Search results for: average information ratio

Authors: Atnafu Ashenef Wondim

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In today’s highly competitive business environment, employees are considered a source of competitive advantage. Researchers have looked into job autonomy's effect on organizational commitment and declared superior organizational performance strongly depends on the effort and commitment of employees. The purpose of this study was to explore the relationship between job autonomy and organizational commitment from newcomer’s point of view. The mediation role of employee engagement (physical, emotional, and cognitive) was also examined in the case of Ethiopian Commercial Banks. An exploratory survey research design with mixed-method approach that included partial least squares structural equation modeling and Fuzzy-Set Qualitative Comparative Analysis technique were using to address the sample size of 348 new employees. In-depth interviews with purposive and convenientsampling techniques are conducted with new employees (n=43). The results confirmed that job autonomy had positive, significant direct effects on physical engagement, emotional engagement, and cognitive engagement (path coeffs. = 0.874, 0.931, and 0.893).The results showed thatthe employee engagement driver, physical engagement, had a positive significant influence on affective commitment (path coeff. = 0.187) and normative commitment (path coeff. = 0.512) but no significant effect on continuance commitment. Employee engagement partially mediates the relationship between job autonomy and organizational commitment, which means supporting the indirect effects of job autonomy on affective, continuance, and normative commitment through physical engagement. The findings of this study add new perspectives by positioning it within a complex organizational African setting and by expanding the job autonomy and organizational commitment literature, which will benefit future research. Much of the literature on job autonomy and organizational commitment has been conducted within a well-established organizational business context in Western developed countries.The findings lead to fresh information on job autonomy and organizational commitment implementation enablers that can assist in the formulation of a better policy/strategy to efficiently adopt job autonomy and organizational commitment.

Keywords: employee engagement, job autonomy, organizational commitment, social exchange theory

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Authors: Mustapha Ouardouz, Mina Amharref, Abdessamed Bernoussi

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The future energy, for limited energy resources countries, goes through renewable energies (solar, wind etc.). The renewable energies constitute a major component of the energy strategy to cover a substantial part of the growing needs and contribute to environmental protection by replacing fossil fuels. Indeed, sustainable development involves the promotion of renewable energy and the preservation of the environment by the use of clean energy technologies to limit emissions of greenhouse gases and reducing the pressure exerted on the forest cover. So the impact studies, of the energy use on the environment and farm-related risks are necessary. For that, a global approach integrating all the various sectors involved in such project seems to be the best approach. In this paper we present an approach based on the multi criteria analysis and the realization of one pilot to achieve the development of an innovative geo-intelligent environmental platform. An implementation of this platform will collect, process, analyze and manage environmental data in connection with the nature of used energy in the studied region. As an application we consider a region in the north of Morocco characterized by intense agricultural and industrials activities and using diverse renewable energy. The strategic goals of this platform are; the decision support for better governance, improving the responsiveness of public and private companies connected by providing them in real time with reliable data, modeling and simulation possibilities of energy scenarios, the identification of socio-technical solutions to introduce renewable energies and estimate technical and implantable potential by socio-economic analyzes and the assessment of infrastructure for the region and the communities, the preservation and enhancement of natural resources for better citizenship governance through democratization of access to environmental information, the tool will also perform simulations integrating environmental impacts of natural disasters, particularly those linked to climate change. Indeed extreme cases such as floods, droughts and storms will be no longer rare and therefore should be integrated into such projects.

Keywords: renewable energies, decision aided tool, environment, simulation

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Authors: E. M. Mhlongo, E. Lutge

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In 2010, South Africa’s National Department of Health (NDoH) launched national primary health care (PHC) initiative to strengthen health promotion, disease prevention, and early disease detection. The strategy, called Re-engineering Primary Health Care (rPHC), aims to support a preventive and health-promoting community-based PHC model by using community-based outreach teams (known in South Africa as Ward-based Primary Health Care Outreach teams or WBPHCOTs). These teams provide health education, promote healthy behaviors, assess community health needs, manage minor health problems, and support linkages to health services and health facilities. Ward based primary health care outreach teams are supervised by a professional nurse who is the outreach team leader. In South Africa, the WBPHCOTs have been established, registered, and are reporting their activities in the District Health Information System (DHIS). This study explored and described the challenges faced by outreach team leaders in supporting and supervising the WBPHCOTs. Qualitative data were obtained through interviews conducted with the outreach team leaders at a sub-district level. Thematic analysis of data was done. Findings revealed some challenges faced by team leaders in day to day execution of their duties. Issues such as staff shortages, inadequate resources to carry out health promotion activities, and lack of co-operation from team members may undermine the capacity of team leaders to support and supervise the WBPHCOTs. Many community members are under the impression that the outreach team is responsible for bringing the clinic to the community while the outreach teams do not carry any medication/treatment with them when doing home visits. The study further highlights issues around the challenges of WBPHCOTs at a household level. In conclusion, the WBPHCOTs are an important component of National Health Insurance (NHI), and in order for NHI to be optimally implemented, the issues raised in this research should be addressed with some urgency.

