Search results for: business intelligence and analytics

Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Roni Ramon-Gonen, Amir Dori, Shahar Shelly

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Background and objectives: Healthcare professionals produce abundant textual information in their daily clinical practice. The extraction of insights from all the gathered information, mainly unstructured and lacking in normalization, is one of the major challenges in computational medicine. In this respect, text mining assembles different techniques to derive valuable insights from unstructured textual data, so it has led to being especially relevant in Medicine. Neurological patient’s history allows the clinician to define the patient’s symptoms and along with the result of the nerve conduction study (NCS) and electromyography (EMG) test, assists in formulating a differential diagnosis. Past medical history (PMH) helps to direct the latter. In this study, we aimed to identify relevant PMH, understand which PMHs are common among patients in the referral cohort and documented by the medical staff, and examine the differences by sex and age in a large cohort based on textual format notes. Methods: We retrospectively identified all patients with abnormal NCS between May 2016 to February 2022. Age, gender, and all NCS attributes reports were recorded, including the summary text. All patients’ histories were extracted from the text report by a query. Basic text cleansing and data preparation were performed, as well as lemmatization. Very popular words (like ‘left’ and ‘right’) were deleted. Several words were replaced with their abbreviations. A bag of words approach was used to perform the analyses. Different visualizations which are common in text analysis, were created to easily grasp the results. Results: We identified 5282 unique patients. Three thousand and five (57%) patients had documented PMH. Of which 60.4% (n=1817) were males. The total median age was 62 years (range 0.12 – 97.2 years), and the majority of patients (83%) presented after the age of forty years. The top two documented medical histories were diabetes mellitus (DM) and surgery. DM was observed in 16.3% of the patients, and surgery at 15.4%. Other frequent patient histories (among the top 20) were fracture, cancer (ca), motor vehicle accident (MVA), leg, lumbar, discopathy, back and carpal tunnel release (CTR). When separating the data by sex, we can see that DM and MVA are more frequent among males, while cancer and CTR are less frequent. On the other hand, the top medical history in females was surgery and, after that, DM. Other frequent histories among females are breast cancer, fractures, and CTR. In the younger population (ages 18 to 26), the frequent PMH were surgery, fractures, trauma, and MVA. Discussion: By applying text mining approaches to unstructured data, we were able to better understand which medical histories are more relevant in these circumstances and, in addition, gain additional insights regarding sex and age differences. These insights might help to collect epidemiological demographical data as well as raise new hypotheses. One limitation of this work is that each clinician might use different words or abbreviations to describe the same condition, and therefore using a coding system can be beneficial.

Keywords: abnormal studies, healthcare analytics, medical history, nerve conduction studies, text mining, textual analysis

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Authors: Proud Arunrangsiwed

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Crisis management strategies could be found in various curriculums, not only in schools of business, but also schools of communication. Young students, such as freshmen and sophomores of undergraduate schools, may not care about learning crisis management strategies. Moreover, crisis management strategies are not a topic art students are familiar with. The current paper discusses a way to adapt entertainment media into a crisis management lesson, and the importance of learning crisis management strategies in the school of animation. Students could learn crisis management strategies by watching movies with content about a crisis and responding to crisis responding. The students should then participate in follow up discussions related to the strategies that were used to address the crisis, as well as their success in solving the crisis.

Keywords: situational crisis communication theory, crisis response strategies, media effect, unintentional effect

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Authors: Daniel Stabler, Anne-Laure Mention, Henri Hakala, Ahmad Alaassar

