Search Results (118,162)

Gliders are engineless aircraft capable of maintaining altitude for extended periods and achieving long ranges. This paper presents an optimal aerodynamic design method for flying wing gliders, leveraging a combination of artificial neural networks (ANNs) as a surrogate model and genetic algorithms (GAs) for optimization. Data for training the ANN is generated using the vortex-lattice method (VLM). The study identifies optimal aerodynamic shapes for two objectives: maximum flight endurance and maximum range. A key finding is the inherent conflict between aerodynamic performance and static stability in tailless designs. By introducing a stability constraint via a penalty function, we successfully generate stable and high-performance configurations. For instance, the stabilized RG15 airfoil design achieves a maximum glide ratio of 24.1 with a robust 5.1% static margin. This represents a calculated 11.5% performance reduction compared to its unstable theoretical optimum, quantitatively demonstrating the crucial trade-off between stability and performance. The methodology provides a computationally efficient path to designing practical, high-performance, and inherently stable flying wing gliders. Full article

The terahertz (THz) frequency range holds critical importance for next-generation, wireless communications and biomedical sensing applications. However, conventional metamaterial design approaches suffer from computationally intensive simulations and optimization processes that can extend over several months. This work presents an intelligent inverse design framework integrating deep neural network (DNN) surrogate modeling with success-history-based adaptive differential evolution (SHADE) for tunable graphene-based THz metasurfaces. Our DNN surrogate model achieves an exceptional coefficient of determination (R² = 0.9984) while providing a four-order-of-magnitude acceleration compared with conventional electromagnetic solvers. The SHADE-integrated framework demonstrates 96.7% accuracy in inverse design tasks with an average convergence time of 10.2 s. The optimized configurations exhibit significant tunability through graphene Fermi level modulation, as validated by comprehensive electromagnetic field analysis. This framework represents a significant advancement in automated electromagnetic design and establishes a robust foundation for intelligent photonic systems across diverse frequency regimes. Full article

Implantable medical devices present several technological challenges, one of the most critical being how to provide power supply and communication capabilities to a device hermetically sealed within the body. Using a battery as a power source represents a potential harm for the individual’s health because of possible toxic chemical release or overheating, and it requires periodic surgery for replacement. This paper proposes a batteryless implantable device powered by an inductive link and equipped with bidirectional wireless communication channels. The device, designed in a 180 nm CMOS process, is based on two different pairs of mutually coupled inductors that provide, respectively, power and a low-bitrate bidirectional communication link and a separate, high-bitrate, one-directional upstream connection. The main link is based on a 13.56 MHz carrier and allows power transmission and a half-duplex two-way communication at 106 kbps (downlink) and 30 kbps (uplink). The secondary link is based on a 27 MHz carrier, which provides one-way communication at 2.25 Mbps only in uplink. The low-bitrate links are needed to send commands and monitor the implanted system, while the high-bitrate link is required to receive a continuous stream of information from the implanted sensing devices. The microchip acts as a hub for power and data wireless transmission capable of managing up to four different neural recording and stimulation front ends, making the device employable in a complex, distributed, bidirectional neural prosthetic system. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article

Gilles de la Tourette syndrome (GTS) is a neurodevelopmental disorder characterized by motor and phonic tics, often including attention deficit, hyperactivity, and obsessive–compulsive behaviours. The pathophysiology involves the dysfunction of cortico-striato-thalamo-cortical circuits, primarily implicating dopaminergic hyperactivity, but also involving multiple different neurotransmitter systems. Treatment of GTS is complex, highly individualized, and influenced by considerable variability in symptom presentation. Behavioural approaches, such as Habit Reversal Therapy (HRT), play a key role, especially in milder cases. Pharmacological therapy is largely empirical and varies across countries, influenced by drug availability and the perceived risks of certain classes of drugs, particularly dopamine receptor blocking agents. Drug options for managing tics include dopamine receptor antagonists, monoamine depleting agents, and alpha-2 agonists, all of which require close monitoring for metabolic, cardiovascular, and neurological side effects. Botulinum toxin injections represent an effective solution for focal tics that are resistant to systemic treatments. Cannabinoids and antiepileptics have limited efficacy, yet they may still offer relevant therapeutic potential in selected cases. Serotonergic drugs are useful for treating obsessive–compulsive symptoms. For patients with refractory tics, deep brain stimulation (DBS) represents an intervention of last-resort; however, DBS remains off-label and consensus on optimal targets is lacking. This narrative review draws on both the relevant literature and extensive personal clinical experience to explore the complexities of managing GTS, with a focus on evidence-based treatments for tics and associated neuropsychiatric symptoms. A therapeutic algorithm is proposed, emphasizing a “start low, go slow” approach, combining pharmacological interventions with cognitive behavioural and surgical therapies, when needed. We underscore the importance of tailoring treatments to individual patient profiles and symptom variability over time, highlighting the need for further research in GTS management. Full article

