Search results for: Competitive Intelligence

Authors: Dimitrios Kafetzopoulos

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Nowadays, companies are more concerned with adopting their own strategies for increased efficiency and sustainability. Dynamic environments are fertile fields for developing operational changes. For this purpose, organizations need to implement an advanced management philosophy that boosts changes to companies’ operation. Changes refer to new applications of knowledge, ideas, methods, and skills that can generate unique capabilities and leverage an organization’s competitiveness. So, in order to survive and compete in the global and niche markets, companies should incorporate the adoption of operational changes into their strategy with regard to their products and their processes. Creating the appropriate culture for changes in terms of products and processes helps companies to gain a sustainable competitive advantage in the market. Thus, the purpose of this study is to investigate the role of both incremental and radical changes into operations of a company, taking into consideration not only product changes but also process changes, and continues by measuring the impact of these two types of changes on business efficiency and sustainability of Greek manufacturing companies. The above discussion leads to the following hypotheses: H1: Radical operational changes have a positive impact on firm efficiency. H2: Incremental operational changes have a positive impact on firm efficiency. H3: Radical operational changes have a positive impact on firm sustainability. H4: Incremental operational changes have a positive impact on firm sustainability. In order to achieve the objectives of the present study, a research study was carried out in Greek manufacturing firms. A total of 380 valid questionnaires were received while a seven-point Likert scale was used to measure all the questionnaire items of the constructs (radical changes, incremental changes, efficiency and sustainability). The constructs of radical and incremental operational changes, each one as one variable, has been subdivided into product and process changes. Non-response bias, common method variance, multicollinearity, multivariate normal distribution and outliers have been checked. Moreover, the unidimensionality, reliability and validity of the latent factors were assessed. Exploratory Factor Analysis and Confirmatory Factor Analysis were applied to check the factorial structure of the constructs and the factor loadings of the items. In order to test the research hypotheses, the SEM technique was applied (maximum likelihood method). The goodness of fit of the basic structural model indicates an acceptable fit of the proposed model. According to the present study findings, radical operational changes and incremental operational changes significantly influence both efficiency and sustainability of Greek manufacturing firms. However, it is in the dimension of radical operational changes, meaning those in process and product, that the most significant contributors to firm efficiency are to be found, while its influence on sustainability is low albeit statistically significant. On the contrary, incremental operational changes influence sustainability more than firms’ efficiency. From the above, it is apparent that the embodiment of the concept of the changes into the products and processes operational practices of a firm has direct and positive consequences for what it achieves from efficiency and sustainability perspective.

Keywords: incremental operational changes, radical operational changes, efficiency, sustainability

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Authors: Swati Tomar, Sunil Kumar Gupta

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Anammox is a novel and promising technology that has changed the traditional concept of biological nitrogen removal. The process facilitates direct oxidation of ammonical nitrogen under anaerobic conditions with nitrite as an electron acceptor without the addition of external carbon sources. The present study investigated the feasibility of anammox hybrid reactor (AHR) combining the dual advantages of suspended and attached growth media for biodegradation of ammonical nitrogen in wastewater. The experimental unit consisted of 4 nos. of 5L capacity AHR inoculated with mixed seed culture containing anoxic and activated sludge (1:1). The process was established by feeding the reactors with synthetic wastewater containing NH4-H and NO2-N in the ratio 1:1 at HRT (hydraulic retention time) of 1 day. The reactors were gradually acclimated to higher ammonium concentration till it attained pseudo steady state removal at a total nitrogen concentration of 1200 mg/l. During this period, the performance of the AHR was monitored at twelve different HRTs varying from 0.25-3.0 d with increasing NLR from 0.4 to 4.8 kg N/m3d. AHR demonstrated significantly higher nitrogen removal (95.1%) at optimal HRT of 1 day. Filter media in AHR contributed an additional 27.2% ammonium removal in addition to 72% reduction in the sludge washout rate. This may be attributed to the functional mechanism of filter media which acts as a mechanical sieve and reduces the sludge washout rate many folds. This enhances the biomass retention capacity of the reactor by 25%, which is the key parameter for successful operation of high rate bioreactors. The effluent nitrate concentration, which is one of the bottlenecks of anammox process was also minimised significantly (42.3-52.3 mg/L). Process kinetics was evaluated using first order and Grau-second order models. The first-order substrate removal rate constant was found as 13.0 d-1. Model validation revealed that Grau second order model was more precise and predicted effluent nitrogen concentration with least error (1.84±10%). A new mathematical model based on mass balance was developed to predict N2 gas in AHR. The mass balance model derived from total nitrogen dictated significantly higher correlation (R2=0.986) and predicted N2 gas with least error of precision (0.12±8.49%). SEM study of biomass indicated the presence of the heterogeneous population of cocci and rod shaped bacteria of average diameter varying from 1.2-1.5 mm. Owing to enhanced NRE coupled with meagre production of effluent nitrate and its ability to retain high biomass, AHR proved to be the most competitive reactor configuration for dealing with nitrogen laden wastewater.

