Search results for: train routing

Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Yuliya Kovalenko

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Given the social nature of people, it is interesting to explore strategies of responding to psycho-traumatic situations in individuals of different ethnic and religious identity. This would allow to substantially expand the idea of human behavior in general, and coping behavior, in particular. This paper investigated the weighted impact of ethnic and religious identities on the patterns of coping behavior. This cross-cultural research empirically revealed intergroup differences in coping strategies and behavior in the samples of young students and teachers of different ethnic identities (Egyptians N=216 and Ukrainians N=109) and different religious identities (Egyptian Muslims N=147 and Christians, including Egyptian Christians N=68 and Ukrainian Christians N = 109). The empirical data were obtained using the questionnaires SACS and COPE. Statistical analysis and interpretation of the results were performed with IBM SPSS-23.0. It was found that, compared to the religious identity, the ethnic identity of the subjects appeared more predictive of coping behavior. It was shown that the constant exchange of information and the unity of biological and social contributed to a more homogeneous picture in the society where Christians and Muslims were integrated into a single cultural space. It was concluded that depending on their ethnic identity, individuals would form a specific hierarchy of coping strategies resulting in a specific pattern of coping with certain stressors. The Egyptian subjects revealed the following pattern of coping with various kinds of academic stress: 'seeking social support', 'problem solving', 'adapting', 'seeking information'. The coping pattern demonstrated by the Ukrainian subjects could be presented as 'seeking information', 'adapting', 'seeking social support', 'problem solving'. There was a tendency in the group of Egyptians to engage in more collectivist coping strategies (with the predominant coping strategy 'religious coping'), in contrast to the Ukrainians who displayed more individualistic coping strategies (with 'planning' and 'active coping' as the mostly used coping strategies). At the same time, it was obvious that Ukrainians should not be unambiguously attributed to the individualistic coping behavior due to their reliance on 'seeking social support' and 'social contact'. The final conclusion was also drawn from the peculiarities of developing religious identity, including religiosity, in Egyptians (formal religious education of both Muslims and Christians) and Ukrainians (more spontaneous process): Egyptians seem to learn to resort to the religious coping, which could be an indication that, in principle, it is possible and necessary to train individuals in desirable coping behavior.

Keywords: coping behavior, cross-cultural research, ethnic and religious identity, hierarchical pattern of coping

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Authors: Glaoui Hachemi

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Wind has been proven as a cost effective and reliable energy source. Technological advancements over the last years have placed wind energy in a firm position to compete with conventional power generation technologies. Algeria has a vast uninhabited land area where the south (desert) represents the greatest part with considerable wind regime. In this paper, an analysis of wind energy utilization as a viable energy substitute in six selected sites widely distributed all over the south of Algeria is presented. In this presentation, wind speed frequency distributions data obtained from the Algerian Meteorological Office are used to calculate the average wind speed and the available wind power. The annual energy produced by the Fuhrlander FL 30 wind machine is obtained using two methods. The analysis shows that in the southern Algeria, at 10 m height, the available wind power was found to vary between 160 and 280 W/m2, except for Tamanrasset. The highest potential wind power was found at Adrar, with 88 % of the time the wind speed is above 3 m/s. Besides, it is found that the annual wind energy generated by that machine lie between 33 and 61 MWh, except for Tamanrasset, with only 17 MWh. Since the wind turbines are usually installed at a height greater than 10 m, an increased output of wind energy can be expected. However, the wind resource appears to be suitable for power production on the south and it could provide a viable substitute to diesel oil for irrigation pumps and electricity generation. In this paper, a model of the wind turbine (WT) with permanent magnet generator (PMSG) and its associated controllers is presented. The increase of wind power penetration in power systems has meant that conventional power plants are gradually being replaced by wind farms. In fact, today wind farms are required to actively participate in power system operation in the same way as conventional power plants. In fact, power system operators have revised the grid connection requirements for wind turbines and wind farms, and now demand that these installations be able to carry out more or less the same control tasks as conventional power plants. For dynamic power system simulations, the PMSG wind turbine model includes an aerodynamic rotor model, a lumped mass representation of the drive train system and generator model. In this paper, we propose a model with an implementation in MATLAB / Simulink, each of the system components off-grid small wind turbines.

Keywords: windgenerator systems, permanent magnet synchronous generator (PMSG), wind turbine (WT) modeling, MATLAB simulink environment

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Authors: Rema Vazhappilly, Tapas Das

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Oleic acid is a cis unsaturated fatty acid and is known to be a partially essential fatty acid due to its limited endogenous synthesis during pregnancy and lactation. Previous studies have demonstrated the role of oleic acid in neuronal differentiation and brain phospholipid synthesis. These evidences indicate a major role for oleic acid in learning and memory. Interestingly, oleic acid has been shown to enhance hippocampal long term potentiation (LTP), the physiological correlate of long term synaptic plasticity. However the effect of oleic acid on short term synaptic plasticity has not been investigated. Short term potentiation (STP) is the physiological correlate of short term synaptic plasticity which is the key underlying molecular mechanism of short term memory and neuronal information processing. STP in the hippocampal CA1 region has been known to require the activation of N-methyl-D-aspartate receptors (NMDARs). The NMDAR dependent hippocampal STP as a potential mechanism for short term memory has been a subject of intense interest for the past few years. Therefore in the present study the effect of oleic acid on NMDAR dependent hippocampal STP was determined in mouse hippocampal slices (in vitro) using Multi-electrode array system. STP was induced by weak tetanic Stimulation (one train of 100 Hz stimulations for 0.1s) of the Schaffer collaterals of CA1 region of the hippocampus in slices treated with different concentrations of oleic acid in presence or absence of NMDAR antagonist D-AP5 (30 µM) . Oleic acid at 20 (mean increase in fEPSP amplitude = ~135 % Vs. Control = 100%; P<0.001) and 30 µM (mean increase in fEPSP amplitude = ~ 280% Vs. Control = 100%); P<0.001) significantly enhanced the STP following weak tetanic stimulation. Lower oleic acid concentrations at 10 µM did not modify the hippocampal STP induced by weak tetanic stimulation. The hippocampal STP induced by weak tetanic stimulation was completely blocked by the NMDA receptor antagonist D-AP5 (30µM) in both oleic acid and control treated hippocampal slices. This lead to the conclusion that the hippocampal STP elicited by weak tetanic stimulation and enhanced by oleic acid was NMDAR dependent. Together these findings suggest that oleic acid may enhance the short term memory and neuronal information processing through the modulation of NMDAR dependent hippocampal short-term synaptic plasticity. In conclusion this study suggests the possible role of oleic acid to prevent the short term memory loss and impaired neuronal function throughout development.

