Search results for: demand model

Authors: Andrew Freiburger, Sergi Molins

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Freshwater is a vital resource; yet, the supply of clean freshwater is diminishing as the consequence of melting snow and ice from global warming, pollution from industry, and an increasing demand from human population growth. The unsustainable trajectory of diminishing water resources is projected to jeopardize water security for billions of people in the 21st century. Membrane desalination technologies may resolve the growing discrepancy between supply and demand by filtering arbitrary feed water into a fraction of renewable, clean water and a fraction of highly concentrated brine. The leading hindrance of membrane desalination is fouling, whereby the highly concentrated brine solution encourages micro-organismal colonization and/or the precipitation of occlusive minerals (i.e. scale) upon the membrane surface. Thus, an understanding of brine formation is necessary to mitigate membrane fouling and to develop efficacious desalination technologies that can bolster the supply of available freshwater. This study presents a reactive transport simulation of brine formation and scale deposition during reverse osmosis (RO) desalination. The simulation conceptually represents the RO module as a one-dimensional domain, where feed water directionally enters the domain with a prescribed fluid velocity and is iteratively concentrated in the immobile layer of a dual porosity model. Geochemical PHREEQC code numerically evaluated the conceptual model with parameters for the BW30-400 RO module and for real water feed sources – e.g. the Red and Mediterranean seas, and produced waters from American oil-wells, based upon peer-review data. The presented simulation is computationally simpler, and hence less resource intensive, than the existent and more rigorous simulations of desalination phenomena, like TOUGHREACT. The end-user may readily prepare input files and execute simulations on a personal computer with open source software. The graphical results of fouling-potential and brine characteristics may therefore be particularly useful as the initial tool for screening candidate feed water sources and/or informing the selection of an RO module.

Keywords: desalination, PHREEQC, reactive transport, scaling

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Authors: Jiunde Lee, Meng-Yu Wun

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The present study attempted to explore how the range differences of ‘Geometric Field of Vision’ (GFOV) and differences in camera control in 3D simulation games, OMSI—The Bus Simulator of the 2013 PC version, affected players’ cognitive load, anxiety, and task performances. The study employed a between-subjects factorial experimental design. A total of 80 subjects completed experiment whose data were eligible for further analysis. The results of this study showed that in the difference of field of view, players had better task performances in a spacious view. Although cognitive resources consumed more of the players’ ‘mental demand,’ ‘physical demand’, and ‘temporal demand’, they had better performances in the experiment, and their anxiety was effectively reduced. On the other hand, in the narrow GFOV, players thought they spent more cognitive resources on ‘effort’ and ‘frustration degree,’ and had worse task performances, but it was not significant enough to reduce their anxiety. In terms of difference of camera control, players had worse performances since the fixed lens restricted their dexterous control. However, there was no significant difference in the players’ subjective cognitive resources or anxiety. The results further illustrated that task performances were affected by the interaction of GFOV and camera control.

Keywords: geometric field of view, camera lens, cognitive load, anxiety

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Authors: Mahdiyeh Zafaranchi, Ethan S. Cantor, William T. Riddell, Jess W. Everett

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The New Jersey Department of Military and Veteran’s Affairs (NJ DMAVA) operates over 50 facilities throughout the state of New Jersey, U.S. NJDMAVA is under a mandate to move toward decarbonization, which will eventually include eliminating the use of natural gas and other fossil fuels for heating. At the same time, the organization requires increased resiliency regarding electric grid disruption. These competing goals necessitate adopting the use of on-site renewables such as photovoltaic and geothermal power, as well as implementing power control strategies through microgrids. Planning for these changes requires a detailed understanding of current and future electricity use on yearly, monthly, and shorter time scales, as well as a breakdown of consumption by heating, ventilation, and air conditioning (HVAC) equipment. This paper discusses case studies of two buildings that were simulated using the QUick Energy Simulation Tool (eQUEST). Both buildings use electricity from the grid and photovoltaics. One building also uses natural gas. While electricity use data are available in hourly intervals and natural gas data are available in monthly intervals, the simulations were developed using monthly and yearly totals. This approach was chosen to reflect the information available for most NJ DMAVA facilities. Once completed, simulation results are compared to metrics recommended by several organizations to validate energy use simulations. In addition to yearly and monthly totals, the simulated peak demands are compared to actual monthly peak demand values. The simulations resulted in monthly peak demand values that were within 30% of the measured values. These benchmarks will help to assess future energy planning efforts for NJ DMAVA.

