Search results for: distributed type-2 fuzzy algorithm

Authors: R. Vigneswaran, S. Thilakanathan

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Variable time-stepping algorithms for solving dynamical systems performed poorly for long time computations which pass close to a fixed point. To overcome this difficulty, several authors considered phase space error controls for numerical simulation of dynamical systems. In one generalized phase space error control, a step-size selection scheme was proposed, which allows this error control to be incorporated into the standard adaptive algorithm as an extra constraint at negligible extra computational cost. For this generalized error control, it was already analyzed the forward Euler method applied to the linear system whose coefficient matrix has real and negative eigenvalues. In this paper, this result was extended to the linear system whose coefficient matrix has complex eigenvalues with negative real parts. Some theoretical results were obtained and numerical experiments were carried out to support the theoretical results.

Keywords: adaptivity, fixed point, long time simulations, stability, linear system

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Authors: Hussaini Doko Ibrahim, Hamilton Cyprian Chinwenyi, Henrietta Nkem Ude

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In this paper, efforts were made to examine and compare the algorithmic iterative solutions of the conjugate gradient method as against other methods such as Gauss-Seidel and Jacobi approaches for solving systems of linear equations of the form Ax=b, where A is a real n×n symmetric and positive definite matrix. We performed algorithmic iterative steps and obtained analytical solutions of a typical 3×3 symmetric and positive definite matrix using the three methods described in this paper (Gauss-Seidel, Jacobi, and conjugate gradient methods), respectively. From the results obtained, we discovered that the conjugate gradient method converges faster to exact solutions in fewer iterative steps than the two other methods, which took many iterations, much time, and kept tending to the exact solutions.

Keywords: conjugate gradient, linear equations, symmetric and positive definite matrix, gauss-seidel, Jacobi, algorithm

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Hurgesa Hundera, Samuel Shibeshibikeko, Tarike Daba, Tesfaye Ganamo

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The study aimed at examining the ongoing adaptation strategies used by smallholder farmers in response to climate variability in Boset district. It also assessed the socioeconomic factors that influence the choice of adaptation strategies of smallholder farmers to climate variability risk. For attaining the objectives of the study, both primary and secondary sources of data were employed. The primary data were obtained through a household questionnaire, key informant interviews, focus group discussions, and observations, while secondary data were acquired through desk review. Questionnaires were distributed and filled by 328 respondents, and they were identified through systematic random sampling technique. Descriptive statistics and binary logistic regression model were applied in this study as the main analytical methods. The findings of the study reveal that the sample households have utilized multiple adaptation strategies in response to climate variability, such as cropping early mature crops, planting drought resistant crops, growing mixed crops on the same farm lands, and others. The results of the binary logistic model revealed that education, sex, age, family size, off farm income, farm experience, access to climate information, access to farm input, and farm size were significant and key factors determining farmers’ choice of adaptation strategies to climate variability in the study area. To enable effective adaptation measures, Ministry of Agriculture and Natural Resource, with its regional bureaus and offices and concerned non–governmental organizations, should consider climate variability in their planning and budgeting in all levels of decision making.

Keywords: adaptation strategies, boset district, climate variability, smallholder farmers

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Authors: Siddique Ullah Baig, Alisha Manzoor

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The remotest, mountainous, and icy Broghil Valley is a high-profile protected area as a national park, which hosts one of the highest altitude permanent human settlements on the earth. This park hosts a distributed but diverse range of habitats. Due to a lack of infrastructures, higher altitudes, and harsh environmental conditions, poverty-stricken inhabitants mostly rely on its resources, causing ecological dis-balance. This study aims to investigate the environmental degradation of natural resources of the park based on empirical evidence from stakeholders and geoinformatics. The result shows that one-fourth of the park is a gently undulating basin dotted with water bodies / grass, and agricultural land and three fourth is entirely rugged with steep mountains and glaciers. There are virtually no forests as the arid cold tundra climate and high altitude prevent tree growth. Rapid three-decadal land cover changes have led to ecological disequilibrium of the park, narrowing the traditional diverse food base, decreasing the resilience of biodiversity and local livelihoods as crop-land has shifted towards fallow, alpine-grass to peat-land and snow/glacial ice area to bare-soil/rocks. The local community believes in exploiting whatever vegetation or organic material is available for use as food, fodder, and fuel. The permanent presence of the community and limited cost-effective options in the park will be a challenge forever to maintain undisturbed natural processes as the objective of a national park.