The accurate extraction of water bodies from remote sensing imagery is crucial for water resource monitoring and flood disaster warning. However, this task faces significant challenges due to complex land cover, large variations in water body morphology and spatial scales, and spectral similarities between water and non-water features, leading to misclassification and low accuracy. While deep learning-based methods have become a research hotspot, traditional convolutional neural networks (CNNs) struggle to represent multi-scale features and capture global water body information effectively. To enhance water feature recognition and precisely delineate water boundaries, we propose the AMU-Net model. Initially, an improved residual connection module was embedded into the U-Net backbone to enhance complex feature learning. Subsequently, a multi-scale attention mechanism was introduced, combining grouped channel attention with multi-scale convolutional strategies for lightweight yet precise segmentation. Thereafter, a dual-attention gated modulation module dynamically fusing channel and spatial attention was employed to strengthen boundary localization. Furthermore, a cross-layer geometric attention fusion module, incorporating grouped projection convolution and a triple-level geometric attention mechanism, optimizes segmentation accuracy and boundary quality. Finally, a triple-constraint loss framework synergistically optimized global classification, regional overlap, and background specificity to boost segmentation performance. Evaluated on the GID and WHDLD datasets, AMU-Net achieved remarkable IoU scores of 93.6% and 95.02%, respectively, providing an effective new solution for remote sensing water body extraction. Full article

Cell motility is a process central to life and is undoubtedly influenced by mechanical and chemical signals. Even so, other stimuli are also involved in controlling cell migration in vivo and in vitro. Among these, electric fields have been shown to provide a powerful and programmable cue to manipulate cell migration. There is now a clear consensus that the electromigration of membrane components represents the first response to an external electric field, which subsequently activates downstream signals responsible for controlling cell migration. Here, we focus on a specific mode of electrotaxis: frictionless, amoeboid-like migration. We used the Finite Element Method to solve an active gel model coupled with a mathematical model of the electromigration of aquaporins and investigate the effect of electric fields on ameboid migration. We demonstrate that an electric field can polarize aquaporins in a cell and, consequently, that the electromigration of aquaporins can be exploited to regulate water flux across the cell membrane. Our findings indicate that controlling these fluxes allows modulation of cell migration velocity, thereby reducing the cell’s migratory capacity. Our work provides a mechanistic framework to further study the impact of electrotaxis and to add new insights into specific modes by which electric fields modify cell motility. Full article

Background/Objectives: Fixed prosthetic dentures (FPD) represent a reliable treatment option to rehabilitate oral function and aesthetics. The concentration of loads at a certain level of the bridge causes overloading of the bridge components. The objectives of this study were to identify the frequency and severity of complications in FPDs associated with occlusal overload for patients prosthetically treated with metal–ceramic and metal–acrylic FPDs. Methods: This retrospective cross-sectional clinical study included 306 patients of both sexes, aged 30+, with an FPD requiring rehabilitation. The following data were collected: general demographic data of the patients, data on dental bridges, data on occlusal overloads, and complications of FPDs according to severity. Statistical processing was performed in SPSS. Results: Metal–ceramic FPDs had more abutment teeth (p = 0.035), fewer intermediaries (p < 0.0005), and less severe complications (p = 0.001). Maxillary FPDs had a higher number of abutment teeth, compared to mandible FPDs (p < 0.001). As the life duration of the FPDs increased, the number of intermediaries and the severity grade also increased (p < 0.001). Compared to intercalated and pontics, cantilever pontics of FPDs had a longer duration (p < 0.001), a lower number of intermediaries (p < 0.001), and a higher severity grade (p < 0.001). The presence of occlusal interferences was associated only with a lower FPD duration (p = 0.007). The presence of unbalanced occlusion planes was associated with a higher severity grade, while the presence of shortened arches was associated with a longer FPD duration (p < 0.001), a higher number of intermediaries (p = 0.005), and a higher severity grade (p < 0.001). Conclusions: The most severe complications of FPDs were recorded in the case of shortened arches and cantilever bridges. This study draws attention to the need for preprosthetic and postprosthetic occlusal balancing and the need for complete rehabilitation of the dental arches. Full article

