Search Results (3,198)

Background: Mild cognitive impairment (MCI) affects up to 40% of patients with Parkinson’s disease (PD), yet conventional rehabilitation often lacks engagement. The OPERA project developed a novel Bio-cooperative Robotic Platform (PRoBio), integrating a service robot and a virtual reality-based rehabilitation for personalized cognitive training. This work presents two preliminary user-centred studies aimed to assess PRoBio usability and acceptability. Methods: to gather qualitative feedback on robotic and virtual reality technologies, through ad hoc questionnaires, developed according to participatory design principles and user-centered evaluation literature, Study 1 (Focus group) involved 23 participants: 10 PD patients (F = 6; mean age = 68.9 ± 8.2 years), 5 caregivers (F = 3; mean age = 49.0 ± 15.5), 8 healthcare professionals (F = 6; mean age = 40.0 ± 12.0). Study 2 (Living Lab) tested the final version of PRoBio platform with 6 healthy volunteers (F = 3; mean age = 50.3 ± 11.0) and 8 rehabilitation professionals (F = 3; mean age = 32.8 ± 9.9), assessing usability and acceptability through validated questionnaires. Results: The focus group revealed common priorities across the three groups, including ease of use, emotional engagement, and personalization of exercises. Living Lab unveiled PRoBio as user-friendly, with high usability, hedonic quality, technology acceptance and low workload. No significant differences were found between groups, except for minor concerns on system responsiveness. Discussion: These preliminary findings support the feasibility, usability, and emotional appeal of PRoBio as a cognitive rehabilitation tool. The positive convergence among the groups suggests its potential for clinical integration. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

Background: Zoonotic fascioliasis and schistosomiasis, caused by trematode parasites transmitted by freshwater snails, are neglected tropical diseases of both medical and veterinary importance. There are critical knowledge gaps regarding the transmission dynamics of these infections in humans and animals, particularly in endemic African communities. Therefore, the current study aimed to determine the burden of human zoonotic schistosomiasis and fascioliasis among different age groups, focusing on the Lake Victoria zone and the Southern Highlands of Tanzania. Methods: A cross-sectional study was conducted among preschool-aged children, school-aged children, and adults. A total of 1557 stool and urine samples were collected, 400 from preschool children, 804 from school-aged children, and 353 from adults. Stool samples were processed using the Kato–Katz technique and the formol-ether concentration method to detect Schistosoma mansoni and Fasciola spp., respectively. Urine samples were examined for Schistosoma haematobium infection using the urine filtration method. Data were analyzed using Stata version 17. The t-tests or one-way ANOVA were used to assess statistical differences in the mean egg counts of S. mansoni and S. haematobium between exposure groups. Results: The overall prevalence of S. haematobium was 4.9%, S. mansoni was 1.2% with no significant differences across age groups, but with a statistically significant difference between sexes 1.8%. Males had a higher prevalence of both S. haematobium and S. mansoni infections compared to females. The prevalence of Fasciola infection was 0.9%, with the highest prevalence found in adults (≥18 years). Conclusions: Zoonotic schistosomiasis and fascioliasis are prevalent in the study area, affecting individuals across all age groups. This is the first study to report the presence of Fasciola infection in both the Lake Victoria zone and the Southern Highlands of Tanzania. These findings call for the Ministry of Health, through the Tanzania NTD Control Program, to recognize fascioliasis as a high-priority disease and include it in the national master plan. Full article

The development of algal bioplastics offers a promising pathway toward sustainable materials that can mitigate reliance on fossil fuel-derived plastics. This article reviews recent advances in algal cultivation, strain optimization, biopolymer extraction, and processing technologies, alongside techno-economic and life cycle assessments. Special emphasis is placed on integrated biorefinery models, innovative processing techniques, and the role of government–industry–academia partnerships in accelerating commercialization. The analysis incorporates both demonstrated algal systems and theoretical applications derived from established microbial processes, reflecting the emerging nature of this field. The environmental advantages, market readiness, and scalability challenges of algal bioplastics are critically evaluated, with reference to peer-reviewed studies and industrial pilot projects. The analysis underscores that while technical feasibility has been demonstrated, economic viability and large-scale adoption depend on optimizing yield, reducing production costs, and fostering collaborative frameworks. Future research priorities include enhancing strain performance via AI-enabled screening, expanding product valorization streams, and aligning regulatory standards to support global market integration. Full article

