Search Results (27,197)

In recent years, climate change has become increasingly urgent, and governments are intensifying efforts to regulate carbon-intensive industries through policy innovations. The transport sector faces particularly acute decarbonisation challenges due to its reliance on fossil fuels. This study investigates road-rail intermodal transport as a strategic solution that synergises the flexibility of trucking with the superior energy efficiency of rail. A novel arc-path 0-1 nonlinear model is developed, optimising profit maximisation while incorporating hard constraints on transport due dates. The predominant carbon emission policies—command-and-control regulations and carbon pricing mechanisms—are analysed, and the corresponding extended models are constructed. Next, the linearisation techniques are introduced. In the end, a numerical example is built to test the validity of the model and compare the optimisation decisions of the basic model and the extended models. Furthermore, a sensitivity analysis of key parameters is conducted to provide operational recommendations for enterprises to balance carbon emissions and profits. Full article

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems. Full article

High-performance speed regulation of interior permanent magnet synchronous motor (IPMSM) drives in electric vehicle (EV) applications becomes particularly challenging in the field-weakening region, where voltage constraints, parameter variations, and nonlinear aerodynamic loads significantly affect the closed-loop stability. To address these challenges, this paper proposes a stability-aware adaptive fractional-order speed control framework for EV traction systems. The framework integrates a fractional-order PI (FOPI) core to provide iso-damping robustness, a bounded fuzzy gain-scheduling mechanism for real-time adaptation, and an offline multi-objective optimization layer for systematic parameter tuning. A Lyapunov-based qualitative analysis is provided to justify closed-loop ultimate boundedness under adaptive gain modulation and field-weakening constraints. The fuzzy scheduler is explicitly structured to regulate the error energy dissipation rate by modulating the proportional and integral gains while preserving the gain boundedness. The controller parameters are optimized using a diversity-driven fractional-order multi-objective PSO algorithm to balance the tracking accuracy and control effort. The proposed framework was validated using a high-fidelity MATLAB/Simulink–CarSim 2023 co-simulation platform under the aggressive US06 driving cycle. The results demonstrated a zero-overshoot transient response, robustness against a 2.5× inertia mismatch, and sustained performance under flux-linkage and inductance variations in deep field-weakening operation. Compared with conventional PI-based strategies, the proposed approach reduced the speed RMSE by 82%, lowered the current THD from 18.5% to 3.2%, and reduced the cumulative DC-link current-squared index by 6.7%. These results validate the practical robustness and computational feasibility of the proposed stability-aware framework for EV traction control. Full article

The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art accuracy and robustness for Permanent Magnet Synchronous Motor (PMSM) temperature forecasting. Our methodology first calibrates a Lumped-Parameter Thermal Network (LPTN) to serve as a physics engine for generating physically consistent data augmentations, which then pre-trains a Temporal Convolutional Network (TCN) encoder via self-supervision, with the final prediction assembled from the physics model’s baseline guess and a correction learned by an ensemble of gradient boosting models on a rich, multi-modal feature set. Evaluated against a suite of strong baselines, our hybrid ensemble achieves a state-of-the-art Root Mean Squared Error of 5.24 °C on a challenging OOD stress test composed of the most chaotic operational profiles. Most compellingly, our model’s performance improved by an unprecedented −10.68% under these extreme stress conditions where standard, purely data-driven models collapsed. This demonstrated robustness, combined with a statistically valid Coverage Under Shift (CUS) Gap of only 1.43%, provides a complete blueprint for building high-performance, trustworthy AI, enabling safer and more efficient control of critical cyber-physical systems and motivating future research into physics-guided pre-training for other industrial assets. Full article

