Search Results (12,001)

The simultaneous measurement of strain and temperature using Fiber Bragg Grating (FBG) sensors presents a significant challenge due to the intrinsic cross-sensitivity of the Bragg wavelength. While recent studies have increasingly employed “black-box” machine learning algorithms to address this ambiguity, such approaches often overlook the physical limitations of the sensor’s spectral response. This paper challenges the assumption that advanced algorithms alone can compensate for data that is physically ambiguous. We propose a “Sensor-Algorithm Co-Design” methodology, demonstrating that robust discrimination is achievable only when the sensor architecture exhibits a unique, orthogonal physical signature. Using a rigorous Transfer Matrix Method (TMM) and 4 × 4 polarization analysis, we evaluate three distinct architectures. Quantitative analysis reveals that a standard Quadratically Chirped FBG (QC-FBG) functions as an “ill-conditioned baseline” failing to distinguish measurands due to feature space collapse (K_{cond >} 4600). Conversely, we validate two robust co-designs: (1) An Amplitude-Modulated Superstructure FBG (S-FBG) paired with an Artificial Neural Network (ANN), utilizing thermally induced duty-cycle variations to achieve high accuracy (~3.4 °C error) under noise; and (2) A Polarization-Diverse Inverse-Gaussian FBG (IG-FBG) paired with a 4 × 4 K-matrix, exploiting strain-induced birefringence (K_cond ≈ 64). Furthermore, we address the data scarcity issue in AI-driven sensing by introducing a Physics-Informed Neural Network (PINN) strategy. By embedding TMM physics directly into the loss function, the PINN improves data efficiency by 2.2× compared to standard models, effectively bridging the gap between physical modeling and data-driven inference, addressing the critical data scarcity bottleneck identified in recent optical sensing roadmaps. Full article

Advances in AI are accelerating intelligent ship autonomy, yet robust trajectory tracking remains challenging under nonlinear dynamics and persistent environmental disturbances. Traditional model-based guidance becomes tuning-sensitive and loses robustness under strong disturbances, while data-driven approaches like reinforcement learning often suffer from poor generalization to unseen dynamics and brittleness in out-of-distribution conditions. To address these limitations, we propose a guidance architecture embedding a Large Language Model (LLM) directly within the closed-loop control system. Using in-context prompting with a structured Chain-of-Thought (CoT) template, the LLM generates adaptive k-step heading reference sequences conditioned on recent navigation history, without model parameter updates. A latency-aware temporal inference mechanism synchronizes the asynchronous LLM predictions with a downstream Model Predictive Control (MPC) module, ensuring dynamic feasibility and strict actuation constraints. In MMG-based simulations of the KVLCC2, our framework consistently outperforms conventional model-based baselines. Specifically, it demonstrates superior path-keeping accuracy, higher corridor compliance, and faster disturbance recovery, achieving these performance gains while maintaining comparable or reduced rudder usage. These results validate the feasibility of integrating LLMs as predictive components within physical control loops, establishing a foundation for knowledge-driven, context-aware maritime autonomy. Full article

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions. Full article

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique. Full article

Land consolidation requires reliable and objective land valuation to ensure transparency and fairness in the reallocation process. This study introduces a data-driven method for assessing agricultural site productivity based on vegetation indices derived from multispectral imagery, supported by Sentinel satellite data and validated using handheld chlorophyll meter measurements. Site productivity, defined as the land’s ability to generate yield and biological value, is determined by natural and environmental factors that directly influence economic worth. Vegetation indices (NDVI, SAVI) obtained from UAV imagery showed a strong correlation with chlorophyll content, confirming the reliability of this non-invasive assessment. The analysis, conducted in Poland and Slovakia, demonstrated the method’s applicability under two different land consolidation systems: a market-based model in Poland and an ecologically oriented approach in Slovakia. The proposed framework proved easy to implement and provided consistent results even without the use of ground control points. By reducing fieldwork time and costs while improving valuation accuracy, this method enhances the objectivity and transparency of land consolidation procedures. The findings confirm the potential of vegetation indices to support data-driven and environmentally informed land valuation across diverse consolidation contexts. Full article

