Search Results (6,772)

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km². The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m³ per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)₁₂ model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m³ (13 ± 93). A SARIMA (2,1,0)(2,0,0)₁₂ model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m³ and exhibited an SARIMA (0,1,1)(2,0,0)₁₂ model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends. Full article

Multi-spectral temperature measurement technology has been found to have extensive applications in engineering practice. Addressing the challenges posed by unknown emissivity in multi-spectral temperature measurement data processing, this paper adds emissivity constraints to the objective function. It proposes a multi-spectral radiation temperature measurement data processing model realized through a particle swarm optimization algorithm improved based on multiple strategies. This paper simulates six material models with distinct emissivity trends. The simulation results indicate that the algorithm calculates an average relative temperature error of less than 0.3%, with an average computation time of merely 0.24 s. When applied to the temperature testing of silicon carbide and tungsten, experimental data further confirmed its accuracy: the absolute temperature error for silicon carbide (tungsten) is less than 4 K (7 K), and the average relative error is below 0.4% (0.3%), while two materials maintain an average computation time of 0.33 s. In summary, the improved particle swarm optimization algorithm demonstrates strong performance and high accuracy in multi-spectral radiation thermometry, making it a feasible solution for addressing multi-spectral temperature measurement challenges in practical engineering applications. Additionally, it can be extended to other multi-spectral systems. Full article

Understanding the spatial variability and driving mechanisms of humus horizon thickness (HHT) degradation is crucial for effective soil degradation prevention in black soil regions. The study compared ordinary kriging interpolation (OK), inverse distance weighted interpolation (IDW), and regression kriging interpolation (RK) using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and relative RMSE to select the most accurate model. Environmental variables were then integrated to predict HHT characteristics. Results indicate that: (1) RK was superior to OK and IDW in characterizing HHT with the smallest ME (11.45), RMSE (14.98), MAE (11.45), and RRMSE (0.44). (2) The average annual temperature (0.29), precipitation (0.27), and digital elevation model (DEM) (0.21) were the primary factors influencing the spatial variability of HHT. (3) The HHT exhibited notable variability, with an increasing trend from the southeast towards the central and northern directions, being the thinnest in the southeast. It was thicker in the northeast and southwest regions, thicker but less dense along the southern Bohai coast, thicker yet sporadically distributed in the northwest (especially Chaoyang and Fuxin), and thick with aggregated distribution over a smaller area in the northeastern direction (e.g., Tieling). These findings provide a scientific basis for accurate soil management in Liaoning Province. Full article

ZG25MnCrNiMo steel samples were prepared by squeeze casting under pressure ranging from 0 to 150 MPa. The effects of pressure on the microstructure, low-temperature toughness, hardness, and impact wear performance of the prepared steels were experimentally investigated. The experimental results indicated that the samples fabricated under pressure exhibited finer grains and a significant ferrite content compared to those produced without pressure. Furthermore, the secondary dendrite arm spacing of the sample produced at 150 MPa decreased by 45.3%, and the ferrite content increased by 39.1% in comparison to the unpressurized sample. The low-temperature impact toughness of the steel at −40 °C initially increased and then decreased as the pressure varied from 0 MPa to 150 MPa. And the toughness achieved an optimal value at a pressure of 30 MPa, which was 65.4% greater than that of gravity casting (0 MPa), while the hardness decreased by only 6.17%. With a further increase in pressure, the impact work decreased linearly while the hardness increased slightly. Impact fracture analysis revealed that the fracture of the steel produced without pressure exhibited a quasi-cleavage morphology. The samples prepared by squeeze casting under 30 MPa still exhibited a large number of fine dimples even at −40 °C, indicative of ductile fracture. In addition, the impact wear performance of the steels displayed a trend of initially decreasing and subsequently increasing across the pressure range of 0–150 MPa. The wear resistance of samples prepared without pressure and at 30 MPa was superior to that at 60 MPa, and the wear resistance deteriorated when the pressure increased to 60 MPa, after which it exhibited an upward trend as the pressure continued to rise. The wear mechanisms of the samples predominantly consisted of impact wear, adhesive wear, and minimal abrasive wear, along with notable occurrences of plastic removal, furrows, and spalling. Full article

