Search results for: machine tools

Authors: Supriya Akerkar

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The UN Decade on Healthy Ageing (2021-2030) provides a new opportunity to address ageing and gender issues in different societies. The concept of gender has been used to unpack and analyse the power and constructions of gender relations in different societies. Such analysis has been employed and used to inform policy and practices of governments and non-governmental organisations to further gender equalities in their work. Yet, experiences of older women and men are often left out of such mainstream gender analysis, marginalising their existence and issues. This paper argues that new critical analytical tools are needed to capture the realities and issues of interest to older women and men. In particular, it argues that gender analysis needs to integrate analytical concepts of ageing and lifecycle approach in its framework. The paper develops such a framework by critical interrogation of the gender analysis tools that are currently applied for framing gender issues in international development and humanitarian work. Informed by the realities and experiences of older women and men, developed through a synthesis of available literature, the paper will develop a new framework for gender analysis that can be used by governments and non-government organisations in their work to further gender justice across the life cycle.

Keywords: ageing, gender, older people, social inclusion

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Authors: Lucas Paganin, Viliam Makis

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With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy.

Keywords: maintenance, semi-Markov decision process, statistical process control, Xbar control chart

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Authors: Kuribachew Gizaw Tohiye, Monica Garfield

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Personal knowledge management is the aspect of knowledge management that relates to the way in which individuals organize and manage their own set of knowledge. While in that respect, there has been research in this area for the past 25 years, it is at present necessary to speculate upon what research has been done and what we have discovered about this arena of knowledge management. In contrast to organizational knowledge management, which focuses on a firm’s profitability and competitiveness, personal knowledge management (PKM) is concerned with the person’s self-effectiveness, competence and success. People are concerned in managing their knowledge in order to become more efficient in a variety of personal and organizational interests. This study presents a systematic review of PKM studies. Articles with PKM concepts are reviewed with the objective of clearly defining PKM, identifying the benefits of PKM, classifying the tools that enable PKM and finding the research gaps to indicate future research directions in the area. Consequently, we have developed a definition of PKM and identified the benefits of PKM, including an understanding of who seeks PKM and for what. Tools enabling PKM are identified and classified under three categories Web 1.0, 2.0 and 3.0 and finally the research gap and future directions are suggested. Research which facilitates collaboration by using semantic technologies is suggested to be studied further to improve PKM effectiveness.

Keywords: personal knowledge management, knowledge management, organizational knowledge management, systematic review

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Authors: Mohd Anuar Awang Idris, Muhammad Nubli Abdul Wahab, Nora Yusma Mohamed Yusoff

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Wudu’ (Ablution) and Zikr are amongst some of the spiritual tools which may help an individual control his mind, emotion and attitude. These tools are deemed to be able to deliver a positive impact on an individual’s psychophysiology. The main objective of this research is to determine the effects of Wudu’ (Ablution) and Zikr therapy using the biofeedback emWave application and technology. For this research, 13 students were selected as samples from the students’ representative body at the University Tenaga National, Malaysia. The DASS (Depression Anxiety Stress Scale) questionnaire was used to help with the assessment and measurement of each student’s ability in controlling his or her emotions before and after the therapies. The biofeedback emWave technology was utilized to monitor the student’s psychophysiology level. In addition, the data obtained from the Heart rate variability (HRV) test have also been used to affirm that Wudu’ and Zikr had had significant impacts on the student’s success in controlling his or her emotional pressure.

Keywords: biofeedback EmWave, emotion, psychophysiology, wudu’, zikr

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Authors: Till Gramberg

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In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Alberto Abello Moreno-Cid

