Search results for: ion torrent personal genome machine (PGM)

Authors: Corina E. Niculaescu, Ivan Sangiorgi, Adrian R. Bell

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The COVID-19 pandemic has been life-changing in many aspects of people’s daily and social lives, but has it also changed attitudes towards investments? This paper explores the effect of the COVID-19 pandemic on retail investors’ levels of investments in the U.S. during the first COVID-19 wave in summer 2020. This is an unprecedented health crisis, which could lead to changes in investment behavior, including irrational behavior in retail investors. As such, this study aims to inform policymakers of what happened to investment decisions during the COVID-19 pandemic so that they can protect retail investors during extreme events like a global health crisis. The study aims to answer two research questions. First, was the level of investments affected by the COVID-19 pandemic, and if so, why? Second, how were investments affected by retail investors’ personal experience with COVID-19? The research analysis is based on primary survey data collected on the Amazon Mechanical Turk platform from a representative sample of U.S. respondents. Responses were collected between the 15th of July and 28th of August 2020 from 1,148 U.S. retail investors who hold mutual fund investments and a savings account. The research explores whether being affected by COVID-19, change in the level of savings, and risk capacity can explain the change in the level of investments by using regression analysis. The dependent variable is changed in investments measured as decrease, no change, and increase. For this reason, the methodology used is ordered probit regression models. The results show that retail investors in the U.S. increased their investments during the first wave of COVID-19, which is unexpected as investors are usually more cautious in crisis times. Moreover, the study finds that those who were affected personally by COVID-19 (e.g., tested positive) were more likely to increase their investments, which is irrational behavior and contradicts expectations. An increase in the level of savings and risk capacity was also associated with increased investments. Overall, the findings show that having personal experience with a health crisis can have an impact on one’s investment decisions as well. Those findings are important for both retail investors and policymakers, especially now that online trading platforms have made trading easily accessible to everyone. There are risks and potential irrational behaviors associated with investment decisions during times of crisis, and it is important that retail investors are aware of them before making financial decisions.

Keywords: COVID-19, financial decision-making, health crisis retail investors, survey

Procedia PDF Downloads 177

Authors: Nouf Nasser, Nada Alotaibi, Jazzal Kandiel

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This research study is about the use of artificial intelligence in PhotoRoom. When an individual selects a photo, PhotoRoom automagically removes or separates the background from other parts of the photo through the use of artificial intelligence. This will allow an individual to select their desired background and edit it as they wish. The methodology used was an observation, where various reviews and parts of the app were observed. The review section's findings showed that many people actually like the app, and some even rated it five stars. The conclusion was that PhotoRoom is one of the best photo editing apps due to its speed and accuracy in removing backgrounds.

Keywords: removing background, app, artificial intelligence, machine learning

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Authors: Matthias Steffan, Franz Haas

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The production process grinding is one of the latest steps in a value-added manufacturing chain. Within this step, workpiece geometry and surface roughness are determined. Up to this process stage, considerable costs and energy have already been spent on components. According to the current state of the art, therefore, large safety reserves are calculated in order to guarantee a process capability. Especially for non-circular grinding, this fact leads to considerable losses of process efficiency. With present technology, various non-circular geometries on a workpiece must be grinded subsequently in an oscillating process where X- and Q-axis of the machine are coupled. With the approach of RPM-Synchronous Noncircular Grinding, such workpieces can be machined in an ordinary plung grinding process. Therefore, the workpieces and the grinding wheels revolutionary rate are in a fixed ratio. A non-circular grinding wheel is used to transfer its geometry onto the workpiece. The authors use a worldwide unique machine tool that was especially designed for this technology. Highest revolution rates on the workpiece spindle (up to 4500 rpm) are mandatory for the success of this grinding process. This grinding approach is performed in a two-step process. For roughing, a highly porous vitrified bonded grinding wheel with medium grain size is used. It ensures high specific material removal rates for efficiently producing the non-circular geometry on the workpiece. This process step is adapted by a force control algorithm, which uses acquired data from a three-component force sensor located in the dead centre of the tailstock. For finishing, a grinding wheel with a fine grain size is used. Roughing and finishing are performed consecutively among the same clamping of the workpiece with two locally separated grinding spindles. The approach of RPM-Synchronous Noncircular Grinding shows great efficiency enhancement in non-circular grinding. For the first time, three-dimensional non-circular shapes can be grinded that opens up various fields of application. Especially automotive industries show big interest in the emerging trend in finishing machining.