Increasing demands for improved energy efficiency and resource recovery in wastewater management have driven intensified research on microalgal–bacterial consortia (M-BC). This technological approach represents one of the most promising and continuously evolving concepts for integrated wastewater treatment and energy recovery. M-BC systems exploit complementary processes, including photosynthesis, oxygen production, nutrient uptake by microalgae, as well as heterotrophic degradation of organic contaminants and CO₂ generation by bacteria. Laboratory- and pilot-scale studies demonstrate that such integration can substantially reduce energy demand while significantly improving technological performance. Metabolic synergy, metabolite exchange, intercellular communication, and the specific aggregate architecture collectively determine the stability and high productivity of these consortia. Depending on operational conditions, M-BC may occur as suspended cultures, biofilm-based systems, or granules, which differ in process characteristics and biomass recovery potential. Available evidence indicates that M-BC biomass can serve as a highly efficient substrate for anaerobic digestion (AD). The methane production potential of M-BC reaches 350–365 mL CH₄/gVS, and following pretreatment may increase to 530–560 mL CH₄/gVS, exceeding typical ranges reported for conventional sewage sludge. These values were obtained under specific process conditions and depend on biomass characteristics, consortium structure, inoculum type, and operational parameters; therefore, their generalisation should be interpreted with caution. However, practical implementation remains constrained by process-related barriers directly affecting AD performance, including extracellular polymeric substance (EPS)-mediated hydrolysis limitation and nitrogen-associated inhibition linked to low C/N ratios and ammonia accumulation. Additional challenges include seasonal variability in biomass composition and incomplete understanding of M-BC behaviour under anaerobic conditions, particularly at scale. This paper provides a comprehensive and integrative analysis of the structure and biochemistry of M-BC biomass, their ecological mechanisms, technological configurations, and current knowledge regarding their susceptibility to anaerobic digestion. The review identifies the key biological, chemical, and process-related barriers and highlights research directions required for future integration of M-BC into circular wastewater treatment systems and energy-oriented biomass valorisation. Full article

Dual fuel engines utilize two different fuels consisting of a high reactivity fuel (HRF) injected into the cylinder and a low reactivity fuel (LRF), typically fumigated into the intake manifold. To reduce engine-out emissions of oxides of nitrogen (NO_x), early start of injection (SOI) of HRF may be employed in dual fuel combustion, albeit at the expense of higher engine-out emissions of unburned hydrocarbons (HC) and carbon monoxide (CO). This study compares performance and emissions of diesel–propane and poly-oxy methylene dimethyl ether (POMDME)-propane dual fuel combustion for a heavy-duty single-cylinder research engine (SCRE) platform based on a production PACCAR MX-11 engine at a low load of 5 bar IMEPg and a constant speed (“B Speed”) of 1339 rpm. While POMDME-natural gas combustion has been explored in previous work, the novelty of the present work lies in the direct comparison of diesel–propane and POMDME–propane combustion for the same SCRE under fixed constraints of NO_x < 1 g/kWh, COV of IMEP < 5%, and a maximum pressure rise rate < 10 bar/CAD. By optimizing HRF injection parameters, boost pressure, and propane energy substitution, the present work demonstrates diesel–propane HC and CO emissions improvements of ~86% and ~67%, respectively, while POMDME–propane HC and CO emissions improved by ~91% and ~86% respectively, compared to the corresponding unoptimized baseline values. These improvements were obtained while achieving very low engine-out NO_x emissions (diesel–propane ~0.7 g/kWh, POMDME–propane ~0.1 g/kWh) and very good gross indicated fuel conversion efficiencies (diesel–propane ~51%, POMDME–propane ~48%). Additionally, POMDME–propane demonstrated near-zero measurable smoke emissions for all engine operating conditions. Full article

This paper proposes a single-stage three-phase AC-DC converter based on an LLC resonant topology utilizing a front-end matrix switch. Unlike traditional two-stage solutions, the proposed topology synthesizes a fluctuating equivalent DC voltage from the three-phase input, achieving direct power conversion with high efficiency. To maintain a stable DC output voltage against the time-varying input, a trajectory-based Pulse Frequency Modulation (PFM) control strategy is developed. By employing State-Plane Analysis (SPA), the operational trajectory is divided into four calculation segments, allowing precise derivation of the switching frequency and duty cycles for both boost and buck modes within a single line cycle. Furthermore, to improve power density and reduce parasitic parameters, a high-frequency planar inductor with interleaved windings and a planar transformer are designed for 500 kHz operation. A pipeline control architecture based on a single DSP is implemented to handle the complex real-time computations. A 500 W prototype is built and tested under 100 V input and 130 V output conditions. Experimental results demonstrate that the converter achieves a peak efficiency of 97%, a power factor of 0.99, and a grid current Total Harmonic Distortion (THD) of 3.95%, validating the effectiveness of the proposed topology and control scheme. Full article

