Search Results (3,298)

This work addresses the challenges of inadequate data acquisition and the limited availability of labeled samples for shearer rocker arm bearing faults by developing a data augmentation methodology that synergistically incorporates the Genetic Algorithm-optimized Wavelet Transform Symmetrical Dot Pattern (GA-WT-SDP) with a Wasserstein Conditional Generative Adversarial Network (WCGAN). In the initial step, the Genetic Algorithm (GA) is employed to refine the mapping parameters of the Wavelet Transform Symmetrical Dot Pattern (WT-SDP), facilitating the transformation of raw vibration signals into advanced and discriminative graphical representations. Thereafter, the Wasserstein distance in conjunction with a gradient penalty mechanism is introduced through the WCGAN, thereby ensuring higher-quality generated samples and improved stability during model training. Experimental results validate that the proposed approach yields accelerated convergence and superior performance in sample generation. The augmented data significantly bolsters the generalization ability and predictive accuracy of fault diagnosis models trained on small datasets, with notable gains achieved in deep architectures (CNNs, LSTMs). The research substantiates that this technique helps overcome overfitting, enhances feature representation capacity, and ensures consistently high identification accuracy even in complex working environments. Full article

Spinel LiNi_0.5Mn_1.5O₄ (LNMO) has emerged as a highly competitive cobalt-free cathode material for higher-energy-density lithium-ion batteries. However, its practical application is hindered by severe capacity degradation, particularly under high-voltage operation. To solve this problem, we put forward a surface modification strategy employing a Li_0.4La_0.54TiO₃ (LLTO) coating. The LLTO coating forms a protective cathode–electrolyte interphase that helps to inhibit interfacial side reactions, enabling enhanced electrochemical performance up to 5 V. As a result, the optimized 1 wt% LLTO-coated LNMO exhibits a remarkable capacity retention of 96.5% after 200 cycles at 0.1 C and delivers a high-rate capacity of 103.5 mAh g⁻¹ at 2 C, significantly outperforming its pristine counterpart (86.8% and 89.6 mAh g⁻¹, respectively). This work provides a viable and efficient surface modification approach for achieving robust high-voltage LNMO cathode material, underscoring its great potential for next-generation energy storage systems. Full article

Parallel laying of high-voltage cables will generate a zero-sequence current, due to spatial electromagnetic induction, which reduces the cable’s current-carrying capacity, causing heating and corrosion of the grounding points and deteriorating grounding performance. Currently, there is a lack of effective control measures. This article establishes a calculation model for the cable sheath current under the condition of double circuit cable cross interconnection grounding, analyzes the causes of a zero-sequence grounding current in a double circuit cable sheath, and proposes an optimal phase sequence selection method, considering load changes with the goal of maximizing the probability of the cable sheath current, not exceeding the standard. The results show that when the double circuit cable is evenly distributed in the cross interconnection section, the zero-sequence grounding current will be generated on the metal sheath of the cable, causing an excessive total grounding current. By applying the proposed probability-based phase-sequence optimization, the likelihood that both circuits simultaneously satisfy the sheath-current criterion can be significantly improved; for example, under representative layouts and load distributions, the “both-within-limit” probability can reach 53.3% (horizontal layout), 76.2% (horizontal equilateral triangle layout), 90.5% (vertical layout), and 81.6% (vertical equilateral triangle layout). For different working conditions, selecting the optimal load phase sequence combination by maximizing the probability of the sheath current and not exceeding the standard within the current carrying area can help to reduce the cable sheath current. Full article

Chinese local governments have long financed public services through land-sale revenues. The shift from selling undeveloped land to redeveloping existing urban areas has disrupted this traditional financing model, exposing a critical tension between the pursuit of immediate revenue and the assurance of long-term fiscal health. The continued dependence on land-based finance has led many local governments to overlook long-term public service obligations and the long-term operating deficits associated with intensive urban development. Thus, by examining the relationship between the development rights allocation and the sustainable fiscal capacity of the government, the study evaluates both short-term revenue generation and long-term expenditure commitments in urban redevelopment contexts. However, existing research has yet to provide actionable tools to reconcile this structural mismatch between short-term revenues and long-term liabilities. We employ a comprehensive analytical framework that integrates fiscal impact modeling with the optimization of development rights allocation. Based on this framework, we construct a quantitative, dual-period fiscal balance model using mathematical programming to analyze various combinations of land development rights supply strategies for achieving fiscal equilibrium. Our results identify multiple feasible supply combinations that can maintain fiscal balance while supporting sustainable urban development. The findings demonstrate that strategic development rights allocation functions as an effective tool for balancing short-term revenue needs with long-term obligations in local land finance systems. Our study contributes to establishing a sustainable land finance framework, particularly for jurisdictions lacking comprehensive land value capture mechanisms. The proposed approach offers an alternative to traditional land rights transfer models and provides guidance for avoiding long-term fiscal distress caused by excessive land transfer. The framework supports more sustainable urban redevelopment financing while maintaining fiscal responsibility across temporal horizons. Full article

