Real-time Monitoring System Research Articles

RETRACTED ARTICLE: Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

RETRACTED ARTICLE: Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

H-ADCP-based real-time sediment load monitoring system using support vector regression calibrated by global optimization technique and its applications

Reliable and continuous measurement of sediment load in rivers is essential for understanding and managing sediment transport dynamics. However, traditional sediment load monitoring methods depend on labor-intensive field sampling techniques, leading to limitations in spatiotemporal coverage. This study introduces a novel approach for machine learning-aided real-time suspended sediment concentration monitoring using a horizontal acoustic Doppler current profiler. Support vector regression models for suspended sediment concentration estimation are derived from eight flow monitoring stations with sediment sampling data. A new model selection framework is proposed, integrating a global optimization algorithm in order to calibrate input variables and hyperparameters simultaneously. The three-fold cross-validation score is utilized to enhance the generalization accuracy and reliability of sediment load estimation. This work assessed the impact of considering additional input variables in addition to the backscattering signal. The findings confirm that incorporating flowrate, water level, and their time derivatives during model training improves predictability. Spatial differences between flow monitoring and sediment sampling locations significantly influence the model accuracy. This study also discusses the potential application of the H-ADCP-based monitoring system to obtain suspended and total sediment loads at an arbitrary flow monitoring station. By offering real-time monitoring capabilities and reduced reliance on labor-intensive field sampling, this work contributes to the advancement of sediment transport data acquisition methods.

Scope of practice regulation in medicine: balancing patient safety, access to care and professional autonomy.

Scope of practice regulation in medicine is crucial for ensuring patient safety, access to care and professional autonomy. This paper explores the impact of scope of practice regulation on healthcare delivery, professional responsibilities and patient outcomes. It discusses the variability in standards for safe practice, the challenges in defining boundaries between medical specialties and the recent controversies in cosmetic surgery practice. The paper also examines the potential benefits and drawbacks of rigorous scope of practice regulations, including their impact on clinical innovation, flexibility and access to care. Furthermore, it delves into the implications of defensive medicine and the consequences of restrictive regulations on patient care. The author proposes implementing a proactive, national, artificial intelligence-powered, real-time outcome monitoring system to address these challenges. This system aims to cover every patient undergoing a surgical procedure and could be gradually extended to non-surgical conditions, benefiting all key stakeholders in the health system. The paper emphasises the need for a balanced approach to scope of practice regulation to avoid stifling clinical innovation and professional autonomy, while ensuring patient safety and professional accountability.

Review on mechanism and remediation strategies of dissolved oxygen abnormal in surface water.

Dissolved oxygen (DO) is an important index to evaluate the quality of surface water environments. In recent years, anomalies in DO level have emerged as a major contributor to the decline of surface water quality. These anomalies have triggered several ecological and environmental challenges such as biodiversity loss, the degradation of water environmental quality, intensification of eutrophication, and an exacerbation of the greenhouse effect. Understanding the mechanisms underlying DO anomalies and devising targeted remediation strategies holds paramount importance in the scientific pursuit of water pollution control and aquatic ecosystem restoration. We explored and summarized the fluctuations and abnormal mechanism of DO concentration in surface water, focusing on factors like oxygen solubility, reoxygenation rates, and oxygen consumption by water bodies. We compiled a range of approaches for addressing DO anomalies, including pollution source management, artificial oxygenation, and the reconfiguration of aquatic ecosystems. Ultimately, we underscored the emerging significance of monitoring and regulating DO level in surface waters. Future research in this realm should encompass the establishment of distinct quality standards for surface water, the development of a comprehensive real-time spatial monitoring system for DO levels across watersheds, and the formulation of standardized procedures and technical norms.

