The COVID-19 pandemic over the past years has shown a great need for rapid, low-cost and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild type SARS-CoV-2 spike protein (SP) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S protein of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

This study addresses a gap in the literature by conducting a comparative analysis of statistical and artificial intelligence techniques for predicting bankruptcy in small and medium-sized enterprises (SMEs). Using a comprehensive database of 7,104 Belgian SMEs, our findings demonstrate that intelligent techniques offer superior predictive accuracy for bankruptcy. Notably, our results indicate that a firm's position in the global value chain significantly affects prediction accuracy, with lower performance for firms operating upstream in the production process. This suggests that bankruptcy risk may spread upward along the value chain. This insight is particularly valuable for practitioners, as it highlights the importance of focusing on specific performance factors based on the type of firms they manage, advise, or collaborate with.

Soil naturally contains fluorine, but concerns arise when its levels or bioavailability are increased by human activities or specific compounds. South Korea faces challenges in regulating soil fluorine pollution despite implementing stricter total fluorine standards than many developed countries. Moreover, these regulations can be economically unfeasible because of the naturally high background levels of fluorine in the soil in Korea. This review explores the global landscape of soil fluorine management strategies and examines potential solutions that reconcile environmental protection with economic concerns. We recommend a shift towards regulating specific and hazardous fluorine compounds rather than total content, and prioritizing remediation efforts based on assessments of bioavailable fluorine. This approach may help Korea establish a more effective and sustainable strategy for managing soil fluorine pollution.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarante. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with power and energy balance. Drawing upon the principles of conditional value at risk theory, the risk indexes of load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment and the risk cost of load shedding, a distributed robust optimal scheduling model based on flexible ramp reserve of thermal power is established.

This paper presents the results of quantitative research examining the impacts of demographic and socioeconomic factors on community resilience to disasters. The survey was carried out utilising a questionnaire distributed and subsequently collected online from 321 participants during January 2024. The study employed an adapted version of the '5S' social resilience framework, encompassing five sub-dimensions – social structure (10 variables), social capital (9 variables), social mechanisms (17 variables), social equity and diversity (13 variables), and social belief (13 variables). This customized framework includes 62 indicators, providing a thorough assessment of community (social) disaster resilience in the research context. To explore the relationship between predictors and community (social) disaster resilience in Serbia, their attitudes towards the mentioned five sub-dimensions, as well as their engagement in preventive measures and their perception of resilience to various disasters with a particular focus on demographic and socio-economic impacts, statistical methods including t-tests, one-way ANOVA, Pearson’s correlation, and multivariate linear regression were employed. The results of the multivariate regressions across various community disaster resilience subscales indicate that age emerged as the most significant predictor for the social structure subscale. At the same time, education stood out as the primary predictor for the social capital subscale. Additionally, employment status proved to be the most influential predictor for both social mechanisms and social equity-diversity subscales, with property ownership being the key predictor for the social beliefs sub-scale. Despite being considered in the analyses, gender, marital status, income level, and number of household members did not emerge as the most significant predictors across the investigated community (social) disaster resilience subscales. The determined mean value of the community (social) disaster resilience index is M = 2.62, and the findings reveal that participants assigned the highest ratings to the social beliefs subscale (M = 2.76), while the lowest values were observed in the social structure subscale (M = 2.46). Findings can be used to create strategies and interventions aimed at enhancing the resilience of communities in Serbia by addressing the intricate interplay between demographic characteristics, socio-economic factors, and their ability to withstand, adapt to, and recover from different disasters.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, anxiety, hyperactivity, and restricted interest for specific subjects. In addition to the genetic factors, multiple environmental factors have been related to the development of ASD. Animal models can serve as crucial tools for understanding the complexity of ASD and identifying potential therapeutic strategies. In this study, a chemical model of ASD has been developed in zebrafish by exposing embryos to valproic acid (VPA) from 4 to 48 hours post-fertilization. After the exposure, embryos were transferred to fish water and the effects on behavior and neurotransmitters profile were analyzed at both larval and adult stages. Larvae from VPA-treated embryos showed hyperactivity and decreased visual and vibrational escape responses, as well as an altered neurotransmitters profile, with increased glutamate and decreased acetylcholine and norepinephrine levels. Adults from VPA-treated embryos exhibited impaired social behavior characterized by larger shoal sizes and a decreased interest for their conspecifics. Neurotransmitter analysis revealed a significant decrease in dopamine and GABA levels in the brain. These results support the interest of this model in the study of ASD.

A low cost, scalable and robust process has been proposed as innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminium nitrides, previously obtained on the laboratory scale. This work presents the scaling-up of fabrication process from laboratory to prototypal scale and pre-liminary results of outdoor self-cleaning solar mirror prototypes testing in the demonstrative CSP plant ENEASHIP located in Casaccia (Rome) ENEA research centre. Prototypes 50x40cm have shown stability in external conditions: no degradation occurs during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows the utilization of a reduced quantity of water for cleaning operations with respect to uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as glass envelopes of heat collecting elements, PV panels and everywhere are required self-cleaning performance combined to optical ones.

This study is based on the clamor of fish farmers for a cheaper fish feed alternative to current commercial feeds. At present, 70-80% of aquaculture costs come from feed cost, keeping farmers' income below their expectation. This study aimed to find alternative feeds or supplementary feeds that can perform similarly to the existing commercial feeds thereby reducing farmer expense and cutting down their cost of production and increasing their profitability. To do that, various indigenous raw materials were selected based on an earlier study which could result in a better feed alternative. The formulations for the study included four grow-out treatments mainly: control (commercial feed), taro+control (powdered Colocasia esculenta+commercial fish feed), bloodmeal (formulated fish feed based on bloodmeal), banana stem+control (chopped banana stem+commercial fish feed). A total of 12,000 fingerlings initially weighing 16 g (TL=10 cm) individually were assigned randomly to the four treatments (3000 in each cage measuring 6 x 6 m). Week zero was used for acclimation period, and fingerlings were fed with commercial fish feed. Then the first week used the treatment diets which were adjusted based on average body weight and sampled weekly (ABW=23g; TL=11 cm). The result of the study first showed significant differences between treatments on their fourth week in terms of the diet treatments (MS=0.103, df=3, F=4.12, P=0.0076) with the control and the taro+control showing no difference in terms of weight and length (TC vs CF; P=0.4524). But during the fifth week, the bloodmeal treatment showed increased weight and length for fish sampled (MS=0.109, df=3, F=4.10, P=0.0076) and provided similar weight and length as those from the taro+control and the control (TC vs CF; P=0.3721 and BM vs CF; P=0.0587). During the succeeding weeks, there were no significant differences between feed treatments from 6th weeks onwards, whether with weight [MS=0.130, df=3, F=1.14, P=0.333] or total length of the treatments [MS=0.008, df=3, F=0.40, P=0.75]. The FCR ratio showed taro (1.81) as lowest, followed by blood meal (2.09), and control (2.24) and then banana stem (2.84). In terms of weight gain, fish from the bloodmeal treatment gained the highest (236.97 g), followed by control (223.33 g), then taro (217.67 g) and banana stem (196.99 g). As a result, we find that the tested treatments such as bloodmeal formulated feed, taro+commercial feed, and banana stem+commercial feed can serve as an alternative to purely commercial fish feeds. Cost-benefit analysis showed that a better gross profit margin of 42% and cost-benefit ratio of 1.37 was observed in taro+commercial feed diet.

The recent availability of commercial platforms for behavioral analysis in zebrafish larvae based on video-tracking technologies has exponentially increased the number of studies analyzing different behaviors in this model organism to assess neurotoxicity. Among the most commonly used assays in zebrafish larvae are basal locomotor activity (BLA) and visual motor response (VMR). However, the effect of different intrinsic and extrinsic factors that can significantly alter the outcome of these assays is still not well understood. In this work we have analyzed in 143 larvae the influence of age (5-8 days post-fertilization), time of day (8:00, 10:00, 12:00, 14:00; 16:00, 18:00, and 20:00 h), and experiment (three experiments performed at different days) on BLA and VMR results (4,004 analyses for each behavior). Results from both behaviors were adjusted to a Random-Effects linear regression model using generalized least squares (GLS), including in the model the effect of the three variables, the second-way interactions between them and the three-way interaction. The results presented in this manuscript show a specific effect of all three intrinsic factors and their interactions on both behaviors, supporting the view that the most stable time period for performing these behavioral assays is from 10:00 am to 04:00 pm, with some differences depending on age of the larva and the behavioral test.

Tegucigalpa, the capital city of Honduras, has the highest number of landslides recorded in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. The present study focused on assessing the physical vulnerability of houses and the risk linked to landslides in these types of soils. As an input for this assessment, the study generated landslide risk vulnerability functions based on empirical data. The study's findings suggest that the estimated Annual Average Losses associated with landslides in this region is USD 7.26 million. Moreover, the study developed a novel approach for the spatial and temporal estimation of landslide hazard probability, which overcomes some of the limitations identified in previous landslide risk assessment studies.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

The intelligent elderly care model places new demands on the skills of aging caregivers. The identification and development of forward-thinking, high-potential nursing talent is essential for advancing the intelligent elderly care sector. Multi-benchmarking selection and competitive analysis is used to evaluate and identify nursing personnel in the rehabilitation treatment center of a hospital in Hefei City. Using proxy evaluation and democratic evaluation model, three types of benchmarks such as single item benchmark, individual benchmark and group benchmark were identified and selected, and the professional nursing team was constructed based on the evaluation results. Different from the traditional other-directed evaluation, multi-benchmarking selection and competitive analysis is self-directed evaluation, this method is more conducive to the identification of high-potential and digital nursing talents by intelligent elderly service organizations, and facilitates the formation of professional nursing teams with complementary strengths and cross-fertilization, so as to satisfy the diversified and multi-level needs of the elderly for intelligent elderly services.

