Performance Monitoring System Research Articles

Pilot Study of a Modified DOPS Scale for Insulin Pump and CGM Installation Training in Chinese Medical Students During Endocrinology Rotations.

Direct Observation of Procedural Skills (DOPS) is a clinical assessment tool that enables trainers to observe medical students' procedural abilities in real-time clinical settings. It assesses students' knowledge application, decision-making, and skill proficiency during clinical tasks. This study modifies the DOPS to evaluate the operation of insulin pumps (PUMP) and continuous glucose monitoring systems (CGMS) in diabetes management. Key elements of the modified DOPS include 1) Knowledge Assessment: Evaluating understanding of PUMP and CGMS, including interpreting CGMS data for insulin adjustments; 2) Operational Skills: Assessing correct PUMP needle insertion, programming, and adjustments; 3) Patient Safety: Ensuring safe and aseptic procedures; 4) Feedback: Providing constructive feedback to help students improve their skills. Training through DOPS led to significant improvements in all domains, overall performance scores, and reduced execution time for each domain. Correlations between domains showed that PUMP indication scores were linked to all other domains and execution times, including re-evaluation. Communication skills and seeking assistance were crucial factors influencing other domains. Multilinear regression analysis revealed that while DOPS-CGMS (R square 1.0) fully explained performance scores, DOPS-PUMP (R square 0.984) indicated that additional personal qualities significantly impacted students' PUMP operation performance. This customized DOPS form offers insights into students' abilities in managing diabetes with PUMP and CGMS, while emphasizing the need for training on both technical skills and interpersonal skills in future educational models.

Application of DC distributed bus bar power systems on ships

Operation of vessels sailing along the Northern Sea Route is associated with numerous risks that determine increased requirements for the reliability of the propulsion system. In this regard, it is important to use ship electrical networks based on the distribution of direct current (DC) electricity, the operation of which provides a number of advantages in comparison with AC distribution systems. Conducting technical control of imported ship equipment due to the lack of information received from foreign manufacturers is an urgent need. Using the example of a propulsion electric plant with Azipod-type equipment, methods for assessing the technical condition of the propulsion electric motor, protecting the shaft from parasitic voltages and currents that can accelerate the destruction of bearings have been described. Methods for troubleshooting semiconductor elements included in the frequency converter have been considered. Probable errors in the operation of the nacelle position monitoring system have been presented. The possibilities of remote assessment of the technical condition of the system have been determined. The aim of the work is to develop stages of technical control and evaluation of the obtained parameters for the development of expert decisions on the operational reliability and prospects for further operation of equipment and elements in Azipod-type propulsion electric systems relevant on numerous vessels.

XGBoost-enabled low-complexity approach for signal integrity assessment in coherent communication systems

Abstract Error vector magnitude (EVM) provides critical information for assessing signal integrity and system performance in optical communication systems. In this research, a regression approach using boosting algorithms is developed to retrieve EVM information from complex signal constellation structures applicable to various modulation formats. Amplitude histograms are created and collected at different OSNR levels, launch powers, and transmission distances using an offline preprocessing approach. The impact of various evaluation techniques, including mean absolute error, coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) is discussed in detail. The results show that the proposed extreme gradient boosting framework considerably increases estimation precision compared to categorical boosting and light gradient boosting machine, especially under diverse transmission conditions, with MAPE staying below 1.7% and RMSE below 0.5, thereby enhancing overall performance monitoring in optical communication systems. This research provides a comprehensive and accurate representation of the proposed EVM estimation technique, making it a valuable resource for the development of advanced optical performance monitoring systems in the future.