Compound meta-optics, characterized by the unprecedented complex optical architectures containing single or multiple meta-optics elements, has emerged as a powerful paradigm for overcoming the physical limitations of single-layer metasurfaces. This review systematically examines the recent progress in this burgeoning field, primarily focusing on the development of high-performance optical systems for imaging, display, sensing, and computing. We first focus on the design of compound metalens architectures that integrate metalenses with additional elements such as iris, refractive optics, or other meta-optics elements. These configurations effectively succeed in providing multiple high-quality image quality metrics simultaneously by correcting monochromatic and chromatic aberrations, expanding the field of view, enhancing overall efficiency, and so on. Thus, the compound approach enables practical applications in next-generation cameras and sensors. Furthermore, we explore the advancement of cascaded metasurfaces in the realm of wave-optics, specifically for advanced meta-holography and optical computing. These multi-layered systems facilitate complex wavefront engineering, leading to significant increases in information capacity and functionality for security and analog optical computing applications. By providing a comprehensive overview of fundamental principles, design strategies, and emerging applications, this review aims to offer a clear perspective on the pivotal role of compound meta-optics in devising and optimizing compact, multifunctional optical systems to optics engineers with a variety of professional knowledge backgrounds and techniques. Full article

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

Background: Solid organ transplantation (SOT) is a life-saving treatment for patients with end-stage diseases and/or organ failure. However, access to healthy organs is often limited by challenges in organ preservation. Furthermore, upon transplantation, ischemia–reperfusion injury (IRI) can lead to increased organ rejection or graft failures. The work presented aims to address both challenges using an innovative nanomedicine platform for simultaneous drug and oxygen delivery. In recent studies, resveratrol (RSV), a natural antioxidant, anti-inflammatory, and reactive oxygen species (ROS) scavenging agent, has been reported to protect against IRI by inhibiting ferroptosis. Here, we report the design, development, and scalable manufacturing of the first-in-class dual-function perfluorocarbon-nanoemulsion (PFC-NE) perfusate for simultaneous oxygen and antioxidant delivery, equipped with a near-infrared fluorescence (NIRF) reporter, longitudinal, non-invasive NIRF imaging of perfusate flow through organs/tissues during machine perfusion. Methods: A Quality-by-Design (QbD)-guided optimization was used to formulate a triphasic PFC-NE with 30% w/v perfluorooctyl bromide (PFOB). Drug-free perfluorocarbon nanoemulsions (DF-NEs) and RSV-loaded nanoemulsions (RSV-NEs) were produced at 250–1000 mL scales using M110S, LM20, and M110P microfluidizers. Colloidal attributes, fluorescence stability, drug loading, and RSV release were evaluated using DLS, NIRF imaging, and HPLC, respectively. PFC-NE oxygen loading and release kinetics were evaluated during perfusion through the BMI OrganBank^® machine with the MEDOS HILITE^® oxygenator and by controlled flow of oxygen. The in vitro antioxidant activity of RSV-NE was measured using the oxygen radical scavenging antioxidant capacity (ORAC) assay. The cytotoxicity and ferroptosis inhibition of RSV-NE were evaluated in RAW 264.7 macrophages. Results: PFC-NE batches maintained a consistent droplet size (90–110 nm) and low polydispersity index (<0.3) across all scales, with high reproducibility and >80% PFOB loading. Both DF-NE and RSV-NE maintained colloidal and fluorescence stability under centrifugation, serum exposure at body temperature, filtration, 3-month storage, and oxygenation. Furthermore, RSV-NE showed high drug loading and sustained release (63.37 ± 2.48% at day 5) compared with the rapid release observed in free RSV solution. In perfusion studies, the oxygenation capacity of PFC-NE consistently exceeded that of University of Wisconsin (UW) solution and demonstrated stable, linear gas responsiveness across flow rates and FiO₂ (fraction of inspired oxygen) inputs. RSV-NE displayed strong antioxidant activity and concentration-dependent inhibition of free radicals. RSV-NE maintained higher cell viability and prevented RAS-selective lethal compound 3 (RSL3)-induced ferroptosis in murine macrophages (macrophage cell line RAW 264.7), compared to the free RSV solution. Morphological and functional protection against RSL3-induced ferroptosis was confirmed microscopically. Conclusions: This study establishes a robust and scalable PFC-NE platform integrating antioxidant and oxygen delivery, along with NIRF-based non-invasive live monitoring of organ perfusion during machine-supported preservation. These combined features position PFC-NE as a promising next-generation acellular perfusate for preventing IRI and improving graft viability during ex vivo machine perfusion. Full article

