Search results for: game outcome prediction

Authors: Mayank, Navneet Kaur, Narinder Singh

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The V599 mutations in the BRAF protein are extremely oncogenic, responsible for countless of malignant conditions. Along with wild type, V599E, V599D, and V599R are the important mutated variants of the BRAF proteins. The BRAF inhibitory anticancer agents are continuously developing, and sorafenib is a BRAF inhibitor that is under clinical use. The crystal structure of sorafenib bounded to wild type, and V599 is known, showing a similar interaction pattern in both the case. The mutated 599th residue, in both the case, is also found not interacting directly with the co-crystallized sorafenib molecule. However, the IC50 value of sorafenib was found extremely different in both the case, i.e., 22 nmol/L for wild and 38 nmol/L for V599E protein. Molecular docking study and MMGBSA binding energy results also revealed a significant difference in the binding pattern of sorafenib in both the case. Therefore, to explore the role of distinctively situated 599th residue, we have further conducted comprehensive computational studies. The molecular dynamics simulation, residue interaction network (RIN) analysis, and residue correlation study results revealed the importance of the 599th residue on the therapeutic outcome and overall dynamic of the BRAF protein. Therefore, although the position of 599th residue is very much distinctive from the ligand-binding cavity of BRAF, still it has exceptional control over the overall functional outcome of the protein. The insight obtained here may seem extremely important and guide us while designing ideal BRAF inhibitory anticancer molecules.

Keywords: BRAF, oncogenic, sorafenib, computational studies

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

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Authors: Nilanchali Singh, Alka Kriplani, Reeta Mahey, Garima Kachhawa

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Objective: Resection of larger uterine septa does improve obstetric performance but whether smaller septa need resection and their impact on obstetric outcome is not clear. We wanted to evaluate the role of septal resection of septa of various sizes in obstetric performance. Methods: This retrospective cohort study comprised of 107 patients with uterine septum. The patients were categorized on the basis of extent of uterine septum into four groups: a) Subsepta (< 1/3rd), b) Septum > 1/3 to ½, c) Septum>1/2 to whole uterine cervix, d) Septum traversing whole of uterine cavity and cervix. Out of these 107 patients, 74 could be contacted telephonically and outcomes recorded. Sensitivity and specificity of investigative modalities were calculated. Results: Infertility was seen in maximum number of cases in complete septa (100%), whereas abortions were seen more commonly, in subsepta (18%). MRI had maximum sensitivity and positive predictive value, followed by hysteron-salpingography. Tubal block, fibroid, endometriosis, pelvic adhesions, ovarian pathologies were seen in some but no definite association of these pathologies was seen with any subgroup of septa. Almost five-year follow-up was recorded in all the subgroups. Significant reduction in infertility was seen in all septal subgroup (p=0.046, 0.032 & 0.05) patients except in subsepta (< 1/3rd uterine cavity) after septum resection. Abortions were significantly reduced (p=0.048) in third subgroup (i.e. septum > ½ to upto internal os) after hysteroscopic septum resection. Take home baby rate was 33% in subsepta and around 50% in the remaining subgroups of septa. Conclusions: Septal resection improves obstetric performance in patients with uterine septa of various sizes. Whether septal resection improves obstetric performance in patients with subsepta or very small septa, is controversial. Larger studies addressing this issue need to be planned.

Keywords: septal resection, obstetric outcome, infertility, septum size

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Authors: Pongsakorn Komutpol, Pravej Serichetaphongse, Soontra Panmekiate, Atiphan Pimkhaokham