Keywords: oleic acid, short-term potentiation, memory, field excitatory post synaptic potentials, NMDA receptor

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Authors: Francesca Perri

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This paper describes a proposal for a teachers’ training course, focused on the introduction of corpora in the EFL didactics (English as a foreign language) of some Italian secondary schools. The training course is conceived as a part of a TEDD participant’s five months internship. TEDD (Technologies for Education: diversity and devices) is an advanced course held by the Department of Engineering and Information Technology at the University of Trento, Italy. Its main aim is to train a selected, heterogeneous group of graduates to engage with the complex interdependence between education and technology in modern society. The educational approach draws on a plural coexistence of various theories as well as socio-constructivism, constructionism, project-based learning and connectivism. TEDD educational model stands as the main reference source to the design of a formative course for EFL teachers, drawing on the digitalization of didactics and creation of learning interactive materials for L2 intermediate students. The training course lasts ten hours, organized into five sessions. In the first part (first and second session) a series of guided and semi-guided activities drive participants to familiarize with corpora through the use of a digital tools kit. Then, during the second part, participants are specifically involved in the realization of a ML (Mistakes Laboratory) where they create, develop and share digital activities according to their teaching goals with the use of corpora, supported by the digital facilitator. The training course takes place into an ICT laboratory where the teachers work either individually or in pairs, with a computer connected to a wi-fi connection, while the digital facilitator shares inputs, materials and digital assistance simultaneously on a whiteboard and on a digital platform where participants interact and work together both synchronically and diachronically. The adoption of good ICT practices is a fundamental step to promote the introduction and use of Corpus Linguistics in EFL teaching and learning processes, in fact dealing with corpora not only promotes L2 learners’ critical thinking and orienteering versus wild browsing when they are looking for ready-made translations or language usage samples, but it also entails becoming confident with digital tools and activities. The paper will explain reasons, limits and resources of the pedagogical approach adopted to engage EFL teachers with the use of corpora in their didactics through the promotion of digital practices.

Keywords: digital didactics, education, language learning, teacher training

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Authors: Zeynep Aykul Yavuz

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Misconception about design is about making a product pretty. However, the holistic approaches such as design thinking or human-centered design could take the design from making things nice to things inspired by real people and work with real-world limitations. Design thinking helps companies to understand not only problem area but also opportunities. It can be used by any people from any background which provide a space for companies where employees from different departments work together to solve the same problem. While demanding skills changing year to year into the market, previous technical skills are commons anymore. The frontier companies in the sectors look for interactive methods to solve problems. Moreover, the recruiter aims to understand the candidate’s design thinking skills (. The study includes a case study where a 21st century creative services company “ATÖLYE” offers innovation transformation with design thinking to a banking company. Both companies are located in İstanbul in Turkey. The banking company contacted with the ATÖLYE in January 2018 because they heard design thinking in different markets and how it transformed the way of working. The transformation process had 3 phases which were basic training of teams while getting coaching from ATÖLYE’s employees, coaching training with graduates of basic training, facilitator training. Employees built new skills while solving the banking company’s strategic problems. ATÖLYE offered experiential learning which helped employees’ making sense of new skills and knowledge. One day workshops were organized to create awareness about the practice of design thinking. In addition to these, a community of practice was built to create an environment to make reflections and discuss good practice. Not only graduates from the training program but also other employees from the company participated in the community gatherings. ATÖLYE did not train some employees in the company. Rather than that, its aim was to build a contemporary organization for the company. This provided a sustainable system in terms of human resources and motivation. At the beginning of 2020, employees from the first cohort in the basic training who took coaching training and facilitator training have started to design training for different groups in the company. They have considered what could be better in their training experience and designed new ones according to that, so they have been using design thinking to design the design training. This is one of the outcomes which shows the impact of all process clearly.

Keywords: design thinking, learning community, professional development, training, organizational transformation

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Authors: Yiu K. C. Jacky, Tang S. K. Eric, W. Y. Tsang, C. Y. Li, C. K. Leung

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Objectives : (1) To enhance the competence of nurses on using IPMOE for drug administration; (2) To ensure the transition on implementation of IPMOE in safer and smooth way hospital-wide. Methodology: (1) Well-structured Governance: To make provision for IPMOE implementation, multidisciplinary governance structure at Corporate and Local levels are well established. (2) Staff Engagement: A series of staff engagement events were conducted including Staff Forum, IPMOE Hospital Visit, Kick-off Ceremony and establishment of IPMOE Webpage for familiarizing the forthcoming implementation with frontline staff. (3) Well-organized training program: from Workshop to Workplace Two different IPMOE training programs were tailor-made which aimed at introducing the core features of administration module. Fifty-five identical training classes and six train-the-trainer workshops were organized at 2-3Q 2015. Lending Scheme on IPMOE hardware for hands-on practicing was launched and further extended the training from workshop to workplace. (4) Standard Guidelines and Workflow: the related workflow and guidelines are developed which facilitates users to acquire the competence towards IPMOE and fully familiarize with the standardized contingency plan. (5) Facilities and Equipment: The installations of IPMOE hardware were promptly arranged for rollout. Besides, IPMOE training venue was well-established for staff training. (6) Risk Management Strategy: UCH Medication Safety Forum is organized in December 2015 for sharing “Tricks & Tips” on IPMOE which further disseminate at webpage for arousal of medication safety. Hospital-wide annual audit on drug administration was planned to figure out the compliance and deliberate the rooms for improvement. Results: Through the comprehensive training plan, over 1,000 UCH nurses attended the training program with positive feedback. They agreed that their competence on using IPMOE was enhanced. By the end of November 2015, 28 wards (over 1,000 Inpatient-bed) involving departments of M&G, SUR, O&T and O&G have been successfully rolled out IPMOE in 5-month. A smooth and safe transition of implementation of IPMOE was achieved. Eventually, we all get prepared for embedding IPMOE into daily nursing and work altogether for medication safety at UCH.

