Search results for: lean tools and techniques

Authors: Gulnaz Erkomaishvili

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Introduction. One of the most important issues of state economic policy in the 21st century is the problem of environmental protection. The Georgian government considers the green economy as one of the most important means of sustainable economic development and takes the initiative to implement voluntary measures to promote sustainable development. In this context, it is important to promote the development of ecosystem services, clean production, environmental education and green jobs.The development of the green economy significantly reduces the inefficient use of natural resources, waste generation, emissions into the atmosphere and the discharge of untreated water into bodies of water.It is, therefore, an important instrument in the environmental orientation of sustainable development. Objectives.The aim of the paper is to analyze the current status of the green economy in Georgia and identify effective ways to improve the environmental, economic policy of sustainable development. Methodologies: This paper uses general and specific methods, in particular, analysis, synthesis, induction, deduction, scientific abstraction, comparative and statistical methods, as well as experts’ evaluation. bibliographic research of scientific works and reports of organizations was conducted; Publications of the National Statistics Office of Georgia are used to determine the regularity between analytical and statistical estimations. Also, theoretical and applied research of international organizations and scientist-economists are used. Contributions: The country should implement such an economic policy that ensures the transition to a green economy, in particular, revising water, air and waste laws, strengthening existing environmental management tools and introcing new tools (including economic tools). Perfecting the regulatory legal framework of the environmental impact assessment system, which includes the harmonization of Georgian legislation with the requirements of the European Union. To ensure the protection and rational use of Georgia's forests, emphasis should be placed on sustainable forestry, protection and restoration of forests.

Keywords: green economy, environmental protection, environmental protection economic policy, environmental protection policy challanges

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

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Authors: Khaled M. Alhawiti

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This paper aims to analyze the role of natural language processing (NLP). The paper will discuss the role in the context of automated data retrieval, automated question answer, and text structuring. NLP techniques are gaining wider acceptance in real life applications and industrial concerns. There are various complexities involved in processing the text of natural language that could satisfy the need of decision makers. This paper begins with the description of the qualities of NLP practices. The paper then focuses on the challenges in natural language processing. The paper also discusses major techniques of NLP. The last section describes opportunities and challenges for future research.

Keywords: data retrieval, information retrieval, natural language processing, text structuring

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Authors: A. Zatir, A. Mokhtari, A. Foufa, S. Zatir

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The city of Oran, like any other region of northern Algeria, is subject to frequent seismic activity, the study presented in this work will be based on an analysis of urban and architectural context of the city of Oran before the date of the earthquake of 1790, and then try to deduce the differences between the old city before and after the earthquake. The analysis developed as a specific objective to tap into the seismic history of the city of Oran parallel to its urban history. The example of the citadel of Oran indicates that constructions presenting the site of the old citadel, may present elements of resistance for face to seismic effects. Removed in city observations of these structures, showed the ingenuity of the techniques used by the ancient builders, including the good performance of domes and arches in resistance to seismic forces.

Keywords: earthquake, citadel, performance, traditional techniques, constructions

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Authors: K. Jahani, J. Razavi

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Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone

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Authors: Gaurav Bhanjana, Ganga Ram Chaudhary, Sandeep Kumar, Neeraj Dilbaghi

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Main sources of endocrine disrupting compounds in the ecosystem are hormones, pesticides, phthalates, flame retardants, dioxins, personal-care products, coplanar polychlorinated biphenyls (PCBs), bisphenol A, and parabens. These endocrine disrupting compounds are responsible for learning disabilities, brain development problems, deformations of the body, cancer, reproductive abnormalities in females and decreased sperm count in human males. Although discharge of these chemical compounds into the environment cannot be stopped, yet their amount can be retarded through proper evaluation and detection techniques. The available techniques for determination of these endocrine disrupting compounds mainly include high performance liquid chromatography (HPLC), mass spectroscopy (MS) and gas chromatography-mass spectrometry (GC–MS). These techniques are accurate and reliable but have certain limitations like need of skilled personnel, time consuming, interference and requirement of pretreatment steps. Moreover, these techniques are laboratory bound and sample is required in large amount for analysis. In view of above facts, new methods for detection of endocrine disrupting compounds should be devised that promise high specificity, ultra sensitivity, cost effective, efficient and easy-to-operate procedure. Nowadays, electrochemical sensors/biosensors modified with nanomaterials are gaining high attention among researchers. Bioelement present in this system makes the developed sensors selective towards analyte of interest. Nanomaterials provide large surface area, high electron communication feature, enhanced catalytic activity and possibilities of chemical modifications. In most of the cases, nanomaterials also serve as an electron mediator or electrocatalyst for some analytes.

