Search Results (25,176)

Full-bridge resonant converters have gained widespread adoption due to their excellent performance characteristics. However, these converters face limitations in terms of gain expansion. To address this issue, this paper proposes a switch-multiplexed resonant converter with wide voltage gain. By employing an auxiliary capacitor and inductor, the converter can achieve pseudo partial power processing and effectively expand its voltage gain. To regulate the voltage of the converter, the duty cycle of the switching devices is designed to be modulated, which is different from the conventional resonant converter. By optimizing the design parameters, the resonant current of converter can operate in discontinuous current mode (DCM), eliminating reverse recovery losses and/or minimizing reactive current. All switching devices achieve soft switching, thereby removing switching losses and further enhancing efficiency. Finally, a 1 kW prototype is built to validate the effectiveness of the proposed converter. Full article

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the Fibonacci spiral sampling technique optimizes pose selection, ensuring end-effector poses fully cover the manipulator’s workspace to enhance identification convergence. By combining the local convergence capability of the Levenberg–Marquardt (LM) algorithm with the global search characteristics of Particle Swarm Optimization (PSO), we propose a novel hybrid PSO-LM optimization algorithm, achieving synergistic enhancement of global exploration and local refinement. An experimental platform using a laser tracker as the metrology reference was constructed, with a 6-degree-of-freedom (6-DOF) space manipulator selected as a validation case. Experimental results demonstrate that the proposed method significantly reduces the average positioning error from 10.87 mm to 0.47 mm, achieving a 95.7% improvement in relative accuracy. These findings validate that the parameter identification approach can precisely determine the actual geometric parameters of space manipulators, providing critical technical support for high-precision on-orbit operations. Full article

The elbow suction conduit plays a decisive role in determining inflow conditions, thereby influencing a pump’s efficiency and cavitation characteristics. The complex three-dimensional swirling and separating flow makes pinpointing the sources and mechanisms of energy dissipation challenging. This study aims to accurately diagnose the sources of hydraulic losses within the elbow suction conduit and conduct effective geometric optimization to enhance overall pump performance. Entropy production theory was integrated with three-dimensional Reynolds-averaged Navier-Stokes simulations to quantitatively analyze the irreversible energy dissipation in different parts of the conduit. Results reveal that energy dissipation is predominantly concentrated at the inlet section, wall surfaces, outer curvature of the bend, and the inner conical diffuser. Key geometric parameters were systematically optimized. Compared to the baseline design, the optimized configuration not only reduced entropy generation induced by wall shear and turbulent fluctuations but also improved the spatio-temporal uniformity of the outflow. Consequently, this translated directly into enhanced overall pump performance: the optimized design shows a 0.34% increase in efficiency and a 3.6% reduction in the inception cavitation coefficient at the rated condition, leading to lower energy consumption and enhanced operational reliability. The effectiveness of entropy production analysis for the hydraulic optimization of pumps was demonstrated. Full article

Omniphobic membranes have gained extensive attention for mitigating membrane wetting in robust membrane separation owing to the super-repulsion toward water and oil. In this study, a Teflon/PAI composite membrane with omniphobic characteristics was prepared by a vacuum-assisted dip-coating strategy on the PAI hollow fiber membrane. A series of characterizations on morphological structure, surface chemical composition, wettability, permeability, mechanical properties, and stability were systematically investigated for pristine PAI and Teflon/PAI composite membranes. Subsequently, the experiment was conducted to explore the oil–gas separation performance of membranes, with standard transformer oil containing dissolved gas as the feed. The results showed that the Teflon AF2400 functional layer was modified, and C-F covalent bonds were introduced on the composite membrane surface. The Teflon/PAI composite membrane exhibited excellent contact angles of 156.3 ± 1.8° and 123.0 ± 2.5° toward DI water and mineral insulating oil, respectively, indicating omniphobicity. After modification, the membrane tensile stress at break increased by 23.0% and the mechanical performance of the composite membrane was significantly improved. In addition, the Teflon/PAI composite membrane presented satisfactory thermal and ultrasonic stability. Compared to the previous membranes, the Teflon/PAI composite membrane presented a thinner Teflon AF2400 separation layer. Furthermore, the omniphobic membrane demonstrated anti-wetting performance by reaching the dynamic equilibrium within 2 h for the dissolved gases separated from the insulating oil. This suggests an omniphobic membrane as a promising alternative for oil–gas separation in monitoring the operating condition of oil-filled electrical equipment online. Full article

