Search Results (1,091)

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

The estimation of the state of health (SOH) of lithium-ion batteries is a critical technology for enhancing battery lifespan and safety. When estimating SOH, it is essential to select representative features, commonly referred to as health indicators (HIs). Most existing studies primarily focus on HIs related to capacity degradation and internal resistance increase. However, due to the complexity of lithium-ion battery degradation mechanisms, the relationships between these mechanisms and health indicators remain insufficiently explored. This paper provides a comprehensive review of core methodologies for SOH estimation, with a particular emphasis on the classification and extraction of health indicators, direct measurement techniques, model-based and data-driven SOH estimation approaches, and emerging trends in battery management system applications. The findings indicate that capacity, internal resistance, and temperature-related indicators significantly impact SOH estimation accuracy, while machine learning models demonstrate advantages in multi-source data fusion. Future research should further explore composite health indicators and aging mechanisms of novel battery materials, and improve the interpretability of predictive models. This study offers theoretical support for the intelligent management and lifespan optimization of lithium-ion batteries. Full article

As Chinese open-pit mines progressively transition to deeper operations, challenges such as rising stripping ratios, declining slope stability, and environmental degradation have become increasingly pronounced. The sustainability of traditional open-pit mining models faces substantial challenges. Underground mining, offering higher resource recovery rates and minimal environmental disruption, is emerging as a pivotal technological pathway for the green transformation of mining. Consequently, the transition from open-pit to underground mining has emerged as a central research focus within mining engineering. This paper provides a comprehensive review of key technological advancements in this transition, emphasizing core issues such as mine development system selection, mining method choices, slope stability control, and crown pillar design. A typical case study of the Anhui Xinqiao Iron Mine is presented to analyze its engineering approaches and practical experiences in joint development, backfilling mining, and ecological restoration. The findings indicate that the mine has achieved multi-objective optimization of resource utilization, environmental coordination, and operational capacity while ensuring safety and recovery efficiency. This offers a replicable and scalable technological demonstration for the green transformation of similar mines around the world. Full article

As a key tropospheric photochemical pollutant, ground-level ozone (O₃) poses significant threats to ecosystems through its strong oxidative capacity. With China’s rapid industrialization and urbanization, worsening O₃ pollution has emerged as a critical environmental concern. This study examines O₃’s impacts on forest ecosystems in Southwestern China (Yunnan, Guizhou, Sichuan, and Chongqing), which harbors crucial forest resources. We analyzed high-resolution monitoring data from over 200 stations (2019–2023), employing spatial interpolation to derive the regional maximum daily 8 h average O₃ (MDA8-O₃, ppb) and accumulated O₃ exposure over 40 ppb (AOT40) metrics. Through AOT40-based exposure–response modeling, we quantified the forest relative yield losses (RYL), economic losses (ECL) and ECL/GDP (GDP: gross domestic product) ratios in this region. Our findings reveal alarming O₃ increases across the region, with a mean annual MDA8-O₃ anomaly trend of 2.4% year⁻¹ (p < 0.05). Provincial MDA8-O₃ anomaly trends varied from 1.4% year⁻¹ (Yunnan, p = 0.059) to 4.3% year⁻¹ (Guizhou, p < 0.001). Strong correlations (r > 0.85) between annual RYL and annual MDA8-O₃ anomalies demonstrate the detrimental effects of O₃ on forest biomass. The RYL trajectory showed an initial decline during 2019–2020 and accelerated losses during 2020–2023, peaking at 13.8 ± 6.4% in 2023. Provincial variations showed a 5-year averaged RYL ranging from 7.10% (Chongqing) to 15.85% (Yunnan). O₃ exposure caused annual ECL/GDP averaging 4.44% for Southwestern China, with Yunnan suffering the most severe consequences (ECL/GDP averaging 8.20%, ECL averaging CNY 29.8 billion). These results suggest that O₃-driven forest degradation may intensify, potentially undermining the regional carbon sequestration capacity, highlighting the urgent need for policy interventions. We recommend enhanced monitoring networks and stricter control methods to address these challenges. Full article

