Search Results (1,132)

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. Full article

To identify the complex time patterns in the process data and monitor the effect of wastewater treatment by predicting effluent chemical oxygen demand more accurately, a soft-sensor modeling method based on the multi-scale temporal convolutional network (MSTCN) was proposed in this paper. Data at different time scales are reconstructed according to the main frequencies determined by the Fourier transform approach, and the correlations between variables during that period are calculated and stored in the corresponding adjacency matrix. The specific temporal convolutional network (TCN) is built to learn the temporal dependencies within each sequence at the current scale, while the graph convolutional layer (GCN) captures the relationships among variables. Finally, predictions with less error can be obtained by integrating output features from GCN and TCN layers. The proposed model is validated on an annual dataset collected from a wastewater treatment plant employing biological processes for organic matter removal. The experimental results indicate that the proposed MSTCN reduces RMSE by 35.71% and 22.56% compared with the convolutional neural network and TCN, respectively. Moreover, MSCTN shortens the training period by 6.3 s and improves RMSE by 30.41% when compared to the long short-term memory network, which is outperformed in extracting temporal dynamic characteristics. Full article

The galvanic dissolved oxygen sensor finds widespread applications in multiple critical fields due to its high precision and excellent stability. As its core sensing components, the oxygen-permeable membrane, electrode, and electrolyte significantly impact the sensor’s performance. To systematically investigate the comprehensive effects of these core sensing components on the performance of galvanic dissolved oxygen sensors, this study selected six types of oxygen-permeable membranes made from two materials (Perfluoroalkoxy Polymer (PFA) and Fluorinated Ethylene Propylene Copolymer (FEP)) with three thicknesses (0.015 mm, 0.03 mm, and 0.05 mm). Additionally, five concentrations of KCl electrolyte were configured, and four different proportions of lead–tin alloy electrodes were chosen. Single-factor and crossover experiments were conducted using the OxyGuard dissolved oxygen sensor as the experimental platform. The experimental results indicate that under the same membrane thickness conditions, PFA membranes provide a higher output voltage compared to FEP membranes. Moreover, the oxygen permeability of FEP membranes is more significantly affected by temperature. Furthermore, the oxygen permeability of the membrane is inversely proportional to its thickness; the thinner the membrane, the better the oxygen permeability, resulting in a corresponding increase in sensor output voltage. When the membrane thickness is reduced from 0.05 mm to 0.015 mm, the sensor output voltage for PFA and FEP membranes increases by 86% and 74.91%, respectively. However, this study also observed that excessively thin membranes might compromise measurement accuracy. In a saturated, dissolved oxygen environment, the sensor output voltage corresponding to the six oxygen-permeable membranes used in the experiment exhibits a highly linear inverse relationship with temperature (correlation coefficient ≥ 98%). Meanwhile, the lead–tin ratio of the electrode and electrolyte concentration have a relatively minor impact on the sensor output voltage, demonstrating good stability at different temperatures (coefficient of variation ≤ 0.78%). In terms of response time, it is directly proportional to the thickness of the oxygen-permeable membrane, especially for PFA membranes. When the thickness increases from 0.015 mm to 0.05 mm, the response time extends by up to 2033.33%. In contrast, the electrode material and electrolyte concentration have a less significant effect on response time. To further validate the practical value of the experimental results, the best-performing combination of core sensing components from the experiments was selected to construct a new dissolved oxygen sensor. A performance comparison test was conducted between this new sensor and the OxyGuard dissolved oxygen sensor. The results showed that both sensors had the same response time (49 s). However, in an anaerobic environment, the OxyGuard sensor demonstrated slightly higher accuracy by 2.44%. This study not only provides a deep analysis of the combined effects of oxygen-permeable membranes, electrodes, and electrolytes on the performance of galvanic dissolved oxygen sensors but also offers scientific evidence and practical guidance for optimizing sensor design. Full article

