Search Results (969)

Background: Antibiotic overuse in hospitals is common and linked to adverse outcomes and antimicrobial resistance. Antimicrobial stewardship programs (ASP) aim to optimize prescribing and require context-specific adaptation. Objectives: To describe the experience of implementing and strengthening ASP in hospitals from six Latin American countries by using an integrated framework. Methods: The intervention included a point-prevalence survey (PPS) of antibiotic use, a baseline checklist, a continuous online education program, and individual facility meetings to share SWOT analyses and recommendations. The latter was performed based on PPS and checklist results. The checklist covers six domains (authorities’ commitment, organization, structure, and accountability; interventions; education and training; monitoring and surveillance; and internal communication). The training program spanned 12–18 months and addressed core ASP components. Results: The PPS across 67 hospitals showed an antibiotic use prevalence of 47.9%, with 63% of prescriptions deemed appropriate. The median checklist score was 61.2%. Among the categories assessed, monitoring and surveillance achieved the highest score (median 75.0; IQR 63.9–84.0), while education received the lowest (median 43.8; IQR 29.7–62.5). A total of 80 country groups and 35 individual hospital meetings were held. Conclusions: An integrated, data-driven framework combining PPS, checklists, individual hospital meetings, and sustained training provides a scalable approach to strengthening ASP in diverse Latin American hospitals, aligning with Pan American Health Organization (PAHO) guidance and global recommendations. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Road freight transport (RFT) faces growing pressure from increasing freight demand, stricter environmental requirements, and persistent driver shortages. Automation technologies (ATes)—especially semi-autonomous driving—are increasingly viewed as a practical pathway toward improving the sustainability performance of freight operations; however, their effects depend strongly on infrastructure and operational conditions. This study evaluates the sustainability potential of autonomous and semi-autonomous trucks through an integrated framework combining (i) a structured review of technical and regulatory developments, (ii) surveys of transport enterprises (TEes) and road users (RUs), (iii) SWOT/TOWS analysis, and (iv) a cost minimization logistics model that links operational feasibility to infrastructure readiness (IR). The proposed model minimizes cost per tonne-kilometre and introduces an Infrastructure Readiness Score (IRS) to represent the share of a route that can be operated in automated mode; it also accounts for fuel savings from platooning and higher maintenance and capital costs of semi-autonomous vehicles (SAVs). Results indicate that, as IRS increases, semi-autonomous operations achieve higher daily mileage and lower unit costs, with a break-even point at approximately IRS ≈ 0.125. Beyond this threshold, unit costs decline from EUR 0.0433 to EUR 0.0348 per tonne-kilometre as IRS rises toward 0.6, after which further infrastructure improvements yield diminishing mileage gains. These cost and utilization improvements imply sustainability benefits via improved energy efficiency and reduced emissions intensity per tonne-kilometre. Nevertheless, survey evidence highlights major adoption barriers, including insufficient IR, regulatory uncertainty, technological reliability concerns, and limited public trust in fully autonomous systems. Overall, the findings support semi-autonomous trucking as the most feasible near-term stage of transition, while emphasizing that infrastructure upgrades and governance mechanisms are critical for scaling sustainability gains. Full article

Against the backdrop of global energy pattern restructuring, the advancement of dual-carbon goals and large-scale development of unconventional oil and gas, complex oil and gas fields are confronted with practical challenges including harsh geological conditions and diversified development objectives. Traditional development planning methods for oil and gas fields suffer from single evaluation dimensions, strong subjectivity in decision making and insufficient dynamic adaptability, which make them unable to meet the full-process development requirements. To realize scientific, quantitative and systematic development planning of complex oil and gas fields, a development planning evaluation method suitable for complex oil and gas fields is established by integrating multidisciplinary theories. First, a multilevel evaluation model for oil and gas field development planning is constructed according to the characteristics and difficulties of development planning evaluation for complex oil and gas fields. The model consists of five core modules: external analysis, internal analysis, corporate development strategy selection, business planning and risk assessment. Secondly, a development planning evaluation method is established through a closed-loop process including special quantitative IFE/EFE analysis, IE matrix strategic positioning, SWOT alternative strategy pool and QSPM priority ranking. Then, the strategic priority ranking is dynamically adjusted by considering the impact of stepped oil prices. Finally, combined with the analytic hierarchy process (AHP), a comprehensive risk index evaluation model is established to realize quantitative assessment and traceability of risk levels. A case application in Block M demonstrates that its strategic positioning belongs to the growth type. Under low–medium–high tiered oil prices, the strategic combinations with the highest strategic priority are W+O strategy, S+O strategy and S+O, respectively. The development risk level is moderate risk. This study fills the gap in the whole-process evaluation system of complex oil and gas fields, and realizes the transformation of development planning from qualitative analysis to quantitative decision making. It provides theoretical methods and practical references for ensuring high-quality development of complex oil and gas fields and energy security. Full article

