Search Results (9,616)

Based on the field survey and reference data of the sea area of the Zhongjieshan Islands from 2021 to 2022, the Ecopath model was used to analyze the energy flow structure of the marine ecosystem of the sea area of the Zhongjieshan Islands; the energy structure of the marine ecosystem was divided into 21 functional groups, and its nutrient structure, energy flow, and total system characteristics were analyzed. The results show that the credibility of the model is 0.414, which is at a medium level. The trophic level of each functional group of the ecosystem in the sea area of Zhongjieshan Islands was 1–3.48, the energy flow structure of the system was mainly concentrated in the first five grades, and the trophic level was relatively simple, with the average energy transfer efficiency of the system being 8.11%, the energy flow range being 2.81–13.04%, the energy transfer efficiency of the primary producers of the system being 7.25%, and the energy conversion efficiency of the system debris being 9.12%. The total system throughput was 2125.96 t·km⁻²; The analysis of the overall characteristics of the ecosystem showed that the system connectance index and the system omnivory index were 0.45 and 0.24, respectively, while the Finn’s cycling index was 8.24, the Finn’s mean path length of the system was 2.72, and the total primary production/total respiration was 1.71. In this study, the marine ecosystem model of the sea area of the Zhongjieshan Islands was studied to understand the trophic structure and ecosystem status of the sea area, which is conducive to the sustainable utilization and scientific management of fishery resources in the sea area. Full article

The current work investigates a recently introduced unidirectional wave model, applicable in science and engineering to understand complex systems and phenomena. This investigation has two primary aims. First, it employs a novel modified Sardar sub-equation method, not yet explored in the literature, to derive new solutions for the governing model. Second, it analyzes the complex dynamical structure of the governing model using bifurcation, chaos, and sensitivity analyses. To provide a more accurate depiction of the underlying dynamics, they use quantum mechanics to explain the intricate behavior of the system. To illustrate the physical behavior of the obtained solutions, 2D and 3D plots, along with a phase plane analysis, are presented using appropriate parameter values. These results validate the effectiveness of the employed method, providing thorough and consistent solutions with significant computational efficiency. The investigated soliton solutions will be valuable in understanding complex physical structures in various scientific fields, including ferromagnetic dynamics, nonlinear optics, soliton wave theory, and fiber optics. This approach proves highly effective in handling the complexities inherent in engineering and mathematical problems, especially those involving fractional-order systems. Full article

The routine assessment of pilot fatigue is paramount to ensuring aviation safety. However, current designs of pilot fatigue factors often lack the comprehensiveness needed to fully account for the dynamic and cumulative nature of fatigue. To bridge this gap, this study introduces a biomathematical fatigue model (BFM) that leverages system dynamics theory, integrating a dynamic feedback mechanism for fatigue information. The novelty of this approach lies in its capability to continuously capture and model fatigue fluctuations driven by varying operational demands. A comparative analysis with international methodologies for evaluating cumulative fatigue on weekly and monthly scales demonstrates that the proposed BFM effectively reproduces variations in pilot fatigue characteristics. Moreover, the pilot fatigue coefficient derived from the model provides a robust differentiation of fatigue profiles across diverse work types, making it particularly suitable for estimating cumulative fatigue over monthly intervals. This BFM-based approach offers valuable insights for the strategic planning of flight schedules and establishes an innovative framework for utilizing BFMs in fatigue management. By employing a scientifically grounded evaluation method rooted in system dynamics and the BFM, this study rigorously assesses cumulative pilot fatigue, confirming the model's accuracy in replicating fatigue patterns and validating the efficiency and reliability of the derived fatigue coefficient. Full article

