Search Results (4,976)

The global polymer industry faces increasing pressure to diminish its environmental impact. However, traditional Environmental Impact Assessment (EIA) frameworks evaluate sustainability incrementally rather than as an integrated system, and empirical evidence from rapidly industrializing Gulf economies remains limited. This study investigates the Sustainable Conceptual Model for Environmental Impact Assessment in Polymer Production (SCM-EIAPP), founded on the Resource-Based View, which correlates five operational practices—Choice of Feedstock, Process Optimization, Energy Efficiency, Waste Management, and Recycling Options—with perceived EIA outcomes. Data obtained from a survey of 207 professionals within the polymer industry in the United Arab Emirates were analyzed through confirmatory factor analysis, ordinary least squares regression, partial least squares structural equation modelling (PLS-SEM), and mediation analysis. The measurement model exhibited acceptable fit and reliability. Collectively, the five practices explained approximately 39.6% of the variance in perceived EIA outcomes (PLS-SEM R² = 0.396), with the OLS estimate closely aligned at R² = 0.376. Among these, Recycling Options was the strongest predictor (β = 0.303), followed by Waste Management (β = 0.261), Process Optimization (β = 0.170), and Energy Efficiency (β = 0.168). Choice of Feedstock showed a marginal, method-dependent association (PLS-SEM β = 0.103; OLS p = 0.086). Two mediation pathways were analyzed. The hypothesized mediation of the Waste Management–EIA link via Process Optimization was not statistically significant. Conversely, Process Optimization exhibits a significant indirect effect in the Energy Efficiency–EIA relationship, with approximately 24% of Energy Efficiency’s total association operating through the process capability pathway—aligning with Resource-Based View capability spillover theory. Practices that are internally controllable—particularly Energy Efficiency—appear to be reliable levers, while material recovery practices (Recycling and Waste Management) showed the strongest overall associations for environmental improvement within resource-constrained Gulf contexts. These insights offer industry leaders and policymakers a data-driven basis for prioritizing investments in sustainability and advancing circular economy initiatives. Full article

Accurate monocular pose estimation of spacecraft is essential for on-orbit servicing and proximity operations, yet this remains challenging because of large variations in the target scale and limited onboard computational resources. To address these challenges, this paper proposes SAPose, a lightweight geometry-guided pose-estimation framework that integrates multi-task keypoint prediction with geometric pose recovery. The proposed network jointly performs spacecraft detection and semantic keypoint localization using a stage-aware lightweight backbone and a scale-adaptive feature fusion structure, thereby enhancing geometric feature representation across different target scales. For distant spacecraft occupying only a small portion of the image, an adaptive coarse-to-fine inference strategy selectively activates ROI-based secondary refinement to recover weakened structural details while avoiding unnecessary computation for medium- and large-scale targets. In addition, confidence-ranked keypoint selection and object-space nonlinear refinement are employed to improve the stability and accuracy of pose recovery. Experiments on the Spacecraft Pose Estimation Dataset (SPEED) and the Spacecraft Keypoint Dataset (SKD) demonstrate that SAPose achieves competitive pose estimation accuracy with a compact model size and efficient inference, providing a practical balance between accuracy and computational cost for monocular spacecraft pose estimation. Full article

Agricultural machinery operates under variable loads, impacts, dust, and changing soil–crop interactions, allowing faults to propagate through energy, material, information, and control pathways. This qualitative review synthesizes 195 research publications across fault formation, trustworthy diagnosis, prognostics and proactive risk control, maintenance and recovery, and case-based evidence assessment. Empirical findings are reported separately from review-derived recommendations, the authors’ conceptual requirements, and mandatory provisions of applicable standards or law. The literature most consistently supports controlled-fault identification, selected single-machine field monitoring, and localized operational compensation. Evidence is weaker for transfer across machines and seasons, calibrated prognostics, safety authorization, post-repair verification, and fleet-scale deployment. On this basis, the review recommends mission profile-specific fault boundaries, traceable diagnostic outputs, explicit uncertainty and abstention, and risk decisions linked to remaining work and resources. It also proposes a diagnostic passport, risk evolution trajectory, repair-effectiveness label, case-evidence matrix, and closed-loop repair workflow as review-level organizing constructs. No included study continuously followed the same machine or fleet through diagnosis, prognosis, authorization, maintenance, acceptance, and feedback; the framework therefore connects evidence-supported stages theoretically rather than claiming end-to-end validation. Full article

