The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.

As organizations are vulnerable to cyber attacks, their protection becomes a significant issue. Capability Maturity Models can enable organizations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the UK. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security and privacy regulations and best practices that HEIs must be compliant to and can be used as a self-assessment or a cybersecurity audit tool.

This review aimed to update the main aspects of aflatoxin production, occurrence and incidence in selected countries, and associated aflatoxicosis outbreaks. Means to reduce aflatoxin incidence in crops were also presented with an emphasis on the environment-friendly technology using atoxigenic strains of Aspergillus flavus. Aflatoxins are unavoidable widespread natural contaminants of foods and feeds with serious impact on health, agricultural and livestock productivity, and food safety. They are secondary metabolites produced by Aspergillus species distributed on three main sections of the genus (section Flavi, section Ochraceorosei, and section Nidulantes). Poor economic status of a country exacerbates the risk and the extent of crop contamination due to faulty storage conditions that are usually suitable for mold growth and mycotoxin production: temperature of 22 to 29°C and water activity of 0.90 to 0.99. This situation paralleled the prevalence of high liver cancer and the occasional acute aflatoxicosis episodes that have been associated with these regions. Risk assessment studies revealed that Southeast Asian and Sub-Saharan African countries remain at high risk and that, apart from the regulatory standards revision to be more restrictive, other actions to prevent or decontaminate crops are to be taken for adequate public health protection. Indeed, a review of publications on the incidence of aflatoxins in selected foods and feeds from countries whose crops are classically known for their highest contamination with aflatoxins, reveals that despite the intensive efforts made to reduce such an incidence, there has been no clear tendency, with the possible exception of South Africa, towards sustained improvements. Nonetheless, a global risk assessment of the new situation regarding crop contamination with aflatoxins by international organizations with the required expertise is suggested to appraise where we stand presently.

Improving the efficiency of the supply process in prefabricated components is challenging and requires accounting for a variety of risks involved in the management of the suppliers. The purpose of this study is to present a method to account for the systematic trade-offs between several supplier alternatives. A novel framework is presented for the whole assessment of supplier alternatives by taking advantage of the information extracted from customized building information modeling (BIM) and a database required for assessment of impacts. A data library related to assessment criteria for supply alternatives is built to facilitate the storage and sharing of information. Analytic hierarchy process (AHP) is used to select the optimal supplier that is able to provide the most satisfaction for the determined criteria. The proposed framework was also illustrated by the implementation in a mega project. The study implication is that BIM-enabled supplier selection can indeed lead to more benefits and higher values for all stakeholders.

This paper addresses the development of knowledge and assessment-centered learning approaches within a reflective learning framework in a first year physics class in a university faculty. The quality of students’ reflections was scored using a Self-reporting Reflective Learning Appraisal Questionnaire at the end of each learning approach. The results showed the differences between the approaches based on reflections on the learning control through self-knowledge, by connecting experience and knowledge, as well as through self-reflection and self-regulation. Assessment-centered activities fundamentally help students identify aspects of their attitudes towards, as well as regulate, their sustainability learning education.

The social criteria of sustainable development have remained underexplored. Moreover, a large number of green building assessment tool and social sustainability documentations have been developed which, has had a direct impact on social criteria issues, but there seems to be a substantial gap in the study of social criteria in green building assessment tools. In examining the problem facing social sustainability, taking into consideration social sustainability in sustainable development reviews and green building assessment tool towards social aspects. This paper through analysis identified a centripetal conceptual framework composed of seven key components equity, education, participation & control, social cohesion, health & safety, accessibility & satisfaction, and cultural values. The interpretation of the social sustainability in green building assessment tool would impact building practitioners towards implementing social criteria in GBAT. The aim was to identify social categories as well as consider a starting point for the development of an effective social criteria assessment tool for green building.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles, fortifications, etc.), has few conservation plans, with many remains included on the Red List of Spanish Heritage ly-ing abandoned. This poses a challenge for landscape conservation and the optimal use of the province’s tourism potential. In this paper we present the early implementation of an ongoing methodological approach designed to alleviate this situation. It takes advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS, GIS and fieldwork that will be applied across five archaeological case studies. It has been tested for the first time in the village of Magaña. Three main objectives and phases of this methodological approach have been designed: 1. Preparation of an Environmental Risk Chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteris-tics affected the state of conservation of certain heritage sites?” 2. Preparation of HBIM models to foster monitorisation and conservation policies for the main archaeological remains and improving their protection based on digital and technological tools. 3. Building a Smart Tourism App for mobile devices aimed at promoting smart tourism by digitalising and virtualising tourist itineraries and archaeological remains. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective man-ner.

This study evaluates whether a combination of photodiode sensor measurements, taken during laser powder bed fusion (L-PBF) builds, can be used to predict the resulting build quality via a purely data-based approach. We analyse the relationship between build density and features that are extracted from sensor data collected from three different photodiodes. The study uses a Singular Value Decomposition to extract lower-dimensional features from photodiode measurements, which are then fed into machine learning algorithms. Several unsupervised learning methods are then employed to classify low density (< 99% part density) and high density (≥ 99% part density) specimens. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density. Using the unsupervised clustering approaches, applied to features extracted from both photodiode sensor data as well as observations relating to the energy transferred to the material, build density was predicted with up to 93.54% accuracy. With regard to the supervised regression approach, a Gaussian Process algorithm was capable of predicting the build density with a RMS error of 3.65%. The study shows, therefore, that there is potential for machine learning algorithms to predict indicators of L-PBF build quality from photodiode build-measurements. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantification in machine-learnt predictions of L-PBF build quality.

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is used widely in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application.

To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method is proposed. First, a scenario generation scheme considering waterlogged faults of power facilities is constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios will be merged to several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram-Charlier level expansion methods are adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution is performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance are considered to quantitatively access the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method is verified in case studies based on the IEEE 24-bus system.

The accelerated pace with which new diagnostic possibilities are produced and the budget re-strictions of the healthcare sector require that the decision-making process of acquiring new technologies becomes systematic in nature. There are some methodologies such as the Health Technology Assessment (HTA) that encompasses technological domains, clinical effectiveness, patient safety, organizational and financial aspects, among others. However, these methods do not contemplate the learning curve (LC) which is a differential factor in operator-dependent technologies. An assessment model was conceived based on the assessment domains of the AdHopHTA project with the addition of LC. The model was validated and calibrated through System Dynamics (SD) and using as case study the assessment of Point-of-care Ultrasound (POCUS) equipment in dengue screening. The model helped identify the influence of the LC and patient demand on the income and costs for the healthcare system regarding dengue screening as well as the cost-benefit indicator. The control charts LC-CUSUM and CUSUM are graphic tools to intuitively assess the learning curve which can be used for real-time monitoring once students reach a certain skill level.

Consumer products contain the chemical substances that threaten human health. The modeling methods and experimental methods have been used to estimate the inhalation exposure concentration by the consumer products. The model and measurement methods have the spatial property problem and time/cost consuming problem, respectively. For solving the problems due to the conventional methodology, this study performed the feasibility of applying CFD for evaluation of inhalation exposure by comparing the experiment results and the zero-dimensional results with CFD results. To calculate the aerosol concentration, the CFD was performed by combined the 3D Reynolds averaged Navier Stoke’s equation and discrete phased model using ANSYS FLUENT. As a result of comparing the three methodologies performed under the same simulation/experimental conditions, we found the zero-dimensional spray model shows approximately 5 times underestimated inhalation exposure concentration when compared with the CFD results and measurement results in near field. Also, the results of the measured concentration of aerosols at five locations and the CFD results at the same location were compared to show the possibility of evaluating inhalation exposure at various locations using CFD instead of experimental method. The CFD results according to measurement positions can predict rationally the measurement results with low error. In conclusion, in the field of exposure science, a guideline for exposure evaluation using CFD was found that complements the shortcomings of the conventional methodology, the zero-dimensional spray model and measurement method.

The fatigue life of concrete is affected by many interwoven factors whose effect is nonlinear. Be-cause of its unique self-learning ability and strong generalization capability, the Bayesian regu-larized backpropagation neural network (BR-BPNN) is proposed to predict concrete behavior in tensile fatigue. The optimal model was determined through various combinations of network parameters. The average relative impact value (ARIV) was constructed to evaluate the correla-tion between fatigue life and its influencing parameters (maximum stress level Smax, stress ratio R, static strength f, failure probability P). ARIV results were also compared with other factor as-sessment methods (weight equation and multiple linear regression analyses). Using BR-BPNN, S-N curves were then obtained for the combinations of R=0.1, 0.2, 0.5; f=5, 6, 7MPa; P=5%, 50%, 95%. The tensile fatigue results under different testing conditions were finally compared for compatibility. It was concluded that Smax has the most significant negative effect on fatigue life; the degree of influence of R, P, and f, which positively correlate with fatigue life, decreases suc-cessively. ARIV is confirmed as a feasible way to analyze the importance of parameters and could be recommended for future applications. The tensile fatigue performance of plain concrete under different stress states (flexural tension, axial tension, splitting tension) does not differ sig-nificantly. Besides utilizing the valuable fatigue test data scattered in the literature, insights gained from this work could provide a reference for subsequent fatigue test program design and fatigue evaluation.

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

This study aimed to investigate the occurrence of the artificial sweetener aspartame (E951) in a wide range of foods and beverages sampled by food control authorities in Germany between 2000 and 2022. The dataset was obtained through the Consumer Information Act. The results showed that aspartame was present in a wide range of product groups tested, most commonly in powdered drink bases (84%) and flavored milk drinks (78%). In the solid food groups, the highest mean aspartame content was found in chewing gum (1543 mg/kg, n=241), followed by sports foods (1453 mg/kg, n=125), fiber supplements (1248 mg/kg, n=11), powdered drink bases (1068 mg/kg, n=162), and candies (437 mg/kg, n=339). Liquid products generally had the highest aspartame content in diet soft drinks (91 mg/l, n=2021), followed by regular soft drinks (59 mg/l, n= 574), flavored milk drinks (48 mg/kg, n=207), and mixed beer drinks (24 mg/l, n=40). These results suggest that aspartame is widely used in foods and beverages in Germany and that certain product groups contain higher concentrations of the sweetener than others. However, the levels of aspartame found were generally within the legal limits set by the European Union. These findings provide the first comprehensive overview of aspartame in the German food market and may be particularly useful in informing the forthcoming working groups of the WHO International Agency for Research on Cancer (IARC) and the WHO/FAO Joint Expert Committee on Food Additives (JECFA), which are in the process of evaluating the human health hazards and risks associated with the consumption of aspartame.

Although South Africa is well known for its incredible biodiversity, targeted efforts are still re-quired to accelerate the discovery and description of the still-unknown species in this floristically mega-diverse country. In addition, new species discovery is a new opportunity for the devel-opment of ethnobotanical knowledge. In the present study, we collected data on current knowledge of plant richness in the country and expert taxonomists’ opinions and used statistical modelling technique to predict what might still be missing in South Africa’s flora. According to experts’ opinions and our modelling, we might be missing 1400-1575 plant species in the country. We predicted that we might take 40-45 years to identify and describe these species and that be-tween 64 and 315 taxonomists will be required. The pool of taxonomists who took part in this study have spent a total of R 680 670 000 (US $40 039 411) to describe 419 species. This implies that, in theory, R 1 624 510.74 (US $95 559) was spent, on average, to describe 1 species. At this rate, R 2 558 604 415.00 (US $150 506 142) would be required to describe the 1575 (modelling) or R 2 274 315 036.00 (US $133 783 237) for the 1400 remaining species (expert opinion) in the current context where both full- and part-time taxonomists are committed to biodiversity assessment in the country. It is important to highlight that this estimate does not correspond to what is required specifically for only species description but does integrate all connected activities, e.g., running cost, bursary, salaries, grants, etc. Furthermore, we must bear in mind that these estimates do not account for the possibility of taxonomic revision which, on its own, needs to be funded, nor do they account for molecular laboratory requirement. However, if we consider that 15% of the predicted funds are spent solely on taxonomic activities, this means that we would need ~R243 673 (US$ 14 334) on 1 species. Overall, our study provides an important figure that can inform policy development including funding and recruitment strategies of taxonomists to fuel efforts towards a comprehensive assessment of the unique South Africa’s biodiversity. The implication of new species discovery is that it opens windows for traditional knowledge development.

