Search results for: support sector machine

Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Julien Benomar

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The World Bank report published in April 2023 called “Migrants, Refugees and Society” allows us to first distinguish migrants in search of economic opportunities and refugees that flee a situation of danger and choose their destination based on their immediate need for safety. Amongst those two categories, the report distinguished people having professional skills adapted to the labor market of the host country and those who have not. Out of that distinction of four categories, we choose to focus our research on refugees that do not have professional skills adapted to the labor market of the host country. Given that refugees generally have no recourse to public assistance schemes and cannot count on the support of their entourage or support network, we propose to examine the extent to which external assistance, such as international humanitarian action, is likely to accompany refugees' transition to financial empowerment through entrepreneurship. To this end, we propose to carry out a case study structured in three stages: (i) an exchange with a Non-Governmental Organisation (NGO) active in supporting refugee populations from Congo and Burundi to Rwanda, enabling us to (i.i) define together a financial empowerment income, and (i. ii) learn about the content of the support measures taken for the beneficiaries of the humanitarian project; (ii) monitor the population of 118 beneficiaries, including 73 refugees and 45 Rwandans (reference population); (iii) conduct a participatory analysis to identify the level of performance of the project and areas for improvement. The case study thus involved the staff of an international NGO active in helping refugees from Rwanda since 2015 and the staff of a Luxembourg NGO that has been funding this economic aid project through entrepreneurship since 2021. The case study thus involved the staff of an international NGO active in helping refugees from Rwanda since 2015 and the staff of a Luxembourg NGO, which has been funding this economic aid through an entrepreneurship project since 2021, and took place over a 48-day period between April and May 2023. The main results are of two types: (i) the need to associate indicators for monitoring the impact of the project on the indirect beneficiaries of the project (refugee community) and (ii) the identification of success factors making it possible to bring concrete and relevant responses to the constraints encountered. The first result thus made it possible to identify the following indicators: Indicator of community potential ((jobs, training or mentoring) promoted by the activity of the entrepreneur), Indicator of social contribution (tax paid by the entrepreneur), Indicator of resilience (savings and loan capacity generated, and finally impact on social cohesion. The second result made it possible to identify that among the 7 success factors tested, the sector of activity chosen and the level of experience in the sector of the future activity are those that stand out the most clearly.

Keywords: entrepreuneurship, refugees, financial empowerment, international aid

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Authors: Abdullah Al-Alaian, Ahmad Al-Enzi, Hasan Al-Herz, Ahmad Bakri, Shant Tatorian, Amr Nounou

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Kuwait is a country that has an imbalanced economy since most of its national outcome comes from the oil trade. It is so risky for a country to be dependent on a single source for income, and this increases the need to diversify its economy. In addition, according to the Public Authority for Industry, the contribution of the industrial sector to the current Gross Domestic Product (GDP) of Kuwait is low which is about 4.33%. Therefore, the development of the industrial sector can be one of the means to diversify the economy and increase the industry's contribution to the national outcome. This is in accordance with Kuwait’s vision of 2035 which aims at increasing the contribution of the industrial sector to the GDP to 12%. In order to do so, this study aims at proposing a strategic plan that will accomplish certain objectives when implemented. It is based on analyzing the industrial sectors in Kuwait taking into consideration studying the strengths, weaknesses, opportunities, and threats facing them. At the same time, it tends to gain from the experience of leading models and neighboring countries regarding the development of the industrial sector. In this study, the SWOT analysis technique will be conducted on all industrial sectors based on evaluation criteria in which it is determined whether any of them has a potential for improvement or not. In other words, it is determined whether the sectors are able to compete locally, regionally, or globally. Based on the results of the SWOT analysis, certain sectors will be chosen, assessed based on an assessment scheme, and their potentials for improvement will be aligned with the overall objectives. To ensure the achievement of the study’s objectives, an action plan will be proposed regarding recommendations for the related authorities, and for entrepreneurs. In addition, monitoring tools are going to be provided for the purpose of periodically checking the progress made in the implementation of the plan.