This study examines how green finance influences high-quality economic development, with a particular focus on its spatial spillover mechanisms. Specifically, we investigate the competing roles of technology spillover and the pollution haven effect. Using provincial panel data from China (2010–2021) and applying a Spatial Durbin Model (SDM), we deconstruct the total effect of green finance into three distinct components: the local technological progress effect, the positive technology spillover effect, and the negative pollution haven effect. While acknowledging limitations related to the macro-level data granularity and the indirect nature of the mechanism tests, our analysis yields three main findings. First, green finance development shows significant regional disparities. It has progressed most rapidly in the eastern region, remained relatively stable in the central region, and declined in the western region. Second, green finance exerts a strong positive direct effect on local high-quality economic development. This promoting effect becomes even stronger in more developed regions. Third, green finance generates significant negative spatial spillovers on neighboring regions. These are primarily driven by the pollution haven effect, which involves the cross-regional relocation of polluting industries. However, local technological progress partially mitigates these adverse externalities. Overall, our findings reveal the dual nature of the spatial externalities associated with green finance. They also highlight the urgency of coordinated regional environmental governance to prevent “green leakage” and to promote balanced, high-quality economic development. Full article

Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure-based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making. Full article

Accurately identifying electric vehicle (EV) users with high demand response (DR) potential is critical for grid stability but remains challenging due to behavioral heterogeneity, data sparsity, and the subjectivity of expert-dependent methods. In particular, the absence of behavior labels and the low temporal frequency of EV charging events limit the effectiveness of conventional rule-based and clustering approaches. To address these issues, we propose a novel unsupervised framework that integrates deep behavioral modeling with multi-source indicator fusion. Our approach begins by developing a behavior recognition model robust to sparse data, effectively characterizing user charging patterns. Subsequently, a multi-dimensional potential feature system is established. A key innovation lies in our unsupervised weighting mechanism, which automatically learns the importance of each indicator by assessing inter-indicator correlations, thereby eliminating subjective bias. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank users comprehensively based on their fused potential scores. Case studies on a large-scale real-world EV charging dataset demonstrate that the proposed method can effectively distinguish high-potential users from low-potential ones. The results indicate clear separability across multiple behavioral and willingness-related dimensions. This provides a practical and data-driven basis for targeted DR incentive design and user-side flexible resource planning. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return (ALS_D) and ALS full-waveform (ALS_FW), photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) is a biologically heterogeneous malignancy with poor outcomes in advanced disease. Increasing evidence indicates that the tumor microenvironment, particularly cancer-associated fibroblasts (CAFs), plays an important role in tumor progression and immune regulation. However, the diversity of CAF subsets and their clinical relevance in HNSCC remain incompletely understood. This study aimed to characterize CAF heterogeneity and assess the prognostic significance of CAF subset-specific transcriptional programs. Methods: Single-cell RNA sequencing data from HNSCC tumors were analyzed to identify CAF subsets based on differentially expressed genes. CAF subset-specific gene signatures were used to construct prognostic risk models for overall survival (OS) and progression-free survival (PFS) in The Cancer Genome Atlas HNSCC cohort, with validation in an independent dataset. CAF-driven prognostic groups were defined, and their immune landscapes and biological pathways were evaluated. Bulk RNA sequencing of primary CAF cultures was performed for validation. Results: Six CAF subsets were identified, including myofibroblastic (myCAF), inflammatory (iCAF), antigen-presenting, and extracellular matrix-related CAFs. Risk scores derived from inflammatory CAF subsets consistently predicted shorter OS across independent cohorts, whereas PFS prediction showed greater cohort dependency. CAF-based stratification identified patient subgroups with distinct immune profiles and pathway enrichment patterns. These results were supported by validation analyses and by bulk RNA sequencing of primary CAFs, demonstrating preservation of myCAF- and iCAF-like transcriptional programs ex vivo. Conclusions: CAF heterogeneity has important prognostic and immunological implications in HNSCC. Inflammatory CAF-related transcriptional programs represent robust markers of patient survival and may complement tumor-intrinsic biomarkers. Full article