Keywords: anammox, filter media, kinetics, nitrogen removal

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Authors: Bill Wang, Paul Childerhouse, Yuanfei Kang

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Purpose: The research is to extend resource orchestration theory (ROT) into supply chain management (SCM) area to investigate the dyadic relationships at both individual and organizational levels in supply chain integration (SCI). Also, we try to explore the interaction mechanism between inter-personal relationships (IPRs) and inter-organizational (IORs) during the whole SCI process. Methodology/approach: The research employed an exploratory multiple case study approach of four New Zealand companies. The data was collected via semi-structured interviews with top, middle, and lower level managers and operators from different departments of both suppliers and customers triangulated with company archival data. Findings: The research highlights the important role of both IPRs and IORs in the whole SCI process. Both IPRs and IORs are valuable, inimitable resources but IORs are formal and exterior while IPRs are informal and subordinated. In the initial stage of SCI process, IPRs are seen as key resources antecedents to IOR building while three IPRs dimensions work differently: personal credibility acts as an icebreaker to strengthen the confidence forming IORs, and personal affection acts as a gatekeeper, whilst personal communication expedites the IORs process. In the maintenance and development stage, IORs and IPRs interact each other continuously: good interaction between IPRs and IORs can facilitate SCI process while the bad interaction between IPRs can damage the SCI process. On the other hand, during the life-cycle of SCI process, IPRs can facilitate the formation, development of IORs while IORs development can cultivate the ties of IPRs. Out of the three dimensions of IPRs, Personal communication plays a more important role to develop IORs than personal credibility and personal affection. Originality/value: This research contributes to ROT in supply chain management literature by highlighting the interaction of IPRs and IORs in SCI. The intangible resources and capabilities of three dimensions of IPRs need to be orchestrated and nurtured to achieve efficient and effective IORs in SCI. Also, IPRs and IORs need to be orchestrated in terms of breadth, depth, and life-cycle of whole SCI process. Our study provides further insight into the rarely explored inter-personal level of SCI. Managerial implications: Our research provides top management with further evidence of the significance roles of IPRs at different levels when working with trading partners. This highlights the need to actively manage and develop these soft IPRs skills as an intangible competitive resource. Further, the research identifies when staff with specific skills and connections should be utilized during the different stages of building and maintaining inter-organizational ties. More importantly, top management needs to orchestrate and balance the resources of IPRs and IORs.

Keywords: case study, inter-organizational relationships, inter-personal relationships, resource orchestration, supply chain integration

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Authors: Shehu Isyaku

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The general objective of the study was to investigate ‘the relationship between the external business environment and the sustainability of micro, small and medium enterprises (MSMEs) in Jigawa state’, Nigeria. Specifically, the study was to examine the relationship between 1) the economic environment, 2) the social environment, 3) the technological environment, and 4) the political environment and the sustainability of MSMEs in Jigawa state, Nigeria. The study was drawn on Resource-Based View (RBV) Theory and Knowledge-Based View (KBV). The study employed a descriptive cross-sectional survey design. A researcher-made questionnaire was used to collect data from the 350 managers/owners who were selected using stratified, purposive and simple random sampling techniques. Data analysis was done using means and standard deviations, factor analysis, Correlation Coefficient, and Pearson Linear Regression analysis. The findings of the study revealed that the sustainability potentials of the managers/owners were rated as high potential (economic, environmental, and social sustainability using 5 5-point Likert scale. Mean ratings of effectiveness of the external business environment were; as highly effective. The results from the Pearson Linear Regression Analysis rejected the hypothesized non-significant effect of the external business environment on the sustainability of MSMEs. Specifically, there is a positive significant relationship between 1) economic environment and sustainability; 2) social environment and sustainability; 3) technological environment and sustainability and political environment and sustainability. The researcher concluded that MSME managers/owners have a high potential for economic, social and environmental sustainability and that all the constructs of the external business environment (economic environment, social environment, technological environment and political environment) have a positive significant relationship with the sustainability of MSMEs. Finally, the researcher recommended that 1) MSME managers/owners need to develop marketing strategies and intelligence systems to accumulate information about the competitors and customers' demands, 2) managers/owners should utilize the customers’ cultural and religious beliefs as an opportunity that should be utilized while formulating business strategies.

