Search Results (4,388)

Terminal lakes in arid regions face severe degradation due to the dual pressures of climate change and anthropogenic water consumption. Traditional single-threshold methods for defining ecological water requirements often fail to balance ecosystem stability with water scarcity. To address this, this study constructs a comprehensive framework coupling “Morphometric Stability–Ecological Security Reliability–Resource Use Efficiency” to delineate the suitable ecological interval for Ebinur Lake, the largest saltwater lake in Xinjiang. Using multi-source remote sensing data (Landsat, Sentinel, ICESat, CryoSat), we reconstruct the long-term hydrological dynamics from 2001 to 2023. Results indicate a significant shrinking trend in the lake area, driven primarily by reduced inflow. We jointly consider the lake morphometric breakpoint, the ecological security baseline, and the lower bound of ecosystem service water use efficiency (ESWUE) to determine a minimum suitable ecological area of 500 km²; the regulation upper limit is set at 740 km² based on the marginal peak of ESWUE. However, monitoring data reveal that the lake falls below the minimum 500 km² baseline in approximately 40% of months, highlighting a severe ecological deficit risk. Furthermore, ESWUE analysis shows a peak in April (10 CNY/m³), suggesting that, under current climate conditions, a “Spring Surplus and Autumn Deficit” regulation strategy—advancing the replenishment window to the spring windy season—can maximize dust suppression benefits at a lower evaporative cost. This study provides a theoretical basis and methodological paradigm that will contribute to the sustainable management of shrinking terminal lakes globally. Full article

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article

Accurate modelling of hydrodynamic damping remains a critical challenge in the dynamic analysis of floating offshore wind turbines (FOWTs), particularly when motion coupling between degrees of freedom is significant. This study addresses the limitations of conventional single-degree-of-freedom damping identification techniques by proposing a novel multi-degree-of-freedom identification procedure capable of including off-diagonal coupling terms in the estimation of both linear and quadratic damping matrices. The aim is to assess whether viscous cross-coupling effects can be explicitly identified within a multi-degree-of-freedom lumped-parameter framework and to evaluate their impact on motion prediction. The methodology employs a hybrid optimisation approach, combining a genetic algorithm with a gradient-based solver. The procedure is applied to a taut-leg moored semi-submersible floating platform, focusing on surge–pitch coupling and using both experimental wave-basin data and high-fidelity CFD free-decay simulations. The results show that diagonal damping coefficients can be robustly identified even under coupled free-decay conditions, whereas the inclusion of off-diagonal viscous terms does not significantly improve the reconstruction of free-decay responses. Moreover, the simultaneous calibration of the added mass matrix enabled by the proposed procedure further improves agreement with the reference data. Although the findings highlight limited identifiability of viscous cross-coupling effects from free-decay tests, this paper provides a flexible tool for more advanced damping identification in operational and extreme conditions. Full article

Modern reliability experiments frequently face operational constraints that require balancing test duration, precision, and removal strategies, rendering classical censoring schemes inadequate for contemporary multidisciplinary applications. This study introduces a novel multi-progressive generalized Type-II censoring (MP-GC-T2) framework that unifies and extends existing progressive and generalized censoring structures through the integration of staged failure-proportion controls, dual temporal termination thresholds, and adaptive withdrawal of surviving units. The proposed mechanism provides enhanced flexibility in experiment design while retaining analytical tractability for statistical inference. Assuming Weibull lifetimes, we develop a complete inferential framework including maximum likelihood estimation, asymptotic interval construction, and Bayesian estimation via hybrid Metropolis–Hastings–Gibbs sampling with informative gamma priors, together with multiple interval estimation strategies for reliability characteristics. Extensive Monte Carlo investigations assess estimator bias, precision, coverage behaviour, and interval efficiency across diverse censoring configurations, demonstrating robustness and inferential gains relative to conventional schemes. Furthermore, optimal progressive-removal planning criteria are explored to guide practitioners in selecting censoring patterns that maximize inferential accuracy under practical constraints. The versatility and practical relevance of the MP-GC-T2 design are illustrated through applications to heterogeneous real datasets arising from clinical, chemical, geological, physical, and petroleum sciences, confirming its adaptability to distinct reliability structures and data-generation mechanisms. Collectively, the proposed methodology contributes a unified experimental and inferential platform that advances censoring design, reliability estimation, and cross-disciplinary statistical modelling. Full article

