Search results for: digital business models

Authors: Stehr Melanie

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The availability of apposite information is essential for the decision-making process of organizational buyers. Due to the constraints of the Covid-19 crisis, information channels that emphasize face-to-face contact (e.g. sales visits, trade shows) have been unavailable, and usage of digitally-driven information channels (e.g. videoconferencing, platforms) has skyrocketed. This paper explores the question in which areas the pandemic induced shift in the use of information channels could be sustainable and in which areas it is a temporary phenomenon. While information and buying behavior in B2C purchases has been regularly studied in the last decade, the last fundamental model of organizational buying behavior in B2B was introduced by Johnston and Lewin (1996) in times before the advent of the internet. Subsequently, research efforts in B2B marketing shifted from organizational buyers and their decision and information behavior to the business relationships between sellers and buyers. This study builds on the extensive literature on situational factors influencing organizational buying and information behavior and uses the economics of information theory as a theoretical framework. The research focuses on the German woodworking industry, which before the Covid-19 crisis was characterized by a rather low level of digitization of information channels. By focusing on an industry with traditional communication structures, a shift in information behavior induced by an exogenous shock is considered a ripe research setting. The study is exploratory in nature. The primary data source is 40 in-depth interviews based on the repertory-grid method. Thus, 120 typical buying situations in the woodworking industry and the information and channels relevant to them are identified. The results are combined into clusters, each of which shows similar information behavior in the procurement process. In the next step, the clusters are analyzed in terms of the post and pre-Covid-19 crisis’ behavior identifying stable and dynamic information behavior aspects. Initial results show that, for example, clusters representing search goods with low risk and complexity suggest a sustainable rise in the use of digitally-driven information channels. However, in clusters containing trust goods with high significance and novelty, an increased return to face-to-face information channels can be expected after the Covid-19 crisis. The results are interesting from both a scientific and a practical point of view. This study is one of the first to apply the economics of information theory to organizational buyers and their decision and information behavior in the digital information age. Especially the focus on the dynamic aspects of information behavior after an exogenous shock might contribute new impulses to theoretical debates related to the economics of information theory. For practitioners - especially suppliers’ marketing managers and intermediaries such as publishers or trade show organizers from the woodworking industry - the study shows wide-ranging starting points for a future-oriented segmentation of their marketing program by highlighting the dynamic and stable preferences of elaborated clusters in the choice of their information channels.

Keywords: B2B buying process, crisis, economics of information theory, information channel

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Authors: Shwadhin Sharma

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As there has been increasing focus on the shorter attention span of the millennials students, there is a relative absence of instructional tools on behavioral assessments in learning information technology skills within the information systems field and textbooks. This study uses project-based learning in which students gain knowledge and skills related to information technology by working on an extended project that allows students to find a real business problem design information systems based on information collected from the company and develop an information system that solves the problem of the company. Eighty students from two sections of the same course engage in the project from the first week of the class till the sixteenth week of the class to deliver a small business information system that allows them to employ all the skills and knowledge that they learned in the class into the systems they are creating. Computer Information Systems related courses are often difficult to understand and process especially for the Under Representative Minorities students who have limited computer or information systems related (academic) experiences. Project-based learning demands constant attention of the students and forces them to apply knowledge learned in the class to a project that helps retaining knowledge. To make sure our assumption is correct, we started with a pre-test and post-test to test the students learning (of skills) based on the project. Our test showed that almost 90% of the students from the two sections scored higher in post-test as compared to pre-test. Based on this premise, we conducted a further survey that measured student’s job-search preparation, knowledge of data analysis, involved with the course, satisfaction with the course, student’s overall reaction the course and students' ability to meet the traditional learning goals related to the course.

Keywords: project-based learning, job-search preparation, satisfaction with course, traditional learning goals

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Authors: Bukra Kalayci, Geraldine Brennan

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Introduction: Over the last decade, a proliferation of literature related to textile and fashion business in the context of sustainable production and consumption has emerged. However, the economic and environmental benefits of solid waste recovery have not been comprehensively searched. Therefore at the end-of-life or end-of-use textile waste management remains a gap. Solid textile waste reuse and recycling principles of the circular economy need to be developed to close the disposal stage of the textile supply chain. The environmental problems associated with the over-production and –consumption of textile products arise. Together with growing population and fast fashion culture the share of solid textile waste in municipal waste is increasing. Focusing on post-consumer textile waste literature, this research explores the opportunities, obstacles and enablers or success factors associated with closed-loop textile business systems. Methodology: A systematic literature review was conducted in order to identify best practices and gaps from the existing body of knowledge related to closed-loop post-consumer textile waste initiatives over the last decade. Selected keywords namely: ‘cradle-to-cradle ‘, ‘circular* economy* ‘, ‘closed-loop* ‘, ‘end-of-life* ‘, ‘reverse* logistic* ‘, ‘take-back* ‘, ‘remanufacture* ‘, ‘upcycle* ‘ with the combination of (and) ‘fashion* ‘, ‘garment* ‘, ‘textile* ‘, ‘apparel* ‘, clothing* ‘ were used and the time frame of the review was set between 2005 to 2017. In order to obtain a broad coverage, Web of Knowledge and Science Direct databases were used, and peer-reviewed journal articles were chosen. The keyword search identified 299 number of papers which was further refined into 54 relevant papers that form the basis of the in-depth thematic analysis. Preliminary findings: A key finding was that the existing literature is predominantly conceptual rather than applied or empirical work. Moreover, the enablers or success factors, obstacles and opportunities to implement closed-loop systems in the textile industry were not clearly articulated and the following considerations were also largely overlooked in the literature. While the circular economy suggests multiple cycles of discarded products, components or materials, most research has to date tended to focus on a single cycle. Thus the calculations of environmental and economic benefits of closed-loop systems are limited to one cycle which does not adequately explore the feasibility or potential benefits of multiple cycles. Additionally, the time period textile products spend between point of sale, and end-of-use/end-of-life return is a crucial factor. Despite past efforts to study closed-loop textile systems a clear gap in the literature is the lack of a clear evaluation framework which enables manufacturers to clarify the reusability potential of textile products through consideration of indicators related too: quality, design, lifetime, length of time between manufacture and product return, volume of collected disposed products, material properties, and brand segment considerations (e.g. fast fashion versus luxury brands).

