Search results for: virtual machine migration

Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Hamisu Idi

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The present study focuses on studying the impact of influencer recommendation in the quality of cement production. Exploratory research was done on monthly basis, where data were obtained from secondary source i.e. the record kept by an automated recompilation machine. The machine keeps all the records of the mills downtime which the process manager checks for validation and refer the fault (if any) to the department responsible for maintenance or measurement taking so as to prevent future occurrence. The findings indicated that the product of the Ashaka Cement Plc. were considered as qualitative, since all the production processes were found to be in control (preset specifications) with the exception of the natural cause of variation which is normal in the production process as it will not affect the outcome of the product. It is reduced to the bearest minimum since it cannot be totally eliminated. It is also hopeful that the findings of this study would be of great assistance to the management of Ashaka cement factory and the process manager in particular at various levels in the monitoring and implementation of statistical process control. This study is therefore of great contribution to the knowledge in this regard and it is hopeful that it would open more research in that direction.

Keywords: cement, quality, variation, assignable cause, common cause

Procedia PDF Downloads 261

Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

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The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

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Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems.

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Iqbaldeep Kaur, Amarjeet Kaur

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Software Reusability is important part of software development. So component based software development in case of software testing has gained a lot of practical importance in the field of software engineering from academic researcher and also from software development industry perspective. Finding test cases for efficient reuse of test cases is one of the important problems aimed by researcher. Clustering reduce the search space, reuse test cases by grouping similar entities according to requirements ensuring reduced time complexity as it reduce the search time for retrieval the test cases. In this research paper we proposed approach for re-usability of test cases by unsupervised approach. In unsupervised learning we proposed k-mean and Support Vector Machine. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

Keywords: software testing, reusability, clustering, k-mean, SVM

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Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Rania Ahmed Kadry Abdel Gawad Birry, Mohamed El-Habrouk

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This paper presents a brief survey of the techniques used for sign language recognition along with the types of sensors used to perform the task. It presents a modified method for identification of an isolated sign language gesture using Microsoft Kinect with the OpenNI framework. It presents the way of extracting robust features from the depth image provided by Microsoft Kinect and the OpenNI interface and to use them in creating a robust and accurate gesture recognition system, for the purpose of ASL translation. The Prime Sense’s Natural Interaction Technology for End-user - NITE™ - was also used in the C++ implementation of the system. The algorithm presents a simple finger counting algorithm for static signs as well as directional Finite State Machine (FSM) description of the hand motion in order to help in translating a sign language gesture. This includes both letters and numbers performed by a user, which in-turn may be used as an input for voice pronunciation systems.

Keywords: American sign language, finger counting, hand tracking, Microsoft Kinect

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Merey M. Sarsengeldin, Alexandr S. Kolokhmatov, Galiya Seidaliyeva, Alexandr Ozerov, Sanim T. Imatayeva

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This series of interdisciplinary studies is an attempt to investigate and develop a scalable model of fair socioeconomic relations on the base of blockchain using positive psychology techniques and Machine Learning algorithms for data analytics. In this particular study, we use hyperinteraction approach and intuition to investigate their influence on 'wisdom of crowds' via created mobile application which was created for the purpose of this research. Along with the public blockchain and private Decentralized Autonomous Organization (DAO) which were elaborated by us on the base of Ethereum blockchain, a model of fair financial relations of members of DAO was developed. We developed a smart contract, so-called, Fair Price Protocol and use it for implementation of model. The data obtained from mobile application was analyzed by ML algorithms. A model was tested on football matches.

