Search Results (3,077)

Time-fractional diffusion equations describe subdiffusive transport in heterogeneous media, but their numerical treatment is complicated by the nonlocal Caputo derivative and by the weak singularity that the solution develops at the initial time. We study a global-in-time discretization that combines spectral collocation in time—on the fractional power basis ${\left\{t^{\mathsf{\ell }\alpha }\right\}}_{\mathsf{\ell }=0}^N$, evaluated at Chebyshev–Gauss–Lobatto nodes, which reproduces the leading terms of the singular expansion of the solution—with a second-order conservative finite-difference stencil in space that uses harmonic averaging of the diffusivity at the cell faces and therefore remains accurate across discontinuous media. The resulting fully discrete problem is a large, nonsymmetric, dense-in-time linear system whose two-norm condition number grows like the inverse square of the spatial mesh size, so that Krylov subspace iteration without preconditioning stalls under refinement. Exploiting the Kronecker sum structure of the discrete operator, we build a preconditioner by fast diagonalization of the spatial factor through the discrete sine transform. For constant diffusivity the preconditioner reproduces the operator exactly and yields a direct solver; for variable diffusivity it is spectrally equivalent to the operator, and we prove that the eigenvalues of the preconditioned system cluster in a disk centered at one whose radius depends only on the coefficient contrast, and not on the mesh, the number of temporal degrees of freedom, or the fractional order. Numerical experiments in one and two space dimensions confirm second-order spatial accuracy and a preconditioned iteration count that stays flat—twelve iterations from $M=32$ up to $M=1024$ in one dimension and eleven up to $M=256$ per direction in two—while the unpreconditioned count grows by more than two orders of magnitude. In time, the accuracy is spectral until round-off in the ill-conditioned Vandermonde matrix of the power basis takes over: the barrier is reached at $N=9,10,13$ for $\alpha =0.3,0.5,0.7$, where the attainable error is about ${10}^{-6}$. A benchmark against the L1 scheme on uniform and graded meshes, the Alikhanov L2-$1_{\sigma }$ scheme and Grünwald–Letnikov convolution quadrature quantifies when the global approach pays: on forced problems and on modes with $\kappa \lambda T^{\alpha }\lesssim 2$ it reaches a prescribed accuracy one to two orders of magnitude faster and with several times less memory, while for strongly damped modes the fractional power basis converges only algebraically and graded time marching is preferable below a relative error of ${10}^{-2}$. Full article

Digital transformation is reshaping the mining industry by improving resource efficiency and environmental performance. However, empirical evidence explaining how, why, and under which organizational and institutional conditions digitalisation enhances sustainable operational performance (SOP) in Sub-Saharan African mining remains limited, despite the region’s strategic role in global mineral supply. This study examines the effect of digitalisation on sustainable operational performance using longitudinal panel data from 48 mining companies operating in Sub-Saharan Africa between 2013 and 2022. Digitalisation is conceptualized as a multidimensional organizational capability and measured through a Digitalisation Index. The index was systematically derived from corporate annual environmental, social and governance reports using transparent coding procedures and Principal Component Analysis, enhancing measurement transparency and reproducibility. SOP is measured using a composite index encompassing operational efficiency, equipment utilization and maintenance effectiveness, resource utilization and environmental sustainability, and occupational health and safety. Fixed effects panel regression serves as the primary estimator, while the two-step System Generalized Method of Moments addresses endogeneity and dynamic persistence, with robustness analyses confirming result stability. The findings show that digitalisation significantly enhances sustainable operational performance by transforming digital resources into organizational capabilities that strengthen operational resilience, optimize resource allocation, and improve sustainability outcomes. By integrating the Resource-Based View, Dynamic Capabilities Theory, the TOE framework, and the Natural Resource-Based View into a unified explanatory framework, this study advances theory while providing practical guidance for digital capability development and Industry 4.0 investment and informing policies that strengthen digital infrastructure, institutional readiness, and regulatory support for sustainable mining in Sub-Saharan Africa. Full article

