Search Results (2,416)

Material conveying and collection are critical to harvesting efficiency, crop quality, energy consumption, and operational continuity in combine harvesters. However, existing studies mainly focus on individual technologies, while systematic criteria for technology comparison and selection remain insufficient. This review critically analyzes major conveying and collection technologies, mechanism-based simulation methods, and intelligent sensing and control strategies. Screw, clamping-flexible, chain/vibrating, and pneumatic conveying systems are compared in terms of conveying efficiency, crop damage and material loss, energy consumption, reliability, and adaptability. DEM, dynamic/vibro-acoustic analysis, and CFD–DEM are further evaluated according to their applicable mechanisms, physical fidelity, and computational cost. Recent advances in multi-source sensing, data-driven prediction, and feedforward–feedback control are summarized. Based on these comparisons, a system-level optimization framework is proposed, emphasizing efficiency, quality preservation, and energy efficiency while maintaining operational reliability and adaptability. The review indicates that no single technology is universally optimal; technology selection should be matched to crop properties, operating conditions, and dominant performance objectives. Future research should focus on material-property-informed technology selection, mechanism–data hybrid modeling, and adaptive closed-loop control for intelligent harvesting systems. Full article

Wood and wood-based materials represent low-carbon sustainable alternatives to petroleum sound absorbers, yet their baseline sound absorption coefficient varies drastically with wood species, anatomical cutting orientation and pore connectivity due to strong structural anisotropy. This review systematically integrates multiscale structural regulation, porous acoustic theories and data-driven optimization into a unified framework, revealing that broadband high sound absorption relies on the synergistic coordination of impedance matching, thermo-viscous dissipation and low-frequency resonant mechanisms, rather than simply maximizing porosity. We quantitatively compare state-of-the-art wood absorbers: directionally frozen wood aerogels achieve near-perfect absorption (α = 0.95–1.00, NRC = 0.82) across 520–6300 Hz, marking the current performance benchmark, while multifunctional superhydrophobic wood aerogels deliver moderate absorption (α ≈ 0.40) but stand out as all-biomass weather-resistant composites. Rigid-frame JCA/JCAL and poroelastic Biot models are clarified for wood’s distinct stiffness characteristics, and existing data-driven approaches are categorized, highlighting that most neural surrogates rely solely on FEM simulation without physical impedance-tube validation. Critical unresolved challenges including poor moisture/fire durability, insufficient industrial scalability and incomplete material databases are summarized, and targeted research priorities covering gradient manufacturing, hybrid physics–machine learning models and lifecycle environmental evaluation are proposed. Full article

Accurate prediction of transportation carbon emissions is essential for identifying emerging emission pressures and supporting the transition toward sustainable transportation systems. This study develops a hybrid prediction framework that integrates Elastic Net Regression (ENR), Beetle Antennae Search (BAS), and Generalized Regression Neural Network (GRNN) to estimate transportation-sector monthly carbon emissions from 2008 to 2023 in Anhui Province, China. ENR is employed to identify key influencing factors, while BAS optimizes GRNN parameters to enhance model convergence and generalization. In an ex-post out-of-sample evaluation using observed explanatory variables from 2023, comparative experiments show that the ENR–BAS–GRNN model achieves the highest accuracy (R² = 0.99, ARE < 1%), outperforming all benchmark models. The results indicate that integrating feature selection with intelligent optimization can effectively capture nonlinear emission dynamics. Beyond improving estimation accuracy, the proposed framework provides a quantitative tool for monitoring transportation-related carbon emissions and diagnosing the factors associated with emission changes. When coupled with scenario-based projections of explanatory variables, the framework can support ex-ante forecasting. The study therefore contributes a computationally lightweight and data-driven approach for integrating carbon emission measurement, monitoring, and policy support into regional sustainable transportation governance. Full article

