Search Results (4,434)

This study presents an analysis of transverse vibration behavior of a fluid-conveying pipe mounted on an elastic foundation, incorporating both classical analytical techniques and modern physics-informed neural network (PINN) methodologies. A partial differential equation (PDE) architecture is developed to approximate the solution by embedding the physics PDE, initial, and boundary conditions directly into the loss function of a deep neural network. A one-dimensional fourth-order PDE is employed to model governing transverse displacement derived from Euler–Bernoulli beam theory, with additional terms representing fluid inertia, flow-induced excitation, and stochastic force modelled as Gaussian white noise. The governing PDE is decomposed via separation of variables into spatial and temporal components, and modal analysis is employed to determine the natural frequencies and mode shapes under free–free boundary conditions. The influence of varying flow velocities and excitation frequencies on critical buckling behavior and mode shape deformation is analyzed. The network is trained using the Resilient Backpropagation (RProp) optimizer. A preliminary validation study is presented in which a baseline PINN is benchmarked against analytical modal solutions for a fluid-conveying pipe on an elastic foundation under deterministic excitation. The results demonstrate the capability of PINNs to accurately capture complex vibrational phenomena, offering a robust framework for data-driven modelling of fluid–structure interactions in engineering applications. Full article

Lysobacter enzymogenes is well known for producing extracellular enzymes and bioactive molecules that suppress a wide range of plant pathogens, including fungi such as Rhizoctonia and Fusarium spp. and oomycetes such as Phytophthora infestans. It also exhibits antagonistic effects against Gram-negative bacteria through the type IV secretion system. Interestingly, L. enzymogenes JCK1421, isolated from the rhizosphere of pine forests, showed neither antifungal nor antibacterial activity, in contrast to other L. enzymogenes strains. However, foliar application of JCK1421 significantly reduced disease symptoms in tomato seedlings challenged with Ralstonia solanacearum. To elucidate the underlying defense mechanisms, comparative transcriptome analysis integrated with network and pathway enrichment approaches was performed. Comparative transcriptome and network analyses identified signaling modules activated by JCK1421 in pathogen-free plants and further enhanced upon R. solanacearum challenge. In challenged plants, JCK1421 treatment strongly induced resistance-related genes, including those encoding Ca²⁺-dependent proteins and ion channels, hormone biosynthesis components, and mitogen-activated protein kinase cascades—hallmarks of plant immune responses. These findings demonstrate that JCK1421 provides an effective model for investigating microbe-associated defense activation in plants, highlighting its potential as an eco-friendly agent for sustainable crop protection. Full article

A new criticality indicator for Water Distribution Networks (WDNs) is presented. The new indicator is based on the minimum pressure (MP) model, which relies on the assumption that air can enter the pipes, e.g., when failure occurs in water scarcity scenarios, and maintain a minimum pressure equal to zero in the whole network. The proposed indicator properly integrates topological features, provided by structural hole theory, with the hydraulic constraints provided by the WDN steady-state solution, with a particular focus on pipes where occurring free surface flow leads to a serious reduction in the quality of the network service. The new indicator leads to a new criterion for the prioritized maintenance of pipes in existing networks, as well as for the design and planning of new ones, which is different from the one derived from other popular indicators. Three real-life WDNs are selected as test cases. Full article

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article

WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to ‘coverage holes’ in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is slow, making it difficult to maintain high coverage in real time. This study focuses on the coverage optimization problem of wireless sensor networks (WSNs) and proposes improvements to the Flamingo Search Optimization Algorithm (FSA). Specifically, the algorithm is enhanced by integrating the elite opposition-based learning strategy and the stagewise step-size control strategy, which significantly improves its overall performance. Additionally, the introduction of a cosine variation factor combined with the stagewise step-size control strategy enables the algorithm to effectively break free from local optima constraints in the later stages of iteration. The improved Flamingo Algorithm is applied to optimize the deployment strategy of sensing nodes, thereby enhancing the coverage rate of the sensor network. First, an appropriate number of sensing nodes is selected according to the target area, and the population is initialized using a chaotic sequence. Subsequently, the improved Flamingo Algorithm is adopted to optimize and solve the coverage model, with the coverage rate as the fitness function and the coordinates of all randomly distributed sensing nodes as the initial foraging positions. Next, a search for candidate foraging sources is performed to obtain the coordinates of sensing nodes with higher fitness; the coordinate components of these candidate foraging sources are further optimized through chaos theory to derive the foraging source with the highest fitness. Finally, the coordinates of the optimal foraging source are output, which correspond to the coordinate values of all sensing nodes in the target area. Experimental results show that after 100 and 200 iterations, the coverage rate of the improved Flamingo Search Optimization Algorithm is 7.48% and 5.68% higher than that of the original FSA, respectively. Furthermore, the findings indicate that, by properly configuring the Flamingo population size and the number of iterations, the improved algorithm achieves a higher coverage rate compared to other benchmark algorithms. Full article

