Search results for: squared prediction risk

Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Imtiaz Ali, Imam Mansoor

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Investigations as a part of the academic study were undertaken to identify and evaluate the significant risks associated with the construction of K-span buildings in the region of UAE. Primary field data was collected through questionnaires obtaining specific open and close-ended questions from carefully selected construction firms, civil engineers and, construction manager regarding risks associated to K-span building construction. Historical data available for other regions of the same construction technique was available which was compared for identifying various non-critical and critical risk parameters by comparative evaluation techniques to come up with important risks and potential sources for their control and minimization in K-Span buildings that is increasing in the region. The associated risks have been determined with their Relative Importance Index (RII) values of which Risk involved in Change of Design required by Owners carries the highest value (RII=0.79) whereas, Delayed Payment by Owner to Contractor is one of the least (RII=0.42) value. The overall findings suggest that most relative risks as quantified originate or associated with the contractors. It may be concluded that project proponents undertaking K-span projects in planning and budgeting the cost and delays should take into account of risks on high account if changes in design are also required any delays in the material by the supplier would then be a major risk in K-span project delay. Since projects are, less costly, so owners have limited budgets, then they hire small contractors, which are not highly competent contractors. So study suggests that owner should be aware of these types of risks associated with the construction of K-span buildings in order to make it cost effective.

Keywords: k-span buildings, k-span construction, risk management, relative improvement index (RII)

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Authors: Saleh Alzahrani, Halim Boussabaine

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The Public Private Partnership (PPP) contracts are produced taking into account the reason that the configuration, development, operation, and financing of an open undertaking is to be recompensed to a private gathering inside a solitary contractual structure. PPP venture dangers are ordinarily connected with the improvement and development of another resource and in addition its operation for a considerable length of time. Without a doubt, the most genuine outcomes of dangers amid the development period are value and time overwhelms. These occasions are amongst the most extensively utilized situations as a part of worth for cash investigation dangers. The wellsprings of danger change over the life cycle of a PPP venture. In customary acquirement, the general population segment ordinarily needs to cover all value trouble from these dangers. At any rate there is bounty confirmation to recommend that cost pain is a standard in a percentage of the tasks that are conveyed under customary obtainment. This paper means to research the effect of endogenous dangers on expense overwhelm in KSA PPP ventures. The paper displays a brief writing survey on PPP danger evaluating systems, and after that presents an affiliation model between danger occasions and expense invade in KSA. The paper finishes up with considerations for future examination.

Keywords: PPP, risk pricing, impact of risk, Endogenous risks

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Authors: Ndou Rembuluwani Moddy, Daniel Ter Goon, Takalani Grace Tshitangano, Lindelani Fhumudzani Mushaphi

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Obesity is a global public health problem. The study aimed to determine the risk factors associated with and the consequences of obesity among residents of Tshikota, Makhado Municipality, Limpopo Province, South Africa. A cross-sectional study involving 318 randomly selected adults aged 18-45 years residing at Tshikota, Makhado Local Municipality, South Africa. Sociodemographic information includes age, gender, educational level, occupation, behavioral lifestyle, environmental, psychological, and family history. Anthropometric, blood pressure, and blood glucose measurements followed standard procedure. The prevalence of obesity and overweight was 35.5% and 28.6%, respectively. About 75.2% of obese do not engage in physical activity. Most participants (63.5%) take meals three times a day, and 19.2% do not skip breakfast. Most participants do not have access to fruits and vegetables. Participants who were pre-hypertensive were 92(28.9%) and 32(10.1%) were in Stage 1 hypertension. Of the participants with Class 1 obesity, 40.9% were pre-hypertensive, and 15.2% were in Stage 1 hypertension. In Class 2 obesity, 37.8% were pre-hypertensive, and 26.7% were in Stage 1 hypertension. There was a significant difference between BMI and blood pressure among participants (p=0.00). About 6.1% of the participants in Class 1 obesity were at high risk, and 3.0% were at very high risk of glucose levels. Regarding cholesterol levels, 65 (20.4%) were at borderline, and 17(5.3%) were at high risk. There was no significant difference in BMI and cholesterol levels among participants (p= 0.20). The prevalence of obesity and overweight was high among residents of this setting. Age, marital and educational status, and employment were significantly associated with obesity. An obesity awareness campaign is crucial, and the availability of supermarkets and full-service grocery stores would provide accessibility to healthy food such as fruits and vegetables.