Post-conflict reconstruction under prolonged blockade and governance fragmentation has rarely been examined empirically at the neighborhood scale. This qualitative single-case study investigates housing reconstruction in Al-Qubba, Gaza City, following the near-total destruction of the 2014 conflict. A three-dimensional analytical framework- physical quality, community agency, governance, and structural constraints is applied through the cross-cutting lens of Gaza’s blockade ecology. Data were triangulated across semi-structured interviews (n = 25), GIS spatial analysis, Space Syntax assessment (DepthmapX), and institutional document review. On physical quality, reconstruction met the minimum Build Back Better (BBB) threshold: reinforced-concrete roofing replaced pre-war structures, streets were standardized to 10 m and over 75% were paved, while 94.7% of residents expressed satisfaction; however, Space Syntax analysis revealed that Global Integration recovered only to 1.07 against a pre-war baseline of 1.28, indicating functional recovery without spatial optimisation, and public space provision was entirely unrealised. Regarding community agency, the hybrid owner-driven model produced multi-stage participation in 14/20 households (70%), substantially exceeding agency-driven equivalents, with participation depth positively associated with satisfaction through a perceived legitimacy mechanism; however, neighborhood-scale collective participation was absent. On governance, GRM material delays averaged three to six months; approximately 6/20 households (32%) were undercompensated by donor floor-area caps; and only 8/20 respondents (40%) reported concurrence between official and self-assessed damage valuations. The findings characterize Al-Qubba as demonstrating incremental resilience-building rather than transformative regeneration, and contribute four theoretical constructs: a two-tier BBB for siege contexts; blockade ecology as a BBB modifier; incremental resilience under siege; and configurational BBB assessment. As a single qualitative case study, findings are analytically transferable to comparable constrained reconstruction contexts rather than statistically generalizable. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

With the increasing penetration of wind and photovoltaic generation, microgrids are playing an increasingly important role in promoting renewable energy accommodation, enhancing operational flexibility, and enabling low-carbon energy management. However, the strong uncertainty of renewable generation and load demand, together with the coupling effects of electricity, hydrogen, and carbon markets, poses significant challenges to the optimal planning and operation of microgrid energy storage systems. To address these issues, this paper proposes a bi-level optimal sizing framework for an electric–hydrogen hybrid energy storage system (EHH-ESS) in a microgrid under multi-market coupling. First, typical wind–solar–load scenarios are generated using a Wasserstein generative adversarial network with gradient penalty (WGAN-GP), so as to capture the stochastic characteristics and temporal correlations of renewable generation and load demand. Then, a multi-market coupling index (MCI), integrating electricity price, hydrogen price, and carbon price signals, is constructed to characterize time-varying economic and low-carbon operating incentives and to guide coordinated dispatch decisions. On this basis, a bi-level multi-objective optimization model is established. The upper level determines the optimal capacities of battery storage, electrolyzers, fuel cells, and hydrogen tanks, while the lower level performs hourly coordinated operation of the microgrid under multi-market conditions. The model considers annual equivalent total cost, renewable energy curtailment rate, and carbon emissions as objective functions, and is solved using the NSGA-III algorithm. Compared with the no-storage benchmark, the proposed scheme improves the annual operating economics and renewable-energy accommodation under the studied market conditions. The proposed method significantly reduces annual operating cost and improves renewable energy accommodation. However, under the current carbon price and grid emission factor settings, the optimal economic solution increases carbon emissions relative to the baseline, indicating a trade-off between economic arbitrage and low-carbon operation. Full article

Accurate parameterization of the photovoltaic single-diode model is usually obtained from complete current–voltage (I-V) measurements. However, full I-V curve tracing is not always available in real monitoring environments, where the most accessible variables are irradiance and module temperature. This paper proposes a hybrid methodology for reconstructing the five parameters of the single-diode model from irradiance and temperature data. The method first estimates the maximum-power point and the remaining remarkable points of the I-V curve as well as the photocurrent ($I_{ph}$) through regression models calibrated on measured data. These predicted points are sufficient to solve the SDM equation. A numerical approach is then used to identify the five SDM parameters while enforcing physical admissibility constraints. The method is validated using NREL outdoor datasets from three locations and several photovoltaic technologies. The results show that the maximum-power current is estimated with very high reliability, with $R^2$ values close to unity in almost all cases. Voltage estimation is less stable and depends more strongly on technology and temperature sensor location. The reconstructed I-V curves are physically admissible for most crystalline silicon, HIT, and CdTe modules, whereas CIGS and amorphous silicon modules exhibit lower admissibility. The proposed method should therefore be understood as an environmental-data-driven reconstruction tool when complete I-V curves are unavailable, rather than as a replacement for direct full-curve fitting techniques such as TSLLS or Reduced Form. Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

