Search Results (1,190)

The high-temperature proton ceramic membranes with simultaneous separation of hydrogen and oxygen exhibit promising applications in the catalytic conversion field. However, their separation performance often relies on external electrical circuits, which limits practical application. To overcome this limitation, doping strategies have emerged as a viable approach to develop triple-conducting (H⁺/e⁻/O²⁻) membranes for simultaneous hydrogen and oxygen separation in non-electrochemical mode. In this study, honeycomb-structured hollow fiber membranes were fabricated, and the effects of varying Fe³⁺ and Y³⁺ doping concentrations on hydrogen and oxygen permeation fluxes were systematically investigated. At the Fe³⁺ doping level of 0.2 mol, the oxygen permeation flux of 0.692 mL min⁻¹ cm⁻² in BaCe_0.6Zr_0.2Fe_0.2O_3−δ (BCZF) was achieved at 1000 °C, while the hydrogen permeation flux was 0.201 mL min⁻¹ cm⁻². The BaCe_0.55 Fe_0.05Zr_0.2Y_0.2O_3−δ (Fe-BCZY) hollow fiber membrane can enhance the hydrogen permeation flux by 75% at 1000 °C. Furthermore, during the simultaneous permeation of hydrogen and oxygen, a 1.7-fold enhancement in hydrogen permeation performance was achieved for the Fe-BZCY hollow fiber membrane at 1000 °C, and with oxygen permeation flux of 1.76 mL min⁻¹ cm⁻² at the same temperature. More significantly, a hydrogen permeation flux of 0.34 mL min⁻¹ cm⁻² can be achieved at 700 °C under simultaneous hydrogen and oxygen permeation, which is favorable for the application of membrane reactors in catalytic reactions. Full article

The development of high-performance composite supercapacitors remains challenging because the specific capacitance of composite electrodes is jointly governed by electronic percolation, ion accessibility, and interfacial contact, all of which are strongly affected by the balance among active materials, conductive agents, and binders. Traditional equivalent circuit modeling and empirical trial-and-error methods are often inadequate for describing these non-linear relationships and optimizing electrode design. To address this limitation, we establish a physics-guided and interpretable machine learning (ML) framework for predicting the specific capacitance of composite electrodes. Unlike traditional methods that rely on macroscopic mass fractions, our approach constructs a feature space comprising ten descriptors, including two newly introduced binder-related proxy descriptors—Binder-to-Conductive Ratio (BCR) and Specific Binder Loading (SBL)—to better represent the influence of binder content. By systematically evaluating 17 machine learning algorithms on a high-fidelity dataset, we identify the XGBoost model, optimized via Bayesian optimization, as the best predictor, achieving a coefficient of determination (R²) of 0.981 and a low mean absolute percentage error (MAPE) of 14.49%. Importantly, interpretability analysis using Shapley Additive Explanations (SHAP) provides physically interpretable statistical insights, revealing that high BCR suppresses specific capacitance through an insulating barrier effect, whereas lattice distortion in the filler material promotes ion transport. This study offers a robust, data-driven framework for optimizing composite electrode performance, demonstrating the potential of interpretable ML models for the rational design of advanced energy-storage materials. Full article

