Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims at dissecting the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic methods can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. Ethical considerations related to patient privacy, biases, and lack of reliability and generalizability are the main issues. Furthermore, there is a need for high-quality real-word validation as well as the development of standardized protocols and policy rules to guide the implementation of these technologies in diverse clinical settings.

This study presents a fit-for-purpose evaluation of commercially available portable sensor systems, measuring PM, NO2 and/or BC, through extensive lab- and field benchmarking. The main aim of the study is to identify sensor systems that can be used for mobile monitoring by citizens to assess dynamic exposure in ambient air. After an initial literature study and market search resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked in laboratory and real-word conditions. We evaluated comparability to reference analyzers, sensor precision and sensitivity to temperature, humidity and O3. Moreover, we evaluated if accuracy can be improved by the lab and field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM and BC, but maturity of the tested NO2 sensors is still low and additional efforts are needed in terms of signal noise and calibration. The horizontal accuracy of the build-in GPS was generally good achieving a <10m horizontal accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and exposome impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

Seawater contamination is a global challenge due to its hazardous effects on marine organisms and human health. Twenty-three surface seawater samples were collected from Al Lith intertidal area along the Saudi Red Sea coast to evaluate the ecological risks and document the potential sources of Cr, Cd, Fe, Zn, and Pb. Contamination factor (CF), contamination degree (Cd), water quality index (WQI), and heavy metal pollution index (HPI), as well as multivariate tools were applied. The averages of HMs (μg/L) had the following order: Zn (6.616) > Pb (0.284) > Cd and Cr (0.268) > Fe (0.197). Results of CF showed moderate contamination of seawater with Cd and low contamination for Cr, Fe, Zn, and Pb. However, 26.09% of the samples showed considerable contamination for Cd. Average Cd values revealed low contamination for HMs, while 17.39% of the samples showed moderate contamination. HPI average values indicated medium pollution for Al Lith seawater, meanwhile 13 samples reported high pollution. The higher HPI values were reported in samples characterized by higher concentration of HMs, particularly Cd and Zn. Correlation matrix and principal component analysis suggested anthropogenic sources for Pb and Zn, mostly from industrial and agricultural effluents, landfilling and domestic wastewaters, apart from their natural sources.

The Galápagos Islands are one of the most emblematic protected areas on the planet, and also one of the most studied. Their main economic activity is tourism, which has grown rapidly in recent years. The increase in tourists is associated with the increase in population and the introduction of invasive species, which puts conservation at risk. This makes adequate management even more necessary and relevant on an international scale, since the economy of Galápagos depends on the good state of conservation of its ecosystems and species. Numerous studies have shown that social factors, rather than physical-natural ones, determine the success or failure of a marine protected area (MPA), so they must be urgently incorporated into all phases of MPA management. In this study we have analyzed the management of the protected areas of Galápagos: the National Park and the Marine Reserve. The methodology used presents five prescribed scenarios in which priority is given to one or other factors, each of which has an impact on biophysical and socio-economic systems that are influenced by the MPA. These scenarios have been called: proactive, interactive, centralized, learning, and formal management. The results show that the archipelago's protected areas follow a proactive management model, with a continuous feedback loop. This feedback is a key element in any adaptive management process, which also allows practitioners to anticipate future problems. Both public participation and strategic planning are essential.

The Roma population constitutes the largest ethnic minority in Europe, however, data regarding the prevalence of non- communicable diseases are still scarce in the medical literature. The aim of this study was to assess the clinical and metabolic particularities of a Roma population with diabetes, in comparison with a group of Romanian Caucasians. We conducted an observational, transversal study and evaluated 808 adult patients with diabetes mellitus, from a tertiary hospital providing diabetes care, aged between 18 and 89 years old, of whom the majority were men (52.84%). Most of them had type 2 diabetes mellitus (T2DM), reaching 95.1% the Caucasian group and 87.8% among the Roma patients. 81.1% of the Caucasian patients had hypertension, approximately 15% higher than the Roma group, while more than half of the patients in both groups had obesity. There were no differences concerning myocardial infarction, however, the number of patients with history of stroke was 2.1 times higher in the Roma group compared to Caucasians. Chronic kidney disease was present in 22.1% of the Roma patients, 33.25% had retinopathy, and 72.7% presented polyneuropathy. The prevalence of cardiovascular risk factors, cardiovascular disease and microvascular complications among the study Roma population are quite significant, underscoring the importance of ethnic disparities in approaching healthcare management strategies.

Due to various industrial developments and artificial inputs, there are emerging environmental contaminats(ECs) of serious concern in a wide range of environmental media. With recent advancements in scientific knowledge and industrial technology, the definition of emerging environmental contaminats has expanded. Among them, emerging contaminants are typically present in trace amounts and pose serious ecological risks and potential hazards to human health, animals and plants, with cumulative effects on organisms. However, Traditional treatment techniques have proven ineffective in eliminating and preventing subsequent environmental problems from new pollutants such as microplastics, antibiotics and fluorocarbons. Consequently, extensive research has been conducted to address these issues. Effective removal methods prioritize not only removal efficiency, but also environmental friendliness and regenerative capacity. The purpose of this paper is to summarize the sources, forms of presence, environmental and human health hazards, and removal technologies of ECs, and to provide various examples of different sources.

The authors present a framework for assessing seasonal events which may influence maritime operations. Most maritime-related research tends to focus on single events, such as a storm, but maritime systems operate within complex systems that have some predictable patterns. These predictable patterns due to natural events, such as weather and water levels, can influence operations, while other factors, such as cargo peaks or cultural activities, could also shape maritime systems. By integrating data from emergency management databases and weather information sources with other inputs, the authors created a matrix of regionally specific predictable events that may occur within a region by time of year. The matrix was vetted with various stakeholder groups to verify the information in the matrix. The main findings were that a seasonal event matrix is helpful as a reference for examining operational patterns in a river for various uses, such as training, operational planning, and emergency response coordination.

Background: Visual acuity (VA) assessments are crucial in ophthalmology but traditionally rely on in-clinic evaluations. The emergence of telemedicine has spurred interest in creating dependable self-administered VA tests for use beyond standard clinical environments. This study evaluates the practicality and validity of a self-administered Near VA card test against traditional Snellen and Rosenbaum Pocket Vision Screener (RPVS) methods for home monitoring and enhancing clinical workflow. Methods: In a cross-sectional study a near VA card (Hadassah Self-Visual Acuity Screener-HSVA) was developed with written and videotaped instructions for self-use. Patients with a minimal best corrected VA (BCVA) of 1.0 LogMAR in at least one eye were recruited from Ophthalmology and Optometry clinics. Outcomes included mean BCVA difference between the self-administered and those obtained by the examiner, and correlations among BCVA values obtained by Snellen, RPVS, HSVA and previous distance BCVA according to the patient electronic medical records. Results: A total of 275 participants (mean age: 42.5±19.4 years, range: 18-89 years, 47% female) were included. Test-retest reliability analysis for the HSVA demonstrated very good correlation and repeatability (n=38 patients.; Rs=1.0, P

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modelling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the "Kaplan-Meier Estimator," a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to repurpose this methodology to assess drought risk by treating past droughts as "events" and evaluating the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) and map these results for a better understanding of the drought risks within larger spatial scales such as a river basin. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope get steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps which expose more vulnerable areas within the river basin.

The rise in both the frequency of natural disasters and the ubiquity of artificial intelligence has led to novel applications of new technologies in improving disaster response processes, such as the labor-intensive assessment of disaster damages. Assessment of residential and commercial structure damages, a precursory step to government agencies being able to provide most of their financial assistance, has benefited from aerial and satellite imagery-based computer vision models; however, limitations of using such imagery include the ground structures being obscured by clouds or smoke, as well as the lack of resolution to distinguish individual structures from others. Using a different data source, we propose a damage severity classification model using ground-level imagery, focusing on residential structures damaged by wildfires. This classifier, a Vision Transformer (ViT) model trained on over 18,000 professionally labeled images of damaged homes from the 2020-2022 California wildfires, has achieved an accuracy score of over 95%. Further, we have open sourced the training dataset–the first of its kind and scale–as well as built a publicly available web application prototype, which we demoed to and received feedback from disaster response officials, both of which further contribute to the broader literature beyond the proposed model.

In modern automobiles, Infotainment High-Performance Computing (HPC) systems play a vital role in enhancing the capabilities of drivers and passengers by providing advanced features consisting of music, navigation, communication, entertainment, etc. However, as the use of information technology in vehicles increases, it results in cybersecurity threats such as data breaches and the loss of sensitive information. To improve the security of the infotainment system in automotive vehicles, the research conducted threat modeling at the component level using Microsoft’s STRIDE tool and performed risk assessment by using SAHARA (Safety-Aware Hazard Analysis and Risk Assessment) and DREAD methodologies to evaluate associated risks. It provides a systematic representation of threats, associated risks, and generic mitigation strategies to counter cybersecurity attacks. Through the threat modeling process, 34 potential security threats were identified. The study also provided a comparative analysis to calculate the risk values of the threats to prioritize for treatment. These identified threats and associated risks need to be considered to avoid potential cyberattacks, before deploying the infotainment HPC system in real-world automotive vehicles.

Although agricultural crops production systems in subtropical plateau regions currently pose high environmental risks, especially in developing countries, their environmental impacts and emission-reduction potentials remain unclear, and the development potential of such systems is likely huge. We conducted a case study in Yunnan Province, China, to quantify the environmental impacts from crop production during 2002–2021. The life cycle assessment method was employed to identify factors driving the environmental impacts, and potential mitigation strategies were proposed. The yield and total nutrient input of grain crops in Yunnan Province increased over the 20-year period, and environmental footprint of crops production in Yunnan Province was higher than that of other regions. Average annual mean greenhouse gas (GHG) emissions and soil acidification potentials (AP) and water eutrophication potentials (EP) of crops production during 2002–2021 were 732 kg CO2-eq·Mg-1, 15.8 kg SO2-eq·Mg-1, and 2.7 kg PO4-eq Mg-1, respectively. GHG emissions from g crops mainly originated from applying agricultural materials during the crop life cycle. There was a significant correlation between surplus nitrogen and environmental impacts. Scenario testing showed that through crop sowing, nutrient regulation, and crop management, the crop yield could be increased and environmental costs could be reduced. The greatest reductions were found in wheat production: 53.1%, 67.9%, and 67.7% for GHG emissions, AP, and EP, respectively. Adopting comprehensive agricultural management measures could reduce the negative impact of crops production on the environment in subtropical plateau areas and achieve sustainable agricultural development.

Certain services can help avoid environmental impact in larger contexts. However, there is no commonly agreed bottom-up methodology for calculation of the total net reduction effect of specific digital services. Life Cycle Assessment (LCA) is a common denominator for most methodologies. The most common method is the Attributional LCA (ALCA), and recently the emerging handprint ALCA estimating so-called positive environmental impacts. Moreover, Consequential LCA (CLCA) can be used to capture market effects. The third conceptual approach is Input-Output LCA. The purpose is to propose and test a new method based on some of the existing ones. The existing concepts are compared and a synthesis is made to create a practical but still useful method. The new method is applied to two illustrative cases in the Information and Communication Technology (ICT) domain; the introduction of a 5G enabled drone for pipe inspection and the 5G enabled health consultation. Compared to simplified ALCA, the difference between the absolute score for the baseline system and the target system changes up to 15% when the proposed simplified CLCA (SCLCA) method is used. The results show that SCLCA, when combined with analytical methods for expressing digital ICT services’ own impact, is a fruitful approach which is both practical and feasible.

