Search results for: cloud computing adoption

Authors: Ali Safaeianpour, Nima Shamandi

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As air traffic expands, many airports have evolved into transit centers for people, information, and commerce, and technology implementation is an absolute part of airport development. Several challenges are in the way of implementing technology in an airport. Airport 4.0 proposes the "Smart Airport" concept, which focuses on using modern technologies such as Big Data, the Internet of Things (IoT), advanced biometric systems, blockchain, and cloud computing to alter and enhance passengers' journeys. Several common IoT concrete topics as partial keys to smart airports are discussed and introduced, ranging from automated check-in systems to exterior tracking processes, with the goal of enlightening more and more insightful ideas and proposals about smart airport solutions. IoT will dramatically alter people's lives by infusing intelligence, boosting the quality of life, and assembling it smarter. This paper reviews the approaches to transforming an airport into a smart airport and describes several enabling components of IoT and challenges that can hinder the implementation of a smart airport's function, which require to be addressed.

Keywords: airport 4.0, digital airport, smart airport, IoT

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: M. Armenteros, A. Domínguez, M. Fernández, A. J. Benítez

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The following paper describes the video database tool used by the Fédération Internationale de Football Association (FIFA) as part of the research project developed in collaboration with the Carlos III University of Madrid. The database project began in 2012, with the aim of creating an educational tool for the training of instructors, referees and assistant referees, and it has been used in all FUTURO III courses since 2013. The platform now contains 3,135 video clips of different match situations from FIFA competitions. It has 1,835 users (FIFA instructors, referees and assistant referees). In this work, the main features of the database are described, such as the use of a search tool and the creation of multimedia presentations and video quizzes. The database has been developed in MySQL, ActionScript, Ruby on Rails and HTML. This tool has been rated by users as "very good" in all courses, which prompt us to introduce it as an ideal tool for any other sport that requires the use of video analysis.

Keywords: assistants referees, cloud computing, e-learning, instructors, FIFA, referees, soccer, video database

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Authors: Hafiz Md. Hasan Babu, Khandaker Mohammad Mohi Uddin, Nitish Biswas, Sarreha Tasmin Rikta, Nuzmul Hossain Nahid

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Nanomedicine and bioengineering use biological systems that can perform computing operations. In a biocomputational circuit, different types of biomolecules and DNA (Deoxyribose Nucleic Acid) are used as active components. DNA computing has the capability of performing parallel processing and a large storage capacity that makes it diverse from other computing systems. In most processors, the multiplier is treated as a core hardware block, and multiplication is one of the time-consuming and lengthy tasks. In this paper, cost-effective DNA multipliers are designed using algorithms of molecular DNA operations with respect to conventional ones. The speed and storage capacity of a DNA multiplier are also much higher than a traditional silicon-based multiplier.

Keywords: biological systems, DNA multiplier, large storage, parallel processing

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Authors: Mazen Zaki, Sherwat E. Ibrahim

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Renewable energy has become a very important and critical topic all around the world due to the limited resources that led to shifting to the trend of renewable energy and its integration with the conventional ones. This paper investigates the adoption of the solar energy technology for up-scale residential sector in Cairo, Egypt. The technology acceptance model uses several stakeholder points’ of views to develop vignettes to be used in examining the intention and attitude of the householders to adopt the solar energy technology.

Keywords: solar energy, technology acceptance model, TAM, stakeholder analysis, vignette, residential sector

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Authors: Tosin Deborah Akanbi, Adeyemi Oluwaseun Adepoju, Hameed Olusegun Adebambo, Akinloye Fatai Lawal