Keywords: Broghil National Park, natural resources, environmental degradation, land cover

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: A. Nuchitprasittichai, N. Lerdritsirikoon, T. Khamsing

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Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO₂ liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments.

Keywords: central composite design, CO2 liquefaction, latin hypercube sampling, simulation-based optimization

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Authors: Yu-Jen Chang, Yun Chen, Hei-Lam Wong

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The Economic Lot Scheduling Problem (ELSP) is a valuable mathematical model that can support decision-makers to make scheduling decisions. The basic period approach is effective for solving the ELSP. The assumption for applying the basic period approach is that a product must use its maximum production rate to be produced. However, a product can lower its production rate to reduce the average total cost when a facility has extra idle time. The past researches discussed how a product adjusts its production rate under the common cycle approach. To the best of our knowledge, no studies have addressed how a product lowers its production rate under the basic period approach. This research is the first paper to discuss this topic. The research develops a simple fixed rate approach that adjusts the production rate of a product under the basic period approach to solve the ELSP. Our numerical example shows our approach can find a better solution than the traditional basic period approach. Our mathematical model that applies the fixed rate approach under the basic period approach can serve as a reference for other related researches.

Keywords: economic lot, basic period, genetic algorithm, fixed rate

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Authors: S. J. Higgs, E. Van Eck, K. Heynis, S. H. Broadberry

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Engagement with non-human animals is a rapidly-growing field of study within the animal science and social science sectors, with human-interactions occurring in many forms; interactions, encounters and animal-assisted therapy. To our knowledge, there has been a wide array of research published on domestic and livestock human-animal interactions, however, there appear to be fewer publications relating to zoo animals and the effect these interactions have on the animal, human and establishment. The aim of this study was to identify if there were any perceivable benefits from the human-animal interaction for the cheetah, the human and the establishment. Behaviour data were collected before, during and after the interaction on the behaviour of the cheetah and the human participants to highlight any trends with nine interactions conducted. All 35 participants were asked to fill in a questionnaire prior to the interaction and immediately after to ascertain if their perceptions changed following an interaction with the cheetah. An online questionnaire was also distributed for three months to gain an understanding of the perceptions of human-animal interactions from members of the public, gaining 229 responses. Both questionnaires contained qualitative and quantitative questions to allow for specific definitive answers to be analysed, but also expansion on the participants perceived perception of human-animal interactions. In conclusion, it was found that participants’ perceptions of human-animal interactions saw a positive change, with 64% of participants altering their opinion and viewing the interaction as beneficial for the cheetah (reduction in stress assumed behaviours) following participation in a 15-minute interaction. However, it was noted that many participants felt the interaction lacked educational values and therefore this is an area in which zoological establishments can work to further improve upon. The results highlighted many positive benefits for the human, animal and establishment, however, the study does indicate further areas for research in order to promote positive perceptions of human-animal interactions and to further increase the welfare of the animal during these interactions, with recommendations to create and regulate legislation.

Keywords: Acinonyx jubatus, encounters, human-animal interactions, perceptions, zoological establishments

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Authors: Nermeen Elbeltagy, Sara Hassan, Tamer Hosny, Mostafa Abdelaziz

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Introduction: Adequate postoperative analgesia after caesarean section (CS) is crucial as it impacts the distinct surgical recovery needs of the parturient. Over recent years, there has been increased interest in regional nerve block techniques with promising results on efficacy. These techniques reduce the need for additional analgesia, thereby lowering the incidence of drug-related side effects. As postoperative pain after cesarean is mainly due to abdominal incision, the transverses abdomenis plane ( TAP ) block is a relatively new abdominal nerve block with excellent efficacy after different abdominal surgeries, including cesarean section. Objective: The main objective is to compare ultrasound-guided TAP block provided by the anesthesiologist with TAP provided by the surgeon through a caesarean incision regarding the duration of postoperative analgesia, intensity of analgesia, timing of mobilization, and easiness of the procedure. Method: Ninety pregnant females at term who were scheduled for delivery by elective cesarean section were randomly distributed into two groups. The first group (45) received spinal anesthesia and postoperative ultrasound guided TAP block using 20ml on each side of 0.25% bupivacaine which was provided by the anesthesiologist. The second group (45) received spinal anesthesia plus a TAP block using 20ml on each side of 0.25% bupivacaine, which was provided by the surgeon through the cesarean incision. Visual Analogue Scale (VAS) was used for the comparison between the two groups. Results: VAS score after four hours was higher among the TAP block group provided by the surgeon through the surgical incision than the postoperative analgesic profile using ultrasound-guided TAP block provided by the anesthesiologist (P=0.011). On the contrary, there was no statistical difference in the patient’s dose of analgesia after four hours of the TAP block (P=0.228). Conclusion: TAP block provided through the surgical incision is safe and enhances early patient’s mobilization.

