Search results for: artificial stock market

Authors: Bi-Huei Tsai

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Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightly-distressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models, are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the two-stage model incorporating financial ratios, corporate governance and market factors has the lowest misclassification error rate. The two-stage model is more accurate than the one-stage model as its distressed cut-off indicators are adjusted according to the macroeconomic-based credit cycle index.

Keywords: Multinomial logit model, corporate governance, company failure, reorganization, bankruptcy

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Authors: Olivier Mesly

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This research presents a conceptual framework termed “Consumer Financial Spinning” (CFS) to analyze consumer behavior in the financial/economic markets. This phenomenon occurs when consumers of high-stakes financial products accumulate unsustainable debt, leading them to detach from their initial financial hierarchy of needs, wealth-related goals, and preferences regarding their household portfolio of assets. The daring actions of these consumers, forming a dark financial triangle, are characterized by three behaviors: overconfidence, the use of rationed rationality, and deceitfulness. We show that we can incorporate CFS into the traditional CAPM and Markovitz’ portfolio optimization models to create a framework that explains such market phenomena as the global financial crisis, highlighting the antecedents and consequences of ill-conceived speculation. Because this is a conceptual paper, there is no methodology with respect to ground studies. However, we apply modeling principles derived from the data percolation methodology, which contains tenets explicating how to structure concepts. A simulation test of the proposed framework is conducted; it demonstrates the conditions under which the relationship between expected returns and risk may deviate from linearity. The analysis and conceptual findings are particularly relevant both theoretically and pragmatically as they shed light on the psychological conditions that drive intense speculation, which can lead to market turmoil. Armed with such understanding, regulators are better equipped to propose solutions before the economic problems become out of control.

Keywords: consumer financial spinning, rationality, deceitfulness, overconfidence, CAPM

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Authors: Geetha Baskaran, Andrzej Barjiela, Rong Qu

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Motivation: Nurse scheduling is a complex combinatorial optimization problem. It is also known as NP-hard. It needs an efficient re-scheduling to minimize some trade-off of the measures of violation by reducing selected constraints to soft constraints with measurements of their violations. Problem Statement: In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. Approach: This approach satisfies the rules of a typical hospital environment based on a standard benchmark problem. Generating good work schedules has a great influence on nurses' working conditions which are strongly related to the level of a quality health care. Domain transformation that combines the strengths of operation research and artificial intelligence was proposed for the solution of the problem. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) package is used to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave for Windows to solve this problem. Results: The scheduling problem has been solved in the proposed formalism resulting in a high quality schedule. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers an advantage of efficient and easily understandable solutions as well as offering deterministic reproducibility of the results. We note, however, that it does not guarantee the global optimum.

Keywords: domain transformation, nurse scheduling, information granulation, artificial intelligence, simulation

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Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

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Authors: Mirjana Dokmanovic

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The paper will introduce main findings of the research of the responses of the Central European and South Eastern European (CEE/SEE) countries to the global economic and financial crisis in 2008 from human rights and gender perspectives. The research methodology included desk research and qualitative analysis of the available data, studies, statistics, and reports produced by the governments, the UN agencies, international financial institutions (IFIs) and international network of civil society organizations. The main conclusion of the study is that the governments in the region missed to assess the impacts of their anti-crisis policies both ex ante and ex post from the standpoint of human rights and gender equality. Majority of the countries have focused their efforts solely on prompting up the banking and financial sectors, and construction business sectors. The tremendous debt which the states have accumulated for the rescue of banks and industries lead to further cuts in social expenses and reduction of public services. Decreasing state support to health care and social protection and declining family incomes made social services unaffordable for many families. Thus, the economic and financial crisis stirred up the care crisis that was absorbed by women’s intensifying unpaid work within a family and household to manage household survival strategy. On the other hand, increased burden of the care work weakened the position of women in the labour market and their opportunities to find a job. The study indicates that the artificial separation of the real economy and the sphere of social reproduction still persist. This has created additional burden of unpaid work of women within a family. The aim of this paper is to introduce the lessons learnt for future: (a) human rights may not be derogated in the times of crisis; (b) the obligation of states to mitigate negative impacts of economic policies to population, particularly to vulnerable groups, must be prioritized; (c) IFIs and business sector must be liable as duty bearers with respect to human rights commitments.