Identification of Climate Change Risks to Surface and Groundwater Resources across the Central African Republic and Resilience Measures

The extensive hydrological and hydrogeological systems across the Central African Republic (CAR) provide essential urban and rural water supplies for the populations across the country. Hydrological and hydrogeological studies during the last 20 years are extremely limited, in part due to the complex security and socio-economic context. As a result, significant uncertainties exist related to water resource availability and infrastructure management. This paper examined the recent evolution of the meteorological, hydrological, and hydrogeological regimes across CAR and the potential future impacts of climate change on surface and water resource availability. This was undertaken by analyzing the available regional hydrometry data and using regional climate change projections to evaluate changes to rainfall and evapotranspiration patterns by 2050. A lack of long-term monitoring data and operational monitoring systems prevents a detailed assessment of these risks at local levels. Although the national scale findings suggest that water resources may slightly increase over the next 40 years, significant local risks remain due to intensifying, increasingly variable rainfall, and increasing demographic pressures. The lack of functional, national-scale water resource monitoring systems contributes to the fragile resilience of the populations across CAR in the face of increased climate-associated risks. Recommendations are included for measures to help characterize and manage the potential risks in the long-term including the implementation of robust water resource monitoring systems

Photoelectric sensor-based belt-type high-speed seed guiding device performance monitoring method and system

The belt-type high-speed seed guiding device encounters difficulties in seed guidance due to its distinctive seed transporting belt structure. The monitoring point is situated between the seed belt support plate and the seeds, which pass through it, making it difficult to distinguish the characteristics of the seed pulses. This presents challenges in monitoring the device’s performance. This study proposes a monitoring method for the belt-type high-speed seed guiding device using photoelectric sensors. The monitoring module employs a photoelectric sensor as a signal source and designs hardware and software for the monitoring module. The research introduces the deviation pulse extraction algorithm (DPEA), which utilises the seed guidance performance monitoring mechanism. This achieves precise monitoring of seed transport on the transporting seed belt, thereby eliminating the increased multiple index caused by “pulse sinking”. Simulation experiments demonstrate that the optimal deviation pulse extraction algorithm (ODPEA) is highly effective in identifying and eliminating “pulse sinking”, resolving the issue of increased multiple index. The DPEA is unable to achieve the same effect. The results of comparative tests of seeding monitoring accuracy indicate that both algorithms exhibit average monitoring accuracy above 96% across varying operating speeds. The DPEA, however, demonstrates heightened sensitivity to changes in operating speed. It can be observed that the operating speed has a significant impact on the monitoring accuracy of both algorithms. The monitoring system performance evaluation comparative test reveals that the DPEA-based monitoring system registers the smallest deviations in qualified feed index, miss index, and multiple index at 0.25 percentage points, 0.10 percentage points, and 0.35 percentage points, respectively. In contrast, the ODPEA-based system shows minimum deviations in qualified feed index and miss index at 0.12 percentage points each, and no deviation in multiple index. Both algorithms demonstrate minimal influence of operating speed on missing and multiple seeding rates. The impact of changes in operating speed on these metrics is significantly lower. The efficacy of the ODPEA in eliminating ’pulse sinking’ enhances the algorithm’s ability to assess multiple seeding rates and overall accuracy. The ODPEA-based monitoring system exhibits superior performance, providing theoretical support for the development of monitoring systems in high-speed belt-type seed guiding devices.

A Digital Twin Comprehensive Monitoring System for Ship Equipment

Abstract In this study, a comprehensive digital twin monitoring system for ship equipment was designed and implemented, including the system architecture, key technologies, and applications. Through data-driven models and operational monitoring system analysis, our PSO-SVM-based time series prediction method demonstrated excellent predictive capabilities for catamaran equipment, achieving efficient fault warnings using a threshold method. The digital twin model and virtual scenarios constructed here provide a visualisation and simulation platform for equipment status monitoring, enhanced fault diagnosis and support for maintenance decisions. The system integrates real-time monitoring, fault warning, and data analysis, and testing results show good stability and accuracy. In addition, the system optimises the user experience through multi-round feedback testing, and ensures data security and privacy protection through multi-layer encryption, identity verification, and role-based access control. A case study indicates that the proposed system effectively monitors equipment status and provides fault warnings, and has broad application prospects and practical value. Future work will focus on optimising the functionality and improving the applicability and security of the system.

