Search Results (1,445)

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R²) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. Full article

Electrodermal activity (EDA) is a powerful anchor for assessing human sympathetic nervous system (SNS) arousal. However, EDA alone is only one facet of physiological response. Researchers have increasingly moved away from single-sensor analysis to multimodal wearable systems, integrating EDA with other signals such as heart rate variability (HRV), photoplethysmography (PPG), skin temperature (SKT), blood oxygen (SpO₂) and more. This critical shift in methodology is not yet reflected in current reviews of the literature. Existing surveys thoroughly cover EDA as a standalone measure, but the combination of sensor technologies has been largely unexamined. In this context, multimodal refers to integrating EDA with complementary biosignals (HRV, PPG, SKT, SpO₂, etc.) commonly captured by modern wearable platforms. This review provides a comprehensive analysis focused on multimodal systems for assessing SNS arousal. A total of 58 studies met the inclusion criteria. We map the landscape, from single signal methods to complex sensor-fusion, and highlight advances in multimodal sensor models, physiological modeling, and context-aware sensing. We also examine recent advances in signal processing and machine learning that enhance multimodal SNS arousal inference, outlining current capabilities and identifying open directions for future work. By providing a framework of this emerging field, this paper serves as a resource for all researchers aiming to build and deploy the next generation of context-aware SNS arousal-sensing technology. Full article

Dichloromethane, as a widely used highly volatile industrial solvent, has neurotoxicity and hepatotoxicity and is suspected of being a carcinogen to humans. Therefore, it is necessary to develop a detection method that is more convenient for users, responds faster and is more efficient than traditional analytical techniques. In cataluminescence (CTL) technology, as a promising alternative, the performance of CTL sensors critically depends on the design of high-performance sensitive materials. In this study, by rationally designing two typical metal–organic frameworks (MOFs), UIO-66 (zirconium-based) and HKUST-1 (copper-based), UIO-66/HKUST-1 nanocomposites for dichloromethane CTL detection were prepared by using a simple hydrothermal method. The experimental results show that when the composition ratio of UIO-66 is 2%, this composite exhibits the strongest CTL response to dichloromethane. Under optimized conditions, this sensor exhibits high selectivity, excellent stability (RSD = 3.98%), and a rapid response advantage for dichloromethane. The response time and recovery time are 5 and 19 s, respectively. It shows a good linear relationship within the concentration range of 8.4–84 ppm, along with a detection limit as low as 1.71 ppm. Analysis indicates that the enhanced performance stems from the formation of high-concentration oxygen vacancies and significantly strengthened synergistic effects at the UIO-66/HKUST-1 composite. This increases the concentration of surface reactive oxygen species, thereby providing more active sites for catalytic reactions. This work provides a robust and efficient sensing strategy for dichloromethane detection. Full article

The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems. Full article

The physicochemical controls on gold precipitation in orogenic gold deposits remain poorly constrained, with traditional fluid inclusion and isotopic studies often yielding ambiguous results due to overprinting or incomplete records. This study addresses this challenge using chlorite—a sensitive mineral proxy for fluid conditions—as a quantitative sensor in the Jinshan orogenic gold deposit (>200 t Au) of the Jiangnan orogenic belt, South China. Hosted in Neoproterozoic phyllite within NE–NNE-trending ductile–brittle shear zones, Jinshan features auriferous quartz–polymetallic sulfide veins with prominent chlorite alteration. Integrating high-resolution SEM-EPMA analyses of multi-generational chlorite with thermodynamic modeling, we reconstruct the temporal evolution of temperature, oxygen fugacity (fO₂), pH and sulfur fugacity (fS₂) during ore formation. Four paragenetic stages are identified: Stage 1 (ankerite–quartz), Stage 2 (pyrite–arsenopyrite–quartz), Stage 3 (quartz–gold–polymetallic sulfide), and Stage 4 (chlorite–carbonate–quartz). Electron microprobe analysis reveals that the chlorite composition changes from Fe-rich chamosite (Stage 2) to Mg-rich clinochlore (Stage 3) and then to Fe-rich chamosite (Stage 4). Chlorite from Stage 2 (Chl-1) formed metasomatically at low fluid/rock ratios, while Stage 3 and 4 chlorites (Chl-2 and Chl-3) precipitated directly from higher fluid/rock ratio fluids. Chlorite compositions record a critical Stage 2–3 transition involving cooling from ~320 °C to ~260 °C, reduction (log fO₂ from −33.6 to −39.7), and alkalinization, and sulfur fugacity remained stable within a narrow range (log fS₂ = −13.6 to −8.0), followed in Stage 4 by minor reheating to ~280 °C, re-acidification, and a slight rebound in oxygen fugacity. Thermodynamic simulations reveal that the destabilization of Au(HS)₂⁻ complexes, primarily driven by the synergistic effects of cooling, pH increase, and decreasing oxygen fugacity, triggered gold precipitation during the main ore stage. Results demonstrate that abrupt cooling coupled with fluid alkalinization and reduction exerted the dominant control on gold precipitation in Jinshan, resolving long-standing debates on ore-forming mechanisms and highlighting chlorite as a robust quantitative sensor for fluid evolution. Full article

