Search Results (13,453)

Comminution is the most energy-intensive stage in mineral processing and a major source of indirect greenhouse gas (GHG) emissions in mining. This study evaluates the impact of Ultra-High-Intensity Blasting (UHIB) on downstream comminution energy demand and associated GHG emissions under conditions representative of large-scale Chilean mining. Fragmentation from conventional blasting and UHIB was simulated using JKSimBlast, and the resulting particle size distributions were used as input for four comminution circuit configurations modeled in JKSimMet. Two ore hardness scenarios were analyzed: hard ore (Bond Work Index, BWI = 19 kWh/t) and soft ore (BWI = 11 kWh/t). Power draw of crushers and mills was used to estimate specific energy consumption and GHG emissions based on the Chilean electrical system emission factor. Results show that UHIB enables significant reductions in comminution energy demand, reaching approximately 18% for hard ore and over 30% for soft ore. These reductions are primarily associated with circuit simplification, including the removal of energy-intensive stages such as primary crushing and SAG milling. The results demonstrate that improved fragmentation can reduce downstream energy demand and carbon intensity, highlighting UHIB as an effective mine-to-mill strategy for energy efficiency and emission reduction. Full article

This study presents a headset-type biofluorometric gas sensor incorporating a CMOS camera for continuous, non-invasive monitoring of transcutaneous ethanol from the ear canal. The sensor employs alcohol dehydrogenase (ADH) to catalyze the NAD⁺-to-NADH conversion during ethanol oxidation, enabling quantitative measurement through NADH fluorescence detection (λ_ex = 340 nm, λ_em = 490 nm). The integrated system comprises a wireless CMOS camera, an ADH-immobilized cotton mesh enzyme membrane, UV-LED excitation source, optical bandpass filters, and a dual convex lens assembly housed in a 3D-printed headset powered by a lithium battery. Key improvements include a 3.5-fold enhancement in fluorescence collection efficiency achieved through optimized dual convex lens configuration. Systematic screening of seven cotton mesh materials identified Iwatsuki cotton mesh as the optimal enzyme immobilization substrate, exhibiting minimal autofluorescence and 14.2-fold higher water retention capacity compared to H-PTFE membranes. The glutaraldehyde-crosslinked ADH-immobilized cotton mesh maintained enzymatic activity for over 45 min with a 10-fold improvement in signal-to-noise ratio. The system demonstrated a dynamic detection range spanning 10 ppb to 10 ppm for gaseous ethanol and exhibited high selectivity against interfering volatile organic compounds in skin gas, including methanol, acetaldehyde, formaldehyde, and acetone. Human experiments validated the system’s practical performance. Following alcohol consumption, subjects wore the device for 50 min while real-time fluorescence monitoring captured dynamic ethanol concentration changes in the ear canal. The dose-dependent fluorescence response—approximately 2-fold higher at 0.4 g/kg versus 0.04 g/kg alcohol intake—correlated well with calibration data. This headset-type biofluorometric sensor enables unrestrained continuous monitoring of ear canal ethanol, providing a novel wearable platform for alcohol metabolism assessment with potential applications in health monitoring and clinical research. Full article

Laser surface hardening is a technique that improves various mechanical characteristics of different materials. The methods are being extensively used in the automobile, aerospace, tool manufacturing, and construction industries for various components. The present review highlights the hardness and hardened surface depth improvement of different steels and non-ferrous alloys in as-bought and pre-heat treatment conditions. Diode and fibre lasers have rendered higher surface hardness and hardened depth, while consuming higher power. Nd:YAG lasers have resulted in a precise increase in hardness and a very minimal 0.8 in ferrous and 2 mm in surface-hardened depth of non-ferrous alloys, proving a better efficiency. The pre-heat treatments are selected to enhance mechanical properties and reduce the deformations and defects. An increase of 300.43 and 282.38% of surface hardness due to laser hardening as compared to the core material of AISI 420 was observed using a high-power diode laser. A huge 281.41% of increase in surface hardness was observed for ICD-5 tool steel using Nd:YAG lasers. The annealing pre-heat treatment has also affected the hardenability, resulting in high hardness. Non-ferrous alloys such as titanium and A356 alloys have recorded 200 and 125% increase in surface hardness compared to their core using Nd:YAG lasers. Full article