Keywords: building energy modeling, eQUEST, peak demand, smart meters

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Authors: A. Kumar, V. Devadas

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Energy is the basic element for any country’s economic development. India is one of the most populated countries, and is dependent on fossil fuel and nuclear-based energy generation. The energy sector faces huge challenges and is dependent on the import of energy from neighboring countries to fulfill the gap in demand and supply. India has huge setbacks for efficient energy generation, distribution, and consumption, therefore they consume more quantity of energy to produce the same amount of Gross Domestic Product (GDP) compared to the developed countries. Technology and technique use, availability, and affordability in the various sectors are varying according to their economic status. In this paper, an attempt is made to quantify the domestic electrical energy consumption in Jodhpur, India. Survey research methods have been employed and stratified sampling technique-based households were chosen for conducting the investigation. Pre-tested survey schedules are used to investigate the grassroots level study. The collected data are analyzed by employing statistical techniques. Thereafter, a multiple regression model is developed to understand the functions of total electricity consumption in the domestic sector corresponding to other independent variables including electrical appliances, age of the building, household size, education, etc. The study resulted in identifying the governing variable in energy consumption at the household level and their relationship with the efficiency of household-based electrical and energy appliances. The analysis is concluded with the recommendation for optimizing the gap in peak electrical demand and supply in the domestic sector.

Keywords: appliance, consumption, electricity, households

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Authors: I. C. Tolias, A. G. Venetsanos, N. Markatos

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In the present work, CFD simulations of a large scale open deflagration experiment are performed. Stoichiometric hydrogen-air mixture occupies a 20 m hemisphere. Two combustion models are compared and are evaluated against the experiment. The Eddy Dissipation Model and a Multi-physics combustion model which is based on Yakhot’s equation for the turbulent flame speed. The values of models’ critical parameters are investigated. The effect of the turbulence model is also examined. k-ε model and LES approach were tested.

Keywords: CFD, deflagration, hydrogen, combustion model

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Authors: J. Rhodes, V. Cheng, P. Lok

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The aim of this study is to develop a parsimonious model that explains the effect of different stimulus on a tourist’s intention to visit a new destination. The model consists of destination trust and interest as the mediating variables. The model was tested using two different types of stimulus; both studies empirically supported the proposed model. Furthermore, the first study revealed that advertising has a stronger effect than positive online reviews. The second study found that the peripheral route of the elaboration likelihood model has a stronger influence power than the central route in this context.

Keywords: advertising, electronic word-of-mouth, elaboration likelihood model, intention to visit, trust

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Authors: Ahmad Sarfaraz, Raiyad Herwies

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In this paper, a multi-criteria decision making analysis is used to help any organization selects the best KM tool that fits and serves its needs. The AHP model is used based on a previous study to highlight and identify the main criteria and sub-criteria that are incorporated in the selection process. Different KM tools alternatives with different criteria are compared and weighted accurately to be incorporated in the GP model. The main goal is to combine the GP model with the AHP model to ensure that selecting the KM tool considers the resource constraints. Two important issues are discussed in this paper: how different factors could be taken into consideration in forming the AHP model, and how to incorporate the AHP results into the GP model for better results.

Keywords: knowledge management, analytical hierarchy process, goal programming, multi-criteria decision making

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: A. Hol

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21^st century brought waves of business and industry eTransformations. The impact of change is also being seen in education. To identify the extent of this, scenario analysis methodology was utilised with the aim to assess business transformations across industry sectors ranging from craftsmanship, medicine, finance and manufacture to innovations and adoptions of new technologies and business models. Firstly, scenarios were drafted based on the current eTransformation models and its dimensions. Following this, eTransformation framework was utilised with the aim to derive the key eTransformation parameters, the essential characteristics that have enabled eTransformations across the sectors. Following this, identified key parameters were mapped to the transforming domain-education. The mapping assisted in deriving a cognitive eTransformation framework for education sector. The framework highlights the importance of context and the notion that education today needs not only to deliver content to students but it also needs to be able to meet the dynamically changing demands of specific student and industry groups. Furthermore, it pinpoints that for such processes to be supported, specific technology is required, so that instant, on demand and periodic feedback as well as flexible, dynamically expanding study content can be sought and received via multiple education mediums.

