Search Results (13,475)

To address the major safety hazard that heat released from mining polyurethane (PU) reinforcement materials may induce spontaneous combustion of residual coal in goaf, this study selected No. 3 coal from Wangzhuang Coal Mine, Shanxi Lu’an, as the research object. A self-developed large-capacity, large-scale experimental system was used to conduct programmed heating experiments on 2.0 kg multi-particle-size coal-PU mixed samples. The effects of PU content on characteristic gas release, crossing point temperature (CPT), residue morphology, and TGA-DSC characteristic temperatures were systematically investigated, and the reaction-kinetic evolution was further analyzed using the distributed activation energy model (DAEM). The results show that coal and PU exhibit a significant synergistic enhancement effect during co-heating. As the PU content increased, the release concentrations of CO, C₂H₄, and C₂H₆ increased markedly, and their initial release temperatures decreased, whereas CH₄ generation was inhibited by hydrogen-radical competition; no C₂H₂ was produced below 400 °C. The CPT decreased linearly with an increasing PU content, with an average decrease of approximately 8.5 °C for every 10% increase in PU content. Residue morphology showed clear critical features: glassy agglomerates appeared when the PU content exceeded 16.67%, and dense bulk coking occurred when the PU/coal mass ratio was greater than 1:10. TGA-DSC analysis showed that when the PU/coal ratio was lower than 1:10, the ignition temperature of the mixed sample was higher than that of pure coal, indicating an inhibitory synergistic effect. When the ratio exceeded 1:10, the ignition temperature decreased significantly, and the synergy shifted to promotion; increasing the heating rate shifted the characteristic temperatures to higher values and increased the reaction intensity. DAEM analysis further confirmed that when the PU ratio exceeded 1:10, the apparent activation energy of the mixed samples was lower than that of pure coal. Coal powder also acted as a physical skeleton that effectively dispersed molten PU, eliminated the activation-energy peaks of pure PU in the conversion ranges of 30–50% and 70–90%, and substantially improved combustion stability. Mechanistically, low-temperature PU melting and coating optimized heat and mass transfer, medium-temperature pyrolysis released active radicals and combustible gases that altered coal pyrolysis pathways and the radical reaction environment, and high-temperature hydrogen-radical competition reshaped the gas-product distribution. Together, these processes form a complete chain of synergistic spontaneous combustion. This study identifies key safety threshold parameters for PU reinforcement materials, recommends a PU content of ≤9.10%, and identifies CO and C₂H₄ as priority early-warning gases, providing direct experimental evidence for characteristic-gas-based early warning and mine fire prevention. Full article

This study presented a comprehensive optimization of a piezoelectric injector specifically designed for pure methanol compression-ignition engines. As a fuel for compression-ignition engines, methanol exhibits broad application prospects. To overcome the challenges posed by methanol’s low cetane number and energy density, a co-optimization strategy was implemented, targeting the actuator, drive waveform, and internal flow geometry. The redesigned injector exhibited superior dynamic performance, featuring significantly faster response times and enhanced operational stability, which were critical for precise fuel delivery control. Furthermore, the optimized internal flow path increased the effective flow rate, ensuring sufficient fuel supply across all engine operating conditions. The upgraded injector was rigorously tested on an engine bench, demonstrating substantial performance gains. Brake thermal efficiency improved from 38.9% to 40.4% at low load and from 43.68% to 46.07% at high load. Emissions of CO, formaldehyde, acetaldehyde, and unburned methanol were consistently reduced, with the maximum reduction reaching 23.1%, confirming markedly enhanced combustion completeness. This improvement was directly attributed to the injector’s refined spray characteristics and precise control, although it led to a slight increase in NOx emissions due to higher peak combustion temperatures. Full article