The article describes the methodology of the experimental analysis of operational and cost-effective assessment using geographic information system (GIS) technologies, as opposed to the assessment of the tourism potential of the nature of the Fergana Valley in Uzbekistan using long term and costly classical approaches. Based on the ArcGIS (10.8) grid index approach of 20 natural geographical indicators of the valley, such as geographical location, relief, climate, hydrographic and biological, the level of tourism comfort was determined and the location of 6 tourist-recreation zones were defined. The level of accuracy of the tourist-recreational zones all situated utilizing GIS technologies was confirmed by field-expedition approaches.

Background: School meals represent a significant supply of nutrients for children in Brazil, especially those in conditions of social vulnerability. Objectives: This study aimed to assess the levels of arsenic (As), cadmium (Cd), and lead (Pb) in meals served in public elementary schools in four municipalities in the state of Bahia, Brazil, and assess the risk posed to children's health. Methods: Ninety-six samples were collected from 16 schools, freeze-dried, and subjected to microwave-assisted digestion. The As, Cd, and Pb levels were determined by graphite furnace atomic absorption spectrometry. The risk assessment was based on calculating each element's hazard quotient (HQ). Results: None of the samples reached or exceeded the tolerable levels for the elements analyzed. Pb was the metal that obtained the most significant result, reaching maximum levels of 39-157 µg.kg-¹. Conclusions: No element exceeded the PTWI proposed by JECFA; thus, the toxic metal content in school meals poses a negligible risk to children´s health.

Post-earthquake fire is considered as a catastrophic secondary disaster to personal and property safety. In this article, an evacuation assessment of a complex rail transit station under the post-earthquake fire for sustainable buildings was conducted from the internal environment and external rescue based on Building Information Modeling (BIM) and Fire Dynamic Simulation (FDS). The internal environment evacuation assessment simulation experiments were conducted in six hypothetical high-risk scenarios. Besides, the external rescue assessment was based on investigation of the route and the rescue time required during different periods of holidays and workdays. The results show that: (1) The influence caused by different size of fire area in the power distribution room is smaller than those in the train at the platform floor. (2) In the case of fire scenarios with the same fire area but different fire locations, the impact on temperature is greater than that on CO concentration in power distribution room. (3) It shows slight differences between single-floor fire and double-floor fire on evacuation of small area fire in power distribu-tion room. Meanwhile, optimized design recommendations are proposed to reduce the risk of emergency evacuation in both internal and external environments of rail transit stations for sustainable future buildings.

Comorbidities can have a cumulative effect on hospital outcomes of care, such as the length of stay (LOS), and hospital mortality. This study examines patients hospitalized with Congestive Heart Failure (CHF), a life-threatening condition, which, when it coexists with a burdened disease profile, the risk for negative hospital outcomes increases. Since coexisting conditions co-interact, with a variable effect on outcomes, clinicians should be able to recognize these joint effects. In order to study CHF comorbidities, we used medical claims data from CMS. After extracting the most frequent cluster of CHF comorbidities, we: (i) Calculated, step-by-step, the conditional probabilities for each disease combination inside this cluster (ii) Estimated the cumulative effect of each comorbidity combination on the LOS and hospital mortality (iii) Constructed (a) Bayesian, scenario-based graphs and (b) Bayes-networks to visualize results. Results show that, for CHF patients, different comorbidity constructs have variable effect on the LOS and hospital mortality. Therefore, dynamic comorbidity risk assessment methods should be implemented for informed clinical decision making in any ongoing effort for quality of care improvements.

Soil quality is related to food security and human survival and development. In recent years, due to the acceleration of urbanization and the increase of abandoned land, land degradation occurs, poor topsoil quality. In this study, the minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics and influencing reasons of topsoil quality in Tieling County. The re-sults showed that MDS included total potassium (TK), Clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu). The MDS indicators can well replace all indicators to evaluate the topsoil quality in the study area. The overall soil quality of Tieling County showed a trend of low in the east and high in the west, and gradually increased from the hilly area to the plain area. The evaluation results are consistent with field research, which can provide reference for other topsoil quality evaluation, and it also provide a basis for the formulation of soil quality improvement measures.

Under the “Korean emission trading system in the forestry sector (KETSF)” initiative, the South Korean government has developed several greenhouse gas (GHG) emissions reduction programs that include forestry activities as the cornerstones of the initiative. Forest management is deemed to be a major strategy to implement KETSF; this has been confirmed by most participants in the program, who have shown their preference for forest management projects as the most effective and encouraging strategy to participate in the KETSF program. For a successful implementation of KETSF projects is essential to explore methods that optimize the positive impacts of such strategies, thereby maximizing the economic returns and carbon stocks that result from the implementation of forest management activities. Thus, this study investigated several value-added KETSF projects in South Korea, which included simulated scenarios under two main forest management strategies: one based on an extension of the rotation age, and a second one based on reforestation with new species. Five forest management scenarios were examined and evaluated in their ability to maximize carbon stocks and economic returns. Based on the results, Scenarios 2 and 4 were identified as the best KETSF projects in terms of carbon stock increments. Additionally, the results indicated that projects including reforestation with new species added more economic value than projects that considered an extension of the rotation age. The study also revealed that KETSF projects generated revenue in both scenarios, by either extending the rotation age or by implementing reforestation with new species.

COVID‑19 pandemic, also known as the coronavirus pandemic, has spread rapidly across the globe since the end of 2019 and brought impressive changes in our life and living through partial or full lockdown and reduced anthropogenic activities. Hence, it is imperative to investigate the impacts and consequences of COVID-19 on global eco-system and environment. The present study accordingly addresses the impacts and consequences of COVID-19 by ten environmental indicators; viz., global warming (or greenhouse gas emission), stratospheric ozone depletion, ozone formation (on human health), fine particulate matter formation, smog, ionizing radiation, human carcinogenic toxicity, human non-carcinogenic toxicity, water resource consumption, and effect on human health. The overall study has been performed in agreement with the standard principle and guidelines of life cycle assessment. Worldwide changes in consumption of fossil fuels, viz., petroleum, coal and natural gas, as a result of COVID-19 pandemic, has been the core theme of the study. Outcomes from the study show that COVID-19 has been a ‘blessing in disguise’ for the global environment with most of the above-mentioned indicators declining by approximately 20-30% in 2020 in comparison to the 2019 level.

Presepsin is a diagnostic and prognostic biomarker of sepsis; however, elevated presepsin levels have also been documented without sepsis. This study aims to retrospectively analyze the laboratory parameters and Sequential Organ Failure Assessment (SOFA) score affecting presepsin levels in 567 patients. Some patients with elevated presepsin levels exhibited renal dysfunction or elevation of biliary enzymes despite a low SOFA score. The univariate regression analysis revealed a close correlation between presepsin levels and SOFA score, serum creatinine (CRE), blood urea nitrogen, and biliary enzymes. In addition, a multivariate regression analysis revealed that SOFA score, alkaline phosphatase (ALP), and CRE independently affected presepsin levels significantly. The analysis of covariance (ANCOVA) revealed that presepsin levels were significantly higher in patients with hepatobiliary disease. Besides, we found that patients who presented with the dilatation of intra- or extrahepatic bile ducts and the elevation of ALP or total bilirubin exhibited remarkable high presepsin levels in the bile. Furthermore, the presepsin production in the liver’s Kupffer cells was established by immunostaining in patients who received surgical liver resection. Overall, this study elucidates that biliary enzymes’ elevation affects presepsin levels, presepsin exists in high concentrations in the bile, and is positive in Kupffer cells.

This article investigates if public investments in rural basic infrastructure represent the best strategy for boosting the local economy of rural communities from Romania. The article focuses on one specific program implemented under the Cohesion policy in the framework of the National Plan for Rural Development called Measure 322. Geographically, the research included a sample of rural communes from the North-Western Region of Romania. Moreover, the study also looks at other determinants of local economic development (LED) than infrastructure investments, with a focus on certain feature characterizing Romanian rural communities such as population size, isolation from urban centers, connection with European and national roads networks, educational stock, etc. The research included three steps, namely the construction of the LED Index, a cvasi-experimental research, and a regression model. Our main findings seem to suggest that while investments in infrastructure help the development gap between beneficiaries and non-beneficiaries remains relatively the same. In terms of determinants of LED, percentage of population with a university degree and connection to a European road are the most significant in the Romanian rural context.

The electrification of process industries is one of the main challenges when building a low-carbon society since they consume huge amounts of fossil fuel generating different emissions. Heat pumps are one of the instruments to perform industrial sector of carbon-neutral market players. This paper proposes an approach to improve the economic feasibility of heat pumps within the process plants. Initial energy targeting with Grand Composite Curves was used and supplemented with the detailed design of the evaporator and compressor for different condensation and evaporation pressures. The trade-off between the capital cost of the heat pump and the electricity cost was investigated and optimal configurations were selected. The case study investigates the gas fractioning unit of a polymer plant where three heat pumps were integrated into distillation columns. The results demonstrated that the heat recovery is 174 MW and requires an additional 37.9 MW of electricity to reduce hot utility by 212 MW. The selection of evaporation and condensation pressure of heat pumps allows saving of 21.5 M€/y for 7 years of plant operation.

This study presents the development and pilot implementation of an assessment and shared deci-sion-making methodology called the Gezamenlijk Inschatten van Zorgbehoeften (GIZ), designed for youth (health) care professionals working with children and families. The three-phase approach included a needs assessment, development of the GIZ methodology, and pilot implementation in a preventive youth health care and youth care setting, involving parents, adolescents, and youth (health) care professionals. The needs assessment produced nine criteria that the GIZ methodology needed to meet. Based on these criteria, the GIZ methodology was developed and described using the Template for Intervention Description and Replication (TIDieR). The pilot implementation demonstrated high client engagement, satisfaction, and feasibility in daily practice. The GIZ ap-proach promotes appropriate support for families by improving the joint assessment and shared decision-making process in youth (health) care. This study supports the implementation of GIZ in youth (health) care practice and highlights the need for further focus on implementation and sus-tainability.

Among others, road traffic, industrial emissions, commercial activities, smoking and cooking are considered as major contributing factors for the increasing levels of pollutants in atmosphere. High levels of potentially toxic metals and microbes in atmosphere, especially in indoor air, may pose serious threat to human health. Therefore, concentration and associated health risks of potentially toxic trace metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their risk to human health, and microbial load in indoor air was assessed using air condition (AC) filter dust samples collected from 5 locations representing residential, agricultural and industrial settings of Eastern Province, Kingdom of Saudi Arabia. The levels of trace metals varied considerably among sampling areas, with the highest levels of Cr and Cd recorded in the Industrial-area sites followed by the Agricultural and Urban-Residential sites. The highest levels of Pb and Fe were found in the Agricultural area sites followed by the Industrial and Urban-Residential area sites. The metals in dust sample, especially Cd, Cr and Pb, showed a considerable health risk through dermal pathway. Among the sites, the highest hazard quotient for these metals was found for Al-Qatif-Industrial areas sites and among the metals it was the highest for Cd. The cancer risk from the metals contained in AC filter dust was negligible. Samples collected from Agricultural and Industrial area sites were substantially contaminated with bacteria and fungi, respectively. Bacterial contaminants were mostly Gram Negative, with considerable antibiotic resistance and haemolytic activity. Thus, indoor air quality as assessed by AC filter dust depicted that a considerable health risk could be posed by the trace heavy metals and microorganisms for a long-term exposure. Furthermore, this study demonstrated that AC filters dust could be a unique and reliable test sample for the assessment of indoor environment.