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

Pragmatic trials aim to assess intervention efficacy in usual patient care settings, contrasting with explanatory trials conducted under controlled conditions. In aging research, pragmatic trials are important designs for obtaining real-world evidence in elderly populations, which are often underrepresented in trials. In this review, we discuss statistical considerations from a frequentist approach for the design and analysis of pragmatic trials. When choosing the dependent variable, it is essential to use an outcome that is highly relevant to usual medical care while also providing sufficient statistical power. Besides traditionally used binary outcomes, ordinal outcomes can provide pragmatic answers with gains in statistical power. Cluster randomization requires careful consideration of sample size calculation and analysis methods, especially regarding missing data and outcome variables. Mixed effects models and generalized estimating equations (GEEs) are recommended for analysis to account for center effects, with tools available for sample size estimation. Multi-arm studies pose challenges in sample size calculation, requiring adjustment for design effects and consideration of multiple comparison correction methods. Secondary analyses are common but require caution due to the risk of reduced statistical power and false-discovery rates. Safety data collection methods should balance pragmatism and data quality. Overall, understanding statistical considerations is crucial for designing rigorous pragmatic trials that evaluate interventions in elderly populations under real-world conditions. In conclusion, this review focuses on various statistical topics of interest to those designing a pragmatic clinical trial, with consideration of aspects of relevance in the aging research field.

Background: Despite its global eradication in 1977, smallpox remains a concern owing to its potential as a biological agent, thereby prompting the ongoing development and utilization of its vaccine. Vaccination with the Vaccinia virus induces immunity against variola virus, the causative agent of smallpox; however, this immunity does not extend to viruses of different genera within the Poxviridae family. In this study, we aimed to assess the efficacy of an enzyme-linked immunosorbent assay (ELISA) method utilizing Vaccinia virus and recombinant A27L antigen for detecting antibodies against smallpox. Methods. Analysis of serum from 20 individuals pre- and post-vaccination with the CJ strain (CJ50300) revealed neutralizing antibodies, which were confirmed using the plaque reduction neutralization test (PRNT). The ELISA method, validated with a PRNT50 cut-off value of >4, exhibited a sensitivity and specificity of >95% and was particularly reactive with the inactivated virus. Furthermore, adherence to the smallpox vaccination policy revealed significant differences in Orthopoxvirus antibody levels among 300 individuals of different age groups. These findings highlight the reliability and efficacy of the ELISA method in detecting post-vaccination antibodies and contribute significantly to diagnostic methods to prepare for potential smallpox resurgence and bioterrorism threats.

The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.

Vernonia mespilifolia is widely used in folk medicine in the Eastern Cape Province, South Africa. The aim of this study was to evaluate the biological activity of the acetone, aqueous and ethanol extracts of Vernonia mespilifolia using brine shrimp hatchability and lethality assay. The result showed hatching success in this order: aqueous extract (48.6%) > acetone extract (38.2%) > ethanol extract (26.8%). The LC₅₀ of the lethality assay were in this order: acetone extract (67.8 µg/mL) > aqueous extract (132 µg/mL) > ethanol extract (383 µg/mL). According to Meyer’s toxicity index (using brine shrimps), LC₅₀ < 1000 µg/ mL is toxic. Therefore, the results of the three solvent extracts could be said to be toxic as do have LC₅₀ < 1000 µg/ mL. However, the toxicity of the crude extracts could suggest or confer some antitumor properties, hence further in vitro, in vivo and antitumour assays are recommended to further substantiate these claims.

As for most Mediterranean countries, groundwater is the main resource for irrigation and drinking supply in most parts of Syria, however this resource suffers from mismanagement. In the study area (Northeast of Mt. Hermon), the lack of information makes water management in this area extremely difficult. Assessing groundwater pollution risk is the most essential issue for water resources management, especially in the regions where complex interaction between climate, geology, geomorphology, hydrogeology, water scarcity and water resource mismanagement exist. This complexity leads to significant complication in determining pollution risk of studied system. In the present work, we adopted an Integrative Approach to assess groundwater pollution risk in the study area. This methodology is based on the analysis of hydrogeological characteristic of aquifer system and the available information about socio-economic context and physio-chemical groundwater condition that might affect this system. This approach allowed us to delineate the groundwater pollution risk map based on the analysis of concerning parameters/ indicators. The degree of risk was assessed based as the sum and average of rating of these parameters and indicators for each subarea. Typically, very high pollution risk index was identified over the Quaternary/Neogene horizon, i.e shallow and unconfined aquifer and in the lower part of Jurassic aquifer. In these two parts, the majority of anthropogenic activities are concentrated. Low pollution risk index was found for the outcropping of low permeable Quaternary basalt at the Southern part of the study area. A moderate pollution index was identified for the low/moderate permeability of silt, clay and marly limestone rich horizons of the major part of Neogene aquifer outside of the intersected zones with Quaternary aquifer and for the Paleogene formations. The spatial analysis shows that about 50% of the study area is characterized as being at very high and high pollution risk index. Hence, the overall natural protective capacity of this area is still poor. This study demonstrates the flexibility of the proposed approach to assess groundwater pollution risk in local complex aquifer system characterized by lack of information and data in order to reduce the risk of future groundwater pollution.

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.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

Occupational accidents, despite continuous safety updates, are still a scourge in the occupational and forensic spheres, constituting, among other things, the subject of a large share of litigation. Demographic data can help to understand the areas where the application of health surveillance is lacking. This meta-analysis sets out to analyze data from studies on accidents at work, focusing on the correlation between the areas in which accidents occur and whether or not personal safety equipment is used, about the different regulations in force. For the selection of the data, a systematic review was carried out according to the PRISMA guidelines, with the primary objective of identifying the trend of occupational accidents in specific geographical areas, which differ in terms of the attention paid to preventive aspects. The data we highlighted showed, regarding the type of accident, substantial differences between low-income countries and industrialized countries (stratified according to the Human Development Index) and, an overall indifference as to whether or not individual safety devices were used, revealing that, despite the continuous normative evolution in the field of safety at work, even today, the investigative data on the actual application of the regulations, during accidents at work, is underestimated and little researched.

The analysis of the key factors which prevent or contribute to the promotion and development of the Power Purchase Agreements (PPAs) in order to meet the sustainability goals represents an important issue which worth being investigated, especially in the situation in which entering in bilateral relations inside this contract has a powerful meaning in achieving new climate targets, developing and improving the use of green energy, and promoting a sustainable green economy. The main goal of this research is to identify and analyze the main factors which help or pre-vent the promotion and development of a PPA, in order to meet the sustainability goals by promoting a green economy. For this research survey based on comparative analysis, questionnaires and interviews with energy experts, Spearman’s correlation matrix and IBM SPSS Statistics for Window are also used. The results suggest that although there are positive interest and an open attitude towards the PPA from both sides (sellers and buyers), but there is still place for improvement, the lack of knowledge or the insufficient measures taken so far being two of the reasons way until now in Romania we do not have a developed PPA market and the level of knowledge regarding this type of mechanism is quite low.

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

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.

Extreme climates such as droughts and hot extremes, may result in marked damage to crop yields and threaten regional and global food security. Maize is a major grain crop in Liaoning Province, severely affected by drought and hot events. The objective of this study is to advance an approach to estimate the risk of crop yield reduction under the compound dry-hot events (CDHE) in Liaoning Province. Results show that the ratio of stations experiencing compound dry-hot events during the twelfth leaf and maturity stage tended to increase on the time scale. On a spatial scale, the frequency of compound dry-hot events in central Liaoning Province was relatively low during the whole and various reproductive periods of maize. In addition to the twelfth leaf, western Liaoning Province is prone to compound dry-hot events. The high-risk area for the occurrence of the compound dry-hot events in maize is Chaoyang City, located in the western part of Liaoning Province, and the cities in the low-risk area account for about 64.3% of the total number of cities in Liaoning Province, mainly in the central and northern parts of Liaoning Province. Generally, this study emphasizes focusing on the impacts of compound dry-hot events on agricultural production and provides an index to assess the risk of compound dry-hot events on maize production.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.

This research deals with risk assessment for human life, man-made infrastructure, and agriculture in a landslide-prone area using GIS-based spatial methods. The study area landslide inventory map was prepared based on previous landslide information, aerial photograph analysis, and several field observations. A total of 550 landslides have been included with 182 debris flow and 368 soil slides. All included landslides were classified into two groups by random selection; half were used for model calibration and the rest were used for cross-validation. In the analysis, fourteen causative factors were vastly used, such as aspect, slope, curvature, elevation, topographic wetness index, forest timber diameter, forest type, forest crown density, forest age, land-use, geology, soil drainage, soil depth, and soil texture. Moreover, to identify the interaction between occurred landslides and causative factors, the affected pixels were divided into different sub-classes using a frequency ratio method. Based on the total dataset, three landslide susceptibility maps were constructed using Bayesian prediction, likelihood ratio, and fuzzy set method. By evaluating cross-validation and success rate curve, model susceptibility results were plotted with a receiver operating characteristic (ROC) curve and the area under the curve (AUC) was estimated. In addition, for risk assessment, each social data layer such as agriculture, house, industry, business, road, river, population intensity, monetary value, and vulnerability level was added based on the local standard and incident time and was converted into US dollars. During the analysis, each method hazard map was used with a specific group of thematic data layers. Subsequently, for preparing the probability table, study area total pixels and predictive landslide affected pixels were considered. Matching with the affected pixels, a standard of 5000 pixels was selected to run the final evaluation. Based on the results, the agricultural field showed the highest vulnerability and estimated risk of US $ 16.3 million. Further, the man-made infrastructure map showed a risk of US $ 31.3 million. The total estimated population casualties were 6.77, which was relatively similar to the published data.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

Innovation and sustainability are key elements gaining important positions in public and private organizations’ agenda in recent years. Whilst innovation drives for more productivity, efficiency and competitiveness, sustainability incorporation into the development of the organizations outlines as a counterbalance. It helps directing the innovative endeavors of the organizations towards more responsible activities in terms of environmental, economic and social impacts. To help achieve that, standardization offers valuable tools to orient organizational development efforts towards sustainability. This research aims at exploring the plausible connection between use or implementation of standards and pursuit of sustainability in an important industrial sector in Romania – food industry. The objective of the research is to determine which of the two iden-tified hypothesis can be scientifically supported using the quantitative research methods. The expected results are that more than simply following short-term objectives, companies that con-sciously use recognized standards in their activity pursue long-term sustainability.

