Search results for: prognosis prediction

Authors: Yoshio Kurosawa

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The applications of soundproof materials for reduction of high frequency automobile interior noise have been researched. This paper presents a sound pressure prediction technique including wind noise by Hybrid Statistical Energy Analysis (HSEA) in order to reduce weight of acoustic insulations. HSEA uses both analytical SEA and experimental SEA. As a result of chassis dynamo test and road test, the validity of SEA modeling was shown, and utility of the method was confirmed.

Keywords: vibration, noise, road noise, statistical energy analysis

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Authors: Marina Vicario, Amadeo Argüelles, Pilar Gómez, Carlos Hernández

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In this paper, the Computer Research Network of the National Polytechnic Institute of Mexico proposes a paradigmatic model for the inference of cognitive patterns in digital learning systems. This model leads to metadata architecture useful for analysis and prediction in online learning systems; especially on MOOc's architectures. The model is in the design phase and expects to be tested through an institutional of courses project which is going to develop for the MOOc.

Keywords: cognitive footprints, learning analytics, predictive learning, digital learning, educational computing, educational informatics

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Authors: Doaa M. Elghannam, Nashwa Khayrat Abousamra, Doaa A. Shahin, Enas F. Goda, Hanan Azzam, Emad Azmy, Manal Salah El-Din

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Background: The pathogenesis of acute myeloid leukemia (AML) involves the cooperation of mutations promoting proliferation/survival and those impairing differentiation. Point mutations of the NRAS gene are the most frequent somatic mutations causing aberrant signal-transduction in acute myeloid leukemia (AML). Aim: The present work was conducted to study the frequency and prognostic significance of NRAS gene mutations (NRASmut) in de novo Egyptian adult AML. Material and methods: Bone marrow specimens from 150 patients with de novo acute myeloid leukemia and controls were analyzed by genomic PCR-SSCP at codons 12, 13 (exon 1), and 61 (exon 2) for NRAS mutations. Results: NRAS gene mutations was found in 19/150 (12.7%) AML cases, represented more frequently in the FAB subtype M4eo (P = 0.028), and at codon 12, 13 (14of 19; 73.7%). Patients with NRASmut had a significant lower peripheral marrow blasts (P = 0.004, P=0.03) and non significant improved clinical outcome than patients without the mutation. Complete remission rate was (63.2% vs 56.5%; p=0.46), resistant disease (15.8% vs 23.6%; p=0.51), three years overall survival (44% vs 42%; P = 0.85) and disease free survival (42.1% vs 38.9%, P = 0.74). Multivariate analysis showed that age was the strongest unfavorable factor for overall survival (relative risk [RR], 1.9; P = .002), followed by cytogenetics (P = .004). FAB types, NRAS mutation, and leukocytosis were less important. Conclusions: NRAS gene mutation frequency and spectrum differ between biologically distinct subtypes of AML but do not significantly influence prognosis and clinical outcome.

Keywords: NRAS Gene, egyptian adult, acute myeloid leukemia, cytogenetics

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Jitendra Jain, Cassandra Hidajat, Hansraj Riteesh Bookun

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Graft patency underpins long-term prognosis after coronary artery bypass grafting surgery (CABG). The benefits of the combined use of only the left internal mammary artery and radial artery, referred to as total arterial revascularisation (TAR), on long-term clinical outcomes and quality of life are relatively unknown. The aim of this study was to identify whether there were differences in long term clinical outcomes between recipients of TAR compared to a cohort of mostly arterial revascularization involving the left internal mammary, at least one radial artery and at least one saphenous vein graft. A retrospective analysis was performed on all patients who underwent TAR or were re-vascularized with supplementary saphenous vein graft from February 1996 to December 2004. Telephone surveys were conducted to obtain clinical outcome parameters including major adverse cardiac and cerebrovascular events (MACCE) and Short Form (SF-36v2) Health Survey responses. A total of 176 patients were successfully contacted to obtain postop follow up results. The mean follow-up length from time of surgery in our study was TAR 12.4±1.8 years and conventional 12.6±2.1. PCS score was TAR 45.9±8.8 vs LIMA/Rad/ SVG 44.9±9.2 (p=0.468) and MCS score was TAR 52.0±8.9 vs LIMA/Rad/SVG 52.5±9.3 (p=0.723). There were no significant differences between groups for NYHA class 3+ TAR 9.4% vs. LIMA/Rad/SVG 6.6%; or CCS 3+ TAR 2.35% vs. LIMA/Rad/SVG 0%.

