Search Results (7,811)

Neuroligin-2 (NLGN2) is a cell adhesion molecule implicated in neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and intellectual disability (ID). While NLGN2 is well known as a postsynaptic organizer of predominantly inhibitory synapses, accumulating evidence suggests that NLGN family proteins are also involved in neuronal morphogenesis during early developmental stages. However, a gap remains in our understanding of how loss of function in NLGN2 potentially leads to abnormal neuronal morphogenesis. Here, we investigated the molecular basis of excessive neuronal process formation induced by depletion of NLGN2 using the N1E-115 cell line, an established model of neuronal differentiation. Clustered regularly interspaced short palindromic repeat (CRISPR)/Cas13-mediated knockdown of NLGN2 promoted neuronal process elongation and neuronal differentiation marker expression. Mechanistically, NLGN2 knockdown resulted in activation of coiled-coil and hook domain-containing protein 88A (CCDC88A, also known as Girdin or GIV), a non-receptor guanine nucleotide exchange factor for heterotrimeric G proteins. Transfection of the regulator of G protein signaling (RGS) domain of RGS3, a negative regulator of G proteins, or the G protein-binding domain of engulfment and cell motility 1 (ELMO1) effectively decreased the excessive process elongation phenotype. Similar effects were observed in primary cortical neurons. Furthermore, these interventions normalized elevated Rac1 activity induced by NLGN2 knockdown. Collectively, our findings identify the CCDC88A-G protein-ELMO signaling pathway as a key mediator of excessive neuronal morphogenesis following NLGN2 knockdown. These results provide valuable insight into the mechanisms by which NLGN2 dysfunction may contribute to abnormal neuronal morphogenesis and suggest potential recovery strategies. Full article

The development of sustainable coatings that combine reprocessability with active functionalities remains a central challenge for the composites sector. In this work, a healable, electrically conductive coating was formulated using epoxidized castor oil (ECO) as a bio-based matrix, dibutyl phosphate (DBP) as a transesterification catalyst, and short recycled carbon fibres (RCFs, 2 mm in length) as a conductive filler at loadings of 10 and 20 phr. Formulations were UV-cured via cationic photopolymerization and characterized across the full liquid-to-solid processing chain. FT-IR and photo-DSC showed that increasing RCF content progressively reduced curing rate and conversion, an effect attributed to light scattering/absorption by the fibres and restricted chain mobility, although gel content remained above 98% in all cases. DMTA showed that RCF did significantly affect the glass transition temperature but markedly increased the rubbery storage modulus and apparent crosslink density, consistent with a physical reinforcement mechanism. Stress relaxation tests confirmed the dynamic bond exchange behaviour in all formulations, with the apparent activation energy decreasing from 112 kJ/mol for the neat resin to 33–34 kJ/mol upon RCF incorporation. This significant reduction suggests that the presence of RCF facilitates the bond-exchange process, potentially through interfacial interactions between the polymer network and the fibre surface. However, the specific molecular mechanism responsible for this effect cannot be established from the present data. Electrical conductivity peaked at 10 phr RCF (3.6 × 10⁻³ S/m), enabling measurable Joule heating, while the 20 phr formulation showed reduced conductivity linked to voids and lower conversion. Thermally triggered healing at 120 °C for 6 h restored mechanical integrity, which is higher than reference values, demonstrating the coating’s capacity for repeated repair through its dynamic covalent network. Full article

Poly(β-L-malic acid) (PMLA) is a water-soluble, biodegradable aliphatic polyester whose pendant carboxyl groups support chemical functionalization for biomedical, packaging, and materials applications. Microbial fermentation can use pure sugars and biomass-derived carbon sources under mild conditions, but industrial translation remains constrained by feedstock cost and variability, strain performance, oxygen and pH control, pretreatment-derived inhibitors, and downstream recovery. This review therefore focuses on the fermentative production of PMLA from refined and renewable carbon sources, the microorganisms and metabolic routes involved, and the process variables that govern titer, yield, productivity, molecular weight, and purification. Agricultural and forestry feedstocks are compared according to their actual carbohydrate class and processing requirements. Bamboo shoot shell hydrolysate is treated as an emerging case study rather than an established production platform: one accepted shake-flask study directly demonstrated PMLA production by Aureobasidium pullulans NRRL Y-2311-1, but controlled bioreactor validation, reproducibility, techno-economic analysis, and application-specific product qualification remain to be further investigated. The review also examines autohydrolysis, low-molecular-weight PMLA for biomedical use, furan inhibition, membrane and ion-exchange purification, and the limits of current economic comparisons. This evidence-based framing identifies where bamboo-processing residues may contribute to renewable PMLA production while distinguishing laboratory feasibility from industrial readiness. Full article

