Search Results (5,433)

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller. Full article

Balancing low segment wear with stable cutting forces remains a challenge in granite sawing. This work compares diamond-impregnated saw segments produced by microwave– hybrid sintering (MWHS) and hot pressing (HP) when cutting Rosa Porriño granite. Tests were performed under tap-water cooling (22 L min⁻¹) while varying peripheral speed (20–40 m s⁻¹), feed speed (22–38 mm s⁻¹), and cutting depth (9–18 mm). Cutting forces were recorded during successive slots, and wear was quantified as mass loss per machined area (1.2–3.0 m² per test). MWHS segments exhibited lower wear than HP segments, with reductions up to ~20%, consistent with improved diamond retention and reduced binder exposure to debris-driven abrasion. Under higher cutting severity, however, MWHS segments developed higher forces, indicating reduced grit renewal and progressive blunting, plausibly linked to stronger diamond–matrix bonding and the low-friability diamond grade used. In contrast, HP segments operated at lower forces but showed higher wear, consistent with greater surface renewal through controlled grit release. Tuning diamond friability and matrix compliance in MWHS is proposed to stabilize forces while preserving the wear advantage. Overall, MWHS is a viable route for granite cutting segments, but further optimization is required to achieve HP-equivalent behavior across the tested conditions. Full article

Continuous monitoring of livestock vocalizations offers a non-invasive tool for welfare assessment, but deploying current deep learning models in resource-constrained farm environments remains challenging due to high computational demands. This study proposes a feature-based machine learning pipeline optimized for edge computing to classify caprine vocalizations. Using the VOCAPRA dataset, which comprises 4147 labeled caprine vocalizations categorized into eight distinct welfare states and contexts, a hybrid feature extraction framework was applied to derive 156 spectral, temporal, and bioacoustic descriptors. Dimensionality reduction and a comprehensive comparative screening of 18 algorithms identified the CatBoost Classifier and a Multilayer Perceptron (MLP) as the optimal models. The CatBoost ensemble achieved a robust accuracy of 85.2%, while the optimized MLP reached 87.2% overall accuracy. An edge deployment benchmark revealed that the MLP was the best candidate with for real-time application, featuring a memory footprint of just 0.639 MB and near-instantaneous inference speeds of under 0.005 milliseconds per sample. Furthermore, feature importance and SHAP analyses revealed that mel-frequency cepstral coefficients heavily drove model decisions, particularly for identifying extreme physical distress and maternal reunion. The proposed methodology achieves competitive classification performance while dramatically reducing pre-processing and computational loads compared to image-based deep learning approaches, demonstrating the viability of lightweight-model, energy-efficient, real-time bioacoustic monitoring for precision livestock farming. Full article

Speech Emotion Recognition (SER) is a key enabling technology for advanced human–computer interaction and affective computing. This paper presents an adaptive hybrid SER framework that combines a deep neural feature extraction module with a heterogeneous ensemble of machine learning classifiers, including XGBoost, Support Vector Machines (SVMs), and Random Forest. To overcome the limitations of static fusion strategies, a confidence-gated meta-classification mechanism is introduced to dynamically weight the contribution of each base classifier according to its instance-level reliability. The proposed approach is evaluated on two widely adopted benchmark datasets, EmoDB and SAVEE, achieving competitive accuracies of 98.88% and 91.92%, respectively. Experimental results demonstrate that the proposed fusion strategy significantly improves robustness against inter-speaker variability and emotional ambiguity, while maintaining low computational complexity suitable for real-time implementation. These findings highlight the effectiveness of the proposed framework as a robust and efficient solution for speech emotion recognition. While the model is evaluated on benchmark datasets, it is intended as a foundational component for future emotion-aware systems, including applications in human–computer interaction. Full article

In accordance with the national strategy of “carbon peaking by 2030 and carbon neutrality by 2060” and Hunan Province’s target of achieving carbon peaking in the construction sector by 2030, this study uses carbon emission data from Hunan’s construction sector for the period 2005–2022 as a research sample to conduct research on carbon emission accounting, analysis of influencing factors, and peak prediction. The carbon emission coefficient method was employed to calculate industry-wide carbon emissions. Using the STIRPAT model combined with ridge regression, we identified and quantified the driving factors of carbon emissions. A CNN-LSTM-Attention hybrid deep learning model was constructed, and three development scenarios—high-carbon, baseline, and low-carbon—were established to simulate the evolution of carbon emissions in Hunan’s construction industry from 2023 to 2040. The results indicate that carbon emissions from Hunan’s construction industry showed an overall upward trend during the study period, with indirect emissions constituting the primary component. Through variable optimization, the core positive drivers and negative restraints of carbon emissions in the construction industry were identified. The constructed hybrid model demonstrated excellent fitting performance, with prediction accuracy significantly higher than that of traditional machine learning and single deep learning models. Carbon emission trends varied significantly across different development scenarios, with the low-carbon development scenario identified as the optimal path for achieving the industry’s carbon peak target. These findings provide a theoretical basis and data support for the low-carbon transition of Hunan Province’s construction sector, as well as for the formulation and optimization of carbon peaking implementation plans. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

