Search results for: artificial intelligence in semiconductor manufacturing

Authors: Liubov Magerramova, Mikhail Petrov, Vladimir Isakov, Liana Shcherbinina, Suren Gukasyan, Daniil Povalyukhin, Olga Klimova-Korsmik, Darya Volosevich

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Aircraft engines are developing along the path of increasing resource, strength, reliability, and safety. The building of gas turbine engine body parts is a complex design and technological task. Particularly complex in the design and manufacturing are the casings of the input stages of helicopter gearboxes and central drives of aircraft engines. Traditional technologies, such as precision casting or isothermal forging, are characterized by significant limitations in parts production. For parts like housing, additive technologies guarantee spatial freedom and limitless or flexible design. This article presents the results of computational and experimental studies. These investigations justify the applicability of additive technologies (AT) to reduce the weight of aircraft housing gearbox parts by up to 32%. This is possible due to geometrical optimization compared to the classical, less flexible manufacturing methods and as-casted aircraft parts with over-insured values of safety factors. Using an example of the body of the input stage of an aircraft gearbox, visualization of the layer-by-layer manufacturing of a part based on thermal deformation was demonstrated.

Keywords: additive technologies, gas turbine engines, topological optimization, synthesis process

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Authors: Zakir Husain

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Main objective of a power transformer design optimization problem requires minimizing the total overall cost and/or mass of the winding and core material by satisfying all possible constraints obligatory by the standards and transformer user requirement. The constraints include appropriate limits on winding fill factor, temperature rise, efficiency, no-load current and voltage regulation. The design optimizations tasks are a constrained minimum cost and/or mass solution by optimally setting the parameters, geometry and require magnetic properties of the transformer. In this paper, present the above design problems have been formulated by using genetic algorithm (GA) and simulated annealing (SA) on the MATLAB platform. The importance of the presented approach is stems for two main features. First, proposed technique provides reliable and efficient solution for the problem of design optimization with several variables. Second, it guaranteed to obtained solution is global optimum. This paper includes a demonstration of the application of the genetic programming GP technique to transformer design.

Keywords: optimization, power transformer, genetic algorithm (GA), simulated annealing technique (SA)

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Authors: Eliana Bergamin

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This philosophical inquiry, influenced by the seminal works of Annemarie Mol and Jeannette Pols, critically examines the transformative impact of artificial intelligence (AI) on emotional caregiving practices within virtual healthcare. Rooted in the traditions of philosophy of care, philosophy of emotions, and applied philosophy, this study seeks to unravel nuanced shifts in the moral and emotional fabric of healthcare mediated by AI-powered technologies. Departing from traditional empirical studies, the approach embraces the foundational principles of care ethics and phenomenology, offering a focused exploration of the ethical and existential dimensions of AI-mediated emotional caregiving. At its core, this research addresses the introduction of AI-powered technologies mediating emotional and care practices in the healthcare sector. By drawing on Mol and Pols' insights, the study offers a focused exploration of the ethical and existential dimensions of AI-mediated emotional caregiving. Anchored in ethnographic research within a pioneering private healthcare company in the Netherlands, this critical philosophical inquiry provides a unique lens into the dynamics of AI-mediated emotional practices. The study employs in-depth, semi-structured interviews with virtual caregivers and care receivers alongside ongoing ethnographic observations spanning approximately two and a half months. Delving into the lived experiences of those at the forefront of this technological evolution, the research aims to unravel subtle shifts in the emotional and moral landscape of healthcare, critically examining the implications of AI in reshaping the philosophy of care and human connection in virtual healthcare. Inspired by Mol and Pols' relational approach, the study prioritizes the lived experiences of individuals within the virtual healthcare landscape, offering a deeper understanding of the intertwining of technology, emotions, and the philosophy of care. In the realm of philosophy of care, the research elucidates how virtual tools, particularly those driven by AI, mediate emotions such as empathy, sympathy, and compassion—the bedrock of caregiving. Focusing on emotional nuances, the study contributes to the broader discourse on the ethics of care in the context of technological mediation. In the philosophy of emotions, the investigation examines how the introduction of AI alters the phenomenology of emotional experiences in caregiving. Exploring the interplay between human emotions and machine-mediated interactions, the nuanced analysis discerns implications for both caregivers and caretakers, contributing to the evolving understanding of emotional practices in a technologically mediated healthcare environment. Within applied philosophy, the study transcends empirical observations, positioning itself as a reflective exploration of the moral implications of AI in healthcare. The findings are intended to inform ethical considerations and policy formulations, bridging the gap between technological advancements and the enduring values of caregiving. In conclusion, this focused philosophical inquiry aims to provide a foundational understanding of the evolving landscape of virtual healthcare, drawing on the works of Mol and Pols to illuminate the essence of human connection, care, and empathy amid technological advancements.

