Search results for: Wiener model

Authors: Alejandro Molina-Villegas, Silvia Fernández-Sabido, Eduardo Mendoza-Vargas, Fátima Miranda-Pestaña

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The automatic detection of indigenous languages ​​in digital texts is essential to promote their inclusion in digital media. Underrepresented languages, such as Maya, are often excluded from language detection tools like Google’s language-detection library, LANGDETECT. This study addresses these limitations by developing a hybrid language detection solution that accurately distinguishes Maya (YUA) from Spanish (ES). Two strategies are employed: the first focuses on creating a profile for the Maya language within the LANGDETECT library, while the second involves training a Naive Bayes classification model with two categories, YUA and ES. The process includes comprehensive data preprocessing steps, such as cleaning, normalization, tokenization, and n-gram counting, applied to text samples collected from various sources, including articles from La Jornada Maya, a major newspaper in Mexico and the only media outlet that includes a Maya section. After the training phase, a portion of the data is used to create the YUA profile within LANGDETECT, which achieves an accuracy rate above 95% in identifying the Maya language during testing. Additionally, the Naive Bayes classifier, trained and tested on the same database, achieves an accuracy close to 98% in distinguishing between Maya and Spanish, with further validation through F1 score, recall, and logarithmic scoring, without signs of overfitting. This strategy, which combines the LANGDETECT profile with a Naive Bayes model, highlights an adaptable framework that can be extended to other underrepresented languages in future research. This fills a gap in Natural Language Processing and supports the preservation and revitalization of these languages.

Keywords: indigenous languages, language detection, Maya language, Naive Bayes classifier, natural language processing, low-resource languages

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Authors: Walter Vera-Ortega, Idoia Busnadiego, Sam J. Wilson

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Introduction: Human Immunodeficiency Virus type 1 (HIV-1) is responsible for the acquired immunodeficiency syndrome (AIDS), a leading cause of death worldwide infecting millions of people each year. Despite intensive research in vaccine development, therapies against HIV-1 infection are not curative, and the huge genetic variability of HIV-1 challenges to drug development. Current animal models for HIV-1 research present important limitations, impairing the progress of in vivo approaches. Macaques require a CD8+ depletion to progress to AIDS, and the maintenance cost is high. Mice are a cheaper alternative but need to be 'humanized,' and breeding is not possible. The development of an HIV-1 clone able to replicate in mice is a challenging proposal. The lack of human co-factors in mice impedes the function of the HIV-1 accessory proteins, Tat and Rev, hampering HIV-1 replication. However, Tat and Rev function can be replaced by constitutive/chimeric promoters, codon-optimized proteins and the constitutive transport element (CTE), generating a novel HIV-1 clone able to replicate in mice without disrupting the amino acid sequence of the virus. By minimally manipulating the genomic 'identity' of the virus, we propose the generation of an HIV-1 clone able to replicate in mice to assist in antiviral drug development. Methods: i) Plasmid construction: The chimeric promoters and CTE copies were cloned by PCR using lentiviral vectors as templates (pCGSW and pSIV-MPCG). Tat mutants were generated from replication competent HIV-1 plasmids (NHG and NL4-3). ii) Infectivity assays: Retroviral vectors were generated by transfection of human 293T cells and murine NIH 3T3 cells. Virus titre was determined by flow cytometry measuring GFP expression. Human B-cells (AA-2) and Hela cells (TZMbl) were used for infectivity assays. iii) Protein analysis: Tat protein expression was determined by TZMbl assay and HIV-1 capsid by western blot. Results: We have determined that NIH 3T3 cells are able to generate HIV-1 particles. However, they are not infectious, and further analysis needs to be performed. Codon-optimized HIV-1 constructs are efficiently made in 293T cells in a Tat and Rev independent manner and capable of packaging a competent genome in trans. CSGW is capable of generating infectious particles in the absence of Tat and Rev in human cells when 4 copies of the CTE are placed preceding the 3’LTR. HIV-1 Tat mutant clones encoding different promoters are functional during the first cycle of replication when Tat is added in trans. Conclusion: Our findings suggest that the development of an HIV-1 Tat-Rev independent clone is challenging but achievable aim. However, further investigations need to be developed prior presenting our HIV-1 clone as a candidate model for research.

Keywords: codon-optimized, constitutive transport element, HIV-1, long terminal repeats, research model

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Authors: John Ayoade

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Radio Frequency Identification (RFID) has gained a lot of popularity in all walks of life due to its usefulness and diverse use of the technology in almost every application. However, there have been some security concerns most especially in regards to how authentic readers and tags can confirm their authenticity before confidential data is exchanged between them. In this paper, Kerberos protocol is adopted for the mutual authentication of RFID system components in order to ensure the secure communication between those components and to realize the authenticity of the communicating components.

Keywords: RFID, security, mutual authentication, Kerberos

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Authors: Asim Alsharif, Christophe Pinna, Hassan Ghadbeigi

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The development of next-generation advanced high strength steels (AHSS) used in the automotive industry requires a better understanding of local deformation and damage development at the scale of their microstructures. This work is focused on dual-phase DP1000 steels and involves micro-mechanical tensile testing inside a scanning electron microscope (SEM) combined with digital image correlation (DIC) to quantify the heterogeneity of deformation in both ferrite and martensite and its evolution up to fracture. Natural features of the microstructure are used for the correlation carried out using Davis LaVision software. Strain localization is observed in both phases with tensile strain values up to 130% and 110% recorded in ferrite and martensite respectively just before final fracture. Damage initiation sites have been observed during deformation in martensite but could not be correlated to local strain values. A finite element (FE) model of the microstructure has then been developed using Abaqus to map stress distributions over representative areas of the microstructure by forcing the model to deform as in the experiment using DIC-measured displacement maps as boundary conditions. A MATLAB code has been developed to automatically mesh the microstructure from SEM images and to map displacement vectors from DIC onto the FE mesh. Results show a correlation of damage initiation at the interface between ferrite and martensite with local principal stress values of about 1700MPa in the martensite phase. Damage in ferrite is now being investigated, and results are expected to bring new insight into damage development in DP steels.

