Search Results (64,771)

Polymer-based dressings deriving from natural biomaterials have advantages such as nontoxicity, biocompatibility, and mechanical stability, which are essential for efficient wound healing and microbial infection diagnostics. Here, we designed a prototype of an intelligent hydrogel dressing on the base of bacterial cellulose (BC) for monitoring wound microbial infection due to the uploaded natural pH dye-sensor, anthocyanins (ANC) of elderberry fruit (Sambucus nigra L.). The highest sensor responses to bacterial metabolites for ANC immobilized to BC were observed at pH 5.0 and 6.0. The detection limit of the sensor signals was 3.45 A.U., as it was evaluated with a smartphone-installed application. The FTIR spectral analysis of the hybrid BC/ANC hydrogel films has proved the presence of anthocyanins within the BC matrix. Hybrid films differed from the control ones by thicker microfibrils and larger pores, as detected with scanning electron microscopy. Halochromic BC/ANC films exhibited antimicrobial activities mainly against gram-positive bacteria and yeast. They showed no cytotoxicity for the in vitro human cell lines and mouse fibroblasts within a selected range of anthocyanin concentrations released from the BC/ANC film/dressing prototype. Compared to the control, the in vitro healing test showed overgrowth of primary mouse fibroblasts after applying 0.024–2.4 µg/mL ANC. Full article

Graphene, known for its outstanding physical and chemical properties, is widely used in various fields, including electronics and biomedicine. Reduced graphene oxide (rGO) is preferred for electrochemical applications due to its enhanced water solubility and dispersion. Electrochemically reduced graphene oxide (ErGO) is particularly advantageous as it can be prepared under mild conditions and simplifies sensor fabrication; however, ErGO-based electrochemical sensors often lack specificity. Bioreceptors like proteins, enzymes, and DNA/RNA aptamers are incorporated to provide high specificity. This study introduces a guanine (G)/cytosine (C)-modified ErGO electrode (G/C@ErGO-GCE) for the sensitive electrochemical detection of doxorubicin (DOX) with good selectivity. The G/C mixture acts as a bioreceptor and is anchored on the ErGO-GCE surface via π-π interactions. The G/C@ErGO-GCE was characterized using scanning electron microscopy, contact angle measurement, Raman spectroscopy, and electrochemical methods. The sensor demonstrated excellent dynamic range (DPV: 10 nM to 1 µM, CA: 30 nM to 1.3 µM), sensitivity (DPV: 2.17 µA/µM, CA: 6.79 µA/µM), limit of detection (DPV: 84 nM, CA: 34 nM), and selectivity for DOX detection, highlighting its potential for biomedical applications and pharmacokinetic studies. Full article

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3). Full article

Histone deacetylase inhibitors (HDACi) show high antineoplastic potential in preclinical studies in various solid tumors, including gastric carcinoma; however, their use in clinical studies has not yet yielded convincing efficacies. Thus, further studies on cellular/molecular effects of HDACi are needed, for improving clinical efficacy and identifying suitable combination partners. Here, we investigated the role of oxidative stress in gastric cancer cells upon treatment with HDACi. A particular focus was laid on the role of the Nrf2 pathway, which can mediate resistance to cell-inhibitory effects of reactive oxidative species (ROS). Using fluorescence-based ROS sensors, oxidative stress was measured in human gastric cancer cell lines. Activation of the Nrf2 pathway was monitored in luciferase reporter assays as well as by mRNA and proteomic expression analyses of Nrf2 regulators and Nrf2-induced genes. Furthermore, the effects of ROS scavenger N-acetyl-L-cysteine (NAC) and Nrf2-knockdown on HDACi-dependent antiproliferative effects were investigated in colorimetric formazan-based and clonogenic survival assays. HDACi treatment led to increased oxidative stress levels and consequently, treatment with NAC reduced cytotoxicity of HDACi. In addition, vorinostat treatment stimulated expression of a luciferase reporter under the control of an antioxidative response element, indicating activation of the Nrf2 system. This Nrf2 activation was only partially reversible by treatment with NAC, suggesting ROS independent pathways to contribute to HDACi-promoted Nrf2 activation. In line with its cytoprotective role, Nrf2 knockdown led to a sensitization against HDACi. Accordingly, the expression of antioxidant and detoxifying Nrf2 target genes was upregulated upon HDACi treatment. In conclusion, oxidative stress induction upon HDAC inhibition contributes to the antitumor effects of HDAC inhibitors, and activation of Nrf2 represents a potentially important adaptive response of gastric cancer cells in this context. Full article