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Mummification was the process through which immortality was granted to the deceased, so it was of extreme importance to the Egyptians. The techniques of embalming had evolved over the centuries, and specialists created increasingly sophisticated tools. However, due to its eminently religious nature, knowledge about everything related to this practice was jealously preserved, and the testimonies that have survived to our time are scarce. For this reason, embalming instruments found in archaeological excavations are uncommon. The tomb of the Vizier Amenhotep Huy (AT No. -28-), located in the el-Assasif necropolis that is being excavated since 2009 by the team of the Institute of Ancient Egyptian Studies, has been the scene of some discoveries of this type that evidences the existence of mummification practices in this place after the New Kingdom. The clysters or enemas are the fundamental tools in the second type of mummification described by the historian Herodotus to introduce caustic solutions inside the body of the deceased. Nevertheless, such objects only have been found in three locations: the tomb of Ankh-Hor in Luxor, where a copper enema belonged to the prophet of Ammon Uah-ib-Ra came to light; the excavation of the tomb of Menekh-ib-Nekau in Abusir, where was also found one made of copper; and the excavations in the Bucheum, where two more artifacts were discovered, also made of copper but in different shapes and sizes. Both of them were used for the mummification of sacred animals and this is the reason they vary significantly. Therefore, the object found in the tomb No. -28-, is the first known made of faience of all these peculiar tools and the oldest known until now, dated in the Third Intermediate Period (circa 1070-650 B.C.). This paper bases its investigation on the study of those parallelisms, the material, the current archaeological context and the full analysis and reconstruction of the object in question. The key point is the use of faience in the production of this item: creating a device intended to be in constant use seems to be a first illogical compared to other samples made of copper. Faience around the area of Deir el-Bahari had a strong religious component, associated with solar myths and principles of the resurrection, connected to the Osirian that characterises the mummification procedure. The study allows to refute some of the premises which are held unalterable in Egyptology, verifying the utilization of these sort of pieces, understanding its way of use and showing that this type of mummification was also applied to the highest social stratum, in which case the tools were thought out of an exceptional quality and religious symbolism.

Keywords: clyster, el-Assasif, embalming, faience enema mummification, Theban necropolis

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Stephanie Liu Lu

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With the proliferation of artificial intelligence tools in the field of education, it is crucial to explore their impact on language learning outcomes. This paper examines the use of AI tools, such as ChatGPT, in practical writing within business Chinese teaching to investigate how AI can enhance practical writing skills and teaching effectiveness. The study involved third and fourth-year university students majoring in accounting and finance from a university in Hong Kong within the context of a business correspondence writing class. Students were randomly assigned to a control group, who completed business letter writing independently, and an experimental group, who completed the writing with the assistance of AI. In the latter, the AI-assisted business letters were initially drafted by the students issuing commands and interacting with the AI tool, followed by the students' revisions of the draft. The paper assesses the performance of both groups in terms of grammatical expression, communicative effect, and situational awareness. Additionally, the study collected dialogue texts from interactions between students and the AI tool to explore factors that affect text generation and the potential impact of AI on enhancing students' communicative and identity awareness. By collecting and comparing textual performances, it was found that students assisted by AI showed better situational awareness, as well as more skilled organization and grammar. However, the research also revealed that AI-generated articles frequently lacked a proper balance of identity and writing purpose due to limitations in students' communicative awareness and expression during the instruction and interaction process. Furthermore, the revision of drafts also tested the students' linguistic foundation, logical thinking abilities, and practical workplace experience. Therefore, integrating AI tools and related teaching into the curriculum is key to the future of business Chinese teaching.

Keywords: AI-assistance, business Chinese, textual analysis, language education

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: M. H. Arslan, U. K. Sahin, H. Acikgoz-Tufan, I. Gocek, I. Erdem