Keywords: blockchain, Naïve Bayes algorithm, hyperinteraction, intuition, wisdom of crowd, decentralized autonomous organization

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Authors: Manasi Mahanty

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In the current phase of globalization, the mobility of capital facilitates outsourcing and subcontracting of production processes to the developing economies for cheap and flexible labour force. In such process, the globalization of production networks operates at multi-locational points within the nation. Under the new quota regime in the globalization period, the Indian manufacturing exporters came under the influence of corporate buyers and large retailers from the importing countries. As part of such process, the garment manufacturing sector is expected to create huge employment opportunities and to expand the export market in the country. While following these, expectations, the apparel and garment industries mostly target to hire female migrant workers with a purpose of establishing more flexible industrial relations through the casual nature of employment contract. It leads to an increasing women’s participation in the labour market as well as the rise in precarious forms of female paid employment. In the context, the main objective of the paper is to understand the wider dynamics of globalization of production and its link with informalization, feminization of labour force and internal migration process of the country. For this purpose, the study examines the changing labour relations in the KINFRA Apparel Park at Kerala’s Special Economic Zone which operates under the scheme ‘Apparel Parks for Export’ (APE) of the Government of India. The present study was based on both quantitative and qualitative analysis. In the first, the secondary sources of data were collected from the source location (SEAM centre) and destination (KINFRA Park). The official figures and data were discussed and analyzed in order to find out the various dimensions of labour relations under globalization of production. In the second, the primary survey was conducted to make a comparative analysis of local and migrant female workers. The study is executed by taking 100 workers in total. The local workers comprised of 53% of the sample whereas the outside state workers were 47%. Even personal interviews with management staff, and workers were also made for collecting the information regarding the organisational structure, nature, and mode of recruitment, work environment, etc. The study shows the enormous presence of rural women migrant workers in KINFRA Apparel Park. A Public Private Partnership (PPP) arranged migration system is found as Skills for Employment in Apparel Manufacturing (SEAM) from where young women and girls are being sent to work in garment factories of Kerala’s KINFRA International Apparel Park under the guise of an apprenticeship based recruitment. The study concludes that such arrangements try to avoid standard employment relationships and strengthen informalization, casualization and contractualization of work. In this process, the recruitment of women migrant workers is to be considered as best option for the employers of private industries which could be more easily hired and fired.

Keywords: female migration, globalization, informalization, KINFRA apparel park

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Authors: Dimpy Shah

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The global economy has substantially changed in last three decades. Now almost all markets are transparent and visible for global customers. The corporates are now no more reliant on local markets for trade. The information technology revolution has changed business dynamics and marketing practices of corporate. The markets are divided into two different formats: traditional and virtual. In very short span of time, many e-commerce portals have captured global market. This strategy is well supported by global delivery system of multinational logistic companies. Now the markets are dealing with global supply chain networks, which are more demand driven and customer oriented. The corporate have realized importance of supply chain integration and marketing in this competitive environment. The Indian markets are also significantly affected with all these changes. In terms of population, India is in second place after China. In terms of demography, almost half of the population is of youth. It has been observed that the Indian youth are more inclined towards e-commerce and prefer to buy goods from web portal. Initially, this trend was observed in Indian service sector, textile and electronic goods and now further extended in other product categories. The FMCG companies have also recognized this change and started integration of their supply chain with e-commerce platform. This paper attempts to understand contemporary marketing practices of corporate in e-commerce business in Indian food and beverages segment and also tries to identify innovative marketing practices for proper execution of their strategies. The findings are mainly focused on supply chain re-integration and brand building strategies with proper utilization of social media.

Keywords: FMCG (Fast Moving Consumer Goods), ISCM (Integrated supply chain management), RFID (Radio Frequency Identification), traditional and virtual formats

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Authors: Lakshminarayanan, Vijayaraghavan, Krishnamurthy

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The ever-increasing demand for high efficiency in automotive and aerospace applications requires new materials to suit to high temperature applications. The Ceramic Matrix Composites nowadays find its applications for high strength and high temperature environments. In this paper, Al2O3 and Sic ceramic materials are taken in particulate form as matrix and reinforcement respectively. They are blended together in Ball Milling and compacted in Cold Compaction Machine by powder metallurgy technique. Scanning Electron Microscope images are taken for the samples in order to find out proper blending of powders. Micro harness testing is also carried out for the samples in Vickers Micro Hardness Testing Equipment. Surface grinding of the samples is also carried out in Surface Grinding Machine in order to find out grinding force estimates. The surface roughness of the grounded samples is also taken in Surface Profilometer. These are yielding promising results.

