Search results for: Support Vector Regression

Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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Authors: Jyoti Srivastava, Sandhya S. Kaushik, Mallika Tewari, Hari S. Shukla

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Little information is available about the development of psychological well being over time among women who have been undergoing treatment for breast cancer. The aim of this study was to identify the trajectories of depression anxiety and stress among women with early-stage breast cancer. Of the 48 Indian women with newly diagnosed early-stage breast cancer recruited from surgical oncology unit, 39 completed an interview and were assessed for depression anxiety and stress (Depression Anxiety Stress Scale-DASS 21) before their first course of chemotherapy (baseline) and follow up interviews at 3, 6 and 9 months thereafter. Growth mixture modeling was used to identify distinct trajectories of Depression Anxiety and Stress symptoms. Logistic Regression analysis was used to evaluate the characteristics of women in distinct groups. Most women showed mild to moderate level of depression and anxiety (68%) while normal to mild level of stress (71%). But one in 11 women was chronically anxious (9%) and depressed (9%). Young age, having a partner, shorter education and receiving chemotherapy but not radiotherapy might characterize women whose psychological symptoms remain strong nine months after diagnosis. By looking beyond the mean, it was found that several socio-demographic and treatment factors characterized the women whose depression, anxiety and stress level remained severe even nine months after diagnosis. The results suggest that support provided to cancer patients should have a special focus on a relatively small group of patient most in need.

Keywords: psychological well being, growth mixture modeling, logistic regression analysis, socio-demographic factors

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Authors: Fred Alinda, Marjorie Negesa, Gerald Karyeija

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Like elsewhere in the world, youth unemployment remains a big problem more so to the most educated youth and female. In Rwanda, unemployment is estimated at 13.2% among youth graduates compared to 10.9% and 2.6 among secondary and primary graduates respectively. Though empirical evidence elsewhere associate youth unemployment with education level, relevance of skills and access to business support opportunities, mixed evidence still exist on the significance of these factors to youth employment. As youth employment strategies in countries like Rwanda continue to recognize the potential role university education can play to enhance employment, there is a need to understand the catalysts or barriers. This paper, therefore, draws empirical evidence from a survey on the influence of education qualification, skills relevance and access to business support opportunities on employment of the youth university graduates in Masaka sector, Rwanda. The analysis tested four hypotheses; access to university education significantly affects youth employment, Relevance of university education significantly contributes to youth employment; access to business support opportunities significantly contributes to youth employment, and significant gender differences exist in the employment of youth university graduates. A cross-section survey was used in lieu of the need to explore the prevailing status of youth employment and contributing factors across the sector. A questionnaire was used to collect data on a large sample of 269 youth to allow statistical analysis. This was beefed up with qualitative views of leaders and technical officials in the sector. The youth University graduates were selected using simple random sampling while the leaders and technical officials were selected purposively. Percentages were used to describe respondents in line with the variables under while a regression model for youth employment was fitted to determine the significant factors. The model results indicated a significant influence (p<0.05) of gender, education level and access to business support opportunities on employment of youth university graduates. This finding was also affirmed by the qualitative views of key informants. Qualitative views pointed to the fact that university education generally equipped the youth with skills that enabled their transition into employment mainly for a salary or wage. The skills were, however, deficient in technical and practical aspects. In addition, the youth generally lacked limited access to business support opportunities particularly guarantees for loans, business advisory, and grants for business as well as training in business skills that would help them gain salaried employment or transit into self-employment. The study findings bear an implication on the strategy for catalyzing youth employment through university education. The findings imply that university education should be embraced but with greater emphasis on or supplementation with specialized training in practical and technical skills as well as extending business support opportunities to the youth. This will accelerate the contribution of university education to youth employment.