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

The study investigates the economic feasibility and farmer acceptance of utilizing treated wastewater (TWW) for agricultural irrigation, particularly in the Northern Jordan Valley. Despite its potential to mitigate water scarcity, concerns persist regarding its impact on soil health, crop yield, and land utilization, hindering widespread adoption. The research aims to measure farm profitability and farmers' willingness to embrace TWW through various blending scenarios with traditional surface water sources. The yield response function to salinity was incorporated into profit function to simulate crop profitability and water values. Results indicate that the introduction of TWW negatively affects salt-sensitive crops like citrus but positively impacts others such as date palm, olives, tomatoes, and peppers due to increased fertilizer content in irrigation water. Economic performance indicators reveal a gradual decrease in water net productivity for citrus with increasing blending ratios of TWW, with the water value decreasing accordingly. Higher percentages of treated water do not consistently lead to increased productivity, profitability, or net value added, emphasizing the need for careful consideration and understanding of economic risks associated with specific crops and water quality. To mitigate the impact of water salinity on citrus production, growers are advised to improve soil quality, periodically flush the soil with low-salinity water, and use salt-tolerant rootstocks. Regular monitoring of soil and water salinity levels is crucial for informed decision-making. While using TWW, water may offer cost savings.

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

in our increasingly interconnected world, sensor networks are critical in gathering and sending data for various applications, from environmental monitoring and industrial automation to healthcare and smart cities. However, as sensor networks expand in importance, so does the need to solve the multidimensional concerns of security, privacy, and forensics. This article explores the complex world of sensor network security, the delicate balance between data privacy and utility, and the emerging area of sensor network forensics. This article focuses on risk assessment of a network.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Attention to the work-related musculoskeletal disorders (WMSDs) involve also statistical surveys showing an increasing trend in the incidence of WMSDs. Technological development retrieves new tools and methods for the assessment of physical load at work. In addition to the conventional questionnaires and scaling assessment systems, these methods are mostly based on the direct sensing of appropriate parameters, which allows more precise quantification. The aim of this paper is to compare the assessment of ergonomic risk through current legislative regulations. A Captiv wireless sensor system was used at a car headlight quality control assembly workplace for sensing, data acquisition and data processing. During the evaluation of the postures and movements at work, we discovered differences in the applicable standards: Decree 542/2007 Coll. (Slovak legislation), STN EN 1005-4+A1, French standards default in the Captiv system. Standards define the thresholds for hazardous postures with significant differences in several evaluated body segments, which also affects the final evaluation of the measurements. Based on the results of our research, we found that respondents were in an acceptable position for approximately 63% of the total duration of the work activity in terms of the Legislative method, 44% with Captiv and 27% with Standards method.

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3 or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small sized business incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factors 1 and 2. Factor 3, although positive, it is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

Our study is part of the ongoing discussion concerning influencer marketing as there seems to have been a shift and social media marketing is bigger than ever. Social media outlets are excellent vehicles for fostering relationships with consumers who can be susceptible to influencer advertising. Therefore, companies now pay for posts on users’ profiles to generate popularity for products. How this approach affects consumers’ buying behavior is an interesting research question. We reference Herbert Simon’s economics of attention as an appropriate theoretical construct for describing the reality of the internet. Based on unique survey data from 513 Polish respondents, the study applies econometric methods and supervised and unsupervised machine learning techniques to identify the primary consumer groups with different characteristics of engagement and sensitivity to influencer activities, revealing the possible drivers of purchase intent. The findings provide valuable insights into consumers’ informational preferences, demonstrating how social media influencers may attract attention and drive sales.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

Abstract: Global ecological concern regarding the transfer of fouling organisms to ship hulls is increasing. This study investigated the species composition, dominant species, distribution patterns, community structure, and life-cycle differences of hull-fouling macroinvertebrates on five research vessels (R/Vs: Isabu, Onnuri, Eardo, Jangmok 1, and Jangmok 2) operated by the Korea Institute of Ocean Science and Technology (KIOST). Hull-fouling macroinvertebrates were collected three to five times on quadrats from the upper and middle sectors of the hull sides, bottom, and niche areas (the propellers and shafts and the thrusters). A total of 47 macroinvertebrate species were identified, represented by 8,519 individuals(ind.)/m2 and a biomass of 1,967 gWWt/m2 on the five vessels. The number of species, density, and biomass were greater on the coastal vessels the Eardo, Jangmok 1, and Jangmok 2 than on the ocean-going vessels the Isabu and Onnuri. Among the coastal vessels, barnacles were the most abundant and had the greatest density, while mollusks had the highest biomass. Differences between hull sectors showed that the highest species abundance and density appeared on all hulls in ports and bays where the Jangmok 1 operated, while the highest species abundance, density, and biomass were identified in the niche areas of the Eardo, which operated farther from the coast. The hull-fouling macroinvertebrates that exceeded 1% of all organisms were the barnacles Amphibalanus amphitrite, Balanus trigonus, and Amphibalanus improvisus; the polychaete Hydroides ezoensis; the bivalves Magallana gigas and Mytilus galloprovincialis; and the amphipod Jassa slatteryi. The dominant species were cosmopolitan and globally distributed, and many of them were cryptogenic. Six native species were identified: M. gigas, H. ezoensis, the amphipod Melita koreana, the isopod Cirolana ko-reana, and the barnacle B. trigonus and F. kondakovi. Eight non-indigenous species (NIS) were de-tected: barnacles A. amphitrite and A. improvisus, the bivalve M. galloprovincialis, the polychaete Perinereis nuntia, amphipods J. slatteryi and Caprella californica, and bryozoans Bugulina californica and Bugula neritina. Of the fouling macroinvertebrates found on the vessel hulls, 13% were native and 17% were NIS. The nMDS ordination analysis of the five vessels showed four clusters of fouling macroinvertebrate communities: two ocean clusters (the Isabu and Onnuri), one coastal cluster (the Eardo and Jangmok 2), and one local cluster (the Jangmok 1). More diverse communities developed on the hulls of vessels that operated locally rather than globally or in deep oceans. Spearman rank correlation showed that biological indices such as number of species, density, and biomass correlated highly and positively with the numbers of days on coastal operations and negatively with the number days on ocean operations. The species diversity index correlated positively with the total number of anchoring days and coastal operation days and negatively with the total number of operation days and ocean operation days. The macroinvertebrates differed by the area of operation, the port of anchorage, the number of days in operation and at anchor, and the hull sectors. There is no previous research data on hull-fouling macroinvertebrates in the Republic of Korea, and this study provides a basis for future studies to identify introduced species and their differences based on operation area.

Food insecurity remains a significant developmental challenge, particularly in rural areas, despite ongoing efforts to mitigate it. To better understand the persistence of this issue, this study investigates the causes of household food insecurity and the coping strategies employed by households in Zorkor Goo, Zorkor Kanga, and Zorkor Kadaare communities within the Bongo District of Ghana’s Upper East Region. A descriptive cross-sectional survey was conducted, sampling 196 households systematically from these communities. Data were collected through a structured questionnaire covering socio-demographic factors, causes of food insecurity, household coping strategies, and dietary intake. The analysis was performed using SPSS (Version 21.0), with food security status and dietary intake assessed via the FANTA Household Food Insecurity Access Scale (HFIAS) and Food Consumption Score, respectively. The study identified key factors contributing to food insecurity, including soil infertility, poor rainfall patterns, food spoilage, extravagant funerals, large family size, high unemployment, and lack of education. Major coping strategies among households included meal skipping, reducing meal size and frequency, migration, asset sales, and basket weaving. The findings revealed that 96.4% of households were severely food insecure, and over two-thirds (67.3%) exhibited poor dietary consumption. Notably, household employment levels, education, and food consumption scores were significantly associated with food insecurity (P&lt;0.001). These findings underscore the need for targeted interventions addressing the root causes of food insecurity and enhancing household resilience in these communities.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

The purpose of this study is to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrate that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect generalisation of the study findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interplay to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

The formula Chong-Lou-Yao-Fang (CLYF) is an herbal medicinal formulation developed by the indigenous Naxi people for treating liver cancer. This study is to reveal the biological activity, potential targets, and molecular mechanisms of CLYF for cancer treatment through network pharmacology, molecular docking and in vitro experiments. 35 key compounds were screened from 176 active ingredients in CLYF, which played a vital role in the treatment of cancer, 20 core targets were filtered through protein–protein interaction (PPI) network analysis. The enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that PI3K-Akt, MAPK, hepatitis B, hepatitis C might be effective in liver cancer treatment. Mo-lecular docking revealed stable binding between EGFR, TP53, and AKT1 with active ingredients. Experiments with cell cultures confirmed that CLYF-A suppressed the proliferation of HepG2 cells, which is consistent with the network pharmacology approach undertaken and the predictions of molecular docking. CLYF-A also induced significant apoptosis and cell cycle arrest in HepG2 cells, which was associated with the loss of mitochondrial membrane potential. This study explored the biological activity, potential targets, and molecular mechanisms of CLYF for cancer treatment. Our results may provide a scientific basis for the clinical use of CLYF for cancer treatment and have important implications for developing pharmaceutical preparations, which also need more phar-macological experiments.