Keywords: obesity, overweight, risk factors, adults.

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Peifang Hsieh

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New Taipei city faces increasing number of migrant families, in which the needs of children are sometimes neglected due to insufficient support from communities. Moreover, the traditional mindset of disengagement discourages citizens from preemptively identifying families in need in their communities, resulting in delay of prompt intervention from authorities concerned. To safeguard these vulnerable families, New Taipei city develops the 'Integrated Safety-Net Program for High-Risk Families' from 2011 by implementing the following measures: (A) New attitude and action: Instead of passively receiving reported case of high-risk families, the program takes proactive and preemptive approach to detect and respond at early stage, so the cases are prevented from worsening. In addition, cross-departmental integration mechanism is established to meet multiple needs of high-risk families. The children number added to the government care network is greatly increased to over 10,000, which is around 4.4 times the original number before the program. (B) New service points: 2000 city-wide convenience stores are added as service stations so that children in less privileged families can go to any of 24-hour convenience stores across the city to pick up free meals. This greatly increases the approachability to high-risk families. Moreover, the social welfare institutes will be notified with information left in convenience stores by children and follow up with further assistance, greatly enhancing chances of less privileged families being identified. (C) New Key Figures: Mobilize community officers and volunteers to detect and offer on-site assistance. Volunteer organizations within communities are connected to report and offer follow-up services in a more active manner. In total, from 2011 to 2015, 54,789 cases are identified through active care, benefiting 82,124 children. In addition, 87.49% family-cases in the program receiving comprehensive social assistance are no longer at high risk.

Keywords: cross department, high-risk families, public-private partnership, integrated safety net

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Authors: Rui Miguel Costa, Jose Pestana, David Costa, Paula Mangia, Catarina Correia, Mafalda Pinto Coelho

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There is lack of studies examining the relation of different heart rate variability (HRV) parameters with the risk of erectile dysfunction (ED) in younger men. Thus, the present study aimed at examining, in a nonclinical sample of men aged 19-39 years old (mean age = 23.98 years, SD = 4.90), the relations of risk of ED with the standard deviation of the heart rate (SD of HR), high and low frequency power of HRV, and low-to-high frequency HRV ratio. Eighty-three heterosexual Portuguese men completed the 5-item version of the International Index of Erectile Function (IIEF-5) and HRV parameters were calculated from a 5-minute resting period. Risk of ED was determined by IIEF-5 scores of 21 or less. Fifteen men (18.1%) reported symptoms of ED (14 with mild and one with mild to moderate symptoms). Univariate analyses of variance revealed that risk of ED was related to lesser SD of HR and lesser low-frequency power, the two HRV parameters that express a coupling of higher vagal and sympathetic tone. Risk of ED was unrelated to high-frequency power and low-to-high frequency HRV ratio. Further, in a logistic regression, the risk of ED was independently predicted by older age and lower SD of HR, but not by low-frequency power, having a regular sexual partner, and cohabiting. The results provide preliminary evidence that, in younger men, a coupling of higher vagal and sympathetic tone, as indexed by the SD of HR, is important for erections. Greater resting SD of HR might reflect better vascular and interpersonal function via vagal tone coupled with greater motor mobilization to pursue sexual intercourse via sympathetic tone. Many interventions can elevate HRV; future research is warranted on how they can be tailored to treat ED in younger men.

