Search results for: measurement accuracy

Authors: Xi Vivien Wu, Emily Neo Kim Ang, Yi Jung Tung, Wenru Wang

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Background and Objective: The increase in life expectancy and emphasis on self-reliance for the older adults are global phenomena. As such, lifelong learning in the community is considered a viable means of promoting successful and active aging. This systematic review aims to examine various lifelong learning programs for community-dwelling older adults and to synthesize the contents and outcomes of these lifelong learning programs. Methods: A systematic search was conducted in July to December 2016. Two reviewers were engaged in the process to ensure creditability of the selection process. Narrative description and analysis were applied with the support of a tabulation of key data including study design, interventions, and outcomes. Results: Eleven articles, which consisted of five randomized controlled trials and six quasi-experimental studies, were included in this review. Interventions included e-health literacy programs with the aid of computers and the Internet (n=4), computer and Internet training (n=3), physical fitness programs (n=2), music program (n=1), and intergenerational program (n=1). All studies used objective measurement tools to evaluate the outcomes of the study. Conclusion: The systematic review indicated lifelong learning programs resulted in positive outcomes in terms of physical health, mental health, social behavior, social support, self-efficacy and confidence in computer usage, and increased e-health literacy efficacy. However, the lifelong learning programs face challenges such as funding shortages, program cuts, and increasing costs. A comprehensive lifelong learning program could be developed to enhance the well-being of the older adults at a more holistic level. Empirical research can be done to explore the effectiveness of this comprehensive lifelong learning program.

Keywords: community-dwelling older adults, e-health literacy program, lifelong learning program, the wellbeing of the older adults

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Authors: Maha Saliba Foster

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Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: G. Ravindranath, S. Savitha

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This paper presents heat transfer analysis of single horizontal bare tube and heat transfer analysis of staggered arrangement of bare tube bundles bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on experimental data. Fluidized bed provide nearly isothermal environment with high heat transfer rate to submerged objects i.e. due to through mixing and large contact area between the gas and the particle, a fully fluidized bed has little temperature variation and gas leaves at a temperature which is close to that of the bed. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold bubbling air-fluidized bed of size 0.305 m. x 0.305 m. Studies were conducted for single horizontal Bare Tube of length 305mm and 28.6mm outer diameter and for bare tube bundles of staggered arrangement using beds of large (average particle diameter greater than 1 mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter ( Dp), Fluidizing Velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 out puts. The network predictions are found to be in very good agreement with the experimental observed values of bare heat transfer coefficient (hb) and nusselt number of bare tube (Nub).

Keywords: fluidized bed, large particles, particle diameter, ANN

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

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Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: Bouchou Aïssa, Mohamed Akbi

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Contact materials used for electrical breakers are often made with silver alloys. Mechanical and thermo dynamical properties as well as electron emission of such complicated alloys present a lack of reliable and accurate experimental data. This paper deals mainly with electron work function (EWF) measurements about silver-metal oxide (Ag-MeO) electrical contacts (Ag-ZnO (92/8), before and after surface heat treatments at 296 K  813 K, under UHV conditions (residual gas pressure of 1.4 x 10-7 mbar). The electron work function (EWF) of silver zinc oxide materials was measured photoelectrically, using both Fowler’s method of isothermal curves and linearized Fowler plots. In this paper, we present the development of a method for measuring photoelectric work function of contact materials. Also reported in this manuscript are the results of experimental work whose purpose has been the buildup of a reliable photoelectric system and associated monochromatic ultra-violet radiations source, and the photoelectric measurement of the electron work functions (EWF) of contact materials. In order to study the influence of annealing temperature on the EWF, a vacuum furnace was used for heating the metallic samples up to 800 K. The EWF of the silver – zinc oxide materials were investigated to study the influence of annealing temperature on the EWF. In the present study, the photoelectric measurements about Ag-ZnO(92/8) contacts have shown a linear decrease of the EWF with increasing temperature, i.e. the temperature coefficient is constant and negative: for the first annealing # 1, in the temperature range [299 K  823 K]. On the contrary, a linear increase was observed with increasing temperature (i.e. , being constant and positive), for the next annealing # 2, in the temperature range [296 K  813 K]. The EWFs obtained for silver-zinc oxide Ag-ZnO(92/8) show an obvious dependence on the annealing temperature which is strongly associated with the evolution of the arrangement on ZnO nano particles on the Ag-ZnO contact surface as well as surface charge distribution.