Chlorophyll, a natural photosynthetic pigment, is gaining interest for its sustainable and eco-friendly applications in renewable energy, particularly as a photosensitizer in dye-sensitized solar cells (DSSCs). This study investigates the feasibility of chlorophyll extracted from Moringa oleifera as a natural photosensitizer in DSSCs, building on our previous work demonstrating its high chlorophyll content and long-term stability. Chlorophyll was extracted using acetone under optimal conditions (45 °C, 60 min) and applied in DSSCs comprising a TiO₂ photoanode, iodide/triiodide electrolyte, and platinum counter electrode. The TiO₂ photoanode was characterised using UV-Vis spectroscopy, FE-SEM, XRD, and Raman spectroscopy, confirming the presence of pure anatase phase TiO₂ with uniform spherical nanoparticle morphology. The fabricated DSSCs achieved a short-circuit current density of 0.197 mA cm⁻², an open-circuit voltage of 0.44 V, a fill factor of 32%, and a photoconversion efficiency (PCE) of 0.027%. While this performance is lower than the highest reported chlorophyll-based DSSC efficiency (4.6%), the results demonstrate that M. oleifera is a viable and sustainable source of chlorophyll for DSSC applications. The findings highlight the importance of dye–semiconductor interactions and suggest that further optimisation through co-sensitization, TiO₂ surface modification, and improved dye anchoring could enhance device performance. Full article

This study examines the processes of technology adoption and appropriation among older adults participating in two community-based digital inclusion workshops (LAB65+) in Madrid, exploring how digital technologies are appropriated within community learning environments and identifying the social, relational, and pedagogical factors that shape this process, with particular attention to the role of mutual support, warm experts, and community learning dynamics. Drawing on a series of workshops and group interaction recordings conducted with regular attendees, the research identifies a set of factors that consistently shape participants’ engagement with digital tools. Particular attention is given to socio-educational background, previous work experience, and prior exposure to technology, as well as to the everyday motivations associated with the use of mobile phones for communication through WhatsApp, online purchasing, access to health services, and routine banking procedures. Across both labs, the findings reveal that successful and sustained engagement with technology among older adults depends less on technical training per se than on elements related to motivation, self-efficacy, meaningful instruction, and the creation or reinforcement of social ties in familiar environments. Although minor differences emerge between the two settings, the evidence consistently underscores the centrality of these relational and contextual factors over purely operational or skill-based considerations. The study highlights the need for community-oriented approaches that recognize and build upon the social dimensions of learning and using technology in later life. Full article

Shale gas production is governed by complex geological and engineering factors, and its production dynamics are often highly variable. Conventional methods, which can incorporate only a limited number of production-related variables, often struggle to provide accurate forecasts under fluctuating operating conditions. Focusing on the natural flowing stage of shale gas wells, this study proposes a probabilistic forecasting framework that integrates physical decline characteristics with dynamic production data. A dual-branch TCN–LSTM network constrained by decline features is constructed, and Student’s t-distribution is introduced to quantify the uncertainty caused by short-term production fluctuations. The results show that embedding physical decline constraints into the deep learning architecture helps bridge the gap between conventional models with limited parameter representation and purely data-driven models with insufficient interpretability. The proposed method improves forecasting accuracy while preserving the physical meaning of the predictions, and it can generate noise-robust confidence intervals with stable coverage. This method provides decision support for short-term production tracking and production-regime adjustment in shale gas wells. Full article

Post-extractive alveolar ridge reconstruction with resorbable barrier membranes aims to preserve ridge dimensions and support future implant placement, yet evidence on soft tissue healing with magnesium-based membranes remains limited. This prospective observational pilot study evaluated mucosal healing following alveolar ridge reconstruction using a resorbable pure magnesium membrane (NOVAMag^®) combined with a xenogeneic bone graft and a collagen dermal matrix overlay in five consecutively enrolled patients. Soft tissue healing was assessed with the Wound Healing Index (WHI) at 7, 14, 30, 90, and 180 days postoperatively. Secondary outcomes included postoperative pain (VAS) and oral health-related quality of life (OHIP-14) at 7 days. Mean WHI scores progressed from 4.4 ± 0.8 at day 7 to 4.8 ± 0.4 at day 14, with complete excellent healing (WHI 5.0 ± 0.0) achieved in all patients by day 90 and maintained through day 180. One case of partial wound dehiscence resolved spontaneously without infection or graft loss. Mean VAS was 1.86 ± 1.89 cm, and mean OHIP-14 was 20.8 ± 5.23, indicating limited and transient patient-reported impact. These preliminary findings support the clinical feasibility of the NOVAMag^® membrane as part of a combined regenerative approach in post-extractive ridge reconstruction and warrant validation in larger controlled trials. Full article