Keywords: CEE/SEE region, global financial and economic crisis, international financial institutions, human rights commitments, principle of non-derogability of human rights

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Authors: Elvira Loibl

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In Western countries, the demand for adoptable children, especially healthy babies, has been considerably high for several years. Rising infertility rates, liberal abortion politics, the widespread use of contraception, and the increasing acceptance of unmarried motherhood are factors that have decreased the number of infants available for domestic adoption in the U.S. and Europe. As a consequence, many involuntarily childless couples turn to intercountry adoption as a viable alternative to have a child of their own. However, the demand for children far outpaces the supply of orphans with the desired characteristics. The imbalance between the number of prospective adopters and the children available for intercountry adoption results in long waiting lists and high prices. The inordinate sums of money involved in the international adoption system have created a commercial ‘underbelly’ where unethical and illicit practices are employed to provide the adoption market with adoptable children. Children are being purchased or abducted from their families, hospitals or child care institutions and then trafficked to receiving countries as ‘orphans’. This paper aims to uncover and explain the factors of the German adoption system that are conducive to child trafficking for adoption purposes. It explains that the tension between money and integrity as experienced by German adoption agencies, blind trust in the authorities in the sending countries as well as a lenient control system encourage and facilitate the trafficking in children to Germany.

Keywords: child trafficking, intercountry adoption, market in adoptable babies, German adoption system

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Authors: Jangje Park, So Young Kim

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The market demand for audio quality in mobile devices continues to increase, and audible buzz noise generated in time division communication is a chronic problem that goes against the market demand. In the case of time division type communication, the RF Power Amplifier (RF PA) is driven at the audio frequency cycle, and it makes various influences on the audio signal. In this paper, we measured the ground bounce noise generated by the peak current flowing through the ground network in the RF PA with the audio frequency; it was confirmed that the noise is the cause of the audible buzz noise during a call. In addition, a grounding method of the microphone device that can improve the buzzing noise was proposed. Considering that the level of the audio signal generated by the microphone device is -38dBV based on 94dB Sound Pressure Level (SPL), even ground bounce noise of several hundred uV will fall within the range of audible noise if it is induced by the audio amplifier. Through the grounding method of the microphone device proposed in this paper, it was confirmed that the audible buzz noise power density at the RF PA driving frequency was improved by more than 5dB under the conditions of the Printed Circuit Board (PCB) used in the experiment. A fundamental improvement method was presented regarding the buzzing noise during a mobile phone call.

Keywords: audio frequency, buzz noise, ground bounce, microphone grounding

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Syed Qasim Gilani, Muhammad Umair, Muhammad Noman, Syed Bilawal Shah, Aqib Abbasi, Muhammad Waheed

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Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time.

Keywords: cardiovascular disease, classification, electrocardiogram, blood pressure

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Authors: Christopher Benedikt Jakob

