Search results for: innovative approaches

Authors: Hossein Ramezani Ali-Akbari

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This paper presents an innovative method to control the rotational speed of a satellite solar panel during its deployment phase. A brushed DC motor has been utilized in the passive spring driven deployment mechanism to reduce the deployment speed. In order to use the DC motor as a damper, its connector terminals have been connected with an external resistance in a closed circuit. It means that, in this approach, there is no external power supply in the circuit. The working principle of this method is based on the back electromotive force (or back EMF) of the DC motor when an external torque (here the torque produced by the torsional springs) is coupled to the DC motor’s shaft. In fact, the DC motor converts to an electric generator and the current flows into the circuit and then produces the back EMF. Based on Lenz’s law, the generated current produced a torque which acts opposite to the applied external torque, and as a result, the deployment speed of the solar panel decreases. The main advantage of this method is to set an intended damping coefficient to the system via changing the external resistance. To produce the sufficient current, a gearbox has been assembled to the DC motor which magnifies the number of turns experienced by the DC motor. The coupled electro-mechanical equations of the system have been derived and solved, then, the obtained results have been presented. A full-scale prototype of the deployment mechanism has been built and tested. The potential application of brushed DC motors as a rotational speed damper has been successfully demonstrated.

Keywords: back electromotive force, brushed DC motor, rotational speed damper, satellite solar panel deployment mechanism

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Authors: Koko Wangjam, Naresh Kumar Sharma

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The concern for most public health policies and decision- makers is the equitable distribution of health care resource of the nation. Also, in public health care system, the primary aim is assuaging the burden of the disease. Objective: This paper captures and evaluates some important theories in equity in health with its relevance with the ART program in India. Methodology: The paper is exploratory and descriptive study based on secondary data. The sources of secondary data are published official reports from NACO (National AIDS Control Organisation), United Nations AIDS Program (UNAIDS), World Health Organisation (WHO) etc. Observation: The roll-out of the ART program in 2004 by the Govt. of India made a paradigm shift in HIV/AIDS scenario in the country. Conclusion: There are many theoretical injunctions in most of the principles and approaches in existing theories of health equity. The enervation of HIV infection by taking ART drugs had helped in curbing the prevalence and the fact that it is provided at free of cost has proven this program to be an epitome in distributive justice in public health.

Keywords: art program, burden of the disease, health equity, hiv/aids

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Sepideh Samadzadehyazdi, Mohammad Javad Khalili, Sarvenaz Samadzadehyazdi, Mohammad Javad Mahdavinejad

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The term ‘Windphil Architecture’ refers to the building that facilitates natural ventilation by architectural elements. Natural ventilation uses the natural forces of wind pressure and stacks effect to direct the movement of air through buildings. Natural ventilation is increasingly being used in contemporary buildings to minimize the consumption of non-renewable energy and it is an effective way to improve indoor air quality. The main objective of this paper is to identify the strategies of using natural ventilation in Iranian modern buildings. In this regard, the research method is ‘descriptive-analytical’ that is based on comparative techniques. To simulate wind flow in the interior spaces of case studies, FLUENT software has been used. Research achievements show that it is possible to use natural ventilation to create a thermally comfortable indoor environment. The natural ventilation strategies could be classified into two groups of environmental characteristics such as public space structure, and architectural characteristics including building form and orientation, openings, central courtyards, wind catchers, roof, wall wings, semi-open spaces and the heat capacity of materials. Having investigated modern buildings of Iran, innovative elements like wind catchers and wall wings are less used than the traditional architecture. Instead, passive ventilation strategies have been more considered in the building design as for the roof structure and openings.

