Search Results (7,850)

Land use and land cover (LULC) dynamics are critical indicators of environmental transformation and anthropogenic pressure on regional landscapes. Mirzapur, located in the transitional zone between the Indo-Gangetic Plain and the Vindhyan uplands in Northern India, represents a region characterized by ecological sensitivity, mineral-based industries, agricultural dependency, and rapid infrastructural growth. In recent decades, Northern India has experienced accelerated urbanization, population pressure, land fragmentation, and environmental stress, thus making systematic LULC monitoring crucial for sustainable resource management and policy planning. The present study examines the spatio-temporal changes in land use and land cover in Mirzapur for the years 2004, 2014, and 2024. The study employed a cloud-based platform and the Random Forest algorithm for supervised classification of multi-temporal satellite imagery. LULC maps were generated and post classification comparison was used to assess changes across the selected years. Accuracy assessment was conducted using standard validation metrics, including the Kappa coefficient, to evaluate classification. From 2004 to 2024, urban areas expanded by a relative increase of 169.36%, largely through the conversion of cropland, although the overall cropland area showed a slight increase due to agricultural expansion in other parts of the study area. A slight increase in forest cover was also observed during this period. Water bodies and barren lands declined, indicating ecological stress in the region. These changes reflect rapid urbanization, demographic pressure, and evolving socio-economic activities within the district. The LULC classification achieved overall accuracies of 96.50% (2004), 97.52% (2014), and 96.08% (2024), showing the reliability of the generated maps. The study demonstrates the effectiveness of cloud-based geospatial analysis combined with a machine learning algorithms for long-term LULC monitoring and provides valuable insights for sustainable land management and regional planning. Full article

Compressor surge poses a critical safety risk for aero-engines. However, conventional physics-driven detection methods—relying on single-domain features and fixed empirical thresholds—struggle to adapt across varying compressor configurations, wide operating ranges, and complex interference environments. This paper proposes a multi-feature fusion adaptive surge detection method that integrates time-domain amplitude, frequency-weighted power and slope features within a joint threshold criteria, enabling reliable and adaptive surge detection according to the statistical characteristics of the signal itself. A wavelet-based preprocessing strategy is established with the db4 wavelet and four-level decomposition identified as the optimal setting through systematic evaluation. A novel feature FWP is introduced herein, which applies frequency-dependent weighting to the power spectral density to suppress noise components while amplifying energy changes within surge-relevant bands, achieving 1.7 to 6.1 times greater magnitude variation near the surge point compared with total spectral power. The slope feature is further discovered to distinguish surge from transient interferences such as rapid valve throttling, fuel stepping and rapid acceleration. Among 100 samples, the three-feature joint detection strategy integrated with adaptive threshold criteria improves accuracy from 61% to 98%. A Bayesian optimization framework using Gaussian process surrogate models is developed for efficient cross-engine hyperparameter tuning, converging to optimal solutions within merely 11 to 13 iterations across two distinct compressors. Lastly, the method is implemented on an NI cRIO-based real-time platform and validated on two distinct ten-stage high-pressure compressors, covering surge tests across a wide speed range of 45% to 98%. Comparative tests against an industry-standard reference device demonstrate earlier warning lead times of 41 to 99 ms. The results confirm that the proposed method herein achieves high accuracy, strong robustness against operational interferences, and good cross-platform adaptability for practical application. Full article