Sustainable supply chains are increasingly vulnerable to systemic risks, such as geopolitical conflicts at critical trade routes like the Strait of Hormuz or climate disasters, which reveal deep Environmental, Social, and Governance (ESG) weaknesses. Conventional optimization often fails in these “deep uncertainty” contexts, where reliable historical data are often scarce and qualitative factors are paramount. This study introduces a simulation-optimization framework that reframes risk management as a decision process rather than a purely computational one. Portfolios are parameterized across five key characteristics—prevention, vulnerability, resilience, recovery, and detection—to enable a genetic algorithm (GA) to generate a diverse ensemble of high-performing strategies. Instead of providing one “best” answer, the GA allows managers to evaluate multiple options against quantitative tail-risk measures and qualitative institutional factors. The framework produces a “trade-off map,” or Pareto frontier, visualizing the cost of protecting against downside risks. By adjusting the GA’s settings, decision makers can toggle between improving current plans and exploring new, structurally different strategies. The numerical results demonstrate that the GA consistently identifies high-performing portfolios, achieving at least 99.55% of the true optimal performance across all metrics while requiring only 25% of the computational evaluation budget of an exhaustive search space. Furthermore, the framework successfully generates a structurally diverse menu of near-optimal alternatives across all performance metrics, consistently outperforming Monte Carlo sampling in the quality of near-optimal solutions identified, particularly for tail-risk measures such as conditional value-at-risk. Ultimately, this approach integrates the manager’s professional judgment regarding non-quantifiable factors, such as political stability and social responsibility, with simulation data to support the selection of a robust, sustainable portfolio. Full article

Octopus maya is a fast-growing species from the Yucatán Peninsula with high economic relevance, accounting for a major share of regional fishery production. However, a significant fraction of the organism, rich in type I collagen, is discarded as by-products, representing a promising and underutilized source for sustainable biomaterials. This study evaluated, through a 3² factorial design, the effect of two factors on the rheological and dynamic mechanical properties of film-forming solutions (FFS). The first factor was the type of food-grade polysaccharide: chitosan (Ch), hydroxypropyl methylcellulose (HPMC), or starch (S). The second factor was the proportion of each polysaccharide blended with ultrasound-extracted Octopus maya insoluble collagen (CIPM), using polysaccharide ratios of 30:70, 50:50, and 70:30 (w/w). This approach aims to valorize octopus by-products through the recovery and functional utilization of collagen. Rheological properties were determined by rotational and oscillatory rheometry at 25 °C, with flow curves fitted to the Carreau-Yasuda model. All formulations exhibited pseudoplastic behavior (n < 1), with viscosity decreasing as shear rate increased. Pure CIPM showed high viscosity (190.36 Pa·s at 1 s⁻¹), which decreased (0.3–10.44 Pa·s) in HPMC and chitosan systems, suggesting their potential suitability for applications requiring fluidity, such as spray coatings or film-forming solutions, based on their rheological properties. In contrast, starch-based systems exhibited higher viscosities (33.54–197.53 Pa·s) and a more structured viscoelastic profile (G′ > G″), suggesting potential suitability for thick coatings or gels requiring structural stability, although these applications were not experimentally validated. These results demonstrate that CIPM-polysaccharide systems enable tunable rheological properties, supporting the use of Octopus maya collagen as a sustainable functional material for advanced food and biomaterial design. Full article