Keywords: applied philosophy, artificial intelligence, healthcare, philosophy of care, philosophy of emotions

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Authors: Zzibu Mark Brian

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As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

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Authors: Nermin Noshi Esraeil Abdalla

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Inclusive schooling offerings for college kids with Autism stays in its early developmental levels in Thailand. despite many greater youngsters with autism are attending schools since the Thai authorities brought the training Provision for human beings with Disabilities Act in 2008, the services students with autism and their families obtain are typically missing. This quantitative examine used attitude and Preparedness to educate college students with Autism Scale (APTSAS) to investigate 110 number one faculty teachers’ attitude and preparedness to educate college students with autism inside the widespread training school room. Descriptive statistical evaluation of the records discovered that scholar behavior changed into the most good sized factor in constructing teachers’ terrible attitudes students with autism. the majority of teachers additionally indicated that their pre-service schooling did not put together them to fulfill the mastering needs of children with autism especially, folks who are non-verbal. The take a look at is substantial and offers path for enhancing trainer education for inclusivity in Thailand.

Keywords: attitude, autism, teachers, sports activities, movement skills, motor skills

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Authors: Thomas Rockstuhl, Soon Ang, Kok Yee Ng, Linn Van Dyne

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Intercultural interactions are often challenging and fraught with conflicts. To shed light on how to interact effectively across cultures, academics and practitioners alike have advanced a plethora of intercultural competence models. However, the majority of this work has emphasized distal outcomes, such as job performance and cultural adjustment, rather than proximal outcomes, such as how individuals resolve inevitable intercultural conflicts. As a consequence, the processes by which individuals negotiate challenging intercultural conflicts are not well understood. The current study advances theorizing on intercultural conflict resolution by exploring antecedents of how people resolve intercultural conflicts. To this end, we examine creativity – the generation of novel and useful ideas – in the context of resolving cultural conflicts in intercultural interactions. Based on the dual-identity theory of creativity, we propose that individuals with greater international experience will display greater creativity and that the relationship is accentuated by individual’s cultural intelligence. Two studies test these hypotheses. The first study comprises 84 senior university students, drawn from an international organizational behavior course. The second study replicates findings from the first study in a sample of 89 executives from eleven countries. Participants in both studies provided protocols of their strategies for resolving two intercultural conflicts, as depicted in two multimedia-vignettes of challenging intercultural work-related interactions. Two research assistants, trained in intercultural management but blind to the study hypotheses, coded all strategies for their novelty and usefulness following scoring procedures for creativity tasks. Participants also completed online surveys of demographic background information, including their international experience, and cultural intelligence. Hierarchical linear modeling showed that surprisingly, while international experience is positively associated with usefulness, it is unrelated to novelty. Further, a person’s cultural intelligence strengthens the positive effect of international experience on usefulness and mitigates the effect of international experience on novelty. Theoretically, our findings offer an important theoretical extension to the dual-identity theory of creativity by identifying cultural intelligence as an important individual difference moderator that qualifies the relationship between international experience and creative conflict resolution. In terms of novelty, individuals higher in cultural intelligence seem less susceptible to rigidity effects of international experiences. Perhaps they are more capable of assessing which aspects of culture are relevant and apply relevant experiences when they brainstorm novel ideas. For utility, individuals high in cultural intelligence are better able to leverage on their international experience to assess the viability of their ideas because their richer and more organized cultural knowledge structure allows them to assess possible options more efficiently and accurately. In sum, our findings suggest that cultural intelligence is an important and promising intercultural competence that fosters creative resolutions to intercultural conflicts. We hope that our findings stimulate future research on creativity and conflict resolution in intercultural contexts.