Keywords: efficiency enhancement, finishing machining, non-circular grinding, rpm-synchronous grinding

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Authors: Mahmoud Mohammad Salem Al-Suod, Ushkarenko O. Alexander, Dorogan I. Olga

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New algorithms using microprocessor systems have been proposed for protection the diesel-generator unit in autonomous power systems. The software structure is designed to enhance the control automata of the system, in which every protection module of diesel-generator encapsulates the finite state machine.

Keywords: diesel-generator unit, protection, state diagram, control system, algorithm, software components

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Authors: Barbora Chmelikova

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Current financial markets offer a wide range of financial products to the consumers. However, access to the financial products is not always provided or guaranteed, particularly in less developed countries. For this reason, financial inclusion is an important component in the modern society. This paper investigates financial inclusion and what financial products are held by university students majoring in finance fields. The OECD methodology was used to examine the awareness and use of financial products. The study was conducted via online questionnaire at Masaryk University in the Czech Republic among finance students. The results show that the students use current and savings accounts more than any other financial products.

Keywords: financial inclusion, financial products, personal finance, university students

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Authors: Alyaa Abdlwahab

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Cutaneous leishmaniasis represents a serious health problem in Syria; this problem has become noticeably aggravated after the civil war in the country. Leishmania tropica parasite is the main cause of cutaneous leishmaniasis in Syria. In order to control the disease, we need an effective vaccine against leishmania parasite. DNA vaccination remains one of the favorable approaches that have been used to face cutaneous leishmaniasis. Ribosomal protein S4 is responsible for important roles in Leishmania parasite life. DNA vaccine based on S4 gene has been used against infections by many species of Leishmania parasite but leishmania tropica parasite, so this gene represents a good candidate for DNA vaccine construction. After proving the existence of ribosomal protein S4 gene in a Syrian strain of Leishmania tropica (LCED Syrian 01), sequencing it and cloning it into pCI plasmid, BALB/C mice were inoculated with pCI-S4 DNA vaccine. The immune response was determined by monitoring the lesion progression in inoculated BALB/C mice for six weeks after challenging mice with Leishmania tropica (LCED Syrian 01) parasites. IL-12, IFN-γ, and IL-4 were quantified in draining lymph nodes (DLNa) of the immunized BALB/C mice by using the RT-qPCR technique. The parasite burden was calculated in the final week for the footpad lesion and the DLNs of the mice. This study proved the existence and the expression of the ribosomal protein S4 gene in Leishmania tropica (LCED Syrian 01) promastigotes. The sequence of ribosomal protein cDNA S4 gene was determined and published in Genbank; the gene size was 822 bp. Expression was also demonstrated at the level of cDNA. Also, this study revealed that pCI-S4 DNA vaccine induces TH1\TH2 response in immunized mice; this response prevents partially developing a dermal lesion of Leishmania.

Keywords: ribosomal protein S4, DNA vaccine, Leishmania tropica, BALB\c

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Authors: Nur Hidayah Iwani Mohd Kamal

