Search Results (2,968)

Two-dimensional (2D) materials offer exceptional electrical, optical, and mechanical properties but face challenges in terms of scalability, stability, and integration. Hybridizing 2D materials with polymers provides an effective route to overcome these limitations by enabling tunable interfaces, mechanical compliance, chemical functionality, and three-dimensional device processability. This review summarizes the fundamental structural configurations of 2D–polymer hybrids, including embedded composites, stacked heterostructures, covalently functionalized interfaces, polymer-encapsulated layers, and fiber–network architecture, and describes how their interfacial interactions dictate charge transport, environmental robustness, and mechanical behavior. We also highlight major fabrication strategies, such as solution dispersion, in situ polymerization, and vapor-phase deposition. Finally, we discuss emerging applications in sensors, optoelectronics, neuromorphic systems, and energy devices, demonstrating how synergistic coupling between 2D materials and functional polymers enables enhanced sensitivity, programmable electronic states, broadband photodetection, and improved electrochemical performance. These insights provide design guidelines for future multifunctional and scalable 2D–polymer hybrid platforms. Full article

The ultrasensitive detection of microRNA-17 (miRNA-107) is required for clinical diagnosis. In this work, an aggregation-induced electrochemiluminescence (AIECL) sensor was developed for the quantification of miRNA-107, in which AIECL-active polymer dots (Pdots) were characterized by transmission electron microscopy, ultraviolet–visible spectroscopy, and cyclic voltammetry and used as ECL emitters. Black hole quencher-labeled hairpin DNA (HP-BHQ) was modified on the Pdot surfaces, resulting in the ECL signal of the Pdots being in the “off” state due to the resonant energy transfer (RET) between the BHQ and Pdots. In the presence of miRNA-107, HP-BHQ opened through RNA-DNA hybridization. Subsequently, the introduced duplex-specific nuclease (DSN) facilitated the cleavage of DNA in the RNA–DNA hybrid chain and led to the detachment of HP-BHQ from the electrode surface. The ECL signal of the Pdots recovered, i.e., to the “on” state. The variation in the ECL signal was related to the concentration of the target miRNA-107. As a result, the AIECL biosensor exhibited a wide linear response to miRNA-107 concentrations ranging from 1.0 fM to 10.0 pM, and a low detection limit of 0.82 fM. This work provides a novel platform for the sensitive analysis of miRNA. Full article

Forest structural parameters, such as canopy closure, stand density, diameter at breast height, tree height, leaf area index, stand age, and biomass, are fundamental for quantifying forest ecosystem functioning and supporting sustainable forest management. Remote sensing has become an indispensable tool for forest structural parameter estimation. Commonly used data sources include optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), unmanned aerial vehicles (UAVs), and multisource data fusion. Correspondingly, modeling approaches have evolved from empirical and statistical methods to machine learning, deep learning, and hybrid physical-data-driven models, enabling improved characterization of nonlinear and complex forest structures. Each data source and modeling strategy offers unique strengths and limitations with respect to accuracy, scalability, interpretability, and transferability. This review provides a concise synthesis of recent advances in remote sensing data sources and model algorithms for forest structural parameter estimation, evaluates the strengths and limitations of different sensors and algorithms, and highlights key challenges related to uncertainty, scalability, transferability, and model interpretability. Finally, future research directions are discussed, emphasizing cross-scale integration, multisource data fusion, and physically informed deep learning frameworks as promising pathways toward more accurate, robust, and ecologically interpretable forest structural parameter modeling at regional to global scales. Full article

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

Marine scientific observation missions operate over disrupted, high-loss links and must keep heterogeneous sensor, image, and log data confidential and verifiable under fragmented, out-of-order delivery. This paper proposes an end-to-end encryption–verification co-design that integrates HMR integrity structuring with EMR hybrid encapsulation. By externalizing block boundaries and maintaining a minimal receiver-side verification state, the framework supports block-level integrity/provenance verification and selective recovery without continuous sessions, enabling multi-hop and intermittent connectivity. Experiments on a synthetic multimodal ocean dataset show reduced storage/encapsulation overhead (10.4% vs. 12.8% for SHA-256 + RSA + AES), lower hashing latency (6.8 ms vs. 12.5 ms), and 80.1 ms end-to-end encryption–decryption latency (21.2% lower than RSA + AES). Under fragmentation, verification latency scales near-linearly with block count (R² = 0.998) while throughput drops only slightly (11.8 → 11.3 KB/ms). With 100 KB blocks, transmission latency stays below 1.024 s in extreme channels and around 0.08–0.10 s in typical ranges, with expected retransmissions < 0.25. On Raspberry Pi 4, runtime slowdown remains stable at ~3.40× versus a PC baseline, supporting deployability on resource-constrained nodes. Full article