Quality-of-life (QoL) needs among gynecologic cancer survivors are multifaceted and culturally mediated, yet limited research has examined how survivors in the Middle East prioritize key domains such as sexual function, emotional well-being, and relational quality. This study aimed to identify subgroups of survivors based on the perceived importance of these domains and to explore demographic and clinical predictors of subgroups within the Saudi Arabian context. We conducted a cross-sectional, survey-based study among 129 women with a history of breast or cervical cancer attending a tertiary oncology center in Jeddah, Saudi Arabia. Participants rated the importance of sexual, emotional, and relational QoL domains using a 4-point Likert scale. Latent class analysis (LCA) was used to segment survivors based on their perceived domain importance. Differences in demographic and clinical characteristics across classes were assessed using chi-square tests. A decision tree classifier was employed. Three latent classes emerged: Class 0 (48.8%) prioritized all domains highly; Class 1 (17.8%) reported low importance across domains; and Class 2 (33.3%) emphasized emotional and relational domains while downplaying sexual function. Class group was significantly associated with age (p = 0.001), education (p = 0.04), nationality (p = 0.03), and number of children (p < 0.001). Decision tree analysis identified number of children, age, and marital status as the strongest predictors of high-importance class group. Gynecologic cancer survivors in Saudi Arabia hold diverse priorities regarding QoL domains, primarily shaped by sociocultural context than clinical variables. Tailored survivorship interventions that reflect survivors’ lived values, particularly in relation to age, family structure, and cultural norms, are critical for person-centered oncology care in the region. Full article

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport. Full article

Aquaculture has rapidly become a vital sector for ensuring global food security by meeting the growing demand for animal protein. Bangladesh, one of the world’s leading aquaculture producers, recorded a production of 4.91 million MT in 2022–2023, largely driven by inland farming systems. Despite this remarkable growth, the sector is highly vulnerable to disease outbreaks, which are aggravated by different factors. Pathogens such as bacteria, viruses, fungi, and parasites cause significant losses, while conventional disease diagnosis in Bangladesh still depends mainly on visual assessment and basic laboratory techniques, limiting early detection. This narrative review highlights recent advances in diagnostics as molecular tools, immunodiagnostics, nanodiagnostics, machine learning, and next-generation sequencing (NGS) that are widely applied globally but remain limited in Bangladesh due to infrastructure gaps, lack of skilled manpower, and resource constraints. Current treatment strategies largely rely on antibiotics and aquaculture medicinal products (AMPs), often misused without proper diagnosis, contributing to antimicrobial resistance (AMR). Promising alternatives, including probiotics, immunostimulants, vaccines, and enhanced biosecurity, require greater adoption and farmer awareness. The near-term priorities for Bangladesh include standardized disease and AMR surveillance, prudent antibiotic stewardship, phased adoption of validated rapid diagnostics, and investment in diagnostic and human capacity. Policy-level actions, including a national aquatic animal health strategy, stricter antimicrobial regulation, strengthening diagnostic infrastructure in institution, are crucial to achieve sustainable disease management and ensure long-term resilience of aquaculture in Bangladesh. Full article

