Search results for: risk prediction model

Authors: L. Madouni, M. Ould Ouali, N. E. Hannachi

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Two constitutive models for concrete are available in ABAQUS/Explicit, the Brittle Cracking Model and the Concrete Damaged Plasticity Model, and their suitability and limitations are well known. The aim of the present paper is to implement a damage-friction concrete constitutive model and to evaluate the performance of this model by comparing the predicted response with experimental data. The constitutive formulation of this material model is reviewed. In order to have consistent results, the parameter identification and calibration for the model have been performed. Several numerical simulations are presented in this paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated model.

Keywords: Abaqus, concrete, constitutive model, numerical simulation

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Authors: Mahmoud Zakeri Niri, Saber Moazami, Arman Abdollahipour, Hossein Ghalkhani

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The objective of this research is to hydrological evaluation of four widely-used satellite precipitation products named PERSIANN, TMPA-3B42V7, TMPA-3B42RT, and CMORPH over Zarinehrood basin in Iran. For this aim, at first, daily streamflow of Sarough-cahy river of Zarinehrood basin was simulated using IHACRES rainfall-runoff model with daily rain gauge and temperature as input data from 1988 to 2008. Then, the model was calibrated in two different periods through comparison the simulated discharge with the observed one at hydrometric stations. Moreover, in order to evaluate the performance of satellite precipitation products in streamflow simulation, the calibrated model was validated using daily satellite rainfall estimates from the period of 2003 to 2008. The obtained results indicated that TMPA-3B42V7 with CC of 0.69, RMSE of 5.93 mm/day, MAE of 4.76 mm/day, and RBias of -5.39% performs better simulation of streamflow than those PERSIANN and CMORPH over the study area. It is noteworthy that in Iran, the availability of ground measuring station data is very limited because of the sparse density of hydro-meteorological networks. On the other hand, large spatial and temporal variability of precipitations and lack of a reliable and extensive observing system are the most important challenges to rainfall analysis, flood prediction, and other hydrological applications in this country.

Keywords: hydrological evaluation, IHACRES, satellite precipitation product, streamflow simulation

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Authors: Marinko Maslaric, Maja Jokic

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When searching for fast and long term responses, sustainable logistics and supply chain applications have developed irrefutable theories and hypotheses towards market requirements. Nevertheless, there are certain misunderstandings on how the implementation of sustainability principles (social, economical, and environmental) and concepts should work in practice, more specifically, within a humanitarian supply chain management context. This paper will focus on the review and analysis of risk management concepts in humanitarian supply chain in order to identify their compliance with sustainable principles. In this direction, the study will look for strategies that suggest: minimization of environmental impacts throughout the reduction of resources consumption, depreciation of logistics costs, including supply chain ones, minimization of transportation and service costs, elaboration of quality performance of supply chain and logistics, and reduction of supply chain delivery time. On the side of meeting all defense, trades and humanitarian logistics needs, the research will be aligned to UN Sustainable Development Goals, standards, and performances. It will start with relevant strategies for identification of risk indicators and it will end with suggestion of valuable strategic approaches for their minimization or total prevention. Finally, a content analysis will propose a suitable methodological structure for the creation of most sustainable strategy in risk management of humanitarian supply chain. Content analysis will accompany thorough, consistent and methodical approach of literature review for potential disaster risk management plan. Thereupon, the propositions of this research will look for contemporary literature gaps, with respect to operate the literature analysis and to suggest the appropriate sustained risk low master plan. The indicated is here to secure the high quality of logistics practices in hazardous events.