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Statement of Problem: The important parameter of esthetic assessment in anterior maxillary implant include pink esthetic of gingiva and white esthetic of restoration. While the 3 dimensional (3D) implant position are recently concerned as a key for succeeding in implant treatment. However, to our knowledge, the authors did not come across any publication that demonstrated the relations of esthetic outcome and 3D implant position. Objectives: To investigate the correlation between positional accuracy of single-tooth implant restoration (STIR) in all 3 dimensions and their esthetic outcomes. Materials and Methods: 17 patients’ data who had a STIR at central incisor with pristine contralateral tooth were included in this study. Intraoral photographs, dental models, and cone beam computed tomography (CBCT) images were retrieved. The esthetic outcome was assessed in accordance with pink esthetic score and white esthetic score (PES/WES). While the number of correct position in each dimension (mesiodistal, labiolingual, apicocoronal) of the implant were evaluated and defined as 'right' or 'wrong' according to ITI consensus conference by one investigator using CBCT data. The different mean score between right and wrong position in all dimensions was analyzed by Mann-Whitney U test with 0.05 was the significant level of the study. Results: The average score of PES/WES was 15.88 ± 1.65 which was considered as clinically acceptable. The average PES/WES score in 1, 2 and 3 right dimension of the implant position were 16.71, 15.75 and 15.17 respectively. None of the implants placed wrongly in all three dimensions. Statistically significant difference of the PES/WES score was found between the implants that placed right in 3 dimensions and 1 dimension (p = 0.041). Conclusion: This study supported the principle of 3D position of implant. The more properly implant was placed, the higher esthetic outcome was found.

Keywords: accuracy, dental implant, esthetic, 3D implant position

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Authors: Aboussalih Amira, Zarza Tahar, Fedaoui Kamel, Hammoudi Saleh

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This work focuses on the simulation of the prediction of the behaviour of austenitic stainless steel (SS) 304L under complex loading in stress and imposed strain. The Chaboche model is a cable to describe the response of the material by the combination of two isotropic and nonlinear kinematic work hardening, the model is implemented in the ZébuLon computer code. First, we represent the evolution of the axial stress as a function of the plastic strain through hysteresis loops revealing a hardening behaviour caused by the increase in stress by stress in the direction of tension/compression. In a second step, the study of the ratcheting phenomenon takes a key place in this work by the appearance of the average stress. In addition to the solicitation of the material in the biaxial direction in traction / torsion.

Keywords: damage, 304L, Ratcheting, plastic strain

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Authors: Jon Cobb, Nasir

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Due to higher switching frequencies, the conducted Electromagnetic interference (EMI) noise is generated in a converter. It degrades the performance of a switching converter. Therefore, it is an essential requirement to mitigate EMI noise of high performance converter. Moreover, it includes two types of emission such as common mode (CM) and differential mode (DM) noise. CM noise is due to parasitic capacitance present in a converter and DM noise is caused by switching current. However, there is dire need to understand the main cause of EMI noise. Hence, we propose a novel method to predict conducted EMI noise of different converter topologies during early stage. This paper also presents the comparison of conducted electromagnetic interference (EMI) noise due to different SMPS topologies. We also make an attempt to develop an EMI noise model for a converter which allows detailed performance analysis. The proposed method is applied to different converter, as an example, and experimental results are verified the novel prediction technique.

Keywords: EMI, electromagnetic interference, SMPS, switch-mode power supply, common mode, CM, differential mode, DM, noise

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Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

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Authors: M. Ben Slimene, M.-S. Ouali

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Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

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Authors: Osama Moustafa Zayed