The rapid expansion of livestock production sector to meet the world population demand is posing a big challenge to environmental sustainability. Plant-based feed additives extracted from food by-products could potentially result in a double outcome: improving animal zootechnical performances and mitigating environmental impacts of meat production chains. This presented study was carried out to assess the environmental impacts of the supplementation of a commercial Citrus extract feed additive (CEFA) in swine and broiler farming. Life cycle assessment (LCA) was applied to assess the impact of manufacturing and distributing 1 bag of 25 kg of CEFA and its use in feed in broiler and swine productions. With regard to CEFA manufacturing and distribution, results showed that the majority of impact came from the production of CEFA ingredients, accounting for 70% impact generated. The remaining 30% effect was divided to transportation to customer (25%), CEFA packaging (3%), CEFA manufacture, and production loss (2%). When enlarging the scope, the use of CEFA in piglet and broiler diets showed improving the measured environmental indicators, compared to such standard systems. Indeed, feed supplemented CEFA have demonstrated in elevating zootechnical performances, hence saving feed consumed. Consequently, that helped reduce environmental issues from animal feed ingredients’ agriculture. To be more specific, the supplementation of 25 kg of CEFA in feed led to a reduction of 6 tons of CO2 emitted along the life cycle of broiler production and 5 tons in the case of fattening pig. 0.7 hectares of land used had been saved as well as, 201 m3 of water (broiler) and 82 m3 (swine) were economized. Environmental performance assessment showed the interest in using this CEFA in swine and broiler diets to mitigate the environmental impacts.

Introduction With the global population increasingly relying on the internet for information, YouTube has emerged as a significant platform for communication and information sharing, particularly in the field of medicine. However, concerns persist regarding the reliability and quality of medical information on YouTube, given the lack of regulation and guidelines. This study aims to systematically evaluate the reliability of health-related information on YouTube. Methods: Data collection was conducted in September 2023 on YouTube, with stringent selection criteria for videos. Videos were evaluated using the "Internet Health Information Certification Standards" by the Korean Medical Association and the Global Quality Scale. A plastic surgeon assessed information quality based on specific criteria. The study analyzed the influence of keywords on information accessibility and compared the evaluation tools. Results: 76 Korean search results were analyzed using keywords. Certified South Korean medical providers appeared more in "ptosis surgery" results (50%) than "droopy eyelid diagnosis" (5%). Medical professionals produced 86.7% of videos, with some non-medical content. "Ptosis surgery" had the highest KMA scale score (16.3), "droopy eyelid diagnosis" the lowest (13.8). Out of 76 videos, 39 by plastic surgeons, 20 by ophthalmologists, and 5 by dermatologists. A GQS comparison showed specialists with higher scores (3.12) than non-specialists (1.53). Conclusion: YouTube and similar platforms offer valuable access to medical information, but quality and reliability remain significant concerns. Collaborative efforts from government agencies, medical organizations, and users are essential to enhance the quality of online health information. Users should also cultivate critical thinking skills to discern trustworthy information.

Pasture-based production systems are predominant in major beef-producing countries, however, these systems lack validated protocols to assess animal welfare under commercial conditions. The objective of this study was to validate an animal welfare protocol for fattening Zebu cattle farms in tropical pasture systems. The initial protocol was developed with the participation of producers, professionals, the general public, and the Colombian health authority, through workshops with a participatory approach and collaborative knowledge management. The validation was carried out in 24 pasture-based commercial Zebu cattle farms in the middle Magdalena region of Colombia. Visits were made with an average duration of 2.5 hours, which included the evaluation of 788 fattening cattle. The protocol evaluated animal-based, resource-based, and livestock management indicators through a questionnaire-guided interview to evaluate cattle handling and health, animal-based measurements, and documentation management. A protocol validation process was carried out by selecting indicators that remained unchanged, adjusting those that were feasible to implement, and eliminating inadequate or unnecessary indicators. The application of the protocol demonstrated that there are valid and feasible measures to include in the evaluation protocols of pasture-based fattening systems. Likewise, the active participation of producers is crucial to achieving a greater commitment to the implementation of this protocol.

The contractual relationship between the processing firm and the broiler smallholder presents incessant conflicts of interest and inequality due to technical and economic discrepancies, leading to an undervaluation of the producers' remuneration. This study aims to deepen the discussions in searching for a more balanced monetary exchange between processing firms and broiler smallholders based on scientific aspects. For this, the Emergy theory and its concepts were used in considering a representative broiler production system at Concórdia, Santa Catarina, Brazil. This study suggests the inclusion of cultural information in the Emergy-based model calculation. For the broiler smallholder, cultural information showed the highest Emergy contribution (62.95%; transformity = 1.73 ×10^8). However, only considering cultural information was not sufficient to increase the sustainability of the broiler production system. The results highlight an imbalance of the monetary exchange between agroindustry and broiler smallholder payment for both the economic and Emergy-based payment. Both estimated payments were higher than the practiced payment value (0.24 USD/broiler as well as 0.32 USD/broiler and 1.62 EmUSD/broiler, respectively). Thus, evaluating the “(eco)cost” in Emergy-based accounting recognizes that production depends not only on tangible physical resources but also on knowledge, skills and information (“iceberg of value” thinking). Policy and decision makers must, therefore consider the promotion of public policies that subside initiatives, including social and environmental welfare programmes.

Radon, a carcinogenic radioactive gas, is a leading cause of lung cancer according to the World Health Organization. European Member States are required to develop and implement National Radon Action Plans (RAPs) to address its dangerous health effects. However, assessing the effectiveness of these RAPs presents challenges for authorities. This study aims to explore the possibility for a systematic and standardised assessment method to evaluate the effectiveness of RAPs strategies and its implementation. The method involved analysing the strategies of 27 EU Member States and the UK, conducting legal document analysis and group interviews with responsible authorities. Additionally, four regional workshops and one final European workshop were held. The research took place from March 2021 to May 2023. Findings indicate that evaluating RAP effectiveness is challenging due to limited existing common criteria or indicators. To address this, the study proposes guiding questions for each element required by the EU Directive, as well as additional questions related to education and training. This contribution benefits RAP owners and European regulatory authorities, supporting the development of effectiveness indicators for RAPs. BY improving assessment methods, we can enhance the effectiveness of strategies in mitigating the risks associated with radon exposure.

The campuses of Nigerian universities especially the old ones, like the University of Ibadan, comprise old trees which can pose a threat to pedestrians, structures, and roads within the school environments. However, there is no adequate information on these avenue trees thereby impeding their sustainable management. Therefore, this study provides the basic information needed by developing a geodatabase for the avenue trees along Benue, Emotan and Oduduwa roads within the University of Ibadan, Ibadan, Nigeria. The coordinates of all the avenue trees, and some within the selected roads were obtained. Tree growth data collected include; diameter at the breast height (dbh), diameter at the base, middle and at the top, crown height, crown diameter, and total height of all the avenue trees. Tree visual evaluation for diseases (TVED) was carried out for root decay, and termite attack on any part of the tree (stem, leaves and visible part of the root). Risk rate was assessed as low, medium and high, depending on the potential target (human, vehicles, structures). The risk rate depended on the incidence of leaning towards a building or road, cracked branches, root attack by termites, basal decay or branch decay. The growth data were used to compute the tree volume while the avenue trees were georeferenced. The physical health status of the avenue trees was determined using TVED data. The spatial distribution of the avenue trees was developed in GIS while all the analysed data were pulled together to develop a geodatabase for the avenue tress using design templates of BOOTSTRAP, comprising of Hypertext Markup Language, Cascade Style Sheets, JavaScripts and Hypertext Preprocessor. A total of 121 individual avenue trees belonging to 14 species were identified in the study area. The analysis of the geospatial distribution shows that Emotan Road had a lesser spatial distribution of avenue trees compared to Benue and Oduduwa roads. Furthermore, the health risk assessment of the trees in the study area indicated that 17.35% of the individual avenue trees had defects. The database created is user-friendly, suitable and has the ability for easy data storage and quick information retrieval on the avenue trees to enhance their maintenance and risk management. This can be accessed using the link http://159.203.29.155/rebs/rebs.php.

Since the global request for freight transportation is continuously increasing to face the requirements of the modern economy, 5G infrastructures and companion technologies are becoming essential to support the digital transformation in logistics and transportation, in particular for their capability to allow data collection through a multitude of different sources across the supply chain, improving the connectivity and making more effective the secure and reliable data sharing. The objective of this work is to assess the penetration and implementation levels of innovative technologies to optimize port operations, by interviewing a set of actors and stakeholders directly involved in the logistics chain of ports.

The Attentional Networks Test for Interactions and Vigilance–executive and arousal components (ANTI-Vea) is a computerized task of 32 min duration in the standard format. The task simultaneously assesses the main effects and interactions of the three attentional networks (i.e., phasic alertness, orienting, and executive control) and two dissociated components of vigilance with reasonable reliability (executive and arousal vigilance). We present this free and publicly accessible resource (ANTI-Vea-UGR; https://anti-vea.ugr.es/) developed to easily run, collect, and analyze data with the ANTI-Vea (or its subtasks measuring some attentional and/or vigilance components embedded in the ANTI-Vea). Available in six different languages, the platform allows for adaptation of stimuli timing and procedure to facilitate data collection from different populations (e.g., clinical patients, children). Collected data can be freely downloaded and easily analyzed with the provided scripts and tools, including a Shiny app. We discuss previous evidence supporting that attention and vigilance components can be assessed in typical lab conditions as well as online and outside the laboratory. We hope this tutorial will help researchers interested in measuring attention and vigilance with a tool useful to collect data from large sample sizes and easy to use in applied contexts.

Mild traumatic brain injury (mTBI) is the most common brain injury, seen in sports, falls, vehicle, or workplace injuries. Concussion is the most common type of mTBI. Assessment of impairments from concussion are evolving, with oculomotor testing suggested as a key component in a multi-modality diagnostic protocol. The aim of this study was to evaluate the reliability of one eye-tracking system, the EyeGuide Focus. Seventy-five healthy adolescent and adult participants (adolescents: n=28; female=11, male=17, mean age 16.5 ± 1.4 years; adults n=47; female=22; male=25, mean age 26.7 ± 7.0 years) completed three repetitions of the EyeGuide Focus within the one session. Intraclass correlation coefficient (ICC) analysis showed the EyeGuide Focus had overall good reliability (ICC 0.79, 95%CI: 0.70, 0.86). However, a familiarization effect showing improvements in subsequent trials was noticeable in both cohorts (p<0.001) with adolescent participants showing greater familiarisation effects than adults. No differences were observed between sexes (p=0.69). Overall, the EyeGuide Focus was found to be reliable, but the observation of familiarization effects suggests that a standardized protocol of a minimum of three trials be employed, similar to other eye-movement testing applications.

Community co-creation is increasingly crucial to the building of sustainable societies. Communities must proactively and independently create value chains in the face of many wicked problems. However, practical frameworks and tools for facilitating community co-creation require further development. As such, for this paper, we propose a theoretical framework for analyzing community co-creation value chain mechanisms based on a narrative review of literature on Collective Impact, social impact assessment, and Community Capital from the perspective of collective impact, a framework with a fair amount of previous research. We argue for the importance of applying knowledge of social impact assessment, particularly for the collective impact conditions of common agenda and shared measurement system. We discuss its relationship with the Theory of Change and the use of systems thinking to mitigate the subjectivity and arbitrariness of design thinking. Furthermore, we emphasize the value of employing the community capital concept as a framework for analyzing interrelationships among elements. By integrating multiple concepts, we enhance the collective impact framework and outline potential avenues for future research on analyzing and effectively implementing co-creation value chain mechanism in a community-based approach.

CKD in HF patients is very common condition, their dysfunction is closely linked and influence each other, so their management required multidisciplinary and personalized approaches. The diagnosis of HF and CDK relies on signs and symptoms. Several tools, such as blood-based biomarkers and echography help us to clarify and discriminate the main characteristics of these patients. Evidence in improving survival due to new drug-employment in HF, has increasingly challenged physicians to manage patients with multiple diseases, especially in patients with CKD. The difficulty is in the safe administration of these drugs in patients with HF and CKD. Knowing up to which values ​​of creatinine or renal clearance any drug can be administered is fundamental. We wanted to summarize, on this sizable and complex topic, the experiences of various prior study to get clearer ideas and a more precise reference about the assessment and management of HF and CKD.

