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Search Results (966)

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12 pages, 22317 KiB  
Article
Biomimetic Cooling: Functionalizing Biodegradable Chitosan Films with Saharan Silver Ant Microstructures
by Markus Zimmerl, Richard W. van Nieuwenhoven, Karin Whitmore, Wilfried Vetter and Ille C. Gebeshuber
Biomimetics 2024, 9(10), 630; https://doi.org/10.3390/biomimetics9100630 - 17 Oct 2024
Viewed by 319
Abstract
The increasing occurrence of hot summer days causes stress to both humans and animals, particularly in urban areas where temperatures can remain high, even at night. Living nature offers potential solutions that require minimal energy and material costs. For instance, the Saharan silver [...] Read more.
The increasing occurrence of hot summer days causes stress to both humans and animals, particularly in urban areas where temperatures can remain high, even at night. Living nature offers potential solutions that require minimal energy and material costs. For instance, the Saharan silver ant (Cataglyphis bombycina) can endure the desert heat by means of passive radiative cooling induced by its triangular hairs. The objective of this study is to transfer the passive radiative cooling properties of the micro- and nanostructured chitin hairs of the silver ant body to technically usable, biodegradable and bio-based materials. The potential large-scale transfer of radiative cooling properties, for example, onto building exteriors such as house facades, could decrease the need for conventional cooling and, therefore, lower the energy demand. Chitosan, a chemically altered form of chitin, has a range of medical uses but can also be processed into a paper-like film. The procedure consists of dissolving chitosan in diluted acetic acid and uniformly distributing it on a flat surface. A functional structure can then be imprinted onto this film while it is drying. This study reports the successful transfer of the microstructure-based structural colors of a compact disc (CD) onto the film. Similarly, a polyvinyl siloxane imprint of the silver ant body shall make it possible to transfer cooling functionality to technically relevant surfaces. FTIR spectroscopy measurements of the reflectance of flat and structured chitosan films allow for a qualitative assessment of the infrared emissivity. A minor decrease in reflectance in a relevant wavelength range gives an indication that it is feasible to increase the emissivity and, therefore, decrease the surface temperature purely through surface-induced functionalities. Full article
(This article belongs to the Special Issue The Latest Progress in Bionics Research)
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Graphical abstract

Graphical abstract
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<p>Incoming and outgoing radiation energy intensity and the absorption spectrum of the atmosphere. The bulk of the outgoing energy lies within the atmospheric window from 8 to 13 µm [<a href="#B17-biomimetics-09-00630" class="html-bibr">17</a>].</p>
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<p>(<b>a</b>) SEM micrograph of cross-sections cut with a focused ion beam (FIB) through triangular chitin hairs of a Sahara silver ant gaster (hind part). Scale bar—2 µm. (<b>b</b>) Illustration of the triangular cross-section of a silver ant hair. Incoming solar radiation undergoes Mie scattering at the small indentations of the top sides. The light that enters the silver ant hair can be reflected on the bottom side when the conditions for total reflection are met (incidence angle and difference in refractive index between silver ant hair and air gap) [<a href="#B14-biomimetics-09-00630" class="html-bibr">14</a>]. Scale bar—1 µm.</p>
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<p>(<b>a</b>) SEM sample holder with exposed and unexposed shrimp shell sample, as well as silver ant gaster (rear segment of the silver ant). Scale bar—1 cm. (<b>b</b>) Climate chamber cycles (programmed).</p>
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<p>Process of creating a copy of the silver ant surface structure in chitosan with the help of a PVS stamp.</p>
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<p>(<b>a</b>) Confocal image of scratched shrimp shell before and after exposure in the climate chamber. Scale bar—200 µm. (<b>b</b>) Chitosan film with iridescent microstructures transferred from a CD. Scale bar—0.5 cm.</p>
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<p>Confocal images of (<b>a</b>) an ’unstructured’ area of the PVS stamp, which shows the structure of the cardboard that surrounded the silver ant gaster. (<b>b</b>) The PVS–cardboard structure transferred onto chitosan. (<b>c</b>) A structured area of the PVS stamp structured with an ant gaster. (<b>d</b>) The PVS–ant structure transferred onto chitosan. Scale bars—200 µm.</p>
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<p>(<b>a</b>) Average reflectance (* in relation to a reference gold mirror) of structured and unstructured chitosan films. The structured areas in both samples exhibit a slightly higher reflectance for wavelengths greater than 6 µm. However, this difference is less than the calculated standard deviation. Below 6 µm, the two samples feature inconsistent differences in reflectance. (<b>b</b>) Zoom into the respective region of the atmospheric window in (<b>a</b>).</p>
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19 pages, 2326 KiB  
Article
Storing Excess Solar Power in Hot Water on Household Level as Power-to-Heat System
by Ivar Kotte, Emma Snaak and Wilfried van Sark
Energies 2024, 17(20), 5154; https://doi.org/10.3390/en17205154 - 16 Oct 2024
Viewed by 242
Abstract
PV technology has become widespread in the Netherlands, reaching a cumulative installed capacity of 22.4 GWp in 2023 and ranking second in the world for solar PV per capita at 1268 W/capita. Despite this growth, there is an inherent discrepancy between energy supply [...] Read more.
PV technology has become widespread in the Netherlands, reaching a cumulative installed capacity of 22.4 GWp in 2023 and ranking second in the world for solar PV per capita at 1268 W/capita. Despite this growth, there is an inherent discrepancy between energy supply and demand during the day. While the netting system in the Netherlands can currently negate the economic drawbacks of this discrepancy, grid congestion and imbalanced electricity prices show that improvements are highly desirable for the sustainability of electricity grids. This research analyzes the effectiveness of a Power-to-Domestic-Hot-Water (P2DHW) system at improving the utilization of excess PV electricity in Dutch households and compares it to similar technologies. The results show that the example P2DHW system, the WaterAccu, compares favorably as a low cost and flexible solution. In particular, for twelve different households differing in size (1–6 occupants), PV capacity (2.4–8 kWp), and size of hot water storage boiler (50–300 L), it is shown that the total economic benefits for the period 2024–2032 vary from −€13 to €3055, assuming the current net metering scheme is abolished in 2027. Only for large households with low PV capacity are the benefits a little negative. Based on a multi-criteria analysis, it is found that the WaterAccu is the cheapest option compared to other storage options, such as a home battery, a heat pump boiler, and a solar boiler. A sensitivity study demonstrated that these results are overall robust. Furthermore, the WaterAccu has a positive societal impact owing to its peak shaving potential. Further research should focus on the potential of the technology to decrease grid congestion when implemented on a neighborhood scale. Full article
(This article belongs to the Special Issue Advanced Solar Technologies and Thermal Energy Storage)
18 pages, 7500 KiB  
Article
Evaluation of Thermal Comfort Conditions in the Working Environments of Seasonal Agricultural Workers in Csa Koppen Climate Type
by Nihat Karakuş, Serdar Selim, Ceren Selim, Rifat Olgun, Ahmet Koç, Zeynep R. Ardahanlıoğlu, Sülem Şenyiğit Doğan and Nisa Ertoy
Sustainability 2024, 16(20), 8903; https://doi.org/10.3390/su16208903 - 14 Oct 2024
Viewed by 390
Abstract
This study focuses on determining the thermal comfort conditions of seasonal agricultural workers during the hot periods of the year when agricultural production is intense in the Aksu/Türkiye region, which is characterized by the Csa climate type according to the Köppen–Geiger climate classification. [...] Read more.