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The higher education institutions in Malaysia is challenged with a more socially and culturally diverse student population than ever before, especially with the increasing number of international students studying in Malaysia in the recent years. First year university is a critical time in students’ lives. Students are not only developing intelectually, they are also establishing and maintaining personal relationships, developing an identity, deciding about career and lifestyle, maintaining personal health and wellness, and developing an integrated philosohy of life. The higher education institutions work as a diverse community of learners to provide a supportive environment for these first year students in assisting them in their transition from high school to university. Although many universities are taking steps to improve the first year experience for their new local and international students, efforts must be taken to ensure organized and coordinated manner in order for the initiatives to be successful. The objectives of the study are to examine the international students’ perceptions and interpretation of their first year experiences in shaping and determining their attitudes toward study and the quality of their entire undergraduate academic career; and identify an appropriate mechanism to encounter the international students’ adjustment in the new environment in order to facilitate cross-functional communication and create a coherent and meaningful first year experience. A key construct in this study is that if universities wish to recruiting and retaining international students, it is their ethical responsibility to determine how they can best meet their needs at the academic and social level, create a supportive ‘learning community’ as a foundation of their educational experience, hence facilitate cross-cultural communication and create a coherent and meaningful first year experience. This study is simultaneously frames in relation to focus on the factors that influence a successful and satisfying transition to university life by the first year international students. The study employs a mixed-method data collection involving semi-structured interviews, questionnaire, classroom observation and document analysis. This study provides valuable insight into the struggles that many international students face as they attempt to make the adjustment not only to a new educational system but factors such as psychosocial and cultural problems. It would discuss some of the factors that impact the international students during their first year in university in their quest to be academically successful. It concludes with some recommendations on how Malaysian universities provide these students with a good first year experience based on some the best practices of universities around the world.

Keywords: first year experience, Malaysian universities, international students, education

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Authors: L. Agneni, F. Carta, C. Micheletta, V. Tersigni

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The development of a qualitative method to profile jobseekers is needed to improve the quality of the Public Employment Services (PES) in Italy. This is why the National Agency for Active Labour Market Policies (ANPAL) decided to introduce a Qualitative Profiling Service in the context of the activities carried out by local employment offices’ operators. The qualitative profiling service provides information and data regarding the jobseeker’s personal transition status, through a semi-structured questionnaire administered to PES clients during the guidance interview. The questionnaire responses allow PES staff to identify, for each client, proper activities and policy measures to support jobseekers in their reintegration into the labour market. Data and information gathered by the qualitative profiling tool are the following: frequency, modalities and motivations for clients to apply to local employment offices; clients’ expectations and skills; difficulties that they have faced during the previous working experiences; strategies, actions undertaken and activated channels for job search. These data are used to assess jobseekers’ personal and career characteristics and to measure their employability level (qualitative profiling index), in order to develop and deliver tailor-made action programmes for each client. This paper illustrates the use of the above-mentioned qualitative profiling service on the national territory and provides an overview of the main findings of the survey: concerning the difficulties that unemployed people face in finding a job and their perception of different aspects related to the transition in the labour market. The survey involved over 10.000 jobseekers registered with the PES. Most of them are beneficiaries of the “citizens' income”, a specific active labour policy and social inclusion measure. Furthermore, data analysis allows classifying jobseekers into a specific group of clients with similar features and behaviours, on the basis of socio-demographic variables, customers' expectations, needs and required skills for the profession for which they seek employment. Finally, the survey collects PES staff opinions and comments concerning clients’ difficulties in finding a new job and also their strengths. This is a starting point for PESs’ operators to define adequate strategies to facilitate jobseekers’ access or reintegration into the labour market.

Keywords: labour market transition, public employment services, qualitative profiling, vocational guidance

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Authors: Imane El Karaoui, Mohammed Maaroufi, Hamid Chaikhy

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Doubly Fed Induction Generator (DFIG) is one of the most reliable wind generator. Major problem in wind power generation is to generate Sinusoidal signal with very low THD on variable speed caused by inverter two levels used. This paper presents a multi-level inverter whose objective is to reduce the THD and the dimensions of the output filter. This work proposes a three-level NPC-type inverter, the results simulation are presented demonstrating the efficiency of the proposed inverter.