Keywords: community health worker, national health insurance, primary health care, ward-based primary health care outreach teams

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Authors: Anushtha Saxena

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This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

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Authors: Maryam Sayadi, Rana Sedighpour, Hossein Rezaie

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In this study, in order to evaluate water quality of Gamasiab and Gharehsoo rivers located in Kermanshah province, the information of a 5-year statistical period during the years 2014-2018 was used. To evaluate the hydrochemistry of water, first the type and hydrogeochemical facies of river water were determined using Stiff and Piper diagrams. Then, based on Gibbs diagram and combination diagrams, the factors controlling the chemical parameters of the two rivers were identified. Saturation indices were used to predict the possibility of dissolution and deposition of some minerals. Then, in order to classify water in different sections, fourteen water quality indicators for different uses along with WHO standard were used. Finally, factor analysis was used to determine the processes affecting the hydrochemistry of the two rivers. The results of this study showed that in both rivers, the predominant type and facies are bicarbonate of calcite. Also, the main factor in changing the chemical quality of water in both Gamasiab and Gharehsoo rivers is the water-rock reaction. According to the results of factor analysis in both rivers, two factors have the greatest impact on water quality in the region. Among the parameters of Gamasiab river in the first factor, HCO3-, Na+ and Cl-, respectively, had the highest factor loads, and in the second factor, SO42- and Mg2+ were selected as the main parameters. The parameters Ca2+, Cl- and Na have the highest factor loads in the first factor and in the second factor Mg2+ and SO42- have the highest factor loads in Gharehsoo river. The dissolution of carbonate formations due to their abundance and expansion in the two basins has a more significant effect on changing water chemistry. It has saturated the water of rivers with aragonite, calcite and dolomite. Due to the low contribution of the second factor in changing the chemical parameters, the water of both rivers is saturated with respect to evaporative minerals such as gypsum, halite and anhydrite in all stations. Based on Schoeller diagrams, Wilcox and other quality indicators in these two sections, the amount of main physicochemical parameters are in the desired range for drinking and agriculture. The results of Langelier, Ryznar, Larson-Skold and Puckorius indices showed that water is corrosive in industry.

Keywords: factor analysis, hydrochemical, saturation index, surface water quality

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Authors: Jameson Mirimu, Edward Mawejje, Ibra Twinomujuni

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Introduction: Adolescent girls and young women (AGYW) are a vulnerable category whose risk of acquiring HIV is 20 times compared to the general population accounting for 25% of the new infections. Despite proven scientific evidence of preventing HIV acquisition, Oral Pre-Exposure Prophylaxis (PreP) is less used as one of the biomedical interventions among the AGYW. By 2020, only 31000-32000 of the targeted 90,000 persons in Uganda enrolled on Oral PreP LPHS-EC project employed a combination of Expanded Peer Outreach Approach (EPOA) and Effective client follow-up to increase PreP initiation (PrEP_NEW) and continuation for more than three months (PrEP_CT). Method: Quantitatively, data from National Key population Combination tracker retrospectively analyzed by M&E, focused group discussion with AGYWs and Health care workers to identify barriers. Barriers found; hesitancy of AGYW, misconceptions about Oral PrEP, inadequate knowledge and skills in handling adolescent and Data quality issues. To address the mentioned barriers, youth friendly corners initiated in study sites, identified PrEP Champions among the AGYW, oral PrEP dialogues, group Antenatal counselling, CQI Projects initiated, weekly perfomance meetings to track performance. Results: Routine program data review PrEP_NEW and PrEP_CT increased from 5% (4/80) and 4% (2/54), respectively, in July 2022 to 90% (72/80) and 79% (43/54) respectively for PrEP_NEW and PrEP_CT at the end of March 2023. Lessons Learnt: Demystifying misconception about oral Prep through provision of adequate information by involving health care workers through skills enhancement, CQI projects are critical intervention. Conclusion: With improved safe spaces, skills enhancement of health workers, stakeholders’ engagement through Oral Prep dialogues is critical in improving PreP uptake and continuity among the AGYWS.