Keywords: industrial sector, SWOT analysis, productivity, competitiveness, GDP, Kuwait, economy

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Authors: Elena Valionienė, Genutė Kalvaitienė

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The issue of resilience of an individual, an organisation, or an entire ecosystem of organisations has recently become an integral part of the education system, where the uncertainties that lead to societal development in the short term create economic, social, and psycho-emotional instability. The Maritime Transport Sector (MTS) is no exception, and the aim of the article is to model the possibilities of enhancing the professional, sociocultural, and psycho-emotional resilience of MTS specialists to proactively respond to crises caused by uncertainties. The research consists of theoretical model creation that helps to identify general maritime business resilience factors and critical success factors. This can develop high resilience and achieve business excellence in a highly volatile, uncertain, complex, and ambiguous (VUCA) environment.

Keywords: maritime transport sector, resilience, uncertainties, VUCA

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Authors: Sanja Seljan, Ivan Dunđer

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The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

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Authors: Shashi Pingolia

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The paper discusses best practices developed and followed by 07 'Maharatna' Central Public sector Enterprises in India. The paper begins with brief analyses of the contribution of ‘Maharatna’ companies in the growth story of India Inc. Progressively; it enlists Human Resource practices and approach of these 'Maharatna' companies in the areas such as Recruitment, Pay structure, Employee Benefits and Development, Rewards and Recognition practices, Performance Management Systems, etc. In the later part of the paper, HR factors that led some of these 'Maharatna' companies from average employers to 'Best Place at Work' are discussed in brief.

Keywords: central public sector enterprises in India, Maharatna companies in India, human resource management, best place to work

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Authors: Yasmin Nanabhay

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The paper scrutinizes the literature on accountability and non-accountability, and then presents an analysis of a South African case which demonstrated consequences of a lack of accountability. Ethical conduct displayed by members of the public sector is integral to creating a sustainable democratic government, which upholds the constitutional tenets of accountability, transparency and professional ethicality. Furthermore, a true constitutional democracy emphasises and advocates the notion of service leadership that nurtures public participation and engages with citizens in a positive manner. Ethical conduct and accountability in the public sector earns public trust; hence these are key principles in good governance. Yet, in the years since the advent of democracy in South Africa, the government has been plagued by rampant corruption and mal-administration by public officials and politicians in leadership positions. The control measures passed by government in an attempt to ensure ethicality and accountability within the public sector include codes of ethics, rules of conduct and the enactment of legislation. These are intended to shape the mindset of members of the public sector, with the ultimate aim of an efficient, effective, ethical, responsive and accountable public service. The purpose of the paper is to analyse control systems and accountability within the public sector and to present reasons for non-accountability by means of a selected case study. The selected case study is the corruption trial of Jackie Selebi, who served as National Commissioner of the South African Police Service but was dismissed from the post. The reasons for non-accountability in the public sector as well as recommendations based on the findings to enhance accountability will be undertaken. The case study demonstrates the experience and impact of corruption and/or mal-administration, as a result of a lack of accountability, which has contributed to the increasing loss of confidence in political leadership in the country as elsewhere in the world. The literature is applied to the erstwhile National Commissioner of the South African Police Service and President of Interpol, as a case study of non-accountability.

Keywords: corruption, internal control, maladministration, non-compliance, oversight mechanisms, public accountability, public sector

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Authors: Katejarinporn Chaiya, Thawit Janbanklong

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The purposes of this study were to design and find users’ satisfaction after using the decision support system for tourism in the Northern part of Thailand, which can provide tourists with touristic information and plan their personal voyage. Such information can be retrieved systematically based on personal budget and provinces. The samples of this study were five experts and users: 30 "white collars" in Bangkok. This decision support system was designed via ASP.NET. Its database was developed by using MySQL, for administrators to effectively manage the database. The application outcome revealed that the innovation works properly as sought in objectives. Specialists and white collars in Bangkok have evaluated the decision support system; the result was satisfactorily positive.