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Building information modelling (BIM) is a process that starts with the development of a sequential 3D design and encourages data administration, organization, and visualization throughout the life span of a facility (drawings, construction, and supervision). The implementation of building information modelling has been slow in recent years, and this is due to some prominent barriers that hinder its adoption. In this regard, the study aims to examine the significant barriers that influence the adoption of building information modelling in the Lagos state construction industry. Data were gathered through a questionnaire survey with 332 construction professionals in the study area. Three online structured interviews were conducted to support and validate the findings of the quantitative analysis. The results revealed that interest (lack of awareness and understanding of BIM, absence of in-house BIM competent professionals, and unavailability of BIM competent professionals in the labour market), legal (lack of policies and regulations on copyright ownership and lack of enforcement from government agencies and industry leaderships) and professional (people’s inability or refusal to learn new technologies and processes, waste in time and human resource and lack of clarity of professional roles in BIM) barriers are the major barriers influencing the adoption of BIM. The results also revealed that six final themes were generated, namely: finance barriers, industry barriers, interest barriers, leadership barriers, legal barriers, and professional barriers. Thus, there is a need for policymakers to design and implement policies (regulatory, economic, and information) to promote financial schemes to support construction firms and professionals and to reduce financial barriers. It is also important for the government to lay down rules and regulations that must be enforced among the construction professionals and firms in the Lagos state construction industry.

Keywords: BIM barriers, BIM adoption characteristics, construction industry, Lagos State Nigeria

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Authors: Y. Kong, I. Masud, M. H. Nyaso

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This paper seeks to examine consumers’ intention to adopt and use mobile payment method in Ghana. A conceptual framework was adopted from the extant literature using the Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA) as the theoretical bases. Data for the study was obtained from a sample of 425 respondents through online and direct surveys using structured questionnaire. Structural Equation Modeling was used to analyse the data through SPSS v.22 and SmartPLS v.3. Findings with regards to the determinants of mobile payment system adoption indicate that subjective norm, perceived ease of use, attitude, and perceived usefulness play active roles in consumers’ decision to adopt mobile payment system in Ghana. Also, perceived usefulness and perceived ease of use have a significant and positive influence on consumers’ attitude towards mobile payment adoption in Ghana. Further, subjective norm was found to influence perceived usefulness and perceived ease of use of mobile payment adoption in Ghana. The study contributes to literature on mobile payment system from developing country context. The study proffered some recommendations.

Keywords: consumer behaviour, mobile payment, subjective norm, theory of planned behavior

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Authors: Innocent Pangapanga, Eric Mungatana

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Tropical cyclones–related floods (TCRFs) in Malawi have devastating effects on smallholder agriculture, thereby threatening the food security agenda, which is already constrained by poor agricultural innovations, low use of improved varieties, and unaffordable inorganic fertilizers, and fragmenting landholding sizes. Accordingly, households have engineered and indigenously implemented sustainable landscape management (SLM) innovations to contain the adverse effects of TCRFs on farm productivity. This study, therefore, interrogated the efficacy of SLM adoption on farm productivity under varying TCRFs, while controlling for the potential selection bias and unobservable heterogeneity through the application of the Endogenous Switching Regression Model. In this study, we further investigated factors driving SLM adoption. Substantively, we found TCRFs reducing farm productivity by 31 percent, on the one hand, and influencing the adoption of SLM innovations by 27 percent, on the other hand. The study also observed that households that interacted SLM with TCRFs were more likely to enhance farm productivity by 24 percent than their counterparts. Interestingly, the study results further demonstrated that multiple adoptions of SLM-related innovations, including intercropping, agroforestry, and organic manure, enhanced farm productivity by 126 percent, suggesting promoting SLM adoption as a package to appropriately inform existing sustainable development goals’ agricultural productivity initiatives under intensifying TCRFs in the country.