Background: Falls risk assessment tools are used in hospital inpatient settings to identify pa-tients at increased risk of falls (which may be related to muscle loss/sarcopenia) to guide and target interventions for falls prevention. In 2022, Western Health, Melbourne, Australia, intro-duced a new falls risk assessment tool, the Western Health St. Thomas’ Risk Assessment Tool (WH-STRATIFY), a modified version of The Northern Hospital’s risk tool (TNH-STRATIFY), which replaced the Peninsula Health Risk Screening Tool (PH-FRAT). Aims: To determine the predictive accuracy of three falls risk assessment tools (PH-FRAT, TNH-STRATIFY and WH-STRATIFY) on admission to Geriatric Evaluation Management (GEM) units. Method: A retrospective observational study was conducted on four GEM units. Data was col-lected on 54 consecutive patients who fell during admission and 62 randomly sampled patients who did not fall between December 2020 and June 2021. Participants were scored against three falls risk assessment tools. The event rate Youden (Youden IndexER) indices were calculated and compared using default and optimal cut points to determine which tool was most accurate for predicting falls. Results: Using default cut points to compare falls assessment tools, TNH-STRATIFY had the highest predictive accuracy (Youden IndexER = 0.20, 95% confidence interval CI = 0.07, 0.34). The PH-FRAT (Youden IndexER = 0.01 and 95% CI = -0.04, 0.05) and WH-STRATIFY (Youden IndexER = 0.00 and 95% CI = -0.04, 0.03) were statistically equivalent and not predictive of falls compared to TNH-STRATIFY. When calculated optimal cut points were applied, predictive accuracy im-proved for PH-FRAT (Cut point 17, Youden IndexER = 0.14 and 95% CI = 0.01, 0.29) and WH-STRATIFY (Cut point 7, Youden IndexER = 0.18 and 95% CI = 0.00, 0.35). Overall, all tools had low predictive accuracy for falls. Conclusion: TNH-STRATIFY had the highest predictive accuracy for falls. The predictive accu-racy of WH-STRATIFY improved and was significant when the calculated optimal cut point was applied. The optimal cut points of falls risk assessment tools should be determined and validated in different clinical settings to optimise local predictive accuracy, enabling targeted falls risk mitigation strategies and resource allocation.

Tungiasis has been included in the WHO list of Neglected Tropical Diseases, but systematic data on the occurrence are scarce. We analyzed data on the occurrence of tungiasis in the 184 munici-palities of Ceará State in Northeast Brazil, based on an online open rapid assessment question-naire focused on health professionals and other key stakeholders. Questions included the occur-rence of tungiasis, severe cases, cases in animals and seasonal variations of the disease. There were in total 1,265 individual data entries available (1 to 104 per municipality) from all munici-palities of the state. A total of 181 (98.3%) of municipalities reported the occurrence of tungiasis in the past or currently, 120 (65.2%) reported current occurrence, 155 (84.2%) severe cases in the past or currently; and 47 (25.5%) reported severe cases currently. A total of 132 (71.7%) municipalities reported tungiasis in animals, most commonly in dogs (97), pigs (80), cats (50), horses (20), cattle (8) and goats (6). Most municipalities mentioned the seasonality of tungiasis (n=146; 79.3%). There was no report of any specific tungiasis control program. Our data show that tungiasis and severe tungiasis commonly occur in Ceará state and that tungiasis is a significant public health issue for both humans and animals. An integrated One Health approach is needed to reduce dis-ease burden, including human and animal health, and the environment.

In this paper, we have investigated the impact of an early learning assessment on students' motivation for improving their performance throughout the semester. An observation analysis was conducted on an entry level mechanical engineering course in which students are enrolled in during their first semester of engineering work. This study analyzes the effect that a first exam, with an average below a passing grade, has on student's outcome in the course. It was hypothesized that students were motivated to achieve their desired grade outcomes following inadequate performance on the first exam. This was investigated by diving into the results of the course and referencing initial performance to the remaining exam and assessment outcomes. Students were placed into grade bands ranging from 0 to 100 in 20% increments. Their results were tracked and it was shown that for the second mechanics exam, averages jumped 43.333%, 35.35%, and 30.055% for grade bands of 0 to 20, 20 to 40, and 40 to 60 respectively. Assessment grades increased as well with the remaining assessments being averaged to a score of 91.095%. Variables contributing to student performance came from both with-in and outside the classroom. Learning communities, material differentiation, and student and professor adaptation all contributed to the rise in performance. It was concluded that the internal and external variables acted in combination with one another to increase student dedication to achieve success.

Despite laws and directives for the regulation and restriction of pesticide in farming, the large use of Plant Protection Products (PPPs) in paddy fields is a relevant worldwide cause of contamination of the environment. The aim of this work is to evaluate the environmental impact due to the use of PPPs by using an integrated approach based on chemical analyses and ecotoxicological hazard assessment, supported by statistical tools, in order to overcome the issues related to the traditional tabular evaluation. Samples of soil and water of seven conventional and organic paddies located in Northern Italy have been examined for two years by extensive chemical and ecotoxicological analyses. The results evidenced a direct relationship between the presence of Oxadiazon in water and bioassays responses as the main cause of the toxicity measured. This phenomenon affected both biological and conventional rice fields, due to the free circulation of water through irrigation canals. Therefore, the implementation of organic districts with a water circulation isolated from conventional fields represents a simple and effective countermeasure to safeguard the agricultural practices of organic crops.

The COVID-19 pandemic has highlighted the vitalness of telehealth in our medical world, where considering a restructuring of healthcare services has become paramount. In fact, telemedicine has recently earned a valuable place in many specialties; and its implications in cardiology and cardiovascular medicine were among the leading interests. In this letter, we gathered previous evidence supporting the merit of telemedicine in the fields of cardiology and cardiovascular medicine—medical branches in which patients require meticulous care and continuous monitoring—as well as protrusions of concerns about the uncertainty regarding the efficacy of telemedicine’s future implications and technologies. In sum, in the context of this still on-going pandemic, medical institutions must strive to improve telehealth technologies and implement solid future research directions in this growing field—to be able to persevere in meeting the needs of the patients. As long as no conclusive evidence exists regarding the fields where telemedicine is most worthwhile, healthcare systems will always keep the dread of wasting resources on developing ineffective programs. We conclude that telemedicine has been attributed a considerable attention in managing cardiac and cardiovascular conditions; nevertheless, further studies with solid designs are still needed to confirm its validity and utility in those specialties.

The present paper deals with the investigation, at conceptual level, of the performance of short-medium-range aircraft with hydrogen propulsion. The attention is focused on the relationship between figures of merit related to transport capability, such as passenger capacity and flight range, and the parameters which drive the design of liquid hydrogen tanks and their integration with a given aircraft geometry. The reference aircraft chosen for such purpose is a box-wing short-medium-range airplane, object of study within a previous European research project called PARSIFAL, capable to cut the fuel consumption per passenger-kilometre up to 22%. By adopting a retrofitting approach, non-integral pressure vessels are sized to fit into the fuselage of the reference aircraft, under the assumption that the main aerodynamic, flight mechanic and structural characteristics are not affected. A parametric model is introduced to generate a wide variety of fuselage-tank cross-section layouts, from a single tank with the maximum diameter compatible with a catwalk corridor to multiple tanks located in the cargo deck , and an assessment work-flow is implemented to perform the structural sizing of the tanks and analyse their thermodynamic behaviour during the mission. This latter is simulated with a time-marching approach that couples the fuel request from engines with the thermodynamics of the hydrogen in the tanks, which is constantly subject to evaporation and, depending on the internal pressure, vent-ed-out in gas form. Each model is presented in detail in the paper and results are provided through sensitivity analyses to both the technology parameters of the tanks and the geometric parameters influencing their integration. The guidelines resulting from the analyses indicate that light materials, such as the Aluminium alloy AA2219 for tanks’ structure and polystyrene foam for the insulation, should be selected. Preferred values are also indicted for the aspect ratios of the vessel components, i.e. central tube and endcaps, as well as suggestions for the integration layout to be adopted depending on the desired trade-off between passenger capacity, as for the case of multiple tanks in the cargo deck, and achievable flight ranges, as for the single tank in the section.

The present study was conducted to estimate the exposure and risk characterization of the children population of Serbia to Aflatoxin M1 (AFM1) from milk and milk-based food. A total of 3404 samples comprising of milk and different milk-based food samples were collected from various regions of Serbia from 2017 to 2019. Evaluation of the AFM1 exposure was carried out using the deterministic method, whereas risk characterization was evaluated with the calculation of the Margin of Exposure (MOE) and the risk of Hepatocellular Carcinoma (HCC). Detection rates for AFM1 in milk and milk-based food samples ranged between 2% and 79%, with the highest incidence (79%) and mean level (22.34  0,018 ng kg-1) of AFM1 was detected in pasteurized and UHT milk. According to the three consumption estimates, the values of EDI were higher for toddlers as compared with children. Children aged 1–3 years had the highest risk of exposure to AFM1 in milk, with an estimated daily intake of 0.164 and 0.193 ng kg-1 bw day-1 using a lower bound (LB) and the upper bound (UB) exposure scenarios, respectively. Such difference could result from the higher consumption versus weight. Based on the EDI found in this study, the risk of AFM1 due to consumption of milk and milk-based food was low since MOE values obtained were 10000. In addition, the risk of HCC cases/year/105 individuals of different age groups showed that the value of HCC using potency estimates of 0.0017 (mean) was maximum (0.00034) in the age group 1–3 years which indicates no health risk for the evaluated groups. The present study revealed the importance of controlling and preventing AFM1 contamination in milk through continuous monitoring and regular inspection to reduce the risk of AFM1 exposure, especially in children.

The global trend towards increased demand for organic food, greener environments, and the integration of biological control agents into pest management strategies has greatly enhanced the need for biological pesticides (biopesticides). Biopesticides are generally environmentally friendly and are made from micro-organisms or other natural substances. Despite their great potential, relatively few have been registered and commercialised in Nigeria compared to other African countries such as South Africa and Kenya. Biological active agents are so diverse such that ap-plying the same safety standards or environmental conditions to all of them is almost impossible. A review of risk assessment processes and comparative assessments of Nigeria's biopesticide regulations with other developing African countries and developed regions was conducted. Prolonged field testing, lack of bridged risk assessments and technical checklists have been identified as key factors hampering the timely development and commercialisation of biopesti-cides in Nigeria. Recommendations on necessary changes to the existing Nigeria biopesticide regulations have been made. Risk assessment matrices for microbial and biochemical biopesti-cides and a scientific/technical checklist have also been developed. Harmonisation and data ex-change among other countries in the region will also enhance the advancement of scientific and technical knowledge for sustainable regulation and cross-border trade.

Tinnitus is a phantom sound perception in the ears or head and can arise from many different medical disorders. Currently, there is no standard treatment for tinnitus that reliably reduces tinnitus. Individual patients reported that acupressure at various points around the ear can help to reduce tinnitus, which was investigated here. With this longitudinal observational study, we report a systematic evaluation of auricular acupressure on 39 tinnitus sufferers, combined with a self-help smartphone app. The participants were asked to report about tinnitus, stress, mood, neck and jaw muscle tensions twice a day using an ecological momentary assessment study design for six weeks. On average, 123.6 questionnaires per person were provided and used for statistical analysis. The treatment responses of the participants were heterogeneous. On average, we ob-served significant negative trends for tinnitus loudness (Cohen’s d effect size: -.861), tinnitus dis-tress (d = -.478), stress (d = -.675), and tensions in the neck muscles (d = -.356). Comparison with a matched control group revealed significant improvements for tinnitus loudness (p = .027) and self-reported stress level (p = .003). The positive results of the observational study motivate fur-ther research including a randomized clinical trial and long-term assessment of the clinical im-provement.

Female sexual functioning and satisfaction are impaired by the presence of sexual difficulties. Current study aims to analyze the differences on sexual satisfaction in women according to types of sexual problems (absence of sexual problems, self-perceived and assessed by the FSFI cut-off). A total of 329 women answered to a specific question about presence of self-perceived sexual problems, the Sexual Satisfaction Scale for Women, and the Female Sexual Functioning Index. Main findings revealed that sexually health women had better levels of sexual satisfaction when compared to women who self-report sexual difficulties and women who scored clinical levels in FSFI. Also, women who scored clinical levels in FSFI scored lower on sexual satisfaction compared to women who self-report sexual difficulties. Female sexual satisfaction was negatively affected by sexual difficulties assessed according to the FSFI cut-off. Although with lower impact, self-perceived sexual problems also affect negatively sexual satisfaction in women.