Keywords: circular fashion, closed loop business, product service systems, solid textile waste elimination

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Authors: Abdul Ganiyu Iddrisu

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Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth, and reducing poverty. Yet compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, low-income flows among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector, however, empirical evidence on digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We therefore argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa focusing on the role of digitization, and financial sector development. First, we assess how digitization influence financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on 2 economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improves economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, economic growth, financial sector development, Africa

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Authors: Kassahun Birhanu Tadesse, Megersa Olumana Dinka

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Reliable future river flow information is basic for planning and management of any river systems. For data scarce river system having only a river flow records like the Waterval River, a univariate time series models are appropriate for river flow forecasting. In this study, a univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied for forecasting Waterval River flow using GRETL statistical software. Mean monthly river flows from 1960 to 2016 were used for modeling. Different unit root tests and Mann-Kendall trend analysis were performed to test the stationarity of the observed flow time series. The time series was differenced to remove the seasonality. Using the correlogram of seasonally differenced time series, different SARIMA models were identified, their parameters were estimated, and diagnostic check-up of model forecasts was performed using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AIc) and Hannan-Quinn (HQc) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 was selected as the best model for Waterval River flow forecasting. Therefore, this model can be used to generate future river information for water resources development and management in Waterval River system. SARIMA model can also be used for forecasting other similar univariate time series with seasonality characteristics.

Keywords: heteroscedasticity, stationarity test, trend analysis, validation, white noise

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Authors: Patricia Huedo, Maria José Ruá, Raquel Agost-Felip

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Ensuring sustainable development of urban environments means guaranteeing adequate environmental conditions, being resilient and meeting conditions of safety and inclusion for all people, regardless of their condition. All existing buildings should meet basic safety conditions and be equipped with safe and accessible routes, along with visual, acoustic and tactile signals to protect their users or potential visitors, and regardless of whether they undergo rehabilitation or change of use processes. Moreover, from a social perspective, we consider the need to prioritize buildings occupied by the most vulnerable groups of people that currently do not have specific regulations tailored to their needs. Some residential models in operation are not only outside the scope of application of the regulations in force; they also lack a project or technical data that would allow knowing the fire behavior of the construction materials. However, the difficulty and cost involved in adapting the entire building stock to current regulations can never justify the lack of safety for people. Hence, this work develops a simplified model to assess compliance with the basic safety conditions in case of fire and its compatibility with the specific accessibility needs of each user. The purpose is to support the designer in decision making, as well as to contribute to the development of a basic fire safety certification tool to be applied in inclusive residential models. This work has developed a methodology to support designers in adapting Social Services Centers, usually intended to vulnerable people. It incorporates a checklist of 9 items and information from sources or standards that designers can use to justify compliance or propose solutions. For each item, the verification system is justified, and possible sources of consultation are provided, considering the possibility of lacking technical documentation of construction systems or building materials. The procedure is based on diagnosing the degree of compliance with fire conditions of residential models used by vulnerable groups, considering the special accessibility conditions required by each user group. Through visual inspection and site surveying, the verification model can serve as a support tool, significantly streamlining the diagnostic phase and reducing the number of tests to be requested by over 75%. This speeds up and simplifies the diagnostic phase. To illustrate the methodology, two different buildings in the Valencian Region (Spain) have been selected. One case study is a mental health facility for residential purposes, located in a rural area, on the outskirts of a small town; the other one, is a day care facility for individuals with intellectual disabilities, located in a medium-sized city. The comparison between the case studies allow to validate the model in distinct conditions. Verifying compliance with a basic security level can allow a quality seal and a public register of buildings adapted to fire regulations to be established, similarly to what is being done with other types of attributes such as energy performance.

