Search results for: adaptive educational digital learning environments

Authors: Rabab A. El Wahsh, Hatem M. Marey, Maha Yousif, Asmaa M. Ibrahim

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Background: OSA and COPD are associated with microvascular changes. Retinal microvasculature can be directly and non-invasively examined. Aim: to evaluate retinal vascular tortuosity in patients with COPD, OSA, and overlap syndrome. Subjects and method: Sixty subjects were included; 15 OSA patients, 15 COPD patients, 15 COPD-OSA overlap patients, and 15 matched controls. They underwent digital retinal photography, polysomnography, arterial blood gases, spirometry, ESS, and stop-bang questionnaires. Results: Tortuosity of most retinal vessels was higher in all patient groups compared to the control group; tortuosity was more marked in overlap syndrome. There was a negative correlation between tortuosity of retinal vessels and PO2, O2 saturation, and minimum O2 desaturation, and a positive correlation with PCO2, AHI, O2 desaturation index, BMI and smoking index. Conclusion: Retinal vascular tortuosity occurs in OSA, COPD and overlap syndrome. Retinal vascular tortuosity is correlated with arterial blood gases parameters, polysomnographic findings, smoking index and BMI.

Keywords: OSA, COPD, overlap syndrome, retinal vascular tortuosity

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Authors: Natalie Thorburn

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New Zealand legalised and regulated prostitution 13 years ago with the hope of eradicating unsafe or exploitative practices in the sex trade, but the extent to which this has been successful has been hotly contested, with the New Zealand Government denying any existence of sex trafficking and evidence generally indicating the success of the 2004 reform. The aim of the research was therefore to establish the circumstances in which sex trafficking may be occurring without using any previously instrumental gatekeepers of the New Zealand sex industry. 14 survivors of gang, family, or intimate partner trafficking (all of whom had first been trafficked prior to the age of 16) were interviewed, as well as several key informants. It was found that there was a perceived lack of commitment by Police to investigate instances of trafficking, and this was considered to be linked to the legal status of prostitution. The lack of recognition at both community and political levels of the existence and prevalence of trafficking also meant that medical and social service practitioners were unaware trafficking was occurring, and would not know who to refer to if it was disclosed. Participants commonly normalised coercion into sex, seeing this as a continuation of prior sexually abusive experiences that were prevalent in their childhood and early adolescent environments. Their experiences with the helping services were typically either negative or non-existent, and they expressed frustration regarding the absence of justice, the lack of awareness, and health and outcomes they suffered in relation to their experiences of having been trafficked. Barriers to engagement and strategies to facilitate meaningful and sustainable engagement with this population group are therefore presented.

Keywords: legalisation, regulation, service access, socio-political context

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Authors: Maari Sugawara

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This arts-based research is about "self-torture": the interplay of seemingly opposing elements of pain, pleasure, submission, and power. It asserts that "self-torture" can be considered a nontrivial mediation between the aesthetic and the sociopolitical. It explores what the author calls queered self-torture; "self-torture" marked by an ambivalence that allows the oppressed to resist, and their counter-valorization occasionally functions as therapeutic solutions to the problems they highlight and condense. The research goal is to deconstruct normative self-torture and propose queered self-torture as a fertile ground for considering the complexities of desire that allow the oppressed to practice freedom. While “self-torture” manifests in many societies, this research focuses on cultural and national identity in post-WWII Japan using this lens of self-torture, as masochism functions as the very basis for Japanese cultural and national identity to ensure self-preservation. This masochism is defined as an impulse to realize a sense of pride and construct an identity through the acceptance of subordination, shame, and humiliation in the face of an all-powerful Other; the dominant Euro-America. It could be argued that this self-torture is a result of Japanese cultural annihilation and the trauma of the nation's defeat to the US. This is the definition of "self-torturous thresholds," the author’s post-WWII Japan psycho-historical diagnosis; when this threshold is crossed, the oppressed begin to torture themselves; the oppressors no longer need to do anything to maintain their power. The oppressed are already oppressing themselves. The term "oppressed" here refers to Japanese individuals and residents of Japan who are subjected to oppressive “white” heteropatriarchal supremacist structures and values that serve colonialist interests. There are three stages in "self-torturous thresholds": (1) the oppressors no longer need to oppress because the oppressed voluntarily commit to self-torture; (2) the oppressed find pleasure in self-torture; and (3) the oppressed achieve queered self-torture, to achieve alternative futures. Using the conceptualization of "self-torture," this research examines and critiques pleasure, desire, capital, and power in postwar Japan, which enables the discussion of the data-colonizing “Moonshot Research and Development program”. If the oppressed want to divest from the habits of normative self-torture, which shape what is possible in both our present and future, we need methods to feel and know that the alternative results of self-torture are possible. Phase three will be enacted using Sarah Ahmed's queer methodology to reorient national and cultural identity away from heteronormativity. Through theoretical analysis, textual analysis, archival research, ethnographic interviews, and digital art projects, including experimental documentary as a method to capture the realities of the individuals who are practicing self-torture, this research seeks to reveal how self-torture may become not just a vehicle of pleasure but also a mode of critiquing power and achieving freedom. It seeks to encourage the imaginings of queer Japanese futures, where the marginalized survive Japan’s natural and man-made disasters and Japan’s Imperialist past and present rather than submitting to the country’s continued violence.

