Search results for: risk prediction model

Authors: Riffat Iqbal, Sidra Ihsan, Andleeb Batool, Maryam Mukhtar

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Glaucoma is a gathering of optic neuropathies described by dynamic degeneration of retinal ganglionic cells. It is clinically and innately heterogenous illness containing a couple of particular forms each with various causes and severities. Primary open-angle glaucoma (POAG) is the most generally perceived kind of glaucoma. This study investigated the genetic association of single nucleotide polymorphisms (SNPs; rs10483727 and rs33912345) at the SIX1/SIX6 locus with primary open-angle glaucoma (POAG) in the Pakistani population. The SIX6 gene plays an important role in ocular development and has been associated with morphology of the optic nerve. A total of 100 patients clinically diagnosed with glaucoma and 100 control individuals of age over 40 were enrolled in the study. Genomic DNA was extracted by organic extraction method. The SNP genotyping was done by (i) PCR based restriction fragment length polymorphism (RFLP) and sequencing method. Significant genetic associations were observed for rs10483727 (risk allele T) and rs33912345 (risk allele C) with POAG. Hence, it was concluded that Six6 gene is genetically associated with pathogenesis of Glaucoma in Pakistan.

Keywords: genotyping, Pakistani population, primary open-angle glaucoma, SIX6 gene

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Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Jozef Mindas, Jaroslav Skvarenina, Jana Skvareninova

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Study of the effects of climate change on Norway Spruce (Picea abies) forests has mainly focused on the diversity of tree species diversity of tree species as a result of the ability of species to tolerate temperature and moisture changes as well as some effects of disturbance regime changes. The tree species’ diversity changes in spruce forests due to climate change have been analyzed via gap model. Forest gap model is a dynamic model for calculation basic characteristics of individual forest trees. Input ecological data for model calculations have been taken from the permanent research plots located in primeval forests in mountainous regions in Slovakia. The results of regional scenarios of the climatic change for the territory of Slovakia have been used, from which the values are according to the CGCM3.1 (global) model, KNMI and MPI (regional) models. Model results for conditions of the climate change scenarios suggest a shift of the upper forest limit to the region of the present subalpine zone, in supramontane zone. N. spruce representation will decrease at the expense of beech and precious broadleaved species (Acer sp., Sorbus sp., Fraxinus sp.). The most significant tree species diversity changes have been identified for the upper tree line and current belt of dwarf pine (Pinus mugo) occurrence. The results have been also discussed in relation to most important disturbances (wind storms, snow and ice storms) and phenological changes which consequences are little known. Special discussion is focused on biomass production changes in relation to carbon storage diversity in different carbon pools.

Keywords: biodiversity, climate change, Norway spruce forests, gap model

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Authors: Amirhossein Chambari

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This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

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Authors: Kwok W. Mui, Ling T. Wong, Chi T. Cheung

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Cooling energy consumption in the residential sector, different from shopping mall, office or commercial buildings, is significantly subject to occupant decisions where in-depth investigations are found limited. It shows that energy consumptions could be associated with housing types. Surveys have been conducted in existing Hong Kong public housings to understand the housing characteristics, apartment electricity demands, occupant’s thermal expectations, and air–conditioning usage patterns for further cooling energy-saving assessments. The aim of this study is to develop a hybrid cooling energy prediction model, which integrated by EnergyPlus (EP) and artificial neural network (ANN) to estimate cooling energy consumption in public residential sector. Sensitivity tests are conducted to find out the energy impacts with changing building parameters regarding to external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control respectively. Assessments are performed to investigate the relationships between cooling demands and occupant behavior on thermal environment criteria and air-conditioning operation patterns. The results are summarized into a cooling energy calculator for layman use to enhance the cooling energy saving awareness in their own living environment. The findings can be used as a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioral air–conditioning operation and criteria of energy-saving incentives.

