Search results for: scoring based risk assessment method

Authors: H. Benaldjia, N. Habib, F. Djefaflia, A. Nait-Merzoug, A. Harat, J. El-Haskouri, O. Guellati

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Currently, the technology has attracted knowledge of energy storage sources similar to batteries, capacitors and super-capacitors because of its very different applications in many fields with major social and economic challenges. Moreover, hydroxides have attracted much attention as a promising and active material choice in large-scale applications such as molecular adsorption/storage and separation for the environment, ion exchange, nanotechnology, supercapacitor for energy storage and conversion, electro-biosensing, and catalysts, due to their unique properties which are strongly influenced by their composition, microstructure, and synthesis method. In this context, we report in this study the synthesis of hydroxide-based nanomaterials precisely based on Ni and Fe using a simple hydrothermal method with mono and bi precursors at optimized growth conditions (6h-120°C). The obtained products were characterized using different techniques, such as XRD, FTIR, FESEM and BET, as well as electrochemical measurements.

Keywords: energy storage, Supercapacitors, nanocomposites, nanohybride, electro-active materials.

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Authors: Ange Weinrabe, James Tran, Ian B. Hickie

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Risk-taking behaviour is common during youth. In the time between adolescence and early adulthood, young people (aged 15-25 years) are more vulnerable to mood disorders, such as anxiety and depression. What impact does an emerging mood disorder have on decision-making in youth at critical decision points in their lives? In this article, we explore the impact of risk and ambiguity on youth decision-making in a clinical setting using a well-known economic experiment. At two time points, separated by six to eight weeks, we measured risky and ambiguous choices concurrently with findings from three psychological questionnaires, the 10-item Kessler Psychological Distress Scale (K10), the 17-item Quick Inventory of Depressive Symptomatology Adolescent Version (QIDS-A17), and the 12-item Somatic and Psychological Health Report (SPHERE-12), for young help seekers aged 16-25 (n=30, mean age 19.22 years, 19 males). When first arriving for care, we found that 50% (n=15) of participants experienced severe anxiety (K10 ≥ 30) and were severely depressed (QIDS-A17 ≥ 16). In Session 2, taking attrition rates into account (n=5), we found that 44% (n=11) remained severe across the full battery of questionnaires. When applying multiple regression analyses of the pooled sample of observations (N=55), across both sessions, we found that participants who rated severely anxious avoided making risky decisions. We suggest there is some statistically significant (although weak) (p=0.09) relation between risk and severe anxiety scores as measured by K10. Our findings may support working with novel tools with which to evaluate youth experiencing an emerging mood disorder and their cognitive capacities influencing decision-making.

Keywords: anxiety, decision-making, risk, adolescence

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Somveer Singh, Vineet Kumar Singh

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We present a new model based on viscoelasticity for the Non-Newtonian fluids.We use a matrix formulated algorithm to approximate solutions of a class of partial integro-differential equations with the given initial and boundary conditions. Some numerical results are presented to simplify application of operational matrix formulation and reduce the computational cost. Convergence analysis, error estimation and numerical stability of the method are also investigated. Finally, some test examples are given to demonstrate accuracy and efficiency of the proposed method.

Keywords: Legendre Wavelets, operational matrices, partial integro-differential equation, viscoelasticity

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Authors: Honggang Qu, Yong Xu, Rongmei Liu, Zhenji Gao, Bin Wang

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Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: Rebecca-Lee Kuhnert, Ron Rapee

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Up to 15% of Australian youth will experience an emotional disorder, yet relatively few get the help they need. Schools provide an ideal environment through which we can identify young people who are struggling and provide them with appropriate help. Universal mental health screening is a method by which all young people in school can be quickly assessed for emotional disorders, after which identified youth can be linked to appropriate health services. Despite the obvious logic of this process, universal mental health screening has received little scientific evaluation and even less application in Australian schools. This study will develop methods for Australian education systems to help identify young people (aged 9-17 years old) who are struggling with existing and emerging emotional disorders. Prior to testing, a series of focus groups will be run to get feedback and input from young people, parents, teachers, and mental health professionals. They will be asked about their thoughts on school-based screening methods and and how to best help students at risk of emotional distress. Schools (n=91) across New South Wales, Australia will be randomised to do either immediate screening (in May 2021) or delayed screening (in February 2022). Students in immediate screening schools will complete a long online mental health screener consisting of standard emotional health questionnaires. Ultimately, this large set of items will be reduced to a small number of items to form the final brief screener. Students who score in the “at-risk” range on any measure of emotional health problems will be identified to schools and offered pathways to relevant help according to the most accepted and approved processes identified by the focus groups. Nine months later, the same process will occur among delayed screening schools. At this same time, students in the immediate screening schools will complete screening for a second time. This will allow a direct comparison of the emotional health and help-seeking between youth whose schools had engaged in the screening and pathways to care process (immediate) and those whose schools had not engaged in the process (delayed). It is hypothesised that there will be a significant increase in students who receive help from mental health support services after screening, compared with baseline. It is also predicted that all students will show significantly less emotional distress after screening and access to pathways of care. This study will be an important contribution to Australian youth mental health prevention and early intervention by determining whether school screening leads to a greater number of young people with emotional disorders getting the help that they need and improving their mental health outcomes.