Fermentation is a complex biochemical process that transforms brewer’s wort into beer. Beer fermentation is driven by yeast and is influenced by process parameters such as the content of fermentable sugars in wort, temperature, and pH. Traditional methods of modelling this process rely heavily on empirically tuned kinetic models. However, these models tend to be recipe-specific and often require retuning when processes change. This paper proposes a data-driven approach using a Long Short-Term Memory (LSTM) network, a type of recurrent neural network, to model beer fermentation dynamics. By training the LSTM model on real-world fermentation data (1305 fermentations across ales, IPAs, lagers, and mixed-culture beers), including variables such as apparent extract (derived from specific gravity), temperature, and pH, we demonstrate that this technique can accurately predict key fermentation trajectories and support process monitoring and optimisation. When evaluated on representative medoid fermentations as one-step-ahead roll-outs over 0–300 h, the model produces accurate predictions with low errors and minimal residuals. These results show that the LSTM-based model provides accurate and robust predictions across beer styles and operating conditions, offering a practical alternative to traditional mechanistic kinetic models. This work highlights the potential of LSTM networks to enhance our understanding, monitoring, and control of fermentation processes, providing a scalable and efficient tool for both research and industrial applications. The findings suggest that LSTM models can be effectively adapted to model other fermentation processes in beverage production, opening new possibilities for advancing food science and engineering. Full article

A key barrier to achieving sustainability in 15 minute cities is the efficiency of supply-chain logistics, particularly in historic urban districts characterized by dense and heritage-protected urban forms. This article presents a data-driven urban methodology to optimize last-mile logistics in Ciutat Vella (Valencia, Spain). Within the ENACT 15 min cities project, a Spatial Decision Support System (SDSS) was developed, combining iterative geospatial adjustments to the logistics network under changing boundary conditions with a demand-estimation model derived from the Valencia open-data platform. Using cadastral and field-survey data, the workflow simulates and optimizes the selection of vacant commercial premises as urban logistics hubs. A genetic algorithm minimizes oversupply, maximizes demand coverage, and improves spatial balance. The methodology also estimates the resulting carbon footprint, demonstrating that the optimized configuration enhances sustainability and service efficiency in dense historic settings. The approach is generalized to other urban contexts. Full article

The establishment of a young farmer in the rural economy is a key stage in the process of farm succession in the rural development environment. In this matter, Pillar II of the Common Agricultural Policy (CAP) has a distinct approach related to financing the initiatives of this establishment. A young farmer can obtain funds for their agricultural activity by submitting a funding project proposal to the national agency. The success of a funding project proposal depends on various factors. In this paper, a model of prediction and classification using supervised learning algorithms, primarily Random Forest (RF) and Logistic Regression (LR), was developed to predict project selection outcomes and identify the key determinants of success. This was developed in relation to proposals submitted in the period 2014–2021 through Sub-Measure 6.1 and through the intervention for the young farmer installation intervention under the 2023–2027 CAP Strategic Plan (DR-30 (2023–2027)—Young Farmer Installation, indicated in this paper as DR 30) for the period of 2023–2027. Using the historical data related to this proposal, several models that use automated learning were developed in order to predict the success of a proposal based on specific determinants. In addition, a classification model was used to determine patterns in the proposal data, obtaining several project proposal clusters with common characteristics. The variables and selection criteria with the greatest impact on the final score and probability of acceptance were identified, highlighting the differences between sub-measures and the implications for generational renewal policies in rural areas. The novelty of this study lies in the integration of predictive modeling, classification, and clustering within a unified, policy-oriented analytical framework applied to real administrative data. The results reveal that project selection outcomes are driven primarily by formal scoring components, while structural characteristics such as farm economic size and planned investment play a secondary but consistent role across programming periods. These findings provide actionable insights for refining selection criteria and advisory mechanisms under the Common Agricultural Policy. Full article

