Search results for: marketing intelligence

Authors: Saad Zagloul Shokri Melika

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The London College of Communication is one of the only universities in the world to offer a lifestyle journalism master’s degree. A hybrid originally constructed largely out of a generic journalism program crossed with numerous cultural studies approaches, the degree has developed into a leading lifestyle journalism education attracting students worldwide. This research project seeks to present a framework for structuring the degree as well as to understand how students in this emerging field of study value the program. While some researchers have addressed questions about journalism and higher education, none have looked specifically at the increasingly important genre of lifestyle journalism, which Folker Hanusch defines as including notions of consumerism and critique among other identifying traits. Lifestyle journalism, itself poorly researched by scholars, can relate to topics including travel, fitness, and entertainment, and as such, arguably a lifestyle journalism degree should prepare students to engage with these topics. This research uses the existing Masters of Arts and Lifestyle Journalism at the London College of Communications as a case study to examine the school’s approach. Furthering Hanusch’s original definition, this master’s program attempts to characterizes lifestyle journalism by a specific voice or approach, as reflected in the diversity of student’s final projects. This framework echoes the ethos and ideas of the university, which focuses on creativity, design, and experimentation. By analyzing the current degree as well as student feedback, this research aims to assist future educators in pursuing the often neglected field of lifestyle journalism. Through a discovery of the unique mix of practical coursework, theoretical lessons, and broad scope of student work presented in this degree program, researchers strive to develop a framework for lifestyle journalism education, referring to Mark Deuze’s ten questions for journalism education development. While Hanusch began the discussion to legitimize the study of lifestyle journalism, this project strives to go one step further and open up a discussion about teaching of lifestyle journalism at the university level.

Keywords: Journalism, accountability, education, television, publicdearth, investigative, journalism, Nigeria, journalismeducation, lifestyle, university

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Authors: Dimitra Riza

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This paper examines different ways of exploring a city by using smart phones' applications while walking, and the way this new attitude will change our perception of the urban environment. By referring to various examples of such applications we will consider options and possibilities that open up with new technologies, their advantages and disadvantages, as well as ways of experiencing and interpreting the urban environment. The widespread use of smart phones gave access to information, maps, knowledge, etc. at all times and places. The city tourism marketing takes advantage of this event and promotes the city's attractions through technology. Mobile mediated walking tours, provide new possibilities and modify the way we used to explore cities, for instance by giving directions proper to find easily destinations, by displaying our exact location on the map, by creating our own tours through picking points of interest and interconnecting them to create a route. These apps act as interactive ones, as they filter the user's interests, movements, etc. Discovering a city on foot and visiting interesting sites and landmarks, became very easy, and has been revolutionized through the help of navigational and other applications. In contrast to the re-invention of the city as suggested by the Baudelaire's Flâneur in the 19th century, or to the construction of situations by the Situationists in 60s, the new technological means do not allow people to "get lost", as these follow and record our moves. In the case of strolling or drifting around the city, the option of "getting lost" is desired, as the goal is not the "wayfinding" or the destination, but it is the experience of walking itself. Getting lost is not always about dislocation, but it is about getting a feeling, free of the urban environment while experiencing it. So, on the one hand, walking is considered to be a physical and embodied experience, as the observer becomes an actor and participates with all his senses in the city activities. On the other hand, the use of a screen turns out to become a disembodied experience of the urban environment, as we perceive it in a fragmented and distanced way. Relations with the city are similar to Alberti’s isolated viewer, detached from any urban stage. The smartphone, even if we are present, acts as a mediator: we interact directly with it and indirectly with the environment. Contrary to the Flaneur and to the Situationists, who discovered the city with their own bodies, today the body itself is being detached from that experience. While contemporary cities turn out to become more walkable, the new technological applications tend to open out all possibilities in order to explore them by suggesting multiple routes. Exploration becomes easier, but Perception changes.

Keywords: body, experience, mobile apps, walking

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Authors: Dona Shaini Abhilasha Nanayakkara, Kurugamage Jude Pravinda Gregory Perera

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E-commerce platforms have revolutionized the shopping experience, offering convenient ways for consumers to make purchases. To improve interactions with customers and optimize marketing strategies, it is essential for businesses to understand user behavior, preferences, and needs on these platforms. This paper focuses on recommending businesses to customize interactions with users based on their behavioral patterns, leveraging data-driven analysis and machine learning techniques. Businesses can improve engagement and boost the adoption of e-commerce platforms by aligning behavioral patterns with user goals of usability and satisfaction. We propose TheAnalyzer, a clustering-based system designed to enhance business productivity by analyzing user-profiles and improving human-computer interaction. The Analyzer seamlessly integrates with business applications, collecting relevant data points based on users' natural interactions without additional burdens such as questionnaires or surveys. It defines five key user analytics as features for its dataset, which are easily captured through users' interactions with e-commerce platforms. This research presents a study demonstrating the successful distinction of users into specific groups based on the five key analytics considered by TheAnalyzer. With the assistance of domain experts, customized business rules can be attached to each group, enabling The Analyzer to influence business applications and provide an enhanced personalized user experience. The outcomes are evaluated quantitatively and qualitatively, demonstrating that utilizing TheAnalyzer’s capabilities can optimize business outcomes, enhance customer satisfaction, and drive sustainable growth. The findings of this research contribute to the advancement of personalized interactions in e-commerce platforms. By leveraging user behavioral patterns and analyzing both new and existing users, businesses can effectively tailor their interactions to improve customer satisfaction, loyalty and ultimately drive sales.

