Search Results (11,469)

Known as the “Ear of the Earth”, Lop Nor has become one of China’s four largest uninhabited areas due to environmental changes. Lop Nor is rich in mineral resources, including potassium salt, which has good quality and has been largely mined since 2002. This study focuses on the surrounding area of the Lop Nor Potash Salt Field, which covers an area of 80,036.39 square kilometers, spanning from 39.29° N to 41.84° N and 88.92° E to 92.26° E. The research is based on 1 km resolution precipitation, potential evapotranspiration, temperature data, and 250 m resolution NDVI data spanning 2002–2022. This study is devoted to exploring the trend of precipitation changes in the region surrounding the Lop Nor salt field since the start of the construction of the salt field, exploring the climatic impacts of the construction of the salt field on the surrounding region, and analyzing the correlations related to the changes in precipitation by selected meteorological factors. The Sen and Trend-Free Pre-Whitening Mann–Kendall trend analysis method was used to analyze the trend of precipitation data over the years. Combining with the data of the salt field location, the influence of the development of the salt field on regional precipitation was analyzed both temporally and spatially. The bias correlation analysis method was used to explore the correlation between maximum temperature, potential evapotranspiration, Normalized Difference Vegetation Index, and precipitation. The results of this analysis indicate that between 2002 and 2022, the study area exhibited both increasing and decreasing trends in precipitation. The region experiencing decreasing precipitation is predominantly located in the southwestern part of the study area, encompassing approximately 62% of the total area. Conversely, the area showing increasing precipitation is situated in the northeastern part, accounting for 38% of the total area. Field visits and survey data further corroborated the observed trend of increased precipitation in the northeastern region. Based on these findings, it is hypothesized that the development of salt flats has contributed to the increased precipitation, thereby alleviating regional drought conditions. Additionally, a partial correlation analysis of meteorological factors and precipitation revealed significant correlation. Temperature, potential evapotranspiration (PET), and the Normalized Difference Vegetation Index (NDVI) all exhibited varying degrees of correlation with precipitation. Temperature and potential evapotranspiration were the primary meteorological factors showing significant individual correlations. This study discusses the impact of salt field development and other climatic factors on the drought situation in Lop Nor and quantitatively analyzes the trend of precipitation changes in the study area and the factors affecting it. Water resources are scarce in China’s desert areas, and this research can provide a scientific basis for the state to formulate long-term plans for ecological protection and desert management, and it can also provide guidance for industrial development in desert areas. At the same time, it can provide important data and cases for global climate change research, offering experience and technical support for international cooperation in desertification control. Full article

The Aldehyde Dehydrogenase (ALDH) superfamily comprises a group of NAD⁺ or NADP⁺-dependent enzymes that play essential roles in responding to abiotic stresses in plants. In Brassica napus L., however, the increasing frequency of extremely low temperatures during winter in recent years has significantly affected both yield and quality. This study conducted a genome-wide screening of ALDH superfamily genes, analyzing their gene structures, evolutionary relationships, protein physicochemical properties, and expression patterns under low-temperature stress to explore the function of the ALDH superfamily gene in cold tolerance in Brassica napus L. A total of six BnALDH genes with significant differences in expression levels were verified utilizing quantitative real-time polymerase chain reaction (qRT-PCR), revealing that BnALDH11A2, BnALDH7B2, BnALDH3F5, BnALDH12A3, BnALDH2B6, and BnALDH7B3 all exhibited higher expression in cold-tolerant material 24W233 compared with cold-sensitive material 24W259. Additionally, a single nucleotide polymorphism (SNP) in the BnALDH11A2 promoter region shows differences between the cold-tolerant (24W233) and the cold-sensitive (24W259) Brassica napus varieties, and it may be associated with the cold tolerance of these two varieties. This comprehensive analysis offers valuable insights into the role of ALDH family genes in low-temperature stress adaptation in Brassica napus and offers genetic resources for the development of novel cold-tolerant cultivars. Full article