Keywords: tropical cyclones–related floods, sustainable landscape management innovations, farm productivity, endogeneity, endogenous switching regression model, panel data, smallholder agriculture

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Authors: Mihretu Erjabo

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Halaba special district is characterized by drought, soil erosion, high population pressure, poor livestock production, lack of feed for livestock, very deep water table, very low productivity of crops and food insufficiency. In order to address these problems, the woreda agricultural development office along with other management practices such as soil physical conservation measures agroforestry was introduced decades ago as a means to alleviate the problem. However, the level of agroforestry adoption remains low. Objective of this study was to identify the factors that influence adoption of agroforestry technologies by farmers in the district. Random sampling was employed to select two kebele administrations and respondents. Data collection was conducted by rural household questionnaire survey, participatory rural appraisal, questionnaires for local and woreda extension staff, secondary data resources and field observation. A sample of 12 key informants, 6 extension staffs, and 182 households, were used in the data collection. Chi square test used to determine significant relationships between adoption of agroforestry and 15 selected variables. Out of which eleven were found to be significant to affect farmers’ adoptiveness. These were frequency of visits of farmers (13.39%), participation in training (11.49%), farmers’ attitude towards agroforestry practices (10.61%), frequency of visits of extensionists (10.38%), participation in extension meeting (10.34%), participation in field day (10.28%), land holding size (9.29%), level of literacy (8.78%), awareness about the importance of agroforestry technology packages (7.06%), time taken from their residence to nearest extension (5.04%) and gender of respondents (3.34%). This study also identified various factors that result in low adoption rates of agroforestry including fear of competition, seedling, rainfall and labour shortage, free grazing, financial problem, expecting trees as soil degrader and long span of trees and lack of need ranking. To improve farmers’ adoption, the factors identified should be well addressed by launching a series and recurrent outreach extension program appropriate and suitable to farmers need.

Keywords: farmers attitude, farmers participation, soil degradation, technology packages

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Fouzia Shersad, Sabeena Salam

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Introduction: Quality assurance cycles like RADAR, PDCA, ADRI are cycles of planning, implementation, assessment and improvement. These cycles are required when institutions apply for reaccreditation to accreditation bodies and for adoption of holistic models of institutional quality. Method of Study: The adoption of these cycles at the higher education institutions under the Dubai Medical University is studied to explore the feasibility and the benefits in institutions outcomes. After adequate faculty training, these steps were incorporated in all new activities and embedded in every new initiative and approach undertaken at unit and institutional levels. Conclusions: Improvement in student satisfaction rates and performance levels has been achieved. Wherever weaknesses or deficits have been identified, improvement strategies are implemented in a timely manner. The feedback has become an incentive for faculty members to implement new ideas. Implementation of these cycles for core processes at micro and macro levels have ensured that a systematic mechanism for corrective actions existed. This has led to increasing adoption of innovative initiatives. Another outcome was the recognition through national level awards for the overall institutions which have been certified by external reviewers.

Keywords: higher education, quality, accreditation, institutional improvement

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Grid computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using Virtual Organization (VO). Security is a critical issue due to the open nature of the wireless channels in the grid computing which requires three fundamental services: authentication, authorization, and encryption. The privacy and anonymity are considered as an important factor while communicating over publicly spanned network like web. To ensure a high level of security we explored an extension of onion routing, which has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity. To improve the performance of encrypting the layers, the elliptic curve cryptography is used. Compared to traditional cryptosystems like RSA (Rivest-Shamir-Adelman), ECC (Elliptic Curve Cryptosystem) offers equivalent security with smaller key sizes which result in faster computations, lower power consumption, as well as memory and bandwidth savings. This paper presents the estimation of the performance improvements of onion routing using ECC as well as the comparison graph between performance level of RSA and ECC.

Keywords: grid computing, privacy, anonymity, onion routing, ECC, RSA

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Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.

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Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.

Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud

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Authors: Faisal Zaman, Ya Ping Wong, Boon Yian Ng

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Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this, we present a novel approach using modified kernel density estimation (KDE) technique with bilateral filtering to remove noisy points and outliers. First we present a method for estimating optimal bandwidth of multivariate KDE using particle swarm optimization technique which ensures the robust performance of density estimation. Then we use mean-shift algorithm to find the local maxima of the density estimation which gives the centroid of the clusters. Then we compute the distance of a certain point from the centroid. Points belong to outliers then removed by automatic thresholding scheme which yields an accurate and economical point surface. The experimental results show that our approach comparably robust and efficient.