The intense changes in glaciers in the southeastern Tibetan Plateau (SETP) have essential impacts on regional water resource management. In order to study the seasonal fluctuations of glaciers in this region and their relationship with climate change, we focus on the Yigong Zangbo River Basin in the SETP, extract the annual and seasonal variations of glaciers in the basin during 2018–2023, and analyze their spatio-temporal characteristics through the seasonal-trend decomposition using the LOESS (STL) method. Finally, combining the Extreme Gradient Boosting (XGBoost) model and the Shapley additive explanations (SHAP) model, we assess the comprehensive impact of meteorological factors such as temperature and snowfall on glacier changes. The results indicate that glaciers in the Yigong Zangbo River Basin experienced remarkable mass loss during 2018–2023, with an average annual melting rate of −0.83 ± 0.12 m w.e.∙yr⁻¹. The glacier mass exhibits marked seasonal fluctuations, with increases in January–March (JFM) and April–June (AMJ) and noticeable melting in July–September (JAS) and October–December (OND). The changes over these four periods are 2.12 ± 0.04 m w.e., 0.93 ± 0.15 m w.e., −1.58 ± 0.19 m w.e., and −1.32 ± 0.17 m w.e., respectively. Temperature has been identified as the primary meteorological driver of glacier changes in the study area, surpassing the impact of snowfall. This study uses advanced altimetry data and meteorological data to monitor and analyze glacier changes, which provides valuable data for cryosphere research and also validates a set of replicable research methods, which provides support for future research in related fields. Full article

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), permanganate index (COD_Mn), and 5-day biochemical oxygen demand (BOD₅)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH₃-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment. Full article

It is important to determine the ventilation required in the construction of deep and long tunnels and the variation law of tunnel temperature fields to reduce the numbers of high-temperature disasters and serious accidents. Based on a tunnel project with a high ground temperature, with the help of convection heat transfer theory and the theoretical analysis and calculation method, this paper clarifies the contribution of various heat sources to the air demand during tunnel construction, and reveals the important environmental parameters that determine the ventilation value by changing the construction conditions. The results show that increasing the fresh air temperature greatly increases the required air volume, and the closer the supply air temperature is to 28 °C, the more the air volume needs to be increased. The air temperature away from the palm face is not significantly affected by changes in the supply air temperature. Adjusting the wall temperature greatly accelerates the rate of temperature growth. The supply air temperature rose from 15 to 25 °C, while the tunnel temperature at 800 m only increased by 1.5 °C. Over a 50 m range, the wall temperature rose from 35 to 60 degrees Celsius at a rate of 0.0842 to 0.219 degrees Celsius per meter. The total air volume rises and the surface heat transfer coefficient decreases as the tunnel’s cross-section increases. For every 10 m increase in the tunnel diameter, the temperature at 800 m from the tunnel face drops by about 0.5 °C. Changing the distance between the air duct and the tunnel face has little influence on the temperature distribution law. The general trend is that the farther the air duct outlet is from the tunnel face, the higher the temperature is, and the maximum difference is within the range of 50 m~250 m from the tunnel face. The maximum difference between the air temperatures at 12 m and 27 m is 0.79 °C. The geological structure and geothermal background have the greatest influence on the temperature prediction of high geothermal tunnels. The prediction results are of great significance for guiding tunnel construction, formulating cooling measures, and ensuring construction safety. Full article

Light use efficiency (LUE) characterizes the efficiency of vegetation in converting photosynthetically active radiation (PAR) into biomass energy through photosynthesis and is a critical parameter for gross primary productivity (GPP) in terrestrial ecosystems. Based on the eddy covariance measurements of a Chinese cork oak plantation ecosystem in northern China, the temporal variations in LUE were investigated, and biophysical factors were examined at time scales ranging from hours to years. Our results show that diurnal LUE first increased sharply before 8:30 and then decreased gradually until 12:00, thereafter increasing gradually and reaching the maximum value at sunset during the growing season. The daily and monthly LUE first increased and then decreased within a year and showed a substantial drop around June. The annual LUE ranged from 0.09 to 0.17 g C mol photon⁻¹, and the multiyear mean maximal LUE was 0.30 g C mol photon⁻¹ during 2006–2019. Only GPP (positive) and clearness index (CI) (negative) had consistent effects on LUE at different time scales, and the effects of the remaining biophysical factors on LUE were different as the time scale changed. The effects of air temperature, vapor pressure deficit, precipitation, evaporative fraction, and normalized difference vegetation index on LUE were mainly indirect (via PAR and/or GPP). When CI decreased, an increased ratio of diffuse PAR to PAR produced a more uniform irradiance in the canopy, which ultimately resulted in a higher LUE. Due to climate change in our study area, the annual LUE may decrease in the future but improving management practices may slow or even reverse this trend in the annual LUE in the studied Chinese cork oak plantation. Full article