Keywords: children and young people, early intervention, mental health, mental health screening, prevention, school-based mental health

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Authors: P. L. Cheng, I. N. Umar

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Online discussion forum is a popular e-learning technique that allows participants to interact and construct knowledge. This study aims to examine the levels of participation, categories of participants and the structure of their interactions in a forum. A convenience sampling of one course coordinator and 23 graduate students was selected in this study. The forums’ log file and the Social Network Analysis software were used in this study. The analysis reveals 610 activities (including viewing forum’s topic, viewing discussion thread, posting a new thread, replying to other participants’ post, updating an existing thread and deleting a post) performed by them in this forum, with an average of 3.83 threads posted. Also, this forum consists of five at-risk participants, six bridging participants, four isolated participants and five leaders of information. In addition, the network density value is 0.15 and there exist five reciprocal interactions in this forum. The closeness value varied between 28 and 68 while the eigen vector centrality value varied between 0.008 and 0.39. The finding indicates that the participants tend to listen more rather than express their opinions in the forum. It was also revealed that those who actively provide supports in the discussion forum were not the same people who received the most responses from their peers. This study found that cliques do not exist in the forum and the participants are not selective to whom they response to, rather, it was based on the content of the posts made by their peers. Based upon the findings, further analysis with different method and population, larger sample size and a longer time frame are recommended.

Keywords: e-learning, learning management system, online forum, social network analysis

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Authors: Sıdıka Kaya, Gülay Sain Güven, Seda Karsavuran, Onur Toka

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This study is part of an ongoing research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 114K404, and participation to this conference was supported by Hacettepe University Scientific Research Coordination Unit under Project Number 10243. Evaluation of hospital readmissions is gaining importance in terms of quality and cost, and is becoming the target of national policies. In Turkey, the topic of hospital readmission is relatively new on agenda and very few studies have been conducted on this topic. The aim of this study was to determine 30-day readmission rates and risk factors for readmission. Whether readmission was planned, related to the prior admission and avoidable or not was also assessed. The study was designed as a ‘prospective cohort study.’ 472 patients hospitalized in internal medicine departments of a university hospital in Turkey between February 1, 2015 and April 30, 2015 were followed up. Analyses were conducted using IBM SPSS Statistics version 22.0 and SPSS Modeler 16.0. Average age of the patients was 56 and 56% of the patients were female. Among these patients 95 were readmitted. Overall readmission rate was calculated as 20% (95/472). However, only 31 readmissions were unplanned. Unplanned readmission rate was 6.5% (31/472). Out of 31 unplanned readmission, 24 was related to the prior admission. Only 6 related readmission was avoidable. To determine risk factors for readmission we constructed Chi-square automatic interaction detector (CHAID) decision tree algorithm. CHAID decision trees are nonparametric procedures that make no assumptions of the underlying data. This algorithm determines how independent variables best combine to predict a binary outcome based on ‘if-then’ logic by portioning each independent variable into mutually exclusive subsets based on homogeneity of the data. Independent variables we included in the analysis were: clinic of the department, occupied beds/total number of beds in the clinic at the time of discharge, age, gender, marital status, educational level, distance to residence (km), number of people living with the patient, any person to help his/her care at home after discharge (yes/no), regular source (physician) of care (yes/no), day of discharge, length of stay, ICU utilization (yes/no), total comorbidity score, means for each 3 dimensions of Readiness for Hospital Discharge Scale (patient’s personal status, patient’s knowledge, and patient’s coping ability) and number of daycare admissions within 30 days of discharge. In the analysis, we included all 95 readmitted patients (46.12%), but only 111 (53.88%) non-readmitted patients, although we had 377 non-readmitted patients, to balance data. The risk factors for readmission were found as total comorbidity score, gender, patient’s coping ability, and patient’s knowledge. The strongest identifying factor for readmission was comorbidity score. If patients’ comorbidity score was higher than 1, the risk for readmission increased. The results of this study needs to be validated by other data–sets with more patients. However, we believe that this study will guide further studies of readmission and CHAID is a useful tool for identifying risk factors for readmission.

