Search Results (30,452)

Modern technologies, tools, and services are rapidly penetrating life, requiring a radical shift in traditional education and the rapid implementation of interdisciplinary STEM (science, technology, engineering, and mathematics) learning approaches. Hence, numerous studies have been initiated to explore key aspects of modern STEM methods and existing opportunities for their evaluation, personalization, and optimization. The article presents results from qualitative research conducted through semi-structured interviews with schoolteachers who have experience in implementing innovative STEM methods in their educational practice. The focus of the study is on the qualitative analysis of modern methods and good practices applied in teaching STEM subjects in Bulgaria. The formulated research questions address the readiness to apply STEM educational methods in Bulgarian schools, focusing on prerequisites such as institutional support, availability of technology infrastructure and resources, and teachers’ competencies. Additionally, these research questions aim to explore teachers’ views on modern teaching methods and approaches currently utilized in STEM education, as well as their perceived effectiveness, engagement, and applicability. The key themes and insights that emerged from the interviews also shed light on the state-of-the-art of STEM education in Bulgarian schools and the current use of teaching methods and techniques for STEM education. The findings revealed that teachers miss time in the curriculum dedicated to STEM lessons and need more integrated learning resources and additional qualifications to apply STEM methods effectively. The interpretation of results analyzes the significance of the findings and their implications for teaching practices and policies in STEM education. Full article

Biofilms are the source of numerous issues in the food, pharmaceutical, and production industries, making their control a major component of economic and public health. Among anti-biofilm strategies, enzyme-based products that target the biofilm matrix have proven effectiveness against multiple bacterial species. We tested the efficacy of a commercial product, Baso Bionil SL40^® (SL40; Stockmeier France, Saint-Jacques de la lande, France), against biofilms of Pseudomonas aeruginosa under various conditions of temperature, concentration, pH, and incubation time. SL40 contains two enzymes: a subtilisin protease and an α-amylase glycosidase. Our results showed that SL40 removed up to 85% of the biofilm biomass compared to tris solutions. SL40’s efficacy was strongly influenced by the presence of the enzymes and both temperature and concentration. Enzymatic activity was maintained from 20 °C to 60 °C and at pH values ranging from 5 to 9, with effective concentrations corresponding to SL40 dilutions from 3/200 to 1/200 in 50 mM tris solutions. Additionally, we observed that the P. aeruginosa biofilm biomass after pH 9 tris solution treatment was twice compared to a H₂O washing. Our results confirm the potential of enzymes against biofilms, highlight the need to define optimal application conditions, and support their integration into combined strategies for complete biofilm removal. Full article

This study examines cross-national differences in parental involvement, work hours, and gender ideologies among parents in the UK and Portugal. Findings revealed that UK parents, particularly mothers, reported higher childcare involvement, while Portuguese parents worked more paid hours, reflecting fewer structural childcare constraints. Despite mothers in both countries endorsing more egalitarian gender ideologies than fathers, Portuguese parents overall held more egalitarian views and lower essentialist beliefs. Surprisingly, British fathers reported greater involvement in physical childcare than their Portuguese counterparts. Gender ideologies negatively predicted partner childcare hours, indicating compensatory dynamics, with significant mediation by work hours only in the UK. This suggests that egalitarian gender beliefs alone might be insufficient for achieving equality in family roles without corresponding sociopolitical frameworks to enable equitable practices. These results highlight the influence of national context and gender beliefs on family labour divisions and underscore the need for policies that support more equitable sharing of paid and unpaid responsibilities across both countries. Full article

Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates their application to multi-omics for the molecular characterization of hematological malignancies, an area with unmet clinical need. We searched PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore, and Web of Science from January 2015 to December 2024. Two reviewers screened records, extracted data, and used a modified appraisal emphasizing explainability, performance, reproducibility, and ethics. From 2847 records, 89 studies met inclusion criteria. Studies focused on acute myeloid leukemia (34), acute lymphoblastic leukemia (23), and multiple myeloma (18). Other hematological diseases were less frequently studied. Methods included Support Vector Machines, Random Forests, and deep learning (28, 25, and 24 studies). Multi-omics integration was reported in 23 studies. External validation occurred in 31 studies, and explainability in 19. The median diagnostic area under the curve was 0.87 (interquartile range 0.81 to 0.94); deep learning reached 0.91 but offered the least explainability. Artificial Intelligence and machine learning show promise for molecular characterization, yet gaps in validation, interpretability, and standardization remain. Priorities include external validation, interpretable modeling, harmonized evaluation, and standardized reporting with shared benchmarks to enable safe, reproducible clinical translation. Full article