Keywords: cultural Intelligence, intercultural conflict, intercultural creativity, international experience

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Authors: Nelvin Joseph, K. Krishna Milan Rao, Praveen Dwarakanath

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The explosive growth of Smart phones around the world has led to the shift of the primary engagement tool for entertainment from traditional consoles and music players to an all integrated device. Augmented Reality is the next big shift in bringing in a new dimension to the play. The paper explores the construct and working of the community engine in Delta T – an Augmented Reality game that allows users to evolve rules in the game basis collective bargaining mirroring democracy even in a gaming world.

Keywords: augmented reality, artificial neural networks, mobile application, human computer interaction, community engine

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Authors: Nael Y. Zabel, Maged E. Georgy, Moheeb E. Ibrahim

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A knowledge-based expert system with the acronym RASPE is developed as an application tool to help decision makers in construction companies make informed decisions about managing risks in pipeline construction projects. Choosing to use expert systems from all available artificial intelligence techniques is due to the fact that an expert system is more suited to representing a domain’s knowledge and the reasoning behind domain-specific decisions. The knowledge-based expert system can capture the knowledge in the form of conditional rules which represent various project scenarios and potential risk mitigation/response actions. The built knowledge in RASPE is utilized through the underlying inference engine that allows the firing of rules relevant to a project scenario into consideration. This paper provides an overview of the knowledge acquisition process and goes about describing the knowledge structure which is divided up into four major modules. The paper shows one module in full detail for illustration purposes and concludes with insightful remarks.

Keywords: expert system, knowledge management, pipeline projects, risk mismanagement

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Authors: J. S. Dhillon, K. K. Dhaliwal

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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Rady Farag Aziz Ibrahim

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The gap between Islamic terrorism and human rights has become an important issue in the fight against Islamic terrorism worldwide. This situation is repeated because terrorism and human rights are interconnected in such a way that when the former begins, the latter becomes subject to violence. This unknown relationship was recognized in the Vienna Declaration and Program of Action adopted at the International Conference on Human Rights held in Vienna on 25 June 1993, confirming that terrorist acts, in all their forms and manifestations, aim to destroy the rights of individuals. humanity to destroy. Therefore, Islamic terrorism is a violation of basic human rights. For this purpose, the first part of the article will focus on the relationship between terrorism and human rights and the synergy between these two concepts. The second part then explores the emerging concept of cyber threats and how they exist. Additionally, technology analysis will be conducted against threats based on human rights. This will be achieved through analysis of the concept of 'securitization' of human rights and by striking a balance between counter-terrorism measures and the protection of human rights at all costs. This article concludes with recommendations on how to balance terrorism and human rights today.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development

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Authors: Nidhi Sharotri, Dhiraj Sud

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Water pollution by pesticides constitutes a serious ecological problem due to their potential toxicity and bioaccumulation. The widespread use of pesticides in industry and agriculture along with their resistance to natural decomposition, biodegradation, chemical and photochemical degradation under typical environmental conditions has resulted in the emergence of these chemicals and their transformed products in natural water. Among AOP’s, heterogeneous photocatalysis using TiO2 as photocatalyst appears as the most emerging destructive technology for mineralization of the pollutant in aquatic streams. Among the various semiconductors (TiO2, ZnO, CdS, FeTiO3, MnTiO3, SrTiO2 and SnO2), TiO2 has proven to be the most efficient photocatalyst for environmental applications due to its biological and chemical inertness, high photo reactivity, non-toxicity, and photo stability. Semiconductor photocatalysts are characterized by an electronic band structure in which valence band and conduction band are separated by a band gap, i.e. a region of forbidden energy. Semiconductor based photocatalysts produces e-/h+ pairs which have been employed for degradation of organic pollutants. The present paper focuses on modification of TiO2 photocatalyst in order to shift its absorption edge towards longer wavelength to make it active under natural light. Semiconductor TiO2 photocatalysts was prepared by doping with anion (N), cation (Mn) and double doped (Mn, N) using greener approach. Titanium isopropoxide is used as titania precursor and ethanedithiol, hydroxyl amine hydrochloride, manganous chloride as sulphur, nitrogen and manganese precursors respectively. Synthesized doped TiO2 nanomaterials are characterized for surface morphology (SEM, TEM), crystallinity (XRD) and optical properties (absorption spectra and band gap). EPR data confirms the substitutional incorporation of Mn2+ in TiO2 lattice. The doping influences the phase transformation of rutile and anatase phase crystal and thereby the absorption spectrum changes were observed. The effect of variation of reaction parameters such as solvent, reaction time and calcination temperature on the yield, surface morphology and optical properties was also investigated. The TEM studies show the particle size of nanomaterials varies from 10-50 nm. The calculated band gap of nanomaterials varies from 2.30-2.60 eV. The photocatalytic degradation of organic pollutant organophosphate pesticide (Quinalphos) has been investigated by studying the changes in UV absorption spectrum and the promising results were obtained under visible light. The complete mineralization of quinalphos has occurred as no intermediates were recorded after 8 hrs of degradation confirmed from the HPLC studies.