Keywords: humanitarian logistics, sustainability, supply chain risk, risk management plan

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Authors: Qing Wu, Xuchun Ye, Kirsten Corazzini

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Objective: It is increasingly recognized that patients’ rational and meaningful engagement in healthcare could make important contributions to their health care and safety management. However, recent evidence indicated that patients' actual roles in healthcare didn’t match their desired roles, and many patients reported a less active role than desired, which suggested that patient engagement in healthcare may be influenced by various factors. This study aimed to analyze influencing factors on patient engagement and explore the influence mechanism, which will be expected to contribute to the strategy development of patient engagement in healthcare. Methods: On the basis of analyzing the literature and theory study, the research framework was developed. According to the research framework, a cross-sectional survey was employed using the behavior and willingness of patient engagement in healthcare questionnaire, Chinese version All Aspects of Health Literacy Scale, Facilitation of Patient Involvement Scale and Wake Forest Physician Trust Scale, and other influencing factor related scales. A convenience sample of 580 patients was recruited from 8 general hospitals in Shanghai, Jiangsu Province, and Zhejiang Province. Results: The results of the cross-sectional survey indicated that the mean score for the patient engagement behavior was (4.146 ± 0.496), and the mean score for the willingness was (4.387 ± 0.459). The level of patient engagement behavior was inferior to their willingness to be involved in healthcare (t = 14.928, P ＜ 0.01). The influencing mechanism model of patient engagement in healthcare was constructed by the path analysis. The path analysis revealed that patient attitude toward engagement, patients’ perception of facilitation of patient engagement and health literacy played direct prediction on the patients’ willingness of engagement, and standard estimated values of path coefficient were 0.341, 0.199, 0.291, respectively. Patients’ trust in physician and the willingness of engagement played direct prediction on the patient engagement, and standard estimated values of path coefficient were 0.211, 0.641, respectively. Patient attitude toward engagement, patients’ perception of facilitation and health literacy played indirect prediction on patient engagement, and standard estimated values of path coefficient were 0.219, 0.128, 0.187, respectively. Conclusions: Patients engagement behavior did not match their willingness to be involved in healthcare. The influencing mechanism model of patient engagement in healthcare was constructed. Patient attitude toward engagement, patients’ perception of facilitation of engagement and health literacy posed indirect positive influence on patient engagement through the patients’ willingness of engagement. Patients’ trust in physician and the willingness of engagement had direct positive influence on the patient engagement. Patient attitude toward engagement, patients’ perception of physician facilitation of engagement and health literacy were the factors influencing the patients’ willingness of engagement. The results of this study provided valuable evidence on guiding the development of strategies for promoting patient rational and meaningful engagement in healthcare.

Keywords: healthcare, patient engagement, influencing factor, the mechanism

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Authors: A. Asambe

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A study was conducted to determine the level of compliance with sanitary measures in piggeries, and awareness and risk factors of African swine fever in Benue State, Nigeria. Questionnaires were distributed to 74 respondents consisting of piggery owners and attendants in different piggeries across 12 LGAs to collect data for this study. Sanitary measures in piggeries were observed to be generally very poor, though respondents admitted being aware of ASF. Piggeries located within a 1 km radius of a slaughter slab (OR=9.2, 95% CI - 3.0-28.8), piggeries near refuse dump sites (OR=3.0, 95% CI - 1.0-9.5) and piggeries where farm workers wear their work clothes outside of the piggery premises (OR=0.2, 95% CI - 0.1-0.7) showed higher chances of ASFV infection and were significantly associated (p < 0.0001), (p < 0.05) and (p < 0.01), and were identified as potential risk factors. The study concluded that pigs in Benue State are still at risk of an ASF outbreak. Proper sanitary and hygienic practices is advocated and emphasized in piggeries, while routine surveillance for ASFV antibodies in pigs in Benue State is strongly recommended to provide a reliable reference data base to plan for the prevention of any devastating ASF outbreak.

