Search Results (2,684)

High-voltage unipolar square wave pulsed electric fields (PEFs) can cause cell membrane rupture and cell death during a process termed irreversible electroporation (IRE). PEF effects are influenced by pulse parameters like number of pulses (NP), voltage (PV), width (PW), and interval (PI). This study systematically evaluates their effects on the conductivity and relative conductivity changes between untreated and PEF-treated regions of potato tissue across a frequency range of 1 Hz to 5 MHz by means of electrochemical impedance spectroscopy (EIS), using a custom-made four-point EIS probe with RG58/U coaxial cables. Potatoes were chosen as a plant-based PEF model to reduce animal experiments and untreated tissue showed minimal conductivity variation across regions. Relative conductivity changes were maximal at 1000 Hz. At 1000 Hz, significant conductivity differences between untreated and PEF-treated regions were observed from PV = 200 V, NP = 10, PW = 10 µs, and PI = 50 ms onwards (most significant changes occurred for PV = 700 V; NP = 70; PW = 70 µs; PI = 250 ms and 500 ms). Our results may be beneficial for multiphysics modelling of IRE with specific electrical properties, conductivity mapping with optimal contrast—such as in electrical impedance tomography—and development of IRE procedures. Full article

Upper extremity amputations significantly impact an individual’s physical capabilities, psychosocial well-being, and overall quality of life. The level at which an amputation is performed influences residual limb function, prosthetic compatibility, and long-term patient satisfaction. While surgical guidelines traditionally emphasize maximal limb preservation, prosthetists often advocate for amputation sites that optimize prosthetic fit and function, highlighting the need for a collaborative approach. This review examines the discrepancies between surgical and prosthetic recommendations for optimal amputation levels, from digit amputations to shoulder disarticulations, and explores their implications for prosthetic design, functionality, and patient outcomes. Various prosthetic options, including passive functional, body-powered, myoelectric, and hybrid devices, offer distinct advantages and limitations based on the level of amputation. Prosthetists emphasize the importance of residual limb length, not only for mechanical efficiency but also for achieving symmetry with the contralateral limb, minimizing discomfort, and enhancing control. Additionally, emerging technologies such as targeted muscle reinnervation (TMR) and advanced myoelectric prostheses are reshaping rehabilitation strategies, further underscoring the need for precise amputation planning. By integrating insights from both surgical and prosthetic perspectives, this review highlights the necessity of a multidisciplinary approach involving surgeons, prosthetists, rehabilitation specialists, and patients in the decision-making process. A greater emphasis on preoperative planning and interprofessional collaboration can improve prosthetic outcomes, reduce device rejection rates, and ultimately enhance the functional independence and well-being of individuals with upper extremity amputations. Full article

As electric vehicles (EVs) continue to advance toward widespread adoption, innovations in power electronics are playing a pivotal role in improving efficiency, performance, and sustainability. This review presents recent progress in bidirectional converters and regenerative braking systems (RBSs), highlighting their contributions to energy recovery, battery longevity, and vehicle-to-grid integration. Bidirectional converters support two-way energy flow, enabling efficient regenerative braking and advanced charging capabilities. The integration of wide-bandgap semiconductors, such as silicon carbide and gallium nitride, further enhances power density and thermal performance. The paper evaluates various converter topologies, including single-stage and multi-stage architectures, and assesses their suitability for high-voltage EV platforms. Intelligent control strategies, including fuzzy logic, neural networks, and sliding mode control, are discussed for optimizing braking force and maximizing energy recuperation. In addition, the paper explores the influence of regenerative braking on battery degradation and presents hybrid energy storage systems and AI-based methods as mitigation strategies. Special emphasis is placed on the integration of RBSs in advanced electric vehicle platforms, including autonomous systems. The review concludes by identifying current challenges, emerging trends, and key design considerations to inform future research and practical implementation in electric vehicle energy systems. Full article

