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Search Results (1,363)

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32 pages, 7797 KiB  
Review
Sustainability in Global Agri-Food Supply Chains: Insights from a Comprehensive Literature Review and the ABCDE Framework
by Gaofeng Wang, Yingying Wang, Shuai Li, Yang Yi, Chenming Li and Changhoon Shin
Foods 2024, 13(18), 2914; https://doi.org/10.3390/foods13182914 - 14 Sep 2024
Viewed by 699
Abstract
The sustainability of global agricultural produce supply chains is crucial for ensuring global food security, fostering environmental protection, and advancing socio-economic development. This study integrates bibliometric analysis, knowledge mapping, and the ABCDE framework to conduct a comprehensive qualitative and quantitative analysis of 742 [...] Read more.
The sustainability of global agricultural produce supply chains is crucial for ensuring global food security, fostering environmental protection, and advancing socio-economic development. This study integrates bibliometric analysis, knowledge mapping, and the ABCDE framework to conduct a comprehensive qualitative and quantitative analysis of 742 relevant articles from the Web of Science core database spanning January 2009 to July 2023. Initially, bibliometric analysis and knowledge mapping reveal the annual progression of research on the sustainability of global agricultural produce supply chains, the collaborative networks among research institutions and authors, and the geographic distribution of research activities worldwide, successfully pinpointing the current research focal points. Subsequently, the ABCDE framework, constructed from the quantitative findings, helps us identify and comprehend the antecedents, barriers and challenges, impacts, and driving forces affecting the sustainability of these supply chains. The study identifies globalization and technological advancement as the primary forces shaping the sustainability of agricultural produce supply chains, despite them also posing challenges such as resource constraints and environmental pressures. Moreover, the application of innovative technologies, the optimization of organizational models, and active stakeholder engagement are key to propelling supply chains toward more sustainable development, exerting a profound impact on society, the environment, and the economy. In conclusion, this study suggests future research directions. The integrated methodology presented offers new perspectives and deep insights into the complexities of sustainable global agricultural produce supply chains, demonstrating its potential to foster knowledge innovation and practical applications, providing valuable insights for academic research and policy formulation in this domain. Full article
(This article belongs to the Special Issue Food Insecurity: Causes, Consequences and Remedies—Volume II)
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<p>Sample literature screening flow chart. Chart source: self-made by the author.</p>
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<p>Trends in the number of articles issued, from January 2009 to July 2023. Data source: based on the WOS core database; chart source: self-made by the author.</p>
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<p>Mapping of author collaboration networks. Chart source: self-made by the author.</p>
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<p>School organizations and institutions mapping chart. Chart source: self-made by the author.</p>
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<p>Statistics of publication numbers and centrality of the top ten organizations. Data source: based on the WOS core database; Chart source: self-made by the author.</p>
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<p>Mapping of cooperation among issuing countries. Chart source: self-made by the author.</p>
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<p>Number of publications and centrality statistics for the top ten countries. Data source: based on the WOS core database. Chart source: self-made by the author.</p>
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<p>Keyword co-occurrence map. Chart source: self-made by the author.</p>
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<p>Knowledge graph of keyword clustering in the global sustainable supply chain of agricultural products. Chart source: self-made by the author.</p>
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<p>Global sustainable supply chain research timeline. Data source: based on the WOS core database. Chart source: self-made by the author.</p>
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<p>Atlas of emerging words in the global sustainable supply chain of agricultural products. Data source: based on the WOS core database. Chart source: self-made by the author.</p>
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<p>The ABCDE framework for sustainable global agri-food supply chains. Chart source: self-made by the author.</p>
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20 pages, 27362 KiB  
Article
SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
by Michele Fiori, Giuliano Fois, Marco Secondo Gerardi, Fabio Maggio, Carlo Milesi and Andrea Pinna
Appl. Sci. 2024, 14(18), 8275; https://doi.org/10.3390/app14188275 - 13 Sep 2024
Viewed by 284
Abstract
The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, [...] Read more.
The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis. Full article
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<p>A schematic description of the back-end and front-end of the SMARTerra decision support system.</p>
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<p>The digital elevation model (DEM) of Sardinia, with the island’s main rice-growing areas (<b>left</b>). The DEM was obtained by processing <math display="inline"><semantics> <mrow> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> resolution rasters downloaded from the geoportal of the region of Sardinia [<a href="#B14-applsci-14-08275" class="html-bibr">14</a>]. On the (<b>right</b>), the locations of the meteorological stations of the Regional Meteorological Network (RUR) are shown in yellow, and the nodes of the grid points for which the BOLAM model provides weather forecasts are shown in white.</p>
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<p>A schematic description of the InfluxDB time-series-oriented database storing the weather measurements from the RUR network and BOLAM forecasts. After preprocessing, the data are put into the database and organized into measurements and fields. Then, the data become available for the interpolation techniques by query via API.</p>
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<p>Temperature distribution for a selected geographic window at a specific day and time. Enabling the “Station” flag displays all stations (shown as blue dots) within the window. Detailed variations of the selected variable are shown for each station (dark popup), along with the relative trend of the value from the nearest BOLAM grid point (blue popup). The white dots represent the location of the 4 nodes of the BOLAM model closest to the selected station.</p>
