Search Results (865)

As the prevalence of public discourse pertaining to loneliness increases, digital interventions, such as artificial intelligence companions, are being introduced as methods for fostering connection and mitigating individual negative experiences of loneliness. These tools, while increasing in volume and popularity, operate within and are shaped by the same engagement-driven systems that have been found to contribute to loneliness. This meta-narrative review examines how algorithmic infrastructures, which are optimized for retention, emotional predictability, and behavioural nudging, not only mediate responses to loneliness but participate in its ongoing production. Flattening complex social dynamics into curated, low-friction interactions, these systems gradually displace relational agency and erode users’ capacity for autonomous social decision making. Drawing on frameworks from communication studies and behavioural information design, this review finds that loneliness is understood both as an emotional or interpersonal state and as a logical consequence of hegemonic digital and technological design paradigms. Without addressing the structural logics of platform capitalism and algorithmic control, digital public health interventions risk treating loneliness as an individual deficit rather than a systemic outcome. Finally, a model is proposed for evaluating and designing digital public health interventions that resist behavioural enclosure and support autonomy, relational depth, systemic accountability, and structural transparency. Full article

This paper develops a belief-based dynamic optimisation framework to explain career continuation decisions in settings characterised by monopsonistic employment and asymmetric performance evaluation. Extending Holmström’s career concerns model, we consider agents who must decide whether to continue or exit their vocation based on subjective beliefs updated from noisy signals. Unlike the original framework, our model assumes a single institutional employer and limited feedback transparency, turning the agent’s decision into an optimal stopping problem governed by evolving belief thresholds. Analytical results demonstrate how greater signal noise, higher effort costs, and more attractive outside options raise the probability of exit. To validate the framework, we confront belief-based dropout decisions using original survey data from over 8000 football referees in Europe, showing that threats, unmet development expectations, and perceived stagnation significantly predict dropout. The results offer practical insights for institutions, such as sports federations, academic bodies, and civil services, on how to improve retention through increased transparency and better support structures. This study contributes to the literature by integrating optimal stopping theory and dynamic labor models in a novel context of constrained career environments. Full article

Climate change and rapid urbanization exert significant impacts on ecosystem services (ESs). The rational assessment and prediction of ESs are crucial for urban sustainable development. This study analyzes the spatiotemporal changes in land use in Shanghai from 2000 to 2020 and evaluates the key ESs, including water yield, soil retention, carbon storage, and habitat quality. Furthermore, integrated “climate change-land use” scenarios were constructed to systematically simulate the response characteristics of ESs under different climate change and development pathways. The results indicate that Shanghai’s land use from 2000 to 2020 was characterized by continuous expansion of built-up land and a significant reduction in cropland. Ecological land exhibited a low and fragmented coverage. By 2040, the ecological protection (EP) scenario could effectively curb the disorderly expansion of built-up land and maintain the stability of cropland and woodland, whereas the natural development (ND) scenario would exacerbate urban sprawl towards the east and further fragment ecological land. From 2000 to 2020, water yield in Shanghai showed an increasing trend, soil retention initially decreased followed by a gradual recovery, carbon sequestration experienced minor fluctuations, and habitat quality exhibited a continuous decline. By 2040, the EP scenarios will effectively maintain water yield and soil retention functions, steadily enhance carbon sequestration and habitat quality, and mitigate the negative impacts of climate change. In contrast, the ND scenarios show an unstable trend of initial increase followed by decrease. Spatially, the western and northern regions consistently remain high-value ESs zones under both scenarios. In 2040, Shanghai’s ESs will exhibit distinct administrative district disparities, characterized by “peripheral sensitivity and central stability”. This pattern underscores the necessity for implementing zone-specific regulation strategies in future urban planning. Full article

The integration of emulsion gels in 3D food printing has emerged as a promising strategy to enhance both the structural fidelity and functional performance of printed foods. Emulsion gels, composed of proteins, polysaccharides, lipids, and their complexes, can provide tunable rheological and mechanical properties suitable for extrusion and shape retention. This review explores the formulation strategies, including phase behavior (O/W, W/O, and double emulsions); stabilization methods; and post-printing treatments, such as enzymatic, ionic, and thermal crosslinking. Advanced techniques, including ultrasound and high-pressure homogenization, are highlighted for improving gel network formation and retention of active compounds. Functional applications are addressed, with a focus on meat analogs, bioactive delivery systems, and personalized nutrition. Furthermore, the role of the oil content, interfacial engineering, and protein–polysaccharide interactions in improving print precision and post-processing performance is emphasized. Despite notable advances, challenges remain in scalability, regulatory compliance, and optimization of print parameters. The integration of artificial intelligence can also provide promising advances for smart design, predictive modeling, and automation of the 3D food printing workflow. Full article