Keywords: drug administration, inpatient medication order entry system, medication safety, nursing informatics

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Authors: Ehsan Moradi, Parisa Paikhaste, Amir Alam Beigi, Seyedeh Somayeh Bathaei

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The need for entrepreneurial sustainability is as important as the entrepreneurship category itself. By transferring competencies in a sustainable entrepreneurship framework, entrepreneurship education can make a significant contribution to the effectiveness of businesses, especially for start-up entrepreneurs. This study analyzes the essential competencies of students in the development of sustainable entrepreneurship. It is an applied causal study in terms of nature and field in terms of data collection. The main purpose of this research project is to study and explain the dimensions of sustainability entrepreneurship competencies among agricultural students. The statistical population consists of 730 junior and senior undergraduate students of the Campus of Agriculture and Natural Resources, University of Tehran. The sample size was determined to be 120 using the Cochran's formula, and the convenience sampling method was used. Face validity, structure validity, and diagnostic methods were used to evaluate the validity of the research tool and Cronbach's alpha and composite reliability to evaluate its reliability. Structural equation modeling (SEM) was used by the confirmatory factor analysis (CFA) method to prepare a measurement model for data processing. The results showed that seven key dimensions play a role in shaping sustainable entrepreneurial development competencies: systems thinking competence (STC), embracing diversity and interdisciplinary (EDI), foresighted thinking (FTC), normative competence (NC), action competence (AC), interpersonal competence (IC), and strategic management competence (SMC). It was found that acquiring skills in SMC by creating the ability to plan to achieve sustainable entrepreneurship in students through the relevant mechanisms can improve entrepreneurship in students by adopting a sustainability attitude. While increasing students' analytical ability in the field of social and environmental needs and challenges and emphasizing curriculum updates, AC should pay more attention to the relationship between the curriculum and its content in the form of entrepreneurship culture promotion programs. In the field of EDI, it was found that the success of entrepreneurs in terms of sustainability and business sustainability of start-up entrepreneurs depends on their interdisciplinary thinking. It was also found that STC plays an important role in explaining the relationship between sustainability and entrepreneurship. Therefore, focusing on these competencies in agricultural education to train start-up entrepreneurs can lead to sustainable entrepreneurship in the agricultural higher education system.

Keywords: sustainable entrepreneurship, entrepreneurship education, competency, agricultural higher education

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Authors: Naif Mohsen Alharbi

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This paper present failure occurred on flexible coupling installed at oil anf gas operation. Also it presents maintenance ideas implemented on the flexible coupling installed to transmit high torque from gearbox to pump. Basically, the machine train is including steam turbine which drives the pump and there is gearbox located in between for speed reduction. investigation are identifying the root causes, solving and developing the technology designs or bad actor. This report provides the study intentionally for continues operation optimization, utilize the advanced opportunity and implement a improvement. Objective: The main objectives of the investigation are identifying the root causes, solving and developing the technology designs or bad actor. Ultimately, fulfilling the operation productivity, also ensuring better technology, quality and design by solutions. This report provides the study intentionally for continues operation optimization, utilize the advanced opportunity and implemet improvement. Method: The method used in this project was a very focused root cause analysis procedure that incorporated engineering analysis and measurements. The analysis method extensively covers the measuring of the complete coupling dimensions. Including the membranes thickness, hubs, bore diameter and total length, dismantle flexible coupling to diagnose how deep the coupling has been affected. Also, defining failure modes, so that the causes could be identified and verified. Moreover, Vibration analysis and metallurgy test. Lastly applying several solutions by advanced tools (will be mentioned in detail). Results and observation: Design capacity: Coupling capacity is an inadequate to fulfil 100% of operating conditions. Therefore, design modification of service factor to be at least 2.07 is crucial to address this issue and prevent recurrence of similar scenario, especially for the new upgrading project. Discharge fluctuation: High torque flexible coupling encountered during the operation. Therefore, discharge valve behaviour, tuning, set point and general conditions revaluated and modified subsequently, it can be used as baseline for upcoming Coupling design project. Metallurgy test: Material of flexible coupling membrane (discs) tested at the lab, for a detailed metallurgical investigation, better material grade has been selected for our operating conditions,

Keywords: high speed machine, reliabilty, flexible coupling, rotating equipment

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Authors: Louise Bunce, Jill Childs, Adam J. Lonsdale, Naomi King

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A major concern for the Social Work profession concerns the frequency of burn-out and high turnover of staff. The characteristic of resilience has been identified as playing a crucial role in social workers’ ability to have a satisfying and successful career. Thus a critical role for social work education is to develop resilience in social work students. We currently need to know more about how to train resilient social workers who will also increase the academic standing of the profession. The specific aim of this research was to quantify characteristics that may contribute towards resilience and academic success among student social workers in order to mitigate against the problems of burn-out and low academic standing. These three characteristics were competence (effectiveness at mastering the environment), autonomy (sense of control and free will), and relatedness (interacting and connecting with others), as specified in Self-Determination Theory (SDT). When these three needs are satisfied, we experience higher degrees of motivation to succeed and wellbeing. Thus when these three needs are met in social work students, they have the potential to raise academic standards and promote wellbeing characteristics that contribute to the development of resilience. The current study tested the hypothesis that higher levels of autonomy, competence, and relatedness, as defined by SDT, will predict levels of academic success and resilience in social work students. Two hundred and ten social work students studying at a number of universities completed well-established questionnaires to assess autonomy, competence, and relatedness, level of academic performance and resilience (The Brief Resilience Scale). In this scale, students rated their agreement with items e.g., ‘I bounce back quickly after hard times’ and ‘I usually come through difficult times with little struggle’. After controlling for various factors, including age, gender, ethnicity, and course (undergraduate or postgraduate) preliminary analysis revealed that the components of SDT provided useful predictive value for academic success and resilience. In particular, autonomy and competence provided a useful predictor of academic success while relatedness was a particularly useful predictor of resilience. This study demonstrated that SDT provides a valuable framework for helping to understand what predicts academic success and resilience among social work students. This is relevant because the psychological needs for autonomy, competence and relatedness can be affected by external social and cultural pressures, thus they can be improved by the right type of supportive teaching practices and educational environments. These findings contribute to the growing evidence-base to help build an academic and resilient social worker student body and workforce.

