Search results for: prediction of publications

Authors: Maria F. Seran

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In second language interaction, an EFL student always carries his knowledge of targeted language and sometimes gets influenced by his first language cultures which makes him transfer his utterances from the first language to the second language. The influence of L1 cultures somehow can lead to face-threatening act when it comes to responding on speech act, for instance, compliment. A speaker praises a compliment to show gratitude, and in return, he expects for compliment respond uttered by the hearer. While Western people use more acceptance continuum on compliment response, Indonesians utter more denial continuum which can somehow put the speakers into a face-threating situation and offense. This study investigated compliment response employed by EFL students and English native speakers. The study was distinct as none compliment response studies had been conducted to compare the compliment response between English native speakers and two different Indonesian EFL proficiency groups in which this research sought to meet this need. This study was significant for EFL teachers because it gave insight on cross-cultural understanding and brought pedagogical implication on explicit pragmatic instruction. Two research questions were set, 1. How do Indonesian EFL students and English native speakers respond compliments? 2. Is there any correlation between Indonesia EFL students’ proficiency and their compliment response use in English? The study involved three groups of participants; 5 English native speakers, 10 high-proficiency and 10 low-proficiency Indonesian EFL university students. The research instruments used in this study were as follows, an online TOEFL prediction test, focusing on grammar skill which was modified from Barron TOEFL exercise test, and a discourse completion task (DCT), consisting of 10 compliment respond items. Based on the research invitation, 20 second-year university students majoring in English education at Widya Mandira Catholic University, Kupang, East Nusa Tenggara, Indonesia who willingly participated in the research took the TOEFL prediction test online from the link provided. Students who achieved score 75-100 in test were categorized as high-proficiency students, while, students who attained score below 74 were considered as low-proficiency students. Then, the DCT survey was administered to these EFL groups and the native speaker group. Participants’ responses were coded and analyzed using categories of compliment response framework proposed by Tran. The study found out that 5 native speakers applied more compliment upgrades and appreciation token in compliment response, whereas, Indonesian EFL students combined some compliment response strategies in their utterance, such as, appreciation token, return and compliment downgrade. There is no correlation between students’ proficiency level and their CR responds as most EFL students in both groups produced less varied compliment responses and only 4 Indonesian high-proficiency students uttered more varied and were similar to the native speakers. The combination strategies used by EFL students can be explained as the influence of pragmatic transfer from L1 to L2; therefore, EFL teachers should explicitly teach more compliment response strategies to raise students’ awareness on English culture and elaborate their speaking to be more competence as close to native speakers as possible.

Keywords: compliment response, English native speakers, Indonesian EFL students, speech acts

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Authors: Ihsane Modhaffar

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Fiber orientation is essential for the physical properties of composite materials. The theoretical parameters of a given reinforcement are usually known and widely used to predict the behavior of the material. In this work, we propose an image processing approach to estimate true principal directions and fiber orientation during injection molding processes of short fiber reinforced thermoplastics. Generally, a group of fibers are described in terms of probability distribution function or orientation tensor. Numerical techniques for the prediction of fiber orientation are also considered for concentrated situations. The flow was considered to be incompressible, and behave as Newtonian fluid containing suspensions of short-fibers. The governing equations, of this problem are: the continuity, the momentum and the energy. The obtained results were compared to available experimental findings. A good agreement between the numerical results and the experimental data was achieved.

Keywords: injection, composites, short-fiber reinforced thermoplastics, fiber orientation, incompressible fluid, numerical simulation