Keywords: arts-based research, Japanese studies, interdisciplinary arts, queer studies, cultural studies, popular culture, BDSM, sadomasochism, sexuality, VR, AR, digital art, visual arts, speculative fiction

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Kaowen Grace Chang

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Urban green spaces play an important role in promoting wellbeing through the sense of relaxation for urban residents. Among many designing factors, what the principal ones that could effectively influence people’s sense of relaxation? And, what are the relationship between the sense of relaxation and those factors? Regarding those questions, there is still little evidence for sufficient support. Therefore, the purpose of this study, based on individual responses to environmental information, is to investigate the landscape factors that relate to well-being through the sense of relaxation in mixed-use urban environments. We conducted the experimental design and model construction utilizing choice-based conjoint analysis to test the factors of plant arrangement pattern, plant trimming condition, the distance to visible automobile, the number of landmark objects, and the depth of view. Through the operation of balanced fractional orthogonal design, the goal is to know the relationship between the sense of relaxation and different designs. In a result, the three factors of plant trimming condition, the distance to visible automobile, and the depth of view shed are significantly effective to the sense of relaxation. The stronger magnitude of maintenance and trimming, the further distance to visible automobiles, and deeper view shed that allow the users to see further scenes could significantly promote green space users’ sense of relaxation in urban green spaces.

Keywords: urban green space, landscape planning and design, sense of relaxation, choice model

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Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

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This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence

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Authors: B. Kirubakaran, C. Rajasekaran

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Reconfigurable techniques are used in many engineering and industrial applications for the efficient data transmissions through the wireless sensor networks. Nowadays most of the industrial applications are work for try to minimize the size and cost. During runtime the reconfigurable technique avoid the unwanted hang and delay in the system performance. In recent world Field Programmable Gate Array (FPGA) as one of the most efficient reconfigurable device and widely used for most of the hardware and software reconfiguration applications. In this paper, the work deals with whatever going to make changes in the hardware and software during runtime it’s should not affect the current running process that’s the main objective of the paper our changes be done in a parallel manner at the same time concentrating the cost and power transmission problems during data trans-receiving. Analog sensor (Temperature) as an input for the controller (PIC) through that control the FPGA digital sensors in generalized manner.

Keywords: field programmable gate array, peripheral interrupt controller, runtime reconfigurable techniques, wireless sensor networks

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Authors: Anuradha Ranawakage, Nimalashanithi Amarasekara

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Supply chain resilience and sustainability have received great attention from both academia and business professionals since last few decades. However, the relationship between supply chain resilience and sustainability has not been empirically tested in the apparel industry, where both concepts play a crucial role. Thus, this study aims to investigate how supply chain resilience strategies (digital connectivity, inventory and reserve capacity, and collaboration) impact the supply chain sustainability of export-oriented apparel manufacturing companies in Sri Lanka. An online questionnaire was used to collect data on the impact of supply chain resilience strategies on the supply chain sustainability of 99 apparel companies operating in Sri Lanka. This research makes a significant contribution to the field of supply chain management by assessing the impact of supply chain resilience strategies on supply chain sustainability in the context of the developing country, Sri Lanka, where economic crises and the pandemic have had a profound impact on the apparel industry. The findings have important theoretical and managerial implications for maintaining congruence between supply chain resilience and supply chain sustainability in the long run.

