Search Results (719)

With the acceleration of urbanization in China, water resources have become a key factor restricting regional sustainable development. Current research primarily examines the temporal or spatial variations in the water resources ecological footprint (WREF), with limited emphasis on the integration of both spatial and temporal scales. In this study, we collected the data and information from the 2005–2022 Statistical Yearbook and Water Resources Bulletin of the Yangtze River Delta Urban Agglomeration (YRDUA), and calculated evaluation indicators: WREF, water resources ecological carrying capacity (WRECC), water resources ecological pressure (WREP), and water resources ecological surplus and deficit (WRESD). We primarily analyzed the temporal and spatial variation in the per capita WREF and used the method of Geodetector to explore factors driving its temporal and spatial variation in the YRDUA. The results showed that: (1) From 2005 to 2022, the per capita WREF (total water, agricultural water, and industrial water) of the YRDUA generally showed fluctuating declining trends, while the per capita WREF of domestic water and ecological water showed obvious growth. (2) The per capita WREF and the per capita WRECC were in the order of Jiangsu Province > Anhui Province > Shanghai City > Zhejiang Province. The spatial distribution of the per capita WREF was similar to those of the per capita WRECC, and most areas effectively consume water resources. (3) The explanatory power of the interaction between factors was greater than that of a single factor, indicating that the spatiotemporal variation in the per capita WREF of the YRDUA was affected by the combination of multiple factors and that there were regional differences in the major factors in the case of secondary metropolitan areas. (4) The per capita WREF of YRDUA was affected by natural resources, and the impact of the ecological condition on the per capita WREF increased gradually over time. The impact factors of secondary metropolitan areas also clearly changed over time. Our results showed that the ecological situation of per capita water resources in the YRDUA is generally good, with obvious spatial and temporal differences. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

Traditional Chinese villages, which have long supported villagers’ comfort level of daily activities, are increasingly affected by global climate change and rural reconstruction, prompting growing research interest in their outdoor microclimate design. This systematic review aims to synthesize and evaluate the outdoor microclimate spatial design mechanism studies in traditional Chinese villages noted for their uniqueness and complexity. Following the PRISMA method, this study was carried out on November 27, 2024, by retrieving studies from the Scopus and CNKI databases and applying predefined inclusion and exclusion criteria; 42 empirical studies were systematically reviewed. It identifies current research trends, summarizes concepts, frameworks, indicators, and methodologies with a focus on the design mechanisms considering scales, contexts, and user groups, and outlines directions for future research. The findings reveal a growing number of publications, with case studies predominantly concentrated on three concepts: physical microclimates, human comfort, and behavioral responses, characterized as distributed in south-east areas. Based on these concepts and their correlations, this study proposes a classification framework based on multiple scales, contexts, and user groups. Within this framework, the study found that relative humidity and PET (physiological equivalent temperature) emerge as the most commonly used indicators, while field measurements, simulations, surveys, and observations are identified as the primary methods. The review further reveals that unique outdoor spatial design characteristics shape physical microclimates, human comfort, and behavior indicators influenced by contexts and users from the macro to the micro scale. Future research should advance existing studies by enriching the current contextual framework and explore more microclimatic factors. This review offers a comprehensive overview and actionable insights for outdoor microclimate design, policymaking, and the promotion of climate adaptation and villagers’ public health in different traditional rural settings. Full article