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Aim of the work: To evaluate the effectiveness of venous glucose, levels of serum lactate and base deficit in polytraumatized patients as simple parameters to predict the mortality in these patients. Compared to the predictive value of Trauma and injury severity (TRISS) and Acute Physiology And Chronic Health Evaluation IV (APACHE IV). Introduction: Trauma is a serious global health problem, accounting for approximately one in 10 deaths worldwide. Trauma accounts for 5 million deaths per year. Prediction of mortality in trauma patients is an important part of trauma care. Several trauma scores have been devised to predict injury severity and risk of mortality. The trauma and injury severity score (TRISS) was most common used. Regardless of the accuracy of trauma scores, is based on an anatomical description of every injury and cannot be assigned to the patients until a full diagnostic procedure has been performed. So we hypothesized that alterations in admission glucose, lactate levels and base deficit would be an early and easy rapid predictor of mortality. Patient and Method: a comparative cross-sectional study. 282 Polytraumatized patients attended to the Emergency Department(ED) of the Suez Canal university Hospital constituted. The period from 1/1/2012 to 1/4/2013 was included. Results: We found that the best cut off value of TRISS probability of survival score for prediction of mortality among poly-traumatized patients is = 90, with 77% sensitivity and 89% specificity using area under the ROC curve (0.89) at (95%CI). APACHE IV demonstrated 67% sensitivity and 95% specificity at 95% CI at cut off point 99. The best cutoff value of Random Blood Sugar (RBS) for prediction of mortality was>140 mg/dl, with 89%, sensitivity, 49% specificity. The best cut off value of base deficit for prediction of mortality was less than -5.6 with 64% sensitivity, 93% specificity. The best cutoff point of lactate for prediction of mortality was > 2.6 mmol/L with 92%, sensitivity, 42% specificity. Conclusion: According to our results from all evaluated predictors of mortality (laboratory and scores) and mortality based on the estimated cutoff values using ROC curves analysis, the highest risk of mortality was found using a cutoff value of 90 in TRISS score while with laboratory parameters the highest risk of mortality was with serum lactate > 2.6 . Although that all of the three parameter are accurate in predicting mortality in poly-traumatized patients and near with each other, as in serum lactate the area under the curve 0.82, in BD 0.79 and 0.77 in RBS.

Keywords: APACHE IV, emergency department, polytraumatized patients, serum lactate

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Authors: Gauri Shankar Shah

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Objectives: The Type I diabetes mellitus in children is frequently a missed diagnosis and children presents in emergency with diabetic ketoacidosis having significant morbidity and mortality. The present study was done to find out the clinical presentation and outcome at a tertiary-care centre. Methods: This was retrospective analysis of data of Type I diabetes mellitus reporting to our centre during last one year (2012-2013). Results: There were 12 patients (8 males) and the age group was 4-14 years (mean ± 3.7). The presenting symptoms were fever, vomiting, altered sensorium and fast breathing in 8 (66.6%), 6 (50%), 4 (33.3%), and 4 (33.3%) cases, respectively. The classical triad of polyuria, polydypsia, and polyphagia were present only in two patients (33.2%). Seizures and epigastric pain were found in two cases each (33.2%). The four cases (33.3%) presented with diabetic ketoacidosis due to discontinuation of insulin doses, while 2 had hyperglycemia alone. The hemogram revealed mean hemoglobin of 12.1± 1.6 g/dL and total leukocyte count was 22,883.3 ± 10,345.9 per mm3, with polymorphs percentage of 73.1 ± 9.0%. The mean blood sugar at presentation was 740 ± 277 mg/ dl (544–1240). HbA1c ranged between 7.1-8.8 with mean of 8.1±0.6 %. The mean sodium, potassium, blood ph, pCO2, pO2 and bicarbonate were 140.8 ± 6.9 mEq/L, 4.4 ± 1.8mEq/L, 7.0 ± 0.2, 20.2 ± 10.8 mmHg, 112.6 ± 46.5 mmHg and 9.2 ± 8.8 mEq/L, respectively. All the patients were managed in pediatric intensive care unit as per our protocol, recovered and discharged on intermediate insulin given twice daily. Conclusions: Thus, it shows that these patients have uncontrolled hyperglycemia and often presents in emergency with ketoacidosis and deranged biochemical profile. The regular administration of insulin, frequent monitoring of blood sugar and health education are required to have better metabolic control and good quality of life.

