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In this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well. [ABSTRACT FROM AUTHOR]

Electrochemistry forms the base of large-scale production of various materials, encompassing numerous applications in metallurgical engineering, chemical engineering, electrical engineering, and material science. This field is important for energy harvesting applications, especially supercapacitors (SCs) and photovoltaic (PV) devices. This review examines various electrochemical techniques employed to fabricate and characterize PV devices and SCs. Fabricating these energy harvesting devices is carried out by electrochemical methods, including electroreduction, electrocoagulation, sol-gel process, hydrothermal growth, spray pyrolysis, template-assisted growth, and electrodeposition. The characterization techniques used are cyclic voltammetry, electrochemical impedance spectroscopy, photoelectrochemical characterization, galvanostatic charge-discharge, and I–V curve. A study on different recently reported materials is also presented to analyze their performance in various energy harvesting applications regarding their efficiency, fill factor, power density, and energy density. In addition, a comparative study of electrochemical fabrication techniques with others (including physical vapor deposition, mechanical milling, laser ablation, and centrifugal spinning) has been conducted. The various challenges of electrochemistry in PVs and SCs are also highlighted. This review also emphasizes the future perspectives of electrochemistry in energy harvesting applications. [ABSTRACT FROM AUTHOR]

In the latest years the capacity and complexity of climate and environmental modeling has increased considerably. Therefore, tools and criteria for model performance evaluation are needed to ensure that different users can benefit from model selection. Among graphical tools, Taylor's diagram is widely used to provide evaluation and comparison of model performances, with particular emphasis on climate models. Taylor's diagram accounts for different statistical features of model outputs and observations, including correlation, variability and centered root mean square error. Not included is model bias, which is an essential feature for climate model evaluations, and it is usually calculated separately to complement the information embedded in Taylor's diagram. In this paper a new diagram is proposed, referred to as Aras' diagram, which allows for visual assessments of the correspondence between model outputs and reference data in terms of total error, correlation, as well as bias and variability ratios through an easy-to-interpret two-dimensional (2D) plot, allowing for proper weighting of different model features. The strengths of the new diagram are exemplified in a case study of performance evaluation of EURO-CORDEX historical experiment over Southern Italy using E-OBS as reference dataset, for three hydrological variables (i.e. daily precipitation, daily surface minimum temperature, and daily maximum surface temperature), and four popular climate indices (i.e. total annual precipitation, annual maxima of daily precipitation, annual minima of daily minimum temperatures, and annual maxima of daily maximum temperatures). The proposed diagram shows interesting properties, in addition to those already included in Taylor's diagram, which may help promoting climate model evaluations based on their accuracy in reproducing the climatological patterns observed in time and space. [ABSTRACT FROM AUTHOR]

Concrete-filled steel tubular (CFST) columns have been widely used in multi-story and high-rise frame structures. During the service period, they may suffer vehicle impact due to traffic accidents or terrorist attacks. This paper numerically evaluates the performance of CFST columns under vehicle impact and investigates the effects of carbon FRP (CFRP) wrapping arrangements on performance improvement of the columns. Before that, a numerical model was developed to simulate the responses of CFST columns without and with FRP wrapping under vehicle impact and post-impact axial compression, and then calibrated by reported tests. Evaluation results show that the performance of CSFT columns under vehicle impact is divided into five levels, i.e., no repair required, rapid repair required, minor repair needed, major repair needed, and replacement needed. The performance level decreases with the increase in the vehicle weight or speed and increases with the increase in the column diameter or steel tube thickness. The column height has little effects on the performance level. A higher axial load ratio, e.g., 0.5, might reduce the performance level. Besides, a CFST column tends to fail in flexure mode when hit by F800 medium truck, while it may fail in flexure & shear mode when hit by C2500 pickup truck. Investigation results indicate that FRP wrapping with each layer orientation of 90° (i.e., in the longitudinal direction) and 0° (i.e., in the hoop direction) present the best performance improvement for a CFST column possibly undergoing flexure & shear and flexure failure, respectively. The increase of the number of FRP layers effectively improves the performance levels of CFST columns but the excessive demand may be not economical. It is not necessary to employ an FRP wrapping range of 100% for improving the vehicular impact performance level of a CFST column to the expected one. [ABSTRACT FROM AUTHOR]

Background: Mastery and performance goals are typically measured as trait‐like abstract goals. However, in their daily academic pursuits, students pursue more concrete goals. The pursuit of these goals is replete with obstacles that can lead to an action crisis. Aims: We examined how mastery and performance goals affect progress, effort and well‐being during academic goal pursuit. We also asked whether these goals moderated relations between an action crisis and goal pursuit. Sample: 154 Israeli students (average age = 23.59 years, SD = 2.17; 62% female) preparing for an exam. Methods: In this daily diary study, participants first reported the date of their most stressful exam, two related goals, and their trait‐like achievement goals (mastery, performance). They then completed daily questionnaires assessing their progress, effort, well‐being and action crisis on the 10 days leading up to the exam. Results: Multilevel modelling showed mastery goals positively predicted daily goal effort and progress and negatively predicted daily action crises, while performance goals positively predicted daily negative affect and action crises. An action crisis was negatively correlated with next‐day goal progress and positive affect among students with high‐performance goals. In students with low‐performance goals, an action crisis positively predicted next‐day positive affect. Finally, for students high on mastery goals, an action crisis was unrelated to negative affect on the same day. Conclusions: Different trait‐like achievement goals can be mapped onto a common personal goal and affect its pursuit differently. Mastery goals predict optimal goal pursuit, and performance goals are associated with non‐optimal goal pursuit. [ABSTRACT FROM AUTHOR]