Keywords: fire safety, inclusive housing, universal accessibility, vulnerable people

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Authors: Jamaiah H. Yahaya, Aziz Deraman, Abdul Razak Hamdan, Yusmadi Yah Jusoh

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One of the most essential issues in software products is to maintain it relevancy to the dynamics of the user’s requirements and expectation. Many studies have been carried out in quality aspect of software products to overcome these problems. Previous software quality assessment models and metrics have been introduced with strengths and limitations. In order to enhance the assurance and buoyancy of the software products, certification models have been introduced and developed. From our previous experiences in certification exercises and case studies collaborating with several agencies in Malaysia, the requirements for user based software certification approach is identified and demanded. The emergence of social network applications, the new development approach such as agile method and other varieties of software in the market have led to the domination of users over the software. As software become more accessible to the public through internet applications, users are becoming more critical in the quality of the services provided by the software. There are several categories of users in web-based systems with different interests and perspectives. The classifications and metrics are identified through brain storming approach with includes researchers, users and experts in this area. The new paradigm in software quality assessment is the main focus in our research. This paper discusses the classifications of users in web-based software system assessment and their associated factors and metrics for quality measurement. The quality model is derived based on IEEE structure and FCM model. The developments are beneficial and valuable to overcome the constraints and improve the application of software certification model in future.

Keywords: software certification model, user centric approach, software quality factors, metrics and measurements, web-based system

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Authors: Mohammad Fereidoonnezhad, Seán Leen, Stephen Nash, Patrick McGarry

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From the perspective of research innovation, the tidal energy industry is still in its early stages. While a very small number of turbines have progressed to utility-scale deployment, blade breakage is commonly reported due to the enormous hydrodynamic loading applied to devices. The aim of this study is the development of computer simulation technologies for the design of next-generation fibre-reinforced composite tidal turbines. This will require significant technical advances in the areas of tidal turbine testing and multi-scale computational modelling. The complex turbine blade profiles are designed to incorporate non-linear distributions of airfoil sections to optimize power output and self-starting capability while reducing power fluctuations. A number of candidate blade geometries are investigated, ranging from spiral geometries to parabolic geometries, with blades arranged in both cylindrical and spherical configurations on a vertical axis turbine. A combined blade element theory (BET-start-up model) is developed in MATLAB to perform computationally efficient parametric design optimisation for a range of turbine blade geometries. Finite element models are developed to identify optimal fibre-reinforced composite designs to increase blade strength and fatigue life. Advanced fluid-structure-interaction models are also carried out to compute blade deflections following design optimisation.

Keywords: tidal turbine, composite materials, fluid-structure-interaction, start-up capability

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Authors: Kwak, Hyo-Gyung, Gang, Han Gul

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In the context of an increasing need for reliability and safety in concrete structures under blast and impact loading condition, the behavior of concrete under high strain rate condition has been an important issue. Since concrete subjected to impact loading associated with high strain rate shows quite different material behavior from that in the static state, several material models are proposed and used to describe the high strain rate behavior under blast and impact loading. In the process of modelling, in advance, mesh dependency in the used finite element (FE) is the key problem because simulation results under high strain-rate condition are quite sensitive to applied FE mesh size. It means that the accuracy of simulation results may deeply be dependent on FE mesh size in simulations. This paper introduces an improved criterion which can minimize the mesh-dependency of simulation results on the basis of the fracture energy concept, and HJC (Holmquist Johnson Cook), CSC (Continuous Surface Cap) and K&C (Karagozian & Case) models are examined to trace their relative sensitivity to the used FE mesh size. To coincide with the purpose of the penetration test with a concrete plate under a projectile (bullet), the residual velocities of projectile after penetration are compared. The correlation studies between analytical results and the parametric studies associated with them show that the variation of residual velocity with the used FE mesh size is quite reduced by applying a unique failure strain value determined according to the proposed criterion.

Keywords: high strain rate concrete, penetration simulation, failure strain, mesh-dependency, fracture energy

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Samuel Fekadu A., Esayas Alemayehu B., Bultum Oljira D., Seid Tiku D., Dessalegn Dadi D., Bart Van Der Bruggen A.

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The peroxi-photoelectrocoagulation process was evaluated for the removal of chemical oxygen demand (COD) and color from healthcare wastewater. A 2-level full factorial design with center points was created to investigate the effect of the process parameters, i.e., initial COD, H₂O₂, pH, reaction time and current density. Furthermore, the total energy consumption and average current efficiency in the system were evaluated. Predictive models for % COD, % color removal and energy consumption were obtained. The initial COD and pH were found to be the most significant variables in the reduction of COD and color in peroxi-photoelectrocoagulation process. Hydrogen peroxide only has a significant effect on the treated wastewater when combined with other input variables in the process like pH, reaction time and current density. In the peroxi-photoelectrocoagulation process, current density appears not as a single effect but rather as an interaction effect with H₂O₂ in reducing COD and color. Lower energy expenditure was observed at higher initial COD, shorter reaction time and lower current density. The average current efficiency was found as low as 13 % and as high as 777 %. Overall, the study showed that hybrid electrochemical oxidation can be applied effectively and efficiently for the removal of pollutants from healthcare wastewater.

