Search Results (1,644)

As part of our technology-enhanced learning (TEL) strategy, we developed a field-based augmented reality (AR) learning system for biodiversity education among senior elementary school students. Using a 2D illustration style to present the appearance of the species and a situational interactive design, the AR app focused on common wild animals in Taiwan. They also gained insight into wild animal species in outdoor settings, gained knowledge about the phenomenon of roadkill and the rescue of wild animals, and promoted their awareness of ecological conservation. Using the design-based research (DBR) method, we integrated user-oriented design processes and iteratively modified the system functions and interface through expert review and field usability testing. During this activity, 26 senior elementary school students were recruited to participate in an interactive AR game designed for a single player. As part of the learning content, students must collect images of species, recognize roadkill, and learn about wildlife rescue. To evaluate the effect of the activity on knowledge learning and the app’s usability, data were collected through pre- and post-test paper tests, questionnaires, and so on. Based on the research results, this system can significantly enhance students’ learning interests and contextual understanding of biodiversity topics as an effective technology-assisted learning tool. Students reported high levels of immersion and learning motivation, and the teachers agreed that it promoted inquiry-based and independent learning. The results of this study contribute to the field of educational and environmental education. Consequently, context-aware AR tools may enhance students’ situational learning experience and environmental literacy. In addition, it provides a practical design reference for future AR educational applications, demonstrating that gamification and outdoor learning can enhance the learning outcomes of traditional science education. Full article

Core body temperature (CBT) is a fundamental physiological parameter tightly regulated by thermoregulatory mechanisms and is critically important for heat stress assessment, clinical management, and circadian rhythm research. Although invasive measurements such as pulmonary artery, esophageal, and rectal temperatures provide high accuracy, their practical use is limited by invasiveness, discomfort, and restricted feasibility for continuous monitoring in daily-life or field environments. Consequently, extensive efforts have been devoted to developing non-invasive CBT measurement and estimation techniques. This review provides an application-oriented synthesis of invasive reference methods and representative non-invasive approaches, including in-ear sensors, infrared thermography, ingestible telemetric sensors, heat-flux-based techniques, and model-based estimation using wearable physiological signals. For each approach, measurement principles, accuracy, invasiveness, usability, and application domains are comparatively examined, with particular emphasis on trade-offs between measurement fidelity and real-world implementability. Rather than ranking methods by absolute performance, this review highlights their relative positioning across clinical, occupational, and daily-life contexts. While no single non-invasive technique can universally replace invasive gold standards, recent advances in wearable sensing, heat-flux modeling, and multimodal estimation demonstrate growing potential for practical CBT monitoring. Overall, the findings suggest that future CBT assessment will increasingly rely on hybrid and context-aware systems that integrate complementary methods to enable reliable monitoring under real-world conditions. This review is intended for researchers and practitioners who need to select or design CBT monitoring systems. Full article

The decarbonization of the building sector is a critical challenge for achieving Japan’s net-zero targets. However, comprehensive assessments comparing residential construction methods and building heights at the national scale remain limited. This study applies Environmentally Extended Input–Output Analysis (EEIOA) to evaluate the embodied CO₂ emissions associated with four distinct residential construction methods. The results reveal that, when accounting for carbon storage, the net CO₂ emissions per unit of floor area were significantly lower for wooden houses (195 kg-CO₂/m²) compared to steel-reinforced concrete (1109 kg-CO₂/m²), reinforced concrete (857 kg-CO₂/m²), and steel-framed houses (803 kg-CO₂/m²). A further analysis based on building height indicates a structural divergence: while wooden houses account for the majority of emissions in one- to three-story buildings due to their high market share, reinforced concrete houses dominate emissions in four- to nine-story buildings driven by their high carbon intensity. These findings suggest that promoting timber construction, particularly in taller buildings, is a vital strategy for climate change mitigation. Consequently, policy support focusing on technological advancement, cost reduction, and consumer awareness is essential to accelerate the adoption of wooden architecture. Full article