In modern distribution networks (DNs), extreme weather events and cascading faults pose severe challenges to operational safety. However, existing defense mechanisms struggle with a core question: How to maintain high-fidelity situational awareness and make precise active decisions when physical parameters drift and historical fault data is scarce? To address this, this paper proposes a situational deduction and active defense framework based on a service-oriented digital twin. First, regarding the modeling fidelity gap, a data–physics fusion mechanism is constructed. By integrating Kirchhoff’s laws with data-driven error correction, it dynamically calibrates time-varying parameters to resolve mapping distortion. Second, regarding the data scarcity bottleneck, a predictive perception method is introduced. Utilizing the digital twin as a generative engine, it augments rare fault samples to enable super-real-time deduction of future trends. Third, regarding the decision-making passivity, a service-driven simulation model is established. It transforms abstract indicators (safety, economy, resilience) into executable constraints, shifting the paradigm from ‘passive response’ to ‘active defense.’ Case studies on a modified IEEE 123-node system demonstrate that the proposed method significantly enhances resilience and decision accuracy under complex conditions. Full article

Smart transportation infrastructure is increasingly deployed, and cooperative perception using stationary Light Detection and Ranging (LiDAR) sensors installed at intersections and along roadsides is becoming more important. However, infrastructure LiDAR often suffers from sparse point-cloud data (PCD) at long ranges and frequent occlusions, which can degrade the stability of inter-frame displacement and speed estimation. This paper proposes a real-time vehicle speed estimation method that operates robustly under sparse and partially observed conditions. The proposed approach extracts boundary points from clustered vehicle PCD and removes outliers, and then fits a 2D rectangle to the vehicle contour via Gauss–Newton optimization by minimizing distance-based residuals between boundary points and rectangle edges. To further improve robustness, we incorporate Hessian augmentation terms that account for boundary states and size variations, thereby alleviating excessive boundary violations and abnormal deformation of the width and height parameters during iterations. Next, from the fitted rectangles in consecutive frames, we construct a nearest corner with respect to the LiDAR origin and an auxiliary point, and perform 2D SVD-based alignment using only these two representative points. This enables efficient computation of inter-frame displacement and speed without full point-cloud registration (e.g., iterative closest point (ICP)). Experiments conducted at an intersection in K-City (Hwaseong, Republic of Korea) using a 40-channel LiDAR, a test vehicle (Genesis G70), and a real-time kinematic (RTK) system (MRP-2000) show that the proposed method stably preserves representative points and fits rectangles, even in sparse regions where only about two LiDAR rings are observed. Using CAN-based vehicle speed as the reference, the proposed method achieves an MAE of 0.76–1.37 kph and an RMSE of 0.90–1.58 kph over the tested speed settings (30, 50, and 70 kph, as well as high speed (~90 kph)) and trajectory scenarios. Furthermore, per-object processing-time measurements confirm the real-time feasibility of the proposed algorithm. Full article

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper presents a detection pipeline for malicious websites and investigates the feasibility of Small Language Models (SLMs) using raw HTML code and URLs. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used SLMs, ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption. While SLMs underperform compared to state-of-the-art proprietary LLMs, the gap is moderate: the best SLM achieves an F1-score of 0.893 (Llama3.3:70B), compared to 0.929 for GPT-5.2, indicating that open-source models can provide a viable and scalable alternative to external LLM services. Full article

Nth-root gates allow for a paced application of two-qubit operations. We apply them in quantum thermodynamic protocols for operating a quantum heat engine. A set of circuits for two and three qubits is compared by considering maximum work production and related efficiency. Our results show that for all circuits considered and most regions of initial parameter space, quantum coherence of one of the qubits strongly increases the maximum work production and improves the system’s performance as a quantum heat engine. In such circuits, coherence is initially imprinted into one of the qubits, improving the overall maximum extractable work. We focus here on the efficiency of such work extraction, assuming the initialisation of the qubits is a free resource. For the novel protocol that employs fractional control gates, work is generated with 84% up to 100% efficiency. Further, we uncover a strong linear correlation between work production and many-body correlations in the working medium generated by these gates. Full article

Carbon dioxide (CO₂) emissions resulting from natural gas flaring are significant contributors to atmospheric greenhouse gases, posing a substantial risk to the Earth’s climate by exacerbating global warming. As a response, both the oil industry and government authorities are actively exploring cost-effective strategies to address this issue through carbon capture, utilization, and storage (CCUS), as well as reducing natural gas flaring and CO₂ leaks in the oil fields to mitigate the adverse consequences of greenhouse gas emissions. This study presents a numerical investigation of CO₂ utilization for enhanced oil recovery (EOR) and associated CO₂ retention in unconventional reservoirs, using the Bakken Formation as a representative case. A compositional reservoir model is developed to simulate CO₂ Huff-n-Puff (HnP) processes in a fractured horizontal well. The model incorporates dual-porosity and dual-permeability formulations, fluid–rock interactions, and an equation-of-state-based compositional framework to capture multiphase flow behavior. Key operational parameters, including reservoir pressure, injection rate, injection duration, and CO₂ molecular diffusion, are systematically evaluated to assess their impact on oil recovery and CO₂ retention. The results show that lower bottom-hole pressures enhance oil recovery through increased drawdown, while operating pressures near the minimum miscibility pressure (MMP) improve CO₂ solubility and overall retention. Extended injection durations and higher diffusion coefficients increase CO₂ dissolution in the oil phase but exhibit diminishing marginal benefits beyond an optimal injection time. The study quantifies residual and solubility trapping mechanisms during the operational timeframe of CO₂-EOR and provides mechanistic insights into optimizing CO₂-HnP performance in tight formations. The proposed framework establishes a technical basis for integrating CO₂-EOR with emission mitigation strategies in unconventional reservoirs. Full article