A reliable prediction of the remaining useful life of critical electronic components, such as insulated gate bipolar transistors, is necessary for preventing failures in many industrial applications. Recently, diverse machine-learning techniques have been used for this task. However, they are generally focused on capturing the temporal dependencies or on representing the probabilistic nature of the degradation of the device. This work proposes a neural network architecture that combines long short-term memory and mixture density networks to address both targets simultaneously when modeling the remaining useful life. The proposed model was trained and evaluated using a real dataset of insulated gate bipolar transistors, demonstrating a high capacity for predicting the remaining useful life of the validation devices. The proposed model outperformed the other algorithms considered in the study in terms of root mean squared error and coefficient of determination. In general terms, an average reduction of at least 18% of the root mean squared error was obtained when compared with the second-best model among those considered in this work, but in some specific cases, the root mean squared error during the prediction of remaining useful life decreased up to 21%. In addition to the high performance obtained, the characteristics of the network output also facilitated the creation of confidence intervals, which are more informative than solely exact values for decision-making. Full article

Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship. Full article

Large language models (LLMs) pose significant challenges to content authentication, as their sophisticated generation capabilities make distinguishing AI-produced text from human writing increasingly difficult. Current detection methods suffer from limited information capture, poor rate–distortion trade-offs, and vulnerability to adversarial perturbations. We present CurveMark, a novel dual-channel detection framework that combines probability curvature analysis with dynamic semantic watermarking, grounded in information-theoretic principles to maximize mutual information between text sources and observable features. To address the limitation of requiring prior knowledge of source models, we incorporate a Bayesian multi-hypothesis detection framework for statistical inference without prior assumptions. Our approach embeds imperceptible watermarks during generation via entropy-aware, semantically informed token selection and extracts complementary features from probability curvature patterns and watermark-specific metrics. Evaluation across multiple datasets and LLM architectures demonstrates 95.4% detection accuracy with minimal quality degradation (perplexity increase < 1.3), achieving 85–89% channel capacity utilization and robust performance under adversarial perturbations (72–94% information retention). Full article

Phosphorylated cellulose is proposed as a bio-resin for the removal of heavy metals, as a substitute for synthetic polymer-based materials. Phosphorylation is carried out using kraft pulp fibers as the cellulose source, with phosphate esters and urea as reactants to prevent significant fiber degradation. Herein, phosphorylated fibers, with three types of counterions (sodium, ammonium, or hydrogen), are used in adsorption trials involving four individual metals: nickel, copper, cadmium, and lead. The Langmuir isotherm model is applied to determine the maximum adsorption capacities at four different temperatures (10, 20, 30, and 50 °C), enabling the calculation of the Gibbs free energy (ΔG), entropy (ΔS), and enthalpy (ΔH) of adsorption. The results show that the adsorption capacity of phosphorylated fibers is equal or even higher than that of commercially available resins (1.7–2.9 vs. 2.4–2.6 mmol/g). However, the nature of the phosphate counterion plays an important role in the adsorption capacity, with the alkaline form showing a superior ion exchange capacity than the hybrid form and acid form (2.7–2.9 vs. 2.3–2.7 vs. 1.7–2.5 mmol/g). The thermodynamic analysis indicates the spontaneous (ΔG = (-)16–(-)30 kJ/mol) and endothermic nature of the adsorption process with positive changes in enthalpy (0.45–15.47 kJ/mol) and entropy (0.07–0.14 kJ/mol·K). These results confirm the high potential of phosphorylated lignocellulosic fibers for ion exchange applications, such as the removal of heavy metals from process or wastewaters. Full article

Ultra-high-performance concrete (UHPC) jackets present a promising solution for enhancing the seismic resilience of seismically deficient reinforced concrete (RC) bridge columns. This study investigates jacket thickness optimization through integrated experimental and numerical analyses. Quasi-static cyclic tests on a control column and a specimen retrofitted with a 30-mm UHPC jacket over the plastic hinge region demonstrated significant performance improvements: delayed damage initiation, controlled cracking, a 24.6% increase in lateral load capacity, 139.5% higher energy dissipation at 3% drift, and mitigated post-peak strength degradation. A validated OpenSees numerical model accurately replicated this behavior and enabled parametric studies of 15-mm, 30-mm, and 45-mm jackets. Results identified the 30-mm thickness as optimal, balancing substantial gains in lateral strength (~12% higher than other thicknesses), ductility, and energy dissipation while avoiding premature failure modes—insufficient confinement in the 15-mm jacket and strain incompatibility-induced brittle failure in the 45-mm jacket. These findings provide quantitative design guidance, establishing 30 mm as the recommended thickness for efficient seismic retrofitting of existing RC bridge columns. Full article