Keywords: decision Support System, ASP.NET, MySQL, white collars

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Authors: Dammar Bahadur Adhikari

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Community forestry in Nepal has got disproportionately high level of support from government and other actors in forestry sector. Even though master plan for forestry sector (1989) has highlighted community and private forestry as one component, the government policies and other intervention deliberately left out private forestry in its structure and programs. The study aimed at providing the pathway for formulating appropriate policies to address need of different kind of forest management regimes in Rautahat district, Nepal. The key areas the research focused were assessment of current status of private forestry, community forest users' understanding on private forestry; criteria for choosing species of private forestry and factors affecting establishment of private forestry in the area. Qualitative and quantitative data were collected employing questionnaire survey, rapid forest assessment and key informant interview. The study found out that forest policies are imposed due to intense pressure of exogenous forces than due to endogenous demand. Most of the local people opine that their traditional knowledge and skills are not sufficient for private forestry and hence need training on the matter. Likewise, local use, market value and rotation dictate the choice of species for plantation in private forests. Currently district forest office is the only government institution working in the area of private forestry all other governmental and non-governmental organizations have condoned. private forestry. Similarly, only permanent settlers in the area are found to establish private forests other forest users such as migrants and forest encroachers follow opportunistic behavior to meet their forest product need from community and national forests. In this regard, the study recommends taking appropriate step to support other forest management system including private forestry provide community forestry the benefits of competition as suggested by Darwin in 18th century, one and half century back and to help alleviate poverty by channelizing benefits to household level.

Keywords: community forest, forest management, poverty, private forest, users’ group

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Authors: Rishabh Agrawal, S. C. Kaushik, T. S. Bhatti

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In order to reduce energy consumption for sustainable development, continuous energy consumption tracking of building energy systems are essential. In this paper an assessment study has been done to identify the energy consumption & energy conservation potential for commercial buildings sector in Karnataka state, India. There are a total of 326 commercial buildings in the state of Karnataka who has qualified as designated consumers (i.e., having a Contract Demand ≥ 600 KVA), was consider for the study. It has estimated that the annual electricity sale to commercial sector is 3.62 Billion Units (BU) in alone Karnataka State, India, which is an account for 9.57 % of the total electricity sold. The commercial sector constitutes Government & private establishments, hospitals, hotels, restaurants, educational institutions, malls etc. Total 326 commercial buildings in the state accounting for annual energy consumption of 1295.72 Million Units (MU) which works out to about 35% of the sectoral consumption. The annual energy savings potential for 326 commercial buildings is assessed to be 0.25 BU.

Keywords: commercial buildings, connected load, energy conservation studies, energy savings, energy efficiency, energy conservation strategy, energy efficiency, thermal energy, HVAC system

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Authors: Lameck Zetu Khonje, Mulala Danny Simatele

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The tourism industry in Malawi has grown tremendously during the past twenty-five years. This growth is attributed to the change in the political system which opened doors to international tourist and investment opportunities in the country which previously was under a strict repressive one-party political system. This research paper focuses on the developments that took place in the accommodation sector during the same period and the impact that it has partly caused on an environmental metabolic rift in the country which is now vulnerable to climate change-related catastrophes. Respondents from the government departments and the hotel sector were recruited for in-depth interviews. These interviews were conducted between July and November 2015 and follow up interviews were conducted between September and December 2017. Both results indicated there were minimal efforts pursued from the public sector to cartel capitalistic development tendencies in the accommodation sector. The results from the hotel revealed there were considerable efforts pursued driven by operating cost-cutting motive. Applying systems thinking the paper recommends that the policing machinery needs improvement to ensure that the industry also focuses on environmental wellbeing instead of profit maximization. This paper contributes to the body of knowledge on tourism development and climate change.