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In Europe, 2.2 billion tons of waste annually generate severe environmental damage and economic burdens, and negatively impact human health. A stark illustration of the problem is found within the consumer electronics industry, which reflects one of the most complex global waste streams. Of the 5.3 billion globally discarded mobile phones in 2022, only 17% were properly recycled. To address these pressing issues, Europe has made significant strides in developing waste management strategies, Circular Economy initiatives, and Right to Repair policies. These endeavors aim to make product repair and maintenance more accessible, extend product lifespans, reduce waste, and promote sustainable resource use. European countries have introduced Right to Repair policies, often in conjunction with extended producer responsibility legislation, repair subsidies, and consumer repair indices, to varying degrees of regulatory rigor. Changing societal trends emphasizing sustainability and environmental responsibility have driven consumer demand for more sustainable and repairable products, benefiting repair-focused consumer electronics businesses. In academic research, much of the literature in Management studies has examined the European Circular Economy and the Right to Repair from firm-level perspectives. These studies frequently employ a business-model lens, emphasizing innovation and strategy frameworks. However, this study takes an institutional perspective, aiming to understand the adoption of Circular Economy and repair-focused business models within the European consumer electronics market. The concepts of the Circular Economy and the Right to Repair align with institutionalism as they reflect evolving societal norms favoring sustainability and consumer empowerment. Regulatory institutions play a pivotal role in shaping and enforcing these concepts through legislation, influencing the behavior of businesses and individuals. Compliance and enforcement mechanisms are essential for their success, compelling actors to adopt sustainable practices and consider product life extension. Over time, these mechanisms create a path for more sustainable choices, underscoring the influence of institutions and societal values on behavior and decision-making. Institutionalism, particularly 'neo-institutionalism,' provides valuable insights into the factors driving the adoption of Circular and repair-focused business models. Neo-institutional pressures can manifest through coercive regulatory initiatives or normative standards shaped by socio-cultural trends. The Right to Repair movement has emerged as a prominent and influential idea within academic discourse and sustainable development initiatives. Therefore, understanding how macro-level societal shifts toward the Circular Economy and the Right to Repair trigger firm-level responses is imperative. This study aims to answer a crucial question about the impact of European Right to Repair initiatives had on the financial and environmental performance of European consumer electronics companies at the firm level. A quantitative and statistical research design will be employed. The study will encompass an extensive sample of consumer electronics firms in Northern and Western Europe, analyzing their financial and environmental performance in relation to the implementation of Right to Repair mechanisms. The study's findings are expected to provide valuable insights into the broader implications of the Right to Repair and Circular Economy initiatives on the European consumer electronics industry.

Keywords: circular economy, right to repair, institutionalism, environmental management, european union

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Authors: Aishwarya Rai

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This study explores the transformative influence of generative AI on startups, businesses, and industries. We will explore how large businesses can benefit in the area of customer operations, where AI-powered chatbots can improve self-service and agent effectiveness, greatly increasing efficiency. In marketing and sales, generative AI could transform businesses by automating content development, data utilization, and personalization, resulting in a substantial increase in marketing and sales productivity. In software engineering-focused startups, generative AI can streamline activities, significantly impacting coding processes and work experiences. It can be extremely useful in product R&D for market analysis, virtual design, simulations, and test preparation, altering old workflows and increasing efficiency. Zooming into the retail and CPG industry, industry findings suggest a 1-2% increase in annual revenues, equating to $400 billion to $660 billion. By automating customer service, marketing, sales, and supply chain management, generative AI can streamline operations, optimizing personalized offerings and presenting itself as a disruptive force. While celebrating economic potential, we acknowledge challenges like external inference and adversarial attacks. Human involvement remains crucial for quality control and security in the era of generative AI-driven transformative innovation. This talk provides a comprehensive exploration of generative AI's pivotal role in reshaping businesses, recognizing its strategic impact on customer interactions, productivity, and operational efficiency.

Keywords: generative AI, digital transformation, LLM, artificial intelligence, startups, businesses

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Authors: Kayode Julius Oni

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Without mincing words, democracy is by far the most popular form of government in the world today. No matter how we look at it, and regardless of the variant, most leaders in the world today wish to be seen or labeled as Democrats. Perhaps, its attractions in terms of freedom of allocation, accountability, smooth successions of leadership and a lot more, account for its appeal to the ordinary people. The governance style in Nigeria since 1999 cannot be said to be different from the military. Elections are manipulated, judicial processes abused, and the ordinary people do not have access to the dividends of democracy. The paper seeks to address the existing failures experienced under democratic rule in Nigeria which have to transcend into violation of human rights in the conduct of government business. The paper employs the primary and secondary sources of data collection, and it is highly descriptive and critical.