Friction-induced unstable vibration caused by nonlinear stator–rotor friction and electromechanical coupling is a critical dynamic stability issue in aircraft electric braking systems, potentially degrading braking performance and operational safety. In this study, a novel nonlinear dynamic model of an aircraft electric braking system is developed by considering nonlinear stator–rotor friction, the nonlinear meshing force of the gear pair, and the nonlinear axial contact stiffness of the ball screw pair. The effects of braking conditions, negative friction–velocity slope, and transmission mechanism parameters on the stability and global nonlinear dynamic behavior of the system are systematically investigated. The results indicate that the negative friction–velocity slope has a critical influence on system stability. Reducing its magnitude simplifies the steady-state response and improves system stability, while the corresponding instability boundary depends on the braking conditions and system parameters. In addition, increasing the screw lead reduces the vibration intensity and simplifies the vibration modes of the system. The time-varying meshing stiffness and backlash of the transmission mechanism significantly affect the impact response and vibration intensity of the transmission mechanism, but have little influence on the vibration response of the disc brake. These findings provide theoretical guidance for vibration suppression, stability-oriented design, and parameter optimization of aircraft electric braking systems. Full article

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made. Full article

The operational reliability of shaft systems in hydropower units has become increasingly critical as these units are frequently engaged in grid regulation under new power systems. This paper presents a sensor-driven method for operational status assessment and trend prediction of Francis turbine generator unit shaft systems using vibration and swing signals. Time domain features are extracted from the sensor-acquired signals to construct a multi-dimensional quantitative index system for characterizing the operational state, and a combined Entropy Weight–Coefficient of Variation–TOPSIS model with dynamic health thresholds is established for adaptive condition assessment. To address the nonlinear and non-stationary characteristics inherent in such signals, a decomposition–prediction–reconstruction fusion framework is developed, incorporating Variational Mode Decomposition (VMD) for signal decomposition and noise reduction, iTransformer for capturing global multi-variable interactions, and Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal feature extraction. The hybrid model achieves a coefficient of determination R² of 0.9845 on complex vibration and swing signals, demonstrating its superior prediction capability. Based on the prediction results, health scores and dynamic thresholds are calculated to perform trend analysis and health early warning. A case study is conducted using real-world monitoring data from a 306 MW Francis turbine unit. The results demonstrate that the proposed method effectively characterizes the shaft system operational state, achieving a closed-loop integration from condition monitoring to fault diagnosis and predictive maintenance. The operational status assessment and trend prediction analyses are in good agreement with actual operating conditions, providing reliable technical support for the intelligent health management of hydropower units. Full article

This study presents a small-scale application-oriented vehicle vision-sharing prototype for blocked-view observation in vehicle-following scenarios. The system was implemented using two DuckieBot vehicles and integrates socket-based image transmission, JPEG image compression, You Only Look Once (YOLO)-based object detection, road-plane-aligned image fusion, and fuzzy proportional–integral–derivative (fuzzy-PID) motor control. These modules are adopted as existing techniques and integrated for prototype-level experimental evaluation rather than proposed as new perception, compression, fusion, or control algorithms. Experiments were conducted under controlled small-scale indoor conditions. JPEG compression was quantitatively evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), encoded file size, and processing time. Q75 provided a mean PSNR of 39.8777 dB, a mean SSIM of 0.970522, and an average encoded size of 27.78 KB, representing a practical trade-off between reconstructed image quality and encoded data size. The YOLOv8n obstacle detector achieved a precision of 0.9724, a recall of 0.9571, an mAP@0.5 of 0.9851, and an mAP@0.5:0.95 of 0.8585 on an independent test set. Image-fusion evaluation showed that α = 0.60 produced the highest global mean PSNR, whereas α = 0.90 produced the highest global mean SSIM, indicating that the preferred blending coefficient depends on the selected image-quality criterion. A system-level ablation further distinguished shared-view visualization from a warning-only configuration, with the expected obstacle information presented in all 35 positive trials and no false alarms observed in 10 negative trials. The vehicle-following experiment verified the functional operation of the complete perception-to-control pipeline. The results should be interpreted within the controlled miniature-vehicle setting and should not be directly generalized to full-scale vehicles or real-road advanced driver assistance systems. Full article

