Search Results (1,425)

A multi-tier enterprise software system, the 3-4-5 framework, was designed to optimize workforce allocation and streamline training pipelines in vocational engineering education. The system decouples workflows into four interoperable platforms, ranging from virtualized campus simulations to distributed industrial internship data pipelines. An application programming interface-driven gateway synchronizes telemetry and resource dispatch across five groups: colleges, enterprises, families, government, and social organizations. The deployment results show significant improvements in tracking throughput, data consistency, and user literacy. The system provides a scalable reference model for digital learning ecosystems and enhances efficiency in human capital development pipelines. Full article

Reliable solar insecticidal lamp Internet of Things (SIL-IoT) equipment underpins green pest control and precise pest monitoring. Healthy ranges of light, photovoltaic and thermal variables shift with daylight, weather, energy availability and installation conditions, while labelled faults remain scarce and imbalanced. We introduce a diagnosability-aware operating-state health baseline (DA-OHB) for calibration-based cross-node diagnosis of data-observable faults in SIL-IoT equipment. Candidate faults were screened by data observability, mechanistic expressibility and availability as curated telemetry event labels. Photovoltaic–light, electrical-box/air-temperature, power and rolling-state features were combined with operating-state gates, direction-sensitive evidence scores and target-node healthy false-alarm calibration. We evaluated DA-OHB on July–August 2025 field records from four devices deployed in Chuzhou, China, for three maintenance-relevant faults: light-intensity sensor open circuit, light-intensity/solar-panel-current mismatch, and electrical-box/air-temperature mismatch. Under leave-one-device-out aggregation with an early healthy calibration subset from each target node, mean F1-scores were 0.996, 0.823 and 0.770; mean area under the precision–recall curve values were 1.000, 0.890 and 0.963. Across five seeds, DA-OHB F1-scores were 0.996 ± 0.000, 0.823 ± 0.000 and 0.763 ± 0.004. F1 reflects the target-node mechanism with sufficient fault evidence, whereas F2 and F3 demonstrate cross-node evidence from multiple devices. Field diagnosis of SIL-IoT equipment thus benefits from linking alarms to valid operating states, fault directions and node-specific healthy calibration. DA-OHB provides an interpretable basis for SIL-IoT maintenance under curated telemetry labels; effects on pest-count estimates and agricultural decisions require separate evaluation. Full article

This paper presents a decoupled Extended Reality (XR) and Digital Twin (DT) framework for robotic manipulators, leveraging mathematical, physical, and topological symmetries to achieve stable cyber–physical synchronization. Traditional robotic teleoperation architectures suffer from tight coupling between low-level control, communication, and visualization interfaces, making them vulnerable to network latency and platform dependencies. We propose an open, modular framework that structurally decouples physical and virtual counterparts. By establishing a system-theoretic formulation of Digital Twin symmetry, we represent the virtual robot as an isomorphic state-space mirror of the physical manipulator. Communication is mediated by an asynchronous, network topology using a lightweight Message Queuing Telemetry Transport (MQTT) broker, ensuring topic-based communication symmetry. This decoupled structure enables a highly flexible, many-to-many evaluation paradigm, allowing researchers to interchangeably pair a single physical robot with diverse user interfaces (UIs) or control multiple heterogeneous robots (physical and simulated) with a single virtual interface. Experimental validation on a 7-Degree-of-Freedom (7-DOF) Franka Emika Panda robot and a unified data collector confirms high-fidelity tracking, with a symmetric wired and wireless Round-Trip Times (RTT) averaging 47 ms. Finally, we discuss the challenges of transitioning this laboratory-scale architecture to high-concurrency industrial settings, addressing single-broker vulnerabilities, network jitter, and dynamic calibration drift. Full article