Keywords: democracy, human rights, Nigeria, politics, republic

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Authors: Michelle J. Miller

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In recent year, there has been a rise of two emerging issues that impact the global employment and business market that the legal community must review closer: offshore outsourcing and data privacy. These two issues intersect because employment opportunities are shifting due to offshore outsourcing and some States, like the United States, anti-outsourcing legislation has been passed or presented to retain jobs within the country. In addition, the legal requirements to retain the privacy of data as a global employer extends to employees and third party service provides, including services outsourced to offshore locations. For this reason, this paper will review the intersection of these two issues with a specific focus on data privacy.

Keywords: outsourcing, data privacy, international compliance, multinational corporations

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Authors: Dennis Kreutzer, Sarah Müller-Abdelrazeq, Ingrid Isenhardt

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Resource scarcity, energy transition and the planned climate neutrality pose enormous challenges for manufacturing companies. In order to achieve these goals and a holistic sustainable development, the European Union has listed the circular economy as part of the Circular Economy Action Plan. In addition to a reduction in resource consumption, reduced emissions of greenhouse gases and a reduced volume of waste, the principles of the circular economy also offer enormous economic potential for companies, such as the generation of new circular business models. However, many manufacturing companies, especially small and medium-sized enterprises, do not have the necessary capacity to plan their transformation. They need support and strategies on the path to circular transformation, because this change affects not only production but also the entire company. Maturity models offer an approach, as they enable companies to determine the current status of their transformation processes. In addition, companies can use the models to identify transformation strategies and thus promote the transformation process. While maturity models are established in other areas, e.g. IT or project management, only a few circular economy maturity models can be found in the scientific literature. The aim of this paper is to analyse the identified maturity models of the circular economy through a systematic literature review (SLR) and, besides other aspects, to check their completeness as well as their quality. Since the terms "maturity model" and "readiness model" are often used to assess the transformation process, this paper considers both types of models to provide a more comprehensive result. For this purpose, circular economy maturity models at the company (micro) level were identified from the literature, compared, and analysed with regard to their theoretical and methodological structure. A specific focus was placed, on the one hand, on the analysis of the business units considered in the respective models and, on the other hand, on the underlying metrics and indicators in order to determine the individual maturity level of the entire company. The results of the literature review show, for instance, a significant difference in the holism of their assessment framework. Only a few models include the entire company with supporting areas outside the value-creating core process, e.g. strategy and vision. Additionally, there are large differences in the number and type of indicators as well as their metrics. For example, most models often use subjective indicators and very few objective indicators in their surveys. It was also found that there are rarely well-founded thresholds between the levels. Based on the generated results, concrete ideas and proposals for a research agenda in the field of circular economy maturity models are made.

Keywords: maturity model, circular economy, transformation, metric, assessment

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Authors: Kim Sang Hyuck, Y. Park Sung

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The outbreak of national disasters stimulates the decrease of the both internal and domestic tourism demands, causing bad effects on the hospitality industry. The effective and efficient risk management regarding national disasters are being increasingly required from the hospitality industry practitioners and the tourism policymakers. To establish the effective and efficient risk management strategy on national disasters, the most essential prerequisite condition is the correct estimation of national disasters’ effects in terms of the size and duration of the damages occurred from national disaster on hospitality industry. More specifically, the national disasters are twofold: natural disaster and social disaster. In addition, the hospitality industry has consisted of several types of business, such as hotel, restaurant, travel agency, etc. As reasons of the above, it is important to consider how each type of national disasters differently influences on the performance of each type of hospitality industry. Therefore, the purpose of this study is examining the effects of national disaster on hospitality industry in Korea based on the types of national disasters as well as the types of hospitality business. The monthly data was collected from Jan. 2000 to Dec. 2016. The indexes of industrial production for each hospitality industry in Korea were used with the proxy variable for the performance of each hospitality industry. Two national disaster variables (natural disaster and social disaster) were treated as dummy variables. In addition, the exchange rate, industrial production index, and consumer price index were used as control variables in the research model. The impulse response analysis was used to examine the size and duration of the damages occurred from each type of national disaster on each type of hospitality industries. The results of this study show that the natural disaster and the social disaster differently influenced on each type of hospitality industry. More specifically, the performance of airline industry is negatively influenced by the natural disaster at the time of 3 months later from the incidence. However, the negative impacts of social disaster on airline industry occurred not significantly over the time periods. For the hotel industry, both natural disaster and social disaster negatively influence the performance of hotel industry at the time of 5 months and 6 months later, respectively. Also, the negative impact of natural disaster on the performance of restaurant industry occurred at the time of 5 months later, as well as for both 3 months and 6 months later for the social disaster. Finally, both natural disaster and social disaster negatively influence the performance of travel agency at the time of 3 months and 4 months later, respectively. In conclusion, the types of national disasters differently influence the performance of each type of hospitality industry in Korea. These results would provide an important information to establish the effective and efficient risk management strategy for the national disasters.