As modern technologies, particularly home assistant devices, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information in 15-minute or even 5-minute intervals, including weather forecasts, outputs from Renewable Energy Sources (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the Local Energy Market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by Large Language Models (LLM), Scikit-llm and Zero-Shot Classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy.

This research work focused on the risks we are faced with on our day to day activities, either directly or indirectly, which can gradually accumulates and results to communal crisis, religious crisis, socio-economic agitations and tribal crises. Nasarawa Eggon Local Government Area of Nasarawa State was used as case study and a well structure close–ended questionnaire was used for data collection as it relates to Controlled Self Risk Assessment (CSRA) and linear programming technique was used to analyze. The results proved that many of this processes were neglected in these communities, making them to be prone to incessant communal crisis. The study recommend that Government should strongly, as a matter of urgency address the unemployment rate, distribution of political position, schools, primary health care and equip security personnel with the modern technology facilities, which happen to record the highest risk level.

Background/Objectives: This study aimed to identify various risk factors associated with breast cancer and explore different treatment strategies based on socio-economic, hormonal, and clinical perspectives.; Methods: We analyzed socio-economic, hormonal, genetic, and clinical variables using data from 356 questionnaires obtained from breast cancer patients at Hassan II Regional Hospital in Agadir, Morocco ; Results:The study reveals that the average age of breast cancer patients is 50 ± 10.72 years, with a mode of 47 years and an age range from 29 to 94 years. Notably, women aged 38 are most frequently diagnosed with triple-negative breast cancer. Significant socio-economic trends were observed among the participants, with the majority being housewives (91%) and nearly half residing in rural areas (48.60%). High rates of illiteracy (74.72%) and financial difficulties (54.2%) pose challenges in accessing healthcare services. Psychological distress is prevalent, with 40.7% experiencing anxiety and 30.1% experiencing sadness. The use of oral contraceptives varies, with implications for breast cancer risk. Additionally, statistics on menarche and menopause suggest hormonal influences on breast cancer development. Histological analysis indicates a predominance of grade II invasive ductal carcinoma and left breast localization. Metastases are present in nearly 38.20% of cases. Notably, surgery as the primary treatment effectively treats medium-sized tumors while limiting metastasis. Furthermore, a comparison of treatment strategies reveals a preference for letrozole among postmenopausal patients and tamoxifen among premenopausal patients; Conclusions:These findings highlight the complexity of breast cancer risk factors and therapeutic approaches, emphasizing the importance of personalized care and targeted interventions.

The purpose of this review is to elucidate the actual threat of the most prevalent mycotoxins in agricultural commodities and human/animal food/feed for appearance of foodborne ailments and diseases. The underestimated hazard of joint mycotoxin uptake of animals or humans is critically discussed with regards to synergistic or additive interaction between some target mycotoxins. The real toxicity of target mycotoxin combinations as it happens in practice is evaluated and possible lower limit values or control measures are suggested in such cases. Some critical points in adequate risk assessment, hygiene control and regulation of mycotoxns are discussed. The efficiency of current mycotoxin regulations and control measures is evaluated in regards to human/animal health hazard. The risk assessment in the case of multiple mycotoxin exposure of humans/animals via the food/feed or agricultural commodities is evaluated and some suggestions are proposed in such cases. Appropriate control measures and food safety issues throughout the food supply chain are proposed in order to prevent target foodborne diseases. Some preventive measures and possible veterinary hygiene control or risk assessment are proposed in some practical cases of foodborne diseases for preventing mycotoxin contamination of animal products designed for human consumption and to avoid possible public health issues.

Debates regarding human capital disclosure (HCD) emphasise the usefulness of people-related information in stimulating investor confidence and building stakeholder trust. Despite this, human capital (HC) information is interwoven in most international sustainability reporting standards such as the global reporting initiative (GRI), thereby concealing the impact of human resource (HR) practices in shareholder value creation. The aim of this study is to develop a taxonomy to assess HCD in the sustainability reports to capture the true value of HC distinctively. A content analysis-based study with a deductive approach was followed by utilising the GRI standards to isolate and classify people-related information into an HCD sustainability taxonomy. 40% HC metrics were extracted from the GRI standards and subsequently classified into 7 dimensions, namely, human capital allocation, human capital wellbeing, human capital investment, human capital network, human capital governance, human capital risk and human capital earnings. Furthermore, 40 GRI content indexes of the companies listed in the Johannesburg Stock Exchange (JSE) revealed that most disclosures are covered in the human capital governance and human capital earnings categories. HCD sustainability provides investors and HR professionals with a useful tool to assess people-related information from the sustainability reports for isolating the true value of HC.

In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.

This study aimed to develop a state-of-the-art method for assessing chemical risks. The method combined desk research findings and the authors' practical knowledge to identify the major shortcomings in performing risk assessments at the workplace. Data were collected from nine SMEs across three sectors: agriculture, laboratory research, and conservation and restoration laboratories. The results of the discussions with the workers and on-site observations were used to develop the method to assess chemical risks. The method uses numerical factors to account for the level of conformity and the duration of exposure to support an estimation of the probability of an incident. The developed method was designed to enhance risk assessment practices and tackle common issues encountered by enterprises, including SMEs.

The importance of frailty in older people is getting constant recognition as an important aspect both in terms of public health, as well in personal level for the appropriate management of an older person health condition. This is reflected by the continuously increasing number of research studies carried out in several settings across different countries. Sometimes this is very solid, but in other cases there is a considerable gap in terms of accurate and well grounded documentation of frailty status. This is the case in Greece, where we are missing clinically validated tools to approach frailty. We are missing frailty screening tools, such as for instance PRISMA 7, the gold standard tool of Fried criteria is somehow problematic since the question referring to physical activity originates from a questionnaire that has not been translated and validated, while Clinical Frailty Scale has been validated for the translation but not for the capacity to detect frailty. Aim of this study is to validate these tools for their accuracy to detect frailty by using a measurable index of frailty, previously proposed for use in clinical studies: the Short Physical Performance Battery (SPPB). 74 male and female participants have been evaluated for their frailty status using different tools. We observed that PRISMA 7 translation detects frailty only when one question is removed at a cut-off of ≥ 2 and indicated a sensitivity of 88.1%, and specificity of 99.9% with a good correlation with SPPB measurements (r = 0.858; p &lt; 0.001). When CFS was validated using SPPB it demonstrated a very good correlation (r = -0.838; p &lt; 0.001 respectively) as it was the case for the modified Fried Criteria (r = -0.725; p &lt; 0.001). All items demonstrated a good correlation between them. We can here propose that we can accurately assess frailty status in the community setting by using a modified version of Fried criteria, Clinical Frailty Scale translation in Greek and we can screen for frailty by using the Greek translation of PRISMA 7 only after removing the item 6 of the questionnaire.

The causal networks are important for understanding the signaling alterations in diseases. To reveal the network pathways affected in epithelial-mesenchymal transition (EMT) and cancer stem cells (CSCs), which are related to poor prognosis of cancer, the molecular networks and gene expression in diffuse- and intestinal-type gastric cancer (GC) have been analyzed. The network pathways in GC were analyzed in Ingenuity Pathway Analysis (IPA). The analysis of the probe sets of which gene expression has significant differences between diffuse- and intestinal-type GC in RNA Sequencing publicly available data identified 1099 causal networks in diffuse- and intestinal-type GC. Master regulators of the causal networks included lenvatinib, pyrotinib, histone deacetylase 1 (HDAC1), mir-196, and erb-b2 receptor tyrosine kinase 2 (ERBB2). The analysis of the HDAC1-interacting network identified the involvement of regulation of EMT by growth factors pathway, coronavirus pathogenesis pathway, and vorinostat. The network had RNA-RNA interactions with microRNAs such as mir-10, mir-15, mir-17, mir-19, mir-21, mir-217, mir-223, mir-25, mir-27, and mir-34. These molecular networks revealed in the study may lead to the identification of drug targets for GC.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

This analysis examines the wide range of occupational and environmental health risks that affect people in diverse settings. This article emphasizes the importance of conducting exposure and risk assessments to safeguard public health by analyzing various hazards, including arsenic, oil accidents, lead, welding fumes, and mycotoxins. Various methodologies are utilized in these research articles to conduct the study, emphasizing hazard identification, exposure assessment, and risk characterization. The results uncover harmful substances, vulnerable demographics, and occupational duties linked to increased hazards. The report suggests various mitigation strategies, such as targeted interventions, enhanced community efforts, and improved exposure estimates. Notwithstanding the constraints identified in certain studies, the review emphasizes the necessity for continuous investigation to improve assessments of exposure, comprehend the cumulative effects, and examine the wider societal consequences. The significance of sector-specific risk assessments, protective measures, and ongoing monitoring is underscored to ensure mental and physical health protection.

This study introduces an integrated method for managing process risks in a Business Process Reengineering (BPR) project, using Robust Data Envelopment Analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA: Severity, Occurrence, and Detection (S-O-D), and incorporating two additional factors (Breakdown Cost and Breakdown Duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an au-tomotive sector company, and the results showed the impact of uncertainty on values by com-paring different approaches such as RPN, PFMEA-DEA, and PFMEA-RDEA. A new reduced risk categorization was achieved, who allowed for decision-makers to focus on necessary actions for reengineering.