This study focuses on determining the thermal comfort conditions of seasonal agricultural workers during the hot periods of the year when agricultural production is intense in the Aksu/Türkiye region, which is characterized by the Csa climate type according to the Köppen–Geiger climate classification. In this study, the thermal comfort conditions of seasonal agricultural workers working on open farmlands were evaluated in ten-day, monthly, and seasonal periods for 6 months between 5:00 and 21:00 h using the modified Physiological Equivalent Temperature (mPET) index in the Rayman Pro software according to their activity energy during work. The results of the study reveal that increased activity energy leads to a decrease in thermal comfort conditions of agricultural workers, mPET values of agricultural workers engaged in soil cultivation (Group II) are 2.1 to 2.9 °C higher than the mPET values of workers engaged in plant care and harvesting (Group I), and the agricultural workers in Group II are exposed to more heat stress. The thermal comfort conditions of agricultural workers in Group I deteriorate between 09:00 and 16:00 h with mPET values between 34.1 and 35.3 °C and those of agricultural workers in Group II deteriorate between 08:00 and 17:00 h with mPET values between 34.3 and 37.7 °C. In this context, the daily comfortable working time in the morning and afternoon was found to be 9 h for Group I and 7 h for Group II. Overall, determining the comfortable working hours of agricultural workers in regions with different climate types in future studies will be an important resource for decision-makers in developing strategies to protect the health and increase the productivity of agricultural workers. Full article
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Figure 1
<p>Geographic location of the study area.</p>
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<p>Air temperature graph of the study area in 2023. The daily range of reported temperatures (gray bars) and 24-h highs (red ticks) and lows (blue ticks) are placed over the daily average high (faint red line) and low (faint blue line) temperature [<a href="#B45-sustainability-16-08903" class="html-bibr">45</a>].</p>
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<p>Hourly average climate data for temperature and relative humidity.</p>
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<p>Hourly average climate data for wind speed/velocity and cloud cover.</p>
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<p>Ten-day mPET results for seasonal agricultural workers.</p>
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<p>Monthly mPET results of seasonal agricultural workers.</p>
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<p>mPET results for seasonal agricultural workers between April and September.</p>
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<p>Comfortable and uncomfortable working hours of seasonal agricultural workers.</p>
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15 pages, 1184 KiB  
Article
The Addition of Hot Water Extract of Juncao-Substrate Ganoderma lucidum Residue to Diets Enhances Growth Performance, Immune Function, and Intestinal Health in Broilers
by Yu-Yun Gao, Xiao-Ping Liu, Ying-Huan Zhou, Jia-Yi He, Bin Di, Xian-Yue Zheng, Ping-Ting Guo, Jing Zhang, Chang-Kang Wang and Ling Jin
Animals 2024, 14(20), 2926; https://doi.org/10.3390/ani14202926 - 11 Oct 2024
Viewed by 316
Abstract
The purpose of this experiment was to investigate the effects of Hot Water Extract of Juncao-substrate Ganoderma lucidum Residue (HWE-JGLR) on the immune function and intestinal health of yellow-feather broilers. In an animal feeding experiment, 288 male yellow-feather broilers (1 day old) were [...] Read more.
The purpose of this experiment was to investigate the effects of Hot Water Extract of Juncao-substrate Ganoderma lucidum Residue (HWE-JGLR) on the immune function and intestinal health of yellow-feather broilers. In an animal feeding experiment, 288 male yellow-feather broilers (1 day old) were randomly allocated to four treatment groups with six replicates of 12 birds each. The control (CON) group was fed a basal diet. HJ-1, HJ-2, and HJ-3 were fed a basal diet supplemented with 0.25%, 0.50%, and 1.00% HWE-JGLR, respectively. The feeding trial lasted for 63 d. The results showed increased ADFI (p = 0.033) and ADG (p = 0.045) of broilers in HJ-3, compared with the CON group. Moreover, higher contents of serum IL-4 and IL-10 and gene expression of IL-4 and IL-10 in jejunum mucosa and lower contents of serum IL-1β and gene expression of IL-1β in jejunum mucosa in HJ-3 were observed (p < 0.05). Additionally, the jejunal mucosal gene expression of Claudin-1 and ZO-1 in HJ-2 and HJ-3 was higher than that in the CON group (p < 0.05). As for the microbial community, compared with the CON group, the ACE index, Shannon index, and Shannoneven index of cecal microorganisms in HJ-2 and HJ-3 were elevated (p < 0.05). PCoA analysis showed that the cecal microbial structure of broilers in HJ-2 and HJ-3 was different from the CON group (p < 0.05). In contrast with the CON group, the broilers in HJ-2 and HJ-3 possessed more abundant Desulfobacterota at the phylum level and unclassified Lachnospiraceae, norank Clostridia vadinBB60 group and Blautia spp. at the genus level, while Turicibacter spp. and Romboutsia spp. were less (p < 0.05). In conclusion, dietary supplementation with HWE-JGLR can improve growth performance, enhance body immunity and intestinal development, and maintain the cecum microflora balance of yellow-feather broilers. Full article
(This article belongs to the Section Animal Nutrition)
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Figure 1
<p>Effects of HWE-JGLR on serum cytokines (<b>A</b>) and immunoglobulins (<b>B</b>) contents of broilers. Values are presented as a mean ± SD. Letters in the case of each cytokine describe significant differences between groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of HWE-JGLR on gene expression of jejunal mucosal cytokine (<b>A</b>) and jejunal tight-junction protein (<b>B</b>) in broilers. Values are presented as a mean ± SD. Letters in the case of each cytokine or jejunal tight-junction protein describe significant differences between groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Rarefaction curves of sequencing read (<b>A</b>) and Venn diagram (<b>B</b>) of ASVs in broilers. PCoA analysis based on the Bray-Curtis distance (<b>C</b>). Relative abundance at phylum level (<b>D</b>) and genus level (<b>E</b>) in cecum microbiota. In (<b>B</b>–<b>D</b>) A represents HJ-1, B represents HJ-2, and C represents HJ-3.</p>
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36 pages, 50364 KiB  
Article
MITIGATING THE URBAN HEAT ISLAND EFFECT: The Thermal Performance of Shade-Tree Planting in Downtown Los Angeles
by Yuzhou Zhu and Karen M. Kensek
Sustainability 2024, 16(20), 8768; https://doi.org/10.3390/su16208768 - 11 Oct 2024
Viewed by 739
Abstract
The intensifying urban heat island (UHI) effect presents a growing challenge for urban environments, yet there is a lack of comprehensive strategies that account for how multiple factors influence tree-cooling effectiveness throughout the year. While most studies focus on the effects of individual [...] Read more.
The intensifying urban heat island (UHI) effect presents a growing challenge for urban environments, yet there is a lack of comprehensive strategies that account for how multiple factors influence tree-cooling effectiveness throughout the year. While most studies focus on the effects of individual factors, such as tree shading or transpiration, over specific time periods, fewer studies address the combined impact of various factors—such as seasonal variations, building shading, transpiration rates, tree placement, and spacing—on tree cooling across different seasons. This study fills this gap by investigating the thermal environment in downtown Los Angeles through ENVI-met simulations. A novel tree-planting strategy was developed to enhance cooling performance by adjusting tree positions based on these key factors. The results show that the new strategy reduces Universal Thermal Climate Index (UTCI) temperatures by 2.2 °C on the hottest day, 0.97 °C on the coldest day, and 1.52 °C annually. The study also evaluates the negative cooling effects in colder months, demonstrating that, in cities with climates similar to Los Angeles, the benefits of tree cooling in hot weather outweigh the drawbacks during winter. These findings provide a new method for optimizing tree placement in urban planning, contributing to more effective UHI mitigation strategies. Full article
(This article belongs to the Special Issue A Systems Approach to Urban Greenspace System and Climate Change)
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Figure 1

Figure 1
<p>The range of research area. The red dashed box formed by ABCD represents the study area for this research.</p>
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<p>Daily temperature data of downtown Los Angeles in 2022.</p>
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<p>With or without tree in date comparison diagram. The green area indicates the locations of trees in the simulation.</p>
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<p>Tree under the shade of building’s simulation diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p>
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<p>Tree’s eight location’s simulation diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p>
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<p>Trees’ canopies separated by 15-feet diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p>
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<p>Trees touching canopies diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p>
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<p>Overlapping trees’ canopies diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p>
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<p>No tree and one tree modeling in ENVI-met.</p>
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<p>Hottest day UTCI diagram comparison by date factor at 10 a.m., 12 p.m., and 2 p.m.</p>
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<p>Coldest day UTCI diagram comparison by date factor at 10 a.m., 12 p.m., and 2 p.m.</p>
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<p>Hottest and coldest days reduced UTCI comparison (center of the tree).</p>
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<p>Hottest and coldest days reduced UTCI comparison (shadow area of the tree).</p>
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<p>Each month reduced UTCI comparison.</p>
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<p>Building shade without a tree and with a tree.</p>
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<p>Hottest day UTCI diagram comparison by building shade factor at 10 a.m., 12 p.m., and 2 p.m.</p>
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<p>Cooling ability reduction comparison.</p>
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<p>Building shade without a tree and with a tree for transpiration analysis.</p>
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<p>Hottest day UTCI diagram comparison by transpiration factor at 10 a.m.</p>
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<p>Coldest day UTCI diagram comparison by transpiration factor at 10 a.m.</p>
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<p>Transpiration cooling effect on UTCI comparison.</p>
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<p>Trees in different eight locations surrounding a building diagram.</p>