Feasibility study of one-dimensional imaging with an optical fiber for radiation dose-rate monitoring system in the decommissioning process

Real-time dose-rate monitoring system under the high dose-rate situation is required for the decommissioning of Fukushima Daiichi Nuclear Plants to remove the debris remaining inside the plants. We have proposed a dose-rate monitor consisting of a scintillator, over 100-m long optical fiber and CCD spectrometer. Some noises with a dominant emission wavelength of below 550 nm originating from the optical fiber itself must be separated from the signal from the scintillator, and the signal region has been defined as 650–1000 nm to use significant data, up to now. In this paper, we investigated if the noise region defined as below 550 nm can be supporting data for the monitoring system, because such noises are expected to include position information through the fiber. In the emission spectrum, the intensities of the noise emission or absorption band are expected to depend on the position of the fiber, and we demonstrated such dependence, “one-dimensional dose-rate distribution”, with 20 m-long optical fiber and CCD spectrometer. Optical fiber was placed around a 60Co source with an activity of approximately 60 TBq, and emission spectra of the fiber were acquired with different dose-rate conditions from 10 to 700 Gy/h. The noise intensities were well fitted with the power function as a function of the product of fiber length and dose rate, and we found that fiber noise has rate information in one-dimensional imaging.

Development and application of a mathematical model for calculating the discharge of non-standard thin-plate weirs in urban combined sewer overflow systems: a case study

Abstract The aim of this study is to address the issue of difficulty in evaluating the combined sewer overflow (CSO) pollution effectively, especially for the monitoring of overflow from non-standard thin-plate weirs (NTPWs). In order to construct a discharge calculation mathematical model (DCMM) of NTPWs in an urban combined sewer overflow system (UCSOS), which can be used to construct a real-time monitoring and early warning system, a physical model constructed firstly for obtaining hydraulic characteristic sets under three flow conditions (free, submerged, pressurized flow). Then, the coefficient function expressions are obtained under three flow conditions through genetic algorithm (GA) fitting, and a DCMM of UCSOS is established subsequently. The results indicate that DCMM exhibits high accuracy, with mean relative error (MRE) at 0.0326, root mean square error (RMSE) at 1.569, and Nash–Sutcliffe efficiency (NSE) coefficient at 0.9387. The DCMM can not only evaluate the discharge capacity under different flow conditions, but also be used to realize reliable and convenient discharge monitoring. Although the DCMM constructed in this case is not universal, the modeling method of combining physical models with GA is an effective new approach to evaluate the discharge capacity of NTPW, which also holds value for application in other cities.

Anticancer Activity of Iso-Mukaadial Acetate on Pancreatic and Colon Cancer Cells.

Pancreatic cancer and colon cancer pose significant challenges in treatment, with poor prognoses. Natural products have long been explored for their potential as anticancer agents. Iso-mukaadial acetate has shown promise in inducing apoptosis in breast and ovarian cancer cells. The objective of this study was to investigate the effect of Iso-mukaadial acetate on pancreatic (MIA-PACA2) and colon (HT29) cancer cell lines. Pancreatic (MIA-PACA2) cancer cells, colon (HT29) cancer cells, normal embryonic kidney cells (HEK 293), and normal lung cells (MRC5) were cultured and treated with Iso-mukaadial acetate (IMA) for 24 hours. The viability assays were conducted using Alamarblue reagent and a real-time cell viability monitoring system, xCELLigence. The IC50 values were determined, followed by assessments of ATP production, caspase 3/7 activation, mitochondrial function, morphological changes using a light microscope, and gene expression changes via RT-PCR. This study indicates that Iso-mukaadial acetate exhibited concentration-dependent cytotoxic effects, slowing cellular proliferation in both cancer cell lines. Activation of the mitochondrial apoptotic pathway and caspase 3/7 suggests induction of apoptosis. Reduced ATP production and altered gene expression further support its anticancer properties. Morphological changes after treatment with Iso-mukaadial acetate showed apoptotic characteristics which may suggest that apoptosis was induced. According to the results obtained, Iso-mukaadial acetate shows potential as an anticancer agent, evidenced by its effects on cellular viability, mitochondrial function, ATP production, caspase activation, and gene expression in pancreatic and colon cancer cells. These findings highlight its promise for further investigation and potential in the development of therapeutic agents.