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Mariam Vardiashvili

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The economic value of the asset impairment process is quite large. Impairment reflects the reduction of future economic benefits or service potentials itemized in the asset. The assets owned by public sector entities bring economic benefits or are used for delivery of the free-of-charge services. Consequently, they are classified as cash-generating and non-cash-generating assets. IPSAS 21 - Impairment of non-cash-generating assets, and IPSAS 26 - Impairment of cash-generating assets, have been designed considering this specificity.  When measuring impairment of assets, it is important to select the relevant methods. For measurement of the impaired Non-Cash-Generating Assets, IPSAS 21 recommends three methods: Depreciated Replacement Cost Approach, Restoration Cost Approach, and  Service Units Approach. Impairment of Value in Use of Cash-Generating Assets (according to IPSAS 26) is measured by discounted value of the money sources to be received in future. Value in use of the cash-generating asserts (as per IPSAS 26) is measured by the discounted value of the money sources to be received in the future. The article provides classification of the assets in the public sector  as non-cash-generating assets and cash-generating assets and, deals also with the factors which should be considered when evaluating  impairment of assets. An essence of impairment of the non-financial assets and the methods of measurement thereof evaluation are formulated according to IPSAS 21 and IPSAS 26. The main emphasis is put on different methods of measurement of the value in use of the impaired Cash-Generating Assets and Non-Cash-Generation Assets and the methods of their selection. The traditional and the expected cash flow approaches for calculation of the discounted value are reviewed. The article also discusses the issues of recognition of impairment loss and its reflection in the financial reporting. The article concludes that despite a functional purpose of the impaired asset, whichever method is used for measuring the asset, presentation of realistic information regarding the value of the assets should be ensured in the financial reporting. In the theoretical development of the issue, the methods of scientific abstraction, analysis and synthesis were used. The research was carried out with a systemic approach. The research process uses international standards of accounting, theoretical researches and publications of Georgian and foreign scientists.

Keywords: cash-generating assets, non-cash-generating assets, recoverable (usable restorative) value, value of use

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Authors: Diego Tancara, Raul Coto, Ariel Norambuena, Hoseein T. Dinani, Felipe Fanchini

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Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models.

Keywords: quantum, machine learning, kernel, non-markovianity

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Authors: Sarah Nasser, Anne Moreau, Alain Hedoux, Romain Jeantet, Guillaume Delaplace

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Background: Spray dryed powders containing some caseins are commonly produced in dairy industry. It is widely admitted that the structure of casein evolves during powder storage, inducing a loss of solubility. However few studies evaluate accurately the destabilization mechanisms at molecular and mesoscopic level, in particular for Native Phospho Casein powder (NPC). Consequently, at the state of the art, it is very difficult to assess which secondary structure change or crosslinks initiate insolubility during storage. To address this issue, controlled ageing conditions have been applied to a NPC powder (which was obtained by spray drying a concentrate containing a higher content of casein (90%), whey protein (8%) and lactose (few %)). Evolution of structure and loss of solubility, with the effects of temperature and time of storage were systematically reported. Methods: FTIR spectroscopy, Raman and Circular Dichroism were used to monitor changes of secondary structure in dry powder and in solution after rehydration. Besides, proteomic tools and electrophoresis have been performed after varying storage conditions for evaluating aggregation and post translational modifications, like lactosylation or phosphorylation. Finally, Tof Sims and MEB were used to follow in parallel evolution of structure in surface and skin formation due to storage. Results + conclusion: These results highlight the important role of storage temperature in the stability of NPC. It is shown that this is not lactosylation at the heart of formation of aggregates, as advanced in others publications This is almost the rise of multitude post translational modifications (chemical cross link), added to disulphide bridges (physical cross link) wich contribute to the destabilisation of structure and aggregation of casein. A relative quantification of each kind of cross link, source of aggregates, is proposed. In addition, it has been proved that migration of lipids and formation of skin in surface during the ageing also explains the evolution of structure casein and thus the alterations of functional properties of NPC powder.

Keywords: casein, cross link, powder, storage

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Maxim A. Kadatskiy, Konstantin V. Khishchenko

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Iron alloys are widespread components in various types of structural materials which are exposed to intensive thermal and mechanical loads. Various quantum-statistical cell models with the approximation of self-consistent field can be used for the prediction of the behavior of these materials under extreme conditions. The application of these models is even more valid, the higher the temperature and the density of matter. Results of Hugoniot calculation for iron alloys in the framework of three quantum-statistical (the Thomas–Fermi, the Thomas–Fermi with quantum and exchange corrections and the Hartree–Fock–Slater) models are presented. Results of quantum-statistical calculations are compared with results from other reliable models and available experimental data. It is revealed a good agreement between results of calculation and experimental data for terra pascal pressures. Advantages and disadvantages of this approach are shown.

