Search results for: food frequency and biomedical data

Authors: Mansouri Ouarda, Abdennour Cherif, Saidi Malika

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Since the industrial revolution, many anthropogenic activities have caused environmental, considerable and overall changes. The lead represents a very dangerous disruptive for the functioning of the body. In this context the current study aims at evaluating a natural therapy by the use of the plant grass in wheat (Triticum durum) against the toxicity of lead in rat wistar male. The rats were divided into three groups: the control group, the group treated with 600 mg /kg food of lead only (Pb) is the group treated with the combination of 600 mg/kg of food and 9g/rat /day of the plant grass in wheat (Pb-bl). The duration of the treatment is 6 weeks. The results of the biometrics of the organs (thyroid, kidney, testis and epididymis) show no significant difference between the three groups. The dosage of a few parameters and hormonal biochemical shows a decrease in the concentration of the hormone T3 and TSH levels among the group pb alone compared to the control and Pb-Bl. These results have been confirmed by the study of histological slices. A morphological changes represented by a shrinking volume of vesicles with the group treated with Pb alone. A return to the normal state of the structure of the follicles was observed. The concentration in serum testosterone, urea and creatinine was significantly increased among the group treated by Pb only in relation to the control and Pb-Bl. whereas the rate of glucose did not show any significant difference. The histology study of the kidney, testis and epididymal weights show no modification at the group Pb-bl comparing to the control. The parenchyma of the kidney shows a dilation of tubes distal and proximal causing a tubular nephropathy for the batch processed by Pb only. The testicles have marked a destruction or absence of germ cells and the light of some seminiferous are almost empty. Conclusion: The supplementation of the plant Triticum durum has caused a considerable improvement which ensures the return of parameters investigated in the normal state.

Keywords: creatinine, glucose, histological sections, T3, TSH, testosterone

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: Salihah Alghamdi, Surajit Ray

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Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

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Authors: P. Daniels, T. Adonis, P. September-Brown, R. Comalie

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The community engagement unit at the University of the Western Cape was tasked with establishing a community engagement database. The database would store information of all community engagement projects related to the university. The wealth of knowledge obtained from the various disciplines would be used to facilitate interdisciplinary collaboration within the university, as well as facilitating community university partnership opportunities. The purpose of this qualitative study was to explore electronic data collection through the development of a database. Two types of electronic data collection platforms were used, namely online questionnaire and email. The semi structured questionnaire was used to collect data related to community engagement projects from different faculties and departments at the university. There are many benefits for using an electronic data collection platform, such as reduction of costs and time, ease in reaching large numbers of potential respondents, and the possibility of providing anonymity to participants. Despite all the advantages of using the electronic platform, there were as many challenges, as depicted in our findings. The findings suggest that certain barriers existed by using an electronic platform for data collection, even though it was in an academic environment, where knowledge and resources were in abundance. One of the challenges experienced in this process was the lack of dissemination of information via email to staff within faculties. The actual online software used for the questionnaire had its own limitations, such as only being able to access the questionnaire from the same electronic device. In a few cases, academics only completed the questionnaire after a telephonic prompt or face to face meeting about "Is higher education in South Africa ready to embrace electronic platform in data collection?"

Keywords: community engagement, database, data collection, electronic platform, electronic tools, knowledge sharing, university

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Authors: Divya Juneja, Sukhjeet Kaur Matharu

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Purpose: The paper is aimed at identifying the challenging factors experienced by the women entrepreneurs in India in operating their enterprises amidst the challenges posed by the COVID-19 pandemic. Methodology: The sample for the study comprised 396 women entrepreneurs from different regions of India. A purposive sampling technique was adopted for data collection. Data was collected through a self-administered questionnaire. Analysis was performed using the SPSS package for quantitative data analysis. Findings: The results of the study state that entrepreneurial characteristics, resourcefulness, networking, adaptability, and continuity have a positive influence on the resiliency of women entrepreneurs when faced with a crisis situation. Practical Implications: The findings of the study have some important implications for women entrepreneurs, organizations, government, and other institutions extending support to entrepreneurs.