The rapid expansion of smart agriculture and precision aquaculture necessitates continuous, high-resolution environmental monitoring to optimize ecosystem stability and prevent catastrophic biomass loss. Conventional Internet of Things (IoT) solutions routinely treat atmospheric and aquatic parameters as isolated domains, neglecting the dynamic physicochemical coupling occurring across the air–water boundary layer. To overcome this domain fragmentation, this study presents an integrated edge-cloud telemetry architecture designed for concurrent, multi-domain environmental monitoring and cross-domain spatial-statistical analysis. The proposed framework employs low-cost, multi-sensor edge nodes integrated with Narrowband IoT (NB-IoT) cellular communication, achieving high signal penetration, energy-efficient operation, and direct base-station connectivity without local gateway dependencies. The system continuously acquires atmospheric parameters (temperature, relative humidity, particulate matter ${\mathrm{PM}}_{1.0}$/${\mathrm{PM}}_{2.5}$/${\mathrm{PM}}_{10}$, ozone ${\mathrm{O}}_3$, total volatile organic compounds TVOC, equivalent ${\mathrm{CO}}_2$, Air Quality Index AQI, and Ultraviolet Index UVI) alongside aquatic indicators (water temperature, pH, dissolved oxygen DO, electrical conductivity EC, and turbidity). Telemetry is streamed via Message Queuing Telemetry Transport (MQTT) to a centralized MySQL cloud database, providing real-time Grafana dashboards, spatial Inverse Distance Weighting (IDW) mapping, and automated multi-channel alerting via LINE Notify and email. The architecture was deployed and validated across the Tunghai University aquatic research facility, capturing n = 14,400 synchronized 1-min observations (with an initial raw Packet Delivery Rate of $99.24\%$). Statistical evaluations accounting for temporal autocorrelation ($N_{\mathrm{eff}}\approx 892$) and False Discovery Rate correction revealed significant cross-domain associations ($p_{\mathrm{adj}}<0.001$), notably an inverse association ($r=-0.782$, $\rho =-0.794$, $\tau =-0.612$) between ambient air temperature and aquatic dissolved oxygen physically consistent with Henry’s Law of gas solubility, an inverse association ($r=-0.763$) between atmospheric humidity and dissolved oxygen, and a positive association ($r=+0.789$, $\rho =+0.812$) between humidity and aquatic turbidity. First-order partial correlation analysis ($r_{T_a,\mathrm{DO}\mid T_w}=-0.172$) confirmed that water temperature serves as the primary thermal mediator of dissolved oxygen depletion. By synergizing low-power NB-IoT telemetry with robust multi-domain analytics, this work provides a scalable, empirical foundation for transitioning from reactive threshold alerting to proactive predictive management in precision aquaculture. Full article

Industry 4.0 depends on the ability to connect heterogeneous systems, yet students and practitioners rarely have a low-risk environment in which to practice this kind of systems integration. This article presents a virtual-prototyping architecture developed and tested, linking a discrete-event simulation tool with external machine learning models through industrial communication protocols. The resulting artifacts and method are a contribution to systems engineering education, practice, and development. A two-phase empirical virtual-prototyping approach was used. First, Open Platform Communications Unified Architecture and Message Queuing Telemetry Transport were prototyped and compared as communication layers between Siemens Tecnomatix Plant Simulation and Python-based machine learning clients. Second, for this project, the more suitable protocol was applied to three increasingly complex use cases, addressing automated guided vehicle capacity, conveyor speed control, and process bottleneck identification. The use cases were assessed against the Technology Readiness Level scale. Open Platform Communications Unified Architecture provided reliable, real-time, bidirectional data exchange, while Message Queuing Telemetry Transport proved less stable for this application. The three use cases each demonstrated feasible simulation-machine learning integration, and the overall prototype reached Technology Readiness Level 4. Beyond its contribution to I4.0 practice, the staged research design, the use of Technology Readiness Levels as a maturity and reflection instrument, and the low-cost, risk-free nature of virtual prototyping constitute a transferable pedagogical pattern for systems engineering curricula, capstone projects, and competency-based training. Full article

IoT deployments handle credential hygiene reactively: cloned, replayed, or stale credentials are typically discovered only after misuse, and revocation state is often propagated through centralized lists whose integrity cannot be independently verified. This article introduces the Yang–Baxter IoT Consistency Gateway (YB-IoT-CG), a Zero-Trust admission-control framework that pushes an algebraic layer of credential screening to the edge and anchors security evidence on a modeled permissioned-ledger architecture. YB-IoT-CG operates after conventional secret-based authentication and Yang–Baxter equality is used as an execution-consistency invariant rather than as proof of device identity or message authenticity. Each authenticated message is hashed into a digest and reduced to an algebraic transcript that is verified over two Yang–Baxter traversal paths. Beyond equality checking, chain-proximity metrics inspired by time–memory trade-off analysis, nonce and timestamp freshness heuristics, and contextual risk scoring identify credentials that should be proactively challenged or revoked before telemetry is admitted. The proactive risk component is deterministic and policy-based rather than a learned predictive model. Decisions are batched into Merkle trees whose roots are represented through a permissioned-ledger model, credential revocation is propagated through a modeled on-chain registry, and device identities are bound to W3C Decentralized Identifiers (DIDs) with verifiable credentials. This design provides tamper-evident audit support while keeping ledger interaction off the packet decision path. Validation is based on a controlled Python 3.13.7 packet-level simulation and a parameterized ledger model, not on a physical IoT or live Hyperledger Fabric deployment. Across 60 seeded simulation runs, the complete post-authentication screening pipeline obtained a median incremental decision time of 7.8 $\mathsf{\mu }$s, 99.4% aggregate decision accuracy for the modeled attack classes, a 0.43% false rejection rate, and a 31.4 ms amortized ledger service-time equivalent per decision for a batch size of 64. The 99.4% value is a property of the composed freshness/context/registry/YB policy and is not a YB-only detection rate; the YB-specific positive guarantee is limited to the isolated fault class of Proposition 7. These results provide simulation-based evidence supporting a lightweight, explainable, auditable, and proactive approach to credential hygiene at the edge. Full article