This paper reviews artificial intelligence (AI) applications in the design and optimization of proton exchange membrane (PEM) materials for hydrogen fuel cells. Clean energy conversion is a substantial benefit of PEM fuel cells, which conventional membrane development struggles with due to time-consuming trial-and-error methods, which are not adequate in capturing the different interdependencies of the membrane structure, and environmental variables. The review establishes foundational design principles of PEMs and outlines their challenges and computational methodologies are constructed to address them. Various advanced AI methods have been highlighted which include graph neural networks, multitask frameworks, and physics-informed models that facilitate rapid prediction of polymer properties. Optimization methods have been reported with 10–30% performance improvements, for instance, NSGA-II frameworks achieving 13–27% gains in power density. Experimental requirements are reduced by 40–60%, as seen with Bayesian optimization, identifying optimal designs within as few as 40 iterations. Current challenges include data availability, generalizability, and scalability, which are closely assessed in this review. Full article

Surveying and register administration are core to land administration, and accordingly, land surveying and registration are essential to socio-economic development due to their potential accuracy and efficiency. Until now, customary land surveying and registration have relied on human input, which is a situation that undermines efficiency and is prone to errors in data handling. During the last decade, the exponential growth in artificial intelligence (AI), in particular, geospatial artificial intelligence (GeoAI), has provided new methodologies that can overcome these deficiencies. This review examines AI in cadastral management by analyzing technical solutions and trends across three areas including data collection, modeling, and common applications. This review aims to provide a comprehensive survey of the current use of AI in cadastral management to the extent of defining a future research avenue. Based on the comprehensive review of literature, this study has reached the following three conclusions. (1) Automated extraction of parcel boundaries has been achieved through deep learning in data collection and processing, removing the bottlenecks of manual interpretation. Models such as convolutional neural networks (CNNs) and Transformers have been used for pixel-level semantic segmentation of high-resolution remote sensing images, leading to significant improvements in efficiency and accuracy. (2) Non-spatial data have been processed with natural language processing techniques to automatically extract information and construct relationships, thus overcoming the limitations of paper-based archives and traditional relational databases. (3) Deep learning models have been applied to automatically detect parcel changes and to enable integrated analysis of spatial and non-spatial data, which has supported the transition of cadastral management from two-dimensional to three-dimensional. However, several challenges remain, including differences in multi-temporal data processing, spatial semantic ambiguity, and the lack of large-scale, high-quality annotated data. Future research can focus on improving model generalization, advancing cross-modal data fusion, and providing recommendations for the development of a reliable and practical intelligent cadastral system. Full article

Interactions among plant roots, soil, and microorganisms in the rhizosphere regulate nutrient cycling, plant health, and ecosystem resilience. Recent advances in DNA sequencing and multi-omics are contributing to a shift from primarily descriptive surveys toward more mechanistic and predictive frameworks. This review synthesizes methodological developments and conceptual insights spanning microbial ecology, functional genomics, and agricultural applications. We first summarize DNA-based approaches—marker-gene sequencing, shotgun metagenomics, and quantitative nucleic acid assays—and then complementary omics layers, including metatranscriptomics, metaproteomics, metabolomics, epigenomics, ionomics, and phenomics. We next outline computational advances in data integration, network modeling, and visualization that help represent complex multi-layered datasets as biologically interpretable systems. Applications relevant to climate resilience and sustainable agriculture are discussed, including the design of synthetic microbial communities, the identification of biomarkers for soil health and stress tolerance, and case studies in which rhizosphere multi-omics informs crop breeding and soil management strategies. Overall, these developments underscore the potential of treating microbes as functional and, to some extent, manageable components of the plant holobiont. Looking ahead, we identify key research gaps involving standardized workflows, cross-scale causal inference, and real-time monitoring pipelines that integrate molecular diagnostics with remote sensing and edge–cloud analytics. By linking ecological mechanisms with translational practice, multi-omics frameworks may support the development of more sustainable, data-driven agriculture that better aligns productivity with environmental stewardship. Full article