Body-Esteem Scale is an assessment tool for adolescents and adults that evaluate three dimensions of self-evaluations of one’s body. Body-Esteem Scale has been translated and validated in some countries since America down to Europe. Lack of translation and reliability evidence in Portugal was detected. This study aimed to translate and test the validity and reliability of the Body Esteem Scale for Adolescents and Adults (BESAA) in students in the context of Portuguese higher educa-tion. A total of 173 students (60.7% are female) with a mean age of 19.7 (standard deviation = 2.2) years participated. Categorical Principal Component Analysis was used to assess the underlying dimensions of BESAA. Construct validity was evaluated through correlation with the Appearance Schemas Inventory – Revised and a three-factor model (“Appearance”, ‘‘Weight’’ and “Attribu-tion’’) was established. Confirmatory factor analysis was performed to verify the construct validity of the instrument. Items that had factor weights (λ)<.40 were removed, as well as those that were considered redundant by the modification indices estimated by the Lagrange Multipliers (LM) method (LM>11, p<.001). We observed high correlations between theoretically similar factors, and low correlations between different factors. The Portuguese BESAA showed adequate validity and reliability.

BACKGROUND: To present a new method of urethral pressures examination, and to evaluate diagnostic capabilities of three dimensional profilometry , as an alternative to classical urethral profile (UPP) .Using four channel catheters and dedicated software global urethral pressures image is obtained. The method eliminates the main limitation of classical urethral profilometry where the catheter orientation determines the pressures picture limited to only one point in the uretral circumference while we observed even 50% differences in pressure measures depending on the point of measurement. METHODS: This is a preliminary study containing method presentation and analisys of the use in varied clinical cases of either healthy patients and patients with LUTS. The article includes technique and equipment decription, full evaluation of selected cases, including three dimensional urethral pressures distribution graphics. RESULTS AND CONCLUSIONS:Three dimensional profilometry compared to classical technique is comparable regarding the time, cost, technical difficulty and patient discomfort. At the same time we obtain much more data on the urethral pressures and its distribution.The results are easy for interpretation due to 3D movable graphics created automatically through the dedicated software.

In public health literature the risk of death or disease associated with a dietary, environmental of behavioral factor is most commonly denoted by odds ratio (OR), hazard ratio (HR) or risk ratio (RR). The ratio indices have several desirable statistical properties. However, the most important question is whether there are some evolved innate norms of perception of risk that people use and what they are. We conducted a simple one question survey of 98 individuals with different age, sex, educational and professional backgrounds. The respondents were asked to judge the relative perceived risk of four different hypothetical habits for which data on the percentage of people affected by the disease with and without the habit was given. They were asked to rank the risks for the four habits. Results showed that the habits that had the highest difference between probability of acquiring the disease were ranked high on risk perception. The probability ratios did not affect risk perception significantly. Further age, sex, profession or formal training in statistics did not affect the response significantly. Even individuals that were formally trained to use OR and HR as risk indicators, preferred using probability differences over ratios for judging their own risk in the perceived context. This preliminary inquiry into intuitive statistical perception suggests that designing statistical indices based on people’s innate perception may be a better strategy than trying to train people to understand the indices designed by expert statisticians.

Even though the rules for free movement of machinery within the European Union market have existed for more than 30 years, accidents related to their activities have constantly been achieving significant value. When designing the machine, a designer must stem from risk assessment, whereas all stages of its life cycle and ways of its use must be taken into consideration. In industrial operations, there is old machinery, which, although fulfilling its function reliably, the safety level is not in accordance with the developing requirements for their safe operation. The proposed methodology of assessment of the machinery safety condition comes out from the presupposition of the right application of steps of risk assessment and their reduction mainly by means of implementation of both effective and efficient preventive measures. The aim of the research applied in 3 operations, was to verify the method of machinery safety management. The created methodology based on 19 requirements for safety evaluates the level of the actual measures by means of the so-called criterion of current status and total efficiency of measures. Its output is the assessment of the efficiency level of implemented safety of each machine as well as of the whole operation.

Currently there is no multi-hazard risk assessment tool for determining the level of complexity to swiftwater and flood rescue incidents. Traditionally, the International Scale of River Difficulty is used but it is primarily for paddlers for use in a recreational context, without much consideration to the multitude of hazards faced in swiftwater and flood rescue environments. In response to this gap, the ECHO risk assessment tool has been developed and undergone initial testing. This tool provides for simple and rapid codification of multiple hazards and response considerations and is globally applicable. The tool also assigns a final risk assessment colour making the interpretation of the assessment easy to understand and communicate. Though the proposed tool shows potential, further research is needed before it should be operationalised.

Agricultural intensification is considered essential for meeting growing demand for food and biomass for energy and material purposes. Intensifying grasslands is a so-far under-represented although promising option, given their large land area and relatively low management levels. This study quantifies the bioenergy potential from intensifying temporary grasslands in Europe and the integral greenhouse gas emission effects in 2030. We first conduct a literature review of intensification options for European grasslands and then apply the environmental impact assessment model MITERRA-Europe to implement the key intensification option of using multi-species grass mixtures. The results show that 853 kha (or 8%) of temporary grassland could be made sustainably available for additional biomass production. This can be translated into a bioethanol potential of 23 PJ/yr and an emission mitigation potential of 5.8 Mt CO2-eq/yr (if conventional grass mixture from surplus temporary grassland is used for energy) or 72 PJ/yr and 4.0 Mt CO2-eq/yr (if surplus temporary grassland is used for grassy energy crops). Although the bioenergy potential is limited, the intensification measure’s key advantage is that it results in a better environmental performance of temporary grasslands. This makes it a key option for sustainably producing bioenergy in areas with high shares of temporary grasslands.

The Matāura River is the sixth largest river system in New Zealand and has long been subject to agricultural, industrial, and residential land use activities. The catchment has geographic and economic value and is of great cultural importance for local Māori, who have concerns over potential adverse impacts that anthropogenic stressors exert on the health of the river. There is a dearth of information on the impacts of these stressors towards the health of native species such as the longfin eel Anguilla dieffenbachii. This study assessed the environmental status of the Matāura River using biological and chemical methodologies incorporating A. dieffenbachii as a bioindicator species for exposure to combined anthroprogenic stressors. A range of biomarker endpoints were measured in caged and wild-caught eels (when available) to characterize site-specific responses to combined anthropogenic stressors. While there was no clear indication of cumulative impacts moving from pristine headwaters to the lower reaches of the Matāura River biomarkers of xenobiotic metabolization were induced in A. dieffenbachia and there was evidence of chemical contamination in sediments and tissues.

Substantial studies have focused on early detection of Alzheimer's disease (AD). Cerebral amyloid beta (Aβ), is hallmark of AD, can be observed in vivo via positron emission tomography imaging using amyloid tracer or cerebrospinal fluid assessment, but costly expensive. The current study aims to identify and compared predictability in magnetic resonance imaging (MRI) markers and neuropsychological markers to predict cerebral Aβ status in AD cohort using machine learning (ML) approaches. The predictability in candidate markers for cerebral Aβ status was examined by analyzing 724 participants from the ADNI-2 cohort. Demographic variables, structural MRI markers, and neuropsychological test scores were used as input in several ML algorithms to predict cerebral Aβ positivity. Out of five combination of candidate markers, neuropsychological markers with demographics showed the most cost-efficient result. A feature selection model could distinguish abnormal levels of Aβ with the predictability of 0.85, indicating the same performance with MRI-based models. The result has first to identified the predictability in MRI markers using ML approaches, and secondary to demonstrate the neuropsychological model with demographics could predict Aβ positivity, suggesting a more cost-efficient method for detecting cerebral Aβ status compared to MRI markers.

Investments in digital infrastructure in marginalised communities are set to increase in the next decade. These are premised on the potential of digital technologies to contribute towards solving societal problems, including the fragility of food value chains in rural areas. Although there are mixed empirical findings on the impact of these digital infrastructure investments, huge investments are continuing amid changing ICT policies in most developing countries. This paper, using a case study of a local livestock value chain in a rural community in Zimbabwe, argues for the application of non-conventional approaches towards digital infrastructure transformation impact assessment. Using selected theories and frameworks (socio-ecological systems framework, choice framework and technology affordances theory) as well as empirical data from a project in a rural community, the paper shows that real-time impact assessment using context-specific metrics may reveal hidden digital infrastructure transformation impacts, positive and negative, that are often overlooked when traditional impact assessment approaches are employed. The findings of this study contribute towards improving approaches towards ICT impact assessment. Practitioners engaging in impact assessment are challenged to move beyond dependence on traditional metrics (e.g. access) to the adoption of participatory processes to decipher context-appropriate metrics.

The importance of the contribution from tourism to climate change was pointed out by the International Tourism Organization (UNWTO). By combining process-based Life Cycle Assessment (LCA) and Input-output analysis, several researches have tried to evaluate the impacts of the tourism industry as well as its products and services. Indeed, the tourism sector has a wide range of industries including travel and tour, transportation, accommodation, food and beverage, amusement, souvenirs etc. However, the existing cases did not show a breakdown of the impact on climate change. In this paper, the carbon footprint (CFP) of the Japanese tourism industry was calculated based on tourist consumption, using the Japanese Input-output table and the Japanese tourism industry. It was shown that the total emissions were approximately 136 million t-CO2 per year. The contribution ratio of each stage is as follows: Transport 56.3%, Souvenirs 23.2%, Petrol (direct emissions) 16.9%, Accommodation 9.8%, Food and Beverage 7.5%, Activities 3.0%. Then, in the breakdown, the impact is high in the following order Air transport 24.7%, Petrol (direct emissions) 16.9%,Accommodation 9.8%, Food and Beverage 7.5%, Petrol 6.1%, Textile products 5.3%, Food items 4.9%, Confectionery 4.8%, Rail transport 3.9%, Cosmetics 1.9%, Footwear 1.8%, etc. In addition to transportation, this research also highlighted especially the contribution from souvenirs, accommodation, food and beverages.

The complex systems that require safety are the Safety Critical Systems. Maintaining these systems is a big challenge. Now a days, safety is a very critical requirement for the latest systems. Safety critical systems must be safe. Different approaches to ensure quality and safety in safety critical systems has been discussed in this paper. A comparison is also conducted between these various approaches. Safety critical systems must remain more influential in future.

The study of the distribution and health risk assessment is meaningful to provide basic data for environmental management．To investigate the pollution of potential toxic organics and their health risk to human beings, water samples were collected at 7 sites of main surface water of Beijing during wet and dry seasons respectively. The targeted 92 organics were detected, including phthalates (PAEs), volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs) and phenols. The results showed that: there were 56 organics detected out, and the number ratios of detected compounds to the total compounds of the same kind increased as the following: VOCs, phthalates, phenols, PAHs. 8 VOCs were detected in wet season, and 3 in dry season. The concentration of 2,2-Dichloropropane was highest as 10.62ug/L, while the concentrations of other VOCs were below 5ug/L; There were 11 phthalates detected during dry season. The content of Bis(2-methoxyethyl) phthalate was highest as 188.47ng/L; 17 phenols were found in samples during wet season, and the highest concentration was 1244.73ng/L for 4-nitrophenol; PAHs could be detected in all samples, and the detected compounds and the corresponding average concentrations were higher in wet seasons than those in dry seasons, which indicated that non-point pollution was possibly the main pollution source. The health risk assessment of the detected 56 pollutants by using a model from US EPA showed that, the risk caused by the four kinds of toxic organics in this study was in the acceptable ranges.