Understanding how globalization, energy transition, and resource dependence shape environmental pressure remains critical for resource-rich economies seeking sustainable development. This study investigates the determinants of ecological footprint per capita in the United Arab Emirates from 1992Q1 to 2020Q4 by extending the STIRPAT framework to incorporate scale effects, structural composition, technological mitigation, and a globalization–renewable energy interaction channel. The empirical strategy combines ARDL cointegration modeling, Ridge regression and annual frequency estimations for robustness assessment, wavelet coherence analysis, and ARDL-ECM Granger causality tests. The results show that economic growth increases ecological footprint in the short run, reflecting persistent affluence-related scale effects. In the long run, economic globalization and natural resource rents significantly increase ecological footprint, suggesting that trade- and hydrocarbon-driven composition effects outweigh potential efficiency gains during the study period. Renewable energy consumption exerts a negative long-run elasticity, indicating its technological mitigation role. However, the positive globalization–renewable energy interaction indicates that expanding economic integration partially offsets the environmental benefits associated with renewable energy deployment. Wavelet coherence analysis reveals that these relationships vary across time and frequency horizons, with globalization exhibiting leading associations with ecological pressure at medium-term frequencies, while Granger causality identifies significant predictive pathways toward ecological footprint dynamics. The findings remain consistent across robustness assessments and suggest that renewable energy contributes to reducing ecological pressure, but achieving substantial ecological decoupling requires both fossil fuel substitution and structural transformation in globalization and resource-dependent development pathways. This study provides evidence-based insights for supporting sustainability transitions in resource-dependent economies and advancing progress toward the SDGs. Full article

The conventional fabrication of concrete block moulds is characterised by persistent challenges related to standardisation, protracted redesign processes, and an absence of structural validation, all of which undermine regulatory compliance. This study proposes a standardised parametric modelling process aimed at ensuring compliance with the technical criteria of the INEN-3066 and ASTM C90 standards. An integrative methodology combining QFD/VOC matrices with CAD-CAE tools was used to parameterise three commercial mould configurations (10, 15, and 20 cm) in SolidWorks 2023. A finite element analysis (FEA) was subsequently conducted in ANSYS 2025 R1 under iterative overloads of up to 10,000 N, complemented by a rheological analysis in SolidWorks Plastics. The results show that the “male” (punch) components exhibit consistently high stiffness, maintaining fatigue safety factors above 1.61 across all three configurations. In contrast, the “female” (die) components are the more vulnerable link in the assembly: the fatigue safety factor of the 10 cm die drops below the required threshold of 1.0 at 4000 N, compared with 6175.6 N and 9254 N for the 15 and 20 cm configurations, respectively. The rheological analysis further confirmed the feasibility of an ultrafast injection cycle, with cavity filling times below 0.11 s and injection pressures ranging from 6.105 to 12.9 MPa across all formats. It is posited that, in accordance with the parametric model, a reinforced-wall geometry should be adopted for the 10 cm die, characterised by an augmentation of wall thickness by 15% and enlarged fillet radii. This is projected to elevate its fatigue-critical load beyond 4500 N without necessitating any alteration in the external block dimensions. These findings indicate that parametric CAD-CAE-CFD digitalisation can anticipate structural failures before manufacturing, offering a computational pathway toward regulatory compliance that should be confirmed through physical prototype testing. Full article

Provincial cold-chain investment decisions must reconcile food-loss prevention, digital visibility, logistics capability and the environmental burden of freight-intensive growth. This study develops a sustainability-oriented readiness–intensity framework for 31 provincial-level regions in mainland China. The baseline model uses 14 auditable public-data criteria and combines Criteria Importance Through Intercriteria Correlation (CRITIC) with the standard Combined Compromise Solution (CoCoSo) algorithm. Because the observations combine 2024 statistics, a 2023 digital-finance index and the cumulative 2020–2025 cold-chain-base list, the design is described as an asynchronous cross-sectional snapshot rather than a single-year panel. Municipal sewage and green-space variables are interpreted as regional enabling capacity, not direct cold-chain environmental performance; road freight turnover relative to gross domestic product is treated as a cost-type freight-intensity transition-pressure proxy. A separate diagnostic replaces the earlier inverse-size term with logistics residuals conditional on agri-food output. Shandong, Guangdong, Jiangsu, Henan and Zhejiang form the leading demonstration-readiness group. Equal-weight CoCoSo closely matches the CRITIC result (Spearman ρ = 0.996), while TOPSIS and VIKOR retain the broad ordering but expose local method sensitivity. Dropping either digital criterion, removing the three indirect green proxies, winsorizing the normalization range, varying the CoCoSo compromise parameter and substituting 2022 digital data do not alter the leading pattern. Under an assumed 5% indicator-error perturbation, Shandong and Guangdong remain within ranks 1–2, whereas the ordering of several adjacent provinces is less secure. The framework supports sequenced investment packages rather than a deterministic league table and distinguishes demonstration-ready, scale-led, intensity-led and coverage-building contexts. Full article