Keywords: advanced high strength steels, digital image correlation, finite element modelling, micro-mechanical testing

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Authors: El Ghazi Safae, Cherkaoui Mounia

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Within companies, emotions have been forgotten as key elements of successful management systems. Seen as factors which disturb judgment, make reckless acts or affect negatively decision-making. Since management systems were influenced by the Taylorist worker image, that made the work regular and plain, and considered employees as executing machines. However, recently, in globalized economy characterized by a variety of uncertainties, emotions are proved as useful elements, even necessary, to attend high-level management. The work of Elton Mayo and Kurt Lewin reveals the importance of emotions. Since then emotions start to attract considerable attention. These studies have shown that emotions influence, directly or indirectly, many organization processes. For example, the quality of interpersonal relationships, job satisfaction, absenteeism, stress, leadership, performance and team commitment. Emotions became fundamental and indispensable to individual yield and so on to management efficiency. The idea that a person potential is associated to Intellectual Intelligence, measured by the IQ as the main factor of social, professional and even sentimental success, was the main problematic that need to be questioned. The literature on emotional intelligence has made clear that success at work does not only depend on intellectual intelligence but also other factors. Several researches investigating emotional intelligence impact on performance showed that emotionally intelligent managers perform more, attain remarkable results, able to achieve organizational objectives, impact the mood of their subordinates and create a friendly work environment. An improvement in the emotional intelligence of managers is therefore linked to the professional development of the organization and not only to the personal development of the manager. In this context, it would be interesting to question the importance of emotional intelligence. Does it impact organizational performance? What is the importance of emotional intelligence and how it impacts organizational performance? The literature highlighted that measurement and conceptualization of emotional intelligence are difficult to define. Efforts to measure emotional intelligence have identified three models that are more prominent: the mixed model, the ability model, and the trait model. The first is considered as cognitive skill, the second relates to the mixing of emotional skills with personality-related aspects and the latter is intertwined with personality traits. But, despite strong claims about the importance of emotional intelligence in the workplace, few studies have empirically examined the impact of emotional intelligence on organizational performance, because even though the concept of performance is at the heart of all evaluation processes of companies and organizations, we observe that performance remains a multidimensional concept and many authors insist about the vagueness that surrounds the concept. Given the above, this article provides an overview of the researches related to emotional intelligence, particularly focusing on studies that investigated the impact of emotional intelligence on organizational performance to contribute to the emotional intelligence literature and highlight its importance and show how it impacts companies’ performance.

Keywords: emotions, performance, intelligence, firms

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Authors: João Coutinho-Rodrigues, Nuno Sousa, Luís Alçada-Almeida

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Transportation infrastructure systems are fundamental in modern society and economy. However, they need modernizing, maintaining, and reinforcing interventions which require large investments. In many countries, accumulated intervention delays arise from aging and intense use, being magnified by financial constraints of the past. The decision problem of managing the renewal of large backlogs is common to several types of important transportation infrastructures (e.g., railways, roads). This problem requires considering financial aspects as well as operational constraints under a multidimensional framework. The present research introduces a linear programming multiobjective model for managing railway infrastructure asset renewal. The model aims at minimizing three objectives: (i) yearly investment peak, by evenly spreading investment throughout multiple years; (ii) total cost, which includes extra maintenance costs incurred from renewal backlogs; (iii) priority delays related to work start postponements on the higher priority railway sections. Operational constraints ensure that passenger and freight services are not excessively delayed from having railway line sections under intervention. Achieving a balanced annual investment plan, without compromising the total financial effort or excessively postponing the execution of the priority works, was the motivation for pursuing the research which is now presented. The methodology, inspired by a real case study and tested with real data, reflects aspects of the practice of an infrastructure management company and is generalizable to different types of infrastructure (e.g., railways, highways). It was conceived for treating renewal interventions in infrastructure assets, which is a railway network may be rails, ballasts, sleepers, etc.; while a section is under intervention, trains must run at reduced speed, causing delays in services. The model cannot, therefore, allow for an accumulation of works on the same line, which may cause excessively large delays. Similarly, the lines do not all have the same socio-economic importance or service intensity, making it is necessary to prioritize the sections to be renewed. The model takes these issues into account, and its output is an optimized works schedule for the renewal project translatable in Gantt charts The infrastructure management company provided all the data for the first test case study and validated the parameterization. This case consists of several sections to be renewed, over 5 years and belonging to 17 lines. A large instance was also generated, reflecting a problem of a size similar to the USA railway network (considered the largest one in the world), so it is not expected that considerably larger problems appear in real life; an average of 25 years backlog and ten years of project horizon was considered. Despite the very large increase in the number of decision variables (200 times as large), the computational time cost did not increase very significantly. It is thus expectable that just about any real-life problem can be treated in a modern computer, regardless of size. The trade-off analysis shows that if the decision maker allows some increase in max yearly investment (i.e., degradation of objective ii), solutions improve considerably in the remaining two objectives.