Background: Malnutrition is a frequent condition in the elderly and is associated with prolonged hospitalization and increased mortality. However, the impact of malnutrition among elderly patients with acute myocardial infarction has not been clarified yet. Methods and Results: We have enrolled 174 patients aged 65 years and over, admitted with the diagnosis of acute myocardial infarction (AMI) who underwent to the evaluation of nutritional status by Mini Nutritional Assessment (MNA) and of mortality risk by Grace score 2.0. All-cause mortality was the outcome considered for this study. Over a mean follow-up of 24.5 ± 18.2 months, 43 deaths have been registered (24.3%). Non-survivors were more likely to be older, with worse GFR, lower SBP, lower albumin and MNA score, higher prevalence of Killip classification III-IV grade, and higher Troponin I levels. Multivariate Cox proportional analysis revealed that Grace score and MNA showed a significant and independent impact on mortality, (HR = 1.76, 95% CI = 1.34–2.32 and HR = 0.56, 95% CI = 0.42–0.73, respectively). Moreover, the clinical decision curve revealed a higher clinical net benefit when the MNA was included compared to the partial models without MNA. Conclusions: Nutritional status is an independent predictor of long-term mortality among elderly patients with AMI. MNA score in elderly patients with AMI may help prognostic stratification and identification of patients with/at risk of malnutrition in order to apply interventions to improve nutritional status and maybe survival in this population.

The three heat-induced coffee contaminants acrylamide, furfuryl alcohol (FA) and 5-hydroxymethylfurfural (HMF) were analyzed in a collective of commercial samples as well as in Coffea arabica seeds roasted under controlled conditions from very light Scandinavian style to very dark Neapolitan style profiles. Regarding acrylamide, average contents in commercial samples were lower than in a previous study in 2002 (196 compared to 303 µg/kg). The roasting experiment confirmed the inverse relationship between roasting degree and acrylamide content, i.e. the lighter the coffee the higher the acrylamide content. However, FA and HMF were inversely related to acrylamide and found in higher contents in darker roasts. Therefore, mitigation measures must consider all contaminants and not be focused isolatedly on acrylamide, specifically since FA and HMF are contained in much higher contents with lower margins of exposure compared to acrylamide.

Videogames have become educational, communicative and social tools among the youngest, favouring the acquisition of skills, abilities and values, encompassing an endless number of themes, and help to experience and to face, in the first person, a great diversity of environmental situations and ecology problems. Thus, the present article aims: a) the evaluation of a sample of 20 educational videogames about water, making use of some empirical criteria of quality; and b) the design, validation and application of an integrate quality indicator of educational videogames on water, based on the aspects of narrative, gameplay and didactics to that sample, which allows us to obtain a ranking. The findings reflect a ranking of games allows to suggest that the nature of the game (simulation, adventures, platforms or questions) does not determine the quality of the game, although generally simulations and adventure games are placed in a range of medium or high quality, as well as those games that pursue objectives related to the design and management of a territory in a sustainable way. The paper provides teachers with quality criteria based on narrative and gameplay that complement and enriches the pedagogical dimension.

Breakwaters influence coastal wave climate and circulation by blocking and dissipating wave energy. Accurate representation of these effects is essential to the determination of coastal circulation and wave processes. MIKE21SW and SWAN are two third-generation spectral wave models which are used widely in coastal research and engineering applications. Recent improved versions of the models are able to consider the influence of breakwater structures. In this study, we used available observations to evaluate the accuracy of model simulations of waves in New Haven Harbor, Connecticut, USA, an estuary with three detached breakwaters near the mouth. We then compare the accuracy and computational efficiency of MIKE21SW and SWAN. Both models were executed on the same unstructured triangular grid. The boundary conditions were derived from a bottom mounted ADCP on the offshore side of the breakwaters. Wind forcing was applied using data from the Central Long Island Sound buoy. We find that both models are largely consistent with observations during storms. The MIKE21SW significant wave height and wave direction simulations were slightly superior; however, SWAN is more efficient and faster due to its implementation of a fully implicit technique for time integration.

Optoelectronic devices are gold standard for 3D evaluation in clinics but due to the complexity of such kind of hardware and the lack of access for patients affordable, transportable and easy to use systems must be developed to be largely used in daily clinics. The Kinect^TM sensor presents various advantages compared to optoelectronic devices: price, transportability but also some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points that make 3D evaluation impossible. To overcome these limitations a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises allowing functional evaluation while performing physical rehabilitation. To validate this new approach a double step method was used. The first step is a laboratory validation where the results obtained with the Kinect^TM have been compared with results obtained with an optoelectronic device, 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of such kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns can be observed. The new methodology and the different steps of the validations are presented in this paper.

The shoulder region has the highest incidence of acute injuries in the sport of surfing. Little is known about the strength profile at the shoulder in a surfing cohort. The primary aim of this study was to establish the reliability of a rotator cuff strength testing procedure for surfers with a secondary aim of providing a profile of internal (IR) and external rotation (ER) strength in a competitive surfing cohort. Shoulder IR and ER isometric strength was measured using a hand-held dynamometer in 13 competitive surfers. Intra-class coefficient values ranged from 0.97 to 0.98 for intra rater reliability and were lower for inter rater reliability ranging from 0.80 to 0.91. Normalised force (N/Kg) for IR strength was significantly greater than ER strength bilaterally (dominant, p= 0.007, non-dominant, p<0.001). No significant differences (p< 0.79) were found in IR strength (N/Kg) between the dominant and non-dominant arms. ER strength (N/Kg) was significantly weaker on the non-dominant arm compared with the dominant arm (p<0.02). The non-dominant arm ER to IR ratio (0.82 ± 0.15) was significantly (p=0.025) lower than the dominant (0.88 ± 0.14) The current procedure is reliable with the same clinician, results indicate musculature asymmetry specific to the external rotators.

The PILAR model provides a dynamical systems perspective on collaboration. Two studies are performed using peer assessment data, both testing empirical support for the five Pillars (prospects, involved, liked, agency, respect) that constitute member’s perceptions of collaboration viability. The first study analyses peer-assessment data collected online from 458 first-year engineering students (404 males; 54 females). A nine-item instrument was inherited from past year’s usage in the course, expanded with four additional items to elaborate upon the agency and liked Pillars. Exploratory factor analysis was conducted on student responses to test whether they thematically aligned to constructs consistent with the five Pillars. As anticipated, twelve of the thirteen items grouped into five components, each aligned with a Pillar, providing empirical evidence that the five Pillars represent perceptions of collaboration. The second study replicated the first study using a retrospective analysis of 87 items included in the Comprehensive Assessment of Team Member Effectiveness (CATME) peer-assessment tool. The associated factor analyses resulted in five components and conceptual alignment of these components with Pillars was evident for three of five CATME components. We recommend a peer-assessment instrument based upon PILAR as potentially more parsimonious and reliable than an extensive list of behaviours, such as employed by CATME. We also recommend including items that target inter-rater bias, which is aligned with the liked Pillar, that instruments such as CATME exclude.

SLEUTH Model (slope, landuse, exclusion, urban extent, transportation and hillshade) is an important tool for landuse planning and land policy. To evaluate the performance of SLEUTH model, implementation of Sensitivity Analysis (SA) is essential. The main limitation of SA in SLEUTH application is a lack of insight into model input self-modification parameters (SMPs) variation, namely, uncertainty involved in the model transfer metrics and model presumptions, which often misled the decision makers and model users. To address this issue, this study divided the forward process into two stages. Firstly, during the transfer process ①, the contribution scores of five SMPs were drawn, and parameters highly sensitive to model output were given. Apart from that, the recommended initial value for SMPs of 0.11, 0.2, 0.87, 1.13, 15, 1.01, 0.49 were found to be subordinated to such a heterogeneous urban area simulation. Secondly, during the transfer process ②, SMP caused imagery metrics indicated the disparity between parameters with Fixed Reference and with Successive Reference. Reversely, it derives reasonable threshold for the best fit values of five prediction coefficients’ initialization by comparing the real image with the predicted one. The framework of SLEUTH model transfer mechanism not only could distinguish highly sensitive SMPs with higher contribution scores, but also could give parametric analysis for simulation imagery based on metrics. The study was found to be a practical tool for quantization response of model input variables for modelling complex urban systems. So, this insight can help geographic information scientists decide how to find out the inner forward transfer mechanism of SLEUTH model for further make good use of it and improve the model.

The hazard and the carcinogenic risks due to the exposure to some potentially toxic elements by the Santiago Island (Cape Verde) population where calculated, considering soil ingestion, inhalation and dermal contact as exposure pathways. The topsoil of Santiago Island is enriched in Co, Cr, Cu, Ni, V, Zn, Mn and Cd to upper crust values. Hazard indices (HI) were calculated for these metals and As exposures, of Santiago Island population and the calculations were performed for children and adults. For children HI are higher than 1 for Co, Cr and Mn. So there is indication of potential non-carcinogenic risk for children, due to the high Co (HI=2.995), Cr (HI=1.329) and Mn (HI=1.126), values in soils. For the other elements and for adults there is no potential non-carcinogenic risk. Cancer risk was calculated for As, Cd, Cr and Ni exposures, for adults and children and the results are always lower than the carcinogenic target risk of 1x10-6, for As, Cd, and Ni. However, cancer risk are higher than the carcinogenic target risk for Cr, for adults. Regarding As, for children the fraction due to Riskingestion represents 51.6%, while Riskinhalation represents 48.0% and Riskdermalcontact represents only 0.4% of total risk. For adults Riskinhalation represents 81.3%, Riskingestion represents 16.6% and Riskdermal contact represents 2.1%. These results reflect the higher daily ingestion dose for children and the higher inhalation rate and higher dermal contact surface for adults. For the other elements and for adults the cancer risk due to Cr, Ni and Cd inhalation is always higher than for children, reflecting the higher inhalation rate for adults.

The article focuses on mining of non-energy mineral resources with minimum environmental impacts. It issues from research results of a project Competence Centre for Effective and Ecological Mining of Mineral Resources implemented at the Faculty of Mining and Geology at VŠB-Technical University of Ostrava, Czech Geological Survey, a company Watrad ltd., a state enterprise Diamo, a company RPS Ostrava plc and a company Sedlecký kaolin plc. The paper starts with a partial analysis of the existing legal norms related to mining and processing of mineral resources. Next, it analyses mineral resource mining options free of negative environmental impacts. The fundamental tool to assess potential environmental impacts of mining is the implementation of Environmental Impact Assessment (EIA) process for a given mineral resource. In the Czech Republic environmental impact assessment is carried out by course of Act 100/2001 Coll. Its important amendment is Act 39/2015 Coll. claiming, inter alia, that the environmental impact assessment is rigidly connected with other permits and procedures, such as the zoning process and building construction permits. The article describes the environmental impacts of mining of non-energy mineral resources, including the following factors: appropriation of land, impacts on surface water, ground water and soil, noise, influence on the landscape character, and air pollution. The paper also includes a case study summarizing information on the environmental factors that may play a role in potential underground mining of graphite in the deposit Český Krumlov - Městský Vrch and the deposit Lazec - Křenov.

Post-industrial recycling (PIR) and post-consumer recycling (PCR) are measures to sustain re-sources by improving materials’ circularity and sustainability. Currently, circularity is mostly measured as the degree of reutilization of a material from 0 to 100 % at product or company lev-el. This lacks in assessing the resources’ usage over multiple product life cycles. Therefore, we propose to assess circularity as (i) the frequency resources are used in products (effective circu-larity eC) and (ii) a vehicle to reduce virgin resource use and environmental impacts (environ-mentally efficient circularity eeC). Besides, to compare the environmental impacts of using recy-cled materials from PIR or PCR, we analyse their impact reduction potential (IRP) indicating the environmental benefits of recycling in relation to virgin material put onto the market. We demonstrate the suggested indicators for a case study material: polypropylene. For this polymer type, the eC ranges between 0.93 and 9.08 uses of the resource on average depending on collec-tion, sorting, and recycling rates. Likewise, the eeC ranges between 0.31 and 1.50 uses per kg of CO2 equivalents emitted. PCR has a higher IRP regarding climate change impacts than PIR in all analysed scenarios. The results reveal the relevance of PCR and PIR beyond the product life cycle.

Epoxy asphalt (EA) concrete is widely used in constructing long-span steel bridge pavements (SBDPs). This study aims to derive a fatigue damage evolution law, conducting an experimental investigation of SBDP. First, a general theoretical form of the fatigue damage evolution law of materials is established based on the thermal motion of atoms. Then, fatigue experiments demonstrate that this evolution law well represents the known damage–life relationships of SBDP. Taking into account the experimental relationships between damage and fatigue life under symmetrical cyclic loadings with different overload amplitudes and temperature variations, a detailed damage evolution law is deduced. Finally, the role of damage accumulation is discussed on the basis of the proposed damage evolution law for the extreme situation of heavy overload and severe environments. The results show that both heavy loading and falling temperatures increase the fatigue damage of SBDP considerably; therefore, SBDP should avoid heavy loading combined with winter temperatures. EA shows a fatigue life two to three times longer than that of modified matrix asphalt (SMA) or guss asphalt (GA). For the same thickness, EA pavement is demonstrated to be more suitable for an anti-fatigue design of large-span SBDP under high traffic flows and low temperatures.