Keywords: business environment, sustainability, small and medium enterprises, external business environment

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Authors: W. A. Nalaka Wijesooriya

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The increasing paddy production, stabilization of domestic rice consumption and the increasing dynamism of rice processing and domestic markets call for a rethinking of the general direction of the rice milling industry in Sri Lanka. The main purpose of the study was to explore levels of concentration in rice milling industry in Polonnaruwa and Hambanthota which are the major hubs of the country for rice milling. Concentration indices reveal that the rice milling industry in Polonnaruwa operates weak oligopsony and is highly competitive in Hambanthota. According to the actual quantity of paddy milling per day, 47 % is less than 8Mt/Day, while 34 % is 8-20 Mt/day, and the rest (19%) is greater than 20 Mt/day. In Hambanthota, nearly 50% of the mills belong to the range of 8-20 Mt/day. Lack of experience of the milling industry, poor knowledge on milling technology, lack of capital and finding an output market are the major entry barriers to the industry. Major problems faced by all the rice millers are the lack of a uniform electricity supply and low quality paddy. Many of the millers emphasized that the rice ceiling price is a constraint to produce quality rice. More than 80% of the millers in Polonnaruwa which is the major parboiling rice producing area have mechanical dryers. Nearly 22% millers have modern machineries like color sorters, water jet polishers. Major paddy purchasing method of large scale millers in Polonnaruwa is through brokers. In Hambanthota major channel is miller purchasing from paddy farmers. Millers in both districts have major rice selling markets in Colombo and suburbs. Huge variation can be observed in the amount of pledge (for paddy storage) loans. There is a strong relationship among the storage ability, credit affordability and the scale of operation of rice millers. The inter annual price fluctuation ranged 30%-35%. Analysis of market margins by using series of secondary data shows that farmers’ share on rice consumer price is stable or slightly increases in both districts. In Hambanthota a greater share goes to the farmer. Only four mills which have obtained the Good Manufacturing Practices (GMP) certification from Sri Lanka Standards Institution can be found. All those millers are small quantity rice exporters. Priority should be given for the Small and medium scale millers in distribution of storage paddy of PMB during the off season. The industry needs a proper rice grading system, and it is recommended to introduce a ceiling price based on graded rice according to the standards. Both husk and rice bran were underutilized. Encouraging investment for establishing rice oil manufacturing plant in Polonnaruwa area is highly recommended. The current taxation procedure needs to be restructured in order to ensure the sustainability of the industry.

Keywords: conduct, performance, structure (SCP), rice millers

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Authors: Maria A. Castillo H., Andrés R. Leandro C.

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During the last decades, agricultural productivity in Latin America has increased with precision agriculture and more efficient agricultural technologies. The use of automated systems, satellite images, geographic information systems, and tools for data analysis, and artificial intelligence have contributed to making more effective strategic decisions. Twenty years ago, the state of Mérida, located in the Venezuelan Andes, reported the largest area covered by greenhouses in the country, where certified seeds of potatoes, vegetables, ornamentals, and flowers were produced for export and consumption in the central region of the country. In recent years, it is estimated that production under greenhouses has changed, and the area covered has decreased due to different factors, but there are few historical statistical data in sufficient quantity and quality to support this estimate or to be used for analysis and decision making. The objective of this study is to compare data collected about geoposition, use, and covered areas of the greenhouses in 2007 to data available in 2021, as support for the analysis of the current situation of horticultural production in the main municipalities of the state of Mérida. The document presents the development of the work in the diagnosis and integration of geographic coordinates in GIS and data analysis phases. As a result, an evaluation of the process is made, a dashboard is presented with the most relevant data along with the geographical coordinates integrated into GIS, and an analysis of the obtained information is made. Finally, some recommendations for actions are added, and works that expand the information obtained and its geographical traceability over time are proposed. This study contributes to granting greater certainty in the supporting data for the evaluation of social, environmental, and economic sustainability indicators and to make better decisions according to the sustainable development goals in the area under review. At the same time, the methodology provides improvements to the agricultural data collection process that can be extended to other study areas and crops.