Keywords: alloy, Hugoniot, iron, terapascal pressure

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Authors: Teng Li, Kamran Mohseni

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This paper presents an inviscid regularization technique that is capable of regularizing the shocks and sharp interfaces simultaneously in the shock-interface interaction simulations. The direct numerical simulation of flows involving shocks has been investigated for many years and a lot of numerical methods were developed to capture the shocks. However, most of these methods rely on the numerical dissipation to regularize the shocks. Moreover, in high Reynolds number flows, the nonlinear terms in hyperbolic Partial Differential Equations (PDE) dominates, constantly generating small scale features. This makes direct numerical simulation of shocks even harder. The same difficulty happens in two-phase flow with sharp interfaces where the nonlinear terms in the governing equations keep sharpening the interfaces to discontinuities. The main idea of the proposed technique is to average out the small scales that is below the resolution (observable scale) of the computational grid by filtering the convective velocity in the nonlinear terms in the governing PDE. This technique is named “observable method” and it results in a set of hyperbolic equations called observable equations, namely, observable Navier-Stokes or Euler equations. The observable method has been applied to the flow simulations involving shocks, turbulence, and two-phase flows, and the results are promising. In the current paper, the observable method is examined on the performance of regularizing shocks and interfaces at the same time in shock-interface interaction problems. Bubble-shock interactions and Richtmyer-Meshkov instability are particularly chosen to be studied. Observable Euler equations will be numerically solved with pseudo-spectral discretization in space and third order Total Variation Diminishing (TVD) Runge Kutta method in time. Results are presented and compared with existing publications. The interface acceleration and deformation and shock reflection are particularly examined.

Keywords: compressible flow simulation, inviscid regularization, Richtmyer-Meshkov instability, shock-bubble interactions.

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Authors: Jevonte Abioye, Dylan Savary

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The aim of this research is to present a disaggregated geostatistical analysis of the subnational provincial trends of child mortality variation in Zimbabwe from a child health policy perspective. Soon after gaining independence in 1980, the government embarked on efforts towards promoting equitable health care, namely through the provision of primary health care. Government intervention programmes brought hope and promise, but achieving equity in primary health care coverage was hindered by previous existing disparities in maternal health care disproportionately concentrated in urban settings to the detriment of rural communities. The article highlights policies and programs adopted by the government during the millennium development goals period between 1990-2015 as a response to the inequities that characterised the country’s maternal health care. A longitudinal comparative method for a spatial variation on child mortality rates across provinces is developed based on geostatistical analysis. Cross-sectional and time-series data was extracted from the World Health Organisation (WHO) global health observatory data repository, demographic health survey reports, and previous academic and technical publications. Results suggest that although health care policy was uniform across provinces, not all provinces received the same antenatal and perinatal services. Accordingly, provincial rates of child mortality growth between 1994 and 2015 varied significantly. Evidence on the trends of child mortality rates and maternal health policies in Zimbabwe can be valuable for public child health policy planning and public service delivery design both in Zimbabwe and across developing countries pursuing the sustainable development agenda.

Keywords: antenatal care, perinatal care, infant mortality rate, neonatal mortality rate, under-five mortality rate, millennium development goals, sustainable development agenda

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Authors: Raluca Ana Maria Viziteu, Anna Prudnikova

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Industrial control systems (ICS) have received much attention in recent years due to the convergence of information technology (IT) and operational technology (OT) that has increased the interdependence of safety and security issues to be considered. These issues require ICS-tailored solutions. That led to the need to creation of a methodology for supporting ICS device manufacturers and system integrators in carrying out threat modeling of embedded ICS devices in a way that guarantees the quality of the identified threats and minimizes subjectivity in the threat identification process. To research, the possibility of creating such a methodology, a set of existing standards, regulations, papers, and publications related to threat modeling in the ICS sector and other sectors was reviewed to identify various existing methodologies and methods used in threat modeling. Furthermore, the most popular ones were tested in an exploratory phase on a specific PLC device. The outcome of this exploratory phase has been used as a basis for defining specific characteristics of ICS embedded devices and their deployment scenarios, identifying the factors that introduce subjectivity in the threat modeling process of such devices, and defining metrics for evaluating the minimum quality requirements of identified threats associated to the deployment of the devices in existing infrastructures. Furthermore, the threat modeling methodology was created based on the previous steps' results. The usability of the methodology was evaluated through a set of standardized threat modeling requirements and a standardized comparison method for threat modeling methodologies. The outcomes of these verification methods confirm that the methodology is effective. The full paper includes the outcome of research on different threat modeling methodologies that can be used in OT, their comparison, and the results of implementing each of them in practice on a PLC device. This research is further used to build a threat modeling methodology tailored to OT environments; a detailed description is included. Moreover, the paper includes results of the evaluation of created methodology based on a set of parameters specifically created to rate threat modeling methodologies.