Keywords: supply chain resilience, supply chain sustainability, apparel, supply chain

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Authors: Shri Devi

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This paper was conducted to see the ground water potential zones in Paramakudi taluk, Ramanathapuram,Tamilnadu India with a total areal extent of 745 sq. km. The various thematic map have been prepared for the study such as soil, geology, geomorphology, drainage, land use of the particular study area using the Toposheet of 1: 50000. The digital elevation model (DEM) has been generated from contour interval of 10m and also the slope was prepared. The ground water potential zone of the region was obtained using the weighted overlay analysis for which all the thematic maps were overlayed in arc gis 10.2. For the particular output the ranking has been given for all the parameters of each thematic layer with different weightage such as 25% was given to soil, 25% to geomorphology and land use land cover also 25%, slope 15%, lineament with 5% and drainage streams with 5 percentage. Using these entire potential zone maps was prepared which was overlayed with the village map to check the region which has good, moderate and low groundwater potential zone.

Keywords: GIS, ground water, Paramakudi, weighted overlay analysis

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Authors: Miraç Savaş Turhan, Ali Danişman

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Recently scholars of new institutional theory have used institutional logics perspective to explain the contradictory practices in modern western societies. Accordingly, distinct institutional logics are embedded in central institutions such as the market, state, democracy, family, and religion. They guide individual and organizational actors and constraint their behaviors in a particular organizational field. Through this perspective, actors are assumed to have a situated, embedded, boundedly intentional, and adaptive role against the structure in social, cultural and political context. On the other hand, over a decade, there is an emerging attempt focusing on the role of actors on creating, maintaining, and changing the institutions. Such attempts brought out the concept of institutional entrepreneurs to explain the role of individual actors in relation to institutions. Institutional entrepreneurs are individuals, groups of individuals, organizations or groups of organizations that are able to initiate some actions to build, maintain or change institutions. While recent studies on institutional logics perspective have attempted to explain roles of entrepreneurial actors who have resources and skills, little is known about the effects of multiple institutional logics on the ability of institutional entrepreneurs. In this study, we aim to find out that how multiple institutional logics affect the ability of institutional entrepreneurs during the process of institutional change. We examine this issue in the Turkish Education Field. While institutional logics were identified based on the previous studies in the education field, the actions taken by Turkish National Education Ministry from 2003 to 2013 was examined through content analysis The early results indicate that there are remarkable shift and contradictions in the ability of institutional entrepreneur in taking actions to change the field in relationship to balance of power shift among the carriers of institutional logics.

Keywords: institutional theory, institutional logics, institutional entrepreneurs, Turkish national education

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Authors: Silvia C. Ambag, Racidon P. Bernarte, Jacquelyn B. Buccahi, Jessica R. Lacaron, Charlyn L. Mangulabnan

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This study aimed to answer the query, “What is the impact of Students Online Evaluation on PUP Faculty’s Performance?” The problem of the study was resolve through the objective of knowing the perceived impact of students’ online evaluation on PUP faculty’s performance. The objectives were carried through the application of quantitative research design and by conducting survey research method. The researchers utilized primary and secondary data. Primary data was gathered from the self-administered survey and secondary data was collected from the books, articles on both print-out and online materials and also other theses related study. Findings revealed that PUP faculty in general stated that students’ online evaluation made a highly positive impact on their performance based on their ‘Knowledge of Subject’ and ‘Teaching for Independent Learning’, giving a highest mean of 3.62 and 3.60 respectively., followed by the faculty’s performance which gained an overall means of 3.55 and 3.53 are based on their ‘Commitment’ and ‘Management of Learning’. From the findings, the researchers concluded that Students’ online evaluation made a ‘Highly Positive’ impact on PUP faculty’s performance based on all Four (4) areas. Furthermore, the study’s findings reveal that PUP faculty encountered many problems regarding the students’ online evaluation; the impact of the Students’ Online Evaluation is significant when it comes to the employment status of the faculty; and most of the PUP faculty recommends reviewing the PUP Online Survey for Faculty Evaluation for improvement. Hence, the researchers recommend the PUP Administration to revisit and revise the PUP Online Survey for Faculty Evaluation, specifically review the questions and make a set of questions that will be appropriate to the discipline or field of the faculty. Also, the administration should fully orient the students about the importance, purpose and impact of online faculty evaluation. And lastly, the researchers suggest the PUP Faculty to continue their positive performance and continue on being cooperative with the administrations’ purpose of addressing the students’ concerns and for the students, the researchers urged them to take the online faculty evaluation honestly and objectively.

