Search Results (461)

The Wadi Al-Ahsaba watershed is an arid to semi-arid catchment situated in southwestern Saudi Arabia, characterized by intermittent surface flow, high evaporation and low rainfall, and a dam reservoir built for flood control. The work aims to assess hydrological and anthropogenic controls on surface and groundwater quality, pollution status, and human health risks using an integrated approach of hydrogeochemical analysis, multivariable statistics, and water quality and contamination indices. A total of 21 water samples (15 surface water, 6 groundwater) were analyzed for general chemistry, major ions, and trace elements. Hydrogeochemical analysis and principal component analysis (PCA) were implemented to differentiate the geogenic from anthropogenic control on water quality. The pollution status and associated risk were evaluated using water quality index (WQI), contamination degree (Cd), Hazard Quotient (HQ), and Hazard Index (HI). Results suggest limited surface–groundwater interaction, with surface water dominated by Ca–Mg–HCO₃ facies, indicating recent recharge and limited water–rock interaction, whereas groundwater exhibits mixed Ca–Mg–Cl and Ca–Na–Cl–SO₄ types, revealing longer residence time and water–rock interaction. Nitrate (9.5–109 mg/L) and TDS (522–1003 mg/L) exceeded drinking water standards in 90% and 95% of tested samples, respectively, and WQI ranged from 43 to 134, reflecting excellent to poor water. High non-carcinogenic risk from nitrate was observed, especially for infants. The study concluded that the geogenic processes (water–rock interaction, evaporation, and mineral dissolution) control the general chemistry of tested water, while anthropogenic input from wastewater and agriculture input are likely contributors to nitrate contamination. The study contributes to the understanding of arid wadi-dam systems by revealing how limited recharge, hydrological connectivity, and episodic flow control contaminant transport and persistence, underscoring the critical role of integrated hydrological analysis and land use management in safeguarding freshwater resources in arid environments. Full article

We investigate the onset of hyperbolicity in Jensen polynomials $J_{d,n}$ associated with the Riemann $\Xi$-function and identify a robust parity-driven bifurcation with a natural geometric interpretation. Numerical analysis for degrees $5\le d\le 16$ reveals two distinct regimes. For even d, the roots form a compact complex cluster whose imaginary extent decreases smoothly, and the transition to hyperbolicity is governed by a single complex-conjugate pair, consistent with a low-dimensional (tame) geometric structure. For odd d, a hierarchy of more intricate onset mechanisms emerges, including single-event transitions ($d=11$) and intermittent regimes ($d\ge 13$) with decoupled geometric invariants, suggestive of dynamics on decorated (wild) character varieties. We interpret this dichotomy through a connection with the $P_{\mathrm{III}}\left(D_6\right)$ tau-function arising in the Painlevé confluence diagram. Defining $\tau \left(t\right)=\Xi ({\displaystyle \frac{1}{2}}+\sqrt{-t})/\Xi \left({\displaystyle \frac{1}{2}}\right)$, we construct a generating function $B\left(w\right)={\sum }_{j\ge 0}{b}_j{w}^j$ from the cumulants of $log\Xi ({\displaystyle \frac{1}{2}}+z)$ using high-precision Cauchy/DFT methods (280–400-digit arithmetic), without explicit use of the zero expansion. Two independent numerical diagnostics indicate that B exhibits Stieltjes-type behavior: (i) positivity of Hankel determinants up to order $N=30$ and (ii) Padé approximants whose poles converge to ${\gamma }_k^2$ (squares of Riemann-zero ordinates) with stabilizing residues. These results provide strong evidence that the parity bifurcation observed in Jensen polynomial onset reflects a finite-dimensional manifestation of an underlying moment-based positivity structure. Motivated by this correspondence, we formulate a conjecture relating the Stieltjes nature of $B\left(w\right)$ to the eventual hyperbolicity of Jensen polynomials. This conjecture suggests a bridge between finite-dimensional root geometry and an infinite-dimensional kernel-based positivity framework, while leaving open the problem of establishing such positivity independently of the zero expansion. Full article