Keywords: accommodation sector, climate change, metabolic rift, Malawi, tourism industry

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Authors: Gor Muradyan

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The paper is about friction and wear, including mechanisms, modeling, characterization, measurement and testing case in Bangladesh. Bangladesh is a country under development, A lot of people live here, approximately 145 million. The territory of this country is very small. Therefore buildings are very close to each other. As the pipe lines are very old, and people get almost dirty water, there are a lot of ongoing projects under ADB. In those projects the contractors using HDD machines (Horizontal Directional Drilling ) and grundoburst. These machines are working underground. As ground in Bangladesh is very sludge, machine can't work relevant because of big friction in the soil. When drilling works are finished machine is pulling the pipe underground. Very often the pulling of the pipes becomes very complicated because of the friction. Therefore long section of the pipe laying can’t be done because of a big friction. In that case, additional problems rise, as well as additional work must be done. As we mentioned above it is not possible to do big section of the pipe laying because of big friction in the soil, Because of this it is coming out that contractors must do more joints, more pressure test. It is always connected with additional expenditure and losing time. This machine can pull in 75 mm to 500 mm pipes connected with the soil condition. Length is possible till 500m related how much friction it will had on the puller. As less as much it can pull. Another machine grundoburst is not working at this soil condition at all. The machine is working with air compressor. This machine are using for the smaller diameter pipes, 20 mm to 63 mm. Most of the cases these machines are being used for the installing of the house connection pipes, for making service connection. To make a friction less contractors using bigger pulling had then the pipe. It is taking down the friction, But the problem of this machine is that it can't work at sludge. Because of mentioned reasons the friction has a big mining during this kind of works. There are a lot of ways to reduce the friction. In this paper we'll introduce the ways that we have researched during our practice in Bangladesh.

Keywords: Bangladesh, friction and wear, HDD machines, reducing friction

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Authors: Edi Artono, Budi Tamtomo, Gema Wahyudi Purnama

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The world energy demand increasing extreamly high time by time, including domestic demand. That is impossible to avoid because energy a country demand proportional to surge in the number of residents, economic growth and addition of industrial sector. Domestic Oil and gas conventional reserves depleted naturally while production outcome from reservoir also depleted time to time. In the other hand, new reserve did not discover significantly to replace it all. Many people are investigating to looking for new alternative energy to answer the challenge. There are several option to solve energy fossil needed problem using Unconventional Hydrocarbon. There are four aspects to consider as a management reference in order that Unconventional Hydrocarbon business can work properly, divided to: 1. Legal aspect, 2. Environmental aspect, 3. Technical aspect and 4. Economy aspect. The economic aspect as the key to whether or not a project can be implemented or not in Oil and Gas business scheme, so do Unconventional Hydorcarbon business scheme. The support of regulation are needed to buttress Unconventional Hydorcarbon business grow up and make benefits contribute to Government.

Keywords: alternative energy, unconventional hydrocarbon, regulation support, management strategy

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Authors: H. Y. Jung, Ju H. Park, S. M. Lee

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A sampled-data controller is presented for solid oxide fuel cell systems which is expressed by a sector bounded nonlinear model. The sector bounded nonlinear systems, which have a feedback connection with a linear dynamical system and nonlinearity satisfying certain sector type constraints. Also, the sampled-data control scheme is very useful since it is possible to handle digital controller and increasing research efforts have been devoted to sampled-data control systems with the development of modern high-speed computers. The proposed control law is obtained by solving a convex problem satisfying several linear matrix inequalities. Simulation results are given to show the effectiveness of the proposed design method.