Keywords: CABG; MACCEs; quality of life; total arterial revascularisation

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Authors: Xiaoping Su, Gabriel G. Malouf

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Introduction: Breast cancer is a heterogeneous disease that can be classified in 4 subgroups using transcriptional profiling. The role of lncRNA expression in human breast cancer biology, prognosis, and molecular classification remains unknown. Methods and results: Using an integrative comprehensive analysis of lncRNA, mRNA and DNA methylation in 900 breast cancer patients from The Cancer Genome Atlas (TCGA) project, we unraveled the molecular portraits of 1,700 expressed lncRNA. Some of those lncRNAs (i.e, HOTAIR) are previously reported and others are novel (i.e, HOTAIRM1, MAPT-AS1). The lncRNA classification correlated well with the PAM50 classification for basal-like, Her-2 enriched and luminal B subgroups, in contrast to the luminal A subgroup which behaved differently. Importantly, estrogen receptor (ESR1) expression was associated with distinct lncRNA networks in lncRNA clusters III and IV. Gene set enrichment analysis for cis- and trans-acting lncRNA showed enrichment for breast cancer signatures driven by breast cancer master regulators. Almost two third of those lncRNA were marked by enhancer chromatin modifications (i.e., H3K27ac), suggesting that lncRNA expression may result in increased activity of neighboring genes. Differential analysis of gene expression profiling data showed that lncRNA HOTAIRM1 was significantly down-regulated in basal-like subtype, and DNA methylation profiling data showed that lncRNA HOTAIRM1 was highly methylated in basal-like subtype. Thus, our integrative analysis of gene expression and DNA methylation strongly suggested that lncRNA HOTAIRM1 should be a tumor suppressor in basal-like subtype. Conclusion and significance: Our study depicts the first lncRNA molecular portrait of breast cancer and shows that lncRNA HOTAIRM1 might be a novel tumor suppressor.

Keywords: lncRNA profiling, breast cancer, HOTAIRM1, tumor suppressor

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Authors: Masao Fujisawa, Hirohito Ikeda, Tomonori Ohata, Miho Yukawa, Hatsumi Aki, Takayoshi Kimura

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We attempted to predict adsorption coefficients by utilizing dispersion energies. We performed liquid-phase free energy calculations based on gas-phase geometries of organic compounds using the DFT and studied the relationship between the adsorption of organic compounds by activated carbon and dispersion energies of the organic compounds. A linear correlation between absorption coefficients and dispersion energies was observed.

Keywords: activated carbon, adsorption, prediction, dispersion energy

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Authors: Á. L. Bendes

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By the end of the second decade of the 21st century, artificial intelligence (AI) has become an unavoidable part of everyday life and has necessarily aroused the interest of researchers in almost every field of science. This is no different in the case of jurisprudence, whose main task is not only to create its own theoretical paradigm related to AI. Perhaps the biggest impact on digital crime is artificial intelligence. In addition, the need to create legal frameworks suitable for the future application of the law has a similar importance. The prognosis according to which AI can reshape the practical application of law and, ultimately, the entire legal life is also of considerable importance. In the past, criminal law was basically created to sanction the criminal acts of a person, so the application of its concepts with original content to AI-related violations is not expected to be sufficient in the future. Taking this into account, it is necessary to rethink the basic elements of criminal law, such as the act and factuality, but also, in connection with criminality barriers and criminal sanctions, several new aspects have appeared that challenge both the criminal law researcher and the legislator. It is recommended to continuously monitor technological changes in the field of criminal law as well since it will be timely to re-create both the legal and scientific frameworks to correctly assess the events related to them, which may require a criminal law response. Artificial intelligence has completely reformed the world of digital crime. New crimes have appeared, which the legal systems of many countries do not or do not adequately regulate. It is considered important to investigate and sanction these digital crimes. The primary goal is prevention, for which we need a comprehensive picture of the intertwining of artificial intelligence and digital crimes. The goal is to explore these problems, present them, and create comprehensive proposals that support legal certainty.