Monitoring water quality in the Amazon River remains a significant challenge due to limited accessibility, high sediment loads, intermittent connectivity, and the lack of continuous data in remote regions. This study presents the development and experimental validation of an IoT-based system for real-time water quality monitoring. The platform integrates an STM32WL-based embedded architecture with multiparameter sensing, LoRaWAN communication, and configurable monitoring strategies to enable autonomous operation in dynamic environments. The system was validated through a comparative study involving 698 manually collected samples over eight months and 49,570 automated measurements collected during a three-month field deployment. The evaluation considered measurement consistency, variability and operational autonomy based on CONAMA Resolution No. 357/2005. The results showed good agreement between manual and automated measurements, with MAE/RMSE values of 0.18/0.20 °C for water temperature, 0.36/0.44 for pH, and 12.99/20.09 NTU for turbidity. Additionally, the energy analysis demonstrated autonomous operation under variable solar irradiance, achieving self-sufficiency under typical conditions and maintaining operation for up to 4.9 days without solar input. Taken together, the study provides a robust and scalable framework for continuous monitoring in sediment-rich tropical river systems. Full article

As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 ± 0.0086 to 0.0048 ± 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder. Full article

Kimchi fermentation involves dynamic physicochemical and microbial changes; however, conventional monitoring methods are generally dependent on intermittent measurements, resulting in limitations in the real-time detection of abnormal fermentation. In this study, a Wireless Sensor Network (WSN)-based Fermentation Monitoring System (WFMS) and a Long Short-Term Memory (LSTM)-based Anomaly Detection System (LADS) were developed to continuously monitor internal pressure changes during kimchi fermentation. Kimchi samples were prepared under normal fermentation conditions (CON) and glucose-added conditions (GLU-6). Pressure data were collected at 10 min intervals using 15 psi and 30 psi pressure sensors connected to an Arduino Nano 33 IoT board and were transmitted to the ThingSpeak platform. During the fermentation period, pressure data were collected stably, while the external temperature was maintained at approximately 25 °C. Both CON and GLU-6 samples exhibited a rapid increase in internal pressure during the early fermentation stage, followed by a gradual decrease. However, relatively larger pressure fluctuations were observed in the middle and late fermentation stages of the GLU-6 samples. An LSTM autoencoder model trained using CON data established a reconstruction error-based threshold of 0.0025 and successfully detected anomalies in the GLU-6 samples. Anomalies were mainly identified during the initial fermentation stage and between fermentation days 2 and 4. These results demonstrate that pressure-based real-time monitoring combined with LSTM autoencoder analysis can be effectively applied for the non-destructive tracking of kimchi fermentation and the early detection of abnormal fermentation patterns. Full article

Obstructive sleep apnea (OSA) constitutes a chronic disorder characterized by recurrent upper airway collapse during sleep. This condition is prevalent among patients with cardiac rhythm disturbances and represents a potent independent risk factor for arrhythmia. Although most studies have concentrated on the association between OSA and atrial fibrillation (AF), numerous investigations have established connections with ventricular and supraventricular arrhythmias. Arrhythmogenesis in OSA represents a complex multifactorial phenomenon. Acute mechanisms involve induction of negative intrathoracic pressure during the effort to breathe, which triggers recurrent episodes of hypoxia, hypercapnia, alterations in carbon dioxide and acid–base equilibrium, as well as surges in sympathetic nervous system activity. Chronic intermittent hypoxia (CIH) and negative thoracic pressure (NTP) induce atrial stretch, chronic structural remodeling, and elevated vagal tone, thereby heightening susceptibility to bradycardic and conduction arrhythmias. Intermediate pathways through which OSA may precipitate arrhythmia encompass heightened systemic inflammation, oxidative stress, a prothrombotic state, and vascular dysfunction. Long-term OSA is linked with atrial enlargement and fibrosis, ventricular hypertrophy, hypertension, and coronary artery disease. These factors predispose to cardiac arrhythmias through the following mechanisms: shortening of the atrial effective refractory period, abnormal automaticity, promotion of slowed and heterogeneous conduction, enhancement of reentrant arrhythmia persistence, and prolongation of the QT interval. In this paper, we aim to present the pathophysiological mechanisms underpinning the association between obstructive sleep apnea and cardiac arrhythmias. Understanding the precise pathophysiological pathways by which obstructive sleep apnea contributes to arrhythmogenesis will enable targeted preventive stratification of patients at risk for cardiovascular events and promote the development of innovative therapies to attenuate OSA-induced arrhythmogenicity. Full article