Keywords: artificial neural network, cooling energy, occupant behavior, residential buildings, thermal environment

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Authors: Shubham Dwivedi, Raghavender Medishetti, Rita Rani, Aarti Sevilimedu, Pushkar Kulkarni, Yogeeswari Perumal

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder of early onset, characterized by impaired sociability, cognitive function and stereotypies. There is a significant urge to develop and establish new animal models with ASD-like characteristics for better understanding of underlying mechanisms. The aim of the present study was to develop a cost and time effective zebrafish model with quantifiable parameters to facilitate mechanistic studies as well as high-throughput screening of new molecules for autism. Zebrafish embryos were treated with valproic acid and a battery of behavioral tests (anxiety, inattentive behavior, irritability and social impairment) was performed on larvae at 7th day post fertilization, followed by study of molecular markers of autism. This model shows a significant behavioural impairment in valproic acid treated larvae in comparison to control which was again supported by alteration in few marker genes and proteins of autism. The model also shows a rescue of behavioural despair with positive control drugs. The model shows robust parameters to study behavior, molecular mechanism and drug screening approach in a single frame. Thus we postulate that our 7 days zebrafish larval model for autism can help in high throughput screening of new molecules on autism.

Keywords: autism, zebrafish, valproic acid, neurodevelopment, behavioral assay

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Authors: Huda Al Shuaily, Karen Renaud

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Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: pattern, SQL, learning, model

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Authors: C. Surmei

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Autism Spectrum Disorder (ASD) is a complex developmental disorder that can affect an individual’s (but is not limited to) cognitive development, emotional development, language acquisition and the capability to relate to others. Ecological Systems Theory is a sociocultural theory that focuses on environmental systems with which an individual interacts. The SCERTS Model is an educational approach and multidisciplinary framework that addresses the challenges confronted by individuals on the autism spectrum and other developmental disabilities. To aid the understanding of ASD and educational philosophies for families, educators, and the global community alike, a Comparative Analysis was undertaken to examine key variables (the child, society, education, nurture/care, relationships, communication). The results indicated that the Ecological Systems Theory and the SCERTS Model were comparable in focus, motivation, and application, attaining to a viable and notable relationship between both theories. This paper unpacks two child development philosophies and their relationship to each other.

Keywords: autism spectrum disorder, ecological systems theory, education, SCERTS model

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Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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Authors: Winnie Kung, Xinhua Liu, Debbie Huang, Patricia Kim, Keon Kim, Xiaoran Wang, Lawrence Yang

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Considerable Asian Americans were exposed to the World Trade Center attack due to the proximity of the site to Chinatown and a sizeable number of South Asians working in the collapsed and damaged buildings nearby. Few studies focused on Asians in examining the disaster’s mental health impact, and even less longitudinal studies were reported beyond the first couple of years after the event. Based on the World Trade Center Health Registry, this study examined the trajectory of PTSD of individuals directly exposed to the attack from 2-3 to 5-6 years after the attack, comparing Asians against the non-Hispanic White group. Participants included 2,431 Asians and 31,455 Whites. Trajectories were delineated into the resilient, chronic, delayed-onset and remitted groups using PTSD checklist cut-off score at 44 at the 2 waves. Logistic regression analyses were conducted to compare the poorer trajectories against the resilient as a reference group, using predictors of baseline sociodemographic, exposure to the disaster, lower respiratory symptoms and previous depression/anxiety disorder diagnosis, and recruitment source as the control variable. Asians had significant lower socioeconomic status in terms of income, education and employment status compared to Whites. Over 3/4 of participants from both races were resilient, though slightly less for Asians than Whites (76.5% vs 79.8%). Asians had a higher proportion with chronic PTSD (8.6% vs 7.4%) and remission (5.9% vs 3.4%) than Whites. A considerable proportion of participants had delayed-onset in both races (9.1% Asians vs 9.4% Whites). The distribution of trajectories differed significantly by race (p<0.0001) with Asians faring poorer. For Asians, in the chronic vs resilient group, significant protective factors included age >65, annual household income >$50,000, and never married vs married/cohabiting; risk factors were direct disaster exposure, job loss due to 9/11, lost someone, and tangible loss; lower respiratory symptoms and previous mental disorder diagnoses. Similar protective and risk factors were noted for the delayed-onset group, except education being protective; and being an immigrant a risk. Between the 2 comparisons, the chronic group was more vulnerable than the delayed-onset as expected. It should also be noted that in both comparisons, Asians’ current employment status had no significant impact on their PTSD trajectory. Comparing between Asians against Whites, the direction of the relationships between the predictors and the PTSD trajectories were mostly the same, although more factors were significant for Whites than for Asians. A few factors showed significant racial difference: Higher risk for lower respiratory symptoms for Whites than Asians, higher risk for pre-9/11 mental disorder diagnosis for Asians than Whites, and immigrant a risk factor for the remitted vs resilient groups for Whites but not for Asians. Over 17% Asians still suffered from PTSD 5-6 years after the WTC attack signified its persistent impact which incurred substantial human, social and economic costs. The more disadvantaged socioeconomic status of Asians rendered them more vulnerable in their mental health trajectories relative to Whites. Together with their well-documented low tendency to seek mental health help, outreach effort to this population is needed to ensure follow-up treatment and prevention.