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<p>Detail of the rice blast (“brusone”) risk map on a given day, highlighting the paddy field areas. The dark popup allows users to view specific information about the selected parcel, including the name of the farm, technical features like area, the rice cultivar, and other relevant details.</p>
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<p>Daily maximum temperature (<b>left</b>) and daily rainfall accumulation (<b>right</b>) spatially interpolated from measured data with the KED technique.</p>
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<p>Thumbnails from the PDF report. From (<b>top left</b>) to (<b>bottom right</b>), the hourly mean temperature, mean relative humidity and rainfall from the measured and forecast data, the daily maximum and mean temperature and rainfall from the measured data, the risk indices and alert levels for the rice blast disease, and the threshold dates and accumulation totals for the locust egg hatching prediction.</p>
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<p>Risk indices (<b>top</b>, <b>bottom left</b>) and alert levels (<b>bottom right</b>) maps for the rice blast disease obtained from interpolated measured and forecast weather data. See <a href="#sec2dot2dot5-applsci-14-08275" class="html-sec">Section 2.2.5</a> for details.</p>
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<p>The cumulative sum of degree days (<b>left</b>), starting for each pixel of the map once the rain accumulation threshold <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>th</mi> </msub> <mo>=</mo> <mn>100</mn> <mo> </mo> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math> has been reached, and the predicted dates for locust egg hatching (<b>right</b>). The white areas indicate regions where the summation of <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>th</mi> </msub> <mo>=</mo> <mn>200</mn> <mo> </mo> <mrow> <mo>°</mo> <mi mathvariant="normal">C</mi> </mrow> </mrow> </semantics></math> degree days has not yet occurred. Compare with the former map, where the darkest red areas correspond to regions where the summation of degree days is below the threshold <math display="inline"><semantics> <msub> <mi>D</mi> <mi>th</mi> </msub> </semantics></math>. See <a href="#sec2dot2dot6-applsci-14-08275" class="html-sec">Section 2.2.6</a> for details.</p>
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<p>Comparison of observed and predicted alert levels for rice blast disease in the Oristano rice districts. Observed (<b>top left</b>) and predicted (<b>top right</b>) alert levels for 17 July 2023. Observed (<b>bottom left</b>) and predicted (<b>bottom right</b>) alert levels for 18 July 2023.</p>
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<p>Spatial distribution of <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>d</mi> </msub> </semantics></math> for egg hatching predicted by the default (literature) model (<b>left</b>) and a slightly modified model (<b>right</b>), both with a superimposed OpenStreetMap layer (<b>top</b>), a map of the dates for <math display="inline"><semantics> <msub> <mi>day</mi> <mn>2</mn> </msub> </semantics></math> when the rain threshold <math display="inline"><semantics> <msub> <mi>R</mi> <mi>th</mi> </msub> </semantics></math> is reached and the accumulation of degree days starts (<b>middle</b>), and a map representing <math display="inline"><semantics> <msub> <mi>day</mi> <mn>3</mn> </msub> </semantics></math>, the predicted dates of the hatching of locust eggs (<b>bottom</b>). The region of interest is partitioned into hexagons, each of which is assigned a color according to the average of the <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>d</mi> </msub> </semantics></math> values of the records within the area of the hexagon itself.</p>
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15 pages, 7454 KiB  
Article
Spatial Analysis on the Service Coverage of Emergency Facilities for Fire Disaster Risk in an Urban Area Using a Web Scraping Method: A Case Study of Chiang Rai City, Thailand
by Saharat Arreeras, Suchada Phonsitthangkun, Tosporn Arreeras and Mikiharu Arimura
Urban Sci. 2024, 8(3), 140; https://doi.org/10.3390/urbansci8030140 - 13 Sep 2024
Viewed by 248
Abstract
Emergency service facilities play a pivotal role in mitigating the impact of fire disasters in urban areas. This research article delves into the critical aspects of analyzing service coverage for emergency facilities in relation to fire disaster risk in Chiang Rai city—a strategic [...] Read more.
Emergency service facilities play a pivotal role in mitigating the impact of fire disasters in urban areas. This research article delves into the critical aspects of analyzing service coverage for emergency facilities in relation to fire disaster risk in Chiang Rai city—a strategic hub in northern Thailand. Focusing on fire disaster risk merchandise and shops, categorized by the type of hazardous materials they store and sell, this study leverages facility location data obtained through web scraping from Google Maps. Utilizing spatial analysis and Geographic Information Systems (GISs), this research evaluates the reachability of emergency services, assessing travel times and coverage efficiency. The findings reveal significant disparities, particularly within the critical 3 min response window, highlighting the need for strategic improvements. This study offers actionable insights for urban planners and policymakers, advancing the integration of spatial technology in urban disaster management to enhance public safety and resilience. Full article
(This article belongs to the Special Issue Advances in Urban Spatial Analysis, Modeling and Simulation)
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<p>The study’s roadmap.</p>
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<p>Facility GIS data scraping from Google Maps using Instant Data Scraper application. Source: Google Maps.</p>
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<p>Map of Chiang Rai city (study area), Chiang Rai province, Thailand. Source: OpenStreetMap and QGIS.</p>
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<p>Facilities’ overlay for spatial analysis. Source: OpenStreetMap and QGIS.</p>
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<p>Travel time analysis (<b>a</b>) and travel distance analysis (<b>b</b>) from emergency facilities to fire-risk facilities, grouped by emergency facilities.</p>
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<p>Map of emergency facilities’ service coverage based on different time thresholds, timeframe: 3, 5, and 10 min. Source: OpenStreetMap and QGIS.</p>
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24 pages, 210044 KiB  
Article
Scale- and Resolution-Adapted Shaded Relief Generation Using U-Net
by Marianna Farmakis-Serebryakova, Magnus Heitzler and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2024, 13(9), 326; https://doi.org/10.3390/ijgi13090326 - 12 Sep 2024
Viewed by 206
Abstract
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) [...] Read more.