Keywords: artificial intelligence, chat forums, defamation, international criminal cooperation, social networking, virtual sites

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: Selam Mulugeta, Barkot Milkias, Mesfin Araya, Abel Worku, Eyasu Mulugeta

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In Ethiopia, there is inadequate information on non-adherence to antidepressant treatment in patients with depressive disorders. Having awareness of the pattern of adherence is important in future prognosis, quality of life, and functionality in these patients. This hospital-based cross-sectional quantitative study was done on a sample of 216 consecutive outpatients with depressive disorders. Data were collected using questionnaires through in-person and phone call interviews. The 8-item Morisky scale was used to assess the pattern of medication adherence. Other specially developed tools were used to obtain sociodemographic and clinical information from electronic medical records and patient interviews. Data were analyzed using the Statistical Package for the Social Sciences Version - 25. Univariate and multivariable analyses were carried out to assess factors associated with non-adherence. 90% of the participants had a primary diagnosis of major depressive disorder. Based on the 8-item Morisky Medication Adherence Scale, the prevalence of non-adherence was found to be 84.7%. Living distance between 11 to 50 km from the hospital (AOR= 11, 95% CI (29,46.6)), post-secondary level of education (AOR= 8.3, 95% CI (1, 64.4)) and taking multiple medications (AOR= 6.1, 95% CI (1, 34.9)) were found to have significantly increased odds of non-adherence. Non-adherence was significantly associated with factors such as increased living distance from the hospital, relatively higher educational level, and polypharmacy. Proper and patient-centered psychoeducation, addressing the communication gap between patients and doctors, adherence to prescribing guidelines, avoiding polypharmacy unless indicated & working on accessibility of treatment is essential to decrease non-adherence.

Keywords: depressive disorders, Ethiopia, medication adherence, Addis Ababa

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Authors: Nanine Fouche

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The Cape Flats is a low-lying sand-covered expanse of approximately 460 square kilometres, situated to the southeast of the central business district of Cape Town in the Western Cape of South Africa. The aeolian sands masking this area are often loose and compressible in the upper 1m to 1.5m of the surface, and there is a general exceedance of the maximum allowable settlement in these sands. The settlement of shallow foundations on Cape Flats sands is commonly predicted using the results of in-situ tests such as the SPT or DPSH due to the difficulty of retrieving undisturbed samples for laboratory testing. Varying degrees of accuracy and reliability are associated with these methods. More recently, shear wave velocity (Vs) profiles obtained from seismic testing, such as continuous surface wave tests (CSW), are being used for settlement prediction. Such predictions have the advantage of considering non-linear stress-strain behaviour of soil and the degradation of stiffness with increasing strain. CSW tests are rarely executed in the Cape Flats, whereas SPT’s are commonly performed. For this reason, and to facilitate better settlement predictions in Cape Flats sand, equations representing shear wave velocity (Vs) as a function of SPT blow count (N60) and vertical effective stress (v’) were generated by statistical regression of site investigation data. To reveal the most appropriate method of overburden correction, analyses were performed with a separate overburden term (Pa/σ’v) as well as using stress corrected shear wave velocity and SPT blow counts (correcting Vs. and N60 to Vs1and (N1)60respectively). Shear wave velocity profiles and SPT blow count data from three sites masked by Cape Flats sands were utilised to generate 80 Vs-SPT N data pairs for analysis. Investigated terrains included sites in the suburbs of Athlone, Muizenburg, and Atlantis, all underlain by windblown deposits comprising fine and medium sand with varying fines contents. Elastic settlement analysis was also undertaken for the Cape Flats sands, using a non-linear stepwise method based on small-strain stiffness estimates, which was obtained from the best Vs-N60 model and compared to settlement estimates using the general elastic solution with stiffness profiles determined using Stroud’s (1989) and Webb’s (1969) SPT N60-E transformation models. Stroud’s method considers strain level indirectly whereasWebb’smethod does not take account of the variation in elastic modulus with strain. The expression of Vs. in terms of N60 and Pa/σv’ derived from the Atlantis data set revealed the best fit with R2 = 0.83 and a standard error of 83.5m/s. Less accurate Vs-SPT N relations associated with the combined data set is presumably the result of inversion routines used in the analysis of the CSW results showcasing significant variation in relative density and stiffness with depth. The regression analyses revealed that the inclusion of a separate overburden term in the regression of Vs and N60, produces improved fits, as opposed to the stress corrected equations in which the R2 of the regression is notably lower. It is the correction of Vs and N60 to Vs1 and (N1)60 with empirical constants ‘n’ and ‘m’ prior to regression, that introduces bias with respect to overburden pressure. When comparing settlement prediction methods, both Stroud’s method (considering strain level indirectly) and the small strain stiffness method predict higher stiffnesses for medium dense and dense profiles than Webb’s method, which takes no account of strain level in the determination of soil stiffness. Webb’s method appears to be suitable for loose sands only. The Versak software appears to underestimate differences in settlement between square and strip footings of similar width. In conclusion, settlement analysis using small-strain stiffness data from the proposed Vs-N60 model for Cape Flats sands provides a way to take account of the non-linear stress-strain behaviour of the sands when calculating settlement.