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Various exterior factors have perpetual effects on textile materials during wear, use and laundering in everyday life. In accordance with their frequency of use, textile materials are required to be laundered at certain intervals. The medium in which the laundering process takes place have inevitable detrimental physical and chemical effects on textile materials caused by the unique parameters of the process inherently existing. Connatural structures of various textile materials result in many different physical, chemical and mechanical characteristics. Because of their specific structures, these materials have different behaviors against several exterior factors. By modeling the behavior of commercial and test textiles as group-wise against laundering process, it is possible to disclose the relation in between these two groups of materials, which will lead to better understanding of their behaviors in terms of similarities and differences against the washing parameters of the laundering. Thus, the goal of the current research is to examine the behavior of two groups of textile materials as commercial textiles and as test textiles towards the main washing machine parameters during laundering process such as temperature, load quantity, mechanical action and level of water amount by concentrating on shrinkage, pilling, sewing defects, collar abrasion, the other defects other than sewing, whitening and overall properties of textiles. In this study, cotton fabrics were preferred as commercial textiles due to the fact that garments made of cotton are the most demanded products in the market by the textile consumers in daily life. Full factorial experimental set-up was used to design the experimental procedure. All profiles always including all of the commercial and the test textiles were laundered for 20 cycles by commercial home laundering machine to investigate the effects of the chosen parameters. For the laundering process, a modified version of ‘‘IEC 60456 Test Method’’ was utilized. The amount of detergent was altered as 0.5% gram per liter depending on varying load quantity levels. Datacolor 650®, EMPA Photographic Standards for Pilling Test and visual examination were utilized to test and characterize the textiles. Furthermore, in the current study the relation in between commercial and test textiles in terms of their performance was deeply investigated by the help of statistical analysis performed by MINITAB® package program modeling their behavior against the parameters of the laundering process. In the experimental work, the behaviors of both groups of textiles towards washing machine parameters were visually and quantitatively assessed in dry state.

Keywords: behavior against washing machine parameters, performance evaluation of textiles, statistical analysis, commercial and test textiles

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Hector G. Perez-Gonzalez, Miriam Vazquez-Escalante, Sandra E. Nava-Muñoz, 
 Francisco E. Martinez-Perez, Alberto S. Nunez-Varela

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The software engineering teaching process has been the subject of many studies. To improve this process, researchers have proposed merely illustrative techniques in the classroom, such as topic presentations and dynamics between students on one side or attempts to involve students in real projects with companies and institutions to bring them to a real software development problem on the other hand. Simulators and serious games have been used as auxiliary tools to introduce students to topics that are too abstract when these are presented in the traditional way. Most of these tools cover a limited area of the huge software engineering scope. To address this problem, we have developed D6tions, an educational serious game that simulates the software engineering process and is designed to experiment the different stages a software engineer (playing roles as project leader or as a developer or designer) goes through, while participating in a software project. We describe previous approaches to this problem, how D6tions was designed, its rules, directions, and the results we obtained of the use of this game involving undergraduate students playing the game.

Keywords: serious games, software engineering, software engineering education, software engineering teaching process

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Authors: Ahmad Yame, Ahad Ali, Badih Jawad, Daw Al-Werfalli Mohamed Nasser, Sabah Abro

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Industries in general have a lot of waste. Wool textile company, Baniwalid, Libya has many complex problems that led to enormous waste generated due to the lack of lean strategies, expertise, technical support and commitment. To successfully address waste at wool textile company, this study will attempt to develop a methodical approach that integrates lean manufacturing tools to optimize performance characteristics such as lead time and delivery. This methodology will utilize Value Stream Mapping (VSM) techniques to identify the process variables that affect production. Once these variables are identified, Design of Experiments (DOE) Methodology will be used to determine the significantly influential process variables, these variables are then controlled and set at their optimal to achieve optimal levels of productivity, quality, agility, efficiency and delivery to analyze the outputs of the simulation model for different lean configurations. The goal of this research is to investigate how the tools of lean manufacturing can be adapted from the discrete to the continuous manufacturing environment and to evaluate their benefits at a specific industrial.

Keywords: lean manufacturing, DOE, value stream mapping, textiles

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Authors: Sultan Samah A Almjlad

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The translation industry in Saudi Arabia is experiencing significant shifts driven by Vision 2030, which aims to diversify the economy and enhance international engagement. This change highlights the need for translators who are skilled in various languages and cultures, playing a crucial role in the nation's global integration efforts. However, there's a notable gap between the skills taught in academic institutions and what the job market demands. Many translation programs in Saudi universities don't align well with industry needs, resulting in graduates who may not meet employer expectations. To tackle this challenge, it's essential to thoroughly analyze the market to identify the key skills required, especially in sectors like legal, medical, technical, and audiovisual translation. At the same time, existing translation programs need to be evaluated to see if they cover necessary topics and provide practical training. Involving stakeholders such as translation agencies, professionals, and students is crucial to gather diverse perspectives. Identifying discrepancies between academic offerings and market demands will guide the development of targeted strategies. These strategies may include enriching curricula with industry-specific content, integrating emerging technologies like machine translation and CAT tools, and establishing partnerships with industry players to offer practical training opportunities and internships. Industry-led workshops and seminars can provide students with valuable insights, and certification programs can validate their skills. By aligning academic programs with industry needs, Saudi Arabia can build a skilled workforce of translators, supporting its economic diversification goals under Vision 2030. This alignment benefits both students and the industry, contributing to the growth of the translation sector and the overall development of the country.