Black-hole accretion systems exhibit a characteristic coexistence of activities: broad-band X-ray variability, hot coronae, wide-angle winds, and both steady and discrete jets. This coexistence suggests a persistently time-dependent magnetic background in which noisy fluctuations and explosive release are both essential. In this paper, we connect them all to the storage, organization, and intermittent reconnection-mediated release of magnetic energy, and we propose a Synchronized Spin Model (SSM) in which multiple local dynamos in a rotating accretion flow are represented as interacting macro-spins. Their synchronization, partial synchronization, excursion, and reversal define a compact set of collective variables that organize both timing statistics and large-scale morphology. In this picture, multiscale magnetic reconnection converts stored magnetic energy into coronal heating, flares, intermittent outflows, and discrete jet activity, while the same synchronization dynamics produce amplitude modulation and demodulation, providing a route to 1/f-like variability, rms–flux/Taylor-like scaling, and approximately log-normal statistics of the demodulated envelope. We further argue that, although the continuous flux distribution in black-hole systems is more naturally discussed in multiplicative or log-normal terms, broader event-catalog statistics remain useful for describing suitably defined burst hierarchies, particularly by analogy with solar and stellar flare systems. The hard/soft cycle of X-ray binaries is then interpreted as motion through magnetic state space. Full article

Sinonasal malignancies are rare, accounting for fewer than 3% of head and neck cancers, but their early presentation often overlaps with benign rhinosinusitis. Unilateral nasal obstruction, rhinorrhoea, facial pressure, and intermittent epistaxis may initially appear inflammatory, which contributes to diagnostic delay and late-stage presentation. Recent clinical guidance emphasizes that persistent unilateral symptoms, especially when accompanied by bleeding, focal endoscopic abnormalities, or orbital, dental, or neurologic features, should prompt specialist assessment rather than repeated empiric treatment. This commentary argues that the central clinical problem is not failure to recognize advanced disease, but failure to reconsider a benign working diagnosis when “sinusitis” stops behaving like sinusitis. This commentary proposes a pragmatic triage framework for unilateral or refractory sinonasal disease that prioritizes pattern recognition, focused nasal endoscopy, appropriate imaging, and timely biopsy where indicated. Its contribution is to connect three clinically relevant observations: sinonasal malignancy is rare and therefore easily deprioritized; unilateral, progressive, refractory, bleeding, orbital, dental, or neurologic features should prompt earlier cancer exclusion; and emerging AI-assisted endoscopy should currently be viewed only as a triage adjunct, not a substitute for imaging, histopathology, or multidisciplinary assessment. Full article

The convergence of the Internet of Things (IoT), edge computing, and artificial intelligence (AI) is reshaping cyber defense in distributed cyber–physical environments. IoT-edge systems expose heterogeneous, resource-constrained, and intermittently connected devices to threats that unfold close to sensing and control processes, making purely signature-based or rule-based defenses increasingly insufficient. This article presents a structured review of AI for cybersecurity in IoT-edge systems from a systems-oriented perspective. Rather than surveying AI for IoT security in general, it organizes the literature around four practical lenses: AI methods, datasets and benchmarks, evaluation practice, and deployment constraints. The review reconstructs a workspace-verifiable corpus of 96 references, emphasizes literature published between January 2023 and April 2026 while retaining foundational benchmark papers, and uses a conservative 26-paper empirical subset for paper-level gap coding. Because this subset was purposively sampled and the original retrieval logs were not preserved, coded counts are interpreted as recoverable reporting signals and comparability indicators rather than field-level prevalence estimates. The revised synthesis further stratifies the coded evidence by task, model family, dataset, application scenario, metric type, and deployment signal, and translates deployment feasibility into a minimum reporting checklist and edge-hardware decision matrix. Within this evidence boundary, recent work remains dominated by intrusion and anomaly detection, with continued use of traditional machine learning, deep learning, federated learning, explainable AI, and graph-based approaches. However, experimentation remains concentrated around a small set of public benchmarks, while latency, memory, energy, communication overhead, operational robustness, and reproducibility are reported inconsistently. The field is therefore constrained less by classifier novelty than by benchmark concentration, weak deployment reporting, limited response-and-mitigation analysis, undercoverage of authentication, access-control, and trust-management tasks, and limited reproducible edge-aware evaluation. Full article