CAR-T cell therapy remains ineffective in most solid tumours because effector cells infiltrate poorly, undergo exhaustion, and face antigen escape within an immunosuppressive microenvironment. To address this, we developed a hybrid framework that combines a mechanistic spatiotemporal model with machine learning for limited individual-level mechanistic personalisation under data constraints. At its core, we employed a reaction–diffusion–chemotaxis model describing functional and exhausted CAR-T cells, antigen-positive and antigen-negative tumour subpopulations, a chemoattractant, an immunosuppressive factor, and hypoxia. Gradient boosting combined with nested cross-validation was used to recover model-consistent latent-parameter pseudo-labels generated by a limited inverse problem. Within this surrogate-target setting, parameters characterising the tumour microenvironment and CAR-T cell exhaustion were reproduced most robustly, whereas antigen escape and individualised initial conditions were substantially less well constrained. As an auxiliary reference point, we also considered a direct empirical baseline for binary clinical outcomes. This baseline indicated that the observed clinical features contained a more stable signal for disease control than for objective response. A favourable response was associated with high CAR-T cell infiltration and cytotoxic potency, whereas resistance was linked to exhaustion, antigen escape, and a suppressive microenvironment. Overall, the proposed approach should be interpreted as an internally validated, hypothesis-generating proof-of-concept platform for mapping clinical features to mechanistically interpretable surrogate latent targets, rather than as evidence for validated recovery of true patient-specific biological parameters. Full article

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production management, reliant on empirical fixed-cycle schedules, fails to adapt to dynamic reservoir behavior and wellbore conditions, leading to suboptimal energy efficiency and recovery. This study presents a physics-constrained, data-driven framework for adaptive intermittent production optimization, specifically designed to address the coupled geological-engineering complexities of such reservoirs. The methodology integrates three core innovations: (1) a hybrid flowing bottomhole pressure (FBHP) decline model coupling a “Three-Segment” wellbore pressure calculation with inflow performance relationship (IPR) curves, enabling dynamic characterization of pressure depletion; (2) a shut-in pressure buildup prediction framework combining a physically interpretable dual-exponential recovery mechanism—representing near-wellbore elastic expansion and far-field formation recharge—with a Random Forest Regression algorithm to capture the influence of geological and operational heterogeneity; and (3) a “Dual-Curve Intersection Method” that autonomously determines optimal pumping and shut-in durations by intersecting predicted pressure decline and recovery curves under geological constraints. Field implementation on 15 low-production wells in the Jiyuan Oilfield—a representative low-permeability asset—demonstrated robust performance: average pump efficiency improved from 14.3% to 14.49%, and average single-well electricity savings reached 15.61%. This work establishes a closed-loop intelligent control framework that bridges reservoir geology, wellbore hydraulics, and machine learning, offering a scalable solution for enhancing energy efficiency and production sustainability in low-permeability and unconventional resources. Full article

In the framework of Agriculture 4.0, the modernization and predictive maintenance of legacy heavy machinery are essential for ensuring food security and operational efficiency. This study presents a non-invasive automated diagnostic system for classifying the operational status of mechanical diesel injection pumps in agricultural tractors through vibration analysis and machine learning. A rigorous experimental setup was conducted on an International 523 tractor to acquire vibration signals under controlled fuel pressure conditions ranging from 1 to 4 bar, with 2 bar established as the optimal nominal pressure. The signal processing methodology employed a hybrid feature extraction approach, integrating spectral components from the Fast Fourier Transform (FFT) with time-domain statistical variables. After evaluating 33 classification algorithms, a Support Vector Machine (SVM) model demonstrated superior performance, achieving a training accuracy of 96.7% and Area Under the Curve (AUC) values exceeding 0.90 across all classes. Notably, the model achieved perfect identification (AUC = 1.0) of critical low-pressure faults (1 bar), which significantly compromise engine start-up and combustion efficiency. Validation with an independent dataset confirmed the robustness of the system, maintaining a 95% accuracy rate. These findings validate the proposed approach as a reliable, low-cost solution for condition monitoring, facilitating the integration of conventional tractors into digital maintenance ecosystems. Full article

Real-time traffic signal control represents a key challenge in modern intelligent transportation systems, particularly under highly variable traffic flows and the presence of priority vehicles. This study proposes a hybrid framework for adaptive signal plan control at a signalized intersection. The framework integrates deep learning-based traffic prediction, surrogate-based performance evaluation, and reinforcement learning-based adaptive control. Short-term traffic flow is predicted using recurrent neural networks, providing anticipatory information for traffic control decisions. Based on predicted flows and generated candidate signal plans, a machine learning surrogate model enables fast estimation of key performance indicators, including average vehicle delay and queue length. Adaptive control is implemented using the Proximal Policy Optimization algorithm within the SUMO environment via TraCI, which enables real-time fine-tuning of signal phases. A dedicated priority and stability module ensures effective emergency vehicle preemption and adaptive public transport priority while preserving intersection stability. Simulation results show that the proposed framework reduces average vehicle delay by up to 35% compared with FT and by up to 15% compared with standalone RL, while also improving traffic flow efficiency and priority vehicle performance. Full article