Keywords: data clustering, data standardization, dimensionality reduction, human computer interaction, user profiling

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Authors: I. Rodionov, A. Semenov, E. Gosteva, O. Sokolova

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Currently, the venture capital becomes more and more advanced and effective source of the innovation project financing, connected with a high-risk level. In the developed countries, it plays a key role in transforming innovation projects into successful businesses and creating prosperity of the modern economy. Actually, in Russia there are many necessary preconditions for creation of the effective venture investment system: the network of the public institutes for innovation financing operates; there is a significant number of the small and medium-sized enterprises, capable to sell production with good market potential. However, the current system does not confirm the necessary level of efficiency in practice that can be substantially explained by the absence of the accurate plan of action to form the national venture model and by the lack of experience of successful venture deals with profitable exits in Russian economy. This paper studies the influence of various factors on the venture industry development by the example of the IT-sector in Russia. The choice of the sector is based on the fact, that this segment is the main driver of the venture capital market growth in Russia, and the necessary set of data exists. The size of investment of the second round is used as the dependent variable. To analyse the influence of the previous round such determinant as the volume of the previous (first) round investments is used. There is also used a dummy variable in regression to examine that the participation of an investor with high reputation and experience in the previous round can influence the size of the next investment round. The regression analysis of short-term interrelations between studied variables reveals prevailing influence of the volume of the first round investments on the venture investments volume of the second round. Because of the research, the participation of investors with first-class reputation has a small impact on an indicator of the value of investment of the second round. The expected positive dependence of the second round investments on the forecasted market growth rate now of the deal is also rejected. So, the most important determinant of the value of the second-round investment is the value of first–round investment, so it means that the most competitive on the Russian market are the start-up teams which can attract more money on the start, and the target market growth is not the factor of crucial importance.

Keywords: venture industry, venture investment, determinants of the venture sector development, IT-sector

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Authors: Håkon Riiber

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This paper explores the extent to which Russian ‘Active Measures’ play a role in contemporary Russian foreign policy and in what way the legacy of the Soviet Union is still apparent in these practices. The analysis draws on a set of case studies from the 21st century to examine these aspects, showing which ‘Active Measures’ features are old and which are new in the post-Cold War era. The paper highlights that the topic has gained significant academic and political interest in recent years, largely due to the aggressive posture of the Russian Federation on the world stage, exemplified through interventions in Estonia, Georgia, and Ukraine and interference in several democratic elections in the West. However, the paper argues that the long-term impact of these measures may have unintended implications for Russia. While Russia is unlikely to stop using Active Measures, increased awareness of the exploitation of weaknesses, institutions, or other targets may lead to greater security measures and an ability to identify and defend against these activities. The paper contends that Soviet-style ‘Active Measures’ from the Cold War era have been modernized and are now utilized to create an advantageous atmosphere for further exploitation to support contemporary Russian foreign policy. It offers three key points to support this argument: the reenergized legacy of the Cold War era, the use of ‘Active Measures’ in a number of cases in the 21st century, and the applicability of AM to the Russian approach to foreign policy. The analysis reveals that while this is not a new Russian phenomenon, it is still oversimplified and inaccurately understood by the West, which may result in a decreased ability to defend against these activities and limit the unwarranted escalation of the ongoing security situation between the West and Russia. The paper concludes that the legacy of Soviet-era Active Measures continues to influence Russian foreign policy, and modern technological advances have only made them more applicable to the current political climate. Overall, this paper sheds light on the important issue of Russian ‘Active Measures’ and the role they play in contemporary Russian foreign policy. It emphasizes the need for increased awareness, understanding, and security measures to defend against these activities and prevent further escalation of the security situation between the West and Russia.

Keywords: Russian espionage, active measures, disinformation, Russian intelligence

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Authors: Irma Wani Othman

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The internationalization of higher education in Malaysia mainly focuses to place the implementation of the strategic, comprehensive and integrated range of stakeholders in order to highlight the visibility of Malaysia as a hub of academic excellence. While the concept of 'global citizenship' is used as a two-pronged strategy of aggressive marketing by universities which includes; (i) the involvement of the academic expatriates in stimulating international activities of higher education and (ii) an increase in international student enrollment capacity for the enculturation of science and the development of first class mentality. In this aspect, aspirations for a transnational social movement through global citizenship status to establish the identity of the university community without borders (borderless universities) - regardless of skin colour, thus rationalize and liberalize the universal principles of life and cultural traditions of a nation. The education system earlier referred by the spirit of nationalism is now progressing due to globalization, hence forming a system of higher education that is relevant and generated by the need of all time. However, debates arose when the involvement of global citizenship is said to threaten the ultimate university autonomy in determining the direction of academic affairs and governance of their human resources. Stemming from this debate, this study aims to explore the experience of 'global citizenship' that the academic expatriates and international students in shaping the university's strategic needs and interests which are in line with the transition of contemporary higher education. The objective of this study is to examine the acculturation experience of the global citizen in the form of transnational higher education system and suggest policy and policing IHE which refers directly to the experience of the global citizen. This study offers a detailed understanding of how the university communities assess their expatriation experience, thus becoming useful information for learning and transforming education. The findings also open an advanced perspective on the international mobility of human resources and the implications on the implementation of the policy of internationalization of higher education. The contribution of this study is expected to give new input, thus shift the focus of contextual literature for the internationalization of the education system. Instead of focusing on the purpose of generating income of a university, to a greater understanding of subjective experience in utilizing international human resources hence contributing to the prominent transnational character of higher education.