Keywords: point preprocessing, outlier removal, surface reconstruction, kernel density estimation

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Authors: D. Madhushanka, Y. Liu, H. C. Fernando

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Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

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Authors: Monica Santi, Evita Puspitasari

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International Financial Reporting Standard (IFRS) has developed the principle based accounting standard. Based on this, IASB then eliminated the conservatism concept within accounting framework. Conservatism concept represents a prudent reaction to uncertainty to try to ensure that uncertainties and risk inherent in business situations are adequately considered. The conservatism concept has two ingredients: conditional conservatism or ex-post (news depending prudence) and unconditional conservatism or ex-ante (news-independent prudence). IFRS in substance disregards the unconditional conservatism because the unconditional conservatism can cause the understatement assets or overstated liabilities, and eventually the financial statement would be irrelevance since the information does not represent the real fact. Therefore, the IASB eliminate the conservatism concept. However, it does not decrease the practice of unconditional conservatism in the financial statement reporting. Therefore, we expected the earnings quality would be affected because of this situation, even though the IFRS implementation was expected to increase the earnings quality. The objective of this study was to provide empirical findings about the unconditional conservatism and the earnings quality before and after the IFRS adoption. The earnings per accrual measure were used as the proxy for the unconditional conservatism. If the earnings per accrual were negative (positive), it meant the company was classified as the conservative (not conservative). The earnings quality was defined as the ability of the earnings in reflecting the future earnings by considering the earnings persistence and stability. We used the earnings response coefficient (ERC) as the proxy for the earnings quality. ERC measured the extant of a security’s abnormal market return in response to the unexpected component of reporting earning of the firm issuing that security. The higher ERC indicated the higher earnings quality. The manufacturing companies listed in the Indonesian Stock Exchange (IDX) were used as the sample companies, and the 2009-2010 period was used to represent the condition before the IFRS adoption, and 2011-2013 was used to represent the condition after the IFRS adoption. Data was analyzed using the Mann-Whitney test and regression analysis. We used the firm size as the control variable with the consideration the firm size would affect the earnings quality of the company. This study had proved that the unconditional conservatism had not changed, either before and after the IFRS adoption period. However, we found the different findings for the earnings quality. The earnings quality had decreased after the IFRS adoption period. This empirical results implied that the earnings quality before the IFRS adoption was higher. This study also had found that the unconditional conservatism positively influenced the earnings quality insignificantly. The findings implied that the implementation of the IFRS had not decreased the unconditional conservatism practice and has not altered the earnings quality of the manufacturing company. Further, we found that the unconditional conservatism did not affect the earnings quality. Eventhough the empirical result shows that the unconditional conservatism gave positive influence to the earnings quality, but the influence was not significant. Thus, we concluded that the implementation of the IFRS did not increase the earnings quality.

Keywords: earnings quality, earnings response coefficient, IFRS Adoption, unconditional conservatism

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Authors: Muganyizi Jonas Bisheko, G. Rejikumar

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Reducing post-harvest losses could be a sustainable solution to enhance the food and income security of smallholder farmers in developing countries. While various research institutions have come up with a number of innovative post-harvest technologies for reducing post-harvest losses, most of them have not been extensively adopted by smallholder farmers. Despite this gap, the synthesized information about the major constraints of post-harvest technology is scarce. This study has been conducted to fill this gap and show the implications of the findings for future post-harvest research. The developed search strategy retrieved 2201 studies. However, after excluding duplicates, title, abstract and full article screening, a total of 41 documents were identified. The major findings are: (i) there is an outstanding deficiency of systematic evidence of the effect of climate change, off-farm income and sources of post-harvest information on the adoption of improved post-harvest technologies; (ii) there is very limited information on adoption constraints pertaining to matters of policy, rules and regulations; (iii) there is very thin literature on behavioral constraints associated with limited adoption of improved post-harvest technologies; (iv) most of the studies focused on post-harvest storage technologies (47%) followed by overall post-harvest management practices (25%), processing technologies (19%) and packaging technologies (3%). Much of the information was found on Cereals (58%), especially maize (44%); (v) geographically, Sub-Saharan Africa accounted for 79% of the reviewed interventions, while South Asia occupied only 21%. The findings of this review are intended to guide various post-harvest technologists and decision-makers in addressing the challenge of huge post-harvest losses.

