Search results for: hardy cross networks accuracy

Authors: Priyajeet Kaur Kaleka

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Introduction: A dentist is often the first responder in the battle for a patient’s healthy body and maybe the first health professional to observe signs of child abuse, be it physical, emotional, and/or sexual mistreatment. Therefore, it is an ethical responsibility for the dental clinician to detect and report suspected cases of child abuse and neglect (CAN). The main reasons for not reporting suspected cases of CAN, with special emphasis on the third: 1) Uncertainty of the diagnosis, 2) Lack of knowledge of the reporting procedure, and 3) Child abuse and neglect somewhat remained the subject of ignorance among dental professionals because of a lack of advance clinical training. Given these epidemic proportions, there is a scope of further research about dental school curriculum design. Purpose: This study aimed to assess the knowledge and attitude of dentists in Canada regarding signs and symptoms of child abuse and neglect (CAN), reporting procedures, and whether educational strategies followed by dental schools address this sensitive issue. In pursuit of that aim, this abstract summarizes the evidence related to this question. Materials and Methods: Data was collected through a specially designed questionnaire adapted and modified from the author’s previous cross-sectional study on (CAN), which was conducted in Pune, India, in 2016 and is available on the database of PubMed. Design: A random sample was drawn from the targeted population of registered dentists and dental students in Canada regarding their knowledge, professional responsibilities, and behavior concerning child abuse. Questionnaire data were distributed to 200 members. Out of which, a total number of 157 subjects were in the final sample for statistical analysis, yielding response of 78.5%. Results: Despite having theoretical information on signs and symptoms, 55% of the participants indicated they are not confident to detect child physical abuse cases. 90% of respondents believed that recognition and handling the CAN cases should be a part of undergraduate training. Only 4.5% of the participants have correctly identified all signs of abuse due to inadequate formal training in dental schools and workplaces. Although nearly 96.3% agreed that it is a dentist’s legal responsibility to report CAN, only a small percentage of the participants reported an abuse case in the past. While 72% stated that the most common factor that might prevent a dentist from reporting a case was doubt over the diagnosis. Conclusion: The goal is to motivate dental schools to deal with this critical issue and provide their students with consummate training to strengthen their capability to care for and protect children. The educational institutions should make efforts to spread awareness among dental students regarding the management and tackling of CAN. Clinical Significance: There should be modifications in the dental school curriculum focusing on problem-based learning models to assist graduates to fulfill their legal and professional responsibilities. CAN literacy should be incorporated into the dental curriculum, which will eventually benefit future dentists to break this intergenerational cycle of violence.

Keywords: abuse, child abuse and neglect, dentist knowledge, dental school curriculum, problem-based learning

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Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

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The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

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Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

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Authors: Magda Milleyde de Sousa Lima, Letícia Lima Aguiar, Marina Guerra Martins, Erika Veríssimo Dias Sousa, Lizandra Sampaio de Oliveira, Lívia Moreira Barros, Joselany Áfio Caetano

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Patients with chronic kidney disease are vulnerable to episodes which make the safety of their health vulnerable, mainly due to the treatment process that exposes them to high rates of interventions during hemodialysis sessions. Some factors associated with health care contribute to the risk of death and complications. However, there are a small number of scientific studies evaluating the level of safety of hemodialysis clinics, and the sociodemographic characteristics of patients and professionals associated with this safety. Therefore, the present study aims to examine the level of patient safety in hemodialysis clinics in the Brazilian capital, to identify the sociodemographic and clinical factors of patients and nursing staff associated with the level of safety. This is an observational, descriptive and quantitative research conducted in three hemodialysis clinics placed in the city of Fortaleza-CE, Brazil, from September to November 2019. The sample was formed after a sample calculation for finite inhabitants of correlation with 200 chronic renal patients, 30 nursing technicians and seven nurses. Conventional sampling was used based on the inclusion criteria: being present at the hemodialysis session on the day the researcher performed the data collection and being 18 years of age or older. Participants who presented communication difficulties to listen to and/or answer the sociodemographic and clinical questionnaire were excluded. Two instruments were applied: sociodemographic and clinical characterization form and Chronic Renal Patient Safety Assessment Scale on Hemodialysis (EASPRCH). The data were analyzed using the Kruskal Walls Test for categorical variables and Spearman correlation coefficient for non-categorical variables, using the Statistical Package SPSS version 20.0. The present study respected the ethical and legal principles determined by resolution 466/2012 of the National Health Council, under the approval of the Ethics and Research Committee with an opinion number: 3,255,635. The results showed that a hemodialysis clinic presented unsafe care practices of 32 points in the EASPRCH (p=0.001). A statistical association was identified between the level of safety and the variables of the patients: level of education (p=0.018), family income (p=0.049), type of employment (p=0.012), venous access site (p=0.009), use of medication during the session (p=0.008) and time of hemodialysis (p=0.002). When evaluating the profile of nurses, a statistical association was evidenced between the level of safety with the variables: marital status (p=0.000), race (p=0.017), schooling (p= 0.000), income (p=0.013), age (p=0.000), clinic workload (p=0.000), time working with hemodialysis (p=0.000), time working in the clinic (p= 0.007) and clinic sizing (p=0.000). In order, the sociodemographic factors of nursing technicians associated with the level of patient safety were: race (p= 0.001) and weekly workload at (p=0.010). Therefore, it is concluded that there is a non-conformity in the level of patient safety in one of the clinics studied and, that sociodemographic and clinical factors of patients and health professionals corroborate the level of safety of the health unit.

Keywords: hemodialysis, nursing, patient safety, quality improvement

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

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Authors: Enrico Maria la Forgia

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Since the 80ies, in specific situations, France’s Muslims have enacted political actions to reply to attacks on their identity or assimilation attempts, using their religious affiliation as a resource for the organization and expression of collective claims. Indeed, despite Islam's internal sectarian and ethnic differences, religion may be politicized when minorities’ social and cultural rights are under attack. French Civil Society organizations, in this specific case with an Islamic background (ICSO - Islamic Civil Society Organizations), play an essential role in defending Muslims’ social and cultural rights. As a matter of fact, Civil Society organized on an ethnic or religious base is a way to strengthen minoritarian communities and their role as political actors, especially in multicultural contexts. Since the first 1983’s “Marche des Beurs” (slang word referring to French citizens with foreign origins), which involved many Muslims, the development of ICSO contributed to the strenghtening of Islam in France, here meant as a Social Movement aiming to constitute a French version of Islam, defending minorities’ cultural and religious rights, and change the perception of Islam itself in national society. However, since a visible and stigmatized minority, ICSO do not relate only to protests as a strategy to achieve their goals: on several occasions, pressure on authorities through personal networks and connections, or the introduction into public debates of bargaining through the exploitation of national or international crisis, might appear as more successfully - public discourses on minorities and Islam are generally considered favorable conditions to advance requests for cultural legitimation. The proposed abstract, based on a literary review and theoretical/methodological reflection on the state of knowledge on the topic, aims to open a new branch of studies and analysis of Civil Society and Social Movements in Europe, focusing on the French Islamic community as a political actor relating on ICSO to pressure society, local, and national authorities to improve Muslims' rights. The opted methodology relies on a qualitative approach based on ethnography and face-to-face interviews addressing heads and middle-high level activists from ICSO, in an attempt to individuate the strategies enacted by ICSO for mobilizing Muslims and build relations with, on one hand, local and national authorities; into the other, with actors belonging to the Civil Society/political sphere. The theoretical framework, instead, relies on the main Social Movements Theories (resources mobilization, political opportunity structure, and contentious/non-contentious movements), aiming to individuate eventual gaps in the analysis of Islamic Social Movements and Civil Society in minoritarian contexts.

