Search results for: neural perception.

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: B. S. Choudri

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Introduction: Climate change is a major threat to rural livelihoods and to food security in the developing world, including Oman. The aim of this study is to provide a basis for policymakers and researchers in order to understand the impacts of climate change on agriculture and developing adaptation strategies in Oman. Methodology: The data was collected from different agricultural areas across the country with the help of a questionnaire survey among farmers, discussion with community, and observations at the field level. Results: The analysis of data collected from different areas within the country shows a shift in the sowing period of major crops and increased temperatures over recent years. Farmer community is adopting through diversification of crops, use of heat-tolerant species, and improved measures of soil and water conservation. Agriculture has been the main livelihood for most of the farmer communities in rural areas in the country. Conclusions: In order to reduce the effects of climate change at the local and farmer communities, risk reduction would be important along with an in-depth analysis of the vulnerability. Therefore, capacity building of local farmers and providing them with scientific knowledge, mainstreaming adaptation into development activities would be essential with additional funding and subsidies.

Keywords: agriculture, climate change, vulnerability, adaptation

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Authors: N. C. Sarkar, A. Bhaskar, Z. Zheng

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The driving behavior in area-based (i.e., non-lane based) traffic is induced by the presence of other individuals in the choice space from the driver’s visual perception area. The driving behavior of a subject vehicle is constrained by the potential leaders and leaders are frequently changed over time. This paper is to determine a stochastic model for a parameter of modified intelligent driver model (MIDM) in area-based traffic (as in developing countries). The parametric and non-parametric distributions are presented to fit the parameters of MIDM. The goodness of fit for each parameter is measured in two different ways such as graphically and statistically. The quantile-quantile (Q-Q) plot is used for a graphical representation of a theoretical distribution to model a parameter and the Kolmogorov-Smirnov (K-S) test is used for a statistical measure of fitness for a parameter with a theoretical distribution. The distributions are performed on a set of estimated parameters of MIDM. The parameters are estimated on the real vehicle trajectory data from India. The fitness of each parameter with a stochastic model is well represented. The results support the applicability of the proposed modeling for parameters of MIDM in area-based traffic flow simulation.

Keywords: area-based traffic, car-following model, micro-simulation, stochastic modeling

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Authors: Mahera Abdulrahman

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Objective: Healthcare transformation from traditional to modern in the country recently prompted the need to address career choices, accreditation perception and satisfaction among medical residents. However, a concerted nationwide study to understand and address burnout in the medical residency program has not been conducted in the UAE and the region. Methods: A nationwide, multicenter, cross-sectional study was designed to evaluate professional burnout and depression among medical residents in order to address the gap. Results: Our results indicate that 75.5% (216/286) of UAE medical residents had moderate to high emotional exhaustion, 84% (249/298) had high depersonalization, and 74% (216/291) had a low sense of personal accomplishment. In aggregate, 70% (212/302) of medical residents were considered to be experiencing at least one symptom of burnout based on a high emotional exhaustion score or a high depersonalization score. Depression ranging from 6-22%, depending on the specialty was also striking given the fact the Arab culture lays high emphasis on family bonding. Interestingly 83% (40/48) of medical residents who had high scores for depression also reported burnout. Conclusion: Our data indicate that burnout and depression among medical residents is epidemic. There is an immediate need to address burnout through effective interventions at both the individual and institutional levels. It is imperative to reconfigure the approach to medical training for the well-being of the next generation of physicians in the Arab world.

Keywords: mental health, Gulf, Arab, residency training, burnout, depression

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Authors: Chaudhary Muhammad Aqdus Ilyas, Matthias Rehm, Kamal Nasrollahi, Thomas B. Moeslund

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This paper presents the investigation of data collection procedures, associated with robots when placed with traumatic brain injured (TBI) patients for rehabilitation purposes through facial expression and mood analysis. Rehabilitation after TBI is very crucial due to nature of injury and variation in recovery time. It is advantageous to analyze these emotional signals in a contactless manner, due to the non-supportive behavior of patients, limited muscle movements and increase in negative emotional expressions. This work aims at the development of framework where robots can recognize TBI emotions through facial expressions to perform rehabilitation tasks by physical, cognitive or interactive activities. The result of these studies shows that with customized data collection strategies, proposed framework identify facial and emotional expressions more accurately that can be utilized in enhancing recovery treatment and social interaction in robotic context.