This paper investigates travellers’ intentions, with regard to preferences for a green and/or ethical hotel, boycotting hotels accused of extreme environmental damages or over-exploitation of workers, and sharing relevant information on social media. Questioning the claim that intentions to prefer a green hotel are based mainly or even solely on practical criteria, this study focuses on examining the influencing power of values. The Values-Beliefs-Norms model was employed and modified as the New Environmental Paradigm was replaced by climate change risk perception. Personal interviews were conducted with consumers in the urban area of Thessaloniki, Greece, using a structured questionnaire for data collection. Area sampling, in combination with quota sampling, in terms of gender and age, was used. Results provided that egoistic and altruistic values were excluded from the final structural model, and just biospheric values indicated a statistically significant positive relationship with Risk Perception. The other hypothesised consecutive relationships between Biospheric Values (BV), Risk Perception (RP), Awareness of Consequences (AC), Ascription of Responsibility (AR), Personal Norms (PN) and Intentions (Int) were found to be statistically significant and positive. Overall, 80.9% of the variance in Intentions was explained, while Personal Norms indicated the stronger impact on Intentions among all other relationships in the chain. Eventually, theoretical and practical implications, as well as future research directions, are suggested. Full article

The use of cannabis during pregnancy is increasing, in line with its growing societal acceptance and legalization. Cannabis use mainly concerns its active components Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD). While cannabis has therapeutic effects on pain, nausea, and vomiting, its impact on fetal development remains a significant public health concern. Given the existence of a local endocannabinoid system (ECS) in the placenta, with proven effects on placental development and blood flow, it is likely that THC and CBD exert effects via interference with the placental ECS. This review summarizes how cannabis use affects the placental ECS and describes the consequences of such use on placental function and fetal development. It starts with discussing the placental ECS, the effects of THC and CBD on placental function, and the pharmacokinetics of cannabinoids during pregnancy. It then describes the effects of both paternal and maternal cannabis use and provides epidemiological data linking placental insufficiency, impaired fetal growth, and preeclampsia to cannabis use. It also raises awareness for the possibility that cannabis use, by altering DNA methylation, might result in transgenerational effects. It is concluded that current evidence supports abstaining from cannabis use during preconception, pregnancy, and lactation to optimize maternal, fetal, and intergenerational health outcomes. Full article

The COVID-19 pandemic has revealed profound social–psychological vulnerabilities and strengths across societies worldwide. Beyond its immediate health implications, the pandemic has triggered a wave of mental health issues, disrupted social cohesion, and challenged community resilience. This paper synthesizes the current literature, critically discusses five recent studies as part of the Special Issue “Mental Health Consequences of COVID-19: The Role of Social Determinants”, and articulates an agenda for future research within a social–psychological framework. Moving beyond mere negative effects such as anxiety, this review highlights the role of resilience, prosocial behavior, (digital) mental health interventions, and community social capital. Correspondingly, I advocate for interdisciplinary efforts to enhance awareness, preparedness, and adaptive capacity during health crises, emphasizing the need for a clearer focus on vulnerable social groups. In sum, recognizing the evolving global landscape, this work underscores the urgency of integrating psychological insights into public health policies to build resilient societies capable of confronting future pandemics and health emergencies. Full article

Spectral data obtained from upstream remote sensing tasks contain abundant complementary information. Infrared images are rich in radiative information, and visible images provide spatial details. Effective fusion of these two modalities improves the utilization of remote sensing data and provides a more comprehensive representation of target characteristics and texture details. The majority of current fusion methods focus primarily on intensity fusion between infrared and visible images. These methods ignore the chrominance information present in visible images and the interference introduced by infrared images on the color of fusion results. Consequently, the fused images exhibit inadequate color representation. To address these challenges, an infrared and visible image fusion method named Chrominance-Aware Multi-Resolution Network (CMNet) is proposed. CMNet integrates the Mamba module, which offers linear complexity and global awareness, into a U-Net framework to form the Multi-scale Spatial State Attention (MSSA) framework. Furthermore, the enhancement of the Mamba module through the design of the Chrominance-Enhanced Fusion (CEF) module leads to better color and detail representation in the fused image. Extensive experimental results show that the CMNet method delivers better performance compared to existing fusion methods across various evaluation metrics. Full article

