Search Results (5,746)

In Latin America, sustainable commitments towards decarbonizing hard-to-abate industrial sectors have identified hydrogen (H2) as a key enabler for the energy transition. This study develops a policy analytical framework to enhance the green H2 economy, using Argentina as the central case study. Key insights from this study include identifying often-overlooked social challenges within the H2 economy and proposing the integration of social indicators into policy design, with a particular focus on the territorial dynamics of Patagonia, labor conditions, Indigenous participation, governance, and community impacts. Drawing from Social Life Cycle Assessment (S-LCA) guideline standards and H2 justice approach, this study highlights key social hotspots that existing S-LCA tools overlook due to their lack of specific focus on regional territories and their communities. The analysis combines six social impact categories, namely, human rights, working conditions, health and safety, cultural heritage, governance, and socio-economic repercussions as recommended by the United Nations Environmental Program (UNEP), analyzed at three levels, and complemented by the H2 justice approach for Argentina’s potential green H2 production sector. These policy recommendations aim to foster a more resilient and sustainable development of the green H2 industry. Full article

Digital village construction (DVC) is an important tool for promoting rural revitalization and increasing farmers’ income. This paper selects panel data at the county level and employs the difference-in-differences (DID) method, combined with mediation effect models, heterogeneity tests, and multi-dimensional robustness tests, to systematically explore the impact of DVC on farmers’ income and its internal transmission path. According to the research, the DVC has a positive impact on farmers’ income at the 1% significance level, a conclusion that remains valid after robustness tests such as PSM-DID and substitution of the explained variable. Industrial restructuring, agricultural mechanization, and enterprise agglomeration are positively significant at the 5%, 1%, and 1% levels, respectively, indicating that these three are the core intermediary mechanisms for increasing farmers’ income, promoting farmers’ income growth by releasing structural dividends, efficiency dividends, and agglomeration dividends, respectively. The income-increasing effect of DVC exhibits significant heterogeneity, being positively significant at the 5% and 1% levels in areas with a deep digital divide and non-grain-producing areas, but not significant in areas with a shallow digital divide and major grain-producing areas. Therefore, policy recommendations are to optimize resource allocation, broaden income-increasing pathways, and implement differentiated policies. Full article

The evaluation of biodiversity is an essential tool for conservation, management of natural resources, and assessment of ecosystem functioning. Choosing an appropriate and understandable diversity metric is critical to ultimately make better decisions and apply more sustainable resource management. However, biodiversity metrics are numerous, and care must be taken when using them. So, should one consider all these metrics to obtain the right information? If not, how should one choose? This paper aims to demonstrate the importance of understanding and selecting the appropriate diversity metrics to reach accurate conclusions. We simulated theoretical plant communities for which calculations of different biodiversity metrics were carried out to understand why and how to use them. We explored Richness, Evenness and Disparity components of biodiversity using both scales of diversity partitioning (i.e., alpha and beta diversity). In doing so, a decision tree is proposed to select diversity metrics according to user objectives. We also suggest an add-in term if alpha metrics are calculated with subsamples to better reflect biodiversity. Finally, we recommend that when dealing with ecosystem functioning or conservation concerns, species-dependent metrics should be used, as they reflect Disparity. However, there is a critical need to increase knowledge and data availability on species traits or phylogeny to be able to better analyze Disparity. Full article

Many physics educators seek to improve their courses but feel constrained by traditional post-secondary structures and norms. Instructors often perceive a false tension between fostering inclusive learning environments and maintaining the rigor central to the discipline. The Effective Practices for Physics Programs (EP3) Guide synthesizes decades of research-based recommendations for improving physics education. However, it offers limited guidance on how to integrate these diverse recommendations into a coherent, course-level approach—a responsibility that falls to individual instructors, whose graduate training prepared them primarily as researchers rather than as educators. This paper begins by motivating and introducing the Practicing Professionalism Framework (PPF), a course design framework developed in alignment with EP3 recommendations that encourages development of professional skills in a way that connects students’ interests and values to the broader physics community. We present the PPF in sufficient detail to enable motivated faculty to adopt and adapt it as a research-informed tool for aligning their course design with both their professional values and instructional goals. Next we present the PPF implemented in two very different instructional contexts, demonstrating how the PPF can offer a structured pathway for making courses more inclusive while preserving disciplinary rigor. We conclude with observations across the two case studies. Full article