Keywords: education, resilience, self-determination theory, student social workers

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Authors: Jovana Vukcevic, Sanja Pekovic, Djurdjica Perovic, Tatjana Stanovcic

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This paper presents a critical inquiry into the role of uncomfortable heritage in nation branding with the particular focus on the specificities of the politics of memory, forgetting and revisionism in the post-communist post-Yugoslavia. It addresses legacies of unwanted, ambivalent or unacknowledged past and different strategies employed by the former-Yugoslav states and private actors in “rebranding” their heritage, ensuring its preservation, but re-contextualizing the narrative of the past through contemporary tourism practices. It questions the interplay between nostalgia, heritage and market, and the role of heritage in polishing the history of totalitarian and authoritarian regimes in the Balkans. It argues that in post-socialist Yugoslavia, the necessity to limit correlations with former ideology and the use of the commercial brush in shaping a marketable version of the past instigated the emergence of the profit-oriented heritage practices. Building on that argument, the paper addresses these issues as “commodification” and “disneyfication” of Balkans’ ambivalent heritage, contributing to the analysis of changing forms of memorialisation and heritagization practices in Europe. It questions the process of ‘coming to terms with the past’ through marketable forms of heritage tourism, fetching the boundary between market-driven nostalgia and state-imposed heritage policies. In order to analyse plurality of ways of dealing with controversial, ambivalent and unwanted heritage of dictatorships in the Balkans, the paper considers two prominent examples of heritage commodification in Serbia and Montenegro, and the re-appropriations of those narratives for the nation branding purposes. The first one is the story of the Tito’s Blue Train, the landmark of the socialist past and the symbol of Yugoslavia which has nowadays being used for birthday parties and marriage celebrations, while the second emphasises the unusual business arrangement turning the fortress Mamula, former concentration camp through the Second World War, into a luxurious Mediterranean resort. Questioning how the ‘uneasy’ past was acknowledged and embedded into the official heritage institutions and tourism practices, study examines the changing relation towards the legacies of dictatorships, inviting us to rethink the economic models of the things past. Analysis of these processes should contribute to better understanding of the new mnemonics strategies and (converging?) ways of ‘doing’ past in Europe.

Keywords: commodification, heritage tourism, totalitarianism, Serbia, Montenegro

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Alberto Jiménez-Vargas, Manuel de Jesús Palacios-Gallegos, Miguel Ángel Hernández-López, Rafael Campos-Amezcua, Julio Cesar Solís-Sanchez

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An optimal wind turbine blade design must be able of capturing as much energy as possible from the wind source available at the area of interest. Many times, an optimal design means the use of large quantities of material and complicated processes that make the wind turbine more expensive, and therefore, less cost-effective. For the construction and installation of a wind turbine, the blades may cost up to 20% of the outline pricing, and become more important due to they are part of the rotor system that is in charge of transmitting the energy from the wind to the power train, and where the static and dynamic design loads for the whole wind turbine are produced. The aim of this work is the develop of a blade fluid-structure interaction (FSI) simulation that allows the identification of the major damage zones during the normal production situation, and thus better decisions for design and optimization can be taken. The simulation is a dynamic case, since we have a time-history wind velocity as inlet condition instead of a constant wind velocity. The process begins with the free-use software NuMAD (NREL), to model the blade and assign material properties to the blade, then the 3D model is exported to ANSYS Workbench platform where before setting the FSI system, a modal analysis is made for identification of natural frequencies and modal shapes. FSI analysis is carried out with the two-way technic which begins with a CFD simulation to obtain the pressure distribution on the blade surface, then these results are used as boundary condition for the FEA simulation to obtain the deformation levels for the first time-step. For the second time-step, CFD simulation is reconfigured automatically with the next time-step inlet wind velocity and the deformation results from the previous time-step. The analysis continues the iterative cycle solving time-step by time-step until the entire load case is completed. This work is part of a set of projects that are managed by a national consortium called “CEMIE-Eólico” (Mexican Center in Wind Energy Research), created for strengthen technological and scientific capacities, the promotion of creation of specialized human resources, and to link the academic with private sector in national territory. The analysis belongs to the design of a rotor system for a 5 kW wind turbine design thought to be installed at the Isthmus of Tehuantepec, Oaxaca, Mexico.