Water-lubricated bearings (WLB), due to their pollution-free nature and low noise, are increasingly becoming critical components in aerospace, marine applications, high-speed railway transportation, precision machine tools, etc. However, in practice, water-lubricated bearings suffer severe friction and wear due to low-viscosity water, harsh conditions, and contaminants like sediment, which can compromise the lubricating film and shorten their lifespan. The implementation of micro-textures has been demonstrated to improve the tribological performance of water-lubricated bearings to a certain extent, leading to their widespread adoption for enhancing the frictional dynamics of sliding bearings. The shape, dimensions (including length, width, and depth), and distribution of these micro-textures have a significant influence on the frictional performance. Therefore, this study aims to explore the coupling effect of different micro-texture shapes and distributions on the frictional performance of water-lubricated sliding, using the computational fluid dynamics (CFD) analysis. The results indicate that strategically arranging textures across multiple regions can enhance the performance of the bearing. Specifically, placing linear groove textures in the outlet of the divergent zone and triangular textures in the divergent zone body maximize improvements in the load-carrying capacity and frictional performance. This specific configuration increases the load-carrying capacity by 7.3% and reduces the friction coefficient by 8.6%. Overall, this study provided critical theoretical and technical insights for the optimization of WLB, contributing to the advancement of clean energy technologies and the extension of critical bearing service life. Full article

Subsurface drainage and biochar application are conventional measures for improving saline–alkali soils. However, their combined effects on the fate of nitrogen (N) fertilizers remain unclear. This study investigated the combined effects of subsurface drainage and biochar amendment on the fate of nitrogen (N) in coastal saline–alkali soils, where these conventional remediation measures’ combined impacts on fertilizer N dynamics remain seldom studied. Using ¹⁵N-labeled urea tracing in an alfalfa–soil system, we examined how different drainage spacings (0, 6, 12, and 18 m) and biochar application rates (5, 10, and 15 t/ha) influenced N distribution patterns. Results demonstrated decreasing in drainage spacing and increasing in biochar application; these treatments enhanced ¹⁵N use efficiency on three harvested crops. Drainage showed more sustained effects than biochar. Notably, the combination of 6 m drainage spacing with 15 t/ha biochar application achieved optimal performance of ¹⁵N use, showing N utilization efficiency of 46.0% that significantly compared with most other treatments (p < 0.05). ¹⁵N mass balance analysis revealed that the plant absorption, the soil residual and the loss of applied N accounted for 21.6–46.0%, 38.6–67.5% and 8.5–18.1%, respectively. These findings provide important insights for optimizing nitrogen management in coastal saline–alkali agriculture, demonstrating that strategic integration of subsurface drainage (6 m spacing) with biochar amendment (15 t/ha) can maximize N use efficiency, although potential N losses warrant consideration in field applications. Full article

In recent times, blades that have the ability to change shape passively or actively have garnered interest due to their ability to optimize blade performance for varying flow conditions. Various versions of morphing exist, from simple chord length changes to full blade morphing with multiple degrees of freedom. These blades can incorporate smart materials or mechanical actuators to modify the blade shape to suit the wind conditions. Morphing blades have shown an ability to improve performance in simulations. These simulations show increased performance in Region 2 (partial load) operating conditions. This study focuses on the effects of the wake for a flexible wind turbine with actively variable twist angle distribution (TAD) to improve the energy production capabilities of morphing structures. These wake effects influence wind farm performance for locally clustered turbines by extracting energy from the free stream. Hence, the development of better wake models is critical for better turbine design and controls. This paper provides an outline of some approaches available for wake modeling. FLORIS (FLow Redirection and Induction Steady-State) is a program used to predict steady-state wake characteristics. Alongside that, the Materials and Methods section shows different modeling environments and their possible integration into FLORIS. The Results and Discussion section analyzes the 20 kW wind turbine with previously acquired data from the National Renewable Energy Laboratory’s (NREL) AeroDyn v13 software. The study employs FLORIS to simulate steady-state non-linear wake interactions for the nine TAD shapes. These TAD shapes are evaluated across Region 2 operating conditions. The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. The Results and Discussion section compares these TAD shapes to the original blade design regarding the wake characteristics. The project aims to enhance the understanding of FLORIS for studying wake characteristics for morphing blades. Full article