Keywords: education, employment, self-employment, youth

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

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Authors: Esther Mafunda, Simbarashe Muparangi

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The present paper tests the applicability of the Regression Hypothesis on the pathological language dissolution of a Shona male adult with Broca’s aphasia. It particularly assesses the prediction of the Regression Hypothesis, which states that the process according to which language is forgotten will be the reversal of the process according to which it will be acquired. The main aim of the paper is to find out whether mirror symmetries between L1 acquisition and L1 dissolution of tense in Shona and, if so, what might cause these regression patterns. The paper also sought to highlight the practical contributions that Linguistic theory can make to solving language-related problems. Data was collected from a 46-year-old male adult with Broca’s aphasia who was receiving speech therapy at St Giles Rehabilitation Centre in Harare, Zimbabwe. The primary data elicitation method was experimental, using the probe technique. The TART (Test for Assessing Reference Time) Shona version in the form of sequencing pictures was used to access tense by Broca’s aphasic and 3.5-year-old child. Using the SPSS (Statistical Package for Social Studies) and Excel analysis, it was established that the use of the future tense was impaired in Shona Broca’s aphasic whilst the present and past tense was intact. However, though the past tense was intact in the male adult with Broca’s aphasic, a reference to the remote past was made. The use of the future tense was also found to be difficult for the 3,5-year-old speaking child. No difficulties were encountered in using the present and past tenses. This means that mirror symmetries were found between L1 acquisition and L1 dissolution of tense in Shona. On the basis of the results of this research, it can be concluded that the use of tense in a Shona adult with Broca’s aphasia supports the Regression Hypothesis. The findings of this study are important in terms of speech therapy in the context of Zimbabwe. The study also contributes to Bantu linguistics in general and to Shona linguistics in particular. Further studies could also be done focusing on the rest of the Bantu language varieties in terms of aphasia.

Keywords: Broca’s Aphasia, regression hypothesis, Shona, language dissolution

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Authors: Kasirga Yildirak, Ismail Gur

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We propose a model to measure hail risk of an Agricultural Insurance portfolio. Hail is one of the major catastrophic event that causes big amount of loss to an insurer. Moreover, it is very hard to predict due to its strange atmospheric characteristics. We make use of parcel based claims data on apricot damage collected by the Turkish Agricultural Insurance Pool (TARSIM). As our ultimate aim is to compute the loadings assigned to specific parcels, we build a portfolio risk model that makes use of PD and the severity of the exposures. PD is computed by Spherical-Linear and Circular –Linear regression models as the data carries coordinate information and seasonality. Severity is mapped into integer brackets so that Probability Generation Function could be employed. Individual regressions are run on each clusters estimated on different criteria. Loss distribution is constructed by Panjer Recursion technique. We also show that one risk-one crop model can easily be extended to the multi risk–multi crop model by assuming conditional independency.

Keywords: hail insurance, spherical regression, circular regression, spherical clustering

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Authors: Lotha Valan, Åsa Hörnsten, Ulf Isaksson

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Introduction: Sweden has a national child health care program (CHCP) where all parents are offered support to raise their children and support them for lifelong health. A systematic review concludes that there is a request for guidance in using the internet effectively for the health purposes of their children. However, a study about internet use among young mothers means that the internet is not always easy to navigate for parents, and they may need support. To fill this gap and develop a digital channel to complement the child health care (CHC) for the support of parents of children within CHC, there is a demand to investigate parents' needs in relation to this purpose. Methods: The study had a qualitative approach using focus group interviews with parents. The interview data were analyzed using qualitative content analysis. Results: The main theme highlights that parents expected that a digital support channel would be something that might strengthen them toward independence concerning the care of their children in a positive way. However, they also felt that they needed personal support and that relationships with other parents and the child health care nurse were significant and meaningful. Another parental desire that emerged was that a future digital channel would facilitate and simplify access to care, and they suggested having both planned and urgent times available for parents to book. The digital channel was expected to make this possible and be a good complement to the physical contacts the traditional child healthcare currently offers. Discussion/conclusions: The parents in this study believed that digital solutions could increase their parental power in relation to the care of their children. Examples were given as nurse-led parent groups where parents with similar problems and experiences around their children could support each other and were expected to strengthen them over time. The parents stressed that a planned digital support channel also needs satisfactory solutions for both contact and response. It was suggested that there should be bookable times for both planned and urgent needs and also the possibility of rescheduling visits.