Keywords: transport infrastructure, asset renewal, railway maintenance, multiobjective modeling

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Bhenjamin Jordan L. Ona

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Weather modification is an act of altering weather systems that catches interest on scientific studies. Cloud seeding is a common form of weather alteration. On the same principle, tropical cyclone mitigation experiment follows the methods of cloud seeding with intensity to account for. This study will present the effects of aerosol to tropical cyclone cloud microphysics and intensity. The framework of Weather Research and Forecasting (WRF) model incorporated with Thompson aerosol-aware scheme is the prime host to support the aerosol-cloud microphysics calculations of cloud condensation nuclei (CCN) ingested into the tropical cyclones before making landfall over the Philippine coast. The coupled microphysical and radiative effects of aerosols will be analyzed using numerical data conditions of Tropical Storm Ketsana (2009), Tropical Storm Washi (2011), and Typhoon Haiyan (2013) associated with varying CCN number concentrations per simulation per typhoon: clean maritime, polluted, and very polluted having 300 cm-3, 1000 cm-3, and 2000 cm-3 aerosol number initial concentrations, respectively. Aerosol species like sulphates, sea salts, black carbon, and organic carbon will be used as cloud nuclei and mineral dust as ice nuclei (IN). To make the study as realistic as possible, investigation during the biomass burning due to forest fire in Indonesia starting October 2015 as Typhoons Mujigae/Kabayan and Koppu/Lando had been seeded with aerosol emissions mainly comprises with black carbon and organic carbon, will be considered. Emission data that will be used is from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The physical mechanism/s of intensification or deintensification of tropical cyclones will be determined after the seeding experiment analyses.

Keywords: aerosol, CCN, IN, tropical cylone

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Authors: Iveta Ludviga, Agita Kalvina

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This paper aims to link together the concepts of job satisfaction, work engagement, trust, job meaningfulness and loyalty to the organisation focusing on specific type of employment–academic jobs. The research investigates the relationships between job satisfaction, work engagement and loyalty as well as the impact of trust and job meaningfulness on the work engagement and loyalty. The survey was conducted in one of the largest Latvian higher education institutions and the sample was drawn from academic staff (n=326). Structured questionnaire with 44 reflective type questions was developed to measure toe constructs. Data was analysed using SPSS and Smart-PLS software. Variance based structural equation modelling (PLS-SEM) technique was used to test the model and to predict the most important factors relevant to employee engagement and loyalty. The first order model included two endogenous constructs (loyalty and intention to stay and recommend, and employee engagement), as well as six exogenous constructs (feeling of fair treatment and trust in management; career growth opportunities; compensation, pay and benefits; management; colleagues; teamwork; and finally job meaningfulness). Job satisfaction was developed as second order construct and both: first and second order models were designed for data analysis. It was found that academics are more engaged than satisfied with their work and main reason for that was found to be job meaningfulness, which is significant predictor for work engagement, but not for job satisfaction. Compensation is not significantly related to work engagement, but only to job satisfaction. Trust was not significantly related neither to engagement, nor to satisfaction, however, it appeared to be significant predictor of loyalty and intentions to stay with the University. This paper revealed academic jobs as specific kind of employment where employees can be more engaged than satisfied and highlighted the specific role of job meaningfulness in the University settings.

Keywords: job satisfaction, job meaningfulness, higher education, work engagement

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Authors: Faris Tarlochan, Siva Mahesh Tangutooru

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Lateral Geniculate Nucleus (LGN) is the relay center in the visual pathway as it receives most of the input information from retinal ganglion cells (RGC) and sends to visual cortex. Low threshold calcium currents (IT) at the membrane are the unique indicator to characterize this firing functionality of the LGN neurons gained by the RGC input. According to the LGN functional requirements such as functional mapping of RGC to LGN, the morphologies of the LGN neurons were developed. During the neurological disorders like glaucoma, the mapping between RGC and LGN is disconnected and hence stimulating LGN electrically using deep brain electrodes can restore the functionalities of LGN. A computational model was developed for simulating the LGN neurons with three predominant morphologies, each representing different functional mapping of RGC to LGN. The firings of action potentials at LGN neuron due to IT were characterized by varying the stimulation parameters, morphological parameters and orientation. A wide range of stimulation parameters (stimulus amplitude, duration and frequency) represents the various strengths of the electrical stimulation with different morphological parameters (soma size, dendrites size and structure). The orientation (0-1800) of LGN neuron with respect to the stimulating electrode represents the angle at which the extracellular deep brain stimulation towards LGN neuron is performed. A reduced dendrite structure was used in the model using Bush–Sejnowski algorithm to decrease the computational time while conserving its input resistance and total surface area. The major finding is that an input potential of 0.4 V is required to produce the action potential in the LGN neuron which is placed at 100 µm distance from the electrode. From this study, it can be concluded that the neuroprostheses under design would need to consider the capability of inducing at least 0.4V to produce action potentials in LGN.

Keywords: Lateral Geniculate Nucleus, visual cortex, finite element, glaucoma, neuroprostheses

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Authors: Tess Hartland, Moitree Banerjee, Sue Churchill, Antonina Pereira, Ian Tyndall, Ruth Lowry

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Background: Sexual health services and policies for middle-aged and older adults are underdeveloped, while global sexually transmitted infections in this age group are on the rise. The Interreg cross-Europe Sexual Health In Over 45s (SHIFT) project aims to increase participation in sexual health services and improve sexual health and wellbeing in people aged over 45, with an additional focus on disadvantaged groups. Methods: A two-pronged mixed-methodology is being used to develop a model for good service provision in sexual health for over 45s. (1) Following PRISMA-ScR guidelines, a scoping review is being conducted, using the databases PsychINFO, Web of Science, ERIC and PubMed. A key search strategy using terms around sexual health, good practice, over 45s and disadvantaged groups. The initial search for literature yielded 7914 results. (2) Surveys (n=1000) based on the Theory of Planned Behaviour are being administered across the UK, Belgium and Netherlands to explore current sexual health knowledge, awareness and attitudes. Expected results: It is expected that sexual health needs and potential gaps in service provision will be identified in order to inform good practice for sexual health services for the target population. Results of the scoping review are being analysed, while focus group and survey data is being gathered. Preliminary analysis of the survey data highlights barriers to access such as limited risk awareness and stigma. All data analysis will be completed by the time of the conference. Discussion: Findings will inform the development of a model to improve sexual health and wellbeing for among over 45s, a population which is often missed in sexual health policy improvement.