Keywords: greenhouses, geographic information system, protected agriculture, data analysis, Venezuela

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Authors: Mounir Soule, Hindatou Saidou, Razafimahefa, Mohamed Thani Ibouroi

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High levels of habitat fragmentation and loss are the main drivers of plant species extinction. They reduce the habitat quality, which is a determining factor for the reproduction of plant species, and generate strong selective pressures for habitat selection, with impacts on the reproduction and survival of individuals. The Comorian’s yam (Dioscorea comorensis) is one of the most threatened plant species of the Comoros archipelago. The species faces one of the highest rates of habitat loss worldwide (9.3 % per year) and is classified as Endangered in the IUCN red list. Despite the nutritional potential of this tuber, the Comorian’s yam cultivation remains neglected by local populations due probably to lack of knowledge on its nutritional importance and the factors driving its spatial distribution and development. In this study, we assessed the nutritional characteristics of Dioscorea comorensis and the drivers of spatial distribution and abundance to propose conservation measures and improve crop yields. To determine the nutritional characteristics, the Kjeldahl method, the Soxhlet method, and Atwater's specific calorific coefficients methods were applied for analyzing proteins, lipids, and caloric energy respectively. In addition, atomic absorption spectrometry was used to measure mineral particles. By combining species occurrences with ecological (habitat types), climatic (temperature, rainfall, etc.), and physicochemical (soil types and quality) variables, we assessed habitat suitability and spatial distribution of the species and the factors explaining the origin, persistence, distribution and competitive capacity of a species using a Species Distribution Modeling (SDM) method. The results showed that the species contains 83.37% carbohydrates, 6.37% protein, and 0.45% lipids. In 100 grams, the quantities of Calcium, Sodium, Zinc, Iron, Copper, Potassium, Phosphorus, Magnesium, and Manganese are respectively 422.70, 599.41, 223.11, 252.32, 332.20, 780.41, 444.17, 287.71 and 220.73 mg. Its PRAL index is negative (- 9.80 mEq/100 g), and its Ca/P (0.95) and Na/K (0.77) ratios are less than 1. This species provides an energy value of 357.46 Kcal per 100 g, thanks to its carbohydrates and minerals and is distinguished from others by its high protein content, offering benefits for cardiovascular health. According to our SDM, the species has a very limited distribution, restricted to forests with higher biomass, humidity, and clay. Our findings highlight how distribution patterns are related to ecological and environmental factors. They also emphasize how the Comoros yam is beneficial in terms of nutritional quality. Our results represent a basic knowledge that will help scientists and decision-makers to develop conservation strategies and to improve crop yields.

Keywords: Dioscorea comorensis, nutritional characteristics, species distribution modeling, conservation strategies, crop yields improvement

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Authors: Fatih Frank Alparslan

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The aviation industry is at a crucial turning point due to modern technologies, environmental concerns, and changing ways of transporting people and goods globally. The paper examines these challenges and opportunities comprehensively. It emphasizes the role of innovative management and advanced technology in shaping the future of air travel. This study begins with an overview of the current state of the aviation industry, identifying key areas where innovation and technology could be highly beneficial. It explores the latest advancements in airplane design, propulsion, and materials. These technological advancements are shown to enhance aircraft performance and environmental sustainability. The paper also discusses the use of artificial intelligence and machine learning in improving air traffic control, enhancing safety, and making flight operations more efficient. The management of these technologies is critically important. Therefore, the research delves into necessary changes in organization, culture, and operations to support innovation. It proposes a management approach that aligns with these modern technologies, underlining the importance of forward-thinking leaders who collaborate across disciplines and embrace innovative ideas. The paper addresses challenges in adopting these innovations, such as regulatory barriers, the need for industry-wide standards, and the impact of technological changes on jobs and society. It recommends that governments, aviation businesses, and educational institutions collaborate to address these challenges effectively, paving the way for a more innovative and eco-friendly aviation industry. In conclusion, the paper argues that the future of aviation relies on integrating new management practices with innovative technologies. It urges a collective effort to push beyond current capabilities, envisioning an aviation industry that is safer, more efficient, and environmentally responsible. By adopting a broad approach, this research contributes to the ongoing discussion about resolving the complex issues facing today's aviation sector, offering insights and guidance to prepare for future advancements.