Keywords: ceramic matrix composite, cold compaction, material characterization, particulate and surface grinding

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Authors: Ying-Yi Laio, Yea-Ru Yang, Yih-Ru Wu, Ray-Yau Wang

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Background: Virtual reality (VR) systems are proved to increase motor performance in stroke and elderly. However, the effects have not been established in patients with Parkinson’s disease (PD). Purpose: To examine the effects of VR based training in improving muscle strength, sensory integration ability and walking abilities in patients with PD by a randomized controlled trial. Method: Thirty six participants with diagnosis of PD were randomly assigned to one of the three groups (n=12 for each group). Participants received VR-based Wii Fit exercise (VRWii group) or traditional exercise (TE group) for 45 minutes, followed by treadmill training for another 15 minutes for 12 sessions in 6 weeks. Participants in the control group received no structured exercise program but fall-prevention education. Outcomes included lower extremity muscle strength, sensory integration ability, walking velocity, stride length, and functional gait assessment (FGA). All outcomes were assessed at baseline, after training and at 1-month follow-up. Results: Both VRWii and TE groups showed more improvement in level walking velocity, stride length, FGA, muscle strength and vestibular system integration than control group after training and at 1-month follow-up. The VRWii training, but not the TE training, resulted in more improvement in visual system integration than the control. Conclusions: VRWii training is as beneficial as traditional exercise in improving walking abilities, sensory integration ability and muscle strength in patients with PD, and such improvements persisted at least for 1 month. The VRWii training is then suggested to be implemented in patients with PD.

Keywords: virtual reality, walking, sensory integration, muscle strength, Parkinson’s disease

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Hassan Mahdi

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The integration of computer-assisted translation (CAT) tools in translation creates several opportunities for translators. However, this integration is not useful in all types of English structures. This study aims at examining the impact of using CAT tools in translating collocations. Seventy students of English as a foreign language participated in this study. The participants were divided into three groups (i.e., CAT tools group, Machine Translation group, and the control group). The comparison of the results obtained from the translation output of the three groups demonstrated the improvement of translation using CAT tools. The results indicated that the participants who used CAT tools outscored the participants who used MT, and in turn, both groups outscored the control group who did not use any type of technology in translation. In addition, there was a significant difference in the use of CAT for translation different types of collocations. The results also indicated that CAT tools were more effective in translation fixed and medium-strength collocations than weak collocations. Finally, the results showed that CAT tools were effective in translation collocations in both types of languages (i.e. target language or source language). The study suggests some guidelines for translators to use CAT tools.

Keywords: machine translation, computer-assisted translation, collocations, technology

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Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead

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Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: classification, falls, health risk factors, machine learning, older adults

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Authors: Shaoying Guo, Yanyun Xu, Meng Zhang, Weiqing Huang

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The wireless communication network is developing rapidly, thus the wireless security becomes more and more important. Specific emitter identification (SEI) is an vital part of wireless communication security as a technique to identify the unique transmitters. In this paper, a SEI method based on multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The algorithms of MDE and RCMDE are used to extract features for identification of five wireless devices and cross-validation support vector machine (CV-SVM) is used as the classifier. The experimental results show that the total identification accuracy is 99.3%, even at low signal-to-noise ratio(SNR) of 5dB, which proves that MDE and RCMDE can describe the communication signal series well. In addition, compared with other methods, the proposed method is effective and provides better accuracy and stability for SEI.

