Search results for: General Linear Model

Authors: Mouna Baassi, Mohamed Moussaoui, Hatim Soufi, Sanchaita RajkhowaI, Ashwani Sharma, Subrata Sinha, Said Belaaouad

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Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability, allowing a comparative study of the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of Darunavir and may be used for further experimental studies. Our study may also be used as a pipeline to search and design new potential inhibitors of HIV-1 proteases.

Keywords: QSAR, ADMET properties, molecular docking, molecular dynamics simulation.

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Authors: S.I.Mukhin, S. Seidov, A. Mukherjee

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The Dicke model is a key tool for the description of correlated states of quantum atomic systems, excited by resonant photon absorption and subsequently emitting spontaneous coherent radiation in the superradiant state. The Dicke Hamiltonian (DH) is successfully used for the description of the dynamics of the Josephson Junction (JJ) array in a resonant cavity under applied current. In this work, we have investigated a generalized model, which is described by DH with a frustrating interaction term. This frustrating interaction term is explicitly the infinite coordinated interaction between all the spin half in the system. In this work, we consider an array of N superconducting islands, each divided into two sub-islands by a Josephson Junction, taken in a charged qubit / Cooper Pair Box (CPB) condition. The array is placed inside the resonant cavity. One important aspect of the problem lies in the dynamical nature of the physical observables involved in the system, such as condensed electric field and dipole moment. It is important to understand how these quantities behave with time to define the quantum phase of the system. The Dicke model without frustrating term is solved to find the dynamical solutions of the physical observables in analytic form. We have used Heisenberg’s dynamical equations for the operators and on applying newly developed Rotating Holstein Primakoff (HP) transformation and DH we have arrived at the four coupled nonlinear dynamical differential equations for the momentum and spin component operators. It is possible to solve the system analytically using two-time scales. The analytical solutions are expressed in terms of Jacobi's elliptic functions for the metastable ‘bound luminosity’ dynamic state with the periodic coherent beating of the dipoles that connect the two double degenerate dipolar ordered phases discovered previously. In this work, we have proceeded the analysis with the extended DH with a frustrating interaction term. Inclusion of the frustrating term involves complexity in the system of differential equations and it gets difficult to solve analytically. We have solved semi-classical dynamic equations using the perturbation technique for small values of Josephson energy EJ. Because the Hamiltonian contains parity symmetry, thus phase transition can be found if this symmetry is broken. Introducing spontaneous symmetry breaking term in the DH, we have derived the solutions which show the occurrence of finite condensate, showing quantum phase transition. Our obtained result matches with the existing results in this scientific field.

Keywords: Dicke Model, nonlinear dynamics, perturbation theory, superconductivity

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Authors: Martin Cermak, Tomas Karasek, Jaroslav Rojicek

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The key role in phenomenological modelling of cyclic plasticity is good understanding of stress-strain behaviour of given material. There are many models describing behaviour of materials using numerous parameters and constants. Combination of individual parameters in those material models significantly determines whether observed and predicted results are in compliance. Parameter identification techniques such as random gradient, genetic algorithm, and sensitivity analysis are used for identification of parameters using numerical modelling and simulation. In this paper genetic algorithm and sensitivity analysis are used to study effect of 4 parameters of modified AbdelKarim-Ohno cyclic plasticity model. Results predicted by Finite Element (FE) simulation are compared with experimental data from biaxial ratcheting test with semi-elliptical loading path.

Keywords: genetic algorithm, sensitivity analysis, inverse approach, finite element method, cyclic plasticity, ratcheting

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Authors: Clementine Colpaert, Boris L. Nikitenko