Microbial bio-inoculants have been proposed as management tools to enhance crop performance under variable environmental conditions; however, their effectiveness is often influenced by site-specific factors. This study evaluated the effects of bio-inoculation on seed germination and seedling vigor of pea under osmotic stress induced by polyethylene glycol (PEG 6000), and its interaction with two fertilization levels (75% and 100% of the recommended dose) under field conditions in the Amazonas region of Peru. Under laboratory conditions, germination percentage remained high across all treatments (93.3–100%) and was not affected by bio-inoculation or osmotic potential; however, osmotic stress altered germination dynamics, increasing mean germination time from 1.85–2.09 days at 0 MPa to 2.26–2.43 days at −0.8 MPa, while germination synchrony and seedling vigor decreased as stress increased. The seedling vigor index reached maximum values at −0.2 MPa (4.47–5.29) and declined at −0.8 MPa (1.50–2.00), and multivariate analyses showed that variation in germination responses was mainly associated with germination timing and vigor rather than seed viability. Under field conditions, no significant effects of fertilization level, microbial bio-inoculation, or their interaction were detected on agronomic traits or yield, although variability between locations was observed; plant height ranged from 38.5–46.3 cm in Lamud and from 100.6–108.3 cm in Molinopampa, while grain yield varied from 698–1846 kg/ha and 8771–9919 kg/ha, respectively. Overall, environmental conditions exerted a stronger influence than microbial bio-inoculation on germination dynamics and field productivity, while the findings provide practical guidance for improving pea production with bio-inoculants and optimized fertilization. Full article

Background: Digitalization and artificial intelligence-supported clinical decision support systems (AI-DSS), defined here as tools that generate patient-specific alerts, risk estimates, prioritization prompts, documentation suggestions, or related recommendation outputs intended to support rather than replace professional nursing judgment, can improve clinical decision-making, yet they may also amplify technostress and burnout, with downstream effects on missed nursing care and implementation readiness. Methods: We surveyed 239 registered nurses from a tertiary-care hospital in Timișoara, Romania (January–March 2025), including critical care (n = 60) and general wards (n = 179). Measures included a 15-item technostress scale, eHEALS, Maslach Burnout Inventory–Human Services Survey (MBI-HSS), Safety Attitudes Questionnaire (SAQ) teamwork and safety climate subscales, a 10-item missed nursing care inventory, and a six-item AI-DSS acceptance scale reflecting perceived usefulness, trust, and stated willingness to use such tools if available as an attitudinal readiness outcome rather than as routine observed use. Multivariable regression, exploratory mediation models, cluster analysis, and exploratory ROC analysis were performed. Results: Higher technostress was associated with higher emotional exhaustion (r = 0.52) and more missed care (r = 0.41), whereas eHealth literacy correlated with higher AI-DSS acceptance (r = 0.35) and lower technostress (r = −0.34). In adjusted models, technostress (per 10 points) was associated with higher missed care (β = 0.28, p < 0.001) (equivalent to 0.14 points per 5-point increase) and higher odds of low AI-DSS acceptance (OR = 1.38, p = 0.001), while eHealth literacy was associated with lower odds of low acceptance (OR = 0.71 per 5 points, p < 0.001). Burnout and the safety climate statistically accounted for approximately 35% of the technostress–missed care association. Three workflow phenotypes were identified, with the high-strain/low-literacy cluster showing the most missed care (3.5 ± 1.8) and the lowest AI acceptance (19.7 ± 5.2). An exploratory in-sample ROC model for intention to leave achieved an AUC of 0.82. Conclusions: Higher technostress clustered with worse nurse well-being, more care omissions, and lower AI-DSS acceptance, whereas eHealth literacy appeared protective. Interventions combining digital skills support, usability-focused redesign, and a stronger safety climate may reduce missed care and support safer AI implementation. Full article

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)₃, ATH), Magnesium Hydroxide (Mg(OH)₂, MDH), Calcium Aluminate Sulfate (3CaO·Al₂O₃·3CaSO₄·32H₂O, CAS), and Magnesium Sulfate Heptahydrate (MgSO₄·7H₂O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions. Full article