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) relates to the neural network process and the maps used for training. Currently, there is no clear guidance on which DEM resolution to use to generate relief shading at specific scales. To address this gap, we trained the U-Net models on swisstopo manual relief shadings of Switzerland at four different scales and using four different resolutions of SwissALTI3D DEM. An interactive web application designed for this study allows users to outline a random area and compare histograms of varying brightness between predictions and manual relief shadings. The results showed that DEM resolution and output scale influence the appearance of the relief shading, with an overall scale/resolution ratio. We present guidelines for generating relief shading with neural networks for arbitrary areas and scales. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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<p>Relief shading generalisation at various scales; Säntis group, Swiss Alps, swisstopo manual relief shadings.</p>
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<p>Extents of the swisstopo manual relief shadings of Switzerland with the Swiss border (red) and the tiles footprints (green).</p>
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<p>Extents of the swisstopo manual relief shadings of Switzerland with the Swiss border (red) and the tiles footprints (green).</p>
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<p>Training points and tiles, where green is a base and pink is a padding.</p>
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<p>Training with 80% of the training points; the polygon outlined in red represents the testing area and the polygon outlined in blue shows the clipped area in the canton of Ticino for predictions in the next table.</p>
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<p>Interactive web tool allowing users to outline an area and show histograms of varying brightness between manual and neural shadings (<b>left</b>), histograms of difference values (<b>right</b>), overlaid statistical values, and a confusion matrix of grayscale values.</p>
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<p>Neural relief shadings trained with 80% of the points (<b>left</b>) and 100% of the points (<b>right</b>) subtracted from the manual relief shadings with the minimum and maximum tonal difference values for that specific area at (<b>a</b>) 1:25,000 and 12.5 m resolution, (<b>b</b>) 1:50,000 and 25 m resolution, (<b>c</b>) 1:100,000 and 50 m resolution, and (<b>d</b>) 1:200,000 and 100 m resolution.</p>
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<p>The darkest values on both manual relief shadings and predictions for the clipped area of Switzerland in the y range 0–100.</p>
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<p>The brightest values on manual relief shadings and predictions for the clipped area of Switzerland in the y range 0–100 for the predictions.</p>
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<p>Outliers and missing tonal values in the manual relief shadings and predictions.</p>
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<p>Confusion matrices of grey values as heat maps with manual values in the <span class="html-italic">x</span>-axis and neural values in the <span class="html-italic">y</span>-axis: (<b>a</b>) a slope, (<b>b</b>) a larger area, and (<b>c</b>) the whole of Switzerland at 1:25,000 and 12.5 m resolution.</p>
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<p>Mean and standard deviation values plotted against different resolutions for each of the scales (80% of the training points) with the bars outlined in green according to <a href="#ijgi-13-00326-t001" class="html-table">Table 1</a>.</p>
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<p>Mean and standard deviation values plotted against different resolutions for each of the scales (100% of the training points) with the bars outlined in green according to <a href="#ijgi-13-00326-t001" class="html-table">Table 1</a>.</p>
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<p>Neural shadings of multi-resolution DEMs for Valdez area, Alaska, USA, using the models trained at 1:25,000 and 12.5 m, 1:50,000 and 25 m, 1:100,000 and 50 m, and 1:200,000 and 100 m, all trained with 80% of the training points.</p>
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<p>Neural shadings of multi-resolution DEMs for Valdez area, Alaska, USA, using the models trained at 1:25,000 and 12.5 m, 1:50,000 and 25 m, 1:100,000 and 50 m, and 1:200,000 and 100 m, all trained with 80% of the training points.</p>
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<p>Neural shadings of multi-resolution DEMs for the Gore Range, Colorado, USA, using the models trained at 1:25,000 and 12.5 m, 1:50,000 and 25 m, 1:100,000 and 50 m, and 1:200,000 and 100 m, all trained with 80% of the training points.</p>
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<p>Neural shadings of multi-resolution DEMs for the Gore Range, Colorado, USA, using the models trained at 1:25,000 and 12.5 m, 1:50,000 and 25 m, 1:100,000 and 50 m, and 1:200,000 and 100 m, all trained with 80% of the training points.</p>
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<p>Guidelines on using the trained models for different scales and resolutions.</p>
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50 pages, 3004 KiB  
Review
Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review
by Angelly de Jesus Pugliese Viloria, Andrea Folini, Daniela Carrion and Maria Antonia Brovelli
Remote Sens. 2024, 16(18), 3374; https://doi.org/10.3390/rs16183374 - 11 Sep 2024
Viewed by 426
Abstract
With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to [...] Read more.
With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to model susceptibility of hazardous events. This study consists of a systematic review of the ML/DL techniques applied to model the susceptibility of air pollution, urban heat islands, floods, and landslides, with the aim of providing a comprehensive source of reference both for techniques and modelling approaches. A total of 1454 articles published between 2020 and 2023 were systematically selected from the Scopus and Web of Science search engines based on search queries and selection criteria. ML/DL techniques were extracted from the selected articles and categorised using ad hoc classification. Consequently, a general approach for modelling the susceptibility of hazardous events was consolidated, covering the data preprocessing, feature selection, modelling, model interpretation, and susceptibility map validation, along with examples of related global/continental data. The most frequently employed techniques across various hazards include random forest, artificial neural networks, and support vector machines. This review also provides, per hazard, the definition, data requirements, and insights into the ML/DL techniques used, including examples of both state-of-the-art and novel modelling approaches. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Remote Sensing 2023-2024)
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<p>Literature review methodology.</p>
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<p>Number of selected articles per year.</p>
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<p>The structure of a fully connected ANN.</p>
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<p>The structure of a CNN and an example of convolution.</p>
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<p>The structure of an RNN.</p>
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<p>The structure of a CNN–LSTM hybrid.</p>