Keywords: adult health, disease prevention, health promotion, over 45s, sexual health

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Authors: Natsuki Tsuda, Kiyotaka Obunai, Kazuya Okubo, Hideyuki Tashiro, Yoshinori Yamaji, Hideyuki Kato

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The objective of this study is to investigate the effect of belt stiffness on the performance of the CVT unit, such as the required pulley thrust force and the ratio coverage. The CVT unit consists of the V-grooved pulleys and the rubber CVT belt. The width of the driving pulley groove was controlled by the stepper motor, while that of the driven pulley was controlled by the hydraulic pressure. The generated mechanical power on the motor was transmitted from the driving axis to the driven axis through the CVT unit. The rotational speed and the transmitting torque of both axes were measured by the tachometers and the torque meters attached with these axes, respectively. The transmitted, mechanical power was absorbed by the magnetic powder brake. The thrust force acting on both pulleys and the force between both shafts were measured by the load cell. The back face profile of the rubber CVT belt along with width direction was measured by the 2-dimensional laser displacement meter. This paper found that when the stiffness of the rubber CVT belt in the belt width direction was reduced, the thrust force required for shifting was reduced. Moreover, when the stiffness of the rubber CVT belt in the belt width direction was reduced, the ratio coverage of the CVT unit was reduced. Due to the decrement of stiffness in belt width direction, the excessive concave deformation of belt in pulley groove was confirmed. Because of this excessive concave deformation, apparent wrapping radius of belt would have been reduced. Proposed model could be effectively estimated the difference of ratio coverage due to concave deformation. The proposed model could also be utilized for designing the rubber CVT belt with optimal bending stiffness in width direction.

Keywords: CVT, countinuously variable transmission, rubber, belt stiffness, transmission

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Authors: Murtala Gada Abubakar, Aliyu T. Umar

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A substantial part of Nigeria is part of semi-arid areas of the world, underlain by basement complex (hard) rocks which are very poor in both transmission and storage of appreciable quantity of water. Recently, a growing attention is being paid on the need to develop water resources in these areas largely due to concerns about increasing droughts and the need to maintain water security challenges. While there is ample body of knowledge that captures the hydrological behaviours of the sedimentary part, reported research which unambiguously illustrates water distribution in the basement complex of the Sokoto basin remains sparse. Considering the growing need to meet the water requirements of those living in this region necessitated the call for accurate water balance estimations that can inform a sustainable planning and development to address water security challenges for the area. To meet this task, a one-dimensional soil water balance model was developed and utilised to assess the state of water distribution within the Sokoto basin basement complex using measured meteorological variables and information about different landscapes within the complex. The model simulated the soil water storage and rates of input and output of water in response to climate and irrigation where applicable using data from 2001 to 2010 inclusive. The results revealed areas within the Sokoto basin basement complex that are rich and deficient in groundwater resource. The high potential areas identified includes the fadama, the fractured rocks and the cultivated lands, while the low potential areas are the sealed surfaces and non-fractured rocks. This study concludes that the modelling approach is a useful tool for assessing the hydrological behaviour and for better understanding the water resource availability within a basement complex.

Keywords: basement complex, hydrological processes, Sokoto Basin, water security

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Authors: Ebru Dural, M. Zulfu Asık

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Laminated glass is a kind of safety glass, which is made by 'sandwiching' two glass sheets and a polyvinyl butyral (PVB) interlayer in between them. When the glass is broken, the interlayer in between the glass sheets can stick them together. Because of this property, the hazards of sharp projectiles during natural and man-made disasters reduces. They can be widely applied in building, architecture, automotive, transport industries. Laminated glass can easily undergo large displacements even under their own weight. In order to explain their true behavior, they should be analyzed by using large deflection theory to represent nonlinear behavior. In this study, a nonlinear mathematical model is developed for the analysis of laminated glass cylindrical shell which is free in radial directions and restrained in axial directions. The results will be verified by using the results of the experiment, carried out on laminated glass cylindrical shells. The behavior of laminated composite cylindrical shell can be represented by five partial differential equations. Four of the five equations are used to represent axial displacements and radial displacements and the fifth one for the transverse deflection of the unit. Governing partial differential equations are derived by employing variational principles and minimum potential energy concept. Finite difference method is employed to solve the coupled differential equations. First, they are converted into a system of matrix equations and then iterative procedure is employed. Iterative procedure is necessary since equations are coupled. Problems occurred in getting convergent sequence generated by the employed procedure are overcome by employing variable underrelaxation factor. The procedure developed to solve the differential equations provides not only less storage but also less calculation time, which is a substantial advantage in computational mechanics problems.