This work offers a reliability framework for a five-subsystem hybrid series-parallel system representing smart factory monitoring infrastructure under heat and vibration stressors. A central programmable logic controller (1-out-of-1), pressure and temperature sensors (2-out-of-5), wireless communication units (2-out-of-4), solar power modules (3-out-of-6), and a cold standby database server (1-out-of-2) are connected in series. Unit failures are modeled using exponential distributions with component-specific failure rates and two environmental failure rates for thermal stress (${\alpha }_6$) and vibration stress (${\alpha }_7$) which go beyond the single-parameter models used in prior research. Repair of degraded states is governed by general distributions. The Gumbel–Hougaard copula family deals with total failure states, permitting positive repair time dependence due to common maintenance resources and environmental recovery. The state probabilities are obtained in closed form by using Laplace transforms and the supplementary variable method. Those state probabilities are used to find system availability, reliability, $MTTF$, sensitivity, indices and profit for three cases: copula-based repair, general distribution repair and a reduction technique with parameter $\rho$. Numerical analysis reveals steady state availability of 96.80%when using copula repair, and 99.35% when using the reduction technique ($\rho$ = 0.2). Sensitivity analysis reveals that the solar power module subsystem is the main cause of $MTTF$ degradation, however cost analysis reveals that proactive quality enhancement is more profitable than reactive repair options at all maintenance expenditure levels. Full article

Patient recovery prediction and hospital length of stay estimation remain critical challenges in healthcare resource allocation and clinical decision-making. Inaccurate discharge planning drives substantial avoidable hospital costs, while existing machine learning models remain limited by narrow, single-domain data that fail to capture the multimodal complexity of modern smart ward environments. This study proposes IoT-ClinXAI, an explainable multimodal framework that fuses IoT environmental data, wearable physiological signals, and clinical records for accurate and transparent patient recovery prediction in smart hospital wards. A Multi-domain Hierarchical Consensus Feature-Selection method groups features into structured domains, applies Borda–Kemeny weighted consensus within each domain, and reduces cross-domain redundancy while preserving complementary information. A Snake Optimization–tuned Random Forest Regressor optimizes predictive performance through adaptive hyperparameter search, while a multi-scale SHAP framework provides global and patient-level explanations for transparent clinical inference. Experiments were conducted on a real-world IoT-enabled smart ward dataset comprising patient data and recovery duration. The proposed framework achieved strong predictive performance on the held-out test set, with $R^2$ of 0.964, RMSE of 0.476 days, MAE of 0.342 days, and MAPE of 3.13%, yielding a 23.3% RMSE reduction over the unselected baseline and outperforming all feature-selection methods by 10.9–17.6% in RMSE. Statistical superiority was consistently confirmed across all pairwise comparisons using Wilcoxon signed-rank tests with Bonferroni correction ($p<0.001$). SHAP analysis identified ward allocation, respiratory rate, and oxygen saturation as the dominant recovery predictors, while environmental IoT variables showed minimal predictive contribution. These results highlight IoT-ClinXAI as a reliable and useful framework for supporting bed management, discharge planning, and hospital resource optimization in resource-constrained healthcare settings. Full article