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In our current society, there is no need to take a look at the wristwatch to know the exact time. Smartphones, the watch in the car or the computer watch, inform us about the time too. Over hundreds of years, luxury watches have held a fascination for human beings. Consumers buy watches that cost thousands of euros, although they could buy much cheaper watches which also fulfill the function to indicate the correct time. This shows that the functional value has got a minor meaning with reference to the buying-reasons as regards luxury watches. For a few years, people have an increased demand to track data like their walking distance per day or to track their sleep for example. Smart watches enable consumers to get information about these data. There exists a trend that people intend to optimise parts of their social life, and thus they get the impression that they are able to optimise themselves as human beings. With the help of smart watches, they are able to optimise parts of their productivity and to realise their targets at the same time. These smart watches are also offered as luxury models, and the question is: how will customers of traditional luxury watches react? Therefore this study has the intention to give answers to the question why people are willing to spend an enormous amount of money on the consumption of luxury watches. The self-expression model, the relationship basis model, the functional benefit representation model and the means-end-theory are chosen as an appropriate methodology to find reasons why human beings purchase specific luxury watches and luxury smart watches. This evaluative approach further discusses these strategies concerning for example if consumers buy luxury watches/smart watches to express the current self or the ideal self and if human beings make decisions on expected results. The research critically evaluates that relationships are compared on the basis of their advantages. Luxury brands offer socio-emotional advantages like social functions of identification and that the strong brand personality of luxury watches and luxury smart watches helps customers to structure and retrieve brand awareness which simplifies the process of decision-making. One of the goals is to identify if customers know why they like specific luxury watches and dislike others although they are produced in the same country and cost comparable prices. It is very obvious that the market for luxury watches especially for luxury smart watches is changing way faster than it has been in the past. Therefore the research examines the market changing parameters in detail.

Keywords: buying-behaviour, brand management, consumer, luxury watch, smart watch

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Zahra Mila Elmi, Mahsa Khanekheshi

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Almost half of the population and potential manpower in the country and rural areas are women. Manpower especially educated people, plays an important role in the production and economic growth. Also, the potential of rural areas to create employment should not be overlooked. In this research, the effects of socio-economic and demographic factors on women's economic participation in rural areas of Iran's provinces will be studied. Therefore, this study was performed by using the results of the rural households income and expenditure surveys -has been taken in 2016- in the framework of pseudo panel data. This study used the logit model and the maximum likelihood method to study the rural women's participation, with 28,265 observations. Results show the inverted U-shaped relationship between age and the probability of female participation; In other words, young women are more likely to participate in labor markets more than the other groups. Divorced and single woman has more chance of participation in comparison with who was being married. With increasing the divorce rate and singleness in Iran, economic policymakers must provide appropriate solutions for this challenge in the coming years. On the base of the results, being a student and the presence of an infant under the age of 6 in the household has a negative effect on the possibility of women's participation in the labor market. The women's education level has a U-shaped relationship with their participation rate. Illiteracy and high education have a strong positive effect on the economic participation of rural women. This shows the dual labor market for women in Iran. Illiterate women are attracted to service jobs, and educated woman are more attracted to education and health jobs. Increasing household income has a small but positive and significant effect on the probability of rural female participation. In the overlook, due to the frequency of the women population in the age group of 25 to 35 years, and more willingness of women in the age 35 to 44 years to participate in the labor market, and studying ofa significant portion of the rural women, the increase of rural female participation is expected in the years ahead. Thus, it is expected policy maker to create new job opportunities for the employment of educated women and take the necessary plan to improve the current situation for women.

Keywords: female participation rate, rural area, provincial data, pseudo-panel data method

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Authors: Vijay Kumar, Mack Moyo, Johannes Van Staden

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Lachenalia viridiflora is a critically endangered bulbous plant with high potential on the international floriculture market. In the present study, an efficient protocol for in vitro plantlet regeneration through somatic embryogenesis was developed. Embryogenic callus was established on Murashige and Skoog (MS) basal medium supplemented with various concentrations and combinations of picloram and thidiazuron (TDZ). A high number of SEs (28.5 ± 1.49) with at different developmental stages of somatic embryos (SEs: globular embryos, torpedo and cotyledon embryo with bipolar characteristics) was obtained on Murashige and Skoog (MS) (Murashige and Skoog 1962) medium with 2.5 μM picloram, and 1.0 μM TDZ. Histological and scanning electron microscopic (SEM) analysis confirmed the presence of somatic embryos. Mature somatic embryos germinated and developed into plantlets after 6 weeks on half/full strength MS medium. High plant regeneration frequency (91.11 %) was achieved on full-strength MS medium supplemented with 5 μM phloroglucinol (PG). Well-developed healthy plantlets were successfully acclimatized in the greenhouse with a survival rate of 80%. The result of this study is beneficial in the mass propagation of high-quality Lachenalia viridiflora clonal plants for the commercial horticultural market and also provides a platform for future genetic transformation studies on the plant.