In a PV array, mismatch among modules changes the shape of the P–V curve and may create several power peaks. This makes maximum power point tracking (MPPT) more difficult, especially when the tracker needs to distinguish the global maximum power point (GMPP) from local peaks. This paper studies this problem with SVM-ILEA, a hybrid MPPT method that combines support vector machine (SVM) regression and an improved love evolution algorithm (ILEA). The SVM model takes module irradiance and temperature as inputs and predicts a voltage close to the GMPP. ILEA uses this voltage as the search center and avoids scanning the full voltage range. The modified convergence factor and adaptive distance factor further adjust the voltage movement during iteration, giving wider search steps at the early stage and smaller corrections near the optimum to reduce steady-state power oscillations. The simulation setup in MATLAB/Simulink R2019b includes standard test conditions (STC) and static partial shading with non-uniform irradiance and temperature distributions, as well as dynamic operating conditions. Across the four static conditions, SVM-ILEA achieves mean tracking times of 0.0233–0.0303 s and mean steady-state power fluctuations of 0.0111–0.0500 W. Across the three dynamic tests, the mean MPPT efficiency ranges from 97.9057% to 98.2991%. The results obtained demonstrate fast GMPP tracking, small power fluctuation, and stable re-tracking under complex PV operating conditions. Full article

Hydropower units operating in isolated grids are characterized by low rotational inertia and weak damping, making it difficult to balance rapid frequency regulation and overshoot suppression. To address this issue, this paper proposes a GA–SQP hybrid optimization control strategy for multiple operating conditions based on a high-fidelity nonlinear dynamic model. Deep feedforward neural networks are first employed to reconstruct the nonlinear torque and discharge characteristics of the hydro-turbine, providing smooth and continuously differentiable mappings for subsequent gradient-based optimization. An improved performance index combining the Integral of Time-Cubed Absolute Error (ITCAE) with a transient overshoot penalty is then formulated to suppress long-tail errors and prioritize smooth responses with reduced transient overshoot. A two-stage optimization framework is further developed, in which the Genetic Algorithm (GA) performs global exploration to identify a promising parameter region, followed by Sequential Quadratic Programming (SQP) for high-precision local refinement. Comparative simulations under low-, rated-, and high-head high-load conditions show that the proposed strategy achieves higher optimization accuracy with fewer iterative resources. Within the investigated operating range, the optimized controller maintains a very low overshoot level while preserving satisfactory response speed, effectively improving the balance between rapidity and stability in isolated-grid frequency regulation. Full article

As images of the supermassive black holes Messier 87* (M87*) and Sagittarius A* (Sgr A*) from the Event Horizon Telescope (EHT) grow in number, we gain a better understanding of the typical conditions of the near-horizon region of these objects, and slowly approach a converged time-average of the black hole image. In this article, we briefly review the signatures of black hole spin that manifest most apparently in these time-averaged images. We divide our discussion between coarse spatial features that are accessible with time averages from terrestrial very-long-baseline interferometry (VLBI) at 230 and 345 GHz, and fine spatial features which require either the extension of VLBI at these frequencies to space or the operation of global VLBI at much higher frequencies. We find that, while both coarse spatial features available from the ground and fine spatial features available only from space can both greatly inform spin, the primary difference is the degree of sensitivity to unknown astrophysical conditions, with photon ring-dominated observables benefitting from the exponential suppression of the details of the accretion disk. We conclude that spin measurements from resolved features of strong lensing are likely to provide sharp (∼±10%) spin measurements of both M87* and Sgr A* in the near future. Full article