Keywords: sampled-data control, fuel cell, linear matrix inequalities, nonlinear control

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Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: Martin Kittel, Alexander Roth

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The continuous integration of variable renewable energy sources (VRE) in the power sector is required for decarbonizing the European economy. Power sectors become increasingly exposed to weather variability, as the availability of VRE, i.e., mainly wind and solar photovoltaic, is not persistent. Extreme events, e.g., long-lasting periods of scarce VRE availability (‘VRE droughts’), challenge the reliability of supply. Properly accounting for the severity of VRE droughts is crucial for designing a resilient renewable European power sector. Energy system modeling is used to identify such a design. Our analysis reveals the sensitivity of the optimal design of the European power sector towards VRE droughts. We analyze how VRE droughts impact optimal power sector investments, especially in generation and flexibility capacity. We draw upon work that systematically identifies VRE drought patterns in Europe in terms of frequency, duration, and seasonality, as well as the cross-regional and cross-technological correlation of most extreme drought periods. Based on their analysis, the authors provide a selection of relevant historical weather years representing different grades of VRE drought severity. These weather years will serve as input for the capacity expansion model for the European power sector used in this analysis (DIETER). We additionally conduct robustness checks varying policy-relevant assumptions on capacity expansion limits, interconnections, and level of sector coupling. Preliminary results illustrate how an imprudent selection of weather years may cause underestimating the severity of VRE droughts, flawing modeling insights concerning the need for flexibility. Sub-optimal European power sector designs vulnerable to extreme weather can result. Using relevant weather years that appropriately represent extreme weather events, our analysis identifies a resilient design of the European power sector. Although the scope of this work is limited to the European power sector, we are confident that our insights apply to other regions of the world with similar weather patterns. Many energy system studies still rely on one or a limited number of sometimes arbitrarily chosen weather years. We argue that the deliberate selection of relevant weather years is imperative for robust modeling results.

Keywords: energy systems, numerical optimization, variable renewable energy sources, energy drought, flexibility

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Authors: Ehsan Tuzcuoğlu, Muhammed Emin Çoban, Songül Byraktar

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One of the main targets of the washing machine is to remove any dirt and stains from the clothes. Especially, the stain removal is significantly important in the Far East market, where the high percentage of the consumers use the top load washing machines as washing appliance. They use all pretreatment methods (i.e. soaking, prewash, and heavy functions) to eliminate the stains from their clothes. Therefore, with this study it is aimed to study experimentally the stain removal performance of 3 different Top-Loading washing machines of the Far East market with 24 different types of stains which are mostly related to Far East culture. In the meanwhile, the mechanical damge on laundry is examined for each machine to see the mechanical effect of the related stain programs on the textile load of the machines. The test machines vary according to have a heater, moving part(s)on their impeller, and to be in different height/width ratio of the drum. The results indicate that decreasing the water level inside the washing machine might result in better soil removal as well as less textile damage. Beside this, the experimental results reveal that heating has the main effect on stain removal. Two-step (or delayed) heating and a lower amount of water can also be considered as the further parameters

Keywords: laundry, washing machine, top load washing machine, stain removal, textile damage, mechanical textile damage

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Authors: Olanrewaju Johnson Akinduntire

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This paper investigates the statistical evaluation of books, citations, articles, journals and other publications in accounting and finance on leveraging technology to mitigate the impact of COVID-19 on business. The research proffers an appraisal of the impact of computerized accounting systems in pre and post pandemic era on activities of the formal and informal sectors, it analyzes the concept of computerized accounting systems, and it seeks to determine the impact of computerized of the overall activities of the informal sector. A special focus of this ICT strategy should be to demystify and promote the diffusion of ICT as a general-purpose technology to the informal sector. It is believed that the use of new technologies can be crucial to meeting the Millennium Development Goals (MDGs) in a timely and effective fashion. Consequent to these, there is a need to prevent the further marginalization of the informal sector by availing ICT services which are mixed appropriately and also properly located. By implication, this will help them access markets and other business information, which can enable or make their economic activities more vibrant and facilitate the availability of information about new opportunities. Conclusively, for one to understand the application of ICT and their locational dynamics in informal sector clusters, there is a need to comprehend and acknowledge the drivers and pressures leading to the adoption of new technology.