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<p>Example of a support vector machine.</p>
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<p>Scheme of the bagging ensemble method.</p>
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<p>Scheme of the boosting ensemble method.</p>
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<p>Scheme of the stacking ensemble method.</p>
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<p>Hazard modelling workflow. * The solid connections suggest that it is a mandatory step, while the dotted connections suggest that it is an optional one. ** The blue colour groups the data, the yellow groups the data processing (preprocessing and feature selection), the purple groups the modelling process and the model, the green groups the susceptibility map as a product, and lastly, in orange groups the final optional steps.</p>
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<p>Air pollution algorithm classes (see <a href="#sec4-remotesensing-16-03374" class="html-sec">Section 4</a>) and frequently used methods. Complete dataset available in the repository (see <a href="#sec3-remotesensing-16-03374" class="html-sec">Section 3</a>). * BPNN = Backpropagation Neural Networks, ELM = Extreme Learning Machine, RNN = Recurrent Neural Networks, DNN = Deep Neural Networks, MLP = Multi-Layer Perceptron, ANN = Artificial Neural Networks, GRU = Gated Recurrent Unit, CNN = Convolutional Neural Network, LSTM = Long Short-Term Memory, LGBM = Light Gradient Boosting Machine, GBDT = Gradient Boosting Decision Trees, XGB = Extreme Gradient Boosting, RF = Random Forest, SVM = Support Vector Machine, LASSO = Least Absolute Shrinkage and Selection Operator Regression, LR = Linear Regression, DT = Decision Trees, KNN = K-Nearest Neighbours, ARIMA = Autoregressive Integrated Moving Average.</p>
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<p>Air pollution articles’ distribution based on the first author’s affiliation.</p>
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<p>Urban heat island algorithm classes count (see <a href="#sec4-remotesensing-16-03374" class="html-sec">Section 4</a>). Complete dataset available in the repository (see <a href="#sec3-remotesensing-16-03374" class="html-sec">Section 3</a>). * XGBR = Extreme Gradient Boosting Regression, SGB = Stochastic Gradient Boosting, AB = AdaBoost, BDT = Bagging Decision Trees, GBRT = Gradient Boosted Regression Trees, RF = Random Forest, RFR = Random Forest Regression, MANN = Model-Averaged Neural Network, DNN = Deep Neural Network, DBN = Deep Belief Network, RESCNN = Residual Convolutional Neural Network, MLP = Multi-Layer Perceptron, ANN = Artificial Neural Network, LUR = Land Use Regression, LR = Linear Regression, SVM = Support Vector Machine, SVR = Support Vector Regression, NB = Naive Bayes, BR = Bayesian Regression, BN = Bayesian Network, RT = Regression Trees, DT = Decision Trees, GMM = Gaussian Mixture Models, KNN = K-Nearest Neighbours.</p>
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<p>Urban heat island articles’ distribution based on THE first author’s affiliation.</p>
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<p>Flooding algorithm classes count (see <a href="#sec4-remotesensing-16-03374" class="html-sec">Section 4</a>). Complete dataset available in the repository (see <a href="#sec3-remotesensing-16-03374" class="html-sec">Section 3</a>). * ERT = Extremely Randomised Trees, GBDT = Gradient Boosted Decision Trees, AB = AdaBoost, BRT = Boosted Regression Tree, XGB = Extreme Gradient Boosting, RF = Random Forest, CNN = Convolutional Neural Network, DNN = Deep Neural Network, ANN = Artificial Neural Network, SVM = Support Vector Machine, KNN = K-Nearest-Neighbours, NB = Naive Bayes, ADT = Alternating Decision Trees, DT = Decision Trees, GLM = General Linear Model, LOGR = Logistic Regression, CB = CatBoost.</p>
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<p>Flooding articles’ distribution based on the first author’s affiliation.</p>
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<p>Landslide algorithm classes count (see <a href="#sec4-remotesensing-16-03374" class="html-sec">Section 4</a>). Complete dataset available in the repository (see <a href="#sec3-remotesensing-16-03374" class="html-sec">Section 3</a>). * AB = AdaBoost, STACK = stack of multiple models, GBDT = Gradient Boosted Decision Trees, XGB = Extreme Gradient Boosting, RF = Random Forest, SVM = Support Vector Machine, MENT = Maximum Entropy, LOGR = Logistic Regression, RNN = Recurrent Neural Network, DNN = Deep Neural Network, CNN = Convolutional Neural Network, ANN = Artificial Neural Network, NB = Naive Bayes, KNN = K-Nearest Neighbours, DT = Decision Trees.</p>
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<p>Landslide articles’ distribution based on the first author’s affiliation.</p>
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17 pages, 1046 KiB  
Article
Ecomuseums in the Mediterranean Area and the Promotion of Sustainable Food Systems
by Nunzia Borrelli, Lisa N. R. Pigozzi and Giulia Mura
Sustainability 2024, 16(18), 7891; https://doi.org/10.3390/su16187891 - 10 Sep 2024
Viewed by 448
Abstract
In recent years, the growing interest in food as a central component of heritage preservation has been paired with a reflection on the sustainability of food systems. At the same time, place-based food governance has undergone processes of hybridization, opening up to a [...] Read more.
In recent years, the growing interest in food as a central component of heritage preservation has been paired with a reflection on the sustainability of food systems. At the same time, place-based food governance has undergone processes of hybridization, opening up to a wider range of stakeholders. We argue that ecomuseums can positively contribute to the promotion of sustainable food systems that can preserve cultural heritage without undermining the development of healthy food systems. To discuss this hypothesis, we conducted an exploratory study to assess the current diffusion and food-related practices of ecomuseums in the Mediterranean area. Integrating the information of existing databases with online research of new institutions, we mapped a large sample of ecomuseums and carried out a Web Content Analysis. The main results of the research are a geolocalized map of Mediterranean ecomuseums and their activities and an index assessing their capacity to engage users on relevant topics through their webpages. The results highlight the existence of an unbalanced distribution of experiences, and the potential for growth, especially in the east and south of the Mediterranean countries. Full article
(This article belongs to the Special Issue Sustainability in the Food System and Consumption)
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<p>Frequency distribution of the Index of Webpage Engagement Capacity.</p>
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<p>Distribution of ecomuseums in the Mediterranean area, with a focus on their work on food and their efficiency in the use of webpages.</p>
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8 pages, 233 KiB  
Protocol
Women’s Vocalization in the First and Second Stages of Labour: A Scoping Review Protocol
by Isabel Pereira, Maria Correia, Margarida Sim-Sim, Ana Cristina Ferrão and Maria Otília Zangão
Women 2024, 4(3), 332-339; https://doi.org/10.3390/women4030025 - 10 Sep 2024
Viewed by 284
Abstract
Taking into account the growing increase in the political and social interest in childbirth, it is critical to identify and explore the instruments that allow and enhance its humanization today. The use of vocalization seems to be a powerful and empowering tool for [...] Read more.
