Search Results (10,238)

This review evaluates urinary catheter sensors designed to warn of crystalline encrustation and blockage or to monitor infection-related microbial, interfacial, and host-response states. A source-to-claim framework links each measured state and sampling compartment with its readout, reference endpoint, and supportable clinical claim. The analysis also considers whether catheter integration preserves the relationship between source, readout, and claim. pH-responsive systems have the strongest human feasibility evidence, although the studies are small, use different sampling configurations, and include few blockage events. Evidence for urease sensing is confined to analytical studies and controlled in vitro experiments. Mineral-deposition and hydraulic approaches rely mainly on catheter models or adjacent-device studies. Most infection-related platforms are sample-based assays, prototypes, or short-duration systems that measure clinically non-equivalent states. Analyte detection alone cannot establish a diagnosis or predict a later event in either application. Interpretation also changes with sensor location and with the effects of transport, fouling, and mechanical loading on the recognition interface. Translation requires validation over complete catheter episodes against an endpoint matched to the intended claim. An additional channel is warranted only if it resolves a prespecified uncertainty and improves clinically relevant performance against a fixed comparator. Full article

Metal oxide semiconductor (MOS) gas sensors are widely used for low-cost chemical detection, but their practical performance is still limited by high operating temperature, insufficient selectivity, signal drift, and device-to-device variation. Recent advances in microelectromechanical systems (MEMS), dynamic sensing protocols, and artificial intelligence (AI) provide new opportunities to improve MOS gas sensing from both hardware and data-processing perspectives. MEMS micro-hotplates enable miniaturized devices, low-power heating, rapid thermal control, temperature-modulated operation, and compatibility with integrated readout and interface circuits, while AI methods extract multivariate, nonlinear, and temporal information from cross-sensitive sensor responses. This review summarizes the fundamentals of MOS sensing materials, MEMS micro-hotplate platforms, material–device integration strategies, and AI-assisted data-processing methods ranging from classical statistical analysis to deep learning. Representative strategies are discussed, including single-sensor feature extraction, sensor-array recognition, temperature-modulated sensing, drift compensation, and AI-guided material design. Application studies in food quality assessment, agriculture, medical diagnostics, environmental monitoring, and public safety are further reviewed to show how sensing tasks evolve from odor-fingerprint discrimination to nonlinear feature interpretation, dynamic response analysis, domain adaptation, and deployable intelligent monitoring. Particular attention is given to the role of high-consistency integration of MOS sensing layers on MEMS platforms, since reproducible material loading, morphology, electrode coverage, and thermal coupling are essential for reliable datasets and transferable AI models. Finally, key challenges are discussed, including dataset heterogeneity, long-term drift, edge deployment, and material–device reproducibility. This review highlights that future AI-assisted MOS/MEMS gas sensors require coordinated design of sensing materials, device platforms, fabrication processes, operating protocols, and data-driven models. Full article

Intelligent seedbed preparation requires more than an optimized soil-engaging component: field condition must be diagnosed, a controllable setting must be adjusted, and the resulting soil zone must be verified. This critical review synthesizes 132 unique sources across soil mechanics, component design, numerical modeling, field validation, and sensing-control research for water-limited cropping systems. Evidence was compared as bounded within-study contrasts rather than pooled effects because outcome definitions, soils, operating regimes, and validation scales differ. Four recurring conflicts organize the synthesis: fracture versus draft, tilth versus evaporative exposure, residue retention versus blockage, and immediate loosening versus persistence. Reported examples include an increase in the <50 mm aggregate fraction from 76.1% to 88.0%, a study-specific fragmentation index increase from 83.0% to 94.54%, a 43–47% reduction in penetration resistance after geometry-optimized loosening, and rainfall-dependent water-use-efficiency gains of 6.0–11.7% after subsoiling. These values are retained as study-specific anchors, not universal settings. The proposed framework adds value by linking four decision stages—field diagnosis, component design, controlled operation, and post-pass verification—while explicitly separating conventional optimization, monitored operation, and closed-loop intelligent control. Full article