Keywords: horticultural plant, Lachenalia viridiflora, phloroglucinol, somatic embryogenesis, thidiazuron

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Authors: Jesus Kombaya, Nadia Hamani, Lyes Kermad

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The preservation of a company’s place on the market in such aggressive competition is becoming a survival challenge for manufacturers. In this context, survivors are only those who succeed to satisfy their customers’ needs as quickly as possible. The production system should be endowed with a certain level of flexibility to eliminate or reduce the rigidity of the production systems in order to facilitate the conversion and/or the change of system’s features to produce different products. Therefore, it is essential to guarantee the quality, the speed and the flexibility to survive in this competition. According to literature, this adaptability is referred to as the notion of "change". Indeed, companies are trying to establish a more flexible and agile manufacturing system through several reconfiguration actions. Reconfiguration contributes to the extension of the manufacturing system life cycle by modifying its physical, organizational and computer characteristics according to the changing market conditions. Reconfigurability is characterized by six key elements that are: modularity, integrability, diagnosability, convertibility, scalability and customization. In order to control the production systems, it is essential for manufacturers to make good use of this capability in order to be sure that the system has an optimal and adapted level of reconfigurability that allows it to produce in accordance with the set requirements. This document develops a measure of customization of reconfigurable production systems. These measures do not only impact the production system but also impact the product design and the process design, which can therefore serve as a guide for the customization of manufactured product. A case study is presented to show the use of the proposed approach.

Keywords: reconfigurable manufacturing systems, customization, measure, flexibility

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Authors: Laiba Amin, Rashna H. Sukhia, Mubassar Fida

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Introduction: This study evaluates the accuracy of responses from ChatGPT, Google Bard, and Microsoft Copilot regarding dentofacial orthopedic appliances. As artificial intelligence (AI) increasingly enhances various fields, including healthcare, understanding its reliability in specialized domains like orthodontics becomes crucial. By comparing the accuracy of different AI models, this study aims to shed light on their effectiveness and potential limitations in providing technical insights. Materials and Methods: A total of 110 questions focused on dentofacial orthopedic appliances were posed to each AI model. The responses were then evaluated by five experienced orthodontists using a modified 5-point Likert scale to ensure a thorough assessment of accuracy. This structured approach allowed for consistent and objective rating, facilitating a meaningful comparison between the AI systems. Results: The results revealed that Google Bard demonstrated the highest accuracy at 74%, followed by Microsoft Copilot, with an accuracy of 72.2%. In contrast, ChatGPT was found to be the least accurate, achieving only 52.2%. These results highlight significant differences in the performance of the AI models when addressing orthodontic queries. Conclusions: Our study highlights the need for caution in relying on AI for orthodontic insights. The overall accuracy of the three chatbots was 66%, with Google Bard performing best for removable Class II appliances. Microsoft Copilot was more accurate than ChatGPT, which, despite its popularity, was the least accurate. This variability emphasizes the importance of human expertise in interpreting AI-generated information. Further research is necessary to improve the reliability of AI models in specialized healthcare settings.

Keywords: artificial intelligence, large language models, orthodontics, dentofacial orthopaedic appliances, accuracy assessment.

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Authors: Diego Saldo Alves, Marcelo Paveck Ayub

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The quality of accounting information is very important for the decision-making of managers, investors government and other information users. The opinion of the independent audit has a significant influence on the decision-making, especially the investors. Therefore, the aim of this study is to analyze the reasons that companies listed on Brazilian Stock Exchange B3, if they received qualified opinion and disclaimer of opinion of the independent auditors. We analyzed the reports of the independent auditors of 23 Brazilian companies listed in B3 that received qualified opinion and disclaimer of opinion between the years 2012 and 2017. The findings show that the companies do not comply the International Financial Reporting Standard, IFRS, also they did not provide documentation to prove the operations performed, did not account expenses, problems in corporate governance and internal controls.