Drying is widely used to stabilize foods, but long processing times and thermal exposure increase energy demand and can impair color, texture, nutrients, and flavor. Cold plasma (CP) pretreatment has attracted interest as a nonthermal strategy for accelerating moisture removal while maintaining product quality. This critical review examines CP pretreatment-assisted drying across food materials by linking structural remodeling with redox regulation and metabolic responses. Current evidence shows that changes in surface wettability, cuticular barriers, cell-wall and membrane integrity, and pore connectivity can facilitate water migration and shorten drying. CP-associated modulation of browning enzymes, oxidative status, and bioactive or flavor-related metabolites may also influence color, antioxidant capacity, nutrient retention, and flavor. However, these effects vary with discharge mode, treatment intensity, gas composition, pressure, temperature, and food-matrix properties. Excessive exposure can instead aggravate oxidation and diminish product quality. Current mechanistic evidence is strongest for plant foods and edible fungi and remains limited for animal-source foods. Together, these findings link plasma-generated chemical and physical agents to structural, biochemical, and drying responses. They provide a basis for defining material-specific operating windows and developing reproducible, safe, and scalable CP pretreatment-assisted drying of foods. Full article

This study investigates the empirical links between climatic/socio-economic factors and Consumer Price Food Indices (CPFIs) across 47 advanced and emerging economies from 2001 to 2022. Utilizing a balanced panel dataset compiled from the World Bank’s World Development Indicators, FAO databases, and UNDP Human Development Reports, the analysis applies fixed-effects and random-effects panel data models to evaluate the drivers of food price dynamics. The empirical results reveal that socio-economic variables, specifically the Human Development Index (HDI), food price inflation, and agricultural productivity, are consistently associated with food indices, underscoring the critical role of structural development and nominal inflationary pressures. Climatic factors, particularly temperature anomalies, also exert a significant impact: a one-degree Celsius increase in temperature anomalies is associated with a 0.82-unit rise in the food price index level, whereas expansions in forest area and agricultural land are linked to price reductions. These findings indicate that food price dynamics are shaped by the intricate interplay of climate variability and socio-economic conditions. Consequently, policy recommendations emphasize targeted investments in socio-economic development, climate adaptation measures (including support for climate-resilient agriculture), and sustainable land management (forest conservation and optimized land use) to enhance food system resilience and support long-term food security. Full article

To address the critical role of armature–rail contact in electromagnetic railguns, a comprehensive electromagnetic–thermal–mechanical coupled model is developed. In contrast to existing coupled railgun models, this work uniquely introduces the dynamic mechanical contact state and contact resistance as coupling variables and explicitly accounts for the interference−fit process during armature loading, enabling a full−cycle simulation from assembly to launch. The simulation results are compared with open−bore experimental measurements, and the model is applied to simulate the launch process. The results reveal a characteristic evolution of contact resistance: a rapid initial decrease followed by a gradual increase, maintaining relatively stable conditions until muzzle exit. Mechanistically, the early−stage decrease is attributed to transverse Lorentz forces that enlarge the contact area, while the later−stage stability is governed by thermal expansion, preserving contact pressure. Parametric studies further elucidate the influence of operating conditions on contact resistance. Full article

Terrestrial vegetation cover is a critical component of the global carbon cycle, annually assimilating a substantial fraction of anthropogenic CO₂ emissions. However, regional estimates of gross primary production (GPP) and net primary production (NPP) remain insufficiently studied, especially for ecologically sensitive areas such as the sub-Mediterranean landscapes of southeastern Crimea. The aim of this study is to calculate and map the spatio-temporal distribution of GPP and NPP across southeastern Crimea over the period 2001–2025 using Earth remote sensing data and geoinformation modeling. This study employed MODIS products (MOD17A2H collection 061) processed in the Google Earth Engine cloud platform, together with temperature and precipitation data (ClimateEU, CHIRPS). Statistical analysis included calculation of the carbon use efficiency (CUE) coefficient and correlation analysis. The results show that the mean GPP for southeastern Crimea is 1.13 kg C/m² and the mean NPP is 0.61 kg C/m², which exceed global average values. Maximum productivity is characteristic of natural forest communities (sessile oak, beech and juniper forests), whereas anthropogenically transformed landscapes (agricultural land, urban coenoses) exhibit the lowest values. The mean CUE is 0.54, with the highest values (0.63–0.66) recorded for agrocoenoses and steppes, and the lowest (0.49–0.57) for forests. A positive correlation between productivity and precipitation and a negative correlation with air temperature were identified, especially for forest ecosystems. This study fills a gap in regional primary productivity assessments and can serve as a basis for ecosystem monitoring under climate change and anthropogenic pressure. Full article