Modular shipbuilding, as a cutting-edge ship construction paradigm, enables parallel manufacturing across workshops and stages—a core advantage that significantly shortens the total shipbuilding cycle, making it pivotal for modern shipyards to enhance productivity. However, this mode decomposes the integrated shipbuilding project into a large number of interdependent sub-activities spanning three key stages (fabrication, logistics, and assembly). Further, the duration of these sub-activities is inherently uncertain, primarily due to the extensive manual operations, variable on-site conditions, and supply chain fluctuations inherent in shipbuilding. These characteristics collectively pose a formidable challenge to project planning that pursues both high efficiency and low cost. To address this challenge, this paper proposes a Strategy-Group Evolution algorithm. First, the modular shipbuilding process scheduling problem is mathematically formulated as a resource-constrained three-stage multi-objective optimization model, where triangular fuzzy numbers are employed to characterize the uncertain sub-activity durations. Second, a two-layered Strategy-Group Evolution algorithm is designed for solving this model: the inner layer comprises 12 practical priority rules tailored to modular shipbuilding’s multi-stage features, while the outer layer adopts a genetic algorithm-based evolution policy to schedule and optimize the assignment of inner-layer rules to activity groups. The core of the Strategy-Group Evolution algorithm lies in dynamically assigning suitable strategies to different activity groups and evolving these assignments toward optimality—this avoids the limitation of a single priority rule for all stages, thereby facilitating the search for global optimal solutions. Finally, validation tests on real cruise ship construction projects and benchmark datasets demonstrate the efficacy and superiority of the proposed Strategy-Group Evolution algorithm. Full article

As the transformative influence of novel technologies sweeps across industries, organisations are called upon to position their staff in the equally dynamic operational environment, which includes embedding technical and legal communication skills in their training programs. For many organisations, internal and external communication of data modelling and related concepts, reporting, and monitoring still pose major challenges. The aim of this research is to develop an effective data training framework for learners with or without mathematical or computational maturity. It also addresses subtle aspects such as the legal and ethical implications of dealing with organisational data. Data was collected from a training course in Python, delivered to government employees in different departments in the United Arab Emirates (UAE). A structured questionnaire was designed to measure the effectiveness of the training program using Python, from the employees’ perspective, based on three key attributes: their personal characteristics, professional characteristics, and technical knowledge. A descriptive analysis of aggregations, deviations, and proportions was used to describe the data attributes gathered for the study. The main findings revealed a huge knowledge gap across disciplines regarding the core skills of big data analytics. In addition, the findings highlighted that previous knowledge about statistical methods of data analysis along with prior programming knowledge made it easier for employees to gain skills in data analytics. While the results of this study showed that their training program was beneficial for the vast majority of participants, responses from the survey indicate that providing a solid knowledge of technical communication, legal and ethical aspects would offer significant insights into the big data analytics field. Based on the findings, we make recommendations for adapting conventional data analytics approaches to align with the complexity or the attainment of the non-orthogonal United Nations Sustainable Development Goals (SDG). Associations of selected responses from the survey with some of the key data attributes indicate that the research highlights vital roles that technology and data-driven skills will play in ensuring a more prosperous and sustainable future for all. Full article