Keywords: Islam, islamophobia, civil society, social movements, sociology, qualitative methodology, Islamic activism in social movement theory, political change, Islam as social movement, religious movements, protest and politics, France, Islamic civil society

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Authors: Sarah Elshahat, Tina Moffat

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Suboptimal mental health is a considerable global public health challenge that leads to considerable inequalities worldwide. Newcomers are at elevated risk for developing mental health issues as a result of social exclusion, stigmatization, racism, unequal employment opportunities, and discrimination. The problem can be especially serious amongst Arabic-speaking immigrants and refugees (ASIR) whose mental wellness may have already been affected by exposure to political violence, persecution, hunger or war in their countries of origin. A scoping review was conducted to investigate pre- and post-migration mental health determinants amongst ASIR in North America (the U.S. and Canada), who are a rapidly growing population in both regions. Pertinent peer-reviewed papers and grey literature were located through a systematic search of five electronic databases (Medline, Embase, PsycINFO, Anthropology Plus, and Sociology Database). A stakeholder consultation was implemented to validate the analyzed findings of the included 44 studies. About 80% of the studies were carried out in the US, underscoring a lack of Canadian ASIR-mental health research. A gap in qualitative, mixed-method, and longitudinal research was detected, where approximately two-thirds of the studies adopted a cross-sectional method. Pre-migration determinants of mental health were related to the political unrest, violence and armed conflict in the Arab world, increasing post-traumatic stress disorder and psychological distress levels among ASIR. English language illiteracy and generational variations in acculturation patterns were major post-migration mental health triggering factors. Exposure to domestic violence, stigmatization, poverty, racialization, and harassment were significant post-migration mental health determinants that stem from social inequalities, triggering depression, and distress amongst ASIR. Family conflicts linked to child-rearing and gendered norms were considered as both pre- and post-migration mental health triggering factors. Most post-migration mental health protective factors were socio-culturally related and included the maintenance of positive ethnic identity, faith, family support, and community cohesion. Individual resilience, articulated as self-esteem and hope, was a significant negative predictor of depression and psychological distress among ASIR. Community-engaged, mixed-methods, and longitudinal studies are required to address the current gap in mental health research among ASIR in North America. A more thorough determination of potential mental health triggers and protective factors would help inform the development of mental wellness and resilience-promoting programs that are culturally sensitive to ASIR. On the policy level, the Health in All Policies framework of the World Health Organization can be potentially useful for addressing social and health inequalities among ASIR, reducing mental health challenges.

Keywords: depression, post-traumatic stress disorder, psychological distress, resilience

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Authors: Mengjiao Fan, Thomson W. L. Wong

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It is unavoidable that older adults may have to deal with aging-related motor problems. Aging is highly likely to affect motor learning and control as well. For example, older adults may suffer from poor motor function and quality of life due to age-related eye changes. These adverse changes in vision results in impairment of movement automaticity. Reaching is a fundamental component of various complex movements, which is therefore beneficial to explore the changes and adaptation in visuomotor behaviors. The current study aims to explore how aging affects visuomotor behaviors by comparing motor performance and gaze behaviors between two age groups (i.e., young and older adults). Visuomotor behaviors in reaching under providing or blocking online visual feedback (simulated visual deficiency) conditions were investigated in 60 healthy young adults (Mean age=24.49 years, SD=2.12) and 37 older adults (Mean age=70.07 years, SD=2.37) with normal or corrected-to-normal vision. Participants in each group were randomly allocated into two subgroups. Subgroup 1 was provided with online visual feedback of the hand-controlled mouse cursor. However, in subgroup 2, visual feedback was blocked to simulate visual deficiency. The experimental task required participants to complete 20 times of reaching to a target by controlling the mouse cursor on the computer screen. Among all the 20 trials, start position was upright in the center of the screen and target appeared at a randomly selected position by the tailor-made computer program. Primary outcomes of motor performance and gaze behaviours data were recorded by the EyeLink II (SR Research, Canada). The results suggested that aging seems to affect the performance of reaching tasks significantly in both visual feedback conditions. In both age groups, blocking online visual feedback of the cursor in reaching resulted in longer hand movement time (p < .001), longer reaching distance away from the target center (p<.001) and poorer reaching motor accuracy (p < .001). Concerning gaze behaviors, blocking online visual feedback increased the first fixation duration time in young adults (p<.001) but decreased it in older adults (p < .001). Besides, under the condition of providing online visual feedback of the cursor, older adults conducted a longer fixation dwell time on target throughout reaching than the young adults (p < .001) although the effect was not significant under blocking online visual feedback condition (p=.215). Therefore, the results suggested that different levels of visual feedback during movement execution can affect gaze behaviors differently in older and young adults. Differential effects by aging on visuomotor behaviors appear on two visual feedback patterns (i.e., blocking or providing online visual feedback of hand-controlled cursor in reaching). Several specific gaze behaviors among the older adults were found, which imply that blocking of visual feedback may act as a stimulus to seduce extra perceptive load in movement execution and age-related visual degeneration might further deteriorate the situation. It indeed provides us with insight for the future development of potential rehabilitative training method (e.g., well-designed errorless training) in enhancing visuomotor adaptation for our aging population in the context of improving their movement automaticity by facilitating their compensation of visual degeneration.

Keywords: aging effect, movement automaticity, reaching, visuomotor behaviors, visual degeneration

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Authors: Katharina Raab, Ralf Wagner

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With the decrease of poverty, the focus on the solid waste challenge shifts away from affluent, mostly Westernized consumers to the base of the pyramid. The relevance of considering the disposal behavior of impoverished people arises from improved welfare, leading to an increase in consumption opportunities and, consequently, of waste production. In combination with the world’s growing population the relevance of the topic increases, because solid waste management has global impacts on consumers’ welfare. The current annual municipal solid waste generation is estimated to 1.9 billion tonnes, 30% remains uncollected. As for the collected 70% is landfilling and dumping, 19% is recycled or recovered, 11% is led to energy recovery facilities. Therefore, aim is to contribute by adding first insights about poor people's disposal behaviors, including the framing of their rationalities, emotions and cognitions. The study provides novel empirical results obtained from qualitative semi-structured in-depth interviews near Guatemala City. In the study’s framework consumers have to choose from three options when deciding what to do with their obsolete possessions: Keeping the product: The main reason for this is the respondent´s emotional attachment to a product. Further, there is a willingness to use the same product under a different scope when it loses its functionality–they recycle their belongings in a customized and sustainable way. Permanently disposing of the product: The study reveals two dominant disposal methods: burning in front of their homes and throwing away in the physical environment. Respondents clearly recognized the disadvantages of burning toxic durables, like electronics. Giving a product away as a gift supports the integration of individuals in their peer networks of family and friends. Temporarily disposing of the product: Was not mentioned–to be specific, rent or lend a product to someone else was out of question. Contrasting the background to which extend poor people are aware of the consequences of their disposal decisions and how they feel about and rationalize their actions were quite unexpected. Respondents reported that they are worried about future consequences with impacts they cannot anticipate now–they are aware that their behaviors harm their health and the environment. Additionally, they expressed concern about the impact this disposal behavior would have on others’ well-being and are therefore sensitive to the waste that surrounds them. Concluding, the BoP-framed life and Westernized consumption, both fit in a circular economy pattern, but the nature of how to recycle and dispose separates these two societal groups. Both systems own a solid waste management system, but people living in slum-type districts and rural areas of poor countries are less interested in connecting to the system–they are primarily afraid of the costs. Further, it can be said that a consumer’s perceived effectiveness is distinct from environmental concerns, but contributes to forecasting certain pro-ecological behaviors. Considering the rationales underlying disposal decisions, thoughtfulness is a well-established determinant of disposition behavior. The precipitating events, emotions and decisions associated with the act of disposing of products are important because these decisions can trigger different results for the disposal process.