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The Upper Jurassic foraminiferal assemblages of the East European Platform have been strongly investigated through the 20th century with biostratigraphical and in smaller degree palaeoecological and palaeobiogeographical purposes. Over the Late Jurassic, the platform was a shallow epicontinental sea that extended from Tethys to the Artic through the Pechora Sea and further toward the northeast in the West Siberian Sea. Foraminiferal assemblages of the Russian Sea were strongly affected by sea-level changes and were controlled by alternated Boreal to Peritethyan influences. The central part of the East European Platform displays very rich and diverse foraminiferal assemblages. Two sections have been analyzed; the Makar'yev Section in the Moscow Depression and the Gorodishi Section in the Yl'yanovsk Depression. Based on the evolution of foraminiferal assemblages, palaeoenvironment has been reconstructed, and sea-level changes have been refined. The aim of this study is to understand palaeoceanographical changes throughout the Oxfordian – Kimmeridgian of the central part of the Russian Sea. The Oxfordian was characterized by a general transgressive event with intermittency of small regressive phases. The platform was connected toward the south with Tethys and Peritethys. During the Middle Oxfordian, opening of a pathway of warmer water from the North-Tethys region to the Boreal Realm favoured the migration of planktonic foraminifera and the appearance of new benthic taxa. It is associated with increased temperature and primary production. During the Late Oxfordian, colder water inputs associated with the microbenthic community crisis may be a response to the closure of this warm-water corridor and the disappearance of planktonic foraminifera. The microbenthic community crisis is probably due to the increased sedimentation rate in the transition from the maximum flooding surface to a second-order regressive event, increasing productivity and inputs of organic matter along with sharp decrease of oxygen into the sediment. It is following during the Early Kimmeridgian by a replacement of foraminiferal assemblages. The almost all Kimmeridgian is characterized by the abundance of many common with Boreal and Subboreal Realm. Connections toward the South began again dominant after a small regressive event recorded during the Late Kimmeridgian and associated with the abundance of many common taxa with Subboreal Realm and Peritethys such as Crimea and Caucasus taxa. Foraminiferal assemblages of the East European Platform are strongly affected by palaeoecological changes and may display a very good model for biofacies typification under Boreal and Subboreal environments. The East European Platform appears to be a key area for the understanding of Upper Jurassic big scale palaeoceanographical changes, being connected with Boreal to Peritethyan basins.

Keywords: foraminifera, palaeoceanography, palaeoecology, upper jurassic

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Authors: C. B. Abhilash, Kavi Mahesh

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Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: Redha Elhuni, M. Munir Ahmad

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The main purpose of this research is to construct a generic model for successful implementation of Total Quality Management (TQM) in oil sector, and to find out the effects of this model on the organizational sustainability development (OSD) performance of Libyan oil and gas companies using the structured equation modeling (SEM) approach. The research approach covers both quantitative and qualitative methods. A questionnaire was developed in order to identify the quality factors that are seen by Libyan oil and gas companies to be critical to the success of TQM implementation. Hypotheses were developed to evaluate the impact of TQM implementation on O SD. Data analysis reveals that there is a significant positive effect of the TQM implementation on OSD. 24 quality factors are found to be critical and absolutely essential for successful TQM implementation. The results generated a structure of the TQMSD implementation framework based on the four major road map constructs (Top management commitment, employee involvement and participation, customer-driven processes, and continuous improvement culture).

Keywords: total quality management, critical success factors, oil and gas, organizational sustainability development (SD), Libya

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Authors: Atiwat Khatpibunchai, Taweesak Kritjaroen

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A social enterprise is the innovative hybrid organizations where its ultimate goal is to generate revenue and use it as a fund to solve the social and environmental problem. Although the evidence shows the clear value of economic, social and environmental aspects, the limitations of most of the social enterprises are the expanding impact of social and environmental aspects through the normal market mechanism. This is because the major sources of revenues of social enterprises derive from the business advocates who merely wish to support society and environment by using products and services of social enterprises rather than expect the satisfaction and the distinctive advantage of products and services. Thus, social enterprises cannot reach the achievement as other businesses do. The relevant concepts from the literature review revealed that dynamic capability is the ability to sense, integrate and reconfigure internal resources and utilize external resources to adapt to changing environments, create innovation and achieve competitive advantage. The objective of this research is to study the influence of dynamic capability that affects competitive advantage and sustainable performance, as well as to determine important elements of dynamic capability. The researchers developed a conceptual model from the related concepts and theories of dynamic capability. A conceptual model will support and show the influence of dynamic capability on competitive advantage and sustainable performance of social enterprises. The 230 organizations in South-East Asia served as participants in this study. The results of the study were analyzed by the structural equation model (SEM) and it was indicated that research model is consistent with empirical research. The results also demonstrated that dynamic capability has a direct and indirect influence on competitive advantage and sustainable performance. Moreover, it can be summarized that dynamic capability consists of the five elements: 1) the ability to sense an opportunity; 2) the ability to seize an opportunity; 3) the ability to integrate resources; 4) the ability to absorb resources; 5) the ability to create innovation. The study recommends that related sectors can use this study as a guideline to support and promote social enterprises. The focus should be pointed to the important elements of dynamic capability that are the development of the ability to transform existing resources in the organization and the ability to seize opportunity from changing market.

