Search Results (14,194)

Underwater wireless sensor networks (UWSNs) are increasingly used in marine monitoring and naval coastal surveillance, where limited bandwidth, long propagation delays, and physically exposed nodes make efficient authentication critical. This paper analyzes the maritime-surveillance-oriented protocol of Jain and Hussain and identifies vulnerabilities to physical capture, replay, and denial-of-service (DoS) attacks. We propose a PUF-assisted mutual authentication and session key agreement protocol for UWSNs. The design relies on lightweight symmetric primitives (one-way hash and XOR) and uses a fuzzy extractor to support stable PUF-based key material. In addition, a lightweight continuous authentication procedure is introduced to facilitate fast re-authentication under intermittent link disruptions commonly observed in underwater communication. Security is evaluated using BAN logic, the Real-or-Random (ROR) model, and security verification with the Scyther tool. An analytical overhead evaluation reports a computational cost of 5.972 ms per mutual authentication and a 1152-bit communication overhead, supporting a practical security–efficiency trade-off for resource-constrained UWSN deployments. Full article

The use of ships and boats as sea-state (SS) measurement platforms has the potential to expand ocean observations while providing actionable information for real-time operational decision-making at sea. Within the framework of the Wave Buoy Analogy (WBA), this work develops an inverse approach in which efficient simulations of wave-induced motions of an advancing vessel are used to train a neural network (NN) to predict SS parameters across a broad range of wave climates. We show that a reduced set of novel motion discriminant variables (MDVs)—computed from short time series of heave, roll, and pitch motions measured by an onboard inertial measurement unit (IMU), together with the vessel’s forward speed—provides sufficient and robust information for accurate, near-real-time SS estimation. The methodology targets small, barge-like tugboats whose operations are SS-limited and whose motions can become large and strongly nonlinear near their upper operating limits. To accurately model such responses and generate training data, an efficient nonlinear time-domain seakeeping model is developed that includes nonlinear hydrostatic and viscous damping terms and explicitly accounts for forward-speed effects. The model is experimentally validated using a scaled physical model in laboratory wave-tank tests, demonstrating the necessity of these nonlinear contributions for this class of vessels. The validated model is then used to generate large, high-fidelity datasets for NN training. When applied to independent numerically simulated motion time series, the trained NN predicts SS parameters with errors typically below 5%, with slightly larger errors for SS directionality under relatively high measurement noise. Application to experimentally measured vessel motions yields similarly small errors, confirming the robustness and practical applicability of the proposed framework. In operational settings, the trained NN can be deployed onboard a tugboat and driven by IMU measurements to provide real-time SS estimates. While results are presented for a specific vessel, the methodology is general and readily transferable to other ship geometries given appropriate hydrodynamic coefficients. Full article

In this paper, we explore the spiking encoding methodology within spiking neural networks for affective state recognition, deriving inspiration from the principles of quantum entanglement. A pioneering encoding strategy is proposed based on the strategic utilization of the quantum mechanical phenomenon of entanglement. By integrating quantum mechanisms into the spike-encoding pipeline, we aim to match the accuracy of existing encoders on emotion-classification tasks while retaining the inherently low-power advantage of spiking neural networks. Notably, leveraging the superposition of quantum bits and their potential quantum entanglement of adjacent values in feature space during encoding calculations, this quantum-inspired encoding paradigm holds substantial promise for augmenting information processing capabilities in brain-like neural networks. Through quantum observation, we derive spike trains characterized by quantum states, thereby establishing a foundation for experimental validation and subsequent investigative pursuits. We conducted experiments on emotion recognition and validated the effectiveness of our method. Full article

The peak height of the ionospheric F2 layer (hmF2) is a critical parameter in ionospheric physics and high-frequency radio wave propagation research. This study presents a backpropagation neural network (BPNN) enhanced by wavelet transform (WT) decomposition for one-hour-ahead hmF2 forecasting. The WT method decomposes and reconstructs the hmF2 time series, preserving its primary structural characteristics. Subsequently, the BPNN provides high-accuracy predictions. The model is trained and evaluated using 2014 hmF2 measurements from four observation stations in China. Utilizing only hmF2 data, the model produces accurate one-hour-ahead forecasts. The predicted values closely align with observed diurnal variations and exhibit lower fluctuations than those of the IRI and standalone BPNN models. On the test set, the proposed model achieves an average RMSE of 17.16 km, which is 10.10 km and 8.39 km lower than the IRI and BPNN models, respectively. The average RRMSE is 5.72%, representing reductions of 2.88% and 2.64% compared to the IRI and BPNN models, respectively. These findings indicate that the hybrid model is well-suited for the Chinese region and substantially enhances short-term hmF2 forecast accuracy. Full article