Real-Time Monitoring System with Digital Umbrella.

This research project focuses on enhancing the safety and convenience of street sellers and outdoor campers by developing a real-time monitoring system incorporating GSM, GPRS, GPS, and MCU nodes. The system provides live location tracking and an emergency button for immediate assistance. Designed for vegetable sellers, roadside vendors, and tourists, the project integrates security features and amenities like FM radio, mobile charging, lighting, and fan systems into an automated panel powered by solar energy. The solar umbrella captures and stores solar energy during the day to power LED lights and other devices at night, promoting sustainability. This innovative solution combines practicality with advanced technology, ensuring continuous energy supply and real-time emergency alerts, thus significantly improving user safety and convenience.

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Real-time energy monitoring systems: Technological applications in Canada, USA, and Africa

Real-time energy monitoring systems are pivotal for reshaping the global energy landscape, optimizing consumption, and fostering sustainability. This research explores the technological, regional, and socio-economic dimensions of energy monitoring, focusing on Canada, the USA, and Africa. The technological landscape includes sensor technologies, communication protocols, and emerging innovations like AI and blockchain. Regional perspectives highlight diverse challenges and opportunities, while a cross-regional analysis emphasizes collaboration and mutual learning. Challenges such as data security and integration are viewed as catalysts for improvement. Real-time monitoring's impact on sustainability is profound, reducing carbon footprints, optimizing resource utilization, and yielding economic benefits. Improved access to energy and community engagement underscores its positive social impact. The global journey towards sustainability is enhanced through collaboration, technology transfer, and the collective pursuit of a resilient and environmentally conscious energy future.

Reviewing Air Pollutants Generated during the Pyrolysis of Solid Waste for Biofuel and Biochar Production: Toward Cleaner Production Practices

The production of biofuels and biochar through pyrolysis is a promising avenue for sustainable energy generation and waste management. However, this process can inadvertently release various air pollutants into the atmosphere, potentially compromising its environmental benefits. This article provides a comprehensive overview of the gas pollutants associated with pyrolysis for biofuel and biochar production, as well as different variables affecting gas emissions. Key pollutants such as particulate matter (PM), volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), carbon monoxide (CO), and nitrogen oxides (NOx) have been discussed in terms of their formations and emissions during pyrolysis processes. Furthermore, major factors influencing pollutant emissions, including feedstock composition, pyrolysis conditions, and combustion technologies have been examined with up-to-date examples. The review highlights the significance of emission control strategies, such as advanced reactor design, catalyst utilization, and the integration of realtime monitoring systems, in mitigating air pollution from pyrolysis processes. By shedding light on the environmental challenges associated with pyrolysis-based biofuel and biochar production, this article aims to encourage the development of cleaner and more sustainable approaches to harness the potential of solid waste conversion technologies.