Keywords: DFIG, multilevel inverter, NPC inverter, THD, induction machine

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Authors: N. Lakshmipathy, Ch. S. N. Murthy, M. Aruna

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The design of lighting systems for surface mines is not an easy task because of the unique environment and work procedures encountered in the mines. The primary objective of this paper is to identify the major problems encountered in mine lighting application and to provide guidance in the solution of these problems. In the surface mining reflectance of surrounding surfaces is one of the important factors, which improve the vision, in the night hours. But due to typical working nature in the mines it is very difficult to fulfill these requirements, and also the orientation of the light at work site is a challenging task. Due to this reason machine operator and other workers in a mine need to be able to orient themselves in a difficult visual environment. The haul roads always keep on changing to tune with the mining activity. Other critical area such as dumpyards, stackyards etc. also change their phase with time, and it is difficult to illuminate such areas. Mining is a hazardous occupation, with workers exposed to adverse conditions; apart from the need for hard physical labor, there is exposure to stress and environmental pollutants like dust, noise, heat, vibration, poor illumination, radiation, etc. Visibility is restricted when operating load haul dumper and Heavy Earth Moving Machinery (HEMM) vehicles resulting in a number of serious accidents. one of the leading causes of these accidents is the inability of the equipment operator to see clearly people, objects or hazards around the machine. Results indicate blind spots are caused primarily by posts, the back of the operator's cab, and by lights and light brackets. The careful designed and implemented, lighting systems provide mine workers improved visibility and contribute to improved safety, productivity and morale. Properly designed lighting systems can improve visibility and safety during working in the opencast mines.

Keywords: contrast, efficacy, illuminance, illumination, light, luminaire, luminance, reflectance, visibility

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Authors: Ronald Villamar-Torres, Michael Staudt, Christopher Viot

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Cotton Gossypium hirsutum L. is one of the most important industrial crops, producing the world leading natural textile fiber, but is very prone to arthropod attacks that reduce crop yield and quality. Cotton cultivation, therefore, makes an outstanding use of chemical pesticides. In reaction to herbivorous arthropods, cotton plants nevertheless show natural defense reactions, in particular through volatile organic compounds (VOCs) emissions. These natural defense mechanisms are nowadays underutilized but have a very high potential for cotton cultivation, and elucidating their genetic bases will help to improve their use. Simulating herbivory attacks by mechanical wounding of cotton plants in greenhouse, we studied by qPCR the changes in gene expression for genes of the terpenoids biosynthesis pathway. Differentially expressed genes corresponded to higher levels of the terpenoids biosynthesis pathway and not to enzymes synthesizing particular terpenoids. The genes were mapped on the G. hirsutum L. reference genome; their global relationships inside the general metabolic pathways and the biosynthesis of secondary metabolites were visualized with iPath2. The chromatographic profiles of VOCs emissions indicated first monoterpenes and sesquiterpenes emissions, dominantly four molecules known to be involved in plant reactions to arthropod attacks. As a result, the study permitted to identify potential key genes for the emission of volatile terpenoids by cotton plants in reaction to an arthropod attack, opening possibilities for molecular-assisted cotton breeding in benefit of smallholder cotton growers.

Keywords: biosynthesis pathways, cotton, mechanisms of plant defense, terpenoids, volatile organic compounds

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Authors: Kidane workelul, Li xu, Xiaoyang Pang, Jiaping Lv

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Dairy products are exceptionally ideal media for the growth of microorganisms because of their high nutritional content. There are several ways that milk might get contaminated throughout the milking process, including how the raw milk is transported and stored, as well as how long it is kept before being processed. Psychrotrophic bacteria are among the one which can deteriorate the quality of milk mainly their heat resistance proteas and lipase enzyme. For this research purpose 8 selected strains of Psychrotrophic bacteria (Entrococcus hirae, Pseudomonas fluorescens, Pseudomonas azotoformans, Pseudomonas putida, Exiguobacterium indicum, Pseudomonas paralactice, Acinetobacter indicum, Serratia liquefacients)are chosen and try to determine their characteristics based on the research methodology protocol. Thus, the 8 selected strains are cultured, plated incubate, extracted their genomic DNA and genome DNA was amplified, the purpose of the study was to identify their Psychrotrophic properties, lipase hydrolysis positive test, their optimal incubation temperature, designed primer using the noble strain P,flourescens conserved region area in target with lipA gene, optimized primer specificity as well as sensitivity and PCR detection for lipase positive strains using the design primers. Based on the findings both the selected 8 strains isolated from stored raw milk are Psychrotrophic bacteria, 6 of the selected strains except the 2 strains are positive for lipase hydrolysis, their optimal temperature is 20 to 30 OC, the designed primer specificity is very accurate and amplifies for those strains only with lipase positive but could not amplify for the others. Thus, the result is promising and could help in detecting the Psychrotrophic bacteria producing heat resistance enzymes (lipase) at early stage before the milk is processed and this will safe production loss for the dairy industry.