Keywords: natural ventilation strategies, wind catchers, wind flow, Iranian modern buildings

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Authors: Jeeban Amgain

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This paper investigates the impact of the size and composition of government expenditure and tax on GDP per capita growth in 36 Asian economies over the period of 1991-2012. The research employs the technique of panel regression; Fixed Effects and Generalized Method of Moments (GMM) as well as other statistical and descriptive approaches. The finding concludes that the size of government expenditure and tax revenue are generally low in this region. GDP per capita growth is strongly negative in response to Government expenditure, however, no significant relationship can be measured in case of size of taxation although it is positively correlated with economic growth. Panel regression of decomposed fiscal components also shows that the pattern of allocation of expenditure and taxation really matters on growth. Taxes on international trade and property have a significant positive impact on growth. In contrast, a major portion of expenditure, i.e. expenditure on general public services, health and education are found to have significant negative impact on growth, implying that government expenditures are not being productive in the Asian region for some reasons. Comparatively smaller and efficient government size would enhance the growth.

Keywords: government expenditure, tax, GDP per capita growth, composition

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Authors: Tanya Beran, Michelle Drefs, Ghazwan Altabbaa, Nouf Al Harbi, Noof Al Baz, Elizabeth Oddone Paolucci

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Although research shows that interprofessional practice has desirable effects on patient care, its implementation can present challenges to its team members. In particular, they may feel pressured to agree with or conform to other members who share information that is contrary to their own understanding. Obtaining evidence of this phenomenon is challenging, as team members may underreport their conformity behaviors due to reasons such as social desirability. In this paper, a series of studies are reviewed in which several approaches to assessing conformity in the health care professions are tested. Simulations, questionnaires, and behavior checklists were developed to measure conformity behaviors. Insights from these studies show that a significant proportion of people conform either in the presence or absence of others, express a variety of verbal and nonverbal behaviors when considering whether to conform to others, may shift between conforming and moments later not conforming (and vice versa), and may not accurately report whether they conformed. A new method of measuring conformity using the implicit bias test is also discussed. People at all levels in the healthcare system are encouraged to develop both formal and informal.

Keywords: conformity, decision-making, inter-professional teams, simulation

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Authors: Elaine Wong Yee-Sing, Liaw Wee Tong

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Rapid transformations in demographic and socioeconomic shifts are leading to a growing global demand for health and beauty products and services that demands holistic concepts of well-being. In addition, technological breakthroughs such as internet of things make it convenient and offer innovative solutions for well-being and engage consumers to track their own health conditions and fitness goals. This 'new health economy' encompasses three key concepts: well-being, well-conditioned and well-shaped; which are shaped by wellness segments and goals that influence purchasing decisions of consumers. The research protocol aims to examine the feasibility, challenges, and capabilities in provision for each customer with an ecosystem, or platform, that organizes data and insights to create an individual health and fitness, nutrition, and beauty profile. Convenience sampling of 100 consumers residing in private housing within five major districts in Singapore will be selected to participate in the study. Statistical Package for Social Science 25 will be used to conduct descriptive statistics for quantitative data while qualitative data results using focus interviews, will be translated and transcribed to identify improvements in provision of these services. Rising income in emerging global markets is fuelling the demand for these general wellbeing products and services. Combined with technological advances, it is imperative to understand how these highly personalized services with integrated technology can be designed better to support consumer preferences; provide greater flexibility and high-quality service, and generate better health awareness among consumers.

Keywords: beauty, consumers, health, technology, wellness

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Authors: Nooralhuda Aljlas

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In the ever-growing field of non-governmental organizations, the enhancement of performance management and measurement systems has been increasingly acknowledged by political, economic, social, legal, technological and environmental factors. Within Bahrain Athletics Association, such enhancement results from the key factors leading performance management including collaboration, feedback, human resource management, leadership and participative management. The exploratory, qualitative research conducted reviewed performance management theory. As reviewed, the key factors leading performance management were identified. Drawing on a non-governmental organization case study, the key factors leading Bahrain Athletics Association’s performance management were explored. By exploring the key factors leading Bahrain Athletics Association’s performance management, the research study proposed a theoretical framework of the key factors leading performance management in non-governmental organizations in general. The research study recommended further investigation of the role of the two key factors of command and control and leadership, combining military and civilian approaches to enhancing non-governmental organizations’ performance management.