Background: Pancreaticoduodenectomy (PD) with portal–superior mesenteric vein (PV-SMV) resection is increasingly performed in borderline-resectable periampullary cancer. While conventional PD is the reference standard, robotic PD (RPD) may improve operative ergonomics and recovery; its performance and learning curve in PV-SMV resection remain unclear. Materials and Methods: We retrospectively reviewed consecutive patients undergoing PD with PV-SMV resection at a single tertiary center by a single surgeon (July 2016–September 2022). Twenty-seven patients met the inclusion criteria and were grouped as conventional PD (n = 14) or RPD (n = 13). To assess the learning curve, RPD cases were stratified as early (cases 1–3) versus late (cases 4–13). Primary outcomes were operative time and blood loss; secondary outcomes included 90-day morbidity/mortality, R0 margin, lymph node yield, length of stay, readmission, and overall survival. Results: Baseline characteristics were comparable between conventional PD and RPD. Median operative time was longer with RPD vs. conventional PD (624.0 [IQR 579.0–794.0] vs. 529.5 [456.5–636.5] mins; p = 0.024). Median blood loss trended lower with RPD (350.0 [200.0–1950.0] vs. 1455.0 [630.0–2940.0] mL; p = 0.254). Rates of clinically relevant complications (including POPF, DGE, and hemorrhage), R0 resection (69% vs. 64%), lymph node retrieval, length of stay, 90-day readmission, 90-day mortality, and overall survival were similar between conventional PD and RPD. Within RPD, operative time and blood loss improved from the early to late phases (794.0→601.5 min; 1950.0→275.0 mL), consistent with a learning-curve effect, though not statistically significant in this small cohort. Conclusions: In selected patients, RPD with PV-SMV resection is feasible and achieves oncologic and short-term clinical outcomes comparable to conventional PD, with evidence of efficiency gains as experience accrues. These findings support structured training and case accumulation for the safe adoption of complex robotic pancreatic surgery. Full article

This paper presents a comprehensive machine learning (ML) framework for predicting liquefaction risk, a crucial aspect of seismic hazard assessment. A benchmark geotechnical dataset with multi-dimensional input features was used to evaluate several ML classifiers, followed by hyperparameter optimization through stratified 5-fold cross-validation. Optimized models were combined into a soft Voting Ensemble to enhance stability and accuracy of liquefaction potential prediction. The proposed ensemble model achieved a mean accuracy of 90.12% and a recall of 97.23%, outperforming individual models in most folds. The ensemble’s effectiveness was further evidenced by its precision-recall (PR) and receiver operating characteristic (ROC) curves, with areas under the curve (AUC) of 0.962 and 0.931, respectively—closely matching those of the Gradient Boosting classifier, indicating comparable discriminatory performance. Additionally, SHapley Additive exPlanations (SHAP) analysis was conducted on the ensemble model to assess contributions of each geotechnical inputs to the predictions, revealing that normalized shear wave velocity ($V_{S1}$) as the most influential variable in liquefaction prediction. The proposed framework demonstrates a robust, interpretable, and performance-consistent approach for liquefaction risk assessment. Full article

Background: Assessment of root development and apical closure is critical in dental disciplines, including endodontics, trauma management, and age estimation. This study aims to leverage advances in deep learning Convolutional Neural Networks (CNNs) to automatically evaluate the apical region status of permanent first molars, highlighting a digital health application of AI in dentistry. Methods: In this retrospective study, 262 Cone Beam Computed Tomography (CBCT) scans were reviewed, and 147 anonymized dental images were cropped from pseudopanoramic radiographs, including standard measurements. Tooth regions were resized to 471 × 1075 pixels and split into training (80%) and test (20%) sets. CNN performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves with area under the curve (AUC), demonstrating AI-based image analysis in a dental context. Results: Precision, recall, and F1-scores were 0.79 for open roots and 0.81 for closed roots, with a macro average of 0.80 across all metrics. The overall accuracy and AUC were also 0.80. Conclusions: These results suggest that CNNs can be effectively used to assess apical patency from ROI images derived from pseudopanoramic radiographs. Full article