Keywords: constraints, post-harvest loss, post-harvest technology , smallholder farmer

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Authors: Tawfiq Albishri, Abdulmajeed Alqahtani

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Battery electric vehicles (BEVs) are increasingly seen as a sustainable alternative to internal combustion engine (ICE) vehicles, primarily due to their environmental and economic benefits. Saudi Arabia's interest in investing in renewable energy and reducing greenhouse gas emissions presents significant potential for the widespread adoption of BEVs in the country. However, several factors have hindered the adoption of BEVs in Saudi Arabia, with high ownership costs being the most prominent barrier. This cost discrepancy is primarily due to the lack of localized production of BEVs and their components, leading to increased import costs, as well as the high initial cost of BEVs compared to ICE vehicles. This paper aims to evaluate the feasibility of BEVs compared to ICE vehicles in Saudi Arabia by conducting a cost of ownership analysis. Furthermore, a sensitivity analysis will be conducted to determine the most significant contributor to the ownership costs of BEVs that, if changed, could expedite their adoption in Saudi Arabia.

Keywords: battery electric vehicles, internal combustion engine, renewable energy, greenhouse gas emissions, total cost of ownership

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Authors: Natalia Marulanda Grisales

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The most significant changes in the characteristics of consumers have contributed to the development and adoption of methodologies and tools that enable organizations to be more competitive in the marketplace. One of these methodologies is the integration of Knowledge Management (KM) phases and Environmental Management Systems (EMS). This integration allows companies to manage and share the required knowledge for EMS adoption, from the place where it is generated to the place where it is going to be exploited. The aim of this paper is to identify the relationship between KM phases as a tool for the adoption of EMS in HEI. The methodology has a descriptive scope and a qualitative approach. It is based on a case study and a review of the literature about KM and EMS. We conducted 266 surveys to students, professors and staff at Minuto de Dios University (Colombia). Data derived from the study indicate that if a HEI wants to achieve an adequate knowledge acquisition and knowledge transfer, it must have clear goals for implementing an EMS. Also, HEI should create empowerment and training spaces for students, professors and staff. In the case study, HEI must generate alternatives that enhance spaces of knowledge appropriation. It was found that 85% of respondents have not received any training from HEI about EMS. 88% of respondents believe that the actions taken by the university are not efficient to knowledge transfer in order to develop an EMS.

Keywords: environmental management systems, higher education institutions, knowledge management, training

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Authors: Peter P. K. Chiu

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This paper reports a comparative study of new and old criteria for the professional accreditation of programme by the Hong Kong Institution of Engineers (HKIE). The major change in the criteria is the adoption of the outcome-based accreditation criteria and the use of measurement of attainment of outcomes which is very different from what academic did in the past. This has imposed a lot of difficulty for people in preparation for such exercise. Through this comparative study, the major difference between the two criteria is identified and a methodology is devised to help the academic to handle the issues due to the adoption of the new criteria. Thus it saves a lot of efforts.

Keywords: Hong Kong institution of engineers, outcome-based accreditation, Sydney accord, Washington accord

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Authors: P. K. Purohit, Azad A. Mansoori, Parvaiz A. Khan, Purushottam Bhawre, Sharad C. Tripathi, A. M. Aslam, Malik A. Waheed, Shivangi Bhardwaj, A. K. Gwal