The rapid evolution of AI-driven language technologies has inadvertently widened the gap between high-resource and marginalised languages. Despite significant progress in AI-driven translation for high-resource languages, low-resource languages remain underrepresented due to limited data, a lack of benchmarks, and evaluation challenges. This study presents a comprehensive systematic review of machine translation for low-resource languages, focusing on advances in neural machine translation (NMT) and large language models (LLMs) between 2017 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 63 studies were selected from the 1696 articles in the Scopus, Web of Science, and Google Scholar databases. The review identifies five dominant methodological approaches: data augmentation, back-translation, transfer learning, pre-training, and parameter-efficient fine-tuning. The findings reveal that model performance is highly dependent on resource availability: transformer-based NMT excels in moderate data settings, while LLMs demonstrate promising zero-shot and few-shot capabilities in extremely low-resource scenarios. Hybrid NMT–LLM approaches emerge as a particularly effective paradigm. The study also highlights critical challenges, including the absence of standardised benchmarks, over-reliance on inadequate evaluation metrics such as Bilingual Evaluation Understudy (BLEU), limited human evaluation, and significant geographic and linguistic underrepresentation. Additionally, ethical concerns related to bias, cultural representation, and community engagement are increasingly relevant. The findings contribute to advancing inclusive and equitable AI-driven language technologies. Full article

Geothermal energy and underground thermal storage (UTES) are vital to the low-carbon energy transition, yet their optimization is bottlenecked by multi-scale heterogeneity, coupled thermal–hydraulic–mechanical–chemical (THMC) processes, and the high computational cost of full-physics simulations. This review systematically evaluates machine learning (ML) as a foundational paradigm for overcoming these computational and scale-bridging challenges. We categorize current advances into three key functional roles. First, data-driven upscaling directly maps pore-scale features to macro-scale effective properties, replacing traditional empirical homogenization. Second, deep surrogate models mimic high-fidelity THMC simulations at a fraction of the computational cost, enabling real-time prediction and uncertainty quantification. Third, physics-informed digital twins integrate real-time sensor streams with cloud architectures for dynamic reservoir management. Furthermore, we address the generalization limits of purely data-driven approaches, highlighting physics-informed machine learning (PIML) and hybrid architectures that embed conservation laws as strict constraints. Finally, we outline future pathways toward multimodal data fusion and edge-cloud deployment, marking a shift from static offline modeling to dynamic, physics-safeguarded real-time reservoir optimization. Full article

This study addresses the integration of sustainability-oriented analytics and decision support in CNC milling through a hybrid information system combining structured data management, sustainability KPIs, rule-based expert reasoning, and machine learning models. The proposed framework is evaluated using the publicly available NASA Ames Milling Tool Wear Dataset. Sustainability indicators related to operational energy demand, tool degradation, and vibration/acoustic-emission response are computed from machining parameters and sensor-derived features. Random Forest and Support Vector Machine models are used for tool wear classification. The expert system applies deterministic rules to identify operational risks, which are combined with machine learning predictions through a hierarchical decision-fusion strategy. Under case-wise cross-validation, the Random Forest achieved a mean classification accuracy of 73.8%, while the Support Vector Machine achieved 68.8%. The expert system most frequently identified elevated vibration-index and acoustic-emission conditions, while critical clean-production risks occurred rarely. Overall, the results demonstrate the feasibility of integrating expert knowledge, sustainability KPIs, and data-driven models into a hybrid information system for decision support in CNC milling environments. The proposed framework provides a foundation for future research on hybrid information systems supporting sustainable manufacturing and intelligent decision making. Full article

Remote sensing change detection technology is widely used in land-use monitoring, urban planning, and disaster assessment. However, during imaging and transmission, bi-temporal remote sensing images are vulnerable to Gaussian noise, which makes it difficult for change detection algorithms to distinguish truly changed areas from noise-affected regions. To address this issue, this study proposes an unsupervised Gaussian-noise-robust change detection algorithm, termed FRIH-SEEDSAM. The proposed method first applies the Fast and Robust Fuzzy C-Means (FRFCM) algorithm to perform noise-resistant fuzzy clustering on bi-temporal remote sensing images. To establish reliable correspondences between the clustering results, the Integrated Region Matching (IRM) algorithm is introduced to construct weighted matching relationships while reducing the influence of abnormal memberships. The change intensity of spatially corresponding pixels is then calculated to generate a more stable change intensity map. Subsequently, the change intensity map is input into the Hybrid Conditional Random Field (HCRF) to infer pixel-level change labels, where the object potential function is constructed from the segmentation results of the Superpixels Extracted via Energy-Driven Sampling (SEEDS)-guided Segment Anything Model (SEEDSAM), which uses the centroids of the SEEDS superpixel regions as point prompts for the SAM, thereby enhancing change-label consistency within the same changed object region. The experimental results show that the FRIH-SEEDSAM algorithm maintains stable change detection performance across different datasets and under varying Gaussian noise levels. It outperforms the comparison algorithms in terms of several accuracy evaluation indicators, including Kappa and F1. Furthermore, even when the Gaussian noise variance increases to 0.05, Kappa remains at 0.8 or above on multiple dataset images. Full article

Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, has become an essential close-range remote sensing technique for high-precision engineering surveying, deformation monitoring, industrial inspection, and cultural heritage documentation. The geometric reliability of TLS point clouds strongly depends on the effective compensation of instrumental systematic errors through in situ self-calibration. However, conventional target-based self-calibration often suffers from strong coupling between calibration parameters and exterior orientation parameters, whereas recently developed coplanarity-constrained formulations generally require highly redundant target networks, limiting their field efficiency. To address this limitation, this study proposes a variance inflation factor (VIF)-driven minimal network design strategy for efficient in situ geometric self-calibration of TLS systems. Unlike the commonly used geometric dilution of precision, VIF provides a dimensionless statistical alternative that effectively resolves the dimensional inconsistency inherent in traditional GDOP when handling mixed angular and distance parameters. A differential evolution algorithm is employed to search for hybrid calibration networks that minimize parameter coupling while preserving the physical interpretability of the National Institute of Standards and Technology (NIST) 10-parameter instrumental error model. Five digital twin simulation experiments and a physical validation experiment using a Faro Focus 350 scanner were conducted to evaluate the proposed method. The results show that the optimized network substantially reduces the number of required targets while maintaining high calibration accuracy. The final configuration, which combines VIF-optimized target placement with a dual-station height-difference constraint, reduces the condition number of the normal equations to below 60 and yields a mean system VIF close to 10. The maximum parameter correlation coefficient among the key calibration parameters is constrained to approximately 0.75, indicating near-optimal parameter decoupling under the limited field-of-view geometry of the instrument. These findings demonstrate that the proposed VIF-driven network design provides a highly effective strategy for field-efficient TLS self-calibration and improves the geometric reliability of terrestrial LiDAR point clouds in high-precision remote sensing applications. Full article

The measurement accuracy issue of zero-sequence current transformers (CTs) has long been a critical factor restricting the accuracy of fault line selection in distribution networks. Although existing research methods are relatively mature in theory, their on-site application is limited by the measurement precision of zero-sequence CTs. To address this problem, this paper proposes a fault line selection strategy for distribution networks based on dynamic and accurate measurement of zero-sequence current. Firstly, from the data perspective, this paper analyzes the fault characteristics of various electrical quantities in different operation stages of distribution networks. Combined with system characteristics, an accurate measurement method for zero-sequence current amplitude is subsequently put forward. Afterwards, a distribution network fault line selection algorithm optimized by an attention mechanism-based multi-scale convolutional neural network is constructed. Finally, verification results based on the IEEE standard test system demonstrate that the proposed method enhances the capabilities of feature extraction and side information aggregation, realizes efficient and accurate localization of faulty lines, and exhibits strong robustness under noisy conditions. Full article

The Tianjin Container Freight Index (TCI) is a key benchmark for container shipping prices in northern China and is strongly driven by macroeconomic conditions and trade fluctuations. This study proposes a PCC–CNN–GRU hybrid deep learning framework for TCI forecasting using 25,870 daily observations from 13 April 2015, to 1 January 2024. The model combines Pearson Correlation Coefficient (PCC)-based feature selection, convolutional neural networks (CNN) for local temporal feature extraction, and gated recurrent units (GRU) for capturing long-term dependencies, thereby addressing the nonlinear and nonstationary characteristics of TCI data. Empirical results show that the proposed model achieves an R² of 91.24%, outperforming standalone CNN, GRU, and classical ARIMA and VAR models. The model demonstrates strong robustness to structural changes and noise, enhancing its suitability for complex market environments. The integrated framework provides reliable forecasting support for shipping companies, logistics planners, and policymakers in pricing, capacity planning, and sustainable maritime operations. This study contributes to the growing integration of intelligent forecasting methods with regional freight index analysis and supports the digital transformation of the container shipping industry. Full article