Keywords: quinalphos, doped-TiO2, mineralization, EPR

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Authors: Raquel M. De sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of a higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses an artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of backpropagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this case iodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: artificial neural networks, biodiesel, iodine value, prediction

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Authors: Jalal Abdulkareem Sultan, Abdulhakeem Luqman Hasan

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With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use.

Keywords: bed allocation problem, fuzzy logic, artificial bee colony, multi-objective optimization

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Authors: Matteo Calabrese, Martin Cimmino, Dimos Kapetis, Martina Manfrin, Donato Concilio, Giuseppe Toscano, Giovanni Ciandrini, Giancarlo Paccapeli, Gianluca Giarratana, Marco Siciliano, Andrea Forlani, Alberto Carrotta

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In this paper we describe a machine learning methodology for Predictive Maintenance (PdM) applied on woodworking industrial machines. PdM is a prominent strategy consisting of all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the challenges with PdM approach is to design and develop of an embedded smart system to enable the health status of the machine. The proposed approach allows screening simultaneously multiple connected machines, thus providing real-time monitoring that can be adopted with maintenance management. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime of woodworking machines. The effectiveness of the methodology is demonstrated by testing an independent sample of additional woodworking machines without presenting machine down event.

Keywords: predictive maintenance, machine learning, connected machines, artificial intelligence

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Authors: Agarana Michael C., Akinlabi Esther T., Pule Kholopane

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In order to sustain the advantages of an advanced manufacturing technique, such as laser sintering, minimization of total processing cost of the parts being produced is very important. An efficient time management would usually very important in optimal cost attainment which would ultimately result in an efficient advanced manufacturing process planning and control. During Laser Scanning Process Scanning (SLS) procedures it is possible to adjust various manufacturing parameters which are used to influence the improvement of various mechanical and other properties of the products. In this study, Modelling and mathematical analysis, including sensitivity analysis, of the laser sintering process time were carried out. The results of the analyses were represented with graphs, from where conclusions were drawn. It was specifically observed that achievement of optimal total scanning time is key for economic efficiency which is required for sustainability of the process.

Keywords: modeling and analysis, optimal planning and control, laser sintering process, scanning time

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Authors: Itumeleng Judith Maome, Robert Walter Dumisani Zondo

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The concept of Corporate Social Responsibility (CSR) has gained popularity as a management philosophy in companies. They integrate social and environmental concerns into their operations and interactions with stakeholders. While CSR has mostly been associated with large organisations, it contributes to societal goals by engaging in activities or supporting volunteering or ethically oriented practices. However, small and medium enterprises (SMEs) have been recognised for their contributions to the social and economic development of any country. Consequently, this study examines the effect of CSR practices on human resource performance in the selected manufacturing SME in South Africa. This study was quantitative in design and examined the production and related experiences of the manufacturing SME organisation that had adopted a CSR strategy for human resource improvement. The study was achieved by collecting pre- and post-quarterly data, overtime, for employee turnover and labour absenteeism for analysis using the regression model. The results indicate that both employee turnover and labour absenteeism have no relationship with human resource performance post-CSR implementation. However, CSR has a relationship with human resource performance. Any increase in CSR activities results in an increase in human resource performance.