One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls. The maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent structures. Nowadays, the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of calculation. This paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls, namely eXtreme gradient boosting (XGBoost), least square support vector regressor (LS-SVR), and random forest (RF), to predict maximum lateral displacement of pile walls. The results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669, the highest coefficient of determination 0.9991, and the lowest root mean square error 0.3544. Although the LS-SVR, and RF models were less accurate than the XGBoost model, they provided good prediction results of maximum lateral displacement of pile walls for numerical outcomes. Furthermore, a sensitivity analysis was performed to determine the most effective parameters in the XGBoost model. This analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction. [ABSTRACT FROM AUTHOR]

The precast composite reinforced concrete wall with the advantages of fewer joints, superior impermeability and rapid construction provides an efficient and environmental friendly alternative in the construction of underground utility tunnels in the last few years. To investigate the seismic performance of precast concrete composite walls of utility tunnels with grouting-sleeve connection under out-ofplane loads, a series of quasi-static cyclic tests were performed on the full-scale sidewall specimens with different axial compression ratios in this study. The experimental results including the failure modes, crack distributions, and the influence of different connections on the out-of-plane seismic performance of precast concrete composite wall were carefully examined and compared with those from the cyclic tests of the cast-in-place sidewalls of the utility tunnel. The test results show that the seismic performance of the precast concrete composite sidewall specimen, such as the hysteresis curves, the ultimate bearing capacity, stiffness degradation pattern and the ductility ratio, is basically the same as that of the cast-in-place specimen, indicating that the seismic performance of the prefabricated structure is equivalent to that of the cast-in-place structure. Moreover, the grouting-sleeves of the joints can effectively transfer the reinforcement stress until the failure of the precast concrete composite sidewall specimens, which exhibits excellent out-of-plane ductility and serviceability. [ABSTRACT FROM AUTHOR]

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices.QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively.Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application’s capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results.In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes. [ABSTRACT FROM AUTHOR]

The data and information generated during the design process, such as part parameters, affect the efficiency and quality of product design. Knowledge graph (KG) is often used to express and reuse the above knowledge. However, due to the decentralised and complex nature of the KG, it lacks the quantitative knowledge required for part selection, e.g. the relationship between part type and performance. To complete the knowledge and meet the requirements for rapid and accurate performance evaluation, this paper proposes an automatic KGC method based on a surrogate model. Firstly, the schema layer is established based on the design knowledge ontology. For the sampling process, the query statement is applied to extract knowledge such as design parameters, which define the range of sample points. Secondly, the design of experiments (DOE) method can obtain the data set of the target performance through simulation. Surrogate models are built based on multiple machine learning algorithms. Meanwhile, the hyperparameter optimisation method can further improve the prediction accuracy of the model. Finally, the performance of the design scheme is predicted, and the results are used to complete the KG through knowledge extraction. The method is validated by the selection and evaluation of bogie parts. [ABSTRACT FROM AUTHOR]

Decision accuracy is a crucial factor in the evaluation of refereeing performance. In sports research, officials’ decision-making is frequently assessed outside real games through video-based decision experiments, where they evaluate recorded game situations from a third-person perspective. This study examines whether the inclusion of the first-person perspective influences decision accuracy and certainty. Twenty-four professional officials from the first and second German basketball leagues participated in the study. The officials assessed 50 game situations from both first-person and third-person perspectives, indicating their decisions and certainty levels. The statistical analysis utilises signal detection theory to evaluate the efficacy of the first-person perspective compared to the third-person perspective in identifying rule violations and no-calls in video recordings. The findings indicate that the first-person perspective does not yield superior accuracy in identifying foul calls. However, scenes from the first-person perspective exhibit a significant 9% increase in correctly identifying no-calls. Furthermore, officials report significantly higher levels of decision certainty and comfort when using the first-person perspective. The study suggests that sports officials may benefit from incorporating additional scenes from the first-person perspective into video-based decision training. Future studies should explore whether this additional perspective improves the training effect and translates into enhanced in-game performance. [ABSTRACT FROM AUTHOR]

AbstractSkin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data’s underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data’s complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer. [ABSTRACT FROM AUTHOR]

In this study, an early warning system predicting first‐year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first‐year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition. [ABSTRACT FROM AUTHOR]

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support for the prevention of back pain or the development of MSDs. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons, made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures, and task-specific cognitive load detection from physiological signals. [ABSTRACT FROM AUTHOR]