Keywords: African swine fever, awareness, piggery, risk factors, sanitary measures

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Aisha F. Bushager

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Today we are more exposed than ever to cyber threats and attacks at personal, community, organizational, national, and international levels. More aspects of our lives are operating on computer networks simply because we are living in the fifth domain, which is called the Cyberspace. One of the most sensitive areas that are vulnerable to cyber threats and attacks is the Electronic Banking (e-Banking) area, where the banking sector is providing online banking services to its clients. To be able to obtain the clients trust and encourage them to practice e-Banking, also, to maintain the services provided by the banks and ensure safety, cyber security and risks control should be given a high priority in the e-banking area. The aim of the study is to carry out risk assessment on the e-banking services and determine the cyber threats, cyber attacks, and vulnerabilities that are facing the e-banking area specifically in the Kingdom of Bahrain. To collect relevant data, structured interviews were taken place with e-banking experts in different banks. Then, collected data where used as in input to the risk management framework provided by the National Institute of Standards and Technology (NIST), which was the model used in the study to assess the risks associated with e-banking services. The findings of the study showed that the cyber threats are commonly human errors, technical software or hardware failure, and hackers, on the other hand, the most common attacks facing the e-banking sector were phishing, malware attacks, and denial-of-service. The risks associated with the e-banking services were around the moderate level, however, more controls and countermeasures must be applied to maintain the moderate level of risks. The results of the study will help banks discover their vulnerabilities and maintain their online services, in addition, it will enhance the cyber security and contribute to the management and control of risks that are facing the e-banking sector.

Keywords: cyber security, e-banking, risk assessment, threats identification

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Authors: Makhosazana Dubazana

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Many South Africans commuters use minibus taxis daily and are connected to the informal transport network through metro centres informally known as Taxi Ranks. Taxi ranks form part of an economic nexus for many informal traders, connecting them to commuters, their prime clientele. They work along designated areas along the periphery of the taxi rank and in between taxi lanes. Informal traders are therefore at risk of adverse health effects associated with the inhalation of exhaust fumes from minibus taxis. Of the exhaust emissions, benzene, toluene, ethylbenzene and xylenes (BTEX) have high toxicity. Purpose: The purpose of this study was to conduct a Human Health Risk Assessment for informal traders, looking at their exposure to BTEX compounds. Methods: The study was conducted in a subsection of a taxi rank which is representative of the entire taxi rank. This subsection has a daily average of 400 minibus taxi moving through it and an average of 60 informal traders working in it. In the health risk assessment, a questionnaire was conducted to understand the occupational behaviour of the informal traders. This was used to deduce the exposure scenarios and sampling locations. Three sampling campaigns were run for an average of 10 hours each covering the average working hours of traders. A gas chronographer was used for collecting continues ambient air samples at 15 min intervals. Results: Over the three sampling days, the average concentrations were, 8.46ppb, 0.63 ppb, 1.27ppb and 1.0ppb for benzene, toluene, ethylbenzene, and xylene respectively. The average cancer risk is 9.46E-03. In several cases, they were incidences of unacceptable risk for the cumulative exposure of all four BTEX compounds. Conclusion: This study adds to the body of knowledge on the Human Health Risk effects of urban BTEX pollution, furthermore focusing on the impact of urban BTEX on high risk personal such as informal traders, in Southern Africa.

Keywords: human health risk assessment, informal traders, occupational risk, urban BTEX

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Authors: Yen-An Tsai, Ching-Ling Lin, Jia-Woei Hou, Mei-Lien Chen

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Endocrine-disrupting chemicals (EDCs) adversely affect the endocrine system. Phthalates, also called phthalic acid esters (PAEs), are manmade chemicals that are used as stabilizing agents in personal care products such as perfumes, lotions, and cosmetics. The aim was to explore whether PAEs exposure was associated with central precocious puberty (CPP) or constitutional delays in growth (CDGP). This case-control study included 48 female with CPP, 37 male with constitutional delays in growth, and 127 normal children and was conducted from December 2011 to August 2014. All participants completed a structured questionnaire regarding socio-demographic characteristics, lifestyle, and secondary sexual characteristics. The analytical method was based on ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with isotope dilution for the quantitative detection of several phthalate metabolites in human urine. The risk of CPP with mep, mnbp, LMW >50th percentile were higher than those with 50th percentile were higher than those with <50 percentile in model 2. In model 1, we only found higher CDGP risk in mep, mnbp, and ΣPAEs. It shows that high phthalate exposure may associate with CDGP. In this case-control study, we found PAEs exposure was associated with central precocious puberty (CPP) or constitutional delays in growth.