Keywords: electrochemical, endocrine disruptors, microscopy, nanoparticles, sensors

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Authors: Jafar Mortadha, Imran Qureshi

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This research explains and compares the modern techniques used for measuring the flow angles of a flowing fluid with the traditional technique of using multi-hole pressure probes. In particular, the focus of the study is on four-hole probes, which offer great reliability and benefits in several applications where the use of modern measurement techniques is either inconvenient or impractical. Due to modern advancements in manufacturing, small multi-hole pressure probes can be made with high precision, which eliminates the need for calibrating every manufactured probe. This study aims to improve the range of calibration maps for a four-hole probe to allow high flow angles to be measured accurately. The research methodology comprises a literature review of the successful calibration definitions that have been implemented on five-hole probes. These definitions are then adapted and applied on a four-hole probe using a set of raw pressures data. A comparison of the different definitions will be carried out in Matlab and the results will be analyzed to determine the best calibration definition. Taking simplicity of implementation into account as well as the reliability of flow angles estimation, an adapted technique from a research paper written in 2002 offered the most promising outcome. Consequently, the method is seen as a good enhancement for four-hole probes and it can substitute for the existing calibration definitions that offer less accuracy.

Keywords: calibration definitions, calibration maps, flow measurement techniques, four-hole probes, multi-hole pressure probes

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Authors: Jisun Mo, Paola Boarin

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The impact of common buildings on climate and environment has prompted people to get involved in the green building standards aimed at implementing rating tools or certifications. Thus, green building rating systems were introduced to the construction industry, and the demand for certified green buildings has increased gradually and succeeded considerably in enhancing people’s environmental awareness. However, the existing certification process has been unsatisfactory in attracting stakeholders and/or professionals who are actively engaged in adopting a rating system. It is because they have faced recurring barriers regarding limited information in understanding the rating process, time-consuming procedures and higher costs, which have a direct influence on pursuing green building rating systems. To promote the achievement of green building certifications within the building industry more successfully, this paper aims at designing a Pre-Assessment Tool (PAT) framework that can help stakeholders and/or professionals engaged in the construction industry to clarify their basic knowledge, timeframe and extra costs needed to activate a green building certification. First, taking the first steps towards the rating tool seems to be complicated because of upfront commitment to understanding the overall rating procedure is required. This conceptual PAT framework can increase basic knowledge of the rating tool and the certification process, mainly in terms of all resources or information of each credit requirements. Second, the assessment process of rating tools is generally known as a “lengthy and time-consuming system”, contributing to unenthusiastic reactions concerning green building projects. The proposed framework can predict the timeframe needed to identify how long it will take for a green project to process each credit requirement and the documentation required from the beginning of the certification process to final approval. Finally, most people often have the initial perception that pursuing green building certification costs more than constructing a non-green building, which makes it more difficult to execute rating tools. To overcome this issue, this PAT will help users to estimate the extra expenses such as certification fees and third-party contributions based on the track of the amount of time it takes to implement the rating tool throughout all the related stages. Also, it can prevent unexpected or hidden costs occurring in the process of assessment. Therefore, this proposed PAT framework can be recommended as an effective method to support the decision-making of inexperienced users and play an important role in promoting green building certification.

