Search Results (7,119)

To ensure the sustainability of alloy-based strategies, both compositional design and processing routes must be simplified. Metal additive manufacturing (AM), with its exceptionally rapid, non-equilibrium solidification, offers a unique platform to produce tailored microstructures in simple alloys that deliver superior mechanical properties. In this study, we employ laser powder bed fusion (LPBF) to fabricate 1080 plain carbon steel, a binary alloy comprising only iron and carbon. Deviating from conventional process optimization focusing primarily on density, we optimize LPBF parameters for mechanical performance. We systematically varied key parameters (laser power and scan speed) to produce batches of tensile specimens, which were then evaluated on a high-throughput mechanical testing platform (HTP). Using response surface methodology (RSM), we developed predictive models correlating these parameters with yield strength (YS) and elongation. The RSM models identified optimal and suboptimal parameter sets. Specimens printed under the predicted optimal conditions achieved YS of 1543.5 MPa and elongation of 7.58%, closely matching RSM predictions (1595.3 MPa and 8.32%) with deviations of −3.25% and −8.89% for YS and elongation, respectively, thus validating model accuracy. Comprehensive microstructural characterization, including metallographic analysis and fracture surface examination, revealed the microstructural origins of performance differences and the underlying strengthening mechanisms. This methodology enables rapid evaluation and optimization of LPBF parameters for 1080 carbon steel and can be generalized as an efficient framework for robust LPBF process development. Full article

Aronia juice is well-known for its high nutritional and biological value, due to its polyphenol content, which has a powerful antioxidant effect. However, the high polyphenol content of aronia juice is associated with an astringent flavor, which diminishes consumer acceptance. To improve the flavor of aronia juice, β-cyclodextrin (0–2% w/v) or gelatin (0–0.4 mg/L) were added before pasteurization. The juice samples were first examined organoleptically, and monitored for total phenolic compounds, antioxidant capacity, total flavonoids, total monomeric anthocyanins, polymeric color, pH, total soluble solids, and color. The organoleptic test demonstrated that both β-cyclodextrin and gelatin juice aroma reduced astringency and increased sweetness, whereas β-cyclodextrin also reduced juice aroma. β-cyclodextrin significantly increased polymeric color and total soluble solids (p < 0.05), whereas antioxidant activity, total flavonoids, and monomeric anthocyanins remained unchanged compared to the unpasteurized control. In contrast, the addition of gelatin dramatically reduced total phenolic compounds, antioxidant capacity, and total flavonoids, while enhancing polymeric color and maintaining monomeric anthocyanins with minor decreases relative to pre-pasteurization levels (p < 0.05). A consumer study was conducted with control juice and juices with 2% w/v β-cyclodextrin or 0.4 mg/L gelatin added. The results confirmed the change in flavor profile by masking or removing astringency and astringent aftertaste, as well as increasing sweetness, which significantly improved overall acceptability (p < 0.05). Full article

The Red River Jig is a traditional Métis dance practiced among Indigenous and non-Indigenous Peoples. While exercise improves physical health and fitness, the impacts of cultural dances on wholistic health are less clear. This study aimed to investigate the psychosocial (cultural and mental), social, physical function, and physical fitness benefits of a Red River Jig intervention. In partnership with Li Toneur Nimiyitoohk Métis Dance Group, Indigenous and non-Indigenous adults (N = 40, 39 ± 15 years, 32 females) completed an 8-week Red River Jig intervention. Social support, cultural identity, memory, and mental wellbeing questionnaires, seated blood pressure and heart rate, weight, pulse-wave velocity, heart rate variability, baroreceptor sensitivity, jump height, sit-and-reach flexibility, one-leg and tandem balance, and six-minute walk test were assessed pre- and post-intervention. Community, family, and friend support scores, six-minute walk distance (553.0 ± 88.7 m vs. 602.2 ± 138.6 m, p = 0.002), jump, leg power, and systolic blood pressure low-to-high-frequency ratio increased after the intervention. Ethnic identity remained the same while affirmation and belonging declined, leading to declines in overall cultural identity, as learning about Métis culture through the Red River Jig may highlight gaps in cultural knowledge. Seated systolic blood pressure (116.5 ± 7.3 mmHg vs. 112.5 ± 10.7 mmHg, p = 0.01) and lower peripheral pulse-wave velocity (10.0 ± 2.0 m·s⁻¹ vs. 9.4 ± 1.9 m·s⁻¹, p = 0.04) decreased after the intervention. Red River Jig dance training can improve social support, physical function, and physical fitness for Indigenous and non-Indigenous adults. Full article

