Search Results (82)

Ferritic/martensitic (F/M) steel is a candidate material for key structures in fourth-generation nuclear energy systems (such as fusion reactors and fast reactors). Irradiation hardening behavior is a core index to evaluate the material’s stable performance in a high-neutron-irradiation environment. In this study, based on 2048 composition and property data, a correlation model between key elements and their interactions and irradiation hardening in F/M steel was constructed using a Bayesian optimization neural network, which realized quantitative prediction of the effect of composition on hardening behavior. Studies have shown that the addition of about 9.0% Cr, about 0.8% Si, Mo content higher than about 0.25%, and the addition of Ti, Mn can effectively suppress the irradiation hardening of F/M steel, while the addition of N, Ta, and C will aggravate its irradiation hardening, and the addition of W and V has little effect on the irradiation hardening of F/M steel. There is an interaction between the two elements. C-Cr has a strong synergistic mechanism, which will cause serious hardening when the content is higher than 0.05% and the Cr content is higher than 10%. Cr-Si has a strong antagonistic mechanism, which can achieve the comprehensive irradiation hardening effect in the 9Cr-0.8Si combination. N-Mn needs N controlled lower than 0.01%. Mo-W needs to control Mo content higher than 0.5% to alleviate irradiation hardening. There is a weak synergistic effect in Si-V; when the content is between 0.3% and 0.8% and the V content is between 0.2% and 0.3%, it can assist in optimizing the composition of F/M steel. Through the optimization of multi-element combination, the composition of F/M steel with lower irradiation hardening can be designed. Full article

ZrB₂-HfB₂ composites allow us to obtain materials characterized by the high chemical resistance characteristic of HfB₂ while reducing density and improving sinterability due to the presence of ZrB₂. Since boride composites are difficult-to-sinter materials. One way to achieve high density during sintering is to add phases that activate mass transport processes and, after sintering, remain as composite components that do not degrade and even improve some properties of the borides. The following paper is a comprehensive review of the effects of various and the most commonly used sintering aids, i.e., SiC, B₄C, WC, MoSi₂, and CrSi₂, on the thermo-chemical properties of the ZrB₂-HfB₂ composites. High-density composites with a complex phase composition dominated by (Zr,Hf)B₂ solid solutions were obtained using a hot pressing method. The tests showed differences in the properties of the composites due to the type of sintering additives used. From the point of view of the thermo-chemical properties, the best additive was silicon carbide. The composites containing SiC, when compared to the initial, pure borides, were characterized by high thermal conductivity λ (80–150 W/m·K at 20–1000 °C), a significantly reduced thermal expansion coefficient (CTE ~6.20 × 10⁻⁶ 1/K at 20–1000 °C), and considerably improved oxidation resistance (up to 1400 °C). Full article

This study presents the first investigation into the application of ultrasonic vibration-assisted electrical discharge machining (UV-EDM) using graphite electrodes for external cylindrical surface machining—an essential surface in the production of tablet punches and sheet metal-forming dies. A custom ultrasonic horn was designed and fabricated using 90CrSi material to operate effectively at a resonant frequency of 20 kHz, ensuring stable vibration transmission throughout the machining process. A Box–Behnken experimental design was employed to explore the effects of five process parameters—vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and servo voltage (SV)—on two key performance indicators: material removal rate (MRR) and surface roughness (Ra). The optimization process was conducted in two stages: single-objective analysis to maximize MRR while ensuring Ra < 4 µm, followed by a hybrid multi-objective approach combining NSGA-II and the Analytic Hierarchy Process (AHP). The optimal solution achieved a high MRR of 9.28 g/h while maintaining Ra below the critical surface finish threshold, thus meeting the practical requirements for punch surface quality. The findings confirm the effectiveness of the proposed horn design and hybrid optimization strategy, offering a new direction for enhancing productivity and surface integrity in cylindrical EDM applications using graphite electrodes. Full article