Keywords: blade, dynamic, fsi, wind turbine

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Authors: Armando Cartenì

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Within the design of sustainable transportation engineering, the problem of reducing particulate matter (PM) concentrations in railways metro system was not much discussed. It is well known that PM levels in railways metro system are mainly produced by mechanical friction at the rail-wheel-brake interactions and by the PM re-suspension caused by the turbulence generated by the train passage, which causes dangerous problems for passenger health. Starting from these considerations, the aim of this research was twofold: i) to investigate the particulate matter concentrations in a ‘traditional’ railways metro system; ii) to investigate the particulate matter concentrations of a ‘high quality’ metro system equipped with design devices useful for reducing PM concentrations: platform screen doors, rubber-tyred and an advanced ventilation system. Two measurement surveys were performed: one in the ‘traditional’ metro system of Naples (Italy) and onother in the ‘high quality’ rubber-tyred metro system of Turin (Italy). Experimental results regarding the ‘traditional’ metro system of Naples, show that the average PM10 concentrations measured in the underground station platforms are very high and range between 172 and 262 µg/m3 whilst the average PM2,5 concentrations range between 45 and 60 µg/m3, with dangerous problems for passenger health. By contrast the measurements results regarding the ‘high quality’ metro system of Turin show that: i) the average PM10 (PM2.5) concentrations measured in the underground station platform is 22.7 µg/m3 (16.0 µg/m3) with a standard deviation of 9.6 µg/m3 (7.6 µg/m3); ii) the indoor concentrations (both for PM10 and for PM2.5) are statistically lower from those measured in outdoors (with a ratio equal to 0.9-0.8), meaning that the indoor air quality is greater than those in urban ambient; iii) that PM concentrations in underground stations are correlated to the trains passage; iv) the inside trains concentrations (both for PM10 and for PM2.5) are statistically lower from those measured at station platform (with a ratio equal to 0.7-0.8), meaning that inside trains the use of air conditioning system could promote a greater circulation that clean the air. The comparison among the two case studies allow to conclude that the metro system designed with PM reduction devices allow to reduce PM concentration up to 11 times against a ‘traditional’ one. From these results, it is possible to conclude that PM concentrations measured in a ‘high quality’ metro system are significantly lower than the ones measured in a ‘traditional’ railway metro systems. This result allows possessing the bases for the design of useful devices for retrofitting metro systems all around the world.

Keywords: air quality, pollutant emission, quality in public transport, underground railway, external cost reduction, transportation planning

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Authors: Erva Akin

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– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: Ohud Adnan Saffar

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This study aims to know the effect of using universal design for learning (UDL) to improve the quality of vocational programme with intellectual disabilities (SID) and the challenges facing this method from the teachers' point of view. The significance of the study: There are comparatively few published studies on UDL in emerging nations. Therefore, this study will encourage the researchers to consider a new approaches teaching. Development of this study will contribute significant information on the cognitively disabled community on a universal scope. In order to collect and evaluate the data and for the verification of the results, this study has been used the mixed research method, by using two groups comparison method. To answer the study questions, we were used the questionnaire, lists of observations, open questions, and pre and post-test. Thus, the study explored the advantages and drawbacks, and know about the impact of using the UDL method on integrating SID with students non-special education needs in the same classroom. Those aims were realized by developing a workshop to explain the three principles of the UDL and train (16) teachers in how to apply this method to teach (12) students non-special education needs and the (12) SID in the same classroom, then take their opinion by using the questionnaire and questions. Finally, this research will explore the effects of the UDL on the teaching of professional photography skills for the SID in Saudi Arabia. To achieve this goal, the research method was a comparison of the performance of the SID using the UDL method with that of female students with the same challenges applying other strategies by teachers in control and experiment groups, we used the observation lists, pre and post-test. Initial results: It is clear from the previous response to the participants that most of the answers confirmed that the use of UDL achieves the principle of inclusion between the SID and students non-special education needs by 93.8%. In addition, the results show that the majority of the sampled people see that the most important advantages of using UDL in teaching are creating an interactive environment with using new and various teaching methods, with a percentage of 56.2%. Following this result, the UDL is useful for integrating students with general education, with a percentage of 31.2%. Moreover, the finding indicates to improve understanding through using the new technology and exchanging the primitive ways of teaching with the new ones, with a percentage of 25%. The result shows the percentages of the sampled people's opinions about the financial obstacles, and it concluded that the majority see that the cost is high and there is no computer maintenance available, with 50%. There are no smart devices in schools to help in implementing and applying for the program, with a percentage of 43.8%.

Keywords: universal design for learning, intellectual disabilities, vocational programme, the challenges facing this method

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Authors: Eric Granholm, Christophe Delay, Jason Holden, Peter Link

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Negative symptoms are an important unmet treatment needed for schizophrenia. We conducted an open trial of a novel blended intervention called mobile-assisted cognitive behavior therapy for negative symptoms (mCBTn). mCBTn is a weekly group therapy intervention combining in-person and smartphone-based CBT (CBT2go app) to improve experiential negative symptoms in people with schizophrenia. Both the therapy group and CBT2go app included recovery goal setting, thought challenging, scheduling of pleasurable activities and social interactions, and pleasure savoring interventions to modify defeatist attitudes, a target mechanism associated with negative symptoms, and improve experiential negative symptoms. We tested whether participants with schizophrenia or schizoaffective disorder (N=31) who met prospective criteria for persistent negative symptoms showed improvement in experiential negative symptoms. Retention was excellent (87% at 18 weeks) and severity of defeatist attitudes and motivation and pleasure negative symptoms declined significantly in mCBTn with large effect sizes. We also tested whether pupillary responses, a measure of cognitive effort, predicted improvement in negative symptoms mCBTn. Pupillary responses were recorded at baseline using a Tobii pupillometer during the digit span task with 3-, 6- and 9-digit spans. Mixed models showed that greater dilation during the task at baseline significantly predicted a greater reduction in experiential negative symptoms. Pupillary responses may provide a much-needed prognostic biomarker of which patients are most likely to benefit from CBT. Greater pupil dilation during a cognitive task predicted greater improvement in experiential negative symptoms. Pupil dilation has been linked to motivation and engagement of executive control, so these factors may contribute to benefits in interventions that train cognitive skills to manage negative thoughts and emotions. The findings suggest mCBTn is a feasible and effective treatment for experiential negative symptoms and justify a larger randomized controlled clinical trial. The findings also provide support for the defeatist attitude model of experiential negative symptoms and suggest that mobile-assisted interventions like mCBTn can strengthen and shorten intensive psychosocial interventions for schizophrenia.