Keywords: green building rating tools, Pre-Occupancy Evaluation (PrOE), client’s decision-making, certification

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Authors: Faisal Alshuwaier, Ali Areshey

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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

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Authors: Stephen Kyakulumbye, Vivian Kobusingye

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Generally, World over, COVID-19 has had adverse effects on all sectors but with more debilitating effects on the education sector. After reactive lockdowns, education institutions that could continue teaching and learning had to go a distance mediated by digital technological tools. In Uganda, the Ministry of Education thereby issued COVID-19 Online Distance E-learning (ODeL) emergent guidelines. Despite such guidelines, academic institutions in Uganda and similar developing contexts with academically constrained resource environments were caught off-guard and ill-prepared to transform from face-to-face learning to online distance learning mode. Most academic institutions that migrated spontaneously did so with no deliberate tools, systems, strategies, or software to cause active, meaningful, and engaging learning for students. By experience, most of these academic institutions shifted to Zoom and WhatsApp and instead conducted online teaching in real-time than blended synchronous and asynchronous tools. This paper provides students’ experiences while blending synchronous and asynchronous content-creating and learning tools within a technological resource-constrained environment to navigate in such a challenging Uganda context. These conceptual case-based findings, using experience from Uganda Christian University (UCU), point at the design of learning activities with two certain characteristics, the enhancement of synchronous learning technologies with asynchronous ones to mitigate the challenge of system breakdown, passive learning to active learning, and enhances the types of presence (social, cognitive and facilitatory). The paper, both empirical and experiential in nature, uses online experiences from third-year students in Bachelor of Business Administration student lectured using asynchronous text, audio, and video created with Open Broadcaster Studio software and compressed with Handbrake, all open-source software to mitigate disk space and bandwidth usage challenges. The synchronous online engagements with students were a blend of zoom or BigBlueButton, to ensure that students had an alternative just in case one failed due to excessive real-time traffic. Generally, students report that compared to their previous face-to-face lectures, the pre-recorded lectures via Youtube provided them an opportunity to reflect on content in a self-paced manner, which later on enabled them to engage actively during the live zoom and/or BigBlueButton real-time discussions and presentations. The major recommendation is that lecturers and teachers in a resource-constrained environment with limited digital resources like the internet and digital devices should harness this approach to offer students access to learning content in a self-paced manner and thereby enabling reflective active learning through reflective and high-order thinking.

Keywords: synchronous learning, asynchronous learning, active learning, reflective learning, resource-constrained environment

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Authors: Rohit Mittal, Bright Keswani, Amit Mithal

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This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Reena Murali, David Peter S.

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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

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Authors: M. D. H. Beg, John O. Akindoyo, Suriati Ghazali, Nitthiyah Jeyaratnam

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Over the period of time, several approaches have been undertaken to mitigate the challenges associated with bone regeneration. This includes but not limited to xenografts, allografts, autografts as well as artificial substitutions like bioceramics, synthetic cements and metals. The former three techniques often come along with peculiar limitation and problems such as morbidity, availability, disease transmission, collateral site damage or absolute rejection by the body as the case may be. Synthetic routes remain the only feasible alternative option for treatment of bone defects. Hydroxyapatite (HA) is very compatible and suitable for this application. However, most of the common methods for HA synthesis are either expensive, complicated or environmentally unfriendly. Interestingly, extraction of HA from bio-wastes have been perceived not only to be cost effective, but also environment friendly. In this research, HA was synthesized from bio-waste: namely bovine bones through three different methods which are hydrothermal chemical processes, ultrasound assisted synthesis and ordinary calcination techniques. Structure and property analysis of the HA was carried out through different characterization techniques such as TGA, FTIR, and XRD. All the methods applied were able to produce HA with similar compositional properties to biomaterials found in human calcified tissues. Calcination process was however observed to be more efficient as it eliminated all the organic components from the produced HA. The HA synthesized is unique for its minimal cost and environmental friendliness. It is also perceived to be suitable for tissue and bone engineering applications.