Keywords: dynamic capability, social enterprise, sustainable competitive advantage, sustainable performance

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

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Authors: V. Karamzadeh, J. Adhvaryu, A. Chandrasekaran, M. Packirisamy

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In Bulk Acoustic Wave (BAW) microfluidics, the throughput of particle sorting is dependent on the complex interplay between the geometric configuration of the channel, the size of the particles, and the properties of the fluid medium, which therefore calls for a detailed modeling and understanding of the fluid-particle interaction dynamics under an acoustic field, prior to designing the system. In this work, we propose a simplified Bulk acoustophoretic system that can be used for size dependent particle sorting. A Finite Element Method (FEM) based analytical model has been developed to study the dependence of particle sizes on channel parameters, and the sorting efficiency in a given fluid medium. Based on the results, the microfluidic system has been designed to take into account all the variables involved with the underlying physics, and has been fabricated using an additive manufacturing technique employing a commercial 3D printer, to generate a simple, cost-effective system that can be used for size sensitive particle sorting.

Keywords: 3D printing, 3D microfluidic chip, acoustophoresis, cell separation, MEMS (Microelectromechanical Systems), microfluidics

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Authors: M. Palizvan, M. H. Sadr, M. T. Abadi

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The representative volume element (RVE) plays a central role in the mechanics of random heterogeneous materials with a view to predicting their effective properties. In this paper, a computational homogenization methodology, developed to determine effective linear elastic properties of composite materials, is extended to predict the effective nonlinear elastoplastic response of long fiber reinforced composite. Finite element simulations of volumes of different sizes and fiber volume fractures are performed for calculation of the overall response RVE. The dependencies of the overall stress-strain curves on the number of fibers inside the RVE are studied in the 2D cases. Volume averaged stress-strain responses are generated from RVEs and compared with the finite element calculations available in the literature at moderate and high fiber volume fractions. For these materials, the existence of an RVE is demonstrated for the sizes of RVE corresponding to 10–100 times the diameter of the fibers. In addition, the response of small size RVE is found anisotropic, whereas the average of all large ones leads to recover the isotropic material properties.

Keywords: homogenization, periodic boundary condition, elastoplastic properties, RVE

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Authors: Hyeondal Jeong, Yoonjung Baek

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The main purpose of this paper is to examine the effect of diversity sensitive orientation on job satisfaction and turnover intention. Diversity sensitive orientation is the attitude of the individual to respect and accommodate diversity. This is focused on an individual’s perception of diversity. Although being made from the most diversity related research team and organizational level, this study deals with diversity issues at the individual level. To test the proposed research model and hypothesis, the data were collected from 291 Korean employees. The study conducted a confirmatory factor analysis for the validity test. Furthermore, structural equation modeling (SEM) was employed to test the hypothesized relationship in the conceptual model. The results of this paper were as followings: First, diversity sensitive orientation was positively related to job satisfaction. Second, diversity sensitive orientation was negatively related to turnover intention. In other words, the positive influence of the diversity sensitive orientation has been verified. Based on the findings, this study suggested implications and directions for future research.

Keywords: diversity sensitive orientation, job satisfaction, turnover intention, perception, cognition

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Authors: E. Hovad, J. H. Walther, P. Larsen, J. Thorborg, J. H. Hattel