Surgical wound complications, particularly surgical site infection (SSI), remain common despite advances in perioperative care, and modern dressings—including emerging smart systems—are intended to optimize moisture balance, reduce bioburden, and support monitoring of healing. This narrative review, informed by PRISMA 2020, synthesized comparative clinical evidence on postoperative dressings across surgical specialties. PubMed and Embase were searched for peer-reviewed comparative human studies published in 2015–2025 involving adults undergoing surgery with primary closure or secondary intention healing. Outcomes included SSI, time to epithelialization/closure, scar outcomes, pain, peri-wound skin integrity, and dressing change frequency. Nine studies met the inclusion criteria across orthopedics, general and endocrine surgery, otolaryngology, maxillofacial surgery, and surgical oncology. In hip/knee arthroplasty, hydrofiber dressings were associated with lower SSI rates versus standard/absorbent dressings. A meta-analysis suggested that moist and silver-based dressings generally outperformed gauze, with ionic silver ranking highest for healing and metallic silver for SSI prevention, and hydrocolloids reduced dressing change frequency. Oxygen diffusion therapy improved scar outcomes after cervicotomy, and chitosan gel reduced synechiae after endoscopic sinus surgery. Evidence in oncologic surgery was inconclusive, and heterogeneity in interventions, endpoints, and follow-up limited pooling. Overall, advanced postoperative dressings may improve selected outcomes compared with traditional gauze, but effects appear procedure- and context-dependent; future studies should standardize outcomes, extend follow-up, and incorporate cost-effectiveness and patient-reported measures, alongside evaluation of sensor-enabled smart dressings. Full article

Mitochondria are a key organelle in maintaining metabolic homeostasis. It not only generates most of the cell’s energy through oxidative phosphorylation but also acts as a complex sensor of the redox state and oxygen in the cell. This review thoroughly analyzes the interactions among mitochondrial iron metabolism, mitochondrial reactive oxygen species (mtROS), and lipid peroxidation (LPO), the triggering factors of ferroptosis, an iron-dependent form of programmed cell death. We point out research showing that intrinsic mitochondrial machinery, such as iron–sulfur (Fe-S) cluster assembly and heme metabolism, is both an important cofactor and a master regulator. If these processes are disrupted, they can lead to ferroptosis. Unlike views that focus on the cytosol, we explain that the stability of Fe-S clusters in complexes such as aconitase and respiratory Complex I is crucial for preventing electron leakage and excessive mtROS formation. The Fenton reaction and its direct effect on cardiolipin (CL) oxidation in the inner membrane of mitochondria is a central event in cardiometabolic diseases. Its peroxidation and breakdown make the organelle very unstable and lead to cell death though Ca²⁺ overload and a significantly decreased reduced/oxidized glutathione ratio. Additionally, the functions of essential iron transporters and glutathione homeostasis are examined, and their dysregulation is correlated with ferroptosis-associated progression of cardiometabolic and neurodegenerative disorders, such as obesity and Alzheimer’s disease. This review focused on the need to revisit the classic bioenergetic core of the mitochondria as a key player in the pathophysiology of metabolic and neurodegenerative diseases. Full article