The rapid growth of electric vehicles (EVs) has significantly increased the demand for charging infrastructure, posing a challenge in balancing charging demand and infrastructure supply. The development of battery swapping and charging stations (BSCSs) is crucial for addressing these challenges and serves as a fundamental pillar for the sustainable advancement of EVs. This study develops a bi-level optimization model for the location and route planning of BSCSs. The upper-level model optimizes station locations to minimize total cost and service delay, while the lower-level model optimizes driver travel routes to minimize total time. An updated Non-Dominated Sorting Genetic Algorithm (UNSGA) is applied to enhance solution efficiency. The experimental results show that the bi-level model outperforms the single-level model, reducing total cost by 1.5% and travel time by 6.6%. Compared to other algorithms, the UNSGA achieves 9.43% and 8.23% lower costs than MOPSO and MOSA, respectively. Furthermore, BSCSs, despite 15.42% higher construction costs, reduce driver travel time by 22.43% and waiting time by 71.19%, highlighting their operational advantages. The bi-level optimization method provides more cost-effective decision support for EV infrastructure investors, enabling them to adapt to dynamic drivers’ needs and optimize resource allocation. Full article

Mathematical modeling is a cyclical process in which mathematics is used to represent, explore, and better understand real-world situations by mathematizing a problem and validating the results. Unlike traditional word problems, modeling tasks require learners to make assumptions, define quantities, apply mathematics, interpret results, and revise solutions within authentic contexts. Mathematical modeling is increasingly recognized as essential in mathematics education, highlighted in standards such as the Common Core State Standards for Mathematics through mathematical practice 4 (MP4): model with mathematics. This entry explores the evolving landscape of mathematical modeling education. First, it reviews the diverse conceptions of modeling and the various frameworks used to describe the modeling process, including distinctions between holistic and atomistic approaches. Second, it examines the current state of research, including the documented benefits of modeling for student engagement and access, as well as the challenges teachers face in enacting modeling tasks. Finally, the entry discusses implications for future research and practice, emphasizing the need for targeted teacher preparation and continued theoretical refinement to better support the integration of modeling into mathematics instruction. Full article

Objective: Our aim was to investigate the oral health-related quality of life (OHRQoL) and dental care experiences of adults with Cerebral Palsy (CP). Methods: In 2023, adults with CP and their caregivers from four Australian states completed questionnaires, including the Oral Health Impact Profile-14 (OHIP-14). Non-parametric tests were conducted to analyze associations between demographic and CP-related variables and OHRQoL. Results: A total of 69 respondents participated, including n = 22 adults with CP and n = 47 caregivers of adults with CP. Most adults with CP were diagnosed with a spastic motor type (46/69, 66%), with bilateral spasticity being the most common (30/46, 65%). The mean OHIP-14 score was 10.3 ± 9.3 (mean ± SD). Nearly 70% reported challenges cleaning their teeth, over 25% lacked a dentist, more than 60% found dental exams challenging, and nearly 50% required specialized dental care. In bivariate analysis, OHIP-14 was associated with daily oral care routines (p = 0.012) and “simple dental check-up” (p = 0.017). There was a statistically significant relationship between socio-economic status and scores for the handicap dimension (p = 0.040). Higher OHIP-14 scores were associated with greater levels of impairment regarding gross motor (p = 0.199), manual functioning limitations (p = 0.001), speech (p = 0.123), and communication function scales (p = 0.319). Conclusion: Adults with CP reported challenges participating in and maintaining oral health and accessing dental care, influenced by physical, functional, and socio-economic factors. These findings indicate the need for inclusive care and strategies to support access to services. Full article

The Internet of Things (IoT) is a growing area of interest with an increasing number of applications, including cyber–physical systems (CPS). Emerging threats in the IoT context make software integrity verification a key solution for checking that IoT platforms have not been tampered with so that they behave as expected. Trusted Computing techniques, in particular Remote Attestation (RA), can address this critical need. RA techniques allow a trusted third party (Verifier) to verify the software integrity of a remote platform (Attester). RA techniques rely on the presence of a secure element on the device that acts as a Root of Trust (RoT). Several specifications have been proposed to build RoTs, such as the Trusted Platform Module (TPM), the Device Identifier Composition Engine (DICE), and the Measurement and Attestation RootS (MARS). IoT contexts are often characterized by a highly dynamic scenario where platforms are constantly joining and leaving networks. This condition can be challenging for RA techniques as they need to be aware of the nodes that make up the network. This paper presents the EMBedded Remote Attestation and Verification framEwork (EMBRAVE). It is a TPM-based RA framework designed to provide a dynamic and scalable solution for RA in IoT networks. To support dynamic networks, we designed and developed Join and Leave Protocols, permitting attestation of devices that are not directly under the control of the network owner. This paper discusses the design and open-source implementation of EMBRAVE and presents experimental results demonstrating its effectiveness. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