Keywords: type I diabetes mellitus, hyperglycemia, outcome, glycemic control

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Authors: Caroline E. Mendes, Alberto C. Badino

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Pneumatic reactors have been widely employed in various sectors of the chemical industry, especially where are required high heat and mass transfer rates. This study aimed to obtain correlations that allow the prediction of gas hold-up (Ԑ) and volumetric oxygen transfer coefficient (kLa), and compare these values, for three models of pneumatic reactors on two scales utilizing Newtonian fluids. Values of kLa were obtained using the dynamic pressure-step method, while  was used for a new proposed measure. Comparing the three models of reactors studied, it was observed that the mass transfer was superior to draft-tube airlift, reaching  of 0.173 and kLa of 0.00904s-1. All correlations showed good fit to the experimental data (R2≥94%), and comparisons with correlations from the literature demonstrate the need for further similar studies due to shortage of data available, mainly for airlift reactors and high viscosity fluids.

Keywords: bubble column, internal loop airlift, gas hold-up, kLa

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Authors: Catherine Equey Balzli

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Old age retirement pensions are an important concern among the Swiss but estimating one’s income after retirement is difficult due to the Swiss insurance system’s complexity. This project’s aim is to prepare for developing a digital platform that will allow individuals to plan for retirement in a simplified manner. The main objective of the platform will be to give individuals the tools to check that their savings and retirement benefits will allow them to continue the lifestyle to which they are accustomed once they are retired. The research results from qualitative (focus group) and quantitative (survey) methodologies, recommend the scope and functionalities for a digital platform to be developed. A main outcome is the need to limit the platform’s scope to old-age pension only (excluding survivors’ or disability pensions, for instance). Furthermore, an outcome regarding the functionalities is the proposition of scenarios such as early retirement, changes to income, or modifications to personal status. The development of the digital platform will be a subsequent project.

Keywords: benefit statement, digital platform, retirement financial planning, social insurance

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Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

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Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

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Authors: Gonzalez Carlos, Martinez Fransisco

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The creation of a seismic prediction model that considers all the regional variations and perfectly adjusts its results to the response spectra is very complicated. To achieve statistically acceptable results, it is necessary to process a sufficiently robust data set, and even if high efficiencies are achieved, this model will only work properly in this region. However, when using it in other regions, differences are found due to different parameters that have not been calibrated to other regions, such as the site effect. The fact that impedance contrasts, as well as other factors belonging to the site, have a great influence on the local response is well known, which is why this work, using the residual method, is intended to establish a regional calibration of the corresponding parameters site effect for the Spain region in the global GMPM BSSA 14.

Keywords: GMPM, seismic prediction equations, residual method, response spectra, impedance contrast

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Authors: E. Sahar

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Civic political knowledge is crucial for the stability of democratic countries. In the USA, Americans have poor knowledge about their constitution and their political systems. Some states such as Florida State suffers from a huge decline in civics comparing to the National Average. This study concerns with using new technologies such as augmented reality to engage students in learning civics in classrooms. This is a participatory study, which engage teachers in the process of designing augmented reality civic games. The researcher used survey to find out the materials that teachers struggle with while teaching civics. Four lessons were found the most difficult to teach for middle school students: SS7C1.1 Enlightenment thinkers, SS7C1.2 influencing documents, SS7C1.7-Weakness of the Articles of Confederation, and Forms and systems of governments. For the limited scope of this study, we focused on “Forms and Systems of governments’ as the main project. Augmented Reality is used to help students to engage in learning civics through building a game that is based on the pedagogy constructivism theory. The resulted project meets the educational requirements for civics, provide students with more knowledge in at stake issues such as migration and citizenship, and help them to build leadership skills while playing in groups. The augmented reality game is also designed to test the students learning for each stage. This study helps to generate insightful implications for the use of augmented reality by educators, researchers, instructional designers, and developers who are interested in integrating technology in teaching civics for students in middle school classrooms.