Keywords: electrochemical oxidation, UV, healthcare pollutants removals, factorial design

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Authors: Vineesh Amin, Ananya Agrawal

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In a prolonged period of uncertainty and disruptions in the pre-said normal order, non-cognitive skills, especially entrepreneurial mindsets, have become a pillar that can reform the educational models to inform the economy. Dreamverse Learning Lab’s IVR-based storytelling program -Call-a-Kahaani- is an evolving experiment with an aim to kindle entrepreneurial mindsets in the remotest locations of India in an accessible and engaging manner. At the heart of this experiment is the belief that at every phase in our life’s story, we have a choice which brings us closer to achieving our true potential. This interactive program is thus designed using real-time storytelling principles to empower learners, ages 24 and below, to make choices and take decisions as they become more self-aware, practice grit, try new things through stories, guided activities, and interactions, simply over a phone call. This research paper highlights the framework behind an ongoing scalable, data-oriented, low-tech program to kindle entrepreneurial mindsets in media dark zones supported by iterative design and prototyping to reach 13700+ unique learners who made 59000+ calls for 183900+min listening duration to listen to content pieces of around 3 to 4 min, with the last monitored (March 2022) record of 34% serious listenership, within one and a half years of its inception. The paper provides an in-depth account of the technical development, content creation, learning, and assessment frameworks, as well as mobilization models which have been leveraged to build this end-to-end system.

Keywords: non-cognitive skills, entrepreneurial mindsets, speech interface, remote learning, storytelling

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Authors: David A. Wallace, Salakjit J. Nilboworn

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Secure and reliable transmission and distribution of electrical power is crucial in today’s ever-increasing demand for electricity. Traditional methods of protecting the electrical grid have relied on relaying systems receiving voltage and current inputs from inductive instruments transformers (IT). This method has provided robust and stable performance throughout the years. Today with the advent of new non-conventional transformers (NCIT) and sensors, the electrical landscape is changing. These new systems have to ability to provide the same electrical performance as traditional instrument transformers with the added features of data acquisition, communication, smaller footprint, lower cost and resistance to GMD/GIC events.

Keywords: non-conventional instrument transformers, digital substations, smart grids, micro-grids

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Authors: Parashram Jakappa Patil

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India is the global leader in world cashew business and cashew-nut industry is one of the important food processing industries in world. However India is the largest producer, processor, exporter and importer eschew in the world. India is providing cashew to the rest of the world. India is meeting world demand of cashew. India has a tremendous potential of cashew production and export to other countries. Every year India earns more than 2000 cores rupees through cashew trade. Cashew industry is one of the important small scale industries in the country which is playing significant role in rural development. It is generating more than 400000 jobs at remote area and 95% cashew worker are women, it is giving income to poor cashew farmers, majority cashew processing units are small and cottage, it is helping to stop migration from young farmers for employment opportunities, it is motivation rural entrepreneurship development and it is also helping to environment protection etc. Hence India cashew business is very important agribusiness in India which has potential make inclusive development. World Bank and IMF recognized cashew-nut industry is one the important tool for poverty eradication at global level. It shows important of cashew business and its strong existence in India. In spite of such huge potential cashew processing industry is facing different problems such as lack of infrastructure ability, lack of supply of raw cashew, lack of availability of finance, collection of raw cashew, unavailability of warehouse, marketing of cashew kernels, lack of technical knowledge and especially processing technology and packaging of finished products. This industry has great prospects such as scope for more cashew cultivation and cashew production, employment generation, formation of cashew processing units, alcohols production from cashew apple, shield oil production, rural development, poverty elimination, development of social and economic backward class and environment protection etc. This industry has domestic as well as foreign market; India has tremendous potential in this regard. The cashew is a poor men’s crop but rich men’s food. The cashew is a source of income and livelihood for poor farmers. Cashew-nut industry may play very important role in the development of hilly region. The objectives of this paper are to identify problems of cashew processing and use of processing technology, problems of cashew kernel packaging, evolving of cashew processing technology over the year and its impact on final product and impact of good processing by adopting appropriate technology packaging on international trade of cashew-nut. The most important problem of cashew processing industry is that is processing and packaging. Bad processing reduce the quality of cashew kernel at large extent especially broken of cashew kernel which has very less price in market compare to whole cashew kernel and not eligible for export. On the other hand if there is no good packaging of cashew kernel will get moisture which destroy test of it. International trade of cashew-nut is depend of two things one is cashew processing and other is packaging. This study has strong relevance because cashew-nut industry is the labour oriented, where processing technology is not playing important role because 95% processing work is manual. Hence processing work was depending on physical performance of worker which makes presence of large workforce inevitable. There are many cashew processing units closed because they are not getting sufficient work force. However due to advancement in technology slowly this picture is changing and processing work get improve. Therefore it is interesting to explore all the aspects in context of cashew processing and packaging of cashew business.

Keywords: cashew, processing technology, packaging, international trade, change

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: Yonwaba Atyosi, Moses Cho, Abel Ramoelo, Nobuhle Majozi, Cecilia Masemola, Yoliswa Mkhize