Keywords: device manufacturers, embedded devices, industrial control systems, threat modeling

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Monika Simkute-Bukante

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Teachers significantly influence student achievements, personal development, and academic success. Consequently, they are subject to heightened expectations and scrutiny from governments, school administrations, parents, and even students. Increasing responsibilities and pressures impact teachers’ professional well-being, contributing to a global trend of increased teacher turnover and shortages due to stress and heavy workloads. Given the critical role of teachers in educating young people, it is essential to implement strategies to retain them. School administrations are pivotal in creating an environment conducive to optimal performance. However, there remains a gap in understanding how school administration leadership impacts teachers' professional well-being and its potential for improvement. This research aims to define teacher professional well-being, identify its attributes, and explore the factors influencing it, with a particular focus on the role of school administration. Employing the concept analysis method, this study reviews scholarly publications from 2019 to 2024 to articulate the components of teacher professional well-being. The findings highlight key attributes of teacher well-being, including self-efficacy, work engagement, job satisfaction, relationships with colleagues and students, support from administration, work autonomy, school climate, time pressure, workload, resilience, stress, burnout, and turnover intentions. The analysis demonstrates that school administration leadership directly affects these aspects by providing support in challenging situations, empowering teachers, offering recognition, facilitating open communication, and ensuring autonomy at work. In conclusion, the research shows that teachers' professional well-being is heavily dependent on relationships with school administration, colleagues, and students, as well as the overall school climate. It suggests that by enhancing these elements, school leaders can significantly improve teacher professional well-being. Recommendations are made for developing strategies to support these relationships, thereby fostering an environment that enhances teacher retention and effectiveness.

Keywords: concept analysis, teacher professional well-being, school administration leadership, well-being factors

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

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Authors: Maryam Mirzaei, Sinisa Krajnovic´

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The paper reports a study about the prediction of flows around simplified vehicles using Partially-Averaged Navier-Stokes (PANS). Numerical simulations are performed for two simplified vehicles: A slanted-back Ahmed body at Re=30 000 and a square back Ahmed body at Re=300 000. A comparison of the resolved and modeled physical flow scales is made with corresponding LES and experimental data for a better understanding of the performance of the PANS model. The PANS model is compared for coarse and fine grid resolutions and it is indicated that even a coarse-grid PANS simulation is able to produce fairly close flow predictions to those from a well-resolved LES simulation. The results indicate the possibility of improvement of the predictions by employing a finer grid resolution.

Keywords: partially-averaged Navier-Stokes, large eddy simulation, PANS, LES, Ahmed body

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Authors: Waqas Ali

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Concrete is broadly used as a structural material in the construction of buildings. When the concrete is exposed to elevated temperature, its strength evaluation is very necessary in the existing structure. In this study, the effect of temperature and the reliability of the core test has been evaluated. For this purpose, the cylindrical cores were extracted from High strength concrete (HSC) specimens that were exposed to the temperature ranging from 300 ℃ to 900 ℃ with a constant duration of 4 hr. This study compares the difference between the standard heated cylinders and the cores taken from them after curing of 90 days. The difference of cylindrical control and binary mix samples and extracted cores revealed that there is 12.19 and 12.38% difference at 300℃, while this difference was found to increase up to 12.89%, 13.03% at 500 ℃. Furthermore, this value is recorded as 12.99%, 13.57% and 14.40%, 14.38% at 700 ℃ and 900 ℃, respectively. A total of four equations were developed through a regression model for the prediction of the strength of concrete for both standard cylinders and extracted cores whose R square values were 0.9733, 0.9627 and 0.9473, 0.9452, respectively.