Background: Food properties can directly influence individual dietary intake. Therefore, computer vision-based food recognition could be used to estimate meal contents for patients with metabolic diseases. Food recognition based on deep learning can create opportunities for breakthroughs in dietary interventions for personal health management and have rapidly emerged as dietary assessment strategies. Methods: This study proposed a methodology for automatic food recognition based on fine-grained visual classification, the High-temperaturE Refinement and Background Suppression network (HERBS). A technical investigation was conducted involved in the HERBS model on CNFOOD241 benchmark to verify the model effectiveness. The visualization analysis of HERBS was compared with the VGG16 and RepViT on CNFOOD241. Results: The system achieved classification accuracy of 82.72 % and 97.19 % in Top-1 accuracy and Top-5 accuracy respectively. Data showed that HERBS structure widened the attention area and outperformed VGG16 and RepViT on feature maps. Conclusions: Our findings elucidated that the proposed methodology using HERBS model establishes a new benchmark for SOTA performance in food recognition on CNFOOD 241 dataset. This study proved the feasibility of the proposed approach in a challenging food dataset. We further described a vision-language model structure consisting of a multi-modal vision model, language encoder and a multilayer perceptron classifier for food composition recognition.

This paper proposes a novel hybrid model that integrates Failure Mode and Effects Analysis (FMEA), Rank Order Centroid (ROC) and COmbined COmpromise SOlution (COCOSO) to improve risk assessment and prioritization of failure modes. A case study in the healthcare sector will be conducted to validate the effectiveness of the proposed model. ROC is used to assign weights to the FMEA criteria (severity, occurrence and detectability). COCOSO is then applied to create a robust ranking of failure modes by considering multiple criteria simultaneously. The results of the case study show that the hybrid FMEA-ROC-COCOSO model improves the accuracy and objectivity of risk prioritization. It effectively identifies critical failure modes, outperforming traditional FMEA. The hybrid approach not only improves risk management decision making, leading to better mitigation strategies and higher system reliability, but also reduces the complexity typically found in FMEA hybrid models. This model provides a more comprehensive risk assessment tool suitable for application in different industries.

The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as a way to improve IQA model robustness. This study introduces an enhanced adversarial training approach tailored to IQA models; it adjusts the perceptual quality scores of adversarial images during training to enhance the correlation between an IQA model’s quality and the subjective quality scores. We also propose a new method for comparing IQA model robustness by measuring the Integral Robustness Score; this method evaluates the IQA model resistance to a set of adversarial perturbations with different magnitudes. We used our adversarial training approach to increase the robustness of five IQA models. Additionally, we tested the robustness of adversarially trained IQA models to 16 adversarial attacks and conducted an empirical probabilistic estimation of this feature. The code is available at https://github.com/wianluna/metrics_at.

Text-to-image tasks have gained significant progress, where vivid images are generated from detailed text descriptions. However, existing studies overlook scenarios where the provided text is sparse, neglect the investigation of human preferences, and fail to acknowledge the diversity of aesthetic opinions. To address those issues, we develop a methodology called personalized short-text-to-image generation through aesthetic assessment and human insights. We develop a Personality Encoder (PE) to extract personal information and establish the Big-Five personality traits-based Image Aesthetic Assessment model (BFIAA) for predicting specific human aesthetic preferences. Utilizing the BFIAA model, we fine-tune the Stable Diffusion model to align it more closely with human preferences. Our experiments demonstrate that our BFIAA model can truly reflect human aesthetic preference and the adapted generation model can generate personalized images more preferred by humans.

There is a pressing need for tools that can rapidly predict PM2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA’s BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM2.5-related health risks and estimate economic benefits.

Objective: African Americans (AA) carry the largest burden for almost every type of cancer in the US and are also more likely to die from cancer. Approximately 10% of cancers can be explained by a hereditary factor and detected earlier. Many AA however have inequitable access to hereditary cancer risk assessment (HCRA) tools and information, further exacerbating cancer disparities. Innovative communication strategies to promote community-based HCRA information have promise to bridge AA to optimal primary cancer screening. The current pilot study followed a participatory process where researchers engaged with a Community Advisory Board (CAB) to explore how Communication Asset Mapping could assist Lay Health Advisors with dissemination of evidence-based HCRA information within AA faith communities. Methods: The research team and CAB conducted exploratory community group discussions with residents (N=21) guided by Communication Infrastructure Theory, and a community-engaged mapping process to inform the development of a Communication Asset Mapping (CAM) dissemination strategy. Results: Themes included: (1) Optimal locations (e.g. community centers) within identified networks (specified neighborhood networks) should have representatives who are trusted ambassadors to assist with HCRA information dissemination; (2) Trusted community member voices should fully represent the trusted network; and (3) Well-known and frequented geographic locations should be a true representation of participants’ neighborhoods for creating a robust health information network concerning HCRA. Conclusion: CAB and community residents appreciated the engagement process however, felt the impact was limited due to missing community voices within their neighborhoods to identify communication assets within the network for optimal HCRA information dissemination. CAM therefore is an important public health strategy in identification of trusted networks. The strategy was also helpful in pinpointing people who are critical communicators of emerging health information akin to HCRA.

This study quantitatively assessed the effectiveness of three main options for the adaptation of crop farming to climate change (i.e., shift of planting dates, increase/addition of irrigation, and resilient hybrids/cultivars) in a southern European country (Greece). The potential effect of each option on the yields of several crops in all Greek regions is estimated for 2021-2040 and 2041-2060 and compared with those under the historical local climate of 1986-2005, by using agronomic and statistical regression models, and data from different climatic simulations and climate change scenarios. Our results reveal that all the adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021-2040 when for many regions and crops more than half of the losses can be compensated for. Notably, in some cases during this period, the measures examined can lead to crop yields that are higher than those under the historic climate. However, the effectiveness of the measures was found to greatly diminish in 2041-2060 under very adverse climate change conditions, highlighting the dynamic nature of adaptation. The assessment of the effectiveness of combined adaptation options, and the evaluation of additional criteria (e.g., feasibility) represent main areas for future research.

The rapid proliferation of Internet of Things (IoT) devices has transformed various sectors, from smart homes to industrial systems. However, this growth has introduced significant security risks due to the increased attack surface and diverse nature of IoT devices. This paper presents a comprehensive assessment of IoT security risks and explores effective mitigation strategies. We first categorize the primary risks associated with IoT, including data breaches, device manipulation, and network vulnerabilities. Using a multi-layered approach, we analyze these risks in different contexts, such as consumer IoT, industrial IoT, and healthcare. The study then reviews existing security frameworks and protocols, highlighting their strengths and limitations. We propose a hybrid mitigation strategy combining encryption, access control, and anomaly detection to enhance IoT security. Furthermore, we discuss the role of regulatory standards and best practices in reinforcing IoT security. The findings underscore the need for a proactive and adaptive security posture to address evolving threats in the IoT landscape.

As the number of Small and Medium Enterprises (SMEs) rises in the world, the amount of sensitive data used also increases, making them targets for cyberattacks. SMEs face a host of issues such as lack of resources, and poor cybersecurity talent, resulting in multiple vulnerabilities that increase overall risk. Cybersecurity risk assessment frameworks have been developed by multiple organizations such as the National Institute of Science and Technology (NIST) and the International Organization for Standardization (ISO), but they are complicated to understand, and challenging to implement. This research aimed to create an effective cybersecurity risk assessment framework specifically for SMEs while considering their limitations. This was achieved by first identifying common threats and vulnerabilities and categorizing them according to their importance, and risk. Secondly, popular frameworks like the NIST CSF and ISO 27001/2 were analyzed for their proficiencies and deficiencies while identifying relevant areas for SMEs. Finally, novel techniques catered to SMEs were explored and incorporated to create an effective framework for SMEs. This framework was also developed in the form of a tool, providing an interactive and dynamic environment. The tool was effective and the framework is a promising start but requires more quantitative analysis.

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle-thinking perspective. To this end, facilitating sustainability-oriented de-cision-making for DWM planning requires a sustainability assessment framework for assessing multifaceted criteria. This study develops a BIM-based DWM sustainability assessment ap-proach to facilitate the life cycle assessment (LCA) and decision-making by coupling the enriched IFC model with hybrid Multi-Criteria Decision-Aiding (MCDA) methods using Dynamo visual scripting. To streamline the data-intensive LCA process, this study enriched the BIM properties and accommodated them into the LCA data template to enhance data interoperability, thus achieving seamless data transfer. Moreover, hybrid MCDA methods are integrated into the deci-sion-making workflow for DWM scenario ranking. A pilot study is employed to verify the ap-plicability of the decision-aiding framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the ‘status quo’ in China’s recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the ‘growth curve’ of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the “Australian standard” scenario to the optimal scenario is 18.4%, while the sustainability score merely increases by 2.3%, signal-ling that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined framework and contexts.

Cocoa is a natural resource that plays a very important role globally, being one of the most produced and traded commodities. Being a labor-intensive product and considering that its cultivation involves about 50 million people globally, it seems significant to explore its social sustainability. In light of this, this research aimed to map social risks within the cocoa supply chain from a life cycle perspective. Therefore, according to a Reference Scale Approach, or Type I, the Social Life Cycle Assessment was used, considering the two most influential Countries in its production, i.e. Côte d'Ivoire and Ghana. The results showed that there could be a very high risk that more than half of the cocoa globally is produced through child labour and with wages too low to guarantee workers a decent living, returning incomes of $30-38/month. Forced labour is much less frequent than child labour, while cocoa from Ghana may induce a high risk of improper work, considering the 30.2 hours per week worked by farmers. This is mainly due to the low association power of 10-16%, which reveals a high risk that workers may not organize themselves into trade unions. Finally, at 23-25%, there is also a very high risk of discrimination due to the high presence of migrant labour. The S-LCA results, therefore, showed that the cocoa industry is still characterized by socially unsustainable sourcing.

The transition to renewable energy sources is pivotal in addressing the escalating challenges of climate change and environmental degradation. Solar energy, particularly photovoltaic (PV) technology, stands out as a prominent solution due to its potential for clean and sustainable electricity generation with minimal greenhouse gas emissions. However, accurately assessing the carbon footprint of PV modules is essential for guiding policy, industry practices, and research. This paper reviews the state of current literature and highlights the difficulties in estimating the carbon footprint of PV modules manufactured in China. It emphasizes the inherent limitations of Process-Based Life Cycle Assessment (PLCA), including data collection challenges, dynamic environmental changes, and subjective methodological choices. The study underscores the need for improved transparency, standardization, and reproducibility in Life Cycle Assessments (LCAs) to provide more accurate and reliable environmental impact evaluations.

This study aims to quantify greenhouse gas (GHG) emissions from China's sludge treatment industry and explore the potential of novel technologies to mitigate these emissions, promoting sustainable sludge management practices. Using a life cycle assessment approach, the total GHG emissions from sludge treatment in 2017 were estimated at 18.54 Mt CO2-eq, with significant regional variations. Incineration contributed the highest emissions (1,011.53 kg CO2-eq/t dry sludge), followed by landfill (717.51 kg CO2-eq/t) and land application (276.41 kg CO2-eq/t). The uneven distribution of emissions across cities highlights the need for targeted mitigation strategies that consider local sustainability challenges. Principal component analysis revealed that economic factors and treatment scale significantly influence emissions, emphasizing the importance of integrating sustainability goals with economic development. To reduce the carbon footprint of sludge treatment and enhance sustainability, the mitigation potential and costs of various technologies were evaluated, including thermal conversion, anaerobic digestion, hydrothermal processes, and wet oxidation. These technologies offer GHG emission reductions ranging from 0.09 to 0.46 t CO2-eq/t dry solids compared to conventional methods, while also promoting resource recovery and circular economy principles. However, further optimization is necessary to enhance their environmental and economic performance, ensuring long-term sustainability. By implementing these technologies and adopting optimization strategies, China's sludge treatment industry can significantly contribute to achieving the country's carbon neutrality goal and fostering sustainable development.