Keywords: Bahrain athletics association, exploratory, key factor, performance management

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: Samawi Zepure, Beck Christine, Gallagher Peg

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This community immersion employs the NLN Excellence Model which challenges nursing programs to create student-centered, interactive, and innovative experiences to prepare students for roles in providing high quality care, effective teaching, and leadership in the delivery of nursing services to individuals, families, and communities (NLN, 2006). Senior nursing students collaborate with ethnically and linguistically diverse participants at community-based sites and develop leadership roles of coordination of care linkage within the larger healthcare system, adherence, and self-care management. The immersion encourages students to develop competencies of the NLN Nursing Education Competencies Model (NLN, 2012), proposed to address fast changes in health care delivery, which include values of caring, diversity, and holism; and integrating concepts of context and environment, relationship, and teamwork. Students engage in critical thinking and leadership as they: 1) assess health/illness beliefs, values, attitudes, and practices, explore community resources, interview key informants, and collaborate with community participants to identify learning goals, 2) develop and implement appropriate holistic health promotion and disease prevention teaching interventions promoting continuity, sustainability, and innovation, 3) evaluate interventions through participant feedback and focus groups and, 4) reflect on the immersion experience and future professional role as advocate and citizen.

Keywords: quality of care, health of communities, students as leaders, health promotion

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Authors: Tamar Khardziani, Violeta Berikashvili, Amrosi Chkuaseli, Vladimir Elisashvili

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The main objectives of this proposal are to develop low-cost, innovative, and competitive technologies for the production of mycelial biomass of medicinal mushrooms as a natural food supplement for poultry. To fulfill this task, industrial strains of Lentinus edodes, Ganoderma lucidum, and Pleurotus ostreatus were used in this study. The solid-state fermentation (SSF) of wheat grains, wheat bran, and soy flour was performed in flasks and bags. Among nine mushroom strains, P. ostreatus 2191 appeared to be the most productive in protein biomass accumulation in the SSF of wheat bran. All mushrooms produced exopolysaccharide with the highest yield of 5-8 mg/mL depending on fungal strain and growth substrate. Supplementation of medium with 1% glycerol and 2-4% peptone favored mushroom growth and protein accumulation. Among inorganic nitrogen sources, KNO₃ also provided high biomass and protein production. The SSF of all growth substrates was accompanied by the secretion of cellulase and xylanase activities. The highest CMCase activity (12-13 U/g) was revealed in the cultivation of P. ostreatus 2191 using wheat bran as a growth substrate and ammonium sulfate or yeast extract as a nitrogen source, whereas the highest xylanase activity was detected in the fermentation of soy flour supplemented with peptone. Acknowledgments: This work was supported by the Shota Rustaveli National Science Foundation of Georgia (Grant number STEM-22-2077).

Keywords: mushrooms, plant raw materials, fermentation, biomass protein, cellulase

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Authors: Pinar Ozdemir Ayber, Zeina Hojeij

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Blended Learning (BL) is a rapidly growing teaching and learning approach, which brings together the best of both face-to-face and online learning to expand learning opportunities for students. However, there is limited research on the practices, opportunities and quality of instruction in Blended Classrooms, and on the role of the teaching faculty as well as the learners in these types of classes. This paper will highlight the researchers’ experiences and reflections on blending their classes. It will focus on the importance of designing effective lesson plans that emphasize learner engagement and motivation in alignment with course learning outcomes. In addition, it will identify the changing roles of the teacher and the learners and suggest appropriate variations to the traditional classroom setting taking into consideration the benefits and the challenges of the Blended Classroom. It is hoped that this paper would provide sufficient input for participants to reflect on ways they can blend their own lessons to promote ubiquitous learning and student autonomy. Practical tips and ideas will be shared with the participants on various strategies and technologies that were used in the researchers’ classes.