The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar. [ABSTRACT FROM AUTHOR]

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios. [ABSTRACT FROM AUTHOR]

Cone‐beam computed tomography (CBCT) systems specifically designed and manufactured for dental, maxillofacial imaging (MFI) and otolaryngology (OLR) applications have been commercially available in the United States since 2001 and have been in widespread clinical use since. Until recently, there has been a lack of professional guidance available for medical physicists about how to assess and evaluate the performance of these systems and about the establishment and management of quality control (QC) programs. The owners and users of dental CBCT systems may have only a rudimentary understanding of this technology, including how it differs from conventional multidetector CT (MDCT) in terms of acceptable radiation safety practices. Dental CBCT systems differ from MDCT in several ways and these differences are described. This report provides guidance to medical physicists and serves as a basis for stakeholders to make informed decisions regarding how to manage and develop a QC program for dental CBCT systems. It is important that a medical physicist with experience in dental CBCT serves as a resource on this technology and the associated radiation protection best practices. The medical physicist should be involved at the pre‐installation stage to ensure that a CBCT room configuration allows for a safe and efficient workflow and that structural shielding, if needed, is designed into the architectural plans. Acceptance testing of new installations should include assessment of mechanical alignment of patient positioning lasers and x‐ray beam collimation and benchmarking of essential image quality performance parameters such as image uniformity, noise, contrast‐to‐noise ratio (CNR), spatial resolution, and artifacts. Several approaches for quantifying radiation output from these systems are described, including simply measuring the incident air‐kerma (Kair) at the entrance surface of the image receptor. These measurements are to be repeated at least annually as part of routine QC by the medical physicist. QC programs for dental CBCT, at least in the United States, are often driven by state regulations, accreditation program requirements, or manufacturer recommendations. [ABSTRACT FROM AUTHOR]

Background: Hospitals are the biggest consumers of health system budgets and hence measuring hospital performance by quantitative or qualitative accessible and reliable indicators is crucial. This review aimed to categorize and present a set of indicators for evaluating overall hospital performance. Methods: We conducted a literature search across three databases, i.e., PubMed, Scopus, and Web of Science, using possible keyword combinations. We included studies that explored hospital performance evaluation indicators from different dimensions. Results: We included 91 English language studies published in the past 10 years. In total, 1161 indicators were extracted from the included studies. We classified the extracted indicators into 3 categories, 14 subcategories, 21 performance dimensions, and 110 main indicators. Finally, we presented a comprehensive set of indicators with regard to different performance dimensions and classified them based on what they indicate in the production process, i.e., input, process, output, outcome and impact. Conclusion: The findings provide a comprehensive set of indicators at different levels that can be used for hospital performance evaluation. Future studies can be conducted to validate and apply these indicators in different contexts. It seems that, depending on the specific conditions of each country, an appropriate set of indicators can be selected from this comprehensive list of indicators for use in the performance evaluation of hospitals in different settings. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) superposes several user signals for simultaneous transmission, considerably ameliorating spectral efficiency. To exploit efficiently radio frequency energy sources surrounding NOMA transmitters for improved energy efficiency, NOMA networks further employ energy harvesting, which practically possesses nonlinear characteristic. In addition, multiple antennas can be deployed at users to transfer-and-harvest energy efficiently and to transmit/receive information reliably with antenna array processing. As such, this paper evaluates numerically the throughput and the outage probability of NOMA networks accounting for practical factors such as nonlinear energy harvesting, all multi-antenna users, and imperfect channel information. Illustrative results expose that nonlinear energy harvesting and channel information imperfection considerably degrade system performance, which is governed flexibly by multiple parameters. Notably, NOMA networks can prevent a total outage and achieve the best performance with a proper parameter selection. Moreover, their performance is considerably ameliorated with accreting the number of antennas. [ABSTRACT FROM AUTHOR]

Vehicle lightweighting is a current focus of research in the automotive industry, aiming to reduce vehicle weight, improve fuel economy, and reduce tailpipe emissions. One mechanical joining technology that can improve the performance and sustainability of vehicles is clinching. Clinching is characterized by its lightweight, high strength, and aesthetically pleasing green features, and it is mainly used to join metal or non-metal materials. In this article, a comprehensive overview of clinching is provided, including performance evaluation and testing methods. The influence of section parameters on joint performance is analyzed, and the latest finite element simulation and numerical simulation methods are introduced. Additionally, the relevant testing methods for static and fatigue performance are summarized. Furthermore, new environmental testing and non-destructive testing technologies are introduced. Finally, potential research directions for clinched joining technologies are proposed. [ABSTRACT FROM AUTHOR]