Background: This study aimed to examine the effects of an 8-week plyometric training program on lower-limb explosive strength, jump performance, musculotendinous stiffness, reactive strength index (RSI), and multidirectional speed in elite Polish badminton players. Methods: Twenty-four athletes were randomly assigned to either an experimental group (n = 15), which supplemented their regular badminton training with plyometric exercises, or a control group (n = 15), which continued standard technical training. Performance assessments included squat jump (SJ), countermovement jump (CMJ), single-leg jumps, sprint tests (5 m, 10 m), lateral movements, musculotendinous stiffness, and RSI measurements. Results: The experimental group showed statistically significant improvements in jump height, power output, stiffness, and 10 m sprint and lateral slide-step performance (p < 0.05), with large effect sizes. No significant changes were observed in the control group. Single-leg jump improvements suggested potential benefits for addressing lower-limb asymmetries. Conclusions: An 8-week plyometric intervention significantly enhanced lower-limb explosive performance and multidirectional movement capabilities in young badminton players. These findings support the integration of targeted plyometric training into regular training programs to optimize physical performance, improve movement efficiency, and potentially reduce injury risk in high-intensity racket sports. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $49\%$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background noise, and these features may be scattered over multiple frequency levels, which increases the complexity of extracting important information from them. To address this problem, this paper proposes a Masked and Cascaded Multi-Branch Attention Network (MCMBAN), which combines the Noise Mask Filter Block (NMFB) with the Multi-Branch Cascade Attention Block (MBCAB), and significantly improves the noise immunity of the fault diagnostic model and the efficiency of fault feature extraction. NMFB novelly combines a wide convolutional layer and a top k neighbor self-attention masking mechanism, so as to efficiently filter unnecessary high-frequency noise in the vibration signal. On the other hand, MBCAB strengthens the interaction between different layers by cascading the convolutional layers of different scales, thus improving the recognition of periodic fault signals and greatly enhancing the diagnosis accuracy of the model when processing complex signals. Finally, the time–frequency analysis technique is employed to explore the internal mechanisms of the model in depth, aiming to validate the effectiveness of NMFB and MBCAB in fault feature recognition and to improve the feature interpretability of the proposed modes in fault diagnosis applications. We validate the superior performance of the network model in dealing with high-noise backgrounds by testing it on a standard bearing dataset from Case Western Reserve University and a self-constructed composite bearing fault dataset, and the experimental results show that its performance exceeded six of the top current fault diagnosis techniques. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

Effective species identification is crucial for the conservation and management of marine mammals, particularly in regions such as the Mediterranean Sea, where several cetacean populations are endangered or vulnerable. In this study, we developed and validated a High-Resolution Melting (HRM) analysis protocol for the rapid, cost-effective, and reliable identification of the four representative marine cetacean species that occur in the Mediterranean Sea: the bottlenose dolphin (Tursiops truncatus), the striped dolphin (Stenella coeruleoalba), the sperm whale (Physeter macrocephalus), and the fin whale (Balaenoptera physalus). Species-specific primers targeting mitochondrial DNA regions (cytochrome b and D-loop) were designed to generate distinct melting profiles. The protocol was tested on both tissue and fecal samples, demonstrating high sensitivity, reproducibility, and discrimination power. The results confirmed the robustness of the method, with melting curve profiles clearly distinguishing the target species and achieving a success rate > 95% in identifying unknown samples. The use of HRM offers several advantages over traditional sequencing methods, including reduced cost, speed, portability, and suitability for degraded samples, such as those from the stranded individuals. This approach provides a valuable tool for non-invasive genetic surveys and real-time species monitoring, contributing to more effective conservation strategies for cetaceans and enforcement of regulations against illegal trade. Full article