Keywords: decision tree, hospital, internal medicine, readmission

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Authors: Amro Matyori, Fatimah Sherbeny, Askal Ali, Olayiwola Popoola

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Objectives: The study evaluated the impact of COVID-19 on anxiety levels in the general population in the United States. Methods: The study used an online questionnaire. It adopted the Generalized Anxiety Disorder Assessment (GAD-7) instrument. It is a self-administered scale with seven items used as a screening tool and severity measure for generalized anxiety disorder. The participants rated the frequency of anxiety symptoms in the last two weeks on a Likert scale, which ranges from 0-3. Then the item points are summed to provide the total score. Results: Thirty-two participants completed the questionnaire. Among them, 24 (83%) females and 5 (17%) males. The age range of 18-24-year-old represented the most respondents. Only one of the participants tested positive for the COVID-19, and 39% of them, one of their family members, friends, or colleagues, tested positive for the coronavirus. Moreover, 10% have lost a family member, a close friend, or a colleague because of COVID-19. Among the respondents, there were ten who scored approximately five points on the GAD-7 scale, which indicates mild anxiety. Furthermore, eight participants scored 10 to 14 points, which put them under the category of moderate anxiety, and one individual who was categorized under severe anxiety scored 15 points. Conclusions: It is identified that most of the respondents scored the points that put them under the mild anxiety category during the COVID-19 pandemic. It is also noticed that severe anxiety was the lowest among the participants, and people who tested positive and/or their family members, close friends, and colleagues were more likely to experience anxiety. Additionally, participants who lost friends or family members were also at high risk of anxiety. It is obvious the COVID-19 outcomes and too much thinking about the pandemic put people under stress which led to anxiety. Therefore, continuous assessment and monitoring of psychological outcomes during pandemics will help to establish early well-informed interventions.

Keywords: anxiety and covid-19, covid-19 and mental health outcomes, influence of covid-19 on anxiety, population and covid-19 impact on mental health

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Authors: Rabia Iftikhar, Iqra Tariq

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The current study examined the parental acceptance-rejection and personality assessment of sexually abused children. A sample of 50 control (25 girls and 25 boys) and 50 abused (25 girls and 25 boys) were drawn through the process of purposive sampling (N = 100). The sample consisted of school going children between the ages of 8-16. The sample was taken from non-governmental schools and NGO. Parental Acceptance-Rejection Questionnaire, Personality Assessment Questionnaire and Emotional STROOP task, were used to explore the relationship between the variables. The results showed that girls showed greater parental rejection than boys, were less psychologically and emotionally adjusted than boys. The results also showed that boys were high on psychological abuse while girls were high on physical and sexual abuse. The results of STROOP showed that sexually abused children showed more reaction time than non-abused children.

Keywords: abused, adjustment, Pakistani, stroop

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Authors: Cabangile Ncengeni Ngwane, Gerald Mills

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Anthropogenic climate change is exacerbating natural hazards such as droughts, heat waves and sea-level rise. The associated risks are the greatest in places where socio-ecological systems are exposed to these changes and the populations and infrastructure are vulnerable. Identifying the communities at risk and enhancing local resilience are key issues in responding to the current and project climate changes. This paper explores the types of risks associated with multiple overlapping hazards in Durban, South Africa where the social, cultural and economic dimensions that contribute to exposure and vulnerability are compounded by its history of apartheid. As a result, climate change risks are highly concentrated in marginalized communities that have the least adaptive capacity. In this research, a Geographic Information System is to explore the spatial correspondence among geographic layers representing hazards, exposure and vulnerability across Durban. This quantitative analysis will allow authors to identify communities at high risk and focus our study on the nature of the current human-environment relationships that result in risk inequalities. This work will employ qualitative methods to critically examine policies (including educational practices and financial support systems) and on-the-ground actions that are designed to improve the adaptive capacity of these communities and meet UN Sustainable Development Goals. This work will contribute to a growing body of literature on disaster risk management, especially as it relates to developing economies where socio-economic inequalities are correlated with ethnicity and race.