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<p>Hottest day sunrise, noon, and sunset time UTCI comparison (8 a.m., 12 p.m., and 5 p.m.).</p>
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<p>Hottest day building shadow analysis. The green area indicates the locations of trees in the simulation. The dark gray area represents the locations of buildings in the simulation. The shaded area represents the extent of building shadow coverage. The darker the shade, the longer the duration of the building’s shadow coverage.</p>
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<p>Coldest day building shadow analysis. The green area indicates the locations of trees in the simulation. The dark gray area represents the locations of buildings in the simulation. The shaded area represents the extent of building shadow coverage. The darker the shade, the longer the duration of the building’s shadow coverage.</p>
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<p>Whole-year building shadow analysis. The green area indicates the locations of trees in the simulation. The dark gray area represents the locations of buildings in the simulation. The shaded area represents the extent of building shadow coverage. The darker the shade, the longer the duration of the building’s shadow coverage.</p>
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<p>Trees’ best cooling location ranking.</p>
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<p>Three types of trees’ spacing UTCI comparison at 12 pm (hottest day).</p>
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<p>New trees’ layout 3D map.</p>
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<p>Full-site new trees’ 3D model in ENVI-met.</p>
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<p>Full-site no trees’ UTCI diagram.</p>
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<p>Full-site existing trees’ UTCI diagram.</p>
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<p>Full-site new trees’ UTCI diagram.</p>
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<p>Full-site pedestrian area UTCI comparisons at 12 p.m.</p>
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<p>Reduced UTCI for each month (new scheme).</p>
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<p>Research area 3D model in ENVI-met.</p>
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<p>Assigned materials for building façade and road surface in ENVI-met.</p>
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<p>Climate file setting in ENVI-met (hottest day).</p>
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<p>Climate file setting in ENVI-met (coldest day).</p>
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<p>Daily climate data of each month in downtown Los Angeles.</p>
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<p>February average climate data calculation.</p>
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<p>February air-temperature average data calculation (part).</p>
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<p>February average climate data file in ENVI-met.</p>
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<p>Preliminary simulation UTCI diagram.</p>
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<p>Sidewalk area pixel-count image.</p>
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<p>Coldest day UTCI diagram comparison by building shade factor at 10 a.m., 12 p.m., and 2 p.m.</p>
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<p>Coldest day sunrise, noon, and sunset time UTCI comparison (8 a.m., 12 p.m., and 5 p.m.).</p>
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<p>New tree scheme layout 2D map.</p>
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<p>Full-site no tree 3D model in ENVI-met.</p>
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<p>Full-site existing trees’ 3D model in ENVI-met.</p>
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<p>Full-site no trees’ coldest day at 12 p.m.</p>
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<p>Full-site existing trees coldest day at 12 p.m.</p>
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<p>Full-site new trees’ coldest day at 12 p.m.</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (January).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (February).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (March).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (April).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (May).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (June).</p>
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<p>Existing trees’ and new trees’ UTCI diagram 12 p.m. (July).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (August).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (September).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (October).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (November).</p>
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<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (December).</p>
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19 pages, 5054 KiB  
Article
Impact of Air Conditioning Type on Outdoor Ozone Intrusion into Homes in a Semi-Arid Climate
by James D. Johnston, Seth Van Roosendaal, Joseph West, Hanyong Jung and Darrell Sonntag
Environments 2024, 11(10), 219; https://doi.org/10.3390/environments11100219 - 7 Oct 2024
Viewed by 532
Abstract
Outdoor ozone (O3) is elevated on hot, sunny days when residential air conditioning is used most. We evaluated the impact of direct evaporative coolers (ECs) and vapor-compression air conditioners (ACs) on indoor O3 concentrations in homes (N = 31) in [...] Read more.
Outdoor ozone (O3) is elevated on hot, sunny days when residential air conditioning is used most. We evaluated the impact of direct evaporative coolers (ECs) and vapor-compression air conditioners (ACs) on indoor O3 concentrations in homes (N = 31) in Utah County, Utah, United States of America. Indoor and outdoor O3 concentrations were measured for 24 h at each home using nitrite-impregnated glass-fiber filters. AC homes (n = 16) provided a protective envelope from outdoor O3 pollution. Only one AC home had O3 levels above the limit of detection (LOD). Conversely, EC homes (n = 15) provided minimal protection from outdoor O3. Only one EC home had O3 levels below the LOD. The average indoor O3 concentration in EC homes was 23 ppb (95% CI 20, 25). The indoor-to-outdoor (I/O) ratio for O3 in EC homes was 0.65 (95% CI 0.58, 0.72), while the upper bound for the I/O ratio for AC homes was 0.13 (p < 0.001). Indoor exposure to O3 for residents in EC homes is approximately five times greater than for residents of AC homes. Although ECs offer energy and cost-saving advantages, public health awareness campaigns in O3-prone areas are needed, as well as research into O3 pollution controls for direct ECs such as activated carbon filtration. Full article
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Figure 1
<p>Daily Maximum Temperature (°C) measured at Brigham Young University, and the Daily Maximum 8-h O<sub>3</sub> Concentrations (ppb) measured at the Utah Division of Air Quality Monitors in Lindon and Spanish Fork for 2022 and 2023, with the US EPA Air Quality Index (AQI) levels for O<sub>3</sub> also displayed [<a href="#B13-environments-11-00219" class="html-bibr">13</a>].</p>
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<p>(<b>a</b>) Example outdoor and (<b>b</b>) indoor setup of the O<sub>3</sub> sampling cassettes and pump.</p>
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<p>Average indoor and outdoor O<sub>3</sub> concentrations for each date, home, and visit organized by central air conditioners and evaporative coolers. Missing indoor observations are below the limit of detection (LOD). LOD values ranged between 3 and 6 ppb during the study and are represented with grey points.</p>
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<p>Average indoor and outdoor O<sub>3</sub> concentrations for each visit organized by central air conditioner and evaporative cooler homes. Visits with high flow rate were sampled at 0.5 L/min, visits with a low flow rate were sampled at 0.25 L/min. The limit of detection (LOD) was used for visits where the indoor O<sub>3</sub> concentration is below the LOD. The smooth line is a LOESS fit, and the grey bands are 95% confidence intervals of the mean predicted value.</p>
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<p>Indoor/Outdoor (I/O) O<sub>3</sub> concentrations for each house and visit, organized by central air conditioner (AC) and evaporative cooler (EC) homes. Visits with high flow rate were sampled at 0.5 L/min, visits with low flow-rate were sampled at 0.25 L/min. The limit of detection (LOD) was used for visits where the indoor O<sub>3</sub> concentration is below the LOD. The home visits are ordered by the mean I/O ratio of each home, <span class="html-italic">x<sub>j.</sub></span></p>
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<p>Indoor/Outdoor (I/O) O<sub>3</sub> concentrations for each visit, plotted against the difference in the indoor and outdoor daily minimum relative humidity. Separate panels for central air conditioner and evaporative cooler homes. Visits with a high flow rate were sampled at 0.5 L/min, and visits with a low flow rate were sampled at 0.25 L/min. The limit of detection was used for visits where the indoor O<sub>3</sub> concentration was below the LOD. Linear regression was used to predict the I/O ratio as a function of the difference in minimum relative humidity.</p>
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<p>Indoor/Outdoor (I/O) O<sub>3</sub> concentrations for each visit, plotted against the daily average O<sub>3</sub> concentrations at the closest UDAQ monitor. Separate panels for central air conditioner and evaporative cooler homes. Visits with high flow rate were sampled at 0.5 L/min and visits with low flow rate were sampled at 0.25 L/min. The limit of detection (LOD) was used for visits where the indoor O<sub>3</sub> concentration was below the LOD. The smooth line is a LOESS fit, and the grey bands are 95% confidence intervals of the mean predicted value.</p>
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<p>(<b>a</b>) Boxplot of the representative O<sub>3</sub> I/O ratio for each home, <span class="html-italic">x<sub>j</sub></span>, in the study organized by central air conditioner and evaporative cooler for each home in the study. The limit of detection (LOD) was used for visits where the indoor O<sub>3</sub> concentration was below the LOD. The boxplots are in the style of Tukey; the middle line is the median, the bottom and upper lines are the 25th and 75th percentiles, respectively; whiskers extend to the largest value within 1.5 times the interquartile range; observations beyond the whiskers are labeled individually [<a href="#B41-environments-11-00219" class="html-bibr">41</a>]. (<b>b</b>) Mean (I/O) O<sub>3</sub> concentrations by air conditioning type, <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>x</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> and accompanying 95% confidence intervals of the mean.</p>
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17 pages, 4863 KiB  
Article
Effects of Extreme Climatic Events on the Autumn Phenology in Northern China Are Related to Vegetation Types and Background Climates
by Xinyue Gao, Zexing Tao and Junhu Dai
Remote Sens. 2024, 16(19), 3724; https://doi.org/10.3390/rs16193724 - 7 Oct 2024
Viewed by 580
Abstract
The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, [...] Read more.