Keywords: erectile dysfunction, heart rate variability, standard deviation of the heart rate, younger men

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Authors: Hadi Molaei, Yasavoli Davoud, Keshavarz, Mozhde Poordana

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The present study aimed was to prediction the academic performance based on academic motivation, self-efficacy and Resiliency in the students. The present study was descriptive and correlational. Population of the study consisted of all students in Arak schools in year 1393-94. For this purpose, the number of 304 schools students in Arak was selected using multi-stage cluster sampling. They all questionnaires, self-efficacy, Resiliency and academic motivation Questionnaire completed. Data were analyzed using Pearson correlation and multiple regressions. Pearson correlation showed academic motivation, self-efficacy, and Resiliency with academic performance had a positive and significant relationship. In addition, multiple regression analysis showed that the academic motivation, self-efficacy and Resiliency were predicted academic performance. Based on the findings could be conclude that in order to increase the academic performance and further progress of students must provide the ground to strengthen academic motivation, self-efficacy and Resiliency act on them.

Keywords: academic motivation, self-efficacy, resiliency, academic performance

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Authors: Alon Cohen, Jeffrey Kantor, Shalom Levy

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Technology adoption models are extensively used in the literature to explore drivers and inhibitors affecting the adoption of Computer Assisted Audit Techniques and Tools (CAATTs). Further studies from recent years suggested additional factors that may affect technology adoption by CPA firms. However, the adoption of CAATTs by financial auditors differs from the adoption of technologies in other industries. This is a result of the unique characteristics of the auditing process, which are expressed in the audit risk elements and the risk-based auditing approach, as encoded in the auditing standards. Since these audit risk factors are not part of the existing models that are used to explain technology adoption, these models do not fully correspond to the specific needs and requirements of the auditing domain. The overarching objective of this qualitative research is to fill the gap in the literature, which exists as a result of using generic technology adoption models. Followed by a pretest and based on semi-structured in-depth interviews with 16 Israeli CPA firms of different sizes, this study aims to reveal determinants related to audit risk factors that influence the adoption of CAATTs in audits and proposes a new modeling approach for the successful adoption of CAATTs. The findings emphasize several important aspects: (1) while large CPA firms developed their own inner guidelines to assess the audit risk components, other CPA firms do not follow a formal and validated methodology to evaluate these risks; (2) large firms incorporate a variety of CAATTs, including self-developed advanced tools. On the other hand, small and mid-sized CPA firms incorporate standard CAATTs and still need to catch up to better understand what CAATTs can offer and how they can contribute to the quality of the audit; (3) the top management of mid-sized and small CPA firms should be more proactive and updated about CAATTs capabilities and contributions to audits; and (4) All CPA firms consider professionalism as a major challenge that must be constantly managed to ensure an optimal CAATTs operation. The study extends the existing knowledge of CAATTs adoption by looking at it from a risk-based auditing approach. It suggests a new model for CAATTs adoption by incorporating influencing audit risk factors that auditors should examine when considering CAATTs adoption. Since the model can be used in various audited scenarios and supports strategic, risk-based decisions, it maximizes the great potential of CAATTs on the quality of the audits. The results and insights can be useful to CPA firms, internal auditors, CAATTs developers and regulators. Moreover, it may motivate audit standard-setters to issue updated guidelines regarding CAATTs adoption in audits.

Keywords: audit risk, CAATTs, financial auditing, information technology, technology adoption models

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Authors: Ajay Kumar Rana

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Predicting human behaviour, discerning monozygotic twins or left over remnant tissues/fluids of a single human source remains a big challenge in forensic science. Recent advances in the field of DNA methylations which are broadly chemical hallmarks in response to environmental factors can certainly help to identify and discriminate various single-source DNA samples collected from the crime scenes. In this review, cytosine methylation of DNA has been methodologically discussed with its broad applications in many challenging forensic issues like body fluid identification, race/ethnicity identification, monozygotic twins dilemma, addiction or behavioural prediction, age prediction, or even authenticity of the human DNA. With the advent of next-generation sequencing techniques, blooming of DNA methylation datasets and together with standard molecular protocols, the prospect of investigating and solving the above issues and extracting the exact nature of the truth for reconstructing the crime scene events would be undoubtedly helpful in defending and solving the critical crime cases.