Keywords: internationalization, global citizens, Malaysia higher education, academic expatriate, international students

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Authors: Przemysław Tomczyk, Dagmara Plata-Alf, Piotr Kwiatek

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Literature review is indispensable in research. One of the key techniques used in it is bibliometric analysis, where one of the methods is science mapping. The classic approach that dominates today in this area consists of mapping areas, keywords, terms, authors, or citations. This approach is also used in relation to the review of literature in the field of marketing. The development of technology has resulted in the fact that researchers and practitioners use the capabilities of software available on the market for this purpose. The use of science mapping software tools (e.g., VOSviewer, SciMAT, Pajek) in recent publications involves the implementation of a literature review, and it is useful in areas with a relatively high number of publications. Despite this well-grounded science mapping approach having been applied in the literature reviews, performing them is a painstaking task, especially if authors would like to draw precise conclusions about the studied literature and uncover potential research gaps. The aim of this article is to identify to what extent a new approach to science mapping, variable mapping, takes advantage of the classic science mapping approach in terms of research problem formulation and content/thematic analysis for literature reviews. To perform the analysis, a set of 5 articles on customer ideation was chosen. Next, the analysis of key words mapping results in VOSviewer science mapping software was performed and compared with the variable map prepared manually on the same articles. Seven independent expert judges (management scientists on different levels of expertise) assessed the usability of both the stage of formulating, the research problem, and content/thematic analysis. The results show the advantage of variable mapping in the formulation of the research problem and thematic/content analysis. First, the ability to identify a research gap is clearly visible due to the transparent and comprehensive analysis of the relationships between the variables, not only keywords. Second, the analysis of relationships between variables enables the creation of a story with an indication of the directions of relationships between variables. Demonstrating the advantage of the new approach over the classic one may be a significant step towards developing a new approach to the synthesis of literature and its reviews. Variable mapping seems to allow scientists to build clear and effective models presenting the scientific achievements of a chosen research area in one simple map. Additionally, the development of the software enabling the automation of the variable mapping process on large data sets may be a breakthrough change in the field of conducting literature research.

Keywords: bibliometrics, literature review, science mapping, variable mapping

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Authors: Ishani Maheshika

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The intermediary role performed by freight forwarders in exportation has become significant in fulfilling businesses’ supply chain needs in this dynamic world. Since the success of exporter’s business is at present, highly reliant on supply chain optimization, cost efficiency, profitability, consistent service and responsiveness, the decision of selecting the most beneficial freight forwarder has become crucial for exporters. Although there are similar foreign researches, prior researches covering Sri Lankan setting are not in existence. Moreover, results vary with time, nature of industry and business environment factors. Therefore, a study from the perspective of Sri Lankan exporters was identified as a requisite to be researched. In order to identify and prioritize key factors which have affected the exporter’s decision in selecting freight forwarders in Sri Lankan context, Sri Lankan export industry was stratified into 22 sectors based on commodity using stratified sampling technique. One exporter from each sector was then selected using judgmental sampling to have a sample of 22. Factors which were identified through a pilot survey, was organized under 6 main criteria. A questionnaire was basically developed as pairwise comparisons using 9-point semantic differential scale and comparisons were done within main criteria and subcriteria. After a pre-testing, interviews and e-mail questionnaire survey were conducted. Data were analyzed using Analytic Hierarchy Process to determine priority vectors of criteria. Customer service was found to be the most important main criterion for Sri Lankan exporters. It was followed by reliability and operational efficiency respectively. The criterion of the least importance is company background and reputation. Whereas small sized exporters pay more attention to rate, reliability is the major concern among medium and large scale exporters. Irrespective of seniority of the exporter, reliability is given the prominence. Responsiveness is the most important sub criterion among Sri Lankan exporters. Consistency of judgments with respect to main criteria was verified through consistency ratio, which was less than 10%. Being more competitive, freight forwarders should come up with customized marketing strategies based on each target group’s requirements and expectations in offering services to retain existing exporters and attract new exporters.

Keywords: analytic hierarchy process, freight forwarders, main criteria, Sri Lankan exporters, subcriteria