Introduction: The potential aggravation of the shortage of skilled professionals in surgical specialties presents challenges. The lack of work–life balance and the pressure of training may deter aspiring surgeons. Surgical disciplines still remain predominantly male so that feminization combined with factors such as part-time work and pregnancy-related absence may aggravate workforce shortages. Studies show that the next generation of physicians places more value on work–life balance and seeks a pleasant work environment. This raises the question of whether these developments pose a threat to the future of surgical disciplines or whether generational change may also offer new opportunities. Methodology: This prospective observational study was conducted among a cohort of third-year medical students at a medical university in Germany. A non-validated, self-administered questionnaire was used for data collection. Responses on the Likert scale were dichotomized and the results were statistically analysed using chi-square test and logistic regression. Results: Job expectations differed only marginally across specialties. Students generally rated work–life balance and a pleasant work environment significantly higher than career, income or prestige. Students interested in surgery place significantly less emphasis on work–life balance than non-surgical peers, particularly in orthopedics and trauma surgery (77% vs. 90%, p = 0.025). There was a significant association between interest in surgical specialties and leadership ambitions. Male students were significantly more likely than females to aspire to leadership roles (58.1% vs. 32.7%, p = 0.001) and to choose surgical specialties (46.0% vs. 28.3%, p = 0.018). Female students were not significantly less interested in trauma surgery. Conclusions: Although our data interpretation should be drawn with caution, the increasing feminization of medicine does not appear to exacerbate the shortage of physicians in trauma surgery. In our cohort, we made the indicative suggestion that aspiring surgeons might be willing to trade leisure for career advancement. Specialized curricula could promote identification with the field and develop leadership skills, so that an initial attachment to a specific specialty endures throughout medical studies and results in a corresponding choice of specialty. Full article

Accurate estimation of wireless propagation characteristics is essential for guiding the design and deployment of fifth-generation (5G) communication systems. As network demand increases and 5G infrastructure is introduced in progressive phases, reliable path loss (PL) prediction models are required to refine deployment strategies and improve network efficiency. Conventional propagation models frequently display limited flexibility when applied to diverse environmental conditions and often entail considerable computational expense, reducing their practicality for large-scale 5G planning. Recent developments in data-centric artificial intelligence (AI) have enabled more adaptive and analytically powerful approaches to propagation modeling, resulting in notable gains in PL prediction accuracyThis study employs a comprehensive dataset produced using the NYUSIM channel simulator, integrating a wide spectrum of atmospheric parameters and seasonal variations within South Asian urban microcell environments, complemented by broad empirical observations. The core objective is to construct, optimize, and evaluate four machine learning (ML) models capable of accurately predicting PL at high-frequency bands critical to 5G performance. A fully automated hyperparameter tuning pipeline, based on the Optuna framework, is applied to twelve regression algorithms, including advanced ensemble methods, regularized linear techniques, and classical baseline models. Performance assessment emphasizes predictive reliability, stability, and cross-model generalization. Furthermore, statistical analysis utilizing bootstrap confidence intervals and paired t-tests indicates that all ML methods perform equivalently (p > 0.4), while SHapley Additive exPlanations (SHAP) analysis across all models supports a consistent feature importance distribution, supporting the statistical analysis results. To showcase the superiority of the ML approaches, a comparison with conventional free-space PL modeling methods is presented, with the AI methodology demonstrating robust performance across seasonal variations and a 95.3% improvement. Full article