Single-walled carbon nanotubes (SWCNTs) offer promise as materials for thermoelectric generators (TEGs) due to their flexibility, durability, and non-toxic nature. However, a key barrier to their application lies in their high thermal conductivity, which hampers the generation of temperature differences in TEGs. To address this challenge, we explored a method of enhancing the heat dissipation of SWCNT-based TEGs by coating SWCNT layers onto polymer mesh sheets. During TEG fabrication, achieving stable n-type SWCNT/mesh sheets proved considerably more challenging than their p-type counterparts. This difficulty stemmed from the inferior dispersibility of the n-type SWCNT ink compared to the p-type SWCNT ink. To produce n-type SWCNT/mesh sheets, we initially prepared p-type SWCNT/mesh sheets using p-type SWCNT ink, subsequently doping them with a cationic surfactant solution to induce n-type characteristics. To stabilize the n-type thermoelectric properties in SWCNT/mesh sheets, we applied a fluoropolymer coating to the SWCNT surfaces, mitigating the adsorption of oxygen molecules. This approach yielded n-type SWCNT/mesh sheets capable of long-term maintenance. Furthermore, flexible TEGs fabricated using both p- and n-type SWCNT/mesh sheets demonstrated an output voltage of 15 mV, which can operate IoT sensors using the latest booster circuits, and a maximum power of 100 nW at a temperature difference of 71 K. Full article

TiO₂ is a typical semiconductor material, and it has attracted much attention in the field of humidity sensors. Doping is an efficient way to enhance the humidity response of TiO₂. Eu-doped TiO₂ material was investigated in both theoretical simulations and experiments. In a simulation based on density functional theory, a doped Eu atom can increase the performance of humidity sensors by producing more oxygen vacancies than undoped TiO₂. In these experiments, Eu-doped TiO₂ nanorods were prepared by hydrothermal synthesis, and the results also confirm the theoretical prediction. When the doping mole ratio is 5 mol%, the response of the humidity sensor reaches 23,997.0, the wet hysteresis is 2.3% and the response/recovery time is 3/13.1 s. This study not only improves the basis for preparation of high-performance TiO₂ humidity sensors, but also fills the research gap on rare earth Eu-doped TiO₂ as a humidity-sensitive material. Full article

Glucose is a simple sugar molecule. The chemical formula of this sugar molecule is C₆H₁₂O₆. This means that the glucose molecule contains six carbon atoms (C), twelve hydrogen atoms (H), and six oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally, after eating or drinking, our bodies break down the sugars in food and use them to obtain energy for our cells. To execute this process, our pancreas produces insulin. Insulin “pulls” sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas cannot produce enough insulin. As a result, the level of glucose in their blood rises. This can lead to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack, damage to blood vessels, etc. In this study, a non-invasive and therefore easily usable method for monitoring blood glucose was developed. With the experiment carried out, it was possible to measure glucose levels continuously, thus eliminating the disadvantages of invasive systems. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in blood; these sensors have a wavelength of 940 nm. The sensor was placed on a small black parallelepiped-shaped box on the tip of the finger and the output of the optical sensor was then connected to a microcontroller at the analogue input. Another sensor used, but only to provide more medical information, was the heartbeat sensor, inserted into an armband (along with the microprocessor). After processing and linear regression analysis, the glucose level was predicted, and data were sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods (commercial products). The hardware consisted of a microcontroller, a near-IR optical sensor, a heartbeat sensor, and a Bluetooth module. Another objective of this experiment using low-cost and low-power hardware was to not carry out complex processing of data from the sensors. Our practical laboratory experiment resulted in an error of 2.86 per cent when compared to a commercial product, with a hardware cost of EUR 8 and a consumption of 50 mA. Full article

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels’ lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38% $F_1$-score rates for pH, 96.98% $F_1$-score rates for temp, 97.88% $F_1$-score rates for DO, and 98.11% $F_1$-score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative. Full article