The friction/adhesion between the tool and chip is generally large in metal cutting, and it causes many problems such as high cutting energy/rough surface finish. To suppress this, cutting fluid and tool coating are used in practice, but they are high in energy/cost and environmentally unfriendly. Therefore, this paper investigates the extraordinarily high-speed cutting (EHS cutting) mechanics of mainly soft and highly heat-conductive materials and proposes their application to solve the friction/adhesion problem in an environmentally friendly manner. In order to clarify the EHS cutting mechanics, a simple analytical model is constructed and experiments are conducted with measurement of the cutting temperature and forces. As a result, the following points are clarified/found: (1) heat softening at the secondary plastic deformation zone rather than the primary plastic deformation zone, (2) friction coefficient drop to 0.170 in EHS cutting, and (3) gradually increasing trend of cutting temperature in EHS cutting. Finally, EHS cutting is applied to dry cutting of aluminum alloys with a non-coated carbide tool and compared to conventional wet cutting with a DLC-coated carbide tool, and it is shown that a coating/coolant can be omitted in this region to achieve environmentally friendly cutting. Full article

For advanced aircraft, the temperature environment inside the cabin is very severe due to the high flight speed and the compact concentration of the electronic equipment in the cabin. Accurately predicting the temperature environment induced inside the cabin during the flight of the aircraft can determine the temperature environment requirements of the onboard equipment inside the cabin and provide an accurate input for the thermal design optimization and test verification of the equipment. The temperature environment of the whole aircraft is divided into zones by the cluster analysis method; the heat transfer mechanism of the aircraft cabin is analyzed for the target area; and the influence of internal and external factors on the thermal environment is considered to establish the temperature environment prediction model of the target cabin. The coefficients of the equations in the model are parameterized to extract the long-term stable terms and trend change terms; with the help of the measured data of the flight state, the model coefficients are determined by a stepwise regression method; and the temperature value inside the aircraft cabin is the output by inputting parameters such as flight altitude, flight speed, and external temperature. The model validation results show that the established temperature environment prediction model can accurately predict the change curve of the cabin temperature during the flight of the aircraft, and the model has a good follow-up performance, which reduces the prediction error caused by the temperature hysteresis effect. For an aircraft, the estimated error is 2.8 °C at a confidence level of 95%. Engineering cases show that the application of this method can increase the thermal design requirements of the airborne equipment by 15 °C, increase the low-temperature test conditions by 17 °C, and avoid the problems caused by an insufficient design and over-testing. This method can accurately predict the internal temperature distribution of the cabin during the flight state of the aircraft, help designers determine the thermal design requirements of the airborne equipment, modify the thermal design and temperature test profile, and improve the environmental worth of the equipment. Full article

Xinjiang is an important forest and fruit production area in China, and Cytospora canker, caused by the genus Cytospora Ehrenb., has caused serious losses to forestry production in Xinjiang. In this study, we constructed ensemble models based on Biomod2 to assess the potential geographical distribution of Cytospora chrysosperma, C. nivea, and C. mali in Xinjiang, China and their changes under different climate change scenarios, using species occurrence data and four types of environmental variables: bioclimatic, topographic, NDVI, and soil. The model performance assessment metrics (AUC and TSS) indicated that the ensemble models are highly reliable. The results showed that NDVI had the most important effect on the distribution of all three species, but there were differences in the response patterns, and bioclimatic factors such as temperature and precipitation also significantly affected the distribution of the three species. C. chrysosperma showed the broadest ecological adaptation and the greatest potential for expansion. C. nivea and C. mali also showed expansion trends, but to a lesser extent. The overlapping geographical distribution areas of the three species increased over time and with an intensification of the climate scenarios, especially under the high-emission SSP585 scenario. The centroids of the geographical distribution for all three species generally shifted towards higher latitude regions in the northeast, reflecting their response to climate warming. C. chrysosperma may become a more prevalent forest health threat in the future, and an increase in the overlapping geographical distribution areas of the three species may lead to an increased risk of multiple infections. These findings provide an important basis for understanding and predicting the distribution and spread of the genus Cytospora in Xinjiang and are important for the development of effective forest disease prevention and control strategies. Full article

