Search Results (4,860)

This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLABco-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article

This study investigates the effectiveness of contact and online biology teaching by assessing student performance and gathering perceptions from students, teachers, and parents. Conducted in autumn 2021 with 3035 students, 124 biology teachers, and 719 parents, this study combined post-instruction assessments of student performance in knowledge reproduction and conceptual understanding with questionnaires examining perceptions of contact and online biology teaching effectiveness across students, teachers, and parents. To investigate how various teaching-related factors influence perceived understanding of biological content, we applied a CHAID-based decision tree model to questionnaire responses from students, teachers, and parents. Results indicated that students value engaging, flexible instruction, sufficient time to complete tasks and support for independent thinking. Teachers emphasized their satisfaction with teaching and efforts to support student understanding. In contact lessons, students preferred problem-solving, teacher guidance, and a stimulating environment. In online learning, they preferred low-stress, interesting lessons with room for independent work. Parents emphasized satisfaction with their child’s learning and the importance of a focused, stimulating environment. This comparative analysis highlights the need for student-centered, research-based biology teaching in both formats, supported by teachers and delivered in a motivating environment. The results offer practical insights for improving biology instruction in different teaching modalities. Full article

Background: Transfection is vital for gene therapy, mRNA treatments, CAR-T cell therapy, and regenerative medicine. While viral vectors are effective, non-viral systems like lipid nanoparticles (LNPs) offer safer, more flexible alternatives. This work explores emerging non-viral transfection technologies to improve delivery efficiency and therapeutic outcomes. Methods: This review synthesizes the current literature and recent advancements in non-viral transfection technologies. It focuses on the mechanisms, advantages, and limitations of various delivery systems, including lipid nanoparticles, biodegradable polymers, electroporation, peptide-based carriers, and microfluidic platforms. Comparative analysis was conducted to evaluate their performance in terms of transfection efficiency, cellular uptake, biocompatibility, and potential for clinical translation. Several academic search engines and online resources were utilized for data collection, including Science Direct, PubMed, Google Scholar Scopus, the National Cancer Institute’s online portal, and other reputable online databases. Results: Non-viral systems demonstrated superior performance in delivering mRNA, siRNA, and antisense oligonucleotides, particularly in clinical applications. Biodegradable polymers and peptide-based systems showed promise in enhancing biocompatibility and targeted delivery. Electroporation and microfluidic systems offered precise control over transfection parameters, improving reproducibility and scalability. Collectively, these innovations address key challenges in gene delivery, such as stability, immune response, and cell-type specificity. Conclusions: The continuous evolution of transfection technologies is pivotal for advancing gene and cell-based therapies. Non-viral delivery systems, particularly LNPs and emerging platforms like microfluidics and biodegradable polymers, offer safer and more adaptable alternatives to viral vectors. These innovations are critical for optimizing therapeutic efficacy and enabling personalized medicine, immunotherapy, and regenerative treatments. Future research should focus on integrating these technologies to develop next-generation transfection platforms with enhanced precision and clinical applicability. Full article

The modified Rankin Scale (mRS) is a widely used outcome measure for assessing disability in stroke care; however, its administration is often affected by subjectivity and variability, leading to poor inter-rater reliability and inconsistent scoring. Originally designed for hospital discharge evaluations, the mRS has evolved into an outcome tool for disability assessment and clinical decision-making. Inconsistencies persist due to a lack of standardization and cognitive biases during its use. This paper presents design principles for creating a standardized clinical assessment tool (CAT) for the mRS, grounded in human–computer interaction (HCI) and cognitive engineering principles. Design principles were informed in part by an anonymous online survey conducted with clinicians across Canada to gain insights into current administration practices, opinions, and challenges of the mRS. The proposed design principles aim to reduce cognitive load, improve inter-rater reliability, and streamline the administration process of the mRS. By focusing on usability and standardization, the design principles seek to enhance scoring consistency and improve the overall reliability of clinical outcomes in stroke care and research. Developing a standardized CAT for the mRS represents a significant step toward improving the accuracy and consistency of stroke disability assessments. Future work will focus on real-world validation with healthcare stakeholders and exploring self-completed mRS assessments to further refine the tool. Full article

Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications. Full article

The rise in online learning, accelerated by the COVID-19 pandemic, has led to greater use of synchronous hybrid learning (SHL) in higher education. SHL allows simultaneous teaching of in-person and online learners through videoconferencing tools. Previous studies have identified various benefits (e.g., flexibility) and challenges (e.g., student engagement) to SHL. Whilst systematic reviews have emerged on this topic, few studies have considered the experiences of staff. The aim of this review was threefold: (i) to better understand how staff experiences and perceptions are assessed, (ii) to understand staff experiences in terms of the benefits and challenges of SHL and (iii) to identify recommendations for effective teaching and learning using SHL. In line with the PRISMA guidance, we conducted a systematic review across four databases, identifying 14 studies for inclusion. Studies were conducted in nine different countries and covered a range of academic disciplines. Most studies adopted qualitative methods, with small sample sizes. Measures used were typically novel and unvalidated. Four themes were identified relating to (i) technology, (ii) redesigning teaching and learning, (iii) student engagement and (iv) staff workload. In terms of recommendations, ensuring adequate staff training and ongoing classroom support were considered essential. Additionally, active and collaborative learning were considered important to address issues with interactivity. Whilst these findings largely aligned with previous work, this review also identified limited reporting in research in this area, and future studies are needed to address this. Full article

Background: Mothers’ health significantly affects their well-being and that of their families. The early years of motherhood can be tough and impact mental health. This study examined the associations between mothers’ self-compassion, social support, and self-care behaviors and their physical and mental well-being. Methods: In August 2023, an online cross-sectional survey was conducted among 514 Israeli mothers with children under three. Mothers’ physical and mental health was assessed using SF12. Self-compassion was measured by the Self-Compassion Scale (SCS). Social support was evaluated through the Multidimensional Scale of Perceived Social Support (MSPSS), and self-care was assessed via the Pittsburgh Enjoyable Activities Test (PEAT). Results: Respondents’ average age was 31.5 years. Their self-reported physical health was relatively high, with a mean of 78.36 (SD = 21) on a 0–100 scale (n = 442). Mental health scores were lower, with a mean of 65.88 (SD = 20.28, n = 401). Perceived physical health was higher among Jewish mothers, younger mothers, and those with higher income levels. Additionally, greater social support and self-compassion correlated with better perceived physical health (Adj R² = 0.11, p < 0.001). For mental health, higher scores were observed among Jewish mothers, younger mothers, and full-time employed mothers. Furthermore, higher social support, self-compassion, and self-care practices were associated with improved perceptions of mental health (Adj R² = 0.39, p < 0.001). Conclusions: Promoting the well-being of mothers is crucial for their health, their children’s well-being, and the family unit. Health professionals working with mothers of young children should emphasize and help promote social support, self-compassion, and self-care activities. 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

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

The current proceedings summarize the presentations delivered during the third International Electronic Conference on Microbiology (ECM 2025), which was held online from 1 to 3 April 2025, via the SciForum platform. This virtual event brought together researchers from around the world to share recent advances in microbiological sciences. The ECM 2025 highlighted recent developments across a broad spectrum of microbiological research, including antimicrobial resistance, gut microbiota, infectious diseases, and environmental microbiomes. Participants shared their work through online presentations and abstracts, with selected submissions invited for full publication. The event fostered global collaboration, promoted open-access science, and showcased innovative tools for studying and managing microbial systems in health, agriculture, and industry. The multidisciplinary program was organized into several thematic sessions: S1. Gut Microbiota and Health Disease. S2. Foodborne Pathogens and Food Safety. S3. Antimicrobial Agents and Resistance. S4. Emerging Infectious Diseases. S5. Microbiome and Soil Science. S6. Microbial Characterization and Bioprocess. S7. Microbe–Plant Interactions. This conference report presents summaries of the contributions made by participating authors over the three-day event. Full article