Keywords: child healthcare, parents, digital support, nursing

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Authors: Daohong Yang, Xiang Zhang, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) is a technology that obtains information in the environment for self-positioning and mapping. It is widely used in computer vision, robotics and other fields. Many visual SLAM systems, such as OBSLAM3, employ a constant-speed motion model that provides the initial pose of the current frame to improve the speed and accuracy of feature matching. However, in actual situations, the constant velocity motion model is often difficult to be satisfied, which may lead to a large deviation between the obtained initial pose and the real value, and may lead to errors in nonlinear optimization results. Therefore, this paper proposed a motion model based on acceleration, which can be applied on most SLAM systems. In order to better describe the acceleration of the camera pose, we decoupled the pose transformation matrix, and calculated the rotation matrix and the translation vector respectively, where the rotation matrix is represented by rotation vector. We assume that, in a short period of time, the changes of rotating angular velocity and translation vector remain the same. Based on this assumption, the initial pose of the current frame is estimated. In addition, the error of constant velocity model was analyzed theoretically. Finally, we applied our proposed approach to the ORBSLAM3 system and evaluated two sets of sequences on the TUM dataset. The results showed that our proposed method had a more accurate initial pose estimation and the accuracy of ORBSLAM3 system is improved by 6.61% and 6.46% respectively on the two test sequences.

Keywords: error estimation, constant acceleration motion model, pose estimation, visual SLAM

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Authors: Wenbo Du, Xiaomei Ma

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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: Rosemary E. Huntriss, Lucy Jones

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Obesity and excess weight continue to be significant health problems in England. Traditional weight management programmes offer face-to-face support or group education. Remote care is recognised as a viable means of support; however, its effectiveness has not previously been evaluated in a tier 3 weight management setting. This service evaluation explored the effectiveness of online coaching, telephone support, and face-to-face support as optional management strategies within a tier 3 weight management programme. Outcome data were collected for adults with a BMI ≥ 45 or ≥ 40 with complex comorbidity who were referred to a Tier 3 weight management programme from January 2018 and had been discharged before October 2018. Following an initial 45-minute consultation with a specialist weight management dietitian, patients were offered a choice of follow-up support in the form of online coaching supported by an app (8 x 15 minutes coaching), face-to-face or telephone appointments (4 x 30 minutes). All patients were invited to a final 30-minute face-to-face assessment. The planned intervention time was between 12 and 24 weeks. Patients were offered access to adjunct face-to-face or telephone psychological support. One hundred and thirty-nine patients were referred into the programme from January 2018 and discharged before October 2018. One hundred and twenty-four patients (89%) attended their initial assessment. Out of those who attended their initial assessment, 110 patients (88.0%) completed more than half of the programme and 77 patients (61.6%) completed all sessions. The average length of the completed programme (all sessions) was 17.2 (SD 4.2) weeks. Eighty-five (68.5%) patients were coached online, 28 (22.6%) patients were supported face-to-face support, and 11 (8.9%) chose telephone support. Two patients changed from online coaching to face-to-face support due to personal preference and were included in the face-to-face group for analysis. For those with data available (n=106), average weight loss across the programme was 4.85 (SD 3.49)%; average weight loss was 4.70 (SD 3.19)% for online coaching, 4.83 (SD 4.13)% for face-to-face support, and 6.28 (SD 4.15)% for telephone support. There was no significant difference between weight loss achieved with face-to-face vs. online coaching (4.83 (SD 4.13)% vs 4.70 (SD 3.19) (p=0.87) or face-to-face vs. remote support (online coaching and telephone support combined) (4.83 (SD 4.13)% vs 4.85 (SD 3.30)%) (p=0.98). Remote support has been shown to be as effective as face-to-face support provided by a dietitian in the short-term within a tier 3 weight management setting. The completion rates were high compared with another tier 3 weight management services suggesting that offering remote support as an option may improve completion rates within a weight management service.

Keywords: dietitian, digital health, obesity, weight management

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Hamna Hamid, Kashmala Zaman

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The aim of this study was to evaluate the relationship between self-efficacy, resilience and social support among clinical psychologists, counselors and teachers of special schools. The study also assesses the gender differences on self-efficacy, resilience, social support and vicarious trauma and also vicarious trauma differences among three professions i.e. clinical psychologists, counselors and teachers of special schools. A sample of 150 women and 97 men were handed out a set questionnaire to complete: General Self-Efficacy Scale, Brief Resilience Scale, Multidimensional Scale of Perceived Social Support and Vicarious Trauma Scale. Results showed that there is significant negative correlation between self-efficacy, resilience and vicarious trauma. Women experiences higher levels of vicarious trauma as compared to men. While clinical psychologists and counselors experience higher levels of vicarious trauma as compared to teachers of special schools. Moderation effect of social support is not significant towards resilience and vicarious trauma.