The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring. Full article

The giant squid, despite its abundance as a resource, is underutilized for surimi production due to its distinctive odor and poor gel-forming ability. While soaking (e.g., in sodium citrate) can alleviate the odor, its impact on the gel properties remains unclear. This study employed a comparative approach using pork, a benchmark for high-quality gels, to critically evaluate the gel properties of deodorized giant squid. The rheological, textural, and microstructural properties, as well as the water-holding capacity and water distribution, of squid (after sodium citrate soaking) and pork gels were compared. The results demonstrated that the squid gels exhibited a significantly lower storage modulus and higher tan δ value than pork gels, indicating inferior rheological properties. After cooking, the squid gel exhibited a bent shape and markedly lower hardness (approximately 259.78 g) and chewiness (approximately 226.09 g) compared to the pork gels (approximately 3305.92 g and 2781.27 g, respectively). Microstructurally, the squid gels presented a coarse, porous, and discontinuous network with larger pores, contrasting sharply with the fine, dense, and uniform matrix of the pork gels. Correspondingly, the squid gels had inferior water-holding capacity and a higher proportion of free water. This comparison demonstrates that the gel from sodium citrate-soaked giant squid is weak. More importantly, it provides mechanistic insights by highlighting the specific structural and hydration deficiencies responsible for its poor performance. The findings underscore that targeted strategies to modify the protein network are necessary for the effective utilization of giant squid in surimi production. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied across the perioperative continuum, with potential benefits in efficiency, personalization, and patient safety. Unfortunately, most such tools are developed in isolation, limiting their clinical utility. Multi-Agent Systems for Healthcare (MASH), in which autonomous AI agents coordinate tasks across multiple domains, may provide the necessary framework for integrated perioperative care. This critical review synthesizes current AI applications in anesthesiology and considers their integration within a MASH architecture. This is the first review to advance MASH as a conceptual and practical framework for anesthesiology, uniquely contributing to the AI discourse by proposing its potential to unify isolated innovations into adaptive and collaborative systems. Methods: A critical review was conducted using PubMed and Google Search to identify peer-reviewed studies published between 2015 and 2025. The search strategy combined controlled vocabulary and free-text terms for AI, anesthesiology, perioperative care, critical care, and pain management. Results were filtered for randomized controlled trials and clinical trials. Data were extracted and organized by perioperative phase. Results: The 16 studies (6 from database search, 10 from prior work) included in this review demonstrated AI applications across the perioperative timeline. Preoperatively, predictive models such as POTTER improved surgical risk stratification. Intraoperative trials evaluated systems like SmartPilot and Navigator, enhancing anesthetic dosing and physiologic stability. In critical care, algorithms including NAVOY Sepsis and VentAI supported early detection of sepsis and optimized ventilatory management. In pain medicine, AI assisted with opioid risk assessment and individualized pain-control regimens. While these trials demonstrated clinical utility, most applications remain domain-specific and unconnected from one another. Conclusions: AI has broad potential to improve perioperative care, but its impact depends on coordinated deployment. MASH offers a unifying framework to integrate diverse agents into adaptive networks, enabling more personalized anesthetic care that is safer and more efficient. Full article

Backscatter technologies promise to enable large-scale, battery-free sensor networks by modulating and reflecting ambient radio frequency (RF) carriers rather than generating new signals. Translating this potential into practical deployments—such as distributed photovoltaic (PV) power systems—necessitates realistic modeling that accounts for deployment variabilities commonly neglected in idealized analyses, including uncertain hardware insertion loss, non-ideal antenna gain, spatially varying path loss exponents, and fluctuating noise floors. In this work, we develop a practical model for reliable backscatter communications that explicitly incorporates these impairing factors, and we complement the theoretical development with empirical characterization of each contributing term. To validate the model, we implement a frequency-shift keying (FSK)-based backscatter system employing a non-coherent demodulation scheme with adaptive bit-rate matching, and we conduct comprehensive experiments to evaluate communication range and sensitivity to system parameters. Experimental results demonstrate strong agreement with theoretical predictions: the prototype tag consumes 825 µW in measured operation, and an integrated circuit (IC) implementation reduces consumption to 97.8 µW, while measured communication performance corroborates the model’s accuracy under realistic deployment conditions. Full article