Keywords: blended learning, learner autonomy, learner engagement, learner motivation, mobile learning tools

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Authors: Chien-Wei Hung, Fu-Hau Hsu, Chuan-Sheng Wang, Chia-Hao Lee

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Nowadays, the abuse of keyloggers is one of the most widespread approaches to steal sensitive information. In this paper, we propose an On-Screen Prompts Approach to Keyloggers (OSPAK) and its analysis, which is installed in public computers. OSPAK utilizes a canvas to cue users when their keystrokes are going to be logged or ignored by OSPAK. This approach can protect computers against recoding sensitive inputs, which obfuscates keyloggers with letters inserted among users' keystrokes. It adds a canvas below each password field in a webpage and consists of three parts: two background areas, a hit area and a moving foreground object. Letters at different valid time intervals are combined in accordance with their time interval orders, and valid time intervals are interleaved with invalid time intervals. It utilizes animation to visualize valid time intervals and invalid time intervals, which can be integrated in a webpage as a browser extension. We have tested it against a series of known keyloggers and also performed a study with 95 users to evaluate how easily the tool is used. Experimental results made by volunteers show that OSPAK is a simple approach.

Keywords: authentication, computer security, keylogger, privacy, information leakage

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Authors: Song Xinze, Cai Kejian

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To further explore the application of waste plastics in asphalt pavement, this paper begins with the classification and characteristics of waste plastics. It then provides a state-of-the-art review of the preparation methods and processes of waste plastic modifiers, waste plastic-modified asphalt, and waste plastic-modified asphalt mixtures. The paper also analyzes the factors influencing the compatibility between waste plastics and asphalt and summarizes the performance evaluation indicators for waste plastic-modified asphalt and its mixtures. It explores the research approaches and findings of domestic and international scholars and presents examples of waste plastics applications in pavement engineering. The author believes that there is a basic consensus that waste plastics can improve the high-temperature performance of asphalt. The use of cracking processes to solve the storage stability of waste plastic polymer-modified asphalt is the key to promoting its application. Additionally, the author anticipates that future research will concentrate on optimizing the recycling, processing, screening, and preparation of waste plastics, along with developing composite plastic modifiers to improve their compatibility and long-term performance in asphalt pavements.

Keywords: waste plastics, asphalt pavement, asphalt performance, asphalt modification

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Authors: Sofia Cramerotti, Angela Cattoni, Laura Biancato, Dario Ianes

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The recognition of specific standards for new professionals, within the teaching profile, is a necessary process in order to foster an innovative school vision in accordance with the change that school is experiencing. In line with the reform of the national education and training system and with the National Training Plan for teachers, our Research and Development department developed a training project based on a framework (Syllabus) of skills that each 'Expert Teacher' should master in order to fulfill what the different specific profiles request. The syllabus is a fundamental tool for a training process consistent with the teaching profiles, both to guide the to-become teachers entering in service and to provide the in-service teachers with a system of evaluation and improvement of their skills. According to the national and international literature about professional standards for teachers, we aggregated the skills of the syllabus in three macro areas: (1) Area of professional skills related to the teacher profile and their continuous training; (2) area of teaching skills related to the school innovation; (3) area of organizing skills related to school participation for its improvement. The syllabus is a framework that identifies and describes the skills of the expert teacher in all of their roles. However, the various skills take on different importance in the different profiles involved in the school; some of those skills are determining a role, others could be secondary. Therefore, the characterization of the different profiles is represented by suitably weighted skills sets. In this way, the same skill could differently characterize each profile. In the future, we hope that the skills development and training for the teacher could evolve in a skills development and training for the whole school staff ('Expert Team'). In this perspective, the school will, therefore, benefit from a solid team, in which the skills of the various profiles are all properly developed and well represented.