Keywords: cognitive-behavioral therapy, mobile interventions, negative symptoms, pupillometry schizophrenia

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Authors: Mohd Fairus Humar, Ibrahim Sulaiman, Pedro Cruz, Hasry Harun

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This paper presents the propose knowledge transfer program on railway signaling and communication by Original Equipment Manufacturer (OEM) Thales Portugal. The fundamental issue is that there is no rail related course offered by local universities and colleges in Malaysia which could be an option to pursue student career path. Currently, dedicated trainings related to the rail technology are provided by in-house training academies established by the respective rail operators such as Malaysia Railway Academy (MyRA) and Rapid Rail Training Centre. In this matter, the content of training and facilities need to be strengthened to keep up-to-date with the dynamic evolvement of the rail technology. This is because rail products have evolved to be more sophisticated and embedded with high technology components which no longer exist in the mechanical form alone but combined with electronics, information technology and others. These demand for a workforce imbued with knowledge, multi-skills and competency to deal with specialized technical areas. Talent is needed to support sustainability in Southeast Asia. Keeping the above factors in mind, an Industrial Collaboration Program (ICP) was carried out to transfer knowledge on curricula of railway signaling and communication to a selected railway operators and tertiary educational institution in Malaysia. In order to achieve the aim, a partnership was formed between Technical Depository Agency (TDA), Thales Portugal and MyRA for two years with three main stages of program implementation comprising of: i) training on basic railway signaling and communication for 1 month with Thales in Malaysia; ii) training on advance railway signaling and communication for 4 months with Thales in Portugal and; iii) a series of workshop. Two workshops were convened to develop and harmonize curricula of railway signaling and communication course and were followed by one training for installation equipment of railway signaling and Controlled Train Centre (CTC) system from Thales Portugal. With active involvement from Technical Depository Agency (TDA), railway operators, universities, and colleges, in planning, executing, monitoring, control and closure, the program module of railway signaling and communication course with a lab railway signaling field equipment and CTC simulator were developed. Through this program, contributions from various parties help to build committed societies to engage important issues in relation to railway signaling and communication towards creating a sustainable future.

Keywords: knowledge transfer program, railway signaling and communication, curricula, module and teaching aid simulator

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Authors: Nizamuddin Zainuddin, Adam Mohd Saifudin, Ahmad Yusni Bahaudin, Mohd Hanizan Zalazilah, Roslan Jamaluddin

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Students’ enhanced experience through simulation is a crucial factor that brings reality to the classroom. The enhanced experience is all about developing, enriching and applications of a generic process flow in the field of logistics and transportations. As educational technology has improved, the effective use of simulations has greatly increased to the point where simulations should be considered a valuable, mainstream pedagogical tool. Additionally, in this era of ongoing (some say never-ending) assessment, simulations offer a rich resource for objective measurement and comparisons. Simulation is not just another in the long line of passing fads (or short-term opportunities) in educational technology. It is rather a real key to helping our students understand the world. It is a way for students to acquire experience about how things and systems in the world behave and react, without actually touching them. In short, it is about interactive pretending. Simulation is all about representing the real world which includes grasping the complex issues and solving intricate problems. Therefore, it is crucial before stimulate the real process of inbound and outbound logistics and transportation a generic process flow shall be developed. The paper will be focusing on the validization of the process flow by looking at the inputs gains from the sample. The sampling of the study focuses on multi-national and local manufacturing companies, third party companies (3PL) and government agency, which are selected in Peninsular Malaysia. A simulation flow chart was proposed in the study that will be the generic flow in logistics and transportation. A qualitative approach was mainly conducted to gather data in the study. It was found out from the study that the systems used in the process of outbound and inbound are System Application Products (SAP) and Material Requirement Planning (MRP). Furthermore there were some companies using Enterprises Resources Planning (ERP) and Electronic Data Interchange (EDI) as part of the Suppliers Own Inventories (SOI) networking as a result of globalized business between one countries to another. Computerized documentations and transactions were all mandatory requirement by the Royal Custom and Excise Department. The generic process flow will be the basis of developing a simulation program that shall be used in the classroom with the objective of further enhanced the students’ learning experience. Thus it will contributes to the body of knowledge on the enrichment of the student’s employability and also shall be one of the way to train new workers in the logistics and transportation filed.

Keywords: enhancement, simulation, process flow, logistics, transportation

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Sylvie Sossou, Grégoire Gansou, Ildevert Egue

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Introduction: The mission of the family and the school is to educate and train citizens of the city. But the family’s values , parental roles, respect for life collapse in their traditional African form. Indeed laxity with regard to divorce, liberal ideas about child rearing influence the emotional life of the latter. Several causes may contribute to the decline in academic performance. In order to seek a psychological solution to the issue, a study was conducted in 6 schools at the 9th district in Cotonou, cosmopolitan city of Benin. Objective: To evaluate the impact of single parenthood on the psycho child development. Materials and Methods: Questionnaires and interviews were used to gather verbal information. The questionnaires were administered to parents and children (schoolchildren 4, 5 and six form) from 7 to 12 years in lone parenthood. The interview was done with teachers and school leaders. We identified 209 cases of children living with a "single-parent" and 68 single parents. Results: Of the 209 children surveyed the results showed that 116 children are cut relational triangle in early childhood (before 3 years). The psychological effects showed that the separation has caused sadness for 52 children, anger 22, shame 17, crying at 31 children, fear for 14, the silence at 58 children. In front of complete family’s children, these children experience feelings of aggression in 11.48%; sadness in 30.64%; 5.26% the shame, the 6.69% tears; jealousy in 2.39% and 2.87% of indifference. The option to get married in 44.15% of children is a challenge to want to give a happy childhood for their offspring; 22.01% feel rejected, there is uncertainty for 11.48% of cases and 25.36% didn’t give answer. 49, 76% of children want to see their family together; 7.65% are against to avoid disputes and in many cases to save the mother of the father's physical abuse. 27.75% of the ex-partners decline responsibility in the care of the child. Furthermore family difficulties affecting the intellectual capacities of children: 37.32% of children see school difficulties related to family problems despite all the pressure single-parent to see his child succeed. Single parenthood affects inter-family relations: pressure 33.97%; nervousness 24.88%; overprotection 29.18%; backbiting 11.96%, are the lives of these families. Conclusion: At the end of the investigation, results showed that there is a causal relationship between psychological disorders, academic difficulties of children and quality of parental relationships. Other cases may exist, but the lack of resources meant that we have only limited at 6 schools. Early psychological treatment for these children is needed.