Keywords: Photoemission, Electron work function, Fowler methods, Ag-ZnO contact materials, Vacuum heat treatment

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Kamil Jonski

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Last year vividly demonstrated, that populism and political instability can endanger democratic institutions in countries regarded as democratic transition champions (Poland) or cornerstones of liberal order (UK, US). So called ‘illiberal democracy’ is winning hearts and minds of voters, keen to believe that rule of strongman is a viable alternative to perceived decay of western values and institutions. These developments pose a serious threat to the democratic institutions (including rule of law), proven critical for both personal freedom and economic development. Although scholars proposed some structural explanations of the illiberal wave (notably focusing on inequality, stagnant incomes and drawbacks of globalization), they seem to have little predictive value. Indeed, events like Trump’s victory, Brexit or Polish shift towards populist nationalism always came as a surprise. Intriguingly, in the case of US election, simple rules like ‘Bread and Peace model’ gauged prospects of Trump’s victory better than pundits and pollsters. This paper attempts to compile set of indicators, in order to gauge various democracies’ vulnerability to populism, instability and pursuance of ‘illiberal’ projects. Among them, it identifies the gap between consensus assessment of institutional performance (as measured by WGI indicators) and citizens’ subjective assessment (survey based confidence in institutions). Plotting these variables against each other, reveals three clusters of countries – ‘predictable’ (good institutions and high confidence, poor institutions and low confidence), ‘blind’ (poor institutions, high confidence e.g. Uzbekistan or Azerbaijan) and ‘disillusioned’ (good institutions, low confidence e.g. Spain, Chile, Poland and US). It seems that this clustering – carried out separately for various institutions (like legislature, executive and courts) and blended with economic indicators like inequality and living standards (using PCA) – offers reasonably good watchlist of countries, that should ‘expect the unexpected’.

Keywords: illiberal democracy, populism, political instability, political risk measurement

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Authors: Shaila Bahl, S. P. Lochab, Pratik Kumar

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Nanotechnology and nanomaterials have attracted researchers from different fields, especially from the field of luminescence. Recent studies on various luminescent nanomaterials have shown their relevance in dosimetry of ionizing radiations for the measurements of high doses using the Thermoluminescence (TL) technique, where the conventional microcrystalline phosphors saturate. Ion beams have been used for diagnostic and therapeutic purposes due to their favorable profile of dose deposition at the end of the range known as the Bragg peak. While dealing with human beings, doses from these beams need to be measured with great precision and accuracy. Henceforth detailed investigations of suitable thermoluminescent dosimeters (TLD) for dose verification in ion beam irradiation are required. This paper investigates the TL response of nanocrystalline BaSO4 doped with Eu to 85 MeV carbon beam. The synthesis was done using Co-precipitation technique by mixing Barium chloride and ammonium sulphate solutions. To investigate the crystallinity and particle size, analytical techniques such as X-ray diffraction (XRD) and Transmission electron microscopy (TEM) were used which revealed the average particle sizes to 45 nm with orthorhombic structure. Samples in pellet form were irradiated by 85 MeV carbon beam in the fluence range of 1X1010-5X1013. TL glow curves of the irradiated samples show two prominent glow peaks at around 460 K and 495 K. The TL response is linear up to 1X1013 fluence after which saturation was observed. The wider linear TL response of nanocrystalline BaSO4: Eu and low fading make it a superior candidate as a dosimeter to be used for detecting the doses of carbon beam.

Keywords: radiation, dosimetry, carbon ions, thermoluminescence

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Kalsoom Rani

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Tobacco contains nicotine, which causes addiction to many toxic chemicals. In the world, people consume it in the form of smoke, chew, and sniffing, smoke of it is composed of almost 7000 active chemicals, which are very harmful to human health as well as for eye health, inhalation of tobacco smoke and fumes can accelerate and cause many blinding eye diseases. Dry eye and smoking have not been covered extensively in researches; more studies are required to unveil the relationship between smoking and dry eye. This study was conducted to determine the quantity and quality of tears in smokers. 60 subjects participated in the study, which was divided into two groups on the basis of consumption of cigarettes per day with age matched non smokers of 15-50 years. All participants have gone through a study based questioner, eye examination, and diagnostic 'Dry Eye Tests' for evaporative tears evaluation and measurement of basal tear secretion. Subjects were included in the criteria of 10 cigarettes per day with a minimum duration of 1 year; passive smokers for control groups were excluded. The study was carried out in a Medina Teaching Hospital, Faisalabad, Pakistan, ophthalmology department for the duration of 8 months. Mean values for tear break up time (TBUT), was reported 10sec with SD of +3.74 in controlled group, 5sec with SD + 2.32 in smokers and 4sec SD +3.77 heavy smokers in right eye (RE) and left eye (LE) 10.35sec with SD of +3.88 in controlled 5sec with SD + 2.3 in smokers and much reduced TBUT in heavy smokers was 3.85sec SD+2.20. Smoking has a very strong association with TRUT with a significance of P=.00 both eyes. Mean Schirmer-I value of the subjects was reported 12.6mm with SD + 8.37 in RE and 12.59mm with SD + 8.96 LE. The mean Schirmer-II test value was reported in the right, and left eye with a mean value for control was 20.23mm with SD + 8.93, 20.75mm with SD + 8.84 respectively, and in Smokers 9.90mm with SD + 5.74, and 10.07mm with SD + 6.98, and in heavy smokers 7.7mm, SD + 3.22 and 6.9, SD + 3.50 mm, association with smoking showed p=.001 in RE and .003 in LE. Smoking has deteriorated effect on both evaporative tear and aqueous tear secretion and causing symptoms of dry eye burning, itching, redness, and watering with epithelial cell damage.