Zanthoxylum armatum is an forest tree with apomixis characteristic, and RWP-RKs are plant-specific transcription factors known to regulate nitrogen response and embryonic development. However, the genome-wide analysis of RWP-RK family genes in Z.armatum is unclear. In this study, 36 ZaRWP-RK transcription factors were identified in the genome of Z.armatum, among which 15 genes were belonged to the RKD subfamily and 21 belonged to the NLP subfamily. Duplication events of ZaRWP-RK genes were mainly segmental duplication, and synteny analysis revealed close phylogenetic relationship between Z.armatum and Arabidopsis. The analysis of cis-elements indicated that ZaRWP-RK genes may be involved in the regulation of embryonic development of Z.armatum by responding to plant hormones such as abscisic acid, auxin, and gibberellin. Real-time PCR showed that the expression levels of most ZaRWP-RK genes were significantly increased from flowers to young fruits. Protein-protein interaction network analysis further revealed the potential roles of the ZaRWP-RK proteins in apomixis. Collectively, this study provide a theoretical basis for future investigations into the ZaRWP-RK genes and their regulatory mechanisms in the apomixis process of Z.armatum.

Cannabidiol (CBD), a non-psychotropic main component of the Cannabis plant, is approved as a drug in the European Union (EU) under the name “Epidyolex”. However, the approval process as a food ingredient under the Novel Food Regulation was paused by the European Food Safety Authority (EFSA) due to a lack of safety data. Nevertheless, there is a growing, unregulated market in which CBD is advertised with various health claims and different dosage instructions. Of particular concern is its toxic effect on the liver and possible reproductive toxicity in humans. Studies suitable for calculating a benchmark dose were identified from the available data. Animal studies yielded a Benchmark Dose Lower Confidence Limit (BMDL) of 43 mg/kg bw/day, which translates into a safe human dose of approximately 15 mg/day. Only a Lowest Observed Adverse Effect Level (LOAEL) of 4.3 mg/kg bw/day could be identified from the human data. This updated risk assessment confirmed the Health-based Guidance Value (HBGV) of 10 mg/day based on human LOAEL. Despite existing data gaps, preliminary regulation appears advisable as the current form of the gray CBD market is unacceptable from the standpoint of consumer safety and protection.

: Fluoride toxicity has become a global public health concern, and its significant source of exposure is drinking water. This study aimed to determine groundwater fluoride level and assess its related human health risks. Its level was measured from groundwater samples using an ion-selective electrode. The total hazard index was assessed based on the chronic daily oral intake and dermal absorbed dose of groundwater fluoride. Analyses were conducted using ArcGIS and SPSS. This study reported groundwater fluoride ranged from 0.3 mg/L to 38 mg/L, with a mean annual level of 15 mg/L. Groundwater fluoride level above 5 mg/L was reported in 60 % of the water samples. Total hazard index values among sampled locations varied from 0.18 to 22.56. Half (51.5 %) of infants, 85.5 % of children, 82.5 % of adolescents, and 67.5 % of adults had a risk of developing a non-carcinogenic health effect. This study demonstrated that the fluoride level of the groundwater sources was high, and the water was not suitable for drinking, a result that is of special importance for children and adults since they are a hypersensitive subgroup of the population.

To decrease oral pathogens and increase the number of beneficial bacteria, probiotics and bioactive compounds made via microbial bioconversion are recently used. In addition, the interest regarding probiotics-mediated bioconversion with popular medicinal plants is increasing. These compounds produced during the process exhibit synergistic effect and enhance a variety of biological activities including pathogen inhibition in host. Therefore, we developed bioconverted milk with high inhibitory activity against oral pathogens and α-glucosidase. Lactiplantibacillus plantarum SMFM2016-RK was chosen as the probiotic due to its beneficial characteristics such as high acid and bile tolerance, antioxidant activity, and α-glucosidase inhibition. Based on the minimal bactericidal concentration against three oral pathogens, the following appropriate concentrations of Artemisia herba-alba extract were added to milk: 5 mg/mL of A. herba-alba ethanol extract and 25 mg/mL of A. herba-alba hot-water extract. Four bioconverted milks (BM), BM1, BM2, BM3, and BM4, were produced by combining L. plantarum SMFM2016-RK alone, L. plantarum SMFM2016-RK and ethanol extract, L. plantarum SMFM2016-RK and hot-water extract, and L. plantarum SMFM2016-RK with both extracts. As a result of antimicrobial activity, BM3 inhibited the growth of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis the most, and BM4 suppressed the growth of Fusobacterium nucleatum the most. In addition, bioconverted milk containing A. herba-alba (BM2, BM3, and BM4) inhibited α-glucosidase more effectively than BM1. In conclusion, A. herba-alba-added milk bioconverted by L. plantarum SMFM2016-RK displayed the growth inhibitory effect on oral pathogens and the α-glucosidase inhibitory activity, thus it necessitates to evaluate the effects on the alleviation of oral diseases and glycemic control through future animal experiments.

This study examines the relationship between Gross Domestic Product (GDP) and income inequality in Greece across three key periods: the pre-crisis period (2003-2008), the crisis and memoranda period (2009-2014), and the post-crisis period (2015-2020). Using data from the World Bank Open Data database, the study employs correlation analysis, and linear regression models to analyze how economic changes influence income distribution. The findings reveal that during the pre-crisis period, economic growth was moderately correlated with increasing inequality, although this relationship was not statistically significant, indicating uneven distribution of growth benefits. In the crisis period, a strong and statistically significant negative relationship between GDP and the Gini coefficient was identified, highlighting the sharp rise in inequality as the economy contracted due to austerity measures. In the post-crisis period, the relationship between GDP and inequality weakened, showing a statistically insignificant correlation, suggesting that economic recovery alone did not significantly reduce inequality. The study concludes that economic growth is insufficient to address income inequality without targeted policy interventions, particularly during periods of crisis. These findings underscore the importance of comprehensive social policies to mitigate inequality and suggest areas for future research on the impact of specific interventions on income distribution.

In recent years, the rapid expansion of urban areas has led to changes in land use, a reduction in pervious surfaces, and a disturbance in the balance between surface waters and groundwater. These urban changes, combined with inadequate drainage systems and misdirection of runoff, have resulted in severe urban floods, causing numerous fatalities and damage to urban areas. As a result, investigating the effects of flood occurrences and determining proper mitigation measures to reduce flood damage is essential. This paper aims to establish a novel economic-hydrologic model to evaluate the efficacy of Low-impact Development (LID) measures in urban Bronx River catchment, NYC. The model examines reduced peak discharge of flood and imposed damage. Initially, the EPA’s SWMM is used to simulate urban flooding based on recent extreme events. LID scenarios are then implemented, including individual and combined scenarios, to mitigate the adverse impacts of flood occurrences of high to low frequency. By means of the HEC-GeoRAS tool, highly detailed flood inundation maps are generated and seamlessly incorporated into the flood damage estimation model, namely HAZUS, to obtain estimates of the potential damage caused by floods. Using the reduced monetary damages and the implementation cost of LID measures, the benefit-to-cost (BC) ratio is then calculated. Findings indicated that the combined LID scenario is the most effective approach in peak flow reduction. Moreover, Permeable pavement outperforms infiltration trench, bio retention cell, and rain barrel in terms of benefit-to-cost ratio, with rain barrel showing the lowest ratio. The proposed evaluation system for LID measures, with a focus on flood damage reduction as a key benefit, provides valuable insights for identifying optimal scenarios and facilitating informed decision-making.

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

BACKGROUND Detecting total cholesterol in dried blood spots could aid in identifying individuals with a high likelihood of familial hypercholesterolemia and could be used as a screening measure. This study aims to assess the diagnostic accuracy of dried blood spots on Whatman 903 paper cards using a manual enzymatic technique. METHODS A total of 394 samples collected as serum and dried blood spots were compared. Cholesterol was determined in serum using the automated reference method, while cholesterol on paper was measured using a manual enzymatic method. Within- and between-day diagnostic variability were analyzed. The correlation between both methods was assessed using Passing-Bablok regression and Bland-Altman plot. Internal validation of our correlation formula was performed on 149 samples, along with external validation of the formula proposed by Corso et al. RESULTS The within- and between-day coefficient of variation was found to be lower than 10.14% and 14.09%, respectively. Method comparison yielded an area under the curve of 0.976, with a standard error of 0.009. Passing-Bablok regression indicated a precision of 0.803 and an accuracy of 0.96. Internal validation precision was measured at 0.716. Regarding internal validation, calculated cholesterol with our regression line did not differ from serum cholesterol analysis, while significant differences were observed in calculated cholesterol levels using the external formula. CONCLUSIONS Total cholesterol analysis in dot blood spots demonstrates high precision and reproducibility. This method reliably enables the incorporation of this biological marker into neonatal screening for familial hypercholesterolemia detection.

The COVID-19 pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has exposed the vulnerabilities and unpreparedness of the global healthcare system in dealing with emerging zoonoses. In the past two decades, coronaviruses (CoV) have been responsible for three major viral outbreaks, and the likelihood of future outbreaks caused by these viruses is high and nearly inevitable. Therefore, effective prophylactic universal vaccines targeting multiple circulating and emerging coronavirus strains are warranted. This study utilized an immunoinformatic approach to identify evolutionarily conserved CD4+ (HTL) and CD8+ (CTL) T cells, and B-cell epitopes in the coronaviral spike (S) glycoprotein. A total of 132 epitopes were identified, with the majority of them found conserved in the bat CoV, pangolins CoV, endemic coronaviruses, SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV). Their peptide sequences were then aligned and assembled to identify the overlapping regions. Eventually, two major peptide assemblies were derived based on their promising immune-stimulating properties. In this light, they can serve as lead candidates for universal coronavirus vaccine development, particularly in the search for pan-coronavirus multi-epitope universal vaccines that can confer protection against current and novel coronaviruses.