An indoor aging simulation was carried out to investigate the effect of ultraviolet (UV) aging on the performance of polymer and crumb rubber modified asphalt (PRMA) and its mixtures. At the macro level, the apparent morphology change in the asphalt after UV aging and the changing rates of penetration, ductility, and softening point were analyzed to evaluate the effect of UV aging on the asphalt binder. Fatigue life, dynamic stability, freeze-thaw splitting strength, and failure strain were used to determine the effect of UV aging on the asphalt mixtures. At the micro level, using the large molecular weight and functional group index of asphalt after aging as the evaluation index, the effect of UV aging on the performance of asphalt and asphalt mixtures was quantitatively analyzed. The results for the asphalt binders and mixtures under the same UV aging condition showed that PRMA surface cracking appeared later and crack development was slower; the changing rates of the three indexes of PRMA and the performance reductions, including low- and high-temperature properties and moisture resistance, were smaller. The results also showed that the large molecular weight of the two asphalt binders increased gradually, but the increase in large molecular weight of PRMA appeared later and the range was smaller. Also, the changing rules of the three characteristic functional groups of the two binders were basically the same, and the incorporation of rubber powder delayed UV aging of PRMA to a certain extent. [ABSTRACT FROM AUTHOR]

Multiwalled carbon nanotubes (MWCNTs) improve ultraviolet (UV) aging resistance and storage stability of asphalt modified with styrene-butadiene-styrene (SBS). However, MWCNTs have the disadvantages of poor dispersivity, easy agglomeration, and insolubility in various solvents, which greatly affect the modification effect. The application of silane coupling agent KH550 has the potential to improve the dispersion of original MWCNTs in asphalt. In this research, the high-speed shearing method was used to prepare KH550–modified MWCNTs/SBS composite–modified asphalt, and then Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to observe and evaluate the MWCNTs before and after surface modification. Sensitivity analysis was conducted on the impact of MWCNTs on the performance of SBS-modified asphalt. The softening point test, ductility test, dynamic shear rheology test, and UV aging resistance test were carried out, and the optimal content of MWCNTs after surface modification was determined after comparing with the composite-modified asphalt without coupling agent modification. The results showed that oxidized MWCNTs could be grafted with silane molecules to have a molecular bridging effect on the surface after chemical modification by the silane coupling agent KH550. When the content of SBS was 5% unchanged, the asphalt with 1% surface-modified MWCNTs had excellent performance in softening point, ductility, complex modulus, rutting factor, and UV aging resistance. [ABSTRACT FROM AUTHOR]

• Comparison of portable black carbon monitors with standard aethalometer AE33. • The effect of ambient environment and built-in algorithms were investigated. • Suggestion on application scenario of each micro monitor proposed. Black carbon (BC) is associated with adverse human health and climate change. Mapping BC spatial distribution imperatively requires low-cost and portable devices. Several portable BC monitors are commercially available, but their accuracy and reliability are not always satisfactory during continuous field observation. This study evaluated three models of portable black carbon monitors, C12, MA350 and DST, and investigates the factors that affect their performance. The monitors were tested in urban Beijing, where portable devices running for one month alongside a regular-size reference aethalometer AE33. The study considers several factors that could influence the monitors' performance, including ambient weather, aerosol composition, loading artifacts, and built-in algorithms. The results show that MA350 and DST present considerable discrepancies to the reference instrument, mainly occurring at lower concentrations (0–500 ng/m3) and higher concentrations (2500–8000 ng/m3), respectively. These discrepancies were likely caused by the anomalous noise of MA350 and the loading artifacts of DST. The study also suggests that the ambient environment has limited influence on the monitors' performance, but loading artifacts and accompanying compensation algorithms can result in unrealistic data. Based on the evaluation, the study suggests that C12 is the best choice for unsupervised field measurement, DST should be used in scenarios where frequent maintenance is available, and MA350 is suitable for research purposes with post-processing applicable. The study highlights the importance of assigning portable BC monitors to appropriate applications and the need for optimized real-time compensation algorithms. [Display omitted] [ABSTRACT FROM AUTHOR]

Due to its excellent photocatalytic performance over the last few years, graphitic-like carbon nitride (g-C3N4) has garnered considerable notice as a photocatalyst. Nevertheless, several limitations, including small surface area, the rates at which photo-generated electrons and holes recombine are swift, and the inefficient separation and transport of photoexcited carriers continue to impede its solar energy utilization. To overcome those limitations in single-component g-C3N4, constructing a heterogeneous photocatalytic system has emerged as an effective way. Among the various studies involving the incorporation of hetero composite materials to design heterojunctions, among the most promising approaches is to assemble a Z-scheme photocatalytic configuration. The Z-scheme configuration is essential because it facilitates efficient photocarrier separation and exhibits superior redox ability in separated electrons and holes. Moreover, ternary composites have demonstrated enhanced photocatalytic activities and reinforced photostability. Ternary Z-scheme heterostructures constructed with g-C3N4 possess all the above-mentioned merits and provide a pioneering strategy for implementing photocatalytic systems for environmental and energy sustainability. A summary of the latest technological advancements toward design and fabrication in ternary all-solid-state Z-scheme (ASSZ) and direct Z-scheme (DZ) photocatalysts built on g-C3N4 is presented in this review. Furthermore, the review also discusses the application of ternary Z-scheme photocatalytic architecture established on g-C3N4. [ABSTRACT FROM AUTHOR]