The efficiency of wastewater treatment plants (WWTPs) highly depends on correctly detecting the anomalies hindering their processes. This study investigates the use of hybrid Artificial Neural Networks (A-NNs) for detecting mechanical faults injected in the Dissolved Oxygen (DO) sensor of a WWTP. The hybrid networks are obtained by combining Radial Basis Function Neural Network (RBF-NN) with the specific architectures of Feedforward Neural Network (FF-NN), Long Short-Term Memory Neural Network (LSTM-NN) and Convolutional Neural Network (C-NN), respectively. Each hybrid model is tested on several simulated anomaly scenarios containing both normal and faulty operating conditions of the DO sensor, and evaluated using a comprehensive set of classification metrics, including accuracy (A), precision (P), recall (R), F1-score (F1-S), balanced accuracy (BA), Cohen’s Kappa (CK), Matthew’s Correlation Coefficient (MCC), and the areas under the Receiver Operating Characteristic curve (ROC-AUC) and the Precision–Recall curve (PR-AUC). The results show that the LSTM-NN + RBF hybrid consistently outperforms the other two hybrids, achieving accuracy of 96.04%, precision of 96.78%, recall of 89.51%, F1-score of 92.89%, ROC-AUC of 96.01%, PR-AUC of 94.93%, MCC of 90.25%, CK of 90.03% and BA of 94.16%. These results suggest that the proposed LSTM-NN + RBF hybrid is a promising tool for efficiently detecting mechanical faults in a WWTP. Full article

This study presents a single-case multi-sensor experimental study of thermal loading and emission variation in a high-mileage gasoline engine operating at idle under deliberately impaired cooling until mechanical failure. A production vehicle equipped with a naturally aspirated gasoline engine with a displacement of 1600 cc was operated under relatively steady-state conditions at idle, while gaseous emissions (CO, CO₂, HC, NO_x, and O₂), air–fuel ratio λ, particle number (PN), oil temperature, infrared thermal indicators, and acoustic performance variation were continuously monitored. The results are interpreted primarily in terms of their dependence on the engine oil temperature. They show that despite stable conditions of the air–fuel ratio and an almost constant amount of residual oxygen in the exhaust gases, progressive thermal loading leads to pronounced changes in the behavior of the emissions emitted by the engine during its operation. Hydrocarbon emissions show increased variability and escalation at elevated engine oil temperatures, while nitrogen oxides show a strong temperature-dependent increase, consistent with thermally driven formation mechanisms. The most significant response is observed in the particle number (PN) emissions, which go from low and stable levels to a rapid, multi-step increase in a narrow temperature range preceding mechanical failure. Under the tested cooling impairment scenario, emission behavior was dominated by cumulative thermal stress rather than mixture composition effects. In the investigated case, particle number emissions emerged as a sensitive indicator of system-level thermal instability. The findings provide experimentally documented insight into the system-level progression toward thermal runaway under impaired cooling conditions and its measurable impact on emission behavior in the tested engine. Full article

Adjusting the Schottky barrier height is an important approach to enhancing the gas-sensing performance of TiO₂ Schottky sensors. In this study, micro TiO₂ nanotube Schottky sensors were fabricated via magnetron sputtering and anodic oxidation, with their Schottky barrier height adjusted by varying the annealing temperature. The morphology, phase composition, oxygen vacancy concentration, band structure, and Schottky junction of the samples were investigated using SEM, GIXRD, EPR, Hall effect measurements, XPS, I-V curves, and AC impedance. The sensor annealed at 500 °C demonstrated the highest gas-sensing response, outperforming sensors treated at other temperatures by over 100 times. Its response value to 1 ppm H₂ was 242. The annealing temperature significantly affects the TiO₂ phase and oxygen vacancy concentration, resulting in the highest Schottky barrier height in the 500 °C-annealed sensor, which contributes to its superior sensing performance. AC impedance measurements revealed no significant Fermi-level pinning in TiO₂. Based on the gas-sensing mechanism analysis, the response of the TiO₂ sensor can be divided into three regimes: Schottky junction control, TiO₂ resistance control, and co-control. Full article