Keywords: adaptive capacity, disaster risk reduction, exposure, resilience, South Africa

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Authors: Chinmay V. Deshpande

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Purpose: To assess the efficiency of visual functions and visual skills in strabismic & anisometropic amblyopes and to assess visual acuity and contrast sensitivity in anisometropic amblyopes with spectacles & contact lenses. Method: In a prospective clinical study, 32 children ageing from 5 to 15 years presenting with amblyopia in a pediatric department of Shri Ganapati Netralaya Jalna, India, were assessed for a period of three & half months. Visual acuity was measured with Snellen’s and Bailey-Lovie log MAR charts whereas contrast sensitivity was measured with Pelli-Robson chart with spectacles and contact lenses. Saccadic movements were assessed with SCCO scoring criteria and accommodative facility was checked with ±1.50 DS flippers. Stereopsis was assessed with TNO test. Results: By using Wilcoxon sign rank test p-value < 0.05 (< 0.001), the mean linear visual acuity was 0.29 (≈ 6/21) and mean single optotype visual acuity found to be 0.36 (≈ 6/18). Mean visual acuity of 0.27(≈ 6/21) with spectacles improved to 0.33 (≈ 6/18) with contact lenses in amblyopic eyes. The mean Log MAR visual acuity with spectacles and contact lens were found to be 0.602( ≈6/24) and 0.531(≈ 6/21) respectively. The contrast threshold out of 20 amblyopic eyes shows that mean contrast threshold changed in 9 patients from spectacles 0.27 to contact lens 0.19 respectively. The mean accommodative facility assessed was 5.31(± 2.37). 24 subjects (75%) revealed marked saccadic defects on the test applied. 78% subjects didn’t show even gross stereoscopic ability on TNO test. Conclusion: This study supports the facts about amblyopia and associated deficits in visual skills which are claimed in previous studies. In addition, anisometropic amblyopia can be managed better with contact lenses.

Keywords: strabismus, anisometropia, amblyopia, contrast sensitivity, saccades, stereopsis

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Samuele Abagnato, Lucia Rigamonti

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Life-Cycle-Assessment (LCA) is an effective tool to quantify the environmental impacts of reuse models and recycling technologies for textiles. In this work, publications in the last ten years about LCA on textile waste are classified according to location, goal and scope, functional unit, waste composition, impact assessment method, impact categories, and sensitivity analysis. Twenty papers have been selected: 50% are focused only on recycling, 30% only on reuse, the 15% on both, while only one paper considers only the final disposal of the waste. It is found that reuse is generally the best way to decrease the environmental impacts of textiles waste management because of the avoided impacts of manufacturing a new item. In the comparison between a product made with recycled yarns and a product from virgin materials, in general, the first option is less impact, especially for the categories of climate change, water depletion, and land occupation, while for other categories, such as eutrophication or ecotoxicity, under certain conditions the impacts of the recycled fibres can be higher. Cultivation seems to have quite high impacts when natural fibres are involved, especially in the land use and water depletion categories, while manufacturing requires a remarkable amount of electricity, with its associated impact on climate change. In the analysis of the reuse processes, relevant importance is covered by the laundry phase, with water consumption and impacts related to the use of detergents. About the sensitivity analysis, it can be stated that one of the main variables that influence the LCA results and that needs to be further investigated in the modeling of the LCA system about this topic is the substitution rate between recycled and virgin fibres, that is the amount of recycled material that can be used in place of virgin one. Related to this, also the yield of the recycling processes has a strong influence on the results of the impact. The substitution rate is also important in the modeling of the reuse processes because it represents the number of avoided new items bought in place of the reused ones. Another aspect that appears to have a large influence on the impacts is consumer behaviour during the use phase (for example, the number of uses between two laundry cycles). In conclusion, to have a deeper knowledge of the impacts of a life-cycle approach of textile waste, further data and research are needed in the modeling of the substitution rate and of the use phase habits of the consumers.