The rapid accumulation of plastic waste has become a major environmental concern, while at the same time, it is necessary to create opportunities to rethink how these materials can be reintegrated into productive use, particularly within the construction sector. This study provides a sustainability-oriented review of the reuse of plastic waste, both fossil-based plastics and bioplastics, as building materials, with a specific emphasis on structured decision-support approaches. A systematic literature review was conducted to identify and analyze peer-reviewed studies examining the incorporation of plastic waste into construction applications, including composites, panels, insulation systems, and structural or non-structural components. Particular attention is given to research applying Multi-Criteria Decision Analysis (MCDA) and SWOT analysis as tools for evaluating sustainability performance across environmental, economic, technical, and social dimensions. The findings indicate that recycled plastic and bioplastic-based construction materials can deliver significant advantages, such as diverting waste from disposal pathways, reducing reliance on virgin resources, and, in certain cases, enhancing durability. However, these materials also face important challenges, including limitations in recyclability, concerns related to fire performance, regulatory acceptance, and uncertainties in end-of-life management. MCDA-based studies underscore the critical role of criteria selection and weighting, especially regarding environmental impact reduction and cost competitiveness, in shaping final rankings and decision outcomes. SWOT analyses, in turn, offer complementary strategic insights by highlighting issues related to market readiness, regulatory frameworks, and implementation barriers. By integrating these decision-oriented evaluation approaches, this review contributes to more transparent and evidence-based material selection processes and supports policy development aimed at strengthening circular economy strategies for plastic waste reuse in the built environment. Full article

Sea surface height anomaly (SSHA) observations from Surface Water and Ocean Topography (SWOT) provide a new opportunity for identifying crest lines of internal solitary waves (ISWs). However, L3 LR Unsmoothed SSHA is often affected by residual large-scale trends, rainfall contamination, and stripe noise, which limit segmentation performance. To address this issue, we propose an automatic segmentation workflow for SWOT SSHA. The workflow first applies Gaussian low-pass filtering for scale separation to extract high-frequency SSHA, then uses Otsu adaptive thresholding to segment ISW signals, and finally removes false targets using morphological geometric constraints. Validation based on 230 SWOT images from the northern South China Sea shows that, compared with the conventional method based on subtracting reanalysis fields, the proposed method increases the contrast-to-noise ratio (CNR) of high-frequency SSHA by 1.35 on average (Std = 0.99) and improves signal gain by 13.65 dB on average (Std = 7.71 dB). The method remains robust under complex conditions, including strong typhoons, severe stripe noise, weak shelf signals, and multi-wave interference. In some cases, quasi-synchronous optical imagery further confirms the authenticity of the extracted crest lines. Full article

The global transition to renewable energy underscores the urgent need for safe, efficient, and cost-effective storage solutions for green hydrogen and green ammonia. This review critically examines their fundamental characteristics and unique behaviours, emphasising storage, handling, and integration engineering challenges. To identify the influential studies, knowledge gaps and future trends in the research area, a systematic Scopus-based bibliometric and data analysis is conducted. A novel approach is adopted to identify the better green energy storage option between green hydrogen storage and green ammonia storage, combining a risk-mitigation assessment framework, strengths, weaknesses, opportunities, threats (SWOT) analysis, and a multicriteria taxonomy based on the properties and behaviour of hydrogen and ammonia. This study also analysed and reviewed the performance of the hydrogen and ammonia storage system across technical, thermomechanical, safety/risk and economic dimensions. This review further addresses the limitations in technological bottlenecks, unresolved safety concerns and current simulation approaches to evaluate the performance of green hydrogen and green ammonia. The identified limitations open doors to future research priorities in this research area, including the techno-economic viability, standardisation, and modelling accuracy of hydrogen and ammonia storage systems. The evaluation presented by this study allows for precise identification of suitable energy storage based upon various operational and economic contexts. Full article

This study examines passengers’ perceptions of environmental sustainability practices at Al-Ahsa International Airport and investigates whether these practices are reflected in passenger satisfaction, within the broader policy context of Saudi Arabia’s Vision 2030. It contributes to the emerging literature on perceived environmental sustainability in airport service environments, particularly in regional and developing aviation contexts. The analysis draws on a structured questionnaire administered to 302 passengers, supported by relevant secondary data, and combines descriptive statistics, a SWOT analysis and an ordinal logistic regression model to explore three practical dimensions of environmental performance, namely energy and climate initiatives, waste management practices, and environmentally supportive infrastructure. The results indicate that passengers are generally satisfied with the airport’s environmental performance, with waste management and sustainability-oriented infrastructure showing a statistically significant and positive association with passengers’ satisfaction. Energy and climate practices also exhibit a statistically significant positive effect; however, their impact is comparatively weaker than that of waste management and infrastructure. The findings therefore point to the need to expand clean and renewable energy investments while also making such efforts more visible through targeted awareness activities for passengers and staff, alongside continued improvements in infrastructure that support environmentally responsible behaviour, as part of the airport’s transition towards a greener and more tourism-supportive facility. Full article

