Search Results (1,328)

A pilot anaerobic bioreactor requires near-daily monitoring and frequent maintenance. This study aimed to upgrade a pilot bioreactor into a low-cost IoT device via ESP32 microcontrollers. The methodology was based on remote data acquisition and online monitoring of various parameters towards assessing the anaerobic digestion performance. A semi-continuous tank bioreactor with a 60 L total volume was initially inoculated mainly with livestock manure and fed daily with a mixture of glucose, gelatin, and oleic acid, supplemented with a basic anaerobic medium. Under steady-state conditions, the organic loading rate was 2 g VS L_R⁻¹ d⁻¹. Sensors for pH, temperature, REDOX potential, and ammonium concentration, along with devices measuring biogas volume and methane content, were integrated and validated against analytical methods. Biogas production was recorded accurately, enabling the early detection of production declines through ex-situ data analysis. Methane concentration variance was less than 6% compared to gas chromatography, while temperature and pH deviations were 0.15% and 1.67%, respectively. Ammonia ion measurements required frequent recalibration due to larger fluctuations. This IoT-enhanced system effectively demonstrated real-time monitoring of critical bioreactor parameters, with ESP32 enabling advanced control and monitoring capabilities. Full article

This study presents a blockchain-based traceability system designed specifically for the olive oil supply chain, addressing key challenges in transparency, quality assurance, and fraud prevention. The system integrates Internet of Things (IoT) technology with a decentralized blockchain framework to provide real-time monitoring of critical quality metrics. A practical web application, linked to the Ethereum blockchain, enables stakeholders to track each stage of the supply chain via tamper-proof records. Key functionalities include smart contracts that automate quality checks, ensuring data integrity and providing immediate verification of product authenticity. Initial user feedback highlights the system’s potential to enhance transparency and reduce fraud risks in the olive oil market, supporting consumer trust and regulatory compliance. This approach offers a scalable solution adaptable to other high-value agricultural products, demonstrating the blockchain’s transformative potential for secure and transparent food traceability. Full article

To identify the most used dosimeters for monitoring ultraviolet radiation (UVR) and analyze their reliability and cost for individual UV exposure monitoring, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. An extensive search of the PubMed, Scopus, and Web of Science databases, covering 2005–2023, was conducted, including examining reference lists of retrieved studies. Of the 1202 records, 52 were eligible for analysis. Three types of dosimeters were identified: photosensitive, photochromic, and electronic dosimeters. Photosensitive dosimeters were utilized for 1236 samples across the studies, while photochromic dosimeters were employed for 360 samples. Electronic dosimeters, with a sample size of 3632, were the most extensively studied. This study highlights the variety of resources available for UVR assessment and the significance of specific dosimeter types in this field. Although few studies have explored the costs associated with dosimeter use, electronic dosimeters are the most cost-effective for radiation monitoring and provide the highest accuracy for measuring UVR exposure. Electronic dosimeters, known for real-time data and high precision, are reliable but costly, being approximately 16.5 times more expensive than photosensitive dosimeters and 160 times more expensive than photochromic dosimeters. Photosensitive dosimeters suit large-scale personal use, and photochromic sensors such as polysulphone dosimeters are also reliable. Additional costs for data analysis software, laboratory equipment, or external analysis services may be incurred, especially for advanced research-grade sensors. Full article

Background. Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Results. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Conclusions. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources. Full article