Background: The use of pediatric non-operating room anesthesia (NORA) has increased substantially, yet information regarding current practices in Saudi Arabia remains limited. This study assessed pediatric NORA practices, resources, and reported complications among anesthesiologists in Saudi Arabia. Methods: A cross-sectional online survey was conducted among anesthesiologists practicing in Saudi Arabia. The questionnaire collected information on participant characteristics, indications for pediatric NORA, patient selection criteria, staffing patterns, infrastructure and equipment availability, anesthetic and monitoring practices, recovery arrangements, and post-procedural complications. Results: A total of 234 anesthesiologists participated. Magnetic resonance imaging (97.9%) and computed tomography (85.5%) were the most common indications. Most participants accepted patients with American Society of Anesthesiologists physical status I–III (65.8%) and reported no age restrictions (62.8%). Hospital infrastructure was considered appropriate by 85.0% of respondents. Pulse oximetry (99.6%), electrocardiography (93.2%), capnography (91.5%), and noninvasive blood pressure monitoring (89.7%) were widely used. General anesthesia (80.8%) and deep sedation (70.1%) were the most frequently used techniques. Propofol (88.9%), midazolam (82.5%), and ketamine (73.9%) were the most used sedative agents. Recovery most frequently occurred in dedicated recovery rooms (88.9%). The complications most frequently reported by respondents were delayed recovery (35.5%), agitation or delirium (27.4%), cough (21.8%), and nausea or vomiting (17.5%). Conclusions: Pediatric NORA in Saudi Arabia is commonly performed for imaging procedures and is generally supported by appropriate infrastructure and monitoring practices. Nevertheless, variations in resource availability highlight opportunities for further standardization and quality improvement. Full article

Background/Objectives: Substance use disorders are common in the prison population and are associated with relapse and criminal recidivism, yet qualitative evidence on recovery capital and family support within Eastern European penitentiary systems remains limited. This study explored how incarcerated men with a history of substance use perceive initiation, withdrawal, recovery, and the role of family support as a recovery capital resource. Methods: A qualitative descriptive study was conducted in a Romanian penitentiary with 50 definitively sentenced men with self-reported substance use history, selected through purposive sampling. Data were collected through semi-structured interviews, without audio recording due to institutional security regulations, and reconstructed in detailed written notes validated through member checking, then analyzed using inductive thematic analysis via consensus-based double coding (initially coded by the first researcher and reviewed by a second researcher, with discrepancies resolved through discussion), until thematic saturation was reached. Results: Six themes were identified: initiation of use influenced by peer groups, socioeconomic vulnerability, and criminogenic environments; effects initially perceived as beneficial, followed by progressive deterioration; severe withdrawal and denial of dependence; cognitive mechanisms sustaining use, including externalization of responsibility; the dual role of the family as both a risk and a protective factor; and a recovery process marked by ambivalence toward abstinence, in which family support, employment, and prosocial relationships were central. Abstinence during incarceration was frequently perceived as maintained under institutional constraint rather than as internally motivated recovery. Conclusions: In this single-prison sample, recovery extended beyond abstinence, suggesting the need to strengthen personal, social, and family recovery capital. The findings, which require confirmation in larger samples, support exploring integrated prison–community continuum-of-care interventions centered on addiction treatment, family involvement, and post-release support. Full article

Polymer gels are essential functional materials for subsurface energy operations, playing a critical role in conformance control, fluid diversion, hydraulic fracturing, and leakage mitigation. As reservoirs become increasingly complex and the demand for sustainable energy grows, continued innovation in gel technologies is crucial. This editorial introduces a Special Issue titled “Advances and Applications of Polymer Gels for Subsurface Energy and Storage,” which compiles seven original research articles exploring recent developments in gel synthesis, characterization, and applications. The featured studies highlight the versatility of polymer gels, including nanoparticle-reinforced composites, foam–gel hybrids, recrosslinkable preformed particle gels, and advanced fracturing fluids. The contributions address key challenges across CO₂-enhanced oil recovery, heavy oil production, low-permeability reservoir fracturing, and combined enhanced oil recovery strategies. The findings demonstrate ongoing efforts to tailor gel systems for harsh reservoir conditions, improve sweep efficiency, and reduce formation damage, fostering more efficient and sustainable subsurface engineering practices. This Special Issue serves as a valuable resource for researchers and practitioners advancing polymer gel technologies for energy and storage applications. Full article