Keywords: laminated glass, mathematical model, nonlinear behavior, PVB

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Authors: Dian Guan

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With the wide application of multimedia and the Internet, the development of teaching theories, and the implementation of teaching reforms, many different university English classroom teaching modes have emerged. The university English teaching mode is changing from the traditional teaching mode based on conversation and text to the multimodal English teaching mode containing discussion, pictures, audio, film, etc. Applying university English teaching models is conducive to cultivating lifelong learning skills. In addition, lifelong learning skills can also be called learners' autonomous learning skills. Learners' independent learning ability has a significant impact on English learning. However, many university students, especially art and design students, don't know how to learn individually. When they become university students, their English foundation is a relative deficiency because they always remember the language in a traditional way, which, to a certain extent, neglects the cultivation of English learners' independent ability. As a result, the autonomous learning ability of most university students is not satisfactory. The participants in this study were 60 students and one teacher in their first year at a university in western China. Two observations and interviews were conducted inside and outside the classroom to understand the impact of a multimodal teaching model of university English on students' autonomous learning ability. The results were analyzed, and it was found that the multimodal teaching model of university English significantly affected learners' autonomy. Incorporating classroom presentations and poster exhibitions into multimodal teaching can increase learners' interest in learning and enhance their learning ability outside the classroom. However, further exploration is needed to develop multimodal teaching materials and evaluate multimodal teaching outcomes. Despite the limitations of this study, the study adopts a scientific research method to analyze the impact of the multimodal teaching mode of university English on students' independent learning ability. It puts forward a different outlook for further research on this topic.

Keywords: art university, EFL education, learner autonomy, multimodal pedagogy

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Pratibha Goyal, Nupur Mathur, Anuradha Singh

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Antibiotic resistance is the worldwide threat to human health in this century. Excessive use of antibiotic after their discovery in 1940 makes certain bacteria to become resistant against antibiotics. Most common antibiotic-resistant bacteria include Staphylococcus aureus, Salmonella typhi, E.coli, Klebsiella pneumonia, and Streptococcus pneumonia. Among all Staphylococcus resistant strain called Methicillin-resistant Staphylococcus aureus (MRSA) is responsible for several lives threatening infection in human commonly found in the hospital environment. Our study aimed to isolate bacteriophage against MRSA from the hospital sewage treatment plant and to analyze its efficiency In Vivo in Swiss albino mice model. Sewage sample for the isolation of bacteriophages was collected from SDMH hospital sewage treatment plant in Jaipur. Bacteriophages isolated by the use of enrichment technique and after characterization, isolated phages used to determine phage treatment efficiency in mice. Mice model used to check the safety and suitability of phage application in human need which in turn directly support the use of natural bacteriophage rather than synthetic chemical to kill pathogens. Results show the plaque formation in-vitro and recovery of MRSA infected mice during the experiment. Favorable lytic efficiency determination of MRSA and Salmonella presents a natural way to treat lethal infections caused by Multidrug-resistant bacteria by using their natural host-pathogen relationship.

Keywords: antibiotic resistance, bacteriophages, methicillin resistance Staphylococcus aureus, pathogens, phage therapy, Salmonella typhi

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Authors: Ashish Bhattarai, Bishnu Bhatta, Hem Raj Joshi, Nabin Neupane, Pankaj Yadav

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This report concerns the design, development, and analysis of the combined Darrieus and Savonius wind turbine. Vertical Axis Wind Turbines (VAWT's) are of two type's viz. Darrieus (lift type) and Savonius (drag type). The problem associated with Darrieus is the lack of self-starting while Savonius has low efficiency. There are 3 straight Darrieus blades having the cross-section of NACA(National Advisory Committee of Aeronautics) 0018 placed circumferentially and a helically twisted Savonius blade to get even torque distribution. This unique design allows the use of Savonius as a method of self-starting the wind turbine, which the Darrieus cannot achieve on its own. All the parts of the wind turbine are designed in CAD software, and simulation data were obtained via CFD(Computational Fluid Dynamics) approach. Also, the design was imported to FlashForge Finder to 3D print the wind turbine profile and finally, testing was carried out. The plastic material used for Savonius was ABS(Acrylonitrile Butadiene Styrene) and that for Darrieus was PLA(Polylactic Acid). From the data obtained experimentally, the hybrid VAWT so fabricated has been found to operate at the low cut-in speed of 3 m/s and maximum power output has been found to be 7.5537 watts at the wind speed of 6 m/s. The maximum rpm of the rotor blade is recorded to be 431 rpm(rotation per minute) at the wind velocity of 6 m/s, signifying its potentiality of wind power production. Besides, the data so obtained from both the process when analyzed through graph plots has shown the similar nature slope wise. Also, the difference between the experimental and theoretical data obtained has shown mechanical losses. The objective is to eliminate the need for external motors for self-starting purposes and study the performance of the model. The testing of the model was carried out for different wind velocities.

Keywords: VAWT, Darrieus, Savonius, helical blades, CFD, flash forge finder, ABS, PLA

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Authors: Strahinja Kovačević, Sanja Podunavac-Kuzmanović, Lidija Jevrić, Cristina Prandi, Piermichele Kobauri

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The present study is based on molecular modeling of a series of twelve 5-(4-substituent-phenoxy)-3-methylfuran-2(5H)-one derivatives which have potential of strigolactones mimics toward Striga hermonthica. The first step of the analysis included the calculation of molecular descriptors which numerically describe the structures of the analyzed compounds. The descriptors ALOGP (lipophilicity), AClogS (water solubility) and BBB (blood-brain barrier penetration), served as the input variables in multiple linear regression (MLR) modeling of germination activity toward S. hermonthica. Two MLR models were obtained. The first MLR model contains ALOGP and AClogS descriptors, while the second one is based on these two descriptors plus BBB descriptor. Despite the braking Topliss-Costello rule in the second MLR model, it has much better statistical and cross-validation characteristics than the first one. The ALOGP and AClogS descriptors are often very suitable predictors of the biological activity of many compounds. They are very important descriptors of the biological behavior and availability of a compound in any biological system (i.e. the ability to pass through the cell membranes). BBB descriptor defines the ability of a molecule to pass through the blood-brain barrier. Besides the lipophilicity of a compound, this descriptor carries the information of the molecular bulkiness (its value strongly depends on molecular bulkiness). According to the obtained results of MLR modeling, these three descriptors are considered as very good predictors of germination activity of the analyzed compounds toward S. hermonthica seeds. This article is based upon work from COST Action (FA1206), supported by COST (European Cooperation in Science and Technology).