With the increasing deployment of unmanned aerial vehicle (UAV) swarms in scenarios such as disaster response, environmental monitoring, and military reconnaissance, the need for secure and scalable formation control has become critical. Traditional centralized architectures face challenges such as limited scalability, communication bottlenecks, and single points of failure in large-scale swarm coordination. To address these issues, this paper proposes a blockchain-based decentralized formation control framework that integrates smart contracts to manage UAV registration, identity authentication, formation assignment, and positional coordination. The system follows a leader–follower structure, where the leader broadcasts formation tasks via on-chain events, while followers respond in real-time through event-driven mechanisms. A parameterized control model based on dynamic angle and distance adjustments is employed to support various formations, including V-shape, line, and circular configurations. The transformation from relative to geographic positions is achieved using Haversine and Euclidean methods. Experimental validation in a simulated environment demonstrates that the proposed method achieves lower communication latency and better responsiveness compared to polling-based schemes, while offering enhanced scalability and robustness. This work provides a feasible and secure decentralized control solution for future UAV swarm systems. Full article

Urban economic development together with the concentration of population acts as a major stimulus for changes in land-use configurations, thereby reshaping local ecosystems and influencing habitat quality. Conducting a rigorous evaluation of the temporal–spatial dynamics and the mechanisms underlying these changes is crucial for refining spatial management strategies, improving urban livability, and steering cities toward sustainable pathways. In this research, we established a comprehensive analytical framework that integrates the PLUS model, the InVEST model, and the GeoDetector model to examine shifts in land-use patterns and habitat quality in Busan Metropolitan City during 1988–2019 to pinpoint the principal influencing factors and to project possible trajectories for 2029–2049 under multiple climate change scenarios. The key findings can be summarized as follows: (1) during the last thirty years, the city’s land-use structure underwent substantial transformation, with forested areas and built-up zones becoming the primary categories, indicating continuous urban encroachment and the reduction in ecological land; (2) the average habitat quality dropped by 18.23%, displaying a distinct spatial gradient from low values in plains and coastal areas to higher values in mountainous and inland zones; (3) results from the GeoDetector revealed that variations in land-use type and NDVI exerted the greatest influence on habitat quality differences, reflecting the combined impacts of environmental conditions and socio-economic pressures; (4) scenario projections show that the SSP1-2.6 pathway supports ecological land growth and leads to a notable improvement in habitat quality, while SSP5-8.5 causes ongoing deterioration driven by the expansion of construction land. The SSP2-4.5 pathway demonstrates a relatively moderate pattern, balancing urban development needs with ecological preservation and thus is more consistent with the long-term sustainability objectives of Busan. This study provides a robust scientific basis for understanding historical and projected changes in land cover and habitat quality in Busan and offers theoretical guidance for optimizing land-use structures, strengthening ecological protection, and fostering sustainable development in Busan and other coastal mountainous cities. Full article

Background/Objectives: Sex differences in metabolic dysfunction-associated steatotic liver disease (MASLD) have been established in adult males; however, data on paediatric populations remain scarce. This study aimed to elucidate the sex-specific correlations of serum uric acid (SUA), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels with MASLD in children with obesity. Methods: Clinical data from 262 children with obesity were retrospectively analysed. Participants were categorised by sex and MASLD status (MASLD+ vs. MASLD-). Laboratory tests, including ALT, AST, SUA, fasting glucose, glycated haemoglobin, lipid profile, and insulin levels, were compared. Comparison of significant influencing factors of obesity in children with non-alcoholic fatty liver disease was conducted using multivariable logistic regression analysis by sex. Results: Hyperinsulinemia was significantly associated with MASLD in all children. Sex-based analyses revealed differentiated patterns. In males, elevated SUA (p = 0.008) and ALT (p < 0.05) were independently associated with MASLD; however, in females, only elevated AST (p = 0.003) was significantly associated. Conclusions: While insulin resistance is a common risk factor for MASLD in all children with obesity, this study suggests that its manifestation may differ by sex. Elevated SUA and ALT levels may serve as sex-specific indicators in males, while elevated AST levels appear more relevant in females. These findings support the potential value of sex-specific metabolic markers in early MASLD screening, although further validation is needed. Full article