In a media ecosystem marked by misinformation and disinformation, democratic resilience requires new strategies for digital and media literacy and participation. In the proposed model, the University, through transnational Cooperation Partnerships, activates applied research laboratories that generate high-social-impact communication prototypes. The European case studies Respectnet and DigiFunCollab demonstrate that the conscious use of digital media, transforming students from passive users into conscious creators, reduces vulnerability to cognitive biases, filter bubbles, and echo chambers, thereby limiting manipulation in democratic processes and stimulating civic participation. The imitative diffusion of such practices generates virtuous circles of collective learning. The theoretical framework combines institutional isomorphism, reinterpreted as a virtuous isomorphism of best practices, with democratic resilience and the UNESCO MIL and DigComp 2.2 frameworks. The methodology adopts a mixed-methods design with a quantitative prevalence. The qualitative phase includes focus groups with national stakeholders and a national report (regulatory analysis, training needs, SWOT on social entrepreneurship) preliminary to course design. The quantitative phase involves monitoring training pathways (online course and project work) and a final questionnaire. Indicators include the number of participants, certifications, projects developed, and engagement levels. By systematically implementing this approach, the Academy fuels multi-stakeholder institutional dialogue. Knowledge transfer creates communicative culture and strengthens the democratic capacity of communities. This approach confirms the role of Visual Education as a tool to integrate the University’s three missions, thus structurally reinforcing democratic resilience. Full article

Background: Hospital appointment systems suffer from extended patient waits, manual interventions, and suboptimal resource allocation, reducing satisfaction and efficiency. Methods: This study develops IPAS using Business Process Analysis (BPA), Bizagi modeling for As-Is/To-Be workflows, SWOT analysis, TQM, and Six Sigma DMAIC. It integrates ML/NLP with BioBERT-BiLSTM triage (AUC 0.92, F1 0.87) for symptom analysis, specialist matching, and automated booking, validated via Bizagi simulations. Results: Simulations show booking time was reduced 96.3% (155 to 5.73 min) and human intervention was cut 70%, with enhanced patient satisfaction and process capability. Conclusions: IPAS demonstrates simulation-based gains in scheduling efficiency, pending real-world validation. Full article

The Surface Water and Ocean Topography (SWOT) mission carries a Ka-band interferometric radar altimeter (KaRIn), which enables high-resolution wide-swath measurements of sea surface height, providing new opportunities for deriving high-precision marine gravity fields. The discretization method used by the Scripps Institution of Oceanography (SIO) is one of the simplest methods for deriving deflections of the vertical (DOV), as it avoids parameter estimation and complex mathematical procedures. However, this method only uses adjacent observations for first-order differentiation and ignores diagonal directions, resulting in relatively low data utilization for SWOT/KaRIn data. The optimized discretization method is proposed to take advantage of the two-dimensional characteristics of KaRIn data. Multi-directional data is introduced to estimate the DOV (SWOT_DOV), and the numerical differentiation strategy is extended to higher orders. These significantly improve the solution quality. The standard deviation (STD) of the differences between SWOT_DOV and north_32.1 is 1.60 μrad, and that with east_32.1 is 2.02 μrad. Gravity anomalies are further derived using the inverse Vening-Meinesz formula. Validation using NCEI shipborne gravity data indicates an STD of 3.85 mGal. Further analyses considering seafloor topography gradient, depth, and offshore distance demonstrate that SWOT/KaRIn data have a stable capability to restore high-precision marine gravity field features. Full article

The forest within the Ben En National Park has a diverse flora, which, although protected, remains subject to degradation. The analysis and management strategies for forest degradation within this park were conducted using a phytogeographical approach supplemented by satellite imagery and a SWOT analysis. As a result, the area is characterized by nine distinct vegetation types comprising 1417 vascular plant species (from 902 genera and 196 families). These species belong to endemics from Northern, Central, and all of Vietnam, as well as 16 other phytogeographical elements. Tropical Asian and South China elements dominate the community structure in evergreen broad-leaved closed forests on both limestone and non-limestone mountains. Forest degradation is evident in changes to both floristic composition and vegetation structure. Floristic composition shows a trend of decreasing native elements while simultaneously increasing non-native or introduced elements. This “anthropogenic tropicalization” leads to a declining chain of ecological function from palaeotropical to introduced elements, resulting in biological invasion. For instance, the invasive species, Mimosa pigra, currently occupies about 442 ha in the semi-submerged zone of the Song Muc reservoir, indicating a loss of ecological function and a likely hydrological pathway for further spread. As a consequence of “anthropogenic tropicalization”, the vegetation is fragmented and gradually altered from a natural system to an anthropogenic one through a regressive succession from primary forest to bare land/invaded area. Based on the SWOT analysis, four management actions were proposed: 1—Establish a “sustainable native forest” program and “invasive species control” in the Song Muc reservoir; 2—Launch a “green livelihoods for the buffer zone” initiative; 3—Implement a “Smart forest monitoring” system; and 4—Forge an “ecotourism-conservation-community” alliance. Full article