The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system. Full article

The Internet of Vehicles (IoV) relies on frequent vehicle-to-roadside-unit (RSU) access over open wireless channels, making efficient authentication and key establishment essential. In many existing authentication and key agreement (AKA) schemes, a target RSU receives and processes a request before an invalid sender is rejected, which can waste roadside computation under dense invalid-request traffic. This paper presents EDAKA-IoV, an elliptic-curve-cryptography-based AKA scheme that separates admission filtering from end-to-end session-key establishment. A trusted authority (TA) performs Lightweight Polynomial-based Pre-Verification (LPPV) to discard invalid authentication requests before they reach the target RSU, while the vehicle and RSU establish the final session key. A current–pending dual-state mechanism prevents permanent de-synchronization during dynamic pseudo-identity renewal without adding another communication round. Formal analysis under an eCK-style model, ProVerif verification, and heuristic analysis evaluate session-key secrecy, injective mutual authentication, privacy, and resistance to the considered attacks. Optional offline precomputation moves two fixed-base scalar multiplications outside the online phase and reduces the non-polynomial online computation component by approximately 48.8%. With fixed-length compressed point encoding, the authentication exchange requires 2336 bits. The workload analysis shows that the RSU-side processing reduction is proportional to the invalid-request ratio, while the TA still performs record lookup, hashing, and degree-dependent polynomial evaluation for every received request. EDAKA-IoV therefore provides a balanced authentication solution for resource-sensitive IoV deployments. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

This work proposes a stochastic fractional power-law model for currency exchange rate forecasting. The model incorporates nonlocal temporal effects through the Caputo fractional derivative and nonlinear scaling dynamics via a power-law formulation, providing a flexible framework for representing complex temporal behavior in financial time series. Model parameters are estimated by fitting an explicit analytical calibration expression, constructed under the fractional chain-rule framework adopted in this study, to historical currency exchange-rate data using nonlinear optimization techniques. This expression is employed specifically as a tractable parameterization for model calibration and is not claimed as a general exact closed-form solution of the nonlinear problem involving the standard Caputo derivative. The forecasting stage is carried out through numerical simulations using a FORK-2-based stochastic discretization, with stochastic perturbations incorporated via a Wiener process. The proposed methodology is applied to daily EUR/MXN and EUR/CAD exchange rate series, and forecasts are generated through multiple Monte Carlo simulations over different prediction horizons. The results suggest that the fractional formulation can improve forecasting accuracy when longer training periods are employed. In addition, the nonlinear power-law structure increases the model’s flexibility and provides additional structural flexibility. Nevertheless, the integer-order formulation generally exhibits greater predictive stability, largely independent of the training period and the inclusion of the nonlinear power-law extension. Full article

The Weibei Plain, characterized by its complex stratified aquifer system and extensive brine resources, faces severe groundwater salinization. Unraveling the precise evolutionary mechanisms of diverse hydrochemical types across varying depths and geomorphological zones remains a significant challenge. This study synthesizes a multi-batch hydrochemical dataset with multi-isotopic tracers (δ²H, δ¹⁸O, δ¹¹B, δ⁸¹Br, δ³⁷Cl) to establish a comprehensive groundwater evolutionary model from the piedmont plain to the coastal marine plain. The results indicate distinct hydrochemical zonation governed by geographic geomorphology and historical marine transgressions. Salinization in transitional waters is primarily driven by physical mixing and reverse cation exchange rather than extreme evaporative fractionation. Crucially, isotopic mass balance definitively reveals that deep brine (depth > 60 m) originates not from modern seawater intrusion, but from the extreme surface evaporation of ancient paleo-seawater. This paleo-brine underwent profound isotopic exchange during its gravity-driven downward migration, evidenced by intense clay mineral adsorption (yielding extreme δ¹¹B enrichment up to 64.42‰) and secondary evaporite dissolution. Furthermore, the regional cone of depression formed by intensive brine extraction has profoundly altered deep hydrodynamics, inducing overflow and membrane ultrafiltration across massively thick clay aquitards. This process distinctly drives the isotopic fractionation observed in deep brackish waters. The analysis process in this study combines the isotope method with the regional geomorphological zoning, which can provide a reference for the analysis of groundwater evolution characteristics in other coastal aquifers. Full article