Keywords: aviation innovation, technology integration, environmental sustainability, management strategies, multidisciplinary approach

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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: 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: Shahid Razzaq

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The governments around the globe have started taking into considerations the knowledge management dynamics while formulating, implementing, and evaluating the strategies, with or without the conscious realization, for the different public sector organizations and public policy developments. Health Department of Punjab province in Pakistan is striving to deliver quality healthcare services to the community through an efficient and effective service delivery system. Despite of this struggle some employee performance issues yet exists in the form of challenge to government. To overcome these issues department took several steps including HR strategies, use of technologies and focus of hard issues. Consequently, this study was attempted to highlight the importance of soft issue that is knowledge management in its true essence to tackle their performance issues. Knowledge management in public sector is quite an ignored area in the knowledge management-a growing multidisciplinary research discipline. Knowledge-based view of the firm theory asserts the knowledge is the most deliberate resource that can result in competitive advantage for an organization over the other competing organizations. In the context of our study it means for gaining employee performance, organizations have to increase the heterogeneous knowledge bases. The study uses the cross-sectional and quantitative research design. The data is collected from the knowledge workers of Health Department of Punjab, the biggest province of Pakistan. A total of 341 sample size is achieved. The SmartPLS 3 Version 2.6 is used for analyzing the data. The data examination revealed that knowledge management processes has a strong impact on knowledge worker performance. All hypotheses are accepted according to the results. Therefore, it can be summed up that to increase the employee performance knowledge management activities should be implemented. Health Department within province of Punjab introduces the knowledge management infrastructure and systems to make effective availability of knowledge for the service staff. This knowledge management infrastructure resulted in an increase in the knowledge management process in different remote hospitals, basic health units and care centers which resulted in greater service provisions to public. This study is to have theoretical and practical significances. In terms of theoretical contribution, this study is to establish the relationship between knowledge management and performance for the first time. In case of the practical contribution, this study is to give an insight to public sector organizations and government about role of knowledge management in employ performance. Therefore, public policymakers are strongly advised to implement the activities of knowledge management for enhancing the performance of knowledge workers. The current research validated the substantial role of knowledge management in persuading and creating employee arrogances and behavioral objectives. To the best of authors’ knowledge, this study contribute to the impact of knowledge management on employee performance as its originality.

Keywords: employee performance, knowledge management, public sector, soft issues

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Authors: Ranjith Ihalanayake, Lalith Seelanatha, John Breen