Keywords: PTSD, Asian Americans, World Trade Center Attack, racial differences

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Authors: Prasansha Kumari

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This paper intends to identify and analyze customer adoption and attitudes towards mobile banking facilities. The study uses six perceived characteristics of innovation that can be used to form a favorable or unfavorable attitude toward an innovation, namely: Relative advantage, compatibility, complexity, trailability, risk, and observability. Collected data were analyzed using Pearson Chi-Square test. The results showed that mobile bank users were predominantly males. There is a growing trend among young, educated customers towards converting to mobile banking in Sri Lanka. The research outcomes suggested that all the six factors are statistically highly significant in influencing mobile banking adoption and attitude formation towards mobile banking in Sri Lanka. The major reasons for adopting mobile banking services are the accessibility and availability of services regardless of time and place. Over the 75 percent of the respondents mentioned that savings in time and effort and low financial costs of conducting mobile banking were advantageous. Issue of security was found to be the most important factor that motivated consumer adoption and attitude formation towards mobile banking. Main barriers to mobile banking were the lack of technological skills, the traditional cash‐carry banking culture, and the lack of awareness and insufficient guidance to using mobile banking.

Keywords: compatibility, complexity, mobile banking, observability, risk

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Authors: Irfan Ali, Srikant Gupta, Aquil Ahmed

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Every country needs a sustainable development (SD) for its economic growth by forming suitable policies and initiative programs for the development of different sectors of the country. This paper is comprised of modeling and optimization of different sectors of India that form a multi-criterion model. In this paper, we developed a fractional goal programming (FGP) model that helps in providing the efficient allocation of resources simultaneously by achieving the sustainable goals in gross domestic product (GDP), electricity consumption (EC) and greenhouse gasses (GHG) emission by the year 2030. Also, a weighted model of FGP is presented to obtain varying solution according to the priorities set by the policy maker for achieving future goals of GDP growth, EC, and GHG emission. The presented models provide a useful insight to the decision makers for implementing strategies in a different sector.

Keywords: sustainable and economic development, multi-objective fractional programming, fuzzy goal programming, weighted fuzzy goal programming

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Authors: Saba Arif, Anam Nadeem, Roman Kalvin, Tanzeel Rashid, Burhan Ali, Juntakan Taweekun

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Currently, the energy crisis is taking serious attention. Globally, industries and building are major share takers of energy. 72% of total global energy is consumed by residential houses, markets, and commercial building. Additionally, in appliances air conditioners are major consumer of electricity; about 60% energy is used for cooling purpose in houses due to HVAC units. Energy demand will aid in determining what changes will be needed whether it is the estimation of the required energy for households or instituting conservation measures. Bottom-up model is one of the most famous methods for forecasting. In current research bottom-up model of air conditioners' energy consumption in all income classes in comparison with seasonal variation and hourly consumption is calculated. By comparison of energy consumption of all income classes by usage of air conditioners, total consumption of actual demand and current availability can be seen.