Keywords: impulse response analysis, Korea, national disaster, performance of hospitality industry

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Authors: Mohd Adib Abd Muin, Shuhairimi Abdullah, Azizan Bahari

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Problem: The research problem is lacking of model on social entrepreneurship that focus on Islamic perspective. Objective: The objective of this paper is to analyse the existing model on social entrepreneurship and to identify the research gap on Islamic perspective from existing models. Research Methodology: The research method used in this study is literature review and comparative analysis from 6 existing models of social entrepreneurship. Finding: The research finding shows that 6 existing models on social entrepreneurship has been analysed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: social entrepreneurship, Islamic perspective, research gap, business management

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Authors: P. Kavuma, J. Mukasa, M. Lusunku

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Background and Problem: The widespread poverty in Africa- together with the negative impacts of climate change-are two great global challenges that call for everyone’s involvement including Architects. This in particular places serious challenges on architects to have additional skills in both Entrepreneurship and Environmental Design (SEED). Regrettably, while Architectural Training in most African Universities including those from Uganda lack comprehensive implementation of SEED in their curricula, regulatory bodies have not contributed towards the effective integration of SEED in their professional practice. In response to these challenges, Nkumba University (NU) under Architect Kavuma Paul supported by the Uganda Chambers of Architects– initiated the SEED integration in the undergraduate Architectural curricula to cultivate SEED know-how and examples of best practices. Main activities: Initiated in 2007, going beyond the traditional Architectural degree curriculum, the NU Architect department offers SEED courses including provoking passions for creating desirable positive changes in communities. Learning outcomes are assessed theoretically and practically through field projects. The first set of SEED graduates came out in 2012. As part of the NU post-graduation and alumni survey, in October 2014, the pioneer SEED graduates were contacted through automated reminder emails followed by individual, repeated personal follow-ups via email and phone. Out of the 36 graduates who responded to the survey, 24 have formed four (4) private consortium agencies of 5-7 graduates all of whom have pioneered Ugandan-own-cultivated Architectural social projects that include: fishing farming in shipping containers; solar powered mobile homes in shipping containers, solar powered retail kiosks in rural and fishing communities, and floating homes in the flood-prone areas. Primary outcomes: include being business self –reliant in creating the social change the architects desired in the communities. Examples of the SEED project returns to communities reported by the graduates include; employment creation via fabrication, retail business, marketing, improved diets, safety of life and property, decent shelter in the remote mining and oil exploration areas. Negative outcomes-though not yet evaluated include the disposal of used-up materials. Conclusion: The integration of SEED in Architectural Training has established a baseline benchmark and a replicable model based on best practice projects.