Keywords: phthalates, puberty, delays, growth

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Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Kalpana Rajouriya, Ajay Taneja

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The objective of the present study is to investigate the regional and lobar deposition of size-segregated PM in respiratory tract of human body. PM in different fractions is monitored using the Grimm portable environmental dust monitor during winter season in Firozabad; a Glass city of India. PM10 concentration (200.817g/m³) was 4.46 and 2.0 times higher than the limits prescribed by WHO (45g/m⁻³) and NAAQS (100g/m⁻³) government agencies. PM2.5 concentration (83.538 g/m3) was 5.56 and 1.39 times higher from WHO (15g/m-3) and NAAQS (60g/m⁻³) limits. Results inferred that PM10 and PM2.5 was highest deposited in head region (0.3477-0.5622 & 0.366-0.4704) followed by pulmonary region, especially in the 9-21year old persons. The variation in deposition percentage in our study is mainly due to the airway geometry, PM size, and its deposition mechanisms. The coarse fraction, due to its large size, cannot follow the airway path and mostly gets deposited by inertial impaction in the head region and its bifurcations. The present study results inferred that Coarse and fine PM deposition was highly visualized in 9 (8.45610⁻⁴ g, 2.91110⁻⁴g) year and 3 (1.49610⁻⁴ g, 8.59310⁻⁵g) month age category. So, the 9year children and 3month infants category have high level of health risk.

Keywords: particulate matter, MPPD model, regional deposition, lobar deposition, health risk

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Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: Abiodun Musbau Lawal, Benjamin Oladapo Olley

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Long Distance Truck Drivers (LDTDs) have been found to be a high-risk group in the spread of HIV/AIDS globally; perhaps, due to their high Sexual Risk Behaviours (SRBs). Interventions for reducing SRBs in trucking population have not been fully exploited. A quasi-experimental control group pretest-posttest design was used to assess the efficacy of psycho-education and behavioural skills training in reducing SRBs among LDTDs. Sixteen drivers rivers were randomly assigned into either experimental or control groups using balloting technique. A questionnaire was used as an instrument for data collection. Repeated measures t-test and independent t-test were used to test hypotheses. The intervention had a significant effect on the SRBs among LDTDs at post-test(t{7}=6.01, p<.01) and at followup (t{7}=6.42, p<.01). No significant difference in sexual risk behaviour of LDTDs at post-test and at follow-up stage. Similarly, intervention had significant effects on sexual risk behaviour at post-test (t {14}=- 4.69, p<.05) and at follow-up (t {14}= -9.56, p < .05) respectively. At post-test and follow-up stages, drivers in experimental group reported reduced SRBs than those in the control group. Drivers in an experimental group reported lower sexual risk behaviour a week after intervention as well as at three months follow-up than those in the control group. It is concluded that HIV/AIDS preventive intervention that provides the necessary informational and behavioural skills content can significantly impact long distance truck drivers sexual risk behaviours.

Keywords: HIV/AIDS interventions, long distance truck drivers, Nigeria, sexual risk behaviours

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Authors: Yusarina Mat-Isa, Zuraidah Mohd-Sanusi, Mohd-Nizal Haniff, Paul A. Barnes