Background: Glaucoma is an age-related optic neuropathy frequently accompanied by systemic comorbidities. Vitamin D deficiency (VDD) has been associated with cardiovascular and renal diseases in the general population, yet its relationship with long-term systemic outcomes in glaucoma remains unclear. This study evaluated the association between baseline vitamin D status and subsequent mortality and cardiorenal events in patients with primary glaucoma. Methods: We conducted a retrospective cohort study using deidentified electronic health records from the TriNetX U.S. Collaborative Network, a federated network of participating healthcare organizations. Adults (≥18 years) with incident primary glaucoma (2005–2020) and a serum 25-hydroxyvitamin D (25(OH)D) test within 12 months prior to diagnosis were categorized as VDD (<30 ng/mL) or vitamin D adequacy (VDA; ≥30 ng/mL). After 1:1 propensity score matching across 47 demographic, clinical, medication, and laboratory variables, 11,855 patients per group were followed for up to 5 years. Outcomes included all-cause mortality, major adverse cardiovascular events (MACE), acute kidney injury (AKI), and renal function decline (eGFR < 60 mL/min/1.73 m²). Analyses incorporated Kaplan–Meier curves, Cox models, landmark tests, sensitivity analyses, and competing risk methods. Results: Among the 35,100 eligible patients, the matched cohorts demonstrated higher 5-year risks associated with VDD for all-cause mortality (HR 1.104; 95% CI 1.001–1.217), MACE (HR 1.151; 95% CI 1.078–1.229), and AKI (HR 1.154; 95% CI 1.056–1.261), whereas the risks of renal function decline did not differ (HR 0.972; 95% CI 0.907–1.042). Risk divergence emerged within the first year of follow-up and persisted through the 5-year observation period. Conclusions: In patients with primary glaucoma, vitamin D deficiency was associated with higher long-term risks of mortality and cardiorenal complications, but not renal function decline. Taken together, the results are consistent with vitamin D status serving as a marker of broader systemic vulnerability in glaucoma and highlight the need for prospective studies to further clarify its prognostic significance. Full article

In recent years, with the improvement of unmanned aerial vehicle (UAV) performance, various applications have been explored. In environments such as disaster areas, where existing infrastructure may be damaged, alternative uplink communication for transmitting observation data from UAVs to the ground station (GS) is critical. However, conventional mobile ad hoc network (MANET) routing protocols do not sufficiently account for GS-oriented traffic or the highly mobile UAV topology. This study proposed a flying ad hoc network (FANET) routing protocol that introduces a control option called GS flood, where the GS periodically disseminates routing information, enabling each UAV to efficiently acquire fresh source routes to the GS. Evaluation using NS-3 in a disaster scenario confirmed that the proposed method achieves a higher packet delivery ratio and practical latency compared to the representative MANET routing protocols, namely DSR, AODV, and OLSR, while operating with fewer control IP packets than existing methods. Furthermore, although the multihop throughput between UAVs and the GS in the proposed method plateaued at approximately 40% of the physical-layer maximum, it demonstrated performance exceeding realistic satellite uplink capacities ranging from several hundred kbps to several Mbps. Full article

This study aimed to modify metabolite synthesis in Saccharomyces cerevisiae (S. cerevisiae) under simulated wine fermentation conditions by regulating the glycerol metabolic pathway. We systematically analyzed the effects of overexpressing the aquaporin gene AQY1 and co-expressing AQY1 with the glycerol-3-phosphate dehydrogenase gene GPD1 on the metabolism of ethanol, higher alcohols, and esters. Our results indicate that AQY1 overexpression increased glycerol yield by 6.58%, reduced higher alcohol content by 14.60%, and elevated ester content by 7.15%. The downregulation of related amino acid metabolism genes correlated with the observed decrease in higher alcohol levels. Notably, co-expression of AQY1 and GPD1 further enhanced glycerol yield by 10.66% while decreasing ethanol content by 6.32%. By analyzing changes in gene expression alongside metabolic mechanisms, we hypothesize that the redistribution of carbon flux and NADH toward the glycerol pathway not only decreases the precursors for ethanol synthesis but also directly inhibits the activity of aldehyde dehydrogenase (ALD2/3/4/6), thereby constraining ethanol production. In comparison to AQY1 overexpression alone, the co-expression strategy did not significantly alter glycerol accumulation; however, it reduced both ethanol and ester content by 8.38% and 8.40%, respectively, while markedly increasing higher alcohol content by 22.30%. This increase may result from enhanced glycolytic flux and pyruvate accumulation, which promote metabolic flow toward amino acid synthesis pathways. In summary, this study effectively remodeled the central carbon metabolism network by targeting glycerol metabolism, achieving diverse metabolic product synthesis and providing important references for the selection and breeding of industrial S. cerevisiae strains. Full article