Background/Objectives: Compared to conventional test methods, point-of-care tests (POCTs) offer advantages for optimising care in patient groups at risk of vitamin D deficiency. However, their diagnostic accuracy in clinical settings has not previously been systematically assessed. We conducted a systematic review to assess the diagnostic accuracy of current point-of-care technology (POCT) for diagnosing vitamin D deficiency in adults and children. Methods: We searched Embase, MEDLINE and Web of Science on 3 December 2024 and also conducted forward and backward citation searching. We included studies from all patient groups and clinical settings where the index test had been conducted and processed at point of care, with a comparator of any laboratory reference standard test. We assessed risk of bias and applicability concerns for the included studies using published tools. The review was registered in advance (PROSPERO reference CRD42024618338). Results: After screening, five articles relating to four studies were included. These assessed five index POCTs against reference standard laboratory tests (liquid chromatography tandem mass spectrometry in three of the four included studies). The number of samples per comparison ranged from 6 to 20. There was variation in the level of agreement between POCT and laboratory reference standard tests. We also identified incomplete reporting of key study features, which prevented definitive assessment of several domains of the risk of bias and applicability tools. Conclusions: There is currently insufficient peer-reviewed evidence from clinical evaluations to recommend any particular POCT for vitamin D. Future studies should recruit adequate sample size and complete reporting of study design features and diagnostic accuracy measures. Full article

This study examines the gap concerning occupants’ perceived thermal comfort and objectively measured indoor conditions in a green university building in Riyadh. The purpose is to assess occupant satisfaction with thermal conditions, compare subjective responses with physical measurements, and derive design and operational implications for educational buildings in hot-arid climates. The primary aim was to assess occupant satisfaction with indoor thermal conditions and to measure key environmental parameters to provide a thorough assessment of thermal comfort. A cross-sectional approach was used, combining subjective data from the Center for the Built Environment (CBE) Occupant Indoor Environmental Quality (IEQ) survey with objective measurements of air temperature, relative humidity, mean radiant temperature, and air velocity, which were documented over five consecutive working days during the mid-winter period in Riyadh. These parameters were explored using the CBE Thermal Comfort Tool to calculate Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) indices. Statistical analyses examined the relationship between occupant-reported comfort and measured environmental conditions. Results showed that only 36% of occupants reported satisfaction with thermal comfort, while 48% expressed dissatisfaction. In contrast, objective measurements indicated stable indoor conditions within recommended comfort ranges (average temperature 23 °C, humidity 30–34%, MRT 24 °C, air velocity 0.5–1.0 m/s), with PMV values near neutral (−0.2 to 0.0) and PPD below 6%. The observed discrepancy highlights the influence of regional climate, individual adaptability, and perceived control. These findings emphasize the need to integrate both subjective feedback and objective measurements to develop occupant-centered strategies that enhance comfort and well-being in sustainable educational buildings in hot-arid climates. Full article

Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care. Full article

This study presents one of the first empirical assessments of artificial intelligence (AI) and machine learning (ML) adoption within architectural academia and the Architecture, Engineering, and Construction (AEC) industry in Saudi Arabia. Using a cross-sectional survey of 113 respondents—60 academics and 53 industry professionals—the research examines familiarity, current usage, perceived benefits, challenges, and future readiness for AI/ML integration. Results show high familiarity and strong perceived importance across both sectors, yet actual implementation remains uneven. Very large firms demonstrate the highest adoption capacity, while small and medium-sized firms face financial and organizational constraints. Academic institutions exhibit moderate familiarity but limited curricular and research integration due to faculty expertise gaps, restricted access to tools, and traditional pedagogical structures. Despite these barriers, both sectors consistently identify AI/ML as critical for enhancing creativity, efficiency, and industry preparedness. The study highlights organizational capacity as the primary determinant of adoption. It concludes with recommendations for curriculum reform, faculty training, industry–academia collaboration, and national policy frameworks to accelerate digital transformation aligned with Saudi Vision 2030. This research establishes a foundational baseline for future longitudinal and comparative studies on AI/ML integration in the regional architectural ecosystem. Full article