Keywords: single-parent, psycho child, school, Cotonou

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Authors: G. Natuhwera, M. Rabwoni, P. Ellis, A. Merriman

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Aims: Health workers are likely to document patients’ care inaccurately, especially when using new and revised case tools, and this could negatively impact patient care. This study set out to; (1) assess nurses’ and clinicians’ documentation practices when using a new patients’ continuation case sheet (PCCS) and (2) explore nurses’ and clinicians’ experiences regarding documentation of patients’ information in the new PCCS. The purpose of introducing the PCCS was to improve continuity of care for patients attending clinics at which they were unlikely to see the same clinician or nurse consistently. Methods: This was a mixed methods study. The cross-sectional inquiry retrospectively reviewed 100 case notes of active patients on hospice and palliative care program. Data was collected using a structured questionnaire with constructs formulated from the new PCCS under study. The qualitative element was face-to-face audio-recorded, open-ended interviews with a purposive sample of one palliative care clinician, and four palliative care nurse specialists. Thematic analysis was used. Results: Missing patients’ biogeographic information was prevalent at 5-10%. Spiritual and psychosocial issues were not documented in 42.6%, and vital signs in 49.2%. Poorest documentation practices were observed in past medical history part of the PCCS at 40-63%. Four themes emerged from interviews with clinicians and nurses-; (1) what remains unclear and challenges, (2) comparing the past with the present, (3) experiential thoughts, and (4) transition and adapting to change. Conclusions: The PCCS seems to be a comprehensive and simple tool to be used to document patients’ information at subsequent visits. The comprehensiveness and utility of the PCCS does paper to be limited by the failure to train staff in its use prior to introducing. The authors find the PCCS comprehensive and suitable to capture patients’ information and recommend it can be adopted and used in other palliative and hospice care settings, if suitable introductory training accompanies its introduction. Otherwise, the reliability and validity of patients’ information collected by this PCCS can be significantly reduced if some sections therein are unclear to the clinicians/nurses. The study identified clinicians- and nurses-related pitfalls in documentation of patients’ care. Clinicians and nurses need to prioritize accurate and complete documentation of patient care in the PCCS for quality care provision. This study should be extended to other sites using similar tools to ensure representative and generalizable findings.

Keywords: documentation, information case sheet, palliative care, quality improvement

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Authors: Friman Hen, Banner Ifaa, Shalom-Tuchin Bosmat, Einav Yulia

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The action learning course is an academic course which involves academic learning and social activities. The courses deal with processes and social challenges, reveal different ideologies, and develop critical thinking and pragmatic ideas. Students receive course credits and a grade for being part of such courses. Participating students enroll in courses that involve action and activities to engage in the experiential learning process, thereby creating a dialogue and cross-fertilization between being taught in the classroom and experiencing the reality in the real world. A learning experience includes meeting with social organizations, institutions, and state authorities and carrying out practical work with diverse populations. Through experience, students strengthen their academic skills, formulate ethical attitudes toward reality, develop professional and civilian perspectives, and realize how they can influence their surrounding in the present and the hereafter. Under the guidance and supervision of Dr. Hen Friman, H.I.T. has built an innovative course that combines action and activities to increase the awareness and accessibility of the community in an experiential way. The end goal is to create Green Ambassadors—children with a high level of environmental awareness. This course is divided into two parts. The first part, focused on frontal teaching, delivers knowledge from extensive environmental fields to students. These areas include introduction to ecology, the process of electricity generation, air pollution, renewable energy, water economy, waste and recycling, and energy efficiency (first stage). In addition to the professional content in the environment field, students learn the method of effective and experiential teaching to younger learners (4 to 8 years old). With the attainment of knowledge, students are divided into operating groups. The second part of the course shows how the theory becomes practical and concrete. At this stage, students are asked to introduce to the first- and second-graders of ‘Revivim’ School in Holon a lesson of 90 minutes focused on presenting the issues and their importance during the course (second stage). This course is the beginning of a paradigm shift regarding energy usage in the modern society in Israel. The objective of the course is to expand worldwide and train the first and second-graders, and even pre-schoolers, in a wide scope to increase population awareness rate, both in Israel and all over the world, for a green future.

Keywords: air pollution, green ambassador, recycling, renewable energy

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Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: Reham Khalaf-Nazzal, Imad Dweikat, Paula Gimenez, Iker Oyenarte, Alfonso Martinez-Cruz, Domonik Muller

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Metal cation transport mediator (CNNM) gene family comprises 4 isoforms that are expressed in various human tissues. Structurally, CNNMs are complex proteins that contain an extracellular N-terminal domain preceding a DUF21 transmembrane domain, a ‘Bateman module’ and a C-terminal cNMP-binding domain. Mutations in CNNM2 cause familial dominant hypomagnesaemia. Growing evidence highlights the role of CNNM2 in neurodevelopment. Mutations in CNNM2 have been implicated in epilepsy, intellectual disability, schizophrenia, and others. In the present study, we aim to elucidate the function of CNNM2 in the developing brain. Thus, we present the genetic origin of symptoms in two family cohorts. In the first family, three siblings of a consanguineous Palestinian family in which parents are first cousins, and consanguinity ran over several generations, presented a varying degree of intellectual disability, cone-rod dystrophy, and autism spectrum disorder. Exome sequencing and segregation analysis revealed the presence of homozygous pathogenic mutation in the CNNM2 gene, the parents were heterozygous for that gene mutation. Magnesium blood levels were normal in the three children and their parents in several measurements. They had no symptoms of hypomagnesemia. The CNNM2 mutation in this family was found to locate in the CBS1 domain of the CNNM2 protein. The crystal structure of the mutated CNNM2 protein was not significantly different from the wild-type protein, and the binding of AMP or MgATP was not dramatically affected. This suggests that the CBS1 domain could be involved in pure neurodevelopmental functions independent of its magnesium-handling role, and this mutation could have affected a protein partner binding or other functions in this protein. In the second family, another autosomal dominant CNNM2 mutation was found to run in a large family with multiple individuals over three generations. All affected family members had hypomagnesemia and hypermagnesuria. Oral supplementation of magnesium did not increase the levels of magnesium in serum significantly. Some affected members of this family have defects in fine motor skills such as dyslexia and dyslalia. The detected mutation is located in the N-terminal part, which contains a signal peptide thought to be involved in the sorting and routing of the protein. In this project, we describe heterogenous clinical phenotypes related to CNNM2 mutations and protein functions. In the first family, and up to the authors’ knowledge, we report for the first time the involvement of CNNM2 in retinal photoreceptor development and function. In addition, we report the presence of a neurophenotype independent of magnesium status related to the CNNM2 protein mutation. Taking into account the different modes of inheritance and the different positions of the mutations within CNNM2 and its different structural and functional domains, it is likely that CNNM2 might be involved in a wide spectrum of neuropsychiatric comorbidities with considerable varying phenotypes.