Keywords: education sector, business transformation, eTransformation model, cognitive model, cognitive systems, eTransformation

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Authors: Bilal Ahmad Malik

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With the fall of Soviet Union in 1991 the whole Central Asian region saw a dramatic rise in Muslim identity, a call back to Islamic legacy. Today, many Central Asian Muslims demand, what Islam has termed legal (Halal) and, avoid what Islam has termed illegal (Haram). The process of Islamic resurgence kicked off very quickly soon after the integration of Central Asian republics with other Muslim geographies through the membership of Organization of Islamic Conference (OIC) and other similar organizations. This interaction proved to be a vital push factor to the already existing indigenous reviving trends and sentiments. As a result, along with many other requirements, Muslim customer demand emerged as navel trend in the market in general and in banking and financial sector in particular. To get this demand fulfilled, the governments of CIS states like Kazakhstan, Uzbekistan, Azerbaijan, Turkmenistan, Kyrgyzstan and Tajikistan introduced Halal banking and financial products in the market. Firstly, the present paper would briefly discuss the core composition of Halal banking and financial products. Then, coming to its major theme, it would try to identify and analyze the causes that lead to the emergence of Islamic banking and finance industry in the Muslim majority Post-Soviet CIS States.

Keywords: causes, Central Asia, interest-free banking, Islamic Revival

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Authors: Ajay Kumar, Shamily Jaggi

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In developing countries like India development of modern technologies has been a key determinant in accelerating industrialization and urbanization. But in the pursuit of rapid economic growth, development is considered a top priority, while environmental protection is not given the same importance. Thus, a number of industries sited haphazardly have been established, leading to a deterioration of natural resources like water, soil and air. As a result, environmental pollution is tremendously increasing due to industrialization and mechanization that are serving to fulfill the demands of the population. With the increasing population, demand for bricks for construction work is also increasing, establishing the brick industry as a growing industry. Brick production requires two main resources; water as a source of life, and soil, as a living environment. Water and soil conservation is a critical issue in areas facing scarcity of water and soil resources. The purpose of this review paper is to provide a brief overview of the theoretical frameworks used in the analysis of the adoption and/or acceptance of soil and water conservation practices in the brick industry. Different frameworks and models have been used in the analysis of the adoption and/or acceptance of new technologies and practices; these include the technology acceptance model, motivational model, theory of reasoned action, innovation diffusion theory, theory of planned behavior, and the unified theory of acceptance and use of technology. However, every model has some limitations, such as not considering environmental/contextual and economic factors that may affect the individual’s intention to perform a behavior. The paper concludes that in comparing other models, the UTAUT seems a better model for understanding the dynamics of acceptance and adoption of water and soil conservation practices.

Keywords: brick kiln, water conservation, soil conservation, unified theory of acceptance and use of technology, technology adoption

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Authors: Coskun Serefoglu

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The study aims to analyse how a traditional sector such as agri-food could be mobilized through regional innovation strategies. A principal component analysis as well as qualitative information, such as in-depth interviews, focus group and surveys, were employed to find the priority sectors. An agri-food model was developed which includes both a linear model and interactive model. The model consists of two main components, one of which is technological integration and the other one is agricultural extension which is based on Land-grant university approach of U.S. which is not a common practice in Turkey.

Keywords: regional innovation strategy, interactive model, agri-food sector, local development, planning, regional development

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Sasiporn Rattanasupha, Settapat Chinviriyasit

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The treatment function adopts a continuous and differentiable function which can describe the effect of delayed treatment when the number of infected individuals increases and the medical condition is limited. In this paper, the SEIR epidemic model with treatment function is studied to investigate the dynamics of the model due to the effect of treatment. It is assumed that the treatment rate is proportional to the number of infective patients. The stability of the model is analyzed. The model is simulated to illustrate the analytical results and to investigate the effects of treatment on the spread of infection.