Advancing Pouch Cell Monitoring: The Promise of Surface-Applied and Referenceless Fiber Bragg Grating Sensors

In the rapidly evolving field of lithium-ion battery technology, ensuring safety and optimizing performance have become paramount concerns, especially in applications that demand high performance and reliability, such as electric vehicles and portable electronics [1]. Established and state-of-the-art monitoring of voltage, current, and a sparse distribution of surface temperature sensors are often insufficient for early fault detection [2,3].To address this challenge, this study presents an innovative approach to battery monitoring using referenceless fixed Fiber Bragg Grating (FBG) sensors mounted on the surface of pouch cells. This method takes advantage of the dual functionality of FBG sensors to measure both strain and temperature directly on the exterior of the cell, offering a promising avenue for real-time battery health and safety monitoring [4].Pouch cells, favored for their lightweight and flexible design, present unique challenges for adding robust strain monitoring technologies due to the anisotropic nature of their functionally optimized foil enclosures. Our research addresses these challenges by detailing an innovative and systematic surface application method for FBG sensors that minimizes intrusion while providing stable conditions on the pouch foil. This method's small form factor and cost-effectiveness are significant advantages, providing a non-invasive means of gathering high-density information about the cell's operational state without compromising its structural integrity.A critical aspect of this study is the on-cell calibration technique developed for the FBG sensors, which enables the unambiguous sensor reading across different positions without the usually required additional reference sensors. This breakthrough addresses a common limitation in FBG monitoring systems for batteries by ensuring that sensor outputs are reliably correlated with specific strain and temperature parameters, respective of their placement on the cell. The calibration process, thoroughly documented in our experiments, underpins the sensors' capability to provide accurate, real-time data crucial for predictive maintenance and fault detection. Furthermore, the study explores the limitations imposed by the pouch foil's mechanical properties, presenting a comprehensive analysis of sensor performance concerning these constraints. Through extensive testing, we establish guidelines for optimizing sensor placement and adhesion techniques to ensure reliable measurements without affecting cell performance.Our experimental findings highlight the FBG sensors' dynamic temperature measurement capabilities, demonstrating sufficient sensitivity to detect abnormal strain developments indicative of potential battery faults. The dual-measurement approach not only allows for the early detection of issues such as thermal runaway but also provides valuable insights into the development of mechanical stresses experienced by the cell during operation. Such insights are instrumental in advancing our understanding of battery behavior under various load conditions, contributing to the development of safer and more efficient battery systems.In conclusion, the improved surface application of reference-less fixed FBG sensors on pouch cells represents a significant advancement in battery monitoring technology. The small form factor, cost-efficiency, and high information density offered by this setup make it an attractive solution for manufacturers and researchers alike. By providing a means to accurately monitor strain and temperature in real-time, our approach facilitates improved safety protocols and performance optimization for lithium-ion batteries. Due to the possible omission of an additional sensor, and higher information density compared to just temperature or strain sensors, this study lays the groundwork for industrially used FBG sensor applications and calibration techniques, paving the way for the broader adoption of FBG sensors in battery safety and performance monitoring systems.

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

The development of physical state and exhaustion factors monitoring system with capability of collecting, estimating and analysing data for military personnel