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Tegene Nigussie

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Introduction: Maize is the most domesticated of all the field crops. Wild maize has not been found to date and there has been much speculation on its origin. Regardless of the validity of different theories, it is generally agreed that the center of origin of maize is Central America, primarily Mexico and the Caribbean. Maize in Africa is of a recent introduction although data suggest that it was present in Nigeria even before Columbus voyages. After being taken to Europe in 1493, maize was introduced to Africa and distributed (spread through the continent by different routes. Maize is an important cereal crop in Ethiopia in general, it is the primarily stable food, and rural households show strong preference. For human food, the important constituents of grain are carbohydrates (starch and sugars), protein, fat or oil (in the embryo) and minerals. About 75 percent of the kernel is starch, a range of 60.80 percent but low protein content (8-15%). In Ethiopia, the introduction of modern farming techniques appears to be a priority. However, the adoption of modern inputs by peasant farmers is found to be very slow, for example, the adoption rate of fertilizer, an input that is relatively adopted, is very slow. The difference in socio-economic factors lay behind the low rate of technological adoption, including price & marketing input. Objective: The aim of the study is to determine the optimum application rate or level of different phosphorus fertilizers for the vegetative growth of maize and to identify the effect of different phosphorus rates on the growth and development of maize. Methods: The vegetative parameter (above ground) measurement from five plants randomly sampled from the middle rows of each plot. Results: The interaction of nitrogen and maize variety showed a significant at (p<0.01) effect on plant height, with the application of 60kg/ha and BH140 maize variety in combination and root length with the application of 60kg/ha of nitrogen and BH140 variety of maize. The highest mean (12.33) of the number of leaves per plant and mean (7.1) of the number of nodes per plant can be used as an alternative for better vegetative growth of maize. Conclusion and Recommendation: Maize is one of the popular and cultivated crops in Ethiopia. This study was conducted to investigate the best dosage of phosphorus for vegetative growth, yield, and better quality of maize variety and to recommend a level of phosphorus rate and the best variety adaptable to the specific soil condition or area.

Keywords: leaf, carbohydrate protein, adoption, sugar

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Authors: Aschalew C. Workneh, K. S. Hari Prasad, C. S. P. Ojha

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The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM.

Keywords: adaptive neuro-fuzzy inference system, canopy temperature, crop water stress index, self-organizing map, wheat

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Authors: Salah Belaidi

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We have carried out extensive and in-depth research to search for bioactive compounds based on Algerian plants. A selection of 50 ligands from Algerian medicinal plants. Several compounds used in herbal medicine have been drawn using Marvin Sketch software. We determined the three-dimensional structures of the ligands with the MMFF94 force field in order to prepare these ligands for molecular docking. The 3D protein structure of the SARS-CoV-2 main protease was taken from the Protein Data Bank. We used AutoDockVina software to apply molecular docking. The hydrogen atoms were added during the molecular docking process, and all the twist bonds of the ligands were added using the (ligand) module in the AutoDock software. The COVID-19 main protease (Mpro) is a key enzyme that plays a vital role in viral transcription and mediating replication, so it is a very attractive drug target for SARS-CoV-2. In this work, an evaluation was carried out on the biologically active compounds present in these selected medicinal plants as effective inhibitors of the protease enzyme of COVID-19, with an in-depth computational calculation of the molecular docking using the Autodock Vina software. The top 7 ligands: Phloroglucinol, Afzelin, Myricetin-3-O- rutinosidTricin 7-neohesperidoside, Silybin, Silychristinthat and Kaempferol are selected among the 50 molecules studied which are Algerian medicinal plants, whose selection is based on the best binding energy which is relatively low compared to the reference molecule with binding affinities of -9.3, -9.3, -9, -8.9, -8 .5, 8.3 and -8.3 kcal mol-1 respectively. Then, we analyzed the ADME properties of the best7 ligands using the web server SwissADME. Two ligands (Silybin, Silychristin) were found to be potential candidates for the discovery and design of novel drug inhibitors of the protease enzyme of SARS-CoV-2. The stability of the two ligands in complexing with the Mpro protease was validated by molecular dynamics simulation; they revealed a stable trajectory in both techniques, RMSD and RMSF, by showing molecular properties with coherent interactions in molecular dynamics simulations. Finally, we conclude that the Silybin ligand forms a more stable complex with the Mpro protease compared to the Silychristin ligand.

Keywords: COVID-19, medicinal plants, molecular docking, ADME properties, molecular dynamics

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

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With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: Idowu Olugbenga Adewumi, Foluke Iyabo Oluwatoyinbo

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Presently, there are several discussions on the food security with increase in yield of arable crop throughout the world. This article, briefly present research efforts to create digital interfaces to nature, in particular to area of crop production in agriculture with increase in yield with interest on pervasive computing. The approach goes beyond the use of sensor networks for environmental monitoring but also by emphasizing the development of a system architecture that detect intruder (Intrusion Process) which reduce the yield of the farmer at the end of the planting/harvesting period. The objective of the work is to set a model for setting up the hand held or portable device for increasing the quality and quantity of arable crop. This process incorporates the use of infrared motion image sensor with security alarm system which can send a noise signal to intruder on the farm. This model of the portable image sensing device in monitoring or scaring human, rodent, birds and even pests activities will reduce post harvest loss which will increase the yield on farm. The nano intelligence technology was proposed to combat and minimize intrusion process that usually leads to low quality and quantity of produce from farm. Intranet system will be in place with wireless radio (WLAN), router, server, and client computer system or hand held device e.g PDAs or mobile phone. This approach enables the development of hybrid systems which will be effective as a security measure on farm. Since, precision agriculture has developed with the computerization of agricultural production systems and the networking of computerized control systems. In the intelligent plant production system of controlled greenhouses, information on plant responses, measured by sensors, is used to optimize the system. Further work must be carry out on modeling using pervasive computing environment to solve problems of agriculture, as the use of electronics in agriculture will attracts more youth involvement in the industry.