Against the backdrop of global climate change and energy structure transition, district energy systems have garnered significant attention for their efficiency and sustainability. Accurate load forecasting is crucial for enhancing the operational efficiency of district cooling systems. However, as typical dynamic time-varying systems, building complexes within district energy stations exhibit load characteristics influenced by multi-scale features. Furthermore, traditional load forecasting models employ single-scale analysis without specifically modeling these multi-scale characteristics, resulting in insufficient generalization capabilities of data-driven models under dynamic, time-varying scenarios. This paper proposes a multi-step forecasting model structure based on baseline-morphology decomposition to address the coupling of multi-scale characteristics. By decomposing load into baseline and morphological components, separate prediction models—a backpropagation neural network (BP) and a K-means clustering-decision tree (DT) classification prediction model—are constructed, overcoming the challenge of capturing multi-scale features in traditional methods. The results show that, during the four-month test period from August to November 2024, the proposed model achieves MAPE values ranging from 8.91% to 12.57% under the peak and transitional cooling conditions represented in the dataset. Compared to direct structure, recursive structure, and multi-input multi-output (MIMO) structure, it reduces errors by 1.49% to 19.62% while achieving remarkable advantages in training efficiency. Full article

With the acceleration of the global energy transition, hydrogen is increasingly considered a potential energy carrier for renewable-energy integration, large-scale energy storage, and industrial decarbonization. Proton exchange membrane (PEM) water electrolysis is well suited to variable renewable power because of its fast load response, wide operating range, and compact system structure. However, most existing studies focus on steady-state performance, materials, or model-based analysis, while system-level experimental data on the dynamic behavior of industrial-scale PEM water electrolysis systems remain limited. In this study, the dynamic performance of a 50 kW-class PEM water electrolysis system was experimentally investigated under stepwise load changes, pressure variation, and cold-start conditions. The responses of voltage, temperature, pressure, hydrogen-in-oxygen (HTO), oxygen-in-hydrogen (OTH), and system energy consumption were analyzed. The voltage followed current step changes within seconds, indicating a fast electrical response. In contrast, the thermal response was much slower, and the system required approximately 34 min to approach the rated thermal condition from a cold start. The gas-composition measurements exhibited minute-scale response delays and gradual settling after changes in operating conditions. When the operating pressure increased from 1.2 MPa to 2.9 MPa, the HTO content increased from 0.383% to 0.545%. When the current increased from 300 A to 1200 A, the OTH content decreased from 1001.77 ppm to 5.86 ppm. Energy-flow analysis showed that the total system power consumption under full-load operation was 69.7 kW, including the electrolyzer-related part and balance-of-plant consumption. These results clarify the different response time scales of electrical, thermal, and gas-composition variables in a 50 kW-class PEM water electrolysis system and provide experimental support for dynamic operation under variable renewable power input. Full article

The transition toward a circular economy in the mining sector has increased the need for sustainable strategies capable of recovering value from tailings generated during mining activities and supporting the safe closure of tailings dams. The present study evaluates the environmental performance of reverse mining as a circular economy strategy by comparing two iron ore tailings valorization pathways: mineral reprocessing for iron recovery and reuse as aggregates in cementitious composites. A Life Cycle Assessment (LCA), conducted in accordance with ISO 14040 and ISO 14044 standards, was applied using OpenLCA software and primary operational data collected from a full-scale tailings dam decommissioning project in Minas Gerais, Brazil. Environmental impacts were assessed in ten impact categories using the ReCiPe Midpoint methodology, with emphasis on Global Warming Potential (GWP). Reverse-mined tailings presented a GWP of 1.91 kg CO₂ eq/t, substantially lower than conventionally mined iron ore (6.80 kg CO₂ eq/t) and comparable to natural sand (1.88 kg CO₂ eq/t). Mineral reprocessing reduced the GWP associated with iron ore production by approximately 50%, while the reuse of tailings as construction aggregates proved environmentally competitive under suitable transport conditions. These findings demonstrate that reverse mining can support sustainable tailings dam closure by reducing environmental impacts, recovering secondary resources, and advancing circular economy practices in the mining sector through a practical decision-support framework based on primary industrial data. Full article