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In view that money laundering has become eminent for banking institutions, it is an obligation for the banking institutions to adopt a risk-based approach as the integral component of the accepted policies on anti-money laundering. In doing so, those involved with the banking operations are the most critical group of personnel as these are the people who deal with the day-to-day operations of the banking institutions and are obligated to form a judgement on the level of impending risk. This requirement is extended to all relevant banking institutions staff, such as tellers and customer account representatives for them to identify suspicious customers and escalate it to the relevant authorities. Banking institutions staffs, however, face enormous challenges in identifying and distinguishing money launderers from other legitimate customers seeking genuine banking transactions. Banking institutions staffs are mostly educated and trained with the business objective in mind to serve the customers and are not trained to be “detectives with a detective’s power of observation”. Despite increasing awareness as well as trainings conducted for the banking institutions staff, their competency in assessing money laundering risk is still insufficient. Several gaps have prompted this study including the lack of behavioural perspectives in the assessment of money laundering risk in the banking institutions. Utilizing experimental approach, respondents are randomly assigned within a controlled setting with manipulated situations upon which judgement of the respondents is solicited based on various observations related to the situations. The study suggests that it is imperative that informed judgement is exercised in arriving at the decision to proceed with the banking services required by the customers. Judgement forms a basis of opinion for the banking institution staff to decide if the customers posed money laundering risk. Failure to exercise good judgement could results in losses and absorption of unnecessary risk into the banking institutions. Although the banking institutions are exposed with choices of automated solutions in assessing money laundering risk, the human factor in assessing the risk is indispensable. Individual staff in the banking institutions is the first line of defence who are responsible for screening the impending risk of any customer soliciting for banking services. At the end of the spectrum, the individual role involvement on the subject of money laundering risk assessment is not a substitute for automated solutions as human judgement is inimitable.

Keywords: banking institutions, experimental approach, money laundering, risk assessment

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Authors: Fagner O. Serrano, Danielle R. Bouchard, Todd A. Duhame

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Walking cadence (steps/min) is an effective way to prescribe exercise so an individual can reach a moderate intensity, which is recommended to optimize health benefits. To our knowledge, there is no study on the required walking cadence to reach a moderate intensity for people that present chronic conditions or risk factors for chronic conditions such as Cardiovascular Diseases (CVD). The objectives of this study were: 1- to identify the walking cadence needed for people at risk of CVD to a reach moderate intensity, and 2- to develop and test an equation using clinical variables to help professionals working with individuals at risk of CVD to estimate the walking cadence needed to reach moderate intensity. Ninety-one people presenting a minimum of two risk factors for CVD completed a medically supervised graded exercise test to assess maximum oxygen consumption at the first visit. The last visit consisted of recording walking cadence using a foot pod Garmin FR-60 and a Polar heart rate monitor, aiming to get participants to reach 40% of their maximal oxygen consumption using a portable metabolic cart on an indoor flat surface. The equation to predict the walking cadence needed to reach moderate intensity in this sample was developed as follows: The sample was randomly split in half and the equation was developed with one half of the participants, and validated using the other half. Body mass index, height, stride length, leg height, body weight, fitness level (VO2max), and self-selected cadence (over 200 meters) were measured using objective measured. Mean walking cadence to reach moderate intensity for people age 64.3 ± 10.3 years old at risk of CVD was 115.8  10.3 steps per minute. Body mass index, height, body weight, fitness level, and self-selected cadence were associated with walking cadence at moderate intensity when evaluated in bivariate analyses (r ranging from 0.22 to 0.52; all P values ≤0.05). Using linear regression analysis including all clinical variables associated in the bivariate analyses, body weight was the significant predictor of walking cadence for reaching a moderate intensity (ß=0.24; P=.018) explaining 13% of walking cadence to reach moderate intensity. The regression model created was Y = 134.4-0.24 X body weight (kg).Our findings suggest that people presenting two or more risk factors for CVD are reaching moderate intensity while walking at a cadence above the one officially recommended (116 steps per minute vs. 100 steps per minute) for healthy adults.

Keywords: cardiovascular disease, moderate intensity, older adults, walking cadence

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Authors: Chang Liang, Weizhi Gong, Yan Zhang