Background: Despite the growing demand for amyloid PET quantification, practical challenges remain. As automated software platforms are increasingly adopted to address these limitations, we evaluated the reliability of commercial tools for Centiloid quantification against the original Centiloid Project method. Methods: This retrospective study included 332 amyloid PET scans (165 [¹⁸F]Florbetaben; 167 [¹⁸F]Flutemetamol) performed for suspected mild cognitive impairments or dementia, paired with T1-weighted MRI within one year. Centiloid values were calculated using three automated software platforms, BTXBrain, MIMneuro, and SCALE PET, and compared with the original Centiloid method. The agreement was assessed using Pearson’s correlation coefficient, the intraclass correlation coefficient (ICC), a Passing–Bablok regression, and Bland–Altman plots. The concordance with the visual interpretation was evaluated using receiver operating characteristic (ROC) curves. Results: BTXBrain (R = 0.993; ICC = 0.986) and SCALE PET (R = 0.992; ICC = 0.991) demonstrated an excellent correlation with the reference, while MIMneuro showed a slightly lower agreement (R = 0.974; ICC = 0.966). BTXBrain exhibited a proportional underestimation (slope = 0.872 [0.860–0.885]), MIMneuro showed a significant overestimation (slope = 1.053 [1.026–1.081]), and SCALE PET demonstrated a minimal bias (slope = 1.014 [0.999–1.029]). The bias pattern was particularly noted for FMM. All platforms maintained their trends for correlations and biases when focusing on subthreshold-to-low-positive ranges (0–50 Centiloid units). However, all platforms showed an excellent agreement with the visual interpretation (areas under ROC curves > 0.996 for all). Conclusions: Three automated platforms demonstrated an acceptable reliability for Centiloid quantification, although software-specific biases were observed. These differences did not impair their feasibility in aiding the image interpretation, as supported by the concordance with visual readings. Nevertheless, users should recognize the platform-specific characteristics when applying diagnostic thresholds or interpreting longitudinal changes. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

In Japan’s hilly and mountainous areas, which cover over 60% of the national land area, issues such as population outflow, aging, and regional decline are intensifying. This study explored sustainable decarbonization pathways by examining two representative regions (Maniwa City and Hidakagawa Town), while accounting for diverse regional characteristics. A bottom-up approach was adopted to calculate energy consumption and CO₂ emissions within residential and transportation sectors. Six future scenarios were developed to evaluate emission trends and countermeasure effectiveness in different regions. The key findings are as follows: (1) in the study areas, complex regional issues have resulted in relatively high current levels of CO₂ emissions in these sectors, and conditions may worsen without intervention; (2) if the current trends continue, per-capita CO₂ emissions in both regions are projected to decrease by only around 40% by 2050 compared to 2020 levels; (3) under enhanced countermeasure scenarios, CO₂ emissions could be reduced by >99%, indicating that regional decarbonization is achievable. This study provides reliable information for designing localized sustainability strategies in small-scale, under-researched areas, while highlighting the need for region-specific countermeasures. Furthermore, the findings contribute to the realization of multiple Sustainable Development Goals (SDGs), particularly goals 7, 11, and 13. Full article

In both scientific and industrial fields, there has been a notable increase in attention toward High-Altitude Pseudo-Satellites (HAPSs) in recent years. This surge is driven by their distinct advantages over traditional satellites and Remotely Piloted Aircraft Systems (RPASs). These benefits are particularly evident in critical areas such as intelligent transportation systems, surveillance, remote sensing, traffic and environmental monitoring, emergency communications, disaster relief efforts, and the facilitation of large-scale temporary events. This review provides an overview of key aspects related to the propellers and propulsion systems of HAPSs. To date, propellers remain the most efficient means of propulsion for high-altitude applications. However, due to the unique operational conditions at stratospheric altitudes, propeller design necessitates specific approaches that differ from those applied in conventional applications. After a brief overview of the propulsion systems proposed in the literature or employed by HAPSs, focusing on both the technical challenges and advancements in this emerging field, this review integrates theoretical foundations, historical design approaches, and the latest multi-fidelity optimization techniques to provide a comprehensive comparison of propeller design methods for HAPSs. It identifies key trends, including the growing use of CFD-based simulations methodologies, which contribute to notable performance improvements. Additionally, the review includes a critical assessment of experimental methods for performance evaluation. These developments have enabled the design of propellers with efficiencies exceeding 85%, offering valuable insights for the next generation of high-endurance, high-altitude platforms. Full article