Water-based lubricants (WBLs) are increasingly being considered for electrified drivetrain applications; however, their electrochemical stability toward bearing steels remains insufficiently understood. This study evaluated the corrosion behavior of through-hardened AISI 52100 bearing steel in novel WBLs to elucidate the corrosion kinetics and surface degradation mechanisms. Round steel disks were cleaned and tested in 50 wt% aqueous dilutions of glycerol, ethylene glycol (MEG), polyethylene glycol (PEG), and polyalkylene glycol (PAG). Electrochemical measurements were conducted using a three-electrode cell in accordance with ASTM G3-14, employing open circuit potential (OCP), linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization curves. Among the uninhibited fluids, DI water exhibited the highest corrosion current density (19.85 µA/cm²), while glycerol- and PEG-based systems showed the lowest values (0.79 and 0.85 µA/cm², respectively), attributed to organic adsorption at the steel/electrolyte interface. EIS analysis revealed a single charge-transfer-controlled process across all fluids, consistent with a weak, non-passive interfacial oxide whose protective character is modulated by organic adsorption. The addition of NaNO₃ produced divergent effects depending on the base fluid chemistry: the corrosion activity was reduced in DI water and glycerol systems through enhanced passivation, while PEG- and PAG-based formulations showed increased corrosion current densities and reduced charge transfer resistance, attributed to competitive disruption of the polymer boundary layer by nitrate ions. Surface characterization by SEM/EDAX and white-light interferometry corroborated the electrochemical findings, revealing fluid-dependent corrosion morphologies ranging from uniform attack in DI water to localized pitting in polymer-based systems, with NaNO₃ shifting the corrosion mode in PEG/PAG systems from localized to combined localized and uniform attack. These findings highlight the critical role of fluid chemistry in controlling corrosion processes in water-based lubricants and provide mechanistic insight for the development of corrosion-stable formulations for high-performance electrified drivetrain applications. Full article

Electromagnetic leakage through ventilation interfaces is a critical challenge in photonic instrumentation, where laser controllers and photoelectric readout circuits are highly sensitive to external interference. In photonic instruments, enclosure leakage may couple into laser controller and photoelectric readout circuits, degrading optical signal stability and electromagnetic cleanliness. To enable fast shielding assessment at the packaging stage, this paper proposes an improved semi-analytical shielding-effectiveness model for hexagonal honeycomb waveguides used in laser controller enclosures. Within a transmission matrix formulation, finite-conductivity loss is included through propagation constant correction, and inter-cell interaction is represented by an array-based coupling correction. The model captures high-frequency additional transmission effects that are not reflected in simplified independent-cell formulations. Validation against CST full-wave simulations from 1 to 40 GHz demonstrates improved agreement, particularly near cutoff and in the upper-frequency range, with substantially lower computational cost than full-wave optimization loops. Geometry-dependent trends are further quantified to support EMC co-design of ventilation and enclosure structures in photonic and optoelectronic platforms. Full article

Alzheimer’s disease (AD) is increasingly recognized as a disorder not only of cognition but also of motivation and emotional regulation. Apathy and anhedonia often precede memory deficits, implicating early dysfunction in reward-related circuits. This study investigated whether chronic infusion of palmitoylethanolamide (PEA), a lipid-derived PPARα agonist, could restore motivational behavior and dendritic plasticity in the Tg2576 mouse model of AD. The motivational behavior of mice that received sustained-release PEA pellets for 6 months was assessed by using the conditioned place preference (CPP) paradigm. Morphological and molecular analyses were conducted in the entorhinal cortex (EC), dentate gyrus (DG), and prefrontal cortex (PFC). In Tg2576 mice, PEA significantly rescued CPP performance, increased basal dendritic spines in WT mice in the EC, and both basal and apical dendritic expression in EC and DG from Tg2576 mice, and upregulated the expression of both PPAR-α and brain-derived neurotrophic factor (BDNF) in the PFC. Interestingly, the BDNF increase occurred even in the absence of baseline deficits, suggesting a trophic-enhancement effect. These findings suggest that the PEA-PPARα-BDNF axis may be a potential mechanism for restoring motivation and synaptic integrity in an AD-like mouse model. Lipid-based neuromodulation may therefore offer novel therapeutic routes for addressing non-cognitive symptoms and affective circuitopathy in neurodegenerative diseases. Full article