Adaptive pneumatic soft gripper with embedded flexible bending sensor

Purpose The purpose of this paper is to introduce a variable distance pneumatic gripper with embedded flexible sensors, which can effectively grasp fragile and flexible objects. Design/methodology/approach Based on the motion principle of the three-jaw chuck and the pneumatic “fast pneumatic network” (FPN), a variable distance pneumatic holder embedded with a flexible sensor is designed. A structural design plan and preparation process of a soft driver is proposed, using carbon nanotubes as filler in a polyurethane (PU) sponge. A flexible bending sensor based on carbon nanotube materials was produced. A static model of the soft driver cavity was established, and a bending simulation was performed. Based on the designed variable distance soft pneumatic gripper, a real-time monitoring and control system was developed. Combined with the developed pneumatic control system, gripping experiments on objects of different shapes and easily deformable and fragile objects were conducted. Findings In this paper, a variable-distance pneumatic gripper embedded with a flexible sensor was designed, and a control system for real-time monitoring and multi-terminal input was developed. Combined with the developed pneumatic control system, a measure was carried out to measure the relationship between the bending angle, output force and air pressure of the soft driver. Flexible bending sensor performance test. The gripper diameter and gripping weight were tested, and the maximum gripping diameter was determined to be 182 mm, the maximum gripping weight was approximately 900 g and the average measurement error of the bending sensor was 5.91%. Objects of different shapes and easily deformable and fragile objects were tested. Originality/value Based on the motion principle of the three-jaw chuck and the pneumatic FPN, a variable distance pneumatic gripper with embedded flexible sensors is proposed by using the method of layered and step-by-step preparation. The authors studied the gripper structure design, simulation analysis, prototype preparation, control system construction and experimental testing. The results show that the designed flexible pneumatic gripper with variable distance can grasp common objects.

Complex Real-Time Monitoring and Decision-Making Assistance System Based on Hybrid Forecasting Module and Social Network Analysis

Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99.

Array-Designed Triboelectric Nanogenerator for Healthcare Diagnostics: Current Progress and Future Perspectives

Array-designed triboelectric nanogenerators (AD-TENGs) have firmly established themselves as state-of-the-art technologies for adeptly converting mechanical interactions into electrical signals. Central to the AD-TENG’s prowess is its inherent modularity and the multifaceted, grid-like design that pave the way to robust and adaptable detection platforms for wearables and real-time health monitoring systems. In this review, we aim to elucidate the quintessential role of array design in AD-TENGs for healthcare detection, emphasizing its ability to heighten sensitivity, spatial resolution, and dynamic monitoring while ensuring redundancy and simultaneous multi-detection. We begin from the fundamental aspects, such as working principles and design basis, then venture into methodologies for optimizing AD-TENGs that ensure the capture of intricate physiological changes, from nuanced muscle movements to sensitive electronic skin. After this, our exploration extends to the possible cutting-edge electronic systems that are built with specific advantages in filtering noise, magnifying signal-to-noise ratios, and interpreting complex real-time datasets on the basis of AD-TENGs. Culminating our discourse, we highlight the challenges and prospective pathways in the evolution of array-designed AD-TENGs, stressing the necessity to refine their sensitivity, adaptability, and reliability to perfectly align with the exacting demands of contemporary healthcare diagnostics.

The prototype of a real-time monitoring system for global structural responses of beam structure using wireless sensor network

Recently, the method of structural response analysis using a limited amount of multi-response data (SRALMR) that can estimate global structural responses was developed and verified with numerical analysis models. As a next step of the validation using the numerical analysis model, a prototype of monitoring system for global structural response (PMS-GSR) was established using wireless sensor network and SRALMR in this study. In addition, the graphical user interface was constructed for estimation performance analysis and user convenience. The experimental validation of PMS-GSR was conducted for beam structure under four load cases (LCs) and sensor-layout cases (SLCs). The experimental specimens were 2-span and 3-span beams, and a wireless sensor network including displacement, slope, and strain sensors was used. Finally, the global displacement, slope, and strain were estimated using PMS-GSR, and estimated global responses were analyzed according to the LCs and SLCs.

Biological-based techniques for real-time water-quality studies: Assessment of non-invasive (swimming consistency and respiration) and toxicity (antioxidants) biomarkers of zebrafish