Keywords: sands, settlement prediction, continuous surface wave test, small-strain stiffness, shear wave velocity, penetration resistance

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Authors: Minho Lee, Donghyun Back, Jaemoon Jung, Woojin Park

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The current 3-dimensional (3D) posture prediction models commonly provide only a few optimal postures to achieve a specific objective. The problem with such models is that they are incapable of rapidly providing several optimal posture candidates according to various situations. In order to solve this problem, this paper presents a 3D memory-based posture planning (3D MBPP) model, which is a new digital human model that can analyze the feasible postures in 3D space for reaching tasks that have postural constraints and specific reaching space. The 3D MBPP model can be applied to the types of works that are done with constrained working postures and have specific reaching space. The examples of such works include driving an excavator, driving automobiles, painting buildings, working at an office, pitching/batting, and boxing. For these types of works, a limited amount of space is required to store all of the feasible postures, as the hand reaches boundary can be determined prior to perform the task. This prevents computation time from increasing exponentially, which has been one of the major drawbacks of memory-based posture planning model in 3D space. This paper validates the utility of 3D MBPP model using a practical example of analyzing baseball batting posture. In baseball, batters swing with both feet fixed to the ground. This motion is appropriate for use with the 3D MBPP model since the player must try to hit the ball when the ball is located inside the strike zone (a limited area) in a constrained posture. The results from the analysis showed that the stored and the optimal postures vary depending on the ball’s flying path, the hitting location, the batter’s body size, and the batting objective. These results can be used to establish the optimal postural strategies for achieving the batting objective and performing effective hitting. The 3D MBPP model can also be applied to various domains to determine the optimal postural strategies and improve worker comfort.

Keywords: baseball, memory-based, posture prediction, reaching area, 3D digital human models

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Authors: Md. Shakhawat Hossain, Jimma Hossain, Md. Naushad Ali

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Acute pancreatitis is a dangerous medical emergency in the practice of gastroenterology. Management of acute pancreatitis needs multidisciplinary approach with support starts from emergency to ICU. So, there is a chance of mismanagement in every steps, especially in low resource settings. Other factors such as patient’s financial condition, education, social custom, transport facility, referral system from periphery may also challenge the current guidelines for management. The present study is intended to determine the clinico-pathological profile, severity assessment and challenges of management of acute pancreatitis in a government laid tertiary care hospital to image the real scenario of management in a low resource place. A total 100 patients of acute pancreatitis were studied in this prospective study, held in the Department of Gastroenterology, Rangpur medical college hospital, Bangladesh from July 2017 to July 2018 within one year. Regarding severity, 85 % of the patients were mild, whereas 13 were moderately severe, and 2 had severe acute pancreatitis according to the revised Atlanta criteria. The most common etiologies of acute pancreatitis in our study were gall stone (15%) and biliary sludge (15%), whereas 54% were idiopathic. The most common challenges we faced were delay in hospital admission (59%) and delay in hospital diagnosis (20%). Others are non-adherence of patient party, and lack of investigation facility, physician’s poor knowledge about current guidelines. We were able to give early aggressive fluid to only 18% of patients as per current guideline. Conclusion: Management of acute pancreatitis as per guideline is challenging when optimum facility is lacking. So, modified guidelines for assessment and management of acute pancreatitis should be prepared for low resource setting.