This paper presents the design and experimental validation of a hardware-enforced charging profile selection framework for low-voltage electric all-terrain vehicles (ATVs), implemented on a programmable Delta battery charger operating within a voltage range of 0–120 V and a current range of 0–30 A. Unlike conventional programmable chargers that rely primarily on software-defined configuration or battery management system (BMS)-negotiated parameter setting, the proposed system enforces predefined constant-current–constant-voltage (CC–CV) charging profiles at the hardware execution layer. Vehicle identification is performed using CANopen-based identifiers, while relay-based selection, controlled via Modbus RTU, physically routes the charger output to fixed CC–CV control paths, thereby structurally reducing the risk of misconfiguration and unintended parameter changes. The system integrates layered control using embedded ESP32 nodes, a redPLC supervisory controller, and NodeRED-based orchestration, combined with real-time measurement, logging, and visualization using a time-series database and Grafana dashboards. Experimental validation is conducted using lithium-ion battery packs configured at four nominal voltage levels (24 V, 48 V, 60 V, and 72 V). The results confirm correct automatic profile selection, deterministic relay-based routing, and stable CC–CV charging behavior across repeated charging sessions. Rather than proposing a new charging algorithm, this work contributes a safety-by-design execution-layer charging architecture that complements higher-level smart charging and management protocols and is particularly suited for closed, heterogeneous fleet environments where deterministic behavior, robustness against configuration errors, and transparent verification of charging processes are critical. Full article

Regional flood frequency analysis (RFFA) is a cornerstone for estimating design floods at ungauged or data-scarce sites by pooling information within hydrologically homogeneous regions. This study proposes and evaluates a hybrid RFFA framework that integrates the Index-Flood (IF) technique with a bivariate logistic extreme-value model whose marginal distributions are formulated under both stationary and non-stationary assumptions. Non-stationarity is incorporated through a covariate-dependent location parameter, using time and large-scale climate indices—the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI)—as explanatory variables. The proposed approach is applied to two contrasting hydrological regions in Mexico—RH10 (Sinaloa) and RH23 (Chiapas Coast)—to assess its performance under differing climatic and hydrological regimes. Model adequacy and stability are evaluated using likelihood-based goodness-of-fit criteria (log-likelihood and Akaike Information Criterion) and a leave-one-out (jackknife) cross-validation scheme embedded within the IF regionalization workflow. Results indicate that non-stationary bivariate formulations dominate model selection at most stations and yield stable regional growth curves, providing robust and engineering-relevant performance under cross-validation. Overall, the proposed framework offers a conservative and operational pathway for regional flood quantile estimation that bridges local data scarcity and regional hydrological characterization in environments influenced by climate variability and long-term change. Full article

The problem of multi-criteria selection of network security configurations (NSC) under resource constraints and the necessity to comply with information security (IS) policies is addressed in this study. A formal mathematical model of the problem has been developed, encompassing the definition of a set of possible security mechanism configurations, the formalization of objective functions reflecting security levels, throughput, and deployment costs, and the introduction of constraints on feasible solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm is employed to generate a set of Pareto-optimal solutions, ensuring uniform coverage of compromise configurations. A software package implemented in Python 3 incorporates modules for population generation, fitness evaluation, selection, crossover, mutation operators, and result visualization. Computational experiments (CE) were conducted to validate the effectiveness of the proposed approach. The evolution dynamics of the Pareto hypervolume were analyzed, the uniformity of solution distribution in the objective space was studied, and the impact of algorithm parameters on convergence to the optimal solution was examined. The results demonstrate that the proposed methodology enables the formation of NSC sets that achieve a balanced trade-off between security, throughput, and IS system deployment costs. Full article