Keywords: dairy industry, heat-resistant, lipA, milk, primer and psychrotrophic

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Authors: Ling Liyun

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In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Rehan Mohammad

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Background: Improved Water, Sanitation and Hygiene (WASH) practices in schools ensure children’s health, well-being and cognitive performance. In India under various WASH interventions in schools, teachers, and other staff make every possible effort to educate children about personal hygiene, sanitation practices and harms of open defecation. However, once children get back to their families, they see other practicing inappropriate WASH behaviors, and they consequently start following them. This show disconnect between school behavior and family behavior, which needs to be bridged to achieve desired WASH outcomes. Aims and Objectives: The aim of this study is to assess the factors causing disconnect of WASH-related behaviors between school and the family of children. It also suggests behavior change interventions to bridge the gap. Methodology: The present study has chosen a mixed- method approach. Both quantitative and qualitative methods of data collection have been used in the present study. The purposive sampling for data collection has been chosen. The data have been collected from 20% children in each age group of 04-08 years and 09-12 years spread over three primary schools and 20% of households to which they belong to which is spread over three slum communities in south district of Delhi. Results: The present study shows that despite of several behavior change interventions at school level, children still practice inappropriate WASH behaviors due to disconnect between school and family behaviors. These behaviors show variation from one age group to another. The inappropriate WASH behaviors being practiced by children include open defecation, wrong disposal of garbage, not keeping personal hygiene, not practicing hand washing practices during critical junctures and not washing fruits and vegetables before eating. The present study has highlighted that 80% of children in the age group of 04-08 years still practice inappropriate WASH behaviors when they go back to their families after school whereas, this percentage has reduced to 40% in case of children in the age group 09-12 years. Present study uncovers association between school and family teaching which creates a huge gap between WASH-related behavioral practices. The study has established that children learn and de-learn the WASH behaviors due to the evident disconnect between behavior change interventions at schools and household level. The study has also made it clear that children understand the significance of appropriate WASH practices but owing to the disconnect the behaviors remain unsettled. The study proposes several behavior change interventions to sync the behaviors of children at school and family level to ensure children’s health, well-being and cognitive performance.

Keywords: behavioral interventions, child health, family behavior, school behavior, WASH

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Authors: Douglas N. Simões, Michele Pittol, Vanda F. Ribeiro, Daiane Tomacheski, Ruth M. C. Santana