The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, remain unclear. In this study, we first evaluated the temporal variations in the EOS anomalies in Northern China (NC) based on the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from 2001 to 2018. We then used event coincidence analysis (ECA) to assess the susceptibility of EOS to four ECEs (i.e., extreme heat, extreme cold, extreme wet and extreme dry events). Finally, we examined the dependence of the response of EOS to ECEs on background climate conditions. Our results indicated a slight decrease in the proportion of areas experiencing extreme heat and dry events (1.10% and 0.66% per year, respectively) and a slight increase in the proportion of areas experiencing extreme wet events (0.77% per year) during the preseason period. Additionally, EOS exhibited a delaying trend at a rate of 0.25 days/a during the study period. The susceptibility of EOS to ECEs was closely related to local hydrothermal conditions, with higher susceptibility to extreme dry and extreme hot events in drier and warmer areas and higher susceptibility to extreme cold and extreme wet events in wetter regions. Grasslands, in contrast to forests, were more sensitive to extreme dry, hot and cold events due to their weaker resistance to water deficits and cold stress. This study sheds light on how phenology responds to ECEs across various ecosystems and hydrothermal conditions. Our results could also provide a valuable guide for ecosystem management in arid regions. Full article
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<p>Spatial distribution of mean annual temperature (<b>a</b>), mean annual precipitation (<b>b</b>), elevation (<b>c</b>) and vegetation types (Source: The Vegetation Maps of China with a proportional scale of 1:1,000,000) (<b>d</b>) in Northern China.</p>
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<p>Trends in the proportion of areas affected by extreme climate events in the preseason period during 2001–2018. The lines represent the linear fitting lines and the shaded areas are the 95% confidence bands of the fits.</p>
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<p>Trends in the proportion of areas experiencing EOS anomalies during 2001–2018. The lines represent the linear fitting lines and the shaded areas are the 95% confidence bands of the fits.</p>
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<p>Spatial distributions of the coincidence rates (CRs) between EOS anomalies and extreme climatic events (ECEs) in Northern China. (<b>a</b>) Total CR between the negative EOS anomalies and ECEs. (<b>b</b>) Total CR between the positive EOS anomalies and ECEs. (<b>c</b>) The CRs between the negative EOS anomalies and individual ECEs. (<b>d</b>) The CRs between the positive EOS anomalies with individual ECEs. Only climate extremes with the highest CRs are displayed at each pixel in (<b>c</b>,<b>d</b>).</p>
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<p>Percentage of the pixels of coincidence rates (CRs) between EOS anomalies and extreme climate extremes (ECEs) and the averaged CR for each ECE. The dark and light colors indicate the CRs are significant and non-significant, respectively.</p>
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<p>Distribution in the coincidence rates (CRs) between the negative EOS anomalies and extreme climate extremes along the preseason climate gradients. The black lines in the panels above and to the right of heat maps indicate the mean CRs against the preseason temperature in each bin of 1 °C or preseason precipitation in each bin of 50 mm, respectively. The blue lines represent the linear fitting lines of CRs. ** <span class="html-italic">p</span> &lt; 0.01. * <span class="html-italic">p</span> &lt; 0.05. Pixels that occur less than ten times within a 1 °C and 50 mm precipitation range were excluded to ensure the reliability.</p>
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<p>Distribution in the coincidence rates (CRs) between positive EOS anomalies and extreme climate extremes along the preseason climate gradients. The black lines in the panels above and to the right of heat maps indicate the mean CRs against the preseason temperature in each bin of 1 °C or preseason precipitation in each bin of 50 mm, respectively. The blue lines represent the linear fitting lines of CRs. ** <span class="html-italic">p</span> &lt; 0.01. Pixels that occur less than ten times within a 1 °C and 50 mm precipitation range were excluded to ensure the reliability.</p>
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<p>Distribution of coincidence rates (CRs) between EOS anomalies and extreme climate extremes along preseason temperature and precipitation gradients. (<b>a</b>) CR of negative EOS anomalies and extreme hot events. (<b>b</b>) CR of negative EOS anomalies and extreme cold events. (<b>c</b>) CR of positive EOS anomalies and extreme wet events. (<b>d</b>) CR of negative EOS anomalies and extreme dry events. The shaded areas show the standard deviation. Note: Pixels that were fewer than 10 occurrences within a 1 °C temperature and a 50 mm precipitation range were excluded to ensure the reliability.</p>
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<p>Distribution in sensitivity of EOS to extreme climate events of four vegetation types along preseason temperature and precipitation gradients. The shaded areas represent the standard deviation. Note: Pixels that were fewer than 10 occurrences within a 1 °C temperature and a 50 mm precipitation range were excluded to ensure reliability.</p>
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13 pages, 2006 KiB  
Article
Effects of Acute and One-Week Supplementation with Montmorency Tart Cherry Powder on Food-Induced Uremic Response and Markers of Health: A Proof-of-Concept Study
by Drew E. Gonzalez, Jacob A. Kendra, Broderick L. Dickerson, Choongsung Yoo, Joungbo Ko, Kay McAngus, Victoria Martinez, Megan Leonard, Sarah E. Johnson, Dante Xing, Ryan J. Sowinski, Christopher J. Rasmussen and Richard B. Kreider
Nutrients 2024, 16(19), 3391; https://doi.org/10.3390/nu16193391 - 6 Oct 2024
Viewed by 893
Abstract
Metabolic conditions, such as gout, can result from elevated uric acid (UA) levels. Consuming high-purine meals increases UA levels. Therefore, people with hyperuricemia typically must avoid ingesting such foods. Polyphenols have been shown to reduce uric acid levels and tart cherries (TCs) are [...] Read more.
Metabolic conditions, such as gout, can result from elevated uric acid (UA) levels. Consuming high-purine meals increases UA levels. Therefore, people with hyperuricemia typically must avoid ingesting such foods. Polyphenols have been shown to reduce uric acid levels and tart cherries (TCs) are a rich source of phenolic and anthocyanin compounds. This proof-of-concept study evaluated whether ingesting TCs with a purine-rich meal affects the uricemic response. Methods: A total of 25 adults (15 males and 10 females, 85.0 ± 17 kg, 40.6 ± 9 years, 29.1 ± 4.9 kg/m2) with elevated fasting UA levels (5.8 ± 1.3 mg/dL) donated a fasting blood sample. In a randomized, double-blind, crossover, placebo-controlled, counterbalanced manner, participants ingested capsules containing 960 mg of a placebo (PLA) or concentrated TC powder containing 20.7 mg of proanthocyanins with a serving of hot soup (10 g of carbohydrate, 2 g protein, and 1 g fat) containing 3 g of purines (1 g of adenosine 5′-monophosphate, 1 g of disodium 5′-guanylate, and 1 g of disodium 5′-inosinate). Blood samples were obtained at 0, 60, 120, 180, and 240 min after ingestion to assess changes in uric acid levels and pharmacokinetic profiles. Cell blood counts, a comprehensive metabolic panel, cytokines, inflammatory markers, and subjective side effects ratings were analyzed on baseline (0 min) and post-treatment (240 min) samples. Participants continued consuming two capsules/day of the assigned treatment for one week and then repeated the experiment. Participants observed a 14-day washout and then repeated the experiment while ingesting the alternate treatment. Data were analyzed using general linear model (GLM) statistics with repeated measures, pairwise comparisons, and percentage change from baseline with 95% confidence intervals (CIs). Results: No statistically significant interaction effects or differences between treatments were seen in uric acid levels or PK profiles. Analysis of percent changes from baseline revealed that TC ingestion reduced the blood glucose levels following the ingestion of the high-purine meal (−4.2% [−7.7, −0.7], p = 0017). Additionally, there was some evidence that TC ingestion attenuated the increase from baseline in IL-1β and IL-10 and increased INF-γ. No significant differences were seen in the remaining health markers or subjective side effects ratings. Conclusions: Acute and one-week TC supplementation did not affect the uricemic response to ingesting a high-purine meal in individuals with mildly elevated UA levels. However, there was some evidence that TC supplementation may blunt the glycemic response to ingesting a meal and influence some inflammatory cytokines. Registered clinical trial NCT04837274. Full article
(This article belongs to the Section Nutrition and Metabolism)
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<p>Experimental design and study timeline.</p>
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<p>Consolidated Standards of Reporting Trials (CONSORT) chart. Unblinding revealed that the placebo treatment was Treatment B and tart cherry was Treatment A.</p>
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<p>Percent changes in uric acid levels. PLA = placebo; TC = tart cherry; † = <span class="html-italic">p</span> &lt; 0.05 difference from baseline.</p>
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<p>Percent change in inflammatory cytokines. PLA = placebo; TC = tart cherry; GM-CSF = granulocyte-macrophage colony-stimulating factor; IFN-γ = interferon-gamma; TNF-α = tumor necrosis factor-α; IL = inflammatory interleukins; † = <span class="html-italic">p</span> &lt; 0.05 difference from baseline; ‡ = <span class="html-italic">p</span> &gt; 0.05 to <span class="html-italic">p</span> &lt; 0.10 difference from baseline.</p>
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<p>Blood glucose results. PLA = placebo; TC = tart cherry; † = <span class="html-italic">p</span> &lt; 0.05 effect from baseline value.</p>
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23 pages, 8234 KiB  
Article
Bond Strength and Corrosion Protection Properties of Hot-Dip Galvanized Prestressing Reinforcement in Normal-Strength Concrete
by Petr Pokorný, Tomáš Chobotský, Nikola Prodanovic, Veronika Steinerová and Karel Hurtig
J. Compos. Sci. 2024, 8(10), 407; https://doi.org/10.3390/jcs8100407 - 4 Oct 2024
Viewed by 477
Abstract
Several prestressing reinforced structures have recently collapsed due to chloride-induced steel corrosion. This study investigates the effect of the corrosion of hot-dip galvanized conventional prestressing steel reinforcement under hydrogen evolution on bond strength in normal-strength concrete. The impact of hydrogen evolution on the [...] Read more.