We study a delayed semilinear evolution equation with a Caputo time derivative and the spectral fractional Dirichlet Laplacian on a bounded connected domain. The model separates two forms of memory: the Caputo operator retains the distributed Volterra history, whereas the nonlinear production samples the single past state $u(t-\tau )$. Working in the strongly continuous phase space $C_0(\Omega )$, we prove local well-posedness, positivity, a sup-norm continuation criterion, and a compatible weak formulation. In the delayed-source case with $\mu =0$, the solution exists globally and remains bounded on every finite time interval, while the first Dirichlet mode admits an explicit recursive sequence of positive lower bounds across successive delay windows. In the dissipative case $\mu >0$, $p>q>1$, histories satisfying the explicit smallness conditions remain in an invariant order interval and the $L^2$-energy decays at a Mittag–Leffler rate. The scalar computations are presented only as heuristic first-mode surrogate experiments. In addition, an independent spatially resolved sine spectral-Galerkin/L1 computation of the PDE, with temporal and spectral refinement studies, is included as a numerical illustration. Full article

Fatigue assessment of floating offshore wind turbines (FOWTs) remains challenging because fatigue-sensitive regions may occur outside conventional predefined hotspots. This study applies a previously numerically verified full-field fatigue-screening workflow combining Unit Load Response, submodeling, and Virtual Test Rig concepts to the TaidaFloat semi-submersible FOWT under Taiwan Strait environmental conditions. Reconstructed nodal stress histories are used to map hull fatigue and evaluate occurrence-weighted contributions from 182 environmental bins, including operational and typhoon conditions. The results identify fatigue-sensitive regions not only at conventional column–bracing and column–pontoon connections but also in the upper main column and along the turbine–hull load path. Upper column fatigue is mainly associated with turbine-induced bending, whereas lower column and waterline-adjacent regions are more sensitive to wave-induced global hull bending. Frequently occurring near-rated operational conditions dominate the occurrence-weighted hull fatigue contribution, while selected typhoon conditions produce high short-term damage but limited long-term contributions within the four-year dataset. Approximately 94.6% of hull fatigue damage is captured by 28% of the bins, and a common hull–mooring set captures 97.0% of both contributions using 62% of the bins. These findings support hotspot screening and environmental-bin prioritization rather than detailed or certification-level fatigue life prediction. Full article

This paper presents, to our knowledge, the first framework that jointly and provably optimizes communication and computation energy across an entire fleet of wirelessly connected mobile robots, rather than for a single device or under a fixed offloading policy as in prior work. Battery capacity limits the endurance of autonomous mobile robots, and on-board computation and radio communication increasingly rival locomotion in energy draw; across a fleet, the two are further coupled through shared uplink bandwidth and edge computing capacity. We formulate the joint selection of each robot’s task-offloading ratio, DVFS processor frequency, and transmit power, together with the fleet-wide allocation of bandwidth and edge capacity, subject to hard per-task deadlines. Closed-form inner solutions reduce each robot’s problem to a jointly convex program, coupled fleet-wide only through two linear resource constraints. We exploit this structure in JECCO-M, a distributed price-based algorithm that provably converges to the global fleet optimum while exchanging only a few scalars per iteration. A trajectory-conditioned channel-prediction extension handles robot mobility. Evaluated in simulations against optimization-based and learning-based baselines from the literature and on a physical three-robot testbed with embedded GPU compute, an IEEE 802.11ac uplink, and instrumented power rails, JECCO-M substantially reduces combined electronic energy while meeting all deadlines, and the measured hardware behavior tracks the analytical model closely. The results indicate that treating radio energy, processor energy, and shared edge resources as a single optimization domain is a practical route to extending the operating time of connected robot fleets. Full article