Search Results (3,305)

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

Background: Costal cartilage is the preferred structural material for augmentation rhinoplasty when robust and durable tip support is required. However, conventional full-thickness harvest is associated with significant donor-site morbidity, and commonly employed rigid fixation strategies—such as the septal extension graft—substantially restrict postoperative nasal tip compliance. The present study introduces a novel two-component technique combining a half-harvest costal cartilage procurement method with a pyramidal-shaped columellar strut graft anchored on the floating-tip principle, with the objective of maintaining postoperative nasal tip flexibility while providing structural support following augmentation rhinoplasty. Methods: A retrospective review was performed of consecutive patients who underwent primary or revision augmentation rhinoplasty using the pyramidal costal cartilage columellar strut graft technique by a single surgeon between June 2018 and February 2026. The medial half of the conjoined costal cartilage at the seventh, eighth, or ninth rib was procured via a half-harvest approach, preserving the lateral cortex and perichondrium to minimize donor-site morbidity and potential cartilage regeneration was considered a theoretical benefit. The harvested cartilage was carved into a pyramidal columellar strut and secured to the anterior nasal spine using a floating fixation construct; the inferior base of the strut was rigidly fixed to the nasal septum and anterior nasal spine with a minimum of three PDS 5-0 sutures, while the superior portion remained free to preserve physiologic nasal tip mobility. Adjunctive cap and shield grafts, perichondrial wrapping, and dermal fat grafts were employed as indicated. Primary outcomes included nasal tip projection, postoperative tip mobility, donor-site morbidity, and surgical complication rates. Results: Favorable clinical observations of maintained tip projection were noted throughout follow-up. Manual postoperative examination suggested preservation of tip flexibility in most patients; however, no validated objective mobility assessment tool was available. The revision rate for clinically significant tip deviation was low. No major donor-site adverse events—including pneumothorax or rib fracture—were encountered. Postoperative chest wall pain was minimal and transient, with most patients resuming daily activities within one week of surgery. Conclusions: The pyramidal-shaped costal cartilage columellar strut graft with half-harvest technique is a novel, biomechanically informed, and technically reproducible approach to augmentation rhinoplasty that was developed to address donor-site morbidity and postoperative tip rigidity, two commonly recognized limitations of conventional costal cartilage rhinoplasty: donor-site morbidity and postoperative nasal tip rigidity. Preservation of the lateral cortex and perichondrium during procurement may contribute to reduced postoperative donor-site discomfort, accelerates functional recovery, and may promote endogenous cartilage regeneration over time. The anatomically derived pyramidal strut geometry, combined with floating fixation to the anterior nasal spine, was designed to approximate the native columellar architecture, enabling consistent preservation of physiologic nasal tip mobility. The present series demonstrated a favorable safety profile with a low overall complication rate and an absence of major donor-site adverse events. Prospective studies with validated objective outcome measures are required to confirm these findings, to delineate the optimal patient selection criteria, and to establish evidence-based long-term outcome benchmarks for this technique. Full article

Photoacoustic spectroscopy (PAS), quartz-enhanced photoacoustic spectroscopy (QEPAS), and light-induced thermoelastic spectroscopy (LITES) represent indirect absorption spectroscopy techniques for trace gas sensing, whose performance has long been advanced through hardware-oriented enhancement strategies. However, as hardware technologies continue to advance, conventional hardware-based enhancements are increasingly bottlenecked by weak responses, complex cross-interferences, and coupled multiphysics parameters. To transcend these limitations, algorithm-assisted methods, including traditional algorithms, machine learning, deep learning, and intelligent optimization, are being systematically integrated into these spectroscopic systems. This review summarizes recent progress in intelligent indirect absorption spectroscopy from three interconnected dimensions. First, we outline advanced signal processing and spectral reconstruction strategies designed to achieve weak-signal recovery and background noise suppression. Second, the focus shifts to data-driven parameter inversion, showing how multidimensional artificial intelligence models contribute to concentration retrieval, environmental compensation, multicomponent recognition, spectral-overlap decoupling, and front–back-end collaborative waveform coding and demultiplexing. Third, intelligent system optimization is examined, in which surrogate modeling, swarm-intelligence search, physics-guided topology optimization and multi-objective algorithms are employed to improve the design efficiency of the key elements such as photoacoustic resonators and multipass cells (MPCs). Additionally, prospects for future technological developments are also discussed in the concluding section. Full article