Keywords: hydroxyapatite, bone, calcination, biowaste

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Authors: Muhaned Zaidi, Ian Grout, Abu Khari bin A’ain

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In this paper, a two-stage op-amp design is considered using both Miller and negative Miller compensation techniques. The first op-amp design uses Miller compensation around the second amplification stage, whilst the second op-amp design uses negative Miller compensation around the first stage and Miller compensation around the second amplification stage. The aims of this work were to compare the gain and phase margins obtained using the different compensation techniques and identify the ability to choose either compensation technique based on a particular set of design requirements. The two op-amp designs created are based on the same two-stage rail-to-rail output CMOS op-amp architecture where the first stage of the op-amp consists of differential input and cascode circuits, and the second stage is a class AB amplifier. The op-amps have been designed using a 0.35mm CMOS fabrication process.

Keywords: op-amp, rail-to-rail output, Miller compensation, Negative Miller capacitance

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Authors: Jayanth C., Anirudh Sai Yetikuri, Kavitha S. N.

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This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts.

Keywords: computer vision, pose detection, facial recognition, number plate extraction, machine learning, real-time analysis, OCR, database management

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Authors: José Alberto García Fernández, Zhimin Du, Xinqiao Jin

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Nowadays, data center industry faces strong challenges for increasing the speed and data processing capacities while at the same time is trying to keep their devices a suitable working temperature without penalizing that capacity. Consequently, the cooling systems of this kind of facilities use a large amount of energy to dissipate the heat generated inside the servers, and developing new cooling techniques or perfecting those already existing would be a great advance in this type of industry. The installation of a temperature sensor matrix distributed in the structure of each server would provide the necessary information for collecting the required data for obtaining a temperature profile instantly inside them. However, the number of temperature probes required to obtain the temperature profiles with sufficient accuracy is very high and expensive. Therefore, other less intrusive techniques are employed where each point that characterizes the server temperature profile is obtained by solving differential equations through simulation methods, simplifying data collection techniques but increasing the time to obtain results. In order to reduce these calculation times, complicated and slow computational fluid dynamics simulations are replaced by simpler and faster finite element method simulations which solve the Burgers‘ equations by backward, forward and central discretization techniques after simplifying the energy and enthalpy conservation differential equations. The discretization methods employed for solving the first and second order derivatives of the obtained Burgers‘ equation after these simplifications are the key for obtaining results with greater or lesser accuracy regardless of the characteristic truncation error.

Keywords: Burgers' equations, CFD simulation, data center, discretization methods, FEM simulation, temperature profile

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Okike Benjamin, Garba Ejd

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Today, it has been observed security of information along the superhighway is often compromised by those who are not authorized to have access to such information. In order to ensure the security of information along the superhighway, such information should be encrypted by some means to conceal the real meaning of the information. There are many encryption techniques out there in the market. However, some of these encryption techniques are often easily decrypted by adversaries. The researcher has decided to develop an encryption technique that may be more difficult to decrypt. This may be achieved by splitting the message to be encrypted into parts and encrypting each part separately and swapping the positions before transmitting the message along the superhighway. The method is termed Merged Irregular Transposition Cipher. Also, the research would determine the complexity level in respect to the number of splits of the message.

Keywords: transposition cipher, merged irregular cipher, encryption, complexity level

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Didik Sugiyanto

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Conditions that often occur in the framework of a particular vehicle at a car is a collision or collision with another object, an example of such damage is to the frame or chassis for the required design framework that is able to absorb impact energy. Characteristics of the material are influenced by the value of the stiffness of the material that need to be considered in choosing the material properties of the material. To obtain material properties that can be adapted to the experimental conditions tested the tensile and compression testing. In this study focused on the chassis at an angle of 150, 300, and 450. It is based on field studies that vehicle primarily for freight cars have a point of order light between 150 to 450. Research methods include design tools, design framework, procurement of materials and experimental tools, tool-making, the manufacture of the test framework, and the testing process, experiment is testing the power of the press to know the order. From this test obtained the maximum force on the corner of 150 was 569.76 kg at a distance of 16 mm, angle 300 is 370.3 kg at a distance of 15 mm, angle 450 is 391.71 kg at a distance of 28 mm. After reaching the maximum force the order will occur collapse, followed by a decrease in the next distance. It can be concluded that the greatest strain energy occurs at an angle of 150. So it is known that the frame at an angle of 150 produces the best level of security.