Taking into account the growing increase in the political and social interest in childbirth, it is critical to identify and explore the instruments that allow and enhance its humanization today. The use of vocalization seems to be a powerful and empowering tool for a positive birthing experience when used by women in labour. A scoping review will be developed to map the evidence and knowledge about women’s vocalization in the first and second stages of labour using the Joanna Briggs Institute methodology. The search will be carried out on the Web of Science, EBSCOhost Research Platform (selecting Academic Search Complete, MedicLatina, Cinahl plus with full text, Medline with full text), Willey Online Library, PubMed and Scopus. The National Register of Theses and Dissertations and the Open Scientific Repository of Portugal will also be taken into account. Three reviewers will conduct data analysis, extraction and synthesis independently. The outcomes pretend to be a source for identifying the use of vocalization by women in labour, in order to guide further research on the subject. This study was prospectively registered with the Open Science Framework on the 21 May 2024, with registration number DOI 10.17605/OSF.IO/Z58F4. Full article
21 pages, 21587 KiB  
Article
SAPERI: An Emergency Modeling Chain for Simulating Accidental Releases of Pollutants into the Atmosphere
by Bianca Tenti, Massimiliano Romana, Giuseppe Carlino, Rossella Prandi and Enrico Ferrero
Atmosphere 2024, 15(9), 1095; https://doi.org/10.3390/atmos15091095 - 9 Sep 2024
Viewed by 273
Abstract
Timely forecast of atmospheric pollutants fallout due to accidental fires can provide decision-makers with useful information for effective emergency response, for planning environmental monitoring and for conveying essential alerts to the population to minimize health risks. The SAPERI project (Accelerated simulation of accidental [...] Read more.
Timely forecast of atmospheric pollutants fallout due to accidental fires can provide decision-makers with useful information for effective emergency response, for planning environmental monitoring and for conveying essential alerts to the population to minimize health risks. The SAPERI project (Accelerated simulation of accidental releases in the atmosphere on heterogeneous platforms—from its Italian initials) implements a modeling chain to quickly supply evidence about the dispersion of pollutants accidentally released in the atmosphere, even in the early stages of the emergency when full knowledge of the incident details is missing. The SAPERI modeling chain relies on SPRAY-WEB, a Lagrangian particle dispersion model openly shared for research purposes, parallelized on a GPU to take advantage of local or cloud computing resources and interfaced with open meteorological forecasts made available by the Meteo Italian SupercompuTing PoRtAL (MISTRAL) consortium over Italy. The operational model provides a quantitative and qualitative estimate of the impact of the emergency event by means of a maximum ground level concentration and a footprint map. In this work, the SAPERI modeling chain is tested in a real case event that occurred in Beinasco (Torino, Italy) in December 2021, mimicking its use with limited or missing local input data as occurs when an alert message is first issued. An evaluation of the meteorology forecast is carried out by comparing the wind and temperature fields obtained from MISTRAL with observations from weather stations. The concentrations obtained from the dispersion model are then compared with the observations at three air quality monitoring stations impacted by the event. Full article
(This article belongs to the Special Issue Development in Atmospheric Dispersion Modelling)
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<p>SAPERI modeling chain flow chart: in blue, blocks concerning the user interface; in red, raw data needed by the model; in orange, data processors and in green, the chain core dispersion model.</p>
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<p>Default time modulation of emissions as processed by SAPEMI on the basis of input data.</p>
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<p>Aerial picture of Torino (Italy) with source location (red dot), meteorological (yellow dots) and air quality (blue dots) stations (source: the Italian geo-cartographic portal).</p>
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<p>Maximum ground level concentration field (<b>left</b>) and footprint map (<b>right</b>) for benzene as simulated in the first run (12–14 December). Concentration levels in <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mspace width="4pt"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>. The coordinate reference system of the x- and y-axes is the UTM 32.</p>
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<p>Maximum ground-level concentration field (<b>left</b>) and footprint map (<b>right</b>) for benzene as simulated in the second run (13–15 December). Concentration levels in <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mspace width="4pt"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>. The coordinate reference system of the x- and y-axes is the UTM 32.</p>
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<p>Maximum ground level concentration field (<b>left</b>) and footprint map (<b>right</b>) for benzene as simulated in the third run (14–16 December). Concentration levels in <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mspace width="4pt"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>. The coordinate reference system of the x- and y-axes is the UTM 32.</p>
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<p>Maximum ground level concentration field (<b>left</b>) and footprint map (<b>right</b>) for benzene as simulated in the fourth run (15–17 December). Concentration levels in <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mspace width="4pt"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>. The coordinate reference system of the x- and y-axes is the UTM 32.</p>
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<p>An example of the temperature and wind fields at 00:00 of 12/12/2021 from the Mistral run 12–14 December.</p>
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<p>Temperature (<b>top</b>), wind speed (<b>middle</b>) and wind direction (<b>bottom</b>) comparisons between COSMO-2I forecasts and observations from the Torino Consolata station. Each row corresponds to a different day of the event, from the <math display="inline"><semantics> <mrow> <mn>12</mn> <mi>th</mi> </mrow> </semantics></math> (<b>top</b>) to the <math display="inline"><semantics> <mrow> <mn>16</mn> <mi>th</mi> </mrow> </semantics></math> (<b>bottom</b>). Grey lines and dots are the measured values, colored lines and dots are the model data; each color corresponds to a different model run.</p>
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<p>Temperature (<b>top</b>), wind speed (<b>middle</b>) and wind direction (<b>bottom</b>) comparisons between COSMO-2I forecasts and observations from the Alenia station. Each row corresponds to a different day of the event, from the <math display="inline"><semantics> <mrow> <mn>12</mn> <mi>th</mi> </mrow> </semantics></math> (<b>top</b>) to the <math display="inline"><semantics> <mrow> <mn>16</mn> <mi>th</mi> </mrow> </semantics></math> (<b>bottom</b>). Grey lines and dots are the measured values, colored lines and dots are the model data; each color corresponds to a different model run.</p>
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<p>Temperature (<b>top</b>), wind speed (<b>middle</b>) and wind direction (<b>bottom</b>) comparisons between COSMO-2I forecasts and observations from the Gorini station. Each row corresponds to a different day of the event, from the <math display="inline"><semantics> <mrow> <mn>12</mn> <mi>th</mi> </mrow> </semantics></math> (<b>top</b>) to the <math display="inline"><semantics> <mrow> <mn>16</mn> <mi>th</mi> </mrow> </semantics></math> (<b>bottom</b>). Grey lines and dots are the measured values, colored lines and dots are the model data; each color corresponds to a different model run.</p>
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<p>qq-plot of the observed and modeled benzene hourly concentration for the air quality stations of Beinasco (TRM)-Aldo Mei (<b>a</b>), Torino-Lingotto (<b>b</b>) and Torino-Rubino (<b>c</b>). Dots are original values, crosses are the normalized values and red lines are the best-fit lines.</p>
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<p>Normalized concentration trends for the four simulations (colored dots) compared with measurements of the air quality stations (grey dots) of Beinasco (TRM)-Aldo Mei (<b>a</b>), Torino-Lingotto (<b>b</b>) and Torino-Rubino (<b>c</b>).</p>
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<p>Scatter plot of the observed and normalized model concentrations of benzene for the air quality stations of Beinasco (TRM)-Aldo Mei (<b>a</b>), Torino-Lingotto (<b>b</b>) and Torino-Rubino (<b>c</b>). Red dotted lines represent the factor of two.</p>
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<p>Dioxins mean concentration: model output and measurement values at Garelli kindergarten in Beinasco. Concentrations expressed as <math display="inline"><semantics> <mrow> <mi>fg</mi> <mspace width="4pt"/> <mi mathvariant="normal">I</mi> <mo>−</mo> <mi>TEQ</mi> <mspace width="4pt"/> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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25 pages, 3148 KiB  
Review
Systematic Mapping of Climate Change Impacts on Human Security in Bangladesh
by Ferdous Sultana, Jan Petzold, Sonali John, Verena Muehlberger and Jürgen Scheffran
Climate 2024, 12(9), 141; https://doi.org/10.3390/cli12090141 - 9 Sep 2024
Viewed by 391
Abstract
There is an increasing consensus that climate change undermines human security by exacerbating existing challenges, acting as a “threat multiplier”. Bangladesh is a climate hot spot due to its geographical location, dense population and vulnerable socio-economic infrastructure. Although there is an increasing number [...] Read more.