Keywords: basic reproduction number, local stability, SEIR epidemic model, treatment function

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Authors: Joon-Hyung Kim, Him-Chan Lee, Jin-Hyuk Kim, Yong-Kab Lee, Young-Seok Choi

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The crude oil in an oil well exists in various phases such as gas, seawater, and sand, as well as oil. Therefore, a phase separator is needed at the front of a single-phase pump for pressurization and transfer. On the other hand, the application of a multiphase pump can provide such advantages as simplification of the equipment structure and cost savings, because there is no need for a phase separation process. Therefore, the crude oil transfer method using a multiphase pump is being applied to recently developed oil wells. Due to this increase in demand, technical demands for the development of multiphase pumps are sharply increasing, but the progress of research into related technologies is insufficient, due to the nature of multiphase pumps that require high levels of skills. This study was conducted to verify the reliability of pump performance evaluation using numerical analysis, which is the basis of the development of a multiphase pump. For this study, a model was designed by selecting the specifications of the pump under study. The performance of the designed model was evaluated through numerical analysis and experiment, and the results of the performance evaluation were compared to verify the reliability of the result using numerical analysis.

Keywords: multiphase pump, numerical analysis, experiment, performance evaluation, reliability verification

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Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Tianyu Chen

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China's school district education policy has encouraged parents to purchase properties in school districts with high-quality education resources. Shanghai has implemented "Simultaneous Admission to Public and Private Schools" (SAPPS) since 2018, which has covered all nine-year compulsory education by 2020. This study examines the impact of SAPPS on the housing market, specifically the premium effect of houses located in dual-school districts. Based on the Hedonic Pricing Model and the Signaling Theory, data is collected from 585 second-hand house transactions in Pudong New Area, Shanghai, and it is analyzed with the Difference-in-Differences (DID) model. The results indicate that the implementation of SAPPS has exacerbated the premium of dual school district housing and weakened the effect of the policy to a certain degree. To ensure equal access to education for all students, the government should work both on the supply and demand sides of the education resource equation.

Keywords: simultaneous admission to public and private schools, housing prices, education policy, education equity

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Authors: Rakesh Kumar Mishra, Tarun Kumar Dan

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Lead-Lag based Internal Model Control method is proposed based on Internal Model Control (IMC) strategy. In this paper, we have designed the Lead-Lag based Internal Model Control for binary distillation column for SISO process (considering only bottom product). The transfer function has been taken from Wood and Berry model. We have find the composition control and disturbance rejection using Lead-Lag based IMC and comparing with the response of simple Internal Model Controller.

Keywords: SISO, lead-lag, internal model control, wood and berry, distillation column

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Authors: Vasujeet Singh, Pruthiviraj Nemalipuri, Vivek Vitankar, Harish Chandra Das

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For the correct prediction of thermal and hydraulic performance (bed voidage, suspension density, pressure drop, heat transfer, and combustion kinetics), one should incorporate the correct parameters in the computational fluid dynamics simulation of a fluidized bed gasifier. Scarcity of fossil fuels, and to fulfill the energy demand of the increasing population, researchers need to shift their attention to the alternative to fossil fuels. The current research work focuses on hydrodynamics behavior and gasification of sawdust inside a 2D industrial scale FBG using the Eulerian-Eulerian multifluid model. The present numerical model is validated with experimental data. Further, this model extended for the prediction of gasification characteristics of sawdust by incorporating eight heterogeneous moisture release, volatile cracking, tar cracking, tar oxidation, char combustion, CO₂ gasification, steam gasification, methanation reaction, and five homogeneous oxidation of CO, CH₄, H₂, forward and backward water gas shift (WGS) reactions. In the result section, composition of gasification products is analyzed, along with the hydrodynamics of sawdust and sand phase, heat transfer between the gas, sand and sawdust, reaction rates of different homogeneous and heterogeneous reactions is being analyzed along the height of the domain.

