Search Results (1,080)

Deep coalbed methane (CBM) development faces significant challenges due to extreme geological conditions (high stress, elevated pressure, high temperature) that differ fundamentally from shallow reservoirs. Traditional productivity models developed for shallow CBM often fail to accurately predict deep reservoir performance. The complex “stress-desorption-flow” multi-field coupling mechanism, intensified under deep conditions, critically controls production dynamics but remains poorly understood. This study develops a multi-layer, commingled, coupled geomechanical-flow model for the Hujiertai deep CBM block (2140~2170 m) in Xinjiang, China. The model, integrating gas-water two-phase flow, Langmuir adsorption, and transient geostress evolution, was validated against field production data, achieving a low relative error of 1.2% in the simulated average daily gas rate. Results indicate that: (1) Geomechanical coupling is critical. The dynamic competition between effective stress compaction and matrix shrinkage limits fracture porosity reduction to ~2%, enabling a characteristic “rapid incline, 1–2-year plateau, gradual decline” production profile and significantly enhancing cumulative gas production. (2) Porosity (10~30%) is positively correlated with productivity: a 10-percentage-point increase raises the peak gas rate by 2.1% and cumulative production by 2.8%. Conversely, high initial cleat permeability boosts early rates but accelerates geomechanical damage (cleat closure), lowering long-term productivity. (3) Stimulation parameters show a trade-off. SRV only dictates short-term, near-wellbore production. Higher fracture permeability (peak rate +17% per 500 mD) boosts early output but accelerates depletion and stress-induced closure. The multi-field coupling mechanisms revealed and the robust model developed provide a theoretical basis for optimizing fracturing design and production strategies for analogous deep CBM plays. Full article

Metal halide perovskite (MHP)-based heterojunctions have become a forefront area in the research of optoelectronic functional materials due to their unique layered crystal structure, tunable band gaps, and exceptional optoelectronic properties. Recent studies have demonstrated that interface charge transfer is a crucial factor in determining the optoelectronic performance of the heterojunction devices. By constructing heterojunctions between MHPs and two-dimensional (2D) materials such as graphene, MoS₂, and WS₂, efficient electron–hole separation and transport can be achieved, significantly extending carrier lifetimes and suppressing non-radiative recombination. This results in enhanced response speed and energy conversion efficiency in photodetectors, photovoltaic devices, and light-emitting devices (LEDs). In these heterojunctions, the thickness of the MHP layer, interface defect density, and band alignment significantly influence carrier dynamics. Furthermore, techniques such as interface engineering, molecular passivation, and band engineering can effectively optimize charge separation efficiency and improve device stability. The integration of multilayer heterojunctions and flexible designs also presents new opportunities for expanding the functionality of high-performance optoelectronic devices. In this review, we systematically summarize the charge transfer mechanisms in MHP-based heterojunctions and highlight recent advances in their optoelectronic applications. Particular emphasis is placed on the influence of interfacial coupling on carrier generation, transport, and recombination dynamics. Furthermore, the ultrafast dynamic behaviors and band-engineering strategies in representative heterojunctions are elaborated, together with key factors and approaches for enhancing charge transfer efficiency. Finally, the potential of MHP heterojunctions for high-performance optoelectronic devices and emerging photonic systems is discussed. This review aims to provide a comprehensive theoretical and experimental reference for future research and to offer new insights into the rational design and application of flexible optoelectronics, photovoltaics, light-emitting devices, and quantum photonic technologies. Full article

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques. Full article

The formation of mixed adsorption layers of amyloid fibrils of a plant protein, oat globulin (OG), and a strong polyelectrolyte, sodium polystyrene sulfonate (PSS), at the liquid–gas interface was studied by measurements of the kinetic dependencies of surface tension, dynamic surface elasticity, and ellipsometric angle. The micromorphology of the layers was determined by atomic force microscopy. A strong increase in the surface elasticity was discovered when both components had similar concentrations and formed a network of threadlike aggregates at the interface, thereby explaining the high foam stability in this concentration range. The sequential adsorption of PSS and OG resulted in the formation of thick mixed multilayers and the surface elasticity increased with the number of duplex layers. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