The discharge of synthetic dyes in industrial wastewaters poses a serious environmental threat as they are difficult to degrade naturally and are harmful to aquatic organisms. This study aimed to evaluate the feasibility of using clean untreated rice husk (CRH) as a sustainable and low-cost adsorbent for the removal of methylene blue (MB) from synthetic wastewater. This approach effectively avoids the energy-intensive grinding process by directly using whole unprocessed rice husk, highlighting its potential as a sustainable and cost-effective alternative to activated carbon. A series of batch adsorption experiments were conducted to evaluate the effects of key operating parameters such as initial dye concentration, contact time, pH, ionic strength, and temperature on the adsorption performance. Adsorption kinetics, isotherm models, and thermodynamic analysis were applied to elucidate the adsorption mechanism and behavior. The results showed that the maximum adsorption capacity of CRH for MB was 5.72 mg/g. The adsorption capacity was stable and efficient between pH 4 and 10, and reached the highest value at pH 12. The presence of sodium ions (Na⁺) and calcium ions (Ca²⁺) inhibited the adsorption efficiency, with calcium ions having a more significant effect. Kinetic analysis confirmed that the adsorption process mainly followed a pseudo-second-order model, suggesting the involvement of a chemisorption mechanism; notably, in the presence of ions, the Elovich model provided better predictions of the data. Thermodynamic evaluation showed that the adsorption was endothermic (ΔH° > 0) and spontaneous (ΔG° < 0), accompanied by an increase in the disorder of the solid–liquid interface (ΔS° > 0). The calculated activation energy (Ea) was 17.42 kJ/mol, further supporting the involvement of chemisorption. The equilibrium adsorption data were well matched to the Langmuir model at high concentrations (monolayer adsorption), while they were accurately described by the Freundlich model at lower concentrations (surface heterogeneity). The dimensionless separation factor (RL) confirmed that the adsorption process was favorable at all initial MB concentrations. The results of this study provide insights into the application of agricultural waste in environmental remediation and highlight the potential of untreated whole rice husk as a sustainable and economically viable alternative to activated carbon, which can help promote resource recovery and pollution control. Full article

The European Union is committed to reducing greenhouse gas emissions across all sectors, including the transportation sector. It is possible to assume that road freight transport will need to undergo technological changes, leading to greater use of alternative powertrains. This article builds on previous research on the energy consumption of battery electric trucks (BETs) and assesses the economic efficiency of electric vehicles in freight transport through a cost calculation. The primary objective was to determine the conditions under which a BET becomes cost-effective for a transport operator. These findings are practically relevant for freight carriers. Unlike other studies, this article does not focus on total cost of ownership (TCO) but rather compares the variable and fixed costs of BETs and conventional internal combustion engine trucks (ICETs). In this article, the operating costs of BETs were calculated and modeled based on real-world measurements of a tested vehicle. The research findings indicate that BETs are economically efficient, primarily when state subsidies are provided, compensating for the significant difference in purchase costs between BETs and conventional diesel trucks. This study found that optimizing operational conditions (daily routes) enables BETs to reach a break-even point at approximately 110,000 km per year, even without subsidies. Another significant finding is that battery capacity degradation leads to a projected annual operating cost increase of approximately 4%. Full article

Oral administration remains the preferred drug delivery route but faces formidable gastrointestinal barriers, including enzymatic degradation, solubility limitations, and poor epithelial absorption. Zein-based nanocarriers (ZBNs), derived from maize prolamin, provide a transformative platform to address these challenges. This review synthesizes recent advances in ZBNs’ design, highlighting their intrinsic advantages: structural stability across pH gradients, self-assembly versatility, and a surface functionalization capacity. Critically, we detail how engineered ZBNs overcome key barriers, such as enzymatic/chemical protection via hydrophobic encapsulation, the enhanced mucus penetration or adhesion through surface engineering, and improved epithelial transport via ligand conjugation. Applications demonstrate their efficacy in stabilizing labile therapeutics, enhancing the solubility of BCS Class II/IV drugs, enabling pH-responsive release, and significantly boosting oral bioavailability. Remaining challenges in scalability and translational predictability warrant future efforts toward multifunctional systems, bio-interfacial modeling, and continuous manufacturing. This work positions ZBNs as a potential platform for the oral delivery of BCS Class II–IV drugs’ in the biopharmaceutics classification system. Full article