Keywords: buckling, collapse, body frame, vehicle

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Authors: Ahmed Elzainy, Abir El Sadik, Waleed Al Abdulmonem, Ahmad Alamro, Homaidan Al-Homaidan

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Problem-based learning (PBL) is an educational modality which stimulates critical and creative thinking. PBL has been practiced in the college of medicine, Qassim University, Saudi Arabia, since the 2002s with offline face to face activities. Therefore, crucial technological changes in paperless work were needed. The aim of the present study was to design and implement the digitalization of the PBL activities and to evaluate its impact on students' and tutors’ performance. This approach promoted the involvement of all stakeholders after their awareness of the techniques of using online tools. IT support, learning resources facilities, and required multimedia were prepared. Students’ and staff perception surveys reflected their satisfaction with these remarkable changes. The students were interested in the new digitalized materials and educational design, which facilitated the conduction of PBL sessions and provided sufficient time for discussion and peer sharing of knowledge. It enhanced the tutors for supervision and tracking students’ activities on the Learning Management System. It could be concluded that introducing of digitalization of the PBL activities promoted the students’ performance, engagement and enabled a better evaluation of PBL materials and getting prompt students as well as staff feedback. These positive findings encouraged the college to implement the digitalization approach in other educational activities, such as Team-Based Learning, as an additional opportunity for further development.

Keywords: multimedia in PBL, online PBL, problem-based learning, PBL digitalization

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Authors: Ali Fazeli Moslehabadi

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One of the topics discussed in civil projects, during the execution of the project, which the continuous change of work volumes is usually the characteristics of these types of projects, is how to calculate the volume of done work. The difference in volumes announced by the execution unit with the estimated volume by the technical office unit, has direct effect on the announced progress of the project. This issue can show the progress of the project more or less than actual value and as a result making mistakes for stakeholders and project managers and misleading them. This article intends to introduce some practical methods for calculating the volume of done work in civil projects. It then reviews the strengths and weaknesses of each of them, in order to resolve these contradictions and conflicts.

Keywords: technical skills, systemic skills, communication skills, done work volume calculation techniques

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Authors: Ahuva Even-Zohar

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Context: Families living in poverty often struggle with poor financial management and accumulating debts. Social workers play a crucial role in assisting these families, but they typically receive no formal training in financial management. Research Aim: The aim of this study was to assess the impact of a voluntary organization called Paamonim on social work students' attitudes toward providing material support and assistance in financial management to families living in poverty. Additionally, the study aimed to evaluate the students' perception of the workshop's contribution and their actual use of the tools acquired during the training. Methodology: The study involved 134 social work students, with 55 in a regular program and 79 in a retraining program. The first stage of the study involved the students receiving an explanation of the research, ensuring confidentiality, and signing informed consent. The students completed two questionnaires: The Student Attitudes Questionnaire and a sociodemographic questionnaire. In the second stage, three months after the workshop, the students completed the questionnaires again, along with a feedback questionnaire. Findings: The study found that there were no significant differences in the students' attitudes toward providing material support and proper financial management to families living in poverty before and after the workshop. However, their attitudes remained positive, at a medium level or higher. The students reported that the workshop provided them with tools to assist families in poverty, and they used these tools to some extent in their practical training. They found the workshop interesting and acknowledged its importance in raising awareness about budget management and enriching their knowledge of money management. The students acknowledged the workshop's effectiveness but mentioned that it was too short. Theoretical Importance: This study highlights the importance of developing programs to help families living in poverty manage their household finances. The workshop enriched the students' knowledge and skills, which are vital for their role as social workers working with families in poverty to rehabilitate their financial situations. Data Collection and Analysis Procedures: The students completed self-report questionnaires before and after the workshop, providing insights into their attitudes and perception about the role of social workers in providing material support for families living in poverty and helping them manage their household finances. Statistical analysis was conducted to compare the students' attitudes pre and post-workshop, and a feedback questionnaire assessed the students' perception of the contribution of the workshop. Question Addressed: This study addressed the question of whether a workshop on financial management can positively impact social work students' attitudes and skills in assisting families living in poverty with their household finances. Conclusion: Despite being short and consisting of only one session, the workshop proved to be valuable in enriching the students' knowledge and providing them with important tools for their role as social workers. The students reported positive attitudes toward providing material support and financial assistance to families in need. The practical recommendation is to continue offering such workshops as part of the social work curriculum to further enhance students' abilities to help families manage their finances effectively.