Keywords: women entrepreneurs, analysis, data analysis, positive influence, resiliency

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Maisyatus Suadaa Irfana, Nove Eka Variant Anna, Dyah Puspitasari Sri Rahayu

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Technological developments provide convenience to all the people. Nowadays, the communication of human with the computer is done via text. With the development of technology, human and computer communications have been conducted with a voice like communication between human beings. It provides an easy facility for many people, especially those who have special needs. Voice search technology is applied in the search of book collections in the OPAC (Online Public Access Catalog), so library visitors will find it faster and easier to find books that they need. Integration with Google is needed to convert the voice into text. To optimize the time and the results of searching, Server will download all the book data that is available in the server database. Then, the data will be converted into JSON format. In addition, the incorporation of some algorithms is conducted including Decomposition (parse) in the form of array of JSON format, the index making, analyzer to the result. It aims to make the process of searching much faster than the usual searching in OPAC because the data are directly taken to the database for every search warrant. Data Update Menu is provided with the purpose to enable users perform their own data updates and get the latest data information.

Keywords: OPAC, voice, searching, faster

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

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Authors: Sukalyan Chakraborty

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Arsenic contamination in groundwater has been a matter of serious concern worldwide. Globally, arsenic contaminated water has caused serious chronic human diseases and in the last few decades the transfer of arsenic to human beings via food chain has gained much attention because food represents a further potential exposure pathway to arsenic in instances where crops are irrigated with high arsenic groundwater, grown in contaminated fields or cooked with arsenic laden water. In the present study, the groundwater of Sahibganj district of Jharkhand has been analysed to find the degree of contamination and its probable associated risk due to direct consumption or irrigation. The present study area comprising of three blocks, namely Sahibganj, Rajmahal and Udhwa in Sahibganj district of Jharkhand state, India, situated in the western bank of river Ganga has been investigated for arsenic contamination in groundwater, soil and crops predominantly growing in the region. Associated physicochemical parameters of groundwater including pH, temperature, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), oxidation reduction potential (ORP), ammonium, nitrate and chloride were assessed to understand the mobilisation mechanism and chances of arsenic exposure from soil to crops and further into the food chain. Results suggested the groundwater to be dominantly Ca-HCO3- type with low redox potential and high total dissolved solids load. Major cations followed the order of Ca ˃ Na ˃ Mg ˃ K. The concentration of major anions was found in the order of HCO3− > Cl− > SO42− > NO3− > PO43− varied between 0.009 to 0.20 mg L-1. Fe concentrations of the groundwater samples were below WHO permissible limit varying between 54 to 344 µg L-1. Phosphate concentration was high and showed a significant positive correlation with arsenic. As concentrations ranged from 7 to 115 µg L-1 in premonsoon, between 2 and 98 µg L-1 in monsoon and 1 to 133µg L-1 in postmonsoon season. Arsenic concentration was found to be much higher than the WHO or BIS permissible limit in majority of the villages in the study area. Arsenic was also seen to be positively correlated with iron and phosphate. PCA results demonstrated the role of both geological condition and anthropogenic inputs to influence the water quality. Arsenic was also found to increase with depth up to 100 m from the surface. Calculation of carcinogenic and non-carcinogenic effects of the arsenic concentration in the communities exposed to the groundwater for drinking and other purpose indicated high risk with an average of more than 1 in a 1000 population. Health risk analysis revealed high to very high carcinogenic and non-carcinogenic risk for adults and children in the communities dependent on groundwater of the study area. Observation suggested the groundwater to be considerably polluted with arsenic and posing significant health risk for the exposed communities. The mobilisation mechanism of arsenic also could be identified from the results suggesting reductive dissolution of Fe oxyhydroxides due to high phosphate concentration from agricultural input arsenic release from the sediments along river Ganges.

Keywords: arsenic, physicochemical parameters, mobilisation, health effects

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Authors: Natalia Polkanova, Sergey Kazakov

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In the research, we proposed the set of factors of tourist destinations attractiveness in Spain and determined the factors that have the greatest impact on the positive perception of the tourist destination by Russian tourists; also, we examined what factors create the willingness for Russians to recommend this tourist destination to their friends and relatives. The tourists' comments on the Russian travel sites have been analyzed in order to determine the frequency of attractiveness characteristics references. Additionally, the study will reflect the relationship of variables.