Several prestressing reinforced structures have recently collapsed due to chloride-induced steel corrosion. This study investigates the effect of the corrosion of hot-dip galvanized conventional prestressing steel reinforcement under hydrogen evolution on bond strength in normal-strength concrete. The impact of hydrogen evolution on the porosity of cement paste at the interfacial transition zone (ITZ) is verified through image analysis. The whole surface of prestressing strands is hot-dip galvanized, and their corrosion behavior when embedded in the cement paste is investigated by measuring the time dependence of the open-circuit potential. Concerning the uniformity of the hot-dip galvanized coating and its composition, it is advisable to coat the individual wires of the prestressing reinforcement and subsequently form a strand. It is demonstrated that the corrosion of the coating under the evolution of hydrogen in the cement paste reduces the bond strength of hot-dip galvanized reinforcement in normal-strength concrete. Image analysis after 28 days of cement paste aging indicates insignificant filling of hydrogen-generated pores by zinc corrosion products. Applying an additional surface treatment (topcoat) stable in an alkaline environment is necessary to avoid corrosion of the coating under hydrogen evolution and limit the risk of bond strength reduction. Full article
(This article belongs to the Section Composites Manufacturing and Processing)
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<p>Example of corrosion damage of conventional prestressing steel reinforcement stimulated by chloride anions—archive of Klokner Institute of CTU in Prague.</p>
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<p>Collapse of Troja footbridge in Prague caused by corrosion of conventional prestressing steel reinforcement—archive of Klokner Institute of CTU in Prague.</p>
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<p>Typical composition of hot-dip galvanized coating on low-silicon steel (outside the so-called Sandelin area and the area with significant silicon content in steel)—reprinted from [<a href="#B25-jcs-08-00407" class="html-bibr">25</a>].</p>
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<p>Modelling of sample for measurement E<sub>corr</sub>/FeCr18Ni9 of hot-dip galvanized wire (from prestressing strand) in cement paste: (<b>A</b>) Modelling of sample for measurement; (<b>B</b>) Real view of sample for measurement.</p>
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<p>Figure (<b>A</b>) shows how the steel strand was positioned in the center of the concrete cube using a wooden wedge and a clamp. Figure (<b>B</b>) shows a group of concrete cubes prepared for the pull-out test.</p>
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<p>The pull-out bond strength test between a strand sample and normal-strength concrete: (<b>A</b>) experimental setup schema; (<b>B</b>) the setup of MTS 500 kN loading machine.</p>
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<p>Cement paste specimens were prepared using both types of prestressing steel to determine the porosity of the cement paste at the interface.</p>
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<p>Evaluation of coating formation on individual prestressing wires by optical microscopy – overview.</p>
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<p>Evaluation of continuity of HDG coating between (<b>A</b>) the outer wires of prestressing steel strand—cracks in the coating; (<b>B</b>) the outer and inner wire of prestressing steel strand—presence of discontinuous coating.</p>
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<p>HDG coating on the surface of the prestressing strand—detailed view: (<b>A</b>) The surface of the outer wire; (<b>B</b>) The surface of the inner wire.</p>
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<p>Comparison of surface roughness (R<sub>a</sub>) between uncoated steel (US) and hot-dip galvanized (HDG) prestressing steel wires.</p>
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<p>Time development (250 h of testing) of open-circuit potential for samples: (<b>A</b>) the uncoated (US) prestressing steel in cement paste; (<b>B</b>) HDG prestressing steel in cement paste.</p>
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<p>Time development (250 h of testing) of open-circuit potential for samples: (<b>A</b>) the uncoated (US) prestressing steel in cement paste; (<b>B</b>) HDG prestressing steel in cement paste.</p>
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<p>Results of bond strength tests: (<b>A</b>) bond stress–slip curves overview (curves—mean values, error bars—standard deviation); (<b>B</b>) bond stress–slip curves for low slip values (curves—mean values, error bars—standard deviation) after 28 days of concrete aging.</p>
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<p>Failure pattern after pull-out test for concrete samples with: (<b>A</b>) uncoated (US) prestressing steel; (<b>B</b>) HDG prestressing steel (after 28 days of concrete aging).</p>
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<p>Hot-dip galvanized coating cracks/fracture pattern on surface of prestressing steel after pull-out test: (<b>A</b>) fracture pattern in η phase; (<b>B</b>) fracture pattern in ζ phase (FeZn<sub>13</sub>) after 28 days of concrete aging.</p>
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<p>Interfacial transition zone of uncoated prestressing steel in cement paste after 28 days of aging (overview) and SEM image of detailed porous structure of cement paste from ITZ.</p>
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<p>Interfacial transition zone of hot-dip galvanized prestressing steel in cement paste after 28 days of aging (overview) and SEM image of detailed porous structure of cement paste from ITZ.</p>
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<p>Bar chart of the total area of pores of cement paste taken from different interfacial transition zones (ITZs).</p>
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<p>Diffraction pattern of corrosion products precipitated on surface of hot-dip galvanized prestressing steel after 28 days of curing in cement paste.</p>
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19 pages, 3381 KiB  
Article
Isolation and Identification of Four Strains of Bacteria with Potential to Biodegrade Polyethylene and Polypropylene from Mangrove
by Xilin Fang, Zeming Cai, Xiaocui Wang, Ziyu Liu, Yongkang Lin, Minqian Li, Han Gong and Muting Yan
Microorganisms 2024, 12(10), 2005; https://doi.org/10.3390/microorganisms12102005 - 2 Oct 2024
Viewed by 422
Abstract
With the rapid growth of global plastic production, the degradation of microplastics (MPs) has received widespread attention, and the search for efficient biodegradation pathways has become a hot topic. The aim of this study was to screen mangrove sediment and surface water for [...] Read more.