Keywords: environmental impacts, life-cycle-assessment, textiles recycling, textiles reuse, textiles waste management

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Authors: Navid Daryasafar, Nima Farshidfar

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In this article, we make attempt to hide error in video with an emphasis on the time-wise use of autoregressive (AR) models. To resolve this problem, we assume that all information in one or more video frames is lost. Then, lost frames are estimated using analogous Pixels time information in successive frames. Accordingly, after presenting autoregressive models and how they are applied to estimate lost frames, two general methods are presented for using these models. The first method which is the same standard method of autoregressive models estimates lost frame in unidirectional form. Usually, in such condition, previous frames information is used for estimating lost frame. Yet, in the second method, information from the previous and next frames is used for estimating the lost frame. As a result, this method is known as bidirectional estimation. Then, carrying out a series of tests, performance of each method is assessed in different modes. And, results are compared.

Keywords: error steganography, unidirectional estimation, bidirectional estimation, AR linear estimation

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Authors: C. Puangsawat, C. Limotai, P. Srikhachin

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Bio-psycho-social caring was required for promoting the quality of life of the patients with epilepsy (PWE), despite controlled seizures. Multifaceted issues emerge at the epilepsy clinic. Unpredicted seizures, antiepileptic drug compliance problems/adverse effects, psychiatric, and social problems are all needed to be explored and managed. The Nursing Service System (NSS) at the tertiary epilepsy clinic (TEC) was consequently developed for improving the clinical care for PWE. Case manager concept was integrated as the framework guiding the processes and strategies used for developing the NSS as well as the roles of the multidisciplinary team at the clinic. This study aimed to report the outcomes of the developed NSS integrated case manager concept. The processes of our developed NSS program included 1) screening for patient’s problems using questionnaire prior to seeing epileptologists i.e., assessing the patient’s risk to develop acute seizures at the clinic, issues related to medication use, and uncovered psychiatric and social problems; and 2) assigning the patients at risk to be evaluated and managed by appropriate team. Nurses specializing in epilepsy in coordination with the multidisciplinary team implemented the NSS to promote coordinated work among the team which consists of epileptologists, nurses, pharmacists, psychologists, and social workers. Determination of the role of each person and their responsibilities along with joint care plan were clearly established. One year after implementation, the rate of acute seizure occurrence at the clinic was decreased, and satisfactory feedback from the patients was received. In order to achieve an optimal goal to promote self-management behaviors in PWE, continuing the NSS and systematic assessment of its effectiveness is required.

Keywords: case manager concept, nursing service system, patients with epilepsy, quality of life

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Authors: H. Yousefnia, S. Zolghadri, N. Amraee, Z. Naseri, Ar. Jalilian

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The purpose of this study was to calculate the absorbed dose to each human organ for two new Sm-153 bone-seeking agents in order to evaluate their effectiveness in bone pain palliation therapy. In this work, the absorbed dose of 153Sm-TTHMP and 153Sm-PDTMP to each human organ was evaluated based on biodistribution studies in rats by radiation dose assessment resource (RADAR) method. The highest absorbed dose for 153Sm-TTHMP and 153Sm-PDTMP is observed in trabecular bone with 1.844 and 3.167 mGy/MBq, respectively. Bone/red marrow dose ratio, as the target/critical organ dose ratio, for 153Sm-PDTMP is greater than 153Sm-TTHMP and is compatible with 153Sm-EDTMP. The results showed that these bone-seeking agents, specially 153Sm-PDTMP, have considerable characteristics compared to the most clinically used bone pain palliative radiopharmaceutical, and therefore, can be good candidates for bone pain palliation in patients with bone metastasis; however, further biological studies in other mammals are still needed.

Keywords: internal dosimetry, PDTMP, 153Sm, TTHMP

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Authors: Ferry Kurniawan

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In this paper, we nowcast quarterly output growth in Indonesia by exploiting higher frequency data (monthly indicators) using a mixed-frequency factor model and exploiting both quarterly and monthly data. Nowcasting quarterly GDP in Indonesia is particularly relevant for the central bank of Indonesia which set the policy rate in the monthly Board of Governors Meeting; whereby one of the important step is the assessment of the current state of the economy. Thus, having an accurate and up-to-date quarterly GDP nowcast every time new monthly information becomes available would clearly be of interest for central bank of Indonesia, for example, as the initial assessment of the current state of the economy -including nowcast- will be used as input for longer term forecast. We consider a small scale mixed-frequency factor model to produce nowcasts. In particular, we specify variables as year-on-year growth rates thus the relation between quarterly and monthly data is expressed in year-on-year growth rates. To assess the performance of the model, we compare the nowcasts with two other approaches: autoregressive model –which is often difficult when forecasting output growth- and Mixed Data Sampling (MIDAS) regression. In particular, both mixed frequency factor model and MIDAS nowcasts are produced by exploiting the same set of monthly indicators. Hence, we compare the nowcasts performance of the two approaches directly. To preview the results, we find that by exploiting monthly indicators using mixed-frequency factor model and MIDAS regression we improve the nowcast accuracy over a benchmark simple autoregressive model that uses only quarterly frequency data. However, it is not clear whether the MIDAS or mixed-frequency factor model is better. Neither set of nowcasts encompasses the other; suggesting that both nowcasts are valuable in nowcasting GDP but neither is sufficient. By combining the two individual nowcasts, we find that the nowcast combination not only increases the accuracy - relative to individual nowcasts- but also lowers the risk of the worst performance of the individual nowcasts.