Keywords: self-efficacy, resilience, vicarious trauma, social-support

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Authors: Emily J. Winnall

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It is understood that China has the largest population of people living with dementia in the world; however, little is known about this culturally diverse community, specifically the Chinese Communities, which has been poorly represented in past British research Literature. Further research is needed to gain a greater understanding of the support needs of caregivers caring for a relative living with dementia from the Chinese background. Dementia care and caregivers in Chinese communities are less investigated. The study is a case study of two Chinese community centers and one housing support service. Semi-structured one-to-one interviews and a pilot questionnaire were used as the methods for the study. A toolkit will also be created as a document that provides guidance and signposting to health and social care services for Chinese communities. The findings identified three main themes. Caregivers do not receive any formal support from the UK health and social services, and they felt they would have benefited from getting advice on what support they could access. Furthermore, the data also identified that Chinese organisations do not have the knowledge of dementia, to be able to support those living with dementia and their families. Also, people living with dementia and their families rarely present to Chinese organisations and UK health and social care services, meaning they are not receiving the support they are entitled to or need. Additionally, the community center would like to see workshops/courses around dementia for people from Chinese backgrounds. The study concludes that people from Chinese cultural backgrounds do not have sufficient access to support from UK health and social care services. More information needs to be published that will benefit Chinese communities.

Keywords: Chinese, Chinese organisations, Dementia, family caregivers, social care

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

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Authors: T. Yamazaki, R. Onuma, H. Kaminaga, Y. Miyadera, S. Nakamura

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Web exploration has increasingly diversified in accordance with the development of browsing environments on the Internet. Moreover, advanced exploration often conducted in intellectual activities such as surveys in research activities. This kind of exploration is conducted for a long period with trials and errors. In such a case, it is extremely important for a user to accumulate the search contexts and understand them. However, existing support systems were not effective enough since most systems could not handle the various factors involved in the exploration. This research aims to develop a novel system to support web exploration focusing on the accumulation of the search contexts. This paper mainly describes the outline of the system. An experiment using the system is also described. Finally, features of the system are discussed based on the results.

Keywords: web exploration context, refinement of search intention, accumulation of context, exploration support, information visualization

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Authors: Ghada Soltane

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The objective of this study was to identify factors impacting supply chain collaboration. a quantitative study was carried out on a sample of 84 Tunisian industrial companies. To verify the research hypotheses and test the direct effect of these factors on supply chain collaboration a multiple regression method was used using SPSS 26 software. The results show that there are four factors direct effects that affect supply chain collaboration in a meaningful and positive way, including: trust, engagement, information sharing and information quality

Keywords: supply chain collaboration, factors of collaboration, principal component analysis, multiple regression

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Authors: Uyi Kizito Ehigiamusoe

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The paper examines the impact of money market on economic growth in Nigeria using data for the period 1980-2012. Econometrics techniques such as Ordinary Least Squares Method, Johanson’s Co-integration Test and Vector Error Correction Model were used to examine both the long-run and short-run relationship. Evidence from the study suggest that though a long-run relationship exists between money market and economic growth, but the present state of the Nigerian money market is significantly and negatively related to economic growth. The link between the money market and the real sector of the economy remains very weak. This implies that the market is not yet developed enough to produce the needed growth that will propel the Nigerian economy because of several challenges. It was therefore recommended that government should create the appropriate macroeconomic policies, legal framework and sustain the present reforms with a view to developing the market so as to promote productive activities, investments, and ultimately economic growth.

Keywords: economic growth, investments, money market, money market challenges, money market instruments