The increasing demand for lithium-ion batteries (LIBs) highlights the need for efficient recycling strategies to recover critical materials and reduce environmental impact. This study presents an optimized hydrometallurgical process for the recovery of lithium (Li) and iron (Fe) from spent lithium iron phosphate (LiFePO₄ or LFP) battery cathodes through selective leaching and precipitation. A factorial experimental design was employed to model the leaching process and to optimize the leaching parameters and the best efficiency was achieved at Acid Conc. (1.5 N), Agitation (350 rpm), and solid-to-liquid ratio (15%) and achieved Li (80%), Al (0.28%), and Fe (76%) recovery. The intensified conditions (H₂SO₄ 3 N, 350 rpm, 20% solid-to-liquid ratio) outside the factorial domain for potential interest in scale-up were also applied to achieve 90.8% Li and 98.0% Fe co-extraction at room temperature within only 30 min, in contrast to the elevated temperatures (60–95 °C) typically required in the literature. To enhance Fe–Li separation, various precipitation agents were tested; among these, oxalic acid at the stoichiometric dosage achieved 98.1% Fe removal while limiting Li loss to 2%, making it the most effective purification method. By combining mild, room-temperature leaching with highly selective Fe removal, this approach reduces the energy and reagent requirements associated with conventional thermally assisted LFP recycling routes. Overall, this work provides an effective approach for recovering valuable components from spent LIBs, reducing material losses and contributing to the development of circular resource recovery strategies. Full article

Tantalum and niobium are critical strategic rare refractory metals; however, most associated tantalum–niobium resources are characterized by ultra-low grade, finely disseminated grains, and severe argillization, which causes pronounced slime coating of gangue mica on valuable mineral surfaces and impedes efficient recovery via gravity separation. In this study, an innovative beneficiation route combining sodium silicate pre-dispersion conditioning with shaking-table gravity separation was proposed to recover a spodumene-dominant pegmatitic lithium ore containing trace associated Nb–Ta minerals, with mica as the major slime-forming gangue. Process mineralogy analysis revealed that the raw ore has a total (Nb,Ta)₂O₅ grade of only 0.019%, with the main valuable minerals being columbite-(Mn), columbite–tantalite, and tantalite, whereas layered mica minerals constitute the primary argillization gangue that readily generates micro-fine slimes smaller than 5 μm. Single-factor tests demonstrated that the optimal grinding fineness was 84.4% passing 200 mesh, and the optimal feed pulp density for gravity separation was 28.6%. Adjusting pulp concentration alone, without a dispersant, yielded a Nb₂O₅ concentrate grade of merely 0.144%, as severe slime coating greatly limited separation selectivity. Following the introduction of sodium silicate for pre-dispersion, the separation performance improved markedly at the optimal dosage of 300 g/t: the concentrate Nb₂O₅ grade reached 1.28% (8-fold higher than the blank test), and the Ta₂O₅ grade increased to 0.47% (85-fold higher than the blank test). Multi-scale characterization, including zeta potential measurements, ultraviolet–visible (UV–vis) diffuse reflectance spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), was employed to elucidate the dispersion mechanism of sodium silicate. The results confirmed that sodium silicate undergoes selective chemisorption on mica surfaces by forming Si–O–Al bonds with surface Al–OH active sites, which substantially increases electrostatic repulsion between mineral particles and eliminates mica slime coating on tantalum–niobium minerals. In contrast, sodium silicate exhibits only weak physical adsorption on tantalum–niobium mineral surfaces, with no evident chemical bonding. This work provides a low-cost, eco-friendly pretreatment–gravity separation coupling technology for the efficient enrichment of argillized ultra-low-grade tantalum–niobium associated ores and offers theoretical guidance for the green utilization of complex tantalum–niobium tailings and low-grade mineral resources. Full article