Keywords: audit, disclaimer of opinion, independent auditors, qualified opinion

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Authors: Jovana Dj. Jovanovic, Svetlana M. Stevovic

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The highest priority of so called, projected passive houses is to meet the appropriate energy demand. Every single material and layer which is injected into a dwelling has a certain energy quantity stored. The passive houses include optimized insulation levels with minimal thermal bridges, minimum of air leakage through the building, utilization of passive solar and internal gains, and good circulation of air which leans on mechanical ventilation system. The focus of this paper is on passive house features, benefits and targets, their feasibility and energy demands which are set up during each project. Numerous passive house-standards outline the very significant role of zero-energy dwellings towards the modern label of sustainable development. It is clear that the performance of both built and existing housing stock must be addressed if the population across the world sets out the energy objectives. This scientific article examines passive house features since the many passive house cases are launched.

Keywords: benefits, energy demands, passive houses, sustainable development

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Authors: Beaula, M. Kruger, Silindisipho, T. Belot

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Small informal retailers are considered one of the most important features of developing countries around the world. Those small informal retailers form part of the local communities in South African townships and are estimated to be more than 100,000 across the country. The township economic landscape has changed over time in South Africa. The traditional small informal retailers in South African Townships have been faced with numerous challenges of increasing competition; an increase in the number of local retail shops and foreign-owned shops. There is evidence that the South African personal and disposable income has increased amongst black African consumers. Historically, people residing in townships were restricted to informal retail shops; however, this has changed due to the growing number of formal large retail chains entering into the township market. The larger retail chains are aware of the improved income levels of the middle-income townships residence and as a result, larger retailers have followed certain strategies such as; (1) retail format development; (2) diversification growth strategy; (3) market penetration growth strategy and (4) market expansion. This research did a comparative analysis between the different retail models developed by Pick n Pay, Spar and Shoprite. The research methodology employed for this study was of a qualitative nature and made use of a case study to conduct a comparative analysis between larger retailers. A questionnaire was also designed to obtain data from existing smaller retailers. The study found that larger retailers have developed smaller retail formats to compete with the traditional smaller retailers operating in South African townships. Only one out of the two large retailers offers entrepreneurs a franchise model. One of the big retailers offers the opportunity to employ between 15 to 20 employees while the others are subject to the outcome of a feasibility study. The response obtained from the entrepreneurs in the townships were mixed, while some found their presence as having a “negative impact,” which has increased competition; others saw them as a means to obtain a variety of products. This research found that the most beneficial retail model for both bigger retail and existing and new entrepreneurs are from Pick n Pay. The other retail format models are more beneficial for the bigger retailers and not to new and existing entrepreneurs.

Keywords: Pick n Pay, retailers, shoprite, spar, townships

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Authors: A. Mukrimah, M. Mohd Parid, H. F. Lim

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Wetland ecosystem has valuable resources that contribute to national income generation and public well-being, either directly by resources that have a market value or indirectly by resources that have no market value. Economic approach is used to evaluate the resources to determine the best use of wetland resources and should be emphasized in policy development planning. This approach is to prevent imbalance in the allocation of resources and welfare benefits. A case study was conducted in 2016 to assess the economic value of wetland ecosystem services at Pulau Kukup National Parks (PKNP). This study has applied dichotomous choice survey design Contingent Valuation Method (CVM) to investigate empirically the willingness-to-pay (WTP) by the public. The study interviewed 400 household respondents at Pontian, Johor. Analysis showed 81% of household interviewed were willing to contribute to the Wetland Conservation Trust Fund. The results also indicated that on average a household was willing to pay RM87 annually. By taking into account 21,664 households in Pontian district in 2016, public’s contribution to conserves wetland ecosystem at PKNP was calculated to be RM1, 884,334. From the public’s interest to contribute to the conservation of wetland ecosystem services at PKNP, it indicates that more concerted effort is needed by both the federal and state governments to conserve and rehabilitate the mangrove ecosystem in Malaysia.