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Spectroscopy has been widely used to understand the hyperspectral remote sensing of soils. Accurate and efficient measurement of soil moisture is essential for precision agriculture. The aim of this study was to understand the spectral behavior of soil at different soil water content levels and identify the significant spectral bands for soil moisture content retrieval at field-scale. The study consisted of 60 soil samples from a maize farm, divided into four different treatments representing different moisture levels. Spectral signatures were measured for each sample in laboratory under artificial light using an Analytical Spectral Device (ASD) spectrometer, covering a wavelength range from 350 nm to 2500 nm, with a spectral resolution of 1 nm. The results showed that the absorption features at 1450 nm, 1900 nm, and 2200 nm were particularly sensitive to soil moisture content and exhibited strong correlations with the water content levels. Continuum removal was developed in the R programming language to enhance the absorption features of soil moisture and to precisely understand its spectral behavior at different water content levels. Statistical analysis using partial least squares regression (PLSR) models were performed to quantify the correlation between the spectral bands and soil moisture content. This study provides insights into the spectral behavior of soil at different water content levels and identifies the significant spectral bands for soil moisture content retrieval. The findings highlight the potential of spectroscopy for non-destructive and rapid soil moisture measurement, which can be applied to various fields such as precision agriculture, hydrology, and environmental monitoring. However, it is important to note that the spectral behavior of soil can be influenced by various factors such as soil type, texture, and organic matter content, and caution should be taken when applying the results to other soil systems. The results of this study showed a good agreement between measured and predicted values of Soil Moisture Content with high R2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil samples. The results has significant implications for developing high-resolution and precise field-scale soil moisture retrieval models. These models can be used to understand the spatial and temporal variation of soil moisture content in agricultural fields, which is essential for managing irrigation and optimizing crop yield.

Keywords: soil moisture content retrieval, precision agriculture, continuum removal, remote sensing, machine learning, spectroscopy

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Authors: James Jin, Weisi Xia, Runzhe Gao, Alain Vandal, Darren Svirkis, Andrew Hill

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Background: Post-haemorrhoidectomy pain is concerned by patients/clinicians. Minimizing the postoperation pain is highly interested clinically. Combinations of topical cream targeting three hypothesised post-haemorrhoidectomy pain mechanisms were developed and their effectiveness were evaluated. Specifically, a multi-centred double-blinded randomized clinical trial (RCT) was conducted in adults undergoing excisional haemorrhoidectomy. The primary analysis was conveyed on the data collected to evaluate the effectiveness of the combinations of topical cream targeting three hypothesized pain mechanisms after the operations. Methods: 192 patients were randomly allocated to 4 arms (each arm has 48 patients), and each arm was provided with pain cream 10% metronidazole (M), M and 2% diltiazem (MD), M with 4% lidocaine (ML), or MDL, respectively. Patients were instructed to apply topical treatments three times a day for 7 days, and record outcomes for 14 days after the operations. The primary outcome was VAS pain on day 4. Covariates and models were selected in the blind review stage. Multiple imputations were applied for the missingness. LMER, GLMER models together with natural splines were applied. Sandwich estimators and Wald statistics were used. P-values < 0.05 were considered as significant. Conclusions: The addition of topical lidocaine or diltiazem to metronidazole does not add any benefit. ML had significantly better pain and recovery scores than combination MDL. Multimodal topical analgesia with ML after haemorrhoidectomy could be considered for further evaluation. Further trials considering only 3 arms (M, ML, MD) might be worth exploring.

Keywords: RCT, primary analysis, multiple imputation, pain scores, haemorrhoidectomy, analgesia, lmer

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Authors: David Cobham

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In higher education, a great degree of importance is placed on the internationalization of the student experience. This is seen as a valuable contributor to elements such as building confidence, broadening knowledge, creating networks and connections, and enhancing employability for current students who will become the next generation of managers in technology and business. The COVID-19 pandemic has affected all areas of people’s lives. The limitations of travel coupled with the fears and concerns generated by the health risks have dramatically reduced the opportunity for students to engage with this agenda. Institutions of higher education have been required to rethink fundamental aspects of their business model from recruitment and enrolment through learning approaches, assessment methods, and the pathway to employment. This paper presents a case study which focuses on student mobility and how the physical experience of being in another country, either to study, to work, to volunteer or to gain cultural and social enhancement, has of necessity been replaced by alternative approaches. It considers trans-national education as an alternative to physical study overseas, virtual mobility and internships as an alternative to international work experience, and adopting collaborative online projects as an alternative to in-person encounters. The paper concludes that although these elements have been adopted to address the current situation, the lessons learned and the feedback gained suggests that they have contributed successfully in new and sometimes unexpected ways and that they will persist beyond the present to become part of the 'new normal' for the future. That being the case, senior leaders of institutions of higher education will be required to revisit their international plans and to rewrite their international strategies to take account of and build upon these changes.

Keywords: higher education management, internationalization, transnational education, virtual mobility

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Authors: Ilaria Barletta, Mahesh Mani, Björn Johansson