Keywords: tear break-up time, basal tear secretion, smokers, dry eye

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Authors: C. L. Huang, J. J. Sia

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The interest-based social networks, also known as social bookmark sharing systems, are useful platforms for people to conveniently read and collect internet resources. These platforms also providing function of social networks, and users can share and explore internet resources from the social networks. Providing personalized internet resources to users is an important issue on these platforms. This study uses two types of relationship on the social networks—following and follower and proposes a collaborative recommender system, consisting of two main steps. First, this study calculates the relationship strength between the target user and the target user's followings and followers to find top-N similar neighbors. Second, from the top-N similar neighbors, the articles (internet resources) that may interest the target user are recommended to the target user. In this system, users can efficiently obtain recent, related and diverse internet resources (knowledge) from the interest-based social network. This study collected the experimental dataset from Diigo, which is a famous bookmark sharing system. The experimental results show that the proposed recommendation model is more accurate than two traditional baseline recommendation models but slightly lower than the cosine model in accuracy. However, in the metrics of the diversity and executing time, our proposed model outperforms the cosine model.

Keywords: recommender systems, social networks, tagging, bookmark sharing systems, collaborative recommender systems, knowledge management

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Authors: Fariha Akhter Chowdhury, Sabrina Mahboob, Anamika Saha, Afrin Jahan, Mohammad Nurul Islam

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Urinary tract infection (UTI) is frequently experienced by the female population where the prevalence increases with aging. Escherichia coli, one of the most common UTI causing organisms, retains glutathione defense mechanism that aids the organism to withstand the harsh physiological environment of urinary tract, host oxidative immune response and even to affect antibiotic-mediated cell death and the emergence of resistance. In this study, we aimed to investigate the glutathione reductase activity of uropathogenic E. coli (UPEC) by observing the reduced glutathione (GSH) level alteration under stressful condition. Urine samples of 58 patients with UTI were collected. Upon isolation and identification, 88% of the samples presented E. coli as UTI causing organism among which randomly selected isolates (n=9), obtained from urine samples of female patients, were considered for this study. E. coli isolates were grown under normal and stressful conditions where H₂O₂ was used as the stress-inducing agent. GSH level estimation of the isolates in both conditions was carried out based on the colorimetric measurement of 5,5'-dithio-bis (2-nitrobenzoic acid) (DTNB) and GSH reaction product using microplate reader assay. The GSH level of isolated E. coli sampled from adult patients decreased under stress compared to normal condition (p = 0.011). On the other hand, GSH production increased markedly in samples that were collected from elderly subjects (p = 0.024). A significant partial correlation between age and change of GSH level was found as well (p = 0.007). This study may help to reveal ways for better understanding of E. coli pathogenesis of UTI prevalence in elderly patients.

Keywords: Escherichia coli, glutathione reductase activity, oxidative stress, reduced glutathione (GSH), urinary tract infection (UTI)