Changes in cultivated land use significantly impact food production capacity, which in turn affects food security. Therefore, accurately understanding the spatial and temporal variations in cultivated land use is critical for strategic decision-making regarding national food security. Since the second national soil survey was conducted in around 1980, China has implemented major efforts, such as a nationwide soil testing and fertilization project in around 2005 and the establishment of the National Standards for Cultivated Land Quality Grading in 2016. However, limited research has focused on how cultivated land use has changed during these periods and the mechanisms driving these changes. This study, using Enshi Prefecture in the mountainous region of southwestern Hubei Province as a case study, examines the spatiotemporal changes in cultivated land use during 1980–2018. Land use data from 1980, 2005, and 2018 were combined with statistical yearbook data from Enshi Prefecture, and remote sensing and GIS technology were applied. Indicators such as the dynamic degree of cultivated land use, the relative rate of change in cultivated land use, and a Geoscience Information Atlas model were used to explore these changes. Additionally, principal component analysis was employed to examine the mechanisms influencing these changes. The results show that (1) the area of cultivated land in Enshi Prefecture increased slightly from 1980 to 2005, while from 2005 to 2018, it significantly decreased; compared with the earlier period, the transformation of land use types during 2005–2018 was more intense; (2) the increase in cultivated land area from 1980 to 2005 was mainly due to deforestation, the creation of farmland from lakes, and the reclamation of wasteland, while the decrease in land area was primarily attributed to the conversion of farmland back to forests and grassland. From 2005 to 2018, the main drivers for the increase in cultivated land were deforestation and the reclamation of wasteland, while the return of farmland to forests remained the primary reason for the decrease in land area; (3) from 1980 to 2005, the dynamic degree of cultivated land use in each county and city of Enshi Prefecture was generally low. However, between 2005 and 2018, the dynamic degree increased in most counties and cities except Enshi City and Xianfeng County; (4) there were significant variations in the relative rate of change in cultivated land utilization across counties and cities from 1980 to 2005. However, from 2005 to 2018, the relative rate of change decreased in all counties and cities compared to the previous period; (5) since 1980, nearly 50% of the cultivated land in Enshi Prefecture has undergone land classification conversion, with frequent shifts between different land classes; and (6) economic development, population growth, capital investment, food production, and production efficiency are the dominant socioeconomic factors driving changes in cultivated land use in Enshi Prefecture. The results of this study can provide a scientific basis for the protection and optimization of cultivated land resources in the mountainous regions of southwestern Hubei Province. Full article

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals. Full article

Land use conflict is an inevitable and objective phenomenon during regional development, with significant impacts on both regional economic growth and ecological security. Scientifically assessing the spatiotemporal evolution of these conflicts is essential to optimize land use structures and promote sustainable resource utilization. This study employs multi-period land use/land cover remote sensing data from China to develop a model for the measurement of land use conflict from the perspective of the landscape ecological risk. By applying the optimal landscape scale method to determine the most appropriate analysis scale, this research investigates the spatiotemporal evolution characteristics of land use conflicts in the Central Yunnan Urban Agglomeration from 2000 to 2020. Furthermore, by integrating the Patch-Generating Land Use Simulation (PLUS) model with the Multi-Objective Programming (MOP) algorithm, this study simulates the spatial patterns of land use conflict in 2030 under four scenarios: Natural Development (ID), Economic Development (ED), Ecological Conservation (PD), and Sustainable Development (SD). The findings reveal that, from 2000 to 2020, the proportion of areas with strong and moderately strong conflict levels in the Central Yunnan Urban Agglomeration increased by 2.19%, while the proportion of areas with weak and moderately weak conflict levels decreased by 1.45%, underscoring the growing severity of land use conflict. The predictions for 2030 suggest that the spatial pattern of conflict under various scenarios will largely reflect the trends observed in 2020. Under the ID scenario, areas with weak and moderately weak conflict levels constitute 57.5% of the region; this increases by 0.85% under the SD scenario. Conversely, areas experiencing strong and moderately strong conflict levels, which stand at 33.02% under the ID scenario, decrease by 1.04% under the SD scenario. These projections indicate that the SD scenario, which aims to balance ecological conservation with economic development, effectively mitigates land use conflict, making it the most viable strategy for future regional development. Full article