To optimize expensive black-box functions over networks, one of the most dominant frameworks is distributed Bayesian optimization (DBO), where local information can be exchanged among agents. However, DBO suffers from low evaluation efficiency due to limited data in the initial stage and the high cost of evaluations among multiple objectives. To tackle these obstacles, we propose a prior-informed multi-task collaborative Bayesian optimization (PMCBO) algorithm over networks. Concretely, PMCBO integrates expert prior knowledge about the location of optimum into the distributed multi-task Bayesian optimization framework to reduce the cost of evaluations. Meanwhile, PMCBO combines multi-task Bayesian optimization with a collaborative mechanism to improve the evaluation efficiency. Furthermore, we rigorously prove that the cumulative regret bound of PMCBO can achieve sub-linearly with high probability, where the acquisition functions employ expected improvement (EI) and upper-confidence bound (UCB) based on a Gaussian process surrogate. Finally, we implement various experiments to evaluate the effectiveness of PMCBO. The experimental results demonstrate that PMCBO can achieve state-of-the-art performance and benefit all clients based on diverse benchmarks and prior characteristics. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

Proton exchange membrane fuel cell (PEMFC) parameter identification is a nonlinear computational modeling problem involving strongly coupled electrochemical parameters and partially unobservable polarization processes. Its experimental teaching is further constrained by the cost of fuel cell stacks and the safety requirements associated with hydrogen operation. To address these challenges, this study develops a computational modeling and intelligent simulation framework for a virtual teaching experiment on PEMFC parameter identification. A semi-empirical output-voltage model is established, and the sum of squared errors (SSE) between measured and simulated voltages is formulated as the optimization objective. An enhanced Logistic–Tent reverse snow ablation optimizer (LRSAO), termed RLFDB-LRSAO, is introduced by integrating roulette-wheel-selection-enhanced fitness-distance balance and Lévy flight perturbation. Its methodological novelty lies in the stage-wise coordination of population-diversity enhancement, candidate-selection guidance, and search perturbation within the LRSAO framework, rather than in the individual component strategies themselves. The framework organizes the identification process into mechanism interpretation, model construction, algorithm implementation, parameter configuration, visualization, comparative evaluation, and reflective analysis. Case studies using the NedStack PS6 and Modular SR-12 stacks yield best SSE values of 1.2173340 and 6.13503904, respectively. A small-scale qualitative teaching evaluation involving 20 postgraduate students indicated that the framework supported programming practice, strengthened conceptual understanding of PEMFC parameter identification and intelligent optimization, and provided useful support for research-oriented skills such as engineering problem analysis, technical writing, and innovation-oriented project development. These results provide preliminary evidence of technical and educational feasibility while supporting a cautious, problem-dependent interpretation of the optimizer. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