Previous reviews have investigated machine learning (ML) models used to predict the risk of developing preeclampsia but have not described how the ML models are intended to be deployed throughout pregnancy or feature performance. The aim of this study is to provide an overview of the existing ML models and their intended deployment patterns and performance along with identified features of high importance. This review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. PubMed, Engineering Village, and the Association for Computing Machinery were searched between January and February 2024. A total of 86 studies were found of which 14 were included. Out of 12 studies, eight showed the intent to use the ML model as a single-use, two intended a dual-use, and two intended multiple-use. A total of seven studies listed the features of the highest importance. Systolic and diastolic blood pressure were listed along with mean arterial pressure to be of high importance. Out of four studies intending to use the ML model more than a single-use, three of them were conducted in the years 2023 and 2024, whereas the remaining study is from 2011. No ML model emerged as superior across the subgroups of PE. Utilizing body mass index and either mean arterial pressure or diastolic blood pressure and systolic blood pressure may benefit the performance. The deployment patterns are mainly single use being within the gestation weeks 11+0 to 14+1.

A pivotal strategy in decarbonizing the transport sector is by transitioning from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs), especially if they are charged with renewable energy. To provide an incentive to the common populace to switch to EVs, cities should set up - or enable or even encourage the private sector to do so - electric vehicle charging stations (EVCS) at multiple and suitably accessible locations. This paper aims to present a digitaltwin-enabled methodology study for selecting the most suitable possible locations to establish EVCS with consideration of the low voltage (LV) network operation in terms of power losses, active and reactive power in EV charging demand and the effect of the EVCS on the transformers, based on examples in Newcastle upon Tyne (England, UK). It also evaluates the most suitable type of charger that could be installed at these locations by studying the estimated traffic, cost and impact of the EVCS on the power grid, both at peak and off hours. This paper demonstrates how digitaltwin technologies, such as the web-based tool AutoDesign by Northern Powergrid and EA Technology and open source Pandapower can be used for identifying the most viable and cost-effective locations to connect EVCSs to a Distribution Network Operators (DNO)’s LV network. The results of the analysis indicate that with the installation of the most suitable type of EVCS in a particular location, the local power grid would not be significantly affected at any hour of the day.

This paper investigates the potential of different fermentation feedstocks towards succinic acid production. Three alternative flowsheets were constructed and modelled in SuperPro Designer™, utilizing different fermentation substrates: crude glycerol (a by-product of the biodiesel industry), a commercial 95% glucose syrup and pure glycerol. Techno-economic analysis of the modelled plants showed that all three potential investments can be economically profitable, with the crude glycerol case as the most promising one. The succinic acid production cost for the crude glycerol scenario is estimated at 3.59 $/kg, while a thorough study of the capacity effect to the economics showed that it could drop as low as $2.6 $/kg. Parametric analysis and optimization performed on a flowsheet level, indicated opportunities for lowering the production cost down to 3 $/kg, a value that could pave the framework for a further potential decrease in the selling price of the product.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

Due to the growth of tourism, mining and manufacturing, economy of the Hainan Island, southern China has swiftly expanded, but also poses a risk of soil pollution by mercury (Hg) due to the traffic and mineral processing. In order to investigate the characteristics of soil Hg pollution in the Hainan Island and assess the health risk, a total of 239 samples were gathered from five representative regions across the island. A vehicle-mounted potable mercury analyzer was used to measure the atmospheric Hg and soil Hg concentrations in the collected samples. The results indicate that the soil Hg concentration of the five sub-study areas exhibits notable fluctuations, which was found to be influenced by various factors, including wind direction, mining activities, and economic development conditions. The degree of Hg pollution was assessed using both the Single Pollution Index Method and the Geoaccumulation Index Method. The results showed that although nearly half of the sampling points were contaminated, the current soil Hg level does not pose a non-carcinogenic risk to human health.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

A review of quantitative risk assessment (QRA) models of L. monocytogenes in produce was carried out, with the objective of appraising and contrasting the effectiveness of the control strategies placed along the food chains. Despite nine of the thirteen QRA models recovered focused on fresh or RTE leafy greens, none of them represented important factors or sources of contamination in the primary production, such as type of cultivation, water, fertilisers or irrigation method/practices. Cross-contamination at processing and during consumer’s handling was modelled using transfer rates, which were shown to moderately drive the final risk of listeriosis; highlighting therefore the importance of accurately representing the transfer coefficient parameters. Many QRA models coincided in the fact that temperature fluctuations at retail or temperature abuse at home were key factors contributing to increasing the risk of listeriosis. In addition to a primary module that could help assess current on-farm practices and potential control measures, future QRA models for minimally-processed produce should also contain a refined sanitisation module able to estimate the effectiveness of various sanitisers as a function of type, concentration and exposure time. Finally, L. monocytogenes growth in the products down the supply chain should be estimated by using realistic time-temperature trajectories, and validated microbial kinetic parameters, both of them currently available in the literature.

A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in seafood was performed, with the objective of appraising the effectiveness of the control strategies placed along the food chain. Whereas all the 13 QRA models retrieved were of short scope, covering at most from end of processing to consumption; the majority (85%) focused on smoked or gravad fish. Since the modelled pathways commenced with the packaged product, none of the QRA models addressed cross-contamination events. Many models agreed on the fact that keeping the product’s temperature at 4.0-4.5 ºC leads to greater reductions in the final risk of listeriosis than reducing the shelf life by one week; and that the effectiveness of both measures can be surpassed by reducing the initial occurrence of L. monocytogenes in the product (at the end of processing). It is therefore necessary that future QRA models for RTE seafood contains a processing module that can provide insight into intervention strategies that can retard L. monocytogenes growth, such as the use of bacteriocins, ad-hoc starter cultures and/or organic acids, and other strategies seeking to reduce cross-contamination at the facilities, such as stringent controls for sanitation procedures. Since risk estimates were shown to be moderately driven by growth kinetic parameters, namely, the exponential growth rate, the minimum temperature for growth, and the maximum population density, further work is needed to reduce uncertainties about them.

Air pollution poses a significant risk to public health and ecosystems. The use of sophisticated models to simulate the transformation and dispersion of pollutants in the atmosphere is a recognized method to assess air pollution levels and its sources and inform policy decisions for effective mitigation strategies in urban and industrial environments. The reliability of models depends, in part, on the sensitivity to the variance of input parameters, which can introduce uncertainty into the model's predictions. This uncertainty is especially challenging when the input parameters exhibit inherent variability. In this study, we analyse the uncertainty of an integrated emission/dispersion model by evaluating the relationship between input parameters and model outcomes. We uncover new aspects of this relationship while clarifying some previously known dependencies. Wind speed was found to have the most significant effect on calculated concentrations, with small differences leading to substantial concentration variations. The time of day, which reflects prevailing vertical turbulence conditions, was also found to have a notable impact on concentrations, whereas ambient temperature can also significantly influence model’s results. Based on these findings, we offer specific recommendations for regulators and scientists involved in environmental impact assessment to improve the accuracy and reliability of air pollution modelling.

Behavioural disorders in dogs are common and have severe welfare consequences for dogs. This study aimed to assess the factors that are significant and predictive of behaviour problems in dogs using the Animal Welfare Assessment Grid (AWAG) to further understand what factors influence their welfare. 177 AWAG assessments were undertaken across 129 dogs that clinicians deemed to have a behavioural disorder. Wilcoxon-rank sum tests were used to assess the difference in scores between dogs with behaviour disorders and a cohort of healthy dogs (n=117). This analysis showed that all physical factors besides body condition, all procedural factors besides procedure pain, and all psychological, and environmental factors were significantly different between healthy dogs and dogs with behaviour disorders. Spearman Rank Correlation Coefficient (RS) revealed several significant strong positive correlations including the procedural impact on the dog’s daily routine with aggression towards unfamiliar people and procedure pain, as well as other correlations between the dog’s behaviour during assessment with the frequency at which they encounter fears and anxieties, clinical assessment and procedure pain, and reaction to stressors and social interactions. These findings highlight the interdependent nature of the various influences of welfare. Logistic regression analysis identified that aggression towards the caregiver, fears and anxieties frequency, and choice, control, and predictability were all significant predictors of behaviour disorders. The findings have important implications for veterinary, behaviour, and animal welfare professionals as any changes across these factors may indicate poor welfare linked to emotional disorders in dogs.

Achieving sustainable development will require improved water management, but the main framework to address it, Integrated Water Resources Management (IWRM), lacks clarity and acceptance among a diverse community of many stakeholders. This creates communication problems and obscures issues that people care about like drinking water and pollution prevention. As a process within IWRM, the concept of water resources assessment also suffers from a lack of clarity, which leads to ambiguity and reports that are too general to lead to action. This lack of acceptance of IWRM and water resources assessment saps support for essential data collection, analysis, and reporting. Clarifying IWRM will require work within the water community and better messaging to stakeholders. Methods for water resources assessment can be clarified by linking outcomes to higher level reporting of policy and issues. Acceptance of the role of water resources assessment can be facilitated by adding it to the management instruments in the IWRM Toolbox. Stovepipes among water management agencies require coordination to achieve this, usually through framework studies. Improved messaging will involve what people and stakeholders care about and will require changed approaches to presentation of outcomes by categories, such as drinking water, pollution prevention, and flood risk reduction. Researchers can report outcomes with technical messaging that trends toward uniform language and definitions and the water resources management community can nudge its media outlets to carry unifying messages.

Purpose: To identify factors linked with nutritional risk via the Malnutrition Universal Screening Tool scale assessment among critically ill patients from a hospital in Cali (Colombia). Materials and methods: A retrospective, analytical observational study was conducted with patients admitted to an intensive care unit (ICU). Sociodemographic, clinical, hematological, and biochemical parameters were analyzed, as well as their association with nutritional risk and patient mortality. Binary logistic regression was applied to outline nutritional risk. Results: Overall, 28.4% prevalence of elevated nutritional risk was observed. An association was found between nutritional risk and hemoglobin and hematocrit values, primary diseases accounting for ICU admission, gastrointestinal symptoms, and nutritional support route (p < 0.05). Mortality was 11.6% and it was correlated with nutritional risk, hemoglobin, and blood urea nitrogen. Conclusions: Gastrointestinal symptoms and the type of nutritional support received during hospitalization may increase nutritional risk, whereas polycythemia was a protective factor. We found an associative model to determine nutritional risk with high specificity and an adequate diagnostic validity index.

To ensure sustainability of the future power grid, the rate of expansion of distributed renewable energy resources (DRER) has introduced operational challenges. These includes managing transmission constraints with DRER power injection, dispatching DRER efficiently, managing system frequency and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructure across the United States faces challenges to minimize the impact to maintain system reliability and resiliency. This research embarks on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assesses wildfire-induced risks to power grids, by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation metrics, and system parameters, by employing Principal Component Analysis (PCA) based optimal weights, leading to the formulation of a novel risk factor model. This risk factor has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive tool-set, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery using risk assessment against wildfire.

The study is devoted to analyzing the economic sustainability of the town of Amherst, MA. The city's top employer and core enterprise is the University of Massachusetts Amherst, with over 32,000 students and almost 2,000 staff members. Based on a literature review, a hypothesis was put forward that a university city should have a high level of economic sustainability. To assess economic sustainability, the USCESI Index was developed. It evaluates sustainability in three groups of parameters: society, economy, and ecology. The first group includes the level of racial diversity, the level of education of the population, and the access to medical services. The second group consists of the Gini coefficient by income level, the median cost of housing, and the unemployment rate. The environmental situation is assessed according to the Air Quality Index developed by the US Environmental Protection Agency. For comparison, the town of Braintree, MA, was taken. The USCESI was calculated for both locations. The analysis showed that both Amherst and Braintree have a high degree of economic sustainability. However, it was revealed that proximity to a significant economic center has a more powerful positive impact on economic sustainability than the location of a large university.