Citrus fruit production generates substantial by-products, primarily from juice processing, which represent significant environmental and economic challenges. However, these residues, rich in polysaccharides, flavonoids, essential oils, and enzymes, offer an untapped resource for biotechnological applications. This review explores the potential of citrus by-products as substrates for enzyme production, focusing on key industrial enzymes such as cellulases, pectinases, xylanases, ligninases, lipases, and proteases. Various microbial strains have demonstrated the ability to convert citrus residues into high-value enzymes through solid-state and submerged fermentation. The optimization of fermentation conditions—including temperature, pH, moisture content, and the carbon-to-nitrogen ratio—further enhances enzymatic yields. The valorization of citrus waste aligns with circular economy principles, reducing environmental impacts while supporting sustainable bioproduct development for the food, biofuel, pharmaceutical, and textile industries. Future research should focus on scaling up enzyme production using citrus waste to improve economic feasibility and advance industrial biorefineries. Full article

Farmer professional cooperatives are essential in promoting China’s rural revitalization. However, the widespread occurrence of “empty shell” cooperatives, which are characterized by operational stagnation and human resource depletion, presents significant challenges to achieving this objective. This study explores the role of award-rated demonstration cooperatives in addressing this issue by utilizing a unique dataset of 1570 cooperatives from a particular city in Guizhou Province. The analysis employs mediation and moderation effect models and identifies two primary mechanisms. First, the policy improves cooperatives’ access to government subsidies and loan facilities, which helps mitigate constraints related to human resources and operational risks. Second, cooperative characteristics, including member size, education levels, and leading entity, positively influence the effectiveness of these policy measures. Conversely, the equity structure shows a dual moderating effect, reducing policy benefits in resource retention while enhancing them in operational improvement. These findings highlight the intricate relationship between policy design and cooperative attributes in addressing structural inefficiencies and provide valuable insights for strengthening cooperative governance and advancing rural development. Full article

DeepFake detection models play a crucial role in ambient intelligence and smart environments, where systems rely on authentic information for accurate decisions. These environments, integrating interconnected IoT devices and AI-driven systems, face significant threats from DeepFakes, potentially leading to compromised trust, erroneous decisions, and security breaches. To mitigate these risks, neural-network-based DeepFake detection models have been developed. However, their substantial computational requirements and long training times hinder deployment on resource-constrained edge devices. This paper investigates compression and transfer learning techniques to reduce the computational demands of training and deploying DeepFake detection models, while preserving performance. Pruning, knowledge distillation, quantization, and adapter modules are explored to enable efficient real-time DeepFake detection. An evaluation was conducted on four benchmark datasets: “SynthBuster”, “140k Real and Fake Faces”, “DeepFake and Real Images”, and “ForenSynths”. It compared compressed models with uncompressed baselines using widely recognized metrics such as accuracy, precision, recall, F1-score, model size, and training time. The results showed that a compressed model at 10% of the original size retained only 56% of the baseline accuracy, but fine-tuning in similar scenarios increased this to nearly 98%. In some cases, the accuracy even surpassed the original’s performance by up to 12%. These findings highlight the feasibility of deploying DeepFake detection models in edge computing scenarios. Full article

Generative AI (GenAI) models are designed to produce realistic and natural data, such as images, audio, or written text. Due to their high computational and memory demands, these models traditionally run on powerful remote compute servers. However, there is growing interest in deploying GenAI models at the edge, on resource-constrained embedded devices. Since 2018, the TinyML community has proved that running fixed topology AI models on edge devices offers several benefits, including independence from internet connectivity, low-latency processing, and enhanced privacy. Nevertheless, deploying resource-consuming GenAI models on embedded devices is challenging since the latter have limited computational, memory, and energy resources. This review paper aims to evaluate the progresses made to date in the field of Edge GenAI, an emerging area of research within the broader domain of EdgeAI which focuses on bringing GenAI on edge devices. Papers released between 2022 and 2024 that address the design and deployment of GenAI models on embedded devices are identified and described. Additionally, their approaches and results are compared. This manuscript contributes to understand the ongoing transition from TinyML to Edge GenAI and provides valuable insights to the AI research community on this emerging, impactful, and quite under-explored field. Full article

This comprehensive survey explores recent advancements in scheduling techniques for efficient deep learning computations on GPUs. The article highlights challenges related to parallel thread execution, resource utilization, and memory latency in GPUs, which can lead to suboptimal performance. The surveyed research focuses on novel scheduling policies to improve memory latency tolerance, exploit parallelism, and enhance GPU resource utilization. Additionally, it explores the integration of prefetching mechanisms, fine-grained warp scheduling, and warp switching strategies to optimize deep learning computations. These techniques demonstrate significant improvements in throughput, memory bank parallelism, and latency reduction. The insights gained from this survey can guide researchers, system designers, and practitioners in developing more efficient and powerful deep learning systems on GPUs. Furthermore, potential future research directions include advanced scheduling techniques, energy efficiency considerations, and the integration of emerging computing technologies. Through continuous advancement in scheduling techniques, the full potential of GPUs can be unlocked for a wide range of applications and domains, including GPU-accelerated deep learning, task scheduling, resource management, memory optimization, and more. Full article