A new era in wireless communication has been witnessed by the emergence of fifth generation (5G) technology, characterized by high data rates, ultra-low latency, and massive device connectivity. To address the growing demand for efficient spectrum utilization, Non-Orthogonal Multiple Access (NOMA) has been introduced as a promising multiple access scheme. This study investigates the energy efficiency (EE) and spectral efficiency (SE) performance of NOMA in comparison with Orthogonal Multiple Access (OMA) under varying bandwidth conditions. In addition to conventional analytical and simulation-based evaluations, artificial intelligence (AI) techniques, including Deep Learning (DL), Decision Tree (DT), K-Nearest Neighbours (KNN), and Logistic Regression (LR), are employed to model and predict system performance. The AI models are trained using simulation-generated datasets to capture complex relationships between bandwidth, transmit power, and user distribution. Simulation results demonstrate improvement in SE and EE of NOMA across different bandwidth scenarios. Furthermore, DL and DT models achieve higher prediction accuracy. The consistency between AI predictions and simulation outcomes confirms the robustness of the proposed framework. These findings highlight the superiority of NOMA over OMA and demonstrate the effectiveness of integrating AI techniques for performance optimization in 5G and beyond wireless networks. Full article

In light of the rapid development of the electric vehicle market, permanent magnet synchronous motors (PMSMs) are becoming essential components of propulsion systems. This is due to their high efficiency, remarkable power density, and ability to deliver high torque over a wide speed range. However, the optimal control of these motors under dynamic conditions remains a major challenge due to system nonlinearities, parameter variations, and external disturbances. Conventional strategies such as field-oriented control (FOC), direct torque control (DTC), and fuzzy logic control (FLC) show variable performance in terms of current quality, robustness, and energy efficiency. To overcome these limitations, this study proposes an intelligent control strategy based on artificial neural networks (ANNs), which ensures efficient operation and high control performance under various operating conditions. This approach leverages the learning capabilities of deep neural networks to improve control accuracy, system stability, and overall energy performance. The results obtained show a significant reduction in the current’s total harmonic distortion (THD) as well as an improvement in the stator’s current quality and the electromagnetic torque’s dynamic behavior compared to conventional methods. This improvement reduces overall losses in the electric drive system, thereby contributing to increased vehicle energy efficiency. As a result, the electric vehicle’s range is extended, and the dynamic performance of the PMSM is optimized. These results confirm the potential of artificial intelligence techniques for developing intelligent, robust, and adaptive control systems designed for modern electric propulsion applications. Full article

Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional convolutional neural network (CNN) approaches often struggle with the complex, transient data from these systems, particularly in capturing long-range temporal dependencies and generalizing from limited, imbalanced datasets. This study presents an enhanced diagnostic framework for plunger lift anomaly detection by leveraging the strengths of a pre-trained Vision Transformer (ViT). The methodology transforms one-dimensional time-series pressure data into two-dimensional image representations using the element-wise summation of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), which simultaneously preserves global operational trends and local transient dynamics for vision model analysis. The ViT model, initialized with pre-trained weights, is further optimized using Bayesian optimization (BO) for hyperparameter tuning, and a tailored data augmentation pipeline is employed to improve robustness. Comparative evaluations demonstrate that the proposed ViT-based approach, particularly the ViT + GAF + BO model, significantly outperforms baseline CNN models and their optimized variants, achieving the highest Precision, Recall, and F1-score, with an F1-score of 0.93. Visualizations using t-SNE confirm the ViT’s superior capability in learning discriminative features, showcasing well-separated clusters for different operational conditions compared to CNNs. This research underscores the potential of pre-trained ViTs combined with appropriate data representation and optimization techniques for achieving accurate and reliable anomaly detection in plunger lift systems. Full article