Digital television is an integral part of modern society, and the quality of the service its has exceeded all expectations. Television stations are divided into national and regional licensing categories, governed by the broadcasting regulations of each European Union member state. DVB-T gap fillers are used to provide and enhance the television signal in rural areas using satellite transport streams (TS) as feeds. However, for regional television stations—particularly in areas lacking network coverage—retransmitting their transport streams via standard digital terrestrial reception and rebroadcasting is often insufficient. This direct Re-transmission method frequently suffers from severe signal intermittency and broadcast interruptions. This paper presents the design, field deployment, and long-term evaluation (8760 h) of an ultra-low-cost, license-exempt DVB-over-IP gap filler architecture. The proposed system integrates Commercial-Off-The-Shelf (COTS) devices to convert a pristine DVB-T transport stream into an IP data stream, transmit it via a 5.64 GHz wireless bridge to bypass natural obstacles, and accurately reconstruct the digital TV signal at the remote gap filler. Empirical results demonstrate that the proposed IP stream method yields a 95.51% reduction in total annual downtime, elevating link availability from 86.26% to 99.38%. The system virtually eliminates environmental signal interruptions, with the only recorded downtime caused by a local power outage. Notably, this robust reliability is achieved at approximately 5% of the CEcapital expenditure (CapEx) required for conventional professional microwave backhaul solutions. Furthermore, the residual bandwidth of the wireless IP backbone provides a ready-made foundation for deploying future municipal network services, such as local Wi-Fi hotspots and LoRa-based Internet of Things (IoT) telemetry networks. Full article

Modern data centers underpin smart-city services, yet their control loops must reconcile noisy, missing, or deliberately deceptive telemetry before acting autonomously. We present a two-path agentic architecture in which a deterministic fast path fuses sensor reports through a reliability-based fuzzy-preference OWA operator (FPR–OWA) and a persistent consensus-reaching process (CRP), while an optional reactive or LLM supervisor can only propose typed actions that a deterministic verifier must admit. Across 1890 indexed simulation scenarios and seven attack families, no estimator dominates: averaging attains the lowest global error and sensor redundancy explains most of the accuracy variation, whereas FPR–OWA + CRP provides the strongest anomaly-diagnostic signal. We prove that logistic dominance preserves the reliability ordering and add a zone-dynamic temporal-consistency ablation. In the original safety campaign, no unsafe execution was observed in the verifier-gated episodes, and the unified process-local runtime later passed all 24 deterministic fault-injection cases; both are empirical, not formal, guarantees. An illustrative closed-loop pilot adds standard control metrics and reveals delayed recovery under common-mode sensor bias; a CPU benchmark keeps persistent-CRP median latency below $0.30$ ms at 100 sensors. The result is a reproducible, bounded blueprint for verified autonomy in datacenter infrastructure; hardware validation, production authentication, and energy measurement remain future work. Full article