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The concept of sustainability encompasses economic, environmental, social and institutional considerations. Sustainable manufacturing (SM) is, therefore, a multi-faceted concept. It broadly implies the development and implementation of technologies, projects and initiatives that are concerned with the life cycle of products and services, and are able to bring positive impacts to the environment, company stakeholders and profitability. Because of this, achieving SM-related goals requires a holistic, life-cycle-thinking approach from manufacturing companies. Further, such an approach must rely on a logic of continuous improvement and ease of implementation in order to be effective. Currently, there exists in the academic literature no comprehensively structured frameworks that support manufacturing companies in the identification of the issues and the capabilities that can either hinder or foster sustainability. This scarcity of support extends to difficulties in obtaining quantifiable measurements in order to objectively evaluate solutions and programs and identify improvement areas within SM for standards conformance. To bridge this gap, this paper proposes the concept of a framework for assessing and continuously improving the sustainability of manufacturing companies. The framework addresses strategies and projects for SM and operates in three sequential phases: analysis of the issues, design of solutions and continuous improvement. A set of interviews, observations and questionnaires are the research methods to be used for the implementation of the framework. Different decision-support methods - either already-existing or novel ones - can be 'plugged into' each of the phases. These methods can assess anything from business capabilities to process maturity. In particular, the authors are working on the development of a sustainable manufacturing maturity model (SMMM) as decision support within the phase of 'continuous improvement'. The SMMM, inspired by previous maturity models, is made up of four maturity levels stemming from 'non-existing' to 'thriving'. Aggregate findings from the use of the framework should ultimately reveal to managers and CEOs the roadmap for achieving SM goals and identify the maturity of their companies’ processes and capabilities. Two cases from two manufacturing companies in Australia are currently being employed to develop and test the framework. The use of this framework will bring two main benefits: enable visual, intuitive internal sustainability benchmarking and raise awareness of improvement areas that lead companies towards an increasingly developed SM.

Keywords: life cycle management, continuous improvement, maturity model, sustainable manufacturing

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Authors: Carolin Schulze

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Characterized by a positive interdependence of learners, cooperative learning (CL) is one possibility of successfully dealing with the increasing heterogeneity of students. Various positive effects of CL on the mental, physical and social health of students have already been documented. However, this structure is still rarely used in physical education (PE). Moreover, there is a lack of information about factors that determine the successful implementation of CL in PE. Therefore, the objective of the current study was to find out factors that determine the successful implementation of CL in PE using a digital questionnaire that was conducted from November to December 2022. In addition to socio-demographic data (age, gender, teaching experience, and education level), frequency of using CL, implementation strategies (theory-led, student-centred), and positive and negative effects of CL were measured. Furthermore, teachers were asked to rate the success of implementation on a 6-point rating scale (1-very successful to 6-not successful at all). For statistical analysis, multiple linear regression was performed, setting the success of implementation as the dependent variable. A total of 224 teachers (mean age=44.81±10.60 years; 58% male) took part in the current study. Overall, 39% of participants stated that they never use CL in their PE classes. Main reasons against the implementations of CL in PE were no time for preparation (74%) or for implementation (61%) and high heterogeneity of students (55%). When using CL, most of the reported difficulties are related to uncertainties about the correct procedure (54%) and the heterogeneous performance of students (54%). The most frequently mentioned positive effect was increased motivation of students (42%) followed by an improvement of psychological abilities (e.g. self-esteem, self-concept; 36%) and improved class cohesion (31%). Reported negative effects were unpredictability (29%), restlessness (24%), confusion (24%), and conflicts between students (17%). The successful use of CL is related to a theory-based preparation (e.g., heterogeneous formation of groups, use of rules and rituals) and a flexible implementation tailored to the needs and conditions of students (e.g., the possibility of individual work, omission of CL phases). Compared to teachers who solely implemented CL theory-led or student-adapted, teachers who switched from theory-led preparation to student-centred implementation of CL reported more successful implementation (t=5.312; p<.001). Neither frequency of using CL in PE nor the gender, age, the teaching experience, or the education level of the teacher showed a significant connection with the successful use of CL. Corresponding to the results of the current study, it is advisable that teachers gather enough knowledge about CL during their education and to point out the need to adapt the learning structure according to the diversity of their students. In order to analyse implementation strategies of teachers more deeply, qualitative methods and guided interviews with teachers are needed.

Keywords: diversity, educational technology, physical education, teaching styles

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Authors: Nursuhaili Najwa Masrol, Nur Hafizah Mohammed, Nur Nadhirah Rusyda Rosnan, Vijaya Subramaniam, Sim Choon Cheak

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Replanting in oil palm cultivation is vital to enable the introduction of planting materials and provides an opportunity to improve the road, drainage, terrace design, and planting density. Oil palm replanting is fundamentally necessary every 25 years. The adoption of the digital replanting blueprint is imperative as it can assist the Malaysia Oil Palm industry in addressing challenges such as labour shortages and limited expertise related to replanting tasks. Effective replanting planning should commence at least 6 months prior to the actual replanting process. Therefore, this study will help to plan and design the replanting blueprint with high-precision translation on the ground. With the advancement of geospatial technology, it is now feasible to engage in thoroughly researched planning, which can help maximize the potential yield. A blueprint designed before replanting is to enhance management’s ability to optimize the planting program, address manpower issues, or even increase productivity. In terrace planting blueprints, geographic tools have been utilized to design the roads, drainages, terraces, and planting points based on the ARM standards. These designs are mapped with location information and undergo statistical analysis. The geospatial approach is essential in precision agriculture and ensuring an accurate translation of design to the ground by implementing high-accuracy technologies. In this study, geospatial and remote sensing technologies played a vital role. LiDAR data was employed to determine the Digital Elevation Model (DEM), enabling the precise selection of terraces, while ortho imagery was used for validation purposes. Throughout the designing process, Geographical Information System (GIS) tools were extensively utilized. To assess the design’s reliability on the ground compared with the current conventional method, high-precision GPS instruments like EOS Arrow Gold and HIPER VR GNSS were used, with both offering accuracy levels between 0.3 cm and 0.5cm. Nearest Distance Analysis was generated to compare the design with actual planting on the ground. The analysis revealed that it could not be applied to the roads due to discrepancies between actual roads and the blueprint design, which resulted in minimal variance. In contrast, the terraces closely adhered to the GPS markings, with the most variance distance being less than 0.5 meters compared to actual terraces constructed. Considering the required slope degrees for terrace planting, which must be greater than 6 degrees, the study found that approximately 65% of the terracing was constructed at a 12-degree slope, while over 50% of the terracing was constructed at slopes exceeding the minimum degrees. Utilizing blueprint replanting promising strategies for optimizing land utilization in agriculture. This approach harnesses technology and meticulous planning to yield advantages, including increased efficiency, enhanced sustainability, and cost reduction. From this study, practical implementation of this technique can lead to tangible and significant improvements in agricultural sectors. In boosting further efficiencies, future initiatives will require more sophisticated techniques and the incorporation of precision GPS devices for upcoming blueprint replanting projects besides strategic progression aims to guarantee the precision of both blueprint design stages and its subsequent implementation on the field. Looking ahead, automating digital blueprints are necessary to reduce time, workforce, and costs in commercial production.

