Search Results (850)

The accurate and precise measurement of lubricating oil consumption is critical for developing environmentally friendly internal combustion engines, particularly hydrogen-fueled internal combustion engines. The deuterium tracer method is based on the addition of poly-deuterated base oil tracers to fully formulated oils for precise, accurate, and fast lubricating oil consumption measurements. Previously performed measurements have shown that the use of poly-deuterated poly-alpha olefins has minimal impact on lubricating oil properties, except for a slight drop in oil viscosity. To further reduce the impact on lubricating oil characteristics, a new base oil for the synthesis of a poly-deuterated tracer is introduced, and its influence on the lubricating oil’s chemical, tribological, and rheological properties is analyzed. Furthermore, the influence of the tracer addition on the preignition tendencies of the fully formulated oil is also examined. Based on the analyses, no relevant changes in the lubricating oil properties, such as viscosity, density, and thermal degradation behavior, can be observed. Additionally, the deuterium tracer does not negatively influence combustion anomalies, thus reducing preignition tendencies. These results establish the method’s compatibility with new-generation engines, especially hydrogen-fueled internal combustion engines. Full article

This paper presents a new methodical procedure to monitor in real time the junction temperature of SiC Power MOSFET modules of parallel-connected chips utilized in machine drive systems to develop their reliability modelling and predict their lifetime. The paper implements the on-line measurements of temperature-sensitive electrical parameters (TSEP) approach, particularly the quasi-threshold voltage and the on-state drain to source voltage, to estimate the junction temperature in real time. The proposed procedure firstly applied computational fluid dynamics analysis on the module under study to determine the chip which undergoes the maximum junction temperature during typical operation of the module. Then, a calibration phase, using double-pulse tests on the selected chip, is used to generate look-up tables to relate the TSEPs under study to the junction temperature. Next, the real-time estimation of junction temperature was accomplished during the on-line operation of the three-phase inverter, taking into account the induced distortion/noises due to operation of the parallel-connected chips in the module. After that, a comparison between the two TSEPs under study was provided to demonstrate their advantages/drawbacks. Finally, reliability modelling was developed to predict the lifetime of the studied module based on the estimated junction temperature under a predetermined mission profile. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

High penetration of photovoltaic (PV) generation presents new challenges for voltage regulation in distribution networks (DNs), primarily due to output intermittency and constrained reactive power capabilities. This paper introduces a distributed voltage control method leveraging reactive power compensation from PV inverters. Instead of relying on centralized computation, the proposed method allows each inverter to make local decisions using real-time voltage measurements and delayed communication with neighboring PV nodes. To account for practical asynchronous communication and feedback delay, a Distributed Online Primal–Dual Push–Sum (DOPP) algorithm that integrates a fixed-step delay model into the push–sum coordination framework is developed. Through extensive case studies on a modified IEEE 123-bus system, it has been demonstrated that the proposed method maintains robust performance under both static and dynamic scenarios, even in the presence of fixed feedback delays. Specifically, in static scenarios, the proposed strategy rapidly eliminates voltage violations within 50–100 iterations, effectively regulating all nodal voltages into the acceptable range of [0.95, 1.05] p.u. even under feedback delays with a delay step of 10. In dynamic scenarios, the proposed strategy ensures 100% voltage compliance across all nodes, demonstrating superior voltage regulation and reactive power coordination performance over conventional droop and incremental control approaches. Full article

Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with 328 university students from four districts in Lima, Peru, using an online survey to measure technostress. Confirmatory factor analysis (CFA) was performed to assess the psychometric properties of the TS4US scale, resulting in a refined model with two latent factors and thirteen validated items. Findings indicate that 28% of students experience high technostress levels, while 5% report very high levels, though no significant associations were found between technostress and sociodemographic variables such as campus location, employment status, gender, and academic level. The TS4US instrument had been previously validated in Chile; this study confirms its structure in a new sociocultural context, reinforcing its cross-cultural applicability. These results highlight the need for sustainable strategies to mitigate technostress in higher education, including institutional support, digital literacy programs, and policies fostering a balanced technological environment. Addressing technostress is essential for promoting sustainable education (SDG4) and enhancing student well-being (SDG3). This study directly contributes to the achievement of Sustainable Development Goals 3 (Good Health and Well-being) and 4 (Quality Education) by providing validated tools and evidence-based recommendations to promote mental health and equitable access to digital education in Latin America. Future research should explore cross-country comparisons and targeted interventions, including digital well-being initiatives and adaptive learning strategies, to ensure a resilient and sustainable academic ecosystem. Full article