Keywords: base of the pyramid, disposal behavior, poor consumers, solid waste

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Authors: A. Odoom, A. Salama, H. Ibrahim

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Hydrogen is a clean source of energy for power production and transportation. The main source of hydrogen in this research is biodiesel. Glycerol also called glycerine is a by-product of biodiesel production by transesterification of vegetable oils and methanol. This is a reliable and environmentally-friendly source of hydrogen production than fossil fuels. A typical composition of crude glycerol comprises of glycerol, water, organic and inorganic salts, soap, methanol and small amounts of glycerides. Crude glycerol has limited industrial application due to its low purity thus, the usage of crude glycerol can significantly enhance the sustainability and production of biodiesel. Reforming techniques is an approach for hydrogen production mainly Steam Reforming (SR), Autothermal Reforming (ATR) and Partial Oxidation Reforming (POR). SR produces high hydrogen conversions and yield but is highly endothermic whereas POR is exothermic. On the downside, PO yields lower hydrogen as well as large amount of side reactions. ATR which is a fusion of partial oxidation reforming and steam reforming is thermally neutral because net reactor heat duty is zero. It has relatively high hydrogen yield, selectivity as well as limits coke formation. The complex chemical processes that take place during the production phases makes it relatively difficult to construct a reliable and robust numerical model. Numerical model is a tool to mimic reality and provide insight into the influence of the parameters. In this work, we introduce a finite volume numerical study for an 'in-house' lab-scale experiment of ATR. Previous numerical studies on this process have considered either using Comsol or nodal finite difference analysis. Since Comsol is a commercial package which is not readily available everywhere and lab-scale experiment can be considered well mixed in the radial direction. One spatial dimension suffices to capture the essential feature of ATR, in this work, we consider developing our own numerical approach using MATLAB. A continuum fixed bed reactor is modelled using MATLAB with both pseudo homogeneous and heterogeneous models. The drawback of nodal finite difference formulation is that it is not locally conservative which means that materials and momenta can be generated inside the domain as an artifact of the discretization. Control volume, on the other hand, is locally conservative and suites very well problems where materials are generated and consumed inside the domain. In this work, species mass balance, Darcy’s equation and energy equations are solved using operator splitting technique. Therefore, diffusion-like terms are discretized implicitly while advection-like terms are discretized explicitly. An upwind scheme is adapted for the advection term to ensure accuracy and positivity. Comparisons with the experimental data show very good agreements which build confidence in our modeling approach. The models obtained were validated and optimized for better results.

Keywords: autothermal reforming, crude glycerol, hydrogen, numerical model

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Authors: Parul Kulshreshtha, Subrata Sinha, Rakesh Bhatnagar

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This study was conducted by taking B. anthracis as a model pathogen. On infecting a host, B. anthracis secretes three proteins, namely, protective antigen (PA, 83kDa), edema factor (EF, 89 kDa) and lethal factor (LF, 90 kDa). These three proteins are the components of two anthrax toxins. PA binds to the cell surface receptors, namely, tumor endothelial marker (TEM) 8 and capillary morphogenesis protein (CMG) 2. TEM8 and CMG2 interact with LDL-receptor related protein (LRP) 6 for endocytosis of EF and LF. On entering the cell, EF acts as a calmodulin-dependent adenylate cyclase that causes a prolonged increase of cytosolic cyclic adenosine monophosphate (cAMP). LF is a metalloprotease that cleaves most isoforms of mitogen-activated protein kinase kinases (MAPKK/MEK) close to their N-terminus. By secreting these two toxins, B.anthracis ascertains death of the host. Once the systemic levels of the toxins rise, antibiotics alone cannot save the host. Therefore, toxin-specific inhibitors have to be developed. In this wake, monoclonal antibodies have been developed for the neutralization of toxic effects of anthrax toxins. We created hybridomas by using spleen of mice that were actively immunized with rLFn (recombinant N-terminal domain of lethal factor of B. anthracis) to obtain anti-toxin antibodies. Later on, separate group of mice were immunized with rLFn to obtain a polyclonal control for passive immunization studies of monoclonal antibodies. This led to the identification of one cohort of rLFn-immunized mice that harboured disease-enhancing polyclonal antibodies. At the same time, the monoclonal antibodies from all the hybridomas were being tested. Two hybridomas secreted monoclonal antibodies (H8 and H10) that were cross-reactive with EF (edema factor) and LF (lethal factor), while the other two hybridomas secreted LF-specific antibodies (H7 and H11). The protective efficacy of H7, H8, H10 and H11 was investigated. H7, H8 and H10 were found to be protective. H11 was found to have disease enhancing characteristics in-vitro and in mouse model of challenge with B. anthracis. In this study the disease enhancing character of H11 monoclonal antibody and anti-rLFn polyclonal sera was investigated. Combination of H11 with protective monoclonal antibodies (H8 and H10) reduced its disease enhancing nature both in-vitro and in-vivo. But combination of H11 with LETscFv (an scFv with VH and VL identical to H10 but lacking Fc region) could not abrogate the disease-enhancing character of H11 mAb. Therefore it was concluded that for suppression of disease enhancement, Fc portion was absolutely essential for interaction of H10 with H11. Our study indicates that the protective potential of an antibody depends equally on its idiotype/ antigen specificity and its isotype. A number of monoclonal and engineered antibodies are being explored as immunotherapeutics but it is absolutely essential to characterize each one for their individual and combined protective potential. Although new in the sphere of toxin-based diseases, it is extremely important to characterize the disease-enhancing nature of polyclonal as well as monoclonal antibodies. This is because several anti-viral therapeutics and vaccines have failed in the face of this phenomenon. The passive –immunotherapy thus needs to be well formulated to avoid any contraindications.