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In sand casting of metal parts for the automotive industry such as brake disks and engine blocks, the molten metal is poured into a sand mold to get its final shape. The DISAMATIC molding process is a way to construct these sand molds for casting of steel parts and in the present work numerical simulations of this process are presented. During the process green sand is blown into a chamber and subsequently squeezed to finally obtain the sand mould. The sand flow is modelled with the Discrete Element method (DEM) and obtaining the correct material parameters for the simulation is the main goal. Different tests will be used to find or calibrate the DEM parameters needed; Poisson ratio, Young modulus, rolling friction coefficient, sliding friction coefficient and coefficient of restitution (COR). The Young modulus and Poisson ratio are found from compression tests of the bulk material and subsequently used in the DEM model according to the Hertz-Mindlin model. The main focus will be on calibrating the rolling resistance and sliding friction in the DEM model with respect to the behavior of “real” sand piles. More specifically, the surface profile of the “real” sand pile will be compared to the sand pile predicted with the DEM for different values of the rolling and sliding friction coefficients. When the DEM parameters are found for the particle-particle (sand-sand) interaction, the particle-wall interaction parameter values are also found. Here the sliding coefficient will be found from experiments and the rolling resistance is investigated by comparing with observations of how the green sand interacts with the chamber wall during experiments and the DEM simulations will be calibrated accordingly. The coefficient of restitution will be tested with different values in the DEM simulations and compared to video footages of the DISAMATIC process. Energy dissipation will be investigated in these simulations for different particle sizes and coefficient of restitution, where scaling laws will be considered to relate the energy dissipation for these parameters. Finally, the found parameter values are used in the overall discrete element model and compared to the video footage of the DISAMATIC process.

Keywords: discrete element method, physical properties of materials, calibration, granular flow

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Authors: Shu Hui

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Since 2010, the genre of comedy became predominant in film market in China. However, compared with the huge commercial success, these films received severe public criticism. These films are referred as trash (lan pian) by the public because of the fragment narrative, the non-professional photographing and advocating money warship. The paper aims to explain the contradictive phenomena between the higher box office and the lower mouth of word within hegemony theory. Four popular comedies that ranked top 20 in domestic revenue in the year the film released will be chosen to analyze their popularity in general. Differing from other popular films, these comedies’ popularity is generated from their disruptive pleasures instead of good stories or photographing. The destruction in Confucianism and socialism formulated the public consent or popularity, and caused the public criticism as well. Moreover, the happy-endings restore the normality at the superficial level.

Keywords: Confucianism, destruction, reconcilation, socialism

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Authors: Yapegue Bayogo, Ahmadou Halassi Dicko, Brahima Songore

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In developing countries, the road transportation system occupies an important place because of its flexibility and the low prices of infrastructure and rolling stock. While road transport is necessary for economic development, the movement of people and their goods, it is urgent to use transportation systems that minimize carbon emissions in order to ensure sustainable development. One of the main objectives of OEDC and the Word Bank is to ensure sustainable economic’ development. This paper aims to develop a road transport network taking into account environmental impacts. The methodology adopted consists of formulating a model optimizing the flow of goods and then collecting information relating to the transport of products. Our model was tested with data on product transport in CMDT areas in the Republic of Mali. The results of our study indicate that emissions from the transport sector can be significantly reduced by minimizing the traffic volume. According to our study, optimizing the transportation network, we benefit from a significant amount of tons of CO₂.

Keywords: road transport, transport sustainability, pollution, flexibility, optimized network

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Authors: M. S. Jimah, A. C. Achuenu, M. Momodu

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Group communications over public infrastructure are prone to a lot of security issues. Existing network protocols like Protocol Independent Multicast Sparse Mode (PIM SM) and Protocol Independent Multicast Dense Mode (PIM DM) do not have inbuilt security features. Therefore, any user or node can easily access the group communication as long as the user can send join message to the source nodes, the source node then adds the user to the network group. In this research, a hybrid method of salting and hashing to encrypt information in the source and stub node was designed, and when stub nodes need to connect, they must have the appropriate key to join the group network. Object oriented analysis design (OOAD) was the methodology used, and the result shows that no extra controlled bandwidth overhead cost was added by encrypting and the hybrid model was more securing than the existing PIM SM, PIM DM and Zhang secure PIM SM.