With the rapid growth of the medicinal cannabis sector, there is a growing concern regarding its environmental impact and sustainability. In recent years, life cycle assessment (LCA) studies on medicinal cannabis cultivation and processing have been conducted since 2021. However, there is a lack of comprehensive LCA studies that include all stages of medicinal cannabis cultivation and processing. In this systematic review, various LCA studies conducted from 2021 to 2025 using the ISO 14040/44 methodology are reviewed and discussed in terms of their goal and scope, life cycle inventory (LCI), life cycle impact assessment (LCIA), and result interpretation. Various environmental impact indicators are considered in this review, such as greenhouse gas emissions, energy demand, water usage, eutrophication, acidification, and resource depletion. All of these impact indicators point to a significant environmental impact of indoor cultivation in terms of greenhouse gas emissions, which vary from 2.3 × 10³ to 5.2 × 10³ kg CO₂ eq kg⁻¹ of dried cannabis product. Nevertheless, it is important to note that this is significantly influenced by regional electricity sources. Low-carbon-based electricity sources, especially hydro-based sources, can reduce emissions to a significant level. Cultivation outdoors presents significantly lower emissions of (60–110 kg CO₂ eq kg⁻¹), but fertilizers and substrates used in cultivation contribute significantly to emissions. Also, outdoor plants use 22.7 L plant⁻¹ d⁻¹ water at peak growth, while indoor plants use 9–11 L plant⁻¹ d⁻¹ water. Improvements in the life cycle of cannabis cultivation can be achieved through renewable energy use, water and fertilizers, substrate use and reuse, and inventories for post-harvesting activities like drying and extraction. Botanical parameters including genotype, planting density, and harvesting frequency are identified as significant but under-characterized determinants of LCA outcomes. Ethical and legal barriers are shown to be structural drivers of the LCA data gap. A SWOT analysis contextualizes the opportunities and constraints of the sector. Future research should focus on cradle-to-grave LCA and incorporate socio-economic factors for sustainability in the medicinal cannabis sector. Full article

Wound-healing and skin regeneration are the focus of intensive research, driven by a rapidly expanding global market and the growing clinical demand for more effective interventions engineered to actively direct and enhance tissue regeneration. Recent advances in biomaterial engineering and 3D bioprinting have accelerated the development of highly customized, functional constructs mimicking native tissue. Together, these innovations are reshaping therapeutic strategies and expanding the translational potential of next-generation skin substitutes. This review presents an overview of the evolution of material printing technologies and the different categories of 3D bioprinting techniques and processing methods, followed by an evaluation of the properties of natural biomaterials as bioinks for skin wound-healing and their application in skin tissue engineering. Moreover, we provide a comprehensive global market analysis, with consideration of costs, benefits, and a SWOT analysis to identify the full potential of this technology for the development of novel skin wound-healing products. Recommendations and future perspectives are provided to guide researchers, clinicians, and industry partners on the current state and potential of adopting 3D bioprinting with natural biomaterials for effective wound-healing therapies. Full article

Graphene-related materials (GRMs) possess exceptional electrical, mechanical, thermal, and surface properties, offering significant potential across broad sectors and applications in electronics, energy storage, composites, and environmental technologies. Despite extensive investment in academic research and translation, large-scale industrial adoption of GRMs remains slower than projected. This review systematically analyzes the global graphene manufacturing landscape using available data from 100 commercial producers, with a focused evaluation of manufacturing technology, types and forms of produced GRMs, raw material sources, product forms, industrial quality control and characterization practices. Graphite-based production routes, particularly graphene oxide (GO) and reduced graphene oxide (rGO), dominate in the market due to their scalability and cost advantages. However, substantial inconsistencies in the quality of produced GRMs, characterization and standardization depth, analytical evidence, and technical data sheets (TDSs) remain widespread. A SWOT (strengths, weaknesses, opportunities and threats) analysis of emerging graphene in the industry highlights technological maturity and expanding market demand but reveals critical weaknesses and challenges in quality, standardization and cost–performance alignment. Overall, quality of manufactured materials, quality control transparency, and standardization rather than material manufacturing limitations emerge as the primary barriers to the widespread commercial realization of graphene. Full article