Search Results (49,019)

To address the limitations of conventional reliability assessment methods in handling mesh distribution networks with flexible operation characteristics and complex topologies, namely their poor adaptability and low computational efficiency, this paper proposes a matrix-based analytical approach for reliability assessment of mesh distribution networks. First, a network configuration centered on the soft open points (SOP) is established. Through multi-feeder interconnection and flexible power flow control, a topology capable of fast fault transfer and service restoration is formed. Second, based on the restoration modes of load nodes under fault scenarios, three types of fault incidence matrices (FIM) are proposed. By means of matrix algebra, explicit analytical expressions are derived for the relationships among equipment failure probability, duration, impact range, and reliability indices. This overcomes the drawbacks of iterative search in conventional reliability assessments, significantly improving efficiency while ensuring accuracy. Finally, a modified 44 bus Taiwan test system is used for reliability assessment to verify the effectiveness of the proposed method. The results demonstrate that the proposed matrix-based analytical reliability assessment method enables explicit analytical calculation of both system-level and load-level reliability indices in mesh distribution networks, providing effective support for planning and operational optimization to enhance reliability. Full article

Background/Objective: Craniosynostosis, the premature fusion of one or more cranial sutures, is the second most common craniofacial defect and poses significant diagnostic and therapeutic challenges. Our objective was to systematically evaluate current diagnostic imaging modalities for craniosynostosis and to propose a novel radiation-free ARCANA Protocol as an alternative to conventional screening. Methods: Following PRISMA guidelines, we conducted a systematic review of the literature using PubMed and Cochrane databases from 2015 onwards, restricted to English-language and full-text articles. Inclusion criteria encompassed studies evaluating diagnostic accuracy, radiation exposure, and neurocranial outcomes associated with imaging modalities in craniosynostosis. Quality assessment was performed using QUADAS-2. To evaluate the certainty of evidence supporting each imaging modality, we applied the GRADE framework. Given the extensive number of included studies (n = 70), findings were categorized by diagnostic modality rather than individual studies. Results: Analysis of 70 selected studies demonstrated a continued reliance on 3D computed tomography (3DCT) as the diagnostic gold standard, despite recognized risks of cumulative radiation exposure in pediatric populations. Alternative radiation-free imaging techniques including high-resolution ultrasonography (US), three-dimensional stereophotogrammetry (3DSPG), and advanced magnetic resonance imaging (MRI) have emerged, offering substantial benefits such as eliminating ionizing radiation and providing comprehensive neurocranial assessments. 3DCT demonstrates approximately 90% sensitivity and 90–100% specificity for detecting suture closure; ultrasound achieves 71–100% sensitivity and 86–100% specificity, while advanced MRI techniques such as GA-VIBE report up to 97% sensitivity and 96% specificity. Conclusions: The proposed ARCANA Protocol integrates clinical assessment, 3DSPG, US, and advanced MRI sequences into a unified multimodal framework that eliminates radiation exposure while ensuring comprehensive evaluation of cranial and intracranial anatomy. The protocol emphasizes patient safety and diagnostic accuracy. The main limitations of this study are the heterogeneity of the included studies and the lack of prospective validation, which is essential to confirm diagnostic and clinical effectiveness and to support a potential paradigm shift toward radiation-free assessment of craniosynostosis. Full article

Background/Objectives: Lung nodules present a common diagnostic challenge, particularly when benign and malignant lesions exhibit similar imaging characteristics. Standard evaluation relies on computed tomography (CT), positron emission tomography (PET), or biopsy, all of which have limitations. Quantitative magnetic resonance (MR) relaxometry using native longitudinal relaxation time (T1) and transverse relaxation time (T2) mapping offers a radiation-free alternative reflecting tissue-specific differences. Methods: This prospective, single-center study included 64 patients with 76 histologically or radiologically confirmed lung lesions (25 primary lung cancers, 28 metastases, 9 granulomas, and 14 pneumonic infiltrates). The patients underwent T1 and T2 mapping at 3T. Two independent readers quantified the mean values for each lesion. The pre-specified primary endpoints were (1) benign versus malignant and (2) primary lung cancer versus pulmonary metastases. Results: Significant differences in T1 and T2 values were observed across lesion types. Benign lesions exhibited high T2 values (mean 213.6 ms) and low T1 values (mean 836.6 ms), whereas malignant tumors exhibited lower T2 values (~77–78 ms) and higher T1 values (~1460–1504 ms, p < 0.001). Binary classification yielded 95.7% accuracy (sensitivity 93.8% for malignant, specificity 100% for benign) in an internal 70/30 hold-out validation (no external dataset), with consistent performance confirmed by patient-level and nested cross-validation (balanced accuracy ≈ 0.92–0.94). However, malignant subtypes could not be reliably distinguished (p > 0.05), and multiclass accuracy was 60.9%. Conclusions: Quantitative MR relaxometry allows accurate, radiation-free differentiation of benign and malignant lung lesions and may help reduce unnecessary invasive procedures. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation. Full article