Keywords: acute pancreatitis, challenges of management, severity, prognosis

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Authors: A. M. Tahsini, S. A. Hosseini

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In the present study, the surface temperature history of the adaptor part in a two-stage supersonic launch vehicle is accurately predicted. The full Navier-Stokes equations are used to estimate the aerodynamic heat flux. The one-dimensional heat conduction in solid phase is used to compute the temperature history. The instantaneous surface temperature is used to improve the applied heat flux, to improve the accuracy of the results.

Keywords: aerodynamic heating, heat conduction, numerical simulation, supersonic flight, launch vehicle

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Authors: Pedro Gomes, Lia Ferreira, Sofia Garcia, Jaime Babulal, Luís Costa, Luís Castelo, José Muras, Isabel Gonçalves, Rui Carvalho

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Introduction: Foot ulcers and their complications are an important cause of morbidity and mortality in diabetes. Objectives: The present study aims to evaluate the outcomes in terms of need for hospitalization, amputation, healing time and mortality in patients with new-onset diabetic foot ulcers in subgroups stratified by etiology. Methods: A retrospective study based on clinical assessment of patients presenting with new ulcers to a multidisciplinary diabetic foot consult during 2012. Outcomes were determined until September 2014, from hospital registers. Baseline clinical examination was done to classify ulcers as neuropathic, ischemic or neuroischemic. Results: 487 patients with new diabetic foot ulcers were observed; 36%, 15% and 49% of patients had neuropathic, ischemic and neuroischemic ulcers, respectively. For analysis, patients were classified as having predominantly neuropathic (36%) or ischemic foot (64%). The mean age was significantly higher in the group with ischemic foot (70±12 vs 63±12 years; p <0.001), as well as the duration of diabetes (18±10 vs 16 ± 10years, p <0.05). A history of previous amputation was also significantly higher in this group (24.7% vs 15.6%, p <0.05). The evolution of ischemic ulcers was significantly worse, with a greater need for hospitalization (27.2% vs 18%, p <0.05), amputation (11.5% vs 3.6% p <0.05) mainly major amputation (3% vs. 0%; p <0.001) and higher mean healing time (151 days vs 89 days, p <0.05). The mortality rate at 18 months, was also significantly higher in the ischemic foot group (7.3% vs 1.8%, p <0.05). Conclusions: All types of diabetic foot ulcers are associated with high morbidity and mortality, however, the presence of arterial disease confers a poor prognosis. Diabetic foot can be successfully treated only by the multidisciplinary team which can provide more comprehensive and integrated care.

Keywords: diabetes, foot ulcers, etiology, outcome

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Authors: Ugwu O. C., Mamah R. O., Awudu W. S.

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This work is aimed at enhancing signal reception on a mobile radio network and minimizing outage probability in a mobile radio network using adaptive beamforming antenna arrays. In this research work, an empirical real-time drive measurement was done in a cellular network of Globalcom Nigeria Limited located at Ikeja, the headquarters of Lagos State, Nigeria, with reference base station number KJA 004. The empirical measurement includes Received Signal Strength and Bit Error Rate which were recorded for exact prediction of the signal strength of the network as at the time of carrying out this research work. The Received Signal Strength and Bit Error Rate were measured with a spectrum monitoring Van with the help of a Ray Tracer at an interval of 100 meters up to 700 meters from the transmitting base station. The distance and angular location measurements from the reference network were done with the help Global Positioning System (GPS). The other equipment used were transmitting equipment measurements software (Temsoftware), Laptops and log files, which showed received signal strength with distance from the base station. Results obtained were about 11% from the real-time experiment, which showed that mobile radio networks are prone to signal failure and can be minimized using an Adaptive Beamforming Antenna Array in terms of a significant reduction in Bit Error Rate, which implies improved performance of the mobile radio network. In addition, this work did not only include experiments done through empirical measurement but also enhanced mathematical models that were developed and implemented as a reference model for accurate prediction. The proposed signal models were based on the analysis of continuous time and discrete space, and some other assumptions. These developed (proposed) enhanced models were validated using MATLAB (version 7.6.3.35) program and compared with the conventional antenna for accuracy. These outage models were used to manage the blocked call experience in the mobile radio network. 20% improvement was obtained when the adaptive beamforming antenna arrays were implemented on the wireless mobile radio network.