There is an increasing consensus that climate change undermines human security by exacerbating existing challenges, acting as a “threat multiplier”. Bangladesh is a climate hot spot due to its geographical location, dense population and vulnerable socio-economic infrastructure. Although there is an increasing number of studies on the impacts of climate change in Bangladesh, aggregated research that combines this evidence and provides a comprehensive overview is lacking. The aim of this research is to thoroughly investigate the characteristics of the academic literature on the complex pathways through which climate variability affects different components of human security in Bangladesh, allowing for determination of the trends and research gaps, as well as whether they lead to conflict or cooperation. We used a systematic mapping methodology to search and screen 1839 publications in Web of Science, including 320 publications for the final synthesis. We found a predominant research focus on rural areas, especially in the southwestern region, with declining crop yield, economic loss, migration, water shortage, food scarcity and health hazards being the highlighted impacts of climate change for Bangladesh. The impacts on food, economic, environmental, health and water security have been well studied, but we found significant research gaps in some human security components, especially energy security. Women and the economically disadvantaged are disproportionately affected, and the causal pathways between conflict or cooperation and the ever-changing climate lack research efforts, implying a dire need to focus on under-researched areas before they become more complex and difficult to address. Policies and interventions should prioritise climate-resilient agricultural practices and infrastructure in high-risk areas, building local capacities and integrating climate risk assessments into urban planning, considering the high influx of environmental migrants. This systematic map provides a foundation for future longitudinal studies, establishes a baseline for this era for future comparisons and serves as a reliable database for relevant stakeholders and policy makers. Full article
(This article belongs to the Special Issue Climate Change Impacts at Various Geographical Scales (2nd Edition))
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<p>Stages of the systematic map, adapted from James, Randall and Haddaway [<a href="#B51-climate-12-00141" class="html-bibr">51</a>].</p>
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<p>ROSES (RepOrting standards for Systematic Evidence Syntheses) flow diagram [<a href="#B62-climate-12-00141" class="html-bibr">62</a>].</p>
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<p>Increasing number of publications related to the topic over the period from 2004 to July 2021.</p>
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<p>Number of publications in the six geographically divided regions.</p>
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<p>Methodology adopted by the included publications.</p>
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<p>Settlement type of the research area of the publications.</p>
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<p>Climate hazards that Bangladeshis face as per the retrieved literature.</p>
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<p>Groups of people disproportionately affected by the impacts of climate change.</p>
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<p>Components of human security negatively affected by the impacts of climate change.</p>
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19 pages, 540 KiB  
Systematic Review
Frailty Detection in Older Adults with Diabetes: A Scoping Review of Assessment Tools and Their Link to Key Clinical Outcomes
by Ernesto Guevara, Andreu Simó-Servat, Verónica Perea, Carmen Quirós, Carlos Puig-Jové, Francesc Formiga and María-José Barahona
J. Clin. Med. 2024, 13(17), 5325; https://doi.org/10.3390/jcm13175325 - 9 Sep 2024
Viewed by 645
Abstract
Objectives: With the increasing prevalence of diabetes and frailty among older adults, there is an urgent need for precision medicine that incorporates comprehensive geriatric assessments, including frailty detection. This scoping review aims to map and synthesize the available evidence on validated tools for [...] Read more.
Objectives: With the increasing prevalence of diabetes and frailty among older adults, there is an urgent need for precision medicine that incorporates comprehensive geriatric assessments, including frailty detection. This scoping review aims to map and synthesize the available evidence on validated tools for detecting pre-frailty and frailty in community-dwelling elderly individuals with diabetes and outpatient diabetes patients. Specifically, it addresses: (1) What validated tools are available for detecting pre-frailty and frailty in this population? (2) How are these tools associated with outcomes such as glycemic control, hypoglycemia, and metabolic phenotypes? (3) What gaps exist in the literature regarding these tools? Methods: The review followed PRISMA-ScR guidelines, conducting a systematic search across PubMed, Cochrane Library, and Web of Science. The inclusion criteria focused on studies involving individuals aged 70 years and older with diabetes, emphasizing tools with predictive capacity for disability and mortality. Results: Eight instruments met the inclusion criteria, including the Frailty Index, Physical Frailty Phenotype, and Clinical Frailty Scale. These tools varied in domains such as physical, psychological, and social aspects of frailty and their association with glycemic control, hypoglycemia, and metabolic phenotypes. The review identified significant gaps in predicting diabetes-related complications and their clinical application. Conclusions: Routine management of older adults with diabetes should incorporate frailty detection, as it is crucial for their overall health. Although widely used, the reviewed tools require refinement to address the unique characteristics of this population. Developing tailored instruments will enhance precision medicine, leading to more effective, individualized interventions for elderly individuals with diabetes. Full article
(This article belongs to the Section Endocrinology & Metabolism)
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<p>PRISMA flow diagram.</p>
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14 pages, 1078 KiB  
Review
Prevention and Management of Malnutrition in Patients with Chronic Obstructive Pulmonary Disease: A Scoping Review
by Stefano Mancin, Sara Khadhraoui, Erica Starace, Simone Cosmai, Fabio Petrelli, Marco Sguanci, Giovanni Cangelosi and Beatrice Mazzoleni
Adv. Respir. Med. 2024, 92(5), 356-369; https://doi.org/10.3390/arm92050034 - 6 Sep 2024
Viewed by 343
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is linked to altered nutritional status due to increased catabolism, leading to muscle mass loss. This study aims to identify and map available evidence regarding multidisciplinary interventions focused on prevention, diagnosis and nutrition education, as well as [...] Read more.