Keywords: TAP block, CS, VAS, analgesia

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Authors: Jeaha Yang, Ahmed Khan, Donna L. Rodela, Mohammed A. Qaudeer

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Traditional supply chain silos still exist today due to the difficulty of establishing trust between various partners and technological barriers across industries. Companies lose opportunities and revenue and inadvertently make poor business decisions resulting in further challenges. Blockchain technology can bring a new level of transparency through sharing information with a distributed ledger in a decentralized manner that creates a basis of trust for business. Blockchain is a loosely coupled, hub-style communication network in which trading partners can work indirectly with each other for simpler integration, but they work together through the orchestration of their supply chain operations under a coherent process that is developed jointly. A Blockchain increases efficiencies, lowers costs, and improves interoperability to strengthen and automate the supply chain management process while all partners share the risk. Blockchain ledger is built to track inventory lifecycle for supply chain transparency and keeps a journal of inventory movement for real-time reconciliation. State design patterns are used to capture the life cycle (behavior) of inventory management as a state machine for a common, transparent and coherent process which creates an opportunity for trading partners to become more responsive in terms of changes or improvements in process, reconcile discrepancies, and comply with internal governance and external regulations. It enables end-to-end, inter-company visibility at the unit level for more accurate demand planning with better insight into order fulfillment and replenishment.

Keywords: supply chain management, inventory trace-ability, perpetual inventory system, inventory lifecycle, blockchain, inventory consignment, supply chain transparency, digital thread, demand planning, hyper ledger fabric

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: G. Kaushik, A. Gupta, S. Lalwani, K. D. Soni, S. Kumar, S. Sagar

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Objective: Alcohol is a leading risk factor associated with the disability and death due to RTI. Present study aims to demonstrate the demographic profile, injury pattern, physiological parameters of victims of trauma following alcohol consumption arriving in the emergency department (ED) and mortality in alcohol intoxicated trauma patients admitted to Apex Trauma Center in Delhi. Design and Methods: Present study was performed in randomly selected 182 alcohol breath analyzer tested RTI patients from the emergency department of Jai Prakash Narayan Apex Trauma Center (JPNATC), All India Institute of Medical Sciences, New Delhi for over a period of 3 months started from September 2013 to November 2013. Results: A total 182 RTI patients with blunt injury were selected between 30-40 years of age and equally distributed to male and female group. Of these, 93 (51%) were alcohol negative and 89 (49%) were alcohol positive. In 89 alcohol positive patients, 47 (53%) had Artificial Airway as compared to 17 (18%), (p < 0.001) in the other group. The Glasgow Coma Scale (GCS) score was lower (p < 0.001) and higher Injury Severity Score (ISS) was observed in alcohol positive group as compared to other group (p < 0.03). Increased number of patients (58%) were admitted to Intensive Care Unit (ICU), in alcohol positive group (p < 0.001) and they were in ICU for longer time compare to other group (p < 0.001). The alcohol positive patients were on ventilator support for longer duration as compared to non-alcoholic group (p < 0.001). Mortality rate was higher in alcohol intoxicated patients as compared to non-alcoholic RTI patients, however, the difference was not statistically significant. Conclusion: This study revealed that GCS, mean ISS, ICU stay, ventilation time etc. might have considerable impact on mortality in alcohol intoxicated patients as compared to non-alcoholic group.