Hypoxic pulmonary vasoconstriction (HPV) is a rapid and reversible constrictor response of the pulmonary vasculature, and especially its small muscular precapillary arteries, which is initiated by episodes of local alveolar hypoxia. Acting as a protective homeostatic vasomotor mechanism, HPV enables maximal gas exchange by diverting blood from poorly ventilated alveoli into those rich in oxygen, thereby optimizing oxygen uptake and the ventilation–perfusion (V/Q) ratio so as to maintain the arterial oxygen partial pressure (P_aO₂) within the physiological range. HPV is an intrinsic mechanism of pulmonary artery smooth muscle cells (PASMCs), and requires an O₂ sensor which acts through mediator(s) to trigger effector mechanisms within these cells to evoke constriction. Whereas HPV effector mechanisms are reasonably well defined, the nature of the O₂ sensor and mediators remains in dispute, and a number of proposals have been developed to account for these. Some (but not all) of these share a focus on the concept that hypoxia activates effector mechanisms by inducing a change in the PASMC cytoplasmic redox state. Of these, the Redox Theory, first proposed by Kenneth Weir and Stephen Archer in 1995, proposes that hypoxia inhibits mitochondrial production of reactive oxygen species (ROS), thereby causing the cytoplasm to become more reduced. This inhibits ongoing vasorelaxation maintained by the opening of voltage-gated K⁺ channels. In contrast, according to the Mitochondrial ROS hypothesis, introduced by Paul Schumacker and Naveen Chandel in 2001, hypoxia increases mitochondrial ROS production, causing an oxidizing shift in the cytoplasmic redox state that activates several vasoconstricting pathways. In a third redox-based scenario, developed by Michael Wolin and Sachin Gupte, hypoxia evokes contraction by causing a fall in H₂O₂ production by NADPH oxidase and by activating the pentose phosphate pathway. These effects inhibit basal vasorelaxation maintained by the guanylate cyclase and protein kinase G and also stimulate vasoconstricting mechanisms. In this comprehensive review, we first provide a detailed summary of the key studies contributing to the development of these proposals and then subject the evidence supporting them to a critical appraisal, based in part on how well they accord with the wider literature and recent developments in our understanding of how cells shape and deploy redox mechanisms in order to regulate cell function. Full article

Objective: To determine the inter-session reliability of MOXY-derived muscle oxygenation (SmO₂) in recreationally trained individuals during upper-body strength training. Methods: Eighteen recreationally trained men (mean skinfold thickness at sensor sites = 16.4 ± 9.4 mm) completed two identical experimental sessions. Participants performed five sets to failure at 70% of one-repetition maximum in bench press and row. SmO₂ was recorded from the pectoralis major and latissimus dorsi. Basal SmO₂ prior to each set, SmO₂ consumption during each set, and SmO₂ resaturation during the first 30 s post-set were analyzed. Reliability was assessed using the standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV), and intraclass correlation coefficient (ICC). Results: Reliability was low for all variables. Basal SmO₂ showed SEM = 7.0–17.6%, MDC = 19.5–48.8%, and CV = 10.0–26.5%, with poor ICCs (−0.20 to 0.41). SmO₂ consumption and resaturation demonstrated even lower reliability, with SEM = 10.7–21.7%, MDC = 29.6–60.3%, and CV = 25.0–108.2%, with poor to moderate ICCs (−0.34 to 0.74). Conclusions: MOXY-derived SmO₂ measurements exhibit limited reliability, particularly during and immediately after training sets. These findings highlight the lack of applicability for using MOXY to monitor SmO₂ in recreationally trained individuals during upper-body strength training. Full article