Keywords: immunotherapy, polyclonal, monoclonal, antibody-dependent disease enhancement

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Authors: Abha Gupta, Deepak K. Mishra

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Food security issue in India has surrounded over availability and accessibility of cereal which is regarded as the only food group to check hunger and improve nutrition. Significance of fruits, vegetables, meat and other food products have totally been neglected given the fact that they provide essential nutrients to the body. There is a need to shift the emphasis from cereal-based approach to a more diverse diet so that aim of achieving food security may change from just reducing hunger to an overall health. This paper attempts to analyse how far dietary diversity level has been achieved across different socio-economic groups in India. For this purpose, present paper sets objectives to determine (a) percentage share of different food groups to total food expenditure and consumption by background characteristics (b) source of and preference for all food items and, (c) diversity of diet across socio-economic groups. A cross sectional survey covering 304 households selected through proportional stratified random sampling was conducted in six villages of Aligarh district of Uttar Pradesh, India. Information on amount of food consumed, source of consumption and expenditure on food (74 food items grouped into 10 major food groups) was collected with a recall period of seven days. Per capita per day food consumption/expenditure was calculated through dividing consumption/expenditure by household size and number seven. Food variety score was estimated by giving 0 values to those food groups/items which had not been eaten and 1 to those which had been taken by households in last seven days. Addition of all food group/item score gave result of food variety score. Diversity of diet was computed using Herfindahl-Hirschman index. Findings of the paper show that cereal, milk, roots and tuber food groups contribute a major share in total consumption/expenditure. Consumption of these food groups vary across socio-economic groups whereas fruit, vegetables, meat and other food consumption remain low and same. Estimation of dietary diversity show higher concentration of diet due to higher consumption of cereals, milk, root and tuber products and dietary diversity slightly varies across background groups. Muslims, Scheduled caste, small farmers, lower income class, food insecure, below poverty line and labour families show higher concentration of diet as compared to their counterpart groups. These groups also evince lower mean intake of number of food item in a week due to poor economic constraints and resultant lower accessibility to number of expensive food items. Results advocate to make a shift from cereal based diet to dietary diversity which not only includes cereal and milk products but also nutrition rich food items such as fruits, vegetables, meat and other products. Integrating a dietary diversity approach in food security programmes of the country would help to achieve nutrition security as hidden hunger is widespread among the Indian population.

Keywords: dietary diversity, food Security, India, socio-economic groups

Procedia PDF Downloads 313

Authors: Grant Bifolchi

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As global environmental problems continue to have highly adverse effects, finding long-term, sustainable solutions to combat ecological distress are of growing paramount concern. Ghost Gear—also known as abandoned, lost or otherwise discarded fishing gear (ALDFG) and derelict fishing gear (DFG)—represents one of the greatest threats to the world’s oceans, posing a significant hazard to human health, livelihoods, and global food security. In fact, according to the UN Food and Agriculture Organization (FAO), abandoned, lost and discarded fishing gear represents approximately 10% of marine debris by volume. Around the world, many governments, governmental and non-profit organizations are doing their best to manage the reporting and retrieval of nets, lines, ropes, traps, floats and more from their respective bodies of water. However, these organizations’ ability to effectively manage files and documents about the environmental problem further complicates matters. In Ghost Gear monitoring and management, organizations face additional complexities. Whether it’s data ingest, industry regulations and standards, garnering actionable insights into the location, security, and management of data, or the application of enforcement due to disparate data—all of these factors are placing massive strains on organizations struggling to save the planet from the dangers of Ghost Gear. In this 90-minute educational session, globally recognized Ghost Gear technology expert Grant Bifolchi CET, BBA, Bcom, will provide real-world insight into how governments currently manage Ghost Gear and the technology that can accelerate success in combatting ALDFG and DFG. In this session, attendees will learn how to: • Identify specific technologies to solve the ingest and management of Ghost Gear data categories, including type, geo-location, size, ownership, regional assignment, collection and disposal. • Provide enhanced access to authorities, fisheries, independent fishing vessels, individuals, etc., while securely controlling confidential and privileged data to globally recognized standards. • Create and maintain processing accuracy to effectively track ALDFG/DFG reporting progress—including acknowledging receipt of the report and sharing it with all pertinent stakeholders to ensure approvals are secured. • Enable and utilize Business Intelligence (BI) and Analytics to store and analyze data to optimize organizational performance, maintain anytime-visibility of report status, user accountability, scheduling, management, and foster governmental transparency. • Maintain Compliance Reporting through highly defined, detailed and automated reports—enabling all stakeholders to share critical insights with internal colleagues, regulatory agencies, and national and international partners.

Keywords: ghost gear, ALDFG, DFG, abandoned, lost or otherwise discarded fishing gear, data, technology

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Authors: Suzanne Phibbs, Christine Kenney, Robyn Richardson

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The Sendai Framework for Disaster Risk Reduction called for the inclusion of indigenous people in the design and implementation of all hazard policies, plans, and standards. Ensuring that indigenous knowledge practices were included alongside scientific knowledge about disaster risk was also a key priority. Indigenous communities have specific knowledge about climate and natural hazard risk that has been developed over an extended period of time. However, research within indigenous communities can be fraught with issues such as power imbalances between the researcher and researched, the privileging of researcher agendas over community aspirations, as well as appropriation and/or inappropriate use of indigenous knowledge. This paper documents the process of working alongside a Māori community to develop a successful community-led research project. Research Design: This case study documents the development of a qualitative community-led participatory project. The community research project utilizes a kaupapa Māori research methodology which draws upon Māori research principles and concepts in order to generate knowledge about Māori resilience. The research addresses a significant gap in the disaster research literature relating to indigenous knowledge about collective hazard mitigation practices as well as resilience in rurally isolated indigenous communities. The research was designed in partnership with the Ngāti Raukawa Northern Marae Collective as well as Ngā Wairiki Ngāti Apa (a group of Māori sub-tribes who are located in the same region) and will be conducted by Māori researchers utilizing Māori values and cultural practices. The research project aims and objectives, for example, are based on themes that were identified as important to the Māori community research partners. The research methodology and methods were also negotiated with and approved by the community. Kaumātua (Māori elders) provided cultural and ethical guidance over the proposed research process and will continue to provide oversight over the conduct of the research. Purposive participant recruitment will be facilitated with support from local Māori community research partners, utilizing collective marae networks and snowballing methods. It is envisaged that Māori participants’ knowledge, experiences and views will be explored using face-to-face communication research methods such as workshops, focus groups and/or semi-structured interviews. Interviews or focus groups may be held in English and/or Te Reo (Māori language) to enhance knowledge capture. Analysis, knowledge dissemination, and co-authorship of publications will be negotiated with the Māori community research partners. Māori knowledge shared during the research will constitute participants’ intellectual property. New knowledge, theory, frameworks, and practices developed by the research will be co-owned by Māori, the researchers, and the host academic institution. Conclusion: An emphasis on indigenous knowledge systems within the Sendai Framework for Disaster Risk Reduction risks the appropriation and misuse of indigenous experiences of disaster risk identification, mitigation, and response. The research protocol underpinning this project provides an exemplar of collaborative partnership in the development and implementation of an indigenous project that has relevance to policymakers, academic researchers, other regions with indigenous communities and/or local disaster risk reduction knowledge practices.

