Search Results (26,891)

Wastewater surveillance is a powerful public health tool which gained global prominence during the COVID-19 pandemic. This article describes the development and implementation of the national wastewater surveillance network in France: SUM’EAU. Preliminary work included defining a sampling strategy, evaluating/optimising analytical methods, launching a call for tenders to select network laboratories and producing wastewater monitoring indicators. SUM’EAU was then deployed in three stages: (i) a pilot study, (ii) the transfer of analytical activities from the National Reference Laboratory to four selected network laboratories, and (iii) the extension of the system to additional sampling sites. Currently, SUM’EAU monitors SARS-CoV-2 across 54 wastewater treatment plants in mainland France. Once a week on business days, 24 h flow-proportional composite samples are collected at plant inlets and transported at 5 °C (±3 °C) to partner laboratories for analysis. The analytical process involves sample concentration, RNA extraction, and digital RT-PCR/q-RT-PCR to detect and quantify the presence of the SARS-CoV-2 genome in wastewater. Subsequently, data are transferred to Santé publique France, the French National Public Health Agency, for analysis and interpretation. While SUM’EAU has been instrumental in monitoring the COVID-19 pandemic and holds significant potential for broader application, securing sustainable funding for its operation remains a major challenge. Full article

This study reports on the development and validation of a video-based instrument to assess early childhood education (ECE) teachers’ professional vision (PV) of language-stimulation (LS) interactions. PV refers to noticing and reasoning about key classroom interactions, a skill that can be trained and distinguishes experts from novices. The instrument targets the PV of three language-stimulation (LS) strategies: language input (LI), opportunities for language production (OLP), and feedback (FB). The instrument measures noticing through comparative judgement (CJ) and reasoning through multiple-choice items. Construct validity was assessed using the AERA framework, using three samples: a sample of professionals (n = 22), a pre-service teachers’ sample (n = 107), and a mixed sample with in- and pre-service teachers (n = 6). Reliability and validity were confirmed, with strong reliability scores for the CJ master rank orders (SRR: 0.827–0.866). Think-aloud procedures demonstrated that respondents’ decisions during CJ were mainly based on LS-relevant video features. Decisions unrelated to LS require further study. Multiple-choice reasoning items were developed from professionals’ open-ended feedback. Pre-service teacher reasoning scores showed no significant predictors. Using real classroom videos, this instrument provides an ecologically valid, scalable tool for assessing teachers’ professional vision of LS interactions. This validated instrument offers a foundation for professional development programs aimed at addressing the theory–practice gap in early language education. Full article

-GA 2-oxidases (GA2oxs), a class of enzymes, inhibit the biosynthesis of bioactive gibberellins (GAs) in plants. The GA2 oxidase gene is crucial for regulating the passivation process of active GA and is widely involved in hormone signaling and abiotic stress processes. -To examine the potential effects of the GA2 oxidase gene on Solanum pennellii, one of the important stress-tolerance wild species of tomato, a systematic analysis was performed to study the structure, phylogenetic tree, genomic locus, and upstream cis-regulatory elements of SpGA2ox genes. The expression patterns of the SpGA2ox family in various tissues were analyzed on the basis of published RNA-seq data, and the changes in SpGA2ox expression in the leaves of seedlings were detected under salinity stress and GA treatment by real-time fluorescence quantitative PCR. -We identified nine SpGA2ox genes in S. pennellii. They were located on chromosomes 1, 2, 4, 7, 8, and 10. The SpGA2ox family was clearly divided into three groups through phylogenetic relationship analysis, namely, five in C19-GA2ox class I, one in C19-GA2ox class II, and three in C20-GA2ox class. And cis-element analysis provided the basis for understanding the function of growth, development, hormones, and abiotic stress of GA2ox genes in S. pennellii. The expression patterns of the SpGA2ox family were different in three classes, and SpGA2ox1 exhibited higher expression levels in the stem compared to other tissues. The expression levels of all SpGA2ox genes increased significantly under salt stress and decreased by treatment with GA₃. With the largest changes in relative expression levels, SpGA2ox3 and SpGA2ox8 might exert key effects on the regulation of GA synthesis and the response to salt stress. -The present study may be instrumental for further investigation into the impact of SpGA2oxs on responses to abiotic stress and provide potential targets for the genetic improvement of S. pennellii. Full article