Prefabricated mechanical, electrical, and plumbing (MEP) systems have been increasingly adopted in energy station projects; however, systematic evaluation frameworks capable of integrating construction performance, cost constraints, and uncertain multi-indicator assessments remain limited. To address this gap, this study constructs an Analytic Hierarchy Process (AHP)–Entropy–Fuzzy evaluation framework to assess the comprehensive benefits of BIM-enabled prefabricated MEP construction in energy stations. A hierarchical evaluation system was established based on five dimensions: schedule, quality, cost, safety, and environmental performance, and ten secondary indicators were defined. The Analytic Hierarchy Process was used to determine expert-based subjective weights, the entropy method was applied to capture objective data variability, and multiplicative normalization was employed to obtain combined weights. A fuzzy comprehensive evaluation model was then introduced to transform heterogeneous construction records into comparable benefit levels and scores. The prefabricated method scored 87.80 and was classified as “high”, whereas the conventional method scored 60.85 and was classified as “low”. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based sensitivity analysis further showed that, under 10%, 20%, and 50% criterion-weight perturbations, the prefabricated group consistently achieved higher closeness coefficients than the conventional group. The smallest margin occurred when the schedule weight was reduced by 50%, but the prefabricated group retained a positive advantage. The results demonstrate that Building Information Modeling (BIM)-enabled prefabricated MEP construction can achieve superior overall project performance through the coordinated optimization of schedule, cost, safety, quality, and environmental objectives, offering a practical evaluation framework and decision-support tool for the industrialized delivery of future energy infrastructure projects. Full article

The growing demand for sustainable construction materials has led to significant advancements in ultra-high-performance concrete (UHPC), with a particular focus on geopolymer-based systems as an alternative to conventional cementitious binders. This review explores the latest developments in sustainable Ultra-High-Performance Geopolymer Concrete (UHPGPC) by analysing key material composition, mechanical, durability and microstructural properties. The incorporation of ground granulated blast furnace slag (GGBFS), silica fume (SF), and fly ash (FA) has demonstrated notable improvements in compressive strength, durability, and workability. Additionally, the use of activators such as sodium silicate and sodium hydroxide optimizes geopolymerization, resulting in a denser microstructure and enhanced mechanical performance. This review highlights the critical role of fibre reinforcement in UHPGPC, where steel fibres (SFs) and hybrid fibres significantly enhance compressive and tensile strength, as well as crack resistance. The inclusion of waste materials such as rice husk ash and recycled glass promotes sustainability by reducing CO₂ emissions while maintaining structural integrity. However, higher waste-glass content may adversely affect bonding due to its smooth surface texture. The findings highlight the potential of UHPGC as a high-performance, eco-friendly alternative to traditional cement-based UHPC. By integrating industrial by-products and alternative activation techniques, UHPGPC can contribute significantly to the global shift towards sustainable and low-carbon construction materials. Full article

Network latency remains a fundamental bottleneck in vehicle teleoperation, inducing instability and performance degradation in conventional control methods, while predictive techniques like the Smith Predictor offer a theoretical solution, their efficacy is often compromised by unmodelled dynamics and real-world disturbances. This paper presents the first experimental validation of the Successive Reference-Pose Tracking (SRPT) architecture. By streaming future reference poses rather than direct steering commands, SRPT leverages an onboard Nonlinear Model Predictive Controller to compute optimal vehicle actions while inherently accounting for dynamic constraints and network delays. Real-world human-in-the-loop experiments were conducted with four drivers on a test track featuring cornering, double lane-change, and slalom manoeuvres. Quantitative comparisons at 10 km/h across four modes—manual driving, direct teleoperation, a Smith Predictor, and SRPT—demonstrate that SRPT significantly outperforms other teleoperation methods, reducing cross-track error by up to 66% and yielding smoother, more stable control inputs. Furthermore, SRPT uniquely maintained stability during a proof-of-concept trial at 13 km/h, where it proactively moderated vehicle speed to respect actuator limits—a critical safety behavior absent in other modes. This work provides the first tangible evidence that SRPT is a robust and superior framework for delay-resilient vehicle teleoperation in real-world conditions. Full article