Keywords: nowcasting, mixed-frequency data, factor model, nowcasts combination

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Authors: Muhammad SAFDAR, Mehmet Ozaslan, Musarrat Abbas Khan

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Babesiosis is a haemoparasitic disease due to the multiplication of protozoan’s parasite, Babesia ovis in the red blood cells of the host, and contributes numerous economical losses, including sheep and goat ruminants. The early identification and successful treatment of Babesia Ovis spp. belong to the key steps of control and health management of livestock resources. The objective of this study was to construct a polymerase chain reaction (PCR) based method for the detection of Babesia spp. in small ruminants and to determine the risk factors involved in the spreading of babesiosis infections. A total of 100 blood samples were collected from 50 sheep and 50 goats along with different areas of Muzaffargarh, Pakistan, from randomly selected herds. Data on the characteristics of sheep and goats were collected through questionnaires. Of 100 blood samples examined, 18 were positive for Babesia ovis upon microscopic studies, whereas 11 were positive for the presence of Babesia spp. by PCR assay. For the recognition of parasitic DNA, a set of 500bp oligonucleotide was designed by PCR amplification with sequence 18S rRNA gene for B. ovis. The prevalence of babesiosis in small ruminant’s sheep and goat detected by PCR was significantly higher in female animals (28%) than male herds (08%). PCR analysis of the reference samples showed that the detection limit of the PCR assay was 0.01%. Taken together, all data indicated that this PCR assay was a simple, fast, specific detection method for Babesia ovis species in small ruminants compared to other available methods.

Keywords: Babesia ovis, PCR amplification, 18S rRNA, sheep and goat

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Authors: Saeed Nazari, Masoom Nazari, Ali Hejazi, Siamak Sanoobari Ghazi Jahani, Mohammad Dehghani, Javad Vakili

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More than ever, digitization is an imperative for businesses to keep their competitive advantages, foster innovation and reduce paperwork. To design and successfully implement digital transformation initiatives within process safety management system, employees need to be equipped with the right tool, frameworks, and best practices. we developed a unique full stack application so-called SHEQTool which is entirely dynamic based on our extensive expertise, experience, and client feedback to help business processes particularly operations safety management. We use our best knowledge and scientific methodologies published by CCPS and OSHA Guidelines to streamline operations and integrated them into task management within Petrochemical Companies. We digitalize their main process safety management system elements and their sub elements such as hazard identification and risk management, training and communication, inspection and audit, critical changes management, contractor management, permit to work, pre-start-up safety review, incident reporting and investigation, emergency response plan, personal protective equipment, occupational health, and action management in a fully customizable manner with no programming needs for users. We review the feedback from main actors within petrochemical plant which highlights improving their business performance and productivity as well as keep tracking their functions’ key performance indicators (KPIs) because it; 1) saves time, resources, and costs of all paperwork on our businesses (by Digitalization); 2) reduces errors and improve performance within management system by covering most of daily software needs of the organization and reduce complexity and associated costs of numerous tools and their required training (One Tool Approach); 3) focuses on management systems and integrate functions and put them into traceable task management (RASCI and Flowcharting); 4) helps the entire enterprise be resilient to any change of your processes, technologies, assets with minimum costs (through Organizational Resilience); 5) reduces significantly incidents and errors via world class safety management programs and elements (by Simplification); 6) gives the companies a systematic, traceable, risk based, process based, and science based integrated management system (via proper Methodologies); 7) helps business processes complies with ISO 9001, ISO 14001, ISO 45001, ISO 31000, best practices as well as legal regulations by PDCA approach (Compliance).