Mineral-scale deposition remains a persistent flow-assurance and asset-integrity constraint in oil and gas production. Calcium carbonate, calcium sulfate, barium sulfate, iron sulfide, and mixed inorganic scale deposits reduce tubing internal diameter. They also impair near-wellbore permeability, block safety-critical valves, reduce heat-transfer efficiency, and intensify under-deposit corrosion. Conventional management relies on prediction, chemical inhibition, squeeze treatments, acid dissolution, chelation, mechanical scraping, milling, jetting, and operational water management. These methods are indispensable, but each has a restricted operating envelope. Key limitations include mineral selectivity, corrosion risk, environmental discharge, intervention cost, debris generation, and poor effectiveness against chemically resistant sulfate scales, particularly BaSO₄. Ultrasound has therefore attracted interest as a non-chemical technology. Acoustic cavitation, microstreaming, pressure oscillation, mechanical vibration, and micro jetting may suppress nucleation, disturb boundary layers, weaken adhesion, and fragment brittle deposits. This review critically evaluates ultrasound-assisted scale prevention and removal, with emphasis on production tubing and oilfield relevance. Existing studies show credible mechanistic plausibility and promising laboratory performance for CaCO₃, CaSO₄/gypsum, KCl, NaCl, and membrane or heat-transfer fouling systems. It also compares performance metrics, field cases, and technology-readiness barriers. The evidence is less mature for long steel tubulars operating under high-pressure, high-temperature, multiphase production conditions. Current evidence positions ultrasound at technology-readiness level (TRL) 3–4 for CaCO₃ and CaSO₄ systems, where laboratory and bench-scale validation is established, and at TRL 2–3 for BaSO₄, where mechanistic plausibility exists but controlled experimental validation remains absent. The technology is not yet at the pilot–production transition for downhole tubing applications, but it is approaching that threshold for surface process equipment. Its most credible near-term role is as an intensifier paired with low-dose chemical inhibitors, where acoustic boundary-layer disruption can reduce the minimum inhibitory concentration threshold of inhibitors, and with mild chelating agents for early-stage BaSO₄ management, where ultrasound-enhanced mass transfer may accelerate chelant penetration into deposit microstructure. Advancing ultrasound from its current TRL toward field qualification requires targeted BaSO₄ scale validation in steel tubing systems, acoustic field mapping under HPHT multiphase conditions, mass-removal metrics, and a structured pilot programme. Full article

Cities, being elementary concentrations of socio-economic activities and resource-environmental pressures, confront significant challenges in promoting new quality productive force (NQPF) development because technology, resource, and information conditions may be spatially associated across cities. Although the nexus among cities has gained widespread recognition in China’s high-quality development such as in innovations and low-carbon transformation, a critical gap exists in quantitatively assessing how model-estimated inter-city spatial correlations relate to high-quality development within an integrated framework. To bridge this gap, this study constructs an urban social correlation network (SCN) analysis framework, integrates spatial correlation methods to overcome the limitations of traditional heterogeneity assessment, and applies it to 283 Chinese cities from 2010 to 2023 to measure network characteristics and structural resilience related to NQPF. The results show that the network density rose from 0.0608 in 2010 to 0.1577 in 2023. By 2023, the SCN featured higher connectivity and reciprocity, comparatively high but lower efficiency, low hierarchy, and no obvious core–periphery structure. The 283 cities were divided into four blocks, exhibiting denser intra-regional than inter-regional links, and strengthened inter-regional interactions over time. The displayed node-removal trajectories declined faster under centrality-ordered removal than under one reported random-removal sequence. The network was more vulnerable to intentional attacks than to random attacks. Motif analysis indicates that M1 and M2 dominated and contributed to low network density, while the shift from open structural holes toward a mix of open and closed structures was associated with network evolution. These findings indicate that cultivating key node cities and improving inter-city coordination mechanisms to enhance network resilience are critical pathways for advancing NQPF development with Chinese characteristics, providing quantitative evidence for targeted inter-city coordination policymaking. Full article