Keywords: on-line Evaluation, faculty, performance, Polytechnic University of the Philippines (PUP)

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Authors: Mohammed M. Ibrahim, A. A. Yusuf, Manuel Du, Fiona Mumoki

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The traditional honey bee species of Sudan is Apis mellifera, but in 1985, the dwarf bee Apis florea was introduced to the country, so now there are two species present. However, there are conflicting assessments regarding the subspecies of Apis mellifera colonies in Sudan. Likewise, it is unclear if, in the 40 years since its introduction, Apis florea has already developed regional differences or ecotypes. To shed light on these questions, we performed a morphology study on Sudanese honeybees. Samples of 10 to 20 honeybee workers per colony of the two species were collected from 16 locations, spanning different climatic zones in Sudan during 2021. Measurements were taken from 16 morphometric characteristics using a stereo-microscope equipped with an Image Analysis System (Moticam Image Plus 5.0 Digital Microscope Camera) to study their variability. The results indicate that in both species, the general means of various characters showed significant differences (p < 0.05) within a species between different locations, indicating that there might indeed be regional differences. However, more taxonomic investigation and, ideally also, molecular studies are needed in order to confirm the proper identification of subspecies and their ecotypes.

Keywords: Apis, subspecies, morphology, Sudan

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Yei Mian Adrian Yap

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How we idealize the regular schools to be inclusive as much as possible hinges on mainstream teachers’ attitudes and teaching-efficacy towards the inclusion of students with special needs in the regular schools. This research studies the Japanese and Singaporean mainstream teachers’ attitudes and teaching-efficacy towards the inclusion of students with special needs in the regular classrooms by investigating what key variables influence their attitudes and teaching-efficacy and how they strategize to address their challenges to include their students with special needs in their regular classrooms. In order to understand the nature of teachers’ attitudes and teaching-efficacy towards the inclusive education, a mixed-method research methodology was carried out in Japan and Singapore; it involved an explanatory sequential method of employing quantitative research first before qualitative research. In the quantitative research, 189 Japanese and 183 Singaporean teachers were invited to participate in the questionnaires and out of these participants, 38 Japanese and 15 Singaporean teachers shared their views during their semi-structured interviews. Based on the empirical findings, Japanese teachers’ attitudes and teaching-efficacy were more likely to be influenced by their experiences in teaching students with special needs, knowledge about disability legislation, presence of their disabled family members and level of confidence to teach students with special needs. On the other hand, Singaporean teachers’ attitudes and teaching-efficacy were affected by gender, educational level, received trainings in special needs education, knowledge about disability legislation and level of confidence to teach students with special needs. Both country results also demonstrated that there was a positive correlation between their teaching-efficacy and attitude. Narrative findings further expanded the reasons behind these quantitative factors that shaped teachers’ attitudes and teaching-efficacy. Also it discussed the various problems faced by Japanese and Singaporean teachers and how they identified their coping strategies to circumvent their challenges in including their students with special needs in their regular classrooms. The significance of this research manifests in necessary educational reforms in both countries especially in the context of inclusive education. These findings may not be as definitive as expected but it is believed that it could provide useful information on the current situation about teachers’ concerns towards the inclusive education. In conclusion, this research could potentially make its positive contribution to the body of literature on teachers’ attitudes and teaching-efficacy in the context of Asian developed countries and these findings could posit that regular teachers’ positive attitudes and strong sense of teaching self-efficacy could directly improve the success rate of inclusion of students with special needs in the regular classrooms.