With the rapid growth of global plastic production, the degradation of microplastics (MPs) has received widespread attention, and the search for efficient biodegradation pathways has become a hot topic. The aim of this study was to screen mangrove sediment and surface water for bacteria capable of degrading polyethylene (PE) and polypropylene (PP) MPs. In this study, two strains of PE-degrading bacteria and two strains of PP-degrading candidate bacteria were obtained from mangrove, named Pseudomonas sp. strain GIA7, Bacillus cereus strain GIA17, Acinetobacter sp. strain GIB8, and Bacillus cereus strain GIB10. The results showed that the degradation rate of the bacteria increased gradually with the increase in degradation time for 60 days. Most of the MP-degrading bacteria had higher degradation rates in the presence of weak acid. The appropriate addition of Mg2+ and K+ was favorable to improve the degradation rate of MPs. Interestingly, high salt concentration inhibited the biodegradation of MPs. Results of scanning electron microscopy (SEM), atomic force microscopy (AFM), and Fourier-transform infrared spectroscopy (FTIR) indicated the degradation and surface changes of PP and PE MPs caused by candidate bacteria, which may depend on the biodegradation-related enzymes laccase and lipase. Our results indicated that these four bacterial strains may contribute to the biodegradation of MPs in the mangrove environment. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Results of physiological and biochemical experiments on potentially efficient microplastic-degrading bacteria.</p>
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<p>Phylogenetic tree indicating the relationship between the 16S rRNA gene sequences of (<b>A</b>) <span class="html-italic">Pseudomonas</span> sp. strain GIA7 and Acinetobacter sp. strain GIB8, (<b>B</b>) Bacillus cereus strain GIA17 and Bacillus cereus strain GIB10. The red triangle signs are the four strains of potentially efficient degrading bacteria.</p>
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<p>Weight loss rate of MPs after degradation by potentially efficient microplastic-degrading bacteria (<b>A</b>) at different times, (<b>C</b>) in different pH environments, (<b>E</b>) under different inorganic salt ions, (<b>G</b>) at different salt concentrations. Plot of ΔOD<sub>600</sub> of potentially efficient microplastic-degrading bacteria (<b>B</b>) at different times, (<b>D</b>) in different pH environments, (<b>F</b>) under different inorganic salt ions, (<b>H</b>) at different salt concentrations.</p>
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<p>Laccase and lipase activities of the strains.</p>
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<p>Surface microscopic characterization of PE and PP microplastics before and after degradation by scanning electron microscopy. Untreated PE microplastics (<b>A</b>) and untreated PP microplastics (<b>B</b>). The surface change of PE MPs after 60 days of degradation by GIA7 (<b>C</b>). The surface change of PE MPs after 60 days of degradation by GIA17 (<b>E</b>). The surface change of PP MPs after 60 days of degradation by GIB8 (<b>D</b>). The surface change of PP MPs after 60 days of degradation by GIB10 (<b>F</b>).</p>
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<p>Bacterial attachment of PE and PP MPs observed using SEM after 60 days of incubation. PE MPs after degradation by GIA7 (<b>A</b>) and GIA17 (<b>B</b>). PP MPs after degradation by GIB8 (<b>C</b>) and GIB10 (<b>D</b>).</p>
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<p>Surface microcharacterization of PE and PP microplastics before and after degradation by atomic force microscopy. PE MPs (<b>A</b>) and PP MPs (<b>D</b>) before degradation. PE MPs after degradation by GIA7 (<b>B</b>) and GIA17 (<b>C</b>). PP MPs after degradation by GIB8 (<b>E</b>) and GIB10 (<b>F</b>).</p>
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<p>Chemical modification of PE (<b>A</b>) and PP (<b>B</b>) treated by microplastic-degrading bacteria was analyzed by Fourier infrared spectroscopy.</p>
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20 pages, 5624 KiB  
Article
Application of PVT Coupled Solar Heat Pump System in the Renovation of Existing Campus Buildings
by Bing Liu, Linqing Yang, Tiangang Lv, Li Zhu, Mingda Ji and Weihang Hu
Energies 2024, 17(19), 4922; https://doi.org/10.3390/en17194922 - 1 Oct 2024
Viewed by 495
Abstract
A photovoltaic thermal panel (PV/T) is an integrated module that harnesses both photovoltaic and solar thermal technologies to convert solar energy into electricity and heat, thereby enhancing overall energy efficiency. This paper aims to explore the suitability of PV/T solar heat pump systems [...] Read more.
A photovoltaic thermal panel (PV/T) is an integrated module that harnesses both photovoltaic and solar thermal technologies to convert solar energy into electricity and heat, thereby enhancing overall energy efficiency. This paper aims to explore the suitability of PV/T solar heat pump systems across various climate zones and assess their potential for widespread application. By analyzing the operating principles of an indirect expansion PV/T solar heat pump system in conjunction with the climate characteristics of different regions, MATLAB R2019b/Simulink software was employed to evaluate the photoelectric performance of PV and PV/T systems in representative cities across five distinct climate zones in China during typical winter days. Key metrics, such as power generation, hot water storage tank temperature, indoor temperature, and system COP, were chosen to assess the heating performance of the PV/T solar heat pump system. The findings indicate that the winter ambient temperature significantly affects the photoelectric efficiency of both the PV and PV/T systems. While higher latitudes with lower ambient temperatures yield greater photoelectric efficiency, the southern regions exhibit higher power generation during winter. The winter heating effectiveness of the PV/T solar heat pump system is mainly influenced by indoor and water tank temperatures, with Harbin’s system performing the poorest and failing to meet heating demands, whereas Nanjing’s system shows the best results. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Illustrates the energy consumption and carbon dioxide emissions within the construction industry in China.</p>
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<p>The PV/T solar heat pump system.</p>
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<p>Energy transfer diagram of the PV/T collector.</p>
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<p>Meteorological parameters and fitting of solar radiation intensity diagram.</p>
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<p>Water temperature curves.</p>
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<p>Changes in ambient temperature (<b>a</b>) and light intensity (<b>b</b>) in December in cities in five climate zones in China.</p>
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<p>Shows the photoelectric performance of PV and PV/T systems in the Harbin area.</p>
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<p>Depicts the photoelectric performance of PV and PV/T systems in Tianjin.</p>
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<p>Showcases the photoelectric performance of PV and PV/T systems in Nanjing.</p>
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<p>Illustrates the photoelectric efficiency of photovoltaic (PV) and PV/T systems in Kunming city.</p>
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<p>Shows the photoelectric performance of the PV and PV/T systems in Guangzhou.</p>
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<p>Changes in the power generation of PV/T solar heat pump systems in five typical cities.</p>
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<p>Temperature fluctuations in heat storage tank of PV/T solar heat pump system in five common cities.</p>
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<p>Change in indoor temperature of the PV/T solar heat pump system in five typical cities.</p>
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<p>COP change of PV/T solar heat pump system in five typical cities.</p>
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15 pages, 11810 KiB  
Article
Drying Hot Red Chilies: A Comparative Study of Solar-Gas-Fired, Tunnel, and Conventional Dryers
by Lingdi Tang, Farman Ali Chandio, Sher Ali Shaikh, Abdul Rahim Junejo, Irshad Ali Mari, Hyder Bakhsh Khoso, Li Hao, Tabinda Naz Syed and Fiaz Ahmed
Processes 2024, 12(10), 2104; https://doi.org/10.3390/pr12102104 - 27 Sep 2024
Viewed by 543
Abstract
Drying extends the shelf life of crops; thus, dryers with good designs will help them dry to an optimum level. The present research work was carried out to assess and compare the performance of conventional (CD), solar tunnel (STD), and solar-cum gas-fired dryers [...] Read more.
Drying extends the shelf life of crops; thus, dryers with good designs will help them dry to an optimum level. The present research work was carried out to assess and compare the performance of conventional (CD), solar tunnel (STD), and solar-cum gas-fired dryers (SGD) for drying hot chilies. The Sanam variety of hot chilies was used in this study. Samples were dried using CD, STD, and SGD methods. The drying process was conducted over three days, from 9:00 to 17:00 daily. Results showed significant differences among the drying methods in temperature, relative humidity, and moisture content reduction (p < 0.0001). The SGD consistently outperformed the other methods, achieving the highest temperature (55 °C) and lowest relative humidity (17%), compared to the STD (44 °C, 23%) and CD (34 °C, 31%). The SGD demonstrated superior efficiency, reducing moisture content from 70% to 9.36% in just 36 h, while the STD required 50 h (to 11.37%) and CD took 84 h (to 9.63%). ANOVA and post hoc analyses revealed that the SGD significantly outperformed both the STD (p = 0.0412) and CD (p = 0.0018) in moisture content reduction. Additionally, the SGD and STD better preserved the color of hot chili samples compared to CD, as determined by the Essential Oil Association (EOA) method. It is concluded that the SGD is the most technically suitable method for drying hot chilies, offering improved efficiency and quality retention. It is recommended to use an SGD for optimal results in hot pepper drying. Full article
(This article belongs to the Special Issue Green Technologies for Food Processing)
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<p>Solar-cum gas-fired dryer (<b>A</b>) illustration (<b>B</b>,<b>C</b>) original.</p>
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<p>View of solar tunnel dryer. (<b>A</b>) Inner view; (<b>B</b>) side view.</p>
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<p>Temperature of SGD, STD, and CD on day 1 for drying of hot chilies.</p>
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<p>Temperature of SGD, STD, and CD on day 2 for drying of hot chilies.</p>
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<p>Temperature of SGD, STD, and CD on day 3 for drying of hot chilies.</p>
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<p>Humidity of SGD, STD, and CD during day 1.</p>
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<p>Humidity of SGD, STD, and CD during day 2.</p>
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<p>Humidity of SGD, STD, and CD during day 3.</p>
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<p>Comparison of moisture content of hot chilies for SGD, STD, and CD.</p>
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<p>Comparison of drying rate of hot chilies for SGD, STD, and CD.</p>
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17 pages, 1948 KiB  
Article
Influence of Maternal Dietary Protein during Late Gestation on Performance of Black Bengal Does and Their Kids
by Md Sayaduzzaman Arafath, Mahadi Hasan, Jakia Sultana, Md Hasanur Alam, Asma Khatun and Mohammad Moniruzzaman
Animals 2024, 14(19), 2783; https://doi.org/10.3390/ani14192783 - 26 Sep 2024
Viewed by 369
Abstract
The present study aimed to elucidate the effect of different levels of dietary protein during late pregnancy on the performance of Black Bengal does and their kids. Twelve does were divided into three groups, with four in each, and three diets, i.e., high [...] Read more.