Keywords: beamforming algorithm, adaptive beamforming, simulink, reception

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Authors: Zaki Abiza, Miguel Chavez, David M. Holman, Ruddy Brionnaud

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In this paper, the prediction of the aerodynamic behavior of the flow around a Finned Projectile will be validated using a Computational Fluid Dynamics (CFD) solution, XFlow, based on the Lattice-Boltzmann Method (LBM). XFlow is an innovative CFD software developed by Next Limit Dynamics. It is based on a state-of-the-art Lattice-Boltzmann Method which uses a proprietary particle-based kinetic solver and a LES turbulent model coupled with the generalized law of the wall (WMLES). The Lattice-Boltzmann method discretizes the continuous Boltzmann equation, a transport equation for the particle probability distribution function. From the Boltzmann transport equation, and by means of the Chapman-Enskog expansion, the compressible Navier-Stokes equations can be recovered. However to simulate compressible flows, this method has a Mach number limitation because of the lattice discretization. Thanks to this flexible particle-based approach the traditional meshing process is avoided, the discretization stage is strongly accelerated reducing engineering costs, and computations on complex geometries are affordable in a straightforward way. The projectile that will be used in this work is the Army-Navy Basic Finned Missile (ANF) with a caliber of 0.03 m. The analysis will consist in varying the Mach number from M=0.5 comparing the axial force coefficient, normal force slope coefficient and the pitch moment slope coefficient of the Finned Projectile obtained by XFlow with the experimental data. The slope coefficients will be obtained using finite difference techniques in the linear range of the polar curve. The aim of such an analysis is to find out the limiting Mach number value starting from which the effects of high fluid compressibility (related to transonic flow regime) lead the XFlow simulations to differ from the experimental results. This will allow identifying the critical Mach number which limits the validity of the isothermal formulation of XFlow and beyond which a fully compressible solver implementing a coupled momentum-energy equations would be required.

Keywords: CFD, computational fluid dynamics, drag, finned projectile, lattice-boltzmann method, LBM, lift, mach, pitch

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Authors: Juraj Londák, Radoslav Vargic, Pavol Podhradský

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In this contribution, we focus on the technical and economic aspects of using hybrid storage in microgrids for peak shaving. We perform a feasibility analysis of hybrid storage consisting of conventional supercapacitors and chemical batteries. We use multiple real-life consumption profiles from various industry-oriented microgrids. The primary purpose is to construct a digital twin model for reserved capacity simulation and prediction. The main objective is to find the equilibrium between technical innovations, acquisition costs and energy cost savings

Keywords: microgrid, peak shaving, energy storage, digital twin

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Authors: Eslam Mohammed Abdelkader, Mohamed Marzouk, Tarek Zayed

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Infrastructure systems are crucial to every aspect of life on Earth. Existing Infrastructure is subjected to degradation while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges play a crucial role in urban transportation networks. Moreover, they are subjected to high level of deterioration because of the variable traffic loading, extreme weather conditions, cycles of freeze and thaw, etc. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays especially in the large transportation networks due to the huge variance between the need for maintenance actions, and the available funds to perform such actions. Deterioration models represent a very important aspect for the effective use of BMSs. This paper presents a probabilistic time-based model that is capable of predicting the condition ratings of the concrete bridge decks along its service life. The deterioration process of the concrete bridge decks is modeled using semi-Markov process. One of the main challenges of the Markov Chain Decision Process (MCDP) is the construction of the transition probability matrix. Yet, the proposed model overcomes this issue by modeling the sojourn times based on some probability density functions. The sojourn times of each condition state are fitted to probability density functions based on some goodness of fit tests such as Kolmogorov-Smirnov test, Anderson Darling, and chi-squared test. The parameters of the probability density functions are obtained using maximum likelihood estimation (MLE). The condition ratings obtained from the Ministry of Transportation in Quebec (MTQ) are utilized as a database to construct the deterioration model. Finally, a comparison is conducted between the Markov Chain and semi-Markov chain to select the most feasible prediction model.