Cryopreservation plays a critical role in the long-term preservation of biological materials and has become an indispensable technology in biomedicine, agriculture, animal breeding, and biodiversity conservation. Despite substantial advances in cryobiology, the preservation of cells and tissues at ultra-low temperatures remains limited by cryoinjuries associated with ice crystal formation, osmotic stress, oxidative damage, and cellular dehydration. This review provides an overview of the historical development and fundamental principles of cryopreservation and critically summarizes the key factors affecting cryopreservation efficiency, including cryoprotective agents, antioxidants, osmotic balance, freezing and thawing protocols, and emerging ice-controlling materials. In addition, recent advances in the cryopreservation of diverse biological materials—such as sperm, oocytes, embryos, stem cells, and gonadal tissues from different animal species—are critically discussed, with particular attention to species-specific differences and the distinction between post-thaw survival and functional recovery. Finally, the current limitations of existing cryopreservation technologies and future research directions are highlighted to facilitate the development of safer and more effective preservation strategies for animal genetic resource conservation and biomedical applications. Full article

Wrist fractures present significant challenges in clinical diagnosis, often leading to treatment delays and compromised recoveries. Manual diagnosis is resource-intensive and error-prone. To address these challenges, we develop DeepWristFNet, a compact convolutional architecture designed for end-to-end wrist fracture classification using a small dataset of 193 wrist X-ray images. The DeepWristFNet architecture integrates multi-scale convolutional operations with Fire and Shuffle modules within a compact network design, followed by fully connected layers for binary classification. We applied data pre-processing techniques such as data augmentation, image enhancement, and image resizing to increase the number of images, improve image quality, and resize images to match the DeepWristFNet input size. The proposed method comprised three phases. In the first phase, we trained, validated, and tested end-to-end and achieved validation and testing accuracies of 99.04% and 87.93%, respectively. Testing was performed on a hold-out subset of image instances that was kept separate from model development. The evaluated hold-out images originated from the same dataset distribution and included the corresponding augmented variants. In the second phase, we further evaluated the learned representation by extracting deep features from the first fully connected layer of DeepWristFNet. ReliefF was then used to select informative features, which were subsequently evaluated using 10 conventional machine learning classifiers. Out of 10 classifiers, 5 classifiers, i.e., Efficient linear SVM, quadratic SVM, Narrow NN, wide NN, and medium NN, achieved 100% testing accuracy on unseen samples. In the third phase, an auxiliary Fuzzy Inference System provides an intensity-based foreground-background representation of the X-ray images. This representation provides complementary visual information for interpretation but is not intended to directly classify or localize fractures. Grad-CAM is additionally used to visualize image regions contributing to the DeepWristFNet predictions, providing a model-specific explanation of the classification decision. Additionally, we evaluated how well the proposed DeepWristFNet approach performed against cutting-edge deep transfer learning models. In the evaluated experiments, DeepWristFNet outperformed the compared pre-trained deep learning architectures on the unseen hold-out subset from the same dataset distribution (test set). This study demonstrates the potential of DeepWristFNet for wrist fracture classification under a small-data setting. However, further evaluation on larger, independently collected clinical datasets is required to establish its robustness, generalizability, and suitability for clinical decision support. Full article

As the largest inland river basin in China’s extremely arid region, the stability of the groundwater–vegetatifon system in the Tarim River Basin is crucial for the consolidation of the ecological security barrier in the northwest. To reveal the evolution law of groundwater storage in the watershed from 2003 to 2024 and its response mechanism to vegetation dynamics, this study is based on GRACE gravity satellite, GLDAS land surface assimilation and MODIS remote sensing data. The Theil Sen trend analysis, Hurst index, spatiotemporal Granger causality test, and standardized multiple linear regression model are integrated to systematically analyze the spatiotemporal heterogeneity, future evolution trend, and multi-driving factor contribution pattern of groundwater storage (GWSA) in the watershed. The results showed that: (1) During the study period, the GWSA of the watershed showed a significant downward trend, with a rate of −3.5 mm/a, and experienced a spatial redistribution process of “comprehensive loss local recovery southern compensation northern loss”. The northern and peripheral regions faced new depletion risks. (2) The vegetation condition continues to improve, and the VCI gradually rises from the low to medium range, but the spatial heterogeneity increases synchronously; there is a significant spatial positive correlation between VCI and GWSA, with only a strong lag driving effect in the southwestern region (F > 40). The explanatory power of vegetation factors for groundwater in other regions is limited. (3) Future trend predictions show that over 70% of the region will continue in the direction of historical changes, and the continuous loss trend in the north is difficult to reverse. (4) There is significant spatial differentiation in the contribution rate of driving factors: vegetation conditions (VCI) are the dominant factor, controlling 57.53% of the watershed edge and eastern region; precipitation and temperature dominate the central region (24.94%) and southwestern desert areas (17.53%), respectively. The research results can provide scientific basis for differentiated ecological water delivery and refined management of water resources in the Tarim River Basin. Full article