Keywords: translation industry, briging gap, labor market, requirements

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Hongmei Chi

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The Blockchain has become a necessity for many different societal industries and ordinary lives including cryptocurrency technology, supply chain, health care, public safety, education, etc. Therefore, training our future blockchain developers to know blockchain programming vulnerability and I.T. students' cyber security is in high demand. In this work, we propose a framework including learning modules and hands-on labs to guide future I.T. professionals towards developing secure blockchain programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle following the concept of Secure Software Development Life Cycle (SSDLC). In this research, our goal is to make blockchain programmers and I.T. students aware of the vulnerabilities of blockchains. In summary, we develop a framework that will (1) improve students' skills and awareness of blockchain source code vulnerabilities, detection tools, and mitigation techniques (2) integrate concepts of blockchain vulnerabilities for IT students, (3) improve future IT workers’ ability to master the concepts of blockchain attacks.

Keywords: software vulnerability detection, hands-on lab, static analysis tools, vulnerabilities, blockchain, active learning

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Authors: Safdari Reza, Zahmatkeshan Maryam, Goli Arji

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Background and Aim: Urban health is one of the challenges of the 21st century. Rapid growth and expanding urbanization have implications for health. In this regard, information technology can remove a large number of modern cities’ problems. Therefore, the present article aims to study modern information technologies in the development of urban health. Materials and Methods:. This is a review article based on library research and Internet searches on valid websites such as Science Direct, Magiran, Springer and advanced searches in Google. Some 164 domestic and foreign texts were studied on such topics as the application of ICT tools including cell phones and wireless tools, GIS, and RFID in the field of urban health in 2011. Finally, 30 sources were used. Conclusion: Information and communication technologies play an important role in improving people's health and enhancing the quality of their lives. Effective utilization of information and communication technologies requires the identification of opportunities and constraints, and the formulation of appropriate planning principles with regard to social and economic factors together with preparing the technological, communication and telecommunications, legal and administrative infrastructures.

Keywords: Urban Health, Information Technology, Information & Communication, Technology

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Renga Rao Krishnamoorthy, Raihan Tahir

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The quality of classroom learning and teaching delivery has been and will continue to be debated at various levels worldwide. The regional cooperation programme to improve the quality and labour market orientation of the Technical and Vocational Education and Training (RECOTVET), ‘Deutsche Gesellschaft für Internationale Zusammenarbeit’ (GIZ), in line with the sustainable development goals (SDG), has taken the initiative in the development of quality TVET in the ASEAN region by developing the Quality Toolbox for Better TVET Delivery (Quality Toolbox). This initiative aims to provide quick and practical materials to trainers, instructors, and personnel involved in education and training at an institute to shape the quality of classroom learning and teaching. The Quality Toolbox for Better TVET Delivery was developed in three stages: literature review and development, validation, and finalization. Thematic areas in the Quality Toolbox were derived from collective input of concerns and challenges raised from experts’ workshops through moderated sessions involving representatives of TVET institutes from 9 ASEAN Member States (AMS). The sessions were facilitated by professional moderators and international experts. TVET practitioners representing AMS further analysed and discussed the structure of the Quality Toolbox and content of thematic areas and outlined a set of specific requirements and recommendations. The application exercise of the Quality Toolbox was carried out by TVET institutes among ASM. Experience sharing sessions from participating ASEAN countries were conducted virtually. The findings revealed that TVET institutes use two types of approaches in shaping the quality of learning and teaching, which is ascribed to inductive or deductive, shaping of quality in learning and teaching is a non-linear process and finally, Q-tools can be adopted and adapted to shape the quality of learning and teaching at TVET institutes in the following: improvement of the institutional quality, improvement of teaching quality and improvement on the organisation of learning and teaching for students and trainers. The Quality Toolbox has good potential to be used at education and training institutes to shape quality in learning and teaching.