Keywords: computer vision, convolution neural network- long short term memory network (CNN-LSTM), facial expression and mood recognition, multimodal (RGB-thermal) analysis, rehabilitation, robots, traumatic brain injured patients

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Authors: Teja Koduru

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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

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Authors: Hyun-E Yeom

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Patients’ views about the cause of hypertension may influence their present and proactive behaviors to regulate high blood pressure. This study aimed to examine the internal structure underlying the causal beliefs about hypertension and the influence of causal beliefs on self-care intention and medical compliance in Korean patients with hypertension. The causal beliefs of 145 patients (M age = 57.7) were assessed using the Illness Perception Questionnaire-Revised. An exploratory factor analysis was used to identify the factor structure of the causal beliefs, and the factors’ influence on self-care intention and medication compliance was analyzed using multiple and logistic regression analyses. The four-factor structure including psychological, fate-related, risk and habitual factors was identified and the psychological factor was the most representative component of causal beliefs. The risk and fate-related factors were significant factors affecting lower intention to engage in self-care and poor compliance with medication regimens, respectively. The findings support the critical role of causal beliefs about hypertension in driving patients’ current and future self-care behaviors. This study highlights the importance of educational interventions corresponding to patients’ awareness of hypertension for improving their adherence to a healthy lifestyle and medication regimens.

Keywords: hypertension, self-care, beliefs, medication compliance

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Authors: Sultan Alotaibi, Dmitri Roussinov

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Many governments recently started to change the ways of providing their services by allowing their citizens to access services from anywhere without the need of visiting the location of the service provider. Mobile government (M-government) is one of the techniques that fulfill that goal. It has been adopted by many governments. M-government can be defined as an implementation of Electronic Government (E-Government) by using mobile technology with the aim of improving service delivery to citizens, businesses and all government agencies. There have been several research projects developing models to understand the behavior of individuals towards the adoption of m-government. This paper proposes a model for adoption of m-government services in Saudi Arabia by extending Technology Acceptance Model (TAM) by introducing external factors. This paper also reports on the development of a survey instrument designed to measure user perception of mobile government acceptance. A survey instrument has been developed by using existing scales from prior instruments and a pilot study has been conducted by distributing the survey to 33 participants. As a result, a survey instrument has been refined to retain 43 items. The results also showed that the reliabilities of all the scales in the survey instrument are above the levels acceptable in current academic research, thus the instruments developed by us are capable of analyzing the factors in M-government adoption.

Keywords: TAM, m-government, e-government, model, acceptance, mobile government

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Authors: Heather Clark, Jon Kelly

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Burnout among healthcare professionals has been elevated to a high level of concern. The descriptions of the healthcare workplace often include language such as, stressful, long hours, rotating shifts, weekends and holidays, and exhausting. New graduate healthcare professionals are being sent into the workplace with little to no coping skills, knowledge of signs and symptoms of burnout, or resources that are available. The authors of this study created a university course entitled 'coping in your profession' that enrolled registered nurses, licensed practical nurses, EMTs, nurse assistants, and medical assistants. The course addresses burnout, self-analysis, incivility, coping mechanisms, and organizational responsibilities for employee well-being. The students were surveyed using QualtricsXM that included a pre-course and post-course analysis. Pre-course results showed high levels of individual experiences with burnout and limited knowledge of resources to combat burnout. Post-course results included personal growth and that students’ perception of burnout can be prevented at both the individual and the organization levels. Students also indicated that few to no resources to combat burnout existed at their place of employment. Addressing burnout at the educational level helps prepare graduates with the knowledge and tools to combat burnout at the individual and organization level.