This paper aims to compare the performance and efficiency of fluid-driven (FD) and solid-state (SS) systems, which are used for solar heating using parabolic trough. Sodium nitrite (NaNO2) was considered as phase change material (PCM) and both storages had the same storage capacity. To evaluate system performance, constant heating of 1 kW was provided to both systems to replicate solar energy. The fluid-driven system showed maximum efficiency of around 47% as compared to 20% of solid-state system. Although the operating temperature of FD is higher than SS, as temperature increases flow rate of fluid also increases in FD which results in reducing the melting time of PCM. FD shows potential for providing better efficiency and can help utilize solar energy during evening/night when no sun is present. [ABSTRACT FROM AUTHOR]

Background: The total-body positron emission tomography/computed tomography (PET/CT) system, with a long axial field of view, represents the state-of-the-art PET imaging technique. Recently, the total-body PET/CT system has been commercially available. The total-body PET/CT system enables high-resolution whole-body imaging, even under extreme conditions such as ultra-low dose, extremely fast imaging speed, delayed imaging more than 10 h after tracer injection, and total-body dynamic scan. The total-body PET/CT system provides a real-time picture of the tracers of all organs across the body, which not only helps to explain normal human physiological process, but also facilitates the comprehensive assessment of systemic diseases. In addition, the total-body PET/CT system may play critical roles in other medical fields, including cancer imaging, drug development and immunology. Main body: Therefore, it is of significance to summarize the existing studies of the total-body PET/CT systems and point out its future direction. This review collected research literatures from the PubMed database since the advent of commercially available total-body PET/CT systems to the present, and was divided into the following sections: Firstly, a brief introduction to the total-body PET/CT system was presented, followed by a summary of the literature on the performance evaluation of the total-body PET/CT. Then, the research and clinical applications of the total-body PET/CT were discussed. Fourthly, deep learning studies based on total-body PET imaging was reviewed. At last, the shortcomings of existing research and future directions for the total-body PET/CT were discussed. Conclusion: Due to its technical advantages, the total-body PET/CT system is bound to play a greater role in clinical practice in the future. [ABSTRACT FROM AUTHOR]

To facilitate the integration of education and science, a specialized experiment has been designed to address the research hotspots in the field of energy. This experiment is characterized by collaborative innovation, ensuring both safety and operability. By starting with literature search and experiment design, students are guided to explore the advancements in zinc-air fuel cells, understand their working principles, assembly techniques, and methods for performance evaluation. Through a systematic training program that simulates the scientific research process, students are encouraged to pursue excellence and seek the truth in the scientific field. This program aims to gradually develop their scientific literacy and enhance their practical skills. [ABSTRACT FROM AUTHOR]

Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation. [ABSTRACT FROM AUTHOR]

RPL—Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")—is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent–child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%. [ABSTRACT FROM AUTHOR]

To evaluate the analytical characteristics of a novel high-sensitivity cardiac troponin T (hs-cTnT) test on the automatic light-initiated chemiluminescent assay (LiCA®) system, and validated its diagnostic performance for non-ST-segment elevation myocardial infarction (NSTEMI). Studies included an extensive analytical evaluation and established the 99th percentile upper reference limit (URL) from apparently healthy individuals, followed by a diagnostic performance validation for NSTEMI. Sex-specific 99th percentile URLs were 16.0 ng/L (1.7 % CV: coefficient of variation) for men (21–92 years) and 13.4 ng/L (2.0 % CV) for women (23–87 years) in serum, and 30.6 ng/L (0.9 % CV) for men (18–87 years) and 20.2 ng/L (1.4 % CV) for women (18–88 years) in heparin plasma. Detection rates in healthy individuals ranged from 98.9 to 100 %. An excellent agreement was identified between LiCA® and Elecsys® assays with a correlation coefficient of 0.993 and mean bias of −0.7 % (−1.8–0.4 %) across the full measuring range, while the correlation coefficient and overall bias were 0.967 and −1.1 % (−2.5–0.3 %) for the lower levels of cTnT (10–100 ng/L), respectively. At the specific medical decision levels (14.0 and 52.0 ng/L), assay difference was estimated to be <5.0 %. No significant difference was found between these two assays in terms of area under curve (AUC), sensitivity and specificity, negative predictive value (NPV) and positive predictive value (PPV) for the diagnosis of NSTEMI. LiCA® hs-cTnT is a reliable 3rd-generation (level 4) high-sensitivity assay for detecting cardiac troponin T. The assay is acceptable for practical use in the diagnosis of NSTEMI. [ABSTRACT FROM AUTHOR]

Self-perception and perception by others can diverge. Such differences can play an important role in corporate governance settings, for example in the context of task allocation. We run two experiments to study how predictions about own performance and the performance of others develop over time and how they influence allocation in performance-based decisions. Our findings indicate that people underestimate their own performance, but are too keen to take up performance-dependent tasks. Observers form accurate performance estimates about the performer based on observation. However, when making allocation decisions on the basis of the performer's self-assessment only, they are too hesitant to trust in the performer's success. [ABSTRACT FROM AUTHOR]