Research has provided evidence of the protective characteristics of extended family for U.S. emerging adults, but no research has specifically explored cousin relationships. The current study fills this gap by analyzing qualitative data collected from 192 U.S. 18–29-year-old adults (M age = 25.6 years). As this topic is relatively unexplored, examining qualitative data provides scope and vocabulary for further exploration. Participants completed an online survey asking them to describe interactions with extended family, identifying 561 cousins (M age = 28.2 years). A thematic analysis based on the process defined by Braun and Clark generated four primary themes: (1) emerging adults feel varying degrees of closeness and distance with their cousins, (2) relational maintenance with cousins is both planned and incidental, (3) family membership provides resources, and (4) cousins share the same generational position. These results describe important characteristics of the cousin relationship, including moments of unexpected closeness and shared experience of family. The findings also highlight the relevance of sharing a similar life stage within the same family system. Practitioners can utilize findings to help clients identify extended family members that can be tapped for bonding and support. Full article

The Songliao River Basin (SLRB) is a key agricultural region in China, and understanding precipitation variations can provide crucial support for water resource management and sustainable development. This study used CN05.1 observational data and the Coupled Model Intercomparison Project Phase 6 (CMIP6) data to simulate and evaluate the precipitation characteristics within the SLRB. The optimal model ensemble was selected for future precipitation predictions. We analyzed the historical precipitation characteristics within the SLRB and projected future precipitation variations under SSP126, SSP245, and SSP585, while exploring the driving factors influencing precipitation. The results indicated that EC-Earth3-Veg (0.507) and BCC-CSM2-MR (0.493) from MME2 effectively capture precipitation variations, with MME2 corrected data more closely matching actual precipitation characteristics. From 1971 to 2014, precipitation showed an insignificant increasing trend, with most precipitation concentrated between May and September. Precipitation in the basin decreased from southeast to northwest. From 2026 to 2100, the increasing trend in precipitation became significant. The trend of precipitation growth over time was as follows: SSP126 < SSP245 < SSP585. Future precipitation distribution resembled the historical period, but the area of semiarid regions gradually decreased while the area of humid regions gradually increased, particularly under SSP585. The long-term increase in precipitation will become more pronounced, with a significant expansion of high-precipitation areas. In low-latitude, high-longitude areas, more precipitation events were expected to occur, while the impact of altitude was relatively weaker. From SSP126 to SSP585, the response of precipitation changes to temperature changes within the SLRB shifts from negative to positive. Under SSP585, this response becomes more pronounced, with average precipitation increasing by 4.87% for every 1 °C rise in temperature. Full article

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. Deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware–algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human–machine interactions, and we explore the current challenges and future development directions. Full article

Zoonotic diseases pose a significant global health threat, driven by factors such as globalization, climate change, urbanization, antimicrobial resistance (AMR), and intensified human–animal interactions. The increasing interconnectedness of human, animal, and environmental health underscores the importance of the OH paradigm in addressing zoonotic threats in a globalized world. This review explores the complex epidemiology of zoonotic diseases, the challenges associated with their management, and the necessity for cross-sector collaboration to enhance prevention and control efforts. Key public health strategies, including surveillance systems, infection control measures, and community education programs, play crucial roles in mitigating outbreaks. However, gaps in governance, resource allocation, and interdisciplinary cooperation hinder effective disease management, particularly in low- and middle-income countries (LMICs). To illustrate the effectiveness of the OH approach, this review highlights successful programs, such as the PREDICT project, Rwanda’s National One Health Program, the EcoHealth Alliance, and the Rabies Elimination Program in the Philippines. These initiatives demonstrate how integrating human, animal, and environmental health efforts can enhance early detection, improve outbreak responses, and reduce public health burdens. Strengthening global health governance, enhancing surveillance infrastructure, regulating antimicrobial use, and investing in research and technological innovations are essential steps toward mitigating zoonotic risks. Ultimately, a coordinated, multidisciplinary approach is vital for addressing the dynamic challenges posed by zoonotic diseases and ensuring global health security in an increasingly interconnected world. Full article