Keywords: community resilience, indigenous disaster risk reduction, Maori, research methods

Procedia PDF Downloads 104

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

Procedia PDF Downloads 247

Authors: Yichao Liu, Peter Fransson, Julian Heidecke, Jonas Wallin, Joacim Rockloev

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Dengue, a mosquito-borne viral disease, poses a significant public health challenge in endemic tropical or subtropical countries, including Sri Lanka. To reveal insights into the complexity of the dynamics of this disease and study the drivers, a comprehensive model capable of both robust forecasting and insightful inference of drivers while capturing the co-circulating of several virus strains is essential. However, existing studies mostly focus on only one aspect at a time and do not integrate and carry insights across the siloed approach. While mechanistic models are developed to capture immunity dynamics, they are often oversimplified and lack integration of all the diverse drivers of disease transmission. On the other hand, purely data-driven methods lack constraints imposed by immuno-epidemiological processes, making them prone to overfitting and inference bias. This research presents a hybrid model that combines machine learning techniques with mechanistic modelling to overcome the limitations of existing approaches. Leveraging eight years of newly reported dengue case data, along with socioeconomic factors, such as human mobility, weekly climate data from 2011 to 2018, genetic data detecting the introduction and presence of new strains, and estimates of seropositivity for different districts in Sri Lanka, we derive a data-driven vector (SEI) to human (SEIR) model across 16 regions in Sri Lanka at the weekly time scale. By conducting ablation studies, the lag effects allowing delays up to 12 weeks of time-varying climate factors were determined. The model demonstrates superior predictive performance over a pure machine learning approach when considering lead times of 5 and 10 weeks on data withheld from model fitting. It further reveals several interesting interpretable findings of drivers while adjusting for the dynamics and influences of immunity and introduction of a new strain. The study uncovers strong influences of socioeconomic variables: population density, mobility, household income and rural vs. urban population. The study reveals substantial sensitivity to the diurnal temperature range and precipitation, while mean temperature and humidity appear less important in the study location. Additionally, the model indicated sensitivity to vegetation index, both max and average. Predictions on testing data reveal high model accuracy. Overall, this study advances the knowledge of dengue transmission in Sri Lanka and demonstrates the importance of incorporating hybrid modelling techniques to use biologically informed model structures with flexible data-driven estimates of model parameters. The findings show the potential to both inference of drivers in situations of complex disease dynamics and robust forecasting models.

Keywords: compartmental model, climate, dengue, machine learning, social-economic

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Authors: Staci McGill, Morgan Hayes, Robert Coleman, Kimberly Tumlin

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The interaction and relationships between horses and humans have been shown to be positive for physical, mental, and emotional wellbeing, however equine spaces where these interactions occur do include some environmental risks. There are 1.7 million jobs associated with the equine industry in the United States in addition to recreational riders, owners, and volunteers who interact with horses for substantial amounts of time daily inside built structures. One specialized facility, an “indoor arena” is a semi-indoor structure used for exercising horses and exhibiting skills during competitive events. Typically, indoor arenas have a sand or sand mixture as the footing or surface over which the horse travels, and increasingly, silica sand is being recommended due to its durable nature. It was previously identified in a semi-qualitative survey that the majority of individuals using indoor arenas have environmental concerns with dust. 27% (90/333) of respondents reported respiratory issues or allergy-like symptoms while riding with 21.6% (71/329) of respondents reporting these issues while standing on the ground observing or teaching. Frequent headaches and/or lightheadedness was reported in 9.9% (33/333) of respondents while riding and in 4.3% 14/329 while on the ground. Horse respiratory health is also negatively impacted with 58% (194/333) of respondents indicating horses cough during or after time in the indoor arena. Instructors who spent time in indoor arenas self-reported more respiratory issues than those individuals who identified as smokers, highlighting the health relevance of understanding these unique structures. To further elucidate environmental concerns and self-reported health issues, 35 facility assessments were conducted in a cross-sectional sampling design in the states of Kentucky and Ohio (USA). Data, including air speeds, were collected in a grid fashion at 15 points within the indoor arenas and then mapped spatially using krigging in ARCGIS. From the spatial maps, standard variances were obtained and differences were analyzed using multivariant analysis of variances (MANOVA) and analysis of variances (ANOVA). There were no differences for the variance of the air speeds in the spaces for facility orientation, presence and type of roof ventilation, climate control systems, amount of openings, or use of fans. Variability of the air speeds in the indoor arenas was 0.25 or less. Further analysis yielded that average air speeds within the indoor arenas were lower than 100 ft/min (0.51 m/s) which is considered still air in other animal facilities. The lack of air movement means that dust clearance is reliant on particle size and weight rather than ventilation. While further work on respirable dust is necessary, this characterization of the semi-indoor environment where animals and humans interact indicates insufficient air flow to eliminate or reduce respiratory hazards. Finally, engineering solutions to address air movement deficiencies within indoor arenas or mitigate particulate matter are critical to ensuring exposures do not lead to adverse health outcomes for equine professionals, volunteers, participants, and horses within these spaces.

Keywords: equine, indoor arena, ventilation, particulate matter, respiratory health

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Authors: Izabela P. Vatanabe, Rafaela Peron, Patricia Manzine, Marcia R. Cominetti

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Introduction: Considering the increase in life expectancy of world population, there is an emerging concern in health services to allocate better care and care to elderly, through promotion, prevention and treatment of health. It has been observed that frailty syndrome is prevalent in elderly people worldwide and this complex and heterogeneous clinical syndrome consist of the presence of physical frailty associated with cognitive dysfunction, though in absence of dementia. This can be characterized by exhaustion, unintentional weight loss, decreased walking speed, weakness and low level of physical activity, in addition, each of these symptoms may be a predictor of adverse outcomes such as hospitalization, falls, functional decline, institutionalization, and death. Cognitive frailty is a recent concept in literature, which is defined as the presence of physical frailty associated with mild cognitive impairment (MCI) however in absence of dementia. This new concept has been considered as a subtype of frailty, which along with aging process and its interaction with physical frailty, accelerates functional declines and can result in poor quality of life of the elderly. MCI represents a risk factor for Alzheimer's disease (AD) in view of high conversion rate for this disease. Comorbidities and physical frailty are frequently found in AD patients and are closely related to heterogeneity and clinical manifestations of the disease. The decreased platelets ADAM10 levels in AD patients, compared to cognitively healthy subjects, matched by sex, age and education. Objective: Based on these previous results, this study aims to evaluate whether ADAM10 platelet levels of could act as a biomarker of cognitive frailty. Methods: The study was approved by Ethics Committee of Federal University of São Carlos (UFSCar) and conducted in the municipality of São Carlos, headquarters of Federal University of São Carlos (UFSCar). Biological samples of subjects were collected, analyzed and then stored in a biorepository. ADAM10 platelet levels were analyzed by western blotting technique in subjects with MCI and compared to subjects without cognitive impairment, both with and without presence of frailty. Statistical tests of association, regression and diagnostic accuracy were performed. Results: The results have shown that ADAM10/β-actin ratio is decreased in elderly individuals with cognitive frailty compared to non-frail and cognitively healthy controls. Previous studies performed by this research group, already mentioned above, demonstrated that this reduction is still higher in AD patients. Therefore, the ADAM10/β-actin ratio appears to be a potential biomarker for cognitive frailty. The results bring important contributions to an accurate diagnosis of cognitive frailty from the perspective of ADAM10 as a biomarker for this condition, however, more experiments are being conducted, using a high number of subjects, and will help to understand the role of ADAM10 as biomarker of cognitive frailty and contribute to the implementation of tools that work in the diagnosis of cognitive frailty. Such tools can be used in public policies for the diagnosis of cognitive frailty in the elderly, resulting in a more adequate planning for health teams and better quality of life for the elderly.