Keywords: devolatilization, Eulerian-Eulerian, fluidized bed gasifier, mathematical modelling, sawdust gasification

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Authors: H. Namazi, H. T. N. Kuan

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It is known that in humans, the adaptation to a given odor occurs within a quite short span of time (typically one minute) after the odor is presented to the brain. Different models of human olfaction have been developed by scientists but none of these models consider the diffusion phenomenon in olfaction. A novel microscopic model of the human olfaction is presented in this paper. We develop this model by incorporating the transient diffusivity. In fact, the mathematical model is written based on diffusion of the odorant within the mucus layer. By the use of the model developed in this paper, it becomes possible to provide quantification of the objective strength of odor.

Keywords: diffusion, microscopic model, mucus layer, olfaction

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Authors: R. Horchani

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Optical pumping technique with laser fields combined with photo-association of ultra-cold atoms leads to control on demand the vibrational and/or the rotational population of molecules. Here, we review the basic concepts and main steps should be followed, including the excitation schemes and detection techniques we use to achieve the ro-vibrational cooling of Cs2 molecules. We also discuss the extension of this technique to other molecules. In addition, we present a theoretical model used to support the experiment. These simulations can be widely used for the preparation of various experiments since they allow the optimization of several important experimental parameters.

Keywords: cold molecule, photo-association, optical pumping, vibrational and rotational cooling

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Authors: Yanshuang Zhang, Byungho Jeong

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There exists some time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in calculating efficiency of decision making units (DMU). Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. However, these models can’t discriminate efficient DMUs because of the nature of basic DEA model in which efficiency scores are limited to ‘1’. This problem can be resolved a super-efficiency model. However, a super efficiency model sometimes causes infeasibility problem. This paper suggests an output oriented super-efficiency model for efficiency evaluation under the consideration of time lag effect. A case example using a long term research project is given to compare the suggested model with the MpO model

Keywords: DEA, Super-efficiency, Time Lag, research activities

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Authors: David W. Hill, Linda W. Glass, Jakob L. Vingren

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Maximal accumulated oxygen deficit (MAOD), the gold standard measure of anaerobic capacity, is the difference between the oxygen cost of exhaustive severe intensity exercise and the accumulated oxygen consumption (O2; mL·kg–1). In theory, MAOD can be estimated as the sum of independent estimates of the phosphocreatine and glycolysis contributions, which we refer to as PCr+glycolysis. Purpose: The purpose was to test the hypothesis that PCr+glycolysis provides a valid measure of anaerobic capacity in cycling and running. Methods: The participants were 27 women (mean ± SD, age 22 ±1 y, height 165 ± 7 cm, weight 63.4 ± 9.7 kg) and 25 men (age 22 ± 1 y, height 179 ± 6 cm, weight 80.8 ± 14.8 kg). They performed two exhaustive cycling and running tests, at speeds and work rates that were tolerable for ~5 min. The rate of oxygen consumption (VO2; mL·kg–1·min–1) was measured in warmups, in the tests, and during 7 min of recovery. Fingerprick blood samples obtained after exercise were analysed to determine peak blood lactate concentration (PeakLac). The VO2 response in exercise was fitted to a model, with a fast ‘primary’ phase followed by a delayed ‘slow’ component, from which was calculated the accumulated O2 and the excess O2 attributable to the slow component. The VO2 response in recovery was fitted to a model with a fast phase and slow component, sharing a common time delay. Oxygen demand (in mL·kg–1·min–1) was determined by extrapolation from steady-state VO2 in warmups; the total oxygen cost (in mL·kg–1) was determined by multiplying this demand by time to exhaustion and adding the excess O2; then, MAOD was calculated as total oxygen cost minus accumulated O2. The phosphocreatine contribution (area under the fast phase of the post-exercise VO2) and the glycolytic contribution (converted from PeakLac) were summed to give PCr+glycolysis. There was not an interaction effect involving sex, so values for anaerobic capacity were examined using a two-way ANOVA, with repeated measures across method (PCr+glycolysis vs MAOD) and mode (cycling vs running). Results: There was a significant effect only for exercise mode. There was no difference between MAOD and PCr+glycolysis: values were 59 ± 6 mL·kg–1 and 61 ± 8 mL·kg–1 in cycling and 78 ± 7 mL·kg–1 and 75 ± 8 mL·kg–1 in running. Discussion: PCr+glycolysis is a valid measure of anaerobic capacity in cycling and running, and it is as valid for women as for men.