Background: Aging is the strongest risk factor for prostate cancer (PCa). It is accompanied by progressive epigenomic divergence, known as epigenetic drift, particularly affecting DNA methylation at regulatory regions. However, the extent to which age-associated promoter methylation contributes to coordinated microRNA (miRNA) expression changes in PCa remains incompletely characterized. Methods: We conducted an integrative in silico analysis of 449 primary tumors from the TCGA-PRAD cohort. Age was modeled as a continuous variable. Age-related miRNA expression changes were estimated from miRNA-seq data using DESeq2. Promoter DNA methylation changes (±2 kb from transcription start sites) were assessed using Illumina 450K arrays and linear regression. MiRNAs showing significant age-associated alterations at both expression and methylation levels were classified as concordant or discordant based on directionality and prioritized using an effect size-based concordance score. We analyzed experimentally validated targets of prioritized miRNAs through functional enrichment and network-based approaches to identify convergent regulatory pathways. Results: Initially, we identified 105 age-associated miRNAs. After filtering, 65 candidates remained. Of these, we found 37 miRNAs with significant age-associated changes at both layers, including 20 concordant and 17 discordant miRNAs. These comprised well-characterized cancer-associated miRNAs and lesser-studied candidates enriched in CpG-rich regulatory regions. Network analyses revealed a limited set of genes under convergent regulation by multiple age-associated miRNAs. These implicated pathways are related to cell cycle control, apoptosis, stress response, and epigenetic regulation. Conclusions: Our findings support a model in which age-dependent promoter methylation drift contributes to coordinated miRNA deregulation in PCa. This convergence highlights biologically plausible miRNA biomarkers and age-sensitive epigenetic circuits relevant to prostate carcinogenesis. Full article

In this study, a non-typical luminescent organosilicone was synthesized through a click reaction and used as a cross-linker to cure hydroxyl-terminated dimethylsilicone oil at room temperature via the sol–gel process, followed by application as a coating on a glass surface. This organosilicone film functions effectively as a luminescent down-shifting (LDS) material. Additionally, the presence of methyl groups and voids in the structure imparts a low refractive index, allowing it to serve as an anti-reflective (AR) layer. Optical and structural analyses on organosilicone-coated glass samples were conducted, and the dual-functional layer was applied to the glass cover of a perovskite solar panel to evaluate its performance. The coating not only enhanced light transmission as an AR layer but also converted UV light into blue light, which was absorbed by the solar cell. The results indicated improved solar panel performance, particularly in short-circuit current (Isc), external quantum efficiency (EQE) in the UV wavelength range, and overall efficiency. Overall, this material is a promising candidate for solar panel applications owing to maximized UV absorption for LDS, preserved transparency of the top cover glass, and room-temperature gelation, which facilitates repair of the dual-functional coating. Full article

Silane sol was applied to seal the pores in a micro-arc oxidation coating, with the results proving that the treatment increased the anti-corrosion characteristics of aluminium alloy. Moreover, an electrochemical workstation was employed to test the open-circuit voltage, polarisation potential, and polarisation current of the samples. According to the results, after the aluminium alloy was treated with the micro-arc oxidation coating and underwent subsequent sealing treatment, the open-circuit potential increased from −0.64 to −0.44 V, the corrosion potential from −0.54 to −0.31 V, and the corrosion current density from 56.23 × 10⁻⁷ to 7.76 × 10⁻⁷ A. However, when samples were corroded by 1 mol/L HCl, the corrosion potential and corrosion current density decreased to −0.34 V and 20.42 × 10⁻⁷ A, respectively, proving that sealing the pores on the micro-arc oxidation coating only prevented substrate corrosion for a short time. In addition, slow-strain-rate stretching experiments were conducted to explore the mechanical performances of the samples, determining that the surface treatment had an insignificant effect on the stress of the aluminium alloy but had an important effect on its elongation, and when the surface of the alloy was treated with micro-arc oxidation coating, its elongation decreased from 28% to 26%. Full article