Resonators are passive devices that respond to an excitation signal by oscillating at their natural frequency with exponentially decreasing amplitudes. Physical, chemical and electrical variables can modify the natural frequencies of resonators. If resonators are connected to antennas or other transducers that couple into a communication channel, they enable purely passive sensors that can be read wirelessly. In this manuscript, we use maximum likelihood estimation to analyze the measurement accuracy that can be achieved by the wireless readout of passive resonant sensors as a function of the read signal, the stimulation power and noise figure of the reader, the distance and transducer gain of the transmission channel, and the natural frequency and quality factor of the resonant passive sensor. The Crámer–Rao lower bound characterizes the minimum variance of the natural frequency and decay constant of the resonator. We show the derivation of the Crámer–Rao lower bounds from the Fisher information matrix based on a maximum likelihood estimation of discrete-time samples of an exponentially decaying phasor. This theoretical lower limit of accuracy is almost achieved by an iterative algorithm that approximates the maximum of the measured resonator spectrum with a Lorentz curve. Full article

In an e-learning environment, question levels are based on Bloom’s Taxonomy (BT), which normally classifies a course’s learning objectives into diverse levels. As per the previous literature, the assessment procedure lacks accuracy and results in redundant keywords when automatically assigning Bloom’s taxonomic categories using a keyword-based approach. These assessments are considered challenging as far as e-learning-based students are concerned, as the text feed is the only instrumental testing part. Student assessments are limited to multiple-choice questions and lack an evaluation of students’ text-based input. This paper proposes a natural-language processing-based intelligent deep-learning model that relies on parametric cognitive assessments. By applying class labels to students’ descriptive responses, the proposed approach helps classify a variety of questions mapped to BT levels. The first contribution of this work is a compiled dataset of the assessment items from 300 students, who were tested on 20 questions at each level. Each level is calculated by combining the responses from all students, resulting in 6000 questions per cognitive level for a total of 36,000 records. The second contribution is the development of an intelligent model based on a recurrent neural network (RNN), which not only predicts Bloom’s question level but also learns it over further iterations. The students’ text-based answers are accessed to gauge performance using a refined question pool gathered through the RNN model. The student dataset is mapped and tested using the NLP model for further classification of the students’ cognitive levels. This assessment is related to the formulation of questions and the compilation of Episode 2 for assessment. The third contribution is the comparison and demonstration of the improvements in learning using a parametric cognitive-based assessment in an episodic manner. Improved classification accuracy was attained by adding more processing layers based on the iterative, RNN-based learning model to achieve the vital threshold difference. The cognitive based questions pool classification achieved by RNN results in 98% accuracy. The resulting student assessments, based on performance, increased to an accuracy ratio of 92.16% and a precision ratio of 92.36% at an aggregate level based on the Random Forest classifier. We claim that our work serves as an initiative for effective student evaluations in interactive and e-learning-based environments when handling other types of inputs, like mathematical, graphical, and multimodal inputs. Full article

In a scientific context, the main focus of sustainable supply chain management is on the creation and optimization of product and information flows; however, the management of financial flows receives insufficient attention. All effectively developed supply chain activities may collapse as a result of inadequate management of sustainable supply chain financial processes. In order to successfully develop systematically functioning processes of the international supply chain, it is necessary to analyze how to apply financing instruments in a targeted and effective manner. Adequate financing of the sustainable supply chain is the effect of great prospects and competitive advantage not only on a national scale but also in international markets. The aim of this research was to assess the importance of financing instruments used in international sustainable supply chain finance. Correlation-regression analysis was chosen for the research, which was designed to assess the factors of financial instruments of the dairy industry sustainable supply chain using the example of a company. The results showed that the key factor in the supply chain processes of the dairy products production company was the turnover ratio of buyers’ debts; therefore, in order for the company to improve the indicators of the sustainable supply chain, it should allocate more financing specifically to the turnover ratio of buyers’ debts. Full article

Background/Objectives: Caregivers engage in essential tasks that support patients’ well-being and survival, including administering medications, promoting healthy lifestyle choices, and monitoring and managing symptoms. To date, no valid and reliable instrument is available to assess Caregiver Contribution (CC) to self-care in chronic conditions in middle-income countries such as Albania. Aim: To evaluate the measurement properties (structural and construct validity, internal consistency reliability, stability, and measurement error) of the instrument CC to Self-Care of the Chronic Illness Inventory (CC-SC-CII) in Albanian caregivers caring for elderly people affected by multiple chronic conditions. Methods: A cross-sectional study. We enrolled caregivers of patients with multiple chronic conditions, from August 2020 to April 2021, if they were (a) 18 years of age or older and (b) were identified by the patient as the main unpaid caregiver. Results: Confirmatory factor analysis confirmed the two-factor structure of the CC to Self-Care Maintenance and Management scales and the one-factor structure of the CC to Self-Care Monitoring scale. Reliability estimates were adequate for all (coefficients ranging between 0.827 and 0.961). The construct’s validity was supported. The measurement error was adequate. Conclusions: The Albanian version of the CC to Self-Care of the Chronic Illness Inventory features sound measurement properties and is a valid and reliable instrument for assessing caregiver contribution to patient self-care behaviors in the Albanian population. Full article