Background: Telemedicine (interconsultation between primary and hospital care teams) has been operating in the counties of Central Catalonia Bages, Moianès and Berguedà since 2011 in the specialties of teledermatology, teleulcers, teleeyelids and teleaudiometries. For the period until the end of 2019, a total of 52,198 visits have been recorded. Objective: To analyse the differential costs between telemedicine and usual care in a semi-urban environment. Methodology: A cost-minimization evaluation, including direct and indirect costs from a societal perspective, distinguishing healthcare and user’s costs, within a three-month period. Results: Telemedicine saved € 780,397 over the period analysed. A differential cost favourable to telemedicine of about € 15/visit has been observed, the patient being the largest beneficiary of this saving (by 85%) in terms of shorter waiting times and travel costs. From the healthcare system perspective, moving the time spent in a hospital care consultation to primary care is efficient in terms of the total time devoted per patient. In social terms and in this context, telemedicine is more efficient than usual care. Conclusion: Users’ saving of time in terms of consultation and travel is the main driver of interconsultation between primary and hospital care savings in a semi-urban context. The telemedicine service is also economically favourable for the healthcare system, enabling it to provide a more agile service, which also benefits the healthcare professionals.

Pharmacovigilance databases are showing evidence of injury in the context of the COVID-19 modified mRNA shots. According to recent publications, adverse event reports linked to the mRNA COVID-19 products largely point to the spike protein as an aetiological agent of adverse events, but we propose that the platform itself may be culpable. To assess the safety of current and future mRNA vaccines, further analysis on the risks due to the platform itself, and not specifically the expressed antigen. If harm can be exclusively and conclusively attributed to the spike protein, then it is possible that future mRNA vaccines expressing other antigens will be safe. If harms are attributable to the platform itself, then regardless of the toxicity, or lack thereof, of the chosen payload therein, the platform may be inherently unsafe, pending modification. In this work, we examine previous studies of RNA-based delivery by a lipid nanoparticle and break down the possible etiological elements of harm.

The competitiveness of domestic products at the regional or global market is one of the cornerstones of a country’s internationalcompetitiveness. In this regard, the assessment and analysis of the competitiveness of a country’s international trade becomes an important issue. Thus, development and diversification of the export potential of the country is viewed as one of the most important directions of the international trade policy of the given country. Therefore, another important task is the specialization of the country in the most efficient and competitive segments of the economy. The above-mentioned circumstances embody the fact, that the role of quantitative assessment of potential competitive advantages is important, as it allows determining to what extent certain factors influence on the formation of export capacity in the country. The main objective of the study is to determine the role of export competitiveness in the process of competitive advantages formation of a country. This study mainly focuses on quantitative analysis based on the calculation of Balassa index, determining the extent to which a country has a comparative advantage in producing and exporting certain goods. The study presents the example of the Republic of Armenia, the study covers the period between 2002- 2016. Based on the index calculation, it can be determined whether the country has "revealed" comparative advantage or not. The practical significance of the study lies in the fact that its main findings and conclusions arising from it can be used in assessing the export competitiveness of not only Armenia, but also for other countries or groups of countries.The outcomes and implication of the research can be used to improve the competitiveness of goods and services in the global market.

LoRa and its upper layers definition LoRaWAN is one of the most promising LPWAN technologies for implementing the Internet of Things (IoT). Although being a popular technology, several works in the literature have revealed various weaknesses regarding the security of LoRaWAN v1.0 (the official 1st draft). By using all these recommendations from the academia and industry, the LoRa-Alliance has worked on the v1.0 to develop an enhanced version and provide more secure and trustable architecture. The result of these efforts ended-up with LoRaWAN v1.1, which was released on Oct 11, 2017. This manuscript aims at demystifying the security aspects and provide a comprehensive Security Risk Analysis related to latest version of LoRaWAN. Besides, it provides several remedies to the recognized vulnerabilities. To the best of authors’ knowledge, this work is one of its first kind by providing a detailed security analysis related to latest version of LoRaWAN. According to our analysis, end-device physical capture, rogue gateway and replay attacks are found to be threating for safety operation of the network. Eventually, v1.1 of LoRaWAN is found to be less vulnerable to attacks compared to v1.0, yet possesses several security implications that need to be addressed and fixed for the upcoming releases.

When disposing of waste metal resources in landfills, environmental issues such as soil contamination may arise. Recycling these resources not only recovers valuable metals but also mitigates environmental pollution. Platinum (Pt), a valuable metal used in fuel cells for its high water production activity, will see increased future demand as a fossil fuel alternative. This study analyzes the environmental and resource reduction effects of recycled Pt, considering the growing emphasis on its recycling for stable supply and demand of Pt. The environmental impact and resource consumption of recycled Pt with primary Pt (from natural mines) were compared and analyzed using the Life Cycle Assessment (LCA) technique. Results revealed that resource consumption for primary Pt was 8.25E+01 kg Sb-eq./kg, significantly more than the 5.45E+00 kg Sb-eq./kg for recycled Pt. This represents an environmental reduction effect of approximately 93%. In the case of greenhouse gas emissions, primary Pt emitted 1.35E+04 kg CO2-eq./kg, while recycled Pt emitted 6.94E+02 kg CO2-eq./kg, resulting in an environmental reduction effect of approximately 95%. In conclusion, recycling Pt, compared to primary extraction, offers substantial environmental and resource reduction benefits. This study underscores the significance of recycling and highlights the potential environmental improvements achievable through sustainable practices.

Rice cultivation is the main agricultural activity in Asia, and it results in an enormous amount of agricultural waste, that are usually disposed-off by burning, causing serious environmental health and climate problems. Pakistan is the tenth largest producer in the world and the fourth exporter of rice in the world. To date, there are no studies targeting Pakistan’s profile monitoring its contribution in open burning emissions from rice straw waste. This study presents the characterization of PCDD/Fs, PCBs and PAHs emissions from three samples of rice straw collected from the main rice cultivation regions of Pakistan (Punjab, Sindh and Azad Jammu and Kashmir) in relation to the role of metal-based agricultural additives. PCDD/Fs, PCBs and PAHs emissions were quantified using a fixed grid system biomass plant and measuring the macro-pollutants online and sampling the organic micro-pollutants and metals in isokinetic mode, followed by ISO and CEN standards methods. In addition, role of agrochemicals was monitored in terms of metal catalysis resulted in POPs emissions. This study provides the characterization of most relevant group of pollutants (metals, PCDD/Fs, PCBs and PAHs) as a result of agricultural waste combustion. The rice straw samples from AJK showed high values of organochlorine micro-pollutants compared to the values followed by samples from Punjab and Sindh, i.e., respectively, ∑PCDD/F (2594>1493>856 pg/Nm³) and ∑PCB (41>38>30 pg/Nm³). Whereas the organic micro-pollutants ∑PAH, indicators of incomplete combustion (PICs), were recorded high in the samples from Sindh followed by Punjab and AJK. This study supports the idea that the phenomena that lead to the formation of PCDD/F and PCB are influenced not only by poor combustion but also by the presence of metal catalysts or precursors in the burned fuel.

Scientists observe, discover, justify and eventually share their findings with the scientific community. Dissemination is an integral aspect of scientific discovery since discoveries which go unnoticed have no or little impact on science. Today, peer-review is part of this process of scientific dissemination as it contributes proactively to the quality of a scientific article. As the numbers of scientific journals and scientific articles published therein are increasing steady, processes such as the single-blind or double-blind peer review are facing a near collapse situation. In fact, these traditional forms of reviewing have reached their limits and, because of this, are also increasingly considered as unfair, sloppy, superficial and even biased. In this manuscript we propose forms of Post Publication Public Peer Review (P4R) as valuable alternatives to the traditional blind peer review system. We describe how the journal Sci has explored such an approach and provide first empirical evidence of the benefits and also challenges such a P4R approach is facing.

Radiological risk affect the quality of the environment in buildings since population and workers can be potentially exposed to high level of dose. Radon gas emanated from both subsoil and building materials represents the most important source of radiation exposure for people. This study investigates the sustainability concept of a small rural village of Ischia Island, named Ciglio, in relation to the radiological risk. Radon activity concentration was measured in typical green tuff dwellings and in water samples collected from a local spring using E-Perm devices. Moreover, for green-tuff as building material, the radon emanation coefficient was calculated by gamma spectroscopy. The results highlight the importance to perform environmental radon monitoring and to investigate the radon content of building materials, especially in geographical areas characterized by traditional use of typical stones for constructions. In conclusion, the sustainability development of rural buildings is possible if the radiological risk for inhabitants and workers was assessed.

Green-space resources, in the context of urbanisation, cannot meet the actual needs of residents well, and the study of the balance of green-space resource allocation from the relationship between supply and demand is an urgent problem to be solved. This study quantitatively evaluates the green-space supply level from four dimensions by constructing a framework for assessment in the community life circle. It also evaluates the matching of green space supply and demand, resource distribution fairness, and distribution equilibrium under group differentiation through the supply–demand coupling matrix, Gini coefficient, and the Kruskal–Wallis H non-parametric rank-sum test, respectively. This study shows that (1) a significant spatial imbalance exists in green-space resource allocation in community life circles in different dimensions. (2) The comprehensive green-space supply level in the community life circle matched the total demand of residents to a low degree. (3) There was significant inequality in green-space resource allocation within the community life circle (the Gini coefficient of each evaluation perspective was > 0.4). Based on the study results, we were able to identify community life circles with spatial mismatches, different supply and demand, and other green-space resource allocation problems, which is of great significance to urban green-space research and planning practice under the "green justice" framework.

Building carbon footprint analysis is a key instrument for assessing the impact of different constructions on climate change. Several standards and methodologies are available to calculate the footprint of buildings, including standards and norms, life cycle assessment (LCA), and dedicated software tools. The use of BIM software for these calculations is both scientifically justified and very practical. This scientific publication focusses on the application of a BIM-based research methodology for the analysis of the carbon footprint of a single-family house. The research process included the following steps: (i) design of a single-family house with timber frame construction using Archicad BIM software, (ii) simulation of the building energy performance using the EcoDesigner Star plug-in, (iii) life cycle assessment (LCA) using the plug-in for Archicad, (iv) preparation of a second model with traditional masonry construction for comparison, and (v) comparative analysis of the single-family house models with timber frame and masonry construction. Analysis of the results highlights significant differences in CO2e emissions between masonry and timber buildings and the varying impact of individual elements on the total CO2e emissions of the buildings studied. These findings are relevant for future work on sustainable building design and construction, which aims to minimise negative environmental impacts. The goal of minimising the cumulative carbon footprint of buildings is critical to achieving the Sustainable Development Goals and combating climate change.

While the container crane is an important part of daily port operations, it has received little attention compared with other infrastructures, such as buildings and bridges. Crane collapse due to earthquake affects the operation of the port, and indirectly impacts the economy. This study proposes fragility analyses for various damage levels of the container crane that allow the port owner and partners to better understand the seismic vulnerability presented by container cranes. A large quantity of nonlinear time history analyses was applied for a three-dimensional (3D) finite element model to quantify the vulnerability of the container crane in considering the uplift and derailment behavior. The uncertainty of demand and capacity of the crane structures were also considered through random variables, i.e. elastic modulus of members, ground motion profile, and intensity. The results analyzed in the case of a Korean container crane showed that the probability of exceeding the first uplift with or without derailment is shown before the crane reaches the structure’s limit states. This means that under low seismic excitation, the crane might be derailed without any structural damage. But when the crane reaches the minor damage state, it is always coupled with a certain probability of uplift with or without derailment. This study also proposes the fragility curves developed for different structural periods to enable port stakeholders to assess the risk of their container crane.