This study presents an experimental analysis of the thermodynamic performance of a large-scale magnetic air-conditioning system prototype that uses a combination of La(Fe,Mn,Si) 13 H z and Gd in a sixteen-bed active magnetic regenerator (AMR) assembly. The performance of the system is compared with a previous version which operated in part load with eight regenerator beds and lower flow rates. The experiments were carried out in a relevant environment, characterizing a Technology Readiness Level (TRL) of 6, emulated by a calibrated calorimeter focusing on key performance metrics such as cooling capacity, coefficient of performance, and second-law efficiency. The experimental results demonstrate a maximum cooling capacity of 680 W, along with a temperature span of 8.7 K. Particularly noteworthy is the significant enhancement observed in the performance of the system when accounting for previously disregarded irreversibilities external to the active magnetic regenerators. From an AMR standpoint, the maximum values of the coefficient of performance and second law efficiency are 4.2 and 10.8%, respectively, but from the system perspective, these values are reduced to 0.41 and 0.84%, respectively. [ABSTRACT FROM AUTHOR]

Real world problems in various domains demonstrate different characteristics of changes over time. This is why several researchers have been interested in dynamic optimisation for the last two decades. Since changes occur over time in a dynamic optimisation problem, the goal of a related algorithm becomes tracking the changing optima over time. Evolutionary algorithms and various swarm intelligence techniques have been adapted in the literature to solve dynamic optimisation problems. The Fireworks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for global optimisation of complex static functions that simulates the explosion process of fireworks. Although a set of improvements over the conventional FWA are presented in the literature for the static optimisation problems, the most evident extension is the Enhanced Fireworks Algorithm (EFWA). In this paper, cost effective extensions of the EFWA are proposed for solving dynamic optimisation problems in continuous space. The performance evaluation of our EFWA-based algorithms is validated with the Moving Peaks Benchmark. Empirical studies on different benchmark instances clearly show the applicability of our extensions. Our EFWA-based extensions outperform the related work in terms of both quality of solution and computational cost for a large set of test instances of the benchmark. [ABSTRACT FROM AUTHOR]

Although many scholars say that their algorithms are better than others in the state estimation problem, only a fewer convincing algorithms were applied to engineering practices. The reason is that their algorithms outperform others only in some aspects such as the estimation accuracy or the computation load. To solve the problem of performance evaluation of state estimation algorithms, in this paper, the comprehensive evaluation measures (CEM) for evaluating the nonlinear estimation algorithm (NEA) is proposed, which can comprehensively reflect the performance of the NEAs. First, we introduce three types of the NEAs. Second, the CEM combining the flatness, estimation accuracy and computation time of the NEAs, is designed to evaluate the above NEAs. Finally, the superiority of the CEM is verified by a numerical example, which helps decision makers of nonlinear estimation algorithms theoretically and technically. [ABSTRACT FROM AUTHOR]

Tourism development performance evaluation is one of the key issues in tourism development research, and promoting rural tourism development has become an urgent issue facing tourism development in many places. In order to evaluate the development level of rural tourism in Shangsi Village, this paper uses AHP method and entropy value method to construct a rural tourism development performance evaluation index system from a total of 38 index systems in 5 dimensions, takes Shangsi Village in Urumqi County as the research object for comprehensive evaluation, and determines the weight of rural tourism evaluation system in Shangsi Village. The results showed that the economic development of rural tourism in Shangsi Village had the most obvious results. On this basis, countermeasures and suggestions for improving the performance of rural tourism development in Shangsi Village are put forward. [ABSTRACT FROM AUTHOR]

The long-time test of battery discharge time is the main problem for implementing the performance requirements of the standard IEC 60086. Prediction of battery discharge time can be a solution for the long-time test problem. The discharge test data conceive valuable information of the nonlinear characteristics of the battery to build battery models. An intelligent system with learning algorithm to update the capability of the model from a set of training test data is suitable to predict the battery discharge time. The well-known ANFIS (Adaptive Neuro Fuzzy Inference Systems) is a neuro-adaptive system that has adaptive input nodes and has been applied to many prediction problems through learning and validation of the network from data. In this paper, the empirical test data related to the discharge time are acquired from the laboratory tests and are used to build the persuasive ANFIS model for the discharge time prediction from inputs to 48 recombination output data by means of modified membership functions. [ABSTRACT FROM AUTHOR]