Keywords: burnout, coping, healthcare workers, incivility, resilience

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Paulino V. Tavares, Ana L. Romao

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Public governance, in general, is essential to qualify and educate, pedagogically, the decision-making process of the government in relation to the management of resources and the provision of public services, with transparency and active participation of individuals and citizens for the development of a more democratic environment, besides stimulating control and social empowerment, aiming at the development of the collectivity. In this context, the participation of society in the elaboration, execution, and control of public policies is prominent to strengthen public governance itself. With this, using a multidimensional approach with the application of two questionnaires to a universe of twenty Counselors of the Courts of Auditors (Brazil), twenty professionals of public administration (Brazil), twenty Government/Provincial Counselors (South Africa), and twenty South African professionals of public administration, the preliminary results indicate that the participation of civil society, for both countries, is very low in the elaboration, execution, and control of public policies. At the same time, about 70% of the answers obtained indicate, on average, three possible paths to increase the participation of civil society. With this, it is delineated that developing new horizons to strengthen both public policies how social participation is necessary, but, for both, it is important that governments and civil society, in their respective countries, have an awareness of the effective importance of this interaction.

Keywords: Brazil, civil society, participation, South Africa

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Authors: Asma Ameur, Dhafer Malouche

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Hotel competitiveness research is an essential phase of the marketing strategy for any hotel. Certainly, knowing the hotels' competitors helps the hotelier to grasp its position in the market and the citizen to make the right choice in picking a hotel. Thus, competitiveness is an important indicator that can be influenced by various factors. In fact, the issue of competitiveness, this ability to cope with competition, remains a difficult and complex concept to define and to exploit. Therefore, the purpose of this article is to make an exploratory study to calculate a competitiveness indicator for hotels. Further on, this paper makes it possible to determine the criteria of direct or indirect effect on the image and the perception of a hotel. The actual research is used to look into the right model for hotel ‘competitiveness. For this reason, we exploit different theoretical contributions in the field of machine learning. Thus, we use some statistical techniques such as the Principal Component Analysis (PCA) to reduce the dimensions, as well as other techniques of statistical modeling. This paper presents a survey covering of the techniques and methods in hotel competitiveness research. Furthermore, this study allows us to deduct the significant variables that influence the determination of hotel’s competitors. Lastly, the discussed experiences in this article found that the hotel competitors are influenced by several factors with different rates.

Keywords: competitiveness, e-reputation, hotels' competitors, online hotel’ review, principal component analysis, statistical modeling

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Authors: Wen-Yu Chen, Chia-Yuan Hsu, Sasinee Vongsrikul

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The present study is to explore the perception of Taiwanese females towards medical tourism in Thailand for the development of applicable marketing strategy, integrating travel motivation and cosmetic surgery trend to attract potential medical tourists from Taiwan. Since previous studies relevant to this research issue are limited, qualitative study is firstly employed by using one focus group interview and in-depth interviews with Taiwanese females. Moreover, the present research collected questionnaires from 290 Taiwanese females to provide greater understanding of research results. The top three factors that affect Taiwanese females’ decision for not going to Thailand for medical tourism are “physicians and nurses cannot speak Chinese”, “low quality of the cosmetic surgery product that I want to do”, and “the county does not have laws to protect medical tourists’ right”. The finding of the empirical part would suggest the area in medical tourism industry which Thailand should promote and emphasizes in order to increase its presence as a hub for cosmetic surgery and attract Taiwanese female market. Therefore, the study contributes to the potential development of marketing strategy for medical tourism, specifically in the area of cosmetic surgery in Thailand while targeting Taiwan market.

Keywords: Thailand, Taiwanese female tourists, medical tourism, cosmetic surgery

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Authors: Afaf Manzoor, Abdul Hameed

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Hopes to attend school is the most effective means to overcome the burden of disability and become a self-reliant, productive citizen. The objectives of the study were to develop a valid and reliable scale to measure hopes of out of school children with disabilities and find an association between hopes and various demographic factors such as type of disability, gender, socio-economic status, and locale, etc. Child Hope theory by Snyder (2003) was used as a framework to develop a measure for the hopes of children. According to this theory, hope is defined as a set of cognition that includes self- perception which establish routes to achieve desired goals (pathways) and motivation for achieving the goals (agency). By applying this theory, inclusion hope scale was developed and validated. The data were collected from 361 out of school children with disabilities living in three districts (Lahore, Sheikupura, Kasur) of Lahore Division by using the cluster sampling technique. Findings of the study indicated that children with intellectual challenges were more hopeless as compared to other types of disabilities. Similarly, children living in urban areas have better hopes for inclusion in school. However, no gender disparity was found in terms of being hopeful to attend schools. The study also includes recommendations to improve hopes for educational inclusion among out of school children with disabilities.