The textile industry faces machinery maintenance challenges due to reactive practices, lack of real-time monitoring, and absent integrated management systems, resulting in unplanned downtime, elevated costs, and quality variability. This study addresses these limitations by proposing a hybrid predictive–prescriptive framework integrating XGBoost 3.2.0 and LSTM models with a multi-objective optimization engine to generate data-driven maintenance recommendations. The framework was validated on four critical components, needles, hooks, needle guides, and thread tensioners, using operational data from a textile plant (November 2024–January 2026). Plant-wide Mean Time Between Failures increased by 38% (15–21 to 24–28 h), while Mean Time To Repair decreased by 15% (5.31 to 4.6 h). These improvements yielded 5.5% lower maintenance costs, 9% less fabric waste, and reduced cost per operating hour from $25 to $23.5. The prescriptive module transformed imperfect predictions into robust decisions by evaluating interventions against production constraints, spare parts availability, and risk criteria. Beyond quantitative gains, the framework enabled sustainable practices including data-driven spare parts policies and condition-based inspections. This work demonstrates that integrating prediction with prescription effectively overcomes structural maintenance challenges in textile manufacturing, providing a replicable methodology for broader industrial adoption. Full article

The appearance of technical debt (TD) becomes a critical problem, posing challenges related to software maintainability and its quality within the context of fast modern software development. The presented research focuses on the issue of TD appearance in the context of Python software development by employing a hybrid approach involving perception and predictive approaches. Within the scope of research, the perceptions of 86 IT practitioners and developers have been studied with regard to their reactions, adaptation, and prioritization of different types of TDs. According to the qualitative results, cyclic architectural debt stems from low test coverage, documentation deficiency, and complicated code structure. Based on the aforementioned information, the research team developed a dataset consisting of 130 Python codes in real conditions with the following characteristics: code complexity, comments-to-code ratio, code smells, and software maintainability indexes being used. Thereafter, the application of DT, LR, NB, SVM, KNN, and RF predictive models allowed detecting TDs. The presented results reveal the possibility of predicting TDs with the use of machine learning methods, with optimal performance provided by random forest and optimized logistic regression models. Full article

Accurate estimation of residential building energy consumption and associated CO₂ emissions is essential for refined urban carbon management. This study develops a hybrid framework that integrates physics-based simulation and machine learning to estimate residential building energy use and energy-related CO₂ emissions in Shenzhen in 2020. Representative building archetypes were first simulated and then used to train machine-learning models for large-scale applications. Building-level energy estimates were further combined with a bottom-up inventory to generate high-spatiotemporal-resolution maps of residential CO₂ emissions. The results show that: (1) the selected model achieved good accuracy and temporal robustness, with strong agreement between estimated and reference energy use at daily, monthly, and annual scales; (2) residential energy use was primarily driven by meteorological conditions, especially daily mean temperature and the duration of high-temperature conditions, and exhibited clear weekly and seasonal patterns, with higher values on weekends and in summer; (3) residential CO₂ emissions in Shenzhen reflected the combined effects of scale and intensity, with Longgang and Bao’an contributing the largest total emissions, Self-built residential buildings contributing the largest aggregate emissions, and Old residential buildings showing the highest average emissions per building; (4) emissions were highly concentrated in a small number of high-emission buildings, which were more frequently distributed along road-adjacent block perimeters. Overall, the proposed framework improves the fine-scale characterization of residential building CO₂ emissions and provides a useful basis for hotspot identification and targeted mitigation. Full article

Extreme meteorological phenomena—characterized by gale-force winds, torrential rainfall, and ice-snow accumulation—pose significant threats to the operational safety of high-speed railways, with wind-induced hazards being especially critical. Such events can trigger catastrophic incidents, including train derailments and service disruptions, as evidenced by numerous documented cases worldwide. To bolster the wind resilience of high-speed railway systems, high-precision wind speed prediction has become a cornerstone for ensuring operational safety. This research presents a systematic review of international advancements in railway wind early warning systems, critically evaluating the technical attributes and performance constraints of four primary paradigms: physical numerical models, statistical methods, machine learning algorithms, and hybrid frameworks. Moving beyond a simple taxonomy, this paper delineates the strengths, limitations, and domain-specific applicability of each approach within the high-speed railways context. Furthermore, it assesses the transformative potential of emerging large-scale Artificial Intelligence (AI) meteorological models for wind speed forecasting. A quantitative comparison is provided to facilitate rigorous methodological assessment. The findings reveal four critical technical bottlenecks: (1) low computational efficiency of numerical models; (2) insufficient spatiotemporal resolution of monitoring data; (3) poor generalization of predictive models; and (4) the “black-box” nature and weak interpretability of AI models. To address these, this paper posits that future research should prioritize key technologies including multi-source heterogeneous data fusion, algorithmic optimization, design of intelligent algorithms, probabilistic risk forecasting, and the synergistic integration of AI with numerical weather prediction (NWP). Such advancements will catalyze the development of more robust HSR wind warning systems, ensuring sustained safety and operational efficiency under volatile meteorological conditions. Full article