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Purpose: Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis worldwide. Murine double minute 2 (MDM2) regulates the tumor suppressor p53, increasing cancer risk and accelerating tumor progression. Speckle-type POX virus and zinc finger protein (SPOP), a key of subunit of Cullin-Ring E3 ligase, inhibits tumor genesis and progression by the ubiquitination of its downstream substrates. This study aimed to clarify whether SPOP and MDM2 are mutually regulated in HCC and the correlation between SPOP and MDM2 and the prognosis of HCC patients. Methods: First, the expression of SPOP and MDM2 in HCC tissues were detected by TCGA database. Then, 53 paired samples of HCC tumor and adjacent tissues were collected to evaluate the expression of SPOP and MDM2 using immunohistochemistry. Chi-square test or Fisher’s exact test were used to analyze the relationship between clinicopathological features and the expression levels of SPOP and MDM2. In addition, Kaplan‒Meier curve analysis and log-rank test were used to investigate the effects of SPOP and MDM2 on the survival of HCC patients. Last, the Multivariate Cox proportional risk regression model analyzed whether the different expression levels of SPOP and MDM2 were independent risk factors for the prognosis of HCC patients. Results: Bioinformatics analysis revealed the low expression of SPOP and high expression of MDM2 were related to worse prognosis of HCC patients. The relationship between the expression of SPOP and MDM2 and tumor stem-like features showed an opposite trend. The immunohistochemistry showed the expression of SPOP protein was significantly downregulated while MDM2 protein significantly upregulated in HCC tissue compared to that in para-cancerous tissue. Tumors with low SPOP expression were related to worse T stage and Barcelona Clinic Liver Cancer (BCLC) stage, but tumors with high MDM2 expression were related to worse T stage, M stage, and BCLC stage. Kaplan–Meier curves showed HCC patients with high SPOP expression and low MDM2 expression had better survival than those with low SPOP expression and high MDM2 expression (P < 0.05). A multivariate Cox proportional risk regression model confirmed that a high MDM2 expression level was an independent risk factor for poor prognosis in HCC patients (P <0.05). Conclusion: The expression of SPOP protein was significantly downregulated, while the expression of MDM2 significantly upregulated in HCC. The low expression of SPOP and high expression. of MDM2 were associated with malignant progression and poor prognosis of HCC patients, indicating a potential therapeutic target for HCC patients.

Keywords: hepatocellular carcinoma, murine double minute 2, speckle-type POX virus and zinc finger protein, ubiquitination

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Authors: M. H. Korayem, Sh. Ameri, N. Yousefi Lademakhi

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To ensure the stability of closed-loop nonlinear model predictive control (NMPC) within a finite horizon, there is a need for appropriate design terminal ingredients, which can be a time-consuming and challenging effort. Otherwise, in order to ensure the stability of the control system, it is necessary to consider an infinite predictive horizon. Increasing the prediction horizon increases computational demand and slows down the implementation of the method. In this study, a new technique has been proposed to ensure system stability without terminal ingredients. This technique has been employed in the design of the NMPC algorithm, leading to a reduction in the computational complexity of designing terminal ingredients and computational burden. The studied system is a wheeled mobile robot (WMR) subjected to non-holonomic constraints. Simulation has been investigated for two problems: trajectory tracking and adjustment mode.

Keywords: wheeled mobile robot, nonlinear model predictive control, stability, without terminal ingredients

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Authors: Tai-Yue Wang, Yi-Ho Chen

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In this study, we investigate a two-echelon supply chain with two suppliers and three retailers among which one retailer dominates other retailers. A price competition demand function is used to model this dominant retailer, which is leading market. The promotion strategies and negotiation schemes are integrated to form decision-making models under different scenarios. These models are then formulated into different mathematical programming models. The decision variables such as promotional costs, retailer prices, wholesale price, and order quantity are included in these models. At last, the distributions of promotion costs under different cost allocation strategies are discussed. Finally, an empirical example used to validate our models. The results from this empirical example show that the profit model will create the largest profit for the supply chain but with different profit-sharing results. At the same time, the more risk a member can take, the more profits are distributed to that member in the utility model.

Keywords: supply chain, price promotion, mathematical models, dominant retailer

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Authors: Zerarka Hizia, Akchiche Mustapha, Prunier Florent

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The landslide of Ain El Hammam (AEH) is characterized by a complex geology and a high hydrogeology hazard. AEH's perpetual reactivation compels us to look closely at its triggers and to better understand the mechanisms of its evolution in mass and in depth. This study builds a numerical model to simulate the influencing factors such as precipitation, non-saturation, and pore pressure fluctuations, using Plaxis software. For a finer analysis of instabilities, we use Hill's criterion, based on the sign of the second order work, which is the most appropriate material stability criterion for non-associated elastoplastic materials. The results of this type of calculation allow us, in theory, to predict the shape and position of the slip surface(s) which are liable to ground movements of the slope, before reaching the rupture given by the plastic limit of Mohr Coulomb. To validate the numerical model, an analysis of inclinometer measures is performed to confirm the direction of movement and kinematic of the sliding mechanism of AEH’s slope.