Smallholder poultry farming contributes enormously to rural livelihoods, food security, and nutrition in South Africa, yet the poultry industry remains constrained by limited participation and low ICT utilisation. This study investigated the socioeconomic and demographic factors influencing decisions and choices of smallholder poultry farmers towards the adoption of ICT and market engagement in Jozini Local Municipality, KwaZulu-Natal. A cross-sectional research design was used to collect primary data from respondents. Data were collected through face-to-face surveys from 162 participants, who were randomly selected. Descriptive statistics were employed to profile the use and extent of ICT, while the multivariate probit model was used to analyse the determinants of ICT adoption and market engagement. The findings revealed that most farmers own ICT tools such as mobile phones (98.15%), which they mainly use for communication purposes (98.77%) rather than for accessing production and market related information. Smallholder characteristics like age, faming experience, marital status, and household size significantly influenced farmers decisions and choices to adopt ICT and participate in markets. The study recommends improving the traditional extension through digital integration and farmer support by means of training on ICT and formal market linkages. These interventions can significantly market participation and profitability in smallholder poultry farming, stabilising rural economic development. Full article

Background/Objectives: Low back pain (LBP) is one of the most common musculoskeletal conditions globally and a leading cause of disability. University populations may be particularly vulnerable due to prolonged sitting, academic stress, and frequently suboptimal ergonomics, especially in rapidly expanding higher education systems such as those in Saudi Arabia. This systematic review and meta-analysis aimed to synthesize evidence on the prevalence of LBP among university attendants in Saudi Arabia and to quantify its associations with key demographic and environmental risk factors. Methods: We systematically reviewed observational studies reporting LBP prevalence and/or risk factors among university students and faculty in Saudi Arabia published in English, following Cochrane methodological guidance and PRISMA 2020 reporting recommendations. The protocol was prospectively registered in PROSPERO (CRD420250654048). We searched PubMed, Embase and CINAHL from inception to February 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Joanna Briggs Institute checklist for analytical cross-sectional studies. Random effects meta-analyses were used to pool prevalence estimates across recall periods, regions, populations, and measurement tools, and to calculate pooled odds ratios (ORs) for age, sex, smoking, family history of LBP, and college seating conditions. Heterogeneity, subgroup, and sensitivity analyses were undertaken. Results: Thirteen cross-sectional studies were included. The overall pooled prevalence of LBP was 57% (95% confidence interval [CI] approximately 43–71), with substantial heterogeneity. Prevalence varied by recall period, region, population group, and measurement instrument; pooled prevalence was 58% among students and 50% among faculty. Increasing age (OR 1.17, 95% CI 1.01–1.34) and poor college seating conditions (OR 1.42, 95% CI 1.07–1.76) were significantly associated with LBP. Male gender, smoking, and family history showed non-significant pooled effects. These estimates are limited by substantial between-study heterogeneity, variable measurement tools, and exclusively cross-sectional designs, which restrict causal inference. Conclusions: LBP is prevalent among university attendants in Saudi Arabia, affecting both students and faculty. The consistent associations with age and seating ergonomics highlight the need for ergonomic classroom redesign and age-sensitive preventive strategies. Future work should adopt standardized LBP measures and longitudinal designs to clarify causal pathways and evaluate targeted interventions. Funding: This work was supported by the Deanship of Graduate Studies and Scientific Research at Jouf University (grant DGSSR-2026-NF-01-002). Full article

Fractured tight sandstone reservoirs are promising targets for underground energy storage, but their heterogeneous nature and often-incomplete historical test data pose significant challenges for accurate deliverability prediction and reservoir evaluation. To address this, a novel hybrid methodology is proposed. For wells with complete historical data, deliverability is calculated using a binomial inflow performance relationship (IPR) model. For wells with incomplete data, a weighted fusion model integrating a Random Forest algorithm and least squares regression is developed to predict natural blowout capacity, a key proxy for energy storage injectivity/productivity. The fusion model achieved superior performance with a mean absolute error (MAE) of 7.19 × 10⁴ m³/day and a Mean Relative Error (MRE) of 8.5%, outperforming standalone methods. Based on the predicted deliverability, reservoirs in the Bozi–North block (Kuche Depression, Tarim Basin) were classified into three potential grades (I, II, III). The study provides a data-adaptive framework for deliverability prediction and offers tailored reformation process recommendations (e.g., sand fracturing for Grade I reservoirs), thereby providing a more reliable and practical decision support tool for the efficient development of tight sandstone energy storage reservoirs. Full article