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In this study, we explored the current working capital management practices as applied in small businesses in Victoria, filling an existing theoretical and empirical gap in literature in general and in Australia in particular. Amidst the current global competitive and dynamic environment, the short term insolvency of small businesses is very critical for the long run survival. A firm’s short-term insolvency is dependent on the availability of sufficient working capital for feeding day to day operational activities. Therefore, given the reliance for short-term funding by small businesses, it has been recognized that the efficient management of working capital is crucial in respect of the prosperity and survival of such firms. Against this background, this research was an attempt to understand the current working capital management strategies and practices used by the small scale businesses. To this end, we conducted an internet survey among 220 small businesses operating in Victoria, Australia. The survey results suggest that the majority of respondents are owner-manager (73%) and male (68%). Respondents participated in this survey mostly have a degree (46%). About a half of respondents are more than 50 years old. Most of respondents (64%) have business management experience more than ten years. Similarly, majority of them (63%) had experience in the area of their current business. Types of business of the respondents are: Private limited company (41%), sole proprietorship (37%), and partnership (15%). In addition, majority of the firms are service companies (63%), followed by retailed companies (25%), and manufacturing (17%). Size of companies of this survey varies, 32% of them have annual sales $100,000 or under, while 22% of them have revenue more than $1,000,000 every year. In regards to the total assets, majority of respondents (43%) have total assets $100,000 or less while 20% of respondents have total assets more than $1,000,000. In regards to WCMPs, results indicate that almost 70% of respondents mentioned that they are responsible for managing their business working capital. The survey shows that majority of respondents (65.5%) use their business experience to identify the level of investment in working capital, compared to 22% of respondents who seek advice from professionals. The other 10% of respondents, however, follow industry practice to identify the level of working capital. The survey also shows that more than a half of respondents maintain good liquidity financial position for their business by having accounts payable less than accounts receivable. This study finds that majority of small business companies in western area of Victoria have a WCM policy but only about 8 % of them have a formal policy. Majority of the businesses (52.7%) have an informal policy while 39.5% have no policy. Of those who have a policy, 44% described their working capital management policies as a compromise policy while 35% described their policy as a conservative policy. Only 6% of respondents apply aggressive policy. Overall the results indicate that the small businesses pay less attention into the management of working capital of their business despite its significance in the successful operation of the business. This approach may be adopted during favourable economic times. However, during relatively turbulent economic conditions, such an approach could lead to greater financial difficulties i.e. short-term financial insolvency.

Keywords: small business, working capital management, Australia, sufficient, financial insolvency

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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: 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: Marike Venter de Villiers, Alicia Kruger

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The rapid development of the Internet has facilitated the proliferation of online stores. It has, therefore, become a pertinent issue for online retailers to provide the ultimate experience to customers in an attempt to maintain market share in this competitive landscape. Experiential marketing refers to the sensory dimensions that consumers experience when being faced with a purchase decision, such as getting them to sense, feel, think, act, and relate. The goal of experiential marketing is to provide a holistic experience for customers that allow them to engage in an activity where they may be motivated to purchase the concept behind the product. Creating a unique online experience holds several benefits to brands such as increased customer satisfaction, increased revisit intention, and higher levels of customer loyalty. Although several studies have explored the topic of experiential marketing in an online context, a lack of research exists on South African consumers, an emerging economy that is often overlooked globally. More specifically, the present study focused on professional females and their perceptions of experiential marketing when shopping for fashion products online. The main purpose of this study was to investigate the experiential factors that influence the online purchase intention of fashion products among female professionals. Furthermore, this study aimed to achieve the following objectives: firstly, to gain insight into key website characteristics that consumers value when shopping online for fashion products; secondly, to apply Pine and Gilmore’s (1989) Four Realms of an Experience (entertainment, education, esthetics, and escapism) to ground the study; and thirdly, to gain in-depth insight into the importance of these dimensions and identifying sub-categories that fashion marketers can use to enhance consumers’ online experience. By means of a qualitative study, a focus group was conducted comprising six professional females by using semi-structured questions. Respondents were selected using convenience sampling, and the results were analyzed using thematic analysis. The present research suggests that three of the four realms of experience influence purchase intention of fashion products online, namely, escapism, esthetics, and education. The fourth dimension, pleasure, was present but to a lesser degree. In other words, ‘escapism’ provides online shoppers with a sense of emotional and intellectual pleasure, while ‘esthetics’ refers to the website design, functionality, and product range, and ‘education’ comprises the product information such as the quality, fabric, price and available sizes. The findings of this study provide fashion marketers with insight into how they can maximize on experiential marketing when selling fashion products online. It further provides strategies and techniques for creating an enhanced online experience that ultimately may lead to increased purchase intention.

Keywords: experiential marketing, fashion, online, retail

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Authors: Henning Lahmann, Benjamyn I. Scott, Bart Custers