Background: Chronic obstructive pulmonary disease (COPD) is linked to altered nutritional status due to increased catabolism, leading to muscle mass loss. This study aims to identify and map available evidence regarding multidisciplinary interventions focused on prevention, diagnosis and nutrition education, as well as the role of diet, to prevent and manage malnutrition in patients with COPD. Methods: A scoping review was conducted using the Cochrane, PubMed/Medline, CINAHL, Embase, Scopus, and Web of Science databases. This study adhered to the Arksey and O’Malley framework and JBI methodology. Results: Of the 1761 records identified, 15 were included. Evidence suggests that the Malnutrition Universal Screening Tool and Mini Nutritional Assessment are the most suitable screening scale. Guidelines have highlighted that personalized nutritional counseling is a very common intervention as it allows for a consideration of all physical, psychological, and social aspects of the patient. Conclusions: The role of healthcare professionals is crucial in the early identification of nutrition-related issues and in educating patients about the prevention and management of malnutrition, both in hospital and community settings. Key aspects include early malnutrition detection, personalized counseling and patient education, and a multidisciplinary approach. These findings provide a foundation for developing of targeted patient educational initiatives to improve the nutritional management of COPD patients. Full article
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<p>PRISMA-ScR flow chart of study selection process.</p>
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<p>Pathophysiology of malnutrition in COPD. The figure illustrates the pathogenetic factors contributing to the development of malnutrition in patients with COPD.</p>
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<p>Multidisciplinary dietary counselling. the figure depicts the stages of multidisciplinary dietary treatment in patients with COPD.</p>
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24 pages, 3455 KiB  
Systematic Review
Competitive Advantages of Sustainable Startups: Systematic Literature Review and Future Research Directions
by Adriano Martins de Souza, Fabio Neves Puglieri and Antonio Carlos de Francisco
Sustainability 2024, 16(17), 7665; https://doi.org/10.3390/su16177665 - 4 Sep 2024
Viewed by 780
Abstract
Growing awareness of environmental, social and governance (ESG) issues drives a significant transformation in the global business environment, making sustainability an urgent necessity and a source of competitive advantage. However, despite advances in research, there are still significant gaps in how these practices [...] Read more.
Growing awareness of environmental, social and governance (ESG) issues drives a significant transformation in the global business environment, making sustainability an urgent necessity and a source of competitive advantage. However, despite advances in research, there are still significant gaps in how these practices can confer competitive advantages to startups. We seek to fill this gap by conducting a systematic literature review on the competitive advantages of sustainable startups. We used the PRISMA 2020 protocol to conduct a comprehensive search in the Scopus and Web of Science databases, which led to the inclusion of 44 articles in the final review. The results indicate that sustainable startups align economic and environmental benefits, promote continuous innovation, attract investment, mitigate regulatory risks, and adapt quickly to market changes. The analysis reveals that adopting advanced technologies and circularity strategies is critical to operational efficiency and regulatory compliance. In addition, this study has mapped gaps in the literature, identifying key areas for future research into the competitive advantages of sustainable startups. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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<p>PRISMA flow diagram for systematic reviews (based on Page et al. [<a href="#B23-sustainability-16-07665" class="html-bibr">23</a>]).</p>
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<p>Annual distribution of publications (own elaboration).</p>
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<p>Co-author network analysis (own elaboration).</p>
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<p>Co-author analysis overlay visualization (own elaboration).</p>
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<p>Collaboration network of countries (own elaboration).</p>
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<p>Collaboration overlay visualization of countries (own elaboration).</p>
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<p>Co-occurrence analysis of all keywords (own elaboration).</p>
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<p>Keyword co-occurrence overlay visualization (own elaboration).</p>
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<p>Research methodologies (own elaboration).</p>
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16 pages, 1686 KiB  
Review
Advancements in Phytoremediation Research in South Africa (1997–2022)
by Enoch Akinbiyi Akinpelu and Felix Nchu
Appl. Sci. 2024, 14(17), 7660; https://doi.org/10.3390/app14177660 - 29 Aug 2024
Viewed by 1096
Abstract
Several mining-related pollutions, industrial waste, and soil deterioration define South Africa’s environmental landscape. These have led to the consumption of unhealthy food, contaminated agricultural products, and polluted water. The polluted environment has been linked to numerous diseases among the populace, thus making environmental [...] Read more.