The acoustic startle response (ASR) is an explosive motor response leaded by a sudden and intense acoustic stimulus. ASR has several clinically relevant forms of plasticity, including habituation and sensorimotor gating. Although ASR and its plasticity have been intensively studied in different animal models, including zebrafish (Danio rerio) larvae, information in adult zebrafish is still very scarce. In this manuscript we present Zebra_K, a new automated high-content kinematic analysis platform for assessing ASR, its habituation and prepulse inhibition (PPI), a quantitative measure of sensorimotor gating, in adult zebrafish. The analysis of the kinematic parameters in 1,875 ASR in adult zebrafish has shown a single response wave consistent with the short-latency C-bend described in zebrafish larvae. Moreover, protocols have been designed and validated in Zebra_K for the analysis of sensitivity, habituation and PPI of this response. Then, this platform has been used to determine the effect of pharmacological and physiological modulators of ASR plasticity. As described in other animal models, the NMDA-receptor antagonist ketamine increased the responsiveness to the acoustic stimuli, decreasing habituation and leading to complete abolition of PPI. Moreover, the effect of the gender on zebrafish ASR plasticity has been analyzed for the first time. Females exhibited higher responsiveness and a lower habituation and PPI than males, a result consistent with the gender effect described in other animal models and in humans. These results enhance the interest of using adult zebrafish to assess the potential effect of chemicals or genetic manipulations on ASR plasticity.

Deploying search and rescue (SAR) dogs in disaster situations is crucial for effective rescue missions. This study delves into the challenges and obstacles encountered during the use of SAR dogs, particularly focusing on the earthquake that hit Turkey in February 2023. By employing a case study approach, the research explores various factors that impact the efficiency and safety of SAR dog operations, such as environmental conditions, equipment needs, cultural perceptions, and logistical hurdles. Environmental conditions such as extreme weather, hazardous materials, and unstable structures present significant risks to both the dogs and their handlers. The adequacy of equipment, including protective gear and medical supplies, is critical for ensuring the safety and efficiency of rescue operations. Logistical challenges, including the transportation and care of dogs in disaster zones, further complicate their deployment. Cultural barriers also play a crucial role, as perceptions of dogs vary widely across different communities, potentially affecting the acceptance and integration of canine units in rescue efforts. The case study of the earthquake in Turkey highlights these issues, showcasing the successes and setbacks experienced by international rescue teams. The findings indicate that although SAR dogs are invaluable in locating survivors, they face significant dangers, including hazardous materials, extreme weather, and physical injuries. Additionally, cultural differences and logistical issues complicate their deployment. This paper highlights the necessity for improved training, protective measures, and international standards to ensure SAR dogs' well-being and effectiveness in disaster response efforts. The study's implications suggest that addressing these challenges can markedly enhance the preparedness and resilience of SAR teams, leading to more efficient and safer rescue operations in future disaster scenarios.

A common motivation for increasing open access to research findings and data is the potential to create economic benefits – but evidence is patchy and diverse. This study systematically reviewed the evidence on what kinds of economic impacts (positive and negative) open science can have, how these comes about, and how benefits could be maximized. Use of open science outputs often leaves no obvious trace, so most evidence of impacts is based on interviews, surveys, inference based on existing costs, and modelling approaches. There is indicative evidence that open access to findings/data can lead to savings in access costs, labour costs and transaction costs. There are examples of open science enabling new products, services, companies, research and collaborations. Modelling studies suggest higher returns to R&D if open access permits greater accessibility and efficiency of use of findings. Barriers include lack of skills capacity in search, interpretation and text mining, and lack of clarity around where benefits accrue. There are also contextual considerations around who benefits most from open science (e.g. sectors, small vs larger companies, types of dataset). Recommendations captured in the review include more research, monitoring and evaluation (including developing metrics), promoting benefits, capacity building and making outputs more audience-friendly.

(1) Background: Second Victims are health care workers negatively affected by adverse events and are prone to developing dysfunctional coping strategies compromising not only their own, but also patient safety. An effective way to address this issue is the implementation of peer support programs. This paper reports on the effectiveness of a peer support program at the Austrian Hietzing Clinic (KHI). (2) Methods: Telephone interviews were carried out after peer support sessions using a questionnaire. Descriptive analysis was carried out reporting frequencies and proportions for nominal variables, means with standard deviations for ordinal and interval-scaled variables. (3) Results: So far, 45 peer supporters have completed telephone interviews after a peer support session. The main reasons for the session were bad news from the private sphere and death of a patient. The peer supporters rated the helpfulness of the sessions for their colleagues as very high while not feeling very burdened themselves afterwards. (4) Conclusions: Underutilization of the program is probable. However, the high rating of helpfulness of the carried-out sessions by the peer supporters in combination with them not feeling very burdened, shows that the program is safe for the peer supporters and an upscaling is warranted.

Genetic variability in rice breeding program plays the very crucial role. It provides outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For the understanding of population structure and genetic relationship among the different rice lines is indispensable prior to setting of correlation among dynamic alleles and traits. In the present investigation, genetic diversity and population structure of 116 rice accessions by using 64 polymorphic SSR markers was targeted for the evaluation of the genetic relatedness and diversity. Genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% of the variation was likely due to differences within groups. The study suggests that the population structure and genetic relatedness should be considered when working with the core collection of 116 rice germplasm lines for association mapping, aiming to establish marker-trait associations.

Qualtra, an innovative 10 MW geothermal power plant proposal, employs a closed-loop design to mitigate emissions, ensuring no direct release into the atmosphere. A thorough assessment utilizing energy and exergy analysis, Life Cycle Assessment (LCA), Exergo-economic and Exergo Environmental Analysis (EevA) was conducted. The LCA results, utilizing the ReCiPe 2016 midpoint methodology, encompass all the spectrum of environmental indicators provided. The technology implemented makes it possible to totally avoid direct atmospheric emissions from the Qualtra plant, so the environmental impact is mainly due to indirect emissions over the life cycle. The results obtained for the Global Warming Potential indicator is about 6.6 g CO2/kWh, notably lower compared to other conventional systems. Contribution analysis reveals that the construction phase dominates, accounting for over 90% of the impact in all LCA MidPoint categories. At the component level, a single score calculation incorporating normalization and weighting was executed. The resulting single score is then used into Exergo-Environmental Analysis (EEvA), highlighting the well system as the most impactful contributor, constituting approximately 45% of the total impact. Other substantial contributions to the environmental impact include the condenser (21%), the turbine (17%), and the HEGeo (14%). The Exergo-Economic analysis assesses cost distribution across major plant components, projecting an electricity cost of about 8.3 c€/kWh.

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 study critically explores the legislative frameworks and national regulations of Serbia, Croatia, and Slovenia concerning the engagement, training, and responsibilities of search and rescue dogs and their handlers. The goal is to offer a thorough understanding of how these dogs and their handlers are engaged, trained, and managed in each country. By carefully analyzing the legal foundations, the study aims to clarify the rights, duties, and responsibilities of all parties involved, ensuring that search and rescue dog deployments are conducted within a structured and lawful framework. Such an understanding is crucial for improving disaster management and ensuring a coordinated response to various emergencies. The findings reveal that although each country has strong legal provisions for deploying search and rescue dogs, there are significant differences in the detail and scope of these regulations. Slovenia is notable for its detailed guidelines, regular evaluations, and licensing processes, setting a high standard. Croatia benefits from strong institutional support and active international collaboration, which greatly enhances its disaster response capabilities. On the other hand, Serbia, despite having a solid foundational framework, could improve by better coordinating with non-governmental organizations and aligning more closely with international standards. Key areas for improvement include harmonizing standards, enhancing training programs, increasing public awareness, better resource allocation, and adopting new technologies. By addressing these issues, Serbia, Croatia, and Slovenia can further strengthen their legal and institutional frameworks, ensuring their search and rescue teams are better prepared for disasters. This study emphasizes the need for continuous improvement through collaboration, standardization, and innovation to maximize the effectiveness of search and rescue operations involving dogs.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the creativity of the project, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data was collected from multiple crowdfunding platforms for projects in Africa, that is, cross-country data from 54 African countries, to overcome data limitations from a single country. Econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges in the success of online crowdfunding. The signalling and agency theories support the finding. The findings were limited to crowdfunding projects on the African continent and are not necessarily applicable to some developed countries.

The North China Plain is among the regions most afflicted by ground fissure disasters in China. Recent urbanization has accelerated ground fissure activity in the three counties of the northern North China Plain, posing significant threats to both the natural environment and socioeconomic sustainability. Despite the increased attention, a lack of comprehensive understanding persists due to delayed recognition and limited research. This study conducted field visits and geological surveys across 43 villages and 80 sites to elucidate the spatial distribution patterns of ground fissures in the aforementioned counties. By integrating these findings with regional geological data, we formulated a causative model to explain ground fissure formation. Our analysis reveals a concentration of ground fissures near the Niuxi and Rongxi faults, with the former exhibiting the most extensive distribution. The primary manifestations of ground fissures include linear cracks and patch-shaped collapse pits, predominantly oriented in east-west and north-south directions, indicating tensile failure with minimal vertical displacement. Various factors contribute to ground fissure development, including fault activity, ancient river channel distribution, bedrock undulations, rainfall, and ground settlement. Fault activity establishes a concealed fracture system in shallow geotechnical layers, laying the groundwork for ground fissure formation. Additionally, the distribution of ancient river channels and bedrock undulations modifies regional stress fields, further facilitating ground fissure emergence. Rainfall and differential ground settlement serve as triggering mechanisms, exposing ground fissures at the surface. This research offers novel insights into the causative mechanisms of ground fissures in the northern North China Plain, providing valuable scientific evidence for mitigating ground fissure disasters during the urbanization process in the region.