Keywords: framework, skills, teachers, training

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Authors: Hamza Salih

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Since translation is no longer viewed as a mere replacement of linguistic codes from one language to another, it has increasingly been considered, especially with the advent of the cultural turn in the late 70's, in relation to the broader external context in which it takes place. According to scholars in the field, the translation process is determined by the political, economic and cultural values which exert external pressures on the translator. Correspondingly, the relationship between translation as an act of re-writing the original text and ideology has already been established. This paper addresses the issue of how ideology comes into play in the translational process and what strategies the translator adopts to foreground or circumvent ideological constraints. Along with this, the paper will touch upon the notions of censorship, manipulation, subversion and domestication which are deemed of relevance to this very topic. In fact, after the domination of the empirically-oriented linguistic approaches in translation studies, the relationship between translation and ideology has to be foregrounded to draw attention to the fact that the translation process is not a mere text-to-text linguistic transfer, but, on the contrary, takes place in the midst of economic, political, cultural and religious variables, which some scholars subsume under the category ideology.

Keywords: translation, language, ideology, subversion, censorship and manipulation

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Authors: Rima Zakzouk, Noriko Hiraishi, Mohamed Mahdi Alshahni, Koichi Makimura, Junji Tagami

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Background and Purpose: Silver diamine fluoride (SDF) is used to prevent and arrest dental caries. The aim of this study is to evaluate the effect of SDF on reducing Candida albicans adhesion on dentin. Materials and Methods: Bovine dentin disks (6×6 mm) were cut by Isomet and polished using grit silicon carbide papers down to 2000 in order to obtain flat dentin surfaces. Samples were divided into two groups. The first group (SDF group) was treated with 38% SDF for 3 min, while the other group (control group) did not undergo SDF treatment. All samples were exposed to C. albicans suspension, washed after 6 hours incubation at 30 °C before to be tested using XTT (2,3-Bis-(2-Methoxy-4-Nitro-5-Sulfophenyl)-2H-Tetrazolium-5-Carboxanilide) and real time PCR approaches. Statistical analyses of the results were performed at the significance level α = 0.05. Results: SDF inhibited C. albicans adhesion onto dentin. A significant difference was found between the SDF and control groups in both XTT and real time PCR tests. Conclusion: Using SDF to arrest the caries, could inhibit the Candida growth on dentin.

Keywords: silver diamine fluoride, dentin, real time PCR, XTT

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Authors: Faizan Ahmed, Taimoor Ahmed

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The proliferation of digital technologies has transformed global landscapes, facilitating unprecedented innovation while simultaneously creating new avenues for criminal activity. This paper explores the intersection of criminology and innovation management, emphasizing strategies to combat cybercrime in a rapidly digitizing world. Drawing upon interdisciplinary research, this study investigates the role of proactive management practices and technological solutions in mitigating risks posed by cybercriminals to multinational corporations and government entities. Key areas of focus include the integration of artificial intelligence and blockchain technologies for enhancing cybersecurity, ethical considerations in data management, and cross-jurisdictional collaboration for addressing transnational cybercrime. By proposing a model of "adaptive innovation management," the paper highlights the need for synergistic efforts among policymakers, technologists, and criminologists to ensure secure and ethical digital ecosystems. The findings contribute to innovation policy discourse, aligning technological advancements with sustainable governance practices and interdisciplinary approaches to emerging challenges.

Keywords: cybercrime, innovation management, criminology, artificial intelligence

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Authors: Majid Vafaeipour, Mohamed El Baghdadi, Florian Verbelen, Peter Sergeant, Joeri Van Mierlo, Kurt Stockman, Omar Hegazy

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The Electrical Variable Transmission (EVT), an electromechanical device, can be considered as an alternative solution to the conventional transmission system utilized in Hybrid Electric Vehicles (HEVs). This study present comparisons in terms of fuel consumption, power split, and state of charge (SoC) of an HEV containing an EVT to a conventional parallel topology and a series topology. To this end, corresponding simulations of these topologies are all performed in presence of control strategies enabling battery charge-sustaining and efficient power split. The power flow through the components of the vehicle are attained, and fuel consumption results of the considered cases are compared. The investigation of the results indicates utilizing EVT can provide significant added values in HEV configurations. The outcome of the current research paves its path for implementation of design optimization approaches on such systems in further research directions.