Keywords: augmented reality, games, civics teaching, Florida middle school

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Anshu Radha Aggarwal

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Higher Education (HE) in the India has experienced a series of remarkable changes over the last fifteen years as successive governments have sought to make the sector more efficient and more accountable for investment of public funds. Rapid expansion in student numbers and pressures to widen Participation amongst non-traditional students are key challenges facing HE. Learning outcomes can act as a benchmark for assuring quality and efficiency in HE and they also enable universities to describe courses in an unambiguous way so as to demystify (and open up) education to a wider audience. This paper examines how learning outcomes are used in HE and evaluates the implications for curriculum design and student learning. There has been huge expansion in the field of higher education, both technical and non-technical, in India during the last two decades, and this trend is continuing. It is expected that another about 400 colleges and 300 universities will be created by the end of the 13th Plan Period. This has lead to many concerns about the quality of education and training of our students. Many studies have brought the issues ailing our curricula, delivery, monitoring and assessment. Govt. of India, (via MHRD, UGC, NBA,…) has initiated several steps to bring improvement in quality of higher education and training, such as National Skills Qualification Framework, making accreditation of institutions mandatory in order to receive Govt. grants, and so on. Moreover, Outcome-based Education and Training (OBET) has also been mandated and encouraged in the teaching/learning institutions. MHRD, UGC and NBAhas made accreditation of schools, colleges and universities mandatory w.e.f Jan 2014. Outcome-based Education and Training (OBET) approach is learner-centric, whereas the traditional approach has been teacher-centric. OBET is a process which involves the re-orientation/restructuring the curriculum, implementation, assessment/measurements of educational goals, and achievement of higher order learning, rather than merely clearing/passing the university examinations. OBET aims to bring about these desired changes within the students, by increasing knowledge, developing skills, influencing attitudes and creating social-connect mind-set. This approach has been adopted by several leading universities and institutions around the world in advanced countries. Objectives of this paper is to highlight the issues concerning quality in higher education and quality frameworks, to deliberate on the various education and training models, to explain the outcome-based education and assessment processes, to provide an understanding of the NAAC and outcome-based accreditation criteria and processes and to share best-practice outcomes-based accreditation system and process.

Keywords: learning outcomes, curriculum development, pedagogy, outcome based education

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Authors: Amber Elyse Corrigan, Alexander Smith, Anna Pennington, Ben Carter, Jonathan Hewitt

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Background and Purpose: Morbidity secondary to stroke is highly heterogeneous, but it is important to both patients and clinicians in post-stroke management and adjustment to life after stroke. The consideration of post-stroke morbidity clinically and from the patient perspective has been poorly measured. The patient-reported outcome measures (PROs) in morbidity assessment help improve this knowledge gap. The primary aim of this study was to consider the association between PRO outcomes and stroke predictors. Methods: A multicenter prospective cohort study assessed 549 stroke patients at 19 hospital sites across England and Wales during 2019. Following a stroke event, demographic, clinical, and PRO measures were collected. Prevalence of morbidity within PRO measures was calculated with associated 95% confidence intervals. Predictors of domain outcome were calculated using a multilevel generalized linear model. Associated P -values and 95% confidence intervals are reported. Results: Data were collected from 549 participants, 317 men (57.7%) and 232 women (42.3%) with ages ranging from 25 to 97 (mean 72.7). PRO morbidity was high post-stroke; 93.2% of the cohort report post-stroke PRO morbidity. Previous stroke, diabetes, and gender are associated with worse patient-reported outcomes across both the physical and cognitive domains. Conclusions: This large-scale multicenter cohort study illustrates the high proportion of morbidity in PRO measures. Further, we demonstrate key predictors of adverse outcomes (Diabetes, previous stroke, and gender) congruence with clinical predictors. The PRO has been demonstrated to be an informative and useful stroke when considering patient-reported outcomes and has wider implications for considerations of PROs in clinical management. Future longitudinal follow-up with PROs is needed to consider association of long-term morbidity.