Keywords: architectural training, entrepreneurship, environment, integration

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Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Tamar Arieli

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Cross-border cooperation has long been hailed as a means for stabilizing and normalizing relations between former enemies. Cooperation in problem-solving and realizing of local interests in post-conflict environments can indeed serve as a basis for developing dialogue and meaningful relations between neighbors across borders. Hence the potential for formerly sealed borders to serve as a basis for generating local and national perceptions of interdependence and as a buffer against the resume of conflict. Central questions which arise for policy-makers and third parties are how to facilitate cross-border cooperation and which areas of cooperation best serve to normalize post-conflict border regions. The Israel-Jordan border functions as a post-conflict border, in that it is a peaceful border since the 1994 Israel-Jordan peace treaty yet cross-border relations are defined but the highly securitized nature of the border region and the ongoing Arab-Israel regional conflict. This case study is based on long term qualitative research carried out in the border regions of both Israel and Jordan, which mapped and analyzed cross-border in a wide range of activities – social interactions sponsored by peace-facilitating NGOs, government sponsored agricultural cooperation, municipal initiated emergency planning in cross-border continuous urban settings, private cross-border business ventures and various environmental cooperative initiatives. These cooperative initiatives are evaluated through multiple interviews carried out with initiators and partners in cross-border cooperation as well as analysis of documentation, funding and media. These cooperative interactions are compared based on levels of cross-border local and official awareness and involvement as well as sustainability over time. This research identifies environmental cooperation as the most sustainable area of cross- border cooperation and as most conducive to generating perceptions of regional interdependence. This is a variation to the ‘New Middle East’ vision of business-based cooperation leading to conflict amelioration and regional stability. Environmental cooperation serving the public good rather than personal profit enjoys social legitimization even in the face of widespread anti-normalization sentiments common in the post-conflict environment. This insight is examined in light of philosophical and social aspects of the natural environment and its social perceptions. This research has theoretical implications for better understanding dynamics of cooperation and conflict, as well as practical ramifications for practitioners in border region policy and management.

Keywords: borders, cooperation, post-conflict, security

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Authors: Desiree Llaguno Cerezo, Elizabeth Valdes-Miranda Fernandez

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Women have a critical role in sustaining the economy and in the development of trade. However, such a role has long been invisible due to orthodox conceptions that have ignored the gender variable in commercial analyses. Today, it is generally accepted that neither the economy nor business are gender-neutral and that the performance of these activities often impact negatively the lives of women. Women’s participation in trade, on equal terms as men, in any of the various possible roles -producer, wage earner, consumer, merchant, taxpayer- will not only favour the lives of women but also the performance of the economies in which they participate. Transparency, as a principle of the multilateral trading system, can play a significant role as a strategy for the empowerment of women.

Keywords: trade, human rights, gender equality, transparency, WTO, women workers, women's economic empowerment

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Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

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In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎.

Keywords: mobile edge computing, multi-objective optimization, artificial ‎intelligence ‎approaches, task offloading, resource allocation, ‎ service placement

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Authors: Wee Tong Liaw, Elaine Wong Yee Sing

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The main objective and focus of this study are to establish the significance of a sustained health promoting workplace on stock and portfolio returns focusing on companies listed on the Singapore stock exchange, using a two-factor model comprising of the single factor CAPM and a 'health promoting workplace' factor. The 'health promoting workplace' factor represents the excess returns derived between two portfolios of component stocks that, when combined, would represent a top tier stock market index in Singapore, namely the STI index. The first portfolio represents companies that are independently assessed by the Singapore’s Health Award, SHA, to have a sustained and comprehensive health promoting workplace (SHA-STI portfolio) and the second portfolio represents companies that had not been independently assessed (Non-SHA STI portfolio). Since 2001, many companies in Singapore have voluntarily participated in the bi-annual Singapore HEALTH Award initiated by the Health Promotion Board of Singapore (HPB). The Singapore HEALTH Award (SHA), is an industry-wide award and assessment process. SHA assesses and recognizes employers in Singapore for implementing a comprehensive and sustainable health promotion programme at their workplaces. When using a ten year holding period instead of a one year holding period, excess returns in the SHA-STI portfolio over Non-SHA STI portfolio were consistently being observed over all test periods, during 2001 to 2013. In addition, when applied to the SHA-STI portfolio, results from the Two Factor Model consistently revealed higher explanatory powers across all test periods for the portfolio as well as all the individual component stocks in SHA-STI portfolio, than the single factor CAPM model. However, with respect to attaining higher level of achievement in the Singapore Health Award, this study did not show any incentive for selecting listed companies that have achieved a higher level of award. Results from this study would give further insights to investors and fund managers alike who intend to consider health promoting workplace as a risk factor in their stock or portfolio selection process, in particular for investors who have a preference for STI’s component stocks and with a longer investment horizon. Key micro factors like management abilities, business development strategies and production capabilities that meet the needs of market would create the demand for a company’s product(s) or service(s) and consequently contribute to its top line and profitability. Thereafter, the existence of a sustainable health promoting workplace would be a key catalytic factor in sustaining a productive workforce needed to support the continued success of a profitable business.