Keywords: landslide, second order work, precipitation, inclinometers

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Authors: Sittipong Jarernprasert, Enrique Bazan-Zurita, Paul C. Rizzo

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Currently, seismic probabilistic risk assessments (SPRA) for nuclear facilities use In-Structure Response Spectra (ISRS) in the calculation of fragilities for systems and components. ISRS are calculated via dynamic analyses of the host building subjected to two orthogonal components of horizontal ground motion. Each component is defined as the median motion in any horizontal direction. Structural engineers applied the components along selected X and Y Cartesian axes. The ISRS at different locations in the building are also calculated in the X and Y directions. The choice of the directions of X and Y are not specified by the ground motion model with respect to geographic coordinates, and are rather arbitrarily selected by the structural engineer. Normally, X and Y coincide with the “principal” axes of the building, in the understanding that this practice is generally conservative. For SPRA purposes, however, it is desirable to remove any conservatism in the estimates of median ISRS. This paper examines the effects of the direction of horizontal seismic motion on the ISRS on typical nuclear structure. We also evaluate the variability of ISRS calculated along different horizontal directions. Our results indicate that some central measures of the ISRS provide robust estimates that are practically independent of the selection of the directions of the horizontal Cartesian axes.

Keywords: seismic, directionality, in-structure response spectra, probabilistic risk assessment

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Authors: Mohsen Shirani, Shadi Asadzandi, Micaela Demichela

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This paper presents the results of a study to assess crucial aspects and the strength of the scientific basis of a typically interdisciplinary, applied field: food supply chain risk assessment research. Our approach is based on an advanced scientometrics analysis with novel elements to assess the influence and dissemination of research results and to measure interdisciplinary. This paper aims to describe the quantity and quality of the publication trends in food supply chain risk assessment. The population under study was composed of 266 articles from database web of science. The results were analyzed based on date of publication, type of document, language of the documents, source of publications, subject areas, authors and their affiliations, and the countries involved in developing the articles.

Keywords: food supply chain, risk assessment, scientometrics, web of science

Procedia PDF Downloads 484

Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

Procedia PDF Downloads 100

Authors: Rachel deCatanzaro, Madeline Austen, Ken Tuininga, Kathy St. Laurent, Christina Rohe

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In Canada, the federal Species at Risk Act (SARA) provides protection for wildlife species at risk and a national legislative framework for the conservation or recovery of species that are listed as endangered, threatened, or special concern under Schedule 1 of SARA. Key aspects of the federal species at risk program include the development of recovery documents (recovery strategies, action plans, and management plans) outlining threats, objectives, and broad strategies or measures for conservation or recovery of the species; the identification and protection of critical habitat for threatened and endangered species; and working with groups and organizations to implement on-the-ground recovery actions. Environment Canada’s progress on the development of recovery documents and on the identification and protection of critical habitat in Ontario will be presented, along with successes and challenges associated with on-the ground implementation of recovery actions. In Ontario, Environment Canada is currently involved in several recovery and monitoring programs for at-risk bird species such as the Loggerhead Shrike, Piping Plover, Golden-winged Warbler and Cerulean Warbler and has provided funding for a wide variety of recovery actions targeting priority species at risk and geographic areas each year through stewardship programs including the Habitat Stewardship Program, Aboriginal Fund for Species at Risk, and the Interdepartmental Recovery Fund. Key research needs relevant to the recovery of species at risk have been identified, and include: surveys and monitoring of population sizes and threats, population viability analyses, and addressing knowledge gaps identified for individual species (e.g., species biology and habitat needs). The engagement of all levels of government, the local and international conservation communities, and the scientific research community plays an important role in the conservation and recovery of species at risk in Ontario– through surveying and monitoring, filling knowledge gaps, conducting public outreach, and restoring, protecting, or managing habitat – and will be critical to the continued success of the federal species at risk program.