This paper demonstrates an M/M/C queuing model with Multiple working vacations and also single working vacation under encouraged arrival with impatient customers. The queuing model with the servers adopting multiple working vacation policy and single working vacation are determined separately and it is observed that the servers during working vacation(s) will be serving the customers at a slower service rate when compared during regular busy period. In addition to the above conditions, if there is a rapid increase in the customers' arrival i.e, if encouraged arrival occurs and due to this sudden growth of the queue, there may be a impatience in the behaviour of the customer. With these considerations, an M/M/C Queuing model is analysed with two vacation policies separately by applying PGF method and thus the performance measures for an M/M/C Queue with Multiple Working Vacations and Single Working Vacation under Encouraged arrival with impatient customers are evaluated. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of training on human resource performance in in Al-Wusta Governorate Education Directorate, Ministry of Education, Sultanate of Oman. A questionnaire was used as a data collection tool and was distributed to a sample of 100 employees representing the entire target population. For the study purposes, the descriptiveanalytical approach was followed. The study significant results showed that human resources training scenario and human resources performance in the Directorate of Education in AlWusta Governorate was very high and self-evident. It was also revealed that there was a statistically significant impact at (α < 0.05) of training on the performance of human resources in the Directorate of Education in Al-Wusta Governorate, Sultanate of Oman. The study concluded that there was an impact of management support in terms of training, identifying training needs, and evaluating training on the performance of human resources in the Directorate of Education in Al-Wusta Governorate, Sultanate of Oman. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are often preferred for solving constrained engineering design optimization problems. The most important reason for choosing these algorithms is that they guarantee a satisfactory response within a reasonable time. The swarm intelligence-based manta ray foraging optimization algorithm (MRFO) is a metaheuristic algorithm proposed to solve engineering applications. In this study, the performance of MRFO is evaluated on 19 mechanical engineering optimization problems in the CEC2020 real-world constrained optimization problem suite. In order to increase the MRFO performance, three modifications are made to the algorithm; in this way, the enhanced manta ray foraging optimization (EMRFO) algorithm is proposed. The effects of the modifications made are analyzed and interpreted separately. Its performance has been compared with the algorithms in the literature, and it has been shown that EMRFO is a successful and preferable algorithm for this problem suite. [ABSTRACT FROM AUTHOR]

This paper focuses on the physical and chemical properties of calcium sulfate whiskers and the modification mechanism of asphalt. The research results of surface modification, preparation process and properties of calcium sulfate whiskers are sorted out. The existing problems and future research directions of calcium sulfate whisker modified asphalt are put forward・ It is pointed out that the future research should focus on the preparation process of calcium sulfete whiskers and the research on the flexibility and toughening of composite materials・ Finally, by analyzing the application advantages of calcium sulfate whisker modified asphalt prepared by phosphogypsum, it has broad application prospects for promoting the high value-added utilization of phosphorus solid waste. [ABSTRACT FROM AUTHOR]

The Pygmalion Effect or Rosenthal effect is a psychological phenomenon in which high expectations towards a person lead to improvement in the performance of the individual in a particular area. The Pygmalion/Rosenthal effect was named after the Greek myth of Pygmalion, a sculptor who fell in love with a statue he sculpted and named after the psychologist Robert Rosenthal. In their book, Rosenthal and Lenore Jacobson applied the idea of the phenomenon to teachers' expectations of students, showing how these affect student performance, a view that has been partially undermined by subsequent research. Rosenthal and Jacobson argued that high expectations lead to better performance, and low expectations lead to worse outcomes, both effects leading to the self-fulfillment of prophecies. According to the Pygmalion effect, subjects acquire positive labels and manage to live up to expectations. A similar process works in the opposite direction, in the case of reduced expectations. The idea behind the Pygmalion effect is that an increase in the leader's expectations about the individual's performance will lead to better performance. In sociology, the effect is often invoked in relation to education and social class. [ABSTRACT FROM AUTHOR]

Since the traffic of novel attacks exceeds current knowledge, realistic traffic labeling methods are prone to mislabeling, which has a significant impact on machine learning-based intrusion detection systems. Data cleaning typically relies on the ability of supervised deep neural networks to learn correct knowledge. Under high noise conditions, noisy labels can affect a supervised network and render it ineffective. To clean traffic datasets under high noise conditions, we propose an unsupervised learning-based data cleaning framework (called ULDC) that does not rely on labels and powerful supervised networks, hence reducing the impact of noisy labels. ULDC evaluates the confidence of observed labels through the distribution and similarity of samples in low dimensions. Moreover, ULDC maximizes the retention of hard samples through adaptive intra-class threshold evaluation, preserving more hard samples for training and improving generalization. In evaluations of ULDC on the CIRA-CIC-DoHBrw-2020 dataset, the percentage of data correction reached more than 75% under high noise, which is better than that of the state-of-the-art methods. ULDC is applicable to traffic data cleaning in both traditional networks and novel networks such as the Internet of Things and mobile networks, and it has been validated on datasets including CIC-IDS-2017 and IoT-23. [ABSTRACT FROM AUTHOR]

This research compares AI language models--specifically, OpenAI's ChatGPT, Google's Bard, and Microsoft's Bing--by analyzing their originality, use of external sources, and performance. Conducted on August 5, 2023, the study evaluates how these models respond to different queries, revealing distinct characteristics. ChatGPT stands out with lower similarity scores and lesser reliance on online sources, indicating its potential for creating more unique content. In contrast, Bard and Bing show higher similarity scores, suggesting they draw more from available online content, which could be beneficial for tasks requiring context-rich information. While ChatGPT and Bard excel in grasping context, generating substantial content, and offering relevant insights, there are concerns about accuracy and consistency across queries. Notably, Bing's focus on aiding content creation rather than direct essay generation showcases diverse strengths among AI models. As AI technology progresses, refining these models and addressing inconsistencies will improve their usefulness across various applications. These findings guide users in choosing AI tools that fit their content needs and ensure the credibility of generated outputs. [ABSTRACT FROM AUTHOR]