The optical floating zone technique was utilized to purify chromium and a single-phase chromium–silicon alloy in this work. The impurity content (carbon, nitrogen, and oxygen) can be reduced by decreasing the withdrawal speed of samples during the zone refining process, and the coarsening of grains was also observed. The effect of the impurities on mechanical properties was determined by hardness measurements at room temperature, and the hardness of both chromium and the chromium–silicon alloy decreased with lower concentrations of nitrogen and oxygen. In contrast, brittle material behavior is observed in samples prepared by arc melting process with higher concentrations of impurities. To use chromium–silicon alloys for future high-temperature applications, their brittle behavior must be improved, which can be achieved by reducing their carbon, nitrogen, and oxygen concentrations. Full article

Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes. Full article

Police officers work in high-stress environments that demand emotional resilience, interpersonal skills, and effective communication. Occupational stress can negatively impact their motivation, conflict resolution abilities, and professional effectiveness. Loving-Kindness Meditation (LKM), a mindfulness-based intervention focused on cultivating compassion and empathy, has shown promise in enhancing prosocial attitudes and emotional regulation. With the rise of short video platforms, digital interventions like video-guided LKM may offer accessible mental health support for law enforcement. This study examines the effects of short video app-guided LKM on police officers’ mindfulness, public service motivation (PSM), conflict resolution skills (CRSs), and communication skills (CSSs). It aims to determine whether LKM can enhance these psychological and professional competencies. A randomized controlled trial (RCT) was conducted with 110 active-duty police officers from a metropolitan police department in China, with 92 completing the study. Participants were randomly assigned to either the LKM group (n = 46) or the waitlist control group (n = 46). The intervention consisted of a 6-week short video app-guided LKM program with daily 10 min meditation sessions. Pre- and post-intervention assessments were conducted using several validated scales: the Mindfulness Attention Awareness Scale (MAAS), the Public Service Motivation Scale (PSM), the Conflict Resolution Styles Inventory (CRSI), and the Communication Competence Scale (CCS). A 2 (Group: LKM vs. Control) × 2 (Time: Pre vs. Post) mixed-design MANOVA was conducted to analyze the effects. Statistical analyses revealed significant group-by-time interaction effects for PSM (F(4,177) = 21.793, p < 0.001, η² = 0.108), CRS (F(4,177) = 20.920, p < 0.001, η² = 0.104), and CSS (F(4,177) = 49.095, p < 0.001, η² = 0.214), indicating improvements in these areas for LKM participants. However, no significant improvement was observed for mindfulness (F(4,177) = 2.850, p = 0.930, η² = 0.016). Short video app-guided LKM improves public service motivation, conflict resolution skills, and communication skills among police officers but does not significantly enhance mindfulness. These findings suggest that brief, digitally delivered compassion-focused programs can be seamlessly incorporated into routine in-service training to strengthen officers’ prosocial motivation, de-escalation competence, and public-facing communication, thereby fostering more constructive police–community interactions. Full article

The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed. Full article

The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing strategies for grid-connected PV–battery systems often fail to effectively handle bidirectional power flow between EVs and the grid, particularly in scenarios requiring seamless transitions between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. This paper presents a novel neural network-based model predictive control (NN-MPC) approach for optimizing energy management in a grid-connected PV–battery–EV system. The proposed method combines neural networks for forecasting PV generation, EV load demand, and grid conditions with a model predictive control framework that optimizes real-time power flow under various constraints. This integration enables intelligent, adaptive, and dynamic decision making across multiple objectives, including maximizing renewable energy usage, minimizing grid dependency, reducing transient responses, and extending battery life. Unlike conventional methods that treat V2G and G2V separately, the NN-MPC framework supports seamless mode switching based on real-time system status and user requirements. Simulation results demonstrate a 12.9% improvement in V2G power delivery, an 8% increase in renewable energy utilization, and a 50% reduction in total harmonic distortion (THD) compared to PI control. The results highlight the practical effectiveness and robustness of NN-MPC, making it an effective solution for future smart grids that require bidirectional energy management between distributed energy resources and electric vehicles. Full article