Keywords: high strength, temperature, core, reliability

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Authors: Badiy Elmabrouk, Abdelhamid Boujarif, Zhiguo Zeng, Stephane Borrel, Robert Heidsieck

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In an era where sustainability is paramount, this paper introduces a machine learning-driven testing protocol to accurately predict diode failures, merging reliability engineering with failure physics to enhance repair operations efficiency. Our approach refines the burn-in process, significantly curtailing its duration, which not only conserves energy but also elevates productivity and mitigates component wear. A case study from GE HealthCare’s repair center vividly demonstrates the method’s effectiveness, recording a high prediction of diode failures and a substantial decrease in energy consumption that translates to an annual reduction of 6.5 Tons of CO2 emissions. This advancement sets a benchmark for environmentally conscious practices in the electronics repair sector.

Keywords: maintenance, burn-in, failure physics, reliability testing

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Authors: J. Martin Leal-Graciano, Juan J. Pérez-Gavilán, A. Reyes-Salazar, J. H. Castorena, J. L. Rivera-Salas

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The experimental results about the global behavior of twelve 1:2 scaled reinforced concrete frame subject to in-plane lateral load are presented. The main objective was to generate experimental evidence about the use of steel bars within mortar bed-joints as shear reinforcement in infill walls. Similar to the Canadian and New Zealand standards, the Mexican code includes specifications for this type of reinforcement. However, these specifications were obtained through experimental studies of load-bearing walls, mainly confined walls. Little information is found in the existing literature about the effects of joint reinforcement on the seismic behavior of infill masonry walls. Consequently, the Mexican code establishes the same equations to estimate the contribution of joint reinforcement for both confined walls and infill walls. A confined masonry construction and a reinforced concrete frame infilled with masonry walls have similar appearances. However, substantial differences exist between these two construction systems, which are mainly related to the sequence of construction and to how these structures support vertical and lateral loads. To achieve the objective established, ten reinforced concrete frames with masonry infill walls were built and tested in pairs, having both specimens in the pair identical characteristics except that one of them included joint reinforcement. The variables between pairs were the type of units, the size of the columns of the frame and the aspect ratio of the wall. All cases included tie-columns and tie-beams on the perimeter of the wall to anchor the joint reinforcement. Also, two bare frame with identical characteristic to the infilled frames were tested. The purpose was to investigate the effects of the infill wall on the behavior of the system to in-plane lateral load. In addition, the experimental results were compared with the prediction of the Mexican code. All the specimens were tested in cantilever under reversible cyclic lateral load. To simulate gravity load, constant vertical load was applied on the top of the columns. The results indicate that the contribution of the joint reinforcement to lateral strength depends on the size of the columns of the frame. Larger size columns produce a failure mode that is predominantly a sliding mode. Sliding inhibits the production of new inclined cracks, which are necessary to activate (deform) the joint reinforcement. Regarding the effects of joint reinforcement in the performance of confined masonry walls, many facts were confirmed for infill walls: this type of reinforcement increases the lateral strength of the wall, produces a more distributed cracking and reduces the width of the cracks. Moreover, it reduces the ductility demand of the system at maximum strength. The prediction of the lateral strength provided by the Mexican code is property in some cases; however, the effect of the size of the columns on the contribution of joint reinforcement needs to be better understood.

Keywords: experimental study, Infill wall, Infilled frame, masonry wall

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Authors: Thifhuriwi Emmanuel Madzunye, Patricia Harpur, Ephias Ruhode

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A review of the pertinent literature identified a gap concerning the hindrances and opportunities accompanying the implementation of real-time online learning systems (RTOLs) in rural areas. Whilst RTOLs present a possible solution to teaching and learning issues in rural areas, little is known about the implementation of digital strategies among schools in isolated communities. This study explores associated guidelines that have the potential to inform decision-making where Internet-based education could improve educational opportunities. A systematic literature review has the potential to consolidate and focus on disparate literature served to collect interlinked data from specific sources in a structured manner. During qualitative data analysis (QDA) of selected publications via the application of a QDA tool - ATLAS.ti, the following overarching themes emerged: digital divide, educational strategy, human factors, and support. Furthermore, findings from data collection and literature review suggest that signiant factors include a lack of digital knowledge, infrastructure shortcomings such as a lack of computers, poor internet connectivity, and handicapped real-time online may limit students’ progress. The study recommends that timeous consideration should be given to the influence of the digital divide. Additionally, the evolution of educational strategy that adopts digital approaches, a focus on training of role-players and stakeholders concerning human factors, and the seeking of governmental funding and support are essential to the implementation and success of RTOLs.