Microplastics (MPs) have become a widespread environmental contaminant, raising concern due to their persistence, capacity to transport pollutants, and potential risks to ecosystems and human health. Their increasing global production, prolonged degradation, and ubiquity in aquatic environments underscore the need for improved monitoring and mitigation strategies. Current findings indicate widespread MP contamination, including within the human body, emphasizing significant ecological and health concerns. This review examines the definition, sources, environmental transport mechanisms, associated risks, and current detection methods for MPs in natural and engineered water systems. The methods discussed encompass a broad range of analytical and sensing technologies used to identify, characterize, and quantify MPs across diverse environmental matrices. The review highlights that no single technique is sufficient for comprehensive MP analysis; instead, the combination of multiple methods enhances sensitivity, specificity, and reliability. Progress in automated sample preparation, advanced sensing platforms and standardized methodologies is key to improving detection efficiency and comparability across different studies. In particular, the extensive body of scientific literature underscores the imperative for standardized and harmonized protocols regarding data collection and analysis, as well as homogeneous limits of detection and units of measurement. Reducing MP pollution will require interdisciplinary collaboration, regulatory action, and increased public awareness to protect environmental integrity and human health. Full article

Accurate orchard-scale soil moisture information is a key requirement for efficient irrigation management in perennial crops such as citrus orchards, particularly in Mediterranean environments characterized by water scarcity and strong spatial and temporal variability in soil moisture, canopy structure, and irrigation scheduling. This study proposes a hybrid physical–machine learning methodology for soil moisture estimation that integrates in situ capacitance sensor measurements, Sentinel-1 SAR observations, Sentinel-2 optical imagery, and ERA5-Land agroclimatic variables. Physically based soil moisture estimates were first obtained through the inversion of Sentinel-1 backscatter using integral equation scattering models, a physically based soil dielectric model, and a simplified vegetation attenuation scheme. These physically derived estimates were subsequently incorporated as predictors within supervised machine learning models, together with multi-source remote sensing and meteorological variables. Several algorithms were evaluated, including regularized linear models, support vector regression, random forests, and gradient boosting methods. Model performance was assessed using a strict interannual validation strategy based on independent-year predictions to ensure robust generalization. Within this methodology, tree-based ensemble models achieved the highest and most consistent performance at the orchard scale, with coefficients of determination ranging from 0.55 to 0.76 and root mean square errors typically between 0.7 and 1.1% volumetric soil moisture in the best-performing cases. Benchmarking against a physical-only baseline demonstrated that the hybrid methodology consistently reduced prediction errors and improved temporal robustness under independent-year validation. Overall, the results demonstrate that hybrid physical–machine learning approaches provide a robust and scalable solution for orchard-scale soil moisture monitoring in irrigated citrus orchards using operational data streams, supporting advanced irrigation management and precision agriculture applications in Mediterranean perennial cropping systems. Full article

Multi-criteria decision-making (MCDM) involves evaluating alternatives under uncertain, vague, and conflicting criteria. While fuzzy set theories, such as intuitionistic, pythagorean, fermatean, and q-rung orthopair fuzzy sets have advanced uncertainty modeling, they remain limited to capturing extreme judgments where membership reaches a value of one alongside significant non-membership. The recently introduced q-fractional fuzzy set (q-FrFS) addresses these shortcomings via a flexible constraint, making it suitable for extreme contexts. However, existing q-FrFS methodologies lack robust aggregation mechanisms capable of balancing trade-offs and modulating compensation during information fusion. To overcome this, this study proposes a novel class of Frank-based aggregation operators tailored specifically to q-FrFS environments. Leveraging the parameterized structure of Frank t-norms and t-conorms, we develop two operators: q-FrFFWA (Frank weighted averaging) and q-FrFFWG (Frank weighted geometric) alongside their essential algebraic properties. These operators enhance the representation and fusion of complex and uncertain data. Furthermore, we present a comprehensive MCDM framework utilizing the proposed operators and demonstrate its applicability by selecting optimal vehicle routing software for last-mile delivery. Sensitivity and comparative analyses affirm the stability and credibility of the proposed methodology. This research contributes to the evolving landscape of fuzzy decision-making by integrating the expressive power of q-FrFS with the adaptive flexibility of Frank aggregation, offering a potent tool for modeling and analyzing multidimensional uncertainties in complex decision environments. Full article