According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart’s behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives. Full article

Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of production lines, which is paramount in satisfying stringent quality standards and evolving consumer demands. The investigation into the integration of comprehensive sensor networks and sophisticated algorithmic analytics enriches continuous improvement strategies, markedly enhancing the accuracy and efficiency of production quality monitoring and control mechanisms. By moving beyond the limits of statistical process control to predictive methods enabled by machine learning algorithms, the study presents a transformative leap in manufacturing processes. The presented findings illustrate the critical role of predictive algorithms in navigating the complexities of process variability, thereby ensuring consistent adherence to established quality specifications. This approach not only facilitates immediate and accurate product quality categorization, increasing overall operational efficiency, but also equips manufacturers to swiftly respond to the variable nature of manufacturing requirements. Furthermore, this research delves into the multifaceted impacts of predictive process control on the manufacturing ecosystem. The ability to predict process quality decrease before it occurs, the optimization of resource allocation, and the anticipation of production bottlenecks before they impact output are among the notable benefits of this technological evolution. These developments to predictive process control is instrumental in propelling the manufacturing industry toward a more agile, sustainable, and customer-centric future. This shift not only complements the industry’s drive toward comprehensive digitization but also promises significant strides in achieving superior process improvements and maintaining a competitive edge on the global market. Full article

Objectives: Hypersensitive acupoints in specific body areas are associated with corresponding internal or visceral disorders. Back-shu points are clinically significant for the diagnosis of visceral organ disease, according to the biomechanical characteristics of the acupoints. In this study, we assessed the biomechanical characteristics and pain sensitivities of five back-shu points linked to five visceral organs in healthy participants. Methods: The study included 48 volunteer participants. A myotonometry was used to assess muscle tone and muscle stiffness at five back-shu points associated with visceral organs. Pressure was monitored using a microcontroller and a force sensor. Pain sensitivity was assessed in response to deep pressure pain produced by a constant force. Results: Substantial differences in muscle tone and stiffness were observed at the five back-shu points; muscle tone was highest at BL15, whereas muscle tone and muscle stiffness were lowest at BL23. Moreover, pain sensitivity was significantly different among the acupoints; pain sensitivity was highest at BL23. There was a significant negative correlation between muscle tone and pain sensitivity. Conclusions: We found significant differences in muscle tone, muscle stiffness, and pain sensitivity among five back-shu points associated with visceral organs, which may be attributable to anatomical variations at each point. Our findings suggest that differences at back-shu points should be considered to ensure the accurate diagnosis of visceral disease. Full article

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods. Full article

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications. Full article

Biosensors are a new type of sensor that utilize biologically sensitive materials and microbially active analytes to measure a variety of biological signals. The purpose of monitoring is achieved by combining these sensitive materials with analytes such as proteins, cells, viruses, and bacteria, inducing changes in their physical or chemical properties. The use of nanomaterials in fabricating surface acoustic wave (SAW) biosensors is particularly noteworthy for the label-free detection of organisms due to their compact size, portability, and high sensitivity. Recent advancements in the manufacturing techniques of SAW biosensors have significantly enhanced sensor performance and reliability. These techniques not only ensure precise control over sensor dimensions and material properties but also facilitate scalable and cost-effective production processes. As a result, SAW biosensors are poised to become powerful tools for various clinical and rapid detection applications. Full article

Abstract: When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS. Full article

Audio presentation is often attributed as being capable of influencing a viewer’s feeling of immersion during an audiovisual experience. However, there is limited empirical research supporting this claim. This study aimed to explore this effect by presenting a clip renowned for its immersive soundtrack to two groups of participants with either high-end or basic audio presentation. To measure immersion, a novel method is applied, which utilises a camera instead of an electroencephalogram (ECG) for acquiring a heart rate synchronisation feature. The results of the study showed no difference in the feature, or in the responses to an established immersion questionnaire, between the two groups of participants. However, the camera-sourced HR synchronicity feature correlated with the results of the immersion questionnaire. Moreover, the camera-sourced HR synchronicity feature was found to correlate with an equivalent feature sourced from synchronously recorded ECG data. Hence, this shows the viability of using a camera instead of an ECG sensor to quantify heart rate synchronisation but suggests that audio presentation alone is not capable of eliciting a measurable difference in the feeling of immersion in this context. Full article