Background: Current low milk supply (LMS) definitions use subjective maternal perceptions or arbitrary thresholds for 24 h milk production (MP), potentially misclassifying cases. This study aimed to re-evaluate the definition of LMS using data-driven approaches and investigate associated maternal risk factors. Methods: Lactating mothers 4–26 weeks postpartum (n = 460) provided demographic, obstetric, and infant data and measured 24 h MP and infant milk intake using the test-weighing method. Infant growth was calculated as their weight-for-age z-score. Latent profile analysis, receiver operating characteristic curve analysis, and multinomial logistic regression were used for classification, diagnostic evaluation, and risk factor assessment for LMS. Results: Four milk supply classes emerged: Class 1 with adequate MP, infant intake and infant growth (n = 254); Class 2 with high MP exceeding infant demand and adequate growth (n = 30); Class 3 with slow infant growth despite moderate MP (n = 120); and Class 4 with extremely low MP and high formula intake (n = 56). Classes 1 and 2 were grouped as the normal milk supply group (61.7%), while Classes 3 and 4 formed the LMS group (38.3%). New thresholds were identified for 24 h MP (708 mL/24 h, area under the curve (AUC) = 0.92) and infant breast milk intake (694 mL/24 h, AUC = 0.94) with high diagnostic accuracy. Moreover, practical alternative thresholds for infant average daily weight gain (26 g, AUC = 0.89), formula intake (122 mL/24 h, AUC = 0.89) and formula-to-growth ratio (4 mL/g, AUC = 0.94) were established for the identification of LMS. Minimal breast growth during pregnancy (Odds ratio (OR) = 4.6, 95% confidence interval (CI): 2.3–9.6), advanced maternal age (OR = 2.1, 95% CI: 1.0–4.5), and gestational diabetes mellitus (OR = 2.1, 95% CI: 1.1–4.0) were significant risk factors related to the LMS subgroups. Co-existence of maternal advanced age and overweight showed greatly amplified risk of LMS (OR = 3.7, 95% CI: 1.3–10.5), and a more pronounced risk was observed for the combination of minimal breast growth and advanced maternal age (OR = 9.2, 95% CI: 3.0–28.3). Conclusions: This data-driven classification of LMS and identified risk factors may enhance the precision of LMS diagnosis and guide targeted interventions for lactating mothers. Full article

There are significant differences in the deformation patterns of different parts of arch dams, and there is a common situation of periodic data loss. To accurately analyze the deformation behavior of arch dams, this paper proposes a safety warning and anomaly diagnosis method for arch dam deformation based on the separation of environmental variable effects in different partitions and a knowledge-driven approach. This method combines various techniques such as an optimized ISODATA clustering method, probabilistic principal component analysis (PPCA), square prediction error (SPE) norm control chart, and contribution chart. By defining data forms and rules, existing engineering specifications and experience are transformed into “knowledge” and applied to the operation and management of arch dams, achieving accurate monitoring of arch dam deformation status and timely diagnosis of outliers. Through monitoring data verification of horizontal displacement in a certain arch dam partition, the results show that this method can accurately identify deformation anomalies in the arch dam and effectively separate the influence of environmental variables and noise interference, providing strong support for the safe operation of the arch dam. Accurate deformation monitoring of arch dams is essential for ensuring structural safety and optimizing operational management. However, conventional early warning indicators and empirical models often fail to capture the spatial heterogeneity of deformation and the complex coupling between environmental variables and structural responses. To overcome these limitations, this study proposes a knowledge-driven safety early warning and anomaly diagnosis model for arch dam deformation, based on spatiotemporal clustering and partitioned environmental variable separation. The method integrates the optimized ISODATA clustering algorithm, probabilistic principal component analysis (PPCA), squared prediction error (SPE) control chart, and contribution chart to establish a comprehensive monitoring framework. The optimized ISODATA identifies deformation zones with similar mechanical behavior, PPCA separates environmental influences such as temperature and reservoir level from structural responses, and the SPE and contribution charts quantify abnormal variations and locate potential risk regions. Application of the proposed method to long-term deformation monitoring data demonstrates that the PPCA-based framework effectively separates environmental effects, improves the interpretability of zoned deformation characteristics, and enhances the accuracy and reliability of anomaly identification compared with conventional approaches. These findings indicate that the proposed knowledge-driven model provides a robust and interpretable framework for precise deformation safety evaluation of arch dams. Full article

Ensuring the safe operation of large hydro-generators is essential for energy supply and economic development. Stator-winding single-phase grounding faults are among the most common failures in such generators. Conventional protection methods—such as fundamental voltage protection, third-harmonic voltage saturation, and low-frequency injection—lack fault location capability and cannot assess the fault severity. This paper proposes a stepwise variable-frequency pulse injection method for fault diagnosis and location in large hydro-generator stator windings. A finite element model of a salient-pole hydro-generator is established to analyze magnetic flux density and electromotive force distributions under normal and fault conditions, from which fault characteristics are derived. Equivalent circuit models suitable for low- and high-frequency pulse injection are developed. A bidirectional pulse injection circuit and algorithm are designed to identify the fault phase via terminal current vector characteristics, diagnose the faulty branch based on leakage loop equivalent inductance, and locate the fault point using voltage–current signal slopes. Simulation results validate the effectiveness of the proposed diagnostic approach. Full article