Keywords: AMS, GIZ, RECOTVET, quality tools

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Authors: Rahul Jain, Pradhir Parmar, Dhruvesh Patel

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Planning and drawing are the important aspects of civil engineering. For testing theories about spatial location and interaction between land uses and related activities the computer based solution of urban models are used. The planner’s primary interest is in creation of 3D models of building and to obtain the terrain surface so that he can do urban morphological mappings, virtual reality, disaster management, fly through generation, visualization etc. 3D city models have a variety of applications in urban studies. Gujarat International Finance Tec-City (GIFT) is an ongoing construction site between Ahmedabad and Gandhinagar, Gujarat, India. It will be built on 3590000 m2 having a geographical coordinates of North Latitude 23°9’5’’N to 23°10’55’’ and East Longitude 72°42’2’’E to 72°42’16’’E. Therefore to develop 3D city models of GIFT city, the base map of the city is collected from GIFT office. Differential Geographical Positioning System (DGPS) is used to collect the Ground Control Points (GCP) from the field. The GCP points are used for the registration of base map in QGIS. The registered map is projected in WGS 84/UTM zone 43N grid and digitized with the help of various shapefile tools in QGIS. The approximate height of the buildings that are going to build is collected from the GIFT office and placed on the attribute table of each layer created using shapefile tools. The Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global (30 m X 30 m) grid data is used to generate the terrain of GIFT city. The Google Satellite Map is used to place on the background to get the exact location of the GIFT city. Various plugins and tools in QGIS are used to convert the raster layer of the base map of GIFT city into 3D model. The fly through tool is used for capturing and viewing the entire area in 3D of the city. This paper discusses all techniques and their usefulness in 3D city model creation from the GCP, base map, SRTM and QGIS.

Keywords: 3D model, DGPS, GIFT City, QGIS, SRTM

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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Authors: I. Dumand, C. Clesse, M. Decker, C. Savini, J. Lighezzolo-Alnot

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In the landscape of French psychiatry, long-term institutionalization of patients with severe and disabling chronics disorders is common. Faced with the failures of classical reinsertion, sometimes these users are hurriedly considered as 'insortables'. However, this representation is often swayed by the current behavior of the patient observed through the clinical observation. Unfortunately, it seems that this way of proceeding can not integrate the potentialities of the institutionalized patients and their possible evolution. Therefore, in order not to make hasty conclusions about the life perspectives of these individuals, it seems essential to associate with clinical observation a psycho social rehabilitation diagnosis. Multidisciplinary, it combine all the aspects that make up the life of the subject (the life aspirations, psycho social determinants, family support, cognitive potential, symptoms ...). In this paper, we will rank these different aspects necessary prerequisites to the realization of a psycho social rehabilitation diagnosis. Then, we will specifically speak of the issue of psychological evaluation. By adopting an integrative approach combining neuro psychological tools (Grober and Buschke, Stroop, WCST, AIPSS, WAIS, Eyes test ...) and projective tools interpreted under a psycho dynamic angle (Rorschach, TAT ..) we think that we can grasp the patient in his globality. Thus, during this process we will justify the interest of combining a cognitive and a psycho affective approach, we will identify the different items assessed and their future implications on the everyday life of the users. Finally, we show that this diagnosis can give a chance to reintegration to 30% of patients considered as ''insortables''. In conclusion, we will highlight the importance of this process dear to the community psychology emphasizing in the same time the interests of this approach in terms of empowerment, recovery and quality of life.