Keywords: cross-validation support vector machine, refined com- posite multiscale dispersion entropy, specific emitter identification, transient signal, wireless communication device

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Yuli Hou, Jianping Sun, Mengdan Gao, Hui Liu, Ling Qin, Ang Li, Dongfu Li, Yonghong Zhang, Yan Zhao

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Background and Aims: Interferon-induced transmembrane protein 3 (IFITM3) is a component of ISG (Interferon-Stimulated Gene) family. IFITM3 has been recognized as a key signal molecule regulating cell growth in some tumors. However, the function of IFITM3 rs12252-CC genotype in the hepatocellular carcinoma (HCC) remains unknown to author’s best knowledge. A cohort study was employed to clarify the relationship between IFITM3 rs12252-CC genotype and HCC progression, and cellular experiments were used to investigate the correlation of function of IFITM3 and the progress of HCC. Methods: 336 candidates were enrolled in study, including 156 with HBV related HCC and 180 with chronic Hepatitis B infections or liver cirrhosis. Polymerase chain reaction (PCR) was employed to determine the gene polymorphism of IFITM3. The functions of IFITM3 were detected in PLC/PRF/5 cell with different treated:LV-IFITM3 transfected with lentivirus to knockdown the expression of IFITM3 and LV-NC transfected with empty lentivirus as negative control. The IFITM3 expression, proliferation and migration were detected by Quantitative reverse transcription polymerase chain reaction (qRT-PCR), QuantiGene Plex 2.0 assay, western blotting, immunohistochemistry, Cell Counting Kit(CCK)-8 and wound healing respectively. Six samples (three infected with empty lentiviral as control; three infected with LV-IFITM3 vector lentiviral as experimental group ) of PLC/PRF/5 were sequenced at BGI (Beijing Genomics Institute, Shenzhen,China) using RNA-seq technology to identify the IFITM3-related signaling pathways and chose PI3K/AKT pathway as related signaling to verify. Results: The patients with HCC had a significantly higher proportion of IFITM3 rs12252-CC compared with the patients with chronic HBV infection or liver cirrhosis. The distribution of CC genotype in HCC patients with low differentiation was significantly higher than that in those with high differentiation. Patients with CC genotype found with bigger tumor size, higher percentage of vascular thrombosis, higher distribution of low differentiation and higher 5-year relapse rate than those with CT/TT genotypes. The expression of IFITM3 was higher in HCC tissues than adjacent normal tissues, and the level of IFITM3 was higher in HCC tissues with low differentiation and metastatic than high/medium differentiation and without metastatic. Higher RNA level of IFITM3 was found in CC genotype than TT genotype. In PLC/PRF/5 cell with knockdown, the ability of cell proliferation and migration was inhibited. Analysis RNA sequencing and verification of RT-PCR found out the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin(PI3K/AKT/mTOR) pathway was associated with knockdown IFITM3.With the inhibition of IFITM3, the expression of PI3K/AKT/mTOR signaling pathway was blocked and the expression of vimentin was decreased. Conclusions: IFITM3 rs12252-CC with the higher expression plays a vital role in the progress of HCC by regulating HCC cell proliferation and migration. These effects are associated with PI3K/AKT/mTOR signaling pathway.

Keywords: IFITM3, interferon-induced transmembrane protein 3, HCC, hepatocellular carcinoma, PI3K/ AKT/mTOR, phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin

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Authors: Charlotte Bach, Albrecht Jahn, Mahnaz Motamedi, Maryam Karimi-Ghahfarokhi