A lightweight multi-scale group attention and channel dynamic residual fusion method is proposed to address the challenge of detecting extremely small encoded targets in particle accelerator multipole magnet collimation. In this scenario, the encoded targets occupy merely 0.003% to 0.009% of the total image pixels, rendering conventional visual techniques inadequate for capturing such fine-grained features. The paper proposes MLGA-CDRF-YOLO, an improved detection framework based on YOLOv11s that integrates a lightweight multi-scale group attention mechanism with channel-wise dynamic residual fusion. Specifically, the C2f-CDRF module combines lightweight channel attention with a dynamic residual structure, enhancing feature representation while reducing parameter count and computational complexity. The Multi-scale Lightweight Group Attention (MLGA) module splits input features into groups processed in parallel through a channel attention branch and a multi-scale spatial attention branch, with ChannelShuffle enabling cross-group information exchange, to improve the model’s feature representation capability and generalization performance for complex scenes. Experimental results on the encoded target dataset demonstrate that MLGA-CDRF-YOLO achieves an mAP@0.5 of 95.5%, a precision of 95.8%, and a recall of 88.5%, with only 3.41 M parameters and 14.8 GFLOPs, achieving a competitive and comprehensive balance between accuracy and computational cost. Furthermore, evaluation on the NEU-DET dataset confirms the model’s stable generalization performance across diverse detection tasks. Full article

Liquid chromatography–hydrogen/deuterium exchange–mass spectrometry (LC-HDX-MS) provides orthogonal structural information that complements conventional LC-MS and improves confidence in small-molecule structural elucidation. Although HDX-MS is well established in protein research, its application to small molecules remains less developed, and practical guidance for experimental implementation and data interpretation is limited. Here, we evaluate major LC-HDX-MS strategies, including post-column co-infusion, partial HDX, and full HDX approaches, and assess their analytical performance using reference standards and NIST SRM 1950 human plasma. The effects of deuterated mobile phases on background contamination, labeled/unlabeled signal ratios, retention-time shifts, adduct chemistry, and mass spectral interpretation are examined, and currently available software tools for LC-HDX-MS data processing are discussed. Full HDX provided the most complete deuterium incorporation, whereas deuterated mobile phases increased background signals and generally reduced analyte responses. Retention-time shifts were typically small but should be considered when matching features. HDX-derived information provides an additional experimental structural descriptor by defining the number of exchangeable hydrogen atoms and refining adduct assignments, thereby reducing ambiguity during structural annotation. These findings provide practical recommendations for implementing LC-HDX-MS in untargeted LC-MS workflows and demonstrate its value as an orthogonal tool for small-molecule structural elucidation. Full article

Selective recovery of gold from cyanide leach liquors is hindered by the co-dissolution of iron minerals that generate ferrocyanide complexes which strongly compete with [Au(CN)₂]⁻ at ion-exchange sorbents. Here, we investigate mixed-bed interpolymer systems (IPS) composed of a strong-acid sulfonated polystyrene–divinylbenzene cation exchanger (TC007, Na⁺ form) and a strong-base quaternary ammonium anion exchanger (AV-17-8, Cl⁻ form) as a charge-selective platform for gold cyanide recovery. IPS compositions spanning cation-to-anion molar ratios from 6:0 to 0:6 were evaluated in batch contact with binary model solutions containing 30 mg L⁻¹ each of [Au(CN)₂]⁻ and [Fe(CN)₆]⁴⁻ at pH 10 and 25 °C. The optimal 1:5 IPS achieved an [Au(CN)₂]⁻ extraction degree of 79.88% and a selectivity coefficient β = D_Au/D_Fe = 4.95 at 48 h, whereas the pure AV-17-8 anion exchanger (0:6) reached only 37.55% Au extraction at 48 h, following an atypical delayed-uptake kinetic profile rather than the rapid, near-quantitative capture expected of an unmodified strong-base resin. Sorption kinetics were best described by a pseudo-second-order model (R² = 0.9992), confirming ion exchange at quaternary ammonium sites as the dominant rate-controlling step, with a ~30-fold increase in k₂ for [Au(CN)₂]⁻ in the 1:5 IPS relative to AV-17-8 alone. FTIR spectroscopy and TGA-DSC revealed the incorporation of metal cyanide complexes into the IPS matrix, with diagnostic C≡N stretching bands at 2108.7 and 2034.1 cm⁻¹ and an additional thermal event at 200–280 °C. These findings establish compositionally tunable IPS based on commercially available resins as a charge-selective sorbent platform demonstrating a capacity to regenerate under single-cycle elution conditions for gold cyanide recovery from ferrocyanide-containing process streams while highlighting the need for further evaluation under industrial Fe:Au ratios. Full article