Keywords: morbidity, patient-reported outcome, PRO, stroke

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Authors: Carin Chuang, Kuan-Chou Chen

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An Enterprise Resource Planning (ERP) system is an integrated information system that supports the seamless integration of all the business processes of a company. Implementing an ERP system can increase efficiencies and decrease the costs while helping improve productivity. Many organizations including large, medium and small-sized companies have already adopted an ERP system for decades. Although ERP system can bring competitive advantages to organizations, the lack of proper training approach in ERP implementation is still a major concern. Organizations understand the importance of ERP training to adequately prepare managers and users. The low return on investment, however, for the ERP training makes the training difficult for knowledgeable workers to transfer what is learned in training to the jobs at workplace. Inadequate and inefficient ERP training limits the value realization and success of an ERP system. That is the need to call for a profound change and innovation for ERP training in both workplace at industry and the Information Systems (IS) education in academia. The innovated ERP training approach can improve the users’ knowledge in business processes and hands-on skills in mastering ERP system. It also can be instructed as educational material for IS students in universities. The purpose of the study is to examine the use of ERP simulation games via the ERPsim system to train the IS students in learning ERP implementation. The ERPsim is the business simulation game developed by ERPsim Lab at HEC Montréal, and the game is a real-life SAP (Systems Applications and Products) ERP system. The training uses the ERPsim system as the tool for the Internet-based simulation games and is designed as online student competitions during the class. The competitions involve student teams with the facilitation of instructor and put the students’ business skills to the test via intensive simulation games on a real-world SAP ERP system. The teams run the full business cycle of a manufacturing company while interacting with suppliers, vendors, and customers through sending and receiving orders, delivering products and completing the entire cash-to-cash cycle. To learn a range of business skills, student needs to adopt individual business role and make business decisions around the products and business processes. Based on the training experiences learned from rounds of business simulations, the findings show that learners have reduced risk in making mistakes that help learners build self-confidence in problem-solving. In addition, the learners’ reflections from their mistakes can speculate the root causes of the problems and further improve the efficiency of the training. ERP instructors teaching with the innovative approach report significant improvements in student evaluation, learner motivation, attendance, engagement as well as increased learner technology competency. The findings of the study can provide ERP instructors with guidelines to create an effective learning environment and can be transferred to a variety of other educational fields in which trainers are migrating towards a more active learning approach.

Keywords: business simulations, ERP implementation training, ERPsim, game-based learning, instructional strategy, training innovation

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Authors: Hilary Joseph Watsilla

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Research work is a vital part of the university system; in Nigeria public universities, research is used in measuring the development of individuals and departments within the academic system. Information technology has impacted the way research is carried out by providing easy access to information and improved collaboration between research and other instruments necessary for research activities. However, access to some of these IT facilities is not readily available in most of the public institutions in Nigeria. Research activities are usually tedious and rigorous and any inadequacy in research resources might affect the quality of research outcome. This study aims to identify the IT capability and knowledge management capabilities necessary for academic researchers in public universities in Nigeria, as it will provide more incite to the knowledge creation processes of research. The research will be conducted using an interpretive lens, which will provide a more qualitative understanding of the subject matter. The outcome of the research will provide an empirical understanding of the IT capabilities, which help in the optimization of the knowledge management capabilities of the university.

Keywords: IT capabilities, KM capabilities, universities, academic research

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Authors: Ravneil Nand

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Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

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Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

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To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

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Authors: Nadia Allouache

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The concept of second law is assumed to be important to optimize the energy losses in heat exchangers. The present study is devoted to the numerical prediction of entropy generation due to heat transfer and friction in a double tube heat exchanger partly or fully filled with a porous medium. The goal of this work is to find the optimal conditions that allow minimizing entropy generation. For this purpose, numerical modeling based on the control volume method is used to describe the flow and heat transfer phenomena in the fluid and the porous medium. Effects of the porous layer thickness, its permeability, and the effective thermal conductivity have been investigated. Unexpectedly, the fully porous heat exchanger yields a lower entropy generation than the partly porous case or the fluid case even if the friction increases the entropy generation.