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Suboptimal adoption of AI in defence organisations carries risks for the protection of the freedom, safety, and security of society. Despite the vast opportunities that defence AI-technology presents, there are also a variety of ethical, legal, and societal concerns. To ensure the successful use of AI technology by the military, ethical, legal, and societal aspects (ELSA) need to be considered, and their concerns continuously addressed at all levels. This includes ELSA considerations during the design, manufacturing and maintenance of AI-based systems, as well as its utilisation via appropriate military doctrine and training. This raises the question how defence organisations can remain strategically competitive and at the edge of military innovation, while respecting the values of its citizens. This paper will explain the set-up and share preliminary results of a 4-year research project commissioned by the National Research Council in the Netherlands on the ethical, legal, and societal aspects of AI in defence. The project plans to develop a future-proof, independent, and consultative ecosystem for the responsible use of AI in the defence domain. In order to achieve this, the lab shall devise a context-dependent methodology that focuses on the ‘analysis’, ‘design’ and ‘evaluation’ of ELSA of AI-based applications within the military context, which include inter alia unmanned aircraft. This is bolstered as the Lab also recognises and complements the existing methods in regards to human-machine teaming, explainable algorithms, and value-sensitive design. Such methods will be modified for the military context and applied to pertinent case-studies. These case-studies include, among others, the application of autonomous robots (incl. semi- autonomous) and AI-based methods against cognitive warfare. As the perception of the application of AI in the military context, by both society and defence personnel, is important, the Lab will study how these perceptions evolve and vary in different contexts. Furthermore, the Lab will monitor – as they may influence people’s perception – developments in the global technological, military and societal spheres. Although the emphasis of the research project is on different forms of AI in defence, it focuses on several case studies. One of these case studies is on unmanned aircraft, which will also be the focus of the paper. Hence, ethical, legal, and societal aspects of unmanned aircraft in the defence domain will be discussed in detail, including but not limited to privacy issues. Typical other issues concern security (for people, objects, data or other aircraft), privacy (sensitive data, hindrance, annoyance, data collection, function creep), chilling effects, PlayStation mentality, and PTSD.

Keywords: autonomous weapon systems, unmanned aircraft, human-machine teaming, meaningful human control, value-sensitive design

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Authors: Fanny Desbiolles, Laure Malleret, Isabelle Laffont-Schwob, Christophe Tiliacos, Anne Piram, Mohamed Sarakha, Pascal Wong-Wah-Chung

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Pharmaceuticals in the environment have become a very hot topic in the recent years. This interest is related to the large amounts dispensed and to their release in urine or faeces from treated patients, resulting in their ubiquitous presence in water resources and wastewater treatment plants (WWTP) effluents. Thereby, many studies focused on the prediction of pharmaceuticals’ behaviour, to assess their fate and impacts in the environment. Carbamazepine is a widely consumed psychotropic pharmaceutical, thus being one of the most commonly detected drugs in the environment. This organic pollutant was proved to be persistent, especially with respect to its non-biodegradability, rendering it recalcitrant to usual biological treatment processes. Consequently, carbamazepine is very little removed in WWTP with a maximum abatement rate of 5 % and is then often released in natural surface waters. To better assess the environmental fate of carbamazepine in aqueous media, its photochemical transformation was undertaken in four natural waters (two French rivers, the Berre salt lagoon, Mediterranean Sea water) representative of coastal and inland water types. Kinetic experiments were performed in the presence of light using simulated solar irradiation (Xe lamp 300W). Formation of short-lifetime species was highlighted using chemical trap and laser flash photolysis (nanosecond). Identification of transformation by-products was assessed by LC-QToF-MS analyses. Carbamazepine degradation was observed after a four-day exposure and an abatement of 20% maximum was measured yielding to the formation of many by-products. Moreover, the formation of hydroxyl radicals (•OH) was evidenced in waters using terephthalic acid as a probe, considering the photochemical instability of its specific hydroxylated derivative. Correlations were implemented using carbamazepine degradation rate, estimated hydroxyl radical formation and chemical contents of waters. In addition, laser flash photolysis studies confirmed •OH formation and allowed to evidence other reactive species, such as chloride (Cl2•-)/bromine (Br2•-) and carbonate (CO3•-) radicals in natural waters. Radicals mainly originate from dissolved phase and their occurrence and abundance depend on the type of water. Rate constants between reactive species and carbamazepine were determined by laser flash photolysis and competitive reactions experiments. Moreover, LC-QToF-MS analyses of by-products help us to propose mechanistic pathways. The results will bring insights to the fate of carbamazepine in various water types and could help to evaluate more precisely potential ecotoxicological effects.