Keywords: ADAM10, biomarkers, cognitive frailty, elderly

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Authors: R. Yuan, S. Sivasankaran, N. Dutta, K. Ebrahimi

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This paper investigates the software capability and computer-aided engineering (CAE) method of modelling transient heat transfer process occurred in the vehicle underhood region during vehicle thermal soak phase. The heat retention from the soak period will be beneficial to the cold start with reduced friction loss for the second 14°C worldwide harmonized light-duty vehicle test procedure (WLTP) cycle, therefore provides benefits on both CO₂ emission reduction and fuel economy. When vehicle undergoes soak stage, the airflow and the associated convective heat transfer around and inside the engine bay is driven by the buoyancy effect. This effect along with thermal radiation and conduction are the key factors to the thermal simulation of the engine bay to obtain the accurate fluids and metal temperature cool-down trajectories and to predict the temperatures at the end of the soak period. Method development has been investigated in this study on a light-duty passenger vehicle using coupled aerodynamic-heat transfer thermal transient modelling method for the full vehicle under 9 hours of thermal soak. The 3D underhood flow dynamics were solved inherently transient by the Lattice-Boltzmann Method (LBM) method using the PowerFlow software. This was further coupled with heat transfer modelling using the PowerTHERM software provided by Exa Corporation. The particle-based LBM method was capable of accurately handling extremely complicated transient flow behavior on complex surface geometries. The detailed thermal modelling, including heat conduction, radiation, and buoyancy-driven heat convection, were integrated solved by PowerTHERM. The 9 hours cool-down period was simulated and compared with the vehicle testing data of the key fluid (coolant, oil) and metal temperatures. The developed CAE method was able to predict the cool-down behaviour of the key fluids and components in agreement with the experimental data and also visualised the air leakage paths and thermal retention around the engine bay. The cool-down trajectories of the key components obtained for the 9 hours thermal soak period provide vital information and a basis for the further development of reduced-order modelling studies in future work. This allows a fast-running model to be developed and be further imbedded with the holistic study of vehicle energy modelling and thermal management. It is also found that the buoyancy effect plays an important part at the first stage of the 9 hours soak and the flow development during this stage is vital to accurately predict the heat transfer coefficients for the heat retention modelling. The developed method has demonstrated the software integration for simulating buoyancy-driven heat transfer in a vehicle underhood region during thermal soak with satisfying accuracy and efficient computing time. The CAE method developed will allow integration of the design of engine encapsulations for improving fuel consumption and reducing CO₂ emissions in a timely and robust manner, aiding the development of low-carbon transport technologies.

Keywords: ATCT/WLTC driving cycle, buoyancy-driven heat transfer, CAE method, heat retention, underhood modeling, vehicle thermal soak

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Authors: Amarendar Reddy Addula

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Definition of Artificial Intelligence: Artificial intelligence is the simulation of mortal intelligence processes by machines, especially computer systems. Explicit operations of AI comprise expert systems, natural language processing, and speech recognition, and machine vision. Artificial Intelligence (AI) is an original medium for digital business, according to a new report by Gartner. The last 10 times represent an advance period in AI’s development, prodded by the confluence of factors, including the rise of big data, advancements in cipher structure, new machine literacy ways, the materialization of pall computing, and the vibrant open- source ecosystem. Influence of AI to a broader set of use cases and druggies and its gaining fashionability because it improves AI’s versatility, effectiveness, and rigidity. Edge AI will enable digital moments by employing AI for real- time analytics closer to data sources. Gartner predicts that by 2025, further than 50 of all data analysis by deep neural networks will do at the edge, over from lower than 10 in 2021. Responsible AI is a marquee term for making suitable business and ethical choices when espousing AI. It requires considering business and societal value, threat, trust, translucency, fairness, bias mitigation, explainability, responsibility, safety, sequestration, and nonsupervisory compliance. Responsible AI is ever more significant amidst growing nonsupervisory oversight, consumer prospects, and rising sustainability pretensions. Generative AI is the use of AI to induce new vestiges and produce innovative products. To date, generative AI sweats have concentrated on creating media content similar as photorealistic images of people and effects, but it can also be used for law generation, creating synthetic irregular data, and designing medicinals and accoutrements with specific parcels. AI is the subject of a wide- ranging debate in which there's a growing concern about its ethical and legal aspects. Constantly, the two are varied and nonplussed despite being different issues and areas of knowledge. The ethical debate raises two main problems the first, abstract, relates to the idea and content of ethics; the alternate, functional, and concerns its relationship with the law. Both set up models of social geste, but they're different in compass and nature. The juridical analysis is grounded on anon-formalistic scientific methodology. This means that it's essential to consider the nature and characteristics of the AI as a primary step to the description of its legal paradigm. In this regard, there are two main issues the relationship between artificial and mortal intelligence and the question of the unitary or different nature of the AI. From that theoretical and practical base, the study of the legal system is carried out by examining its foundations, the governance model, and the nonsupervisory bases. According to this analysis, throughout the work and in the conclusions, International Law is linked as the top legal frame for the regulation of AI.

Keywords: artificial intelligence, ethics & human rights issues, laws, international laws

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Authors: Sumiti Saharan, Pallav Patankar, Lily W. Lee

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Understanding the health-seeking behaviors and attitudes of women towards maternal health in the context of gender roles and family dynamics is tremendously crucial for designing effective and impactful interventions aimed at improving maternal and child health outcomes. Further, as the digital world becomes more accessible and affordable, it is imperative to scope the potential of digital technology in enabling access to maternal health information in different socio-economic groups (SEGs). In the summer of 2017, we conducted a study with 500 women across different SEGs in urban India who were pregnant or had had a delivery in the last year. The study was undertaken to assess their maternal health information seeking behavior with a particular focus on probing their use of digital technology for health-related information. The study also measured women's decision-making autonomy in the context of maternal health, awareness of their rights to quality and respectful maternal healthcare, and agency to voice their rights. We probed the impact of key variables including education, age, and socioeconomic status on all outcome variables. In terms of health-seeking behaviors, we found that women heavily relied on medical professionals and/or their mothers and mothers-in-law for all maternal health advice. Digital adoption was found to be high across all SEGs, with around 70% of women from all populations using the internet several times a week. On the other hand, use of the internet for both accessing maternal health information and choosing maternity hospitals were both significantly dependent on SEG. The key reasons reported for not using the internet for health purposes were lack of awareness and lack of trust on content accuracy. Decisions around health practices and type of delivery were found to be jointly made by women and other family members. Almost all women reported their husbands to play a key role in all maternal health decisions and for decisions with a clear financial implication like choice of hospital for delivery, husbands were reported to be the sole decision maker by a majority of women. The agency of women was also found to be low in interactions with maternal healthcare providers with a third of respondents not comfortable with voicing their opinions and preferences to their doctors. Interestingly, we find that this relatively low agency was prominent in both lower middle class and middle-class SEGs. Recognition of the sociocultural determinants of behavior is the first step in developing actionable strategies for improving maternal health outcomes. Our study quantifies the agency and autonomy of women in urban India and the variables that impact them. Our findings emphasize the value of gender normative approaches that factor in the key role husbands play in guiding maternal health decisions. They also highlight the power of digital approaches for catalyzing access to maternal health information. These insights into the attitude and behaviors of mothers in context of their sociocultural environments—and their relationship with digital technology—can help pave the way towards designing effective, scalable maternal and child health programs in developing nations like India.