Silicon carbide (SiC) MOSFETs are promising for high-efficiency, high-power-density power conversion owing to their high breakdown capability, fast switching speeds, and low switching losses. However, parasitic parameters can cause severe voltage/current overshoot and oscillation during high-speed switching, leading to electromagnetic interference and degraded performance. To address this issue, this study analyzes the mechanisms of current overshoot during turn-on and voltage overshoot during turn-off, and presents an adaptive active gate driver chip based on a three-stage driving current control strategy. By identifying key switching intervals and regulating segmented gate-drive current, the proposed chip can effectively suppress overshoot while reducing the switching loss. During turn-on, cross-cycle switching point regulation based on Miller plateau tracking is proposed to achieve adaptive control under different operating conditions, while the turn-off control is realized by peak sampling of the drain–source voltage. The chip was fabricated in the 180 nm BCD process. Compared with a conventional passive driver, the proposed driver reduces turn-on loss by 35.1% at 400 V/40 A under a dv_DS/dt of 4.8 V/ns and reduces turn-off loss by 33.2% under a v_DS overshoot of nearly 50 V. These results show that the proposed chip improves SiC MOSFET switching performance and provides a practical gate-driving solution. Full article

The increasing digitalization of electrical substations, enabled by IEC 61850-based architectures, has improved operational efficiency while expanding the cyber attack surface. This paper introduces a standards-aligned cybersecurity risk mitigation model specifically designed for digital substations and mapped to representative attack scenarios. The model integrates preventive, detective, and application-level controls derived from NIST SP 800-82r3, IEC 62443, and ISO/IEC 27019, and is validated in a laboratory process-bus environment. A baseline risk assessment identified four high-risk scenarios in the studied digital substation architecture. For validation, a selected subset of controls was experimentally evaluated against two representative attack vectors, namely false data injection (FDI) on GOOSE messages and denial-of-service (DoS) against PTP synchronization. For the remaining scenarios, the post-mitigation effects were reassessed analytically based on control coverage, architectural exposure, and standards-aligned cybersecurity reasoning. The experimental validation demonstrated that both empirically tested high-risk scenarios (FDI on GOOSE and DoS on PTP) were effectively mitigated, reducing their residual risk to moderate and low levels, respectively. For the remaining two scenarios, a post-mitigation analytical reassessment based on control coverage and architectural exposure suggested a consistent risk reduction trend, although without direct experimental confirmation. Under this combined empirical–analytical assessment, the number of high-risk scenarios decreased from four to one, corresponding to a 50% experimentally validated reduction in high-risk exposure, complemented by an analytical reassessment of the remaining scenarios. These results provide quantitative evidence about the effectiveness of the model, even with partial implementation. The scientific contribution of this study lies in integrating multistandard cybersecurity requirements into an operational mitigation model tailored to IEC 61850 substations, combined with experimental risk quantification in a realistic process-bus testbed. The proposed model offers practical guidance for utilities and establishes a scalable foundation for advancing cybersecurity in critical power infrastructure. Full article

The production of green hydrogen via aqueous electrolysis of hydrogen sulfide (H₂S) holds significant potential to address challenges related to sustainable energy generation and environmental protection. The electrocatalytic splitting of water polluted with highly toxic H₂S is attractive for industrial applications because the process: (i) is less power-consuming than direct thermal H₂S decomposition; (ii) achieves high Faradaic efficiencies for hydrogen production; and (iii) yields elemental sulfur as an added-value by-product. This review covers a brief discussion on sulfide-containing water sources and electrochemical methods for hydrogen production from H₂S, specifically Direct, Indirect, and Electrochemical Membrane Reactor (EMR) systems. To become commercially and economically attractive, these approaches require improvements in electrolysis efficiency through the development of low-cost electrode materials that are resistant to sulfur poisoning and corrosion, while possessing high catalytic activity, enhanced stability, and durability. Early research focused on carbon-based materials combined with noble metal oxides, transition metal compounds, and related materials. Since their practical performance is limited, investigations have shifted toward nanostructured electrocatalysts with unique crystal structures and designs, which show significantly improved efficiency for H₂S electrolysis. This review highlights the potential of H₂S electrolysis for hydrogen production, giving special attention to recent advancements in electrode materials. Full article

To address the issue of traditional bistable vibration energy harvesters (BVEHs) being prone to becoming trapped in a single potential well—which results in a narrowed energy harvesting bandwidth and reduced efficiency—this paper proposes a method that utilizes the nonlinear electromagnetic force generated during the induction process to modulate the kinematic behavior of the oscillator. The characteristics and influencing factors of the nonlinear force produced during electromagnetic induction are analyzed. A dual-cantilever beam structure is designed, with an iron-core coil and a magnet placed at the respective free ends. A mathematical model of a piezoelectric–electromagnetic coupled bimodal broadband vibration energy harvester is established and numerically simulated. Furthermore, a vertical vibration experimental platform is constructed to conduct frequency sweep tests. The experimental results demonstrate that the proposed piezoelectric–electromagnetic coupled bimodal broadband vibration energy harvester effectively improves energy harvesting efficiency. Within the frequency range of 5–20 Hz, the system exhibits two vibration modes, with resonant frequencies of approximately 7.7 Hz and 15.7 Hz. For a single-layer PVDF piezoelectric film, the maximum output power at the first and second resonance points is 8.9 μW and 9.7 μW, respectively. The electromagnetic module achieves maximum output powers of 0.39 W and 0.71 W. Moreover, within the frequency ranges of 6.3–9.8 Hz and 14–17.7 Hz (a total bandwidth of 7.2 Hz), the device maintains a stable power output. The effective bandwidth is broadened by approximately 80%, demonstrating excellent broadband performance. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control. Full article