Keywords: process, safety, digitalization, management, risk, incident, SHEQTool, OSHA, CCPS

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Krolo Paulina

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The seismic response of moment-resisting steel frames depends on the behavior of the joints, especially when they are considered as ductile zones. The aim of this research is to provide a realistic assessment of the moment-resisting steel frame behavior under seismic loading using nonlinear static pushover analysis (N2 method). The hysteresis behavior of the joints in the frame model was described using a new hysteresis envelope model. The obtained seismic response was compared with the results of the seismic analysis obtained for the same steel frame that takes into account the monotonic model of the joints.

Keywords: beam-to-column joints, hysteresis envelope model, moment-resisting frame, nonlinear static pushover analysis, N2 method

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Eun Kyung Kim, Kyehan Rhee

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Viscoelastic wall properties of the arterial plaques change as the disease progresses, and estimation of wall viscoelasticity can provide a valuable assessment tool for plaque rupture prediction. Cross section of the stenotic coronary artery was modeled based on the IVUS image, and the finite element analysis was performed to get wall deformation under pulsatile pressure. The effects of viscoelastic parameters of the plaque on luminal diameter variations were explored. The result showed that decrease of viscous effect reduced the phase angle between the pressure and displacement waveforms, and phase angle was dependent on the viscoelastic properties of the wall. Because viscous effect of tissue components could be identified using the phase angle difference, wall deformation waveform analysis may be applied to predict plaque wall composition change and vascular wall disease progression.

Keywords: atherosclerotic plaque, diameter variation, finite element method, viscoelasticity

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Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad

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As energetic and environmental issues are getting more and more attention all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascends with an unprecedented rate. This matter causes more uncertainties to appear in the power system context; ergo, the uncertainty analysis of the system performance is an obligation. The uncertainties of any system can be represented probabilistically or possibilistically. Since sufficient historical data about all the system variables is not available, therefore, they do not have a probability density function (PDF) and must be represented possibilistiacally. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is appealed. The first of this two-paper series formulates a new possibilistic-probabilistic tool for the load flow uncertainty assessment. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. This possibilistic- probabilistic formulation is solved in the second companion paper in an uncertain load flow (ULF) study problem.

Keywords: probabilistic uncertainty modeling, possibilistic uncertainty modeling, uncertain load flow, wind turbine generator

Procedia PDF Downloads 566

Authors: Abdelrhman Elagez, Rolla Monib

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This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, an online survey was conducted with 24 project managers and construction professionals, possessing experience ranging from 1 to 30 years (with an average of 6.38 years), to gather industry perspectives and concerns relating to AI integration. The survey results yielded several significant findings. Firstly, respondents exhibited a moderate level of familiarity (66.67%) with AI technologies, while the industry's readiness for AI deployment and current usage rates remained low at 2.72 out of 5. Secondly, the top-ranked barriers to AI adoption were identified as lack of awareness, insufficient knowledge and skills, data quality concerns, high implementation costs, absence of prior case studies, and the uncertainty of outcomes. Thirdly, the most significant risks associated with AI use in construction were perceived to be a lack of human control (decision-making), accountability, algorithm bias, data security/privacy, and lack of legislation and regulations. Additionally, the participants acknowledged the value of factors such as education, training, organizational support, and communication in facilitating AI integration within the industry. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: risk management, construction, artificial intelligence, technology

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Authors: Balázs Ádám, Randi Nørgaard Fløe Pedersen, Jørgen Riis Jepsen

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Volatile chemicals that accumulate and release from freight containers constitute significant health risks. Fumigation to prevent spread of pests and off-gassing of freight are sources of hazardous chemicals. The aim of our study was to investigate the regulation and practice of container handling with focus on preventive measures applied against chemical exposures in Denmark. A comprehensive systematic search of scientific literature and organizational domains of international and Danish regulatory bodies was performed to explore regulations related to safe work with transport containers. The practice of container work was investigated in a series of semi-structured interviews with managers and health and safety representatives of organizations that handle transport containers. Although there are several international and national regulations and local safety instructions that relate to container handling, the provided information is not specific or up-to-date enough to conduct safe practice in many aspects. The interviewees estimate high frequency of containers with chemical exposure and deem that they can potentially damage health, although recognizable health effects are rare. Knowledge is limited about the chemicals and most of them cannot be measured by available devices. Typical preventive measures are passive ventilation and personal protective equipment but their use is not consistent and may not provide adequate protection. Hazardous chemicals are frequently present in transport containers; however, managers, workers and even occupational health professionals have limited knowledge about the problem. Detailed risk assessment and specific instructions on risk management are needed to provide safe conditions for work with containers.