Keywords: air conditioning, bottom up model, income classes, energy demand

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Dheepshika Kodieswaran

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The persisting problem in the world that continuously impose our planet at risk is the increasing amounts of recalcitrant. One such issue is the disposal of the Petroleum Refinery Sludge (PRS) which constitutes hydrocarbons that are hazardous to terrestrial and aquatic life. The comparatively safe approach to handling these wastes is by microbial degradation, while the other chemical and physical methods are either expensive and/or produce secondary pollutants. The bacterial and algal systems have different pathways for the degradation of hydrocarbons, and their growth rates vary. This study shows how different bacterial and microalgal strains degrade the polyaromatic hydrocarbon PAHs individually and their symbiotic influence on degradation as well. In this system, the metabolites and gaseous exchange help each other in growth. This method using also aids in the accumulation of lipids in microalgal cells and from which bio-oils can also be extracted. The bacterial strains used in this experiment are reported to be indigenous strains isolated from PRS. The target PAH studied were anthracene and pyrene for a period of 28 days. The PAH degradation kinetics best fitted the Gompertz model, and the order of the kinetics, rate constants, and half-life was determined.

Keywords: petroleum refinery sludge, co-culturing, polycyclic hydrocarbons, microalgal-bacterial consortia

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Authors: Zhihui Liu, Dongmei Hao, Qian Qiu, Yang An, Lin Yang, Song Zhang, Yimin Yang, Xuwen Li, Dingchang Zheng

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Premature birth, defined as birth before 37 completed weeks of gestation is a leading cause of neonatal morbidity and mortality and has long-term adverse consequences for health. It has recently been reported that the worldwide preterm birth rate is around 10%. The existing measurement techniques for diagnosing preterm delivery include tocodynamometer, ultrasound and fetal fibronectin. However, they are subjective, or suffer from high measurement variability and inaccurate diagnosis and prediction of preterm labor. Electrohysterography (EHG) method based on recording of uterine electrical activity by electrodes attached to maternal abdomen, is a promising method to assess uterine activity and diagnose preterm labor. The purpose of this study is to analyze the difference of EHG signal features between term labor and preterm labor. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Among them, EHG signals from 38 term labor and 38 preterm labor were preprocessed with band-pass Butterworth filters of 0.08–4Hz. Then, EHG signal features were extracted, which comprised classical time domain description including root mean square and zero-crossing number, spectral parameters including peak frequency, mean frequency and median frequency, wavelet packet coefficients, autoregression (AR) model coefficients, and nonlinear measures including maximal Lyapunov exponent, sample entropy and correlation dimension. Their statistical significance for recognition of two groups of recordings was provided. The results showed that mean frequency of preterm labor was significantly smaller than term labor (p < 0.05). 5 coefficients of AR model showed significant difference between term labor and preterm labor. The maximal Lyapunov exponent of early preterm (time of recording < the 26th week of gestation) was significantly smaller than early term. The sample entropy of late preterm (time of recording > the 26th week of gestation) was significantly smaller than late term. There was no significant difference for other features between the term labor and preterm labor groups. Any future work regarding classification should therefore focus on using multiple techniques, with the mean frequency, AR coefficients, maximal Lyapunov exponent and the sample entropy being among the prime candidates. Even if these methods are not yet useful for clinical practice, they do bring the most promising indicators for the preterm labor.

Keywords: electrohysterogram, feature, preterm labor, term labor

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Authors: Manouchehr Amiri

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This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.

Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle

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Authors: Elsheikh Asser

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Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multi-objective model for project scheduling considering different scenarios such as least cost, least time, and target time.