Bioprocess simulation is a powerful tool for leveraging circular economy principles in the analysis of large-scale bioprocesses, enhancing decision-making for efficient and sustainable production. By simulating different process scenarios, researchers and engineers can evaluate the techno-economic feasibility of different approaches. This approach enables the identification of cost-effective and sustainable solutions, optimizing resource use and minimizing waste, thereby enhancing the overall efficiency and viability of bioprocesses within a circular economy framework. In this review, we provide an overview of circular economy concepts and trends before discussing design methodologies and challenges in large-scale bioprocesses. The analysis highlights the application and advantages of using process simulators like SuperPro Designer v.14 in bioprocess development. Process design methodologies have evolved to use specialized software that integrates chemical and biochemical processes, physical properties, and economic and environmental considerations. By embracing circular economy principles, these methodologies evaluate projects that transform waste into valuable products, aiming to reduce pollution and resources use, thereby shifting from a linear to a circular economy. In process engineering, exciting perspectives are emerging, particularly in large-scale bioprocess simulations, which are expected to contribute to the improvement of bioprocess technology and computer applications. Full article

Background: Native vertebral osteomyelitis (NVO) presents diagnostic and therapeutic challenges requiring adherence to complex clinical guidelines. The emergence of large language models (LLMs) offers new avenues for real-time clinical decision support, yet their utility in managing NVO has not been formally assessed. Methods: This study evaluated four LLMs—Consensus, Gemini, ChatGPT-4o Mini, and ChatGPT-4o—using 13 standardized questions derived from the 2015 IDSA guidelines. Each model generated 13 responses (n = 52), which were independently assessed by three orthopedic surgeons for accuracy (4-point scale) and comprehensiveness (five-point scale). Results: ChatGPT-4o produced the longest responses (428.0 ± 45.4 words), followed by ChatGPT-4o Mini (392.2 ± 97.4), Gemini (358.2 ± 60.5), and Consensus (213.2 ± 68.8). Accuracy ratings showed that ChatGPT-4o and Gemini achieved the highest proportion of “Excellent” responses (54% and 51%, respectively), while Consensus received only 20%. Comprehensiveness scores mirrored this trend, with ChatGPT-4o (3.95 ± 0.79) and Gemini (3.82 ± 0.68) significantly outperforming Consensus (2.87 ± 0.66). Domain-specific analysis revealed that ChatGPT-4o achieved a 100% “Excellent” accuracy rating in therapy-related questions. Statistical analysis confirmed significant inter-model differences (p < 0.001). Conclusions: Advanced LLMs—especially ChatGPT-4o and Gemini—demonstrated high accuracy and depth in interpreting clinical guidelines for NVO. These findings highlight their potential as effective tools in augmenting evidence-based decision-making and improving consistency in clinical care. Full article

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO₂/NO_x will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

With the large-scale application and high-quality development demands of optical fiber cables, higher requirements have been placed on the corresponding measurement technologies. In recent years, optical fiber testing has played a crucial role in evaluating cable performance, as well as in the deployment, operation, maintenance, fault repair, and upgrade of optical networks. The Optical Time-Domain Reflectometer (OTDR) is a fiber fault diagnostic tool recommended by standards such as the International Telecommunication Union and the International Electrotechnical Commission. It is used to certify the performance of new fiber links and monitor the status of existing ones, detecting and locating fault events with advantages including simple operation, rapid response, and cost-effectiveness. First, this paper introduces the working principle and system architecture of OTDR, along with a brief discussion of its performance evaluation metrics. Next, a comprehensive review of improved OTDR technologies and systems is provided, categorizing different performance enhancement methods, including the enhanced measurement distance with simple structure and low cost in 2024, and the high spatial resolution measurement of optical fiber reflection events and non-reflection events in 2025. Finally, the development trends and future research directions of OTDR are outlined, aiming to achieve the development of low-cost, high-performance OTDR systems. Full article