Keywords: pervasive computing, intrusion detection, precision agriculture, security, arable crop

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Authors: Brian Fleischli, Shani Robins

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In recent years, the implementation of wisdom skills, including emotional intelligence, mindfulness, empathy, compassion, gratitude, realism (Cognitive-Behavioral Therapy), and humility, within K-12 educational settings has demonstrated significant benefits in reducing stress, anxiety, anger, and conflict among students. This study summarizes the findings of research conducted over several years, showcasing the positive outcomes associated with teaching these skills to elementary and high school students. Additionally, this overview includes an updated synthesis of current literature concerning the application and effectiveness of training these skill sets in K-12 schools. The research outcomes highlight substantial improvements in student well-being and behavior. Demonstrated with treatment group students exhibiting notable reductions in anger, anxiety, depression, and disruptive behaviors compared to control groups. For instance, fourth-grade students showed enhanced empathy, responsibility, and attention, particularly benefiting those with lower initial scores on these measures. Specific interaction effects suggest that older students and males particularly benefit from these interventions, showcasing the nuanced impact of wisdom skill training across different demographics. Furthermore, this presentation emphasizes the critical role of Social and Emotional Learning (SEL) programs in addressing the multifaceted challenges faced by children and adolescents, including mental health issues, academic performance, and social behaviors. The integration of wisdom skills into school curricula not only fosters individual growth and emotional regulation but also enhances overall school climate and academic achievement. In conclusion, the findings contribute to the growing body of empirical evidence supporting the efficacy of teaching wisdom skills in educational settings. The success of these interventions underscores the potential for widespread implementation of evidence-based programs to promote emotional well-being and academic success among students nationwide.

Keywords: wisdom skills, CBT, cognitive behavioral training, mindfulness, empathy, anxiety

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Authors: Kurt Maxwell Kusterer, Sylvain Mignot, Annick Vignes

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To our best knowledge YouTube is the largest video hosting platform in the world. A high number of creators, viewers, subscribers and commentators act in this specific eco-system which generates huge sums of money. Views, subscribers, and comments help to increase the popularity of content creators. The most popular creators are sponsored by brands and participate in marketing campaigns. For a few of them, this becomes a financially rewarding profession. This is made possible through the YouTube Partner Program, which shares revenue among creators based on their popularity. We believe that the role of comments in increasing the popularity is to be emphasized. In what follows, YouTube is considered as a bilateral network between the videos and the commentators. Analyzing a detailed data set focused on French YouTubers, we consider each comment as a link between a commentator and a video. Our research question asks what are the predominant features of a video which give it the highest probability to be commented on. Following on from this question, how can we use these features to predict the action of the agent in commenting one video instead of another, considering the characteristics of the commentators, videos, topics, channels, and recommendations. We expect to see that the videos of more popular channels generate higher viewer engagement and thus are more frequently commented. The interest lies in discovering features which have not classically been considered as markers for popularity on the platform. A quick view of our data set shows that 96% of the commentators comment only once on a certain video. Thus, we study a non-weighted bipartite network between commentators and videos built on the sub-sample of 96% of unique comments. A link exists between two nodes when a commentator makes a comment on a video. We run an Exponential Random Graph Model (ERGM) approach to evaluate which characteristics influence the probability of commenting a video. The creation of a link will be explained in terms of common video features, such as duration, quality, number of likes, number of views, etc. Our data is relevant for the period of 2020-2021 and focuses on the French YouTube environment. From this set of 391 588 videos, we extract the channels which can be monetized according to YouTube regulations (channels with at least 1000 subscribers and more than 4000 hours of viewing time during the last twelve months).In the end, we have a data set of 128 462 videos which consist of 4093 channels. Based on these videos, we have a data set of 1 032 771 unique commentators, with a mean of 2 comments per a commentator, a minimum of 1 comment each, and a maximum of 584 comments.

Keywords: YouTube, social networks, economics, consumer behaviour

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Authors: Batuhan Kocaoglu

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Nowadays digital transformation is a hot topic both in service and production business. For the companies who want to stay alive in the following years, they should change how they do their business. Industry leaders started to improve their ERP (Enterprise Resource Planning) like backbone technologies to digital advances such as analytics, mobility, sensor-embedded smart devices, AI (Artificial Intelligence) and more. Selecting the appropriate technology for the related business problem also is a hot topic. Besides this, to operate in the modern environment and fulfill rapidly changing customer expectations, a digital transformation of the business is required and change the way the business runs, affect how they do their business. Even the digital transformation term is trendy the literature is limited and covers just the philosophy instead of a solid implementation plan. Current studies urge firms to start their digital transformation, but few tell us how to do. The huge investments scare companies with blur definitions and concepts. The aim of this paper to solidify the steps of the digital transformation and offer a roadmap for the companies and academicians. The proposed roadmap is developed based upon insights from the literature review, semi-structured interviews, and expert views to explore and identify crucial steps. We introduced our roadmap in the form of 8 main steps: Awareness; Planning; Operations; Implementation; Go-live; Optimization; Autonomation; Business Transformation; including a total of 11 sub-steps with examples. This study also emphasizes four dimensions of the digital transformation mainly: Readiness assessment; Building organizational infrastructure; Building technical infrastructure; Maturity assessment. Finally, roadmap corresponds the steps with three main terms used in digital transformation literacy as Digitization; Digitalization; and Digital Transformation. The resulted model shows that 'business process' and 'organizational issues' should be resolved before technology decisions and 'digitization'. Companies can start their journey with the solid steps, using the proposed roadmap to increase the success of their project implementation. Our roadmap is also adaptable for relevant Industry 4.0 and enterprise application projects. This roadmap will be useful for companies to persuade their top management for investments. Our results can be used as a baseline for further researches related to readiness assessment and maturity assessment studies.