Keywords: chemometrics, germination activity, molecular modeling, QSAR analysis, strigolactones

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Authors: Jong Yeop Kim, In Kyong Yi, Hyun Jeong Kwak, Sook Young Lee, Sung Yong Park

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Background: Several video laryngoscopes (VLs) were used to facilitate tracheal intubation in the normal and potentially difficult airway, especially by novice personnel. The aim of this study was to compare tracheal intubation performance regarding the time to intubation, glottic view, difficulty, and dental click, by a novice using McGrath VL, Pentax Airway Scope (AWS) and Macintosh laryngoscope in normal and cervical immobilized manikin models. Methods: Thirty-five anesthesia nurses without previous intubation experience were recruited. The participants performed endotracheal intubation in a manikin model at two simulated neck positions (normal and fixed neck via cervical immobilization), using three different devices (McGrath VL, Pentax AWS, and Macintosh direct laryngoscope) at three times each. Performance parameters included intubation time, success rate of intubation, Cormack Lehane laryngoscope grading, dental click, and subjective difficulty score. Results: Intubation time and success rate at the first attempt were not significantly different between the 3 groups in normal airway manikin. In the cervical immobilized manikin, the intubation time was shorter (p = 0.012) and the success rate with the first attempt was significantly higher (p < 0.001) when using McGrath VL and Pentax AWS compared with Macintosh laryngoscope. Both VLs showed less difficulty score (p < 0.001) and more Cormack Lehane grade I (p < 0.001). The incidence of dental clicks was higher with McGrath VL than Macintosh laryngoscope in the normal and cervical immobilized airway (p = 0.005, p < 0.001, respectively). Conclusion: McGrath VL and Pentax AWS resulted in shorter intubation time, higher first attempt success rate, compared with Macintosh laryngoscope by a novice intubator in a cervical immobilized manikin model. McGrath VL could be reduced the risk of dental injury compared with Macintosh laryngoscope in this scenario.

Keywords: intubation, manikin, novice, videolaryngoscope

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Authors: Kerem Rızvanoğlu, Serhat Güney, Emre Kızılkaya, Betül Aydoğan, Ayşegül Boyalı, Onurcan Güden

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This study addresses the re-construction and implementation process of the methodological framework developed to evaluate how locative media applications accompany the urban experiences of international students coming to Istanbul with exchange programs in 2022. The research design was built on a three-stage model. The research team conducted a qualitative questionnaire in the first stage to gain exploratory data. These data were then used to form three persona groups representing the sample by applying cluster analysis. In the second phase, a semi-structured digital diary study was carried out on a gamified task list with a sample selected from the persona groups. This stage proved to be the most difficult to obtaining valid data from the participant group. The research team re-evaluated the design of this second phase to reach the participants who will perform the tasks given by the research team while sharing their momentary city experiences, to ensure the daily data flow for two weeks, and to increase the quality of the obtained data. The final stage, which follows to elaborate on the findings, is the “Walk & Talk,” which is completed with face-to-face and in-depth interviews. It has been seen that the multiple methods used in the research process contribute to the depth and data diversity of the research conducted in the context of urban experience and locative technologies. In addition, by adapting the research design to the experiences of the users included in the sample, the differences and similarities between the initial research design and the research applied are shown.

Keywords: digital diary study, gamification, multi-model research, persona analysis, research design for urban experience, user-centered research, “Walk & Talk”

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Authors: Nendouvhada Livhuwani Portia, Nicole Sibuyi, Kwazikwakhe Gabuza, Adewale Fadaka

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Metabolic dysfunction-associated liver disease (MASLD) is a chronic condition characterized by excessive fat accumulation in the liver, distinct from conditions caused by alcohol, viral hepatitis, or medications. MASLD is often linked with metabolic syndrome, including obesity, diabetes, hyperlipidemia, and hypertriglyceridemia. This disease can progress to metabolic dysfunction-associated steatohepatitis (MASH), marked by liver inflammation and scarring, potentially leading to cirrhosis. However, only 43-44% of patients with steatosis develop MASH, and 7-30% of those with MASH progress to cirrhosis. The exact mechanisms underlying MASLD and its progression remain unclear, and there are currently no specific therapeutic strategies for MASLD/MASH. While anti-obesity and anti-diabetic medications can reduce progression, they do not fully treat or reverse the disease. As an alternative, green-synthesized metal nanoparticles (MNPs) are emerging as potential treatments for liver diseases due to their anti-diabetic, anti-inflammatory, and anti-obesity properties with minimal side effects. MNPs like gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) have been shown to improve metabolic processes by lowering blood glucose, body fat, and inflammation. The study aimed to explore the effects of green-synthesized MNPs on gene expression in an in vitro model of MASH using C3A/HepG2 liver cells. The MASH model was created by exposing these cells to free fatty acids (FFAs) followed by lipopolysaccharide (LPS) to induce inflammation. Cell viability was assessed with the Water-Soluble Tetrazolium (WST)-1 assay, and lipid accumulation was measured using the Oil Red O (ORO) assay. Additionally, mitochondrial membrane potential was assessed by the tetramethyl rhodamine, methyl ester (TMRE) assay, and inflammation was measured with an Enzyme-Linked Immunosorbent Assay (ELISA). The study synthesized AuNPs from Carpobrotus edulis fruit (CeF) and avocado seed (AvoSE) and AgNPs from Salvia africana-lutea (SAL) using optimized conditions. The MNPs were characterized by UV-Vis spectrophotometry and Dynamic Light Scattering (DLS). The nanoparticles were tested at various concentrations for their impact on the C3A/HepG2-induced MASH model. Among the MNPs tested, AvoSE-AuNPs showed the most promise. They reduced cell proliferation and intracellular lipid content more effectively than CeFE-AuNPs and SAL-AgNPs. Molecular analysis using real-time polymerase chain reaction revealed that AvoSE-AuNPs could potentially reverse MASH effects by reducing the expression of key pro-inflammatory and metabolic genes, including tumor necrosis factor-alpha (TNF-α), Fas cell surface death receptor (FAS), Peroxisome proliferator-activated receptor (PPAR)-α, PPAR-γ, and Sterol regulatory element-binding protein (SREBPF)-1. Further research is needed to confirm the molecular mechanisms behind the effects of these MNPs and to identify the specific phytochemicals responsible for their synthesis and bioactivities.