Keywords: DNA methylation, differentially methylated regions, human identification, forensics

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Authors: Olukayode Bamgbose, Feyisola Agboola, Adewale M. Taiwo, Olanrewaju Olujimi Oluwole Terebo, Azeez Soyingbe, Akeem Bamgbade

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The present study evaluated the contents and health risk assessment of metals determined in milk and milk product samples collected from the Abeokuta market. Forty-five milk and milk product (yoghurt) samples were digested and analysed for selected metals using Atomic Absorption Spectrophotometric method. Health risk assessment was evaluated for hazard quotient (HQ), hazard index (HI), and cancer risk (CR). Data were subjected to descriptive and inferential statistics. The concentrations of Zn, which ranged from 3.24±0.59 to 4.35±0.59 mg/kg, were the highest in the samples. Cr and Cd were measured below the detection limit of the analytical instrument, while the Pb level was higher than the Codex Alimentarius Commission value of 0.02 mg/kg, indicating unsafe for consumption. However, the HQ of Pb and other metals in milk and milk product samples was less than 1.0, thereby establishing no adverse health effects for Pb and other metals. The distribution pattern of metals in milk and milk product samples followed the decreasing order of Zn > Fe > Ni > Co > Cu > Mn > Pb > Cd/Cr. The CR levels of meals were also less than the permissible limit of 1.0 x 10-4, establishing no possible development of cancer. Keywords: adverse effects, cancer, metals, milk, milk product, the permissible limit.

Keywords: adverse effects, cancer, metals, milk, milk product, permissible limit

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Authors: Yawen Xia, Rubi Yang, Jing Zhao

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We examine the environmental, social, and governance (ESG) decision-making of companies during turbulent times like COVID-19. We find that firms’ ESG reputation risk comove with their industry and local peers, suggesting that managers may follow their industry and local counterparts in engaging in irresponsible activities. Moreover, the comovement in reputation risk is attenuated during the COVID-19 pandemic. Further analyses suggest that the reduction in comovement varies by state-level partisanship and death rate during the pandemic. Comovement in reputation risk declines more significantly in the Democratic states with stringent social distancing policies and in states with higher infection severity. Our findings suggest that social distancing provisions during COVID-19 may lead to a reduction in social activities and information sharing among enterprise managers.