Keywords: bridge management system, bridge decks, deterioration model, Semi-Markov chain, sojourn times, maximum likelihood estimation

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Authors: Ahmed I. Raheem

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In this thesis, stratified and stratified wavy flow regimes have been investigated numerically for the oil (1.57 mPa s viscosity and 780 kg/m3 density) and water twophase flow in small and large horizontal steel pipes with a diameter between 0.0254 to 0.508 m by ANSYS Fluent software. Volume of fluid (VOF) with two phases flows using two equations family models (Realizable k-

Keywords: CFD, two-phase flow, pressure gradient, volume of fluid, large diameter, horizontal pipe, oil-water stratified and stratified wavy flow

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Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

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Authors: Abhimanyu Thakur, Youngjin Lee

Abstract:

Glioma is a lethal brain cancer whose early diagnosis and prognosis are limited due to the dearth of a suitable technique for its early detection. Current approaches, including magnetic resonance imaging (MRI), computed tomography (CT), and invasive biopsy for the diagnosis of this lethal disease, hold several limitations, demanding an alternative method. Recently, extracellular vesicles (EVs) have been used in numerous biomarker studies, majorly exosomes and microvesicles (MVs), which are found in most of the cells and biofluids, including blood, cerebrospinal fluid (CSF), and urine. Remarkably, glioma cells (GMs) release a high number of EVs, which are found to cross the blood-brain-barrier (BBB) and impersonate the constituents of parent GMs including protein, and lncRNA; however, biophysical properties of EVs have not been explored yet as a biomarker for glioma. We isolated EVs from cell culture conditioned medium of GMs and regular primary culture, blood, and urine of wild-type (WT)- and glioma mouse models, and characterized by nano tracking analyzer, transmission electron microscopy, immunogold-EM, and differential light scanning. Next, we measured the biophysical parameters of GMs-EVs by using atomic force microscopy. Further, the functional constituents of EVs were examined by FTIR and Raman spectroscopy. Exosomes and MVs-derived from GMs, blood, and urine showed distinction biophysical parameters (roughness, adhesion force, and stiffness) and different from that of regular primary glial cells, WT-blood, and -urine, which can be attributed to the characteristic functional constituents. Therefore, biophysical features can be potential diagnostic biomarkers for glioma.

Keywords: glioma, extracellular vesicles, exosomes, microvesicles, biophysical properties

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Authors: Garima Joshi, Pratap Sharan, V. Sreenivas, Nand Kumar, Kameshwar Prasad, Ashima N. Wadhawan

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Schizophrenia is a debilitating disorder and is marked by cognitive impairment, which deleteriously impacts the social and professional functioning along with the quality of life of the patients and the caregivers. Often the cognitive symptoms are in their prodromal state and worsen as the illness progresses; they have proven to have a good predictive value for the prognosis of the illness. It has been shown that intensive cognitive rehabilitation (CR) leads to improvements in the healthy as well as cognitively-impaired subjects. As the majority of population in India falls in the lower to middle socio-economic status and have low education levels, using the existing packages, a majority of which are developed in the West, for cognitive rehabilitation becomes difficult. The use of technology is also restricted due to the high costs involved and the limited availability and familiarity with computers and other devices, which pose as an impedance for continued therapy. Cognitive rehabilitation in India uses a plethora of retraining methods for the patients with schizophrenia targeting the functions of attention, information processing, executive functions, learning and memory, and comprehension along with Social Cognition. Psychologists often have to follow an integrative therapy approach involving social skills training, family therapy and psychoeducation in order to maintain the gains from the cognitive rehabilitation in the long run. This paper reviews the methodologies and cognitive retaining programs used in India. It attempts to elucidate the evolution and development of methodologies used, from traditional paper-pencil based retraining to more sophisticated neuroscience-informed techniques in cognitive rehabilitation of deficits in schizophrenia as home-based or supervised and guided programs for cognitive rehabilitation.

Keywords: schizophrenia, cognitive rehabilitation, neuropsychological interventions, integrated approached to rehabilitation

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Authors: B. Selma

Abstract:

The aim of this study is to test the feasibility of SIPINA method to predict the harmfulness parameters controlling the seismic response. The approach developed takes into consideration both the focal depth and the peak ground acceleration. The parameter to determine is displacement. The data used for the learning of this method and analysis nonlinear seismic are described and applied to a class of models damaged to some typical structures of the existing urban infrastructure of Jassy, Romania. The results obtained indicate an influence of the focal depth and the peak ground acceleration on the displacement.

Keywords: SIPINA algorithm, seism, focal depth, peak ground acceleration, displacement

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Authors: Myung Hwan Na, Ho- Chun Song, EunHee Hong

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We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model.