Spinal cord injury (SCI) is a severe neurological condition that typically results in irreversible loss of motor and sensory function. Emerging evidence indicates that neuroplasticity, the ability of the nervous system to reorganize by forming new neural connections, plays a pivotal role in structural and functional recovery post-injury. This insight lays the groundwork for the development of rehabilitation and therapeutic strategies designed to leverage neuroplasticity. In this review, we offer an exhaustive overview of the neuroplastic alterations and mechanisms that occur following an SCI. We examine the role of neuroplasticity in functional recovery and outline therapeutic approaches designed to augment neuroplasticity post-SCI. The process of neuroplasticity post-SCI involves several physiological processes, such as neurogenesis, synaptic remodeling, dendritic spine formation, and axonal sprouting. Together, these processes contribute to the reestablishment of neural circuits and functional restoration. Enhancing neuroplasticity is a promising strategy for improving functional outcomes post-SCI; however, its effectiveness is influenced by numerous factors, including age, injury severity, time since the injury, and the specific therapeutic interventions employed. A variety of strategies have been suggested to promote neuroplasticity and expedite recovery, including pharmacological treatments, biomaterial-based therapies, gene editing, stem cell transplantation, and rehabilitative training. The combination of personalized rehabilitation programs with innovative therapeutic techniques holds considerable potential for maximizing the benefits of neuroplasticity and enhancing clinical outcomes in SCI management. Full article

With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. Full article

With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization. Full article

We advance a mathematical framework for collective conviction by deriving a continuum theory from the network-based model introduced by us recently. The resulting equation governs the evolution of belief through a degenerate anisotropic logistic–diffusion process, where diffusion slows as conviction saturates. In one spatial dimension, we prove global well-posedness, demonstrate spectral front pinning that arrests the spread of influence at finite depth, and construct explicit traveling-wave solutions. In two dimensions, we uncover a geometric mechanism of curvature–induced quenching, where belief propagation halts along regions of low effective mobility and curvature. Building on this insight, we formulate a variational principle for optimal control under resource constraints. The derived feedback law prescribes how to spatially allocate repression effort to maximize inhibition of front motion, concentrating resources along high-curvature, low-mobility arcs. Numerical simulations validate the theory, illustrating how localized suppression dramatically reduces transverse spread without affecting fast axes. These results bridge analytical modeling with societal phenomena such as protest diffusion, misinformation spread, and institutional resistance, offering a principled foundation for selective intervention policies in structured populations. Full article

Plastic waste is currently a major concern in Romania due to the annual increase in quantities generated from anthropogenic and industrial activities, especially from end-of-life vehicles (ELVs), and the need to reduce environmental impact. This study investigates an alternative valorization route for polypropylene (PP) and polyethylene (PE) plastic waste through microwave-assisted pyrolysis, aiming to maximize conversion into gaseous products, particularly hydrogen-rich gas. A monomode microwave reactor was employed, using layered configurations of plastic feedstock, silicon carbide as a microwave susceptor, and activated carbon as a catalyst. The influence of catalyst loading, reactor configuration, and plastic type was assessed through systematic experiments. Results showed that technical-grade PP, under optimal conditions, yielded up to 81.4 wt.% gas with a hydrogen concentration of 45.2 vol.% and a hydrogen efficiency of 44.8 g/g. In contrast, PE and mixed PP + PE waste displayed lower hydrogen performance, particularly when containing inorganic fillers. For all types of plastics studied, the gaseous fractions obtained have a high calorific value (46,941–55,087 kJ/kg) and at the same time low specific CO₂ emissions (4.4–6.1 × 10⁻⁵ kg CO₂/kJ), which makes these fuels very efficient and have a low carbon footprint. Comparative tests using conventional heating revealed significantly lower hydrogen yields (4.77 vs. 19.7 mmol/g plastic). These findings highlight the potential of microwave-assisted pyrolysis as an efficient method for transforming ELV-derived plastic waste into energy carriers, offering a pathway toward low-carbon, resource-efficient waste management. Full article