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Yuen Man Yan

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Peer support has been a rising prevalent mental health service in the globe. The community-based mental health services employ persons with lived experience of mental illness to be peer support workers (PSWs) to provide peer support service to those who are in the progress of recovery (PIRs). It represents the transformation of mental health service system to a recovery-oriented and person-centered care. Literatures proved the feasibility and effectiveness of the peer support service. Researchers have attempted to explore the unique good qualities of peer support service that benefit the PIRs. Empirical researches found that the strength of the relationship between those who sought for change and the change agents positively related to the outcomes in one-to-one therapies across theoretical orientations. However, there is lack of literature on investigating the relationship building between the PSWs and PIRs in the one-to-one community-based peer support service. This study aims to identify and characterise the relationship in the community-based one-to-one peer support service from the perspectives of PSWs and PIRs; and to conceptualize the components of relationship building between PSWs and PIRs in the community-based one-to-one peer support service. The study adopted the constructivist grounded theory approach. 10 pairs of the PSWs and PIRs participated in the study. Data were collected through multiple qualitative methods, including observation of the interaction and exchange of the PSWs and PIRs in the 1ₛₜ, 3ᵣ𝒹 and 9th sessions of the community-based one-to-one peer support service; and semi-structural interview with the PSWs and PIRs separately after the 3ᵣ𝒹and 9ₜₕ session of the peer support service. This presentation is going to report the preliminary findings of the study. PSWs and PIRs identified their relationship as “life alliance”. Empathy was found to be one of key components of the relationship between the PSWs and the PIRs. Unlike the empathy, as explained by Carl Roger, in which the service provider was able to put themselves into the shoes of the service recipients as if he was the service recipients, the intensity of the empathy was much greater in the relationship between PSWs and PIRs because PSWs had the lived experience of mental illness and recovery. The dimensions of the empathy in the relationship between PSWs and PIRs was found to be multiple, not only related to the mental illness but also related to various aspects in life, like family relationship, employment, interest of life, self-esteem and etc.

Keywords: person with lived experience, peer support worker, peer support service, relationship building, therapeutic alliance, community-based mental health setting

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Authors: H. Booke, R. Nordmeier

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Sudden cardiac arrest is sometimes eye-witnessed by kids. Mostly, their (grand-)parents are affected by sudden cardiac arrest, putting these kids under enormous psychological pressure: Although they are more than desperate to help, they feel insecure and helpless and are afraid of causing harm rather than realizing their chance to help. Even years later, they may blame themselves for not having helped their beloved ones. However, the absolute majority of school children - at least in Germany - is not educated to provide first aid. Teaching young kids (5th grade) in basic life support thus may help to save lives while washing away the kids' fear from causing harm during cardio-pulmonary resuscitation. A teaching of circulatory and respiratory (patho-)physiology, followed by hands-on training of basic life support for every single child, was offered to each school in our district. The teaching was performed by anesthesiologists, and the program was called 'kids can save lives'. However, before enrollment in this program, the entire class must have had lessons in biology with a special focus on heart and circulation as well as lung and gas exchange. More than 1.000 kids were taught and trained in basic life support, giving them the knowledge and skills to provide basic life support. This may help to reduce the rate of failure to provide first aid. Therefore, educating young kids in basic life support may not only help to save lives, but it also may help to prevent any feelings of guilt because of not having helped in cases of eye-witnessed sudden cardiac arrest.

Keywords: teaching, children, basic life support, cardiac arrest, CPR

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Authors: Qiuling Xie, Shanliang Zhu, Zongdi Sun

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In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given.

Keywords: grey relational degree, multiple linear regression, membership function, nonlinear programming

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Authors: Subagyo Yotopranoto, Sri Wijayanti Sulistyawati, Sukmawati Basuki, Budi Armika, Yoes Prijatna Dachlan

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Malaria is a parasitic infectious disease that still remains a health problem in the world, including Indonesia. There is an outbreak happen at West Nusa Tenggara in 2007. A tourist spot in West Nusa Tenggara called West Lombok is mesoendemic area for malaria. Tanjung is the highest malaria morbidity subdistrict in West Lombok. Thus, the research conducted for the presence of a new species of malaria vectors, that are suspected of one factors which caused high morbidity of malaria in this region. The study was conducted in coastal and highland areas. We collected and identified Anopheles larvae from their breeding places. We also collected and identified Anopheles adult mosquitoes with outdoor cow net, indoor and outdoor human bait. In coastal area (Tembobor village), we found Anopheles vagus larvae from rivers as its breeding places. In highland area (Dasan Tengah village), we found An. subpictus from pool, lagoon, and river as its breeding places. In coastal area, with outdoor human bait, we collected An. vagus and An. subpictus adult mosquitoes. With indoor human bait, we collected An. subpictus adult mosquitoes. Whereas with outdoor cow net, we collected An. subpictus and An. maculatus, the first was more dominant. Furthermore, An subpictus strong suspected as malaria vector in coastal area. Anopheles subpictus was an anthropozoophylic mosquitoes, because it was found at indoor and outdoor places.