Keywords: tourism destination, destination attractiveness, destination competitiveness, content analysis, unstructured image

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

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Authors: Gayani Ranawake

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Effective communication is widely regarded as an important aspect of any discipline. This particular study deals with written communication in science. Writing conventions and linguistic choices play a key role in conveying the message effectively to a target audience. Scientists are responsible for conveying their findings or research results not only to their discourse community but also to the general public. Recognizing appropriate linguistic choices is crucial since they vary depending on the target audience. The majority of scientists can communicate effectively with their discourse community, but public engagement seems more challenging to them. There is a lack of research into the language use of scientists, and in particular how it varies by discipline and audience (genre). A better understanding of the different linguistic conventions used in effective science writing by scientists for scientists and by scientists for the public will help to guide scientists who are familiar with their discourse community norms to write effectively for the public. This study investigates the differences and similarities of linguistic choices in biology articles written by scientists for their discourse community and biology magazine articles written by scientists and science communicators for the general public. This study is a part of a larger project investigating linguistic differences in different genres of science academic writing. The sample for this particular study is composed of 20 research articles from the journal Biological Reviews and 20 magazine articles from the magazine Australian Popular Science. Differences in the linguistic devices were analyzed using Hyland’s metadiscourse model for academic writing proposed in 2005. The frequency of the usage of interactive resources (transitions, frame markers, endophoric markers, evidentials and code glosses) and interactional resources (hedges, boosters, attitude markers, self-mentions and engagement markers) were compared and contrasted using the NVivo textual analysis tool. The results clearly show the differences in the frequency of usage of interactional and interactive resources in the two disciplines under investigation. The findings of this study provide a reference guide for scientists and science writers to understand the differences in the linguistic choices between the two genres. This will be particularly helpful for scientists who are proficient at writing for their discourse community, but not for the public.

Keywords: discourse analysis, linguistic choices, metadiscourse, science writing

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Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

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Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Taner Ersoz, Filiz Ersoz

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This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: multiple correspondence analysis, multivariate categorical data, health care services, health satisfaction survey

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Authors: Busiswa Madikazi

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This systematic mapping study explores the underrepresentation of women's contributions to farming in the Global South within the development of Information and Communication Technologies (ICT)-based extension methods. Despite women farmers constituting 70% of the agricultural labour force, their productivity is hindered by various constraints, including illiteracy, household commitments, and limited access to credit and markets. A systematic mapping approach was employed with the aim of identifying evidence gaps in existing ICT extension for women farmers. The data collection protocol follows a structured approach, incorporating key criteria for inclusion, exclusion, search strategy, and coding and the PICO strategy (Population, Intervention, Comparator, and Outcome). The results yielded 119 articles that qualified for inclusion. The findings highlight that mobile phone apps (WhatsApp) and radio/television programming are the primary extension methods employed while integrating ICT with training, field visits, and demonstrations are underutilized. Notably, the study emphasizes the inadequate attention to critical issues such as food security, gender equality, and attracting youth to farming within ICT extension efforts. These findings indicate a significant policy and practice gap, neglecting community-driven approaches that cater to women's specific needs and enhance their agricultural production. Map highlights the importance of refocusing ICT extension efforts to address women farmers’ unique challenges, thereby contributing to their empowerment and improving agricultural practices.

Keywords: agricultural extension, ICT, women farmers, smallholders

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Authors: Saman Naqvi

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Introduction: Telemedicine is the practise of providing remote healthcare to patients via the utilisation of communication technologies. Its application has become increasingly important since the Coronavirus Disease 2019 (COVID-19) pandemic. It is essential to determine the knowledge and attitudes of healthcare professionals concerning its use in order to maximise its application. Purpose: We aim to examine and evaluate the current understanding and perceptions of medical staff toward the use of telemedicine. Methods: In this cross-sectional study, we surveyed 1091 healthcare professionals worldwide. Following an extensive review of the literature, data were gathered using a questionnaire. To depict the participant profile, frequency, percentages, and cumulative percentages were determined. Results: The majority of respondents had either heard of (90.9%), seen (65.3%), or were familiar with (74.6%) how telemedicine is implemented in practice. 72.2% of people were familiar with the tools that could be applied to this technology. Those with a medical degree and experience of under five years were found to be more familiar with telemedicine. Additionally, opinions on providing healthcare remotely were largely favorable, with 80% of respondents stating that it reduced staff burden and 80.6% thinking that it eliminated unnecessary transportation costs. Furthermore, 83% expressed that it saves clinicians' time. However, 20% of participants believed telemedicine adds to staff workload and 40% of healthcare professionals felt it compromises patient privacy and information confidentiality. Conclusion: Despite being a new and developing practice in many countries, telemedicine appears to have a bright future. This is crucial during a pandemic as it provides effective healthcare while maintaining social isolation measures. Moreover, the majority of the participants in this study demonstrated a good understanding and a favorable attitude toward telemedicine.