Financial forecasting is a challenging task due to the complexity and nonlinear volatility that characterize modern financial markets. Machine learning algorithms are very effective at increasing prediction accuracy, thereby supporting data-driven decision making, optimizing pricing strategies, and improving financial risk management. In particular, combining machine learning techniques with metaheuristic algorithms often leads to significant performance improvements across various domains. This study proposes a hybrid framework for cryptocurrency price prediction, where Support Vector Regression (SVR) with radial basis function kernel is used to perform the prediction, while a Firefly algorithm is employed for correlation-based feature selection and hyperparameter tuning. To improve search performance, the parameters of the Firefly algorithm are optimized using the Fully Informed Search Algorithm (FISA) which is an improved version of the parameterless Rao algorithm. The model is applied to hourly data of Bitcoin, Ethereum, Binance, Solana and Ripple, separately. The model’s performance is evaluated by comparison with Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and SVR methods using MSE, MAE, and MAPE metrics, along with statistical validation by Wilcoxon’s signed-rank test. The results show that the proposed model achieves a superior accuracy and demonstrate the critical importance of feature selection and hyperparameter tuning for achieving accurate predictions in volatile markets. Moreover, customizing both feature sets and model configurations for each cryptocurrency allows the model to capture distinct market characteristics and provides deeper insights into intra-day market dynamics. Full article

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience. Full article

Phenylalanine ammonia-lyase (PAL) is the core branch-point enzyme connecting plant primary aromatic amino acid metabolism to the phenylpropanoid pathway, which determines carbon flux redistribution between growth and defense and is essential for plant adaptation to various environments. Extensive research has clarified PAL’s conserved homotetrameric structure, MIO cofactor-dependent catalytic mechanism, and its roles in plant growth, development, and stress responses. However, there is a lack of comprehensive review studies focusing on PAL-mediated carbon metabolic flux redistribution, specifically covering its structural and evolutionary foundations, the links between this flux regulation and plant growth/development, its multi-layered regulatory network, and its roles in stress adaptation, limiting a comprehensive understanding of its evolutionary and functional diversity. This review systematically covers four core aspects: first, the molecular foundation, encompassing PAL’s structural features and catalytic specificity governed by the MIO cofactor; second, evolutionary diversity spanning from algae to angiosperms, with emphasis on unique regulatory mechanisms and evolutionary significance across lineages; third, the multi-layered regulatory network, integrating transcriptional control, post-translational modifications, epigenetic regulation, and functional crosstalk with phytohormones; and fourth, functional dynamics, which elaborate PAL’s roles in organ development, including root lignification, stem mechanical strength, leaf photoprotection, flower and fruit quality formation, and lifecycle-wide dynamic expression, as well as its mediated stress adaptations and regulatory networks under combined stresses. These insights provide a theoretical basis for targeted manipulation of PAL to optimize crop carbon allocation, thus improving growth performance, enhance stress resilience, and promote sustainable agriculture. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

This study examines the impact of vocal fold (VF) lesions located on the superior surface on glottal airflow dynamics and tissue oscillatory behaviors using biomechanical simulations of a two-layered realistic VF model. It is hypothesized that morphological changes in the VFs due to the presence of a lesion cause changes in tissue elasticity and rheological properties, contributing to dysphonia. Previous research has lacked the integration of lesions in computational simulations of anatomically accurate larynx-VF models to explore their effects on phonation and contribution to voice disorders. Addressing the current gap in literature, this paper considers a computational model of a two-layered VF structure incorporating a lesion that represents a hemorrhagic polyp. A three-dimensional, subject-specific, multilayered geometry of VFs is constructed based on STL files derived from a human larynx CT scan, and a fluid–structure interaction (FSI) methodology is employed to simulate the coupling of glottal airflow and VF tissue dynamics. To evaluate the effects of the lesion’s presence, two FSI models, one with a lesion embedded in the cover layer and one without, are simulated and compared. Analysis of airflow dynamics and tissue vibrational patterns between these two models is used to determine the impact of the lesion on the biomechanical characteristics of phonation. The polyp is found to slightly increase airflow resistance through the glottis and disrupt vibratory symmetry by decreasing the vibration frequency of the affected fold, leading to weaker and less rhythmic oscillations. The results also indicate that the lesion increases tissue stress in the affected fold, which agrees with clinical observations. While quantitative ranges depend on lesion size and tissue properties, these consistent and physically meaningful trends highlight the biomechanical mechanisms by which lesions influence phonation. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