This article presents the results of laboratory studies on the smelting of nickel–chromium-containing ferroalloys from low-grade nickel ores from Kazakhstan. X-ray phase analysis was performed on raw materials, which included quartz, nontronite, chromium metahydroxide, goethite, magnetite, iron chromite, and nickel (II) silicate. The reduction reactions of metal oxides with carbon and carbon monoxide were studied as the temperature increased. Experimental smelting was carried out in a Tammann furnace at 1500–1550 °C using three types of reducing agent: RK coke, as well as its mixtures with low-ash Shubarkol coal, in ratios of 75:25 and 50:50. The second option demonstrated the highest economic efficiency, achieving a 91% nickel recovery rate, reduced coke consumption, and a slag-to-metal ratio of 3.07. Chemical analysis showed that the nickel content in the obtained alloys ranged from 2.5% to 6.5%, while chromium content ranged from 2.6% to 4.5%. X-ray phase analysis confirmed the presence of Fe₂Ni_0.6Si, Fe₅Si₃, and Fe₂CrSi phases in the alloy structure. Local element concentrations varied within the following ranges: Fe—55–59%, Ni—2–10%, Cr—2–7%, and Si—29–35%. The results of this study confirmed the feasibility of producing a nickel–chromium-containing alloy with a nickel content of 2–10% and a chromium content of 2–7%. Full article

Medium-entropy alloys (MEAs) allow the formation of different phases, generally in a solid-solution state, and compounds that favor obtaining alloys with properties superior to those of conventional alloys. In this study, medium-entropy CuNiSiCrCoTiNbx alloys were fabricated via melting in a vacuum induction furnace. The influence of the Nb addition (X = 0, 0.5 and 1 wt%) alloying elements on the microstructure, hardness, and wear resistance of the CuNiSiCrCoTiNb₀ (M1), CuNiSiCrCoTiNb_0.5 (M2), and CuNiCoCrSiTiNb₁ (M3) alloys were explored using X-ray diffraction (XRD), scanning electron microscopy (SEM), and a ball-on-disc tribometer, respectively. In general, the results indicated that the incorporation of Nb alloying element promoted the evolution of the microstructure, increased the hardness, and improvement of the wear resistance. The XRD and SEM findings demonstrate that higher Nb addition and aging heat treatment (AT) modification mainly favored the formation of dendritic regions and the precipitation of the Co₂Nb, Cr₃Si, and Ni₂Si phases, which promoted the refinement and strengthening of the microstructure. Significant increases in hardness were recorded: 11.95% increased, promoted by the addition of Nb before (E1) and after (E2, E3, and E4) the heat treatments. The maximum hardness values recorded were 92 ± 0.11 (AC) and 103 ± 0.5 HRB (AT-60 min) for the M3 alloy. The increase in hardness caused by Nb addition and aging heat treatments contributed to the dry sliding wear resistance response, decreasing material loss by 20%. This was related to the high concentration of precipitated phases rich in CoNb, CrSi, and NiSi with high hardness. Finally, the M3 alloy aged for 60 min exhibited the best specific wear rate behavior, with a material loss of 1.29 ${\mathrm{m}\mathrm{m}}^3$. The commercial Cu-Be C17510 alloy experienced a maximum hardness of 83.47 Hardness Rockwell B, HRB, and a high wear rate of 3.34 mm³. Full article

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories. Full article

The utilization of magnesium alloys as lightweight structural materials is becoming increasingly prevalent, particularly within the fields of electronics, automotive engineering, and defense. These alloys display high specific strength and excellent heat dissipation properties. The magnesium–zinc–rare earth alloy ZE52 displays superior formability and strength-ductility when compared to conventional magnesium alloys. A CrSiN film was deposited on the surface using a sputtering technique with the objective of enhancing wear and corrosion resistance for industrial applications. A CrSi buffer layer was deposited onto the ZE52 substrate prior to the deposition of the CrSiN film, with the objective of enhancing the adhesion between the two materials. The sputtering process for CrSiN films entailed the modulation of the substrate bias voltage. The CrSiN films exhibited a nanocomposite structure comprising CrN nanocrystallites embedded within an amorphous Si₃N₄, which resulted in enhanced hardness. Upon adjusting the bias voltage, improvements in mechanical properties were observed, with the film hardness and Young’s modulus increasing to 16.5 GPa and 187.4 GPa, respectively. Among the various CrSiN coatings under investigation, the ZE52 alloy that was coated with a CrSiN film deposited at a bias voltage of −50 V and a substrate temperature of 250 °C demonstrated the most favorable performance, exhibiting the lowest wear rate and superior corrosion resistance. In the tungsten carbide wear test with a loading of 4 N, the coating exhibited the lowest wear rate, at 2.2 × 10⁻⁶ mm³·m⁻¹·N⁻¹. Furthermore, the coating demonstrated remarkable corrosion resistance in a 3.5% NaCl solution, displaying a corrosion current density of 1.23 μA·cm⁻² and a polarization resistance of 1271.4 Ω·cm⁻². Full article