Several mining-related pollutions, industrial waste, and soil deterioration define South Africa’s environmental landscape. These have led to the consumption of unhealthy food, contaminated agricultural products, and polluted water. The polluted environment has been linked to numerous diseases among the populace, thus making environmental remediation an important issue in South Africa. Phytoremediation has been identified as a biological method for the restoration of polluted environments naturally and holistically. Therefore, it is vital to evaluate the level of phytoremediation-related research in South Africa in pursuit of a way out of environmental pollution. Thus, the purpose of this study was to map phytoremediation-related research in South Africa from inception to 2022. Statistical records from the Web of Science Core Collection were analyzed with the bibliometric package in RStudio, while mapping was performed via VOSviewer. Our study showed a low annual growth rate of publication (4.49%). The analysis uncovered that the 39 documents analyzed were written by 112 authors, and the first document was featured in the Journal of Geochemical Exploration in 1997. Kirkham, MB and Liphadzi, MS are the most relevant authors. USA has the strongest collaboration with South Africa, while the International Journal of Phytoremediation, the South African Journal of Botany, and Water SA are the most relevant journals. The result of this study can guide upcoming researchers and policymakers, together with essential facts for enhancing the restoration of the polluted environment in the country. Full article
(This article belongs to the Special Issue Waste Treatment and Sustainable Technologies)
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<p>Annual publication and mean citation per annum on phytoremediation-related research in South Africa since inception to 2022.</p>
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<p>Co-occurrence of authors’ keywords (<b>a</b>) and word cloud (<b>b</b>) in phytoremediation-related research in South Africa.</p>
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<p>Collaboration network of South African researchers with other countries (<b>a</b>) and the country collaboration map (<b>b</b>) in phytoremediation-related research in South Africa.</p>
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<p>Collaboration network of South African researchers with other countries (<b>a</b>) and the country collaboration map (<b>b</b>) in phytoremediation-related research in South Africa.</p>
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<p>Collaboration of authors in phytoremediation-related research in South Africa.</p>
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<p>Bibliographic coupling analysis of journals.</p>
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11 pages, 714 KiB  
Review
Educommunication in Nutrition and Neurodegenerative Diseases: A Scoping Review
by Karla Mônica Dantas Coutinho, Sancha Helena de Lima Vale, Manacés dos Santos Bezerril, Mônica Karina Santos Reis, Almudena Muñoz Gallego, Karilany Dantas Coutinho, Ricardo Valentim, Lucia Leite-Lais and Kenio Costa de Lima
Int. J. Environ. Res. Public Health 2024, 21(8), 1113; https://doi.org/10.3390/ijerph21081113 - 22 Aug 2024
Viewed by 762
Abstract
Neurodegenerative diseases significantly impact individuals’ nutritional status. Therefore, nutritional education plays a crucial role in enhancing the understanding of food and nutrition, preventing or minimizing malnutrition, promoting well-being, and empowering patients and caregivers. Educommunication is a methodology that utilizes communication as a pedagogical [...] Read more.
Neurodegenerative diseases significantly impact individuals’ nutritional status. Therefore, nutritional education plays a crucial role in enhancing the understanding of food and nutrition, preventing or minimizing malnutrition, promoting well-being, and empowering patients and caregivers. Educommunication is a methodology that utilizes communication as a pedagogical tool, with the potential to positively enhance the teaching–learning process. This study aims to identify and map educommunication strategies designed to educate caregivers and patients with neurodegenerative diseases about food and nutrition. Methods: This scoping review followed the JBI Institute Reviewer’s Manual. The search was conducted between June 2022 and March 2023 in databases including PubMed/MEDLINE, Embase, Scopus, and Web of Science. Results: Out of 189 studies identified, 29 met the eligibility criteria, and only 3 were suitable for inclusion in this review. Conclusion: Studies using educommunication for food and nutrition education are scarce. Despite the limited number of studies included in this review, various educommunication strategies utilizing communication and information technologies were used. Educommunication strategies can facilitate knowledge acquisition in food and nutrition and change behaviors, resulting in health benefits for the participants. More studies on this subject are needed. Full article
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<p>Flow diagram showing the scoping review searching and screening processes.</p>
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41 pages, 11807 KiB  
Review
Optimization Control Strategies and Evaluation Metrics of Cooling Systems in Data Centers: A Review
by Qiankun Chang, Yuanfeng Huang, Kaiyan Liu, Xin Xu, Yaohua Zhao and Song Pan
Sustainability 2024, 16(16), 7222; https://doi.org/10.3390/su16167222 - 22 Aug 2024
Viewed by 1252
Abstract
In the age of digitalization and big data, cooling systems in data centers are vital for maintaining equipment efficiency and environmental sustainability. Although many studies have focused on the classification and optimization of data center cooling systems, systematic reviews using bibliometric methods are [...] Read more.
In the age of digitalization and big data, cooling systems in data centers are vital for maintaining equipment efficiency and environmental sustainability. Although many studies have focused on the classification and optimization of data center cooling systems, systematic reviews using bibliometric methods are relatively scarce. This review uses bibliometric analysis to explore the classifications, control optimizations, and energy metrics of data center cooling systems, aiming to address research gaps. Using CiteSpace and databases like Scopus, Web of Science, and IEEE, this study maps the field’s historical development and current trends. The findings indicate that, firstly, the classification of cooling systems, optimization strategies, and energy efficiency metrics are the current focal points. Secondly, this review assesses the applicability of air-cooled and liquid-cooled systems in different operational environments, providing practical guidance for selection. Then, for air cooling systems, the review demonstrates that optimizing the design of static pressure chamber baffles has significantly improved airflow uniformity. Finally, the article advocates for expanding the use of artificial intelligence and machine learning to automate data collection and energy efficiency analysis, it also calls for the global standardization of energy efficiency metrics. This study offers new perspectives on the design, operational optimization, and performance evaluation of data center cooling systems. Full article
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<p>Paper search flowchart.</p>
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<p>Proportion of selected studies in search databases.</p>
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<p>Publication year trends.</p>
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<p>Keyword co-occurrence graph from Scopus.</p>
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<p>Keyword co-occurrence graph from Scopus.</p>
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<p>Keyword co-occurrence graph from Web of Science.</p>
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<p>Temporal clustering of keywords in Scopus.</p>
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<p>Journal publication year bubble chart.</p>
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<p>High-publishing countries graph.</p>
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<p>Country co-occurrence map.</p>
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<p>Issuing authority co-occurrence map.</p>
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<p>Co-citation network of Scopus papers.</p>
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<p>Keyword burstness graph.</p>
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<p>Components of data center energy consumption.</p>
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<p>Basic mechanism of liquid cooling technology.</p>
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<p>Basic mechanism of air cooling technology.</p>
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<p>Schematic diagrams of different microchannel structures.</p>
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