Keywords: Electrical Variable Transmission (EVT), Hybrid Electric Vehicle (HEV), parallel, series, modeling

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Authors: Miran Ismail

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COVID-19 has caused widespread disruption in all economical sectors and industries around the world. The COVID-19 lockdown measures have resulted in production halts, restrictions on persons and goods movement, border closures, logistical constraints, and a slowdown in trade and economic activity. The main subject of this paper is to leverage technology to manage the supply chain effectively and efficiently through the usage of artificial intelligence. The research methodology is based on empirical data collected through a questionnaire survey. One of the approaches utilized is a case study of industrial organizations that face obstacles such as high operational costs, large inventory levels, a lack of well-established supplier relationships, human behavior, and system issues. The main contribution of this research to the body of knowledge is the empirical insights and on supply chain sustainability performance measurement. The results provide guidelines for the selection of advanced technologies to support supply chain processes and for the design of sustainable performance measurement systems.

Keywords: information technology, artificial intelligence, supply chain management, industrial organizations

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Authors: Aishik Deb, Rituparna Sinha

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We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

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Authors: Farouk A.N. Bouhadiba

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Based on Fishman’s Theoretical Paradigm (1991), we shall first discuss his three value positions for the case of the so called minority native languages in Algeria and how they may be included into a global language teaching program in Algeria. We shall then move on to his scale on language loss, language maintenance and language renewal with illustrating examples taken from the Algerian context. The second part of our talk relates to pedagogical issues on how to proceed for a smooth transition from mother tongue to school tongue, what methods or approaches suit best the teaching of mother tongue and school tongue (Immersion Programs, The Natural Approach, Applied Literacy Programs, The Berlitz Method, etc.). We shall end up our talk on how one may reshuffle the current issues on the “Arabic-only” movement and the abrupt transition from mother tongue to school tongue in use today by opting for teaching programs that involve pre-school language acquisition and in-school language acquisition grammars, and thus pave the way to effective language teaching programs and living curricula and pedagogies such as language nests, intergenerational continuity, communication and identity teaching programs, which result in better language teaching models that make language policies become a reality.

Keywords: native languages, language maintenance, mother tongue, school tongue, education, Algeria

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Authors: Yousif Mohamed Y. Abdallah, Mommen A. Alkhir, Amel S. Algaddal

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This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI.

Keywords: improvement, brain, matlab, markers, boundaries

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Jérémy Philippe, Lucien Baldas, Batoul Attar, Jean-Charles Mare