Keywords: access to healthcare information, behavior, digital health, maternal health

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Authors: Erimias Firre

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This study is situated within the religio-cultural milieu of Coptic Orthodox Christians of the Tigrinya ethnic group in Eritrea. With this ethnic group being conservative and traditionally bound, extended family structures dissected along various clans and expansive community networks are the distinguishing mark of its members. Notably, Coptic Tigrinya constitutes the largest percentage of all Christian denominations in Eritrea. As religious, cultural beliefs, rituals and teachings permeate in all aspects of social life, a distinct worldview and traditionalized health and illness conceptualization are common. Accordingly, this study argues that religio-culturally bound illness ideologies immensely determine the perception, help seeking behavior and healing preference of Coptic Tigrinya in Eritrea. The study bears significance in the sense that it bridges an important knowledge gap, given that it is ethno-linguistically (within the Tigrinya ethnic group), spatially (central region of Eritrea) and religiously (Coptic Christianity) specific. The conceptual framework guiding this research centered on the social determinants of mental health, and explores through the lens of critical theory how existing systems generate social vulnerability and structural inequality, providing a platform to reveal how the psychosocial model has the capacity to emancipate and empower those with mental disorders to live productive and meaningful lives. A case study approach was employed to explore the interrelationship between religio-cultural beliefs and practices and perception of common mental disorders of depression, anxiety, bipolar affective, schizophrenia and post-traumatic stress disorders and the impact of these perceptions on people with those mental disorders. Purposive sampling was used to recruit 41 participants representing seven diverse cohorts; people with common mental disorders, family caregivers, general community members, ex-fighters , priests, staff at St. Mary’s and Biet-Mekae Community Health Center; resulting in rich data for thematic analysis. Findings highlighted current religio-cultural perceptions, causes and treatment of mental disorders among Coptic Tigrinya result in widespread labelling, stigma and discrimination, both of those with mental disorders and their families. Traditional healing sources are almost exclusively tried, sometimes for many years, before families and sufferers seek formal medical assessment and treatment, resulting difficult to treat illness chronicity. Service gaps in the formal medical system result in the inability to meet the principles enshrined in the WHO Mental Health Action Plan 2013-2020 to which the Eritrean Government is a signatory. However, the study found that across all participant cohorts, there was a desire for change that will create a culture whereby those with mental disorders will have restored hope, connectedness, healing and self-determination.

Keywords: Coptic Tigrinya, mental disorders, psychosocial model social integration and recovery, traditional healing

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Authors: Valentina Dolmova

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What consumers choose to buy and who voters select on election days are two questions that have captivated the interest of both academics and practitioners for many decades. The importance of understanding what influences the behavior of those groups and whether or not we can predict or control it fuels a steady stream of research in a range of fields. By looking only at the past 40 years, more than 70 thousand scientific papers have been published in each field – consumer behavior and political psychology, respectively. From marketing, economics, and the science of persuasion to political and cognitive psychology - we have all remained heavily engaged. The ever-evolving technology, inevitable socio-cultural shifts, global economic conditions, and much more play an important role in choice-equations regardless of context. On one hand, this makes the research efforts always relevant and needed. On the other, the relatively low number of cross-field collaborations, which seem to be picking up only in more in recent years, makes the existing findings isolated into framed bubbles. By performing systematic research across both areas of psychology and building a parallel between theories and factors of influence, however, we find that there is not only a definitive common ground between the behaviors of consumers and voters but that we are moving towards a global model of choice. This means that the lines between contexts are fading which has a direct implication on what we should focus on when predicting or navigating buyers and voters’ behavior. Internal and external factors in four main categories determine the choices we make as consumers and as voters. Together, personal, psychological, social, and cultural create a holistic framework through which all stimuli in relation to a particular product or a political party get filtered. The analogy “consumer-voter” solidifies further. Leading academics suggest that this fundamental parallel is the key to managing successfully political and consumer brands alike. However, we distinguish additional four key stimuli that relate to those factor categories (1/ opportunity costs; 2/the memory of the past; 3/recognisable figures/faces and 4/conflict) arguing that the level of expertise a person has determines the prevalence of factors or specific stimuli. Our efforts take into account global trends such as the establishment of “celebrity politics” and the image of “ethically concerned consumer brands” which bridge the gap between contexts to an even greater extent. Scientists and practitioners are pushed to accept the transformative nature of both fields in social psychology. Existing blind spots as well as the limited number of research conducted outside the American and European societies open up space for more collaborative efforts in this highly demanding and lucrative field. A mixed method of research tests three main hypotheses, the first two of which are focused on the level of irrelevance of context when comparing voting or consumer behavior – both from the factors and stimuli lenses, the third on determining whether or not the level of expertise in any field skews the weight of what prism we are more likely to choose when evaluating options.

Keywords: buyers’ behaviour, decision-making, voters’ behaviour, social psychology

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Chenyao Shen, Jie Shen

Abstract:

The rating system provides an effective framework for assessing building environmental performance and integrating sustainable development into building and construction processes; as it can be used as a design tool by developing appropriate sustainable design strategies and determining performance measures to guide the sustainable design and decision-making processes. Healthcare buildings are resource (water, energy, etc.) intensive. To maintain high-cost operations and complex medical facilities, they require a great deal of hazardous and non-hazardous materials, stringent control of environmental parameters, and are responsible for producing polluting emission. Compared with other types of buildings, the impact of healthcare buildings on the full cycle of the environment is particularly large. With broad recognition among designers and operators that energy use can be reduced substantially, many countries have set up their own green rating systems for healthcare buildings. There are four main green healthcare building evaluation systems widely acknowledged in the world - Green Guide for Health Care (GGHC), which was jointly organized by the United States HCWH and CMPBS in 2003; BREEAM Healthcare, issued by the British Academy of Building Research (BRE) in 2008; the Green Star-Healthcare v1 tool, released by the Green Building Council of Australia (GBCA) in 2009; and LEED Healthcare 2009, released by the United States Green Building Council (USGBC) in 2011. In addition, the German Association of Sustainable Building (DGNB) has also been developing the German Sustainable Building Evaluation Criteria (DGNB HC). In China, more and more scholars and policy makers have recognized the importance of assessment of sustainable development, and have adapted some tools and frameworks. China’s first comprehensive assessment standard for green building (the GBTs) was issued in 2006 (lately updated in 2014), promoting sustainability in the built-environment and raise awareness of environmental issues among architects, engineers, contractors as well as the public. However, healthcare building was not involved in the evaluation system of GBTs because of its complex medical procedures, strict requirements of indoor/outdoor environment and energy consumption of various functional rooms. Learn from advanced experience of GGHC, BREEAM, and LEED HC above, China’s first assessment criteria for green hospital/healthcare buildings was finally released in December 2015. Combined with both quantitative and qualitative assessment criteria, the standard highlight the differences between healthcare and other public buildings in meeting the functional needs for medical facilities and special groups. This paper has focused on the assessment criteria framework for sustainable healthcare buildings, for which the comparison of different rating systems is rather essential. Descriptive analysis is conducted together with the cross-matrix analysis to reveal rich information on green assessment criteria in a coherent manner. The research intends to know whether the green elements for healthcare buildings in China are different from those conducted in other countries, and how to improve its assessment criteria framework.