In the digital economy, information asymmetry among individuals, data users, and governments limits the full realization of personal data value. To address this, “symmetry enhancement” strategies aim to reduce information gaps, enabling more balanced decision-making and facilitating efficient data flow. This study establishes a tripartite evolutionary game model based on personal data collection and development, conducts simulations using MATLAB R2024a, and proposes countermeasures based on equilibrium analysis and simulation results. The results highlight that individual participation is pivotal, influenced by perceived benefits, management costs, and privacy risks. Meanwhile, data users’ compliance hinges on economic incentives and regulatory burdens, with excessive costs potentially discouraging adherence. Governments must carefully weigh social benefits against regulatory expenditures. Based on these findings, this paper proposes the following recommendations: use personal data application scenarios as a guide, rely on the construction of personal trustworthy data spaces, explore and improve personal data revenue distribution mechanisms, strengthen the management of data users, and promote the maximization of personal data value through multi-party collaborative ecological incentives. Full article

In recent years, automatic segmentation and classification of data from digital surveys have taken a central role in built heritage studies. However, the application of Machine and Deep Learning (ML and DL) techniques for semantic segmentation of point clouds is complex in the context of historic architecture because it is characterized by high geometric and semantic variability. Data quality, subjectivity in manual labeling, and difficulty in defining consistent categories may compromise the effectiveness and reproducibility of the results. This study analyzes the influence of three key factors—annotator specialization, point cloud density, and sensor type—in the supervised classification of architectural elements by applying the Random Forest (RF) algorithm to datasets related to the architectural typology of the Franciscan cloister. The main innovation of the study lies in the development of an advanced feature selection technique, based on multibeam statistical analysis and evaluation of the p-value of each feature with respect to the target classes. The procedure makes it possible to identify the optimal radius for each feature, maximizing separability between classes and reducing semantic ambiguities. The approach, entirely in Python, automates the process of feature extraction, selection, and application, improving semantic consistency and classification accuracy. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

Intercropping legumes offers a sustainable approach to enhance resource efficiency and yields, yet the effects of different legume densities and nitrogen addition levels on soil quality within such systems remain unclear. We conducted a comparative analysis of crop yield, nitrogen use efficiency, and soil quality between intercropping and monoculture systems, and further examined the effects of four planting densities (D1: 210 kg ha⁻¹, six rows; D2: 280 kg ha⁻¹, eight rows; D3: 350 kg ha⁻¹, ten rows) and four nitrogen application levels (N0: 0 kg ha⁻¹; N1: 80 kg ha⁻¹; N2: 160 kg ha⁻¹; N3: 240 kg ha⁻¹) within a jujube–alfalfa (Ziziphus jujuba Mill. and Medicago sativa L. respectively) intercropping system. The results showed that intercropping significantly enhanced land productivity within the agricultural system, with the highest yields (alfalfa: 13790 kg ha⁻¹; jujube: 3825 kg ha⁻¹) achieved at an alfalfa planting density of 280 kg ha⁻¹. While the intercropping systems generally improved productivity, an alfalfa planting density of 350 kg ha⁻¹ resulted in an actual yield loss due to excessive nutrient competition at higher densities. As the planting density of alfalfa increased, its competitive ratio declined, whereas the competitive ratio of jujube trees increased. Compared to monocropping systems, intercropping systems demonstrated a clear trend of enhanced nitrogen utilization efficiency and improved soil quality, particularly at an alfalfa planting density of 280 kg ha⁻¹. At an alfalfa density of 280 kg ha⁻¹, the intercropping system exhibited increases of 15.13% in nitrogen use efficiency (NUE), 46.60% in nitrogen partial factor productivity (NPFP), and 32.74% in nitrogen nutrition index (NNI), as well as improvements in soil quality of 19.53% at a depth of 0–20 cm and 15.59% at a depth of 20–40 cm, compared to the monoculture system. Further analysis revealed that nitrogen utilization efficiency initially increased and then decreased with a rising competitive ratio of alfalfa. Accordingly, soil quality was improved along with the enhanced nitrogen utilization efficiency. Thus, at an alfalfa planting density of 280 kg ha⁻¹, resource use efficiency and soil quality were maximized as a result of optimal interspecific competitiveness and the highest nitrogen use efficiency, with minimal influence from the application of nitrogen fertilizer. Full article