Keywords: attitudes, inclusive education, special education, teaching-efficacy

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Authors: Miramar Damanhouri

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This study examines the relationship between language use in computer-mediated communication and the social identity of the user. The data were collected by surveying 298 Saudi bilingual speakers who are familiar with Arabizi, a blend of Latin characters and Arabic numerals to transliterate Arabic sounds, and then analyzed quantitatively by running tests for statistical confidence in order to determine differences in perceptions between young adults (ages 15-25 years) and middle-aged adults (ages 26-50 years). According to the findings of this study, English is the dominant language among most of the young adults surveyed, and when they do use Arabic, they use Arabizi because of its flexibility, compatibility with modern technology, and its acceptance among people of their age and sociocultural backgrounds. On the other hand, most middle-aged adults surveyed here tend to use Arabic, as they believe that they should show their loyalty to their origin. The results of the study demonstrate a mutual relationship between language use in computer-mediated communication and the user’s social identity, as language is used both to reflect and construct that identity.

Keywords: Arabizi, computer mediated communication, digital communication, language use

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Authors: Ciolomic Ioana Andreea, Farcas Teodora, Tiron-Tudor Adriana

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Investments in research and innovation are widely acknowledged as being crucial drivers for economic growth, for job-creation and to secure social and economic welfare. The aim of this article is to disseminate the results of a Leonardo da Vinci Innovation Transfer project, AdapTykes Adaptation of trainings based up on the Finnish Workplace Development Programme. This project aims to analyses the adaptability of the Finnish model to the economic and political environment of the two emergent countries Romania and Hungary, in order to develop workplace innovation. The focus of this paper is to present the adaptability of the Finnish model to the Romanian context.

Keywords: innovation, human resources, education, tools

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Authors: Wilayat Ali, Li Sheng, Waleed Ahmed

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The ability of the robot to make simultaneously map of the environment and localize itself with respect to that environment is the most important element of mobile robots. To solve SLAM many algorithms could be utilized to build up the SLAM process and SLAM is a developing area in Robotics research. Robot Operating System (ROS) is one of the frameworks which provide multiple algorithm nodes to work with and provide a transmission layer to robots. Manyof these algorithms extensively in use are Hector SLAM, Gmapping and Cartographer SLAM. This paper describes a ROS-based Simultaneous localization and mapping (SLAM) library Google Cartographer mapping, which is open-source algorithm. The algorithm was applied to create a map using laser and pose data from 2d Lidar that was placed on a mobile robot. The model robot uses the gazebo package and simulated in Rviz. Our research work's primary goal is to obtain mapping through Cartographer SLAM algorithm in a static indoor environment. From our research, it is shown that for indoor environments cartographer is an applicable algorithm to generate 2d maps with LIDAR placed on mobile robot because it uses both odometry and poses estimation. The algorithm has been evaluated and maps are constructed against the SLAM algorithms presented by Turtlebot2 in the static indoor environment.

Keywords: SLAM, ROS, navigation, localization and mapping, gazebo, Rviz, Turtlebot2, slam algorithms, 2d indoor environment, cartographer

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Authors: S. Da Costa, D. Páez, F. Sánchez

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We examine the causal role of positive affect on creativity, the association of creativity or innovation in the ideation phase with functional emotional regulation, successful adjustment to stress and dispositional emotional creativity, as well as the predictive role of creativity for positive emotions and social adjustment. The study examines the effects of modification of positive affect on creativity. Participants write three poems, narrate an infatuation episode, answer a scale of personal growth after this episode and perform a creativity task, answer a flow scale after creativity task and fill a dispositional emotional creativity scale. High and low positive effect was induced by asking subjects to write three poems about high and low positive connotation stimuli. In a neutral condition, tasks were performed without previous affect induction. Subjects on the condition of high positive affect report more positive and less negative emotions, more personal growth (effect size r = .24) and their last poem was rated as more original by judges (effect size r = .33). Mediational analysis showed that positive emotions explain the influence of the manipulation on personal growth - positive affect correlates r = .33 to personal growth. The emotional creativity scale correlated to creativity scores of the creative task (r = .14), to the creativity of the narration of the infatuation episode (r = .21). Emotional creativity was also associated, during performing the creativity task, with flow (r = .27) and with affect balance (r = .26). The mediational analysis showed that emotional creativity predicts flow through positive affect. Results suggest that innovation in the phase of ideation is associated with a positive affect balance and satisfactory performance, as well as dispositional emotional creativity is adaptive.