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Background: Maternal and infant deaths are most prevalent in the first month after birth, emphasizing the critical need for quality healthcare services during this period. Immigrant women, who are more susceptible to adverse pregnancy outcomes, often face neglect in accessing proper healthcare. The lack of adequate postpartum care significantly contributes to mortality rates. Therefore, utilizing maternal health care services and implementing postpartum care is crucial in reducing maternal and child mortality. Aims: This study aims to evaluate the assessment of pre- and postnatal care among women with and without migration background. In addition, the study explores the impact of COVID-19 procedures on women's experiences during pregnancy, birth, and the postpartum period. Methods: This research employs a cross-sectional Mixed-Method design. Data collection was facilitated through structured questionnaires administered to participants, alongside the utilization of patient bases, including Maternity and child medical records. Following the assumption that the investigator aimed to gain comprehensive insights, qualitative sampling focused on individuals with substantial experiences related to COVID-19, regarded as rich cases. Results: our study highlighted the influence of educational level, marital status, and consensual partnerships on the likelihood of Cesarean deliveries. Regarding breastfeeding practices, migrant women exhibited higher rates of breastfeeding initiation and continuation. Contraception utilization revealed interesting patterns, with non-migrants displaying higher odds of contraceptive use. The qualitative component of our research adds depth to the exploration of women's experiences during the COVID-19 pandemic, revealing nuanced challenges related to anxiety, hospital restrictions, breastfeeding support, and postnatal ward routines. Conclusion: Dissimilarity among studies toward cesarean rate between migrants and non-migrants underscores the importance of targeted interventions considering the diverse needs of distinct population groups. It also acknowledges potential cultural, contextual, and healthcare system influences on the association between mode of delivery and infant feeding practices. Studies acknowledge the influence of contextual variables on contraceptive preferences among migrants and non-migrants, emphasizing the need for tailored healthcare policies. The findings contribute to existing research, highlighting the need for a nuanced understanding of the impact of birth preparation courses on maternal and infant outcomes. Furthermore, they emphasize the universality of certain maternity care experiences, regardless of pandemic contexts, reinforcing the importance of patient-centred approaches in healthcare delivery.

Keywords: migration background, pregnancy outcome, covid-19, postpartum

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

Procedia PDF Downloads 128

Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

Abstract:

The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

Procedia PDF Downloads 69

Authors: Adnan Mazari, Engin Akcagun, Antonin Havelka, Funda Buyuk Mazari, Pavel Kejzlar

Abstract:

This article experimentally investigates various physical properties of special fire retardant sewing threads under different sewing speeds. The aramid threads are common for sewing the fire-fighter clothing due to high strength and high melting temperature. 3 types of aramid threads with different linear densities are used for sewing at different speed of 2000 to 4000 r/min. The needle temperature is measured at different speeds of sewing and tensile properties of threads are measured before and after the sewing process respectively. The results shows that the friction and abrasion during the sewing process causes a significant loss to the tensile properties of the threads and needle temperature rises to nearly 300^oC at 4000 r/min of machine speed. The Scanning electron microscope images are taken before and after the sewing process and shows no melting spots but significant damage to the yarn. It is also found that machine speed of 2000r/min is ideal for sewing firefighter clothing for higher tensile properties and production.

Keywords: Kevlar, needle temperautre, nomex, sewing

Procedia PDF Downloads 532

Authors: Jawaher A. Alsultan, Allen Feldman

Abstract:

With the rapid switch to remote learning due to the COVID-19 pandemic, science teachers were suddenly required to teach their classes online. This breakneck shift to eLearning raised the question of how teacher educators could support science teachers who wanted to use reform-based methods of instruction while using virtual technologies. In this retrospective self-study, we, two science teacher educators, examined our practice as we worked with science teachers to implement inquiry, discussion, and argumentation [IDA] through eLearning. Ten high school science teachers from a large school district in the southeastern US participated virtually in the COVID-19 Community of Practice [COVID-19 CoP]. The CoP met six times from the end of April through May 2020 via Zoom. Its structure was based on a model of action research called enhanced normal practice [ENP], which includes exchanging stories, trying out ideas, and systematic inquiry. Data sources included teacher educators' meeting notes and reflective conversations, audio recordings of the CoP meetings, teachers' products, and post-interviews of the teachers. Findings included a new understanding of the role of existing relationships, shared goals, and similarities in the participants' situations, which helped build trust in the CoP, and the effects of our paying attention to the science teachers’ needs led to a well-functioning CoP. In addition, we became aware of the gaps in our knowledge of how the teachers already used apps in their practice, which they then shared with all of us about how they could be used for online teaching using IDA. We also identified the need to pay attention to feelings about tensions between the teachers and us around the expectations for final products and the project's primary goals. We found that if we are to establish relationships between us as facilitators and teachers that are honest, fair, and kind, we must express those feelings within the collective, dialogical processes that can lead to learning by all members of the CoP, whether virtual or face-to-face.