Cardiovascular diseases (CVD) are rising rapidly among the postmenopausal woman but they are less likely to identify their risk by an appropriate risk assessment tool. This review evaluates available literature on cardiovascular risk assessment among postmenopausal women to provide a concise view of risk factors and disease burden among them, present risk assessment systems including their drawbacks, emergence of new risk factors and their role in risk prediction, and finally use of hormone replacement therapy during menopause. Results demonstrate that menopause is a transition point for developing CVD not due to physiological changes only but psychosocial factors like depression and marital stress are also responsible. Both conventional and emerging risk factors burden are high among postmenopausal women. Though data regarding CVD risk assessment among postmenopausal population is lacking but existing evidences claimed underestimation or overestimation of risk among women. Moreover application of different tools on same population has revealed significant variation in result. In this regard, recalibration of conventional tools with local data and new risk factors has showed improvement of risk prediction. Hormone replacement therapy during early menopause has reported beneficial to prevent CVD but in secondary prevention it has no role. All of these findings demand further studies on cardiovascular risk assessment, especially in developing countries where women after menopause are not in consideration of health strategy makers.

Surgical smoke is produced by energy-based surgical instruments. The airborne volatile organic compounds (VOCs) from surgical smoke may have potential health risk. This study aimed to evaluate the evidence for the harmful effects on the operating theater staff. An internationally recognized evaluation model of health risk assessment (HRA) was adapted to preliminarily assess the health risks caused by VOCs in surgical smoke. Results of HRA indicated that non-carcinogenic risk indexes of VOCs did not exceed one, indicating that these pollutants didn't cause significant non-carcinogenic harm to the health of the operating theater staff. But the cancer risk indexes exposed to formaldehyde and benzene all exceed 10-5, which was higher than the suggested value of USEPA (10-6) and might cause potential harm to the health of the operating theater staff. Long-term exposure of such surgical smoke will be harmful to the health of the operating theater staff and have a great risk of cancer.

The World Health Organization’s Health Economic Assessment Tool (HEAT) for walking and cycling is a user-friendly web-based tool to assess health impacts of active travel. HEAT, developed over 10 years ago, has been used by researchers, planners and policymakers alike in appraisals of walking and cycling policies of both national and more local scales. HEAT has undergone regular upgrades adopting the latest scientific evidence. This article presents the most recent upgrades of the tool. Health impacts of walking and/or cycling in a specified population are quantified in terms of premature deaths avoided (or caused). In addition to the calculation of benefits from physical activity, HEAT was recently expanded to include assessments of the burden associated with air pollution exposure and crash risks while walking or cycling. Further, impacts on carbon emissions from mode shift to active travel modes can now be assessed. Monetization of impacts using Value of Statistical Life and Social Costs of Carbon now uses country-specific values. As active travel inherently results in often substantial health benefits as well as not always negligible risks, assessments of active travel behaviour or policies are incomplete without considering health implications. The recent developments of HEAT make it easier than ever to obtain ballpark estimates of health impacts and carbon emissions related to walking and cycling.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

Thermal camouflage is a highly sought-after technology to increase the survivability of military equipment against infrared (IR) detectors. Recently, two-dimensional (2D) nanomaterials have shown low IR emissivity, widely tunable opto-electronic properties and compatibility with stealth-applications. Among them, graphene and graphene-like materials are the most attractive 2D materials used for thermal camouflage applications. In particular, in multilayer graphene (MLG) charge density can be effectively tuned through sufficiently intense electric fields or through electrolytic gating. Therefore, MLG optical properties, like infrared emissivity and absorbance, can be controlled in a wide range by voltage bias. The large emissivity modulation achievable with this material makes it suitable in the design of thermal dynamic camouflage devices. Generally, the emissivity modulation in the multilayered graphene medium is governed by an intercalation process of non-volatile ionic liquids under a voltage bias. The electrically-driven reduction of emissivity leads to a decrease in the apparent temperature of the surface, which can match that of the background enabling thermal camouflaging. Since this property is common to other graphene-based materials, here we present a review specifically focused on the recent advances in the field of the thermal camouflage properties of graphene in the form of composite film and aerogel structures. A summary of the current understanding of the working principle of thermal camouflage materials, current limitations, and future opportunities is presented and discussed.

This research focuses on the use of the HEC-RAS model to estimate flash flood risk. It combines advanced hydraulic modeling techniques and precise data entry. The aim is to contribute knowledge to assess flash flood risk and reduce impacts on communities and infrastructure. The selection of study areas with a history of severe flooding, such as the Lam Saphung Basin, Phrom River Basin, and Lam Chern River Basin Part 1 in Thailand, highlights the importance of thorough flash flood risk assessment. By leveraging the HEC-RAS model, the results of this research inform proactive risk management strategies, improve emergency preparedness and help develop sustainable mitigation measures. The results are important in developing emergency response strategies and overall recovery ability in areas at risk of flash floods. The research combines advanced hydrologic modeling and real-world flash flood data. In addition to the flood condition assessment, the extent of the flood area and associated hazards were assessed using a two-case simulation strategy. The study covers future scenarios related to land use changes and retrospective analysis of past flood events. The results have practical implications for improving emergency response strategies, land use planning, and overall resilience in flash flood-prone areas. The integrated approach emphasizes the research output to providing insights and actionable solutions for communities facing ongoing challenges posed by flash floods.

The evaluation of tourism sustainability has become of paramount importance for researchers and decision-makers to better protect tourist destinations, especially with the continuous increase in the number of tourists and visitors that alter the natural components of the environment and degrade ecosystems. Therefore, the use of geographical data is of great significance, particularly in environmental monitoring and land management, due to its ease of use and availability. However, calculating composite indices for sustainability involves several steps and requires a basic understanding of existing models. This article describes the development and design of a package of plugins in QGIS that serves as an easy-to-use tool for processing and automatically calculating economic, environmental, social indices, and the overall index of tourist sustainability. These plugins have been built in Python and designed as QGIS software plugins.

Disasters, whether natural or human-induced, pose significant threats to human lives, property, and infrastructure. This study examines the effectiveness of search and rescue (SAR) dogs in locating survivors during disasters, with a focus on the experiences in Serbia, Croatia, and Slovenia. The primary goals are to evaluate training protocols, operational challenges, success rates, and the overall impact of SAR dogs in disaster scenarios. The findings indicate that SAR dogs greatly enhance the efficiency of disaster response operations. Their superior sense of smell, agility, and ability to manoeuvre through complex terrains make them crucial for quickly locating survivors. However, the performance of SAR dogs is affected by various factors, including the quality of their training, environmental conditions, and the coordination between handlers and dogs. The research highlights several challenges encountered by SAR dogs, such as extreme weather conditions, hazardous terrains, fatigue, and psychological stress. Additionally, limited resources and inconsistent training standards can hinder their effectiveness. The study underscores the importance of standardized training protocols, increased resource allocation, and better coordination among SAR teams. By addressing these challenges, the efficiency of SAR dogs can be further optimized, resulting in more effective disaster responses and higher survival rates. SAR dogs play an essential role in disaster management in Serbia, Croatia, and Slovenia. Ongoing investment in their training and welfare, along with improved operational protocols, will ensure their crucial contribution to saving lives during disasters. This study contributes to a broader understanding of the effectiveness of SAR dogs and provides practical recommendations for enhancing disaster response strategies.

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

Phytoplasma diseases pose a substantial threat to diverse crops of agricultural importance. Management measures are usually implemented only after the disease has already occurred. Early detection of such phytopathogens, prior to disease outbreak, has rarely been attempted but would be highly beneficial for phytosanitary risk assessment, disease prevention and mitigation. In this study we present the implementation of a recently proposed proactive disease management protocol (DAMA: Document, Assess, Monitor, Act) for a group of vector-borne phytopathogens. We used insect samples collected during a recent biomonitoring program in southern Germany to screen for the presence of phytoplasmas. Insects were collected with Malaise traps in different agricultural settings. DNA was extracted from these mass trap samples and subjected to PCR-based phytoplasma detection and COI sequence metabarcoding. In about 1% of the analyzed samples, phytoplasma DNA was detected. Phytoplasma group identification was performed by 16S rRNA gene sequence analysis and the detected phytoplasmas could be assigned to the 16SrI subgroups B and L. Insect species in the sample were identified by DNA metabarcoding. By using established databases, checklists, and archives, we documented historical associations and records of phytoplasmas and its hosts in the study region. For the assessment in the DAMA protocol, phylogenetic triage was performed in order to determine the risk for tri-trophic interactions (plant-insect-phytoplasma) and associated disease outbreaks in the study region. A phylogenetic heat map constitutes the basis for risk assessment and was used here to identify a minimum number of seven leafhopper species suggested to be monitored by stakeholders in this region. A proactive stance in monitoring changing patterns of association between hosts and pathogens can be a cornerstone in capabilities to prevent future phytoplasma disease outbreaks. To our knowledge, this is the first time that the DAMA protocol has been applied in the field of phytopathology and vector-borne plant diseases.

In its 8th Malaysia Plan (MP*), the Malaysian government has given nanotechnology development a high priority and has been made a Strategic Research (SR) programme. Consequently, an exhaustive and long-term approach is essential. Additionally, specific laws are required to provide an optimal health and safety environment for workers. Consequently, the current study intends to assist Malaysian policymakers in promoting improved safety, risk assessment, and management for nanotechnology workplaces, as well as drafting a comprehensive policy for the recently released DOSH guideline in 2018. Utilizing a unique methodology, this study evaluated the assessment and management of nanotechnology health and safety risks in Malaysia using the international risk governance council (IRGC) framework. In addition to document analysis, n = 20 semi-structured interviews were conducted with representatives of several nanotechnology platforms to collect data that was then analyzed thematically. The research evaluated current workplace safety regulations and laws. Consequently, we propose a future dynamic strategy for Malaysian nanotechnology with more favourable upgrades supported by increased concentrations in the following areas: I policy creation, ii) comprehensive management system, iii) knowledge transfer, and iv) infrastructure management. In addition, the findings highlight the need of cross-sectional communication in bridging existing gaps. This study laid the groundwork for a more systematic and comprehensive health and safety strategy.