To investigate the flow-field characteristics of a submerged pre-mixed abrasive water jet impinging on a wall, a physical model of the conical–cylindrical nozzle and computation domain of a submerged pre-mixed abrasive-water-jet flow field were established. Based on the software of FLUENT 2022R2, numerical simulation of the solid–liquid two-phase flow characteristics of the submerged pre-mixed abrasive water jet impinging on a wall was conducted using the DPM particle trajectory model and the realizable $k–\epsilon$ turbulence model. The simulation results indicate that a “water cushion layer” forms when the submerged pre-mixed abrasive water jet impinges on a wall. Tilting the nozzle appropriately facilitates the rapid dispersion of water and abrasive particles, which is beneficial for cutting. The axial-jet velocity increases rapidly in the convergent section of the nozzle, continues to accelerate over a certain distance after entering the cylindrical section, reaches its maximum value inside the nozzle, and then decelerates to a steady value before exiting the nozzle. In addition, the standoff distance has minimal impact on the flow-field characteristic inside the nozzle. When impinging on a wall surface, rapid decay of axial-jet velocity generates significant stagnation pressure. The stagnation pressure decreases with increasing standoff distance for different standoff-distance models. Considering the effects of standoff distance on jet velocity and abrasive particle dynamics, a standoff distance of 5 mm is determined to be optimal for submerged pre-mixed abrasive-water-jet pipe-cutting operations. When the submergence depth is less than 100 m, its effect on the flow-field characteristics of a submerged pre-mixed abrasive water jet impinging on a wall surface remains minimal. For underwater oil pipelines operating at depths not exceeding 100 m, the influence of submergence depth can be disregarded during cutting operations. Full article

This study examines the impact of jet misalignment on the mechanical performance of Pelton turbine runners. A comparative examination of the dynamic response characteristics of the runner under four operational conditions—Undeflected Jet (UJ), Radial offset+ (RO+), Radial offset− (RO−), and Axial offset (AO)—is undertaken based on fluid–structure interaction (FSI) numerical simulations. The findings demonstrate that functioning under misaligned conditions modifies the stress distribution on the runner surface, resulting in considerable stress concentration. The maximum Von-Mises stress attains 129.7 MPa, occurring at the bucket notch region under the RO+ condition. The strain distribution aligns with the stress distribution in the elastic regime, exhibiting a maximum Von-Mises strain of 0.000650 (0.650 × 10⁻³ mm/mm). The distortion of the runner varies from 0.181 mm to 0.190 mm, with the most significant deformation occurring near the trailing edge. The RO+ condition intensifies the risk of high-cycle fatigue in the runner structure, succeeded by RO− and AO situations. The results establish a theoretical foundation for the secure functioning and structural enhancement of Pelton turbines in misalignment scenarios. Full article

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the gasoline production and minimize the coke yield—the latter being directly linked to $\mathrm{C}{\mathrm{O}}_2$ emissions in FCC. A data-driven optimization model leveraging a dual Long Short-Term Memory architecture is developed to capture complex relationships between operating variables and product yields. To efficiently solve the model, an Improved Multi-Objective Whale Optimization Algorithm (IMOWOA) is proposed, integrating problem-specific adaptive multi-neighborhood search and dynamic restart mechanisms. Extensive experimental evaluations demonstrate that IMOWOA achieves superior convergence characteristics and comprehensive performance compared to established multi-objective algorithms. Relative to the yields before optimization, the proposed methodology increases the gasoline yield by 0.32% on average, coupled with an average reduction of 0.11% in the coke yield. For the studied FCC unit with an annual processing capacity of 2.6 million tons, the coke reduction corresponds to an annual $\mathrm{C}{\mathrm{O}}_2$ emission reduction of approximately 10,277 tons, delivering benefits to sustainable FCC operations. Full article