Salmonella is one of the primary causes of foodborne infections worldwide and is often linked to the consumption of poultry products. Despite the implementation of numerous control programmes, the persistence of Salmonella in poultry environments remains a challenge, exacerbated by the emergence of strains resistant to traditional disinfectants. This review examines the key factors associated with the limitations of disinfection and the new strategies employed in poultry production, underscoring the need for more sustainable and effective alternative solutions. Various chemical (nanoparticles), physical (ultraviolet light, heat, pressurised steam, infrared radiation) and biological (bacteriophages, essential oils, and positive biofilm) treatments are examined. Of the various alternatives assessed, some have shown promising antimicrobial activity against Salmonella in vitro and under experimental conditions. However, their application in real-field settings is still limited, and few studies evaluate their effectiveness on a commercial scale. The review emphasises the importance of integrating these alternatives within broader biosecurity programmes, supported by clear regulations to minimise the risk of transmission. In conclusion, the adoption of innovative and sustainable approaches, combined with strengthened biosecurity measures, represents a key strategy to reduce Salmonella contamination in poultry farms, protect public health and promote responsible production systems. Full article

Based on questionnaire data and in-depth interviews with newly arrived students (NAS) from mainland China, this study investigates the construction of their social networks and the mechanisms through which they access social support in the context of migration. Drawing on Berry’s acculturation theory, Bronfenbrenner’s ecological systems theory, and Bourdieu’s concept of social capital, this study provides a theoretically grounded analysis of how NAS balance cultural distance and social needs. The findings reveal that NAS do not form social connections uniformly; rather, they strategically allocate social resources according to the degree of homophily and the strength of social ties. Specifically, weak ties with mainland peers—characterized by high cultural homophily—primarily offer emotional support; strong ties with local Hong Kong peers—marked by low homophily but high interaction frequency—mainly serve instrumental needs such as academic assistance and daily companionship; while strong ties with Hong Kong peers of mainland background combine both emotional and instrumental support, functioning as a core relational bridge in the NAS’s adaptation process. These three types of relationships form a complementary structure within NAS’s social networks. Reliability and validity tests further confirmed that four items (social satisfaction, peer attitude, sense of belonging, integration/adaptation) provide a coherent measure of social integration. The study suggests that NAS’s social practices are not merely about “integration” or “alienation,” but rather represent a dynamic strategy of balancing relational costs, cultural distance, and practical needs in the operation of social capital and characterised by dynamic negotiation and contextual adjustment. Full article

Accurate modelling of near surface wind speeds is essential for robust resource assessment, turbine design, and grid integration. This study presents a unified framework comparing four candidate marginal distributions—Weibull, Gamma, Lognormal, and Generalised Extreme Value (GEV)—across 21 years of daily observations from 11 sites in New South Wales and southern Queensland, Australia. Parameters are estimated by maximum likelihood, with L-moments used when numerical fitting fails. Univariate goodness-of-fit is evaluated via information criteria (Akaike Information Criterion, AIC; Bayesian Information Criterion, BIC) and distributional tests (Anderson–Darling, Cramér–von Mises, Kolmogorov–Smirnov). To capture spatial dependence, we fit an 11-dimensional regular vine (“R-vine”) copula to the probability-integral-transformed data, selecting pair-copula families by AIC and estimating parameters by sequential likelihood. A composite score (70% univariate, 30% copula) ranks distributions per location. Results demonstrate that Lognormal best matches central behaviour at most sites, Weibull remains competitive for bulk modelling, Gamma often excels in moderate tails, and GEV best represents extremes. All turbine yield results presented are illustrative, showing how statistical choices impact energy estimates; they should not be interpreted as operational forecasts. In a case study, 5000 joint simulations from the top-two models drive IEC V90 and E82 power curves, revealing up to 10% variability in annual energy yield due solely to marginal choice. This workflow provides a replicable template for comprehensive wind resource and load hazard analysis in complex terrains. Full article