Keywords: ESG, Covid, peer pressure, local comovement, orporate governance

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: Aliseydi Bozkurt

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Objectives: Benign prostatic hyperplasia (BPH) is the most common benign disease, and prostate cancer (PC) is malign disease of the prostate gland. Transrectal ultrasound-guided biopsy (TRUS-bx) is one of the most important diagnostic tools in PC diagnosis. Identifying men at increased risk for having a biopsy detectable prostate cancer should consider prostate specific antigen density (PSAD), f/t PSA Ratio, an estimate of prostate volume. Method: We retrospectively studied 269 patients who had a prostate specific antigen (PSA) score of 4 or who had suspected rectal examination at any PSA level and received TRUS-bx between January 2015 and June 2018 in our clinic. TRUS-bx was received by 12 experienced urologists with 12 quadrants. Prostate volume was calculated prior to biopsy together with TRUS. Patients were classified as malignant and benign at the end of pathology. Age, PSA value, prostate volume in transrectal ultrasonography, corpuscle biopsy, biopsy pathology result, the number of cancer core and Gleason score were evaluated in the study. The success rates of PV, PSAD, and f/tPSA were compared in all patients and those with PSA 2.5-10 ng/mL and 10.1-30 ng/mL tp foresee prostate cancer. Result: In the present study, in patients with PSA 2.5-10 ng/ml, PV cut-off value was 43,5 mL (n=42 < 43,5 mL and n=102 > 43,5 mL) while in those with PSA 10.1-30 ng/mL prostate volüme (PV) cut-off value was found 61,5 mL (n=31 < 61,5 mL and n=36 > 61,5 mL). Total PSA values in the group with PSA 2.5-10 ng/ml were found lower (6.0 ± 1.3 vs 6.7 ± 1.7) than that with PV < 43,5 mL, this value was nearly significant (p=0,043). In the group with PSA value 10.1-30 ng/mL, no significant difference was found (p=0,117) in terms of total PSA values between the group with PV < 61,5 mL and that with PV > 61,5 mL. In the group with PSA 2.5-10 ng/ml, in patients with PV < 43,5 mL, f/t PSA value was found significantly lower compared to the group with PV > 43,5 mL (0.21 ± 0.09 vs 0.26 ± 0.09 p < 0.001 ). Similarly, in the group with PSA value of 10.1-30 ng/mL, f/t PSA value was found significantly lower in patients with PV < 61,5 mL (0.16 ± 0.08 vs 0.23 ± 0.10 p=0,003). In the group with PSA 2.5-10 ng/ml, PSAD value in patients with PV < 43,5 mL was found significantly higher compared to those with PV > 43,5 mL (0.17 ± 0.06 vs 0.10 ± 0.03 p < 0.001). Similarly, in the group with PSA value 10.1-30 ng/mL PSAD value was found significantly higher in patients with PV < 61,5 mL (0.47 ± 0.23 vs 0.17 ± 0.08 p < 0.001 ). The biopsy results suggest that in the group with PSA 2.5-10 ng/ml, in 29 of the patients with PV < 43,5 mL (69%) cancer was detected while in 13 patients (31%) no cancer was detected. While in 19 patients with PV > 43,5 mL (18,6%) cancer was found, in 83 patients (81,4%) no cancer was detected (p < 0.001). In the group with PSA value 10.1-30 ng/mL, in 21 patients with PV < 61,5 mL (67.7%) cancer was observed while only in10 patients (32.3%) no cancer was seen. In 5 patients with PV > 61,5 mL (13.9%) cancer was found while in 31 patients (86.1%) no cancer was observed (p < 0.001). Conclusions: Identifying men at increased risk for having a biopsy detectable prostate cancer should consider PSA, f/t PSA Ratio, an estimate of prostate volume. Prostate volume in PC was found lower.

Keywords: prostate cancer, prostate volume, prostate specific antigen, free/total PSA ratio

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Authors: Ratri A. Benedictus, Felicia M. Yolanda

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Public transport is a crucial issue for capital city in developing country, such as Jakarta. Inadequate number and low quality of public transport services resulting personal vehicles as the main option. As a result, traffic jams are getting worse in Jakarta. The low quality of public transport, particularly buses, compounded by the risk behavior of the driver. Traffic accidents involving public bus in Jakarta were often the case, even result in fatality. The purpose of this study is to get a description of risk behavior among the public bus drivers in Jakarta. 132 bus drivers become respondent of this study. Risky Driving Behavior scale of Dorn were used. Data were analyzed using descriptive statistics. 51.5% of respondents felt often showing risky behavior while on driving. The highest type of risky driving behavior is still using the unsafe bus (62%). Followed by trespass the bus line (30%), over speed (21%), violate the road signs (15%) and driving with unhealthy physical condition (4%). Results of this study suggested that high understanding of the bus drivers on their risk behaviors have not lead to the emergence of safe driving behavior. Therefore, together with technical engineering and instrumentation work intervention over this issue, psychological aspects also need to be considered, such as: risk perception, safety attitude,safety culture, locus of control and Fatalism.