Enzyme-free flexible glucose sensors hold great promise in the field of wearable health monitoring. However, their performance is limited by the balance between the catalytic interface activity and stability. This paper reports a strategy for interface gradient roughening of screen-printed carbon electrodes (SPCE) via controllable electrochemical exfoliation (EE). It systematically reveals the inherent relationships among the degree of EE treatment, electrode morphology, surface chemistry, and electrochemical performance. On this basis, the deposition of gold nanoparticles (AuNPs) with high density and uniform distribution is achieved, and a high-performance flexible enzyme-free glucose sensor is constructed. The study finds that EE treatment can significantly increase the true surface area of the electrode and introduce abundant oxygen-containing functional groups, thus effectively reducing the charge transfer resistance. Nevertheless, excessive exfoliation leads to the degradation of the conductive network, indicating the existence of a critical “performance window”. The EE-SPCE optimized with 150 cycles has both a high active area and good electrical conductivity, providing an ideal deposition substrate for AuNPs, increasing their distribution density by approximately 158% and reducing the average particle size to 125 nm. The fabricated AuNPs/EE-SPCE sensor exhibits excellent performance in glucose detection: it has a high sensitivity of 550.766 μA·mM⁻¹·cm⁻² in the range of 0.1–3 mM, a detection limit of 0.0998 mM, a wide linear range, excellent selectivity, long-term stability, and good mechanical flexibility. This research not only develops an efficient and scalable method for constructing flexible sensing interfaces but also clarifies the trade-off relationship among “roughening–conductivity–catalytic performance” at the mechanistic level, providing an important theoretical basis and a general strategy for rationally designing high-performance flexible electrochemical devices. Full article

Hydrogen is a key energy carrier for fuel cell vehicles and hydrogen energy systems. However, its colorless and odorless nature, combined with a wide flammability range, poses significant safety risks in the event of leakage. Accordingly, compact and reliable hydrogen sensors capable of low-ppm detection at moderate operating temperatures are essential for early-stage safety monitoring. In this study, a bias-optimized hydrogen gas sensor based on a Pd-functionalized SnO₂ thin film with Mo electrodes and an integrated microheater is designed, fabricated, and systematically characterized. The sensor employs a Mo-based vertical microheater and a multilayer thermal insulation stack, enabling thermally efficient and stable operation at 250–280 °C with low power consumption. The electrical and sensing properties of the SnO₂ layer are optimized by controlling the oxygen partial pressure during reactive sputtering and post-deposition annealing. The Pd catalytic layer promotes hydrogen dissociation and spillover, resulting in pronounced resistance modulation through surface redox reactions and interfacial charge transport effects. By systematically optimizing the sensing bias voltage, a clear trade-off between sensitivity enhancement and electrical noise is identified, which allows stable and repeatable operation in the low-ppm regime. The sensor response follows a power-law dependence on hydrogen concentration, and an automated measurement platform is employed to evaluate repeatability and statistical performance. Based on baseline noise analysis and concentration-dependent resistance variation, a limit of detection of approximately 6.4 ppm is achieved. Furthermore, a concentration-normalized figure of merit that combines response magnitude and concentration dependence is introduced to quantitatively assess low-concentration hydrogen sensing performance. These results demonstrate that the proposed Mo-electrode Pd/SnO₂ thin-film sensor, enabled by bias-optimized operation and integrated thermal control, provides a robust and scalable platform for safety-critical hydrogen leak detection. Full article

Culturing cells and micro-tissue samples in 3D bio-scaffolding structures is gaining popularity; however, precise control of tissue micro-environment in such systems remains challenging. We describe a family of new hybrid bio-scaffolds with 3D O₂-sensing ability, produced by simple means from readily available bio-scaffolding and O₂-sensing materials. Three different types of phosphorescent O₂-sensing materials—polymeric microparticles (MPs), supramolecular probe MitoXpress and nanoparticulate probes NanO2 and Nano-IR (NPs)—were integrated in Matrigel and agarose scaffolding materials and evaluated. Key working characteristics of such hybrid scaffolds, including heterogeneity, stability, cytotoxicity, optical signals and O₂-sensing properties, ease of fabrication and use, were compared. The results show superiority of the Matrigel hybrids with NanO2 and Nano-IR probes. Demonstration experiments were conducted with HCT116 cells and individual spheroids derived from these cells, culturing them in the Matrigel–NP hybrid scaffolds and monitoring oxygenation and local O₂ gradients on a time-resolved fluorescence plate reader and by phosphorescence lifetime imaging microscopy (PLIM). Full article