The purpose of the research analysis of known invasive and non-invasive examination methods, determination of the necessary criteria for assessing the degree of fatigue of submariners, a survey of Navy personnel involved in service on submarine ships was conducted, on the basis of which a justification of research directions was carried out for the subsequent development of a system for collecting, evaluating and analyzing the physiological state and factors affecting the fatigue of military personnel.Methods: the use of an experimental non-invasive research method to determine the degree of fatigue and physiological condition of submariners based on three criteria during the month, the analysis of the results obtained, the establishment of a comparative analysis method during the development, a structural analysis of the system based on previously known devices and sensors.Results. The dependence of the studied indicators on the time of watch keeping and their connection with the increase in fatigue were found. It has been established that in order to develop a system for monitoring the fatigue of submariners, a systematic analysis and objective assessment should be carried out, including: determination of methods for monitoring the condition of military personnel; application of knowledge from medicine, physiology, biophysics, biomechanics, bioengineering, electronics, circuitry and programming, to develop effective methods for monitoring physical condition and fatigue. Based on the analysis of domestic and foreign techniques and devices, a proprietary method for implementing a system for collecting assessments, analyzing the physiological state and factors affecting the fatigue of military personnel is proposed.Conclusion. It is concluded that it is necessary to conduct non-invasive studies of the degree of fatigue, before proposing the development and proposal of its own device and methodology for assessing the degree of fatigue, capable of implementing individual features of the system for operational monitoring in order to improve combat readiness, situational awareness, health, safety, mobility of submariners.The development of such monitoring systems and devices is necessary in the modern world to prevent or prevent disasters without reducing the efficiency of submariners.

Enhancing YOLOv8’s Performance in Complex Traffic Scenarios: Optimization Design for Handling Long-Distance Dependencies and Complex Feature Relationships

In recent years, the field of deep learning and computer vision has increasingly focused on the problem of vehicle target detection, becoming the forefront of many technological innovations. YOLOv8, as an efficient vehicle target detection model, has achieved good results in many scenarios. However, when faced with complex traffic scenarios, such as occluded targets, small target detection, changes in lighting, and variable weather conditions, YOLOv8 still has insufficient detection accuracy and robustness. To address these issues, this paper delves into the optimization strategies of YOLOv8 in the field of vehicle target detection, focusing on the EMA module in the backbone part and replacing the original SPPF module with focal modulation technology, all of which effectively improved the model’s performance. At the same time, modifications to the head part were approached with caution to avoid unnecessary interference with the original design. The experiment used the UA-DETRAC dataset, which contains a variety of traffic scenarios, a rich variety of vehicle types, and complex dynamic environments, making it suitable for evaluating and validating the performance of traffic monitoring systems. The 5-fold cross-validation method was used to ensure the reliability and comprehensiveness of the evaluation results. The final results showed that the improved model’s precision rate increased from 0.859 to 0.961, the recall rate from 0.83 to 0.908, and the mAP50 from 0.881 to 0.962. Meanwhile, the optimized YOLOv8 model demonstrated strong robustness in terms of detection accuracy and the ability to adapt to complex environments.

Android Based Academic Performance Monitoring System For Parents/Guardians

Academic feedback is essential in secondary schools to keep a rap-Port between students, teachers, and parents and guardians. There Are three main factors that contribute towards a student’s progress: Attitude, attendance and aptitude. Monitoring their progress is key To a student’s development in school and allows both teachers and Parents or guardians to support them to a greater extent. Annual Reports are sent to a student’s home to summarise their performance Over the academic year, following set criterion from the government. One aspect of a student’s report is the teacher’s written comment, Providing more details on a student’s attitude towards their learning. However, families whose primary language is not English may Struggle to interpret this information. Working in schools has demonstrated the diversity of students and their wide range of backgrounds, Including– but not limited to– language barriers. This work pro-Poses a system called SENSE (Student performance quantifier using Sentiment analysis) for improving the information conveyed in Secondary school reports through means of natural language processing. By combining the three key features which contribute to-Wards a student’s progress, a numerical representation is produced for an easier interpretation. This reduces the likelihood of a tarnished relationship between home and schools through better means of conveying information and maintains communication between students, teachers and parents or guardians..

Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods

The coefficient of performance (COP) is a crucial metric for evaluating the efficiency of heat pump systems. Real-time monitoring of heat pump system performance necessitates continuously collecting and processing data from various components utilizing multiple sensors and controllers. This process is inherently complex and presents significant challenges. In recent years, artificial intelligence (AI) models have increasingly been applied in refrigeration, heat pump, and air conditioning systems due to their capability to identify and analyze complex patterns and data relationships, demonstrating higher accuracy and reduced computation time. In this study, multilayer perceptron (MLP), support vector machines (SVM), and random forest (RF) are used to develop COP prediction models for solar-assisted heat pumps. By comparing the predictive accuracy and modeling time of the three models built, the results demonstrate that the random forest model achieves the best prediction performance, with a mean absolute error (MAE) of 2.42% and a root mean squared error (RMSE) of 4.01% on the train set. On the test set, the MAE was 2.35% and the RMSE was 3.84%. The modeling time for the RF model was 6.57 s.

A deep kernel regression-based forecasting framework for temperature-induced strain in large-span bridges

The strain data from health monitoring systems of large-span bridges is influenced by various load effects, with the extraction and forecasting of temperature-induced strain being particularly significant for precise analysis and early warning of monitoring data. This paper presents a forecasting framework for temperature-induced strain in large-span bridges, employing a deep kernel regression (DKR) approach that integrates deep learning with Bayesian regression to enhance accuracy and certainty. Initially, this paper addresses the influence of additional response increment induced by vehicle strain effects and employs a robust data smoothing algorithm to extract temperature-induced effect components from measured strain data offline. Subsequently, a DKR model is proposed, integrating a long short-term memory (LSTM) layer with a fully connected layer. The output of the deep learning module serves as the kernel function parameter for the Gaussian process regression (GPR) module, and the GPR module with updated hyperparameters is used for time series forecasting. This method effectively extracts and utilizes time series features from historical data alongside key environmental factors, enabling real-time forecasting of strain effects and significantly improving the performance and utility of health monitoring systems in large-span bridges. Compared to commonly used time series forecasting algorithms, the algorithm proposed in this paper exhibits significantly improved accuracy, stability, and certainty. Through comparing the inference time, it was verified that the algorithm can meet the performance requirements of real-time inference, which underscores the model's potential as a robust tool in bridge structural health monitoring.

Characterizing the impact of emergent technologies on earth communications reliance for crewed deep space missions

Future human expeditions into deep space will encounter unique design challenges posed by the increasing distance from Earth, one of which is the inability to maintain near-continuous communication with ground support teams. As a result, the habitat and crew will require a higher level of operational self-sufficiency informed by onboard self-awareness and decision-making capabilities to accomplish functions that have historically involved extensive support from mission control personnel. These include, for example, task planning and anomaly detection, diagnosis and correction, as well as monitoring of consumable usage rates and system performance. Novel emergent technologies in the domain of ‘smart’ systems and digital twins can be employed to enable the onboard capabilities needed to function with minimal Earth communication. However, these technologies are generally low-TRL and difficult to incorporate through traditional systems engineering methods.This work proposes an extension of utility theory to assess the potential reduction in Earth communication reliance between baseline and emergent technologies, scored through a summation of weighted attributes and evaluated against future deep space mission needs and constraints. Case studies are defined from technology under development in the NASA Habitats Optimized for Missions of Exploration (HOME) Space Technology Research Institute (STRI), a project charged with developing and evaluating technologies to enable highly autonomous deep space habitats, and compared to the current baseline technologies used onboard the International Space Station. Notional deep space mission needs and constraints are informed from current NASA plans for the Moon and Mars. These in turn are broken out into higher resolution attributes that are used to assess the operational capabilities of each emergent technology and whether it offers a potentially viable solution for deep space. These technology attributes are scored through a series of semi-structured subject-matter interviews and compared to notional deep space mission constraints. Additional considerations for technology maturation and integration are also discussed. The design analysis paradigm described and demonstrated here can be adapted to assess the degree to which other emergent technologies rely on Earth-based communication, and is intended to provide trade space considerations for future research and development towards deep space habitat self-sufficiency.