As critical infrastructure for flood control and disaster mitigation, the completeness of a dam spatial database directly impacts regional emergency disaster response. However, existing dam data in some developing countries suffer from severe gaps and outdated information, particularly concerning small- and medium-sized dams, hindering rapid response during disasters. There is an urgent need to improve the physical dam database and implement dynamic monitoring. Yet, current remote sensing identification methods face limitations, including a lack of diverse dam samples, limited analysis of geographical factors, and low efficiency in full-image processing, making it difficult to efficiently enhance dam databases. To address these issues, this study proposes a dam extraction framework integrating comprehensive geographical factor analysis with deep learning detection, validated in Sindh Province, Pakistan. Firstly, multiple geographical factors were fused using the Random Forest algorithm to generate a dam existence probability map. High-probability candidate areas were delineated using dynamic threshold segmentation (precision: 0.90, recall: 0.76, AUC: 0.86). Subsequently, OpenStreetMap (OSM) water body data excluded non-dam potential areas, further narrowing the candidate areas. Finally, a dam image dataset was constructed to train a dam identification model based on YOLOv11, achieving an mAP50 of 0.85. This trained model was then applied to high-resolution remote sensing imagery of the candidate areas for precise identification. Ultimately, 16 previously unrecorded small and medium-sized dams were identified in Sindh Province, enhancing its dam location database. Experiments demonstrate that this method, through the synergistic optimization of geographical constraints and deep learning, significantly improves the efficiency and reliability of dam identification. It provides high-precision data support for dam disaster emergency response and water resource management, exhibiting strong practical utility and regional scalability. Full article

Background: First-Line Nurse Managers (FLNMs) have been recognised as key contributors to achieving organisational objectives, serving as vital intermediaries between management, staff, and patients. Assessing whether the Span of Control (SOC) is appropriate and providing the necessary support for FLNMs to fulfil their responsibilities poses a considerable challenge for healthcare organisations. No tool exists in Spain to guide decisions regarding FLNM’s SOC and resource needs. The aim of this study is to design a tool for assessing the span of control of first-line nurse managers in hospitals. Methods: This study employed a tool development and content validation design to create the EASOC-Nursing instrument (Eliges Aragón SOC tool). The study was conducted in three stages: an integrative literature review, followed by a national Delphi study with 43 experts in nursing management, and finally, focus group discussions. Results: A tool was created to assess first-line nurse managers’ (FLNM) span of control (SOC) using 13 key indicators, organised into four categories: unit (operations and resources, conflicts, and logging and monitoring of activities), professional (staff and competencies), FLNM (autonomy, education, and leadership), and organisation (support systems, education, and research). It includes a total of 31 items and determines SOC adequacy by establishing cut-off points that classify it as below acceptable, appropriate, or excessive. When the SOC is inadequate, the tool provides specific recommendations for support measures, such as the provision of administrative personnel or the appointment of a co-leader. Conclusions: The EASOC-Nursing tool offers a comprehensive evaluation of the core dimensions of the FLNM role and its responsibilities in Spain. Furthermore, it delivers practical guidance on the most suitable types of support to facilitate the attainment of optimal outcomes for both patients and healthcare organisations. In light of the global nursing shortage, the availability of a robust and context-sensitive instrument to assess the SOC enables hospital nursing management to allocate resources more strategically, thereby enhancing working conditions for professionals and contributing to improved patient care outcomes. Full article

Background/Objectives: Chronic low back pain is the leading global cause of disability, with a growing prevalence and socioeconomic burden. Despite strong evidence supporting exercise therapy (ET) as a primary treatment, adherence rates remain low, compromising outcomes and increasing healthcare costs. Research on contributing factors to adherence to ET in non-specific chronic low back pain (nsCLBP) is limited. This systematic review aimed to reconceptualise contributors to adherence, using a conceptual framework to explore their interrelations and complexity by integrating quantitative and qualitative research. Methods: PubMed, Web of Science and Scopus were searched, followed by a two-phase screening process. Risk of Bias (RoB), certainty assessment and level of evidence were assessed independently. Results: Eight qualitative and eleven quantitative studies were included, the latter divided into nine RCTs and two cohort studies. Overall, eight included studies showed low RoB, seven showed some concerns, and four presented high RoB. Synthesis identified internal, external, and intervention-related contributing factors. These factors were presented in a conceptual framework figure, highlighting that adherence should not be viewed as a binary concept but rather as a dynamic behaviour shaped by interrelated factors. Moderate-certainty evidence supports the impact of psychosocial factors, healthcare professional (HCP) characteristics, environmental and time-related factors, program design, progression, home-exercise program (HEP), modalities, and follow-up. Low-to-moderate-certainty evidence suggests beliefs, patient-related characteristics, and treatment setting also impact adherence. Low-certainty evidence indicates that feedback, symptoms and impairments, and confidence possibly impact adherence. Conclusions: This systematic review highlights the complex, context-dependent interplay of factors impacting adherence to ET in individuals with nsCLBP. Overall, these findings underscore the need for personalised, context-sensitive interventions that address the broad spectrum of factors, while future research should focus on validated adherence assessment tools. Full article