Virtual synchronous generator control-based permanent magnetic synchronous generators (VSG-PMSGs) have been widely used for their stable operation in a weak grid and strong voltage and frequency support capacity. However, VSG-PMSGs have complex and time-varying dynamics due to control strategy switching, current limiters, and saturations. Additionally, they are prone to transient angle instability during voltage faults. A dynamic analysis method for VSG-PMSGs considering low-voltage ride-through (LVRT) control is proposed in this paper. First, an improved LVRT control strategy based on active power reference reduction and virtual electromagnetic force (EMF) reset is introduced to mitigate the instability risk of VSG-PMSGs. Then, the mechanisms by which initial power and fault voltages influence the dynamic responses are revealed. The dynamics of VSG-PMSGs under different conditions are classified into four types according to the current and EMF limiters’ state. To predict VSG-PMSG dynamics, we propose a method based on fault steady-state power flow for calculating the fault voltage. Using this approach, fault voltage dips in VSG-PMSGs within a wind farm are computed with an error of less than 0.002 p.u., and the dynamic behavior of each unit is accurately predicted within 10 s. To verify the validity of the proposed method, simulations were conducted across diverse scenarios. The results demonstrate that this method enables accurate and computationally efficient prediction of VSG-PMSG fault dynamics. Full article

L605 cobalt-based superalloy is a typical difficult-to-machine material because of its high strength, pronounced work hardening, and low thermal conductivity. To improve the microgroove machining performance of this alloy, a self-developed water-jet-guided laser (WJGL) system equipped with a multi-focus lens was employed, and single-factor experiments together with a Box–Behnken response surface design were conducted to investigate the effects of laser power, pulse frequency, water pressure, and feed speed on microgroove depth. The results showed that microgroove depth increased with laser power, decreased with pulse frequency and feed speed, and first increased and then decreased with water pressure. Analysis of variance demonstrated that the developed quadratic regression model was significant and fit the data well. A recommended parameter combination of 274.9 W laser power, 3334.9 Hz pulse frequency, 1.636 MPa water pressure, and 0.107 mm/s feed speed corresponded to a predicted microgroove depth of 621.2 μm. Validation experiments yielded an average microgroove depth of 600.2 μm, with a relative error of 3.4%, indicating that the model can be used for microgroove depth prediction and parameter selection in WJGL machining of L605 alloy and may provide guidance for future multi-objective optimization considering both machining quality and efficiency. Full article

Electrocatalytic water splitting powered by renewable energy is a promising route for sustainable hydrogen production. Rather than developing separate catalysts for HER and OER, recent efforts focus on multifunctional electrocatalysts that can efficiently drive both reactions, simplifying system design and improving efficiency. A major limitation of conventional water splitting is the high overpotential and low-value oxygen production in OER. To overcome this, hybrid water splitting replaces OER with more valuable oxidation reactions, such as pollutant degradation or organic upgrading, enhancing overall energy and economic efficiency. This review covers the fundamentals of water splitting and highlights key physicochemical techniques for probing electrocatalyst activity, particularly structural reconstruction under operating conditions. It evaluates noble-metal, nonprecious-metal, and metal-free nanocarbon catalysts in both acidic and alkaline media, with emphasis on their roles in alternative anodic reactions. Finally, it outlines current challenges and future directions for developing efficient, durable, and sustainable electrocatalysts for advanced hydrogen production systems. Full article

In order to achieve more accurate and efficient oil-paper insulation aging assessment, and to improve the operation and maintenance level of oil-paper insulated power equipment, this paper proposes an aging evaluation method of oil-paper insulation based on Raman spectrum and frequency-domain spectroscopy. First, oil-paper insulation samples with different aging degrees were prepared by an accelerated thermal aging test in this experiment. Then, Raman spectroscopy and frequency-domain dielectric spectroscopy were used to examine the samples and analyze the aging characteristics of the samples by LightGBM R2019b. Finally, the gray neural network is used to establish a prediction model for the degree of polymerization of insulating paper based on frequency-domain dielectric features and Raman spectral features. The results of this study showed that there is a certain correlation between the Raman characteristics of insulating oil and the FDS characteristics of insulating paper. The average absolute error of the prediction of the R-F-PGNN model developed in this paper is 20.4. The research in this paper provides a strong support for the development of Raman spectroscopy diagnosis technology for oil-paper insulation aging in the power industry, which has certain academic value and engineering application significance. Full article