Keywords: digital transformation, digital business, ERP, roadmap

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: Ali Al-Shehri, Abdulaziz Al-Qasim, Abdulkarim Sofi, Ali Yousef

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The oil and gas industry has played a major role in improving the prosperity of mankind and driving the world economy. According to the International Energy Agency (IEA) and Integrated Environmental Assessment (EIA) estimates, the world will continue to rely heavily on hydrocarbons for decades to come. This growing energy demand mandates taking sustainability measures to prolong the availability of reliable and affordable energy sources, and ensure lowering its environmental impact. Unlike any other industry, the oil and gas upstream operations are energy-intensive and scattered over large zonal areas. These challenging conditions require unique sustainability solutions. In recent years there has been a concerted effort by the oil and gas industry to develop and deploy innovative technologies to: maximize efficiency, reduce carbon footprint, reduce CO2 emissions, and optimize resources and material consumption. In the past, the main driver for research and development (R&D) in the exploration and production sector was primarily driven by maximizing profit through higher hydrocarbon recovery and new discoveries. Environmental-friendly and sustainable technologies are increasingly being deployed to balance sustainability and profitability. Analyzing technology and its sustainability impact is increasingly being used in corporate decision-making for improved portfolio management and allocating valuable resources toward technology R&D.This paper articulates and discusses a novel workflow to identify strategic sustainable technologies for improved portfolio management by addressing existing and future upstream challenges. It uses a systematic approach that relies on sustainability key performance indicators (KPI’s) including energy efficiency quotient, carbon footprint, and CO2 emissions. The paper provides examples of various technologies including CCS, reducing water cuts, automation, using renewables, energy efficiency, etc. The use of 4IR technologies such as Artificial Intelligence, Machine Learning, and Data Analytics are also discussed. Overlapping technologies, areas of collaboration and synergistic relationships are identified. The unique sustainability analyses provide improved decision-making on technology portfolio management.

Keywords: sustainability, oil& gas, technology portfolio, key performance indicator

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Authors: Giovanna X. De Moura, Fernando A. Starepravo

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In recent years, the discussion of women in sports has been part of the political agenda in several countries. However, in the Brazilian scope, it is possible to say that women's sport has not become a social problem recognized by political actors and, therefore, it has not entered the country's governmental agenda. Thus, this work aimed to analyze why sport for women is not on the Brazilian government's agenda. For this, it was interviewed six women considered to be stakeholders in sports, that is, women who influence or are influenced by sports. The interviews were based on a semi-structured script and carried out in the year 2022. Due to the difficulties of commuting and of the schedule of the interviewees, some interviews were carried out in person, others by video call or telephone and others by WhatsApp. The interviews were transcribed and analyzed using Bardin's Content Analysis. As a result, from the stakeholders' perception, it was ascertained that women's sport is not considered a political problem because both sport and politics are considered masculinized fields, making it difficult for women to be present in both spaces. Besides, not only the sport of women but sport in general, is seen as just a marketing tool and a way of getting financial return for companies, being neglected in government plans. Due to this fact, private institutions, corporative means, federations and confederations have been mobilized in the creation of policies that seek changes in the current scenario. Despite this, two PLs (PL 6263/2019 and PL 5297/2020) have been in the process since 2019 but have not been approved yet due to the failure to submit amendments within the established deadline. In order to change this reality, the ones surveyed suggested that there should be not only different types of women represented on the most varied fronts of sports but also more visibility of the issue of women in this field. Furthermore, they mentioned the importance of the creation of specific plans and policies that guarantee a safe place for women and that are consolidated as State policies. In addition, the need for more women in political decision-making positions was also mentioned. It was concluded that women's sport appears on the agenda at a secondary level since it is included on the legislative, and political agenda but not in the executive branch. In addition, there is not enough movement and mobilization in favor of women's sports for it to become a discussion in the field of politics. Regarding the Multiple Streams Model, women's sport is present only in the ideas stream, as there are solutions and ideas for improvements in this field. Finally, it was pointed that there is still a strong dependence on the State for the creation of policies that seek improvements in the participation of girls and women in sport, hence, being necessary the creation of multicentric policies, including non-governmental agents in the process of elaborating policies.

Keywords: agenda, politics, stakeholders, women’s sport

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Authors: Henry Lau, Dilupa Nakandala, Li Zhao

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All along the length of the supply chain, fresh food firms face the challenge of managing both product quality, due to the perishable nature of the products, and product cost. This paper develops a method to assist logistics managers upstream in the fresh food supply chain in making cost optimized decisions regarding transportation, with the objective of minimizing the total cost while maintaining the quality of food products above acceptable levels. Considering the case of multiple fresh food products collected from multiple farms being transported to a warehouse or a retailer, this study develops a total cost model that includes various costs incurred during transportation. The practical application of the model is illustrated by using several computational intelligence approaches including Genetic Algorithms (GA), Fuzzy Genetic Algorithms (FGA) as well as an improved Simulated Annealing (SA) procedure applied with a repair mechanism for efficiency benchmarking. We demonstrate the practical viability of these approaches by using a simulation study based on pertinent data and evaluate the simulation outcomes. The application of the proposed total cost model was demonstrated using three approaches of GA, FGA and SA with a repair mechanism. All three approaches are adoptable; however, based on the performance evaluation, it was evident that the FGA is more likely to produce a better performance than the other two approaches of GA and SA. This study provides a pragmatic approach for supporting logistics and supply chain practitioners in fresh food industry in making important decisions on the arrangements and procedures related to the transportation of multiple fresh food products to a warehouse from multiple farms in a cost-effective way without compromising product quality. This study extends the literature on cold supply chain management by investigating cost and quality optimization in a multi-product scenario from farms to a retailer and, minimizing cost by managing the quality above expected quality levels at delivery. The scalability of the proposed generic function enables the application to alternative situations in practice such as different storage environments and transportation conditions.