This paper presents a comprehensive review of state-of-health (SoH) estimation methods for lithium-ion batteries, with a particular focus on the specific challenges encountered in hybrid electric vehicle (HEV) applications. As the demand for electric transportation grows, accurately assessing battery health has become crucial to ensuring vehicle range, safety, and battery lifespan, underscoring the relevance of high-precision SoH estimation methods in HEV applications. The paper begins with outlining current SoH estimation methods, including capacity-based, impedance-based, voltage and temperature-based, and model-based approaches, analyzing their advantages, limitations, and applicability. The paper then examines the impact of unique operating conditions in HEVs, such as frequent charge–discharge cycles and fluctuating power demands, which necessitate tailored SoH estimation techniques. Moreover, this review summarizes the latest research advances, identifies gaps in existing methods, and proposes scientifically innovative improvements, such as refining estimation models, developing techniques specific to HEV operational profiles, and integrating multiple parameters (e.g., voltage, temperature, and impedance) to enhance estimation accuracy. These approaches offer new pathways to achieve higher predictive accuracy, better meeting practical application needs. The paper also underscores the importance of validating these estimation methods in real-world scenarios to ensure their practical feasibility. Through systematic evaluation and innovative recommendations, this review contributes to a deeper understanding of SoH estimation for lithium-ion batteries, especially in HEV contexts, and provides a theoretical basis to advance battery management system optimization technologies. Full article

Consumers are regularly exposed to well-known food contaminants (FCs), which are typically assessed for risk on an individual basis. However, there is limited knowledge about the overall levels and combinations of these compounds depending on dietary choices. The goal of this study was to estimate the real-life mixtures of FCs in different dietary models by integrating extensive data from the scientific literature concerning the reliable quantification of FCs in foods. A FAIR database detailing the occurrence of 73 FCs in 16 foods commonly consumed was built. The data were integrated into an omnivorous and a vegetarian dietary model. A weighted estimate of the 25th, 50th, and 75th percentiles of FCs in both dietary models revealed that the omnivorous model presented slightly higher levels of FCs than the vegetarian. At the 25th percentile, the FC levels in both dietary models fall within the European Food Safety Authority (EFSA) reference exposure levels for chemical hazards, except for arsenic, lead, cadmium, fumonisin B1, and OTA. At the 75th percentile, the FC levels exceed the EFSA reference levels for those FCs and additional mycotoxins. Using in vitro models, the 25th percentile can mimic real-life FC exposure, while the 75th percentile simulates a possible worst-case scenario. Full article

Wetlands are crucial for sustainable development, and the evaluation of their GEP is a key focus for governments and scientists. This study created a dynamic accounting model for wetland GEP and assessed the GEP of 39 wetlands in the middle reaches of the Yellow River in Ningxia province. The results indicate that Ningxia province’s wetlands have an average annual GEP of CNY 5.24 billion. Haba wetland contributes the most at 0.52, while Qingtongxia, Sha, and Tenggeli wetlands follow with 0.12, 0.04, and 0.03, respectively. Climate regulation is the most valuable function at 38.24%, with species conservation and scientific research/tourism at 24.93% and 15.11%, respectively. Ningxia’s northern wetlands are vast and shaped by the Yellow River, while the smaller, seasonal southern wetlands are more affected by rainfall and mountain groundwater. Southern wetlands show a strong correlation between GEP and precipitation (0.82), whereas northern wetlands have a moderate correlation between GEP and evapotranspiration (0.52). The effective conservation and management of these wetlands require consideration of their locations and weather patterns, along with customized strategies. To maintain the stability of wetland habitats and provide a suitable environment for various species, it is essential to preserve wetlands within a certain size range. Our study found a strong correlation of 0.85 between the wetland area and the GEP value, indicating that the size of wetlands is a key factor in conserving their GEP. The results provide accurate insights for creating a wetland ecological benefit compensation mechanism. Full article