Keywords: out of school children, disability, hopes, inclusion

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

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Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: attention-based fully convolutional network, optic disc detection and segmentation, retinal fundus image, screening of ocular diseases

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Authors: Nazmul Shahadat, Anthony S. Maida

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In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

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Authors: Larissa Negris de Souza, Ana Regina Mizrahy Cuperschmid, Daniel de Carvalho Moreira

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Augmented Reality (AR) alters the elaboration of the architectural project, which relates to project cognition: representation, visualization, and perception of information. Understanding these features from the earliest stages of the design can facilitate the study of relationships, zoning, and overall dimensions of the forms. This paper’s goal was to explore a new approach for information visualization during the early stages of architectural design using Augmented Reality (AR). A three-dimensional marker inspired by the Rubik’s Cube was developed, and its performance, evaluated. This investigation interwovens the acquired knowledge of traditional briefing methods and contemporary technology. We considered the concept of patterns (Alexander et al. 1977) to outline geometric forms and associations using visual programming. The Design Science Research was applied to develop the study. An SDK was used in a game engine to generate the AR app. The tool's functionality was assessed by verifying the readability and precision of the reconfigurable 3D marker. The results indicated an inconsistent response. To use AR in the early stages of architectural design the system must provide consistent information and appropriate feedback. Nevertheless, we conclude that our framework sets the ground for looking deep into AR tools for briefing design.

Keywords: augmented reality, cuboid marker, early design stages, graphic representation, patterns

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Authors: Deawan Rakin Ahamed Remal, Sinthia Chowdhury, Sharun Akter Khushbu, Sheak Rashed Haider Noori

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Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent.

Keywords: TTR, NSTTR, text to text recognition, deep learning, natural language processing

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Authors: Mark Pizzato

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How do ideas, images, and emotions in stage-plays and videos affect us? Do they evoke a greater awareness (or cognitive reappraisal of emotions) through possible shifts between left-cortical, right-cortical, and subcortical networks? To address these questions, this presentation summarizes the research of various neuroscientists, especially Bernard Baars and others involved in Global Workspace Theory, Matthew Lieberman in social neuroscience, Iain McGilchrist on left and right cortical functions, and Jaak Panksepp on the subcortical circuits of primal emotions. Through such research, this presentation offers an ‘inner theatre’ model of the brain, regarding major hubs of neural networks and our animal ancestry. It also considers recent experiments, by Mario Beauregard, on the cognitive reappraisal of sad, erotic, and aversive film clips. Finally, it applies the inner-theatre model and related research to survey results of theatre students who read and then watched the ‘To be or not to be’ speech in 8 different video versions (from stage and screen productions) of William Shakespeare’s Hamlet. Findings show that students become aware of left-cortical, right-cortical, and subcortical brain functions—and shifts between them—through staging and movie-making choices in each of the different videos.

Keywords: cognitive reappraisal, Hamlet, neuroscience, Shakespeare, theatre

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Authors: Eugene Stepanov, Arkadi Ponossov

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Ordinary differential equations with switching nonlinearities constitute a very useful tool in many applications. The solutions of such equations can usually be calculated analytically if they cross the discontinuities transversally. Otherwise, one has trajectories that slides along the discontinuity, and the calculations become less straightforward in this case. For instance, one of the problems one faces is non-uniqueness of the sliding modes. In the presentation, it is proposed to apply the theory of hybrid dynamical systems to calculate the solutions that are ‘hidden’ in the discontinuities. Roughly, one equips the underlying switched system with an explicitly designed discrete dynamical system (‘automaton’), which governs the dynamics of the switched system. This construction ‘splits’ the dynamics, which, as it is shown in the presentation, gives uniqueness of the resulting hybrid trajectories and at the same time provides explicit formulae for them. Projecting the hybrid trajectories back onto the original continuous system explains non-uniqueness of its trajectories. The automaton is designed with the help of the attractors of the specially constructed adjoint dynamical system. Several examples are provided in the presentation, which supports the efficiency of the suggested scheme. The method can be of interest in control theory, gene regulatory networks, neural field models and other fields, where switched dynamics is a part of the analysis.