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The geomagnetic storm represents the most outstanding example of solar wind-magnetospheric interaction, which causes global disturbances in the geomagnetic field as well as the trigger ionospheric disturbances. We study the behaviour of ionospheric Total Electron Content (TEC) during the geomagnetic storms. For the present investigation we have selected 47 intense geomagnetic storms (Dst ≤ -100nT) that were observed during the solar cycle 23 i.e. during 1998-2006. We then categorized these storms into four categories depending upon their solar sources like Magnetic Cloud (MC), Co-rotating Interaction Region (CIR), SH+ICME and SH+MC. We then studied the behaviour of ionospheric TEC at a mid latitude station Usuda (36.13N, 138.36E), Japan during these storm events produced by four different solar sources. During our study we found that the smooth variations in TEC are replaced by rapid fluctuations and the value of TEC is strongly enhanced during the time of these storms belonging to all the four categories. However, the greatest enhancements in TEC are produced during those geomagnetic storms which are either caused by sheath driven magnetic cloud (SH+MC) or sheath driven ICME (SH+ICME). We also derived the correlation between the TEC enhancements produced during storms of each category with the minimum Dst. We found the strongest correlation exists for the SH+ICME category followed by SH+MC, MC and finally CIR. Since the most intense storms were either caused by SH+ICME or SH+MC while the least intense storms were caused by CIR, consequently the correlation was the strongest with SH+ICME and SH+MC and least with CIR.

Keywords: GPS, TEC, geomagnetic storm, sheath driven magnetic cloud

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Authors: S. R. Dehghani, G. F. Naterer, Y. S. Muzychka

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Wave-impact sea spray creates many droplets which form a spray cloud traveling over marine objects same as marine vessels and offshore structures. In cold climates such as Arctic reigns, sea spray icing, which is ice accretion on cold substrates, is strongly dependent on the wave-impact sea spray. The rate of cooling of droplets affects the process of icing that can yield to dry or wet ice accretion. Trajectories of droplets determine the potential places for ice accretion. Combining two models of trajectories and heat transfer for droplets can predict the risk of ice accretion reasonably. The majority of the cooling of droplets is because of droplet evaporations. In this study, a combined model using trajectory and heat transfer evaluate the situation of a cloud of spray from the generation to impingement. The model uses some known geometry and initial information from the previous case studies. The 3D model is solved numerically using a standard numerical scheme. Droplets are generated in various size ranges from 7 mm to 0.07 mm which is a suggested range for sea spray icing. The initial temperature of droplets is considered to be the sea water temperature. Wind velocities are assumed same as that of the field observations. Evaluations are conducted using some important heading angles and wind velocities. The characteristic of size-velocity dependence is used to establish a relation between initial sizes and velocities of droplets. Time intervals are chosen properly to maintain a stable and fast numerical solution. A statistical process is conducted to evaluate the probability of expected occurrences. The medium size droplets can reach the highest heights. Very small and very large droplets are limited to lower heights. Results show that higher initial velocities create the most expanded cloud of spray. Wind velocities affect the extent of the spray cloud. The rate of droplet cooling at the start of spray formation is higher than the rest of the process. This is because of higher relative velocities and also higher temperature differences. The amount of water delivery and overall temperature for some sample surfaces over a marine vessel are calculated. Comparing results and some field observations show that the model works accurately. This model is suggested as a primary model for ice accretion on marine vessels.

Keywords: evaporation, sea spray, marine icing, numerical solution, trajectory

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Neliswa Dyosi

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Purpose - Using an integrated Technology-Organization-Environment (TOE) framework and the model of technology appropriation (MTA) as a theoretical lens, this interpretive qualitative study seeks to understand and explain the factors that influence the appropriation, non-appropriation, and disappropriation of bitcoin as a payment method by South African retailers. Design/methodology/approach –The study adopts the interpretivist philosophical paradigm. Multiple case studies will be adopted as a research strategy. For data collection, the study follows a qualitative approach. Qualitative data will be collected from the six retailers in various industries. Semi-structured interviews and documents will be used as the data collection techniques. Purposive and snowballing sampling techniques will be used to identify participants within the organizations. Data will be analyzed using thematic analysis. Originality/value - Using the deduction approach, the study seeks to provide a descriptive and explanatory contribution to theory. The study contributes to theory development by integrating the MTA and TOE frameworks as a means to understand technology adoption behaviors of organizations, in this case, retailers. This is also the first study that looks at an integrated approach of the Technology-Organization-Environment (TOE) framework and the MTA framework to understand the adoption and use of a payment method. South Africa is ranked amongst the top ten countries in the world on cryptocurrency adoption. There is, however, still a dearth of literature on the current state of adoption and usage of bitcoin as a payment method in South Africa. The study will contribute to the existing literature as bitcoin cryptocurrency is gaining popularity as an alternative payment method across the globe.