The present study aimed to elucidate the effect of different levels of dietary protein during late pregnancy on the performance of Black Bengal does and their kids. Twelve does were divided into three groups, with four in each, and three diets, i.e., high protein (18% CP), medium protein (14% CP), and low protein (10% CP) were supplied for 50 days, commencing from 100 days post-coitum to parturition. During the first 100 days of pregnancy, uniform rations with similar ingredients were provided to fulfill the nutrient requirements depending on the live weight of does. All three diets were isocaloric (10.0 MJ/kg DM). Data were subjected to one-way ANOVA, and the significance of the difference among means was determined by Duncan’s Multiple Range Test (DMRT). The main effects of diet and sex, as well as their interaction, were analyzed by two-way ANOVA by using the GLM procedure. The relative expression values of qPCR were calculated by using the 2−ΔΔCt analysis method. Live weight gain was significantly (p < 0.05) higher in high-protein-fed dams than other groups during the experimental period. The milk yield of does was significantly (p < 0.05) higher in high-protein-fed goats than in the low-protein group. The lactation length of does was significantly (p < 0.05) higher in the high- and medium-protein-fed does than in the low-protein-fed does. The duration of post-partum anestrus of does was significantly (p < 0.05) higher in the low-protein-fed dams than in the high-protein group. The birth weight of kids tended to be higher in the high-protein group but did not differ significantly among the treatment groups. In male kids, weaning weight, final weight, live weight gain, and average daily gain were significantly (p < 0.05) higher than in female kids. Weaning weight was higher (p < 0.05) in kids of the high-protein-fed does than the low-protein group. Final weight and live weight gain were significantly (p < 0.05) higher in kids of the high-protein-fed does than in the low-protein-fed group. On the other hand, average daily gain was significantly (p < 0.05) higher in kids of the high- and medium-protein-fed does than the low-protein group. The average body length and wither height of kids at the 32nd week was significantly (p < 0.05) higher in kids of high-protein-fed does than those of the low-protein-fed group. The average heart girth of kids at the 32nd week was significantly (p < 0.05) higher in kids of high-protein-fed does than the medium- and low-protein groups. The survival rate of kids was higher in the medium- and high-protein-fed does than in low-protein group. Hot carcass weight and ether extract content of meat were significantly (p < 0.05) higher in the high-protein group than in the other groups. The dressing percentage was significantly (p < 0.05) higher in the kids of high-protein-fed does than low-protein-fed goats. The expression of the H-FABP gene was significantly (p < 0.05) higher in kids of high-protein-fed does than those of the medium- and low-protein groups. In conclusion, maternal dietary protein levels positively influences the production performance of Black Bengal does and their kids. Full article
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<p>Live weight changes of does (<span class="html-italic">n</span> = 4 per group). The line charts represent the average live weight changes of does (kg) from different groups every week. The error bars represent the standard errors of the mean (SEMs) from the replication of the experiments.</p>
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<p>Milk yield of does (<span class="html-italic">n</span> = 4 per group). The line charts represent the average milk yield of does (g) from different groups every week after parturition. The error bars represent the standard errors of the mean (SEMs) from the replication of the experiments. <sup>a,b,c</sup> indicate significant differences among the three groups.</p>
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<p>Effects of maternal dietary protein levels on live weight changes of kids (<span class="html-italic">n</span> = 5 per group). The line charts represent the average body weight changes of kids (kg) from different groups every week. The error bars represent the standard errors of the mean (SEMs) from the replication of the experiments. <sup>a,b</sup> indicate significant differences among the three groups.</p>
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<p>Effects of maternal dietary protein levels on body length of kids (<span class="html-italic">n</span> = 5 per group). The line charts represent the average body lengths changes of kids (cm) from different groups every week. The error bars represent standard errors of the mean (SEMs) from the replication of the experiments. <sup>a,b,c</sup> indicate significant differences among three groups.</p>
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<p>Effects of maternal dietary protein levels on heart girths of kids (<span class="html-italic">n</span> = 5 per group). The line charts represent the average heart girths of kids (cm) from different groups every week. The error bars represent standard errors of the mean (SEMs) from the replication of the experiments. <sup>a,b,c</sup> indicate significant differences among three groups.</p>
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<p>Effects of maternal dietary protein levels on wither heights of kids (<span class="html-italic">n</span> = 5 per group). The line charts represent the average wither heights of kids (cm) from different groups every week. The error bars represent standard errors of the mean (SEMs) from the replication of the experiments. <sup>a,b,c</sup> indicate significant differences among three groups.</p>
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<p>Effects of maternal dietary protein levels on the expression of the heart fatty acid-binding protein (<span class="html-italic">H-FABP</span>) gene related to fat content in meat (<span class="html-italic">n</span> = 3 per group). The bars represent the average fold changes calculated by using the ΔΔ critical threshold method. The error bars represent standard errors of the mean (SEMs) from the replication of the experiments. <sup>a,b</sup> indicate significant differences among three groups.</p>
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18 pages, 12466 KiB  
Article
X-ray Fluorescence Microscopy to Develop Elemental Classifiers and Investigate Elemental Signatures in BALB/c Mouse Intestine a Week after Exposure to 8 Gy of Gamma Rays
by Anthony Smith, Katrina Dobinda, Si Chen, Peter Zieba, Tatjana Paunesku, Zequn Sun and Gayle E. Woloschak
Int. J. Mol. Sci. 2024, 25(19), 10256; https://doi.org/10.3390/ijms251910256 - 24 Sep 2024
Viewed by 385
Abstract
Iron redistribution in the intestine after total body irradiation is an established phenomenon. However, in the literature, there are no reports about the use of X-ray fluorescence microscopy or equivalent techniques to generate semi-quantitative 2D maps of iron in sectioned intestine samples from [...] Read more.