Monitoring water quality in urban rivers is crucial for water resource management since point and non-point source pollution remain a major challenge. However, traditional water quality monitoring methods are costly and limited in frequency and spatial coverage. To optimize the monitoring, techniques such as modeling have been proposed. These methods rely on networks of low-cost multiprobes integrated with IoT networks to offer continuous real-time monitoring, with sufficient spatial coverage. But challenges persist in terms of data quality. Here, we propose a framework to verify the reliability and stability of low-cost sensors, focusing on the implementation of multiparameter probes embedding six sensors. Various tests have been developed to validate these sensors. First of all, a calibration check was carried out, indicating good accuracy. We then analyzed the influence of temperature. This revealed that for the conductivity and the oxygen sensors, a temperature compensation was required, and correction coefficients were identified. Temporal stability was verified in the laboratory and in the field (from 3 h to 3 months), which helped identify the frequency of maintenance procedures. To compensate for the sensor drift, weekly calibration and cleaning were required. This paper also explores the feasibility of LoRa technology for real-time data retrieval. However, with the LoRa gateways tested, the communication distance with the sensing device did not exceed 200 m. Based on these results, we propose a validation method to verify and to assure the performance of the low-cost sensors for water quality monitoring. Full article

In this paper, a Methylene Blue (MB)-coated tapered coreless (TCL) optical fibre sensor is proposed and experimentally investigated for oxygen sensing in the near-infrared (NIR) wavelength range of 993.5 nm. The effect of TCL diameter and MB sol–gel coating thickness on the sensitivity of the sensor was also investigated. A maximum sensitivity of 0.19 dB/O₂% in the oxygen concentration range of 0–37.5% was achieved for a TCL fibre sensor with a 2 µm taper waist diameter and a 0.86 µm MB sol–gel coating thickness, with a response time of 4 min. The sensor provides reproducible results even after 7 days and is shown to be highly selective to oxygen compared to argon and ethanol at the same concentration. Full article

A pulsed detonation chamber (PDC) equipped with Hartmann–Sprenger resonators has been designed and tested for both Hydrogen/air and Hydrogen/Oxygen mixtures. A full-factorial experimental campaign employing four factors with four levels each has been carried out for both mixtures. Instantaneous static pressure has been measured at two locations on the exhaust pipe of the PDC, and the signal has been processed to extract the average and maximum cycle pressures and the operating frequency of the spark plug. The PDC has been shown to be able to reach sustained detonation cycles over a length below 200 mm, measured from the spark plug to the first pressure sensor. The optimal regimes for both air and Oxygen operation have been determined, and the influence of the four factors on the responses is discussed. Full article

In this study, femtosecond (FS) laser irradiation with different laser energy densities of 138, 276, and 414 mJ/cm² is applied to SnO₂-nanowire (NW) gas sensors, and the effect of the FS laser irradiation on the gas sensor response toward toluene (C₇H₈) gas is investigated. The FS laser irradiation causes oxygen deficiency in the SnO₂ NWs and forms SnO and SnO_x. Moreover, an embossing surface with multiple nano-sized bumps is created on the SnO₂ NW surface because of the FS laser irradiation. The FS laser-irradiated SnO₂-NW gas sensor exhibits superior sensing performance compared with the pristine SnO₂-NW gas sensor. Moreover, the FS laser energy density significantly affects gas-sensing performance, and the highest sensor response is achieved by the gas sensor irradiated at 138 mJ/cm². The long-term stability test of the laser-irradiated SnO₂-NW gas sensor is performed by comparing fresh and 6-month-old gas sensors in different gas concentrations and relative humidity levels. Comparable gas-sensing behaviors are examined between the fresh and 6-month-old gas sensor, and this verifies the robustness of the laser-irradiated SnO₂-NW gas sensor. Full article

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates’ determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman’s coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman’s coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables. Full article

Colorimetric biosensor-based nanozymes have received considerable attention in various fields thanks to the advantages of the simple preparation, good stability, and regulable catalytic activity of nanozymes. In this study, a bimetallic nanozyme Fe₃O₄@Co₃O₄/CN was prepared via the high-temperature calcination of Fe₃O₄-PVP@ZIF-67. The material retained its skeletal structure before calcination, which prevented the aggregation of nanoparticles and exposed more active sites of the nanozyme, substantially enhancing the intrinsic dual enzyme-mimetic activities, including peroxidase- and oxidase-like activities. In particular, Fe₃O₄@Co₃O₄/CN with oxidase-like activity catalyzed the colorless tetramethylbenzidine (TMB) to become blue oxTMB with oxygen. Reducing glutathione (GSH) could inhibit the above oxidation reaction. In contrast, with respect to the existence of mercury(II), GSH bound to mercury(II) due to the strong affinity between mercury(II) and -SH, thus eliminating the inhibition and restoring the oxTMB signal. A simple and effective colorimetric sensor was fabricated to detect mercury(II) based on the above principles. The proposed measurement had a linear range of 0.1–15 μM and a limit of detection (LOD) of 0.017 μM. It was shown that the established colorimetric sensing system could be successfully applied to detect mercury(II) in water samples, and the Fe₃O₄@Co₃O₄/CN nanozyme proved to be a promising candidate for biosensing application. Full article