Keywords: environmental economy, economic valuation, choice experiment, Pulau Kukup national parks

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Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: Purva Khurana

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Traditional textile crafts are universally found to have been significantly impeded by the uprise of innovative technologies, but sustained human endeavor, in sync with dynamic market nuances, holds key to these otherwise getting fast-extinct marvels. The metamorphosis of such art-forms into niche markets pre-supposes sharp concentration on adaptability. The author has concentrated on the ancient handicraft of Ikat in Andhra Pradesh (India), a manifestation of their cultural heritage and esoteric cottage industry, so very intrinsic to the development and support of local economy and identity. Like any other traditional practice, ikat weaving has been subjected to the challenges of modernization. However, owing to its unique character, personalize production and adaptability, both of material and process, ikat weaving has stood the test of time by way of judiciously embellishing innovation with contemporary taste. To survive as a living craft as also to justify its role as a universal language of aesthetic sensibility, it is imperative that ikat tradition should lend itself continuous process of experiments, change and growth. Besides, the instant paper aims to examine the contours of ikat production process from its pure form, to more fashion and market oriented production, with upgraded process, material and tools. Over the time, it has adapted well to new style-paradigms, duly matching up with the latest fashion trends, in tandem with the market-sensitivities. Apart, it is an effort to investigate how this craft could respond constructively to the pressure of contemporary technical developments in order to be at cutting edge, while preserving its integrity. In order to approach these issues, the methodology adopted is, conceptual analysis of the craft practices, its unique strength and how they could be used to advance the craft in relation to the emergence of technical developments. The paper summarizes the result of the study carried out by the author on the peculiar advantages of suitably- calibrated vat dyes over natural dyes, in terms of its recycling ability and eco-friendly properties, thus holding definite edge, both in terms of socio-economic as well as environmental concerns.

Keywords: craft, eco-friendly dyes, ikat, metamorphosis

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Authors: Xinlan Zhang, Dandong Ge, Bingying Liu, Haoyang Liang

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With the rapid development of technology and urbanization, innovation has become an important driving force for urban development. Since the late 20th Century, a number of cities and regions have emerged in the world with innovation as the main driving force, and many of them are still the most important innovation centers in the world. Based on the perspective of innovation ecosystem theory, this paper compares Silicon Valley in the United States, Cambridge in the United Kingdom, Tsukuba in Japan and Zhongguancun in China to explore the reasons for the success of innovative regions and their respective characteristics, hoping to provide a reference for the development of other innovative cities. The main conclusions of this study are the following; firstly, different countries have different social backgrounds. The development model of innovative regions is closely related to the regional backgrounds. Secondly, the market force and the government power have important significance for the development of the innovation regions. The influence of the government power in the early stage of development is great, and in the latter stage, development is dominated by the market force. In addition, the self-organizing ability of the region has a great impact on the innovation ability of the region. Strong self-organizing ability is conducive to the development of innovation economy.

Keywords: contrastive study, development model, innovation ecosystem, innovative regions

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Authors: Rachael Olatoun Okunuga

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Developing young people’s employability is a key policy issue for ensuring their successful transition to the labour market and their access to career oriented employment. The youths of today irrespective of their gender need to acquire the knowledge, skills and attitudes that will enable them to create or find jobs as well as cope with unpredictable labour market changes throughout their working lives. In a study carried out to determine the influence of gender on job-competencies requirements of chemical-based industries and undergraduate-competencies acquisition by chemists working in the industries, all chemistry graduates working in twenty (20) chemical-based industries that were randomly selected from six sectors of chemical-based industries in Lagos and Ogun States of Nigeria were administered with Job-competencies required and undergraduate-competencies acquired assessment questionnaire. The data were analysed using means and independent sample t-test. The findings revealed that the population of female chemists working in chemical-based industries is low compared with the number of male chemists; furthermore, job-competencies requirements are found not to be gender sensitive while there is no significant difference in undergraduate-competencies acquisition of male and female chemists. This suggests that females should be given the same opportunity of employment in chemical-based industries as their male counterparts. The study also revealed the level of acquisition of undergraduate competencies as related to the needs of chemical-based industries.