Most pier scour monitoring methods cannot be carried out during floods, and data cannot be recorded in real-time. Since scour holes are often refilled by sediment after floods, the maximum scour depth may not be accurately recorded, making it difficult to derive the equilibrium scour depth. This study proposes a novel approach using 16 proximity sensors (VCNL4200), which are low-cost (less than USD 3 each) and low-power (380 µA in standby current mode), to monitor and record the pier scour depth at eight different positions in a flume as it varies with water flow rate. Based on the regression relationship between PS data and distance, the scour trend related to the equilibrium scour depth can be derived. Through the results of 13 local live-bed sediment scour experiments, this PS module was able to record not only the scour depth, but also the development and geometry of the scour under different water flows. Additionally, based on PS data readings, changes in the topography of the scour hole throughout the entire scouring process can be observed and recorded. Since the maximum scour depth can be accurately recorded and the scour trend can be used to estimate the equilibrium scour depth, observations from the experimental results suggest that the critical velocity derived by Melville and Coleman (2000) may have been underestimated. The experimental results have verified that, beyond achieving centimeter-level accuracy, this method also leverages the Internet of Things (IoT) for the long-term real-time observation, measurement, and recording of the formation, changes, and size of scour pits. In addition to further exploring scouring behavior in laboratory studies, this method is feasible and highly promising for future applications in on-site scour monitoring due to its simplicity and low cost. In future on-site applications, it is believed that the safety of bridge piers can be assessed more economically, precisely, and effectively. Full article

This study aims to enhance elderly fall detection systems by using the YOLO (You Only Look Once) object detection algorithm with pose estimation, improving both accuracy and efficiency. Utilizing YOLOv7-W6-Pose’s robust real-time object detection and pose estimation capabilities, the proposed system can effectively identify falls in video feeds by using a webcam and process them in real-time on a high-performance computer equipped with a GPU to accelerate object detection and pose estimation algorithms. YOLO’s single-stage detection mechanism enables quick processing and analysis of video frames, while pose estimation refines this process by analyzing body positions and movements to accurately distinguish falls from other activities. Initial validation was conducted using several free videos sourced online, depicting various types of falls. To ensure real-time applicability, additional tests were conducted with videos recorded live using a webcam, simulating dynamic and unpredictable conditions. The experimental results demonstrate significant advancements in detection accuracy and robustness compared to traditional methods. Furthermore, the approach ensures data privacy by processing only skeletal points derived from pose estimation, with no personal data stored. This approach, integrated into the NeuroPredict platform developed by our team, advances fall detection technology, supporting better care and safety for older adults. Full article

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed. Full article

This paper investigates the transformative potential of integrating technical and methodological tools such as GraphQL, openEHR, Redis, and Pervasive Business Intelligence in healthcare. Modern healthcare systems face data silos, interoperability, and efficient data communication challenges. The integration of these technologies offers innovative solutions to address these challenges. GraphQL, known for its flexible data retrieval capabilities, simplifies data communication and integration. openEHR, a standards-based approach to healthcare data management, fosters interoperability through a unified data model. Redis, a scalable data storage and caching system, enhances application performance and real-time data processing. Pervasive Business Intelligence empowers healthcare analytics, aiding data-driven decision-making by enabling an integrated Electronic Health Record. This paper explores these technologies’ benefits, integration possibilities, and synergies. The practical implications of this integration are demonstrated through a real-world case study. The findings underscore the potential to revolutionize healthcare data management, communication, and analysis, improving patient care and operational efficiency. Full article

Construction projects in deep underground engineering are associated with the recording of massive amounts of diversified signals during real time and continuous microseismic monitoring given the complexity and specificity of the construction environment. Before the analysis of source information and further prediction of possible disasters, it is generally necessary to perform onset time picking and detection of microseismic signals. To improve the accuracy and efficiency of these tasks, this paper proposes an advanced deep dual-task neural network, which sequentially integrates the two processes. In this method, a score map is used to label the onset time of micro-fracture waveforms to improve the picking accuracy. The proposed model can simultaneously handle the onset time picking and detection tasks of microseismic signals to achieve optimal performance. Based on the similarity of data structures, the output from the onset time picking section is imported into the detection section to classify different types of microseismic waveforms. The onset time picking and detection procedures can be seamlessly integrated, where the score map of the onset time can help improve the detection accuracy. The results show that this method has a good performance for the onset time picking and detection of microseismic waveforms that are polluted by noises of various types and intensities. A comparison of the proposed method with existing methods and applications in underground engineering projects helped demonstrate the excellent performance of this method. The proposed approach can accelerate the automatic processing of microseismic signals and has significant potential for the exploration of seismology and earthquake research. Full article