There is no doubt that leadership is one of the most researched and disseminated topics in recent years, and over time, some distinguished models have developed a solid foundation and a reputable structure. From this perspective, this study analyzes the evidence of validity and reliability of a scale that assesses leadership styles in non-profit institutions. The study had an instrumental design. The sample consisted of 272 workers from nine Latin American countries, aged between 19 and 68 years (M = 34.08 and SD = 8.61), recruited through non-probabilistic sampling. A validity and reliability analysis of the scale confirmed the nine items and three original factors (servant, empowering and shared leadership). The KMO test reached a high level (0.898 > 0.70), and the Bartlett test reached a highly significant level (Sig. = 0.000). The scale also showed good internal consistency (α = 0.918 to 0.956; CR = 0.918 to 0.957; AVE = 0.755 to 0.880). Likewise, for the Confirmatory Factor Analysis, a measurement adjustment was performed, obtaining excellent and acceptable fit indices for Model 2 (CMIN/DF = 1.794; CFI = 0.993; SRMR = 0.023; RMSEA = 0.054; Pclose = 0.369). This study provides a brief and useful tool to measure leadership styles in Latin America, as a scale used specifically for this context would allow for a more accurate and valid assessment. This is crucial for generating effective organizational interventions, fostering the development of authentic leaders, and improving the competitiveness of non-profit institutions. Full article

Participation is an essential instrument for the expression of diverse knowledge and interests in urban planning. It ultimately influences outcomes in the built environment, fostering inclusive, safe, and accessible spaces and environments. However, urban planning is often viewed, by the public and practitioners alike, as an expert-driven, administrative, and semi-closed system, focusing on the provision of resources that are fit for all, seldom targeting the participation of gender expertise in decision-making. Drawing on empirical qualitative research conducted with planning practitioners in Belgrade in 2023 within the UN Women project “Safety of Women and Girls in Public Spaces”, this paper investigates if and how gender perspectives are integrated into urban planning policy and practice in Serbia, and the role of knowledge creation and participation in informing planning about diverse needs at the local level. The research draws on practical insights from practitioners in the fields of urban and spatial planning, traffic, and urban safety. The findings reveal how limitations to modifying the national legislative framework in terms of gender integration could be overcome at the level of planning policy and practice. The importance of including the participation of gender expertise alongside achieving gender parity in knowledge creation is highlighted. Full article

Background and Objectives: Environmental pollution in regions like the Sarno River Basin in southern Italy significantly affects physical and psychological health. This study aimed to validate three novel psychometric tools—REST, HEAL-BDLC, and PEACE—for assessing fatigue, mood disturbances, and sleep quality in environmentally exposed populations. While correlations with heavy metal exposure will be addressed in a separate manuscript, this study focuses solely on psychometric validation. Materials and Methods: The PREVES-STOP Initiative recruited 88 participants aged 30–65 years from the Sarno River Basin. Participants completed psychometric questionnaires tailored to measure fatigue (REST), symptoms of depression and anxiety (HEAL-BDLC), and sleep quality (PEACE). Internal consistency, construct validity, and reliability were analyzed using Cronbach’s alpha, correlation analyses, and principal component analysis (PCA). A subgroup received a nutraceutical intervention for us to explore their responsiveness to change over a two-week period. Results: REST (α = 0.969), HEAL-BDLC (α = 0.962), and PEACE (α = 0.736) demonstrated strong reliability. PCA confirmed the unidimensional structure of REST and the two-component structure of HEAL-BDLC (depression and anxiety dimensions) and PEACE (insomnia and sleep quality). Correlations with established measures, such as the WHO Well-Being Index, supported construct validity. Among the intervention participants, significant improvements were observed in fatigue (−12.5 REST median score), mood (−13.0 HEAL-BDLC median score), sleep (+1.5 PEACE median score), and overall well-being (+4.0 WHO-5 median score). Conclusions: REST, HEAL-BDLC, and PEACE are reliable and valid instruments for assessing nuanced health outcomes in environmentally exposed populations. They hold potential for guiding public health interventions and evaluating environmental remediation impacts. These findings lay the groundwork for future studies linking psychometric outcomes with heavy metal exposure. Full article