Myotonic dystrophy type 1 (DM1) is caused by an expansion of CTG repeats in the DMPK gene. In a proportion of patients, the expanded allele contains variant repeats, which have been associated with later disease onset and different clinical presentation, although their full impact remains incompletely defined. We compared sociodemographic, neuromuscular, and multisystem clinical features between DM1 patients with pure CTG expansions (n = 66) and those with variant repeats (n = 9), who formed a consecutive cohort of unrelated index cases evaluated at the age of diagnosis in routine clinical practice. Patients with variant repeats were nine years older at diagnosis than patients with pure repeat expansions (p = 0.025), had more years of formal education (p = 0.024), and showed reduced muscle strength in proximal lower limbs (p = 0.049). No childhood or juvenile forms were observed among patients with variant repeats. Sex, disease duration, and most other clinical parameters, including multisystem involvement, did not differ between groups. The results of our exploratory study support variant repeats as disease modifiers in both age at onset and pattern of muscle involvement, and imply a two-sequential-component hypothesis in DM1 pathogenesis. Full article

To mitigate error accumulation and long-term drift in unmanned aerial vehicle (UAV) position and attitude estimation using purely inertial measurement unit (IMU) data, this paper presents a dual-branch physics-informed long short-term memory (DPI-LSTM) network incorporating shared temporal encoding, a dual-branch structured regression framework, and physical consistency constraints. The model employs a long short-term memory (LSTM)-based temporal encoder to extract temporal features from IMU time-window sequences. Established inertial kinematic relationships are embedded into the dual-branch LSTM framework as loss constraints, providing physics-based regularisation to guide the network during training. By modelling translational and rotational states separately through the position and attitude branches, the model improves stability and physical interpretability while retaining the advantages of task decoupling. Systematic experiments were conducted on the University of Zurich First-Person View (UZH-FPV) Drone Racing dataset, and comparisons were made with traditional inertial navigation methods and representative deep learning-based inertial odometry approaches. The experimental results indicate that the proposed model demonstrates a measurable reduction in positional root mean square error (RMSE) on the evaluated test sequences, decreasing the RMSE to 0.0654 m, which represents a reduction of more than 20% when compared with inertial odometry network (IONet), convolutional neural network–long short-term memory (CNN–LSTM), and robust neural inertial navigation (RoNIN). Further ablation studies and cross-sequence evaluation indicate that the physical consistency constraints and the dual-branch architecture contribute to improved position estimation stability under the evaluated benchmark sequences. The proposed kinematically constrained framework provides a viable IMU-only position and attitude estimation module, laying the groundwork for future UAV digital twin and precision-agriculture applications where continuous and physically consistent position and attitude information is required. Full article

This study investigates the synthesis–structure relationships governing sol–gel-derived nanostructured MgFe₂O₄ ferrite powders for high-temperature thermochemical energy conversion applications. The effects of key processing parameters, including propylene oxide (PO) concentration, gel aging time, calcination temperature, and calcination duration, were systematically examined to tune the phase composition, specific surface area (SSA), pore volume, crystallite size, and nanoparticle morphology of MgFe₂O₄. Increasing the PO concentration from 5 to 20 mL shortened the gelation time from 585 to 323 s and increased the SSA from 5.30 to 17.88 m²/g, while the pore volume increased from 0.0074 to 0.0210 cm³/g. In contrast, gel aging time between 24 and 120 h produced negligible changes in SSA, pore volume, and crystallite size, indicating that extended aging is not required for microstructural control. Calcination temperature strongly influenced the nanostructure: increasing the temperature from 600 to 1000 °C decreased SSA and pore volume while increasing crystallite size from 21.33 to 48.76 nm. Longer calcination times produced a similar but less pronounced effect, decreasing SSA from 18.83 to 14.89 m²/g and increasing crystallite size from 17.55 to 30.12 nm. Overall, phase-pure MgFe₂O₄ with favorable textural properties was obtained using 20 mL of PO, 24 h of aging, and calcination in the 700–800 °C range. Under the identified synthesis conditions, namely 20 mL of PO, 24 h of aging, and calcination in the range of 700–800 °C for 2 h, phase-pure MgFe₂O₄ nanoparticles with particle sizes of approximately 10–50 nm were obtained. These results establish a processing–microstructure framework for engineering MgFe₂O₄ nanomaterials with tunable textural properties for solar thermochemical redox cycles and related high-temperature energy applications. Full article