Keywords: chemical exposure, fumigation, occupational health and safety regulation, transport container

Procedia PDF Downloads 385

Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

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Authors: Abraham Castellanos, Christophe Durville, Sophie Echenim

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In this article, a portfolio optimization problem is performed in a Solvency II context: it illustrates how advanced optimization techniques can help to tackle complex operational pain points around the monitoring, control, and stability of Solvency Capital Requirement (SCR). The market SCR of a portfolio is calculated as a combination of SCR sub-modules. These sub-modules are the results of stress-tests on interest rate, equity, property, credit and FX factors, as well as concentration on counter-parties. The market SCR is non convex and non differentiable, which does not make it a natural optimization criteria candidate. In the SCR formulation, correlations between sub-modules are fixed, whereas risk-driven portfolio allocation is usually driven by the dynamics of the actual correlations. Implementing a portfolio construction approach that is efficient on both a regulatory and economic standpoint is not straightforward. Moreover, the challenge for insurance portfolio managers is not only to achieve a minimal SCR to reduce non-invested capital but also to ensure stability of the SCR. Some optimizations have already been performed in the literature, simplifying the standard formula into a quadratic function. But to our knowledge, it is the first time that the standard formula of the market SCR is used in an optimization problem. Two solvers are combined: a bundle algorithm for convex non- differentiable problems, and a BFGS (Broyden-Fletcher-Goldfarb- Shanno)-SQP (Sequential Quadratic Programming) algorithm, to cope with non-convex cases. A market SCR minimization is then performed with historical data. This approach results in significant reduction of the capital requirement, compared to a classical Markowitz approach based on the historical volatility. A comparative analysis of different optimization models (equi-risk-contribution portfolio, minimizing volatility portfolio and minimizing value-at-risk portfolio) is performed and the impact of these strategies on risk measures including market SCR and its sub-modules is evaluated. A lack of diversification of market SCR is observed, specially for equities. This was expected since the market SCR strongly penalizes this type of financial instrument. It was shown that this direct effect of the regulation can be attenuated by implementing constraints in the optimization process or minimizing the market SCR together with the historical volatility, proving the interest of having a portfolio construction approach that can incorporate such features. The present results are further explained by the Market SCR modelling.

Keywords: financial risk, numerical optimization, portfolio management, solvency capital requirement

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Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Metabolic syndrome (MetS) is a disease associated with obesity. It is a complicated clinical problem possibly affecting body composition as well as macrominerals. These parameters gain further attention, particularly in the pediatric population. The aim of this study is to investigate the amount of discrete body composition fractions in groups that differ in the severity of obesity. Also, the possible associations with calcium (Ca), phosphorus (P), magnesium (Mg) will be examined. The study population was divided into four groups. Twenty-eight, 29, 34, and 34 children were involved in Group 1 (healthy), 2 (obese), 3 (morbid obese), and 4 (MetS), respectively. Institutional Ethical Committee approved the study protocol. Informed consent forms were obtained from the participants. The classification of obese groups was performed based upon the recommendations of the World Health Organization. Metabolic syndrome components were defined. Serum Ca, P, Mg concentrations were measured. Within the scope of body composition, fat mass, fat-free mass, protein mass, mineral mass were determined by a body composition monitor using bioelectrical impedance analysis technology. Weight, height, waist circumference, hip circumference, head circumference, and neck circumference values were recorded. Body mass index, diagnostic obesity notation model assessment index, fat mass index, and fat-free mass index values were calculated. Data were statistically evaluated and interpreted. There was no statistically significant difference among the groups in terms of Ca and P concentrations. Magnesium concentrations differed between Group 1 and Group 4. Strong negative correlations were detected between P as well as Mg and fat mass index as well as diagnostic obesity notation model assessment index in Group 4, the group, which comprised morbid obese children with MetS. This study emphasized unique associations of P and Mg minerals with diagnostic obesity notation model assessment index and fat mass index during the evaluation of morbid obese children with MetS. It was also concluded that diagnostic obesity notation model assessment index and fat mass index were more proper indices in comparison with body mass index and fat-free mass index for the purpose of defining body composition in children.

Keywords: children, fat mass, fat-free mass, macrominerals, obesity

Procedia PDF Downloads 157