Keywords: communication, digital divide, digital skills, distance, educational strategy, government, ICT, infrastructures, learners, limpopo, lukalo, network, online learning systems, political-unrest, real-time, real-time online learning, real-time online learning system, pass-rate, resources, rural area, school, support, teachers, teaching and learning and training

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Authors: Mohammad Afkhami, Ali Hassanpour, Michael Fairweather

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The work described in this paper was undertaken to gain insight into fundamental aspects of turbulent gas-particle flows with relevance to processes employed in a wide range of applications, such as oil and gas flow assurance in pipes, powder dispersion from dry powder inhalers, and particle resuspension in nuclear waste ponds, to name but a few. In particular, the influence of particle interaction and fluid phase behavior in turbulent flow on particle dispersion in a horizontal channel is investigated. The mathematical modeling technique used is based on the large eddy simulation (LES) methodology embodied in the commercial CFD code FLUENT, with flow solutions provided by this approach coupled to a second commercial code, EDEM, based on the discrete element method (DEM) which is used for the prediction of particle motion and interaction. The results generated by LES for the fluid phase have been validated against direct numerical simulations (DNS) for three different channel flows with shear Reynolds numbers, Reτ = 150, 300 and 590. Overall, the LES shows good agreement, with mean velocities and normal and shear stresses matching those of the DNS in both magnitude and position. The research work has focused on the prediction of those conditions favoring particle aggregation and deposition within turbulent flows. Simulations have been carried out to investigate the effects of particle size, density and concentration on particle agglomeration. Furthermore, particles with different surface properties have been simulated in three channel flows with different levels of flow turbulence, achieved by increasing the Reynolds number of the flow. The simulations mimic the conditions of two-phase, fluid-solid flows frequently encountered in domestic, commercial and industrial applications, for example, air conditioning and refrigeration units, heat exchangers, oil and gas suction and pressure lines. The particle size, density, surface energy and volume fractions selected are 45.6, 102 and 150 µm, 250, 1000 and 2159 kg m-3, 50, 500, and 5000 mJ m-2 and 7.84 × 10-6, 2.8 × 10-5, and 1 × 10-4, respectively; such particle properties are associated with particles found in soil, as well as metals and oxides prevalent in turbulent bounded fluid-solid flows due to erosion and corrosion of inner pipe walls. It has been found that the turbulence structure of the flow dominates the motion of the particles, creating particle-particle interactions, with most of these interactions taking place at locations close to the channel walls and in regions of high turbulence where their agglomeration is aided both by the high levels of turbulence and the high concentration of particles. A positive relationship between particle surface energy, concentration, size and density, and agglomeration was observed. Moreover, the results derived for the three Reynolds numbers considered show that the rate of agglomeration is strongly influenced for high surface energy particles by, and increases with, the intensity of the flow turbulence. In contrast, for lower surface energy particles, the rate of agglomeration diminishes with an increase in flow turbulence intensity.

Keywords: agglomeration, channel flow, DEM, LES, turbulence

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Authors: Andrea Parisi, Samantha Vilkins, Luis Furuya-Kanamori, John A. Crump, Benjamin P. Howden, Darren Gray, Kathryn Glass, Martyn Kirk

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Objectives: Salmonella is a leading cause of foodborne enterocolitis worldwide. Nontyphoidal Salmonella (NTS) infections that are Multi-Drug Resistant (MDR) (non-susceptible to ≥1 agent in ≥3 antimicrobial categories) may result in more severe outcomes, although these effects have not been systematically examined. We conducted a systematic review and meta-analysis to examine impacts of MDR NTS on health in high-income settings. Methods: We systematically reviewed the literature from scientific databases, including PubMed, Scopus and grey literature sources, using PRISMA guidelines. We searched for data from case-control studies, cohorts, outbreaks, reports and theses, imposing no language restriction. We included only publications from January 1990 to September 2016 from high income countries as classified by World Bank. We extracted data from papers on duration of illness, hospitalisation rates, morbidity and mortality for MDR and non-MDR NTS strains. Results: After removing duplicates, the initial search revealed 4258 articles. After further screening, we identified 16 eligible studies for the systematic review, and 9 of these were included in meta-analysis. NTS serotypes differed among the reported studies but serotype Typhimurium, Enteritidis, Newport and Heidelberg were among the most often reported as MDR pathogens. Salmonella infections that were MDR were associated with excess bloodstream infections (OR 1.63; 95%CI 1.18-2.26), excess hospitalisations (OR 2.77; 95%CI 1.47-5.21) and higher mortality (OR 3.54; 95%CI 1.10-11.40). Conclusions: MDR NTS infections are a serious public health concern. With the emergence of MDR Salmonella strains in the high-income countries, it is crucial to restrict the use of antimicrobials both in animals and humans, and intervene to prevent foodborne infections.