Nitrate is among the most widely occurring contaminants in groundwater on a global scale, posing a serious threat to drinking water supplies. With the advancement of urbanization and mountainous agriculture, the nitrate in the groundwater of Wanzhou District in the Three Gorges Reservoir Area has formed a complex combination of pollution sources. To more accurately identify the sources of nitrate in groundwater, this study integrates hydrochemical methods and environmental isotope techniques to analyze the sources and transformation processes in shallow groundwater nitrate under different land-use types. Furthermore, the Bayesian isotope mixing model (MixSAIR) is employed to calculate the contribution rates in various nitrate sources. The results indicate that nitrate is the primary form of inorganic nitrogen in shallow groundwater within the study area, with nitrate concentrations in cultivated groundwater generally higher than those in construction land and forest land. The transformation process of nitrate is predominantly nitrification, with little to no denitrification observed. In cultivated shallow groundwater, nitrate mainly originates from chemical fertilizers (36.3%), sewage and manure (35.4%), and soil organic nitrogen (24.7%); in forested areas, nitrate primarily comes from atmospheric precipitation (35.3%), chemical fertilizers (31.3%), and soil organic nitrogen (22.1%); while in constructed areas, nitrate mainly derives from chemical fertilizers (46.0%) and sewage and manure (32.2%). These results establish a scientific foundation for formulating groundwater pollution control and management strategies in the region and serve as a reference for identifying nitrate sources in groundwater in regions with comparable hydrogeological features and pollution profiles. Full article

Urban vitality is a concept that reflects dynamic changes in economic, social, and cultural aspects, emphasizing the importance of diversified land use and dense population. With technological advancements, research methods on urban vitality are increasingly diverse, particularly with the application of big data and geographic information systems providing new perspectives and tools for such studies. Currently, research on the vitality of inland Central Plains cities in China is relatively limited and largely confined to specific administrative areas, leading to an inadequate understanding of basic economic activities and population distribution within cities. Therefore, this study aims to explore the spatial distribution characteristics of urban vitality and its influencing factors in Zhengzhou’s main urban area, providing a scientific basis for urban planning and sustainable development. This study utilizes methods that include Densi graph curve analysis, the entropy method, and the multiscale geographically weighted regression (MGWR) model, integrating statistical data, geographic information, and remote sensing imagery of Zhengzhou in 2023. The MGWR model analysis reveals: (1) Urban vitality in Zhengzhou’s main urban area exhibits a concentric pattern, with high vitality at the center gradually decreasing toward the periphery, showing significant spatial differences in economic, population, and cultural vitality. (2) Various influencing factors positively correlate with urban vitality in the main urban area, but due to shortcomings in urban development strategies and planning, some factors negatively impact vitality in the central area while positively affecting vitality in peripheral areas. Based on these findings, this study provides relevant evidence and theoretical support for urban planning and sustainable development in Zhengzhou, aiding in the formulation of more effective urban development strategies. Full article

The risk classification of barrier lakes is the key to conducting emergency treatment in a scientific manner. However, risk classification faces difficulties such as a short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly, and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an eight-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lakes and intelligent identification of particles on the surface of barrier bodies were developed, which realized the rapid acquisition of eight-factor evaluation index information in an emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of eight factors. The risk assessment on 15 barrier lakes such as Tangjiashan barrier lake shows that the conclusions drawn for the risk classification method proposed in this paper are basically consistent with those of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss impacts on the risk level of barrier lakes and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable. Full article