Elder abuse is an increasingly common problem in modern society, in the context of rapid population aging. Despite increasing awareness, this phenomenon remains heavily underreported, and effective interventions are yet to be made, thus leading to significant medical, social, and legal implications. The purpose of this review is to present an updated situation of the depths of elder abuse, presenting its prevalence both at the global and European level, the two main environments in which it is the most common (community and institutional settings), different forms of abuse, risk factors, and consequences for each one of them, as well as medico-legal aspects on the matter. A narrative review was conducted based on PubMed/MEDLINE, Scopus, and Web of Science databases, in association with data presented in reports from international organizations. The review included only articles published in English, in peer-reviewed journals, addressing elder abuse in adults aged 60 years and older, and those that didn’t respect the criteria were excluded. Elder abuse comes in different forms, most of the time overlapping, with psychological abuse being the most prevalent. Each one of them has its own risk factors and specific consequences, but all of them will eventually lead to increased morbidity, accelerated cognitive impairment, and functional decline. In community settings, the elders usually experience abuse related to dependency on the family and social isolation, while in institutional settings, abuse is frequently associated with understaffing and inadequate care. From a forensic perspective, functional and cognitive decline complicate the proper documentation of the abuse. Thus, the role of the physician in providing legal support to the victim is essential. Elder abuse continues to be heavily overlooked, losing sight of the fact that its consequences extend beyond immediate physical harm, affecting the general physical and mental health of the victims. A possible solution to this problem is envisioned, with the purpose of raising awareness of this situation and contributing to a change in the perspective from which society looks at the elderly. Full article

A retrospective study on 135 cases of teat and udder surgical conditions in 129 small ruminants is described. On 19 repairs of teat lacerations, a primary- and a secondary-intention healing in 13 (68%) and in 4 (21%) cases, respectively, was observed; 2 (11%) had poor response and consequent mastitis. Good outcome and first-intention healing in 100% of the fistula repairs (2 cases), thelectomies (5 cases), teat neoplasm removals (14), and mastectomies (2 cases) were observed. Among 26 teat curettage cases, all 18 (69%) unilateral lesions treatment had a good outcome versus the 8 (31%) with bilateral lesion that suffered definitive relapse. In 67 skin udder neoplasm removals, a primary- and a secondary-intention healing in 59 (88%) and in 8 (12%) cases, respectively, was observed; however, 2 of the latter suffered mastitis. These procedures are associated with a good prognosis, and the percentage of favorable outcomes was high. Wound infections and dehiscence were the main complications observed. More interest in teat and udder surgery on small ruminants should be encouraged, and farmers should be made aware that the animal can often return into production at a reasonable cost; however, their post-operative care is the key to success. Full article

With growing community awareness of greenhouse gas emissions and their environmental consequences, distributed rooftop photovoltaic (PV) systems have emerged as a sustainable energy alternative in residential settings. However, the high penetration of these systems without effective operational strategies poses significant challenges for local distribution grids. Specifically, the estimation of surplus energy production from these systems, closely linked to complex outdoor weather conditions and seasonal fluctuations, often lacks an accurate forecasting approach to effectively capture the temporal dynamics of system output during peak periods. In response, this study proposes a recurrent neural network (RNN)- based forecasting framework to predict rooftop PV surplus in the context of micro-residential communities over time horizons not exceeding 48 h. The framework includes standard RNN, long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. In this context, the study employed estimated surplus energy datasets from six single-family detached houses, along with weather-related variables and seasonal patterns, to evaluate the framework’s effectiveness. Results demonstrated the significant effectiveness of all framework models in forecasting surplus energy across seasonal scenarios, with low MAPE values of up to 3.02% and 3.59% over 24-h and 48-h horizons, respectively. Simultaneously, BiLSTM models consistently demonstrated a higher capacity to capture surplus energy fluctuations during peak periods than their counterparts. Overall, the developed data-driven framework demonstrates potential to enable short-term smart energy scheduling in micro-residential communities, supporting electric vehicle charging from single-family detached houses through efficient rooftop PV systems. It also provides decision-making insights for evaluating renewable energy contributions in the residential sector. Full article

Aging involves progressive changes in sensory perception, appetite regulation, and metabolic flexibility, which together affect dietary intake, nutrient adequacy, and health-related outcomes. Meanwhile, current wearable technologies allow continuous, minimally invasive monitoring of physiological and behavioral markers relevant to metabolic health, such as physical activity, sleep, heart rate variability, glycemic patterns, and so forth. However, digital nutrition approaches have largely focused on physiological signals while underutilizing the sensory dimensions of eating—taste, smell, texture, and hedonic response—that strongly drive dietary intake and adherence. This narrative review synthesizes evidence on the following: (1) age-related sensory changes and their nutritional consequences, (2) metabolic adaptation and markers of resilience in older adults, and (3) current and emerging wearable technologies applicable to nutritional personalization. Following this, we propose an integrative framework linking subjective (implicit) sensory perception and objective (explicit) wearable-derived physiological responses into adaptive feedback loops to support personalized dietary strategies for healthy aging. In this light, we discuss practical applications, technological and methodological challenges, ethical considerations, and research priorities to validate and implement sensory–physiological integrated models. Merging together sensory science and wearable monitoring has the potential to enhance adherence, preserve nutritional status, and bolster metabolic resilience in aging populations, moving nutrition from one-size-fits-all prescriptions toward dynamic, person-centered, sensory-aware interventions. Full article