Keywords: group communications, multicast, PIM SM, PIM DM, encryption

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Authors: Vijay K. Tomer, Ritu Malik, Satya P. Nehra, Anshu Sharma

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Highly ordered mesoporous WO₃-TiO₂ nanohybrids with large intrinsic surface area and highly ordered pore channels were synthesized using mesoporous silica, KIT-6 as hard template using a nanocasting strategy. The nanohybrid samples were characterized by a variety of physico-chemical techniques including X-ray diffraction, Nitrogen adsorption-desorption isotherms, and high resolution transmission electron microscope. The nanohybrids were tested for detection of important indoor Volatile Organic Compounds (VOCs) including acetone, ethanol, n-butanol, toluene, and xylene. The sensing result illustrates that the nanocomposite sensor was highly responsive towards xylene gas at relatively lower operating temperature. A rapid response and recovery time, highly linear response and excellent stability in the concentration ranges from 1 to 100 ppm was observed for xylene gas. It is believed that the promising results of this study can be utilized in the synthesis of ordered mesoporous nanostructures which can extend its configuration for the development of new age e-nose type sensors with enhanced gas-sensing performance.

Keywords: nanohybrids, response, sensor, VOCs, xylene

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Authors: J. K. Visser, H. T. Malan

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A need exists to identify the sources of risks associated with the process automation systems within petrochemical companies or similar energy related industries. These companies use many different process automation technologies in its value chain. A crucial part of the process automation system is the information technology component featuring in the supervisory control layer. The ever-changing technology within the process automation layers and the rate at which it advances pose a risk to safe and predictable automation system performance. The age of the automation equipment also provides challenges to the operations and maintenance managers of the plant due to obsolescence and unavailability of spare parts. The main objective of this research was to determine the risk sources associated with the equipment that is part of the process automation systems. A secondary objective was to establish whether technology managers and technicians were aware of the risks and share the same viewpoint on the importance of the risks associated with automation systems. A conceptual model for risk sources of automation systems was formulated from models and frameworks in literature. This model comprised six categories of risk which forms the basis for identifying specific risks. This model was used to develop a questionnaire that was sent to 172 instrument technicians and technology managers in the company to obtain primary data. 75 completed and useful responses were received. These responses were analyzed statistically to determine the highest risk sources and to determine whether there was difference in opinion between technology managers and technicians. The most important risks that were revealed in this study are: 1) the lack of skilled technicians, 2) integration capability of third-party system software, 3) reliability of the process automation hardware, 4) excessive costs pertaining to performing maintenance and migrations on process automation systems, and 5) requirements of having third-party communication interfacing compatibility as well as real-time communication networks.

Keywords: distributed control system, identification of risks, information technology, process automation system

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Authors: Jianhua Wang, Ying Zhou, Xi Zhang

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To establish the influential mechanism of learning engagement on interpreter’s performance, the present study submitted a questionnaire to a sample of 927 English major students with 804 valid ones and used the structural equation model as the basis for empirical analysis and statistical inference on the sample data. In order to explore the mechanism for interpreting learning engagement on student interpreters’ performance, a path model of interpreting processes with three variables of ‘input-environment-output’ was constructed. The results showed that the effect of each ‘environment’ variable on interpreting ability was different from and greater than the ‘input’ variable, and learning engagement was the greatest influencing factor. At the same time, peer interaction on interpreting performance has significant influence. Results suggest that it is crucial to provide effective guidance for optimizing learning engagement and interpreting teaching research by both improving the environmental support and building the platform of peer interaction, beginning with learning engagement.

Keywords: learning engagement, interpreting performance, interpreter training, English major students

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Authors: W. Oudad, H. Fikirini, K. Boulenouar

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Material fracture by opening (mode I) is not alone responsible for fracture propagation. Many industrial examples show the presence of mode II and mixed mode I + II. In the present work the three-dimensional and non-linear finite element method is used to estimate the performance of the bonded composite repair of metallic aircraft structures by analyzing the plastic zone size ahead of repaired cracks under mixed mode loading. The computations are made according to Von Mises and Tresca criteria. The extension of the plastic zone which takes place at the tip of a crack strictly depends on many variables, such as the yield stress of the material, the loading conditions, the crack size and the thickness of the cracked component, The obtained results show that the presence of the composite patch reduces considerably the size of the plastic zone ahead of the crack. The effects of the composite orientation layup (adhesive properties) and the patch thickness on the plastic zone size ahead of repaired cracks were analyzed.