The goaf backfilling with the coal gangue is an effective strategy for mitigating the mining-induced surface subsidence and reducing the solid waste accumulation. However, the conventional backfilling methods often suffer from limited transport efficiency, poor material distribution, and high operational cost. The present paper proposes a novel technique using an inclined spiral propeller to propel the gangue particles into the goaf, aiming to improve both the backfill rate and spatial uniformity. A three-dimensional parametric model of the inclined screw conveyor is developed, and the discrete element method (DEM) is employed to simulate the dynamic transport and placement of the gangue particles. An L9 ($3^3$) orthogonal experimental design is implemented to systematically evaluate the effects of the rotational speed (240, 300, 360 r/min), inclination angle ($30°$, $45°$, $60°$), and screw pitch (180, 240, 300 mm) on the two critical performance indicators, namely, filling mass and spreading coverage area. The range analysis and matrix analysis are performed to determine the primary influencing factors and to identify the optimal parameter combination for the multi-objective performance. The results show that the inclination angle is the dominant factor for the filling mass, with a $60°$ angle yielding the highest throughput (38.60 kg). In contrast, the rotational speed is the dominant factor for the spreading coverage area, where an increase from 240 to 360 r/min nearly triples the covered area. The optimal compromise for the comprehensive backfilling performance is the rotational speed 360 r/min, inclination angle $60°$, and screw pitch 300 mm, which simultaneously achieves the high transport capacity (36.65 kg) and the largest spreading area (2.87 ${\mathrm{m}}^2$). The present study provides a theoretical and methodological foundation for the engineering design of efficient, low-cost goaf backfilling systems. Full article

Electrospinning is a highly versatile technique for fabricating nanofibrous membranes with high surface-area-to-volume ratios and tunable porosity. Although polycaprolactone (PCL) is widely utilized in biomedical engineering due to its biocompatibility, its electrospinning traditionally relies on hazardous organic solvents like dichloromethane (DCM) and N,N-dimethylformamide (DMF). This paper details the development of a fully sustainable, green electrospinning process for PCL using a bio-derived binary mixture of acetic acid and formic acid. Processing parameters (applied voltage, tip-to-collector distance, and flow rate) were systematically optimized using a Design of Experiments (DoE) response surface methodology. Scanning electron microscopy (SEM) confirmed the successful fabrication of uniform, bead-free nanofibers with a mean diameter of 247 nm, representing a 37.3% reduction compared to conventional DCM:DMF-spun matrices. Fourier-transform infrared spectroscopy (FTIR) verified complete solvent evaporates. Full article

Additive manufacturing has emerged as a transformative technology for fabricating complex drug-eluting medical devices, offering unprecedented design freedom and functional integration capabilities. This comprehensive review systematically analyzes 3D printing technologies applied to pharmaceutical device manufacturing, focusing on drug-eluting vascular stents as a representative application. This review covers six primary additive manufacturing techniques, ranging from high-resolution vat photopolymerization (25 μm resolution) to direct energy deposition, with a focus on their capabilities for produce pharmaceutical devices with controlled drug release properties. Novel 4D/5D/6D printing technologies introduce stimuli-responsive behaviors enabling programmable drug release profiles and adaptive device functionality. Manufacturing process optimization reveals superior design flexibility compared to conventional methods, with 85–95% reduction in design iteration time and elimination of tooling costs for complex geometries. The material landscape encompasses traditional metals (316L stainless steel, cobalt–chromium), biodegradable polymers (polylactic acid, PLA; polycaprolactone, PCL; poly(lactic-co-glycolic acid), PLGA), shape-memory materials (i.e., polymers and alloys capable of recovering a pre-programmed shape upon exposure to a specific stimulus such as body temperature, moisture, or light), and advanced nanocomposites, each offering distinct drug-loading capacities (100–500 μg/cm²) and release kinetics. Critical challenges include standardization requirements (International Organization for Standardization (ISO) 5840 and American Society for Testing and Materials (ASTM) F2606), pharmaceutical-grade manufacturing protocols, and regulatory pathways for novel drug-device combinations. This review identifies key research priorities including development of biocompatible printing materials, accelerated drug release testing protocols, and scalable manufacturing processes suitable for medical device production. This analysis demonstrates that 3D printing enables integration of multiple pharmaceutical functions within single devices, controlled spatiotemporal drug delivery, and elimination of secondary manufacturing steps for drug coating processes, advancing the development of next-generation therapeutic medical devices. Full article