Performance of Continuous Glucose Monitoring System Among Patients With Acute Ischaemic Stroke Treated With Mechanical Thrombectomy.

Glucose metabolism abnormalities are prevalent in acute ischaemic stroke (AIS) patients and are associated with poor prognosis. The continuous glucose monitoring (CGM) system can provide detailed information on glucose levels and glycaemic excursions. This study aimed to evaluate the feasibility and accuracy of CGM application in the acute phase of AIS patients. This single-centre, prospective, and observational study consecutively enrolled patients with AIS with anterior circulation large vessel occlusion (AC-LVO) and received mechanical thrombectomy (MT) within 24h of symptom onset. A user-retrospectively calibrated iPro2 CGM system was implanted right before the MT procedure started and removed on the fifth day after MT or at discharge. Fingertip glucose was measured as a reference. Accuracy evaluation included the Bland-Altman plot (with a proportion of CGM values within 15/15, 20/20 and 30/30), the absolute relative difference (ARD) and error grid analysis (EGA). The safety and glucose profiles were also evaluated. Of the 183 patients screened, 141 were included, with a median monitoring duration of 4.49days. Compared to reference measurements, 3097 CGM readings were matched with a mean bias of -4.16mg/dL. The proportions of sensor readings meeting the 15/15, 20/20 and 30/30 criteria were 64.55%, 76.07% and 87.21%, respectively. The overall mean and median ARD were 14.60%±14.62% and 9.77% (4.15, 20.00). EGA showed that 98.97%, 99.42% and 99.06% values fall within clinically accurate zones in Clarke, Parkes and continuous glucose EGA, respectively. The CGM system was feasible, safe and accurate for in-hospital use among AIS patients who received MT.

Control of simulated ocean ecosystem indicators by biogeochemical observations

To protect marine ecosystems threatened by climate change and anthropic stressors, it is essential to operationally monitor ocean health indicators. These are metrics synthetizing multiple marine processes relevant to the users of operational services. Here we assess if selected ocean indicators simulated by operational models can be controlled (here meaning constrained effectively) by biogeochemical observations, by using a newly proposed methodological framework. The method consists in firstly screening the sensitivities of the indicators with respect to the initial conditions of the observable variables. These initial conditions are perturbed stochastically in Monte Carlo simulations of one-dimensional configurations of a multi-model ensemble. Then, the models are applied in three-dimensional ensemble assimilation experiments, where the reduction of the ensemble variance corroborates the controllability of the indicators by the observations. The method is applied for ten relevant ecosystem indicators (ranging from inorganic chemicals to plankton production), seven observation types (representing data from satellite and underwater platforms), and an ensemble of five biogeochemical models of different complexity, employed operationally by the European Copernicus Marine Service. We demonstrate that all the indicators are controlled by one or more types of observations. In particular, the indicators of phytoplankton phenology are controlled and improved by the merged observations from the surface ocean colour and chlorophyll profiles. Similar observations also control and reduce the uncertainty of the plankton community structure and production. However, the uncertainty of the trophic efficiency and POC increases when assimilating chlorophyll-a data, though observations were not available to assess whether that was due to a worsen model skill. We recommend that the assessment of controllability proposed here becomes a standard practice in designing operational monitoring, reanalysis and forecast systems, to ultimately provide the users of operational services with more precise estimates of ocean ecosystem indicators.