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The proposed communication deals with the research and development of a rotary direct-drive servo valve for aerospace applications. A key challenge of the project is to downsize the electromagnetic torque motor by reducing the torque required to drive the rotary spool. It is intended to optimize the spool and the sleeve geometries by combining a Computational Fluid Dynamics (CFD) approach with commercial optimization software. The present communication addresses an important phase of the project, which consists firstly of gaining confidence in the simulation results. It is well known that the force needed to pilot a sliding spool valve comes from several physical effects: hydraulic forces, friction and inertia/mass of the moving assembly. Among them, the flow force is usually a major contributor to the steady-state (or Root Mean Square) driving torque. In recent decades, CFD has gradually become a standard simulation tool for studying fluid-structure interactions. However, in the particular case of high-pressure valve design, the authors have experienced that the calculated overall hydraulic force depends on the parameterization and options used to build and run the CFD model. To solve this issue, the authors have selected the standard case of the linear spool valve, which is addressed in detail in numerous scientific references (analytical models, experiments, CFD simulations). The first CFD simulations run by the authors have shown that the evolution of the equivalent discharge coefficient vs. Reynolds number at the metering orifice corresponds well to the values that can be predicted by the classical analytical models. Oppositely, the simulated flow force was found to be quite different from the value calculated analytically. This drove the authors to investigate minutely the influence of the studied domain and the setting of the CFD simulation. It was firstly shown that the flow recirculates in the inlet and outlet channels if their length is not sufficient regarding their hydraulic diameter. The dead volume on the uncontrolled orifice side also plays a significant role. These examples highlight the influence of the geometry of the fluid domain considered. The second action was to investigate the influence of the type of mesh, the turbulence models and near-wall approaches, and the numerical solver and discretization scheme order. Two approaches were used to determine the overall hydraulic force acting on the moving spool. First, the force was deduced from the momentum balance on a control domain delimited by the valve inlet and outlet and the spool walls. Second, the overall hydraulic force was calculated from the integral of pressure and shear forces acting at the boundaries of the fluid domain. This underlined the significant contribution of the viscous forces acting on the spool between the inlet and outlet orifices, which are generally not considered in the literature. This also emphasized the influence of the choices made for the implementation of CFD calculation and results analysis. With the step-by-step process adopted to increase confidence in the CFD simulations, the authors propose a set of best practices and recommendations for the efficient use of CFD to design high-pressure spool valves.

Keywords: computational fluid dynamics, hydraulic forces, servovalve, rotary servovalve

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Authors: Maninder Singh

Abstract:

The Chandigarh city is under the strain of exponential growth of car density across various neighborhood. The consumerist nature of society today is to be blamed for this menace because everyone wants to own and ride a car. Car manufacturers are busy selling two or more cars per household. The Regional Transport Offices are busy issuing as many licenses to new vehicles as they can in order to generate revenue in the form of Road Tax. The car traffic in the neighborhoods of Chandigarh has reached a tipping point. There needs to be a more empirical and sustainable model of cars per household, which should be based on specific parameters of livable neighborhoods. Sector 22 in Chandigarh is one of the first residential sectors to be established in the city. There is scope to think, reflect, and work out a method to know how many cars we need to sell our citizens before we lose the argument to traffic problems, parking problems, and road rage. This is where the true challenge of a planner or a designer of the city lies. Currently, in Chandigarh city, there are no clear visible answers to this problem. The way forward is to look at spatial mapping, planning, and design of car parking units to address the problem, rather than suggesting extreme measures of banning cars (short-term) or promoting plans for citywide transport (very long-term). This is a chance to resolve the problem with a pragmatic approach from a citizen’s perspective, instead of an orthodox development planner’s methodology. Since citizens are at the center of how the problem is to be addressed, acceptable solutions are more likely to emerge from the car and traffic problem as defined by the citizens. Thus, the idea and its implementation would be interesting in comparison to the known academic methodologies. The novel and innovative process would lead to a more acceptable and sustainable approach to the issue of number of car parks in the neighborhood of Chandigarh city.

Keywords: cars, Chandigarh, neighborhood, sustainable living, walkability

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Jainendra Singh, Zaheeruddin

Abstract:

A typical wireless sensor networks (WSNs) consists of several tiny and low-power sensors which use radio frequency to perform distributed sensing tasks. The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While routing protocols are striving to save energy by acting on sensor nodes, recent studies show that network lifetime can be enhanced by further involving sink mobility. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we propose an energy- efficient homogenous clustering (EHC) technique. In this technique, the decision of each sensor is based on their residual energy and an estimate of how many of its neighboring cluster heads (CHs) will benefit from it being a CH. We, also explore the routing algorithm in clustered WSNs. We show that the proposed schemes significantly outperform current approaches in terms of packet delay, hop count and energy consumption of WSNs.

Keywords: wireless sensor network, energy efficiency, clustering, routing

Procedia PDF Downloads 271