Atopic dermatitis (AD) is a multifactorial skin disorder characterized by immune and barrier dysfunction. The gut–skin axis is a bidirectional pathway through which gut and skin influence each other via microbial metabolites. Bioactive metabolites produced by microbial transformation of phytochemicals show potential for AD prevention. This study developed a computational systems biology pipeline that prioritized gut-derived metabolites from Philippine medicinal plants by integrating metabolite prediction, pharmacokinetics, network analysis, and molecular simulations. From 2231 predicted metabolites, 31 satisfied pharmacological criteria and were mapped to 199 AD-associated targets, with ALB, CASP3, and PPARG identified as hub genes. Two metabolites, THPOC and PM38, exhibited complementary target affinities and strong binding stability. THPOC stabilized ALB and CASP3, supporting barrier integrity and apoptosis regulation, while PM38 strongly engaged PPARG, modulating lipid metabolism and anti-inflammatory transcription. They exhibited comparable or superior docking scores, stable MD interactions, and favorable binding free energies, compared to abrocitinib, an approved AD treatment. DFT analysis confirmed electronic stability and donor–acceptor properties linked to target selectivity. These findings highlight THPOC and PM38 as promising immunometabolic modulators acting on key AD-related pathways. Collectively, this study introduces a reproducible systems-based computational discovery framework, offering a novel preventive strategy for AD. Full article

Background: Migraine is a common, disabling neurological disorder with substantial genetic and environmental contributions. Dietary exposures are widely discussed by patients and clinicians as potential triggers or modifiers of attack frequency and severity. We synthesized contemporary evidence on dietary patterns, specific nutrients, and elimination strategies relevant to migraine prevention and management. Methods: We performed a narrative review of PubMed and Google Scholar (inception–August 2025) using combinations of “migraine”, “diet”, “nutrition”, “ketogenic”, “Mediterranean”, “omega-3”, and “gluten”. We prioritized randomized/controlled studies, recent systematic reviews/meta-analyses, and representative observational studies; evidence quality and applicability were appraised descriptively. Results: Higher adherence to a Mediterranean-style diet is associated with lower migraine frequency and disability in observational cohorts. Very low-calorie ketogenic diets significantly reduced monthly migraine attack frequency compared with isocaloric non-ketogenic comparators in an adult randomized controlled trial of participants with overweight or obesity (≥50% responder rate: 74% vs. 6%). Additional supportive evidence from uncontrolled studies, including those involving medium-chain triglyceride supplementation, further corroborates these findings. Omega-3 fatty acids (EPA/DHA) show prophylactic benefit in randomized trials and network meta-analyses, with favorable tolerability. Gluten-free diets may improve headaches in celiac disease and may help selected non-celiac patients. Alcohol (especially red wine) and high, irregular caffeine intake are frequently reported triggers, while evidence for specific foods/additives remains inconsistent. Weight loss and regular physical activity may further reduce burden in people with obesity. Conclusions: Current evidence supports recommending Mediterranean-style eating, consideration of omega-3 supplementation, and selective trials of ketogenic or elimination approaches in appropriate patients, alongside weight management and lifestyle optimization. High-quality, longer-duration RCTs using standardized dietary protocols and adherence biomarkers are needed to define dose–response relationships and enable personalized nutrition in migraine. Full article

To investigate the long-term water stability of graphene-modified permeable asphalt mixtures, in this study, we analysed the effects of single factors and multi-factor coupling. The single-factor water stability was investigated through the free thawing splitting test, standard Cantabro test, and immersion Cantabro test; the experimental indicators were the freeze–thaw cracking ratio (TSR), mass loss rate, and immersion mass loss rate, respectively. The multi-factor water stability was studied through immersion operation tests of mixtures with different degrees of ageing. The dispersion of graphene was examined through Raman mapping, the formation of three-dimensional network structures of graphene and SBS was evaluated via the dynamic shear rheometer test (DSR), and the elemental distribution was quantitatively analysed using energy-dispersive spectroscopy (EDS) and an image pixel algorithm (RGB). The results indicate that an unaged graphene-composite- and SBS-modified permeable asphalt mixture with an optimal graphene content of 0.05% demonstrated a 4.5% improvement in the TSR, alongside reductions in the mass loss rate and water immersion mass loss rate of 25.64% and 23.52%, respectively. Even after prolonged thermal oxygen ageing, its TSR, mass loss rate, and water immersion mass loss rate improved by 5.1%, 23.04%, and 20.70%, respectively. Multi-factor coupling tests confirmed that the water stability met requirements under severe conditions, with better performance at high temperatures. Graphene was uniformly dispersed in the modified asphalt. The appearance of a plateau region at low frequencies in graphene-composite- and SBS-modified asphalt verified the formation of a three-dimensional network structure, and the oxygen content was positively correlated with deepening thermal oxidative ageing. Full article