Keywords: heat exchangers, porous medium, second law approach, turbulent flow

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Authors: Sujay Bisht

Abstract:

The purpose of the study was to assess and examines the pattern of injury among the field hockey players at different field position during practice & game. It was hypothesized that the backfield might have the height rate of injury, followed by midfield. Methods: university level and national level male field hockey (N=60) are selected as a subject and requested to respond an anon questionnaire. Personal characteristics of each and individual players were also collected like (age, height, weight); field hockey professional information (level of play, year of experience, playing surface); players injury history (site, types, cause etc). The rates of injury per athlete per year were also calculated. Result: Around half of the injury occurred were to the lower limbs (49%) followed by head and face (30%), upper limbs (19%) and torso region (2%). Injuries included concussion, wounds, broken nose, ligament sprain, dislocation, fracture, and muscles strain and knee injury. The ligament sprain is the highest rate (40%) among the other types of injuries. After investigation and evaluation backfield players had the highest rate of risk of injury (1.10 injury/athletes-year) followed by midfield players (0.70 injury/athlete-year), forward players (0.45 injury/athlete-year) & goalkeeper was (0.37 injury/athlete-year). Conclusion: Due to the different field position the pattern & rate of injury were different. After evaluation, lower limbs had the highest rate of injury followed by head and face, upper limbs and torso respectively. It also revealed that not only there is a difference in the rate of injury between playing the position, but also in the types of injury sustain at a different position.

Keywords: trauma, sprain, strain, astroturf, acute injury

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

Abstract:

The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

Abstract:

Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

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Authors: Shi-xia Xu, Zai-wen Zhang, Ru-xue Chen, Shan Zhou, Xiang-feng Tang

Abstract:

Objective: The clinical influence of HLA-DPB1 mismatches on clinical outcome of HSCT is less clear. This is the first meta-analysis to study the HLA-DPB1 matching statues on clinical outcomes after unrelated donor HSCT. Methods: We searched the CIBMTR, Cochrane Central Register of Controlled Trials (CENTRAL) and related databases (1995.01–2017.06) for all relevant articles. Comparative studies were used to investigate the HLA-DPB1 loci mismatches on clinical outcomes after unrelated donor HSCT, such as the disease-free survival (DFS), overall survival, GVHD, relapse, and transplant-related mortality (TRM). We performed meta-analysis using Review Manager 5.2 software and funnel plot to assess the bias. Results: At first, 1246 articles were retrieved, and 18 studies totaling 26368 patients analyzed. Pooled comparisons of studies found that the HLA-DPB1 mismatched group had a lower rate of DFS than the DPB1-matched group, and lower OS in non-T cell depleted transplantation. The DPB1 mismatched group has a higher incidence of aGVHD and more severe ( ≥ III degree) aGvHD, lower rate of relapse and higher TRM. Moreover, compared with 1-antigen mismatch, 2-antigen mismatched led to a higher risk of TRM and lower relapse rate. Conclusions: This meta-analysis indicated HLA-DPB1 has important influence on survival and transplant-related complications during unrelated donor HSCT and HLA-DPB1 donor selection strategies have been proposed based on a personalized algorithm.

Keywords: human leukocyte antigen, DPB1, transplant, meta-analysis, outcome

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Authors: James Moir

Abstract:

The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Z. Nurzaireena, K. Azalea, T. Azirawaty, S. Jameela, G. Muralitharan

Abstract:

The aim of this study is the review of the management practices of sickle cell disease patients during pregnancy, as well as the maternal and neonatal outcome at Ampang Hospital, Selangor. The study consisted of a review of pregnant patients with sickle cell disease under follow up at the Hematology Clinic, Ampang Hospital over the last seven years to assess their management and maternal-fetal outcome. The results of the review show that Ampang Hospital is considered the public hematology centre for sickle cell disease and had successfully managed three pregnancies throughout the last seven years. Patients’ presentations, managements and maternal-fetal outcome were compared and reviewed for academic improvements. All three patients were seen very early in their pregnancy and had been given a regime of folic acid, antibiotics and thrombo-prophylactic drugs. Close monitoring of maternal and fetal well being was done by the hematologists and obstetricians. Among the patients, there were multiple admissions during the pregnancy for either a painful sickle cell bone crisis, haemolysis following an infection and anemia requiring phenotype- matched blood and exchange transfusions. Broad spectrum antibiotics coverage during and infection, hydration, pain management and venous-thrombolism prophylaxis were mandatory. The pregnancies managed to reach near term in the third trimester but all required emergency caesarean section for obstetric indications. All pregnancies resulted in live births with good fetal outcome. During post partum all were nursed closely in the high dependency units for further complications and were discharged well. Post partum follow up and contraception counseling was comprehensively given for future pregnancies. Sickle cell disease is uncommonly seen in the East, especially in the South East Asian region, yet more cases are seen in the current decade due to improved medical expertise and advance medical laboratory technologies. Pregnancy itself is a risk factor for sickle cell patients as increased thrombosis event and risk of infections can lead to multiple crisis, haemolysis, anemia and vaso-occlusive complications including eclampsia, cerebrovasular accidents and acute bone pain. Patients mostly require multiple blood product transfusions thus phenotype-matched blood is required to reduce the risk of alloimmunozation. Emphasizing the risks and complications in preconception counseling and establishing an ultimate pregnancy plan would probably reduce the risk of morbidity and mortality to the mother and unborn child. Early management for risk of infection, thromboembolic events and adequate hydration is mandatory. A holistic approach involving multidisciplinary team care between the hematologist, obstetricians, anesthetist, neonatologist and close nursing care for both mother and baby would ensure the best outcome. In conclusion, sickle cell disease by itself is a high risk medical condition and pregnancy would further amplify the risk. Thus, close monitoring with combine multidisciplinary care, counseling and educating the patients are crucial in achieving the safe outcome.

Keywords: anaemia, haemoglobinopathies, pregnancy, sickle cell disease

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Authors: John Rothrock

Abstract:

Background: The role of transthoracic echocardiography (TTE) in the diagnosis and management of patients with acute ischemic stroke remains controversial. While many stroke subspecialist reserve TTE for selected patients, others consider the procedure obligatory for most or all acute stroke patients. This study was undertaken to assess the cost vs. benefit of 'routine' TTE. Methods: We examined a consecutive series of patients who were admitted to a single institution in 2019 for acute ischemic stroke and underwent TTE. We sought to determine the frequency with which the results of TTE led to a new diagnosis of cardioembolism, redirected therapeutic cerebrovascular management, and at least potentially influenced the short or long-term clinical outcome. We recorded the direct cost associated with TTE. Results: There were 1076 patients in the study group, all of whom underwent TTE. TTE identified an unsuspected source of possible/probable cardioembolism in 62 patients (6%), confirmed an initially suspected source (primarily endocarditis) in an additional 13 (1%) and produced findings that stimulated subsequent testing diagnostic of possible/probable cardioembolism in 7 patients ( < 1%). TTE results potentially influenced the clinical outcome in a total of 48 patients (4%). With a total direct cost of $1.51 million, the mean cost per case wherein TTE results potentially influenced the clinical outcome in a positive manner was $31,375. Diagnostically and therapeutically, TTE was most beneficial in 67 patients under the age of 55 who presented with 'cryptogenic' stroke, identifying patent foramen ovale in 21 (31%); closure was performed in 19. Conclusions: The utility of TTE in the setting of acute ischemic stroke is modest, with its yield greatest in younger patients with cryptogenic stroke. Given the greater sensitivity of transesophageal echocardiography in detecting PFO and evaluating the aortic arch, TTE’s role in stroke diagnosis would appear to be limited.

Keywords: cardioembolic, cost-benefit, stroke, TTE

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