In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand and local photovoltaic (PV) generation. We employ the alternating direction method of multipliers (ADMM) to solve the dispatch problem in a distributed manner. Demand response in a 141-bus test system serves as our case study, demonstrating the effectiveness of our approach in shifting power loads to periods of high PV generation. Our results indicate remarkable reductions in the total carbon emission by utilizing more distributed PV generation. Full article

The rapid development of high-power-density semiconductor devices has rendered conventional thermal management techniques inadequate for handling their extreme heat fluxes. This manuscript presents and implements an embedded microchannel cooling solution for such devices. By directly integrating micropillar arrays within the near-junction region of the substrate, efficient forced convection and flow boiling mechanisms are achieved. Finite element analysis was first employed to conduct thermo–fluid–structure simulations of micropillar arrays with different geometries. Subsequently, based on our simulation results, a complete multilayer microstructure fabrication process was developed and integrated, including critical steps such as deep reactive ion etching (DRIE), surface hydrophilic/hydrophobic functionalization, and gold–stannum (Au-Sn) eutectic bonding. Finally, an experimental test platform was established to systematically evaluate the thermal performance of the fabricated devices under heat fluxes of up to 1200 W/cm². Our experimental results demonstrate that this solution effectively maintains the device operating temperature at 46.7 °C, achieving a mere 27.9 K temperature rise and exhibiting exceptional thermal management capabilities. This manuscript provides a feasible, efficient technical pathway for addressing extreme heat dissipation challenges in next-generation electronic devices, while offering notable references in structural design, micro/nanofabrication, and experimental validation for related fields. Full article

Interior-mounted permanent magnet (IPM) machines have been widely used in recent years due to their high efficiency, high torque/power densities, and so on. These machines can produce reluctance torque whereas their surface-mounted (SPM) counterparts cannot. Hence, IPMs are attractive in industrial applications that require high torque density. ${\mathrm{I}}_{\mathrm{d}}=0$ control is commonly adopted to drive permanent magnet (PM) machines, and the strategy is attractive due to its simplicity. However, although it is suitable for SPMs, adopting it in IPMs sacrifices the reluctance torque that can be obtained from the machine. Hence, it is vital to control IPMs using the maximum torque per ampere (MTPA) strategy. This paper adopts the MTPA strategy for a 4.1 kW prototype IPM machine. Test system configuration is discussed step by step by paying particular attention to potential practical issues and inspirational discussions on their solutions. The issues associated with misaligned rotor positions or whistling problems pertinent to inappropriate power conversion strategies are addressed to overcome such issues in practical IPM drives. Comprehensive discussions and extensive comparisons of well-matched simulation and experimental results of both ${\mathrm{I}}_{\mathrm{d}}=0$- and MTPA-controlled drives at different evaluation metrics will be quite insightful to achieve efficiency-optimized IPM drives. Full article

Skin autofluorescence (SAF) detection technology represents a noninvasive, convenient, and cost-effective optical detection approach. It can be employed for the differentiation of various diseases, including metabolic diseases and dermatitis, as well as for monitoring the treatment efficacy. Distinct from diffuse reflection signals, the autofluorescence signals of biological tissues are relatively weak, making them challenging to be captured by photoelectric sensors. Moreover, the absorption and scattering properties of biological tissues lead to a substantial attenuation of the autofluorescence of biological tissues, thereby worsening the signal-to-noise ratio. This has also imposed limitations on the development and application of compact-sized autofluorescence detection systems. In this study, a compact LED light source and a CMOS sensor were utilized as the excitation and detection devices for skin tissue autofluorescence, respectively, to construct a mobile and wireless skin tissue autofluorescence detection system. This system can achieve the detection of skin tissue autofluorescence with a high signal-to-noise ratio under the drive of a simple power supply and a single-chip microcontroller. The detection time is less than 0.1 s. To enhance the stability of the system, a pressure sensor was incorporated. This pressure sensor can monitor the pressure exerted by the skin on the detection system during the testing process, thereby improving the accuracy of the detection signal. The developed system features a compact structure, user-friendliness, and a favorable signal-to-noise ratio of the detection signal, holding significant application potential in future assessments of skin aging and the risk of diabetic complications. Full article

In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R² test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage. Full article