Keywords: genetic algorithms, time-cost trade-off, multi-objective model, project scheduling

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Authors: Babak H. Tabrizi, Seyed Farid Ghaderi

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Concurrent planning of the resource constraint project scheduling and material ordering problems have received significant attention within the last decades. Hence, the issue has been investigated here with the aim to minimize total project costs. Furthermore, the presented model considers different discount options in order to approach the real world conditions. The incorporated alternatives consist of all-unit and incremental discount strategies. On the other hand, a modified version of the genetic algorithm is applied in order to solve the model for larger sizes, in particular. Finally, the applicability and efficiency of the given model is tested by different numerical instances.

Keywords: genetic algorithm, material ordering, project management, project scheduling

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Authors: Ignatius Madu

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The study aims at assessing the environmental and socioeconomic determinants of climate change resilience in rural Nigeria. This is necessary because researches and development efforts on building climate change resilience of rural areas in developing countries are usually made without the knowledge of the impacts of the inherent rural characteristics that determine resilient capacities of the households. This has, in many cases, led to costly mistakes, delayed responses, inaccurate outcomes, and other difficulties. Consequently, this assessment becomes crucial not only to policymakers and people living in risk-prone environments in rural areas but also to fill the research gap. To achieve the aim, secondary data were obtained from the Annual Abstract of Statistics 2017, LSMS-Integrated Surveys on Agriculture and General Household Survey Panel 2015/2016, and National Agriculture Sample Survey (NASS), 2010/2011.Resilience was calculated by weighting and adding the adaptive, absorptive and anticipatory measures of households variables aggregated at state levels and then regressed against rural environmental and socioeconomic characteristics influencing it. From the regression, the coefficients of the variables were used to compute the impacts of the variables using the Stochastic Regression of Impacts on Population, Affluence and Technology (STIRPAT) Model. The results showed that the northern States are generally low in resilient indices and are impacted less by the development indicators. The major determining factors are percentage of non-poor, environmental protection, road transport development, landholding, agricultural input, population density, dependency ratio (inverse), household asserts, education and maternal care. The paper concludes that any effort to a successful resilient building in rural areas of the country should first address these key factors that enhance rural development and wellbeing since it is better to take action before shocks take place.

Keywords: climate change resilience; spatial impacts; STIRPAT model; Nigeria

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Authors: Juliza Hidayati, Yesie M. Sinuhaji, Sawarni Hasibuan

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A company producing drinking water through many incompatibility issues that affect supply chain performance. The study was conducted to determine the factors that affect the performance of the supply chain and improve it. To obtain the dominant factors affecting the performance of the supply chain used Analytic Network Process, while to improve performance is done by using Kaizen. Factors affecting the performance of the supply chain to be a reference to identify the cause of the non-conformance. Results weighting using ANP indicates that the dominant factor affecting the level of performance is the precision of the number of shipments (15%), the ability of the fulfillment of the booking amount (12%), and the number of rejected products when signing (12%). Incompatibility of the factors that affect the performance of the supply chain are identified, so that found the root cause of the problem is most dominant. Based on the weight of Risk Priority Number (RPN) gained the most dominant root cause of the problem, namely the poorly maintained engine, the engine worked for three shifts, machine parts that are not contained in the plant. Improvements then performed using the Kaizen method of systematic and sustainable.

Keywords: analytic network process, booking amount, risk priority number, supply chain performance

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Authors: Mindaye Teshome, Evaldo Muñoz Braz, Carlos M. M. Eleto Torres, Patricia Mattos