Keywords: replanting, geospatial, precision agriculture, blueprint

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Heba M. Jahin, Ali F. Bakr, Zeyad T. Elsayad

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As smart spaces can be defined as any working environment which integrates embedded computers, information appliances and multi-modal sensors to remain focused on the interaction between the users, their activity, and their behavior in the space; hence, smart space must be aware of their contexts and automatically adapt to their changing context-awareness, by interacting with their physical environment through natural and multimodal interfaces. Also, by serving the information used proactively. This paper suggests a dynamic framework through the architectural design process of the space based on the principles of computational methods and context-awareness principles to help in creating a field of changes and modifications. It generates possibilities, concerns about the physical, structural and user contexts. This framework is concerned with five main processes: gathering and analyzing data to generate smart design scenarios, parameters, and attributes; which will be transformed by coding into four types of models. Furthmore, connecting those models together in the interaction model which will represent the context-awareness system. Then, transforming that model into a virtual and ambient environment which represents the physical and real environments, to act as a linkage phase between the users and their activities taking place in that smart space . Finally, the feedback phase from users of that environment to be sure that the design of that smart space fulfill their needs. Therefore, the generated design process will help in designing smarts spaces that can be adapted and controlled to answer the users’ defined goals, needs, and activity.

Keywords: computational methods, context-awareness, design process, smart spaces

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Osman Okumuş

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The pandemic has significantly affected every aspect of life. Especially in recenttimes, as a result of this effect, we have come closer to technology. Distance education has taken the place of formal education rather than supporting formal education. Thiscreatednewexperiencesforbothteachersandstudents. This research focused on revealing the experiences of the same students in distance and formal education, especially in history lessons. In the study, which was designed as a case study, 20 students were interviewed through a semi-structured interview form prepared by the researcher. The results show that both learning environments provide students with important experiences. However, despite the fact that the students developed their digital competencies and experienced different learning environments, they focused on formal education in the name of socialization.

Keywords: history lessons, distance education, pandemic., formal education

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Authors: Danuta Szwajca

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Reputation The exponential growth of the Internet and social media (SM) in the recent years has contributed to changing the communication environment, in which stakeholders: customers, investors, business partners, employees, like their users, may post and distribute their opinions about the company and its products. This generates a number of potential threats to the image and reputation of both people and organizations. Social media create new opportunities not only for rapid and interactive communication but also for organizing themselves into strong pressure groups which may effectively affect the decisions of various organized bodies. Companies cannot ignore this fact and should use SM not only as an additional communication marketing channel but in a broader context - as a tool to build and protect their reputation. This article aims to identify the extent, scope, and directions of the use of SM in the activities of companies operating in the Polish market, as well as to identify threats and opportunities generated by the media in the area of reputation management. The results of research presented in the article showed that Polish companies recognize the potential of SM and try to apply them in their marketing efforts. However, his activity is limited only to maintain communication with customers through two portals: Facebook and Twitter. In the approach to the SM as a communication channel, the traditional way of thinking dominates, in which they are treated as just another promotional tool used by two departments: marketing and PR. This approach is called "silo" and is not integrated. This way of using SM does not allow effective building and protecting reputation in the Internet environment. To achieve this goal, the following research methods were used: the critical analysis of literature, analysis of secondary sources in a form of the report from the research conducted by Harvard Business Review Poland together with Capgemini Poland and case study.

Keywords: corporate reputation, reputation management, social media, risk reputation

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Authors: Mazhar Hamayun

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Organizations need to take a holistic approach to their Zero Trust strategic and tactical security needs. This includes using a framework-agnostic model that will ensure all enterprise resources are being accessed securely, regardless of their location. Such can be achieved through the implementation of a security posture, monitoring the posture, and adjusting the posture through the Identify, Detect, Protect, Respond, and Recover Methods, The target audience of this document includes those involved in the management and operational functions of risk, information security, and information technology. This audience consists of the chief information security officer, chief information officer, chief technology officer, and those leading digital transformation initiatives where Zero Trust methods can help protect an organization’s data assets.