Objective: to evaluate the effects of excitatory repetitive transcranial magnetic stimulation (rTMS) of the international 10-20 system P3 point (intraparietal sulcus region) in chronic patients with a frontal lesion and parietal sparing due to stroke on the impaired upper (UL) and lower limb (LL) as measured by Fugl-Meyer Assessment (FMA). Methods: three patients (C1: 49.83/2.75, C2: 53.17/3.83, C3: 63.33/3.08 years-old at stroke/ years post-stroke, respectively) received two weeks (five days/ week) of rTMS at 10 Hz of P3. A patient was treated in similar conditions with a sham coil (S1: 56.58/4.33). No complimentary therapy was delivered during the study. Patients were evaluated before, after- and two months post-treatment (A1, A2 and A3, respectively). Results: we found increased scores for LL in motor function subsection for C1 and C3 and in sensory function for C2 by A2 that remained at A3. We also found an increased score for UL motor function for C2 and C3, but the score decreased by A3 for C2. C3 score for UL range of motion increased by A3 compared to A1 and A2. Conclusion: In a variable way, P3 excitatory rTMS increased FMA scores in different upper and lower limb subsections of our three treated patients.

With growing global concern of food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world, where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications on national policy, project’s annual budgets, and donor fund-ing. Here we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006-2011) and Landsat 8 OLI (2013-2022) images were used to develop a machine-learning model which classifies irrigated and non-irrigated areas in study areas. The random forest classification achieved overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and the consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine-learning model outperforms the existing global cropland map, highlighting the need of such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated area with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.

Cold brew coffee has emerged as a new trend during the last decade. However, “cold brew” is an extraction style of ground roasted coffee with water at less than body temperature (typically 8°C or room temperature) rather than a beverage per se. Cold brew extraction poses several challenges including the need of specific optimization depending on the multivariate influences of coffee variety and processing, roast degree, grinding, dosage, water composition, turbulence, brew system (drip, immersion etc.), time and temperature. While cold brew is typically characterized by a floral sweetness, over-extraction may lead to abundant acidity and bitterness. To avoid this, an extraction degree of 70% was suggested using rather shorter times (i.e. 2 h at 15°C with 80 g/L coffee with optimized medium roast profiles). Due to the lack of sterilizing temperatures during preparation, cold brew is special in the coffee sector because hygiene and food safety aspects pose specific challenges for food industry. To avoid microbiological contamination and spoilage, cold brew should be as freshly prepared as possible and shelf-life should be minimized.

Disposable absorbent hygiene products have evolved for superior performance, enhancing the convenience of daily lives. Yet the use of disposable hygiene pads has brought safety concerns on chemical exposure, and significant efforts have been made to assess the potential risks associated with use of hygiene pads. This article intends to overview the safety assessment framework of diapers and feminine pads, which includes hazard identification, hazard characterization, exposure assessment, risk characterization, and postmarket risk management. Risk assessment of various constituents are reviewed for quantification methods and conservative estimation of exposure parameters. By reviewing the up-to-date considerations in risk assessment, we aim to provide insightful discussion on safety evaluation of current versions of disposable absorbent products. More clinical testing and postmarket surveillance are needed for continuous monitoring of potential health impacts of advanced products and constituents.

(1) Background: The aim of the work was to characterize the climatic evolution and change based on the Multi Criteria Classification through the dynamics of bioclimatic indices in viticulture across Italy and its regional administrative boundaries, focusing on latitudes and elevations. (2) Methods: The impact of climate change on viticulture in Italy was analysed and spatialized with reference to historical data from 1991 to 2021 and to the Future Scenarios up to 2080 assumed by SSP2-4.5 and SSP5-8.5, taking into account 13 GCMs. (3) Results: The bioclimatic indices have all shown a significant trend in the historical period, with an increase in temperature and a decrease in precipitation, reflecting their effects on the entire Italian territory with respect to HI, up to 44°N for CI and up to 46°N for DI, regardless of altitude. The Future Scenarios highlighted a shift towards the warmer classes of the two temperature-based indices (HI and CI) for both SSPs, especially for altitudes up to 900 m a.s.l.. The DI-based classification based on DI remained relatively stable in Italy over time, although DI values will become increasingly negative in the near future. (4) Conclusions: The climate in Italy is warming, especially in the south and in the coastal regions. By 2080, more areas will be “Very Hot” with “Warm Nights.” Drought will also increase and have a negative impact on viticulture. The importance of higher latitudes and altitudes in mitigating the effects of climate change justifies the continuing trend towards the relocation of vineyards as a medium-term solution and as an alternative to targeted cultivation methods that must be adopted in the short-term to safeguard the suitability of an area to quality viticulture.

In the European Union, Occupational Safety and Health legislation generally refers to European Regulation (CE) n. 1272/2008 to define and classify carcinogens of concern for occupational risk assessment and exposure assessment. In Europe, the current reference is Directive (UE) 2022/431, regarding carcinogen, mutagen and reprotoxic agents (CMRs) exposure. However, at worldwide level, different classification approaches are used to establish carcinogenicity of substances and it is often difficult to compare the classifications of carcinogenicity (CoCs) proposed by different international bodies. This study aims to investigate a list of carcinogens of concern in occupational settings based on the CLP (Classification Labelling Packaging) CoC, and to create a tool that allows a rapid translation-comparison of some international CoCs with the reference one. CoCs proposed by various sources were consulted and used to apply a translation method, to favor an alignment of different CoCs according to a reference. Results outlined that considering diverse sources, CoCs can result in different classifications of the same chemicals. Overall, this may have implications for the hazard assessment process, which is the base of risk assessment. The proposed tool is expected to help risk assessors in the occupational field when it is needed to have a comparison with different CoC systems.

Flooding during the rainy season poses a significant threat to the city of Hargeisa, impacting the well-being of both its residents and the local ecosystems. This research focuses on urban flood control, with a specific emphasis on finding sustainable solutions for managing large stream in Hargeisa. The study identifies five key dimensions, including infrastructure, transportation, environmental soil, life, and property, as critical factors in addressing the issue. By employing a comparative method and collecting data through closed-ended questions, the research aims to analyze and weigh eleven potential solutions, seeking to identify the most effective approaches. The findings of the study indicate that constructing a dam emerges as a promising and sustainable solution, with a weightage of 70 percent. This highlights the potential of a dam in mitigating the impact of floods in the region. Additionally, considering the government's economic capacity, the implementation of check dams is identified as another viable option, with a weightage of 95 percent for reducing the flood power of the stream. These solutions offer practical and effective measures to address the recurring problem of flooding in Hargeisa. To support future decision-making and planning, the research underscores the need for establishing a comprehensive rainfall data database. This database would store relevant information on rainfall patterns, enabling better understanding and prediction of flood events. The study emphasizes the importance of collaboration between local government authorities, environmental organizations, and stakeholders to develop and implement effective flood control strategies. This research provides a systematic approach that can inform and benefit regional and global researchers, policymakers, and municipal governments in their efforts to address urban flood control challenges, not only in Hargeisa but also in similar contexts worldwide.

The present work fronts the human pose estimation problem, developing a method that enables automated ergonomic risk assessment. A research methodology is developed to calculate joint angles from digital snapshots or videos by using computer vision and machine learning techniques to get a more accurate ergonomic risk assessment. Starting from an ergonomic analysis, the study explores the use of a semi-supervised training method to detect the skeletons of workers. The research methodology developed aims to infer the positions and angles of the joints, to calculate the criticality index based on the RULA scores and fuzzy rules. Then, to prevent work-related musculoskeletal disorders (WMSD), improve production capacity and decrease the ergonomic risk, the system uses joint with a collaborative robot to support the worker in carrying out the most critical operations. The method has been tested on a real industrial case in which manual assembly activities of electrical components are conducted. The approach developed can overcome the limitations of recent developments based on computer vision or wearable measurement, sensors by performing an assessment with an objective and flexible ap-proach to postural analysis development.

(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback and teaching. Phase duration itself and its correlation to clinical parameters has not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet. (2) Methods: We assess 100 robot-assisted partial nephrectomy videos for phase duration based on previously defined proficiency metrics. We develop an annotation framework and subsequently compare our annotations to an existing commercial solution (Touch Surgery, Medtronic™). We subsequently explore clinical correlations between phase durations and peri-operative parameters. (3) Results: Objective and uniform phase assessment requires precise definitions derived from an iterative revision process. Comparison to a commercial solution shows large differences in definitions across phases. BMI and duration of renal tumor identification correlate positively, as well as tumor complexity and both tumor excision and renorraphy duration. (4) Conclusions: Surgical phase duration can be correlated with certain clinical outcomes. Further research should investigate if retrieved correlations are also clinically meaningful. This requires increasing dataset sizes and facilitation through intelligent computer vision algorithms. Commercial platforms can facilitate this dataset expansion and help unlock the full potential, provided the disclosure of phase annotation details.

When undertaking wind assessment around buildings using large eddy simulation (LES), the implementation of the integral length scale at the inlet for inflow generation is controversial, as real atmospheric length scales require huge computational domains. While length scales significantly influence inflow generation in the domain, their effect on the downstream flow field has not yet, been investigated. In this paper, we validate the effectiveness and accuracy of implementing a reduced turbulence integral length scale for inflow generation in LES results at the rooftop of low-rise buildings and develop a technique to estimate the real local length scales using simulation results. We measure the wind locally and calculate the turbulence length scales from the energy spectrum of the wind data and simulation data. According to these results, there is an excellent agreement between the length scale from simulation and measurement when they are scaled with their corresponding freestream/inlet value. These results indicate that a reduced integral length scale can be safely used for LES to provide a reliable prediction of the energy spectrum as well as the length scales around complex geometries. The simulation results were confidently employed to obtain the best location for a wind turbine installation on low-rise buildings.

Damage assessment is one of the most crucial issues for bridge engineers, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge. In this regard, the artificial intelligence (AI)-based approach seems a potential way to accomplish those obstacles. This study deploys a comprehensive campaign to determine all dynamic parameters of a pre-damage steel truss bridge structure. Based on the results of mode shape, natural frequency, and damping ratio, a finite element model (FEM) is created and keeps updating. The artificial intelligence network's input data will be analyzed and evaluation from damage cases. The trained artificial neural network model will be curated and evaluated to confirm the approach's feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system is showing good performance in terms of monitoring the structural behavior of the bridge under some unexpected accidents.

Strengthening the analysis and risk assessment of the pig epidemic will help to better prevent and mitigate epidemic risks and promote high-quality development of the pig industry. Based on the systematic grasp of the situation of live pig epidemics, a risk assessment index system was constructed, and the spatial and temporal variation characteristics of the risk of pig epidemics in China were explored by the entropy method. In recent years, the overall trend in pig epidemics in time first increased and then decreased, and in space, the trend in the acceleration of the spread across the country continued to weaken. China still faces challenges, such as many types and a wide range of diseases, large total livestock breeding and weak epidemic prevention and control capacity, and a large risk of introduction of foreign animal epidemics. The spatial and temporal variation in the pig epidemic risk was obvious; there was one high-risk area, two higher-risk areas and 10 medium-risk areas in recent years, and the epidemic risk was highest in Beijing, Hainan, Liaoning, Tibet and Zhejiang. However, there were significant differences in the regional distribution of the risk of pig epidemics in different years. To further build a secure “defense system” for the high-quality development of the pig industry, it is recommended to improve the monitoring and early warning system of pig epidemic risk, perfect the pig epidemic prevention and control system, and strengthen the regional collaboration mechanism of epidemic prevention and control.