Keywords: magnesium transport, autosomal recessive, autism, neurodevelopment, CBS domain

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Authors: S. H. J. Liu

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This paper aims to explore the role of peer Corrective Feedback (CF) in improving written productions by English-as-a- foreign-language (EFL) learners who work together via Wikispaces. It attempted to determine the effect of peer CF on form accuracy in English, such as grammar and lexis. Thirty-four EFL learners at the tertiary level were randomly assigned into the experimental (with peer feedback) or the control (without peer feedback) group; each group was subdivided into small groups of two or three. This resulted in six and seven small groups in the experimental and control groups, respectively. In the experimental group, each learner played a role as an assessor (providing feedback to others), as well as an assessee (receiving feedback from others). Each participant was asked to compose his/her written work and revise it based on the feedback. In the control group, on the other hand, learners neither provided nor received feedback but composed and revised their written work on their own. Data collected from learners’ compositions and post-task interviews were analyzed and reported in this study. Following the completeness of three writing tasks, 10 participants were selected and interviewed individually regarding their perception of collaborative learning in the Computer-Mediated Communication (CMC) environment. Language aspects to be analyzed included lexis (e.g., appropriate use of words), verb tenses (e.g., present and past simple), prepositions (e.g., in, on, and between), nouns, and articles (e.g., a/an). Feedback types consisted of CF, affective, suggestive, and didactic. Frequencies of feedback types and the accuracy of the language aspects were calculated. The results first suggested that accurate items were found more in the experimental group than in the control group. Such results entail that those who worked collaboratively outperformed those who worked non-collaboratively on the accuracy of linguistic aspects. Furthermore, the first type of CF (e.g., corrections directly related to linguistic errors) was found to be the most frequently employed type, whereas affective and didactic were the least used by the experimental group. The results further indicated that most participants perceived that peer CF was helpful in improving the language accuracy, and they demonstrated a favorable attitude toward working with others in the CMC environment. Moreover, some participants stated that when they provided feedback to their peers, they tended to pay attention to linguistic errors in their peers’ work but overlook their own errors (e.g., past simple tense) when writing. Finally, L2 or FL teachers or practitioners are encouraged to employ CMC technologies to train their students to give each other feedback in writing to improve the accuracy of the language and to motivate them to attend to the language system.

Keywords: peer corrective feedback, computer-mediated communication (CMC), second or foreign language (L2 or FL) learning, Wikispaces

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Authors: Mallikarjun Vaddenkeri, Suresh Jange

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This research study reports results of information behaviour by members of faculty and research scholars of various departments of social sciences working at universities with a sample of 300 and Members of Legislative Assembly and Council with 216 samples in Karnataka State, India. The results reveal that 29.3% and 20.3% of Social Scientists indicated medium and high level of awareness of primary sources - Primary Journals are found to be at scale level 5 and 9. The usage of primary journals by social scientists is found to be 28% at level 4, 24% of the respondent’s opined use of primary Conference Proceedings at level 5 as medium level of use. Similarly, the use of Secondary Information Sources at scale 8 and 9 particularly in case of Dictionaries (31.0% and 5.0%), Encyclopaedias (22.3% and 6.3%), Indexing Periodicals (7.0% and 15.3%) and Abstracting Periodicals (5.7% and 20.7%). For searching information from Journals Literature available in CD-ROM version, Keywords (43.7%) followed by Keywords with logical operators (39.7%) have been used for finding the required information. Statistical inference reveals rejection of null hypothesis `there is no association between designation of the respondents and awareness of primary information resources’. On the other hand, educational qualification possessed by Legislative members, more than half of them possess graduate degree as their academic qualification (57.4%) and just 16.7% of the respondents possess graduate degree while only 26.8% of the respondents possess degree in law and just 1.8% possess post-graduate degree in law. About 42.6% indicated the importance of information required to discharge their duties and responsibilities as a Policy Maker in the scale 8, as a Scholar (27.8%) on a scale 6, as a politician (64.8%) on a scale 10 and as a Councillor (51.9%) on a scale 8. The most preferred information agencies/sources very often contacted for obtaining useful information are by means of contacting the people of Karnataka State Legislative Library, listening Radio programmes, viewing Television programmes and reading the newspapers. The methods adopted for obtaining needed information quite often by means of sending their assistants to libraries to gather information (35.2%) and personally visiting the information source (64.8%). The null hypotheses `There is no association between Members of Legislature and Opinion on the usefulness of the resources of the Karnataka State Legislature Library’ is accepted using F ANOVA test. The studies conclude with a note revamp the existing library system in its structure and adopt latest technologies and educate and train social scientists and Legislators in using these resources in the interest of academic, government policies and decision making of the country.

Keywords: information use behaviour, government information, searching behaviour, policy makers

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Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

Abstract:

Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

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