Background/Objectives: This retrospective multicenter study, conducted within the ORCHESTRA Project, investigated SARS-CoV-2 reinfections among 5777 healthcare workers (HCWs) from four University Hospitals (Modena, Verona, Padova and Trieste) in northern Italy, aiming to assess the risk of reinfection and its determinants, comparing the clinical characteristics of reinfections with those of first infections, and examining the impact of preventive measures and vaccination strategies. Methods: HCWs completed an online questionnaire between June and August 2022. The survey collected demographic, occupational, and clinical data, including information on first infections and reinfections. Statistical analyses were performed using SPSS 28.0, through bivariate and multivariate approaches. Results: Response rates were 41.8% for Modena, 39.5% for Verona, 17.9% for Padova, and 17.4% for Trieste. Among the respondents, 4.8% (n = 276) experienced 2 infections and 0.5% (n = 27) reported 3 infections, out of a total of 330 reinfection cases. Additionally, 43.0% (n = 2787) reported only one infection, while 51.5% were never infected. Reinfection rates increased across five study phases (based on the epidemiological context), likely due to the emergence of new SARS-CoV-2 variants. A booster vaccine dose significantly reduced reinfection risk. Higher reinfection risk was found among HCWs aged ≤30 years, those with chronic respiratory diseases, and those working in COVID-19 wards, particularly nurses and allied health professionals. Reinfections were associated with a lower frequency of symptoms both during the period of swab positivity and after a negative swab, as well as with a shorter duration of swab positivity. No significant differences in symptom duration were found between first infections and reinfections. Conclusions: Despite its limitations, the online questionnaire proved a useful tool. Natural infection and vaccination reduced both reinfection risk and symptom severity. Prior infections should be considered in planning vaccination schedules and prioritizing HCWs. Full article

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article

Echocardiography is the primary imaging modality for diagnosing cardiac disease in children, with quantitation largely based on nomograms. Over the past decade, significant efforts have been made to address the numerical and methodological limitations of earlier nomograms. As a result, robust and reliable pediatric echocardiographic nomograms are now available for most two-dimensional anatomical measurements, three-dimensional volumes, and strain parameters. These more recent nomograms are based on adequate sample sizes, strict inclusion and exclusion criteria, and rigorous statistical methodologies. They have demonstrated good reproducibility with minimal differences across different authors, establishing them as reliable diagnostic tools. Despite these advances, some limitations persist. Certain ethnic groups remain underrepresented, and data for preterm and low-weight infants are still limited. Most existing nomograms are derived from European and North American populations, with sparse data from Asia and very limited data from Africa and South America. Nomograms for preterm and low-weight infants are few and cover only selected cardiac structures. Although diastolic parameter nomograms are available, the data remain heterogeneous due to challenges in normalizing functional parameters according to age and body size. The accessibility of current nomograms has greatly improved with the development of online calculators and mobile applications. Ideally, integration of nomograms into echocardiographic machines and reporting systems should be pursued. Future studies are needed to develop broader, more comprehensive, and multi-ethnic nomograms, with better representation of preterm and low-weight populations, and to validate new parameters derived from emerging three- and four-dimensional echocardiographic techniques. Full article

This paper builds on place theory and the psycho-social approach to the study of perceived residential environmental quality to examine the relationship between environmental perceptions and residential action in the neighborhood. An exploratory study on (N = 185) Italian respondents assessed the role of perceived residential environmental quality (i.e., perceived quality of green areas and perceived maintenance levels within the neighborhood), awareness of neighborhood environmental problems, neighborhood descriptive norms, and place attachment (attachment to the neighborhood) as predictors of self-reported individual residential engagement (engagement in improving the environmental quality of the neighborhood). Likert-type measures of the corresponding constructs were included in a structured questionnaire and used to carry out an online survey. Findings showed problem awareness and descriptive norms to directly predict residential engagement. Problem awareness mediated the relationship between perceived maintenance levels and residential engagement. Place attachment was directly predicted by perceived residential quality (quality of green areas), but did not show an independent predictive power vis-à-vis residential engagement. Results suggest new possible research avenues for modelling the individual commitment to improve the environmental quality of one’s own residential architectural and green environment. Full article