Protected horticultural crops are commonly produced at high planting densities and have short production cycles; imbalances in water and fertilizer supply can rapidly affect plant vigor, yield, and quality. Non-destructive diagnostic methods are therefore needed to characterize plant nutritional status under greenhouse conditions. Spectral imaging can simultaneously capture spatial and spectral information associated with pigments, water status, tissue structure, and canopy phenotype. It does not directly detect nutrient ions; rather, it captures physiological and structural responses that may be associated with nutrient status and may also be influenced by water deficit, disease, temperature, salinity, phenology, and genotype. This review focuses on crops grown in soil, substrate, and hydroponic systems under greenhouse conditions. Studies conducted in vertical farms, growth chambers, and open fields are included only as supplementary references for sensor selection, model calibration, and inspection methods. This article synthesizes diagnostic indicators for nitrogen, phosphorus, and potassium, together with their associated physiological responses and spectral characteristics; compares the performance of hyperspectral, multispectral, and machine learning methods at the leaf, plant, and canopy scales; and examines fixed measurement, stop-and-go mobile inspection, continuous motion imaging, and autonomous plant revisitation. Existing studies have established a solid foundation for nutrient content retrieval, deficiency identification, and mobile monitoring. However, several challenges remain inadequately addressed under continuous inspection conditions, including radiometric–geometric joint calibration, plant identity preservation, acquisition of multi-element chemical truth values, model generalization across growth stages and greenhouse types, and long-term performance evaluation. Future work should refine standardized protocols for dynamic data collection and water–fertilizer environmental control, integrate mechanistic constraints with data driven approaches, and incorporate plant re-identification, spatiotemporal registration, uncertainty quantification, and online calibration. These efforts will contribute to constructing a long-term stable and comparable nutritional diagnostic system, thereby advancing the transition of facility vegetable nutritional monitoring from single-time static measurements toward continuous, traceable, and autonomously patrolled systems that may ultimately support precision irrigation and fertilization management after appropriate independent validation. Full article

The 3D geometric documentation of cultural heritage monuments requires spatial datasets that are accurate, complete and suitable for conservation, monitoring, visualization and heritage management. However, complex geometries, occlusions, limited accessibility, vegetation and other field-acquisition constraints often prevent a single surveying technique from providing a complete and metrically reliable representation. In this context, photogrammetry and simultaneous localization and mapping (SLAM)-based mapping provide complementary capabilities, with each method offering advantages and limitations regarding metric accuracy, spatial coverage, detail representation, acquisition flexibility and operational efficiency. This work develops, applies and evaluates a reproducible end-to-end workflow for the metric 3D documentation of complex cultural heritage monuments through multi-sensor integration. The proposed approach combines the metric robustness and visual richness of photogrammetric reconstruction with the rapid acquisition and spatial coverage enabled by SLAM-based mapping, while producing reusable datasets for conservation planning, comparative studies, education and broader heritage applications. The workflow integrates unmanned aerial vehicle (UAV) and close-range photogrammetry, SLAM-based mapping and geodetic control within a common reference system and is demonstrated through the documentation of a historic monastery. Both datasets showed centimetre-level agreement with geodetic observations, while photogrammetry yielded fuller exterior coverage and higher-quality texture, and SLAM enabled rapid interior coverage. The CH-PhotoSLAM3D dataset is released to support reproducibility and further research. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and requires sustained patient engagement to achieve optimal maternal and neonatal outcomes. This narrative review synthesized current evidence on patient engagement strategies and developed clinical principles for implementing patient-centered care across the GDM care continuum. Methods: A domain-based narrative review was conducted using the Consensus literature search platform. A total of 5359 records were identified. After multi-phase screening for relevance and eligibility, 37 studies, including systematic reviews, randomized controlled trials, observational studies, and qualitative research, were included. Evidence was synthesized across four domains: universal screening and linkage to care, whole-person assessment, digital patient engagement, and postpartum transition. Results: Universal screening consistently detected more women with GDM than risk-based approaches, although greater detection alone did not consistently improve maternal or neonatal outcomes. Evidence supported structured patient education, culturally responsive communication, self-management support, and active clinician feedback as key components of effective engagement. Digital health interventions, including telehealth, mobile applications, and remote monitoring, improved adherence, self-management, patient satisfaction, and, in many studies, glycemic control when integrated with clinician oversight. Postpartum follow-up remained a persistent gap despite evidence supporting reminder systems and coordinated care transitions. These findings informed a stepped clinical care pathway tailored to low-resource community health settings. Conclusions: Current evidence supports several components of patient-centered GDM care that informed a proposed stepped clinical care pathway integrating early identification, whole-person assessment, structured education, self-management support, digital engagement, and coordinated postpartum care. Many recommended strategies can be implemented using existing personnel and low-cost digital technologies, although additional implementation research is needed to evaluate culturally tailored interventions and long-term effectiveness in underserved populations. Full article