This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

Practitioners who assess the risk associated with urban trees often factor in the presence or absence of visual tree defects when determining whether a tree may fail. While these defects are a main fixture in many tree risk assessment systems and best management practices, the research supporting their usefulness in predicting tree failure during storms is limited. When looking at past research involving populations of storm-damaged trees, there are several defects that have never predicted failure (or have been associated with reduced rates of failure). In this study, we took a closer look at four such defects: codominant branches; branch unions with included bark; multiple stems originating from the same point; and overextended branches. After Hurricane Ian, we revisited 1519 risk assessed trees where one of these four defects was identified as the primary condition of concern. Fourteen of these trees experienced branch failure during the storm (which hit the study area as a downgraded tropical storm). Upon closer inspection, none of these failures occurred at the defect of concern. Our findings indicate that none of the defects assessed appeared to increase the likelihood of tree failure in the species tested. Our results are in line with past research on these defects derived from post-storm assessments and analysis.

Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has a large potential for further applications, for example in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of the photoplethysmogram. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated to the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as de-terminism and entropy can be estimated with an error lower than 1% even for short photople-thysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.

Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educative counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasability study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone unsupervised during their daily routine over a period of 4 months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in the tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of .408, while the improvements on the THI were much smaller with an effect size of .168. Analysis on the user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively, reported a stronger improvement of the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.

Currently available nationwide prediction models for fine particulate matter (PM_2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM_2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM_2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m³, an R² of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m³ for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

Background and Objectives: Sports medicine, orthopaedic and rehabilitation physicians use gait analysis and goniometry to evaluate and diagnose patients with neuro-musculo-skeletal diseases. Goniometry is a measuring method that shows the joint’s range of motion. Three-dimensional goniometry has been used in order to assess the patients in a kinematic manner, but they are not affordable, so, phone apps come handy to any orthopaedic or rehabilitation physician so we can have a clear image of the progress made by the patient after the rehabilitation program or should something happen and the patient cannot come to the facility, such as the Covid-19 pandemic, when some orthopaedic and most of the rehabilitation facilities have been temporarily closed. Midstance has been chosen as the moment of the gait evaluation in this paper since it has an important role in stability. The objective of this paper is to figure out if the measurements taken during midstance at the knee joint of the subjects can be statistically significant and used during usual examinations. Materials and Methods: Four groups of subjects: patients suffering from hip, knee, hip and knee osteoarthritis and a control group volunteered their participation, being asked to normally walk while their gait was recorded and uploaded into Angles App – a phone based videogoniometer. Results: Patients suffering from hip osteoarthritis have a higher knee angle on the right side than the ones suffering from knee osteoarthritis, hip and knee osteoarthritis and the control group. Female patients suffering from hip osteoarthritis also presented a more flexed knee than the ones suffering from knee osteoarthritis and the ones in the control group, the knee flexion presenting itself as a compensation mechanism. Conclusion: Video goniometry can help us make an orthopaedic, rehabilitation or neurology database for each assessment with constant updates of the evolution of the osteoarthritis patient’s treatment.

Institutional support and quality education are linked in a significant way. During Covid-19, institutional support is critical to closing the huge academic gap that has emerged as physical academic practices have been moved to a virtual education system using technology. This research aims to assess institutional support for online education in Bangladesh during the Covid-19 pandemic. This analysis is focused on the three main elements of the Adapted Model of Institutional Support (AMIS) of Bond et al, 2007, and the Institutional Support Model (ISM) of Valverde and Rodriguez, 2002, namely Financial Support (FS), Technical Support (TS), and Mentoring Support (MS). According to the findings, a few universities in Bangladesh have provided average support for continuing online education, while others have just started taking online classes. Several problems have been discovered, such as the lack of dedicated software for conducting online academic activities, lack of training and grooming, lack of mentoring, poor internet access, lack of smartphones, high internet package rates, and so on. This study concludes with some policy recommendations for a smooth online education system in Bangladesh.

Profiling technologies, such as proteomics, allow the simultaneous measurement and comparison of thousands of plant components without prior knowledge of their identity. The combination of these non-targeted methods facilitates a more comprehensive approach than targeted methods and thus provides additional opportunities to identify genotypic changes resulting from genetic modification, including new allergens or toxins. The purpose of this study was to investigate unintended changes in GM Bt maize grown in South Africa. In the present study, we used bi-dimensional gel electrophoresis based on fluorescence staining, coupled with mass spectrometry in order to compare the proteome of the field-grown transgenic hybrid (MON810) and its near-isogenic counterpart. Proteomic data showed that energy metabolism and redox homeostasis were unequally modulated in GM Bt and non-GM maize variety samples. In addition, a potential allergenic protein – pathogenesis related protein -1 has been identified in our sample set. These finding highlight the suitability of unbiased profiling approaches to complement current GMO risk assessment practices worldwide.

There are uncertainties associated with every phase of the stock assessment process, ranging from the collection of data, assessment model choice, model assumptions and interpretation of risk to the implementation of management advice. The dynamics of fish populations are complex, and our incomplete understanding of those dynamics (and limited observations of important mechanisms) necessitate that models are simpler than nature. The aim is for the model to capture enough of the dynamics to accurately estimate trends and abundance and to provide advice to managers about sustainable harvests. The \textit{status quo} approach to assessment modelling has been to identify the `best' model, based on diagnostics and model selection criteria, and to generate advice from that model, mostly ignoring advice from other model configurations regardless of how closely they performed relative to the chosen model. We review the suitability of the ensemble modelling paradigm to more fully capture uncertainty in stock assessment model building and the provision of advice. We recommend further research to evaluate potential gains in modelling performance and advice from the use of ensemble modelling, while also suggesting revisions to the formal process for reviewing models and providing advice to management bodies.

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

Additive manufacturing (AM) is a promising new technology that is having a very fast growth from home workshops to high-tech cutting-edge factories. As any manufacturing technique, adequate metrology services are needed to assure the quality of items manufactured by AM. One of the most widely used instruments to measure the characteristics of surfaces manufactured with AM is the confocal microscope. In this paper, authors present a whole calibration procedure for confocal microscopes designed to be implemented preferably in workshops or industrial environments rather than in research and development departments. Because of that, it is as simple as possible. The procedure is designed without forgetting any of the key aspects that need to be taken into account and based on classical reference material standards. These standards can be easily found in industrial dimensional laboratories and easily calibrated in accredited calibration laboratories.

In this case study, an active runway of a civilian airport in Zonguldak, Turkey, is used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the ASTER, the AW3D30 m, the SRTM-1”, the SRTM-3”, the SRTM-X, the TanDEM-3”, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line leveling survey of the runway using a Leica Sprinter 150/150M instrument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. The most accurate DEM is the WorldDEMTM followed by the SRTM-3” and TanDEM-3”, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method for identifying the vertical errors in DEMs that is known as the runway method.

This report presents a framework for analysing the risk of alien taxa under South Africa's National Environmental Management: Biodiversity Act of 2004, and the Alien and Invasive Species Regulations of 2014. While the report was initially designed to meet a specific South Africa need, the risk analysis processes developed can, we believe, be transferred to any specified geographic region. In outlining a series of questions related to a taxon’s likelihood of invasion and the consequences thereof, i.e. the potential impacts, the report provides a structure for collating data relevant to the process of listing taxa as well as a process for developing recommendations that is both mathematically sound, transparent, and that explicitly takes uncertainty into account. The framework is based on collating information according to international standards in biological invasions (specifically the IUCN Environmental Impact Classification of Alien Taxa Scheme, the CBD's scheme for classifying invasion pathways, and the Unified Framework for Biological Invasions proposed by Blackburn et al. 2011). The risk analysis framework is currently being implemented in South Africa in an effort to underpin national regulatory lists of invasive species.

Keyhole neurosurgery implies reaching a target area inside the brain through an entry point specified by the neurosurgeon. In order to avoid complications, a risk assessment procedure must be done to establish the minimum risk trajectory from the entry point to the target area. The neurosurgeon establishes the risk values for the brain structure according to the type of intervention. The preset brain structure risk value is used to assess the risk value for each voxel of the brain. This paper proposes an improved risk assessment methodology based on the sum of N maximum risk values for each voxel. Then, risk assessment for a trajectory is done by adding the risk of all voxels that are part of the path. The safest trajectory is defined as the trajectory with the lower risk. Our proposed search trajectory methodology includes a Genetic Algorithm (GA) for finding the safest trajectories. The use of a GA drastically reduces the number of trajectories to analyze, speeding up the planning procedure. The achieved results were qualified by expert neurosurgeons as satisfactory. Our proposed method allows neurosurgeons to calibrate the surgical planning system by allowing them to establish the risk brain structure and the risk value for each structure.

The rapid growth rate of the elderly population is a serious current issue in most countries, affecting them economically through needed medical treatment and healthcare planning. The priority concern is how to reduce the number of elderly people requiring long-term healthcare and raise the number who are able to live independently. This study executed a behavior assessment of elderly person’s self-reported use of electric scooters and analyzed their degree of acceptance of these assisted living tools, partly through a related factor analysis of our survey instrument. We used this questionnaire survey as our research method, applying SPSS22 software for factor analysis that revealed five survey facets.

This paper formulates a new strategy for participatory forest management consisting of encouraging public participation as long as it increases empathy among participants. The strategy requires the homogeneous representation of the opinion of a participant (i.e. to determine how they assess a forest plan and identify the best one). Utility assessments are prepared for participants through pair-comparisons between meaningful points in the territory and from value functions based on forest indicators. The best plan is designed by applying combinatorial optimization algorithms to the utility of a participant. The calculating of empathy -of one participant relative to another - is based on the equivalence of their respective utilities when the current forest plan is modified. This involves calculating the opinions that are due to systematic changes in the collective plan for those participants that each participant supposes will affect the utility of the other participants. Calculating empathy also requires knowing the interactions among participants, which have been incorporated through agent-based simulation models. Application of the above methodology has confirmed the association between increases in empathy and convergence of opinions in different scenarios: well and medium-informed participants and with and without interaction among them, which verifies the proposed strategy. In addition, this strategy is easily integrated into available information systems and its outcomes show advantages over current participatory applications.

Bonfire night is a worldwide phenomenon given to numerous annual celebrations characterised by bonfires and fireworks. Since Thailand has no national ambient air quality standards for metal particulates, it is important to investigate the impacts of particulate injections on elevations of air pollutants and ecological health impacts resulting from firework displays. In this investigation, Pb and Ba were considered potential firework tracers because their concentrations were significantly higher during the episode and lower than/comparable with minimum detection limits during other periods, indicating that their elevated concentrations were principally due to pyrotechnic displays. Pb/Ca, Pb/Al, Pb/Mg, and Pb/Cu can be used to pin-point emissions from firework displays. Air mass backward trajectories (72 h) from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated that areas east and north-east of the study site were the main sources for the air transportation. Although the combined risk associated with levels of Pb, Cr, Co, Ni, Zn, As, Cd, V, and Mn was far below the standards mentioned in international guidelines, the lifetime cancer risks associated with As and Cr levels exceeded US-EPA guidelines, and may expose inhabitants of surrounding areas of Bangkok to elevated cancer risk.