Higher vocational mathematics education is advanced and related to real-time applications providing vast knowledge. Teaching and training peculiar mathematical problems improve their educational and career-focused performance. Therefore optimal performance assessment methods are required for reducing the lack of knowledge in mathematics learning. This article hence introduces an Articulated Performance Assessment Model (APAM) for consenting mathematics assessment. In this model, fuzzy optimization is used for consenting different factors such as understandability, problem-solving, and replication. The understandability is identified using similar problem progression by the students, whereas replication is the application of problem-solving skills for articulated mathematical models. From perspectives, problem-solving and solution extraction is the theme that has to be met by the student. The assessments hence generate a perplexed outcome due to which the fuzzy optimization for high and low-level understandability is evaluated. The optimization recommends the change in varying steps in problem explanation and iterated replication for leveraging the students' performance. This process swings between irrelevant and crisp inputs during fuzzification. In this process, the crisp inputs are the maximum replications produced by the students for better understanding. Therefore, the proposed model is evaluated using efficiency, maximum replication, fuzzification rate, and analytical time. [ABSTRACT FROM AUTHOR]

Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings. [ABSTRACT FROM AUTHOR]

The effectiveness of fireproof sealing systems in preventing the spread of fire in high-rise building cable shafts relies on the properties of various sealing materials and the construction process. Therefore, a comprehensive evaluation is necessary. The authors of this paper propose a comparative test method based on an entity test platform for a performance evaluation of cable shaft fireproof sealing systems in high-rise buildings. The test platform measures changes in temperature, humidity, and smoke mass during fire tests to compare the performance of four sets of fireproof sealing systems in terms of thermal insulation, smoke sealing capacity, and overall integrity. In addition, a fire dynamics simulation (FDS) of fireproof sealing systems was carried out on the entity test platform, and the sealing failure process in the case of cracking in the fireproof sealing system was revealed. The simulation results for the temperature trends in the lower space align with the fire test results. Furthermore, as the gap size increases, the diffusion of smoke and flame accelerates. Consequently, the performance of cable shaft fireproof sealing systems depends not only on the sealing material but also on the construction process. [ABSTRACT FROM AUTHOR]

Low-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2s (PLFs) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM2.5), 10 μm or less (PM10), and nitrogen dioxide (NO2), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r < 0.4). Moderate correlation was observed for one PLF unit at the indoor location and two units at the roadside location when measuring PM2.5, but not for PM10 and NO2 concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R2 values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs. [ABSTRACT FROM AUTHOR]

This article describes a novel model, based on collaboration, dialogue, information exchange, and performance measurement, in order to promote and consolidate a long-term intersectoral partnership in humanitarian and social crises management caused by man-made actions or omissions. The model considered multi-agents and multi-methodologies into three main processes: a) identifying partnership motivators and facilitators; b) elaborating a process; c) monitoring results. A real-life case study demonstrates the model application and provides practical conclusions. Results show most humanitarian partnerships do not have a long-term focus, few humanitarian organizations use performance measures, and, generally, these are restricted to an internal view. This scenario is even worse in the social crises that persist in developing countries. [ABSTRACT FROM AUTHOR]

Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high levels of changing electrical noise and interferences, signal phase couplings (cross-talk phenomena), and the simultaneous presence of various defects and difficulties in localizing and identifying them. In the last few decades, various PD-measuring systems have been developed to deal with these inconveniences and try to achieve the adequate supervision of electrical installations. In the state of the art, one of the main problems that electrical companies and technology developers face is the difficulty in characterizing the measuring system's functionalities in laboratory setups or in real-world facilities, where simulated or real defects must be detected. This is mainly due to the complexity and costs that the laboratory setups entail and the fact that the facilities are permanently in service. Furthermore, in the latter scenario, owners cannot assign facilities to carry out the tests, which could cause irreversible damage. Additionally, with the aforementioned installations, a comparison of results over time in various locations is not possible, and noise conditions cannot be controlled to perform the characterizations in a correct way. To deal with the problems indicated, in this article, an affordable scale modular test platform that simulates an HV installation is presented, where real on-site PD measuring conditions are simulated and controlled. In this first development, the HV installation comprises a cable system connected at both ends to a gas-insulated substation (GIS). As the most common acquisition technique in online applications is based on the placement of high-frequency current transformer (HFCT) sensors in the grounding cables of facilities, the test platform is mainly adapted to carry out measurements with this type of sensor. The designed and developed test platform was validated to assess its features and the degree of convergence with a real installation, showing the convenience of its use for the appropriate and standardized characterization of PD-measuring systems. [ABSTRACT FROM AUTHOR]

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot's power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot's energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area. [ABSTRACT FROM AUTHOR]