Background: Vascular complications, following the application of non-permanent, hyaluronic acid-based tissue fillers are a rare but very serious and rapidly progressive disorder that can, in extreme cases, lead to skin necrosis, blindness, or a stroke. Interest in aesthetic procedures is constantly growing, so awareness and knowledge of the correct and comprehensive treatment of complications are important. The human face is an area characterised by complex innervation and vascularisation. There are high-risk areas in which the application of fillers should be carried out with particular care using appropriate techniques and instruments, as well as preparations with specific rheology. The aetiopathogenesis of vascular complications is complex—involving partial or complete vessel occlusion, the presence of local inflammation in the affected tissues, and potential arteriospasm of the vessels supplying the area, resulting in tissue dysperfusion and ischaemia. Methods: In this article, the case of a patient who developed signs of a vascular compromise on the forehead area after improperly administering filler in the glabellar and nasal areas is presented. Result: The diagnostic and therapeutic management applied, including above all high doses of hyaluronidase, Doppler ultrasound diagnostics, and general medications, as well as a complementary treatment of the distant effects of the complication, i.e., erythema and tissue loss in the forehead area, by autologous injection procedures and laser therapy, resulted in a full recovery and a very good aesthetic result. Conclusions: This case proves that complications after aesthetic medicine procedures, including vascular complications are possible. Education of patients and doctors, proper diagnosis, and initiation of appropriate treatment at an early stage of the problem can bring very good therapeutic results for the patient. Full article

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance. Full article

This systematic review explores the potential of the gamified brain–machine interfaces (BMIs) and brain–computer interfaces (BCIs) to enhance the quality of life for individuals with disabilities. These technologies promise to solve complex problems by delivering customized interventions considering individual needs, ethical dilemmas, and practical constraints. This review follows the PRISMA statement. The search process extensively explored multiple registered databases for studies published between 2015 and 2024. Articles were selected based on strict eligibility criteria, focusing on empirical research evaluating gamified BCIs and BMIs in rehabilitation and learning. The final analysis included 56 studies. A thorough examination emphasizes the transformative potential of gamified BCIs and BMIs for people with disabilities, highlighting the need for interdisciplinary collaboration, user-centered design principles, and ethical consciousness for gamified neurotechnology. These technologies mark a significant change by providing enjoyable and effective treatments for disabled individuals. It also delves into how gamification, neurofeedback, and adaptive learning techniques can enhance motivation, engagement, and overall well-being. This evaluation underscores the efficiency of gamified BCIs and BMIs as potential instruments for improving the quality of life and empowering disabled people. However, despite their apparent potential for rehabilitation and learning, more research is needed to validate their effectiveness, accessibility, and long-term benefits. Full article

High-quality observational datasets are essential for climate research and models, but validating and filtering decades of meteorological measurements is an enormous task. Advances in machine learning provide opportunities to expedite and improve quality control while offering insight into non-linear interactions between the meteorological variables. The Cabauw Experimental Site for Atmospheric Research in the Netherlands, known for its 213 m observation mast, has provided in situ observations for over 50 years. Despite high-quality instrumentation, measurement errors or non-representative data are inevitable. We explore machine-learning-assisted quality control, focusing on wind vane stalling at 10 m height. Wind vane stalling is treated as a binary classification problem as we evaluate five supervised methods (Logistic Regression, K-Nearest Neighbour, Random Forest, Gaussian Naive Bayes, Support Vector Machine) and one semi-supervised method (One-Class Support Vector Machine). Our analysis determines that wind vane stalling occurred 4.54% of the time annually over 20 years, often during stably stratified nocturnal conditions. The K-Nearest Neighbour and Random Forest methods performed the best, identifying stalling with approximately 75% accuracy, while others were more affected by data imbalance (more non-stalling than stalling data points). The semi-supervised method, avoiding the effects of the inherent data imbalance, also yielded promising results towards advancing data quality assurance. Full article

The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the best-known series of standards for this purpose is ISA/IEC 62443. This paper examines the applicability of the ISA/IEC 62443 series of standards, traditionally used for securing industrial automation and control systems, to the IIoT environment. For each requirement described in the ISA/IEC 62443 standards, relevant research on that subject is reviewed and presented in a table-like manner. Based on this table, areas for future research are identified, including system hardening, asset inventory, safety instrumented system isolation, risk assessment methodologies, change management systems, data storage security, and incident response procedures. The focus on future improvement is performed for the area of system hardening, for which research and guidelines already exist but not for the specific area of IIoT environments. Full article