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The increasing demand of thermoplastic elastomers is related to the wide range of applications, such as automotive, footwear, wire and cable industries, adhesives and medical devices, cell phones, sporting goods, toys and others. These materials are susceptible to microbial attack. Moisture and organic matter present in some areas (such as shower area and sink), provide favorable conditions for microbial proliferation, which contributes to the spread of diseases and reduces the product life cycle. Compounds based on SEBS copolymers, poly(styrene-b-(ethylene-co-butylene)-b-styrene, are a class of thermoplastic elastomers (TPE), fully recyclable and largely used in domestic appliances like bath mats and tooth brushes (soft touch). Zinc oxide and zinc ions loaded in personal and home care products have become common in the last years due to its biocidal effect. In that sense, the aim of this study was to evaluate the effect of zinc as antimicrobial agent in compounds based on SEBS/polypropylene/oil/ calcite for use as refrigerator seals (gaskets), bath mats and sink squeegee. Two zinc oxides from different suppliers (ZnO-Pe and ZnO-WR) and one masterbatch of zinc ions (M-Zn-ion) were used in proportions of 0%, 1%, 3% and 5%. The compounds were prepared using a co-rotating double screw extruder (L/D ratio of 40/1 and 16 mm screw diameter). The extrusion parameters were kept constant for all materials. Tests specimens were prepared using the injection molding machine. A compound with no antimicrobial additive (standard) was also tested. Compounds were characterized by physical (density), mechanical (hardness and tensile properties) and rheological properties (melt flow rate - MFR). The Japan Industrial Standard (JIS) Z 2801:2010 was applied to evaluate antibacterial properties against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The Brazilian Association of Technical Standards (ABNT) NBR 15275:2014 were used to evaluate antifungal properties against Aspergillus niger (A. niger), Aureobasidium pullulans (A. pullulans), Candida albicans (C. albicans), and Penicillium chrysogenum (P. chrysogenum). The microbiological assay showed a reduction over 42% in E. coli and over 49% in S. aureus population. The tests with fungi showed inconclusive results because the sample without zinc also demonstrated an inhibition of fungal development when tested against A. pullulans, C. albicans and P. chrysogenum. In addition, the zinc loaded samples showed worse results than the standard sample when tested against A. niger. The zinc addition did not show significant variation in mechanical properties. However, the density values increased with the rise in ZnO additives concentration, and had a little decrease in M-Zn-ion samples. Also, there were differences in the MFR results in all compounds compared to the standard.

Keywords: antimicrobial, home device, SEBS, zinc

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Authors: Prasansha Kumari

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Financial Literacy is the possession of knowledge and understanding of financial matters. Financial Literacy often entails the knowledge of properly making decisions pertaining to certain personal financial areas like real estate, insurance investing, and savings. This paper intends to identify and analyze the financial knowledge among university undergraduates by using 200 undergraduates in four faculties of University of Kelaniya, Sri Lanka. Collected data will be analyzed by descriptive research method using SPSS package. Expected outcomes are considerable percentage of undergraduates have basic knowledge on financial matters while it has a law percentage for advanced financial literacy among undergraduates. Students from faculty of Commerce and Management and Science have good understanding about financial matters than undergraduates in other two faculties

Keywords: advanced finance, undergraduates, financial literacy, savings

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Authors: Andrea Knezevic, Padmini Pai, Julaine Allan, Katarzyna Olcoń, Louisa Smith

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Healthcare staff are on the frontline during times of disaster and are required to support the health and wellbeing of communities despite any personal adversity and trauma they are experiencing as a result of the disaster. This study explored the experiences of healthcare staff trained as ‘Wellness Warriors’ following the 2019-2020 Australian bushfires. The findings indicated that healthcare staff developed interpersonal skills around deep listening and connecting with others which allowed them to feel differently about work and restored their faith in healthcare leadership.

Keywords: Australian bushfires, burnout, health care providers, mental health, occupational trauma, post-disaster, wellbeing, workplace wellness

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Authors: Chetsada Noknoi

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This research aims to study the quality of life of alien workers in Thailand and to compare the quality of work life of alien workers based on personal factors and work factors. Data analysis is performed using frequencies, percentage, mean standard deviation, t-test and ANOVA. Findings will benefit to the relevant authorities to be aware of the quality of life of alien workers in Thailand. This will help to find ways to enhance the quality of life of alien workers. It also brings awareness to the problems and obstacles that alien workers face in their work and life. It is a strategic approach to improve the management of the country's alien workers to be more efficient and effective. Moreover, the knowledge can be the basis of service to the society in different ways.