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Authors: Pontus Backstrom

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In the educational literature on peer effects, attention has been brought to the fact that the mechanisms creating peer effects are still to a large extent hidden in obscurity. The hypothesis in this study is that the Frame Factor Theory can be used to explain these mechanisms. At heart of the theory is the concept of “time needed” for students to learn a certain curricula unit. The relations between class-aggregated time needed and the actual time available, steers and hinders the actions possible for the teacher. Further, the theory predicts that the timing and pacing of the teachers’ instruction is governed by a “criterion steering group” (CSG), namely the pupils in the 10th-25th percentile of the aptitude distribution in class. The class composition hereby set the possibilities and limitations for instruction, creating peer effects on individual outcomes. To test if the theory can be applied to the issue of peer effects, the study employs multilevel structural equation modelling (M-SEM) on Swedish TIMSS 2015-data (Trends in International Mathematics and Science Study; students N=4090, teachers N=200). Using confirmatory factor analysis (CFA) in the SEM-framework in MPLUS, latent variables are specified according to the theory, such as “limitations of instruction” from TIMSS survey items. The results indicate a good model fit to data of the measurement model. Research is still in progress, but preliminary results from initial M-SEM-models verify a strong relation between the mean level of the CSG and the latent variable of limitations on instruction, a variable which in turn have a great impact on individual students’ test results. Further analysis is required, but so far the analysis indicates a confirmation of the predictions derived from the frame factor theory and reveals that one of the important mechanisms creating peer effects in student outcomes is the effect the class composition has upon the teachers’ instruction in class.

Keywords: compositional effects, frame factor theory, peer effects, structural equation modelling

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Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

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Authors: Alongkorn Pekalee, Rossarin Gray

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Various determinants associated with older health include socio-demographic factors and health-promoting behaviors but lack in scholars recommended what factors associated with health status in specific sub-groups of older adults. The current study aims to explore the health-promoting behaviors and to examine and compare the associations of these factors with self-rated health status among three older age cohorts in Thai traditional context. Methods: This study is based on the Survey of Older Persons in Thailand (SOPT), in 2017, conducted by the National Statistical Office (NSO) of Thailand. Participants were classified into three groups by using the Thai contextual recommendation: youngest-old cohort (60-69), old-old cohort (70-79) and oldest old cohort (80 or older). Health promoting behaviors are the behaviors which associated with the health status of older adults include alcohol consumption, smoking, diet, and physical activity. Health status was defined as a subjective measurement by using self-rated health, a simple measure of general health. The socio-demographic factors, health-promoting behaviors, and health status were explained and summarized by descriptive statistics. The binary logistic regression was performed to analyze the data and evaluate the associations between independent and dependent variables. Results: Increase of age contributes to a higher proportion of health-promoting behaviors. All variables were associated with self-reported health status as good health among three older age cohorts statistically significant (p-value = 0.000). However, the influence of income sufficiency on health status is more notable, especially in older adults who aged 60-69 and 70-79. The influence of dietary and physical activity on health status became greater as age increased. Conclusion: the results suggest that income sufficiency should be noted in a plan to promote healthy aging, and co-residence should be more concerned especially in the oldest old cohort. Moreover, the interventions or policies to promote older health behaviors like diet and physical activity should be emphasized in the oldest old cohort more than others.

Keywords: health-promoting behaviors, older adults, self- rated health, Thailand

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Authors: K. K. Kaidarova, Ye. K. Aibuldinov, Zh. B. Iskakova, G. Zh. Alzhanova, S. Zh. Zayrova

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Currently, the existing road infrastructure of Kazakhstan needs the reconstruction of existing highways and the construction of new roads. The solution to this problem can be achieved by replacing traditional building materials with industrial waste, which in their chemical and mineralogical composition are close to natural raw materials and can partially or completely replace some natural binding materials in road construction. In this regard, the purpose of this study is to develop building materials based on the red sludge of the Pavlodar aluminum plant, blast furnace slag of the Karaganda Metallurgical Plant, lime production waste of the Pavlodar Aluminum Plant as a binder for natural loam. Changes in physical and mechanical properties were studied for uniaxial compression strength, linear expansion coefficient, water resistance, and frost resistance of the samples. Nine mixtures were formed with different percentages of these wastes 1-20:25:4; 2-20:25:6; 3-20:25:8; 4-30:30:4; 5-30:30:6; 6-30:30:8; 7-40:35:4; 8-40:35:6; 9-40:35:8 and the mixture identifier were labeled based on the waste content and composition number. The results of strength measurement during uniaxial compression of the samples showed an almost constant increase in strength and amounted to 0.67–3.56 MPa after three days and 3.33–7.38 MPa after 90 days. This increase in compressive strength is a consequence of the addition of lime and becomes more pronounced over time. The water resistance of the developed materials after 90 days was 7.12 MPa, and the frost resistance for the same period was 7.35 MPa. The maximum values of strength determination were shown by a sample of the composition 9-40:35:8. The study of the mineral composition showed that there was no contamination with heavy metals or dangerous substances. It was determined that road materials made of red sludge, blast furnace slag, lime production waste, and natural loam mixture could be used due to their strength indicators and environmental characteristics.