Keywords: carbamazepine, kinetic and mechanistic approaches, natural waters, photodegradation

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Authors: Mohammadreza Bakhtiari, Mehrdad Maghsoudi, Hamidreza Bakhtiari

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This study investigates consumer behavior in the notebook computer market by integrating social media sentiment analysis with expert evaluations. The rapid evolution of the notebook industry has intensified competition among manufacturers, necessitating a deeper understanding of consumer priorities. Social media platforms, particularly Twitter, have become valuable sources for capturing real-time user feedback. In this research, sentiment analysis was performed on Twitter data gathered in the last two years, focusing on seven major notebook brands. The PyABSA framework was utilized to extract sentiments associated with various notebook components, including performance, design, battery life, and price. Expert evaluations, conducted using fuzzy logic, were incorporated to assess the impact of these sentiments on purchase behavior. To provide actionable insights, the TOPSIS method was employed to prioritize notebook features based on a combination of consumer sentiments and expert opinions. The findings consistently highlight price, display quality, and core performance components, such as RAM and CPU, as top priorities across brands. However, lower-priority features, such as webcams and cooling fans, present opportunities for manufacturers to innovate and differentiate their products. The analysis also reveals subtle but significant brand-specific variations, offering targeted insights for marketing and product development strategies. For example, Lenovo's strong performance in display quality points to a competitive edge, while Microsoft's lower ranking in battery life indicates a potential area for R&D investment. This hybrid methodology demonstrates the value of combining big data analytics with expert evaluations, offering a comprehensive framework for understanding consumer behavior in the notebook market. The study emphasizes the importance of aligning product development and marketing strategies with evolving consumer preferences, ensuring competitiveness in a dynamic market. It also underscores the potential for innovation in seemingly less important features, providing companies with opportunities to create unique selling points. By bridging the gap between consumer expectations and product offerings, this research equips manufacturers with the tools needed to remain agile in responding to market trends and enhancing customer satisfaction.

Keywords: consumer behavior, customer preferences, laptop industry, notebook computers, social media analytics, TOPSIS

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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

Procedia PDF Downloads 44

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

Procedia PDF Downloads 50

Authors: Josie West

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This paper focuses on the conflicts and utility of Marxist Feminist frames for sex work research, drawing on findings uncovered through in-depth interviews with online sex workers, alongside critical discourse analysis of media and political commentary. It brings the critical perspective of women into digital workerism and gig economy dialogue who, despite their significant presence within online work, have been overlooked. The autonomist Marxist concept of class composition is adopted to unpack the social, technical and political composition of this often-invisible segment of the service sector. Autonomism makes visible the perspective of workers engaged in processes of mobilization and demobilizaiton. This allows researchers to find everyday forms of resistance which occur within and outside trade unions. On the other hand, Marxist feminist arguments about invisibility politics can generate unhelpful allegories about sex work as domestic labour within the reproductive sphere. Nick Srnicek’s development of Marx’s notion of infrastructure rents helps theorize experiences of unpaid labour within online sex work. Moreover, debates about anti-work politics can cause conflict among sex workers fighting for the labour movement and those rejecting the capitalist work ethic. This illuminates’ tensions caused by white privilege and differing experiences of sex work. The monopolistic and competitive nature of sex work platforms within platform capitalism, and the vulnerable position of marginalised workers within stigmatized/criminalised markets, complicates anti-work politics further. This paper is situated within the feminist sex wars and the intensely divisive question of whether sex workers are victims of the patriarchy or symbols of feminist resistance. Camgirls are shown to engage in radical tactics of resistance against their technical composition on popular sex work platforms. They also engage in creative acts of resistance through performance art, in an attempt to draw attention to stigma and anti-criminalization politics. This sector offers a fascinating window onto grassroots class-action, alongside education about ‘whorephobia.’ A case study of resistance against Only Fans, and a small workers co-operative which emerged during the pandemic, showcases how workers engage in socialist and political acts without the aid of unions. Workers are victims of neoliberalism and simultaneous adopters of neoliberal strategies of survival. The complex dynamics within unions are explored, including tensions with grass-roots resistance, financial pressures and intersecting complications of class, gender and race.

Keywords: autonomist marxism, digital labor, feminism, neoliberalism, sex work, platform capitalism

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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

Procedia PDF Downloads 183

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

Procedia PDF Downloads 223

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

Procedia PDF Downloads 18