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

Background: The global burden of mental disorders continues to escalate, necessitating scalable, evidence-based interventions. Artificial intelligence (AI)-delivered health promotion programs represent a promising approach to addressing treatment gaps by targeting the neuropsychological mechanisms that underlie mental health outcomes. This meta-analysis synthesizes evidence on the effectiveness of AI-delivered interventions in improving executive function, emotion regulation, and clinical outcomes across diverse populations. Methods: A systematic search identified 186 studies (n = 22,755 participants) published between 2020 and 2025. Random-effects meta-analyses estimated pooled effect sizes (Hedges’ g, calculated as between-group standardized mean differences with small-sample correction [J = 1 − 3/(4df − 1)]) for primary outcomes. Between-study heterogeneity was quantified using I² and τ² statistics. To address dependency among effect sizes from studies reporting multiple outcomes, robust variance estimation (RVE) was employed. Subgroup analyses examined intervention modalities, delivery formats, and clinical populations. Moderator analyses explored sources of heterogeneity, including publication year, sample size, intervention duration, control condition type, risk-of-bias rating, geographic region, and AI sophistication tier, and mediational models tested putative therapeutic mechanisms. Results: AI-delivered interventions demonstrated a significant overall effect on health outcomes (g = 0.68, 95% CI [0.58, 0.78]; τ² = 0.12; I² = 73.4%). Executive function outcomes showed moderate effects (g = 0.61, τ² = 0.08), with working memory improvements being strongest (g = 0.72). Emotion regulation outcomes demonstrated moderate-to-large effects (g = 0.61, 95% CI [0.51, 0.70], τ² = 0.006); formal subgroup pooled estimates by emotion regulation strategy were not calculated due to insufficient studies per strategy (k < 3 per category); individual study effect sizes ranged from g = 0.27 to g = 1.11. Among 41 studies examining neuropsychological mechanisms, convergent patterns suggested involvement of prefrontal neural circuits (DLPFC), enhanced alpha-band activity, and improved heart rate variability; however, formal mediation was tested in only 18 studies (9.7%). Among clinical populations, interventions for cognitive impairment yielded the largest effects (g = 1.02; this finding should be interpreted cautiously given modest cumulative sample size [n = 482], potential small-study effects [Egger’s p = 0.08], and trim-and-fill adjusted estimate of g = 0.85), followed by mental health conditions (g = 0.72), while other clinical populations showed smaller but significant improvements (g = 0.19). Mobile applications (g = 0.78) and chatbot-based interventions (g = 0.74) demonstrated the strongest effects among delivery formats. Among studies testing formal mediation, analyses suggested mindfulness (β = 0.42), decentering (β = 0.38), and cognitive reappraisal (β = 0.45) as processes associated with therapeutic outcomes. Conclusions: AI-delivered health promotion programs demonstrate significant effectiveness across executive function, emotion regulation, and clinical outcomes, though substantial heterogeneity (I² = 45–82%) indicates meaningful variability warranting attention to subgroup-specific effects. Given the diversity of intervention types included (chatbots, mobile apps, VR systems, neuromodulation), pooled estimates should be interpreted as characterizing the average effect across this heterogeneous landscape; subgroup-specific estimates provide more precise guidance for clinical decision-making regarding specific modalities. Effects are associated with convergent patterns of neuropsychological mechanisms, though mechanistic conclusions remain preliminary given that only 22% of studies (41/186) examined neuropsychological mechanisms, and formal mediation analyses were conducted in only 18 studies (9.7%); most of the mechanistic evidence is correlational rather than causal. Future research should establish standardized AI taxonomies, optimize adaptive algorithms, conduct adequately powered replication studies in populations with cognitive impairment, prioritize experimental mediation designs to establish causal pathways, and evaluate long-term maintenance effects with a minimum of 6–12-month follow-up periods. Full article

In digital signal processing, the finite impulse response (FIR) filter is a fundamental tool for processing discrete-time signals. This paper explores the implementation of FIR filters using quantum computation methods. In this study, a quantum circuit for the FIR filter is designed using a normalized filter coefficient vector, orthogonal triangular decomposition commonly known as QR decomposition, and the transpilation tools provided by IBM’s software Qiskit SDK V2.3. Then, each block of the input signal is normalized to a unit-norm vector, loaded into a quantum register, and processed by the FIR filter quantum circuit to produce an output state. Quantum measurement is then performed on the output state to obtain a histogram, from which the first-bin data are scaled to compute the output sample of the filter. Finally, signal filtering experiments using FIR mean filters are conducted to demonstrate the effectiveness of the proposed quantum computation approach. Full article