Statins are 3-hydroxy-3-methylglutaryl coenzyme-A (HMG-CoA) reductase inhibitors widely used in the treatment of hyperlipidemia. The inhibition of HMG-CoA reductase in the mevalonate pathway leads to the suppression of cell proliferation and induction of apoptosis. The cyclic GMP-AMP synthase (cGAS) stimulator of the interferon genes (STING) signaling pathway has been suggested to not only facilitate inflammatory responses and the production of type I interferons (IFN), but also activate other cellular processes, such as apoptosis. It has not been studied, however, whether cGAS-STING activation is involved in the apoptosis induced by statin treatment in human colorectal cancer cells. In this study, we reported that lovastatin impaired mitochondrial function, including the depolarization of mitochondrial membrane potential, reduction of oxygen consumption, mitochondrial DNA (mtDNA) integrity, and mtDNA abundance in human colorectal cancer HCT116 cells. The mitochondrial dysfunction markedly induced ROS production in mitochondria, whereas the defect in mitochondria respiration or depletion of mitochondria eliminated reactive oxygen species (ROS) production. The ROS-induced oxidative DNA damage by lovastatin treatment was attenuated by mitochondrial-targeted antioxidant mitoquinone (mitoQ). Upon DNA damage, mtDNA was released into the cytosol and bound to DNA sensor cGAS, thus activating the cGAS-STING signaling pathway to trigger a type I interferon response. This effect was not activated by nuclear DNA (nuDNA) or mitochondrial RNA, as the depletion of mitochondria compromised this effect, but not the knockdown of retinoic acid-inducible gene-1/melanoma differentiation-associated protein 5 (RIG-I/MDA5) adaptor or mitochondrial antiviral signaling protein (MAVS). Moreover, lovastatin-induced apoptosis was partly dependent on the cGAS-STING signaling pathway in HCT116 cells as the knockdown of cGAS or STING expression rescued cell viability and mitigated apoptosis. Similarly, the knockdown of cGAS or STING also attenuated the antitumor effect of lovastatin in the HCT116 xenograft model in vivo. Our findings suggest that lovastatin-induced apoptosis is at least partly mediated through the cGAS-STING signaling pathway by triggering mtDNA accumulation in the cytosol in human colorectal cancer HCT116 cells. Full article

Imbalances in the redox state of the liver arise during metabolic processes, inflammatory injuries, and proliferative liver disorders. Acute exposure to intracellular reactive oxygen species (ROS) results from high levels of oxidative stress (OxS) that occur in response to hepatic ischemia/reperfusion injury (IRI) and metabolic diseases of the liver. Antisense oligonucleotides (ASOs) are an emerging class of gene expression modulators that target RNA molecules by Watson–Crick binding specificity, leading to RNA degradation, splicing modulation, and/or translation interference. Here, we review ASO inhibitor/activator strategies to modulate transcription and translation that control the expression of enzymes, transcription factors, and intracellular sensors of DNA damage. Several small-interfering RNA (siRNA) drugs with N-acetyl galactosamine moieties for the liver have recently been approved. Preclinical studies using short-activating RNAs (saRNAs), phosphorodiamidate morpholino oligomers (PMOs), and locked nucleic acids (LNAs) are at the forefront of proof-in-concept therapeutics. Future research targeting intracellular OxS-related pathways in the liver may help realize the promise of precision medicine, revolutionizing the customary approach to caring for and treating individuals afflicted with liver-specific conditions. Full article