Keywords: road traffic injuries, alcohol, trauma, emergency department

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Authors: Olga Plaza, Katarzyna Kosinska-Kaczynska, Stepan Feduniw, Dominika Pazdzior, Kinga Zebrowska, Katarzyna Kwiatkowska

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Introduction: The main causes of depression among pregnant women remain unclear. However, it is clear that pregnancy carries a higher risk of depression occurrence. Left untreated, prenatal depression can be a cause of serious both maternal and neonatal complications. Aim of the study: The aim of the study was to define potential risk factors of prenatal depression and to assess the frequency of its occurrence among pregnant women. Material and Methods: A prospective cross-sectional study was performed among 346 women. The self- composed questionnaire consisting of 46 questions, was distributed via the Internet between November 2017 and March 2018. The questionnaire contained the Edinburgh Postnatal Depression Scale (EPDS), in which the results of 13 and more points (out of 30) suggested possible prenatal depression. Statistical analysis was performed with Chi2 Pearson. P value < 0.05 was considered significant. Results: 37.57% (n=130) of women had a score of 13 or more points. Women with depressive symptoms (DS) reported lack of support from the partner (46.9% vs. 16.2%; p < 0.001) as well as other family members (40.8% vs. 14.4%; p < 0.001), current pregnancy being unplanned (21.5% vs. 12.5%; p=0.014) and low socio-economic status (10% vs. 0.9%; p < 0.001). Both early and advanced maternal age seemed to play a role in occurrence of DS: in women aged 17-24 40.8% declared symptoms (vs 28.7%; p < 0.01), in mothers aged ≥37 6.2% did (vs 0.5%; p < 0.001). Smoking during pregnancy was also more frequent among patients with DS (31.5% vs. 18.1%; p=0.004). Previous diagnosis of depression or other mood disorders significantly increased a chance of DS occurrence (respectively- 17.7% vs. 4.6%; p < 0.001 and 49.2% vs. 25%; p<0.001). Parental diagnosis of mood disorders and other mental disorders was also more frequent in this group of patients (respectively- 24.6% vs. 15.7%; p= 0.026 and 26.4% vs. 9.7%; p < 0.001). Only 23.8% of women with DS sought help from healthcare professionals, with 21.5% receiving pharmacological treatment. Conclusions: Pregnant women often report having DS. Evaluation of risk factors of DS and possible prenatal depression is essential in proper screening for depression among pregnant women.

Keywords: obstetrics, polish women, prenatal care, prenatal depression, risk factors

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Authors: Wegene Demeke

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Background: The demand for healthcare services is growing in developing and developed countries. Information and communication technology is used to facilitate healthcare services. In the case of developing countries, implementing telemedicine is aimed at providing healthcare for people living in remote areas where health service is not accessible. The implementations of telemedicine in developing countries are unsuccessful. For example, the recent study indicates that 90% of telemedicine projects are abandoned or failed in developing countries. Several researchers reported the technological challenges as the main factor for the non-adoption of telemedicine. However, this research reports the health professionals’ perspectives arising from technical, social and organizational factors that are considered as key elements for the setting and running of telemedicine in Ethiopia. The importance and significance of telemedicine for healthcare is growing. For example, the use of telemedicine in the current pandemic situation becomes an essential strategic element in providing healthcare services in developed countries. Method: Qualitative and quantitative exploratory research methods used to collect data to find factors affecting the adoption of Information and communication technologies for telemedicine use. The survey was distributed using emails and Google forms. The email addresses were collected from personal contact and publicly available websites in Ethiopia. The thematic analysis used to build the barriers and facilitators factors for establishing telemedicine services. A survey questionnaire with open-and-close questions was used to collect data from 175 health professionals. Outcome: The result of this research will contribute to building the key barriers and facilitators factors of telemedicine from the health professional perspectives in developing countries. The thematic analysis provides barriers and facilitators factors arising from technical, organizational, and social sources.

Keywords: telemedicine, ICT, developing country, Ethiopia, health service

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Authors: Farah Fazlinda Binti Mohamad, Ahmad Saifizul Abdullah, Mohamed Rehan Karim , Jamilah Mohamad, Siti Hikmah Musthar