Extreme natural hazard events are increasing across the globe, compelling increased climate research on resiliency. Research concerning issues as integrative as climate change and natural hazard resiliency often requires complex methodologies to account for cumulative influences. Indicators can be used to parse complex data to assess the intersection of inputs and outcomes (i.e., cumulative impacts). The Climate Resilience Screening Index (CRSI) is a good example of an indicator framework as it integrates indicators and their associated metrics into five domains (e.g., natural environment, society, and risk), enabling the index to accommodate a variety of inputs in its assessment of resilience. Indicator research, however, is generally limited by the availability of pertinent data. Natural hazard data concerning exposure, loss, and risk are routinely collected by the Federal Emergency Management Agency (FEMA) to create and update the National Risk Index (NRI), a composite index. The NRI can be disaggregated to obtain individual underlying metrics about natural hazard exposure. Quantifying natural hazard exposure requires extensive computation, with each hazard type requiring multiple modifying considerations, such as meteorological adjustments made by subject matter experts. Commonly available natural hazard exposure data, like that from FEMA, combines the spatial extent of historical natural hazard events and the determined value of the affected area. Exposure-related data were retrieved from the National Risk Index and used to create a new composite value to represent only the spatial extent of natural hazard events. Utilizing this new methodology to represent natural hazard exposure alleviates the burden of complex computation. It allows exposure data to be more expeditiously integrated into research and indices relating to natural hazards. Full article

This study aimed to validate the psychometric properties of the Conflict Resolution Styles Inventory (CRSI) within the context of the university population in Ecuador. The CRSI measures how individuals manage interpersonal conflicts, a critical skill for university students. A sample of 746 university students from various institutions across Ecuador participated in the study. The CRSI, which categorizes conflict resolution styles into five types (competing, avoiding, accommodating, collaborating, and compromising), was translated and culturally adapted for the Ecuadorian context. Psychometric analyses, including factor analysis and reliability testing, were conducted to assess the validity and reliability of the inventory. The factor analysis supported the five-factor structure of the CRSI, confirming that the inventory is suitable for measuring distinct conflict resolution styles in this population. The inventory showed good internal consistency, with Cronbach’s alpha values exceeding 0.70 for all subscales. Additionally, the test–retest reliability indicated stability over time. The validated CRSI provides a robust instrument for understanding and improving conflict resolution skills among university students in Ecuador, contributing to better interpersonal relationships and academic environments. Full article

A novel TiB₂-CeO₂-modified (Nb,Mo,Cr,W)Si₂ coating was prepared on a Nb-5W-2Mo-1Zr alloy substrate using two-step slurry sintering and halide-activated pack cementation to address the limitations of a single NbSi₂ coating in meeting the service requirements of niobium alloys at elevated temperatures. At 1700 °C, the static oxidation life of the coating exceeded 20 h, thus indicating excellent high-temperature oxidation resistance. This was due to the formation of a TiO₂-SiO₂-Cr₂O₃ composite oxide film on the coating surface, which, due to low oxygen permeability, effectively prevented the inward infiltration of oxygen. Additionally, the dense structure of the composite coating further enhanced this protective effect. The composite coating was able to withstand over 1600 thermal shock cycles from room temperature to 1700 °C, and its excellent thermal shock performance could be attributed to the formation of MoSi₂, CrSi₂, and WSi₂ from elements such as Mo, Cr, and W, which were added during modification. In addition to adjusting the difference in thermal expansion coefficients between the layers of composite coatings to reduce the thermal stress generated by thermal shock cycles, the formation of silicide compounds also improved the overall fracture toughness of the coating and thereby improved its thermal shock resistance. Full article