Keywords: crack, elastic-plastic, J integral, patch, plastic zone

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Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: Ethel E. Phiri, Lionel Hartzenberg, Percy Chimwamuromba, Emmanuel Nepolo, Jens Kossmann, James R. Lloyd

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Orphan crops are underresearched and underutilized food plant species that have not been categorized as major food crops, but have the potential to be economically and agronomically significant. They have been documented to have the ability to tolerate extreme environmental conditions. However, limited research has been conducted to uncover their potential as food crop species. The New Partnership for Africa’s Development (NEPAD) has classified Marama bean, Tylosema esculentum, as an orphan crop. The plant is one of the 101 African orphan crops that must have their genomes sequenced, assembled, and annotated in the foreseeable future. Marama bean is a perennial leguminous plant that primarily grows in poor, arid soils in southern Africa. The plants produce large tubers that can weigh as much as 200kg. While the foliage provides fodder, the tuber is carbohydrate rich and is a staple food source for rural communities in Namibia. Also, the edible seeds are protein- and oil-rich. Marama Bean plants respond rapidly to increased temperatures and severe water scarcity without extreme consequences. Advances in molecular biology and biotechnology have made it possible to effectively transfer technologies between model- and major crops to orphan crops. In this research, the aim was to assemble the first transcriptomic analysis of Marama Bean RNA-sequence data. Many model plant species have had their genomes sequenced and their transcriptomes assembled. Therefore the availability of transcriptome data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this research will eventually evaluate the potential use of Marama Bean as a crop species to improve its value in agronomy. data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this researc will eventually evaluate the potential use of Marama bean as a crop species to improve its value in agronomy.

Keywords: 101 African orphan crops, RNA-Seq, Tylosema esculentum, underutilised crop plants

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Authors: Wissem Tighidet, Faïçal Naït Bouda, Moussa Allouche

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The numerical study of the Fluid-Structure Interaction (FSI) in a partially filled flexible tank submitted to a horizontal harmonic excitation motion. It is investigated by using two-way Fluid-Structure Interaction (FSI) in a flexible tank by Coupling between the Transient Structural (Mechanical) and Fluid Flow (Fluent) in ANSYS-Workbench Student version. The Arbitrary Lagrangian-Eulerian (ALE) formulation is adopted to solve with the finite volume method, the Navier-Stokes equations in two phases in a moving domain. The Volume of Fluid (VOF) method is applied to track the free surface. However, the equations of the dynamics of the structure are solved with the finite element method assuming a linear elastic behavior. To conclude, the Fluid-Structure Interaction (IFS) has a vital role in the analysis of the dynamic behavior of the rectangular tank. The results indicate that the flexibility of the tank walls has a significant impact on the amplitude of tank sloshing and the deformation of the free surface as well as the effect of liquid sloshing on wall deformation.

Keywords: arbitrary lagrangian-eulerian, fluid-structure interaction, sloshing, volume of fluid

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Authors: Divij Gupta

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Pendular systems have a range of both mathematical and engineering applications, ranging from modelling the behaviour of a continuous mass-density rope to utilisation as Tuned Mass Dampers (TMD). Thus, it is of interest to study the differential equations governing the motion of such systems. Here we attempt to generalise these equations of motion for the plane compound pendulum with a finite number of N point masses. A Lagrangian approach is taken, and we attempt to find the generalised form for the Euler-Lagrange equations of motion for the i-th bob of the N -bob pendulum. The co-ordinates are parameterized as angular quantities to reduce the number of degrees of freedom from 2N to N to simplify the form of the equations. We analyse the form of these equations up to N = 4 to determine the general form of the equation. We also develop a MATLAB program to compute a solution to the system for a given input value of N and a given set of initial conditions.

Keywords: classical mechanics, differential equation, lagrangian analysis, pendulum

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Salman Iqbal

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Knowledge management (KM) literature has mainly focused on the antecedents of KM. The purpose of this study is to investigate the effect of specific human resource management (HRM) practices on employee knowledge sharing and its outcome as individual knowledge capability. Based on previous literature, a model is proposed for the study and hypotheses are formulated. The cross-sectional dataset comes from a sample of 19 knowledge intensive firms (KIFs). This study has run an item parceling technique followed by Confirmatory Factor Analysis (CFA) on the latent constructs of the research model. Employees’ collaboration and their interpersonal trust can help to improve their knowledge sharing behaviour and knowledge capability within organisations. This study suggests that in future, by using a larger sample, better statistical insight is possible. The findings of this study are beneficial for scholars, policy makers and practitioners. The empirical results of this study are entirely based on employees’ perceptions and make a significant research contribution, given there is a dearth of empirical research focusing on the subcontinent.