Keywords: asset pricing model, company's performance, stock returns, financial risk factor, sustained health promoting workplace

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

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Authors: Nirmani Wijenayake, Louise Lutze-Mann, Lucy Jo, John Wilson, Vivian Yeung, Dean Lovett, Kim Snepvangers

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Sessional academic staff at universities have the most influence and impact on student learning, engagement, and experience as they have the most direct contact with undergraduate students. A blended technology-enhanced program was created for the development and support of sessional staff to ensure adequate training is provided to deliver quality educational outcomes for the students. This program combines innovative mixed media educational modules, a peer-driven support forum, and face-to-face workshops to provide a comprehensive training and support package for staff. Additionally, the program encourages the development of learning communities and peer mentoring among the sessional staff to enhance their support system. In 2018, the program was piloted on 100 sessional staff in the School of Biotechnology and Biomolecular Sciences to evaluate the effectiveness of this model. As part of the program, rotoscope animations were developed to showcase ‘typical’ interactions between staff and students. These were designed around communication, confidence building, consistency in grading, feedback, diversity awareness, and mental health and wellbeing. When surveyed, 86% of sessional staff found these animations to be helpful in their teaching. An online platform (Moodle) was set up to disseminate educational resources and teaching tips, to host a discussion forum for peer-to-peer communication and to increase critical thinking and problem-solving skills through scenario-based lessons. The learning analytics from these lessons were essential in identifying difficulties faced by sessional staff to further develop supporting workshops to improve outcomes related to teaching. The face-to-face professional development workshops were run by expert guest speakers on topics such as cultural diversity, stress and anxiety, LGBTIQ and student engagement. All the attendees of the workshops found them to be useful and 88% said they felt these workshops increase interaction with their peers and built a sense of community. The final component of the program was to use an adaptive e-learning platform to gather feedback from the students on sessional staff teaching twice during the semester. The initial feedback provides sessional staff with enough time to reflect on their teaching and adjust their performance if necessary, to improve the student experience. The feedback from students and the sessional staff on this model has been extremely positive. The training equips the sessional staff with knowledge and insights which can provide students with an exceptional learning environment. This program is designed in a flexible and scalable manner so that other faculties or institutions could adapt components for their own training. It is anticipated that the training and support would help to build the next generation of educators who will directly impact the educational experience of students.

Keywords: designing effective instruction, enhancing student learning, implementing effective strategies, professional development

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

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Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Jonas Colin

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Standing at the vanguard of AI development, Alpha represents an unprecedented synthesis of logical rigor and human abstraction, meticulously crafted to mirror the user's unique persona and personality, a feat previously unattainable in AI development. Alpha, an avant-garde artefact in the realm of artificial intelligence, epitomizes a paradigmatic shift in personalized digital interaction, amalgamating user-specific dialogic patterns with the sophisticated algorithmic prowess of OpenAI's GPT-3.5 to engender a platform for enhanced metacognitive engagement and individualized user experience. Underpinned by a sophisticated algorithmic framework, Alpha integrates vast datasets through a complex interplay of neural network models and symbolic AI, facilitating a dynamic, adaptive learning process. This integration enables the system to construct a detailed user profile, encompassing linguistic preferences, emotional tendencies, and cognitive styles, tailoring interactions to align with individual characteristics and conversational contexts. Furthermore, Alpha incorporates advanced metacognitive elements, enabling real-time reflection and adaptation in communication strategies. This self-reflective capability ensures continuous refinement of its interaction model, positioning Alpha not just as a technological marvel but as a harbinger of a new era in human-computer interaction, where machines engage with us on a deeply personal and cognitive level, transforming our interaction with the digital world.