Accurately obtaining a patient’s respiratory rate is crucial for promptly identifying any sudden changes in their condition during emergencies. Typically, the respiratory rate is assessed through a combination of impedance change measurements and electrocardiography (ECG). However, impedance measurements are prone to interference from body movements. Conversely, a capnometer coupled with a ventilator offers a method of measuring the respiratory rate that is unaffected by body movements. However, capnometers are mainly used to evaluate respiration when using a ventilator or an Ambu bag by measuring the CO₂ concentration at the breathing circuit, and they are not used only to measure the respiratory rate. Furthermore, capnometers are not suitable as wearable devices because they require intubation or a mask that covers the nose and mouth to prevent air leaks during the measurement. In this study, we developed a reliable system for measuring the respiratory rate utilizing a small wearable MOx sensor that is unaffected by body movements and not connected to the breathing circuit. Subsequently, we conducted experimental assessments to gauge the accuracy of the rate estimation achieved by the system. In order to avoid the effects of abnormal states on the estimation accuracy, we also evaluated the classification performance for distinguishing between normal and abnormal respiration using a one-class SVM-based approach. The developed system achieved 80% for both true positive and true negative rates. Our experimental findings reveal that the respiratory rate can be precisely determined without being influenced by body movements. Full article

This comprehensive review examines the phenomena of stress corrosion cracking (SCC) and chloride-induced stress corrosion cracking (Cl-SCC) in materials commonly used in the oil and gas industry, with a focus on austenitic stainless steels. The study reveals that SCC initiation can occur at temperatures as low as 20 °C, while Cl-SCC propagation rates significantly increase above 60 °C, reaching up to 0.1 mm/day in environments with high chloride concentrations. Experimental methods such as Slow Strain Rate Tests (SSRTs), Small Punch Tests (SPTs), and Constant-Load Tests (CLTs) were employed to quantify the impacts of temperature, chloride concentration, and pH on SCC susceptibility. The results highlight the critical role of these factors in determining the susceptibility of materials to SCC. The review emphasizes the importance of implementing various mitigation strategies to prevent SCC, including the use of corrosion-resistant alloys, protective coatings, cathodic protection, and corrosion inhibitors. Additionally, regular monitoring using advanced sensor technologies capable of detecting early signs of SCC is crucial for preventing the onset of SCC. The study concludes with practical recommendations for enhancing infrastructure resilience through meticulous material selection, comprehensive environmental monitoring, and proactive maintenance strategies, aimed at safeguarding operational integrity and ensuring environmental compliance. The review underscores the significance of considering the interplay between mechanical stresses and corrosive environments in the selection and application of materials in the oil and gas industry. Low pH levels and high temperatures facilitate the rapid progression of SCC, with experimental results indicating that stainless steel forms passive films with more defects under these conditions, reducing corrosion resistance. This interplay highlights the need for a comprehensive understanding of the complex interactions between materials, environments, and mechanical stresses to ensure the long-term integrity of critical infrastructure. Full article

The development of photo-/electro-chemical and flexible electronics has stimulated research in catalysis, informatics, biomedicine, energy conversion, and storage applications. Gels (e.g., aerogel, hydrogel) comprise a range of polymers with three-dimensional (3D) network structures, where hydrophilic polyacrylamide, polyvinyl alcohol, copolymers, and hydroxides are the most widely studied for hydrogels, whereas 3D graphene, carbon, organic, and inorganic networks are widely studied for aerogels. Encapsulation of functional species with hydrogel building blocks can modify the optoelectronic, physicochemical, and mechanical properties. In addition, aerogels are a set of nanoporous or microporous 3D networks that bridge the macro- and nano-world. Different architectures modulate properties and have been adopted as a backbone substrate, enriching active sites and surface areas for photo-/electro-chemical energy conversion and storage applications. Fabrication via sol–gel processes, module assembly, and template routes have responded to professionalized features and enhanced performance. This review presents the most studied hydrogel materials, the classification of aerogel materials, and their applications in flexible sensors, batteries, supercapacitors, catalysis, biomedical, thermal insulation, etc. Full article