Background: TRPC6 is recognized as a therapeutically relevant cation channel, whose activation is governed by specific ligand–pocket interactions. Methods: An integrated in silico workflow was employed, comprising structure-based docking, 100-nanosecond molecular dynamics (MD) simulations, and MM-GBSA calculations. Benzoic-acid–based compounds were designed and prioritized for binding to the TRPC6 pocket, using a known literature agonist as a reference for benchmarking. Results: Within the compound series, BT11 was found to exhibit a representative interaction profile, characterized by a key hydrogen bond with Trp680 (~64% occupancy), persistent salt-bridge interactions with Lys676 and Lys698, and π–π stacking with Phe675 and Phe679. A favorable docking score (−11.45 kcal/mol) was obtained for BT11, along with a lower complex RMSD during MD simulations (0.6–4.8 Å), compared with the reference compound (0.8–7.2 Å). A reduction in solvent-accessible surface area (SASA) after ~60 ns was also observed, suggesting decreased water penetration. The most favorable binding energy was predicted for BT11 by MM-GBSA (−67.72 kcal/mol), while SOH95 also ranked highly and slightly outperformed the reference. Conclusions: These convergent computational analyses support the identification of benzoic-acid–derived chemotypes as potential TRPC6 ligands. Testable hypotheses are proposed, along with structure–activity relationship (SAR) guidelines, to inform experimental validation and guide the design of next-generation analogs. Full article

Background: Musculoskeletal pain (MSP) is one of the leading causes of disability worldwide and is frequently reported during the Muslim Hajj Pilgrimage; however, its prevalence and associated risk factors among Kuwaiti pilgrims have not been studied thus far. Methods: This is a retrospective cross-sectional study of Kuwaiti pilgrims conducted during the year 2024. Pilgrims were contacted by phone before and after Hajj to answer a survey regarding MSP during their pilgrimage. Risk ratios were computed using binomial generalised linear models with a log link. Results: A total of 557 participants (Mean BMI 28.0 ± 8.0 kg/m²), comprising 340 women (61%) and 217 men (39%), participated in the study. Most were between 33 and 45 years of age (n = 173, 31%), with 24% of the sample (n = 136) reporting MSP. Our regression analysis revealed that female gender (aRR 1.49, 95% CI 1.08–2.06), short sleep duration (<6 h; aRR 1.37, 95% CI 1.02–1.84), and smoking (aRR 0.66, 95% CI 0.46–0.95) were significantly associated with MSP, while participants who did not report hypertension were also less likely to report MSP (aRR 0.64, 95% CI 0.46–0.89). Conclusions: This study, the first to focus on Kuwaiti pilgrims in this regard, showed that their reported prevalence of MSP during Hajj was lower than reported previously in studies of other nationalities. Several factors that increased the risk of MSP included smoking, hypertension, poor sleep, and female gender. The results of this study emphasise the necessity of both conducting a screening programme before Hajj and raising awareness of the factors that increase the prevalence of MSP, subsequently reducing the readiness of pilgrims. Full article

In open-pit bench blasting design, conventional hole placement methods are limited by their inability to handle irregular blast area boundaries effectively. To address this, an adaptive hole placement algorithm based on Voronoi diagrams is proposed. This algorithm uses Voronoi diagram principles to divide the blast area according to its boundary conditions. Using Lloyd’s algorithm achieves a uniform distribution of blast hole points within the blast zone, enabling the p3rediction of hole coordinates. The algorithm has been developed into a bench blasting design programme using MATLAB R2021a. The programme calculates the required number of blast holes based on coverage area per blast hole charge and blast area. It then completes the entire bench blasting design by incorporating parameters such as the blast area boundary. In practice, this method enables more scientific blast design, demonstrating excellent algorithm stability and computational efficiency. It is particularly adaptable when handling irregular blast area boundaries. Full article