Keywords: affectivity, creativity, induction, innovation, psychological factors

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: Mohamed Laamim, Abdelilah Rochd, Aboubakr Benazzouz, Abderrahim El Fadili

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Smart grids have recently sparked a lot of interest in the energy sector as they allow for the modernization and digitization of the existing power infrastructure. Smart grids have several advantages in terms of reducing the environmental impact of generating power from fossil fuels due to their capacity to integrate large amounts of distributed energy resources. On the other hand, smart grid technologies necessitate many field investigations and requirements. This paper focuses on the major difficulties that governments face around the world and compares them to the situation in Morocco. Also presented in this study are the current works and projects being developed to improve the penetration of smart grid technologies into the electrical system. Furthermore, the findings of this study will be useful to promote the smart grid revolution in Morocco, as well as to construct a strong foundation and develop future needs for better penetration of technologies that aid in the integration of smart grid features.

Keywords: smart grids, microgrids, virtual power plants, digital twin, distributed energy resources, vehicle-to-grid, advanced metering infrastructure.

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Z. Abdollahi Biron, P. Pisu

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Connected vehicles are one of the promising technologies for future Intelligent Transportation Systems (ITS). A connected vehicle system is essentially a set of vehicles communicating through a network to exchange their information with each other and the infrastructure. Although this interconnection of the vehicles can be potentially beneficial in creating an efficient, sustainable, and green transportation system, a set of safety and reliability challenges come out with this technology. The first challenge arises from the information loss due to unreliable communication network which affects the control/management system of the individual vehicles and the overall system. Such scenario may lead to degraded or even unsafe operation which could be potentially catastrophic. Secondly, faulty sensors and actuators can affect the individual vehicle’s safe operation and in turn will create a potentially unsafe node in the vehicular network. Further, sending that faulty sensor information to other vehicles and failure in actuators may significantly affect the safe operation of the overall vehicular network. Therefore, it is of utmost importance to take these issues into consideration while designing the control/management algorithms of the individual vehicles as a part of connected vehicle system. In this paper, we consider a connected vehicle system under Co-operative Adaptive Cruise Control (CACC) and propose a fault diagnosis scheme that deals with these aforementioned challenges. Specifically, the conventional CACC algorithm is modified by adding a Kalman filter-based estimation algorithm to suppress the effect of lost information under unreliable network. Further, a sliding mode observer-based algorithm is used to improve the sensor reliability under faults. The effectiveness of the overall diagnostic scheme is verified via simulation studies.

Keywords: fault diagnostics, communication network, connected vehicles, packet drop out, platoon

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Authors: Dudam Bharath Kumar, Harsh Kumar, Naveed Ahmed

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Vehicular traffic on the road-side plays a significant role in affecting the noise pollution in most of the cities over the world. To assess the correlation of the road-traffic on noise pollution in the city environment, continuous measurements were carried out in an entire daytime starting from 8:00 AM IST to 6:00 PM IST at a single point for each 5 minutes (8:00-8:05, 9:00-9:05, 10:00-10:05 AM, ...) near the KIIT University campus road. Noise levels were observed using a mobile operated app of android cell phone and a handheld noise meter. Calibration analysis shows high correlation about 0.89 for the study location for the day time period. Results show diurnal variability of atmospheric noise pollution levels go hand-in and with the vehicular number which pass through a point of observation. The range of noise pollution levels in the daytime period is observed as 55 to 75 dB(A). As a day starts, sudden upsurge of noise levels is observed from 65 to 71 dB(A) in the early morning, 64 dB(A) in late morning, regains the same quantity 68-71 dB(A) in the afternoon, and rises 70 dB(A) in the early evening. Vehicular number of the corresponding noise levels exhibits 115-120, 150-160, and 140-160, respectively. However, this preliminary study suggests the importance of vehicular traffic on noise pollution levels in the urban environment and further to study population exposed to noise levels. Innovative approaches help curb the noise pollution through modelling the traffic noise pollution spatially and temporally over the city environments.