In recent years, there is a growing concern about the alarming spread of antimicrobial resistance (AMR) in different environments. Increasingly, many species of bacteria, fungi and viruses are becoming immune to the most commonly used pharmaceuticals. One of the causes of the development of the resistance is the persistence of these drugs, excreted by humans, in municipal and hospital wastewater (WW). Consequently, wastewater treatment plants (WWTPs) are a primary source of antimicrobial resistance genes as novel pollutants. This systematic review sought to examine the relevant literature on pharmaceutical residues (PRs) responsible for AMR in municipal and hospital WW in order to propose a classification of the PRs of greatest concern and provide an updated source for AMR management in WWTPs. Among 546 studies collected from four databases, 18 were included in the present review. The internal and external validity of each study was assessed, and the risk of bias was evaluated on a 20-parameter basis. Results were combined in a narrative synthesis discussing influents and effluents PR concentrations at 88 WWTPs, seasonal variations, differences between hospital and municipal WW, environmental risk assessment values of antimicrobial substances and treatment facilities removal efficiencies. Among the 45 PRs responsible for AMR evaluated in this study, the antibiotics ciprofloxacin, clarithromycin, erythromycin, metronidazole, ofloxacin, sulfamethoxazole and trimethoprim constitute a considerable risk in terms of ubiquitous distribution, worrying concentrations, risk quotients values, and resistance to removal treatments. Gaps in knowledge, data and information reported in this review provide a valuable source for managing AMR in WWTPs.

Cheese whey, a significant byproduct of the dairy industry, poses a substantial environmental challenge due to its organic pollutant load and large size demands for effective and affordable valorization methods. The abundance of lactose and other organic compounds in whey contributes to its elevated Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), further straining natural ecosystems. This study aimed to investigate the nutritional potential of whey and its conversion into value-added products: bioethanol and probiotic drinks. To achieve this, Bacillus megaterium and Bacillus subtilis strains, isolated from soil samples in Nepal, were co-cultured with Lactobacillus and Saccharomyces cerevisiae in the whey broth for fermentation. Ethanol distillation was carried out using a Rota-evaporator to maintain the viability of the fermenting organisms within the broth. Moreover, the probiotic criteria of the fermenting strains were extensively examined. Experimental observations revealed a remarkable concentration of 9.2 g/L of ethanol, resulting in an ethanol yield of 0.23 g/L. Extrapolating this rate, it is theoretically possible to produce an annual global bioethanol output of 5 million tons using whey as a sustainable and abundant resource. Furthermore, the study explores novel advancements in the distillation process, demonstrating the successful extraction of ethanol at room temperature. Additionally, the fermentation organisms employed in this research exhibit robust viability, surviving various in-vitro stress-tolerant tests, including exposure to bile salt, pH fluctuations, and gastric juice. These resilient strains also demonstrated long-term stability in fermented whey broth, making them promising candidates for the development of probiotic beverages. This dual approach not only enables the sustainable management of organic waste but also presents an opportunity to create value-added products with positive implications for both the environment and human health.

Nurses experience significant stress and emotional exhaustion, leading to burnout and fatigue. This study assessed how the nurses’ mood and fatigue evolves during their shifts, and the lagged and accumulated factors that influence these phenomena. A two-level design with repeated measures was applied to a sample of 113 nurses, performing an ecological momentary assessment of different parameters and multilevel longitudinal two-level modelling of the data. Accordingly, mood appeared to be explained by effort, by the negative lagged effect of reward and by accumulated effort, each following a quadratic trend, and it was influenced by previously executing a direct care task. By contrast, fatigue was explained by the current and lagged effect of effort, by the lagged effect of reward and by accumulated effort, again following quadratic trends. Fatigue was also associated with direct care, and the prior effect of documentation and communication tasks. Mood was also explained by problem-focused and emotion-focused coping strategies, indicative of negative mood, and by support-seeking and refusal coping strategies. Hence, mood and fatigue do not depend on a single factor like workload but rather, on the evolution and distribution of tasks, as well as on the stress during a shift and how it is handled.

(1) Background: The 2002 Budapest Open Access Initiative recommended on self-archiving of scientific articles in open repositories as the “green road” to open access. Twenty years later, only one part of the researchers deposits their publications in open repositories; moreover, one part of the repositories’ content is not based on self-archived deposits but on mediated nonfaculty contributions. The purpose of the paper is to provide more empirical evidence on this situation and to assess the impact on the future of the green road. (2) Methods: We analyzed the contributions on the French national HAL repository from more than 1,000 laboratories affiliated to the ten most important French research universities, with a focus on 2020, representing 14,023 contributor accounts and 166,939 deposits. (3) Results: We identified seven different types of contributor accounts, including deposits from nonfaculty staff and import flows from other platforms. Mediated nonfaculty contribution accounts for at least 48% of the deposits. We also identified difference between institutions and disciplines. (4) Conclusions: Our empirical results reveal a transformation of open repositories from self-archiving and direct scientific communication towards research information management. Repositories like HAL are somewhere in the middle of the process. The paper describes data quality as the main issue and major challenge of this transformation.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly to most parts of the world, causing high numbers of deaths and significant social and economic impacts. SARS-CoV-2 is a novel coronavirus with a suggested zoonotic origin and with the potential for cross-species transmission among animals. Antarctica can be considered the only continent free of SARS-CoV-2 although at the end of the 2019-2020 tourist season, at least one SARS-CoV-2 positive tourist visited the Antarctic Peninsula. Therefore, concerns have been expressed regarding the potential human introduction of this virus to the continent through the activities of research or tourism with potential effects including those related to human health, but also the potential for virus transmission to Antarctic wildlife. This reverse-zoonotic transmission risk to Antarctic wildlife is assessed considering the available information on host susceptibility, dynamics of the infection in humans, and contact interactions between humans and Antarctic wildlife. Measures to reduce the risk are proposed as well as the identification of knowledge gaps related to this issue.

Threats to natural ecosystems are closely linked to human development, and the lack, insufficiency or inefficiency of public policies are some of the most important drivers of negative effects on the environment. The contribution of the IUCN’s Red List of Ecosystems (RLE) to conservation topics has been discussed in previous studies; however, to date its implications for conservation in public policies have not been addressed. This perspective discusses how the RLE may support the improvement and development of these policies, specifically through the implications for public policy of each of the criteria that substantiate the threat status of ecosystems. We aim to provide a plausible baseline to the operationalization of RLE in public and conservation policy, facilitating the work of governments, practitioners and decision makers. Finally, we provide recommendations and examples as to how to proceed in creating and modifying different public policy instruments, such as land-use planning, spatial zoning, tax reduction, compensation schemes, climate change adaptation plans, management of introduced species, development offsets and restoration investment. This perspective contributes to implement RLE into public policy and to improve ecosystem conservation by expanding the current scope of RLE into practical and political dimensions through plausible actions, policies and strategies.

Background: Appendicular skeletal muscle (or lean) mass (ALM) estimated using dual-energy X-ray absorptiometry (DXA) is considered to be a preferred method for sarcopenia studies. However, DXA is expensive, has limited portability, and requires radiation exposure. Bioelectrical impedance analysis (BIA) is inexpensive, easy to use, and portable; thus BIA might be useful in sarcopenia investigations. However, a large variety of models have been commercially supplied by different companies, and for most consumer products, the equations estimating ALM are not disclosed. It is therefore difficult to use these equations for research purposes. In particular, the BIA equation is often age-dependent, which leads to fundamental difficulty in examining age-related ALM loss. The aims of the current study were as follows: (1) to develop and validate an equation to estimate ALM using multi-frequency BIA (MF-BIA) based on theoretical models, and (2) to establish sarcopenia cutoff values using the equation for the Japanese population. Methods: We measured height (Ht), weight, and ALM obtained using DXA and a standing-posture 8-electrode MF-BIA (5, 50, 250 kHz) in 756 Japanese individuals aged 18 to 86-years-old (222 men and 301 women as developing equation group and 97 men and 136 women as a cross validation group). The traditional impedance index (Ht²/Z₅₀) and impedance ratio of high and low frequency (Z₂₅₀/Z₅) of hand to foot values were calculated. Multiple regression analyses were conducted with ALM as dependent variable in men and women separately. Results: We created the following equations: ALM = (0.6947 × (Ht²/Z₅₀)) + (−55.24 × (Z₂₅₀/Z₅)) + (−10,940 × (1/Z₅₀)) + 51.33 for men, and ALM = (0.6144 × (Ht²/Z₅₀)) + (−36.61 × (Z₂₅₀/Z₅)) + (−9332 × (1/Z₅₀)) + 37.91 for women. Additionally, we conducted measurements in 1624 men and 1368 women aged 18 to 40 years to establish sarcopenia cutoff values in the Japanese population. The mean values minus 2 standard deviations of the skeletal muscle mass index (ALM/Ht²) in these participants were 6.8 and 5.7 kg/m² in men and women, respectively. Conclusion: The current study established and validated a theoretical and age-independent equation using MF-BIA to estimate ALM and provided reasonable sarcopenia cutoff values.

Coastal areas are highly vulnerable to climate change hazards (e.g., sea-level rise, flooding, coastal erosion), which can lead to significant impacts at the ecosystem and societal level. Interest in Ecosystem-based Adaptation (EbA) is gaining importance due to its potential multiple benefits, including social and environmental aspects, when compared to more traditional approaches such as hard engineering interventions. When assessing EbA strategies, further understanding of the nature-society functions, processes, values, and benefits is needed to increase its application. This study contributes to a better knowledge of EbA by developing a systematic literature review of studies performing socio-economic assessments of climate change adaptation in coastal areas. The analysis of 54 publications revealed that most of the studies assessed adaptation solutions through cost-benefit analysis, followed by multi-criteria analysis, and other techniques. Hybrid adaptation strategies based on different combinations of hard, soft and EbA interventions were considered as potential optimal solutions in a significant part of the assessments. This study suggests the potential co-benefits of EbA in the form of ecosystem services, livelihood diversification or biodiversity conservation, but also stresses the need for further research on this topic, as well as on evaluating how EbA perform in the long-term under climate changing conditions scenarios.

The prognostic abilities of artificial intelligence and neuroscience in forensics and the criminal justice system stand as a reformatory paradigm for understanding any criminal conduct. While the use of artificial intelligence has been labeled transformational data analytical capabilities, neural predictive approaches also enable an intricate understanding of culpability and criminal propensities. The literature on the complex nature of neuroprediction and artificial intelligence, its ethical deliberations and its usability in curving recidivism are analyzed. This theoretical review elucidates the complex interplay, nuptial relationships and convergence of these relationships in the quest for justice. The consequences of not protecting individual rights in the criminal justice system are surveyed using grounded theory. The degree of acceptability and dependability of AI-generated evidence in legal proceedings are also reviewed. All these topics are yet to be contemplated under one roof to offer an argumentative view. The author expects to prompt readers and new commers to embrace more sociolegal and technological research before incorporating such research in the Indian Judiciary. The review focuses on the question of whether to blame such technology inclusion wholly or rather to prioritize the acquisition of bias-free pretrained datasets and processing models.

MRI is the gold standard for treatment response assessment in glioblastoma. However, there is no consensus regarding the optimal interval for MRI follow-up during standard treatment. Moreover, reliable assessment of treatment response is hindered by the occurrence of pseudoprogression. It is unknown if a radiological follow-up strategy with 2-3 months intervals actually benefits patients and how it influences clinical decision making about continuation or discontinuation of treatment. This study assessed the consequences of scheduled follow-up scans (post-chemoradiotherapy [post-CCRT], after 3 cycles of adjuvant chemotherapy [TMZ3/6], after completion of treatment [TMZ6/6]) and unscheduled scans on treatment decisions during standard concomitant and adjuvant treatment in glioblastoma patients. Additionally, we evaluated how often follow-up scans resulted in diagnostic uncertainty (tumor progression versus pseudoprogression), and whether perfusion MRI improved clinical decision making. Scheduled follow-up scans during standard treatment in glioblastoma patients rarely resulted in early termination of treatment (2.3% post-CCRT, 3.2% TMZ3/6, and 7.8% TMZ6/6), but introduced diagnostic uncertainty in 27.7% of cases. Unscheduled scans resulted in more major treatment consequences (30%; p&lt;0.001). Perfusion MRI caused less diagnostic uncertainty (p=0.021) but did not influence treatment consequences (p=0.871). This study does not support the current pragmatic follow-up strategy and suggests a more tailored follow-up approach.