Over the last few decades, polymer composites have been rapidly making inroads in critical applications of electrical storage devices such as batteries and supercapacitors. Structural supercapacitor composites (SSCs) have emerged as multifunctional materials capable of storing energy while bearing mechanical loads, offering lightweight and compact solutions for energy systems. This study investigates the functionalization of Bisphenol A-based thermosetting polymers with ionic liquids, aiming to synthesize dual-functional structural electrolytes for SSC fabrication. A multifunctional sandwich structure was subsequently fabricated, in which the fabricated SSC served as the core layer, bonded between two structurally robust outer skins. The core layer was fabricated using carbon fibre layers coated with 10% graphene nanoplatelets (GNPs), while the skin layers contained 0.25% GNPs dispersed in the resin matrix. The developed device demonstrated stable operation up to 85 °C, achieving a specific capacitance of 57.28 mFcm⁻² and an energy density of 179 mWhm⁻² at room temperature. The performance doubled at 85 °C, maintaining excellent capacitance retentions across all experimented temperatures. The flexural strength of the developed sandwich SSC at elevated temperature (at 85 °C) was 71 MPa, which exceeds the minimum requirement for roofing sheets as specified in Australian building standard AS 4040.1 (Methods of testing sheet roof and wall cladding, Method 1: Resistance to concentrated loads). Finite element analysis (FEA) was performed using Abaqus CAE to evaluate structural integrity under mechanical loading and predict damage initiation zones under service conditions. The simulation was based on Hashin’s failure criteria and demonstrated reasonable accuracy. This research highlights the potential of multifunctional polymer composite systems in renewable energy infrastructure, offering a robust and energy-efficient material solution aligned with circular economy and sustainability goals. Full article

To address the problem of inaccurate matching between personalized exercise recommendations and learners’ mastery of knowledge concepts/learning abilities, we propose the Dynamic Multidimensional Memory Augmented knowledge tracing model (DMMA). This model integrates a dynamic key-value memory neural network with the Ebbinghaus Forgetting Curve. By incorporating time decay factors and knowledge concept mastery speed factors, it dynamically adjusts knowledge update intensity, effectively resolving the insufficient personalized recommendation capabilities of traditional models. Experimental validation demonstrates its effectiveness: on Algebra 2006–2007, DMMA achieves 82% accuracy, outperforming CRDP-KT by 6%, while maintaining 53–55% accuracy for cold-start users (0–5 interactions), which is 25% higher than CoKT. The model’s integration of the Ebbinghaus forgetting curve and K-means-based concept classification enhances adaptability. Genetic algorithm optimization yields a diversity score of 0.79, with 18% higher 30-day knowledge retention. The FastDTW–Sigmoid hybrid similarity calculation (weight transition 0.27–0.88) ensures smooth cold-start adaptation, while novelty metrics reach 0.65 via random-forest-driven prediction. Ablation studies confirm component necessity: removing time decay factors reduces accuracy by 2.2%. These results validate DMMA’s superior performance in personalized education. Full article

This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible–near-infrared (VIS–NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and −18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36–36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410–990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R²) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at −18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (p < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Full article

Assessing soil deformation leading to collapse is often conducted through a suction-controlled method, which can be time-intensive. In this study, the collapse deformation of compacted clay was investigated by conducting time-saving and convenient water content-controlled tests. The compacted clay specimens, each with a unique initial void ratio, were subjected to water retention experiments. The water content-controlled oedometer apparatus performed tests involving compression, wetting, and subsequent recompression. Observed experimental results indicate that water content has an inverse relationship with suction, with suction increasing as water content decreases, suggesting an inverse relationship between the two variables. In compression tests performed at a constant water content, water saturation increases and suction decreases as the void ratio decreases. Wetting leads to a decrease in void ratio as the saturation level rises, gradually declining along the wetting path until it aligns with the compression line of fully saturated soil. The compression lines at varying suction levels are established through theoretical analysis of water retention and water content-controlled compression test results. In addition, the collapse deformation is well predicted with a concise formula related to pore gas saturation. In this way, this study provides a quick and effective method for evaluating the hydro-mechanical properties of unsaturated soils. Full article

Odorous emissions from hot-mix asphalt (HMA) plants are a growing environmental concern, particularly due to airborne aldehydes and ketones, which have low odor thresholds and a strong sensory impact. This study presents a field-ready analytical method for monitoring odor-active volatile compounds. The system uses dynamic solid-phase microextraction (SPME and SPME Arrow) with on-fiber derivatization via O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) and is coupled to gas chromatography–mass spectrometry (GC–MS) for direct detection. A flow-cell sampling unit enables the real-time capture of aliphatic aldehydes and ketones under transient emission conditions. Calibration using permeation tubes demonstrated sensitivity (limits of detection (LODs) below 0.13 μg/m³), recovery above 85% and consistent reproducibility. Compound identity was confirmed using retention indices and fragmentation patterns. Uncertainty assessment followed ISO GUM (Guide to the Expression of Uncertainty in Measurement) standards, thereby validating the method’s environmental applicability. Field deployment 200 m from an HMA facility identified measurable concentrations that aligned with CALPUFF model predictions. The method’s dual-isomer resolution and 10 min runtime make it ideal for responding to time-sensitive odor complaints. Overall, this approach supports regulatory efforts by enabling high-throughput on-site chemical monitoring and improving source attribution in cases of odor nuisance. Full article