This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out. Full article

Urban environments encounter urgent challenges in transitioning to net-zero emissions, particularly with respect to the adoption and large-scale incorporation of renewable energy solutions such as photovoltaic (PV) technologies. This study explores the interrelation of digitized energy systems, digital twins, and open-access platforms in accelerating effective PV deployment in cities moving toward carbon neutrality. We examine how digital tools can enhance PV performance, demand-side management, and grid integration, while open-access platforms contribute to data sharing, raising awareness, public engagement, and stakeholder collaboration. We also present BIPV-city—a novel, open-access, digital, and climate-aware platform developed to support and optimize PV integration in building and urban areas. Validations of the solar irradiance calculations against PVGIS for several European cities exhibit a strong agreement, with a root mean square error (RMSE) extending from 3.3 to 7.6. The validation of the standardized BESTEST Case 600 against TRNSYS simulations for three representative climates—Athens, Prague, and Dubai—with tilt variations confirmed substantial alignment for plane-of-array (POA) radiation (within ±2% and ±6% for the global and direct/diffuse components, respectively) and annual PV yield estimations (within ±10%). The findings highlight that the BIPV-city platform is a reliable, user-friendly tool that can harness climate-responsible and scalable BIPV deployment in the built environment through digital innovation. Full article

In this paper, we introduce a novel, self-hosted Syslog collection platform designed specifically to address the challenges that small and medium enterprises (SMEs) face in implementing comprehensive syslog monitoring solutions. Our analysis begins with an assessment of current network observability practices, evaluating enterprise solutions, on-premises systems, and Software as a Service (SaaS) offerings to identify features crucial for SME environments. The proposed platform represents an advancement in the field through the incorporation of modern practices, including GitOps and continuous integration and continuous delivery/deployment (CI/CD), and its implementation onto a self-managed Kubernetes platform, which is an approach not commonly explored in SME-focused solutions. We will explore its scalability by leveraging dynamic templates, which allow us to select the number and type of nodes when deploying networks of various sizes. This architecture ensures organisations can deploy a pre-designed, scalable network monitoring solution without extensive external support. The resilience of the proposed platform is assessed by providing empirical evidence of the scaling performance and reliability under various failure scenarios, including node failure and high network throughput stress. Full article

As a representative high-performance construction material, ultra-high performance concrete (UHPC) is typically prepared using quartz sand and steel fibers. To alleviate the shortage of building materials in island and reef regions, this study employs coral sand for UHPC preparation and investigates the effects of different fibers on its mechanical properties. This study demonstrates that this approach mitigates brittle failure patterns and enhances the durability of structures. To investigate the enhancement effects of PE and steel fibers on the mechanical properties of coral sand ultra-high performance concrete (CSUHPC), 12 mix designs were formulated, including a plain (no fiber) reference group and PE fiber-reinforced, steel fiber-reinforced, and hybrid fiber combinations. Compressive tests, tensile tests, and three-point bending tests on pre-notched beams were conducted. Key parameters such as 28-day compressive strength, tensile strength, and flexural strength and toughness were measured. A multi-criteria evaluation framework was established to comprehensively assess the integrated performance of each group. The experimental results demonstrated that fiber incorporation significantly enhanced the compressive strength and fracture properties of CSUHPC compared to the plain reference group. Steel fiber-only reinforcement exhibited the most pronounced improvement in compressive strength and fracture properties, while hybrid fiber combinations provided superior tensile performance. Through the established multi-criteria evaluation framework, the optimal comprehensive performance was achieved with a 3% steel fiber dosage, achieving improvements of 0.93 times in compressive strength, 2.80 times in tensile strength, 1.84 times in flexural strength, 192.08 times in fracture energy, and 1.84 times in fracture toughness relative to the control group. Full article