Lung cancer remains one of the most formidable health challenges globally, with significant morbidity and mortality rates. Despite advancements in diagnostic and treatment technologies, the disease’s high prevalence, late-stage detection, and complex variations continue to hinder effective management. Early detection and accurate diagnosis play a pivotal role in improving survival rates. Crucially, the clinical and translational relevance of AI-based prediction lies in its potential to significantly reduce the incidence of late-stage diagnoses, thus increasing the chance of successful intervention. Lung cancer was first identified by medical professionals in the mid-19th century. Today, cancer remains a significant global health challenge, affecting an estimated 14 million individuals annually and causing 8.2 million fatalities worldwide. Lung cancer ranks among the leading causes of death associated with cancer. This research aims to bridge gaps in lung cancer diagnosis by exploring various learning methodologies. By focusing on studies from the last 10 years, this survey provides a contemporary understanding of the field, emphasizing the importance of automated diagnostic systems in reducing human error and improving efficiency. The selection of relevant research is based on a rigorous methodology, including specific inclusion and exclusion criteria, which are later discussed in detail with supporting figures and comparative data. Ultimately, this work underscores the critical need for innovative diagnostic solutions and comprehensive screening programs to combat lung cancer, save lives, and advance the field of medical research. Full article

Chest X-ray (CXR) imaging remains the most widely used radiological modality for assessing pulmonary and cardiothoracic disease, yet its interpretation is inherently constrained by tissue superposition, subtle radiographic findings and marked inter-observer variability. Recent advances in artificial intelligence (AI) have driven significant progress in automated CXR analysis, supported by large public datasets, evolving annotation strategies and increasingly expressive deep learning architectures. This review presents a comprehensive synthesis of approaches for pulmonary abnormality detection, encompassing convolutional neural networks, transformers, multimodal and vision–language models and self-supervised representation learning. We critically discuss their strengths, limitations and vulnerability to label noise, domain shift and shortcut learning. In parallel, we examine dataset properties, annotation practices, robustness challenges, explainability methods and the heterogeneity of evaluation protocols that hinder fair comparison and clinical translation. Building on these observations, the review identifies key future directions, including foundation models, multimodal integration, federated and domain-generalized training, longitudinal modeling, synthetic data generation and standardized clinical evaluation frameworks. By integrating methodological and clinical perspectives, this work offers an up-to-date reference for researchers and clinicians and outlines a roadmap toward reliable, interpretable and clinically deployable AI systems for chest radiography. Full article

The brain is a complex structural network. The employment of fluorescent labeling techniques in conjunction with advanced imaging methodologies facilitates comprehensive analysis of multiscale brain anatomy, thereby offering insights into fundamental principles of function and addressing neurological disorders. This review summarizes technological advances in fluorescent labeling methods in the field of neuroscience, and their applications in neural circuit analysis, cerebrovascular imaging, neuronal activity monitoring, and fluorescence-guided treatment of brain tumors. A challenging trend in integrating smart fluorescent labeling with tissue clearing, wide-field 3D imaging, artificial intelligence-assisted data processing/reconstruction, and multimodal information fusion is highlighted and discussed. The future direction of combining high-resolution, low-damage, dynamic imaging with big data analysis is envisioned, providing tools for understanding brain structure and function and their roles in disease. Full article