In this paper, aiming at the safety evaluation of ultimate bearing safety capacity of ultra-high-performance-concrete (UHPC) composite pile bridge foundation, the symmetrical reliability method with forward and inverse analysis considering the randomness of parameters is proposed to evaluate the safety performance of composite pile foundation. The proposed method, consid-ering the randomness of parameters, was used to calculate the safety factor of ultimate bearing capacity of composite pile foundation to meet the prescribed target reliability requirements. An example was used to verify the correctness of the proposed method. The proposed method was applied to an actual engineering example and the sensitivity of the parameters were analyzed, the calculation results show that: ignoring the randomness of the parameters will overestimate the safety of the composite pile foundation; the target reliability index has a great influence on the safety of composite pile foundation; the influence of statistical parameters of random variables on the safety of UHPC composite pile foundation cannot be neglected; the initial iteration value of the safety factor has no effect on the final calculation result. The proposed method has high ac-curacy and applicability, and provides an effective way for the safety factor estimation of ultimate carrying capacity of UHPC composite pile foundation.

Circular economy has recently emerged as a key strategy for promoting sustainability and reducing waste in various industrial sectors. This paper provides an overview of circularity in aerospace, wind energy, transportation, automotive and sports goods using data and information from the literature and the EC funded project "RECREATE". The survey reviews the different definitions, assessment methods and metrics used to explore and evaluate circularity, including assessment frameworks such as Life Cycle Assessment (LCA) and assessment indicators. Furthermore, explores the challenges, possibilities and the available tools for enhancing circularity, such as digital tools. The survey highlights the importance of a holistic and systemic technique to circularity, concerning all stakeholders along the value chain. Overall, this study aims to contribute to a better understanding of circular economy and provides insights for future research.

Machine Learning techniques can be used to identify whether deficits in cognitive functions contribute to antisocial and aggressive behavior. This paper initially presents the results of tests conducted on delinquent and non-delinquent youths to assess their cognitive functions. The dataset extracted from these assessments, consisting of 37 predictor variables and one target. was used to train three algorithms that aim to predict whether the data corresponds to that of a young offender or a non-offending youth. Prior to this, statistical tests were conducted on the data to identify characteristics that exhibited significant differences in order to select the most relevant features and optimize the prediction results. Additionally, other feature selection methods, such as Boruta, RFE, and Filter, were applied, and their effects on the accuracy of each of the three machine learning models used (SVM, RF, and KNN) were compared. 80% of the data were utilized for training, while the remaining 20% were used for validation. The best result was achieved by the K-NN model trained with 19 features selected by the Boruta method, followed by the SVM model trained with 24 features selected by the filter method.

The wastewater circular economy (WW-CE) promises a solution to improve water and sanitation management worldwide. However, the transition from conventional to circular wastewater treatment plants (WWTPs) requires facilitation to aid in decision makers understanding of integral sustainability impacts of alternative WW-CE configurations. This research implemented Life Cycle Sustainability Assessment (LCSA), combining Life Cycle Assessment, Social Life Cycle Assessment and Life Cycle Costing with a Multi-criteria Decision Making (MCDM) model to quantify environmental, socio-cultural, and economic impacts of conventional WWTPs with the WW-CE. Two real WWTPs in Chile have embraced the WW-CEs and adopting the title of Biofactories. These were considered as case studies, compared under three scenarios to demonstrate the sustainability trade-offs of the transition from no sanitation to conventional WWTPs and Biofactory WW-CE configurations. Results demonstrated that the transition to WW-CEs improved integral sustainability according to the LCSA model implemented in both WWPTs. This study highlights the urgent need to adopt sustainable decision-making models to not only improve sanitation coverage, but also improve sustainability performance of the sanitation industry across the globe.

In the absence of a holistic view of Landscape Sustainability, credible data and consistent information are needed to help decision making and support adaptive management. However, consolidated tools (system of standards and controls) of assessment exist, but show a complexity of the references on one hand, and are mostly qualitative and environmental indicators based on the other hand. This review article deals with the theme of both environmental and landscape indicators in general, the principles and the fundamental definitions. The main models used, the prerequisites and various fields of application are also taken into consideration. Last but not least, various environmental and landscape indicators are presented and compared.

The oil spilled from the Gulf war caused land pollution and various petroleum compounds. Among petroleum compounds, polycyclic aromatic hydrocarbons (PAHs) are known for toxicity, and have been designated as carcinogenic, mutagenic, and endocrine disrupting substances by the United States Environmental Protection Agency (USEPA). According to the USEPA, toxic equivalents quotient (TEQ), and mutagenic equivalents quotient (MEQ) can be calculated by multiplying the toxic equivalency factor (TEF) and mutagenic equivalency factor (MEF) by the concentration of PAHs. The USEPA calculates the relative TEF for other PAHs by setting the TEF value of benzo(a)pyrene, which is highly toxic, to 1. Relative MEF is also calculated in the same way. In this study, the TPH concentrations of crude oil-contaminated soils collected from the Burgan in Kuwait were measured to be 5, 8, and 20% and TEQ and MEQ of the soils were calculated by multiplying the 16 PAHs concentrations measured for each sample by TEF and MEF, respectively. When the TPH concentration was increased by 4 times from 5% to 20%, the TEQ was increased by about 9 times and the MEQ was increased by about 10 times. Therefore, it was concluded that as the TPH concentration increases, the carcinogenicity and mutation rate increase greatly. In addition, ecotoxicity assessment was performed using luminescent bacteria to compare the relationship between the calculated TEQ and MEQ and actual ecotoxicity, and benzo(a)pyrene among 16 PAHs showed a coefficient of determination of 0.95. In this study, ecotoxicity in PAHs-contaminated environment can be estimated by analyzing benzo(a)pyrene as a representative substance.

Embryo quality evaluation during in vitro development is vital for the success of assisted reproductive technologies (ART). However, the subjective morphological assessment by embryologists may result in inconsistencies that affect the selection of the best embryo for transfer. To provide a more comprehensive evaluation of embryo quality, we performed the integration of embryo metabolomics with standardized morphokinetic classification. The culture medium of 55 embryos (from 21 couples undergoing ICSI) was collected at two timepoints (day 3 and 5). Samples were split into Good (n=29), Lagging (n=19) and Bad (n=10) according to embryo morphokinetic evaluation. Embryo metabolic performance was assessed by 1H-NMR, monitoring the variation of specific metabolites (pyruvate, lactate, alanine, glutamine, acetate, formate). Adjusted metabolite differentials were observed during the first three days of culture and found to be discriminative of embryo quality at the end of day 5. Pyruvate, alanine, glutamine, and acetate were major contributors to this discrimination. Good and Lagging embryos were found to export and accumulate pyruvate and glutamine in the 3 first days of culture, while Bad embryos consumed them. This suggests that Bad embryos have a less active metabolism than Good and Lagging embryos and these two metabolites as putative biomarkers for embryo quality. This study provides a more comprehensive evaluation of embryo quality and can lead to improvements in ART by enabling the selection of the best embryos. By combining morphological assessment and metabolomics, the selection of high-quality embryos with the potential to result in successful pregnancies may become more accurate and consistent.

As required by the EU legislation and the national risk management duties, the pesticide residues were determined by four specialized laboratories in 9,924 samples taken from 119 crops of economic importance and imported foodstuffs during 2017-2021 in Hungary. The screening method applied covered 622 pesticide residues as defined for enforcement purposes. The limit of detection ranged between 0.002-0.008 mg/kg. The 1.0% violation rate was lower than in the European Union for all commodities. No residue was detectable in 45.9% of the samples. For detailed analyses of the results, eight commodities (apple, cherry, grape, nectarine/peach, sweet peppers, and strawberry) were selected as they were analyzed over 195 samples and contained most frequently residues. Besides testing their conformity with national MRLs, applying 0.3MRL action limits for pre-export control, we found that 73% of the sampled lots would be compliant with ≥90% probability based on second independent sampling. Multiple residues (2-23) in one sample were detected in 36-50% of the tested lots. Noting the provisions of integrated pest management and the major pests and diseases of selected crops, normally 3-4, exceptionally 7-9 active ingredients of different mode of actions should be sufficient for their effective and economic protection.

Pose estimation has various applications in analyzing human movement and behavior, including providing feedback to users about their movements so they could adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human `body' pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 20 articles for methods used to estimate human pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that while there was an improvement, the majority of pose estimation challenges remain. The effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.

Abstract: The need to rapidly reduce GHG has accelerated the adoption of circular models of production. However, this has proved to be a challenging task for SMEs, who lack the financial, organizational and informational capabilities to implement circular business models. In this context, calculating the carbon footprint (CF) of their products could provide the basis for assessing the different circular economy (CE) practices. The aim of this study is to present a CF calculation tool that can be used to calculate the CF of SMEs. The design of the tool was based on the Life Cycle Assessment (LCA) methodology, taking into account the various barriers that SMEs face in adopting CE practices. The tool was tested in a small cheese factory in northern Greece. The pro-duction process was mapped, a GHG inventory was created and the total emissions related to the production of a specific product was estimated. The final aim is to test this tool at a large scale.

Functional somatic disorders (FSD) characterized by persistent and disabling physical symptoms are common in adolescents. Diagnostic uncertainty and insufficient illness explanations are proposed perpetuating factors that may constitute barriers for treatment engagement. This study evaluates the impact of manualized assessment and psychoeducation on diagnostic certainty and various clinical outcomes in adolescents with multi-system FSD. Ninety-one adolescents (15-19 years) included in a randomized trial testing group-based therapy (AHEAD) received systematic assessment (4 hrs.) and a subsequent psychiatric consultation (1.5 hrs.). Clinical outcomes included self-reported physical health, symptom severity, illness perception, illness related behavior, and psychological flexibility assessed before and approximately two months after assessment, prior to specialized treatment. Data were analyzed using t-tests. Immediately following assessment 71 (80.7%) adolescents out of 88 reported a higher diagnostic certainty and 74 (84.1%) reported that attending assessment gave them positive expectations for future treatment. A clinically relevant improvement of physical health was not observed at two months but considerable reductions were seen on symptom severity, illness worry, negative illness perceptions, limiting illness behavior and psychological inflexibility. The results underpin the importance of systematic assessment and psychoeducation which may be effective in their own right in specialized treatment of adolescents with severe FSD.

Peri-urban forests often have extensive networks of greenways that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of flora and fauna and often overlook the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is not as high as expected. Having a quantification of their naturalness would be useful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as "greenways". The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on Geographic Information Systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index, while this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analysing the degree of naturalness of the trails, but if used together, it would be possible to add flexibility to the type of variables incorporated in the analysis.

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for reduction of greenhouse gas emissions, and allowing for natural and imported contributions from other countries and shipping. This paper describes how UKIAM has been used in development of proposed targets for reduction of fine particulate PM2.5 in the Environment Act, exploring scenarios encompassing different levels of ambition up to 2050 with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, to also demonstrate links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating where use of wood for heating is an air quality issue. UKIAM has been validated against air pollution measurements and other modelling, but there are many uncertainties including future energy projections. New work is beginning to link UKIAM directly with the TIMES model addressing future energy projections, to explore different uptake scenarios for hydrogen production and use with respect to air quality.

To evaluate the impact of a new Substance Use Disorder (SUD) education program on medical students' representations, we selected the Substance Abuse Attitude Survey (SAAS) questionnaire, which we adapted to our curriculum and cultural context. To validate this adapted version, we conducted an exploratory factor analysis following the administration of our 29-items bSAAS questionnaire to 657 medical students in Belgium (response rate: 71,1%). 23 items correlated to 3 factors namely "Stereotypes and moralism", "Treatment optimism" and "Specialised treatment" were retained (70% of total variance explained, Cronbach's alpha= 0.80) and constituted the new questionnaire called beSAAS. The factor "Specialised treatment", stood out from previous studies which could be explained by our target population and the impact of the formal, informal, and hidden curriculum in medical education. This study highlighted certain factors influencing stereotypical representations such as age, gender, origin, and personal or professional experience with substance use. Our study allowed us to retain the beSAAS as a good questionnaire to evaluate SUD stigma and highlighted interesting findings to improve SUD training in medicine. Further studies are needed to complete its validity and reliability.