Keywords: noise pollution, vehicular traffic, urban environment, noise meter

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Authors: Lynn Mcquarrie, Charlotte Enns

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The goal of this project was to develop a visually-based, signed language phonological awareness training program and to pilot the intervention with signing deaf children (ages 6 -10 years/ grades 1 - 4) who were beginning readers to assess the effects of systematic explicit American Sign Language (ASL) phonological instruction on both ASL vocabulary and English print vocabulary learning. Growing evidence that signing learners utilize visually-based signed language phonological knowledge (homologous to the sound-based phonological level of spoken language processing) when reading underscore the critical need for further research on the innovation of reading instructional practices for visual language learners. Multiple single-case studies using a multiple probe design across content (i.e., sign and print targets incorporating specific ASL phonological parameters – handshapes) was implemented to examine if a functional relationship existed between instruction and acquisition of these skills. The results indicated that for all cases, representing a variety of language abilities, the visually-based phonological teaching approach was exceptionally powerful in helping children to build their sign and print vocabularies. Although intervention/teaching studies have been essential in testing hypotheses about spoken language phonological processes supporting non-deaf children’s reading development, there are no parallel intervention/teaching studies exploring hypotheses about signed language phonological processes in supporting deaf children’s reading development. This study begins to provide the needed evidence to pursue innovative teaching strategies that incorporate the strengths of visual learners.

Keywords: American sign language phonological awareness, dual language strategies, vocabulary learning, word reading

Procedia PDF Downloads 329

Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

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Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: Nithiya C., Komathi B. J., Praveena N. G., Samuda Prathima

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At present, the ultimate aim in digital system designs, especially at the gate level and lower levels of design abstraction, is power optimization. Adders are a nearly universal component of today's integrated circuits. Most of the research was on the design of high-speed adders to execute addition based on various adder structures. This paper discusses the ideal path for selecting an arithmetic unit for IoT applications. Based on the analysis of eight types of 16-bit adders, we found out Carry Look-ahead (CLA) produces low power. Additionally, multiplier and accumulator (MAC) unit is implemented with the Booth multiplier by using the low power adders in the order of preference. The design is synthesized and verified using Synopsys Design Compiler and VCS. Then it is implemented by using Cadence Encounter. The total power consumed by the CLA based booth multiplier is 0.03527mW, the total area occupied is 11260 um², and the speed is 2034 ps.

Keywords: carry look-ahead, carry select adder, CSA, internet of things, ripple carry adder, design rule check, power delay product, multiplier and accumulator

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Authors: M. J. Suriani, F. Mansor, W. Siti Maizurah, I. Nurizwani

Abstract:

Silicon Carbide (SiC) is one of the Silicon-based materials, which get interested by the researcher. SiC is an emerging semiconductor material, which has received a great deal of attention due to their application in high frequency and high power systems. Although its superior characteristic for a semiconductor material, its outstanding mechanical properties, chemical inertness and thermal stability has gained important aspect for a surface coating for deployment in extreme environments. Very high frequency (VHF)-PECVD technique utilized to deposit nano ns-SiC film in which variation in chamber pressure, substrate temperature, RF power and precursor gases flow rate will be investigated in order to get a good quality of thin film coating. Characterization of the coating performed in order to study the surface morphology, structural information. This performance of coating evaluated through corrosion test to determine the effectiveness of the coating for corrosion prevention. Ns-SiC film expected to possess better corrosion resistance and optical properties, as well as preserving the metal from the marine environment. Through this research project, corrosion protection performance by applying coating will be explored to obtain a great corrosion prevention method to the shipping and oil and gas industry in Malaysia. Besides, the cost of repair and maintenance spending by the government of Malaysia can be reduced through practicing this method.

Keywords: composite materials, marine corrosion, nano-composite, nano structure–coating

Procedia PDF Downloads 462