Timely feedback on whole-body vibration (WBV) exposure requires standardised metrics during measurement, yet many workflows record raw acceleration for offline processing, delaying feedback and increasing the data burden for bandwidth-constrained deployment. This paper presents a sensing node computing ISO 2631-1/AS 2670.1 exposure metrics on-device and transmitting metric records, not waveforms. The node combines an LIS2DH microelectromechanical systems (MEMS) accelerometer with an RP2040 microcontroller to calculate weighted root-mean-square (RMS) acceleration, daily exposure A(8), vibration dose value, maximum transient vibration value and crest factor. Algorithm-level verification showed that the implemented W_k filter reproduced the tabulated ISO 2631-1 response within 1.1% across 0.5–80 Hz. The node was compared under laboratory conditions with a CEM DT-178A datalogger whose 20 Hz recordings were reprocessed through a method-matched causal pipeline. Across ten trials per configuration under vertical excitation in the 6.3 Hz one-third-octave band, mean Z-axis A(8) differences were −4.5% (wired) and −7.9% (wireless). The datalogger’s 20 Hz sampling was included within the comparison chain, and the lower realised W_k gain was consistent with the direction and approximate magnitude of the observed offset. Metric-level telemetry reduced the sustained payload rate by up to four orders of magnitude at the summary cadence, supporting bandwidth-constrained uplinks. Full article

The continuous operation of the geophysical monitoring networks managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV)–Osservatorio Vesuviano relies on the reliability of a highly distributed infrastructure deployed in active volcanic areas. In these contexts, failures affecting power supply, environmental control, or communication systems may lead to interruptions in data transmission and consequent loss of scientific observations. This work presents PROMETEO, an integrated remote-control and infrastructure monitoring system designed to supervise heterogeneous monitoring stations through a multiparametric sensing approach. The system combines distributed sensors and intelligent edge devices for the acquisition of electrical, environmental, and connectivity-related parameters, including battery voltage, load current, cabinet temperature, signal quality, and network reachability. Data are collected and integrated in real time through standard Internet of Things (IoT) and industrial communication protocols, namely Message Queuing Telemetry Transport (MQTT), Simple Network Management Protocol (SNMP), and MODBUS, and centralized within the open-source Home Assistant platform. This architecture enables the fusion of heterogeneous sensor measurements into a unified supervisory framework for real-time visualization, alarm generation, historical storage, and trend analysis. The results show that the multiparametric correlation of sensor data significantly improves diagnostic capability, allowing rapid discrimination between power-related anomalies and communication failures, particularly in remote mobile stations. By reducing diagnostic uncertainty and limiting unnecessary field interventions, PROMETEO enhances the operational resilience of geophysical monitoring infrastructures and supports preventive and predictive maintenance strategies. The proposed system demonstrates how a scalable multiparametric sensing architecture can strengthen the reliability and continuity of monitoring networks operating in complex environmental conditions. Full article

Introduction: Cochlear implant follow-up requires periodic assessments and resources. Remote Check (RC) enables remote assessment of aided thresholds, speech perception in noise, and Impedance Field Telemetry (IFT). This exploratory study investigated relationships between remote and conventional measurements, including numerical and longitudinal comparisons where methodologically appropriate. Methods: This prospective, single-center exploratory study included 12 adults with Nucleus 7/8 cochlear implants. At baseline (T0) and after a mean 4.83-month follow-up (T1), participants underwent free-field (FF) pure-tone audiometry, Matrix Sentence Test (MST), Aided Threshold Test (ATT), Digit Triplet Test (DTT), and IFT; RC assessments were performed under supervised clinical conditions. Analyses included exploratory paired comparisons, correlations, longitudinal analyses, and linear mixed-effects models where appropriate. Results: ATT thresholds were lower than FF thresholds (mean PTA differences: −7.78 dB at T0; −9.23 dB at T1), with no Method × Time interaction. MST and DTT showed a positive association at T0 (Pearson r = 0.833, 95% CI 0.497–0.952), whereas no clear association was observed at T1 (r = 0.074, 95% CI −0.583–0.672). IFT differences were small (T0: +0.35 kΩ; T1: −0.06 kΩ), with high correlations and no Method × Time interaction. Discussion and Conclusions: Under supervised conditions, RC ATT showed numerical differences from conventional FF thresholds, while the relationship between MST and DTT speech-in-noise performance remained uncertain across assessments and remote and conventional IFT measurements showed comparatively small differences. Interpretation is limited by differing measurement paradigms and the small sample. Larger independent studies, including home-based assessments, are required before broader clinical or triage applications can be established. Full article