Mobile networks have advanced significantly, providing high-throughput voice, video, and integrated data access to support connectivity through various services to facilitate high user density. This traffic growth has also increased the complexity of outlier detection (OD) for fraudster identification, fault detection, and protecting network infrastructure and its users against cybersecurity threats. Autoencoder (AE) models are widely used for outlier detection (OD) on unlabeled and temporal data; however, they rely on fixed anomaly thresholds and anomaly-free training data, which are both difficult to obtain in practice. This paper introduces statistical masking in the encoder to enhance learning from nearly normal data by flagging potential outliers. It also proposes a quasidynamic threshold mechanism that adapts to reconstruction errors, improving detection by up to 3% median area under the receiver operating characteristic (AUROC) compared to the standard 95% threshold used in base AE models. Extensive experiments on the Milan Human Telecommunications Interaction (HTA) dataset validate the performance of the proposed methods. Combined, these two techniques yield a 31% improvement in AUROC and a 34% lower computational complexity when compared to baseline AE, long short-term memory AE (LSTM-AE), and seasonal auto-regressive integrated moving average (SARIMA), enabling efficient OD in modern cellular networks. Full article

This study explores the development of bigels (BGs) combining a hydrophilic hydrogel (HG) and a lipophilic oleogel (OG) for co-delivery of two carob fruit extracts (CFEs): I-CFE (inositols) and P-CFE (phenolics). The BGs were formulated in HG:OG ratios of 70:30 and 30:70, using a sodium alginate-based HG and an OG composed of olive pomace oil (OPO) and microcrystalline wax (MW). CFEs were loaded in three modes: I-CFE in HG, P-CFE in OG, and both in their respective phases. Rheological, thermal, physicochemical, and microstructural properties were assessed. All the BGs exhibited solid-like viscoelastic behavior, with greater rigidity in 30:70 formulations. The OG phase enhanced the structural BG network, especially when loaded with P-CFE. At 70:30, I-CFE conferred pseudoplasticity and conformational flexibility, particularly in the absence of P-CFE. At 30:70, both extracts acted synergistically, increasing mechanical strength and network organization. Thermal analysis confirmed MW’s role in structuration, with the BGs showing melting peaks between 40–50 °C. The effects studied affected color and stability. Polarized light microscopy confirmed organized microstructures. This is the first work demonstrating the structuring potential and interactive effects of dual carob extracts (I-CFE and P-CFE) within BGs. All the BGs showed suitable fat-replacer properties, remaining self-standing for 21 days, except the 70:30 I-CFE-free formulation. The findings highlight the potential of CFE-loaded BGs as multifunctional fat replacers in healthier meat products. Full article

With increasing attention to health, plant protein products have gained significant market potential due to their growing consumer demand. This study researches the influence of ultrasonic treatment on the structure and function of pea–oat composite protein gel (POPG) to enhance its elasticity and thermal stability. The ultrasonic treatment parameters were regulated to power (200–600 W for 30 min) and ultrasonic time (20–40 min at 400 W) during the preparation of POPG, and the properties and structure, including gel strength, rheological analysis, water-holding capacity (WHC), thermal characteristics, fluorescence performance, and microstructure, were further evaluated. The results showed that the POPG samples exhibited optimal values in WHC, gel strength, surface hydrophobicity, free sulfhydryl amount, and endogenous fluorescence at 400 W ultrasonic for 30 min compared with the untreated POPG. Rheological analysis indicated that POPG displayed the highest storage modulus and improved viscoelasticity. Ultrasonication resulted in an augmentation in β-sheet content, hence creating a more compact network structure. DSC and TGA revealed improved thermal stability, while SEM and CLSM exhibited a homogeneous and firm gel structure of POPG. This research offers the theory that ultrasonic technology can improve the performance of plant-based composite gels. Full article