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Sustainable forest management planning requires up-to-date information on the structure, standing volume, biomass, and growth rate of trees from a given forest. This kind of information is lacking in many forests in Ethiopia. The objective of this study was to quantify the population structure, diameter growth rate, and standing volume of wood from Juniperus procera trees in the Chilimo forest. A total of 163 sample plots were set up in the forest to collect the relevant vegetation data. Growth ring measurements were conducted on stem disc samples collected from 12 J. procera trees. Diameter and height measurements were recorded from a total of 1399 individual trees with dbh ≥ 2 cm. The growth rate, maximum current and mean annual increments, minimum logging diameter, and cutting cycle were estimated, and alternative cutting cycles were established. Using these data, the harvestable volume of wood was projected by alternating four minimum logging diameters and five cutting cycles following the stand table projection method. The results show that J. procera trees have an average density of 183 stems ha⁻¹, a total basal area of 12.1 m² ha⁻¹, and a standing volume of 98.9 m³ ha⁻¹. The mean annual diameter growth ranges between 0.50 and 0.65 cm year⁻¹ with an overall mean of 0.59 cm year⁻¹. The population of J. procera tree followed a reverse J-shape diameter distribution pattern. The maximum current annual increment in volume (CAI) occurred at around 49 years when trees reached 30 cm in diameter. Trees showed the maximum mean annual increment in volume (MAI) around 91 years, with a diameter size of 50 cm. The simulation analysis revealed that 40 cm MLD and a 15-year cutting cycle are the best minimum logging diameter and cutting cycle. This combination showed the largest harvestable volume of wood potential, volume increments, and a 35% recovery of the initially harvested volume. It is concluded that the forest is well stocked and has a large amount of harvestable volume of wood from J. procera trees. This will enable the country to partly meet the national wood demand through domestic wood production. The use of the current population structure and diameter growth data from tree ring analysis enables the exact prediction of the harvestable volume of wood. The developed model supplied an idea about the productivity of the J. procera tree population and enables policymakers to develop specific management criteria for wood harvesting.

Keywords: logging, growth model, cutting cycle, minimum logging diameter

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Authors: Rodrigo T. Sepulveda-Hirose, Ana B. Carrera-Aguilar, Francisco Andree Ramirez-Casas, Alondra Orozco-Gomez, Miguel Angel Sanchez-Caro, Carlos Herrera-Ventosa

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High concentrations of particulate matter in the atmosphere pose a threat to human health, especially over areas with high concentrations of population; however, field air pollution monitoring is expensive and time-consuming. In order to achieve reduced costs and global coverage of the whole urban area, remote sensing can be used. This study evaluates PM2.5 concentrations, over the Mexico City´s metropolitan area, are estimated using atmospheric reflectance from LANDSAT 8, satellite imagery and historical PM2.5 measurements of the Automatic Environmental Monitoring Network of Mexico City (RAMA). Through the processing of the available satellite images, a preliminary model was generated to evaluate the optimal bands for the generation of the final model for Mexico City. Work on the final model continues with the results of the preliminary model. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated.

Keywords: air pollution modeling, Landsat 8, PM2.5, remote sensing

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: Olivier Mesly

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This article examines the likelihood of deception on the part of borrowers wishing to obtain credit from institutional or private lenders. In our first study, we identify five explanatory variables that account for nearly forty percent of the propensity to act deceitfully: a poor credit history, debt, risky behavior, and to a much lesser degree, irrational behavior and disconnection from the bundle of needs, goals, and preferences. For the second study, we remodeled the initial questionnaire to adapt it to the needs of institutional bankers and borrowers, especially those that engage in money on-line peer-to-peer lending, a growing business fueled by the COVID pandemic. We find that the three key psychological variables that help to indirectly predict the likelihood of deceitful behaviors and possible default on loan reimbursement, i.e., risky behaviors, ir-rationality, and dis-connection, interact with each other to form a loop. This study presents two benefits: first, we provide evidence that it is to some degree possible to tighten control over lending practices. Second, we offer a pragmatic tool: a questionnaire, that lenders can use or adapt to gauge potential borrowers’ deceit, notably by combining their results with standard hard-data measures of risk.