Keywords: corporate social responsibility, employee turnover, human resource, labour absenteeism, manufacturing SME

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Authors: Neil Erick Q. Madariaga, Noel B. Linsangan

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Body Mass Index (BMI) is one of the different ways to monitor the health of a person. It is based on the height and weight of the person. This study aims to compute for the BMI using an Android tablet by obtaining the height of the person by using a camera and measuring the weight of the person by using a weighing scale or load cell. The height of the person was estimated by applying background subtraction to the image captured and applying different processes such as getting the vanishing point and applying Artificial Neural Network. The weight was measured by using Wheatstone bridge load cell configuration and sending the value to the computer by using Gizduino microcontroller and Bluetooth technology after the amplification using AD620 instrumentation amplifier. The application will process the images and read the measured values and show the BMI of the person. The study met all the objectives needed and further studies will be needed to improve the design project.

Keywords: body mass index, artificial neural network, vanishing point, bluetooth, wheatstone bridge load cell

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Authors: Bunty Tomar, Shiva S.

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Fabrication of steel and copper-based functionally graded material (FGM) through cold metal transfer-based wire arc additive manufacturing is a novel exploration. Components combining Cu and steel show significant usage in many industrial applications as they combine high corrosion resistance, ductility, thermal conductivity, and wear resistance to excellent mechanical properties. Joining steel and copper is challenging due to the mismatch in their thermo-mechanical properties. In this experiment, a functionally graded material (FGM) structure of pure copper (Cu) and 316L stainless steel (SS) was successfully developed using cold metal transfer-based wire arc additive manufacturing (CMT-WAAM). The interface of the fabricated samples was characterized under optical microscopy, field emission scanning electron microscopy, and X-ray diffraction techniques. Detailed EBSD and TEM analysis was performed to analyze the grain orientation, strain distribution, grain boundary misorientations, and formation of metastable and intermetallic phases. Mechanical characteristics of deposits was also analyzed using tensile and wear testing. This works paves the way to use CMT-WAAM to fabricate steel/copper FGMs.

Keywords: wire arc additive manufacturing (waam), cold metal transfer (cmt), metals and alloys, mechanical properties, characterization

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Authors: Yingqi Cui, Changran Huang, Raymond Lee

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In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent.

Keywords: artificial intelligence, natural Language processing, knowledge graph, intelligent agents, QA system

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Irving Barragán-Vite

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Wind speed forecasting is an important issue for planning wind power generation facilities. The accuracy in the wind speed prediction allows a good performance of wind turbines for electricity generation. A model based on artificial neural networks is presented in this work. A dataset with atmospheric information about air temperature, atmospheric pressure, wind direction, and wind speed in Pachuca, Hidalgo, México, was used to train the artificial neural network. The data was downloaded from the web page of the National Meteorological Service of the Mexican government. The records were gathered for three months, with time intervals of ten minutes. This dataset was used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The model with the best performance contains three hidden layers and 9, 6, and 5 neurons, respectively; and the coefficient of determination obtained was r²=0.9414, and the Root Mean Squared Error is 1.0559. In summary, the ANN approach is suitable to predict the wind speed in Pachuca City because the r² value denotes a good fitting of gathered records, and the obtained ANN model can be used in the planning of wind power generation grids.

Keywords: wind power generation, artificial neural networks, wind speed, coefficient of determination

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Authors: Ahmed M. Ashteyat

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Artificial Neural Network (ANN) models have been widely used in material modeling, inter-correlations, as well as behavior and trend predictions when the nonlinear relationship between system parameters cannot be quantified explicitly and mathematically. In this paper, ANN was used to predict the residual modulus of elasticity (RME) of self compacted concrete (SCC) damaged by heat. The ANN model was built, trained, tested and validated using a total of 112 experimental data sets, gathered from available literature. The data used in model development included temperature, relative humidity conditions, mix proportions, filler types, and fiber type. The result of ANN training, testing, and validation indicated that the RME of SCC, exposed to different temperature and relative humidity levels, could be predicted accurately with ANN techniques. The reliability between the predicated outputs and the actual experimental data was 99%. This show that ANN has strong potential as a feasible tool for predicting residual elastic modulus of SCC damaged by heat within the range of input parameter. The ANN model could be used to estimate the RME of SCC, as a rapid inexpensive substitute for the much more complicated and time consuming direct measurement of the RME of SCC.