The performance of Peltier-driven atmospheric water generation (AWG) systems depends strongly on the surface geometry and wetting behavior of the condensation structure. Triply periodic minimal surfaces (TPMS) provide high surface area-to-volume ratio and geometric tunability, but their effectiveness as three-dimensional condensation structures requires experimental evaluation. In this study, five additively manufactured TPMS geometries, Gyroid, Diamond, Lidinoid, SplitP, and Schwarz, were evaluated in a Peltier-driven AWG setup under controlled laboratory conditions. The measured water collection response varied among the tested TPMS geometries, which showed different condensation, retention, and collection trends. Water collection was measured with and without surface treatment, while the monitored surface temperature remained below the calculated dew point during testing. Without surface treatment, total water collection ranged from approximately 0.9 to 1.4 g, whereas surface-treated specimens collected approximately 0.6 to 1.2 g. The specimens with surface treatment exhibited predominantly discrete droplets rather than the film-wise morphology observed without surface treatment, but the total water collection did not increase consistently. Gyroid and Lidinoid showed slight increases with surface treatment, while SplitP, Diamond, and Schwarz showed reductions. Water collection also did not scale directly with calculated TPMS surface area, which suggests that effective air exposure, droplet retention, drainage, and coating uniformity contributed strongly to the observed performance. These findings provide experimental insights into additively manufactured TPMS geometry and surface treatment conditions for Peltier-driven AWG. Full article

Continuous monitoring of permafrost dynamics remains challenging in remote high-mountain environments due to logistical constraints, harsh climatic conditions, and the limited availability of spatially distributed observations. In addition to direct temperature observations in boreholes, Autonomous Electrical Resistivity Tomography (A-ERT) offers significant potential for long-term monitoring by providing high temporal resolution observations of subsurface electrical properties, which are highly sensitive to freeze/thaw processes. This study presents the field validation of a low-power A-ERT system designed for long-term autonomous operation in extreme environments. The system was deployed at a high-altitude permafrost site near the Kumtor gold mine in the Central Tien Shan, Kyrgyzstan, representing the first application of continuous A-ERT monitoring in the Central Asian mountain ranges. The system operated continuously under harsh environmental conditions with air temperatures as low as −30 °C. Data quality remained consistently high throughout the monitoring period, with less than 1% of measurements removed during filtering, and inversion results with root-mean-square errors generally ranging between 3% and 4%. Time-lapse resistivity observations revealed strong seasonal freeze–thaw dynamics within the active layer and continued seasonal resistivity variations within the underlying permafrost despite permanently frozen conditions. Analysis of depth-dependent resistivity–temperature relationships revealed increasingly pronounced hysteresis behavior below the active layer, indicating that subsurface electrical properties were not controlled solely by temperature. This behavior likely reflects variations in unfrozen water content and pore connectivity within the fine-grained permafrost, where liquid water can persist at sub-zero temperatures. The results demonstrate the capability of the A-ERT system for reliable long-term autonomous monitoring in remote permafrost environments and investigation of coupled thermal and hydrological processes in permafrost systems. Full article