Excitable neurons are intrinsically capable of firing action potentials (AP), yet a state of hyperexcitability is prevented in the central nervous system by powerful GABAergic inhibition. For this inhibition to be effective, it must occur before excitatory signals can initiate runaway activity, implying the existence of a proactive control system. To test for such proactive inhibition, we used Ca²⁺ imaging and patch-clamp recording to measure how hippocampal neurons respond to depolarization and glutamatergic agonists. In mature hippocampal cultures (14 days in vitro (DIV)) and acute brain slices from two-month-old rats, neurons exhibited non-simultaneous responses to various excitatory stimuli, including KCl, NH₄Cl, forskolin, domoic acid, and glutamate. We observed that the Ca²⁺ rise occurred significantly earlier in GABAergic neurons than in glutamatergic neurons. This delay in glutamatergic neurons was abolished by GABA(A) receptor inhibitors, suggesting a mechanism of preliminary γ-aminobutyric acid (GABA) release. We further found that these early-responding GABAergic neurons express calcium-permeable kainate and AMPA receptors (CP-KARs and CP-AMPARs). Application of domoic acid induced an immediate Ca²⁺ increase in neurons expressing these receptors, but a delayed response in others. Crucially, when domoic acid was applied in the presence of the AMPA receptor inhibitors NBQX or GYKI-52466, the response delay in glutamatergic neurons was significantly prolonged. This confirms that CP-KARs on GABAergic neurons are responsible for the delayed excitation of glutamatergic neurons. In hippocampal slices from two-month-old rats, depolarization with 50 mM KCl revealed two distinct neuronal populations based on their calcium dynamics: a majority group (presumably glutamatergic) exhibited fluctuating Ca²⁺ signals, while a minority (presumably GABAergic) showed a steady, advancing increase in [Ca²⁺]i. This distinction was reinforced by the application of domoic acid. The “advancing-response” neurons reacted to domoic acid with a similar prompt increase, whereas the “fluctuating-response” neurons displayed an even more delayed and fluctuating reaction (80 s delay). Therefore, we identify a subgroup of hippocampal neurons—in both slices and cultures—that respond to depolarization and domoic acid with an early [Ca²⁺]_i signal. Consistent with our data from cultures, we conclude these early-responding neurons are GABAergic. Their early GABA release directly explains the delayed Ca²⁺ response observed in glutamatergic neurons. We propose that this proactive mechanism, mediated by CP-KARs on GABAergic neurons, is a primary means of protecting the network from hyperexcitation. Furthermore, the activity of these CP-KAR-expressing neurons is itself regulated by GABAergic neurons containing CP-AMPARs. Full article

Background/Objectives: Infantile epileptic spasms syndrome (IESS) is a severe epilepsy of infancy. Corticotropin (ACTH) and vigabatrin are the only FDA-approved therapies. The efficacy of ACTH together with the strong convulsant effects of corticotropin-releasing hormone (CRH) suggests that excess CRH, secondary to impaired ACTH feedback, may contribute to spasms. We therefore hypothesized that CRH receptor 1 (CRHR1) antagonists would suppress spasms in a route- and drug-dependent manner. Methods: Using our validated rat model of IESS, in which prenatal priming with betamethasone was followed by postnatal triggering of spasms with N-methyl-D-aspartic acid (NMDA), we tested two CRHR1 antagonists, CP376395 and SN003, delivered intracranially (via intracerebroventricular or intraparenchymal infusion) or systemically. Results: Intracerebroventricular infusion of both antagonists suppressed spasms, with CP376395 providing more consistent effects. Intraparenchymal administration into the hypothalamic arcuate nucleus also reduced spasms, whereas misses into the mammillary bodies were ineffective, highlighting site specificity. Systemic administration yielded divergent results: SN003 robustly suppressed spasms, whereas CP376395 unexpectedly exacerbated them. No sex differences were observed. Conclusions: These findings demonstrate that CRHR1 blockade modifies experimental spasms in a route- and drug-specific manner and implicates discrete hypothalamic circuits, particularly those including the arcuate nucleus, in spasm generation. The divergent systemic responses between CP376395 and SN003 likely reflect differences in CRHR1 engagement (competitive and non-competitive antagonism, respectively) as well as differences in binding properties that may include differential network interactions beyond local CRH signaling or duration of receptor occupancy. In conclusion, SN003 may be a better option than CP376395 for further development as a CRHR1-targeted therapy pending additional pharmacokinetic/pharmacodynamic studies. Further work should explore dosing paradigms of CP376395 to determine if a therapeutic range for CP376395 exists. Full article