Keywords: reliability, tires, field data, linear mixed-effects model

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Authors: Badawia Ibrahim, Iman Hussein, Samar El Sheikh, Fatma Abou Elkasem, Hazem Abo Ismael

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Background: Response of breast carcinoma to neoadjuvant chemotherapy (NAC) varies regarding many factors including hormonal receptor status. Breast cancer is a heterogenous disease with different outcomes, hence a need arises for new markers predicting the outcome of NAC especially for the triple negative group when estrogen, progesterone receptors and Her2/neu are negative. FOXP3 is a promising target with unclear role. Aim: To examine the value of FOXP3 expression in locally advanced triple negative breast cancer tumoral cells as well as tumor infiltrating lymphocytes (TILs) and to elucidate its relation to the extent of NAC response. Material and Methods: Forty five cases of immunohistochemically confirmed to be triple negative breast carcinoma were evaluated for NAC (Doxorubicin, Cyclophosphamide AC x 4 cycles + Paclitaxel x 12 weeks, patients with ejection fraction less than 60% received Taxotere or Cyclophosphamide, Methotrexate, Fluorouracil CMF) response in both tumour and lymph nodes status according to Miller & Payne's and Sataloff's systems. FOXP3 expression in tumor as well as TILs evaluated in the pretherapy biopsies was correlated with NAC response in breast tumor and lymph nodes as well as other clinicopathological factors. Results: Breast tumour cells showed FOXP3 positive cytoplasmic expression in (42%) of cases. High FOXP3 expression percentage was detected in (47%) of cases. High infiltration by FOXP3+TILs was detected in (49%) of cases. Positive FOXP3 expression was associated with negative lymph node metastasis. High FOXP3 expression percentage and high infiltration by FOXP3+TILs were significantly associated with complete therapy response in axillary lymph nodes. High FOXP3 expression in tumour cells was associated with high infiltration by FOXP3+TILs. Conclusion: This result may provide evidence that FOXP3 marker is a good prognostic and predictive marker for triple negative breast cancer (TNBC) indicated for neoadjuvant chemotherapy and can be used for stratifications of TNBC cases indicated for NAC. As well, this study confirmed the fact that the tumour cells and the surrounding microenvironment interact with each other and the tumour microenvironment can influence the treatment outcomes of TNBC.

Keywords: breast cancer, FOXP3 expression, prediction of neoadjuvant chemotherapy effect, triple negative

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Authors: B. V. Feujofack Kemda, N. Barka, M. Jahazi, D. Osmani

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Automobile manufacturers are constantly seeking means to reduce the weight of car bodies. The usage of several steel grades in auto body assembling has been found to be a good technique to enlighten vehicles weight. This few years, the usage of dual phase (DP) steels, transformation induced plasticity (TRIP) steels and boron steels in some parts of the auto body have become a necessity because of their lightweight. However, these steels are martensitic, when they undergo a fast heat treatment, the resultant microstructure is essential, made of martensite. Resistance spot welding (RSW), one of the most used techniques in assembling auto bodies, becomes problematic in the case of these steels. RSW being indeed a process were steel is heated and cooled in a very short period of time, the resulting weld nugget is mostly fully martensitic, especially in the case of DP, TRIP and boron steels but that also holds for plain carbon steels as AISI 1010 grade which is extensively used in auto body inner parts. Martensite in its turn must be avoided as most as possible when welding steel because it is the principal source of brittleness and it weakens weld nugget. Thus, this work aims to find a mean to reduce martensite fraction in weld nugget when using RSW for assembling. The prediction of phase transformation kinetics during RSW has been done. That phase transformation kinetics prediction has been made possible through the modelling of the whole welding process, and a technique called post weld heat treatment (PWHT) have been applied in order to reduce martensite fraction in the weld nugget. Simulation has been performed for AISI 1010 grade, and results show that the application of PWHT leads to the formation of not only martensite but also ferrite, bainite and pearlite during the cooling of weld nugget. Welding experiments have been done in parallel and micrographic analyses show the presence of several phases in the weld nugget. Experimental weld geometry and phase proportions are in good agreement with simulation results, showing here the validity of the model.

Keywords: resistance spot welding, AISI 1010, modeling, post weld heat treatment, phase transformation, kinetics

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Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

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Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

Abstract:

Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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