Efficient management of basal cell carcinoma (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC's perceptual color and texture deviation from perilesional skin. 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach's α, weighted Gwet's AC2, and quadratic Cohen's Kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray-Curtis distance in the YIQ color model and rectified embeddings from the 'fc6' layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: ‘Human-System’ and ‘Human-Human’. The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust, and cost-effective approach to monitoring BCC treatment outcomes in clinical settings.

In recent years, the discharge of various emerging pollutants, chemicals and dyes in water and wastewater has represented one of the prominent human problems. Since water pollution is directly related to human health, highly resistant and emerging compounds in aquatic environments will pose many potential risks to the health of all living. Therefore, water pollution is a very acute problem that has constantly increased in recent years with the expansion of various industries. Consequently, choosing efficient and innovative wastewater treatment methods to remove contaminants is crucial. Among advanced oxidation processes, electrochemical oxidation (EO) is the most common and effective method for removing persistent pollutants from municipal and industrial wastewater. However, there are still many gaps despite the great progress in using EO to treat real wastewater. This is due to the lack of comprehensive information on the operating parameters which affect the process and its operating costs. In this paper, among various scientific articles, the impact of operational parameters on the EO performances, a comparison between different electrochemical reactor configurations, and a report on general mechanisms of electrochemical oxidation of organic pollutants have been reported. Moreover, an evaluation of cost analysis and energy consumption requirements have also been discussed. Finally, the combination process between EO and another important advanced oxidation technology, PEC, called photoelectrocatalysis (PEC), has shortly been discussed and reviewed. This article showed that there is a direct relationship between important operating parameters with the amount of costs and the final removal efficiency of emerging pollutants. Optimal operating conditions can be achieved by paying special attention to reactor design, which can lead to higher efficiency and more efficient treatment. The rapid development of EO for removing emerging pollutants from impacted water and its combination with other green methods can result in more efficient approaches to face the pressing water pollution challenge.

Digitalization is widely recognized as a transformative power for sustainable development. Careful alignment of progress made by digitalization with the globally acknowledged Sustainable Development Goals (SDGs) is crucial for inclusive and holistic sustainable development in the digital era. However, limited reference has been made in SDGs about harnessing the opportunities offered by digitalization capabilities. Moreover, research on inhibiting or enabling effects of digitalization considering its multi-faceted interlinkages with the SDGs and their targets is fragmented. There are only limited instances in the literature examining and categorizing the impact of digitalization on sustainable development. To overcome this gap, this paper introduces a new Digitainability Assessment Framework (DAF) for context-aware practical assessment of the impact of the digitalization intervention on the SDGs. The DAF facilitates in-depth assessment of the many diverse technical, social, ethical, and environmental aspects of a digital intervention by systematically examining its impact on the SDG indicators. Our approach draws on and adapts concepts of the Theory of Change (ToC). The DAF should support developers, users as well policymakers by providing a 360-degree perspective on the impact of digital services or products, as well as providing hints for its possible improvement. We demonstrate the application of the DAF with the three test case studies illustrating how it supports in providing a holistic view of the relation between digitalization and SDGs.

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.

A new green-validated and highly sensitive electrochemical method for the determination of molnupiavir (MOV) has been developed using cyclic voltammetry. The proposed analytical platform involves the use of disposable screen-printed reduced graphene oxide 2.5% electrode (SPrGOE 2.5%) for the first time to measure MOV with high specificity. The surface morphology of the sensor was investigated by using a scanning electron microscope armed with an energy-dispersive X-ray probe. The fabricated sensor attains improved sensitivity when sodium dodecyl sulfate (SDS) surfactant was added to the supporting electrolyte solution. Thus, various factors were investigated in order to select the best supporting electrolyte for voltammetric determination of MOV, and the optimum conditions were achieved by employing 0.04 M Britton-Robinson at pH 2, 10-4 M SDS, and SPrGOE 2.5%. Well-defined MOV oxidation peaks were obtained with no reduction peaks in cyclic voltammetry using diffusion-controlled pathways. The method was validated using differential pulse voltammetry according to ICH guidelines, providing it to be precise, accurate, specific, and robust with linearity over a concentration range of 50-6017 ng/mL. The incorporation of the reduced graphene nanoparticles supported the in situ interaction between electrode surface and MOV, improved the selectivity, and reduced the detection limit to the concentration level of 15.98 ng/mL. The stability investigation demonstrated that SPrGOE 2.5% can provide high-stability behavior throughout a three-month period under refrigeration. The fabricated SPrGOE 2.5% was successfully employed for the measurement of MOV in pharmaceutical capsules and human bio-fluids without the interference of endogenous matrix components as well as the commonly used excipient.

Satellite-based data classification performance remains a challenge for research community in the field of land use/land cover mapping. Here we investigated supervised per-pixel classifications performance under different scenarios, based on single and seasonal multispectral data combi-nations of different sensors (Landsat-8 OLI and Sentinel-2 MSI). In case of Landsat, seasonal spectral indices (EVI and NDMI) were included. A typical Mediterranean watershed with a complex landscape comprised of various forest and wetland ecosystems, crops, artificial surfaces, and lake water was selected to test our approach. All available geospatial data from national databases (Forest Map, LPIS, Natura2000 habitats, cadastral parcels, etc.) are used as ancillary data for clas-sification training and validation. We examined and compared the performance of ML, RF, KNN and SVM classifiers under different scenarios for land use/land cover mapping, according to Copernicus Land Cover (CLC2018) nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2a MSI for an acceptable overall accuracy over 85%. A comparison of the overall classification accuracies shows that Sentinel-2a overall accuracy was slightly higher than Landsat-8 (96.68% vs. 93.02%). Respectively, the best-performed algorithm was ML in Sentinel-2 while in Landsat-8 was KNN. However, machine-learning algorithms have similar results regardless the type of sensor. We concluded that best classification performances achieved using seasonal multispectral data. Future research should be oriented towards inte-grating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping.

Abstract: Objective: To assess both individual and interactive effects of prenatal medical conditions depression and Diabetes, and health behaviors including smoking during pregnancy on infant birth defects. Methods: The data for this research study were collected by the Pregnancy Risk Assessment Monitoring System (PRAMS) in 2018. Birth certificate records were used in each participating jurisdiction to select a sample representative of all women who delivered a live-born infant. Complex sampling weights were used to analyze the data with a weighted sample size of 4,536,867. Descriptive statistics were performed to explore frequencies of the independent and dependent variables. Bivariate and multivariable analyses were conducted to examine associations among the independent and dependent variables. Results: The results indicate and significant interaction between the variables smoking and Depression and Depression and Diabetes (OR= 3.17; p-value <0.001 and OR= 3.13; p-value <0.001 respectively). Depression during pregnancy was found to be strongly associated with delivering an infant with a birth defect (OR= 1.31, P-value < 0.001). Conclusion: Depression during pregnancy and its interaction with smoking and Diabetes are vital in determining birth defects in infants. The results indicate that birth defects in the United States can be lowering Depression in pregnant women.

Total cloud cover (TCC) retrieval from ground-based optical imagery is a problem being tackled by a few generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there is not very much progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose the framework for the assessment of the algorithms characteristics. We present several new algorithms that are based on deep learning techniques: a model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million of all-sky optical images of visible sky dome taken in various regions of the World Ocean. The research campaigns contributed to DASIO collection took place in the Atlantic ocean, the Indian ocean the Red and Mediterranean seas, and also in the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and validation subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver superior quality compared to all previously published schemes. As a key result, we demonstrate considerable drop of the ability to generalize the training data in case of strong covariate shift between training and validation subsets of imagery which may take place in case of region-aware subsampling.

The stability assessment of aged tunnel linings were mainly evaluated based on the visual inspection, and the Tunnel-lining Crack Index (TCI) is one of the most widely used tunnel lining health assessment indexes in Japan. However, the intersection and distribution of cracks, which can influence the stability of tunnel lining greatly, were not considered in the TCI. A new method was proposed for the health assessment of tunnel lining, which evaluate the lining states according to the fractal dimension of cracks. Based on the machine vision-based method, the crack image can be extracted efficiently. The fractal dimension of lining cracks in one span can be obtained in a few minutes. A series of comparative tests and field tests were conducted to evaluate the validity of this new method. The comparative tests confirmed that fractal dimension can characterize the density, width, and distribution of cracks. The results also certificated that the influence of crack width is larger than the crack density. The intersection of cracks, which will increase the risk of lining collapse, can also increase the fractal dimension, whereas the TCI keep constant. The fractal dimensions of tunnel lining cracks were obtained according to the digital inspection test of Hidake Tunnel in Japan for all the 65 spans. Moreover, the TCI was obtained through statistical methods. The correlation between fractal dimension and TCI of tunnel lining was studied. The significance of the new evaluation index is that it can identify some unusual spans of tunnel lining and provide a basis for further internal testing. As a complement to the conventional visual inspection method, the fractal dimension of the cracks is a promising health assessment index.

Indonesia's government anticipates waste problems by managing them through a large number of waste reduction programs. From a governance perspective in Indonesia, it is known that actors from diverse governmental levels and across sectors are involved in waste management, and their involvement largely depends on their institutional goals and problems. Hence, they are expected to coherently collaborate together by developing and implementing sustainable instruments and resources for improving waste management problems. However, it is necessary to understand how supportive and restrictive the governance of waste reduction programs is to encourage successful trash reduction. This study is designed to evaluate and examine the application of Community-Based Solid Waste Management (CBSWM) in Makassar. In this research, The Governance Assessment Tool (GAT) was applied to analyze the governance context of the waste reduction programs in Makassar qualitatively. From the assessment, contexts can be described as either supportive or restrictive of the processes of implementing public policies on waste management. As a result of such an assessment, this study shed light on some opportunities to improve the governance of waste management implementation's impact on reducing waste in Makassar, South Sulawesi, Indonesia. The conclusion of this study depicts that the most important actors are affiliated with the local government and the community. Moreover, this study shows that the assessment of contextual governance is predominantly restrictive.

Purpose – The paper aims to view how researchers have operationalized social impact assessment for construction projects over the last ten years. Design/methodology/approach – A systematic review was used to investigate case studies in the Social Life-Cycle Assessment (S-LCA) to analyze the application of the methodology. A qualitative study of 19 articles published between 2012 and 2022 was used to collect content on multiple categories impeding S-LCA through case studies in the construction industry. Findings – Our results showed the existence of limitations on the qualitative and quantitative aspects in measuring the social indicators. They were associated with the scoring method and the lack of data in some articles. Social implications – From this review, we understood that S-LCA has flaws in terms of the quality of the measurement, scoring method, and the lack of social data. Lack of social data means social impacts are being neglected and not assessed properly since there are several challenges pointed out throughout literature Originality/value – The originality of this research is that it focuses on case studies in the construction industry. It studies the operationalization of the S-LCA in this specific industry showing the different characteristics and challenges in the last 10 years.