High-speed turbulent boundary layers govern the transport of momentum, mass, and energy in compressible flows and play a central role in determining aerodynamic performance, skin-friction drag, aerodynamic heating, flow stability, and thermal protection requirements of advanced aerospace vehicles. Over the past three decades, direct numerical simulation (DNS) has revolutionized the study of compressible wall-bounded turbulence by resolving all dynamically relevant turbulent scales without turbulence-model assumptions, providing benchmark-quality databases and unprecedented physical insight into flow phenomena that remain difficult or impossible to measure experimentally. Together with complementary high-fidelity approaches, DNS has substantially advanced the understanding of turbulence dynamics across a broad range of supersonic and hypersonic flow conditions. This review presents a critical assessment of advances in the high-fidelity simulation of compressible turbulent boundary layers under non-reacting conditions. Particular emphasis is placed on the flow physics of canonical zero-pressure-gradient boundary layers, shock-wave/turbulent-boundary-layer interactions (SWTBLIs), pressure-gradient-driven flows, streamline-curvature effects, and thermochemical non-equilibrium phenomena. Recent developments in numerical methodologies are also briefly examined, including high-order discretization techniques, turbulence inflow generation methods, hybrid continuum-kinetic formulations, and advances in high-performance computing that have enabled DNS at increasingly high Reynolds and Mach numbers. The review highlights the major physical insights emerging from DNS studies, demonstrating that many fundamental characteristics of compressible wall turbulence remain closely related to their incompressible counterparts when appropriate compressibility transformations are employed. At the same time, DNS has revealed the critical influence of wall temperature, pressure gradients, streamline curvature, shock interactions, and finite-rate thermochemistry on turbulence structure, coherent motions, interscale energy transfer, boundary-layer separation, and aerodynamic heating. Full article

The growing demand for clean-label, high-quality, and sustainable meat products has increased interest in non-conventional processing technologies as alternatives to conventional processing methods. Therefore, this review aims to critically evaluate the technological advances, practical benefits, limitations, and industrial implementation potential of cold plasma, high hydrostatic pressure, ultrasound, microwave processing, and ohmic heating in meat and meat products. Studies published between 2016 and 2026 were analyzed, with emphasis on mechanisms of action, effects on physicochemical and microbiological properties, processing performance, and evidence of industrial applicability. Current evidence indicates that these technologies have progressed beyond laboratory-scale investigations in several applications, with HHP showing the highest level of commercial adoption, particularly in ready-to-eat meat products, while ultrasound, cold plasma, microwave processing, and ohmic heating exhibit different degrees of pilot- and industrial-scale development depending on the application. These technologies can enhance microbial safety, improve techno-functional properties, optimize processing efficiency, and contribute to shelf-life extension while reducing reliance on synthetic additives. However, their effectiveness is strongly influenced by processing conditions, product composition, economic feasibility, and technology-specific limitations. Reported challenges include lipid oxidation, color deterioration, texture modifications, heating non-uniformity, high implementation costs, limited process standardization, and regulatory uncertainties. Overall, recent advances demonstrate meaningful progress toward industrial application, but the degree of technological maturity varies substantially among technologies and applications. Further research should prioritize industrial-scale validation, process standardization, techno-economic assessment, regulatory harmonization, and consumer acceptance to facilitate broader commercial adoption. Full article