Keywords: bus driver, psychological factors, public transportation, risky driving behavior

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Authors: Osayomwanbor E. Oghama, John O. Olomukoro

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The school environment, apart from home, is probably the most important indoor environment for children. Children spend as much as 80-90% of their indoor time either at school or at home; an average of 35 - 40 hours per week in schools, hence are at the risk of indoor air pollutants such as volatile organic compounds (VOCs). Concentrations of VOCs vary widely but are generally higher indoors than outdoors. This research was, therefore, carried out to evaluate the levels of VOCs in secondary school classrooms in Benin City, Edo State. Samples were obtained from a total of 18 classrooms in 6 secondary schools. Samples were collected 3 times from each school and from 3 different classrooms in each school using Draeger ORSA 5 tubes. Samplers were left to stay for a school-week (5 days). The VOCs detected and analyzed were benzene, ethlybenzene, isopropylbenzene, naphthalene, n-butylbenzene, n-propylbenzene, toluene, m-xylene, p-xylene, o-xylene, styrene, chlorobenzene, chloroform, 1,2-dichloropropane, 2,2-dichloropropane, tetrachloroethane, tetrahydrofuran, isopropyl acetate, α-pinene, and camphene. The results showed that chloroform, o-xylene, and styrene were the most abundant while α-pinene and camphene were the least abundant. The health risk assessment was done in terms of carcinogenic (CRI) and non-carcinogenic risks (THR). The CRI values of the schools ranged from 1.03 × 10-5 to 1.36 × 10-5 μg/m³ (a mean of 1.16 × 10-5 μg/m³) with School 6 and School 3 having the highest and lowest values respectively. The THR values of the study schools ranged from 0.071-0.086 μg/m³ (a mean of 0.078 μg/m³) with School 3 and School 2 having the highest and lowest values respectively. The results show that all the schools pose a potential carcinogenic risks having CRI values greater than the recommended limit of 1 × 10-6 µg/m³ and no non-carcinogenic risk having THR values less than the USEPA hazard quotient of 1 µg/m³. It is recommended that school authorities should ensure adequate ventilation in their schools, supplementing natural ventilation with mechanical sources, where necessary. In addition, indoor air quality should be taken into consideration in the design and construction of classrooms.

Keywords: carcinogenic risk indicator, health risk, indoor air, non-carcinogenic risk indicator, secondary schools, volatile organic compounds

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Authors: Mohammad H. Yarmohammadian, Masoud Ferdosi, Abbas Haghshenas, Fatemeh Rezaei

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Surgical procedures aim at preserving human life and improving quality of their life. However, there are many potential risk sources that can cause serious harm to patients. For centuries, managers believed that technical competence of a surgeon is the only key to a successful surgery. But over the past decade, risks are considered in terms of process-based safety procedures, teamwork and inter departmental communication. Aims: This study aims to determine how the process- biased surgical risk management should be done in terms of project management tool named ABS (Activity Breakdown Structure). Settings and Design: This study was conducted in two stages. First, literature review and meeting with professors was done to determine principles and framework of surgical risk management. Next, responsible teams for surgical patient journey were involved in following meeting to develop the process- biased surgical risk management. Methods and Material: This study is a qualitative research in which focus groups with the inductive approach is used. Sampling was performed to achieve representativeness through intensity sampling biased on experience and seniority. Analysis Method used: context analysis of interviews and consensus themes extracted from FDG meetings discussion was the analysis tool. Results: we developed the patient journey process in 5 main phases, 24 activities and 108 tasks. Then, responsible teams, transposition and allocated places for performing determined. Some activities and tasks themes were repeated in each phases like patient identification and records review because of their importance. Conclusions: Risk management of surgical departments is significant as this facility is the hospital’s largest cost and revenue center. Good communication between surgical team and other clinical teams outside surgery department through process- biased perspective could improve safety of patient under this procedure.