Effective online bond wire degradation assessment of power modules is crucial for ensuring long-term stability. However, its electrical aging indicators are often influenced by junction temperature ($T_j$), and conventional $T_j$ monitoring methods are also affected by the aging process itself, creating a contradiction. This paper proposes a thermal–electrical co-modeling method designed to reduce reliance on accurate $T_j$. A major challenge of the method is the traditional thermal network models, which rely on case temperature ($T_c$). These models are affected by thermal coupling and have a slow dynamic response, making them difficult to integrate with electrical models. To overcome this, a $T_j$ monitoring method based on in situ sensor fabrication is employed to shorten thermal conduction path and simplify thermal network. This method results in a much faster dynamic process and is unaffected by thermal coupling, as confirmed through both theoretical analysis and finite element simulation. To validate the proposed method, bond wire degradation assessment is conducted using the on-state voltage drop ($V_{ce}$). Tested in practical circuits, this design successfully enables online evaluation of bond wire degradation, which is unaffected by $T_j$. Full article

This study focuses on the zonotope-based set-membership estimation issue for switched Takagi–Sugeno (T-S) fuzzy systems with application to tunnel diode circuits. Given the practical importance of tunnel diodes in radio-frequency, microwave, and high-speed electronic systems, we first model the tunnel diode circuit as a switched T-S fuzzy system to characterize its inherent dynamics. To address the state estimation issue, we propose a zonotopic set-membership estimation framework for the system under mode-dependent average dwell-time (MDADT) switching, which enables tighter state bounding while ensuring $H_{\infty }$ robustness. A mode-dependent observer is designed to attenuate the effects of external disturbances and measurement noise, and the stability of the estimation error system is analyzed based on an appropriate Lyapunov function. Numerical simulations are conducted and the corresponding results show that the estimated boundary can accurately encompass the true state of the system, and the volume of the estimated set is reduced by approximately 28.99% compared with the interval observer method, thus demonstrating the effectiveness and potential of the proposed approach. Full article

Proton exchange membrane fuel cells (PEMFCs) play a key role in hydrogen-based energy systems; however, accurate and practical modelling remains challenging due to system nonlinearities, parameter variability, and degradation effects. This paper presents a low-complexity parameter estimation methodology for a simplified PEMFC equivalent circuit model using current-switching techniques. The approach enables direct extraction of key parameters, including internal resistance and capacitance, from transient voltage responses without requiring complex optimization or large datasets. Experimental validation was conducted using 100 W and 1 kW PEMFC systems under current loading and interruption conditions. The results demonstrate good agreement between measured and simulated voltage responses, with a maximum error below 10% and typical error levels in the range of ~1.4–3%. Compared to conventional mechanistic and data-driven models, the proposed method significantly reduces computational complexity and measurement requirements while maintaining high predictive accuracy. Moreover, the combination of the simplified equivalent circuit model with current-switching-based parameter estimation offers an effective and practical tool for electrical engineers, enabling real-time monitoring, control-oriented modelling, and seamless integration with power electronic systems. The proposed approach is particularly suitable for applications in DC microgrids and digital twin-based monitoring of hydrogen energy systems. Full article

A significant portion of plastic products is not accepted by curbside recycling companies and goes to landfills or incineration, causing an adverse impact on the environment. This study investigated the effects of utilizing post-consumer plastic and e-waste in concrete. A plastic product made of thermoplastic polypropylene (PP) was ground into fine particles and used for 10% volumetric replacement of sand, while bare printed circuit boards (PCBs) were pulverized into powder and used for 10% cement replacement by mass. This study introduces a unique utilization of grounded powder PCBs by partially replacing cement in concrete. Furthermore, reinforced concrete beams with the replacements were constructed and tested under flexure for structural behavior evaluation. The results of this study show an average of 11% reduction in both the compressive strength of concrete and the maximum load capacity of the beams incorporating plastic products. A life cycle assessment study was conducted using a functional unit of 1.0 cubic yard concrete production. The system boundary for the environmental assessment of the concrete in this study includes only the production phase, which is from the cradle to the end gate of the ready-mix concrete plant. The environmental impact estimation of a 10% reduction in constituents of concrete showed a 10% reduction in most LCA measures where cement was replaced compared to a 1% effect for the fine aggregate replacement. Full article