Keywords: cost optimization, food supply chain, fuzzy sets, genetic algorithms, product quality, transportation

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Merih Welay Welesilassie

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Understanding educators' readiness to incorporate AI tools into their teaching methods requires comprehensively examining the influencing factors. This understanding is crucial, given the potential of these technologies to personalise learning experiences, improve instructional effectiveness, and foster innovative pedagogical approaches. This study evaluated factors affecting teachers' adoption of AI tools in their English language instruction by extending the Technology Acceptance Model (TAM) to encompass digital readiness support, perceived risk, and trust. A cross-sectional quantitative survey was conducted with 128 English language teachers, supplemented by qualitative data collection from 15 English teachers. The structural mode analysis indicated that implementing AI tools in Ethiopian higher education was notably influenced by digital readiness support, perceived ease of use, perceived usefulness, perceived risk, and trust. Digital readiness support positively impacted perceived ease of use, usefulness, and trust while reducing safety and privacy risks. Perceived ease of use positively correlated with perceived usefulness but negatively influenced trust. Furthermore, perceived usefulness strengthened trust in AI tools, while perceived safety and privacy risks significantly undermined trust. Trust was crucial in increasing educators' willingness to adopt AI technologies. The qualitative analysis revealed that the teachers exhibited strong content and pedagogical knowledge but needed more technology-related knowledge. Moreover, It was found that the teachers did not utilise digital tools to teach English. The study identified several obstacles to incorporating digital tools into English lessons, such as insufficient digital infrastructure, a shortage of educational resources, inadequate professional development opportunities, and challenging policies and governance. The findings provide valuable guidance for educators, inform policymakers about creating supportive digital environments, and offer a foundation for further investigation into technology adoption in educational settings in Ethiopia and similar contexts.

Keywords: digital readiness support, AI acceptance, perceived risc, AI trust

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: Henrie Yunianto, Dermawan Wibisono

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PT. XYZ is a family business (private company) in Indonesia that provide an educational program and consultation services. Since its establishment in 2011, the company has run without any strategic management system implemented. Though the company could survive until now. The management of PT. XYZ sees the business opportunity for such product is huge, even though the targeted market is very specific (niche), the volume is large (due to large population of Indonesia) and numbers of competitors are low (now). It can be said if the product life cycle is in between ‘Introduction stage’ and ‘growth’ stage. It is observed that nowadays the new entrants (competitors) are increasing, thus PT. XYZ consider reacting in facing the intense business rivalry by conducting the business in an appropriate manner. A Performance Management System is important to be implemented in accordance with the business sustainability and growth. The framework of Performance Management System chosen is Integrated Performance Management System (IPMS). IPMS framework has the advantages of its simplicity, linkage between its business variables and indicators where the company can see the connections between all factors measured. IPMS framework consists of perspectives: (1) Business Result, (2) Internal Processes, (3) Resource Availability. Variables and indicators were examined through deep analysis of the business external and internal environments, Strength-Weakness-Opportunity-Threat (SWOT) analysis, Porter’s five forces analysis. Analytical Hierarchy Process (AHP) analysis was then used to quantify the weight of each variable/indicators. AHP is needed since in this study, PT. XYZ, the data of existing performance indicator was not available. Later, where the IPMS is implemented, the real data measured can be examined to determine the weight factor of each indicators using correlation analysis (or other methods). In this study of IPMS design for PT. XYZ, the analysis shows that with current company goals, along with the AHP methodology, the critical indicators for each perspective are: (1) Business results: Customer satisfaction and Employee satisfaction, (2) Internal process: Marketing performance, Supplier quality, Production quality, Continues improvement; (3) Resources Availability: Leadership and company culture & value, Personal Competences, Productivity. Company and/or organization require performance management system to help them in achieving their vision and mission. Company strategy will be effectively defined and addressed by using performance management system. Integrated Performance Management System (IPMS) framework and AHP analysis help us in quantifying the factors which influence the business output expected.