Enhancing Reliability in Distributed Systems: A Comprehensive Approach to Telemetry and Monitoring

This comprehensive article explores the implementation of telemetry and monitoring in distributed systems, addressing the challenges and opportunities in managing complex, scalable architectures. The article begins by examining the fundamental concepts of telemetry, including data collection methodologies, transmission protocols, and analysis techniques. It then provides an in-depth comparison of popular telemetry tools such as AWS CloudWatch and Prometheus, discussing their features, capabilities, and integration challenges. The article delves into effective monitoring strategies, emphasizing the importance of key performance indicators (KPIs) in distributed environments and the implementation of comprehensive solutions including real-time dashboards, proactive alert systems, and automated remediation techniques. Furthermore, the study investigates emerging trends in AI-driven monitoring and predictive analytics, highlighting their potential to revolutionize system observability. The article also addresses critical challenges facing the field, including scalability concerns in large-scale systems, privacy, and security considerations in telemetry data collection, and the need for balance between monitoring overhead and system performance. By synthesizing current practices with future directions, this article provides valuable insights for practitioners and researchers alike, contributing to the ongoing evolution of telemetry and monitoring practices in the rapidly advancing landscape of distributed computing.

Co-Designing in Cultural Tourism: TExTOUR ICT Services and Performance Monitoring System

The enhancement of cultural heritage for tourism can greatly benefit from the opportunities offered by Information and Communication Technologies (ICTs), promoting greater interaction and integration between all stakeholders and service providers. In recent years, ICT applications and platforms have tended to provide a platform to translate the strategic goals and performance metrics of cultural heritage tourism into comprehensive services tailored to stakeholders’ needs by participating stakeholders. A successful co-designed platform integrates explicit services—requiring specialized software tools—and implicit services, facilitating evaluation processes. This adaptability ensures continuous improvement and alignment with evolving strategies and business objectives. As part of the European project, the development of the TExTOUR open ICT platform marks a significant advancement in leveraging technology within cultural tourism. It exemplifies the creation of a sustainable and adaptable framework, co-designed specifically to support and amplify the strategic objectives of cultural heritage tourism. This approach not only fosters better engagement and management within the sector but also sets a new standard for innovation in cultural tourism practices.

Remote work and human resource management: Challenges and solutions

The rise of remote work, accelerated by global disruptions and technological advancements, has fundamentally reshaped traditional human resource management (HRM) practices. This study investigates the key challenges that organizations face in managing a remote workforce, including maintaining employee engagement, ensuring effective communication, managing productivity, and addressing mental health concerns. Additionally, the research explores the role of technology in overcoming these challenges, highlighting solutions such as virtual collaboration tools, flexible work policies, and performance monitoring systems. Through an analysis of current HR practices in organizations that have successfully adapted to remote work, this paper identifies best practices and strategic adjustments necessary for HR managers to support a distributed workforce. The findings suggest that while remote work offers flexibility and operational efficiency, it also requires a shift in HR strategies toward building a cohesive virtual work culture, offering ongoing training, and ensuring employee well-being. This research contributes to the understanding of how HRM can evolve to meet the demands of the remote work environment, providing actionable insights for organizations navigating this new paradigm.

Self-powered wireless sensor system utilizing a thermoelectric generator for photovoltaic module monitoring application

In this work, we demonstrate a self-powered wireless PV module monitoring system that utilizes a thermoelectric generator (TEG) to convert residual thermal energy from the PV module into electrical power. We investigated the TEG performance with and without the heat sink. Results show that the temperature difference between the hot and cold sides of the TEG increased to 7.2 °C with the heat sink, compared to only 1 °C without it, at a hot side temperature of 50 °C. We integrated the TEG/heat sink with the PV module, which served as the heat source, achieving a maximum output power of 0.981 mW at a voltage of 0.06 V under a temperature gradient of 3.6 °C in a 1 sun condition. We successfully demonstrated a self-powered wireless PV monitoring sensor system by integrating a step-up voltage converter, microcontroller, IR thermometer, Bluetooth communication module, and the TEG/heat sink, which generated sufficient power for the monitoring system operation. The findings introduce a novel solution for wireless PV module monitoring that operates independently of grid connections or battery power. This innovation not only signifies advancements in renewable energy management but also opens new opportunities in the Internet of Things (IoT) sector.