School textbooks are central to the teaching, studying, and learning processes because they mediate the interaction between the prescribed curriculum and the educational experience in the classroom. Evaluating their didactic structure critically allows us to determine the degree to which they align with current curriculum guidelines and promote meaningful learning. This study aimed to analyze the extent to which Ecuadorian natural science textbooks reflect constructivist learning principles and promote the development of key competencies established in the National Priority Curriculum. This curriculum guides the achievement of essential results and strengthens fundamental competencies for students’ comprehensive development. Content analysis was adopted as the methodological approach given its relevance in examining the didactic and curricular dimensions of educational materials. The analysis covered twelve eighth-grade General Basic Education textbooks and their supplementary materials. The analysis was based on two instruments: specialized summary analysis sheets (RAE) and a purpose-built checklist. The ATLAS.ti 25 and IRaMuTeQ programs supported the systematization and visualization of the data. The results showed limited integration of constructivist strategies, such as teaching for comprehension, inquiry-based learning, and problem solving, in most of the analyzed texts. These findings underscore the need to expand and strengthen the incorporation of contextualized, critical, and meaningful learning experiences to improve the didactic design of school textbooks. Such improvements would promote coherent articulation between objectives, content, methods, resources, and assessment in line with constructivist principles of the Ecuadorian curriculum. Furthermore, given these approaches’ affinity with curricular frameworks in other regional countries, the results could offer relevant guidance and starting points for reflection on developing and using textbooks in Latin American contexts with comparable educational characteristics. Full article

Rapid climate change has exacerbated global ecosystem degradation, leading to habitat fragmentation and landscape connectivity loss. Constructing ecological networks (EN) with resilient conduction functions and conservation priorities is crucial for maintaining regional ecological security and promoting sustainable development. However, the spatiotemporal modeling and dynamic resilience assessment of EN under the combined impacts of future climate and land use/land cover (LULC) changes remain underexplored. This study focuses on the Central Yunnan Urban Agglomeration (CYUA), China, and integrates landscape ecology with complex network theory to develop a dynamic resilience assessment framework that incorporates multi-scenario LULC projections, multi-temporal EN construction, and node-link disturbance simulations. Under the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) scenarios, we quantified spatiotemporal variations in EN resilience and identified resilience-based conservation priority areas. The results show that: (1) Future EN patterns exhibit a westward clustering trend, with expanding habitat areas and enhanced connectivity. (2) From 2000 to 2040, EN resilience remains generally stable, but diverges significantly across scenarios—showing steady increases under SSP1-2.6 and SSP5-8.5, while slightly declining under SSP2-4.5. (3) Approximately 20% of nodes and 40% of links are identified as critical components for maintaining structural-functional resilience, and are projected to form conservation priority patterns characterized by larger habitat areas and more compact connectivity under future scenarios. The multi-scenario analysis provides differentiated strategies for EN planning and ecological conservation. This framework offers adaptive and resilient solutions for regional ecosystem management under climate change. Full article

Background/Objectives: The attainment of a stone-free (SF) condition is a fundamental indicator of successful outcomes after flexible ureteroscopy (fURS) for urinary stone disease. External confirmations of preoperative scores remain limited. We externally validated the T.O.HO. and S.T.O.N.E. scores in an independent Japanese cohort and examined calibration, decision curve utility, and threshold-guided use to support personalized planning. Methods: We retrospectively analyzed 361 consecutive patients treated with fURS from March 2018 to August 2023. Postoperative SF status was defined as the absence of residual calculi greater than 2 mm on non-contrast computed tomography performed within three months of surgery. Independent determinants of SF were identified using multivariable logistic regression, predictive performance was quantified by receiver operating characteristic analyses with DeLong’s test, and model calibration and decision curve analysis were additionally assessed. Results: Among the 361 patients, 255 (70.6%) achieved an SF state. A larger stone diameter, the presence of lower-pole calculi, and preoperative pyuria (positive urine WBC) were significant independent predictors of residual fragments. T.O.HO. demonstrated superior discrimination (AUC 0.86) compared with S.T.O.N.E. (AUC 0.77; p < 0.01) and surpassed individual predictors. Both scores showed acceptable calibration. Decision curve analysis demonstrated higher net benefit for T.O.HO. across clinically relevant thresholds. We provide clinically useful cut-offs (e.g., T.O.HO. ≤5: high SF probability; 6: trade-off discussion; ≥7: higher residual risk) to align actions with patient priorities. Conclusions: Beyond discrimination, a calibrated, threshold-aware use of T.O.HO. enables personalized preoperative counseling and shared decision-making, potentially reducing unnecessary staging and enhancing routine fURS planning. Full article