Safe drilling in hydrate-bearing sediments is essential for environmentally responsible natural gas hydrate development. Complex pressure variations, fluid migration, and mechanical disturbances during drilling may increase the risks of gas influx, lost circulation, pipe sticking, and wellbore instability. To improve diagnostic robustness under heterogeneous and noisy drilling conditions while reducing dependence on large-scale manually labeled datasets, this study develops an adaptively coupled data–physics dual-driven diagnostic framework based on a self-organizing map (SOM) and a competitive classifier. Unlike a conventional one-way SOM–classifier cascade, changes in the downstream classification loss are fed back to adjust the SOM neighborhood radius, thereby coupling unsupervised feature mapping with supervised risk classification. In addition, class-conditional pressure-window and torque–drag consistency penalties are linked to the predicted class probabilities so that physical information directly participates in the optimization of applicable fluid-related and pipe-sticking risk predictions. Risk categories without an explicitly available physical residual remain primarily data-driven. Experiments on a hybrid measured–simulated dataset show that the proposed model achieves a test-set accuracy of 97.67%, outperforming representative baseline models. When 20% Gaussian noise is added, the accuracy decreases by only 4.20 percentage points. A three-layer data acquisition–edge-computing–cloud-monitoring early-warning system is implemented through MATLAB/VC integration. In a pilot field trial, a representative well-kick risk was identified 12 min earlier than by a conventional threshold-based alarm, and the missed-alarm rate decreased from 15% to 3%. The proposed method provides an engineering-oriented framework for improving drilling safety and environmental risk control during natural gas hydrate development. Full article

The increased interest in environmental sustainability, driven by the transition to renewable energies, has spotlighted woodchip as an effective alternative to fossil fuel. Despite being readily available, its natural origin results in inherent heterogeneity, affecting every stage of the supply chain up to the combustion. Firstly, moisture content is detrimental for calorific value, leading to ineffective combustion drawbacks in transport and challenging storage. Water content monitoring along the production chain is crucial but its evaluation with standard analyses is limited due to destructive approach, unsuitable schedules and consequent costs. Near-infrared spectroscopy (NIRS), as an alternative technique, provides a non-destructive, repeatable, and faster method for both lab and inline control, overcoming standards limits. Considering the comparation already run with equal method, NIRS performances in predicting moisture content in Italian industrial woodchip simulating an in-line system are evaluated. Steady collection of continuous replicates improves woodchip representativeness, reducing estimation bias and enhancing prediction reliability (determination coefficient ~0.9, error <3%). Given the qualitative screening results, NIRS shows high water detection ability, acting as portable tool for real-time characterization in power plants. The immediate evaluation of water content could support critical steps along the supply chain, contributing to clean energy and market safety. Full article

High-intensity blue light is a powerful regulatory signal for plants, but its excess induces oxidative stress requiring rapid diagnosis. This study evaluated the potential for early detection of changes in the spectral characteristics of microgreen canopy cover caused by high-intensity blue light (PPFD 2500 µmol·m⁻²·s⁻¹, 12 h) in five species of microgreens (Helianthus annuus, Pisum sativum, Eruca sativa, Hordeum vulgare, Raphanus sativus ‘Sango Purple’) using hyperspectral imaging (450–950 nm) and machine learning. A Random Forest model trained on 85 vegetation indices classified light stress with high accuracy (Accuracy > 93%, Kappa > 0.87, F1-score > 93%) and detected spectral changes characteristic of light stress as early as 1–3 h of exposure. SHAP analysis identified carotenoid-sensitive indices (PRI, CCI, PRICI2) and chloroplast movement and stress indices (CMI, LSI_Red, LSI_Norm, Carter5) as the most informative predictors. It is assumed that the primary mechanism underlying early spectral changes was chloroplast avoidance rather than pigment degradation, as confirmed by the reversibility of canopy bleaching, rapid recovery of maximum quantum yield of photosystem II, and unchanged chlorophyll and carotenoid contents. Sunflower and radish were the most sensitive species to high-dose blue light, while barley was the least sensitive. LSI_Red was identified as a reliable qualitative marker of light stress that does not require a control sample, simplifying its use in automated monitoring systems. These findings demonstrate the effectiveness of hyperspectral phenotyping for non-invasive, rapid diagnosis of light stress in microgreens, providing a tool for optimising lighting regimes in controlled environment agriculture. Full article