Keywords: COVID-19 , (MDGs) , ICT, bibliometric

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Authors: Rizwan Fazal

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This study investigates the behavior of external sector of Pakistan economy and its impact on economic growth, using quarterly data for the period 1990:01-2010:04. External sector indices used in this study are financial integration, net foreign assets and trade integration. Augmented Ducky fuller confirms that all variables of external sector are non-stationary at level, but at first difference it becomes stationary. The co-integration test suggests one co-integrating variables in the study. The analysis is based on Vector Auto Regression model followed by Vector Error Correction Model. The empirical findings show that financial integration play important role in increasing economic growth in Pakistan economy while trade integration has negative effect on economic growth of Pakistan in the long run. However, the short run confirms that output lag accounts for error correction. The estimated CUSUM and CUSUMQ stability test provide information that the period of the study equation remains stable.

Keywords: financial integration, trade integration, net foreign assets, gross domestic product

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Authors: Uwe C. Fischer

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Mental health and well-being for employees working in the human service system are getting more and more important given the increasing rate of absenteeism at work. Besides individual capacities, social and community factors seem to be important in the working setting. Starting from a demand resource framework including the classical demand control aspects, social support systems, specific demands and resources of the client work, and work-family conflict were considered in the present study. We state hypothetically, that these factors have a meaningful association with the mental quality of life of employees working in the field of social, educational and health sectors. 1140 employees, working in human service organizations (education, youth care, nursing etc.) were asked for strains and resources at work (selected scales from Salutogenetic Subjective Work Assessment SALSA and own new scales for client work), work-family conflict, and mental quality of life from the German Short Form Health Survey. Considering the complex influences of the variables, we conducted a multiple hierarchical regression analysis. One third of the whole variance of the mental quality of life can be declared by the different variables of the model. When the variables concerning social influences were included in the hierarchical regression, the influence of work related control resource decreased. Excessive workload, work-family conflict, social support by supervisors, co-workers and other persons outside work, as well as strains and resources associated with client work had significant regression coefficients. Conclusions: Social support systems are crucial in the social, educational and health related service sector, regarding the influence on mental well-being. Especially the work-family conflict focuses on the importance of the work-life balance. Also the specific strains and resources of the client work, measured with new constructed scales, showed great impact on mental health. Therefore occupational health promotion should focus more on the social factors within and outside the working place.

Keywords: client interaction, human service system, mental health, social support, work-family conflict

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Authors: Sani AbdulRahman Bala

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This empirical study delves into the realm of forensic accounting practices within the Nigerian Public Sector, seeking to quantitatively analyze their efficacy in addressing the persistent challenges of fraud and financial irregularities. With a focus on empirical data, this research employs a robust methodology to assess the current state of fraud in the Nigerian Public Sector and evaluate the performance of existing forensic accounting measures. Through quantitative analyses, including statistical models and data-driven insights, the study aims to identify patterns, trends, and correlations associated with fraudulent activities. The research objectives include scrutinizing documented fraud cases, examining the effectiveness of established forensic accounting practices, and proposing data-driven strategies for enhancing fraud detection and prevention. Leveraging quantitative methodologies, the study seeks to measure the impact of technological advancements on forensic accounting accuracy and efficiency. Additionally, the research explores collaborative mechanisms among government agencies, regulatory bodies, and the private sector by quantifying the effects of information sharing on fraud prevention. The empirical findings from this study are expected to provide a nuanced understanding of the challenges and opportunities in combating fraud within the Nigerian Public Sector. The quantitative insights derived from real-world data will contribute to the refinement of forensic accounting strategies, ensuring their effectiveness in addressing the unique complexities of financial irregularities in the public sector. The study's outcomes aim to inform policymakers, practitioners, and stakeholders, fostering evidence-based decision-making and proactive measures for a more resilient and fraud-resistant financial governance system in Nigeria.