Keywords: residual modulus of elasticity, artificial neural networks, self compacted-concrete, material modeling

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Authors: A. Ikhlef

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Image noise in computed tomography, is mainly caused by the statistical noise, system noise reconstruction algorithm filters. Since last few years, low dose x-ray imaging became more and more desired and looked as a technical differentiating technology among CT manufacturers. In order to achieve this goal, several technologies and techniques are being investigated, including both hardware (integrated electronics and photon counting) and software (artificial intelligence and machine learning) based solutions. From a hardware point of view, electronic noise could indeed be a potential driver for low and ultra-low dose imaging. We demonstrated that the reduction or elimination of this term could lead to a reduction of dose without affecting image quality. Also, in this study, we will show that we can achieve this goal using conventional electronics (low cost and affordable technology), designed carefully and optimized for maximum detective quantum efficiency. We have conducted the tests using large imaging objects such as 30 cm water and 43 cm polyethylene phantoms. We compared the image quality with conventional imaging protocols with radiation as low as 10 mAs (<< 1 mGy). Clinical validation of such results has been performed as well.

Keywords: computed tomography, electronic noise, scintillation detector, x-ray detector

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Authors: Nicodemus Tiendem, Arrey Mbayong Napoleon

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The heightening of competition and the quest for market share has left business persons and research communities re-examining and reinventing their competitive practices. A case in point is Porter’s generic strategy which has received a lot of criticism lately regarding its inability to maintain a company’s competitive power. This is because it focuses more on the organisation and ignores her external partners, who have a strong bearing on the company’s performance. This paper, therefore, sought to examine Porter’s generic strategies alongside supply chain quality management practices in terms of their effectiveness in building the competitive power of manufacturing companies in Cameroon. This was done with the use of primary data captured from a survey study across the supply chains of 20 manufacturing companies in Cameroon using a five-point Likert scale questionnaire. For each company, four 1st tier suppliers and four 1st tier distributors were carefully chosen to participate in the study alongside the companies themselves. In each case, attention was directed to persons involved in the supply chains of the companies. This gave a total of 180 entities comprising the supply chains of the 20 manufacturing companies involved in the study, making a total of 900 participants. The data was analysed using three multiple regression models to assess the effect of Porter’s generic strategy and supply chain quality management on the marketing performance of the companies. The findings proved that in such a competitive atmosphere, supply chain quality management is a better tool for marketing performance over Porter’s generic strategies and hence building the competitive power of the companies at all levels of the study. Although the study made use of convenience sampling, where sample selectivity biases the results, the findings aligned with many other recent developments in line with building the competitive power of manufacturing companies and thereby made the findings suitable for generalisation.

Keywords: supply chain quality management, Porter’s generic strategies, competitive power, marketing performance, manufacturing companies, Cameroon

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Authors: Nermeen El Kashef, Yasser Fouad, Khaled Mahar

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No AI-complete system can model the human brain or behavior, without looking at the totality of the whole situation and incorporating a combination of senses. This paper proposes a Pattern Recognition model based on Simulation of Chemical Senses (SCS) for separation and classification of sign language. The model based on human taste controlling strategy. The main idea of the introduced model is motivated by the facts that the tongue cluster input substance into its basic tastes first, and then the brain recognizes its flavor. To implement this strategy, two level architecture is proposed (this is inspired from taste system). The separation-level of the architecture focuses on hand posture cluster, while the classification-level of the architecture to recognizes the sign language. The efficiency of proposed model is demonstrated experimentally by recognizing American Sign Language (ASL) data set. The recognition accuracy obtained for numbers of ASL is 92.9 percent.