Keywords: chatbot, GPT 3.5, metacognition, symbiose

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Authors: Qing Cao

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Generally speaking, China has a relatively high level of intellectual property (IP) infringement. Risk assessment is indispensable in the strategic planning process. To complement the current literature in international business, the paper sheds the light on how to assess IP risk for foreign companies in planning market entry to China. Evaluating internal and external IP environment, proposed in the paper, consists of external analysis, internal analysis and further internal analysis. Through position the company’s IP environment, the risk assessment approach enables the foreign companies to either build the corresponding IP strategies or abort the entry plan beforehand to minimize the IP risks.

Keywords: intellectual property, IP environment, risk assessment

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Authors: Pui-Lai To, Chechen Liao, Tzu-Ling Lin

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Virtual communities have created a range of new social spaces in which to meet and interact with one another. Both as a stand-alone model or as a supplement to sustain competitive advantage for normal business models, building virtual communities has been hailed as one of the major strategic innovations of the new economy. However for a virtual community to evolve, the biggest challenge is how to make members actively give information or provide advice. Even in busy virtual communities, usually, only a small fraction of members post information actively. In order to investigate the determinants of information giving willingness of those contributors who usually actively provide their opinions, we proposed a model to understand the reasons for contribution in communities. The study will definitely serve as a basis for the future growth of information giving in virtual communities.

Keywords: information giving, social identity, trust, virtual community

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Authors: J. Berčík, A. Mravcová, A. Rusková, P. Jurčišin, R. Virágh

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The online world is changing every day. With this comes the emergence and development of new business models. One of them is the sale of several types of products in one place. This type of sales in the form of online marketplaces has undergone a positive development in recent years and represents a kind of alternative to brick-and-mortar shopping centres. The main philosophy is to buy several products under one roof. Examples of popular e-commerce marketplaces are Amazon, eBay, and Allegro. Their share of total e-commerce turnover is expected to even double in the coming years. The paper highlights possibilities for testing web applications and online marketplace using emerging methods like stationary eye cameras (eye tracking) and facial analysis (FaceReading).

Keywords: emerging methods, consumer neuroscience, e-commerce, marketplace, user experience, user interface

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Authors: A. V. Popa, C. Barna, V. Mihalache

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Being the largest port at the Black Sea and functioning as a civil and military nodal point between Europe and Asia, Constantza Port has become a potential target on the terrorist international agenda. The authors use qualitative research based on both face-to-face and online semi-structured interviews with relevant stakeholders (top decision-makers in the Romanian Naval Authority, Romanian Maritime Training Centre, National Company "Maritime Ports Administration" and military staff) in order to detect potential vulnerabilities which might be exploited by terrorists in the case of Constantza Port. Likewise, this will enable bringing together the experts’ opinions on potential mitigation measures. Subsequently, this paper formulates various counter-terrorism policies to enhance the robustness of Constantza Port under potential terror attacks and connects them with the attributions in the field of critical infrastructure protection conferred by the law to the lead national authority for preventing and countering terrorism, namely the Romanian Intelligence Service. Extending the national counterterrorism efforts to an international level, the authors propose the establishment – among the experts of the NATO member states of the Wider Black Sea Region – of a platform for the exchange of know-how and best practices in the field of critical infrastructure protection.