Taiwan has one of the highest dialysis prevalences worldwide, making safe and reliable hemodialysis monitoring a critical sensor-based healthcare challenge. Modern hemodialysis machines integrate heterogeneous multimodal sensors (pressure, flow, conductivity, temperature, and cardiovascular signals), but differences in machine brands, data formats, and privacy constraints hinder centralized learning and robust complication prediction. This work proposes a Medical IoT-oriented federated learning framework, PSOFed-HD, that performs dual-layer Particle Swarm Optimization (PSO) to enhance heterogeneous sensor fusion for predicting dialysis-related hypotension and discomfort events. The events are defined as abnormal blood-pressure states, defined as systolic blood pressure <90 mmHg. Each hemodialysis machine is paired with an edge gateway acting as an FL client, where local PSO optimizes CNN feature weights over non-IID sensor subsets, while the central server applies PSO-driven aggregation to adaptively weight client models according to validation performance. Experiments on real-world hemodialysis datasets with 17 most commonly seen HD physiological features demonstrate that standard FedAvg yields an accuracy of 65.24% and F1-score of 0.5318, server-side PSO improves accuracy to 75.11%, and client-side PSO further raises accuracy to 81.97%. The proposed dual-layer PSO framework achieves the best performance, with 90.56% accuracy and an F1-score of 0.8533, along with superior ROC characteristics (AUC = 0.908) and stable cross-validation across 11 folds. State-of-the-art federated learning techniques for non-IID data such as SCAFFOLD and FedProx are also examined using the same heterogeneous HD dataset. The performance is close to our client-only PSO techniques, proving the merits of our dual-layer PSO architecture. These results confirm that jointly optimizing local feature representations and global aggregation weights enables effective fusion of heterogeneous hemodialysis sensor data under privacy-preserving Medical IoT constraints, providing a practical decision-support approach for real-time complication prediction in dialysis units. Future work will incorporate temporal models such as LSTM or Transformer architectures to achieve early event prediction. Full article

Green finance has emerged as a critical mechanism for aligning capital markets with climate and sustainability objectives, particularly as economies face mounting pressure to transition away from carbon-intensive growth models. In hydrocarbon-dependent regions such as the Gulf Cooperation Council (GCC), this transition poses distinctive challenges that require integrated institutional, policy, and financial frameworks. The global transition toward sustainable finance has gathered significant momentum, with green capital markets emerging as a central mechanism for channeling investment toward climate and development objectives. Hydrocarbon-dependent economies face a distinctive challenge in this transition, as they must reconcile resource-based growth models with rising pressures for environmental accountability and low-carbon diversification. This study develops an integrated theoretical framework to examine how Gulf Cooperation Council (GCC) financial systems are transitioning toward green capital markets, drawing on institutional theory, environmental policy pathway analysis, and climate-finance alignment models. Using descriptive statistics from regional stock exchanges covering 2015–2024, the study maps key trends in sustainable asset growth, institutional investor preferences, and regulatory evolution across the GCC. Findings indicate progressive alignment with global ESG norms; sustainable asset valuations grew at 23.5% CAGR (UAE) and 18.7% CAGR (Saudi Arabia). A fixed-effects panel regression with panel-corrected standard errors is estimated across all six GCC economies; regulatory framework maturity emerges as the strongest predictor of green bond issuance (β = 0.47, p < 0.01). Cumulative green bond issuances reached USD 52.6 billion (2015–2024), with renewable energy accounting for 58.1% of the sectoral allocation and green transportation recording a 55.9% CAGR (2020–2024). Policy recommendations focus on GCC-wide harmonization of mandatory ESG disclosure, adoption of a unified green bond taxonomy, and expansion of concessional green financing mechanisms. Substantial cross-country heterogeneity is documented, driven by differences in energy policy commitment, financial market maturity, and institutional capacity. The proposed framework offers specific policy guidance to accelerate green financial integration in the GCC, emphasizing regulatory harmonization, institutional capacity-building, and alignment with SDG targets 7 and 13. The study contributes to the limited evidence base on green finance in hydrocarbon-dependent economies and provides a foundation for future empirical research. Given the small panel dimensions (N = 6 cross-sectional units; T = 10 years), this study is positioned as exploratory rather than confirmatory: the panel-regression estimates and the hypothesized institutional-to-policy-to-finance sequence are interpreted as associational patterns consistent with the proposed framework rather than as definitive causal tests, and the reported coefficients are offered as indicative magnitudes to be re-examined as longer GCC green-finance time series become available. Full article