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, karst ecological vulnerability, and port economic agglomeration”—as a case study. Based on remote sensing image data from 2000 to 2020, a landscape ecological risk index was constructed, and regional landscape ecological risk levels were assessed using ArcGIS spatial analysis tools. On this basis, ecological sources were identified by combining the InVEST model with morphological spatial pattern analysis (MSPA),and an ecological resistance surface was constructed by integrating factors such as land use type, elevation, slope, distance to roads, distance to water bodies, and NDVI. Furthermore, the circuit theory method was applied to identify ecological corridors, ecological pinch points, and barrier points, ultimately constructing the ecological security pattern of the Pinglu Canal Economic Belt. The main findings are as follows: (1) Ecological risks were primarily at low to medium levels, with high-risk areas concentrated in the southern coastal region. Over the past two decades, an overall optimization trend was observed, shifting from high risk to lower risk levels. (2) A total of 15 ecological sources (total area 1313.71 km²), 31 ecological corridors (total length 1632.42 km), 39 ecological pinch points, and 15 ecological barrier points were identified, clarifying the key spatial components of the ecological network. (3) Based on spatial analysis results, a zoning governance plan encompassing “ecological protected areas, improvement areas, restoration areas, and critical areas” along with targeted strategies was proposed, providing a scientific basis for ecological risk management and pattern optimization in the Pinglu Canal Economic Belt. Full article

Background: ALG13-CDG is an X-linked N-linked glycosylation disorder caused by pathogenic variants in the glycosyltransferase ALG13, leading to severe neurological manifestations. Despite the clear CNS involvement, the impact of ALG13 dysfunction on human brain glycosylation and neurodevelopment remains unknown. We hypothesize that ALG13-CDG causes brain-specific hypoglycosylation that disrupts neurodevelopmental pathways and contributes directly to cortical network dysfunction. Methods: We generated iPSC-derived human cortical organoids (hCOs) from individuals with ALG13-CDG to define the impact of hypoglycosylation on cortical development and function. Electrophysiological activity was assessed using MEA recordings and integrated with multiomic profiling, including scRNA-seq, proteomics, glycoproteomics, N-glycan imaging, lipidomics, and metabolomics. X-inactivation status was evaluated in both iPSCs and hCOs. Results: ALG13-CDG hCOs showed reduced glycosylation of proteins involved in ECM organization, neuronal migration, lipid metabolism, calcium homeostasis, and neuronal excitability. These pathway disruptions were supported by proteomic and scRNA-seq data and included altered intercellular communication. Trajectory analyses revealed mistimed neuronal maturation with early inhibitory and delayed excitatory development, indicating an E/I imbalance. MEA recordings demonstrated early network hypoactivity with reduced firing rates, immature burst structure, and shortened axonal projections, while transcriptomic and proteomic signatures suggested emerging hyperexcitability. Altered lipid and GlcNAc metabolism, along with skewed X-inactivation, were also observed. Conclusions: Our study reveals that ALG13-CDG is a disorder of brain-specific hypoglycosylation that disrupts key neurodevelopmental pathways and destabilizes cortical network function. Through integrated multiomic and functional analyses, we identify early network hypoactivity, mistimed neuronal maturation, and evolving E/I imbalance that progresses to compensatory hyperexcitability, providing a mechanistic basis for seizure vulnerability. These findings redefine ALG13-CDG as disorders of cortical network instability, offering a new framework for targeted therapeutic intervention. Full article

Reliable biological datasets, especially those integrating biotic indices such as the Saprobic Index, are scarce, limiting machine and deep learning applications in aquatic ecosystem assessments. This study evaluates Conditional Tabular Generative Adversarial Networks (CTGANs) for generating synthetic datasets that combine physico-chemical parameters with a biological index (Saprobic Index) from multiple monitoring stations in the lower Danube River. Beyond univariate distributional agreement, we assess whether ecologically meaningful multivariate relationships are preserved in the synthetic tables. To support this, we propose an ecology-oriented validation workflow that combines distributional tests with correlation structure and clustering diagnostics across stations. Real monitoring datasets were statistically modelled and recreated using CTGANs, then qualitatively assessed for realism. Comparisons between synthetic and real data employed box plots, Wilcoxon rank-sum tests, correlation matrices, and K-means clustering across stations. Stable variables, including pH, total dissolved solids, and chemical oxygen demand, were well replicated, showing no significant distributional differences (p > 0.05). Conversely, dynamic parameters such as dissolved oxygen, total nitrogen, and suspended solids exhibited notable discrepancies (p < 0.05). Correlation analyses indicated that several strong associations present in the observed data (e.g., total nitrogen–nitrate and total nitrogen–electrical conductivity) were substantially weaker in the synthetic dataset. Overall, a CTGAN can reproduce several marginal patterns but may fail to preserve key ecological linkages, which constrains its use in ecological relationship-dependent inference. While promising for exploratory modelling and general trend analysis, synthetic data should be applied cautiously for studies involving seasonally influenced, biologically significant parameters. Full article