This paper presents a likelihood-consistent joint target-message receiver for a strictly constant-envelope continuous phase modulation (CPM) waveform in pulsed phased-array joint radar–communication (JRC). Station A performs monostatic detection with the transmitted message, while Station B performs bistatic detection and communication with an available, degraded, or absent target-independent reference path. The target-scattered message is included only under the target-present hypothesis. A log-domain 16-state recursion evaluates the exact finite-alphabet CPM message marginal over the complete 46+2-symbol, 92-bit frame and is verified against direct enumeration on a six-bit unit frame to floating-point precision. At a 10 dB reference-path SNR, marginalization increases detection probability relative to single-path max-log reconstruction by 4.67 percentage points (95% paired interval 1.33–8.00) and 8.33 percentage points (4.33–12.33) at target-path SNRs of 10 and 14 dB, respectively; the corresponding joint-success differences are zero. Across 2048 ordered error events, the directed conditional distance has a Spearman correlation of −0.970 with empirical pairwise error probability. The CPM parameterization guarantees a constant active-pulse envelope at every array element, whereas constrained common-phase selection provides only a secondary adjustment of message-dependent ambiguity sidelobes. System-level experiments in a coastal setting use independent 92-bit messages and matched transmission resources for the proposed CPM waveform and RRC-QPSK. Both waveforms recover all evaluated direct-path payloads, showing that the proposed constant-envelope waveform preserves communication reliability at the tested operating point. The same records demonstrate geometry-consistent monostatic and bistatic target recovery, while finite-scatterer tests define the boundary of the single-scattering-center model. Full article

Rapid urbanization across global South megacities has accelerated the overexploitation of urban aquifers, creating complex socio-hydrological crises that threaten long-term water resilience for a vast population. Conventional urban-water management frequently relies on uniform, city-wide regulatory mandates that fail to account for localized hydrogeological heterogeneities and the socio-economic drivers of private extraction. This study performed a seasonal assessment (post- and pre-monsoon) of groundwater levels (GWLs), across the National Capital Territory of Delhi, India, using a 13-year archival dataset (2010–2022) of 77 ‘common’ wells, with a sequential spatial–statistical framework. No statistically significant ‘seasonality’ was found in GWLs, except for isolated years. About 13% of the observations appeared as ‘deep outliers’, the call for more process-level hydrogeologic investigations. Spatial interpolation via the Inverse Distance Weighting (IDW) interpolation technique, alongside Global Moran’s I, Local Indicators of Spatial Association (LISA), and spatially Constrained Hierarchical Cluster Analysis (sHCA), revealed a recurrent spatial pattern: persistent, deep GWLs, within the fracture-dominated, low-yielding Alwar Quartzite (Delhi Ridge) of South and Southeast Delhi. The spatial clustering demonstrates the migration of the deep-GWL hotspots toward the unconfined alluvial aquifers of the Yamuna River floodplains to the east, threatening future baseflow stability. These spatial drawdown patterns represent a structural response to municipal Drinking Water Services Framework (DWSF) deficits, where intermittent supply and informal water markets incentivize the growth of more unregulated and unrestricted private pumping of groundwater. Achieving sustainable urban groundwater governance requires replacing blanket administrative mandates with a more data-driven, micro-zoned socio-hydrological framework across the city—combining area-specific extraction caps, economic instruments for geologically targeted aquifer storage and recovery, informal market regulation, and facilitating more participatory, community-based (Water users Associations, WUA) initiatives in the future to protect groundwater resources in Delhi. However, it requires specialized monitoring data, which is still largely lacking, and detailed investigations involving the aquifer hydrogeology and groundwater pumping patterns. Full article