Keywords: cryptocurrency, bitcoin, payment methods, blockchain, appropriation, online retailers, TOE framework, disappropriation, non-appropriation

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Authors: Hyun-Ho Lee, Kee-Won Kim

Abstract:

The arithmetic operations over GF(2m) have been extensively used in error correcting codes and public-key cryptography schemes. Finite field arithmetic includes addition, multiplication, division and inversion operations. Addition is very simple and can be implemented with an extremely simple circuit. The other operations are much more complex. The multiplication is the most important for cryptosystems, such as the elliptic curve cryptosystem, since computing exponentiation, division, and computing multiplicative inverse can be performed by computing multiplication iteratively. In this paper, we present a parallel computation algorithm that operates Montgomery multiplication over finite field using redundant basis. Also, based on the multiplication algorithm, we present an efficient semi-systolic multiplier over finite field. The multiplier has less space and time complexities compared to related multipliers. As compared to the corresponding existing structures, the multiplier saves at least 5% area, 50% time, and 53% area-time (AT) complexity. Accordingly, it is well suited for VLSI implementation and can be easily applied as a basic component for computing complex operations over finite field, such as inversion and division operation.

Keywords: finite field, Montgomery multiplication, systolic array, cryptography

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Authors: Diego Adrian Cardenas Jorge, Gerardo Mirando Guisado, Alfredo Barrientos Padilla

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A system based on Affective Computing can detect and interpret human information like voice, facial expressions and body movement to detect emotions and execute a corresponding response. This data is important due to the fact that a person can communicate more effectively with emotions than can be possible with words. This information can be processed through technological components like Facial Recognition, Gait Recognition or Gesture Recognition. As of now, solutions proposed using this technology only consider one component at a given moment. This research investigation proposes two solutions based on Affective Computing taking into account more than one component for emotion detection. The proposals reflect the levels of dependency between hardware devices and software, as well as the interaction process between the system and the user which implies the development of scenarios where both proposals will be put to the test in a live environment. Both solutions are to be developed in code by software engineers to prove the feasibility. To validate the impact on society and business interest, interviews with stakeholders are conducted with an investment mind set where each solution is labeled on a scale of 1 through 5, being one a minimum possible investment and 5 the maximum.

Keywords: affective computing, emotions, emotion detection, face recognition, gait recognition

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Authors: Chinedu Peter, Dashrath Singh

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Membrane computing is a computability model which abstracts its structures and functions from the biological cell. The main ingredient of membrane computing is the notion of a membrane structure, which consists of several cell-like membranes recurrently placed inside a unique skin membrane. The emergence of several variants of membrane computing gives rise to the notion of a P system. The paper presents a variant of P systems for arithmetic operations on non-negative integers based on the weak priorities for rule application. Consequently, we obtain deterministic P systems. Two membranes suffice. There are at most four objects for multiplication and five objects for division throughout the computation processes. The model is simple and has a potential for possible extension to non-negative integers and real numbers in general.