Keywords: risk management, activity breakdown structure (ABS), surgical department, medical sciences

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Authors: Ezgi Nevruz, Kasirga Yildirak, Ashis SenGupta

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Comparing or ranking risks is the main motivating factor behind the human trait of making choices. Cumulative prospect theory (CPT) is a preference theory approach that evaluates perception and bias in decision making under risk and uncertainty. We aim to investigate the aggregate claims of different risk classes in terms of their comparability and amenability to ordering when the impact of risk perception is considered. For this aim, we prioritize the aggregate claims taken as actuarial risks by using various stochastic ordering relations. In order to prioritize actuarial risks, we use stochastic relations such as stochastic dominance and stop-loss dominance that are proposed in the frame of partial order theory. We take into account the dependency of the individual claims exposed to similar environmental risks. At first, we modify the zero-utility premium principle in order to obtain a solution for the stop-loss premium under CPT. Then, we propose a stochastic stop-loss dominance of the aggregate claims and find a relation between the stop-loss dominance and the first-order stochastic dominance under the dependence assumption by using properties of the familiar as well as some emerging multivariate claim distributions.

Keywords: cumulative prospect theory, partial order theory, risk perception, stochastic dominance, stop-loss dominance

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Authors: Sarah Müller-Sägebrecht

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Today’s volatile environment challenges executives to make the right strategic decisions to gain sustainable success. Entrepreneurship scholars postulate mainly positive effects of environmental changes on entrepreneurship behavior, such as developing new business opportunities, promoting ingenuity, and the satisfaction of resource voids. A strategic solution approach to overcome threatening environmental changes and catch new business opportunities is business model innovation (BMI). Although this research stream has gained further importance in the last decade, BMI research is still insufficient. Especially BMI barriers, such as inefficient strategic decision-making processes, need to be identified. Strategic decisions strongly impact organizational future and are, therefore, usually made in groups. Although groups draw on a more extensive information base than single individuals, group-interaction effects can influence the decision-making process - in a favorable but also unfavorable way. Decisions are characterized by uncertainty and risk, whereby their intensity is perceived individually differently. The individual risk-willingness influences which option humans choose. The special nature of strategic decisions, such as in BMI processes, is that these decisions are not made individually but in groups due to their high organizational scope. These groups consist of different personalities whose individual risk-willingness can vary considerably. It is known from group decision theory that these individuals influence each other, observable in different group-interaction effects. The following research questions arise: i) How does group interaction shape BMI decision-making from managers’ perspective? ii) What are the potential interrelations among managers’ risk-willingness, group biases, and BMI decision-making? After conducting 26 in-depth interviews with executives from the manufacturing industry, applied Gioia methodology reveals the following results: i) Risk-averse decision-makers have an increased need to be guided by facts. The more information available to them, the lower they perceive uncertainty and the more willing they are to pursue a specific decision option. However, the results also show that social interaction does not change the individual risk-willingness in the decision-making process. ii) Generally, it could be observed that during BMI decisions, group interaction is primarily beneficial to increase the group’s information base for making good decisions, less than for social interaction. Further, decision-makers mainly focus on information available to all decision-makers in the team but less on personal knowledge. This work contributes to strategic decision-making literature twofold. First, it gives insights into how group-interaction effects influence an organization’s strategic BMI decision-making. Second, it enriches risk-management research by highlighting how individual risk-willingness impacts organizational strategic decision-making. To date, it was known in BMI research that risk aversion would be an internal BMI barrier. However, with this study, it becomes clear that it is not risk aversion that inhibits BMI. Instead, the lack of information prevents risk-averse decision-makers from choosing a riskier option. Simultaneously, results show that risk-averse decision-makers are not easily carried away by the higher risk-willingness of their team members. Instead, they use social interaction to gather missing information. Therefore, executives need to provide sufficient information to all decision-makers to catch promising business opportunities.

Keywords: business model innovation, cognitive biases, group-interaction effects, strategic decision-making, risk-willingness

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Authors: Minjuan Sun

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Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by the business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.