Swimming consistency and respiration of fish are recognized as the non-invasive stress biomarkers. Their alterations could directly indicate the presence of pollutants in the water ecosystem. Since these biomarkers are a routine process for fish, it is difficult to monitor their activity manually. For this reason, experts employ engineering technologies to create sensors that can monitor the regular activities of fish. Knowing the importance of these non-invasive stress biomarkers, we developed online biological behavior monitoring system-OBBMS and online biological respiratory response monitoring system-OBRRMS to monitor real-time swimming consistency and respiratory response of fish, respectively. We continuously monitored the swimming consistency and respiration (OCR, CER and RQ) of zebrafish (control and atrazine-treatments) for 7 days using our homemade real-time biological response monitoring systems. Furthermore, we analyzed oxidative stress indicators (SOD, CAT and POD) within the vital tissues (gills, brain and muscle) of zebrafish during stipulated sampling periods. The differences in the swimming consistency and respiratory rate of zebrafish between the control and atrazine treatments could be precisely differentiated on the real-time datasets of OBBMS and OBRRMS. The zebrafish exposed to atrazine toxin showed a concentration-dependent effect (hypoactivity). The OCR and CER were increased in the atrazine treated zebrafish. Both Treatment I and II received a negative response for RQ. Atrazine toxicity let to a rise in the levels of SOD, CAT and POD in the vital tissues of zebrafish. The continuous acquisition of fish signals is achieved which is one of the main merits of our OBBMS and OBRRMS. Additionally, no special data processing was done, the real-time data sets were directly used on statistical tools and the differences between the factors (groups, photoperiods, exposure periods and their interactions) were identified precisely. Hence, our OBBMS and OBRRMS could be a promising tool for biological response-based real-time water quality monitoring studies.

A Wearable Sonification System to Improve Movement Awareness: A Feasibility Study

This paper presents the design, development, and feasibility testing of a wearable sonification system for real-time posture monitoring and feedback. The system utilizes inexpensive motion sensors integrated into a compact, wearable package to measure body movements and standing balance continuously. The sensor data is processed through sonification algorithms to generate real-time auditory feedback cues indicating the user’s balance and posture. The system aims to improve movement awareness and physical conditioning, with potential applications in balance rehabilitation and physical therapy. Initial feasibility testing was conducted with a small group of healthy participants performing standing balance tasks with eyes open and closed. Results indicate that the real-time audio feedback improved participants’ ability to maintain balance, especially in the case of closed eyes. This preliminary study demonstrates the potential for wearable sonification systems to provide intuitive real-time feedback on posture and movement to improve motor skills and balance.

Real-Time Data Acquisition and Monitoring of Three Phase Load Balance in Electricity Distribution

A real-time monitoring mechanism for three-phase electrical power distribution panels is essentially needed to ensure that the power is evenly distributed across all phases, so that the efficiency of energy usage could be maintained. This paper presents monitoring and data acquisition system for three-phase balance distribution. The system ensures balanced distribution of electrical power in order to enhance the efficiency and reliability of power systems. It leverages advanced technologies to collect, analyze, and report data, providing valuable insights into the performance of the power distribution network. Data acquisition is established by using sensor PZEM004T to measure the electricity parameters which is integrated with Arduino microcontroller. In addition, real-time monitoring system is visualized through dashboard data management for more informative data acquisition and analytic purposes. The performance is evaluated by testing the instrument and application at a distribution panel within a span of five days to monitor electrical parameters. The results show that real-time monitoring system for three phase balance works perfectly to provide actual data and phase balance condition. The phase imbalance values are higher during the peak working hours where the loads are dynamics and extensively used.

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm2). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels.

Analysis and experimental research of transient temperature rise characteristics of high-speed cylindrical roller bearing

In this paper, on one hand, the time-varying characteristics of the heat source and thermal boundary conditions of the high-speed spindle system were analyzed considering the thermal-structural coupling effect. And a transient bearing temperature field prediction method combining the thermal network method and finite element method was proposed. Furthermore, the relationships between time step, calculation efficiency and calculation results were analyzed. On the other hand, a online real-time monitoring system of the transient temperature of the cylindrical roller bearing inner ring for maximum speed of 13,000 r/min was designed and implemented using fibre optic sensing technology. Comparing with the conventional static thermal analysis results, it is verified that the simulation method proposed in this paper has higher accuracy. This paper provides a new approach for analysing and testing the thermal characteristics of high-speed spindle system.