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Road safety is an important issue in Malaysia. It become important as it is discussed widely throughout printed and electronic media. Most of the news portrays on road accident and fatalities have increased the concern of everyone. This issue affects everyone's life as everyone shares the roads. The most vulnerable victims are the road user who uses the roads every day. It is appalling when World Health Organization (WHO) reported that in every 100,000 of population in Malaysia, 23 fatalities recorded due to road accident alone. This figure is quite alarming and requires serious attention. Furthermore, research by Malaysian Institute of Road Safety Research concluded that that speeding has contributed to 60% of all road accident in the country. Therefore, this study aims to elucidate the factors that influence road users’ speeding behaviour on selected roads in Peninsular Malaysia. To achieve this, set of questionnaires has distributed to 500 respondents on selected roads in Peninsular Malaysia. The respondents came from various demographic backgrounds in order to have a fair opinion on the issue. Using descriptive analysis, the results have indicated that psychological factors such as emotion and attitude of road user are the prominent factors that influence the road user’s speeding behaviour. Furthermore, the results have shown that male road users were dominant in speeding compared to female, which led to increased vulnerability to road injuries and fatalities. These findings are very useful in order for us to understand road users’ driving behaviour. Relevant authorities should also revise the existing countermeasures and find ways to reduce road accident. Engineers and road experts could cooperate in designing new road specifications for the road user. Nevertheless, it is important to comprehend this speeding issue and factors associated with it. Each road user should take this matter seriously and responsibly as road safety is a responsibility of all.

Keywords: countermeasures, psychological, road safety, speeding

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Authors: Fildzah Badzlina, Mega Puspa Sari

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To rest the body after a full day of activities, the body needs sleep. During sleep, the body's response to external stimuli will be reduced and relatively inactive so that it is used to optimize the body's biological functions that cannot be done when awake. College students often experience poor sleep quality because of the dense activities carried out during the day. In addition, the level of physical activity of college students is also relatively low. Based on previous research, college students who have low physical activity have poor sleep quality. Therefore, the purpose of this study was to determine the relationship between physical activity and sleep quality in college students of the University of Muhammadiyah Prof. Dr. Hamka. This study used a cross-sectional research design with 107 respondents as research subjects. Samples were taken using the purposive sampling technique. The data was taken using a google form which was distributed to all college students in September 2021. The statistical test used was Chi-square. The results of this study showed that 85 (79.4%) college students experienced poor sleep quality during the Covid-19 Pandemic Period. Most respondents were 96 women (89.7%) and 32.7% (35 people) aged 20 years. In the pocket money category, most college students (71%) got pocket money less than 500.000 rupiahs per month. A total of 52 respondents (48.6%) had a moderate level of physical activity category. Poor sleep quality was more common in male students (90.9%) compared to female students (78.1%) (p>0.05). In the group with poor sleep quality, 88.9% of students were categorized in Rp. 500.001 to Rp. 1.000.000 for pocket money, 80.3% of students included in the category Rp. 500.000 or less, and 61.5% of students are included in the category of Rp. 1.000.000 or more. Poor sleep quality was more common among students in the age category 20 years (84.1%), compared to students in the age category > 20 years (71.1%). For the level of physical activity in the poor sleep quality group, 87% were included in the category of heavy physical activity, 82.7% included in the moderate level of physical activity, and 68.8% included in the category of low-level physical activity. There was no significant relationship between gender, pocket money, age, and physical activity with sleep quality (p>0.05).

Keywords: college students, physical activity, sleep quality, university students

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Mounia El Hafyani, Khalid El Himdi

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Precipitation is one of the key variables in water resource planning. The importance of modeling wet and dry durations is a crucial pointer in engineering hydrology. The objective of this study is to model and analyze the distribution of wet and dry durations. For this purpose, the daily rainfall data from 1967 to 2017 of the Moroccan city of Kenitra’s station are used. Three models are implemented for the distribution of wet and dry durations, namely the first-order Markov chain, the second-order Markov chain, and the truncated negative binomial law. The adherence of the data to the proposed models is evaluated using Chi-square and Kolmogorov-Smirnov tests. The Akaike information criterion is applied to assess the most effective model distribution. We go further and study the law of the number of wet and dry days among k consecutive days. The calculation of this law is done through an algorithm that we have implemented based on conditional laws. We complete our work by comparing the observed moments of the numbers of wet/dry days among k consecutive days to the calculated moment of the three estimated models. The study shows the effectiveness of our approach in modeling wet and dry durations of daily precipitation.