Black locust (Robinia pseudoacacia L.), one of the major afforestation species adopted in vegetation restoration, is notable for its rapid root growth and drought resistance. It plays a vital role in improving the natural environment and soil fertility, contributing significantly to soil and water conservation and biodiversity protection. However, compared with natural forests, due to the low diversity, simple structure and poor stability, planted forests including Robinia pseudoacacia L. are more sensitive to the changing climate, especially in the aspects of growth trend and adaptive range. Studying the ecological characteristics and geographical boundaries of Robinia pseudoacacia L. is therefore important to explore the adaptation of suitable niches to climate change. Here, based on 162 effective distribution records in China and 22 environmental variables, the potential distribution of suitable niches for Robinia pseudoacacia L. plantations in past, present and future climates was simulated by using a Maximum Entropy (MaxEnt) model. The results showed that the accuracy of the MaxEnt model was excellent and the area under the curve (AUC) value reached 0.937. Key environmental factors constraining the distribution and suitable intervals were identified, and the geographical distribution and area changes of Robinia pseudoacacia L. plantations in future climate scenarios were also predicted. The results showed that the current suitable niches for Robinia pseudoacacia L. plantations covered 9.2 × 10⁵ km², mainly distributed in the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula, and Liaodong Peninsula. The main environmental variables constraining the distribution included the mean temperature of the driest quarter, precipitation of driest the quarter, temperature seasonality and altitude. Among them, the temperature of the driest quarter was the most important factor. Over the past 90 years, the suitable niches in the Sichuan Basin and Yunnan–Guizhou Plateau have not changed significantly, while the suitable niches north of the Qinling Mountains have expanded northward by 2° and the eastern area of Liaoning Province has expanded northward by 1.2°. In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change. Full article

A series of partially bio-based and biodegradable poly(propylene terephthalate-co-adipate) (PPTA) random copolymers with different components were prepared by the melt polycondensation of petro-based adipic acid and terephthalic acid with bio-based 1,3-propanediol. The microstructure, crystallization behavior, thermal properties, and enzymatic degradation properties were further investigated. The thermal decomposition kinetics was deeply analyzed using Friedman’s method, with the thermal degradation activation energy ranging from 297.8 to 302.1 kJ/mol. The crystallinity and wettability of the copolymers decreased with the increase in the content of the third unit, but they were lower than those of the homopolymer. The thermal degradation activation energy E, carbon residue, and reaction level n all showed a decreasing trend. Meanwhile, the initial thermal decomposition temperature (T_d) was higher than 350 °C, which can meet the requirements for processing and use. The PPTA copolymer material still showed excellent thermal stability. Adding PA units could regulate the crystallinity, wettability, and degradation rate of PPTA copolymers. The composition of PPTA copolymers in different degradation cycles was characterized by ¹H NMR analysis. Further, the copolymers’ surface morphology during the process of enzymatic degradation also was observed by scanning electron microscopy (SEM). The copolymers’ enzymatic degradation accorded with the surface degradation mechanism. The copolymers showed significant degradation behavior within 30 days, and the rate increased with increasing PA content when the PA content exceeded 45.36%. Full article

To address the lack of reliable measurement methods for identifying wear mechanisms and predicting the state of mechanical seal tribo-parts, this study proposes a method for characterizing tribological behavior based on measuring face vibration acceleration. It aims to uncover the source mechanism of mechanical seal face vibration acceleration influenced by tribology and dynamic behavior. This research delves into the dynamic behavior characteristics and vibration acceleration of the mechanical seal stationary ring. We explored the variation pattern of face vibration acceleration root mean square (RMS) with rotation speed, sealing medium pressure, and face surface roughness. The results indicate that under constant medium pressure, an increase in rotation speed leads to a decrease in acceleration RMS and an increase in face temperature. Similarly, under constant rotation speed, an increase in medium pressure results in nonlinear changes in acceleration RMS, forming an “M” shape, along with an increase in face temperature. Furthermore, under conditions of constant medium pressure and rotation speed, an increase in the surface roughness of the rotating ring face corresponds to an increase in acceleration RMS and face temperature. Upon starting the mechanical seal, both acceleration RMS and temperature initially increase before decreasing, a trend consistent with the Stribeck curve. Full article

Floods in Germany have become increasingly frequent and severe over recent decades, with notable events in 2002, 2013, and 2021. This study examines the trends and drivers of flood occurrences in Germany from 1990 to 2024, focusing on the influence of climate-change-related variables, such as temperature, precipitation, and river discharge. Using a comprehensive time series analysis, including Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models and correlation and regression analyses, we identify significant correlations between these climatic variables and flood events. Our findings indicate that rising temperatures (with a mean of 8.46 °C and a maximum of 9 °C) and increased precipitation (averaging 862.26 mm annually)are strongly associated with higher river discharge (mean 214.6 m³/s) and more frequent floods (mean 197.94 events per year). The ANN model outperformed the ARIMA model in flood forecasting, showing lower error metrics (e.g., RMSE of 10.86 vs. 18.83). The analysis underscores the critical impact of climate change on flood risks, highlighting the necessity of adaptive flood-management strategies that incorporate the latest climatic and socio-economic data. This research contributes to the understanding of flood dynamics in Germany and provides valuable insights into future flood risks. Combining flood management with groundwater recharge could effectively lower flood risks and enhance water resources’ mitigation and management. Full article