Short-term wind power forecasting is considered a critical challenge in smart grid management due to the nonlinear, unstable, and multi-scale noise characteristics of wind signals. Although recent advances in hybrid deep learning have improved the accuracy of short-term wind power forecasting, many state-of-the-art models usually consider signal denoising, residual decomposition, and attention mechanisms as independent modules without providing a unified solution. This paper proposes an end-to-end solution, W-HiTS-Attention (Wavelet Transform, N-stacked Hierarchical Interpolation for Time Series, Attention), which coherently integrates wavelet denoising, hierarchical residual learning from N-HiTS (Neural Hierarchical Interpolation), and an in-block self-attention mechanism. The proposed solution outperforms 21 benchmarks in accuracy, including state-of-the-art baselines such as N-BEATS, N-HiTS, TCN, Informer, Autoformer, LSTM, BiLSTM, GRU, and Prophet, achieving an RMSE of 55.56 W and an R² of 0.9918. Furthermore, the results show that the proposed solution is efficient in terms of parameter count (0.033M), latency (0.0036 ms/sample), and training time, making it promising for low-latency inference in resource-constrained environments. The results show that the coherent integration of frequency preprocessing, hierarchical residual forecasting, and attention-based temporal refinement provides a robust, explainable, and deployable solution for short-term wind power forecasting. Full article

This study develops a physics-informed digital twin framework for quasi-real-time assessment and forecasting of marine plate heat exchanger performance under variable environmental and operational conditions. Unlike conventional steady-state or purely data-driven approaches, the proposed framework integrates first-principles thermohydraulic modeling, an iterative successive-approximation solver, and continuous synchronization with operational ship data, enabling adaptive state estimation and degradation tracking. The methodology explicitly accounts for coupled thermal, hydraulic, and fouling processes, and incorporates uncertainty-aware validation under real ship operating conditions. A case study based on a central cooling system of a cargo vessel demonstrates that seawater temperature variations of 3–4 K can induce nonlinear system responses, including up to a fourfold increase in coolant demand, a 10–15% reduction in heat-transfer efficiency, and a 15–25% rise in hydraulic losses. A threshold operating regime is identified, characterized by rapid degradation and fouling amplification. Comparative analysis against a static baseline model shows that the digital twin improves predictive accuracy and enables early detection of performance deterioration. Energy-efficiency assessment indicates that adaptive cooling control supported by the digital twin can reduce auxiliary power demand and contribute to fuel savings. The proposed framework provides a scalable foundation for predictive maintenance and intelligent thermal management in maritime systems. Full article

Background: Corneal organoids derived from pluripotent stem cells have emerged as powerful tools for studying corneal development, disease modeling, and regenerative medicine applications. While previous protocols have successfully generated corneal tissue structures, there remains a need for three-dimensional models that recapitulate the complex cellular architecture and diversity of native human cornea. Methods: We developed a modified spontaneous three-dimensional corneal organoid model using human embryonic stem cells (hESCs) through an adapted Self-formed Ectoderm Autonomous Multi-zone (SEAM) protocol. hESCs were cultured as spheroids in ultra-low-binding plates under normoxic conditions and differentiated over 7–8 weeks. Organoids were characterized using immunofluorescence staining for corneal-specific markers and single-cell RNA sequencing to assess cellular composition and gene expression patterns. Results: Approximately 20% of organoids developed transparent regions characteristic of corneal tissue by day 30 of differentiation. Immunofluorescence analysis revealed spatially organized expression of corneal markers, including ZO-1 and E-cadherin in the outermost epithelial layers, P63α-positive putative limbal stem cells at the epithelial–stromal interface, vimentin-positive stromal cells in the interior, and laminin-1 deposition that suggests Bowman’s membrane formation. The organoids expressed cornea-specific keratins (K3, K12, and K15) and the master regulator PAX6 in appropriate cellular compartments. Single-cell RNA sequencing identified 18 distinct cell clusters, including three corneal epithelium subclusters with differential expression of MUC16, KRT12, and ΔNp63α, two stromal populations with distinct inflammatory profiles, and a corneal endothelium cluster. Transcriptomic analysis confirmed expression of key corneal genes, including AQP3, CDH1, multiple keratins, mucins, and extracellular matrix components (HAS2, CD34, CD44, COL8A1, and KERA). Conclusions: This three-dimensional spheroid-based putative corneal organoid model successfully recapitulates the multilayered architecture and cellular diversity of human cornea, including stratified epithelium, putative limbal stem cells, stroma, and endothelium in spatially appropriate arrangements. The model demonstrates molecular signatures consistent with native corneal tissue and provides a valuable platform for studying corneal development, disease mechanisms, and potential therapeutic applications. Future optimization to improve organoid formation efficiency and functional maturation will enhance the utility of this system for both basic research and translational medicine. Full article