Objectives: To evaluate the reproducibility of measurements of breech progression angle (BPA) by transperineal ultrasound (US) before and after its standardization by applying an image-based checklist. Methods: Eighteen 3-dimensional (3D) volumes of transperineal US from women at 36–40 weeks of gestation with a singleton fetus in breech presentation were provided to eight operators from four maternity units in Spain and Italy. All operators measured the BPA using 3D US volume processing software, and interobserver reproducibility was evaluated using the intraclass correlation coefficient (ICC). Following an online live review of all measurements by the operators, and the identification of sources of disagreement, an image-based scoring system for BPA measurement was collaboratively developed. The checklist included the following: (1) acquisition in the midsagittal plane, avoiding the posterior shadow of the pubic ramus; (2) visualization of the complete “almond-shaped” pubic symphysis; (3) drawing a first line along the longitudinal axis of the symphysis, dividing it equally; (4) extending this line to the inferior edge of the bone; and (5) drawing a second line tangentially from the lower edge of the symphysis to the lowest recognizable fetal part. The BPA measurements were then repeated using this checklist, and reproducibility was reassessed. Results: Eighteen volumes were analyzed by the eight operators, achieving a moderate reproducibility (ICC: 0.70, 95% confidence interval (CI): 0.48 to 0.86). A score was developed to include a series of landmarks for the appropriate assessment of BPA. Subsequently, the same eighteen volumes were reassessed using the new score, resulting in improved reproducibility (ICC: 0.81, 95% CI: 0.66 to 0.92). Conclusions: The measurement of BPA is feasible and reproducible when using a standardized image-based score. Full article

Interest in personal growth is expanding in both the popular press and the scientific literature. These expansions incorporate varied theoretical approaches and multiple areas of life. In the current work, we propose a novel perspective that focuses on managing failure to reach self-improvement goals and improving wellbeing. Specifically, we introduce an iterative mindset, which is the belief that making adaptations combined with deliberate practice and neutralizing of failure is critical for lasting transformations. We seek to contribute to the personal growth and mindset literature in two key ways. First, we developed and validated a new measure, called an Iterative Mindset Inventory (IMI), examining factor structure, reliability, and validity. Second, we investigated the links between iterative mindsets, self-improvement, and wellbeing, extending existing work on the power of beliefs to shape self-development. In both studies (Study 1, N = 871; Study 2, N = 345), we incorporated online samples that resembled the adult population of the United States. In Study 1, we found evidence for the proposed theoretical three-factor structure of an iterative mindset, which we label iterate, practice, and assess. In Study 2, using a longitudinal approach across three weeks, we confirmed the three-factor structure and found high test–retest reliability. Iterative mindsets were also positively linked to weight-loss success across both studies and to self-efficacy and wellbeing in Study 2. Full article

As an emerging e-commerce model, live streaming e-commerce integrates instant interaction, content marketing, and online sales to bring consumers a new shopping experience. However, there are many risks in the process of live e-commerce transactions. Identifying key risk factors and implementing targeted control measures are crucial for promoting the sustainable and healthy development of live streaming e-commerce. This paper firstly constructs a business model of live streaming e-commerce transactions according to the transaction scenario and summarizes 24 risk factors from the three dimensions of live streaming e-commerce platforms, merchants, and anchors based on relevant national standards and other relevant literature. Secondly, the Delphi method is employed to modify and optimize the initial risk factors. On this basis, the social network model of risk factors is constructed to determine the influence relationship among risk factors. By calculating the degree centrality, factor types are segmented, and key risk factors as well as influence paths are identified. Finally, corresponding countermeasures and suggestions are proposed. The results indicate that Credit Evaluation System Perfection, Service Evaluation System Perfection, Qualification Audit Mechanism Perfection, Dispute Complaint Handling Channels Perfection, Risk Identification Mechanism Perfection, Platform Qualification, Merchant Qualification, and Merchant Credit are the critical risk factors affecting live streaming e-commerce transactions. Full article