Food waste undermines the four dimensions of food security, availability, accessibility, utilization, and stability, while imposing adverse economic, social, and environmental impacts on sustainable food systems. Understanding the behavioral determinants of food consumption rationalization is essential for addressing this challenge in the Kingdom of Saudi Arabia. This study examines household food waste behaviors within a knowledge-based framework that integrates three interconnected constructs: awareness of food waste consequences, behavioral knowledge of waste-reduction practices, and actual engagement in conservation strategies. Data were collected from 255 households (response rate: 66%) in Buraydah City through an electronic questionnaire administered in shopping malls. Using Baron and Kenny mediation analysis and multiple linear regression, awareness of waste consequences influences conservation practices both directly (β = 0.132, p < 0.001) and indirectly through behavioral knowledge (accounting for 68.6% of the total effect), explaining 74.9% of the variance in household conservation behaviors (R² = 0.749). The analysis reveals that awareness of waste consequences influences conservation practices both directly and indirectly through behavioral knowledge, establishing a mediation pathway. Together, these knowledge dimensions significantly explain variations in household conservation behaviors. The findings highlight the critical interplay between awareness and practical behavioral knowledge in driving sustainable food consumption practices. These insights provide empirical guidance for policymakers and agencies seeking to develop targeted interventions that integrate consequence messaging with practical behavioral training to effectively reduce household food waste and promote food security in Saudi Arabia. Full article

Background: Maternal overweight and obesity, which show a rising trend globally, are associated with adverse pregnancy outcomes and long-term health risks for both mother and child. Awareness and understanding of these risks among women of reproductive age are essential for effective prevention and early intervention. Methods: We conducted a cross-sectional survey among 958 women planning pregnancy, currently pregnant or breastfeeding to assess their knowledge and attitudes regarding overweight and obesity in the perinatal period. The questionnaire covered lifestyle behaviors, breastfeeding practices, and knowledge related to overweight and obesity in pregnancy. Results: Overall knowledge regarding the consequences of maternal overweight and obesity was low, with notable deficits in understanding the associated health risks and frequent misconceptions about dietary recommendations during pregnancy. Awareness gaps were particularly noticeable in domains related to fetal outcomes and recommended energy requirements across pregnancy. Excessive gestational weight gain was reported in over 75% of pregnancies, including among women with normal body mass index. Participation in antenatal classes, current breastfeeding and older age were significantly associated with higher knowledge; however, these factors together explained only 6.2% of variability. Still, several key aspects were not well recognized despite high educational attainment and frequent contact with maternity care services. Conclusions: Our study highlights a clear and urgent need for better, more targeted educational strategies to improve women’s understanding of metabolic health and nutrition before and during pregnancy. The low explained variance indicates that maternal knowledge is influenced by multifactorial and not easily captured determinants, emphasizing the need for comprehensive and individualized educational approaches. Enhancing maternal awareness could support better health outcomes for both mothers and their offspring. Full article

Synthetic Aperture Radar (SAR) is highly valuable for target detection due to its all-weather, day-night operational capability and certain ground penetration potential. However, traditional SAR target detection methods often directly adapt algorithms designed for optical imagery, simplistically treating SAR data as grayscale images. This approach overlooks SAR’s unique physical nature, failing to account for key factors such as backscatter variations from different polarizations, target representation changes across resolutions, and detection threshold shifts due to clutter background heterogeneity. Consequently, these limitations lead to insufficient cross-polarization adaptability, feature masking, and degraded recognition accuracy due to clutter interference. To address these challenges, this paper systematically reviews recent research advances in SAR target detection, focusing on physical constraints including polarization characteristics, scattering mechanisms, signal-domain properties, and resolution effects. Finally, it outlines promising research directions to guide future developments in physics-aware SAR target detection. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article