Keywords: quality of work life, alien worker, contemporary marketing, management

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Oksana Domkina

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According to the modern economic theory, human capital became one of the main production factors and the most promising direction of investment, as such investment provides opportunity of obtaining high and long-term economic and social effects. Informational technology (IT) sector is the representative of this new economy which is most dependent on human capital as the main competitive factor. So the question for this sector is not whether investment in development of personal should be made, but what are the most effective ways of executing it and who has to pay for the education: Worker, company or government. In this paper we examine the IT sector, describe the labor market of IT workers and its development, and analyze the risks that IT companies may face if they invest in the development of their workers and what factors influence it. The main problem and difficulty of quantitative estimation of risk of investment in human capital of a company and its forecasting is human factor. Human behavior is often unpredictable and complex, so it requires specific approaches and methods of assessment. To build a comprehensive method of estimation of the risk of investment in human capital of a company considering human factor, we decided to use the method of analytic hierarchy process (AHP), that initially was created and developed. We separated three main group of factors: Risks related to the worker, related to the company, and external factors. To receive data for our research, we conducted a survey among the HR departments of Ukrainian IT companies used them as experts for the AHP method. Received results showed that IT companies mostly invest in the development of their workers, although several hire only already qualified personnel. According to the results, the most significant risks are the risk of ineffective training and the risk of non-investment that are both related to the firm. The analysis of risk factors related to the employee showed that, the factors of personal reasons, motivation, and work performance have almost the same weights of importance. Regarding internal factors of the company, there is a high role of the factor of compensation and benefits, factors of interesting projects, team, and career opportunities. As for the external environment, one of the most dangerous factor of risk is competitor activities, meanwhile the political and economical situation factor also has a relatively high weight, which is easy to explain by the influence of severe crisis in Ukraine during 2014-2015. The presented method allows to take into consideration all main factors that affect the risk of investment in human capital of a company. This gives a base for further research in this field and allows for a creation of a practical framework for making decisions regarding the personnel development strategy and specific employees' development plans for the HR departments.

Keywords: risks, personnel development, investment in development, factors of risk, risk of investment in development, IT, analytic hierarchy process, AHP

Procedia PDF Downloads 288

Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Nir Nissim, Erez Shalom, Tomer Lancewiki, Yuval Elovici, Yuval Shahar

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Background: A Medical Device (MD) is an instrument, machine, implant, or similar device that includes a component intended for the purpose of the diagnosis, cure, treatment, or prevention of disease in humans or animals. Medical devices play increasingly important roles in health services eco-systems, including: (1) Patient Diagnostics and Monitoring; Medical Treatment and Surgery; and Patient Life Support Devices and Stabilizers. MDs are part of the medical device eco-system and are connected to the network, sending vital information to the internal medical information systems of medical centers that manage this data. Wireless components (e.g. Wi-Fi) are often embedded within medical devices, enabling doctors and technicians to control and configure them remotely. All these functionalities, roles, and uses of MDs make them attractive targets of cyber-attacks launched for many malicious goals; this trend is likely to significantly increase over the next several years, with increased awareness regarding MD vulnerabilities, the enhancement of potential attackers’ skills, and expanded use of medical devices. Significance: We propose to develop and implement Cyber-Med, a unique collaborative project of Ben-Gurion University of the Negev and the Clalit Health Services Health Maintenance Organization. Cyber-Med focuses on the development of a comprehensive detection framework that relies on a critical attack repository that we aim to create. Cyber-Med will allow researchers and companies to better understand the vulnerabilities and attacks associated with medical devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The Cyber-Med detection framework will consist of two independent, but complementary detection approaches: one for known attacks, and the other for unknown attacks. These modules incorporate novel ideas and algorithms inspired by our team's domains of expertise, including cyber security, biomedical informatics, and advanced machine learning, and temporal data mining techniques. The establishment and maintenance of Cyber-Med’s up-to-date attack repository will strengthen the capabilities of Cyber-Med’s detection framework. Major Findings: Based on our initial survey, we have already found more than 15 types of vulnerabilities and possible attacks aimed at MDs and their eco-system. Many of these attacks target individual patients who use devices such pacemakers and insulin pumps. In addition, such attacks are also aimed at MDs that are widely used by medical centers such as MRIs, CTs, and dialysis engines; the information systems that store patient information; protocols such as DICOM; standards such as HL7; and medical information systems such as PACS. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched against MDs. Very little research has been conducted in order to protect these devices from cyber-attacks, since most of the development and engineering efforts are aimed at the devices’ core medical functionality, the contribution to patients’ healthcare, and the business aspects associated with the medical device.

Keywords: medical device, cyber security, attack, detection, machine learning

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Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

Procedia PDF Downloads 192