Keywords: credit score, digital footprint, Fintech, machine learning

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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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: Siqi Qiu, Yijian Zheng, Xin Guo Ming

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In many reliability and risk analysis, failures of components are supposed to be independent. However, in reality, the ignorance of failure dependencies among components may render the results of reliability and risk analysis incorrect. There are two principal ways to incorporate failure dependencies in system reliability and risk analysis: implicit and explicit methods. In the implicit method, failure dependencies can be modeled by joint probabilities, correlation values or conditional probabilities. In the explicit method, certain types of dependencies can be modeled in a fault tree as mutually independent basic events for specific component failures. In this paper, explicit and implicit methods based on BDD will be proposed to evaluate the reliability of systems considering failure dependencies. The obtained results prove the equivalence of the proposed implicit and explicit methods. It is found that the consideration of failure dependencies decreases the reliability of systems. This observation is intuitive, because more components fail due to failure dependencies. The consideration of failure dependencies helps designers to reduce the dependencies between components during the design phase to make the system more reliable.

Keywords: reliability assessment, risk assessment, failure dependencies, binary decision diagram

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Authors: Moudi Almousa

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This research paper explores Saudi Arabian female consumers’ experiences using 3D body scanning technology and their level of acceptance of possible market applications of this technology to adopt for apparel online shopping. Data was collected for 82 women after being scanned then viewed a short video explaining three possible scenarios of 3D body scanning applications, which include size prediction, customization, and virtual try-on, before completing the survey questionnaire. Although respondents have strong positive responses towards the scanning experience, the majority were concerned about their privacy during the scanning process. The results indicated that size prediction and virtual try on had greater market application potential and a higher chance of crossing the gap based on consumer interest. The results of the study also indicated a strong positive correlation between respondents’ concern with inability to try on apparel products in online environments and their willingness to use the 3D possible market applications.

Keywords: 3D body scanning, market applications, online, apparel fit

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Authors: Samuel Carpintero

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Public-private partnerships (PPP) arrangements have emerged all around the world as a response to infrastructure deficits and the need to refurbish existing infrastructure. The motivations of governments for embarking on PPPs for the delivery of public infrastructure are manifold, and include on-time and on-budget delivery as well as access to private project management expertise. The PPP formula has been used by some State governments in United States and Canada, where the participation of private companies in financing and managing infrastructure projects has increased significantly in the last decade, particularly in the transport sector. On the one hand, this paper examines the various ways used in these two countries in the implementation of PPP arrangements, with a particular focus on risk transfer. The examination of risk transfer in this paper is carried out with reference to the following key PPP risk categories: construction risk, revenue risk, operating risk and availability risk. The main difference between both countries is that in Canada the demand risk remains usually within the public sector whereas in the United States this risk is usually transferred to the private concessionaire. The aim is to explore which lessons can be learnt from both models than might be useful for other countries. On the other hand, the paper also analyzes why the Spanish companies have been so successful in winning PPP contracts in North America during the past decade. Contrary to the Latin American PPP market, the Spanish companies do not have any cultural advantage in the case of the United States and Canada. Arguably, some relevant reasons for the success of the Spanish groups are their extensive experience in PPP projects (that dates back to the late 1960s in some cases), their high technical level (that allows them to be aggressive in their bids), and their good position and track-record in the financial markets. The article’s empirical base consists of data provided by official sources of both countries as well as information collected through face-to-face interviews with public and private representatives of the stakeholders participating in some of the PPP schemes. Interviewees include private project managers of the concessionaires, representatives of banks involved as financiers in the projects, and experts in the PPP industry with close knowledge of the North American market. Unstructured in-depth interviews have been adopted as a means of investigation for this study because of its powers to achieve honest and robust responses and to ensure realism in the collection of an overall impression of stakeholders’ perspectives.

Keywords: PPP, concession, infrastructure, construction

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Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

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Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

Procedia PDF Downloads 55