Background/Objective: The rise in bacterial resistance necessitates novel therapeutic strategies beyond conventional antibiotics. Antimicrobial peptides represent promising candidates but face challenges such as instability, enzymatic degradation, and host toxicity. To overcome these limitations, conjugation and structural modifications are being explored. This study focuses on designing peptide-based inhibitors of the quorum-sensing (QS) regulator LasR in Pseudomonas aeruginosa, a key mediator of biofilm formation and antibiotic resistance. Methods: Rationally designed tripeptides and dipeptides conjugated with coumarin-3-carboxylic acid and dihydro-3-amino-2-(3H)-furanone were evaluated using molecular docking. The most promising ligand‒protein complexes were further analyzed using molecular dynamics (MD) simulations conducted with the CHARMM-GUI and AMBER tools to assess the stability of the ligand‒protein complex systems, and the binding affinities were evaluated using Molecular Mechanics–Poisson Boltzmann Surface Area (MM-PBSA) calculations. Pharmacokinetic and toxicity profiles were predicted using ADMETLab 3.0. Selected compounds were synthesized via solid-phase peptide synthesis, structurally confirmed by ¹H NMR and ESI-MS, and tested for antibacterial and antibiofilm activity against P. aeruginosa ATCC 27853. Results: Computational analyses identified several promising inhibitors with stronger binding affinities than the native autoinducer OdDHL. Coumarin conjugates C004 and C006 showed superior docking scores, while MM-PBSA indicated P004 and C004 had the most favorable binding energies. MD simulations confirmed stable ligand–protein complexes. ADMET predictions highlighted C004 and C006 as having excellent pharmacokinetic properties. Experimental assays showed moderate antibacterial activity (MIC 512–1024 µg/mL) and strong antibiofilm inhibition, particularly for C004 (83% inhibition at ½ MIC). Conclusions: The study demonstrates that peptide–coumarin conjugates, especially C004, are promising tools for disrupting QS and biofilm formation in P. aeruginosa. Further optimization and in vivo validation are needed to advance these compounds toward therapeutic application. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

University course scheduling is a kind of timetable problem and can be mathematically formulated as an integer linear programming problem. Essentially, a university course scheduling problem is an optimization problem that aims at most efficiently minimizing a cost function according to a set of constraints. The huge searching space for the course scheduling problem means a long time will be needed to find the optimal solution. Therefore, some studies have used soft computing approaches to solve course scheduling problems in order to reduce the searching space. However, in order to use soft computing approaches to solve university course scheduling problems, we may need to design algorithms and conduct numerous experiments to achieve maximum efficiency. Thus, in this study, instead of employing soft computing methods, we propose a SWI-PROLOG-based expert system to solve the course scheduling problem. An experiment was conducted using real-world data from a department at a national university in southern Taiwan. During the experiment, each teacher in the department chose five preferential time slots. The experimental results have shown that about 99% of courses were scheduled in teachers’ five preferential time slots with an acceptable computational time of executing SWI-PROLOG (127 milliseconds on a regular personal computer). This study has thus provided a framework for solving course scheduling problems using an expert system. This would be the main contribution of this study. Full article

This paper presents an electromagnetic levitation system that stabilizes a magnetic body using an array of electromagnets controlled by a Hall-effect sensor array and TinyML-based position detection. Departing from conventional optical tracking methods, the proposed design combines finite-element-optimized electromagnets with a microcontroller-optimized neural network that processes sensor data to predict the levitated object’s position with 0.0263–0.0381 mm mean absolute error. The system employs both quantized and full-precision implementations of a supervised multi-output regression model trained on spatially sampled data (40 × 40 × 15 mm volume at 5 mm intervals). Comprehensive benchmarking demonstrates stable operation at 850–1000 Hz control frequencies, matching optical systems’ performance while eliminating their cost and complexity. The integrated solution performs real-time position detection and current calculation entirely on-board, requiring no external tracking devices or high-performance computing. By achieving sub 30 μm accuracy with standard microcontrollers and minimal hardware, this work validates machine learning as a viable alternative to optical position detection in magnetic levitation systems, reducing implementation barriers for research and industrial applications. The complete system design, including electromagnetic array characterization, neural network architecture selection, and real-time implementation challenges, is presented alongside performance comparisons with conventional approaches. Full article