Urban industrial complexes have been expanding worldwide, reducing the spatial separation between agricultural, residential, and industrial zones, particularly in developing nations. Urban road dust contamination, a sensitive indicator of urban environmental quality, primarily originates in urbanization and industrialization. Its detrimental impacts on human health arise not only from particulate matter itself but also from toxic and harmful substances embedded within dust particles. Toxic metals in road dust can pose health risks through inhalation, ingestion and contact. To investigate the seasonal patterns, bioaccessibility levels and the potential human health risks linked to toxic metals (Cadmium (Cd), Nickel (Ni), Arsenic (As), Lead (Pb), Zinc (Zn), Copper (Cu), and Chromium (Cr)), 34 dust samples were collected from key roads in proximity to representative industrial facilities in Wuhan’s Qingshan District. The study found that the concentrations of Cd, Pb, and Cu in road dust were within the limits set by the national standard (GB 15618-2018), while Ni and As were not. Seasonally, Ni, As, Pb, Zn, and Cr exhibited higher concentrations during the summer than in other seasons, whereas Cd levels were lowest in spring and highest in autumn, the opposite of Cu. According to the Simplified Bioaccessibility Extraction Test (SBET), the average bioaccessibility rates of toxic metals were Cd > Zn > Cu > Ni > Cr > As > Pb. An improved health risk assessment model was developed, integrating metal enrichment, bioaccessibility, and parameter uncertainty. Results indicated that Cd, Ni, Zn, Cu, As, and Cr posed no significant non-carcinogenic risk. However, for children, the carcinogenic risks of Cd and As were relatively high, identifying them as priority control metals. Therefore, it is recommended to periodically monitor As and Cd and regulate their potential emission sources, especially in winter and spring. Full article

Widespread casualties and property damage due to structural failures following devastating earthquakes have once again highlighted the critical significance of evaluating the seismic performance of existing buildings. In this context, a fundamental part of modern pre-disaster management is to evaluate the potential seismic risks of existing structures and implementing the necessary precautions. This study focuses on determining regional risk priorities using a rapid assessment methodology applied to a sample of reinforced-concrete (RC) structures in the Centre of Bitlis city, situated in the high-seismic-risk Lake Van Basin. Risk prioritization was made among the buildings based on the Turkish Rapid Assessment technique revised in 2019 for 100 different RC buildings with one to seven stories. The negative parameters utilized in this method were analyzed both in relation to the 6 February 2023, Kahramanmaraş earthquakes and the assessed building stock. Additionally, the study provides a comprehensive review of the existing building inventory across the region and offers recommendations for potential precautions to mitigate earthquake risks. Full article

In pharmaceutical manufacturing, the reliability of machines and utility assets is critical to ensuring product quality, regulatory compliance, and uninterrupted operations. Traditional Risk-Based Maintenance (RBM) models quantify asset criticality using the Risk Priority Number (RPN), calculated from the probability and impact of failure alongside detectability. However, these models often neglect the practical challenges involved in diagnosing and resolving equipment issues, particularly in GMP-regulated environments. This study proposes an enhanced RPN framework that replaces the conventional detectability component with Maintainability Complexity (MC), quantified through two practical indicators: Ease of Diagnosis (ED) and Ease of Resolution (ER). Thirteen Key Performance Indicators (KPIs) were developed to assess Probability, Impact, and MC across 185 pharmaceutical utility assets. To enable objective risk stratification, Jenks Natural Breaks Optimization was applied to group assets into Low, Medium, and High risk tiers. Both multiplicative and normalized averaging methods were tested for score aggregation, allowing comparative analysis of their impact on prioritization outcomes. The enhanced model produced stronger alignment with operational realities, enabling more accurate asset classification and maintenance scheduling. A 3D risk matrix was introduced to translate scores into proactive strategies, offering traceability and digital compatibility with Computerized Maintenance Management Systems (CMMS). This framework provides a practical, auditable, and scalable approach to maintenance planning, supporting Industry 4.0 readiness in pharmaceutical operations. Full article