In this study, we investigate the Suvarṇabhūmi area, corresponding to central–southern Mainland Southeast Asia. We test the hypothesis that this region, located to the south of the Himalayan foothills, can be characterised as a convergence zone in which diverse entities involving humans, animals, and artefacts have significantly diverged from their related counterparts outside the area. We argue that this process of convergence was facilitated by the Maritime Silk Road trade networks, which were particularly active between the 3rd century BCE and the 9th century CE. Comparative data are derived from multiple scientific disciplines, including linguistic typology, onomastics, epigraphy, archaeology, and evolutionary biology. This includes typological features of language, toponyms, inscriptions, glass bead chemistry and related material culture, and phylogenetic data from patterns of endemism to illustrate parallel convergence scenarios observed for each data type. The results reveal recurring patterns of convergence. Linguistic, technological, and biological entities tend to diverge from their original forms and realign with predominant regional types when entering the Suvarṇabhūmi area. The spread of Indic and Sinitic linguistic and cultural elements, the adaptation and development of Brāhmī scripts into distinct local forms, the secondary manufacturing of glass beads, and unique genetic lineages in mammals, amphibians, reptiles, fish, and plants all point to the region’s role as a dynamic interaction sphere. We argue that Suvarṇabhūmi functions as an ecological system, in which trajectories of convergence are notable across a number of individual aspects of cultural and biological diversity. Altogether, these components have contributed to shaping the region’s distinctive natural and cultural history. Full article

Traditional routing algorithms optimizing for distance or travel time are inadequate for electric vehicles (EVs), which require energy-aware planning considering battery constraints and charging infrastructure. This work presents an energy-optimal routing system for EVs that integrates personalized consumption modeling with real-time environmental data. The system employs a Long Short-Term Memory (LSTM) neural network to predict State-of-Charge (SoC) consumption from real-world driving data, learning directly from spatiotemporal features including velocity, temperature, road inclination, and traveled distance. Unlike physics-based models requiring difficult-to-obtain parameters, this approach captures nonlinear dependencies and temporal patterns in energy consumption. The routing framework integrates static map data, dynamic traffic conditions, weather information, and charging station locations into a weighted graph representation. Edge costs reflect predicted SoC drops, while node penalties account for traffic congestion and charging opportunities. An enhanced A* algorithm finds optimal routes minimizing energy consumption. Experimental validation on a Nissan Leaf shows that the proposed end-to-end SoC estimator significantly outperforms traditional approaches. The model achieves an RMSE of 36.83 and an $R^2$ of 0.9374, corresponding to a 59.91% reduction in error compared to physics-based formulas. Real-world testing on various routes further confirms its accuracy, with a Mean Absolute Error in the total route SoC estimation of 2%, improving upon the 3.5% observed for commercial solutions. Full article

Synthetic aperture radar (SAR), with its unique imaging principle and technical characteristics, has significant advantages in surface observation and thus has been widely applied in tasks such as object detection and target classification. However, limited by the lack of labeled SAR image datasets, the accuracy and generalization ability of the existing models in practical applications still need to be improved. In order to solve this problem, this paper proposes a spaceborne SAR image simulation technology and innovatively introduces the concept of bounce number map (BNM), establishing a high-resolution, parameterized simulated data support system for target recognition and classification tasks. In addition, an electromagnetic scattering characteristic-enhanced dual-branch network with simulated image guidance for SAR ship classification (SeDSG) was designed in this paper. It adopts a multi-source data utilization strategy, taking SAR images as the main branch input to capture the global features of real scenes, and using simulated data as the auxiliary branch input to excavate the electromagnetic scattering characteristics and detailed structural features. Through feature fusion, the advantages of the two branches are integrated to improve the adaptability and stability of the model to complex scenes. Experimental results show that the classification accuracy of the proposed network is improved on the OpenSARShip and FUSAR-Ship datasets. Meanwhile, the transfer learning classification results based on the SRSDD dataset verify the enhanced generalization and adaptive capabilities of the network, providing a new approach for data classification tasks with an insufficient number of samples. Full article