Keywords: production waste, uniaxial compression, water resistance of materials, frost resistance of samples

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Authors: Basant Singh Sikarwar, Mukesh Roy, Ayush Goyal, Priya Ranjan

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This work combines two bodies of knowledge which includes biomedical basis of blood stain formation and fluid communities’ wisdom that such formation of blood stain depends heavily on physical properties. Moreover biomedical research tells that different patterns in stains of blood are robust indicator of blood donor’s health or lack thereof. Based on these valuable insights an innovative screening tool is proposed which can act as an aide in the diagnosis of diseases such Anemia, Hyperlipidaemia, Tuberculosis, Blood cancer, Leukemia, Malaria etc., with enhanced confidence in the proposed analysis. To realize this powerful technique, simple, robust and low-cost micro-fluidic devices, a micro-capillary viscometer and a pendant drop tensiometer are designed and proposed to be fabricated to measure the viscosity, surface tension and wettability of various blood samples. Once prognosis and diagnosis data has been generated, automated linear and nonlinear classifiers have been applied into the automated reasoning and presentation of results. A support vector machine (SVM) classifies data on a linear fashion. Discriminant analysis and nonlinear embedding’s are coupled with nonlinear manifold detection in data and detected decisions are made accordingly. In this way, physical properties can be used, using linear and non-linear classification techniques, for screening of various diseases in humans and cattle. Experiments are carried out to validate the physical properties measurement devices. This framework can be further developed towards a real life portable disease screening cum diagnostics tool. Small-scale production of screening cum diagnostic devices is proposed to carry out independent test.

Keywords: blood, physical properties, diagnostic, nonlinear, classifier, device, surface tension, viscosity, wettability

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Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Alireza Dantism

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Presently, describing the function of a protein sequence is one of the most common problems in biology. Usually, this problem can be facilitated by studying the three-dimensional structure of proteins. In the absence of a protein structure, comparative modeling often provides a useful three-dimensional model of the protein that is dependent on at least one known protein structure. Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) mainly based on its alignment with one or more proteins of known structure (templates). Comparative modeling consists of four main steps 1. Similarity between the target sequence and at least one known template structure 2. Alignment of target sequence and template(s) 3. Build a model based on alignment with the selected template(s). 4. Prediction of model errors 5. Optimization of the built model There are many computer programs and web servers that automate the comparative modeling process. One of the most important advantages of these servers is that it makes comparative modeling available to both experts and non-experts, and they can easily do their own modeling without the need for programming knowledge, but some other experts prefer using programming knowledge and do their modeling manually because by doing this they can maximize the accuracy of their modeling. In this study, a web-based tool has been designed to predict the tertiary structure of proteins using PHP and Python programming languages. This tool is called EasyModel. EasyModel can receive, according to the user's inputs, the desired unknown sequence (which we know as the target) in this study, the protein sequence file (template), etc., which also has a percentage of similarity with the primary sequence, and its third structure Predict the unknown sequence and present the results in the form of graphs and constructed protein files.

Keywords: structural bioinformatics, protein tertiary structure prediction, modeling, comparative modeling, modeller

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Nina Dziatzko, Christopher Stehr, Franziska Struve

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Intercultural competence as an explicit required skill nearly never appears in job advertisements in international or even global contexts. But especially those who have to deal with different nationalities and cultures in their everyday business need to have several intercultural competencies and further a global mind-set. This way the question arises how potential future global managers can be trained to learn these competencies. In this regard, it might be helpful to see if different types of intercultural trainings have different effects on those skills. This paper outlines lessons learned based on the evaluation of two different intercultural trainings for management students. The main differences between the observed intercultural trainings are the amount of theoretical input in relation to hands-on experiences, the number of trainers as well as the used methods to teach implicit cultural rules. Both groups contain management students with the willingness and perspective to work abroad or to work in international context. The research is carried out with a pre-training-survey and a post-training-survey which consists of questions referring the international context of the students and a self-estimation of 19 identified intercultural and global mind-set skills, such as: cosmopolitanism, empathy, differentiation and adaptability. Whereas there is no clear result which training gets overall a significant higher increase of skills, there is a clear difference between the focus of competencies trained by each of the intercultural trainings. This way this research provides a guideline for both academicals institutions as well as companies for the decision between different types of intercultural trainings, if the to be trained required skills are defined. Therefore the efficiency and the accuracy of fit of the education of future global managers get optimized.