Keywords: assessment, potentiality, psychosocial rehabilitation diagnosis, tools

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Ilyas Khan. Liliane Pintelon, Harry Martin, Michael Shömig

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The increasing costs of healthcare on one hand, and the rise in aging population and associated chronic disease, on the other hand, are putting increasing burden on the current health care system in many Western countries. For instance, chronic kidney disease (CKD) is a common disease and in Europe, the cost of renal replacement therapy (RRT) is very significant to the total health care cost. However, the recent advancement in healthcare technology, provide the opportunity to treat patients at home in their own comfort. It is evident that home healthcare offers numerous advantages apparently, low costs and high patients’ quality of life. Despite these advantages, the intake of home hemodialysis (HHD) therapy is still low in particular in Germany. Many factors are accounted for the low number of HHD intake. However, this paper is focusing on patients’ safety-related factors of current HHD practices in Germany. The aim of this paper is to analyze the current HHD practices in Germany and to identify risks related factors if any exist. A case study has been conducted in a dialysis center which consists of four dialysis centers in the south of Germany. In total, these dialysis centers have 350 chronic dialysis patients, of which, four patients are on HHD. The centers have 126 staff which includes six nephrologists and 120 other staff i.e. nurses and administration. The results of the study revealed several risk-related factors. Most importantly, these centers do not offer allied health services at the pre-dialysis stage, the HHD training did not have an established curriculum; however, they have just recently developed the first version. Only a soft copy of the machine manual is offered to patients. Surprisingly, the management was not aware of any standard available for home assessment and installation. The home assessment is done by a third party (i.e. the machines and equipment provider) and they may not consider the hygienic quality of the patient’s home. The type of machine provided to patients at home is similar to the one in the center. The model may not be suitable at home because of its size and complexity. Even though portable hemodialysis machines, which are specially designed for home use, are available in the market such as the NxStage series. Besides the type of machine, no assistance is offered for space management at home in particular for placing the machine. Moreover, the centers do not offer remote assistance to patients and their carer at home. However, telephonic assistance is available. Furthermore, no alternative is offered if a carer is not available. In addition, the centers are lacking medical staff including nephrologists and renal nurses.

Keywords: home hemodialysis, home hemodialysis practices, patients’ related risks in the current home hemodialysis practices, patient safety in home hemodialysis

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Authors: R. Pérez Castells, J. M. Carbajo

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Sepiolite is used as a processing aid in the manufacture of fibre cement from the start of the replacement of asbestos in the 80s. Sepiolite increases the inter-laminar bond between cement layers and improves homogeneity of the slurries. A new type of sepiolite processed product, Wollatrop TF/C, has been checked as a retention agent for fine particles in the production of fibre cement in a Hatschek machine. The effect of Wollatrop T/FC on filtering and fine particle losses was studied as well as the interaction with anionic polyacrylamide and microsilica. The design of the experiments were factorial and the VDT equipment used for measuring retention and drainage was modified Rapid Köethen laboratory sheet former. Wollatrop TF/C increased the fine particle retention improving the economy of the process and reducing the accumulation of solids in recycled process water. At the same time, drainage time increased sharply at high concentration, however drainage time can be improved by adjusting APAM concentration. Wollatrop TF/C and microsilica are having very small interactions among them. Microsilica does not control fine particle losses while Wollatrop TF/C does efficiently. Further research on APAM type (molecular weight and anionic character) is advisable to improve drainage.

Keywords: drainage, fibre-reinforced cement, fine particle losses, flocculation, microsilica, sepiolite

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Authors: Akinwale S. O., Koya O. A.

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The need to provide ecologically-friendly and effective lawn mowing solution is crucial for the well-being of humans. This study involved the modification of a human-powered lawn mower designed to cut tall grasses in residential areas. This study designed and fabricated a reel-type mower blade system and a pedal-powered test rig for the blade system. It also evaluated the performance of the machine. The machine was tested on some overgrown grass plots at College of Education Staff School Ilesa. Parameters such as theoretical field capacity, field efficiency and effective field capacity were determined from the data gathered. The quality of cut achieved by the unit was also documented. Test results showed that the fabricated cutting system produced a theoretical field capacity of 0.11 ha/h and an effective field capacity of 0.08ha/h. Moreover, the unit’s cutting system showed a substantial improvement over existing reel mower designs in its ability to cut on both the forward and reverse phases of its motion. This study established that the blade system described herein has the capacity to cut tall grasses. Hence, this device can therefore eliminate the need for powered mowers entirely on small residential lawns.

Keywords: effective field capacity, field efficiency, theoretical field capacity, quality of cut

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Authors: Chao-Ton Su, Li-Fei Chen

Abstract:

The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

Procedia PDF Downloads 129