Keywords: hybrid dynamical systems, singular perturbation analysis, sliding modes, switched dynamics

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Authors: Ng Siew Keow, Cheah Seeh Lee

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Students’ strong engagement in learning increases their motivation and satisfaction to learn, be resilient to combat academic stress. Engagement in learning is even crucial in the open distance learning (ODL) setting, where the adult students are learning remotely, lessons and learning materials are mostly delivered via online platforms. This study aimed to explore the relationship between learning engagement and perceived academic stress levels of adult students who enrolled in ODL learning mode. In this descriptive correlation study during the 2021-2022 academic years, 101 adult students from Wawasan Open University, Malaysia (WOU) were recruited through convenient sampling. The adult students’ online learning engagement levels and perceived academic stress levels were identified through the self-report Online Student Engagement Scale (OSE) and the Perception of Academic Stress Scale (PASS). The Pearson correlation coefficient test revealed a significant positive relationship between online student engagement and perceived academic stress (r= 0.316, p<0.01). The higher scores on PASS indicated lower levels of perceived academic stress. The findings of the study supported the assumption of the importance of engagement in learning in promoting psychological well-being as well as sustainability in online learning in the open distance learning context.

Keywords: student engagement, academic stress, open distance learning, online learning

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Authors: Muhammad Zubair Khan, Yugyung Lee

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Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Shagufta Jahangir, Raisa Jahangir, Nadeemullah

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In many societies sports is considered inappropriate for women. It traditionally associated with mascunity. The proposed study aims at undertaking a critical situation analysis of sports women in Pakistan from a gender perspective by examining various aspects of sports women by gender including wrong social values, unstable economical position, wrong religious perspective and the role of media towards women in sports, while sports can provide a channel for informing women about their social and legal rights as well as their health issues, productive health and others. A major concern of the study is to identify the basic causes which depriving Pakistani women from sports. The Human Rights Commission of Pakistan and the Joint Action Committee for People’s Rights organized a symbolic mini marathon on 21 May 2005 in Pakistan to challenge arbitrary curbs on women’s public participation in sport and to highlight rising violence against women. Historically, sport has engaged the perception of gender-hierarchy in order to reproduce the ideology of male superiority, a notion which is often translated into ‘usual superiority’ within the superior communal order. However, it is argued here that we are presently in a state of communal instability with esteem to women's participation in sport.

Keywords: mascunity, gender, productive health, inappropriate, rights

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Authors: Naomi Sato, Tomonori Sato, Kenji Masui, Rob Stanborough

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To date, there are few studies on the subjective experiences of patients with chronic low-back pain (CLBP). The purpose of this study was to gain a better understanding of CLBP patients’ perceptions and attitudes regarding pain. Individual, semi-constructed interviews were conducted with 7 Japanese and 10 Americans who had been diagnosed with CLBP. The interviews were transcribed verbatim and analyzed based on a content analysis approach. The study proposal was approved by the Institutional Review Board of the first author’s affiliate university. All participants provided written consent. Participants’ ages ranged from 48 to 82. Five main categories were emerged, namely, 'There are no reasons for long-term chronic pain,' 'Just will not worsen,' 'Have something to help me cope,' 'Pain restricts my life,' and 'Have something to relieve me.' Participants lived with CLBP, which could sometimes be avoided as a result of the coping strategies that they employed, and due to which they sometimes felt helpless, despite their efforts. As a result, they had mixed feelings, which included resignation, resoluteness, and optimism. However, their perceptions and attitudes toward pain seemed to differ based on their backgrounds, including biological, social, religious, and cultural status. There is a need for the development of a scale in future studies, to enable quantitative measurement of individuals’ perceptions of and attitudes toward pain. There is also a need for an investigation of factors influencing perceptions and attitudes toward pain.