Keywords: fraud, financial irregularities, nigerian public sector, quantitative investigation

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Authors: Rhiannon Barker, Gregory Hartwell, Matt Egan, Karen Lock

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Background: Evidence suggests that mental health (MH) issues in children and young people (CYP) in the UK are on the rise. Of particular concern is data that indicates that the pandemic, together with the impact of school closures, have accentuated already pronounced inequalities; children from families on low incomes or from black and minority ethnic groups are reportedly more likely to have been adversely impacted. This study aimed to help identify specific support which may facilitate the building of a positive school climate and protect student mental health, particularly in the wake of school closures following the pandemic. It has important implications for integrated working between schools and statutory health services. Methods: The research comprised of three parts; scoping, case studies, and a stakeholder workshop to explore and consolidate results. The scoping phase included a literature review alongside interviews with a range of stakeholders from government, academia, and the third sector. Case studies were then conducted in two London state schools. Results: Our research identified how student MH was being impacted by a range of factors located at different system levels, both internal to the school and in the wider community. School climate, relating both to a shared system of beliefs and values, as well as broader factors including style of leadership, teaching, discipline, safety, and relationships -all played a role in the experience of school life and, consequently, the MH of both students and staff. Participants highlighted the importance of a whole school approach and ensuring that support for student MH was not separated from academic achievement, as well as the importance of identifying and applying universal measuring systems to establish levels of MH need. Our findings suggest that a school’s climate is influenced by the style and strength of its leadership, while this school climate - together with mechanisms put in place to respond to MH needs (both statutory and non-statutory) - plays a key role in supporting student MH. Implications: Schools in England have a responsibility to decide on the nature of MH support provided for their students, and there is no requirement for them to report centrally on the form this provision takes. The reality on the ground, as our study suggests, is that MH provision varies significantly between schools, particularly in relation to ‘lower’ levels of need which are not covered by statutory requirements. A valid concern may be that in the huge raft of possible options schools have to support CYP wellbeing, too much is left to chance. Work to support schools in rebuilding their cultures post-lockdowns must include the means to identify and promote appropriate tools and techniques to facilitate regular measurement of student MH. This will help establish both the scale of the problem and monitor the effectiveness of the response. A strong vision from a school’s leadership team that emphasises the importance of student wellbeing, running alongside (but not overshadowed by) academic attainment, should help shape a school climate to promote beneficial MH outcomes. The sector should also be provided with support to improve the consistency and efficacy of MH provision in schools across the country.

Keywords: mental health, schools, young people, whole-school culture

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Authors: Johnnie Hay

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New Zealand is well known for its natural beauty, diversity of people but also for its strong focus on mental health through the provision of a vast network of psycho-social support services. South African-trained psychologists often make New Zealand their new home when emigrating - as it is relatively simple to slot into the well-established mental health system. South Africa is bigger in size, population, GDP and probably people diversity than New Zealand but struggles to provide adequate educational and psychological support services to schools. This is mainly due to budgetary pressures brought about by the imperative to first ensure that the approximately 13 million learners all have a teacher in front of their classes and at an average ratio of not more than 40 learners per class. In this paper, perspectives on educational and psychological support in New Zealand and South African schools will be shared. Through basic qualitative research encompassing semi-structured interviews with two South African educational psychologists who returned from New Zealand, supplemented by document analysis, the New Zealand situation will be scrutinized. South African perspectives will be obtained through a number of semi-structured interviews and questionnaires administered by education support services specialists working in district-based support teams in three provinces of the country. This research is in process, but preliminary findings indicate large disparities between the two countries' emphasis, funding, post provisioning and structure regarding educational and psychological support services.