Keywords: Constantza Port, counter-terrorism policies, critical infrastructure protection, security, Wider Black Sea Region

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Authors: Salma Krafessi

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The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: Nivedhitha Venkatakrishnan

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Walking down busy temple streets in India is an experience in lifetime. Especially the temple streets are one of the most energetic places not only because of the divinity but also because of the streets itself which provides place for people to relax, meet, shop, linger, just walk around these activities create a set of experience which results in memories that lasts longer. Thinking of any temple street in India the image that comes to anyone’s mind are the elegantly sculpted Gopurams (Gateway) that depicts the craftsmanship and the history of the place, people taking a holy dip in the water, the aroma of the agarbathi’s, flowers with the divine Vedic chants and the sound of the temple bell flock of pigeons flying from the niches of the Gopuram with the sun in the backdrop. It gives a feeling of impulse energy that brings in life to these streets. Any temple street with even any one factor missing would look dead. This will be amiss in the essence in the scene of one’s experiences. These Temple Streets traditionally cater not only for religious purpose but to a wide range of activities. A vibrant street that facilitates such activities are preferred by the public any day. The research seeks to understand and find out the definition of Vibrancy in Indian Context. What is Vibrancy? What brings in the feeling of Vibrancy/Liveliness/Energy? Is it the Built structure and the city? Or is it the people? Or is it the Activity? Or is it Built structure – city – People – Activity put together brings the sense of Vibrancy to a place? How to define Vibrancy? Is it measurable? For which a case of Car Street Udupi, Karnataka is taken. The research is carried out in two stages. ‘Stage One’ makes use of ethnographic fieldwork as a basic method, complimented by structured field observations using a behavioral mapping procedure of the streets. Stage Two’ utilizes surveys that collected. This stage seeks to understand what design characteristics and furniture arrangements are associated with stationary, social and gathering activities of people by each cultural group and all groups collectively. The main conclusion from this research is that retail activities remain the main concern of people in cultural streets. Management and higher-level planning of retail activities on the streets could encourage and motivate possible Shops to enrich the trade variety of the street that provides a means for social and cultural diversity. In addition to business activities, spatial design characteristics are found to have an influence on people’s behavior and activity. The findings of this research suggest that retail and business activities, together with the design and skillful management of the public areas, could support a wider range of static and social activities among people of various ethnic backgrounds.

Keywords: activity, liveliness, temple street, vibrancy

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Authors: Khrystyna Pavlyk

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In the OECD countries, percentage of adults with higher education degrees has increased by 10 % during 2000-2010. Continuously increasing demand for higher education gives universities a chance of becoming key players in socio-economic development of a territory (region or city) via knowledge creation, knowledge transfer, and knowledge spillovers. During previous decade, universities have tried to support spin-offs and start-ups, introduced courses on sustainability and corporate social responsibility. While much has been done, new trends are starting to emerge in search of better approaches. Recently a number of universities created centers that conduct research in a field social entrepreneurship, which in turn underpin educational programs run at these universities. The list includes but is not limited to the Centre for Social Economy at University of Liège, Institute for Social Innovation at ESADE, Skoll Centre for Social Entrepreneurship at Oxford, Centre for Social Entrepreneurship at Rosklide, Social Entrepreneurship Initiative at INSEAD. Existing literature already examined social entrepreneurship centers in terms of position in the institutional structure, initial and additional funding, teaching initiatives, research achievements, and outreach activities. At the same time, Universities can become social enterprises themselves. Previous research revealed that universities use both business and social entrepreneurship models. Universities which are mainly driven by a social mission are more likely to transform into social entrepreneurial institutions. At the same time, currently, there is no clear understanding of what social entrepreneurship in higher education is about and thus social entrepreneurship in higher education needs to be studied and promoted at the same time. Main roles which socially oriented university can play in city development include: buyer (implementation of socially focused local procurement programs creates partnerships focused on local sustainable growth.); seller (centers created by universities can sell socially oriented goods and services, e.g. in consultancy.); employer (Universities can employ socially vulnerable groups.); business incubator (which will help current student to start their social enterprises). In the paper, we will analyze these in more detail. We will also examine a number of indicators that can be used to assess the impact, both direct and indirect, that universities can have on city's economy. At the same time, originality of this paper mainly lies not in methodological approaches used, but in countries evaluated. Social entrepreneurship is still treated as a relatively new phenomenon in post-transitional countries where social services were provided only by the state for many decades. Paper will provide data and example’s both from developed countries (the US and EU), and those located in CIS and CEE region.

Keywords: social enterprise, university, regional economic development, comparative study

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