Keywords: community of practice, facilitators, self-study, action research

Procedia PDF Downloads 126

Authors: Gabriel Malgaresi, Sara Borazjani, Hadi Madani, Pavel Bedrikovetsky

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Suspension-Colloidal flow in porous media occurs in numerous engineering fields, such as industrial water treatment, the disposal of industrial wastes into aquifers with the propagation of contaminants and low salinity water injection into petroleum reservoirs. The main effects are particle mobilization and captured by the porous rock, which can cause pore plugging and permeability reduction which is known as formation damage. Various factors such as fluid salinity, pH, temperature, and rock properties affect particle detachment. Formation damage is unfavorable specifically near injection and production wells. One way to control formation damage is pre-treatment of the rock with nanoparticles. Adsorption of nanoparticles on fines and rock surfaces alters zeta-potential of the surfaces and enhances the attachment force between the rock and fine particles. The main objective of this study is to develop a two-stage mathematical model for (1) flow and adsorption of nanoparticles on the rock in the pre-treatment stage and (2) fines migration and permeability reduction during the water production after the pre-treatment. The model accounts for adsorption and desorption of nanoparticles, fines migration, and kinetics of particle capture. The system of equations allows for the exact solution. The non-self-similar wave-interaction problem was solved by the Method of Characteristics. The analytical model is new in two ways: First, it accounts for the specific boundary and initial condition describing the injection of nanoparticle and production from the pre-treated porous media; second, it contains the effect of nanoparticle sorption hysteresis. The derived analytical model contains explicit formulae for the concentration fronts along with pressure drop. The solution is used to determine the optimal injection concentration of nanoparticle to avoid formation damage. The mathematical model was validated via an innovative laboratory program. The laboratory study includes two sets of core-flood experiments: (1) production of water without nanoparticle pre-treatment; (2) pre-treatment of a similar core with nanoparticles followed by water production. Positively-charged Alumina nanoparticles with the average particle size of 100 nm were used for the rock pre-treatment. The core was saturated with the nanoparticles and then flushed with low salinity water; pressure drop across the core and the outlet fine concentration was monitored and used for model validation. The results of the analytical modeling showed a significant reduction in the fine outlet concentration and formation damage. This observation was in great agreement with the results of core-flood data. The exact solution accurately describes fines particle breakthroughs and evaluates the positive effect of nanoparticles in formation damage. We show that the adsorbed concentration of nanoparticle highly affects the permeability of the porous media. For the laboratory case presented, the reduction of permeability after 1 PVI production in the pre-treated scenario is 50% lower than the reference case. The main outcome of this study is to provide a validated mathematical model to evaluate the effect of nanoparticles on formation damage.

Keywords: nano-particles, formation damage, permeability, fines migration

Procedia PDF Downloads 621

Authors: Gaoguang Yang, Shan Fu

Abstract:

Human-machine interface (HMI) intermediate system information to human operators to facilitate human ability to manage and control the system. Well-designed HMI would enhance human ability. An evaluation must be performed to confirm that the designed HMI would enhance but not degrade human ability. However, the prevalent HMI evaluation techniques have difficulties in more thoroughly and accurately evaluating the suitability and fitness of a given HMI for the wide variety of uncertainty contained in both the existing HMI evaluation techniques and the large number of task scenarios. The first limitation should be attributed to the subjective and qualitative analysis characteristics of these evaluation methods, and the second one should be attributed to the cost balance. This study aims to explore the connection between objective HMI characters and ergonomic safety and step forward toward solving these limitations with objective, characterized HMI parameters. A simulation experiment was performed with the time needed for human operators to recognize the HMI information as characterized HMI parameter, and the result showed a strong correlation between the parameter and ergonomic safety level.