The assessment of seismic risk in urban areas with high seismicity is certainly one of the most important problems that territorial managers have to face. A reliable evaluation of this risk is the basis for the design of both specific seismic improvement interventions and emergency management plans. Unappropriate seismic risk assessments may provide misleading results and induce bad decisions with relevant economic and social impact.The seismic risk in urban areas is mainly linked to three factors, namely, “hazard”, “exposure” and “vulnerability”. Hazard measures the potential of an earthquake to produce harm; exposure evaluates the amount of population exposed to harm; vulnerability represents the proneness of considered buildings to suffer damages in case of an earthquake. Estimates of such factors may not always coincide with the perceived risk of the resident population. The propensity to implement structural seismic improvement interventions aimed at reducing the vulnerability of buildings depends significantly on the perceived risk.This paper investigates on the difference between objective and perceived risk and highlights some critical issues. The aim of this study is to calibrate opportune policies, which allow addressing the most appropriate seismic risk mitigation options with reference to current levels of perceived risk. We propose the introduction of a Seismic Policy Prevention index (SPPi). This methodology is applied to a case-study focused on a densely populated district of the city of Catania (Italy).

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

In recent years, rainfall-induced waterlogging has become a common hazard in the highly urbanized coastal city of Chattogram, Bangladesh resulting in high magnitude of property damage and economic loss. Therefore, the primary objective of this research is to prepare a waterlogging inventory map and understand the spatial variation of the risk by means of hazard intensity, exposure, and vulnerability of waterlogging. In this research, the inventory map and factors influencing waterlogging hazard were determined from a participatory survey and other spatial data including land elevation, population, and structural data were collected from secondary sources. Analytical Hierarchy Process was applied to measure the hazard intensity and the exposure and vulnerability were estimated by overlaying the spatial data onto the hazard intensity map. A total of 58 locations in 22 wards have been identified as waterlogging affected, which covers ~8.42% of the city area. Obtained waterlogging vulnerability index map suggests that ward no. 5, 6, 16, 17, and 33 are greatly vulnerable to waterlogging in terms of their social, infrastructure, critical facilities, economic and environmental vulnerability. We show that ~2.71% of the study area is at very high risk, while the risk score is considerably higher for ward no. 5, 8, 17, 19, and 33.

We present an updated design process for adapting and integrating existing cyber risk assessment approaches for impact assessment for the risk from IoT to the digital economy. The new design process includes a set of changes to the original standards (e.g. NIST) that are adapted for the IoT cyber risk in this paper. This paper also presents a new framework for impact assessment of IoT cyber risk, specific for the digital economy.

Salinity, resulting from climate change and excessive mineral fertilization, burdens farmers and negatively impacts soil and water ecosystems in the Northeastern Nile Delta. Organic and biological approaches are crucial for addressing these issues. This study examined the effects of individual and combined inoculations with cyanobacteria, yeast, and Arbuscular Mycorrhizal Fungi (AMF), with or without K-humate, on enhancing salinity tolerance, increasing yield, and improving crop quality of wheat and faba bean. The aim was to reduce NPK mineral fertilizer application rates to 25% of recommended doses. An economic evaluation was also conducted. In a preliminary laboratory experiment, the interactive effects of these microbiomes on the production of plant antioxidant enzymes and soil enzymes were investigated under salinity stress. Results showed that co-inoculation, especially with K-humate, yielded superior outcomes compared to individual inoculations. The laboratory experiment findings were confirmed by a field trial conducted in saline-alkaline soil in the Northeastern Nile Delta region. The study found that the integrated inoculation of cyanobacteria, yeast, and mycorrhizae with K-humate, along with 25% mineral NPK fertilization enhanced microbial activity, including AMF colonization, spore density, and total bacteria, cyanobacteria, and yeast counts in the plant rhizosphere. It also increased soil enzyme activity, improving soil fertility compared to individual or mineral control treatments. This integration resulted in wheat yields of 2.77 tons/ha and faba bean yields of 4.75 tons/ha. Crop quality improved, with higher levels of nitrogen, potassium, phosphorus, and protein in wheat grains and faba bean seeds. Microbiomes inoculation increased potassium uptake over sodium, enhancing the potassium/sodium ratio and salt stress tolerance. This approach reduces reliance on costly mineral fertilizers by up to 75%, enabling economically viable cultivation in marginal lands. These sustainable practices optimize resource utilization, maximize economic efficiency, and preserve natural resources.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. The research uses advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. The findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. Oil revenues also play a critical role, reflecting the country's economic reliance on oil. The study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. These findings also support the importance of consistent and predictable income sources for sustaining consumption. Additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

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.

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on February 6 2023, caused massive casualties and extensive damage to infrastructure, with the province of Hatay bearing the brunt of the impact. To investigate the potential of nighttime light (NTL) data for earthquake disaster assessment, we performed an analysis of pre- and post-earthquake NPP-VIIRS and SDGSAT-1 NTL data. The NTL change ratio (NCR) was employed to identify the extent of damage in Hatay Province following the earthquake to assess the impact on affected buildings and populations. NPP-VIIRS daily NTL Time-series data was used to establish an economic recovery framework for earthquake disasters in Hatay Province, assessing regional economic recovery after earthquake. The results revealed a strong correlation between the total reduced NTLs of NPP-VIIRS and SDGSAT-1 and the presence of severely and moderately damaged buildings. Conversely, the correlation with slightly damaged buildings was relatively weaker. Furthermore, the reduced NTLs of NPP-VIIRS provide a more precise identification of all earthquake damage compared to the SDGSAT-1 data, achieving an overall accuracy of 86.21% in earthquake damage identification and a Kappa coefficient of 0.72. In accordance with the earthquake economic recovery framework, as of the end of March 2024, neither the provincial economy nor any of its districts have fully restored to their pre-earthquake levels. The findings offered new and valuable insights into earthquake disaster assessment, assisting decision-makers in accurately assessing the level of disaster risk following an earthquake and efficiently allocating post-earthquake relief funds.

Investments in small and medium-sized anaerobic digestion facilities have the potential to boost biogas production in Greece and other EU countries. This study aimed to evaluate the economic feasibility of anaerobic digestion facilities equipped with combined heat and power (CHP) units ranging from 50 to 400 kW, while treating livestock waste. For this purpose, data were gathered from various livestock operations (dairy cattle, poultry, swine, dairy sheep and goat) regarding their annual production, revenues, electricity and fuel usage, and waste generation. Waste samples were then collected and analyzed to assess their biochemical methane production potential. The capital and operational costs of anaerobic digestion facilities, from 50 and 400 kW, were calculated using the equations developed within the “eMT cluster” project. Findings indicate that current feed-in tariffs (FIT) of 0.21 € kWh-1 are insufficient to incentivize investment in anaerobic digestion facilities with capacities below 250 kW, highlighting the need for increased FIT rates or capital expenditure subsidies. Recommendations include shifting towards simplified technology and business models with reduced farmer involvement, coupled with supportive legislative framework and long-term electricity price guarantees. These measures are expected to foster the implementation of anaerobic digestion projects in the animal husbandry sector.

The purpose of this study is to examine the connection between economic growth and unemployment in Liberia between 2001 and 2019. The unit root test and the Augmented Dickey-Fuller (ADF) Co-integration test were used to examine the relationship between unemployment and GDP. The Auto Regressive Distribution Lag (ARDL) bounds test is used to determine if the variables are linked in the long run. According to the results of the ARDL model, there is no long-run relationship between unemployment and economic growth. This study' results have particularly important policy implications for Liberian economic authorities. In both the long and medium term, the observational results showed no meaningful relationship between unemployment and economic growth. The Liberian government should direct its spending toward activities that directly and indirectly promote the creation of employment and decent jobs, a conducive environment and flexible labor market policies or legislation that are not impediments to job creation, and finally, the government should prioritize labor intensive industries.

The COVID-19 outbreak has severely affected the social and economic conditions across this globe. Little is known about the relationship of COVID-19 with countries’ economic and socio-demographic status. Publicly available data on COVID-19 test rate, attack rate, case fatality rate, and recovery rate were analyzed in relation to country’s economic status, population density, median age, and urban population ratio. We also conducted multinomial logistic regression analysis to predict the influence of countries’ social and economic factors on COVID-19. The results revealed that the median age had significant positive correlation with attack rate (r=0.2389, p=0.003), case fatality rate (r=0.3207, p=0.000) and recovery rate (r=0.4847, p=0.000). The urbanization has positive significant correlation with recovery rate (r=0.1957, p= 0.016). The multinomial logistic regression analysis revealed low-income countries are less likely to have an increased recovery rate (p=0.000) and attack rate (p=0.016) compare to high-income countries. The lower-middle-income and upper-middle-income countries are less likely to have an increased recovery rate (p=0.000 and p=0.001, respectively) compared to high-income countries. Based on the result of this study, these economic and socio-demographic factors should consider in designing appropriate preventive measures as a next step. The low and lower-middle-income countries should invest more in health care services to lower the case fatality rate and increase test and recovery rates as part of pandemic preparation like COVID-19. As the number of COVID-19 attacks, death and recovery rates are constantly changing; however, the intensive study is required to obtain a clear picture.

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.