Keywords: artificial intelligence, biocybernetics, gustatory system, sign language recognition, taste sense

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Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Paridhi Agarwal, Kusum M. George

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A cynic is someone upset about the future prematurely. In today’s highly competitive workplace, cynicism has become a prominent concern. It is a controversial issue that brings about psychological disengagement and antagonism towards the management. In organizational sciences, scientific investigation of this negative work behavior is lacking, and so there is no universal definition so far. But most commonly, Organizational Cynicism (OC) has been characterized as an unfavorable attitude towards the organization, encompassing a belief that the organization has low integrity, negative affect, and depreciative behavioral tendencies. Given its prevalence, this study aims to contribute to the existing body of knowledge on OC. This research examines the predictability of OC from two factors- Perceived Organizational Support (POS) and Emotional Intelligence (EI) among millennials in India as well as identify contradictions in today’s scenario. Standardized Organizational Cynicism Scale comprising of three components, Perceived Organizational Support Questionnaire and Goleman’s Emotional Intelligence Test are used on a convenient sample of 104 corporate sector employees in the age range 22-35 years. Correlation test elucidated the relationships, and regression analysis revealed the level of influence of the above variables on OC. Surprisingly, Emotional-Social Awareness had stronger relationships with all dimensions of OC in males as compared to females. It was also seen that EI and POS, together with predicted OC, but separately, only POS accounted for variability in OC, and this impact was much stronger for males, implying that there are other important factors that make females cynical at work. Thus, the over-emphasis on EI training for the millennial generation has also been challenged in this study. It can be said that there are avertible preconditions to the negative attitude- OC. This research has important managerial implications in areas of recruitment, training, and organizational environment.

Keywords: emotional intelligence, millennials, organizational cynicism, perceived organizational support.

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Authors: Tharwat Girgis Farag Girgis

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The link between development and human rights has long been the subject of scholarly debate. As a result, a number of principles have been adopted, from the right to development to the human rights-based development approach, to understand the dynamics between the two concepts. Despite the initiatives taken, the exact relationship between development and human rights remains unclear. However, the rapprochement between the two concepts and the need for development efforts regarding human rights have increased in recent years. On the other hand, the emergence of sustainable development as an acceptable method in development goals and policies makes this consensus even more unstable. The place of sustainable development in the legal debate on human rights and its role in promoting sustainable development programs require further research. Therefore, this article attempts to map the relationship between development and human rights, with particular emphasis on the place given to sustainable development principles in international human rights law. It will continue to investigate whether it recognizes sustainable development rights. The article will therefore give a positive answer to question mentioned here. The jurisprudence and interpretive guidelines of human rights institutions travel to confirm this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Mobarok Hossain Bhuyain

Abstract:

Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

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Authors: Urva Maryam

Abstract:

The information has become an important asset to the current cosmos. Globally, various tactics are being observed to confine the spread of information as it makes people vulnerable to security attacks. Open Source Intelligence (OSINT) is a publicly available source that has disseminated information about users or websites, companies, and various organizations. This paper focuses on the quantitative method of exploring various OSINT tools that reveal public information of personals. This information could further facilitate phishing attacks. Phishing attacks can be launched on email addresses, open ports, and unsecure web-surfing. This study allows to analyze the information retrieved from OSINT tools, i.e. theHarvester, and Maltego that can be used to send phishing attacks to individuals.

Keywords: e-mail spoofing, Maltego, OSINT, phishing, spear phishing, theHarvester

Procedia PDF Downloads 148

Authors: Adrian Travieso Disotuar, J. Antonio Travieso Rodriguez, Ramon Jerez Mesa, Montserrat Vilaseca

Abstract:

The effect of the non-vibration-assisted and vibration-assisted ball burnishing on both the surface and mechanical properties of C300 obtained by Selective Laser Melting additive manufacturing technology is studied in this paper. Different vibration amplitudes preloads, and burnishing strategies were tested. A topographical analysis was performed to determine the surface roughness of the different conditions. Besides, micro tensile tests were carried out in situ on Scanning Electron Microscopy to elucidate the post-treatment effects on damaging mechanisms. Experiments show that vibration-assisted ball burnishing significantly enhances mechanical properties compared to the non-vibration-assisted method. Moreover, it was found that the surface roughness was significantly improved with respect to the reference surface.

Keywords: additive manufacturing, ball burnishing, mechanical properties, metals, surface roughness

Procedia PDF Downloads 80