This study presents a comprehensive assessment of the energy systems in eight Eastern European countries—Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia—focusing on their energy transition, security of supply, decarbonisation, and energy efficiency. Using principal component analysis (PCA) and clustering techniques, we identify three different energy profiles: countries dependent on fossil fuels (e.g., Poland, Bulgaria), countries with a balanced mix of nuclear and fossil fuels (e.g., the Czech Republic, Slovakia, Hungary), and countries focusing mainly on renewables (e.g., Slovenia, Croatia). The sectoral analysis shows that industry and transport are the main drivers of energy consumption and CO₂ emissions, and the challenges and policy priorities of decarbonisation are determined. Regression modelling shows that dependence on fossil fuels strongly influences the use of renewable energy and electricity consumption patterns, while national differences in per capita electricity consumption are influenced by socio-economic and political factors that go beyond the energy structure. The Decarbonisation Level Index (DLI) indicator shows that Bulgaria and the Czech Republic achieve a high degree of self-sufficiency in domestic energy, while Hungary and Slovakia are the most dependent on imports. A typology based on energy intensity and import dependency categorises Romania as resilient, several countries as balanced, and Hungary, Slovakia, and Croatia as vulnerable. The projected investments up to 2030 indicate an annual increase in clean energy production of around 123–138 TWh through the expansion of nuclear energy, the development of renewable energy, the phasing out of coal, and the improvement of energy efficiency, which could reduce CO₂ emissions across the region by around 119–143 million tons per year. The policy recommendations emphasise the accelerated phase-out of coal, supported by just transition measures, the use of nuclear energy as a stable backbone, the expansion of renewables and energy storage, and a focus on the electrification of transport and industry. The study emphasises the significant influence of European Union (EU) policies—such as the “Clean Energy for All Europeans” and “Fit for 55” packages—on the design of national strategies through regulatory frameworks, financing, and market mechanisms. This analysis provides important insights into the heterogeneity of Eastern European energy systems and supports the design of customised, coordinated policy measures to achieve a sustainable, secure, and climate-resilient energy transition in the region. Full article

Background: Neuromuscular training is widely implemented in professional football to enhance performance and reduce injury risk. Although unstable surfaces are commonly used for proprioceptive and rehabilitation purposes, limited evidence supports their effectiveness in improving sport-specific force production and stability in elite athletes. This study aimed to compare the effects of multicomponent neuromuscular training performed on stable versus unstable surfaces on unilateral force production, mobility, and agility in elite male soccer players. Methods: Twenty-seven professional male soccer players from the Spanish first division were randomly assigned to either a stable surface group (SSG; n = 14) or an unstable surface group (USG; n = 13). Both groups completed a 10-week intervention in addition to their regular training routines. Pre- and post-intervention assessments included dorsiflexion range of motion (DFt), Y-Balance Test (YBT), single-leg countermovement jump (SLCMJ), single-leg hop for distance (SLH), side-hop (SH), Speedy Jump (SpJ), Agility T-test (TT), and the Lower Extremity Functional Test (LEFT). A two-way repeated-measures ANOVA and Hedges’ g effect sizes were used for statistical analysis. Results: The SSG showed significant improvements in most performance variables, including DFt, YBT, SLH, SH, SpJ, TT, and LEFT (percent change range: 1.6% to 9.8%; Hedges’ g ranging from 0.52 to 2.57). The USG showed limited improvements, with significant changes only in LEFT (percent change = 1.18%; Hedges’ g = 0.53). Notably, the stable surface group demonstrated enhanced force production and agility, particularly in the non-dominant limb. Conclusions: Multicomponent neuromuscular training on stable surfaces appears more effective than training on unstable surfaces for improving unilateral strength, mobility, and agility in elite soccer players. These findings suggest that stable surface training may provide superior performance benefits and should be considered a priority in high-performance environments. Full article