Keywords: financial literacy, poverty, social work students, workshop

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Fatimah Al-Hayazi, Ehteram. A. Noor, Aisha H. Moubaraki

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The inhibitive effect of Securigera securidaca seed extract (SSE) on mild steel corrosion in 1 M HCl solution has been studied by weight loss and electrochemical techniques at four different temperatures. All techniques studied provided data that the studied extract does well at all temperatures, and its inhibitory action increases with increasing its concentration. SEM images indicate thin-film formation on mild steel when corroded in solutions containing 1 g L-1 of inhibitor either at low or high temperatures. The polarization studies showed that SSE acts as an anodic inhibitor. Both polarization and impedance techniques show an acceleration behaviour for SSE at concentrations ≤ 0.1 g L-1 at all temperatures. At concentrations ≥ 0.1 g L-1, the efficiency of SSE is dramatically increased with increasing concentration, and its value does not change appreciably with increasing temperature. It was found that all adsorption data obeyed Temkin adsorption isotherm. Kinetic activation and thermodynamic adsorption parameters are evaluated and discussed. The results revealed an endothermic corrosion process with an associative activation mechanism, while a comprehensive adsorption mechanism for SSE on mild steel surfaces is suggested, in which both physical and chemical adsorption are involved in the adsorption process. A good correlation between inhibitor constituents and their inhibitory action was obtained.

Keywords: corrosion, inhibition of steel, hydrochloric acid, thermodynamic study

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Authors: Shazia Anwer

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The current TV Talk Shows, especially on domestic politics in Pakistan are following the Aristotelian techniques, including deductive reasoning, three modes of persuasion, and guidelines for communication. The application of “Approximate Truth is also seen when Talk Show presenters create doubts against political personalities or national issues. Mainstream media of Pakistan, being a key carrier of narrative construction for the sake of the primary function of national consensus on regional and extended public diplomacy, is failing the purpose. This paper has highlighted the Aristotelian communication methodology, its purposes and its limitations for a serious discussion, and its connection to the mistrust among the Pakistani population regarding fake or embedded, funded Information. Data has been collected from 3 Pakistani TV Talk Shows and their analysis has been made by applying the Aristotelian communication method to highlight the core issues. Paper has also elaborated that current media education is impaired in providing transparent techniques to train the future journalist for a meaningful, thought-provoking discussion. For this reason, this paper has given an overview of HEC’s (Higher Education Commission) graduate-level Mass Com Syllabus for Pakistani Universities. The idea of ethos, logos, and pathos are the main components of TV Talk Shows and as a result, the educated audience is lacking trust in the mainstream media, which eventually generating feelings of distrust and betrayal in the society because productions look like the genre of Drama instead of facts and analysis thus the line between Current Affairs shows and Infotainment has become blurred. In the last section, practical implication to improve meaningfulness and transparency in the TV Talk shows has been suggested by replacing the Aristotelian communication method with the cognitive semiotic communication approach.