Artificial Intelligence of Things (AIoT) is transforming Internet of Things (IoT) systems from cloud-centric data processing into distributed intelligence across device, edge, fog, and cloud tiers. However, existing reviews often emphasize specific computational layers, learning paradigms, or application domains rather than the cross-domain problem of Artificial Intelligence (AI) workload placement under real deployment constraints. This paper presents a structured integrative review of AI workload placement in AIoT, based on a multi-stage literature search, two-stage screening process, and thematic synthesis of 132 sources. The review does not propose a new physical architecture; instead, it develops a terminology-harmonized and AI-centric perspective for assessing where AI functions should reside according to latency, privacy, bandwidth, power, scalability, resilience, and model complexity. Evidence is synthesized across Industrial Internet of Things (IIoT), smart cities, Internet of Medical Things (IoMT), and smart agriculture. The findings show that placement drivers are domain-dependent: deterministic response and reliability dominate IIoT, interoperability and scale shape smart cities, privacy and human oversight constrain IoMT, and energy scarcity and intermittent connectivity define agriculture. The review concludes that robust AIoT requires hybrid multi-layer architectures combining Tiny Machine Learning (TinyML), edge/fog coordination, cloud-scale optimization, and Federated Learning (FL) where appropriate. Full article

With the rising trend of replacing synchronous generators with inverter-based resources, the grid inertia, frequency control, voltage stability, and fault ride-through are compromised. The current research focuses on the coordinated control of Voltage Source Converter-based HVDC (VSC HVDC) and Battery Energy Storage Systems (BESS) for improving the grid stability in the presence of intermittent sources. Two models are created in the MATLAB/Simulink 2025a environment: one for the grid-connected PV system with the addition of BESS in grid-forming mode (GFM) and grid-following mode (GFL), and the other for the multi-terminal HVDC system with the integration of wind energy from the ocean. The results show that the grid-forming converters perform better than grid-following converters in the event of disturbances, and the coordinated control structure aligns with the IEEE 2800-2022 for low-inertia grids. Full article

Routing in Opportunistic Networks (OppNets) is continuously challenged by intermittent connectivity and severe resource constraints. To address these limitations, this paper proposes CASTRO, a novel routing architecture, alongside its reinforcement learning extension, QL-CASTRO. The primary novelty lies in the mathematical modeling of disconnection intervals (OFF-mode) to extract precise social indicators—Strength, Trend, and Regularity—providing a robust alternative to traditional encounter-frequency metrics. To overcome the latency penalties inherent to conservative social routing, QL-CASTRO integrates a tabular Q-Learning paradigm. This acts as a dynamic acceleration mechanism, fusing social metrics with autonomous delivery delay estimates and strict message retirement policies. Performance was rigorously evaluated using the ONE simulator across dense pedestrian (Helsinki) and sparse vehicular (Manaus) environments. The results demonstrate that both protocols achieve high delivery rates near 90%. Crucially, QL-CASTRO significantly reduces average delivery latency compared to the baseline CASTRO protocol while maintaining moderate overhead and low energy consumption. Ultimately, this hybrid approach offers a scalable, resource-efficient routing solution for dynamic IoT environments where system longevity and information integrity are paramount. Full article