Keywords: Markov chain, rainfall, truncated negative binomial law, wet and dry durations

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Authors: Semih Erdogan

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In this paper, the normal and shear stress distributions in Vlasov beams are determined by two-dimensional triangular finite element formulations. The proposed formulations take into account the warping effects along the beam axis. The shape of the considered beam sections may be arbitrary and varied throughout its length. The stiffness matrices and force vectors are derived for transversal forces, uniform torsion, and nonuniform torsion. The proposed finite element algorithm is validated by comparing the analytical solutions, structural engineering books, and related articles. The numerical examples include beams with different cross-section types such as solid, thick-walled, closed-thin-walled, and open-thin-walled sections. Materials defined in the examples are homogeneous, isotropic, and linearly elastic. Through these examples, the study demonstrates the capability of the proposed method to address a wide range of practical engineering scenarios.

Keywords: Vlasov beams, warping function, nonuniform torsion, finite element method, normal and shear stresses, cross-section properties

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Authors: Badr M. Alshammari, T. Guesmi

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In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO₂, NO_x, and SO₂. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.

Keywords: power dispatch, valve point loading effects, multiobjective optimization, Pareto solutions

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Authors: Mohamed A. Hammad, Dzul Azri Mohamed Noor, Syed Azhar Syed Sulaiman, Ahmed A. Khamis, Abeer Kharshid, Nor Azizah Aziz

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The effect of statins dose intensity (SDI) on glycemic control in patients with existing diabetes is unclear. Also, there are many contradictory findings were reported in the literature; thus, it is limiting the possibility to draw conclusions. This project was designed to compare the effect of SDI on glycated hemoglobin (HbA1c%) control in outpatients with Type 2 diabetes in the endocrine clinic at Hospital Pulau Pinang, Malaysia, between July 2015 and August 2016. A prospective cohort study was conducted, where records of 345 patients with Type 2 diabetes (Moderate-SDI group 289 patients and high-SDI cohort 56 patients) were reviewed to identify demographics and laboratory tests. The target of glycemic control (HbA1c < 7% for patient < 65 years, and < 8% for patient ≥ 65 years) was estimated, and the results were presented as descriptive statistics. From 289 moderate-SDI cohorts with a mean age of 57.3 ± 12.4 years, only 86 (29.8%) cases were shown to have controlled glycemia, while there were 203 (70.2%) cases with uncontrolled glycemia with confidence interval (CI) of 95% (6.2–10.8). On the other hand, the high-SDI group of 56 patients with Type 2 diabetes with a mean age 57.7±12.4 years is distributed among 11 (19.6%) patients with controlled diabetes, and 45 (80.4%) of them had uncontrolled glycemia, CI: 95% (7.1–11.9). The study has demonstrated that the relative risk (RR) of uncontrolled glycemia in patients with Type 2 diabetes that used high-SDI is 1.15, and the excessive relative risk (ERR) is 15%. The absolute risk (AR) is 10.2%, and the number needed to harm (NNH) is 10. Outpatients with Type 2 diabetes who use high-SDI of statin have a higher risk of uncontrolled glycemia than outpatients who had been treated with a moderate-SDI.

Keywords: cohort study, diabetes control, dose intensity, HbA1c, Malaysia, statin, type 2 diabetes mellitus, uncontrolled glycemia

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Authors: Amjad Alsulami, Farah Albishri, Kholod Alzubidi, Lama Almehemadi, Salma Elhag

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Food allergy is a common and rising problem among children. Many students have their first allergic reaction at school, one of these is anaphylaxis, which can be fatal. This study discovered that several schools' processes lacked safety regulations and information on how to handle allergy issues and chronic diseases like diabetes where students were not supervised or monitored during the cafeteria purchasing process. There is no obvious prevention or effort in academic institutions when purchasing food containing allergens or negatively impacting the health status of students who suffer from chronic diseases. Students must always be stable to reflect positively on their educational development process. To address this issue, this paper uses a business reengineering process to propose the automation of the whole food-purchasing process, which will aid in detecting and avoiding allergic occurrences and preventing any side effects from eating foods that are conflicting with students' health. This may be achieved by designing a smart card with an embedded QR code that reveals which foods cause an allergic reaction in a student. A survey was distributed to determine and examine how the cafeteria will handle allergic children and whether any management or policy is applied in the school. Also, the survey findings indicate that the integration of QR technology into the food purchasing process would improve health condition detection. The suggested system would be beneficial to all parties, the family agreed, as they would ensure that their children didn't eat foods that were bad for their health. Moreover, by analyzing and simulating the as-is process and the suggested process the results demonstrate that there is an improvement in quality and time.