Keywords: employees’ collaboration, individual knowledge capability, knowledge sharing, monetary rewards, structural equation modelling

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Authors: Naseem M. Twaissi

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Medical professionals play an important role in society, and in general, they care more about their patients than about their personal well-being. They need to take a professional approach to maintain a work-life balance. Through a collection of primary data from 1020 medical professionals and the application of relevant statistical tools, this paper explores the pressures on medical professionals with reference to their work-life balance. This study highlights how hospital management, in addition to economic reasons, needs to identify variables to enhance the work-life balance of medical professionals so that quality healthcare facilities may be provided to the citizens of Jordan. Results indicate that formulation and implementation of policies for enhancing work-life balance together with career and retention plans for medical professionals would enhance the performance of hospitals and the quality of health care in Jordan, leading to greater societal well-being.

Keywords: work life balance, job environment, job satisfaction, employee well-being, stress, hospital industry

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Authors: Farouq Sule Garo, Yahaya Yusuf

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

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

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Authors: James E. Mahon

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This paper addresses the important question in the philosophy of lies and deception of whether all lying requires an intention to deceive. It does by examining a recent attempt by two philosophers to argue that Immanuel Kant abandoned the view that all lying requires an intention to deceive, in order to be able to claim that lying to God was possible. Ian Proops and Roy Sorensen have recently argued that although Kant always held that it was impossible for anyone to intend to deceive God, late in his life he came to believe that it was possible to lie to God. Kant came to believe that this was possible, they argue, because Kant came to believe that lying is not always deceptive, and that it was possible to tell non-deceptive lies, including non-deceptive lies to God. In this paper their arguments will be broken down and analyzed. Based on a close textual reading of the published works and the Kant’s lectures on ethics, it will be argued that Proops and Sorensen are wrong about what Kant believed about lying in general and lying to God in particular. This paper concludes that Kant never did abandon the Deceptionist position that all lying requires an intention to deceive.

Keywords: Kant, lie, deception, intention, God, ethics, belief, assertion

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Authors: Onesmus Nyaude

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The aim of the study was to investigate participants’ views on how corruption can be combated to enhance learner academic achievement. The study was undertaken on three select public secondary institutions in Zimbabwe. This study also focuses on exploring the various views of educators; parents and the learners on the role played by corruption in perpetuating the seemingly existing learner academic achievement disparities in various educational institutions. The study further interrogates and examines the nexus between the prevalence of corruption in schools and the subsequent influence on the academic achievement of learners. Corruption is considered a form of social injustice; hence in Zimbabwe, the general consensus is that it is perceived rife to the extent that it is overtaking the traditional factors that contributed to the poor academic achievement of learners. Coupled to this, have been the issue of gross abuse of power and some malpractices emanating from concealment of essential and official transactions in the conduct of business. Through proposing robust anti-corruption mechanisms, teaching and learning resources poured in schools would be put into good use. This would prevent the unlawful diversion and misappropriation of the resources in question which has always been the culture. This study is of paramount significance to curriculum planners, teachers, parents, and learners. The study was informed by the interpretive paradigm; thus qualitative research approaches were used. Both probability and non-probability sampling techniques were adopted in ‘site and participants’ selection. A representative sample of (150) participants was used. The study found that the majority of the participants perceived corruption as a social problem and a human right threat affecting the quality of teaching and learning processes in the education sector. It was established that corruption prevalence within institutions is as a result of the perpetual weakening of ethical values and other variables linked to upholding of ‘Ubuntu’ among general citizenry. It was further established that greediness and weak systems are major causes of rampant corruption within institutions of higher learning and are manifesting through abuse of power, bribery, misappropriation and embezzlement of material and financial resources. Therefore, there is great need to collectively address the problem of corruption in educational institutions and society at large. The study additionally concludes that successful combating of corruption will promote successful moral development of students as well as safeguarding their human rights entitlements. The study recommends the adoption of principles of good corporate governance within educational institutions in order to successfully curb corruption. The study further recommends the intensification of interventionist strategies and strengthening of systems in educational institutions as well as regular audits to overcome the problem associated with rampant corruption cases.

Keywords: academic achievement, combating, corruption, good corporate governance, qualitative study

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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