Keywords: gold nanoparticles, green nanotechnology, metal nanoparticles, obesity

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Authors: Hachaichi Mohamed Nour El-Islem, Baouni Tahar

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Environmental sustainability rather than a trans-disciplinary and a scientific issue, is the main problem that characterizes all modern cities nowadays. In developing countries, this concern is expressed in a plethora of critical urban ills: traffic congestion, air pollution, noise, urban decay, increase in energy consumption and CO₂ emissions which blemish cities’ landscape and might threaten citizens’ health and welfare. As in the same manner as developing world cities, the rapid growth of Algiers’ human population and increasing in city scale phenomena lead eventually to increase in daily trips, energy consumption and CO₂ emissions. In addition, the lack of proper and sustainable planning of the city’s infrastructure is one of the most relevant issues from which Algiers suffers. The aim of this contribution is to estimate the carbon deficit of the City of Algiers, Algeria, using the Ecological Footprint Model (carbon footprint). In order to achieve this goal, the amount of CO₂ from fuel combustion has been calculated and aggregated into five sectors (agriculture, industry, residential, tertiary and transportation); as well, Algiers’ biocapacity (CO₂ uptake land) has been calculated to determine the ecological overshoot. This study shows that Algiers’ transport system is not sustainable and is generating more than 50% of Algiers total carbon footprint which cannot be sequestered by the local forest land. The aim of this research is to show that the Carbon Footprint Assessment might be a relevant indicator to design sustainable strategies/policies striving to reduce CO₂ by setting in motion the energy consumption in the transportation sector and reducing the use of fossil fuels as the main energy input.

Keywords: biocapacity, carbon footprint, ecological footprint assessment, energy consumption

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Authors: Abdulmohsen Alruwaili

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A partial differential system featuring momentum and energy balance is often used to describe simulations of flow initiation and thermal shifting in boundary layers. The buoyancy force in terms of temperature is factored in the momentum balance equation. Buoyancy force causes the flow quantity to fluctuate along the streamwise direction 𝑋; therefore, the problem can be, to our best knowledge, analyzed through nonsimilar modeling. In this analysis, a nonsimilar model is evolved for radiative mixed convection of a magnetized power-law nanoliquid flow on top of a vertical plate installed in a stationary fluid. The upward linear stretching initiated the flow in the vertical direction. Assuming nanofluids are composite of copper (Cu) and alumina (Al₂O₃) nanoparticles, the viscous dissipation in this case is negligible. The nonsimilar system is dealt with analytically by local nonsimilarity (LNS) via numerical algorithm bvp4c. Surface temperature and flow field are shown visually in relation to factors like mixed convection, magnetic field strength, nanoparticle volume fraction, radiation parameters, and Prandtl number. The repercussions of magnetic and mixed convection parameters on the rate of energy transfer and friction coefficient are represented in tabular forms. The results obtained are compared to the published literature. It is found that the existence of nanoparticles significantly improves the temperature profile of considered nanoliquid. It is also observed that when the estimates of the magnetic parameter increase, the velocity profile decreases. Enhancement in nanoparticle concentration and mixed convection parameter improves the velocity profile.

Keywords: nanofluid, power law model, mixed convection, thermal radiation

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Authors: Akanimo Emene, Robert Edyvean

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In developing countries, a low cost method of wastewater treatment is highly recommended. Adsorption is an efficient and economically viable treatment process for wastewater. The utilisation of this process is based on the understanding of the relationship between the growth environment and the metal capacity of the biomaterial. Luffa cylindrica (LC), a plant material, was used as an adsorbent in adsorption design system of heavy metals. The chemically modified LC was used to adsorb heavy metals ions, lead and cadmium, from aqueous environmental solution at varying experimental conditions. Experimental factors, adsorption time, initial metal ion concentration, ionic strength and pH of solution were studied. The chemical nature and surface area of the tissues adsorbing heavy metals in LC biosorption systems were characterised by using electron microscopy and infra-red spectroscopy. It showed an increase in the surface area and improved adhesion capacity after chemical treatment. Metal speciation of the metal ions showed the binary interaction between the ions and the LC surface as the pH increases. Maximum adsorption was shown between pH 5 and pH 6. The ionic strength of the metal ion solution has an effect on the adsorption capacity based on the surface charge and the availability of the adsorption sites on the LC. The nature of the metal-surface complexes formed as a result of the experimental data were analysed with kinetic and isotherm models. The pseudo second order kinetic model and the two-site Langmuir isotherm model showed the best fit. Through the understanding of this process, there will be an opportunity to provide an alternative method for water purification. This will be provide an option, for when expensive water treatment technologies are not viable in developing countries.