The accuracy of data perception in Internet of Things (IoT) systems is fundamental to achieving scientific decision-making and intelligent control. Given the frequent occurrence of sensor failures in complex environments, a rapid and accurate fault diagnosis and handling mechanism is crucial for ensuring the stable operation of the system. Addressing the challenges of insufficient feature extraction and sparse sample data that lead to low fault diagnosis accuracy, this study explores the construction of a fault diagnosis model tailored for agricultural sensors, with the aim of accurately identifying and analyzing various sensor fault modes, including but not limited to bias, drift, accuracy degradation, and complete failure. This study proposes an improved dung beetle optimization–support vector machine (IDBO-SVM) diagnostic model, leveraging the optimization capabilities of the former to finely tune the parameters of the Support Vector Machine (SVM) to enhance fault recognition under conditions of limited sample data. Case analyses were conducted using temperature and humidity sensors in air and soil, with comprehensive performance comparisons made against mainstream algorithms such as the Backpropagation (BP) neural network, Sparrow Search Algorithm–Support Vector Machine (SSA-SVM), and Elman neural network. The results demonstrate that the proposed model achieved an average diagnostic accuracy of 94.91%, significantly outperforming other comparative models. This finding fully validates the model’s potential in enhancing the stability and reliability of control systems. The research results not only provide new ideas and methods for fault diagnosis in IoT systems but also lay a foundation for achieving more precise, efficient intelligent control and scientific decision-making. Full article

As talk grows about billions or even trillions of dollars being directed toward potential “Net Zero” activities, it is imperative that the chemistry inherent in or driving those actions make scientific sense. The challenge is to close the mass and energy balances to the carbon and oxygen cycles in the Earth’s atmosphere and oceans. Several areas of climate science have been identified that chemists can investigate through methods that do not require a supercomputer or a climate model for investigation, most notably the following: (1) The carbon cycle, which still needs to be balanced, as many known streams, such as carbon to landfills, carbon in human-enhanced sewage and land runoff streams, and carbon stored in homes and other material, do not seem to have been accounted for in carbon balances used by the IPCC. (2) Ocean chemistry and balances are required to explain the causes of regional and local-scale salinity, pH, and anoxic conditions vs. global changes. For example, local anoxic conditions are known to be impacted by changes in nutrient discharges to oceans, while large-scale human diversions of fresh water streams for irrigation, power, and industrial cooling must have regional impacts on oceanic salinity and pH. (3) Carbon Capture and Storage (CCS) schemes, if adopted on the large scales being proposed (100s to 1000s of Gt net injection by 2100), should impact the composition of the atmosphere by reducing free oxygen, adding more water from combustion, and displacing saline water from subsurface aquifers. Data indicate that atmospheric oxygen is currently dropping at about twice the rate of CO₂ concentrations increasing, which is consistent with combustion chemistry with 1.5 to 2 molecules of oxygen being converted through combustion to 1 molecule of CO₂ and 1 to 2 molecules of H₂O, with reverse reactions occurring as a result of oxygenic photosynthesis by increased plant growth. The CCS schemes will sabotage these reverse reactions of oxygenic photosynthesis by permanently sequestering the oxygen atoms in each CO₂ molecule. Full article

In the context of digital construction, responsibility management in smart city building information modeling (BIM) projects spans the entire building lifecycle. The involvement of numerous BIM designers in project management and frequent data exchanges pose significant challenges for the traceability, immutability, and responsibility attribution of BIM models. To address these issues, this study proposes a blockchain-based responsibility management and collaboration framework for BIM projects using non-fungible tokens (NFTs), aiming to enhance the management of responsibilities and accountability in BIM projects. This research adopts a design science methodology, strictly adhering to scientific research procedures to ensure rigor. First, NFTs based on blockchain technology were developed to generate corresponding digital signatures for BIM model files. This approach ensures that each BIM model file has a unique digital identity, enhancing transparency and traceability in responsibility management. Next, the interplanetary file system (IPFS) was used to generate digital fingerprints, with the content identifier generated by IPFS uploaded to the blockchain to ensure the immutability of BIM model files. This method guarantees the integrity and security of BIM model files throughout their lifecycle. Finally, the proposed methods were validated through a blockchain network. The experimental results indicate that the proposed framework is theoretically highly feasible and demonstrates good applicability and efficiency in practical production. The constructed blockchain network meets the actual needs of responsibility management in smart city BIM projects, enhancing the transparency and reliability of project management. Full article