Keywords: Antimicrobial Resistance, Bloodstream Infection, Health Outcomes, Hospitalisation, Invasive Disease, Multi-Drug Resistance (MDR), Mortality, Nontyphoidal Salmonella

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Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Benedicta Asante, Catherine Trask, Brenna Bath

Abstract:

Background: Waste Collection Workers’ (WCWs) activities contribute greatly to the recycling sector and are an important component of the waste management industry. As the recycling sector evolves, there is the increase in reports of injuries, particularly for common and debilitating musculoskeletal disorders such as low back disorder (LBD). WCWs are likely exposed to diverse work-related hazards that could contribute to LBD. However, there is currently no summary of the state of knowledge on the prevalence and risk factors of LBD within this workforce. Method: A comprehensive search was conducted in Ovid Medline, EMBASE, and Global Health e-publications with search term categories ‘low back disorder’ and ‘waste collection workers’. Two reviewers screened articles at title, abstract, and full-text stages. Data were extracted on study design, sampling strategy, socio-demographics, geographical region, and exposure definition, the definition of LBD, response rate, statistical techniques, LBD prevalence and risk factors. The risk of bias was assessed with a standardized tool. Results: The search of three databases generated 79 studies. Thirty-two studies met the study inclusion criteria for both title and abstract; only thirteen full-text articles met the study criteria and underwent data extraction. The majority of articles reported a 12-month prevalence of LBD between 16-74%. Although none of the included studies quantified relationships between risk factors and LBD, the suggested risk factors for LBD among WCWs included: awkward posture; lifting; pulling; pushing; repetitive motions; work duration; and physical loads. Conclusion: LBD is a major occupational health issue among WCWs. In light of these risks and future growth in this industry, further research should focus on the investigation of risk factors, with more focus on ergonomic exposure assessment, and LBD prevention efforts.

Keywords: low back pain, scavenger, waste pickers, waste collection workers

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Authors: Sumedha Ganjoo, Santosh Goswami

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The rising reliance on technology places national security at the forefront of 21st-century issues. It complicates the efforts of emerging and developed countries to combat cyber threats and increases the inherent risk factors connected with technology. The inability to preserve data securely might have devastating repercussions on a massive scale. Consequently, it is vital to establish national, regional, and global data protection rules and regulations that penalise individuals who participate in immoral technology usage and exploit the inherent vulnerabilities of technology. This study paper seeks to analyse GDPR-inspired Bills in the South Asian Region and determine their suitability for the development of a worldwide data protection framework, considering that Asian countries are much more diversified than European ones. In light of this context, the objectives of this paper are to identify GDPR-inspired Bills in the South Asian Region, identify their similarities and differences, as well as the obstacles to developing a regional-level data protection mechanism, thereby satisfying the need to develop a global-level mechanism. Due to the qualitative character of this study, the researcher did a comprehensive literature review of prior research papers, journal articles, survey reports, and government publications on the aforementioned topics. Taking into consideration the survey results, the researcher conducted a critical analysis of the significant parameters highlighted in the literature study. Many nations in the South Asian area are in the process of revising their present data protection measures in accordance with GDPR, according to the primary results of this study. Consideration is given to the data protection laws of Thailand, Malaysia, China, and Japan. Significant parallels and differences in comparison to GDPR have been discussed in detail. The conclusion of the research analyses the development of various data protection legislation regimes in South Asia.