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies. Full article

Unlike traditional tap changers, which require transformers to be de-energized before making changes, On-Load Tap Changers (OLTCs) can adjust taps while the transformer is in service, ensuring continuous power supply during voltage regulation. OLTCs enhance grid reliability and support load balancing, reducing strain on the network and optimizing power quality. Their importance has grown as the demand for stable voltage and the integration of renewables has increased, making them vital for modern and resilient power systems. While enhanced OLTCs often incorporate stronger materials and improved designs, mechanical components like contacts and diverter switches can still experience wear over time. This can result in longer maintenance intervals. In the era of digitalization, advanced diagnostic techniques capable of detecting early signs of wear or malfunction are essential to enable preventive maintenance for these important components. This contribution introduces a novel method for detecting faults and irregularities in OLTCs, leveraging vibroacoustic signals to enhance OLTC diagnostics. This paper proposes a tolerance-based approach using the envelope of vibroacoustic signals to identify faults. A significant challenge in this field is the limited availability of faulty signal data, which hinders the performance of machine learning algorithms. To address this, this study introduces a nonlinear model utilizing amplitude modulation with a Gaussian carrier to simulate faults by introducing controlled distortions. The dataset used in this study, with data recorded under real operating conditions from 2016 to 2023, is free of anomalies, providing a robust foundation for the analysis. The results demonstrate a marked improvement in the robustness of detecting simulated faults, offering a promising solution for enhancing OLTC diagnostics and preventive maintenance in modern power systems. Full article

Background: SARS-CoV-2 infection is generally associated with less severe forms of disease in children, where most cases only require symptomatic treatment. However, there is a paucity of information regarding the impact and clinical course of COVID-19 in neonate patients. This study aimed to analyze the epidemiological and clinical aspects of COVID-19 in this particular age group who were patients treated in our department. Materials and methods: This is a retrospective observational study that includes neonates (aged less than 1 month) who were diagnosed with COVID-19. The patients were admitted between 1 January 2022 and 31 December 2023, to the Infectious Diseases Pediatric Department of the Hospital Clinic of Pneumophthisiology and Infectious Diseases in Brașov, Romania. All the patients were tested for SARS-CoV-2 infection at admission, using either a real-time PCR (RT-PCR) or rapid antigen testing, according to the national COVID-19 protocol in use at the time. We collected the following data: demographic data, clinical picture and laboratory values at presentation, clinical course, complications, and other significant data. All the data were extracted from existing hospital administrative databases or electronic medical records. Results: Nine neonates were hospitalized with COVID-19, of which five were boys, and four were girls; the mean age was 18.89 days (ranging between 6 and 28 days). The clinical picture at admission mainly consisted of fever (eight cases) and nasal obstruction and cough (five cases each). Only one patient required oxygen support. Co-infections with Streptococcus pneumoniae and Haemophilus influenzae (one case), respiratory syncytial virus (RSV, one case), and rotavirus (one case) were identified. Complications were represented by acute bronchiolitis in three patients. Biologically, lymphopenia was found in three cases, monocytosis in five cases, and increased ferritin values in five cases. The clinical outcome was favorable in all the cases. The patients were discharged in improved condition after an average stay of 5.11 days (ranging between 3 and 10 days). Conclusions: Our data support the observation that infection with SARS-CoV-2 in neonates is a relatively benign condition with a good prognosis. Our study has several limitations and establishes a foundation for future studies on a larger sample of term and premature neonates with different comorbidities. Full article