Keywords: knowledge, skill, attitude, acquired, required, employability

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Authors: K. Almi, S. Lakel, A. Benchabane, A. Kriker

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Several researches are focused on natural resources for the production of biomaterials intended for technical applications. Date palm wood present one of the world’s most important natural resource. Its use as insulating materials will help to solve the severe environmental and recycling problems which other artificial insulating materials caused. This paper reports the results of an experimental investigation on the thermal proprieties of date palm wood from Algeria. A study of physical, chemical, and mechanical properties is also carried out. The goal is to use this natural material in the manufacture of thermal insulation materials for buildings. The local natural resources used in this study are the date palm fibers from Biskra oasis in Algeria. The results have shown that there is no significant difference in the morphological proprieties of the four types of residues. Their chemical composition differed slightly; with the lowest amounts of cellulose and lignin content belong to Petiole. Water absorption study proved that Rachis has a low value of sorption whereas Petiole and Fibrillium have a high value of sorption what influenced their mechanical properties. It is seen that the Rachis and leaflets exhibit high tensile strength values compared to the other residue. On the other hand, the low value of the bulk density of Petiole and Fibrillium leads to a high value of specific tensile strength and young modulus. It was found that the specific young modulus of Petiole and Fibrillium was higher than that of Rachis and Leaflets and that of other natural fibers or even artificial fibers. Compared to the other materials date palm wood provide a good thermal proprieties thus, date palm wood will be a good candidate for the manufacturing efficient and safe insulating materials.

Keywords: composite materials, date palm fiber, natural fibers, tensile tests, thermal proprieties

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Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Emiljan Karma

Abstract:

One of the major problems of the present is young people who are not concerned with employment, education, or training (NEETs). Unfortunately, this group of people in Albania is a considerable part of working-age people, and despite the measures taken, they remain a major problem. NEETs in Albania are very heterogeneous. This is since youth unemployment and inactivity rate are at a very high level (Albania has the highest NEET rate among EU candidates/potential candidates’ countries and EU countries); the high level of NEET rate in Albania means that government agencies responsible for labour market regulation and other social actors interested in the phenomenon (representatives of employees, representatives of employers, non-governmental organizations, etc.) did not effectively materialize the policies in the field of youth employment promotion. The National Agency for Employment and Skills (NAES) delivers measures specifically designed to target unemployed youth, being the key stakeholder in the implementation of employment policies and skills development in Albania. In the context of identifying and assisting NEETs, this role becomes even stronger. The experience of different EU countries (e.g., Youth Guarantee) indicates that there are different policy-making structures and various outreach mechanisms for constraining the youth NEET phenomenon. The purpose of this research is to highlight: (1) The identification of NEETs feature in Albania; (2) The identification of tailored and efficient outreach mechanisms to assist vulnerable NEETs; (3) The fundamental importance of stakeholders’ partnership at central and regional level.

Keywords: labor market, NEETs, non-registered NEETs, unemployment

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Authors: Ridwan Dewayanto Rusli

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We show how prisoner's dilemma-type competition problems can be mitigated through rapid platform diversification and ecosystem expansion. We analyze a ride-hailing company in Southeast Asia, Gojek, whose network grew to more than 170 million users comprising consumers, partner drivers, merchants, and complementors within a few years and has already achieved higher contribution margins than ride-hailing peers Uber and Lyft. Its ecosystem integrates ride-hailing, food delivery and logistics, merchant solutions, e-commerce, marketplace and advertising, payments, and fintech offerings. The company continues growing its network of complementors and App developers, expanding content and gaining critical mass in consumer data analytics and advertising. We compare the company's growth and diversification trajectory with those of its main international rivals and peers. The company's rapid growth and future potential are analyzed using Cusumano's (2012) Staying Power and Six Principles, Hax and Wilde's (2003) and Hax's (2010) The Delta Model as well as Santos' (2016) home-market advantages frameworks. The recently announced multi-billion-dollar merger with one of Southeast Asia's largest e-commerce majors lends additional support to the above arguments.