Keywords: Aristotelian techniques of communication, current affairs talk shows, drama, Pakistan

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Authors: Chen Wang, Gerard Rowe

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The number of undergraduate engineering students enrolled in university has been increasing rapidly recently, leading to challenges associated with increased student-instructor ratios and increased diversity in academic preparedness of the entrants. An increased student-instructor ratio makes the interaction between teachers and students more difficult, with the resulting student ‘anonymity’ known to be a risk to academic success. With increasing student numbers, there is also an increasing diversity in the academic preparedness of the students at entry to university. Conceptual understanding of the entrants has been quantified via diagnostic testing, with the results for the first-year course in electrical engineering showing significant conceptual misunderstandings amongst the entry cohort. The solution is clearly multi-faceted, but part of the solution likely involves greater demands being placed on students to be masters of their own learning. In consequence, it is highly desirable that instructors help students to develop better self-regulated learning skills. A self-regulated learner is one who is capable of setting up their own learning goals, monitoring their study processes, adopting and adjusting learning strategies, and reflecting on their own study achievements. The methods by which instructors might cultivate students’ self-regulated learning abilities is receiving increasing attention from instructors and researchers. The aim of this study was to help students understand fully their self-regulated learning skill levels and provide targeted instructions to help them improve particular learning abilities in order to meet the curriculum requirements. As a survey tool, this research applied the questionnaire ‘Motivated Strategies for Learning Questionnaire’ (MSLQ) to collect first year engineering student’s self-reported data of their cognitive abilities, motivational orientations and learning strategies. MSLQ is a widely-used questionnaire for assessment of university student’s self-regulated learning skills. The questionnaire was offered online as a part of the online-scaffolding-learning tools to develop student understanding of self-regulated learning theories and learning strategies. The online tools, which have been under development since 2015, are designed to help first-year students understand their self-regulated learning skill levels by providing prompt feedback after they complete the questionnaire. In addition, the online tool also supplies corresponding learning strategies to students if they want to improve specific learning skills. A total of 866 first year engineering students who enrolled in the first-year electrical engineering course were invited to participate in this research project. By the end of the course 857 students responded and 738 of their questionnaires were considered as valid questionnaires. Analysis of these surveys showed that 66% of the students thought the online-scaffolding-learning tools helped significantly to improve their self-regulated learning abilities. It was particularly pleasing that 16.4% of the respondents thought the online-scaffolding-learning tools were extremely effective. A current thrust of our research is to investigate the relationships between students’ self-regulated learning abilities and their academic performance. Our results are being used by the course instructors as they revise the curriculum and pedagogy for this fundamental first-year engineering course, but the general principles we have identified are applicable to most first-year STEM courses.

Keywords: academic preparedness, online-scaffolding-learning tool, self-regulated learning, STEM education

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Authors: Shahroz Khan, Erkan Gunpinar, Kemal Mart

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Recently parametric design techniques became a vital concept in the field of Computer Aided Design (CAD), which helps to provide sophisticated platform to the designer in order to automate the design process in efficient time. In these techniques, design process starts by parameterizing the important features of design models (typically the key dimensions), with the implementation of design constraints. The design constraints help to retain the overall shape of the model while modifying its parameters. However, the process of initializing an appropriate number of design parameters and constraints is the crucial part of parametric design techniques, especially for complex surface models such as yacht hull. This paper introduces a method to create complex surface models in favor of parametric design techniques, a method to define the right number of parameters and respective design constraints, and a system to implement design parameters in contract to design constraints schema. For this, in our proposed approach the design process starts by dividing the yacht hull into three sections. Each section consists of different shape lines, which form the overall shape of yacht hull. The shape lines are created using Cubic Bezier Curves, which allow larger design flexibility. Design parameters and constraints are defined on the shape lines in 3D design space to facilitate the designers for better and individual handling of parameters. Afterwards, shape modifiers are developed, which allow the modification of each parameter while satisfying the respective set of criteria and design constraints. Such as, geometric continuities should be maintained between the shape lines of the three sections, fairness of the hull surfaces should be preserved after modification and while design modification, effect of a single parameter should be negligible on other parameters. The constraints are defined individually on shape lines of each section and mutually between the shape lines of two connecting sections. In order to validate and visualize design results of our shape modifiers, a real time graphic interface is created.

Keywords: design parameter, design constraints, shape modifies, yacht hull

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

Procedia PDF Downloads 80