Keywords: Human-Machine Interface (HMI), evaluation, objective, characterization, simulation

Procedia PDF Downloads 66

Authors: Constantinos S. Mammas, Andreas Lazaris, Adamantia S. Mamma-Graham, Georgia Kostopanagiotou, Chryssa Lemonidou, John Mantas, Eustratios Patsouris

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The process to build a better safety culture, methods of error analysis, and preventive measures, starts with an understanding of the effects when human factors engineering refer to remote microscopic diagnosis in surgery and specially in organ transplantation for the evaluation of the grafts. Α high percentage of solid organs arrive at the recipient hospitals and are considered as injured or improper for transplantation in the UK. Digital microscopy adds information on a microscopic level about the grafts (G) in Organ Transplant (OT), and may lead to a change in their management. Such a method will reduce the possibility that a diseased G will arrive at the recipient hospital for implantation. Aim: The aim of this study is to analyze the ergonomics of digital microscopy (DM) based on virtual slides, on telemedicine systems (TS) for tele-pathological evaluation (TPE) of the grafts (G) in organ transplantation (OT). Material and Methods: By experimental simulation, the ergonomics of DM for microscopic TPE of renal graft (RG), liver graft (LG) and pancreatic graft (PG) tissues is analyzed. In fact, this corresponded to the ergonomics of digital microscopy for TPE in OT by applying virtual slide (VS) system for graft tissue image capture, for remote diagnoses of possible microscopic inflammatory and/or neoplastic lesions. Experimentation included the development of an OTE-TS similar experimental telemedicine system (Exp.-TS) for simulating the integrated VS based microscopic TPE of RG, LG and PG Simulation of DM on TS based TPE performed by 2 specialists on a total of 238 human renal graft (RG), 172 liver graft (LG) and 108 pancreatic graft (PG) tissues digital microscopic images for inflammatory and neoplastic lesions on four electronic spaces of the four used TS. Results: Statistical analysis of specialist‘s answers about the ability to accurately diagnose the diseased RG, LG and PG tissues on the electronic space among four TS (A,B,C,D) showed that DM on TS for TPE in OT is elaborated perfectly on the ES of a desktop, followed by the ES of the applied Exp.-TS. Tablet and mobile-phone ES seem significantly risky for the application of DM in OT (p<.001). Conclusion: To make the largest reduction in errors and adverse events referring to the quality of the grafts, it will take application of human factors engineering to procurement, design, audit, and awareness-raising activities. Consequently, it will take an investment in new training, people, and other changes to management activities for DM in OT. The simulating VS based TPE with DM of RG, LG and PG tissues after retrieval, seem feasible and reliable and dependable on the size of the electronic space of the applied TS, for remote prevention of diseased grafts from being retrieved and/or sent to the recipient hospital and for post-grafting and pre-transplant planning.

Keywords: digital microscopy, organ transplantation, tele-pathology, virtual slides

Procedia PDF Downloads 281

Authors: N. Hatraf, L. Merabti, Z. Neffah, W. Taane

Abstract:

The excessive consumption of fossil energies (electrical energy) during summer caused by the technological development involves more and more climate warming. In order to reduce the worst impact of gas emissions produced from classical air conditioning, heat driven solar absorption chiller is pretty promising; it consists on using solar as motive energy which is clean and environmentally friendly to provide cold. Solar absorption machine is composed by four components using Lithium Bromide /water as a refrigerating couple. LiBr- water is the most promising in chiller applications due to high safety, high volatility ratio, high affinity, high stability and its high latent heat. The lithium bromide solution is constitute by the salt lithium bromide which absorbs water under certain conditions of pressure and temperature however if the concentration of the solution is high in the absorption chillers; which exceed 70%, the solution will crystallize. The main aim of this article is to study the phenomena of the crystallization and to evaluate how the dependence between the electric conductivity and the concentration which should be controlled.

Keywords: absorption, crystallization, experimental results, lithium bromide solution

Procedia PDF Downloads 310