Keywords: ride-hailing, prisoner's dilemma, platform and ecosystem strategy, digital applications, diversification, home market advantages, e-commerce

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Authors: Halimat M. Ajose-Adeogun, Zaynab A. Bello

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Artificial Intelligence (AI) and Natural Language Processing (NLP) have transformed computer language comprehension, allowing computers to comprehend spoken and written language with human-like cognition. NLP, a multidisciplinary area that combines rule-based linguistics, machine learning, and deep learning, enables computers to analyze and comprehend human language. NLP applications in medicine range from tackling issues in electronic health records (EHR) and psychiatry to improving diagnostic precision in orthopedic surgery and optimizing clinical procedures with novel technologies like chatbots. The technology shows promise in a variety of medical sectors, including quicker access to medical records, faster decision-making for healthcare personnel, diagnosing dysplasia in Barrett's esophagus, boosting radiology report quality, and so on. However, successful adoption requires training for healthcare workers, fostering a deep understanding of NLP components, and highlighting the significance of validation before actual application. Despite prevailing challenges, continuous multidisciplinary research and collaboration are critical for overcoming restrictions and paving the way for the revolutionary integration of NLP into medical practice. This integration has the potential to improve patient care, research outcomes, and administrative efficiency. The research methodology includes using NLP techniques for Sentiment Analysis and Emotion Recognition, such as evaluating text or audio data to determine the sentiment and emotional nuances communicated by users, which is essential for designing a responsive and sympathetic chatbot. Furthermore, the project includes the adoption of a Personalized Intervention strategy, in which chatbots are designed to personalize responses by merging NLP algorithms with specific user profiles, treatment history, and emotional states. The synergy between NLP and personalized medicine principles is critical for tailoring chatbot interactions to each user's demands and conditions, hence increasing the efficacy of mental health care. A detailed survey corroborated this synergy, revealing a remarkable 20% increase in patient satisfaction levels and a 30% reduction in workloads for healthcare practitioners. The poll, which focused on health outcomes and was administered to both patients and healthcare professionals, highlights the improved efficiency and favorable influence on the broader healthcare ecosystem.

Keywords: natural language processing, artificial intelligence, healthcare communication, electronic health records, patient care

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Authors: Jacquelyn Burkell, Jane Bailey

Abstract:

Artificial intelligence (AI) solutions deployed within the justice system face the critical task of providing acceptable explanations for decisions or actions. These explanations must satisfy the joint criteria of public and professional accountability, taking into account the perspectives and requirements of multiple stakeholders, including judges, lawyers, parties, witnesses, and the general public. This research project analyzes and integrates two existing literature on explanations in order to propose guidelines for explainable AI in the justice system. Specifically, we review three bodies of literature: (i) explanations of the purpose and function of 'explainable AI'; (ii) the relevant case law, judicial commentary and legal literature focused on the form and function of reasons for judicial decisions; and (iii) the literature focused on the psychological and sociological functions of these reasons for judicial decisions from the perspective of the public. Our research suggests that while judicial ‘reasons’ (arguably accurate descriptions of the decision-making process and factors) do serve similar explanatory functions as those identified in the literature on 'explainable AI', they also serve an important ‘justification’ function (post hoc constructions that justify the decision that was reached). Further, members of the public are also looking for both justification and explanation in reasons for judicial decisions, and that the absence of either feature is likely to contribute to diminished public confidence in the legal system. Therefore, artificially automated judicial decision-making systems that simply attempt to document the process of decision-making are unlikely in many cases to be useful to and accepted within the justice system. Instead, these systems should focus on the post-hoc articulation of principles and precedents that support the decision or action, especially in cases where legal subjects’ fundamental rights and liberties are at stake.

Keywords: explainable AI, judicial reasons, public accountability, explanation, justification

Procedia PDF Downloads 126