This study comprehensively evaluated nitrogen (N) management in 27 European countries from 1990 to 2021, utilizing the FAO and LUCAS databases. The EU countries were categorized into four groups based on their agricultural production intensities: low (L), medium–low (ML), medium–high (MH), and high (H). Additionally, a new Sustainable Nitrogen Management Indicator (SNMI) has been introduced to measure the sustainability of agricultural production. The analyses reveal significant variation in nitrogen fertilization intensity among EU countries, which correlates with differences in yield levels. Generally, higher fertilization leads to higher nutrient loss; however, the nitrogen losses per unit of yield show only minor differences between high- and low-intensity countries. From 1990 to 2021, a general improvement was observed in nitrogen management across all groups, as evidenced by a significant decline in the SNMI, indicating that agricultural production has become more sustainable. Notably, low-intensity countries showed the most significant improvement, with increased nitrogen input per hectare since the 1990s, demonstrating that moderate fertilization can enhance N use efficiency. In contrast, high-intensity countries saw decreased nitrogen inputs but still improved SNMI. These trends support the idea of sustainable intensification. The multidimensional SNMI comprehensively assesses eco-efficiency by highlighting environmental threats and production benefits. This paper demonstrates that SNMI is robust and easy to calculate using available datasets, and it can be implemented to assess nitrogen management efficiency at various scales. Full article

Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, most existing land use models emphasize quantity forecasting and spatial allocation, while overlooking the third critical dimension—structural complexity, which is essential for understanding the nonlinear, fragmented evolution of urban systems, thus limiting their ability to fully capture the evolutionary characteristics of urban land systems. To address this gap, this study proposes an integrated SD-PLUS-FD model, which combines System Dynamics, Patch-based Land Use Simulation, and Fractal Dimension analysis to construct a comprehensive three-dimensional framework for simulating and evaluating land use patterns in terms of quantity, spatial distribution, and structural complexity. Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). Fractal dimension analysis further reveals that since 2000, the spatial boundary complexity of all land use types—except forest land—has generally shown an upward trend across multiple scenarios, highlighting the increasingly nonlinear and fragmented nature of urban expansion. The FD values for construction land and cultivated land declined to their historical low in 2005, then gradually increased, reaching their peak under the SSP1-2.6 scenario. Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. Its structural complexity remained generally stable under all scenarios except SSP5-8.5, reflecting higher structural resilience and boundary adaptability under diverse socioclimatic conditions. The SD-PLUS-FD model effectively reveals how land systems respond to different socioclimatic drivers in both spatial and structural dimensions. This three-dimensional framework reveals how land systems respond to socioclimatic drivers across temporal, spatial, and structural scales, offering strategic insights for climate-resilient planning and optimized land resource management in rapidly urbanizing regions. Full article

Microbiological safety and quality consistency are critical challenges in the production of traditional Indian fried snacks, particularly in small-scale food enterprises. With growing export demand, maintaining strict quality control measures is essential. This study assessed the microbiological and physicochemical quality of five traditional Indian fried snacks—Kerala Murukku, Kerala Mixture, Banana Chips, Tapioca Chips, and Achappam—produced in a Food Safety Management System (FSMS)-certified facility over a four-year period (2020–2023). Products were evaluated for moisture, pH, salt content, acid value, and Total Plate Count (TPC). The number of ingredients for each product was recorded from standardized product formulation documents. TPC levels remained within acceptable limits (below 50,000 CFU/g) across all products. Among them, Kerala Mixture consistently showed the highest microbial counts (up to 4.61 log CFU/g) and Achappam the lowest, with no detectable variance (1.00 log CFU/g). Statistically significant year-wise differences (p < 0.05) were observed in all quality parameters. Kerala Mixture showed variation in salt and microbial load; Kerala Murukku varied in moisture, pH, and salt; while Tapioca Chips varied in moisture and salt. PCA identified that TPC, salt content, number of ingredients, and pH were key contributors to product variability. Cluster analysis confirmed Kerala Mixture as the most susceptible product to contamination risk. These findings provide valuable insights into the quality trends within an FSMS-certified environment and highlight the importance of strict post-processing controls. Full article