Keywords: bundle of needs, default, debt, deception, risk, peer-to-peer lending

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Authors: Alia Abdul Ghaffar, Tom Richardson

Abstract:

A model reference adaptive control and a fixed gain LQR control were implemented in the height controller of a quadrotor that has parametric uncertainties due to the act of picking up an object of unknown dimension and mass. It is shown that an adaptive control, unlike a fixed gain control, is capable of ensuring a stable tracking performance under such condition, although adaptive control suffers from several limitations. The combination of both adaptive and fixed gain control in the controller architecture results in an enhanced tracking performance in the presence of parametric uncertainties.

Keywords: UAV, quadrotor, robotic arm augmentation, model reference adaptive control, LQR control

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Authors: Riyadh Alsultani, Ali Majdi

Abstract:

It is necessary to develop a design methodology that includes the possibility of seismic events occurring in a region, the vulnerability of the civil hydraulic structure, and the effects of the occurrence hazard on society, environment, and economy in order to evaluate the flood and earthquake risks of coastal bridge networks. This paper presents a design approach for the assessment of the risk and sustainability of coastal bridge networks under time-variant flood-earthquake conditions. The social, environmental, and economic indicators of the network are used to measure its sustainability. These consist of anticipated loss, downtime, energy waste, and carbon dioxide emissions. The design process takes into account the possibility of happening in a set of flood and earthquake scenarios that represent the local seismic activity. Based on the performance of each bridge as determined by fragility assessments, network linkages are measured. The network's connections and bridges' damage statuses after an earthquake scenario determine the network's sustainability and danger. The sustainability measures' temporal volatility and the danger of structural degradation are both highlighted. The method is shown using a transportation network in Baghdad, Iraq.

Keywords: sustainability, Coastal bridge networks, flood-earthquake risk, structural design

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Authors: Manish Gupta

Abstract:

Following the E-Commerce era, M-Commerce is the next big phase in the technology involvement and advancement. This paper intends to explore how Indian consumers are influenced to adopt the M-commerce. In this paper, the revised Technology Acceptance Model (TAM) has been presented on the basis of the most dominant factors that affect the adoption of M-Commerce in Indian scenario. Furthermore, an analytical questionnaire approach was carried out to collect data from Indian consumers. These collected data were further used for the validation of the presented model. Findings indicate that customization, convenience, instant connectivity, compatibility, security, download speed in M-Commerce affect the adoption behavior. Furthermore, the findings suggest that perceived usefulness and attitude towards M-Commerce are positively influenced by number of M-Commerce drivers (i.e. download speed, compatibility, convenience, security, customization, connectivity, and input mechanism).

Keywords: M-Commerce, perceived usefulness, technology acceptance model, perceived ease of use

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Authors: Jae Woong Sull, Sun Ha Jee

Abstract:

High-density lipoprotein (HDL) cholesterol levels are associated with decreased risk of coronary artery disease. Several genome-wide association studies (GWAS) for HDL cholesterol levels have implicated cholesterol ester transfer protein (CETP) as possibly causal. We tested for the association between single nucleotide polymorphisms (SNPs) in CETP gene and HDL cholesterol levels in Korean population. Subjects were selected from the Korean Metabolic Syndrome Research Initiative study in the Bundang-Gu area. A total of 2,304 individuals from Bundang-Gu were recruited in 2008. Other subjects were selected from the Severance Hospital (N=4,294). SNP rs6499861 in the CETP gene was associated with mean HDL cholesterol levels (effect per allele -2.044 mg/dL, p=7.23×10-7). Subjects with the CG/GG genotype had a 1.46 -fold (range 1.24–1.72-fold) higher risk of having abnormal HDL cholesterol levels (<40 mg/dL) than subjects with the CC genotype. When analyzed by gender, the association of CETP was stronger in women than in men. When analyzed by physical activity behavior, the association with CETP was much stronger in male subjects with low physical activity (OR=1.54, 95% CI: 1.23-1.92, P=0.0001) than in male subjects with high physical activity. This study clearly demonstrates that genetic variants in CETP influence HDL cholesterol levels in Korean adults.

Keywords: CETP, HDL cholesterol, physical activity, polymorphisms

Procedia PDF Downloads 277