Keywords: alactic, anaerobic, cycling, ergometer, glycolysis, lactic, lactate, oxygen deficit, phosphocreatine, running, treadmill

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Authors: Moshe Mello, Hilary Rutto, Tumisang Seodigeng, Itumeleng Kohitlhetse

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The accelerating requirement to reach 0% sulfur content in liquid fuels demand researchers to seek efficient alternative technologies to challenge the predicament. In this current study, the adsorption capabilities of modified Cu(I)-Y zeolite were tested for the removal of organosulfur compounds (OSC) present in tire pyrolytic oil (TPO). The π-complexation-based adsorbent was obtained by ion exchanging Y-zeolite with Cu+ cation using liquid phase ion exchange (LPIE). Preparation of the adsorbent involved firstly ion exchange between Na-Y zeolite with a Cu(NO₃)₂ aqueous solution of 0.5M for 48 hours followed by reduction of Cu²⁺ to Cu+. Fixed-bed breakthrough studies for TPO in comparison with model diesel comprising of sulfur compounds such as thiophene, benzothiophenes (BT), and dibenzothiophenes (DBT) showed that modified Cu(I)-Y zeolite is an effective adsorbent for removal of OSC in liquid fuels. The effect of operating conditions such as adsorbent dosage and reaction time were studied to optimize the adsorptive desulfurization process. For model diesel fuel, the selectivity for adsorption of sulfur compounds followed the order DBT> BT> Thiophene. The Cu(I)-Y zeolite is fully regeneratable and this is achieved by a simple procedure of blowing the adsorbent with air at 350 °C, followed by reactivation at 450 °C in a rich helium surrounding.

Keywords: adsorption, desulfurization, TPO, zeolite

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Authors: Zainab Magaji Musa, Nordin M. A. Rahman, Julaily Aida Jusoh

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The web services applications for digital reference service (WSDRS) of LIS model is an informal model that claims to reduce the problems of digital reference services in libraries. It uses web services technology to provide efficient way of satisfying users’ needs in the reference section of libraries. The formal WSDRS model consists of the Z specifications of all the informal specifications of the model. This paper discusses the formal validation of the Z specifications of WSDRS model. The authors formally verify and thus validate the properties of the model using Z/EVES theorem prover.

Keywords: validation, verification, formal, theorem prover

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Authors: D. Mokatsanyane, J. Jansen Van Rensburg

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This paper analyses the influence and impact of COVID-19 on major agricultural commodity prices in South Africa. According to a World Bank report, the agricultural sector in South Africa has been unable to reduce the domestic food crisis that has been occurring over the past years, hence the increased rate of poverty, which is currently at 55.5 percent as of April 2020. Despite the significance of this sector, empirical findings concluded that the agricultural sector now accounts for 1.88 percent of South Africa's gross domestic product (GDP). Suggesting that the agricultural sector's contribution to the economy has diminished. Despite the low contribution to GDP, this primary sector continues to play an essential role in the economy. Over the past years, multiple factors have contributed to the soaring commodities prices, namely, climate shocks, biofuel demand, demand and supply shocks, the exchange rate, speculation in commodity derivative markets, trade restrictions, and economic growth. The COVID-19 outbursts have currently disturbed the supply and demand of staple crops. To address the disruption, the government has exempted the agricultural sector from closure and restrictions on movement. The spread of COVID-19 has caused turmoil all around the world, but mostly in developing countries. According to Statistic South Africa, South Africa's economy decreased by seven percent in 2020. Consequently, this has arguably made the agricultural sector the most affected sector since slumped economic growth negatively impacts food security, trade, farm livelihood, and greenhouse gas emissions. South Africa is sensitive to the fruitfulness of global food chains. Restrictions in trade, reinforced sanitary control systems, and border controls have influenced food availability and prices internationally. The main objective of this study is to evaluate the behavior of agricultural commodity prices pre-and during-COVID to determine the impact of volatility drivers on these crops. Historical secondary data of spot prices for the top five major commodities, namely white maize, yellow maize, wheat, soybeans, and sunflower seeds, are analysed from 01 January 2017 to 1 September 2021. The timeframe was chosen to capture price fluctuations between pre-COVID-19 (01 January 2017 to 23 March 2020) and during-COVID-19 (24 March 2020 to 01 September 2021). The Generalised Autoregressive Conditional Heteroscedasticity (GARCH) statistical model will be used to measure the influence of price fluctuations. The results reveal that the commodity market has been experiencing volatility at different points. Extremely high volatility is represented during the first quarter of 2020. During this period, there was high uncertainty, and grain prices were very volatile. Despite the influence of COVID-19 on agricultural prices, the demand for these commodities is still existing and decent. During COVID-19, analysis indicates that prices were low and less volatile during the pandemic. The prices and returns of these commodities were low during COVID-19 because of the government's actions to respond to the virus's spread, which collapsed the market demand for food commodities.