This study presents a comprehensive analysis of the marginal capacity credit of utility-scale solar and wind power in South Korea using an effective load-carrying capability-based methodology. This research makes three key contributions distinguishing it from previous works. First, the study introduces the concept of marginal capacity credit to quantify the contributions of newly added renewable energy capacities in power systems that already host significant solar and wind power capacities. Second, it evaluates the interaction effects between solar and wind power, revealing their complementary potential in enhancing system adequacy across different penetration levels. Third, it investigates how integrating energy storage systems mitigates intermittency and aligns renewable generation with peak demand. Results indicate that solar power provides relatively high marginal capacity credit at low penetration levels due to its alignment with peak demand, but its contribution declines as deployment expands and peak hours shift. Conversely, wind power maintains more stable marginal capacity credit and eventually surpasses solar power at higher penetration levels due to its broader generation profile. Storage integration notably enhances marginal capacity credit for both resources, with solar power gaining greater benefit from optimized charging and discharging strategies. These findings provide practical guidance for improving power system reliability and capacity planning under growing renewable penetration. Full article

In cryogenic air liquefaction systems, a major share of the mechanical energy consumption is associated with exergy destruction caused by heat transfer in recuperative heat exchangers. This study investigated the exergetic optimization of cryogenic gas separation systems by focusing on the Claude–Heylandt cycle as an advanced structural modification of the classical Linde–Hampson scheme. An exergy-based analysis demonstrates that minimizing mechanical energy consumption requires a progressive reduction in the temperature difference between the hot forward stream and the cold returning stream toward the cold end of the heat exchanger. This condition was achieved by extracting a fraction of the high-pressure stream and expanding it in a parallel expander, thereby creating a controlled imbalance in the heat capacities between the two streams. The proposed configuration reduces the share of exergy destruction associated with heat transfer in the recuperative heat exchanger from 14% to 3.5%, leading to a fourfold increase in the exergetic efficiency, together with a 3.6-fold increase in the liquefied air fraction compared with the Linde–Hampson cycle operating under identical conditions. The effects of key decision parameters, including the compression pressure, imposed temperature differences, and expander inlet temperature, were systematically analyzed. The study was further extended by integrating an air separation column into the Claude–Heylandt cycle and optimizing its configuration based on entropy generation minimization. The optimal liquid-air feeding height and threshold number of rectification trays were identified, beyond which further structural complexity yielded no thermodynamic benefit. The results highlight the effectiveness of exergy-based optimization as a unified design criterion for both cryogenic liquefaction and separation processes. Full article

The shift towards the integration of and transition to renewable energy has led to an increase in renewable energy communities (RECs) and smart grids (SGs). The significance of these RECs is mainly energy self-sufficiency, energy independence, and energy autonomy. Despite this, in low- and middle-income countries and regions like Pakistan and the Middle East, SGs and RECs are still in their initial stage. However, they have potential for green energy solutions rooted in their unique geographic and climatic conditions. SGs offer energy monitoring, communication infrastructure, and automation features to help these communities build flexible and efficient energy systems. This work provides an overview of Pakistani and Middle Eastern energy policies, goals, and initiatives while aligning with European comparisons. This work also highlights technical, regulatory, and economic challenges in those regions. The main objectives of the research are to ensure that residential service sizes are optimized to maximize the economic and environmental benefits of green energy. Furthermore, in line with SDG 7, affordable and clean energy, the focus in this study is on the development and transformation of energy systems for sustainability and creating synergies with other SDGs. The paper presents insights on the European Directive, including the amended Renewable Energy Directive (RED II and III), to recommend policy enhancements and regulatory changes that could strengthen the growth of RECs in Asian countries, Pakistan, and the Middle East, paving the way for a more inclusive and sustainable energy future. Additionally, it addresses the main causes that hinder the expansion of RECs and SGs, and offers strategic recommendations to support their development in order to reduce dependency on national electric grids. To perform this, a perspective study of Pakistan’s indicative generation capacity by 2031, along with comparisons of energy capacity in the EU, the Middle East, and Asia, is presented. Pakistan’s solar, wind, and hydro potential is also explored in detail. This study is a baseline and informative resource for policy makers, researchers, industry stakeholders, and energy communities’ promoters, who are committed to the task of promoting sustainable renewable energy solutions. Full article