Keywords: malaria, vector, Tanjung, West Nusa Tenggara

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Authors: B. Özlük, A. Karaaslan

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This study has been conducted as a supplementary and relationship seeking study with the purpose of measuring the tendency and success of support among peers amid nursing students studying at university in solving interpersonal problems. The population of the study (N:279) is comprised of nursing students who are studying at one state and one private university in the province of Konya, while its sample is comprised of 231 nursing students who agreed to take part in the study voluntarily. As a result of this study, it has been determined that the peer support and interpersonal problem solving characteristics among students were at medium levels and that the interpersonal problem solving skills of students studying in the third year were higher than those of first and second year students. While the interpersonal problem solving characteristics of students who are aged 20 and over were found to be higher, no difference could be determined in terms of the interpersonal problem solving skills and tendencies among students, based on their gender and where they reside. A positive – to a medium degree – and significant relationship was determined between peer support and interpersonal problem solving skills, and it is possible to say that as peer support increases, so do the skills and tendencies to solve problems.

Keywords: nursing students, peer support, interpersonal problem, problem solving

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

Abstract:

In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Bry X., Trottier C., Mortier F., Cornu G., Verron T.

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We address component-based regularization of a Multivariate Generalized Linear Model (MGLM). A set of random responses Y is assumed to depend, through a GLM, on a set X of explanatory variables, as well as on a set T of additional covariates. X is partitioned into R conceptually homogeneous blocks X1, ... , XR , viewed as explanatory themes. Variables in each Xr are assumed many and redundant. Thus, Generalised Linear Regression (GLR) demands regularization with respect to each Xr. By contrast, variables in T are assumed selected so as to demand no regularization. Regularization is performed searching each Xr for an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in Xr. We propose a very general criterion to measure structural relevance (SR) of a component in a block, and show how to take SR into account within a Fisher-scoring-type algorithm in order to estimate the model. We show how to deal with mixed-type explanatory variables. The method, named THEME-SCGLR, is tested on simulated data.

Keywords: Component-Model, Fisher Scoring Algorithm, GLM, PLS Regression, SCGLR, SEER, THEME

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

Abstract:

Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Sergio Haram Sarmiento, Arcady Ponosov

Abstract:

Based on appropriate multivariate statistical methodology, we suggest a generic framework for efficient parameter estimation for ordinary differential equations and the corresponding nonlinear models. In this framework classical linear regression strategies is refined into a nonlinear regression by a locally linear modelling technique (known as metamodelling). The approach identifies those latent variables of the given model that accumulate most information about it among all approximations of the same dimension. The method is applied to several benchmark problems, in particular, to the so-called ”power-law systems”, being non-linear differential equations typically used in Biochemical System Theory.

Keywords: principal component analysis, generalized law of mass action, parameter estimation, metamodels

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Authors: Kathryn Norgang, Sarah Howrath, Auni Idi Muhire, Pacifique Umubyeyi

Abstract:

Description : A group of nurses address the shortage of trained staff to respond to critical patients at Rwanda Military Hospital (RMH) by developing a training program and a resuscitation response team. Members of the group who received the training when it first launched are now trainer of trainers; all components of the training program are organized and delivered by RMH staff-the clinical mentor only provides adjunct support. This two day training is held quarterly at RMH; basic life support and exposure to interventions for advanced care are included in the test and skills sign off. Seventy staff members have received the training this year alone. An increased number of admission/transfer to ICU due to successful resuscitation attempts is noted. Lessons learned: -Number of staff trained 2012-2014 (to be verified). -Staff who train together practice with greater collaboration during actual resuscitation events. -Staff more likely to initiate BLS if peer support is present-more staff trained equals more support. -More access to Advanced Cardiac Life Support training is necessary now that the cadre of BLS trained staff is growing. Conclusions: Increased access to training, peer support, and collaborative practice are effective strategies to strengthening resuscitation capacity within a hospital.

Keywords: resuscitation, basic life support, capacity building, resuscitation response teams, nurse trainer of trainers

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Authors: V. Rashtchi, H. Bizhani, F. R. Tatari

Abstract:

This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

Abstract:

A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

Procedia PDF Downloads 313