Professional live video production has traditionally been limited by proprietary hardware mixers and monolithic desktop applications, which impose prohibitive costs and restrict scalable, command-line-based implementation for remote integration (REMI) workflows. To address these limitations, this article presents Voctomix 2.0, a containerized and enhanced live video mixing architecture based on the open-source Voctomix framework. This research introduces a production-ready extension layer that adds dynamic multi-layer overlays, operator-level stream-blanking control with coupled audio muting, and an asynchronous AMQP telemetry framework on top of the upstream mixing engine. The architecture was empirically evaluated in local, Docker, and single-node Kubernetes environments under varying workloads, ranging from 1080p25 to 2160p50 (4K). Results under a continuous four-source 1080p25 workload showed that, in a single-node deployment, containerization and orchestration impose no appreciable processing overhead, with a median host-level CPU utilization near 90% and a stable RAM working set of around 13 GB on standard hardware. The median command-to-output latency within the mixer was 293 ms, an internal switching latency that does not include camera capture or wide-area transport. Forced-failure experiments validated the architecture’s self-healing capability, which autonomously recovered blocked camera signals in approximately 1.8 s while maintaining a continuous live program feed. Full article

Smart electric vehicle (EV) charging infrastructures are evolving into large-scale cyber-physical Internet of Things (IoT) systems that depend on distributed communication, real-time sensing, and spatially coordinated charging operations. However, their interconnected communication architecture exposes charging stations, EV communication links, and network gateways to coordinated distributed denial-of-service (DDoS) attacks. Existing intrusion detection approaches primarily rely on localized or static traffic analysis and therefore have limited capability to capture spatially distributed and temporally evolving attack behavior. This study proposes the Spatio-Temporal Attack Graph Guard Network (STAG-GuardNet), an unmanned aerial vehicle (UAV)-assisted spatio-temporal attack graph learning framework for DDoS detection and security monitoring in smart EV charging networks. The framework integrates spatiotemporal signal conditioning, telemetry-adaptive graph aggregation, temporal dependency learning, attack-memory encoding, and adaptive risk-aware attention to model coordinated cyber-physical attack behavior. UAV-assisted telemetry provides complementary spatial and wireless information on communication instability, signal variation, neighboring congestion, and distributed attack-related behavior. A Hybrid Hawk–Manta Adaptive Optimizer (HHMAO) is employed to improve hyperparameter selection and convergence stability under imbalanced, heterogeneous, and nonstationary traffic conditions. The framework is evaluated using a smart-city EV charging cybersecurity dataset and three benchmark IoT intrusion detection datasets, namely TON_IoT, Edge-IIoTset, and X-IIoTID. Experimental results show that STAG-GuardNet achieves 97.7% accuracy, a 97.7% weighted F1-score, and a 98.4% area under the receiver operating characteristic curve (AUC) on the primary dataset. The framework also maintains stable performance under noisy telemetry, missing observations, heterogeneous traffic distributions, and charging-node outages. These findings demonstrate the potential of STAG-GuardNet for resilient and spatially informed security monitoring in UAV-assisted IoT-enabled EV charging infrastructures. Full article

Haul-road condition can affect traction demand and diesel-to-LNG substitution in mining trucks. We evaluated a 140-t truck at the Ekibastuz coal mine using 180 registered cycles (88 diesel-only [DOM], 92 dual-fuel [DGB]), 24 road segments, 36 defect events and 30 matched fuel-mode pairs. Mean ECM-reported substitution was 30.61% across DGB cycles. In matched pairs, diesel use declined from 34.00 to 23.76 L/cycle, a mean saving of 10.24 L/cycle (95% CI 9.53–10.95); calculated combustion-stage CO₂ declined by 7.10%. Total fuel energy, specific fuel-energy consumption and cycle time did not differ significantly (p > 0.30). Across Good-to-Poor road classes, engine load increased from 66.11% to 74.40% and substitution from 28.97% to 32.31%, while specific fuel-energy consumption increased from 5.91 to 6.72 MJ/(t·km). The load association persisted after temperature and wind adjustment and shift-clustered inference. Reference-state haul-road energy penalty was associated with rolling resistance and roughness, but remains dependent on a supplied reference input. The results distinguish diesel displacement from improved transport energy performance and are conditional on the cycle register. They do not establish causal road effects or a life-cycle climate benefit. Full article

This paper presents a 30-day indoor ice rink dataset comprising 1,305,260 observations from twenty signals, a full 43,200-min calendar with acquisition gaps, and a 42,929-row reference preprocessed table produced by deterministic last-observation-carried-forward (LOCF) imputation and variable-specific plausibility screening. The release contains the three tabular stages, preprocessing code, signal mapping, measurement metadata, validation summaries, and per-value provenance. Applications include telemetry preprocessing, short-horizon forecasting with explicit observation-age controls, unsupervised anomaly screening, and refrigeration-monitoring prototype development. Full article