Our research explores the dynamic relationship between CO₂ emissions and four major influencing factors: foreign direct investment (FDI), economic growth (GDP), renewable energy consumption (REN) and population (POP) in the Association of Southeast Asian Nations (ASEAN) and three large Asian countries—China, India and Pakistan, collectively referred to as LACs (larger Asian countries), from 1990 to 2022. The study has three main objectives: (1) to assess the short-run and long-run effects of GDP, FDI, REN and POP on CO₂ emissions; (2) to compare the adjustment speeds and environmental policy responsiveness between ASEAN and LAC regions; and (3) to evaluate the role of renewable energy in mitigating environmental degradation. Against the backdrop of increasing environmental challenges and divergent development paths in Asia, this research contributes to the literature by applying a dynamic heterogeneous panel autoregressive distributed lag (panel ARDL) model. Unlike traditional static panel models, the panel ARDL model captures both long-run equilibrium relationships and short-run adjustments, allowing for country-specific dynamics. The results reveal a significant long-run cointegration among the variables. The error correction term (ECT) indicates a faster adjustment to equilibrium in LACs (−1.18) than ASEAN (−0.37), suggesting LACs respond more swiftly to long-run disequilibria in emissions-related dynamics. This may reflect more responsive policy mechanisms, stronger institutional capacities or more aggressive environmental interventions in LACs. In contrast, the slower adjustment in ASEAN highlights potential structural rigidities or delays in implementing effective policy responses, emphasizing the need for enhanced regulatory frameworks and targeted climate strategies to improve policy intervention efficiency. Results show that GDP and FDI increase emissions in both regions, while REN reduces them. POP is insignificant in ASEAN but increases emissions in LACs. These results provide insights into the relative effectiveness of policy instruments in accelerating the transition to a low-carbon economy, highlighting the need for differentiated strategies that align with each country’s institutional capacity, development stage and energy structure. Full article

Mangroves, as a key component of the blue-carbon ecosystem, have exceptional carbon sequestration capacity and are mainly distributed in tropical coastal regions. In the Solomon Islands, ongoing degradation of mangrove forests, primarily due to land conversion and timber exploitation, highlights an urgent need for high-resolution spatial data to inform effective conservation strategies. The present study introduces an efficient and accurate methodology for mapping mangrove habitats and prioritizing protection areas utilizing open-source satellite imagery and datasets available through the Google Earth Engine platform in conjunction with a U-Net deep learning algorithm. The model demonstrates high performance, achieving an F1-score of 0.834 and an overall accuracy of 0.96, in identifying mangrove distributions. The total mangrove area in the Solomon Islands is estimated to be approximately 71,348.27 hectares, accounting for about 2.47% of the national territory. Furthermore, based on the mapped mangrove habitats, an optimized hotspot analysis is performed to identify regions characterized by high-density mangrove distribution. By incorporating spatial variables such as distance from roads and urban centers, along with mangrove area, this study proposes priority mangrove protection areas. These results underscore the potential for using openly accessible satellite data to enhance the precision of mangrove conservation strategies in data-limited settings. This approach can effectively support coastal resource management and contribute to broader climate change mitigation strategies. Full article

Accurate prediction of lithium-ion battery capacity degradation is crucial for reliable state-of-health estimation and long-term performance assessment in battery management systems. This study presents an empirical modeling approach based on experimental data collected from four lithium iron phosphate (LFP) battery packs cycled over 75 to 100 charge–discharge cycles. Several mathematical models—including linear, quadratic, single-exponential, and double-exponential functions—were evaluated for their predictive accuracy. Among these, the linear and single-exponential models demonstrated strong performance in early-cycle predictions. It was found that using 30 to 40 cycles of data is sufficient for reliable forecasting within a 100-cycle range, reducing the mean absolute error by over 80% compared to using early-cycle data alone. Although these models provide reasonable short-term predictions, they fail to capture the nonlinear degradation behavior observed beyond 80 cycles. To address this, a modified linear model was proposed by introducing an exponentially decaying slope. The modified linear model offers improved long-term prediction accuracy and robustness, particularly when data availability is limited. Capacity forecasts based on only 40 cycles yielded results comparable to those using 100 cycles, demonstrating the model’s efficiency. End-of-life estimates based on the modified linear model align more closely with typical LFP specifications, whereas conventional models tend to underestimate the cycle life. The proposed model offers a practical balance between computational simplicity and predictive accuracy, making it well suited for battery health diagnostics. Full article