Iron redistribution in the intestine after total body irradiation is an established phenomenon. However, in the literature, there are no reports about the use of X-ray fluorescence microscopy or equivalent techniques to generate semi-quantitative 2D maps of iron in sectioned intestine samples from irradiated mice. In this work, we used X-ray fluorescence microscopy (XFM) to map the elemental content of iron as well as phosphorus, sulfur, calcium, copper and zinc in tissue sections of the small intestine from eight-week-old BALB/c male mice that developed gastrointestinal acute radiation syndrome (GI-ARS) in response to exposure to 8 Gray of gamma rays. Seven days after irradiation, we found that the majority of the iron is localized as hot spots in the intercellular regions of the area surrounding crypts and stretching between the outer perimeter of the intestine and the surface cell layer of villi. In addition, this study represents our current efforts to develop elemental cell classifiers that could be used for the automated generation of regions of interest for analyses of X-ray fluorescence maps. Once developed, such a tool will be instrumental for studies of effects of radiation and other toxicants on the elemental content in cells and tissues. While XFM studies cannot be conducted on living organisms, it is possible to envision future scenarios where XFM imaging of single cells sloughed from the human (or rodent) intestine could be used to follow up on the progression of GI-ARS. Full article
(This article belongs to the Special Issue Molecular Research of Biomedical X-ray Fluorescence Imaging (XFI))
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<p>Elemental maps of intestine of the sham irradiated mouse noIR-1 scanned with ~100 nm X-ray beam. Two different areas of the intestine are presented (<b>a</b>,<b>b</b>) and a visible light image of a similar area of the intestine—IHC staining for macrophage marker F4/80 (<b>c</b>). White letters indicate 2D maps of individual elements. Note that P map shows distribution of cellular material, with the strongest P signals in cell nuclei, corresponding with blue hematoxylin staining in (<b>c</b>). Scan step sizes were 100 nm and scan areas in <span class="html-italic">x</span> and <span class="html-italic">y</span> direction were 53 by 84 and 47 by 84 micron. Scale bars in (<b>a</b>,<b>b</b>) indicate 20 and 10 microns, respectively, and scale bar in (<b>c</b>) shows 250 microns.</p>
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<p>Elemental maps of intestine of the irradiated mouse IR-1 scanned with ~100 nm X-ray beam. Three different areas of the intestine are presented (<b>a</b>–<b>c</b>) scanned with 100 nm beam step size and covering areas of 48 by 134, 52 by 120, and 54 by 126 micron, respectively. A visible light image of a similar area of the intestine—IHC staining for macrophage marker F4/80 is shown in (<b>d</b>). White letters indicate 2D maps of individual elements. Scan step sizes were 100 nm and scan areas in <span class="html-italic">x</span> and <span class="html-italic">y</span> were 48 by 134, 52 by 120 and 54 by 126 micron. Scale bars in (<b>a</b>–<b>c</b>) indicate 10 microns, and the scale bar in (<b>d</b>) 250 microns.</p>
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<p>Elemental maps of intestine of the irradiated mouse IR-2 scanned with different step sizes and the same beam spot size. Scanning was performed with ~100 nm X-ray beam, with step size of 250 nm (<b>a</b>) or 100 nm (<b>b</b>,<b>c</b>). White letters indicate 2D maps of individual elements. Yellow rectangles show the sub-regions of a large-step undersampling scan (<b>a</b>) that were subsequently imaged with smaller step sizes generating elemental maps in (<b>b</b>,<b>c</b>). While it is difficult to note differences between these scans for an abundant element such as phosphorus, data density produced with smaller step size scanning is far greater and for elements with low concentrations such as copper (fifth panel in each image), this leads to much better image definition. Scan area sizes were 80 by 136, 52 by 79 and 50 by 56 microns in <span class="html-italic">x</span> and <span class="html-italic">y</span> for scans (<b>a</b>), (<b>b</b>) and (<b>c</b>), respectively. Scale bars in (<b>a</b>–<b>c</b>) indicate 20 microns.</p>
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<p>Phosphorus maps of scans used for this work, with ROIs for crypt cell nuclei marked by colors generated by MAPS program (red, green, navy, turquoise, magenta, yellow, gray, dark gray, tan, orange, light green, dark green). For the IR-2 animal where scans were repeated at different sampling densities, regular scan images are indicated by “more steps” added to sample identification. Please note that the ROI colors do not match between IR-2 and IR-2 more steps images. However, we did not use ROI color as cell label after we extracted the scatter plot pixel spectrum data. Instead, the pixel data were associated with specific cell IDs. Scale bar data is provided in phosphorus maps without labeled ROIs in <a href="#ijms-25-10256-f001" class="html-fig">Figure 1</a>, <a href="#ijms-25-10256-f002" class="html-fig">Figure 2</a> and <a href="#ijms-25-10256-f003" class="html-fig">Figure 3</a> and <a href="#app1-ijms-25-10256" class="html-app">Supplemental Figures S1 and S2</a>.</p>
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<p>Phosphorus maps of all scans used for this work, with interspersed cell nuclei and cytosol ROIs marked by colors generated by MAPS program (red, green, navy, turquoise, magenta, yellow, gray, dark gray, tan, orange, light green, dark green). For the IR-2 animal where scans were repeated at different sampling densities, regular scan images are indicated by “more steps” added to sample identification. Again, please note that the ROI colors do not match between IR-2 and IR-2 more steps images. While ROIs were used to extract the spectrum data for each pixel, the pixel data were associated with specific cell IDs before statistical analysis. Scale bar data is provided in phosphorus maps without labeled ROIs in <a href="#ijms-25-10256-f001" class="html-fig">Figure 1</a>, <a href="#ijms-25-10256-f002" class="html-fig">Figure 2</a> and <a href="#ijms-25-10256-f003" class="html-fig">Figure 3</a> and <a href="#app1-ijms-25-10256" class="html-app">Supplemental Figures S1 and S2</a>.</p>
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<p>Phosphorus maps of scans used for this work, with villus cell nuclei and cytosol ROIs marked by colors generated by MAPS program (red, green, navy, turquoise, magenta, yellow, gray, dark gray, tan, orange, light green, dark green). Scale bar data is provided in phosphorus maps without labeled ROIs in <a href="#ijms-25-10256-f001" class="html-fig">Figure 1</a>, <a href="#ijms-25-10256-f002" class="html-fig">Figure 2</a> and <a href="#ijms-25-10256-f003" class="html-fig">Figure 3</a> and <a href="#app1-ijms-25-10256" class="html-app">Supplemental Figures S1 and S2</a>.</p>
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<p>ROI analyses in this work were conducted with the intention to ask four distinct questions. Q1: Will the statistical analysis recognize same subcellular regions from the same cell from two different scans as same? Q2: Will subcellular regions from several cells in the same tissue region be similar? Q3: What will be the level of similarity between subcellular regions from cells from different tissue regions? Q4: What will be the level of similarity between subcellular regions from cells from the equivalent tissue regions in two different animals, especially if one animal was irradiated and the other one was not?</p>
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<p>Individual pixel phosphorus concentration for all cell ROIs within a single animal: nuclei = teal and cytosol = pink. The data are grouped into three overarching categories on the <span class="html-italic">x</span>-axis: crypt cells, interspersed cells and villus cells. Note that only the nuclei group is represented in crypt cells. For ROI generation, see <a href="#ijms-25-10256-f004" class="html-fig">Figure 4</a>, <a href="#ijms-25-10256-f005" class="html-fig">Figure 5</a> and <a href="#ijms-25-10256-f006" class="html-fig">Figure 6</a>. For corresponding mean numerical values, see <a href="#app1-ijms-25-10256" class="html-app">Supplementary Table S1</a>.</p>
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<p>Individual pixel iron concentrations for all cell ROIs within a single animal: nuclei = teal and cytosol = pink. Three groups of data represent nuclei from crypt cells, cytosol and nuclei from interspersed cells and cytosol and nuclei from villus cells. For ROI generation, see <a href="#ijms-25-10256-f004" class="html-fig">Figure 4</a>, <a href="#ijms-25-10256-f005" class="html-fig">Figure 5</a> and <a href="#ijms-25-10256-f006" class="html-fig">Figure 6</a>. For corresponding numerical values, see <a href="#app1-ijms-25-10256" class="html-app">Supplementary Table S1</a>.</p>
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<p>Individual pixel iron concentrations for separate subcellular compartments (cytosol = top, nuclei = bottom) for individual cell ROIs within each animal. For ROI generation, see <a href="#ijms-25-10256-f004" class="html-fig">Figure 4</a>, <a href="#ijms-25-10256-f005" class="html-fig">Figure 5</a> and <a href="#ijms-25-10256-f006" class="html-fig">Figure 6</a>. Note that <span class="html-italic">y</span>-axis maximum for cytosol is 0.4 micrograms per centimeter square, while it is only 0.15 for nuclei. Another issue worth noting is that majority of data for IR-2 animal comes from undersampled scans—scans generated with ~100 nm focal X-ray spot and 250 nm steps. Thus, IR-2 animal data show lower elemental concentrations than what is actually present in the sample (for more detailed explanations, see <a href="#sec1-ijms-25-10256" class="html-sec">Section 1</a> and <a href="#sec4-ijms-25-10256" class="html-sec">Section 4</a>). Each cellular sub-compartment from each animal is indicated by a unique color.</p>
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<p>UMAP graphs generated for all six elements for crypt cell regions were colored to display iron concentrations as shown in the color bar. The four graphs show modest differences in iron content but no distinctive association between Fe per pixel concentration and a specific UMAP data pattern that correlates to radiation exposure. Elemental concentration scale bar shows colors corresponding to different concentrations of iron expressed in micrograms per centimeter square.</p>
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<p>UMAP graphs generated for all six elements for interspersed cell regions were colored to display iron concentrations as shown in the color bar. These four graphs show differences in iron content as well as distinctive association between Fe per pixel concentration and a specific UMAP data pattern. Elemental concentration scale bar shows colors corresponding to different concentrations of iron expressed in micrograms per centimeter square.</p>
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22 pages, 5464 KiB  
Article
Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables
by Amir Shahcheraghian and Adrian Ilinca
Energies 2024, 17(18), 4714; https://doi.org/10.3390/en17184714 - 22 Sep 2024
Viewed by 723
Abstract
Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across [...] Read more.
Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact. Full article
(This article belongs to the Special Issue Energy Efficiency and Energy Performance in Buildings)
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<p>UBC campus building map [<a href="#B17-energies-17-04714" class="html-bibr">17</a>].</p>
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<p>Total electricity energy and total hot water power of the UBC Campus in 2023.</p>
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<p>Seasonal decomposition of total electrical energy for 2023.</p>
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<p>Correlation matrix for weather and energy in UBC Campus.</p>
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<p>Comparison of MAE and R2 Scores for electricity energy.</p>
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<p>Comparison of MAE and R2 scores for hot water power.</p>
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<p>Comparison of MAE and R2 scores for gas volume.</p>
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<p>Heatmap of R2 squared.</p>
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<p>Training and test loss trends for energy usage prediction models at UBC Vancouver Campus.</p>
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