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

Core body temperature (CBT) is a fundamental physiological parameter tightly regulated by thermoregulatory mechanisms and is critically important for heat stress assessment, clinical management, and circadian rhythm research. Although invasive measurements such as pulmonary artery, esophageal, and rectal temperatures provide high accuracy, their practical use is limited by invasiveness, discomfort, and restricted feasibility for continuous monitoring in daily-life or field environments. Consequently, extensive efforts have been devoted to developing non-invasive CBT measurement and estimation techniques. This review provides an application-oriented synthesis of invasive reference methods and representative non-invasive approaches, including in-ear sensors, infrared thermography, ingestible telemetric sensors, heat-flux-based techniques, and model-based estimation using wearable physiological signals. For each approach, measurement principles, accuracy, invasiveness, usability, and application domains are comparatively examined, with particular emphasis on trade-offs between measurement fidelity and real-world implementability. Rather than ranking methods by absolute performance, this review highlights their relative positioning across clinical, occupational, and daily-life contexts. While no single non-invasive technique can universally replace invasive gold standards, recent advances in wearable sensing, heat-flux modeling, and multimodal estimation demonstrate growing potential for practical CBT monitoring. Overall, the findings suggest that future CBT assessment will increasingly rely on hybrid and context-aware systems that integrate complementary methods to enable reliable monitoring under real-world conditions. This review is intended for researchers and practitioners who need to select or design CBT monitoring systems. Full article

Wireless Body Area Network (WBAN) is an emerging medical health monitoring technology. However, WBANs encounter critical challenges in balancing reliability, energy efficiency, and Quality of Service (QoS) requirements for life-critical medical data. The design of its Medium Access Control (MAC) protocol has challenges since dynamic body-shadowing effects and heterogeneous traffic patterns. In this paper, we propose the Nash Bargaining Rate-optimization MAC (NBR-MAC), a hybrid MAC protocol that integrates TDMA-based Guaranteed Time Slots (GTS) with CSMA/CA-based contention access. Unlike traditional schemes, we model the rate allocation as an Asymmetric Nash Bargaining Game, introducing a rigorous disagreement point to guarantee minimum service for critical nodes. The utility function is normalized to resolve dimensional inconsistencies, incorporating sensor priority, buffer status, and channel quality. The Nash Bargaining solution is derived after proving convexity and verifying the axioms. Superframe time slots are allocated based on sensor data priority. Simulation results demonstrate that the proposed protocol enhances transmission success ratio and throughput while reducing packet age and energy consumption under different load conditions. Full article

This paper presents PLAM (Plovdiv Air Monitoring)—a regional multi-agent platform for air quality monitoring, semantic reasoning, and forecasting. The platform uses a hybrid architecture that combines two types of intelligent agents: classic BDI (Belief-Desire-Intention) agents for complex, goal-oriented behavior and planning, and ReAct agents based on large language models (LLM) for quick response, analysis, and interaction with users. The system integrates data from heterogeneous sources, including local IoT sensor networks and public external services, enriching it with a specialized OWL ontology of environmental norms. Based on this data, the platform performs comparative analysis, detection of anomalies and inconsistencies between measurements, as well as predictions using machine learning models. The results are visualized and presented to users via a web interface and mobile application, including personalized alerts and recommendations. The architecture demonstrates essential properties of an intelligent agent such as autonomy, proactivity, reactivity, and social capabilities. The implementation and testing in the city of Plovdiv demonstrate the system’s ability to provide a more objective and comprehensive assessment of air quality, revealing significant differences between measurements from different institutions. The platform offers a modular and adaptive design, making it applicable to other regions, and outlines future development directions, such as creating a specialized small language model and expanding sensor capabilities. Full article

Soil moisture sensors deployed for long-term monitoring often suffer from prolonged data gaps caused by battery depletion, communication dropouts, or hardware failures. When such gaps overlap with irrigation events, key transient phases are obscured and become difficult for conventional imputers to recover. This study proposes HyDSoil, a hybrid diffusion-based imputation model tailored for event-centered block missingness in multichannel soil moisture time series. HyDSoil is first pretrained on a physically interpretable synthetic generator that mimics the baseline-rise-decay response to irrigation and then fine-tuned on field observations from the Baltimore Ecosystem Study dataset. During reverse diffusion, a mask-guided correction keeps observed values fixed while iteratively denoising missing regions. The denoising backbone integrates one-dimensional convolutions, gated recurrent units, and Transformer components to capture high-frequency event spikes, mid-range temporal dynamics, and long-range cross-depth dependencies, respectively. Experiments on both synthetic and real datasets show that HyDSoil reconstructs irrigation-driven peaks with higher fidelity and achieves consistent improvements over strong baselines in global metrics (MAE and DTW) as well as event-focused metrics (PTE and PAE). Ablation studies further verify the complementary contributions of the convolutional, recurrent, and attention branches, and confirm the benefit of synthetic pretraining for long-duration gaps. Overall, HyDSoil enables more reliable continuous soil moisture monitoring and supports precision irrigation analytics. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems. Full article

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment. Full article

Large sports stadiums require robust real-time monitoring due to high crowd density, complex spatial configurations, and limited network infrastructure. This research evaluates a hybrid edge–cloud architecture implemented in a national stadium in Thailand. The proposed framework integrates diverse surveillance subsystems, including automatic number plate recognition, face recognition, and panoramic cameras, with edge-based processing to enable real-time situational awareness during high-attendance events. A simulation based on the stadium’s physical layout and operational characteristics is used to analyze coverage patterns, processing locations, and network performance under realistic event scenarios. The results show that geometry-informed sensor deployment ensures continuous visual coverage and minimizes blind zones without increasing camera density. Furthermore, relocating selected video processing tasks from the cloud to the edge reduces uplink bandwidth requirements by approximately 50–75%, depending on the processing configuration, and stabilizes data transmission during peak network loads. These findings suggest that processing location should be considered a primary architectural design factor in smart stadium systems. The combination of edge-based processing with centralized cloud coordination offers a practical model for scalable, safety-oriented monitoring solutions in high-density public venues. Full article