Keywords: ZTNA, zerotrust architecture, microsegmentation, NIST SP 800-207

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Authors: Rani Danavath, V. B. Narsimha

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Cloud computing is emerging as a new paradigm of large scale distributed computing. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., three service models, and four deployment networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics models. Load balancing is one of the main challenges in cloud computing, which is required to distribute the dynamic workload across multiple nodes, to ensure that no single node is overloaded. It helps in optimal utilization of resources, enhancing the performance of the system. The goal of the load balancing is to minimize the resource consumption and carbon emission rate, that is the direct need of cloud computing. This determined the need of new metrics energy consumption and carbon emission for energy-efficiency load balancing techniques in cloud computing. Existing load balancing techniques mainly focuses on reducing overhead, services, response time and improving performance etc. In this paper we introduced a Technique for energy-efficiency, but none of the techniques have considered the energy consumption and carbon emission. Therefore, our proposed work will go towards energy – efficiency. So this energy-efficiency load balancing technique can be used to improve the performance of cloud computing by balancing the workload across all the nodes in the cloud with the minimum resource utilization, in turn, reducing energy consumption, and carbon emission to an extent, which will help to achieve green computing.

Keywords: cloud computing, distributed computing, energy efficiency, green computing, load balancing, energy consumption, carbon emission

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Authors: Ankit Tyagi, Reet Kamal Tiwari, Naveen James

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Landslide is the major geo-environmental problem of Himalaya because of high ridges, steep slopes, deep valleys, and complex system of streams. They are mainly triggered by rainfall and earthquake and causing severe damage to life and property. In Uttarakhand, the Tehri reservoir rim area, which is situated in the lesser Himalaya of Garhwal hills, was selected for landslide hazard zonation (LHZ). The study utilized different types of data, including geological maps, topographic maps from the survey of India, Landsat 8, and Cartosat DEM data. This paper presents the use of a weighted overlay method in LHZ using fourteen causative factors. The various data layers generated and co-registered were slope, aspect, relative relief, soil cover, intensity of rainfall, seismic ground shaking, seismic amplification at surface level, lithology, land use/land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), stream power index (SPI), drainage buffer and reservoir buffer. Seismic analysis is performed using peak horizontal acceleration (PHA) intensity and amplification factors in the evaluation of the landslide hazard index (LHI). Several digital image processing techniques such as topographic correction, NDVI, and supervised classification were widely used in the process of terrain factor extraction. Lithological features, LULC, drainage pattern, lineaments, and structural features are extracted using digital image processing techniques. Colour, tones, topography, and stream drainage pattern from the imageries are used to analyse geological features. Slope map, aspect map, relative relief are created by using Cartosat DEM data. DEM data is also used for the detailed drainage analysis, which includes TWI, SPI, drainage buffer, and reservoir buffer. In the weighted overlay method, the comparative importance of several causative factors obtained from experience. In this method, after multiplying the influence factor with the corresponding rating of a particular class, it is reclassified, and the LHZ map is prepared. Further, based on the land-use map developed from remote sensing images, a landslide vulnerability study for the study area is carried out and presented in this paper.

Keywords: weighted overlay method, GIS, landslide hazard zonation, remote sensing

Procedia PDF Downloads 118

Authors: M. O. Adeniyi, R. T. Akinnubi

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The parameterization of turbulent heat fluxes is needed for modeling land-atmosphere interactions in Global Climate Models (GCMs). However, current GCMs still have difficulties with producing reliable turbulent heat fluxes for humid tropical regions, which may be due to inadequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. These roughness lengths are usually expressed in term of excess resistance factor (κB^(-1)), and this factor is used to account for different resistances for momentum and heat transfers. In this paper, a more appropriate excess resistance factor (〖 κB〗^(-1)) suitable for low wind speed condition was developed and incorporated into the aerodynamic resistance approach (ARA) in the GCMs. Also, the performance of various standard GCMs κB^(-1) schemes developed for high wind speed conditions were assessed. Based on the in-situ surface heat fluxes and profile measurements of wind speed and temperature from Nigeria Micrometeorological Experimental site (NIMEX), new κB^(-1) was derived through application of the Monin–Obukhov similarity theory and Brutsaert theoretical model for heat transfer. Turbulent flux parameterizations with this new formula provides better estimates of heat fluxes when compared with others estimated using existing GCMs κB^(-1) schemes. The derived κB^(-1) MBE and RMSE in the parameterized QH ranged from -1.15 to – 5.10 Wm-2 and 10.01 to 23.47 Wm-2, while that of QE ranged from - 8.02 to 6.11 Wm-2 and 14.01 to 18.11 Wm-2 respectively. The derived 〖 κB〗^(-1) gave better estimates of QH than QE during daytime. The derived 〖 κB〗^(-1)=6.66〖 Re〗_*^0.02-5.47, where Re_* is the Reynolds number. The derived κB^(-1) scheme which corrects a well documented large overestimation of turbulent heat fluxes is therefore, recommended for most regional models within the tropic where low wind speed is prevalent.

Keywords: humid, tropic, excess resistance factor, overestimation, turbulent heat fluxes

Procedia PDF Downloads 188