Keywords: analytical hierarchy process, business strategy, differentiation strategy, integrated performance management system

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Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

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Authors: Cynthia Briggs, Kimberly Gerardi

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Over the past six years, the authors have been working together on World Language Collaborations in the Middle School French Program at St. Luke's School in New Canaan, Connecticut, USA. Author 2 brings design expertise to the projects, and both teachers have utilized the fabrication lab, emerging technologies, and collaboration with students. Each year, author 1 proposes a project scope, and her students are challenged to design and engineer a signature project. Both partners have improved the iterative process to ensure deeper learning and sustained student inquiry. The projects range from a 1:32 scale model of the Eiffel Tower that was CNC routed to a fully functional jukebox that plays francophone music, lights up, and can hold up to one thousand songs powered by Raspberry Pi. The most recent project is a Fragrance Marketplace, culminating with a pop-up store for the entire community to discover. Each student will learn the history of fragrance and the chemistry behind making essential oils. Students then create a unique brand, marketing strategy, and concept for their signature fragrance. They are further tasked to use the industrial design process (bottling, packaging, and creating a brand name) to finalize their product for the public Marketplace. Sometimes, these dynamic projects require maintenance and updates. For example, our wall-mounted, three-foot francophone clock is constantly changing. The most recent iteration uses Chat GPT to program the Arduino to reconcile the real-time clock shield and keep perfect time as each hour passes. The lights, motors, and sounds from the clock are authentic to each region, represented with laser-cut embellishments. Inspired by Michel Parmigiani, the history of Swiss watch-making, and the precision of time instruments, we aim for perfection with each passing minute. The authors aim to share exemplary work that is possible with students of all ages. We implemented the reverse engineering process to focus on student outcomes to refine our collaborative process. The products that our students create are prime examples of how the design engineering process is applicable across disciplines. The authors firmly believe that the past and present of World cultures inspire innovation.

Keywords: collaboration, design thinking, emerging technologies, world language

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Authors: Paloma Mendes Saldanha

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Access to information is a prerequisite for democracy while also guiding the material construction of fundamental rights. The exercise of citizenship requires knowing, understanding, questioning, advocating for, and securing rights and responsibilities. In other words, it goes beyond mere active electoral participation and materializes through awareness and the struggle for rights and responsibilities in the various spaces occupied by the population in their daily lives. In times of hyper-cultural connectivity, active citizenship is shaped through ethical trust processes, most often established between humans and algorithms. Automated decisions, so prevalent in various everyday situations, such as purchase preference predictions, virtual voice assistants, reduction of accidents in autonomous vehicles, content removal, resume selection, etc., have already found their place as a normalized discourse that sometimes does not reveal or make clear what violations of fundamental rights may occur when algorithmic explainability is lacking. In other words, technological and market development promotes a normalization for the use of automated decisions while silencing possible restrictions and/or breaches of rights through a culturally modeled, unethical, and unexplained trust process, which hinders the possibility of the right to a healthy, transparent, and complete exercise of citizenship. In this context, the article aims to identify the violations caused by the absence of algorithmic explainability in the exercise of citizenship through the construction of an unethical and silent trust process between humans and algorithms in automated decisions. As a result, it is expected to find violations of constitutionally protected rights such as privacy, data protection, and transparency, as well as the stipulation of algorithmic explainability as a fundamental right in the exercise of Brazilian citizenship in the era of virtualization, facing a threefold foundation called trust: culture, rules, and systems. To do so, the author will use a bibliographic review in the legal and information technology fields, as well as the analysis of legal and official documents, including national documents such as the Brazilian Federal Constitution, as well as international guidelines and resolutions that address the topic in a specific and necessary manner for appropriate regulation based on a sustainable trust process for a hyperconnected world.

Keywords: artificial intelligence, ethics, citizenship, trust

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Authors: Usha Dinakaran, Richa Ganguly

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Gastronomic tourism has evolved into a prominent niche within the travel industry, with tourists increasingly seeking unique culinary experiences as a primary motivation for their journeys. This research explores the intricate relationship between gastronomic tourism and its profound influence on the overall image of travel destinations. It delves into the multifaceted aspects of culinary experiences, tourists' perceptions, and the preservation of cultural identity, all of which play pivotal roles in shaping a destination's image. The primary aim of this study is to comprehensively examine the interplay between gastronomy and tourism, specifically focusing on its impact on destination image. The research seeks to achieve the following objectives: (1) Investigate how tourists perceive and engage with gastronomic tourism experiences. (2) Understand the significance of food in shaping the tourism image. (3.) Explore the connection between gastronomy and the destination's cultural identity Quantify the relationship between tourists' engagement in co-creation activities related to gastronomic tourism and their overall satisfaction with the quality of their culinary experiences. To achieve these objectives, a mixed-method research approach will be employed, including surveys, interviews, and content analysis. Data will be collected from tourists visiting diverse destinations known for their culinary offerings. This research anticipates uncovering valuable insights into the nexus between gastronomic tourism and destination image. It is expected to shed light on how tourists' perceptions of culinary experiences impact their overall perception of a destination. Additionally, the study aims to identify factors influencing tourist satisfaction and how cultural identity is preserved and promoted through gastronomic tourism. The findings of this research hold practical implications for destination marketers and stakeholders. Understanding the symbiotic relationship between gastronomy and tourism can guide the development of more targeted marketing strategies. Furthermore, promoting co-creation activities can enhance tourists' culinary experiences and contribute to the positive image of destinations.This study contributes to the growing body of knowledge regarding gastronomic tourism by consolidating insights from various studies and offering a comprehensive perspective on its impact on destination image. It offers a platform for future research in this domain and underscores the importance of culinary experiences in contemporary travel. In conclusion, this research endeavors to illuminate the dynamic interplay between gastronomic tourism and destination image, providing valuable insights for both academia and industry stakeholders in the field of tourism and hospitality.

Keywords: gastronomy, tourism, destination image, culinary

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