Keywords: global mind-set, intercultural competencies, intercultural training, learning experiences

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Authors: Arshad Alfoqaha, Seth Bard, Dustin Demetriou

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A new multi-physics acoustic modeling approach using ANSYS Mechanical FEA and FLUENT CFD methods is developed for modeling servers mounted to racks, such as IBM Z and IBM Power Systems, in data centers. This new approach allows users to determine the thermal and acoustic conditions that people are exposed to within the data center. The sound pressure level (SPL) exposure for a human working inside a hot aisle containment system inside the data center is studied. The SPL is analyzed at the noise source, at the human body, on the rack walls, on the containment walls, and on the ceiling and flooring plenum walls. In the acoustic CFD simulation, it is assumed that a four-inch diameter sphere with monopole acoustic radiation, placed in the middle of each rack, provides a single-source representation of all noise sources within the rack. Ffowcs Williams & Hawkings (FWH) acoustic model is employed. The target frequency is 1000 Hz, and the total simulation time for the transient analysis is 1.4 seconds, with a very small time step of 3e-5 seconds and 10 iterations to ensure convergence and accuracy. A User Defined Function (UDF) is developed to accurately simulate the acoustic noise source, and a Dynamic Mesh is applied to ensure acoustic wave propagation. Initial validation of the acoustic CFD simulation using a closed-form solution for the spherical propagation of an acoustic point source is performed.

Keywords: data centers, FLUENT, acoustics, sound pressure level, SPL, hot aisle containment, IBM

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Authors: Rafiu Babatunde Ibrahim, Richard Oluseyi Taiwo, Abiodun Toheeb Akintunde

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The relationship between road transport infrastructure and commercial activities in Nigeria has been a topical issue and identified as one of the crucial components for economic development in the country. This study examines road transport infrastructure and commercial activities along selected roads in Ede, Nigeria. The study assesses the characteristics of the selected roads, the condition of road infrastructure, the degree of road network connectivity, maintenance culture for the road infrastructure as well as commercial activities along identified roads in the study area. Stratified Sampling Techniques were used to partition the study area into core, Intermediate and Suburb Township zones. Roads were also classified into Major, Distributor and Access Roads. Field observation and measurement, as well as a questionnaire, were used to obtain primary data from 246 systematically sampled respondents along the roads selected, and they were analyzed using descriptive and inferential statistics. The study revealed that most of the roads were characterized by an incidence of potholes. A total of 448 potholes were observed, where Olowoibida Road accounted for (19.0%), Federal Polytechnic Road (17.4%), and Back to Land Road (16.3%). The majority of the selected roads have no street lights and are of open drainage systems. Also, the condition of road surfaces was observed to be deteriorating. Road network connectivity of the study area was found to be poorly connected with 11% using the alpha index and 40% of Gamma index. It was found that the tailoring business (39) is predominant on major roads and Distributor Roads, while petty trading (35) is dominant on the access road. Results of correlation analysis (r = 0.242) show that there is a low positive correlation between road infrastructure and commercial activities; the significant relationships have indeed explained how important it is in influencing commercial activities across the study area. The study concluded by emphasizing the need for the provision of more roads and proper maintenance of the existing ones. This will no doubt improve the commercial activities along the roads in the study area.

Keywords: road transport, infrastructure, commercial activities, maintenance culture

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Authors: Subir Gupta, Subhas Ganguly

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In this paper, we demonstrate the new area of application of image processing in metallurgical images to develop the more opportunity for structure-property correlation based approaches of alloy design. The present exercise focuses on the development of image processing tools suitable for phrase segmentation, grain boundary detection and recognition of micro-constituents in SEM micrographs of ferrite and pearlite steels. A comprehensive data of micrographs have been experimentally developed encompassing the variation of ferrite and pearlite volume fractions and taking images at different magnification (500X, 1000X, 15000X, 2000X, 3000X and 5000X) under scanning electron microscope. The variation in the volume fraction has been achieved using four different plain carbon steel containing 0.1, 0.22, 0.35 and 0.48 wt% C heat treated under annealing and normalizing treatments. The obtained data pool of micrographs arbitrarily divided into two parts to developing training and testing sets of micrographs. The statistical recognition features for ferrite and pearlite constituents have been developed by learning from training set of micrographs. The obtained features for microstructure pattern recognition are applied to test set of micrographs. The analysis of the result shows that the developed strategy can successfully detect the micro constitutes across the wide range of magnification and variation of volume fractions of the constituents in the structure with an accuracy of about +/- 5%.