Keywords: attitude, chronic low-back pain, perception, qualitative study

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Authors: Eriba Christopher Inyila, Godwin Egena Oga

Abstract:

Statecraft appears to be one of the greatest cultural achievements in the history of man’s civilization. The state itself is often portrayed as the supreme community of the citizen’s collective goodness and will. However, history experience reveals that the state has often been held in captivity permanently in the hand of the political class to almost a total exclusion of the labouring class of workers, artisans and peasants. Consequently, the hallmark of the Nigerian state and society in contemporary era is state of permanent crisis characterized by poverty, unemployment and profound insecurity. A lasting solution to this state of anomie is often touted in terms of ethnic, religious and regional integration which border on non-material perception of realities. A neglected aspect of the approach to the study of recurrent problems in contemporary is the materialist conception of realties through class perspectives of the society. The cutting edge of the approach is found in the attempt to reconcile the contradiction between the productive forces and the social relation of production. In other words, the contemporary state is skewed in favour of ownership of properties/commanding height of economy predominantly in the hands of the few monopoly companies to the total exclusion of majority of Nigerian population classified as peasant, workers and artisan. The lopsided situation creates economic and social disequilibria.

Keywords: national development, class, the state, Nigeria

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Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

Abstract:

Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

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Authors: Che Zarrina Sa'ari, Sheriza Izwa Zainuddin, Hasimah Chik, Sharifah Basirah Syed Muhsin

Abstract:

Palliative care is holistic care for patients with serious illnesses and for the family as well by interdisciplinary specialties to optimize quality of life by preventing, treating, and comforting the suffering and struggling. Palliative care is not a curative treatment but a comprehensive care to ensure the well-being of patients. This study was to identify the perceptions of patients and carers on healthcare needs and any factors related to the needs of palliative patients. Validated questionnaires survey of 254 patients and carers were analysed using a Statistical Package for the Social Sciences (SPSS) version 22. The findings were processed with Cronbach Alpha analysis, frequency, and descriptive to compare the important of each element in healthcare. Open-ended responses were analysed using thematic framework approach. The findings proved that all the items in healthcare needs elements were important because the frequency shown higher values, which were physical needs (5.91), mental needs (6.10), spiritual needs (6.34), emotional needs (6.05), social needs (5.88) and logistics needs (5.05). The total score of Cronbach’s alpha (α) for this study is 0.958, which is suggesting very good internal consistency reliability for the elements for healthcare needs. Professionals and healthcare providers need to ensure healthcare planning is individualised by tailoring it to the values, priorities, and ethnic/cultural/religious context of each person.

Keywords: healthcare, need, holistic, palliative, multi speciality

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Authors: Ali Gheshmi

Abstract:

The highest rate of death, after World War II, is due to the Coronavirus epidemic and more than 2 million people have died since the epidemic outbreak in December 2019, so the word “death” is one of the highest frequency words in social media; moreover, the use of social media has grown due to quarantine and successive restrictions and lockdowns. The most important aspects of the approach used by this study include the analysis of Iranian social media users’ reactions to the images of those who died due to Coronavirus, investigating if seeing such images via social media is effective on the users’ perception of the closeness of death, and evaluating the extent to which the fear of Coronavirus death is instrumental in persuading users to observe health protocols or causing mental problems in social media users. Since the goal of this study is to discover how social media users perceive and react to the images of people who died of Coronavirus, the cultural studies approach is used Receipt analysis method and in-depth interviews will be used for collecting data from Iranian users; also, snowball sampling is used in this study. The probable results would show that cyberspace users experience the closeness of “death” more than any time else and to cope with these annoying images, avoid viewing them or if they view, it will lead them to suffer from mental problems.

Keywords: death, receipt analysis method, mental health, social media, Covid-19

Procedia PDF Downloads 155