Oxidative stress, caused by an imbalance between the production of reactive oxygen species and endogenous antioxidant capacity, is a key etiological factor in numerous pathologies, including neurodegenerative and cardiovascular diseases. The limited clinical efficacy of conventional antioxidants is primarily due to their insufficient accumulation within the mitochondria, the main site of intracellular ROS generation. This article reviews the design and application of Mitochondria-Targeted Antioxidants, which represent a major advance in precision medicine. The design of these compounds involves linking an antioxidant “payload” to a lipophilic cation, such as the triphenylphosphonium group. This positive charge leverages the negative electrochemical gradient across the inner mitochondrial membrane to drive the antioxidant into the organelle. This mechanism allows the drug to reach concentrations over 100 times higher than non-targeted alternatives. The discussion encompasses the structure-activity analysis of the carrier, the payload (e.g., quinone derivatives), and the linker, which determine optimal subcellular partitioning and scavenging efficiency. Preclinical data highlight the therapeutic potential of this approach, showing strong neuroprotection in models of Parkinson’s and Alzheimer’s diseases, as well as improved outcomes in cardiovascular and ocular health. By restoring redox balance specifically within the mitochondria, these targeted therapies offer a more effective way to treat chronic oxidative damage. Full article

Carbon dots (CDs) have emerged as a distinct class of fluorescent nanomaterials distinguished by their tunable physicochemical properties, ultrasmall size, exceptional photoluminescence, versatile surface chemistry, high biocompatibility, and chemical stability, positioning them as promising candidates for biomedical applications ranging from sensing and imaging to drug delivery and theranostics. As CDs increasingly transition toward biological and clinical use, a fundamental understanding of their interactions with biological membranes becomes essential, as cellular membranes govern nanoparticle uptake, intracellular transport, and therapeutic performance. Model membrane systems, such as phospholipid vesicles and liposomes, offer controllable platforms to elucidate CD-membrane interactions by isolating key physicochemical variables otherwise obscured in complex biological environments. Recent studies demonstrate that CD surface chemistry, charge, heteroatom doping, size, and hydrophobicity, together with membrane composition, packing density, and phase behavior, dictate nanoparticle adsorption, insertion, diffusion, and membrane perturbation. In addition, CD-liposome hybrid systems have gained momentum as multifunctional nanoplatforms that couple the fluorescence and traceability of CDs with the encapsulation capacity and biocompatibility of lipid vesicles, enabling imaging-guided drug delivery and responsive theranostic systems. This review consolidates current insights into the mechanistic principles governing CD interactions with model membranes and highlights advances in CD-liposome hybrid nanostructures. By bridging fundamental nanoscale interactions with translational nanomedicine strategies, this work provides a framework for the rational design of next-generation CD-based biointerfaces with optimized structural, optical, and biological performance. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

The advent of Generative Artificial Intelligence (GenAI) has ushered in a transformative wave within the field of language education. However, the applications of GenAI are primarily in language teaching and learning, with assessment receiving much less attention. Drawing on task characteristics identified from a corpus of authentic prior tests, this study investigated the capacity of GenAI tools to develop a short College English Test-Band 4 (CET-4) listening test and examined the degree to which its content, concurrent, and face validity corresponded to those of an authentic, human-generated counterpart. The findings indicated that the GenAI-created test aligned well with the task characteristics of the target test domain, supporting its content validity, whereas sufficient robust evidence to substantiate its concurrent or face validity was limited. Overall, GenAI has demonstrated potential in developing listening tests; however, further optimization is needed to enhance their validity. Implications for language teaching, learning and assessment are therefore discussed. Full article

Fingerprint recognition systems have relied on fragile workflows based on minutiae extraction, which suffer from significant performance losses under real-world conditions such as sensor diversity and low image quality. This study introduces a fully minutiae-free fingerprint recognition framework based on self-supervised Vision Transformers. A systematic evaluation of multiple DINOv2 model variants is conducted, and the proposed system ultimately adopts the DINOv2-Base Vision Transformer as the primary configuration, as it offers the best generalization performance trade-off under conditions of limited fingerprint data. Larger variants are additionally analyzed to assess scalability and capacity limits. The DINOv2 pretrained network is fine-tuned using self-supervised domain adaptation on 64,801 fingerprint images, eliminating all classical enhancement, binarization, and minutiae extraction steps. Unlike the single-sensor protocols commonly used in the literature, the proposed approach is extensively evaluated in a heterogeneous testbed with a wide range of sensors, qualities, and acquisition methods, including 1631 unique fingers from 12 datasets. The achieved EER of 5.56% under these challenging conditions demonstrates clear cross-sensor superiority over traditional systems such as VeriFinger (26.90%) and SourceAFIS (41.95%) on the same testbed. A systematic comparison of different model capacities shows that moderate-scale ViT models provide optimal generalization under limited-data conditions. Explainability analyses indicate that the attention maps of the model trained without any minutiae information exhibit meaningful overlap with classical structural regions (IoU = 0.41 ± 0.07). Openly sharing the full implementation and evaluation infrastructure makes the study reproducible and provides a standardized benchmark for future research. Full article