Background: Osteoporosis and osteopenia are prevalent bone diseases characterized by reduced bone mineral density (BMD) and an increased risk of fractures, particularly in postmenopausal women. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, it has limitations regarding accessibility, cost, and predictive capacity for fracture risk. Machine learning (ML) approaches offer an opportunity to develop automated and more accurate diagnostic models by incorporating both BMD values and clinical variables. Method: This study retrospectively analyzed BMD data from 142 postmenopausal women, classified into 3 diagnostic groups: normal, osteopenia, and osteoporosis. Various supervised ML algorithms—including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN)—were applied. Feature selection techniques such as ANOVA, CHI2, MRMR, and Kruskal–Wallis were used to enhance model performance, reduce dimensionality, and improve interpretability. Model performance was evaluated using 10-fold cross-validation based on accuracy, true positive rate (TPR), false negative rate (FNR), and AUC values. Results: Among all models and feature selection combinations, SVM with ANOVA-selected features achieved the highest classification accuracy (94.30%) and 100% TPR for the normal class. Feature sets based on traditional diagnostic regions (L1–L4, femoral neck, total femur) also showed high accuracy (up to 90.70%) but were generally outperformed by statistically selected features. CHI2 and MRMR methods also yielded robust results, particularly when paired with SVM and k-NN classifiers. The results highlight the effectiveness of combining statistical feature selection with ML to enhance diagnostic precision for osteoporosis and osteopenia. Conclusions: Machine learning algorithms, when integrated with data-driven feature selection strategies, provide a promising framework for automated classification of osteoporosis and osteopenia based on BMD data. ANOVA emerged as the most effective feature selection method, yielding superior accuracy across all classifiers. These findings support the integration of ML-based decision support tools into clinical workflows to facilitate early diagnosis and personalized treatment planning. Future studies should explore more diverse and larger datasets, incorporating genetic, lifestyle, and hormonal factors for further model enhancement. Full article

This research explores the optimization of mechanical properties and predictive modeling of polylactic acid (PLA) reinforced with boron nitride nanoplatelets (BNNPs) using data-driven machine learning (ML) models. PLA-BNNP composites were fabricated through injection molding, with a focus on how key processing parameters influence their mechanical performance. A Taguchi L27 orthogonal array was applied to assess the effects of BNNP composition (0.02 wt.% and 0.04 wt.%), injection temperature (135–155 °C), injection speed (50–70 mm/s), and pressure (30–50 bar) on properties such as tensile strength, Young’s modulus, and hardness. The results indicated that a 0.04 wt.% BNNP loading improved tensile strength, Young’s modulus, and hardness by 18.6%, 32.7%, and 20.5%, respectively, compared to pure PLA. Taguchi analysis highlighted that higher BNNP concentrations, along with optimal injection temperatures, improved all mechanical properties, although excessive temperatures compromised tensile strength and modulus, while enhancing hardness. Analysis of variance (ANOVA) revealed that injection temperature was the dominant factor for tensile strength (68.88%) and Young’s modulus (86.39%), while BNNP composition played a more significant role in influencing hardness (78.83%). Predictive models were built using machine learning (ML) models such as Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost). Among the ML models, XGBoost demonstrated the highest predictive accuracy, achieving R² values above 98% for tensile strength, 92–93% for Young’s modulus, and 96% for hardness, with low error metrics i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE). These findings underscore the potential of using BNNP reinforcement and machine learning-driven property prediction to enhance PLA nanocomposites’ mechanical performance, making them viable for applications in lightweight packaging, biomedical implants, consumer electronics, and automotive components, offering sustainable alternatives to petroleum-based plastics. Full article