Background: Emotion regulation (ER) difficulties are closely linked to maladaptive coping strategies, including impulsive and emotional eating, which undermine health and well-being in young adults. Technical university students are particularly vulnerable due to factors such as a high academic workload, sedentary behavior, and performance-related stress. This study evaluated the effects of a four-week structured physical activity intervention on ER and eating behaviors among engineering students. Methods: Seventy first- and second-year computer science and engineering students (40 males and 30 females, aged 19–25 years) from Politehnica University of Bucharest participated in the study. The intervention included three weekly supervised training sessions and a daily step count requirement (≥6000 steps), verified via weekly smartphone submissions. Pre- and post-intervention assessments employed the Difficulties in Emotion Regulation Scale (DERS-36) and the Adult Eating Behavior Questionnaire (AEBQ-35). Data were analyzed using Kolmogorov–Smirnov tests, Wilcoxon signed-rank tests, and paired-sample t-tests. Results: Significant improvements were observed in five ER domains—non-acceptance of emotional responses, goal-directed behavior, impulse control, access to regulation strategies, and emotional clarity (all p < 0.01). No change occurred in emotional awareness (p > 0.05). Eating behaviors (restrained, emotional, and external eating) showed no significant differences pre- and post-intervention (all p > 0.05). Conclusions: A short-term, structured physical activity program enhanced emotion regulation capacities but did not alter eating behaviors in the short run. These findings highlight the feasibility of embedding low-cost, exercise-based modules into higher education to strengthen students’ psychological resilience. Longer and multimodal interventions may be required to produce measurable changes in eating behaviors. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Micro-computed tomography ($\mu$CT) and X-ray Fluorescence (XRF) were used to investigate a Polymetallic Nodule (PN) from the North-Western Pacific abyssal plain to gather more information concerning the environmental changes that could be reflected by the PN’s internal structure. Despite its small size, for example, an ovoid measured 48 × 38 mm, the $\mu$CT revealed the presence of four concentric layers with varying thicknesses and opacities to X-rays, all developed around a fragment of a tooth, most likely belonging to a Lamniformes shark. The same micro-tomograph, functioning as an XRF spectrometer, allowed for the determination of the mass fractions of Mn and Fe in the first two external layers. To estimate the PN age, a model that considers PN growth rate proportional to the ratio of Mn to the square of Fe mass fractions was used, and, by extrapolating it to the entire PN, its age was estimated at 1.56 ± 0.22 Ma, i.e., Early Pleistocene. Therefore, the correlated use of $\mu$CT and FRX, two noninvasive methods, allowed to highlight a shark tooth fragment as being the PN nucleus as well as determine its absolute age. Full article

The trajectory-tracking capability of autonomous mining trucks is critical for accomplishing transportation tasks efficiently. However, due to the diverse road surfaces and rugged terrains in open-pit mines, the existing vehicle dynamics models struggle to accurately capture the complex tire–ground interactions. As a result, conventional trajectory-tracking control methods that rely on linear vehicle dynamics models suffer from degraded tracking performance. To this end, this paper proposes a novel trajectory-tracking control framework that integrates model predictive control (MPC) with model-free adaptive control (MFAC). A warm-start strategy is employed to improve the computational efficiency of MPC, while MFAC is utilized to provide real-time compensation for the control deviations generated by MPC during the trajectory-tracking process. To validate the effectiveness of the proposed trajectory-tracking control method, co-simulations were conducted on the CarSim and MATLAB/Simulink platforms under various road conditions and driving scenarios. Simulation results demonstrate that the proposed method can effectively enhance the trajectory-tracking performance of autonomous mining trucks. For instance, under the S-condition with Class E road elevation, the proposed method achieves improvements of approximately 90.83%, 15.05%, and 71.93% in the mean error, maximum error, and root mean square error (RMSE), respectively, compared with the conventional LQR-based trajectory-tracking control method. In addition, the computation time of MPC is only 2 ms, which significantly improves the overall performance of the trajectory-tracking controller. Full article