Keywords: P system, binary operation, determinism, weak rule priority

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Authors: Gulshad Azatova, Aleksandr Kapitonov, Natig Aminov

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Multiagent systems have gained significant attention in various fields, such as robotics, autonomous vehicles, and distributed computing, where multiple agents cooperate and communicate to achieve complex tasks. Efficient communication among agents is a crucial aspect of these systems, as it directly impacts their overall performance and scalability. This scholarly work provides an exploration of essential communication elements and conducts a comparative assessment of diverse protocols utilized in multiagent systems. The emphasis lies in scrutinizing the strengths, weaknesses, and applicability of these protocols across various scenarios. The research also sheds light on emerging trends within communication protocols for multiagent systems, including the incorporation of machine learning methods and the adoption of blockchain-based solutions to ensure secure communication. These trends provide valuable insights into the evolving landscape of multiagent systems and their communication protocols.

Keywords: communication, multi-agent systems, protocols, consensus

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Authors: Farouq Sule Garo, Yahaya Yusuf

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The purpose of this study is to investigate why there is a general lack of successful adoption of sustainable energy in Nigeria. This is particularly important given the current global campaign for net-zero emissions. The 26th United Nations Conference of the Parties (COP26), held in 2021, was hosted by the UK, in Glasgow, where, amongst other things, countries including Nigeria agreed to a zero emissions pact. There is, therefore, an obligation on the part of Nigeria for transition from fossil fuel-based economy to a sustainable net-zero emissions economy. The adoption of renewable energy is fundamental to achieving this ambitious target if decarbonisation of economic activities were to become a reality. Nigeria has an abundance of sources of renewable energy and yet there has been poor uptake and where attempts have been made to develop and harness renewable energy resources, there has been limited success. It is not entirely clear why this is the case. When analysts allude to corruption as the reason for failure for successful adoption of renewable energy or project implementation, it is arguable that corruption alone cannot explain the situation. Therefore, there is the need for a thorough investigation into the underlying issues surrounding poor uptake of renewable energy in Nigeria. This pilot study, drawing upon stakeholders’ theory, adopts a multi-stakeholder’ perspectives to investigate the influence and impacts of economic, political, technological, social factors in adoption of renewable energy in Nigeria. The research will also investigate how these factors shape (or fail to shape) strategies for achieving successful adoption of renewable energy in the country. A qualitative research methodology has been adopted given the nature of the research requiring in-depth studies in specific settings rather than a general population survey. There will be a number of interviews and each interview will allow thorough probing of sources. This, in addition to the six interviews that have already been conducted, primarily focused on economic dimensions of the challenges in adoption of renewable energy. The six participants in these initial interviews were all connected to the Katsina Wind Farm Project that was conceived and built with the view to diversifying Nigeria's energy mix and capitalise on the vast wind energy resources in the northern region. The findings from the six interviews provide insights into how the economic factors impacts on the wind farm project. Some key drivers have been identified, including strong governmental support and the recognition of the need for energy diversification. These drivers have played crucial roles in initiating and advancing the Katsina Wind Farm Project. In addition, the initial analysis has highlighted various challenges encountered during the project's implementation, including financial, regulatory, and environmental aspects. These challenges provide valuable lessons that can inform strategies to mitigate risks and improve future wind energy projects.

Keywords: challenges in adoption of renewable energy, economic factors, net-zero emission, political factors

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Authors: Igor Kuzminov

Abstract:

Till now, the nature of lightning is not established. A hypothesis of the origin of lightning is proposed. The lightning charge is formed by electromagnetic induction. The role of the conductor is performed by the air mass of the cloud. This conductor moves in the Earth's magnetic field. The upper and lower edges of the cloud are the plates of the capacitor. Lightning is a special case of electromagnetic processes in an atmosphere. The category of lightning occurs in the process of accumulation of a charge. The process of accumulation goes constantly, but the charge is not fixed. Naturally, the hypothesis demands the carrying out of additional experiments and official acknowledgement. As the proof of a hypothesis can serve that the maximal lighting activity in an equatorial zone where cosφ it is close to 1. An experiment conducted privately showed that there is a potential difference in the atmosphere at different levels. The probability of applied value development of power installation is great.

Keywords: electromagnetic induction, Earth's magnetic field, plates of the capacitors, charge accumulation

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