Keywords: educational psychological support services, support for learners experiencing special needs, education support services, diverse learner population

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Manpreet Kaur

Abstract:

When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

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Authors: Darwin Junnior Sabino Diego, Brando Burgos Guerrero, Diego Arroyo Villanueva

Abstract:

Additive manufacturing, commonly known as 3D printing, has revolutionized modern manufacturing by enabling the agile creation of complex objects. However, challenges persist in the consistency and quality of printed parts, particularly in their mechanical properties. This study focuses on addressing these challenges through the optimization of printing parameters in FDM technology, using Machine Learning techniques. Our aim is to improve the mechanical properties of printed objects by optimizing parameters such as speed, temperature, and orientation. We implement a methodology that combines experimental data collection with Machine Learning algorithms to identify relationships between printing parameters and mechanical properties. The results demonstrate the potential of this methodology to enhance the quality and consistency of 3D printed products, with significant applications across various industrial fields. This research not only advances understanding of additive manufacturing but also opens new avenues for practical implementation in industrial settings.

Keywords: 3D printing, additive manufacturing, machine learning, mechanical properties

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Authors: Israa Sh. Tawfic, Sema Koc Kayhan

Abstract:

In this paper, first, we propose least support orthogonal matching pursuit (LS-OMP) algorithm to improve the performance, of the OMP (orthogonal matching pursuit) algorithm. LS-OMP algorithm adaptively chooses optimum L (least part of support), at each iteration. This modification helps to reduce the computational complexity significantly and performs better than OMP algorithm. Second, we give the procedure for the invisible image watermarking in the presence of compressive sampling. The image reconstruction based on a set of watermarked measurements is performed using LS-OMP.

Keywords: compressed sensing, orthogonal matching pursuit, restricted isometry property, signal reconstruction, least support orthogonal matching pursuit, watermark

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

Abstract:

This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Alaa Alrakaf, Sk. Md. Mizanur Rahman

Abstract:

Since the last two decades’ Arabic natural language processing (ANLP) has become increasingly much more important. One of the key issues related to ANLP is ambiguity. In Arabic language different pronunciation of one word may have a different meaning. Furthermore, ambiguity also has an impact on the effectiveness and efficiency of Machine Translation (MT). The issue of ambiguity has limited the usefulness and accuracy of the translation from Arabic to English. The lack of Arabic resources makes ambiguity problem more complicated. Additionally, the orthographic level of representation cannot specify the exact meaning of the word. This paper looked at the diacritics of Arabic language and used them to disambiguate a word. The proposed approach of word sense disambiguation used Diacritizer application to Diacritize Arabic text then found the most accurate sense of an ambiguous word using Naïve Bayes Classifier. Our Experimental study proves that using Arabic Diacritics with Naïve Bayes Classifier enhances the accuracy of choosing the appropriate sense by 23% and also decreases the ambiguity in machine translation.

Keywords: Arabic natural language processing, machine learning, machine translation, Naive bayes classifier, word sense disambiguation

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Authors: Julianne Macmullen

Abstract:

Due to advances in technology and early detection and treatment of cancer, many cancer patients are surviving longer than five years post-diagnosis. Most cancer patients suffer from depression, anxiety, and post-traumatic stress disorder (PTSD) at some point during diagnosis, treatment, and survivorship. A subgroup of patients will continue to suffer from depression and PTSD and require early intervention. Support groups provide patients with the emotional and informational support they require while also giving survivors a sense of community, friendship, and purpose. This type of support is recognized by researchers to improve the quality of life while also decreasing depression and PTSD symptoms. The gaps in the literature include cultural diversity, minorities, and support groups involving cancer types other than breast cancer. Another gap in the literature includes the perceptions of cancer patients as well as longitudinal studies to determine the relationships between support groups and decreased depression and PTSD rates over time. Future research is required to fill the gaps in the literature mentioned previously. Future research is also needed to analyze the difference in age groups and different types of support groups such as professionally-led, peer-led, and online. Implications for practice involve providers assessing for the symptoms of depression and PTSD in order to offer prompt treatment and support services to those patients. In conclusion, social support by way of support groups improves the quality of life, gives survivors a sense of purpose to help others while also gaining the support they need, and reduces the rate of depressive episodes related to PTSD.

Keywords: cancer survivor, survivorship, post-traumatic stress disorder (PTSD), depression, support groups

Procedia PDF Downloads 170