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations. Full article

This study conducts a technical analysis of frontier generative AI algorithms—including Meta’s Self-Rewarding Language Models, DeepMind’s EVA (Evolving Alignment via Asymmetric Self-Play) framework, and DeepSeek’s pure reinforcement-learning models—in order to examine an intrinsic paradigm shift in the ethical governance of generative artificial intelligence and to advance a physicalist analysis of algorithmic endogenous ethics. Combining a close reading of alignment techniques (RLHF, DPO, iterative DPO, GRPO) with a conceptual analysis grounded in Peter-Paul Verbeek’s theory of technological mediation and moral materialization, the paper traces how value-alignment goals are being “materialized” into internal, dynamic, and evolvable “moral scripts” within the algorithms themselves. The analysis shows that contemporary alignment practices are moving from external ethical discipline toward endogenous norms generated through iterative self-evaluation, asymmetric self-play, and rule-based self-exploration. The paper argues that this trend warrants a re-examination of Verbeek’s framework for its capacity to explain the co-evolution of technology and morality in the digital age, and it envisions a future of human–machine value co-evolution organized around new research directions such as “Setting as Governance” and “value homeostasis mechanisms”. Full article

The electrification of the industrial sector is an important pathway to decarbonizing the industry and achieving a sustainable society. High-temperature heat pumps (HTHPs) are critical devices for providing industrial heat and consuming green electricity. The goal of the present work is the theoretical thermodynamic analysis of a reverse Brayton HTHP that operates with novel working fluids. Specifically, the idea of using mixtures of working fluids with CO₂ is studied for the first time with the aim of suggesting new candidates to increase the performance of industrial HTHPs. A model of an HTHP with an internal heat exchanger is developed and verified in the MATLAB programming language. Nine different mixtures are studied: CO₂/R152a, CO₂/R1234ze(E), CO₂/Propane, CO₂/Butane, CO₂/Isobutane, CO₂/Pentane, CO₂/Isopentane, CO₂/Hexane and CO₂/Heptane. The examined industrial heat production temperatures are 150 °C, 200 °C and 250 °C, while the waste heat stream temperatures that drive the heat pump are considered to be 80 °C and 100 °C. The results prove that the application of the mixtures can enhance the COP, especially in the case of lower temperature lifts. CO₂/R152a seems to be a promising choice compared to pure CO₂, presenting performance enhancements ranging from 4.12% to 64.02% among the studied scenarios. Full article

Aiming at the problem that strip surcharge in coastal soft soil foundations causes lateral squeezing and endangers the safety of adjacent existing bridge pile foundations, the traditional Boussinesq elastic theory cannot reflect the true elastoplastic characteristics of the soil and tends to underestimate the actual horizontal additional stress. This paper establishes a two-dimensional plane strain finite element model and, based on the calibration of pure elastic theoretical solutions, carries out extensive comparative analyses under elastoplastic foundation conditions. Through Pearson correlation and random forest sensitivity analyses, it is clarified that the internal friction angle, load ratio, and normalized distance ratio are the core control variables affecting the redistribution of horizontal additional stress, thereby demonstrating the limitations of the influence of elastic modulus and cohesion. The study reveals the nonlinear amplification mechanism of horizontal stress transfer caused by the penetration of the deep plastic zone within the foundation, as well as the physical evolution law of the stress correction factor, which initially exhibits a Gaussian peak enhancement and subsequently decays exponentially with spatial distance. Based on these mechanisms, a combined prediction formula for the horizontal additional stress correction factor is proposed, achieving an R² = 0.903, and a safety evaluation chart for the correction factor is constructed to quantify high-risk areas. The results indicate that when the normalized distance ratio is greater than or equal to 4, the elastoplastic squeezing effect essentially dissipates. The proposed modification method effectively delineates the applicable boundary of the elastic solution and provides a theoretical basis for the bearing capacity calculation and safety control of passively loaded pile foundations in soft soil regions. Full article