Smart energy meters are widely deployed in modern distribution networks, extending their role beyond revenue billing to real-time monitoring and data-driven smart city applications. However, conventional legal metrology frameworks rely on periodic recalibration and are not intended for the detection of accuracy drift or unexpected malfunctions between scheduled inspections. In scientific publications, various techniques for remote smart meters’ error surveillance are presented, but experimental verification on real distribution network data remains limited. The objective of this study is to experimentally verify two previously proposed power event-driven methods for remote estimation of active power measurement error in individual consumer meters, using a feeder-level sum meter as a reference instrument. One-second resolution electrical readings were collected from a real low-voltage distribution branch using ESP32-based local adapters communicating via MQTT over Wi-Fi, with SNTP-based clock synchronization for power event correlation. Under optimized detection parameters, the linear regression method achieved 0.20% RMSE and 0.75% maximum absolute error, and the neural network method 0.09% RMSE and 0.31%, confirming suitability for Class 1 m accuracy surveillance. Feasibility analysis of three MQTT-based deployment scenarios demonstrates that binary encoding limits local adapter buffers to 2.8 kB and worst-case daily channel demand to 2000 kB, confirming the practical viability of the proposed architecture. Full article

Q420 high-strength steel exhibits pronounced anisotropy due to its rolling process, and conventional uniaxial tensile testing is incapable of acquiring strain field evolution information during the local necking stage. In this study, quasi-static uniaxial tensile tests were conducted on Q420 cold-rolled high-strength steel sheets at six orientations (0°, 15°, 30°, 45°, 60°, and 90°) using Digital Image Correlation (DIC) technology. The evolution of the strain field and the corresponding stress–strain responses at different orientations were systematically investigated. The results show that the DIC technique effectively captured the full-field strain evolution of the specimens from uniform deformation to local necking and final fracture in all directions. Taking the 0° direction as an example, the local maximum engineering strain prior to fracture reached 35.866%, whereas the average fracture strain within the gauge section was only approximately 22.5%, corresponding to a ratio of approximately 1.6 and clearly demonstrating the severe strain concentration within the necking zone. The stress–strain curves corresponding to different rolling directions exhibited pronounced anisotropy. The tensile strength was highest in the 90° direction and lowest in the 0° direction; however, the 0° direction exhibited the best ductility, whereas the 45° direction showed the poorest ductility. Among the six orientations, the midpoint transverse engineering strain exhibited the largest absolute value in the 45° direction, further indicating that this orientation is the most susceptible to plastic instability. In this work, DIC-based full-field measurement was combined with multi-directional tensile testing to quantitatively characterize the relationship between local strain concentration and anisotropy. The findings provide high-precision experimental data for the calibration of anisotropic constitutive models and the optimization of forming processes. Full article

Hemorrhoidal disease is a common anorectal condition that may require treatment when bleeding, prolapse or persistent symptoms fail to respond to conservative or office-based therapy. Radiofrequency ablation (RFA) has emerged as a minimally invasive, tissue-sparing technique for symptomatic internal hemorrhoids, based on controlled delivery of high-frequency energy into hemorrhoidal tissue. The resulting thermal effect induces coagulative necrosis, fibrosis, mucosal fixation and progressive reduction in hemorrhoidal volume, without excisional removal of anoderm or rectal mucosa. This narrative review summarizes the mechanism, technical principles, clinical advantages, comparative evidence and remaining uncertainties surrounding RFA, with particular attention to the Rafaelo procedure and related radiofrequency-based approaches. Current evidence suggests that RFA may reduce postoperative pain, analgesic requirements, wound-related morbidity, hospital stay and time to return to normal activity compared with conventional hemorrhoidectomy, while maintaining acceptable short- and mid-term symptom control in selected patients, especially those with grade II–III internal hemorrhoids. However, available studies remain heterogeneous in design, technique, patient selection, outcome definitions and follow-up duration. The relationship between modern probe-based RFA and earlier radiofrequency-based approaches, including Ellman surface coagulation, Celon bipolar radiofrequency-induced thermotherapy and radiofrequency-assisted hemorrhoidectomy, remains insufficiently standardized in the literature. Further randomized trials, standardized outcome reporting, long-term recurrence data and cost-effectiveness analyses are required to define the optimal indications and therapeutic position of RFA. Full article