Computer vision (CV) systems driven by artificial intelligence (AI) are increasingly replacing manual and conventional rule-based inspection procedures in industrial quality inspection, enabling automated, real-time, and data-driven decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates real-time extrusion monitoring in robotic building construction, where geometric deviations, bead-width variation, surface irregularities, and process inconsistencies are detected during material deposition using vision-based monitoring and image-processing pipelines. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted vision and AI techniques, including image-processing pipelines, convolutional neural networks, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems can outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput when they are supported by reliable acquisition, representative data, and robust industrial integration. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors. Full article

Nanomaterials are increasingly expected to do more than transport a payload, yet added complexity is useful only when it resolves a rate-limiting diagnostic, transport, release, or monitoring problem. This review develops a function-first framework for precision diagnostics and drug delivery in which formation and processing are linked to nanoscale structure, material properties, demonstrated function, route-specific evidence, and translational value. The scope includes AI-assisted plasmonic and terahertz biosensing; biopolymer nanoparticles and hydrogel depots; barrier-directed nose-to-brain and systemic delivery; graphene and carbon nanotube interfaces; lipid nanoparticles for nucleic acid packaging and endosomal escape; nanoporous, magnetic, and plasmonic carriers; and closed-loop theranostic systems. A platform is treated as genuinely multifunctional only when at least two deliberately engineered functions are experimentally supported and either act on distinct rate-limiting steps or close a sensing–intervention–monitoring loop. This review therefore distinguishes total loading from bioavailable payload, cellular uptake from productive delivery, imaging labels from intact carrier fate, and nominal stimulus responsiveness from controlled release in response to a physiologically realistic trigger. Recent independent studies are used to broaden comparisons across material classes and to separate proof-of-concept performance from translational evidence. Artificial intelligence is considered in three distinct roles—sensor interpretation, formulation/material optimization, and prediction of in vivo behavior—with external validation and, where a model is intended to guide decisions, prospective testing treated as essential. The resulting framework emphasizes biological identity, route-specific safety, carrier-versus-payload tracking, critical quality attributes, manufacturing reproducibility, and a minimum-evidence roadmap from concept to product. Full article

Industrial monitoring pipelines that log programmable logic controller (PLC) states to a database for machine learning inherit the noise introduced by the sampling process itself, yet noisy-label learning research is almost always evaluated against synthetically corrupted labels rather than real sampling noise. This study evaluates a physically derived noise-rate predictor, P_edge, by training decision tree and logistic regression classifiers on real, non-synthetic noisy labels from a Festo Modular Production System–Process Automation (MPS PA) industrial automation testbed—a four-station laboratory production line used for Industry 4.0 research and teaching—and assessing them against independently logged true PLC states across four stations. A controlled temporal-subsampling experiment, which varies the effective observation interval while holding the classification task fixed, shows that F1 score (the harmonic mean of precision and recall, used here in place of raw accuracy because of class imbalance in the target signals) against ground truth declines substantially for one process (0.951 to a sweep minimum of 0.679, 0.723 at the highest sampling condition tested), replicated across two target signals and both classifiers, while two other processes remain robust (F1 at or above 0.94), consistent with strong correlated features (correlations at or above 0.98), and one target signal shows no learnable baseline (F1 at or below 0.652) regardless of noise. These results indicate that a physically derived sampling-risk index can help anticipate machine learning sensitivity in industrial automation, but only when the target task is both learnable and lacks a redundant shortcut feature. Full article

IoT devices and robotic technologies can support sensor-based feedback, contextual monitoring, and adaptive control in distributed cyber–physical environments. This article compares three logic design strategies for distributed IoT systems with robotic support: embedded firmware logic, external workflow-based logic, and framework-assisted firmware design. A hybrid architecture is proposed to combine low-latency local control with flexible external orchestration, monitoring, and robotic feedback. To complement the conceptual comparison, a compact validation scenario is introduced, based on sensor reading, local validation, MQTT communication, Node-RED workflow processing, and actuator command execution. The indicative results show that embedded edge logic achieves the shortest response time, with an average value of 1.08 ms, while the hybrid workflow-based path introduces an additional delay, reaching 8.08 ms, but provides greater flexibility, scalability, and multi-device coordination. The findings indicate that hybrid architectures are suitable for inclusive IoT environments when immediate local reactions are combined with configurable external logic and reliable monitoring. Full article