Keywords: SEM micrograph, metallurgical image processing, ferrite pearlite steel, microstructure

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Authors: Dionysios Kakaroumpas, Jesseka Farago, Stephen Webber

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Athletes and spectators depend on the tennis ranking system to represent the truest caliber of athletic prowess; a careful look at the current ranking system though, reveals its main weakness: it undermines expectations of fans and players. Our study proposes several key changes to the existing ranking formula that provide a fair and accurate approach to measure player performance. The study proposes a modification of the system to value: participation, continued advancement, and overall achievement. The new ranking formula facilitates closing the trust gap, encouraging competition equality, engaging the fan base, attracting investment, and promoting tennis involvement worldwide. To probe the crux of our main contention we performed week-by-week comparisons between results procured from the current and proposed formulae. After performing this rigorous case-study of top players of each gender, the findings strongly indicated that there is identifiable inflation in the ranks and enhanced the conviction that the current system should be updated. The new system is accompanied by a web-based software package freely available to anyone involved or interested in tennis rankings. The software package is designed to automatically calculate new player rankings based on a responsive, multi-faceted formula that also generates projected point scenarios and provides separate rankings for the three different court surfaces. By taking a critical look at the current tennis ranking system with consideration to the perspective of fans, players, and businesses involved, an upgrade is in order for it to maintain the balance of trust between fans and the evaluation process. In closure, this proposed solution increases fair play competition, eliminates rank inflation, and better engages fans, players, and sponsors by bringing in a new era of professional tennis.

Keywords: measurement and evaluation, rules and regulations, sports management and marketing, tennis ranking system

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Authors: Moustafa Elwan, Sohier Shehata, Fatma Sedek, Manar Hussine

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One of the most risky factors that lead to a foot injury during physical activities are both high and low arched feet. Normally the medial longitudinal arch of the foot develops in the first 10 years of life, so flexible flat foot has an inversely relationship with age in the first decade, all over the world, the prevalence of flat foot is increasing. In approximately 15% of foot deformities cases, the deformity does not disappear and remains throughout adulthood, 90% of the clinical cases are complaining from foot problems are due to flatfoot. Flatfoot creates subtalar over pronation, which creates tibial and femoral medial rotation, and that is accompanied with increases of pelvic tilting anteriorly, which may influence the lumbar vertebrae alignment by increasing muscle tension and rotation. Objective: To study the impact of the flexible flatfoot on lumbosacral angle (angle of Ferguson). Methods: This experiment included 40 volunteers (14 females &26 males) gathered from the Faculty of Physical Therapy, Modern University of Technology and Information, Cairo, Egypt, for each participant, four angles were measured in the foot( talar first metatarsal angle, lateral talocalcaneal angle, , Calcaneal first metatarsal angle, calcaneal inclination angle) and one angle in the lumbar region (lumbosacral angle). Measurement of these angles was conducted by using Surgimap Spine software (version 2.2.9.6). Results: The results demonstrated that there was no significant correlation betweenFerguson angle and lateral talocalcaneal (r=0.164, p=0.313). Also, there was no significant correlation between Ferguson angle and talo first metatarsal “Meary’s angle" (r=0.007, p=0.968). Moreover, there was no significant correlation between Ferguson angle and calcaneal-first metatarsal angle (r=0.083, p=0.612). Also, there was no significant correlation between Ferguson angle and calcaneal inclination angle (r= 0.032, p= 0.846). Conclusion: It can be concluded that there is no significant correlation between the flexible flat foot and lumbosacral angle So, more study should be conducted in large sample and different ages and conditions of foot problems.

Keywords: calcaneal first metatarsal, calcaneal inclination, flatfoot, ferguson’s angle, lateral talocalcaneal angle, lumbosacral angle, and talar first metatarsal angle

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Authors: Marine Hascoet, Valentina Giaconi, Ludivine Jamain

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The aim of our study is to examine if the family socio-economic status (SES) has an influence on students’ academic achievement. We first make the hypothesis that the more their families have financial and social resources, the more students succeed at school. We second make the hypothesis that this family SES has also an impact on parents’ expectations about their children educational outcomes. Moreover, we want to study if that parents’ expectations play the role of mediator between parents’ socio-economic status and the student’ self-concept and academic outcome. We test this model with a longitudinal design thanks to the census-based assessment from the System of Measurement of the Quality of Education (SIMCE). The SIMCE tests aim to assess all the students attending to regular education in a defined level. The sample used in this study came from the SIMCE assessments done three times: in 4th, 8th and 11th grade during the years 2007, 2011 and 2014 respectively. It includes 156.619 students (75.084 boys and 81.535 girls) that had valid responses for the three years. The family socio-economic status was measured at the first assessment (in 4th grade). The parents’ educational expectations and the students’ self-concept were measured at the second assessment (in 8th grade). The achievement score was measured twice; once when children were in 4th grade and a second time when they were in 11th grade. To test our hypothesis, we have defined a structural equation model. We found that our model fit well the data (CFI = 0.96, TLI = 0.95, RMSEA = 0.05, SRMR = 0.05). Both family SES and prior achievements predict parents’ educational expectations and effect of SES is important in comparison to the other coefficients. These expectations predict students’ achievement three years later (with prior achievement controlled) but not their self-concept. Our model explains 51.9% of the achievement in the 11th grade. Our results confirm the importance of the parents’ expectations and the significant role of socio-economic status in students’ academic achievement in Chile.