The construction sector is a significant contributor to global greenhouse gas (GHG) emissions, necessitating urgent decarbonization efforts to align with international climate goals such as the Paris Agreement and the European Green Deal. This study explores a comprehensive framework for construction companies to map and reduce their GHG emissions through a structured four-step approach: defining emission scopes, conducting GHG inventories, setting reduction targets, and planning actionable reductions. Four key pathways are proposed: electricity decarbonization through renewable energy adoption and energy efficiency measures; direct emissions reduction via fleet electrification and infrastructure optimization; recycling and resource efficiency improvements through waste diversion and material reuse; and supply chain emissions reduction by enforcing sustainability standards and responsible sourcing practices. The analysis highlights the importance of integrating technological, organizational, and policy-driven solutions, such as rooftop photovoltaic systems, virtual power purchase agreements, waste management strategies, and supplier codes of conduct aligned with global sustainability benchmarks. The study concludes that construction companies can achieve significant emission reductions by adopting a structured, multi-pathway approach; emphasizing progress over perfection; and aligning their strategies with national and international climate targets. This research provides actionable insights for the construction sector to transition toward a net-zero future by 2050. Full article

As an efficient clean backup energy source, natural gas hydrates have received high attention from countries around the world, and it is very important to establish models to predict the total amount of regional resources. In response to the complexity and existing shortcomings of current methods in resource exploration and prediction, this article used the volume method evaluation as the basis for predictions. The resource and location information of obtained from 14 wells in the research area were used as data, and k-Nearest Neighbor interpolation (KNN interpolation) was used to estimate the effective area. Through the Kolmogorov–Smirnov test (KS test), we found that the parameters for natural gas hydrate resources roughly follow a Poisson distribution with coordinates. After using a three-dimensional configuration, we were able to characterize the overall distribution pattern and predict the resource quantity of natural gas hydrates in each well and the total regional resource quantity. Finally, we used the Monte Carlo algorithm and genetic algorithm based on the k-Nearest Neighbor interpolation to predict the location of the maximum possible resource quantity within the entire region. In the discussion, we discussed the possible reasons for the occurrence of negative saturation and verified the accuracy of the algorithms and analyzed the applicability of the current algorithm model in different environments. Full article

In-space manufacturing represents a transformative frontier in space exploration and industrial production, offering the potential to revolutionize how goods are produced and resources are utilized beyond Earth. This paper explores the multifaceted aspects of in-space manufacturing, including its evolution, technologies, challenges, and future prospects, while also addressing ethical and legal dimensions critical to its development. Beginning with an overview of its significance and historical context, this paper underscores key concepts such as resource optimization and the reduction of launch costs. It examines terrestrial and space-based manufacturing processes, emphasizing additive manufacturing, advanced materials processing, autonomous robotic systems, and biomanufacturing for pharmaceuticals. Unique challenges posed by the space environment, such as microgravity, vacuum conditions, and radiation exposure, are analyzed alongside issues related to supply chains, quality assurance, and energy management. Drawing from case studies, including missions aboard the International Space Station, this paper evaluates the lessons learned over six decades of innovation in in-space manufacturing. It further explores the potential for large-scale production to support deep-space missions and assesses the commercial and economic feasibility of these technologies. This paper also delves into the policy, legal, and ethical considerations to address as space-based manufacturing becomes integral to future space exploration and the global space economy. Ultimately, this work provides a comprehensive roadmap for advancing in-space manufacturing technologies and integrating them into humanity’s pursuit of sustainable and scalable space exploration. Full article

Scenario generation proves to be an effective approach for addressing uncertainties in stochastic programming for power systems with integrated renewable resources. In recent years, numerous studies have explored the application of deep generative models to scenario generation. Considering the challenge of characterizing renewable resource uncertainty, in this paper, we propose a novel two-stage generative architecture for renewable scenario generation using diffusion models. Specifically, in the first stage the temporal features of the renewable energy output are learned and encoded into the hidden space by means of a representational model with an encoder–decoder structure, which provides the inductive bias of the scenario for generation. In the second stage, the real distribution of vectors in the hidden space is learned based on the conditional diffusion model, and the generated scenario is obtained through decoder mapping. The case study demonstrates the effectiveness of this architecture in generating high-quality renewable scenarios. In comparison to advanced deep generative models, the proposed method exhibits superior performance in a comprehensive evaluation. Full article