Keywords: QR code, smart card, food allergies, business process reengineering, health condition detection

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Authors: Cavidan Yakupoglu, Kurt Rohloff

Abstract:

In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here.

Keywords: lattice cryptography, fully homomorphic encryption, parameter selection, LWE, RLWE

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Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

Abstract:

In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

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Authors: Lyamine Guezouli, Kamel Barka, Zineb Seghir

Abstract:

Wireless sensor and actor networks (WSANs) present a research challenge for different practice areas. Researchers are trying to optimize the use of such networks through their research work. This optimization is done on certain criteria, such as improving energy efficiency, exploiting node heterogeneity, self-adaptability and self-configuration. In this article, we present our proposal for BIFSA (Biologically-Inspired Framework for Wireless Sensor and Actor networks). Indeed, BIFSA is a middleware that addresses the key issues of wireless sensor and actor networks. BIFSA consists of two types of agents: sensor agents (SA) that operate at the sensor level to collect and transport data to actors and actor agents (AA) that operate at the actor level to transport data to base stations. Once the sensor agent arrives at the actor, it becomes an actor agent, which can exploit the resources of the actors and vice versa. BIFSA allows agents to evolve their genetic structures and adapt to the current network conditions. The simulation results show that BIFSA allows the agents to make better use of all the resources available in each type of node, which improves the performance of the network.

Keywords: wireless sensor and actor networks, self-management, genetic algorithm, agent.

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Authors: Flur R. Ismagilov, Irek Kh. Khayrullin, Vyacheslav E. Vavilov, Ruslan D. Karimov, Anton S. Gorbunov, Danis R. Farrakhov

Abstract:

The paper presents another structural scheme of high-temperature starter-generator with external rotor to be installed on High Pressure Shaft (HPS) of aircraft engines (AE) to implement More Electrical Engine concept. The basic materials to make this starter-generator (SG) were selected and justified. Multi-criteria optimization of the developed structural scheme was performed using a genetic algorithm and Pareto method. The optimum (in Pareto terms) active length and thickness of permanent magnets of SG were selected as a result of the optimization. Using the dimensions obtained, allowed to reduce the weight of the designed SG by 10 kg relative to a base option at constant thermal loads. Multidisciplinary computer simulation was performed on the basis of the optimum geometric dimensions, which proved performance efficiency of the design. We further plan to make a full-scale sample of SG of HPS and publish the results of its experimental research.

Keywords: high-temperature starter-generator, more electrical engine, multi-criteria optimization, permanent magnet

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Authors: Ren Moses

Abstract:

The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: Deepika Christopher, Garima Anand

Abstract:

To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Waqas Shafqat Chattha, Muhammad Iqbal, Amir Shakeel

Abstract:

Transcriptional factors are proteins that play a vital role in regulating the transcription of target genes in different biological processes and are being widely studied in different plant species. In the current era of genomics, plant genomes sequencing has directed to the genome-wide identification, analyses and categorization of diverse transcription factor families and hence provide key insights into their structural as well as functional diversity. The DNA-binding with One Finger (DOF) proteins belongs to C2-C2-type zinc finger protein family. DOF proteins are plant-specific transcription factors implicated in diverse functions including seed maturation and germination, phytohormone signalling, light-mediated gene regulation, cotton-fiber elongation and responses of the plant to biotic as well as abiotic stresses. In this context, a genome-wide in-silico analysis of DOF TF family in diploid cotton species i.e. Gossypium raimondii has enabled us to identify 55 non-redundant genes encoding DOF proteins renamed as GrDofs (Gossypium raimondii Dof). Gene distribution studies have shown that all of the GrDof genes are unevenly distributed across 12 out of 13 G. raimondii chromosomes. The gene structure analysis illustrated that 34 out of 55 GrDof genes are intron-less while remaining 21 genes have a single intron. Protein sequence-based phylogenetic analysis of putative 55 GrDOFs has divided these proteins into 5 major groups with various paralogous gene pairs. Molecular evolutionary studies aided with the conserved domain as well as gene structure analysis suggested that segmental duplications were the principal contributors for the expansion of Dof genes in G. raimondii.

Keywords: diploid cotton , G. raimondii, phylogenetic analysis, transcription factor

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