Keywords: healthcare system, global survey, knowledge, attitude, covid 19, telemedicine

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Authors: B. H. Lee

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Nowadays, a grid-connected photovoltaic (PV) inverter is adopted in various places like as home, factory, because grid-connected PV inverter can reduce total power consumption by supplying electricity from PV array. In this paper, design and implementation of a 300 W grid-connected PV inverter are described. It is implemented with TI Piccolo DSP core and operated at 100 kHz switching frequency in order to reduce harmonic contents. The maximum operating input voltage is up to 45 V. The characteristics of the designed system that include maximum power point tracking (MPPT), single operation and battery charging are verified by simulation and experimental results.

Keywords: design, grid-connected, implementation, photovoltaic

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Authors: Orly Yona Drori, Shirley Ben Shlomo

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Objectives: The participants will be able to recognize the predictors of binge eating addiction among women who suffer from psychological abuse in their relationships and will recognize the role of woman's defense mechanisms in moderating the association between psychological abuse and binge eating addiction. Methods: A convenience sample of 380 Israeli women in relationships were located via the Internet, and after consenting to participate in the study, they completed a series of structured questionnaires (The Yale Food Addiction Scale; The Defense Style Questionnaire; psychological maltreatment of women by their male partners; level of differentiation of self; sociodemographic questionnaire). Results: The higher the level of differentiation and mature defense mechanisms, the less addictive a woman is. However, the level of addiction among women who experience psychological abuse within their intimate relations is higher than women who do not experience psychological abuse in their relationship. Among women who experienced psychological abuse in their relations, the defense mechanisms moderate the association between psychological abuse within intimate relations and the extent of the addiction to binge eating. Conclusions: The study contributes to the therapy of women with binge eating addictions, as it raises awareness of therapeutic-related content that could strengthen women and help them to cope with situations in their lives without the need to binge. One of the significant variables for therapeutic work is the level of differentiation of the self. In addition, identifying the types of defense mechanisms might help to match treatment to the woman's emotional needs. The current study found also that it is important to identify the environmental systems by which the addict is surrounded, such as whether woman is in an abusive relationship. Finally the study leads to the recognition that binge eating, which is usually treated with an emphasis on nutritional behavior change, is an addiction, and as such, it requires a combination of mental, nutritional and behavioral therapy. In view of this approach, it is recommended that treating a woman who is addicted to binge eating should involve a multi-disciplinary team comprised of physicians, clinical dietitians, and clinical psychotherapists.

Keywords: binge eating, defence mechanism, food addiction, psychological abuse

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Authors: Paul Dallyn, Ashraf El-Hamalawi, Alessandro Palmeri, Bob Knight

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In order to better understand the long term implications of the grout wear failure mode in large-diameter plain-sided grouted connections, a numerical model has been developed and calibrated that can take advantage of existing operational plant data to predict the wear accumulation for the actual load conditions experienced over a given period, thus limiting the need for expensive monitoring systems. This model has been derived and calibrated based on site structural condition monitoring (SCM) data and supervisory control and data acquisition systems (SCADA) data for two operational wind turbine generator substructures afflicted with this challenge, along with experimentally derived wear rates.

Keywords: grouted connection, numerical model, offshore structure, wear, wind energy

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Authors: Jae-Hyun Ro, Yong-Jun Kim, Chang-Bin Ha, Hyoung-Kyu Song

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In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection.

Keywords: detection, DFE, MIMO-OFDM, ML

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Othman Y. Alothman

Abstract:

The study aims to investigate the effects of blend ratio, VA content and temperature on the rheological properties of PPEVA blends. The results show that all pure polymers and their blends show typical shear thinning behaviour. All neat polymers exhibit power-low type flow behaviour, with the viscosity order as EVA328 > EVA206 > PP in almost all frequency ranges. As temperature increases, the viscosity of all polymers decreases as expected, and the viscosity becomes more sensitive to the addition of EVA. Two different regions can be observed on the flow curve of some of the polymers and their blends, which is thought to be due to slip-stick transition or melt fracture.