This study assesses the financial soundness of Sudanese commercial banks during escalating civil conflict by integrating Altman’s Z-score models with scenario-based stress testing. Using audited financial data from 2016 to 2022 (pre-war) and projections through to 2028, the analysis evaluates resilience under low- and high-intensity conflict scenarios. Altman’s Model 3 (for non-industrial firms) and Model 4 (for emerging markets) are applied to capture liquidity, retained earnings, profitability, and leverage dynamics. The findings reveal relative stability between 2017–2020 and in 2022, contrasted by significant vulnerability in 2016 and 2021 due to macroeconomic deterioration, sanctions, and political instability. Liquidity emerged as the most critical driver of Z-score performance, followed by earnings retention and profitability, while leverage showed a context-specific positive effect under Sudan’s Islamic finance framework. Stress testing indicates that even under low-intensity conflict, rising liquidity risk, capital erosion, and credit risk threaten sectoral stability by 2025. High-intensity conflict projections suggest systemic collapse by 2028, characterized by unsustainable liquidity depletion, near-zero capital adequacy, and widespread defaults. The results demonstrate a direct relationship between conflict duration and systemic fragility, affirming the predictive value of Altman’s models when combined with stress testing. Policy implications include the urgent need for enhanced risk-based supervision, Basel II/III implementation, crisis reserves, contingency planning, and coordinated regulatory interventions to safeguard the stability of the banking sector in fragile states. Full article

The transport behavior of particles in tortuous fractures is prevalent in the oil and gas extraction process and has a profound impact on engineering. However, due to a variety of factors, drilling fluid leakage is prone to occur during drilling and completion, and an evaluation system for the influence of meander characteristics on the kinetic properties of particles has not yet been established. To this end, this paper constructs a numerical model based on CFD-DEM numerical simulation to simulate the particle–fluid two-phase flow in the meandering fracture, investigates the mechanism of surface meandering on particle force, particle transport velocity, and particle residence time, and proposes a mathematical method based on meandering for predicting particle transport velocity and particle residence time in the stable transport phase. The results show that the increase in tortuosity makes the force state of particles in the fracture show significant instability and intensifies the interaction between fluid and particles in the fracture; the effect of the tortuous wall intensifies the inhomogeneity of transport velocity, and the perturbation effect of the complex path structure on the x-direction velocity of particles is stronger; and the increase in tortuosity is not conducive to particle retention in the fracture. The results of the study can provide theoretical guidance for reducing the risk of drilling fluid leakage during drilling and completion. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

Student dropout remains a critical challenge in higher education, especially within public universities that serve diverse and vulnerable populations. This research presents the design and evaluation of an early warning system based on an XGBoost classifier, trained exclusively on data collected at the time of student enrollment. Using a retrospective dataset of nearly 40,000 first-year students (2014–2024) from a Mexican public university, the model incorporated academic, socioeconomic, demographic, and perceptual variables. The final XGBoost model achieved an AUC-ROC of 0.6902 and an F1-score of 0.6946 for the dropout class, with a sensitivity of 88%. XGBoost was chosen over Random Forest due to its superior ability to detect students at risk, a critical requirement for early intervention. The model flagged 59% of incoming students as high-risk, with considerable variability across academic programs. The most influential predictors included age, high school GPA, conditioned admission, and other family responsibilities and economic constraints. This research demonstrates that early warning systems can transform enrollment data into timely and actionable insights, enabling universities to identify vulnerable students earlier and respond more effectively, allocate support more efficiently, and enhance their efforts to reduce dropout rates and improve student retention. Full article

Tungsten disulfide (WS₂), a two-dimensional layered material with favorable electronic properties, has been explored as a promising negative electrode material for calcium-ion batteries (CIBs). Despite its use in monovalent systems, its performance in divalent Ca²⁺ intercalation remains poorly understood. Herein, a combined theoretical and experimental framework is used to elucidate the electronic mechanisms underlying Ca²⁺ intercalation. Theoretical insights were obtained through density functional theory calculations, incorporating periodic simulations using the Vienna Ab initio Simulation Package, and localized orbital-level analysis using the discrete variational Xα method. These approaches reveal that Ca²⁺ insertion induces significant interlayer expansion, lowers diffusion barriers, and narrows the bandgap compared to Li⁺. Orbital analysis revealed strengthened W–S bonding and diminished antibonding interactions, which may contribute to the improved structural resilience. Electrochemical tests validated these predictions; the CaWS₂ electrode delivered an initial discharge capacity of 208 mAh·g⁻¹ at 0.1C, with 61% retention after 50 cycles at 1C. The voltage profile exhibits a distinct plateau near 0.7 V, consistent with a two-phase-like intercalation mechanism, contrasting with the gradual slope observed for Li⁺. These findings suggest that Ca²⁺ intercalation facilitates both rapid ion transport and enhanced structural robustness. This study offers mechanistic insights into multivalent-ion storage and supports the design of high-performance CIB electrodes. Full article