Recommender systems are widely deployed across digital platforms, yet their opacity raises concerns about auditability, fairness, and user trust. To address the gap between predictive accuracy and model interpretability, this study proposes a glass-box architecture for trustworthy recommendation, designed to reconcile predictive performance with interpretability. The framework integrates interpretable tree ensemble model (Random Forest, XGBoost), an NLP sub-model for tag sentiment, prioritising transparency from feature engineering through to explanation. Additionally, a Reality Check mechanism enforces strict temporal separation and removes already-popular items, compelling the model to forecast latent growth signals rather than mimic popularity thresholds. Evaluated on the MovieLens dataset, the glass-box architectures demonstrated superior discrimination capabilities, with the Random Forest and XGBoost models achieving ROC-AUC scores of 0.92 and 0.91, respectively. These tree ensembles notably outperformed the standard Logistic Regression (0.89) and the neural baseline (MLP model with 0.86). Beyond accuracy, the design implements governance through a multi-layered Governance Stack: (i) attribution and traceability via exact TreeSHAP values, (ii) stability verification using ICE plots and sensitivity analysis across policy configurations, and (iii) fairness audits detecting genre and temporal bias. Dynamic threshold optimisation further improves recall for emerging items under severe class imbalance. Cross-domain validation on Amazon Electronics test dataset confirmed architectural generalisability (AUC = 0.89), demonstrating robustness in sparse, high-friction environments. These findings challenge the perceived trade-off between accuracy and interpretability, offering a practical blueprint for Safe-by-Design recommender systems that embed fairness, accountability, and auditability as intrinsic properties rather than post hoc add-ons. Full article

In recent years, the frequent emergence of Android malware has posed a significant threat to user security. The redundancy of features in malicious software samples and the instability of individual model performance have also introduced numerous challenges to malware detection. To address these issues, this paper proposes a malware detection framework named Mass-Droid, based on Multi-feature and Multi-layer Screening for adaptive Stacking integration. First, three types of features are extracted from APK files: permission features, API call features, and opcode sequences. Then, a three-layer feature screening mechanism is designed to effectively eliminate feature redundancy, improve detection accuracy, and reduce the computational complexity of the model. To tackle the problem of high performance fluctuations and limited generalization ability in single models, this paper proposes an adaptive Stacking integration method (Adaptive-Stacking). By dynamically adjusting the weights of base classifiers, this method significantly enhances the stability and generalization performance of the ensemble model when dealing with complex and diverse malware samples. The experimental results demonstrate that the MaSS-Droid framework can effectively mitigate overfitting, improve the model’s generalization capability, reduce feature redundancy, and significantly enhance the overall stability and accuracy of malware detection. Full article

Artificial hybrid pine (Pinus × rigitaeda) plantations, widely established in Northeast Asia for reforestation and timber production, have reached maturity, necessitating an evaluation of their ecological sustainability and successional dynamics. Although numerous studies have examined succession in pure Pinus rigida or Pinus densiflora stands, the long-term structural transition and regeneration potential of hybrid P. × rigitaeda plantations remain poorly understood. This study quantitatively assessed the successional stage and potential transition pathways of P. × rigitaeda stands using an integrated analytical framework combining vegetation classification (TWINSPAN), ordination (NMDS), successional index, survival analysis (Weibull model), and growth–environment modeling (GAM). Multi-layer vegetation data were analyzed to evaluate compositional changes, structural attributes, and nonlinear environmental responses. The results revealed that the dominance of P. × rigitaeda declined markedly while native deciduous species increased in lower strata. The Weibull survival model (k = 1.3) indicated accelerating mortality with stand aging, and the successional index showed the highest value (0.4) for Castanea crenata, followed by other Quercus species, confirming an ongoing shift toward hardwood dominance. GAM analysis confirmed that growth stability was influenced by stand age and precipitation. These findings demonstrate that P. × rigitaeda plantations are not merely artificial production forests but function as self-organizing systems facilitating natural forest recovery. In this respect, the hybrid pine plantation can be interpreted as a spontaneous ecological experiment, highlighting the restoration value of artificial hybrids as transitional stages bridging artificial afforestation and natural forest succession in temperate monsoon regions. Full article

Drilling in aerospace one-up assembly demands high positional accuracy, strong clamping forces, and precise angular compensation to ensure quality in multi-layered stacks. Existing robotic solutions achieve these requirements but are costly, bulky, and unsuitable for flexible or collaborative environments. This work introduces the Advanced Collaborative Multifunctional End-Effector (ACME), a lightweight robotic drilling end-effector designed for integration with collaborative robots (cobots). ACME incorporates vacuum-assisted clamping capable of generating high forces, a passive self-normalization mechanism for angular alignment on double-curvature surfaces, and a compact 5-DoF positioning system for precise positioning and orientation. The system’s kinematics and dynamics were modeled and experimentally verified through frequency response function (FRF) testing, enabling precise behavior prediction. The tool is integrated within a cyber–physical system (CPS) featuring an interactive digital twin that, unlike passive monitoring systems, allows operators to configure workpieces, select drilling locations directly from rendered CAD, and supervise execution without programming expertise. Experiments demonstrated average positional errors of 0.19 mm and normality deviations of 0.29°, both within aerospace standards. The results confirm that ACME effectively extends cobot capabilities for aerospace-grade drilling while improving flexibility, safety, and operator accessibility. Full article