Keywords: conservation biology, habitat protection, species at risk, wildlife recovery

Procedia PDF Downloads 433

Authors: O. O. Olawusi-Peters, K. I. Adejugbagbe

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The concentration of heavy metal (Cd, Pb, Fe, Zn, Cu) in Clarias gariepinus collected from fish markets; Fanibi (Station I) and Fiwasaye (Station II) in Akure metropolis, Ondo state, Nigeria were investigated to ascertain the safety for the consumers. 60 samples were collected from the two markets in three batches (I, II, III) for a period of six months and analyzed for heavy metals in the gills and muscles of the fish. Also, the Health Risk Index (HRI) was used to determine the health risk of these metals to the consumer. The results showed that the investigated metal concentration was higher in station I than station II, except Pb having higher concentration in station II than station I. In both stations, the highest concentration of Fe was recorded in the gills (12.60 ± 1.51; 6.94 ± 1.38) and muscles (3.72 ± 0.09; 3.86 ± 0.33) of samples in batch I. Also, the HRI revealed that consumption of Clarias gariepinus from these study areas did not pose any health risk (HRI < 1). In addition, concentrations of the heavy metals were all below the permissible limits recommended by FAO/WHO.

Keywords: health risk index, heavy metals, clarias gariepinus, akure metropolis, fish monger

Procedia PDF Downloads 133

Authors: Petr Gurný

Abstract:

One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

Procedia PDF Downloads 441

Authors: Eun-Chan Kim, Jeong-Hwan Oh, Seung-Guk Lee

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Billions of tones of ballast water including various aquatic organisms are being carried around the world by ships. When the ballast water is discharged into new environments, some aquatic organisms discharged with ballast water may become invasive and severely disrupt the native ecology. Thus, International Maritime Organization (IMO) adopted the Ballast Water Management Convention in 2004. Regulation A-4 of the convention states that a government in waters under their jurisdiction may grant exemptions to any requirements to ballast water management, but only when they are granted to a ship or ships on a voyage or voyages between specified ports or locations, or to a ship which operates exclusively between specified ports or locations. In order to grant exemptions, risk assessment should be conducted based on the guidelines for risk assessment developed by the IMO. For the risk assessment, it is essential to collect the relevant information and establish a database system. This paper studies the database system for ballast water risk assessment. This database consists of the shipping database, ballast water database, port environment database and species database. The shipping database has been established based on the data collected from the port management information system of Korea Government. For the ballast water database, ballast water discharge has only been estimated by the loading/unloading of the cargoes as the convention has not come into effect yet. The port environment database and species database are being established based on the reference documents, and existing and newly collected monitoring data. This database system has been approved to be a useful system, capable of appropriately analyzing the risk assessment in the all ports of Korea.

Keywords: ballast water, IMO, risk assessment, shipping, environment, species

Procedia PDF Downloads 501

Authors: F. Boudet, V. Galano, D. Gmira, L. Munoz, A. Reina

Abstract:

Commodities pricing needs a specific approach as they are often linked to each other and so are expectedly doing their prices. They are called co-integrated when at least one stationary linear combination exists between them. Though widespread in economic literature, and even if many equilibrium relations and co-movements exist in the economy, this principle of co-movement is not developed in derivatives field. The present study focuses on the following problem: How can the price of a forward agreement on a commodity be simulated, when it is co-integrated with other ones? Theoretical analysis is developed from Gibson-Schwartz model and an analytical solution is given for short maturities contracts and under risk-neutral conditions. The application has been made to crude oil and heating oil energy commodities and result confirms the applicability of proposed method.

Keywords: co-integration, commodities, forward pricing, Gibson-Schwartz

Procedia PDF Downloads 270

Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

Abstract:

The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

Procedia PDF Downloads 104