Keywords: assessment criteria framework, green building design, healthcare building, building performance rating tool

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Authors: Till Gramberg, Erwin Gross, Christoph Birenbaum

Abstract:

This paper presents the approach of the Easy2IoT research project. Easy2IoT aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. It focuses on the development of physical hardware and software to easily capture machine activities from on a sawing machine, benefiting various stakeholders in the SME value chain, including machine operators, tool manufacturers and service providers. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements and potential solutions for smart services are derived. The focus is on providing actionable recommendations, competencies and easy integration through no-/low-code applications to facilitate implementation and connectivity within production networks. At the core of the project is a novel, non-invasive measurement and analysis system that can be easily deployed and made IIoT-ready. This system collects machine data without interfering with the machines themselves. It does this by non-invasively measuring the tension on a sawing machine. The collected data is then connected and analyzed using artificial intelligence (AI) to provide smart services through a platform-based application. Three Smart Services are being developed within Easy2IoT to provide immediate benefits to users: Wear part and product material condition monitoring and predictive maintenance for sawing processes. The non-invasive measurement system enables the monitoring of tool wear, such as saw blades, and the quality of consumables and materials. Service providers and machine operators can use this data to optimize maintenance and reduce downtime and material waste. Optimize Overall Equipment Effectiveness (OEE) by monitoring machine activity. The non-invasive system tracks machining times, setup times and downtime to identify opportunities for OEE improvement and reduce unplanned machine downtime. Estimate CO2 emissions for connected machines. CO2 emissions are calculated for the entire life of the machine and for individual production steps based on captured power consumption data. This information supports energy management and product development decisions. The key to Easy2IoT is its modular and easy-to-use design. The non-invasive measurement system is universally applicable and does not require specialized knowledge to install. The platform application allows easy integration of various smart services and provides a self-service portal for activation and management. Innovative business models will also be developed to promote the sustainable use of the collected machine activity data. The project addresses the digitalization gap between large enterprises and SME. Easy2IoT provides SME with a concrete toolkit for IIoT adoption, facilitating the digital transformation of smaller companies, e.g. through retrofitting of existing machines.

Keywords: smart services, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Sonia A. Stoica, Lexi Frankel, Amalia Ardeljan, Selena Rashid, Ali Yasback, Omar Rashid

Abstract:

Introduction Enterococcus is a vast genus of lactic acid bacteria, gram-positivecocci species. They are common commensal organisms in the intestines of humans: E. faecalis (90–95%) and E. faecium (5–10%). Rare groups of infections can occur with other species, including E. casseliflavus, E. gallinarum, and E. raffinosus. The most common infections caused by Enterococcus include urinary tract infections, biliary tract infections, subacute endocarditis, diverticulitis, meningitis, septicemia, and spontaneous bacterial peritonitis. The treatment for sensitive strains of these bacteria includes ampicillin, penicillin, cephalosporins, or vancomycin, while the treatment for resistant strains includes daptomycin, linezolid, tygecycline, or streptogramine. Enterococcus faecalis CECT7121 is an encouraging nominee for being considered as a probiotic strain. E. faecalis CECT7121 enhances and skews the profile of cytokines to the Th1 phenotype in situations such as vaccination, anti-tumoral immunity, and allergic reactions. It also enhances the secretion of high levels of IL-12, IL-6, TNF alpha, and IL-10. Cytokines have been previously associated with the development of cancer. The intention of this study was to therefore evaluate the correlation between Enterococcus infections and incidence of bone carcinoma. Methods A retrospective cohort study (2010-2019) was conducted through a Health Insurance Portability and Accountability Act (HIPAA) compliant national database and conducted using International Classification of Disease (ICD) 9th and 10th codes for bone carcinoma diagnosis in a previously Enterococcus infected population. Patients were matched for age range and Charlson Comorbidity Index (CCI). Access to the database was granted by Holy Cross Health for academic research. Chi-squared test was used to assess statistical significance. Results A total number of 17,056 patients was obtained in Enterococcus infected group as well as in the control population (matched by Age range and CCI score). Subsequent bone carcinoma development was seen at a rate of 1.07% (184) in the Enterococcal infectious group and 3.42% (584) in the control group, respectively. The difference was statistically significant by p= 2.2x10-¹⁶, Odds Ratio = 0.355 (95% CI 0.311 - 0.404) Treatment for enterococcus infection was analyzed and controlled for in both enterococcus infected and noninfected populations. 78 out of 6,624 (1.17%) patients with a prior enterococcus infection and treated with antibiotics were compared to 202 out of 6,624 (3.04%) patients with no history of enterococcus infection (control) and received antibiotic treatment. Both populations subsequently developed bone carcinoma. Results remained statistically significant (p<2.2x10-), Odds Ratio=0.456 (95% CI 0.396-0.525). Conclusion This study shows a statistically significant correlation between Enterococcus infection and a decreased incidence of bone carcinoma. The immunologic response of the organism to Enterococcus infection may exert a protecting mechanism from developing bone carcinoma. Further exploration is needed to identify the potential mechanism of Enterococcus in reducing bone carcinoma incidence.

Keywords: anti-tumoral immunity, bone carcinoma, enterococcus, immunologic response

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Authors: Zenaida Chitu, Roxana Bojariu, Liliana Velea, Roxana Burcea

Abstract:

A number of studies show that extreme air temperature affects mortality related to cardiovascular diseases, particularly among elderly people. In Romania, the summer thermal discomfort expressed by Universal Thermal Climate Index (UTCI) is highest in the Southern part of the country, where Bucharest, the largest Romanian urban agglomeration, is also located. The urban characteristics such as high building density and reduced green areas enhance the increase of the air temperature during summer. In Bucharest, as in many other large cities, the effect of heat urban island is present and determines an increase of air temperature compared to surrounding areas. This increase is particularly important during heat wave periods in summer. In this context, the researchers performed a temperature-mortality analysis based on daily deaths related to cardiovascular diseases, recorded between 2010 and 2019 in Bucharest. The temperature-mortality relationship was modeled by applying distributed lag non-linear model (DLNM) that includes a bi-dimensional cross-basis function and flexible natural cubic spline functions with three internal knots in the 10th, 75th and 90th percentiles of the temperature distribution, for modelling both exposure-response and lagged-response dimensions. Firstly, this study applied this analysis for the present climate. Extrapolation of the exposure-response associations beyond the observed data allowed us to estimate future effects on mortality due to temperature changes under climate change scenarios and specific assumptions. We used future projections of air temperature from five numerical experiments with regional climate models included in the EURO-CORDEX initiative under the relatively moderate (RCP 4.5) and pessimistic (RCP 8.5) concentration scenarios. The results of this analysis show for RCP 8.5 an ensemble-averaged increase with 6.1% of heat-attributable mortality fraction in future in comparison with present climate (2090-2100 vs. 2010-219), corresponding to an increase of 640 deaths/year, while mortality fraction due to the cold conditions will be reduced by 2.76%, corresponding to a decrease by 288 deaths/year. When mortality data is stratified according to the age, the ensemble-averaged increase of heat-attributable mortality fraction for elderly people (> 75 years) in the future is even higher (6.5 %). These findings reveal the necessity to carefully plan urban development in Bucharest to face the public health challenges raised by the climate change. Paper Details: This work is financed by the project URCLIM which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by Ministry of Environment, Romania with co-funding by the European Union (Grant 690462). A part of this work performed by one of the authors has received funding from the European Union’s Horizon 2020 research and innovation programme from the project EXHAUSTION under grant agreement No 820655.

Keywords: cardiovascular diseases, climate change, extreme air temperature, mortality

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