Keywords: commodities market, commodity prices, generalised autoregressive conditional heteroscedasticity (GARCH), Price volatility, SAFEX

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Authors: Arash Parehkar, S. Jamshid Mousavi, Shoubo Bayazidi, Vahid Karami, Laleh Shahidi, Arash Azaranfar, Ali Moridi, M. Shabakhti, Tayebeh Ariyan, Mitra Tofigh, Kaveh Masoumi, Alireza Motahari

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Water resource systems modelling have constantly been a challenge through history for human being. As the innovative methodological development is evolving alongside computer sciences on one hand, researches are likely to confront more complex and larger water resources systems due to new challenges regarding increased water demands, climate change and human interventions, socio-economic concerns, and environment protection and sustainability. In this research, an automatic calibration scheme has been applied on the Gilan’s large-scale water resource model using mathematical programming. The water resource model’s calibration is developed in order to attune unknown water return flows from demand sites in the complex Sefidroud irrigation network and other related areas. The calibration procedure is validated by comparing several gauged river outflows from the system in the past with model results. The calibration results are pleasantly reasonable presenting a rational insight of the system. Subsequently, the unknown optimized parameters were used in a basin-scale linear optimization model with the ability to evaluate the system’s performance against a reduced inflow scenario in future. Results showed an acceptable match between predicted and observed outflows from the system at selected hydrometric stations. Moreover, an efficient operating policy was determined for Sefidroud dam leading to a minimum water shortage in the reduced inflow scenario.

Keywords: auto-calibration, Gilan, large-scale water resources, simulation

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Authors: Matthew C. Best

Abstract:

Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems.

Keywords: system identification, Kalman filter, linear model, MIMO, model order reduction

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Authors: Jun-ichi Ochiai, Itsuki Noda, Ryo Kanamori, Keiji Hirata, Hitoshi Matsubara, Hideyuki Nakashima

Abstract:

We conducted exhaustive simulations for data assimilation and evaluation of service quality for various setting in a new shared transportation system, called SAVS. Computational social simulation is a key technology to design recent social services like SAVS as new transportation service. One open issue in SAVS was to determine the service scale through the social simulation. Using our exhaustive simulation framework, OACIS, we did data-assimilation and evaluation of effects of SAVS based on actual tax-use data at Tajimi city, Japan. Finally, we get the conditions to realize the new service in a reasonable service quality.

Keywords: on-demand bus sytem, social simulation, data assimilation, exhaustive simulation

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Authors: Tejush Badal, Sanaa Alwidian

Abstract:

Class diagrams are useful tools within the Unified Modelling Language (UML) to model and visualize the relationships between, and properties of objects within a system. As a system evolves over time and space (e.g., products), a series of models with several commonalities and variabilities create what is known as a model family. In circumstances where there are several versions of a model, examining each model individually, becomes expensive in terms of computation resources. To avoid performing redundant operations, this paper proposes an approach for representing a family of class diagrams into Union Models to represent model families using a single generic model. The paper aims to analyze and reason about a family of class diagrams using union models as opposed to individual analysis of each member model in the family. The union algorithm provides a holistic view of the model family, where the latter cannot be otherwise obtained from an individual analysis approach, this in turn, enhances the analysis performed in terms of speeding up the time needed to analyze a family of models together as opposed to analyzing individual models, one model at a time.

Keywords: analysis, class diagram, model family, unified modeling language, union model

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