The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently prove ineffective in delivering both robust harmonic mitigation and expeditious dynamic response. To surmount these constraints, the present paper puts forth an intelligent control solution that is predicated on a fractional-order fuzzy logic (FOFL). The FOFL is integrated into a multi-converter HRPS, comprising a photovoltaic generator, a lithium-ion battery power storage system, and a wind turbine equipped with a permanent magnet synchronous generator. A multifunctional voltage source inverter has been developed to control these parts, which are interfaced via a common DC bus. Through the implementation of MATLAB 2021 simulation studies, the efficacy of the suggested algorithm is verified and evaluated in comparison to the FOPI. The findings indicate that the FOFL enhances system efficacy by minimizing harmonic distortion, improving energy quality, and achieving a faster dynamic response under various circumstances. In the context of grid-connected microgrid environments, the FOFL has been demonstrated to offer superior overall energy management, robustness, and adaptability when compared to other evaluated strategies. Full article

Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV training is impractical in high-risk search-and-rescue (SAR) environments. This study formulates post-disaster victim localisation as a cooperative Dec-POMDP and adapts a model-aided federated multi-agent reinforcement learning framework based on FedQMIX. The proposed pipeline combines a lightweight LoS/NLoS surrogate channel model, PSO-based victim-position estimation, return-to-base and map-feasibility safety checks, an SAR-aligned shaped reward, and a leakage-free centralised training state based on estimated rather than ground-truth victim locations. Each UAV trains locally inside a learned digital-twin simulator and periodically shares only QMIX network parameters, avoiding the exchange of raw trajectories or RSSI logs. The framework is evaluated on two synthetic post-earthquake urban maps representing a compact return-to-base scenario and a larger reach-to-destination scenario. Across five independent seeds per method and map, Model-Aided FedQMIX achieves the highest and most stable victim-localisation performance, with the clearest advantage observed in the larger long-horizon scenario. Additional diagnostic tests examine reward-weight sensitivity, RF channel-shift robustness, BLE/smartphone hardware heterogeneity, non-IID client-data variation, and partial-client FedAvg under missing client updates. The results indicate that combining model-aided localisation cues, decentralised value factorisation, SAR-aligned objective design, and federated parameter sharing can improve the robustness of UAV-based victim-localisation policies. The framework also clarifies deployment considerations for federated SAR coordination, including communication payload, privacy boundaries, heterogeneous client experience, device variability, and intermittent connectivity. This study remains simulation-based, and future validation with real UAVs, BLE devices, and rubble-inspired testbeds is required before operational deployment. Full article

As human space missions become longer and more autonomous, robots are expected to assume broader responsibilities in inspection, maintenance, logistics, scientific support, and crew assistance. Among available robot forms, humanoid robotic astronauts are especially relevant because their anthropomorphic embodiment is compatible with human-centered habitats, tools, interfaces, and procedures. Their deployment in orbital and planetary environments, however, introduces challenges that differ from those of terrestrial humanoids, including floating-base dynamics, intermittent contact, whole-body coordination, constrained perception, and delayed supervision. This review contributes a mission-oriented and astronaut-centered synthesis of humanoid robotic astronauts, distinguishing itself from platform-by-platform or morphology-only surveys. It treats these systems as mission-compatible embodied agents whose feasibility depends on the coupling among mission context, morphology, contact behavior, perception, autonomy, and validation evidence. The primary goals are threefold: to classify representative platforms according to mission context, to synthesize the core technical foundations required for mission-compatible operation, and to identify cross-cutting deployment bottlenecks and benchmarking priorities for future development. Representative systems are organized into intravehicular assistance, extravehicular operations and on-orbit servicing, and surface exploration or transitional scenarios, showing how mission demands shape embodiment, mobility, manipulation, autonomy, and validation strategies. This review further summarizes recent progress in microgravity dynamics and contact mechanics, multimodal perception and scene understanding, whole-body motion planning and control, teleoperation and supervised autonomy, and evaluation and benchmarking methods. The analysis indicates that humanoid robotic astronauts are not simple extensions of terrestrial humanoids but astronaut-oriented embodied systems for mission-constrained environments. Three priorities are identified for future development: contact-rich whole-body intelligence under support transitions, delay-tolerant supervised autonomy with explicit authority handoff, and systematic benchmarking pipelines that connect simulation, ground analogs, short-duration microgravity tests, human-in-the-loop trials, and mission-context demonstrations. Full article