Keywords: Chilean context, parent’s expectations, school achievement, self-concept, socio-economic status

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Authors: Venkatgiri, Ranganath, L. Nagaraja, B. N. Sagar Pandav, S. M. Usturge, D. Dilipkumar, B. V. Shivprakash, B. Bhagwanthappa, D. Jahangir

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A comparative evaluation of Tramadol and Pentazocine as a pre-emptive analgesic in clinical cases of female dogs undergoing ovariohysterectomy was undertaken during this study. During the study, the following parameters were assessed viz., Rectal temperature (ᵒF), Respiratory rate (breaths/min) and Heart rate (beats/min). Hematological and biochemical parameters viz., total erythrocyte count (TEC) (millions/cmm), hemoglobin (g %), otal leucocytes count (TLC) (thousands/cmm), differential leucocytes count (DLC) (%), serum creatinine (mg/dl), plasma protein (mg/dl), blood glucose (mg/dl) was estimated before the surgery and after administration of general anaesthesia and immediate postoperative periods of 0, 12 and 24 hr respectively. Mean Total Pain Score (MTPS) includes measurement of parameters like posture, vocalization, activity level, response to palpation and agitation at different intervals was calculated before surgery and after administration of general anesthesia and post-operative periods of 1, 2, 4, 6, 12hrs and 24 hrs respectively. Mean Total Pain Score (MTPS) was given for each parameter (Posture, Vocalization, Activity Level, Response to Palpation and Agitation) like 0,1,2,3. (maximum score will be given was 4.). Results were revealed in all three groups including control group. There were significant minor alterations in physiological, hematological and biochemical parameters. MTPS (mean total pain score) were revealed and found a significant alteration when compared with control group. In conclusion, Tramadol found to be a better analgesic and had up to 8hrs of analgesic effect and Pentazocine is superior in post-operative pain management when compared to Tramadol because this group of dogs experienced less surgical stress, consumed less anesthetic dose, they recovered early, and they had less MTPS score.

Keywords: dog, pentazocine, tramadol, ovariohysterectomy

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Authors: Ayobami Solomon Popoola

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Yam is majorly a carbohydrate food grown in most parts of the world. It could be boiled, fried or roasted for consumption in a variety of ways. Blanching is an established heat pre-treatment given to fruits and vegetables prior to further processing such as dehydration, canning, freezing etc. Losses of soluble solids during blanching has been a great problem because a reasonable quantity of the water-soluble nutrients are inevitably leached into the blanching water. Without blanching, the high residual levels of reducing sugars after extended storage produce a dark, bitter-tasting product because of the Maillard reactions of reducing sugars at frying temperature. Measurement and prediction of such losses are necessary for economic efficiency in production and to establish the level of effluent treatment of the blanching water. This paper aims at resolving this problem by investigating the effects of cube size and temperature on the rate of diffusional losses of reducing sugars and total sugars during hot water blanching of water-yam. The study was carried out using four temperature levels (65, 70, 80 and 90 °C) and two cubes sizes (0.02 m³ and 0.03 m³) at 4 times intervals (5, 10, 15 and 20 mins) respectively. Obtained data were fitted into Fick’s non-steady equation from which diffusion coefficients (Da) were obtained. The Da values were subsequently fitted into Arrhenius plot to obtain activation energies (Ea-values) for diffusional losses. The diffusion co-efficient were independent of cube size and time but highly temperature dependent. The diffusion coefficients were ≥ 1.0 ×10⁻⁹ m²s⁻¹ for reducing sugars and ≥ 5.0 × 10⁻⁹ m²s⁻¹ for total sugars. The Ea values ranged between 68.2 to 73.9 KJmol⁻¹ and 7.2 to 14.30 KJmol⁻¹ for reducing sugars and total sugars losses respectively. Predictive equations for estimating amount of reducing sugars and total sugars with blanching time of water-yam at various temperatures were also presented. The equation could be valuable in process design and optimization. However, amount of other soluble solids that might have leached into the water along with reducing and total sugars during blanching was not investigated in the study.

Keywords: blanching, kinetics, sugar losses, water yam

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