Keywords: data privacy, GDPR, Asia, data protection laws

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Authors: Xi Yang, Bulent Chavdar, Alan Vonseggern, Taylan Altan

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Fracture in hot precision forging of engine valves was investigated in this paper. The entire valve forging procedure was described and the possible cause of the fracture was proposed. Finite Element simulation was conducted for the forging process, with commercial Finite Element code DEFORMTM. The effects of material properties, the effect of strain rate and temperature were considered in the FE simulation. Two fracture criteria were discussed and compared, based on the accuracy and reliability of the FE simulation results. The selected criterion predicted the fracture location and shows the trend of damage increasing with good accuracy, which matches the experimental observation. Additional modification of the punch shapes was proposed to further reduce the tendency of fracture in forging. Finite Element comparison shows a great potential of such application in the mass production.

Keywords: hotforging, engine valve, fracture, tooling

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Authors: Daniel S. Brox

Abstract:

Starting from earthquake fault dynamic equations, a correspondence between earthquake occurrence statistics in a seismic region before a major earthquake and eigenvalue statistics of a differential operator whose bound state eigenfunctions characterize the distribution of stress in the seismic region is derived. Modeling these eigenvalue statistics with a 2D Coulomb gas statistical physics model, previously reported deviation of seismic activation earthquake occurrence statistics from Gutenberg-Richter statistics in time intervals preceding the major earthquake is derived. It also explains how statistical physics modeling predicts a finite-dimensional nonlinear dynamic system that describes real-time velocity model evolution in the region undergoing seismic activation and how this prediction can be tested experimentally.

Keywords: seismic activation, statistical physics, geodynamics, signal processing

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

Abstract:

The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Aziza Barrek, Mohamed Haythem Msaddek, Ismail Chenini

Abstract:

Excessive groundwater pumping due to the increasing water demand, especially in the agricultural sector, causes groundwater scarcity. Groundwater recharge is the most important process that contributes to the water's durability. This paper is based on the Analytic Hierarchy Process multicriteria analysis to establish a groundwater recharge susceptibility map. To delineate aquifer suitability for groundwater recharge, eight parameters were used: soil type, land cover, drainage density, lithology, NDVI, slope, transmissivity, and rainfall. The impact of each factor was weighted. This method was applied to the El Fahs plain shallow aquifer. Results suggest that 37% of the aquifer area has very good and good recharge suitability. The results have been validated by the Receiver Operating Characteristics curve. The accuracy of the prediction obtained was 89.3%.

Keywords: AHP, El Fahs aquifer, empirical formula, groundwater recharge zone, remote sensing, semi-arid region

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Authors: Anita Chhatwal

Abstract:

Purpose: The purpose of this paper is to evaluate and investigate the scope of usage of Academic Social Networking Websites (ASNs) by the Science faculty members across universities of North India, viz. Panjab University, Punjabi University and University of Delhi, Delhi. Design/Methodology/Approach: The present study is based upon the primary data collected from 81 science faculty participants from three universities of North India. Questionnaire method was used as an instrument for survey. The study is descriptive and research-based to investigate the popular ASNs amongst the participants from three sample universities and the purpose for which they use them along with the problems they encounter while using ASNs. Findings: The findings of the study revealed that majority of the participants were using ASNs for their academic needs. It was observed that majority of the participants (78%) used ASNs to access scientific papers, while 73.8% of the participants used them to share their research publications. ResearchGate (60.5%) and Google Scholar (59.7%) were the top two most preferred and widely used ASNs by the participants. The critical analysis of the data shows that laptops (86.3%) emerged as major tools for accessing ASNs. Shortage of computers was found to be the chief obstacle in accessing ASNs by the participants. Results of the study demonstrate that 56.3% of participants suggested conduct of seminars and training as the most effective method to increase the awareness of ASNs. Research Limitations/Implications: The study in hand absorbed the 81 faculty (Assistant Professors) members from 15 Science teaching departments across three sample universities of North India. The findings of this study will help the Government of India to regulate and simultaneously make effort to develop and enhance ASNs usage among faculty, researchers, and students. The present study will add to the existing library and information science literature and will be advantageous for all the information professionals as well. Originality/Value: This study is original survey based on primary data investigate the usage of ASNs by the academia. This study will be useful for research scholars, academicians and students all over the world.

Keywords: academic social networks, awareness and usage, North India, scholarly communication, web 2.0

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