Keywords: adsorption, luffa cylindrica, metal-surface complexes, pH

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Sarah J. C. Slabbert

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The uptake of research as part of the diffusion or adoption of innovation by practitioners, whether individuals or organisations, has been a popular topic in agricultural development studies for many decades. In the classical, linear model of innovation theory, the innovation originates from an expert source such as a state-supported research organisation or academic institution. The changing context of agriculture led to the development of the agricultural innovation systems model, which recognizes innovation as a complex interaction between individuals and organisations, which include private industry and collective action organisations. In terms of this model, an innovation can be developed and adopted without any input or intervention from a state or parastatal research organisation. This evolution in the diffusion of agricultural innovation has put forward new challenges for state or parastatal research organisations, which have to demonstrate the impact of their research to the legislature or a regulatory authority: Unless the organisation and the research it produces cross the knowledge paths of the intended audience, there will be no awareness, no uptake and certainly no impact. It is therefore critical for such a research organisation to base its communication strategy on a thorough understanding of the knowledge needs, information sources and knowledge networks of the intended target audience. In 2016, the South African Water Research Commission (WRC) commissioned a study to investigate the knowledge needs, information sources and knowledge networks of Water User Associations and commercial irrigators with the aim of improving uptake of its research on efficient water use in irrigation. The first phase of the study comprised face-to-face interviews with the CEOs and Board Chairs of four Water User Associations along the Orange River in South Africa, and 36 commercial irrigation farmers from the same four irrigation schemes. Intermediaries who act as knowledge conduits to the Water User Associations and the irrigators were identified and 20 of them were subsequently interviewed telephonically. The study found that irrigators interact regularly with grower organisations such as SATI (South African Table Grape Industry) and SAPPA (South African Pecan Nut Association) and that they perceive these organisations as credible, trustworthy and reliable, within their limitations. State and parastatal research institutions, on the other hand, are associated with a range of negative attributes. As a result, the awareness of, and interest in, the WRC and its research on water use efficiency in irrigated agriculture are low. The findings suggest that a communication strategy that involves collaboration with these grower organisations would empower the WRC to participate much more efficiently and with greater impact in agricultural innovation networks. The paper will elaborate on the findings and discuss partnering frameworks and opportunities to manage perceptions and uptake.

Keywords: agricultural innovation systems, communication strategy, diffusion of innovation, irrigated agriculture, knowledge paths, research organisations, target audiences, water use efficiency

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Manamba Epaphra

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This paper analyzes the causal effect between domestic private investment, public investment, foreign direct investment and economic growth in Tanzania during the 1970-2014 period. The modified neo-classical growth model that includes control variables such as trade liberalization, life expectancy and macroeconomic stability proxied by inflation is used to estimate the impact of investment on economic growth. Also, the economic growth models based on Phetsavong and Ichihashi (2012), and Le and Suruga (2005) are used to estimate the crowding out effect of public investment on private domestic investment on one hand and foreign direct investment on the other hand. A correlation test is applied to check the correlation among independent variables, and the results show that there is very low correlation suggesting that multicollinearity is not a serious problem. Moreover, the diagnostic tests including RESET regression errors specification test, Breusch-Godfrey serial correlation LM test, Jacque-Bera-normality test and white heteroskedasticity test reveal that the model has no signs of misspecification and that, the residuals are serially uncorrelated, normally distributed and homoskedastic. Generally, the empirical results show that the domestic private investment plays an important role in economic growth in Tanzania. FDI also tends to affect growth positively, while control variables such as high population growth and inflation appear to harm economic growth. Results also reveal that control variables such as trade openness and life expectancy improvement tend to increase real GDP growth. Moreover, a revealed negative, albeit weak, association between public and private investment suggests that the positive effect of domestic private investment on economic growth reduces when public investment-to-GDP ratio exceeds 8-10 percent. Thus, there is a great need for promoting domestic saving so as to encourage domestic investment for economic growth.

Keywords: FDI, public investment, domestic private investment, crowding out effect, economic growth

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Authors: Suhib A. Abu-Seini, Kyung-Doo Kim

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A heat loss model for ATLAS facility was introduced using SPACE code predefined correlations and various dialing factors. As all previous simulations were carried out using a heat loss free input; the facility was considered to be completely insulated and the core power was reduced by the experimentally measured values of heat loss to compensate to the account for the loss of heat, this study will consider heat loss throughout the simulation. The new heat loss model will be affecting SPACE code simulation as heat being leaked out of the system throughout a transient will alter many parameters corresponding to temperature and temperature difference. For that, a Station Blackout followed by a multiple Steam Generator Tube Rupture accident will be simulated using both the insulated system approach and the newly introduced heat loss input of the steady state. Major parameters such as system temperatures, pressure values, and flow rates to be put into comparison and various analysis will be suggested upon it as the experimental values will not be the reference to validate the expected outcome. This study will not only show the significance of heat loss consideration in the processes of prevention and mitigation of various incidents, design basis and beyond accidents as it will give a detailed behavior of ATLAS facility during both processes of steady state and major transient, but will also present a verification of how credible the data acquired of ATLAS are; since heat loss values for steady state were already mismatched between SPACE simulation results and ATLAS data acquiring system. Acknowledgement- This work was supported by the Korean institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea.

Keywords: ATLAS, heat loss, simulation, SPACE, station blackout, steam generator tube rupture, verification

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