With the development of information and communication technologies, analysing new risks of moral harassment at work is becoming increasingly pertinent, especially with the expansion of teleworking and hybrid working (a mix of remote and face-to-face work per week) in the wake of the COVID-19 pandemic. In an attempt to respond to the new issues of labour regulation, this study places special emphasis on new risks of moral harassment in hybrid work and teleworking contexts, considering both the international and European framework and the legal regime in Portugal, identifying its specificities. With the rise in teleworking in the post-pandemic period, the online monitoring of workers has accentuated the difficulty in drawing the line between managerial power and harassment. Moral harassment at work is a persistent challenge and organisations must recognise, prevent and respond to inappropriate behaviour in the organisation. The results of this study—based on the results of an online survey completed by employees (with employment contracts)—show that teleworking employees recognise that they have been pressured, above all, both to respond to messages quickly and pressure to work beyond hours and suggest possible gender differences in the way harassment in hybrid work and teleworking contexts is reported. Full article

This entry explores the multifaceted intersections of gender and digital technologies, offering a comprehensive analysis of how structural inequalities are reproduced, contested, and transformed in digital contexts. It is structured into six interrelated sections that collectively address key dimensions of gendered digital contexts. It begins by addressing the gender digital divide, particularly in the Global South, emphasizing disparities in access, literacy, and sociocultural constraints. The second section examines gendered labor in the tech industry, highlighting persistent inequalities in Science, Technology, Engineering, and Mathematics (STEM) education, employment, and platform-based work. The third part focuses on gender representation in digital spaces, revealing how algorithmic and platform design perpetuate biases. The fourth section discusses gender bias in AI and disinformation, underscoring the systemic nature of digital inequalities. This is followed by an analysis of online gender-based violence, particularly its impact on marginalized communities and participation in digital life. The final section considers the potentials and limitations of digital activism in advancing gender justice. These sections collectively argue for an intersectional, inclusive, and justice-oriented approach to technology policy and design, calling for coordinated global efforts to create equitable digital futures. Full article

This paper explores emotional analysis in Hebrew texts, focusing on improving machine learning techniques for depression detection by integrating psychological feature lexicons. Hebrew’s complex morphology makes emotional analysis challenging, and this study seeks to address that by combining traditional machine learning methods with sentiment lexicons. The dataset consists of over 350,000 posts from 25,000 users on the health-focused social network “Camoni” from 2010 to 2021. Various machine learning models—SVM, Random Forest, Logistic Regression, and Multi-Layer Perceptron—were used, alongside ensemble techniques like Bagging, Boosting, and Stacking. TF-IDF was applied for feature selection, with word and character n-grams, and pre-processing steps like punctuation removal, stop word elimination, and lemmatization were performed to handle Hebrew’s linguistic complexity. The models were enriched with sentiment lexicons curated by professional psychologists. The study demonstrates that integrating sentiment lexicons significantly improves classification accuracy. Specific lexicons—such as those for negative and positive emojis, hostile words, anxiety words, and no-trust words—were particularly effective in enhancing model performance. Our best model classified depression with an accuracy of 84.1%. These findings offer insights into depression detection, suggesting that practitioners in mental health and social work can improve their machine learning models for detecting depression in online discourse by incorporating emotion-based lexicons. The societal impact of this work lies in its potential to improve the detection of depression in online Hebrew discourse, offering more accurate and efficient methods for mental health interventions in online communities. 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

This paper presents the integrated study of the funerary ara of Ofilius Ianuarius, discovered within the burial complex of Via Appia Antica 39, and explores its digital stratigraphic recontextualisation through two 3D semantic workflows. The research aims to evaluate the potential of stratigraphic 3D modelling as a tool for post-excavation analysis and transparent archaeological interpretation. Starting from a set of georeferenced photogrammetric models acquired between 2023 and 2025, the study tests two workflows: (1) an EMF-based approach using the Extended Matrix, Blender, and EMviq for stratigraphic relationship modelling and online visualisation; (2) a semantic integration method using the .gltf format and the CRMArcheo Annotation Tool developed in Blender, exported to the ATON platform. While both workflows enable accurate 3D documentation, they differ in their capacity for structured semantic enrichment and interoperability. The results highlight the value of combining reality-based models with semantically linked stratigraphic proxies and suggest future directions for linking archaeological datasets, ontologies, and interactive digital platforms. This work contributes to the ongoing effort to foster transparency, reproducibility, and accessibility in virtual archaeological reconstruction. Full article