Keywords: polypropylene, ethylene vinyl acetate, blends, rheological properties

Procedia PDF Downloads 467

Authors: Shveta Puri, Shiv Jyoti Pandey, G. M. Bhat, Neha Raina

Abstract:

TheJammu and Kashmir region of the Northwest Himalaya had witnessed many devastating earthquakes in the recent past and has remained unexplored for any kind of seismic investigations except scanty records of the earthquakes that occurred in this region in the past. In this study, we have used local seismic data of year 2013 that was recorded by the network of Broadband Seismographs in J&K. During this period, our seismic stations recorded about 207 earthquakes including two moderate events of Mw 5.7 on 1st May, 2013 and Mw 5.1 of 2nd August, 2013.We analyzed the events of Mw 3-4.6 and the main events only (for minimizing the error) for source parameters, b value and sense of movement through waveform modeling for understanding seismotectonic and seismic hazard of the region. It has been observed that most of the events are bounded between 32.9° N – 33.3° N latitude and 75.4° E – 76.1° E longitudes, Moment Magnitude (Mw) ranges from Mw 3 to 5.7, Source radius (r), from 0.21 to 3.5 km, stress drop, from 1.90 bars to 71.1 bars and Corner frequency, from 0.39 – 6.06 Hz. The b-value for this region was found to be 0.83±0 from these events which are lower than the normal value (b=1), indicating the area is under high stress. The travel time inversion and waveform inversion method suggest focal depth up to 10 km probably above the detachment depth of the Himalayan region. Moment tensor solution of the (Mw 5.1, 02:32:47 UTC) main event of 2ndAugust suggested that the source fault is striking at 295° with dip of 33° and rake value of 85°. It was found that these events form intense clustering of small to moderate events within a narrow zone between Panjal Thrust and Kishtwar Window. Moment tensor solution of the main events and their aftershocks indicating thrust type of movement is occurring in this region.

Keywords: b-value, moment tensor, seismotectonics, source parameters

Procedia PDF Downloads 308

Authors: Dachuan Shi, M. Hecht, Y. Ye

Abstract:

With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

Procedia PDF Downloads 127

Authors: Etty Soesilowati

Abstract:

This research was intended to: (1) designing production systems that produce high quality salt and (2) diversification of salt products. This research used qualitative and quantitative approaches which Garam Mas Ltd. as the research site. The data were analyzed interactively and subjected to laboratory tests. The analyses showed that salt production system using HDPE geomembranes produced whiter and cleaner salts than those produced by conventional methods without HDPE geomembranes. High quality consumption salt contained 97% NaCl and a maximum of 0.05% water, in the form of white minute crystals and usually used for table salt of food and snack seasoning, souses and cheese and vegetable oil industries. Medium grade salt contained 94.7%-97% NaCl and 3%-7% water and usually used for kitchen salt, soy sauce, tofu industries and cattle feeding. Low quality salt contained 90%-94.7% NaCl and 5%-10% water, with dull white color and usually used for fish preservation and agriculture. The quality and quantity of salts production were influenced by temperatures, weather, water concentrations used during production processes and the discipline of salt farmers itself. The use of water temperature less than 23 °Be during the production processes produced low quality salts. Optimizing cultivation of the production process from raw material to end product (consumption salt) should be attempted to produce quality salt that fulfills the Indonesian National Standard. Therefore, the integrated policies among stakeholders are really needed to build strong institutional base at salt farmer level. This might be achieved through the establishment of specific region for salt production.

Keywords: cultivation system, diversification, salt products, high quality salt

Procedia PDF Downloads 395

Authors: Burcu Guvenek, Duygu Baysal Kurt

Abstract:

The impact of foreign trade on economic growth has been discussed since the Classical Economists. Today, foreign trade has become more important for the country's economy with the increasing globalization. When it comes to foreign trade, policies which may vary from country to country and from time to time as protectionism or free trade are implemented. In general, the positive effect of foreign trade on economic growth is alleged. However, as studies supporting this general acceptance take place in the economics literature, there are also studies in the opposite direction. In this paper, the impact of foreign trade on economic growth will be investigated with the help of panel data analysis. For this research, 24 OECD countries’ GDP and foreign trade data, including the period of 1990 and 2010, will be used.

Keywords: foreign trade, economic growth, OECD countries, panel data analysis

Procedia PDF Downloads 380

Authors: Sathapath Kilaso

Abstract:

Nowadays people who live in the city tend to have a pet in order to relief the loneliness more than usual. It can be observed by the growth of the local pet industry. But the essentials of lifestyle of the urban people which is restricted by time and work might not allow the owner to take care of the pet properly. So this article will be about how to develop the prototype of pet care monitoring with Arduino Microcontroller. This prototype can be used to monitor the pet and its environment around the pet such as temperature (both pet’s temperature and outside temperature), humidity, food’s quantity, air’s quality and also be able to reduce the stress of the pet. This prototype can report the result back to the owner via online-channel such as website etc.

Keywords: pet care, Arduino Microcontroller, monitoring, prototype

Procedia PDF Downloads 350