This technical note aims to present a method for developing a Digital Terrain Model (DTM) of the coastal zone based on topobathymetric data from remote sensors. This research was conducted in the waterbody adjacent to the Vistula Śmiała River mouth in Gdańsk, which is characterised by dynamic changes in its seabed topography. Bathymetric and topographic measurements were conducted using an Unmanned Aerial Vehicle (UAV) and two hydrographic methods (a Single-Beam Echo Sounder (SBES) and a manual survey using a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) receiver). The result of this research was the development of a topobathymetric chart based on data recorded by the above-mentioned sensors. It should be emphasised that bathymetric data for the shallow waterbody (less than 1 m deep) were obtained based on high-resolution photos taken by a UAV. They were processed using the “Depth Prediction” plug-in based on the Support Vector Regression (SVR) algorithm, which was implemented in the QGIS software as part of the INNOBAT project. This plug-in allowed us to generate a dense cloud of depth points for a shallow waterbody. Research has shown that the developed DTM of the coastal zone based on topobathymetric data from remote sensors is characterised by high accuracy of 0.248 m (p = 0.95) and high coverage of the seabed with measurements. Based on the research conducted, it should be concluded that the proposed method for developing a DTM of the coastal zone based on topobathymetric data from remote sensors allows the accuracy requirements provided in the International Hydrographic Organization (IHO) Special Order (depth error ≤ 0.25 m (p = 0.95)) to be met in shallow waterbodies. Full article

This comprehensive study investigates the integration of cloud computing and deep learning technologies in medical data analysis, focusing on their combined effects on healthcare delivery and patient outcomes. Through a methodical examination of implementation instances at various healthcare facilities, we investigate how well these technologies manage a variety of medical data sources, such as wearable device data, medical imaging data, and electronic health records (EHRs). Our research demonstrates significant improvements in diagnostic accuracy (15–20% average increase) and operational efficiency (60% reduction in processing time) when utilizing cloud-based deep learning systems. We found that healthcare organizations implementing phased deployment approaches achieved 90% successful integration rates, while hybrid cloud architectures improved regulatory compliance by 50%. This study also revealed critical challenges, with 35% of implementations facing data integration issues and 5% experiencing security breaches. Through empirical analysis, we propose a structured implementation framework that addresses these challenges while maintaining high performance standards. Our findings indicate that federated learning techniques retain 95% model accuracy while enhancing privacy protection, and edge computing reduces latency by 40% in real-time processing. By offering quantitative proof of the advantages and difficulties of combining deep learning and cloud computing in medical data analysis, as well as useful recommendations for healthcare organizations seeking technological transformation, this study adds to the expanding body of knowledge on healthcare digitalization. Full article

Freezing of gait (FOG) is a disabling yet poorly understood paroxysmal gait disorder affecting the vast majority of patients with Parkinson’s disease (PD) as they reach advanced stages of the disorder. Falling is one of the most disabling consequences of a FOG episode; it often results in injury and a future fear of falling, leading to diminished social engagement, a reduction in general fitness, loss of independence, and degradation of overall quality of life. Currently, there is no robust or reliable treatment against FOG in PD. In the absence of reliable and effective treatment for Parkinson’s disease, alleviating the consequences of FOG represents an unmet clinical need, with the first step being reliable FOG prediction. Current methods for FOG prediction and prevention cannot provide real-time readouts and are not sensitive enough to detect changes in walking patterns or balance. To fill this gap, we developed an sEMG system consisting of a soft, wearable garment (pair of shorts and two calf sleeves) embedded with screen-printed electrodes and stretchable traces capable of picking up and recording the electromyography activities from lower limb muscles. Here, we report on the testing of these garments in healthy individuals and in patients with PD FOG. The preliminary testing produced an initial time-to-onset commencement that persisted > 3 s across all patients, resulting in a nearly 3-fold drop in sEMG activity. We believe that these initial studies serve as a solid foundation for further development of smart digital textiles with integrated bio and chemical sensors that will provide AI-enabled, medically oriented data. Full article