Suppliers nowadays can be directly approached in business-to-business (B2B) E-commerce websites. This makes the product and service information of certain suppliers accessible in online reviews. Therefore, online reviews have become important for B2B supplier evaluation and selection. Recently, the sustainability of food packaging has attracted increasing attention from companies and consumers. This study developed a novel multi-criteria decision making (MCDM) method called Percentage Assessment with Synergistic Comparisons And Aggregated Ranks (PASCAAR) to support the selection of sustainable food package suppliers based on online review information in B2B E-commerce websites. Such a method used three different percentage comparisons between alternatives and the minimal options, and then aggregates the comparisons with their ranks. This study confirmed the effectiveness of PASCAAR by applying it to a case study to select the supplier of sustainable food packages (i.e., biodegradable food containers) from six candidates in the B2B E-commerce website by considering multi-dimensional online review information and their own product properties. Using PASCAAR, this study obtained the outcome that the third candidate is the most suitable one, as quantitative results indicate this supplier has the highest PASCAAR score. Based on the results, this study further conducted thorough sensitivity tests to validate the results. It can be found that, compared with the classical MCDM methods in measuring the performance of alternatives and aggregating evaluation scores, the PASCAAR method can have more robust and informative results. This study also developed a PASCAAR Solver to enable easy implementation of this method. This study contributes to the existing literature by providing new ranking and aggregation ideas in MCDM and can offer practitioners a more informative and highly actionable method for supplier selection and decision support system development by utilizing online review information. Full article

Background/Objectives: Schizophrenia Spectrum Disorders (SSDs) carry a debilitating burden of disease which, even after pharmacological and psychological treatment are optimized, remains difficult to fully target. New online-delivered and user-led interventions may provide an appropriate, cost-effective answer to this problem. This study aims to retrieve the currently gathered findings on the efficacy of these interventions across several outcomes, such as symptom severity, social cognition, functioning and others. Methods: A systematic review of the current available literature was conducted. Of 29 potentially relevant articles, 26 were included and assigned at least one of four intervention types: Web-Based Therapy (WBT), Web-Based Psycho-Education (WBP), Online Peer Support (OPS) and Prompt-Based Intervention (PBI). Results: The findings were grouped based on outcome. Of 24 studies evaluating the effects of symptom severity, 14 have achieved statistically significant results, and 10 have not. WBT (such as online-delivered Cognitive Behavioral Therapy, Acceptance and Commitment Therapy, social cognition training and Mindfulness Training) seemed to be the most effective at targeting symptoms. Of 14 studies evaluating functioning, seven achieved significant results, four involving a form of social or neurocognitive training, suggesting a potential pathway towards functional improvements through interventions targeting cognition and motivation. Regarding social cognition, all seven studies measuring the effects of an intervention on this outcome produced significant results, indicating that this outcome lends itself well to remote, online administration. This may be linked with the nature of social cognition exercises, as they are commonly administered through a digital medium (such as pictures, videos and auditory exercises), a delivery method that suits the online-user led model very well. Conclusions: Online user-led interventions show promise as a new way to tackle functional deficits in SSD patients and achieve these improvements through targeting social cognition, a hard-to-reach component of the burden of SSDs which seems to be successfully targetable in a remote, user-led fashion. Symptomatic improvements can also be achievable, through the combination of these interventions with treatment as usual. Full article

Technological developments and the integration of social media into journalistic practices have transformed the media landscape, changing how information is gathered, produced, and shared. This evolution poses challenges, including the lack of clear guidelines and practical tools for ensuring the quality of digital news content. To address these issues, IQJournalism, an intelligent quality prediction advisor, was developed. This paper outlines the methodology for the development of IQJournalism, a platform that leverages advanced AI technologies to process Greek news articles and provide real-time editing recommendations on various dimensions, including language quality, subjectivity level, emotionality, entertainment, and social media engagement. First, a qualitative study was conducted through semi-structured, in-depth interviews with 20 experts, academic researchers and media professionals to identify indicators of perceived quality in journalism. These insights were then transformed into measurable features, which served as training data for explainable machine learning-based models for quality categorization and prediction. Finally, the IQJournalism platform was designed following a user-centered iterative process that included prototyping, testing, and redesigning. The innovative approach aims to serve as a valuable tool for improving journalistic quality, contributing to more reliable and engaging online news content. Importantly, the platform is not limited to the journalistic sector, but can also be used to optimize content in various areas, such as marketing, political, and strategic communication, supporting editors seeking to improve the quality and impact of their writing. Full article