Fossil-based emissions can be avoided through using wood in place of non-renewable raw materials as energy and materials. However, increasing wood harvest influences forest carbon stocks. This effect may reduce the overall climate benefit of wood use significantly but is widely overlooked. We carried out a systematic review of simulation studies and compared differences in forest carbon and amount of wood harvested between more and less intensive wood harvest scenarios for three different time perspectives; short (1-30 years), mid (31-70 years) and long (71-100 years). Out of more than 450 reviewed studies 44 provided adequate data. Our results show that increased harvesting reduced carbon stocks over 100 years in temperate and boreal forests roughly 1.6 (stdev 0.9) t_C per t_C harvested. The value proved to be robust when outliers explicitly influenced by other factors than change in harvest rate, such as increase in fertilization or forest area, were removed. Interestingly, no significant difference in carbon impacts was found for average values of boreal and temperate forests or between short and long time-horizons. However, impacts tend to be greatest in the mid-term. This carbon balance indicator that we estimated can be interpreted as carbon debit of wood harvest in forests. It is significant compared with the typical GHG credits in technosphere generated by avoiding fossil emissions in substitution and increase in carbon storage in harvested wood products, and should not be ignored. Our estimates provide default values that can directly be included in GHG balances of products or assessment of mitigation policies and measures related to wood use. However, more systematic scenarios and transparent data in which different factors influencing forest carbon stocks are separately studied are clearly required to provide better constrained estimates for specific forest types.

With this article, impact of effluent mixed water with the quality of the Mansagar Lake has been established by evaluating the physico-chemical parameters and the heavy-metal contents experimentally. The process of calculating water quality and the metal pollution quality indices is also explained in brief with respect to selected sample locations and varied environmental conditions (Pre- and Post-Monsoon season). Distribution trends of Pearson‘s correlation factor have also been discussed to establish their relation among the physico-chemical parameters and the heavy-metal contents for varied environmental conditions. In the end, detailed discussion on observations made during this study and useful recommendations are also elaborated in details. With this article, we intend to present a document for better understanding of the water quality of this lake in view of futuristic management strategies to be adopted to maintain it heritage values.

Estimating the economic risks of climate shocks and climate stressors on spatially heterogeneous cities over time remain highly challenging. The purpose of this paper is to present a practical methodology to assess the economic risks of climate change in developing cities to inform spatially sensitive municipal climate response strategies. Building on a capital-based framework (CBF), spatially disaggregated baseline and future scenario scores for economic wealth and its exposure to climate change are developed for six different classes of capital and across 77 major suburbs in Cape Town, South Africa. Capital-at-risk was calculated by combining relative exposure and capital scores across different scenarios and with population impacted plotted against the major suburbs and the city’s 8 main planning districts. The economic risk assessment presented here provides a generic approach to assist investment planning and the implementation of adaptation options through an enhanced understanding of relative levels of capital endowment vis-à-vis relative levels of exposure to climate-related hazards over time. An informed climate response strategy in spatially heterogeneous cities need to include spatially sensitive estimates on capital-at-risk and populations disproportionally impacted by climate exposure over time. The economic risk assessment approach presented here helps in advancing to such a goal.

The angle of seismic excitation is a significant factor of the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses using in each one of them different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerograms for the evaluation of seismic response. Thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on Multilayered Feedforward Perceptron Neural Networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class which arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as Pattern Recognition problem. As inputs of networks the ratios of seismic parameters’ values along the two horizontal seismic records' components, as well as appropriately chosen structural parameters, were used. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.

DNA barcoding has emerged as a transformative tool in the realm of biodiversity assessment, facilitating rapid and precise species identification. This review article delves into the evolving landscape of DNA barcoding, with a particular focus on the emergence of mini-barcodes and alternative genes as innovative approaches. Traditional DNA barcoding, anchored in the COI gene, has significantly advanced our understanding of species diversity; however, inherent challenges persist, especially when dealing with degraded samples or taxonomic groups where COI falls short. The introduction of mini-barcodes, characterized by their reduced sequence length, has revolutionized DNA barcoding by expanding its applicability. We explore the utility of mini-barcodes in various scenarios, ranging from DNA extraction from ancient or fragmented specimens to in-field identification using portable sequencing technologies. Case studies and practical insights into their effectiveness are presented, emphasizing their role in overcoming traditional limitations. Alternative genes, a complementary facet of DNA barcoding, offer a novel avenue for enhancing taxonomic resolution and accuracy. This review showcases instances where researchers have harnessed alternative genes to overcome COI's constraints, particularly in diverse taxonomic groups, such as fungi, plants, and microorganisms. The selection of these genes is discussed, along with their potential to uncover hidden biodiversity and improve species delimitation. Examining the broader implications of mini-barcodes and alternative genes, we unveil their pivotal roles in advancing conservation efforts, forensic science, and citizen science initiatives. The adaptable nature of these approaches empowers a diverse range of stakeholders, from scientists conducting critical research to concerned citizens contributing to our collective understanding of the natural world. Looking ahead, we envision an exciting future for DNA barcoding, as these innovations continue to evolve. Integration with cutting-edge sequencing technologies, data analysis methods, and the harnessing of big data hold immense potential. Nonetheless, challenges related to standardization, scalability, and data sharing persist.

Resilient urban water infrastructure (UWI) is essential to maintaining public health and safety in urban areas and preventing consistent disruptions. However, UWI is vulnerable to a wide range of shocks and stresses due to the complex nature and interdependency of its components. The primary objective of this study is to evaluate the advances in resilience assessment of UWI comprising water supply, stormwater, and wastewater systems. This assessment involves examining bibliometric analysis, developed frameworks to understand resilience concepts for infrastructure and society, strategies for improving resilience, and resilience indicators. The study findings indicate that resilience assessment has primarily been conducted in developed countries, highlighting the macroeconomic importance of UWI. Three major areas were identified for analysing resilience in UWI: system design, development of resilience concepts, and implementation of green infrastructure. It was also found that while resilience is commonly defined based on technical approaches, a more thorough understanding of resilience can be obtained through holistic approach. While strategies such as system upgrade, decentralisation, digitalisation, and nature-based solutions can enhance resilience in UWI, they may be insufficient to achieve all resilience indicators. To address the issue of proper comparison of different resilience options, comprehensive and qualified indicators and metrics should be extensively examined in future.

Trees in residential landscapes provide many benefits, but can injure persons and damage property when they fail. In hurricane-prone regions like Florida, USA, the regular occurrence of hurricanes has provided an opportunity to assess factors that influence the likelihood of wind-induced tree failure and develop species failure profiles. We assessed open-grown trees in Naples, Florida, following the passage of Hurricane Irma in September 2017 to determine the effect of relevant factors on the degree of damage sustained by individual trees. Of 4,034 assessed individuals (n = 15 species), 74% sustained no damage, 4% sustained only minor damage (i.e., minimal corrective pruning needed), 6% sustained significant damage (i.e., major corrective pruning needed), and 15% were whole tree failures (i.e., overturned trees or trees requiring removal). The proportion of individuals in each damage category varied among species, stem diameter at 1.4 m above ground, and the presence of utility lines, which was a proxy for maintenance. We compared our results with the findings of seven previous hurricanes in the region to explore species’ resilience in hurricanes.

This study analyzes how Perceived Credibility of a 360-degree feedback and Evaluation of Outcome impact on ratees’ attitudes towards behavioral change. The study adopts a qualitative-quantitative mixed method approach. Reasoned Action Theory was selected as the theoretical framework, and ten 360-degree assessment ratees were interviewed on their perceived credibility, evaluation of outcome and attitude toward behavioral change in the 360-degree assessment practices. The qualitative inputs were analyzed under the methodology of grounded theory. Additional 159 respondents participated in the quantitative research phase of the research to validate the findings in the qualitative research phase. It was validated that Perceived Credibility, Evaluation of Outcome has impact on Attitude Toward Behavioral Change in 360-degree assessment.

The short-term intake (ESTI) of Hungarian consumers to pesticide residues was assessed based on 2331 test results obtained during 2017-2021 monitoring program in the frequently analyzed apples, sour cherries, table grapes, peaches, nectarines, peppers and strawberries (23.5% of all samples taken from 119 crops). The age-specific consumption data were obtained from two representative national food consumption surveys (2009 and 2018-2020). The exposure was characterized with Hazard Quotient and Hazard Index considering the acute reference doses of pesticide residues detected in the samples. When ESTI was calculated with all detected “single“ residues and variability factor of 3.6, recommended for evaluation of monitoring results, the HI only exceeded 1 for children &lt;3 years old eating grape (1.50-1.81). HI was &lt;1 when any of the 6 foods were eaten together within one day. Between forty and fifty percent of samples contained 2-23 residues. Though, the individual residue concentrations were below the corresponding MRLs, multiple residues being present in one sample resulted in maximum HI values in apples (1.14); grapes (6.57); peaches and nectarines (2.57); strawberries (2.74); peppers (10.44). Residues with low ARfD values contributed most. Applying HI is simple, but provides only point estimates; therefore, it should only be used in first-tier risk assessment.

The feasibility of recovering bioactive antioxidants from Phyllantus emblica leaves using ethanol -water mixture (0 – 100%) and heat-assisted extraction technology (HAE-T) was investigated in this present study. To this effect, the process parameter of operating temperature (OT: 30 -50 oC), solid -to-liquid ratio (S:L: 1:20 - 1:60 g/mL) and extraction time (ET: 45 - 180 min) were investigated on the extract total phenolic content (TPC, mg GAE/g d.w), yield (EY, %) and antioxidant activity (μM AAE/g d.w) by employing the Box-Behnken experimental design (BBD) available in response surface methodology (RSM). Multi-objective process optimization using the desirability function algorithm to determine set of process variables that simultaneously optimize process responses of TPC, EY and AA was also investigated. The recovery process was thereafter modeled with BBD-RSM and multi gene genetic programming (MGGP) algorithm and model reliability assessment via Monte Carlo simulation were conducted for the best performing set of models for predicting TPC, EY and AA values. The HPLC characterization of the recovered extract was carried out to ascertain the phenolic compound constituents of the Phyllantus emblica leaf extract. The 50% ethanol solution was found to be the best for the optimal extraction of antioxidants from Phyllantus emblica leaves. The HAE-T was suitable for recovering bioactive extract from Phyllantus emblica leaves and investigated process parameters have impacts on the recovery process. The process parameters that simultaneously gave the optimum EY of 21.6565%, TPC of 67.116 mg GAE/g and AA of 3.68583 µM AAE/g were OT of 41.61 oC, S:L of 1:60 g/mL and ET of 180 min. The HPLC extract profiling revealed that the bioactive constituents present in the recovered extract were betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid. Both the developed BBD-RSM and evolved MGGP-based models predicted the observed process response of TPC, EY and AA satisfactorily with BBD-RSM models marginally better in their predictions. The reliability results showed that the BBD-RSM models predicted TPC, EY and AA values with high certainty of 99.985%, 97.569% and 98.661%, respectively.

Sudden water pollution accident is a tough environmental challenge to solve due to its uncertainty, unpredictability and destructiveness. Therein, the dangerous goods transportation accidents on the cross-tributary bridges are increasing worldwide with the acceleration of industrialization and urbanization, posing threats to the water ecological environment and human health. To prevent and control the risk, this study evaluated the risk of sudden water pollution accidents associated with dangerous goods transportation on the cross-tributary bridges by using the expert judgment fuzzy language method and Bayesian network model. Baiyangdian lake as the largest freshwater shallow lake in North China was chosen as a study case. Different accident scenarios were simulated to put forward risk prevention and control measures. The results showed that the primary risk factors were divided into five categories: personnel factors, vehicle factors, tank factors, weather factors, and road environment factors. The probability of dangerous goods transportation accidents was a possible event (P=0.115), with a high risk level. Furthermore, the Baiyangdian lake would suffer from serious environmental pollution and water quality degradation without prevention and control measures by simulating the oil leakage accident. Therefore, emergency prevention and control measures are expected to be formulated and implemented before and after the occurrence of the sudden water pollution accidents. This study provides useful insights into risk prevention and control strategies for sudden water pollution accidents, and the framework can be made applicable to other rivers facing similar risk challenges worldwide.