Keywords: prep, uptake, continuation, AGYW

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Authors: Sohail Bawani, Kausar S. Khan, Rozina Karmaliani, Shehnaz Mir

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Action research (AR) is one of the types of health systems research (HSR), and participatory action research (PAR) is known for being effective in health systems strengthening (HSS). The current literature on PAR for HSS cites numerous examples and case studies that led to improve health services; build child health information system; increase knowledge and awareness of people about health problems, and identify pathways for institutional and policy change by engaging people in research. But examples of marginalized communities being agents of change in health governance are not common in health systems research (HSR). This approach to PAR is at the heart of Paolo Freire’s Social Transformation Theory and Critical Consciousness building, which was used to design a community-based PAR study in the Northern/mountainous areas of Pakistan. The purpose of the study was to understand the place and role of marginalized communities in strengthening existing health governance structure (health facility and village health committees and health boards) by taking marginalized communities as partners. Community meetings were carried out to identify who is living at the social, political, cultural and economic margins in 40 different villages. Participatory reflection and analysis (PRA) tools were used during the meeting to facilitate identification. Focus group discussions were conducted with marginalized groups using PRA tools and family ethnographies with marginalized families identified through group discussions. Findings of the study revealed that for the marginalized health systems constitute more than just delivery of health services, but it also embraces social determinants that surround systems and its governance. The paper argues that from Frerian perspective people’s participation should not only be limited to knowledge generation. People must be seen active users of the knowledge that they generate for achieving better health outcomes that they want to achieve in the time to come. PAR provides a pathway to the marginalized in playing a role in health governance. The study dissemination planned shall engage the marginalized in a dialogue with service providers so that together a role for the marginalized can be outlined.

Keywords: participatory action research, health systems, marginalized, health services

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Authors: Sopit Tubtimhin, Chaliya Wamaloon, Anchalee Supattagorn

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Background and Significance: Breast cancer is the most frequently diagnosed and leading cause of cancer death among women. This study aims to determine factors potentially predicting recurrence and long-term survival after the first recurrence in surgically treated patients between postmenopausal and younger women. Methods and Analysis: A retrospective cohort study was performed on 498 Thai women with invasive breast cancer, who had undergone mastectomy and been followed-up at Ubon Ratchathani Cancer Hospital, Thailand. We collected based on a systematic chart audit from medical records and pathology reports between January 1, 2002, and December 31, 2011. The last follow-up time point for surviving patients was December 31, 2016. A Cox regression model was used to calculate hazard ratios of recurrence and death. Findings: The median age was 49 (SD ± 9.66) at the time of diagnosis, 47% was post-menopausal women ( ≥ 51years and not experienced any menstrual flow for a minimum of 12 months), and 53 % was younger women ( ˂ 51 years and have menstrual period). Median time from the diagnosis to the last follow-up or death was 10.81 [95% CI = 9.53-12.07] years in younger cases and 8.20 [95% CI = 6.57-9.82] years in postmenopausal cases. The recurrence-free survival (RFS) for younger estimates at 1, 5 and 10 years of 95.0 %, 64.0% and 58.93% respectively, appeared slightly better than the 92.7%, 58.1% and 53.1% for postmenopausal women [HRadj = 1.25, 95% CI = 0.95-1.64]. Regarding overall survival (OS) for younger at 1, 5 and 10 years were 97.7%, 72.7 % and 52.7% respectively, for postmenopausal patients, OS at 1, 5 and 10 years were 95.7%, 70.0% and 44.5 respectively, there were no significant differences in survival [HRadj = 1.23, 95% CI = 0.94 -1.64]. Multivariate analysis identified five risk factors for negatively impacting on survival were triple negative [HR= 2.76, 95% CI = 1.47-5.19], Her2-enriched [HR = 2.59, 95% CI = 1.37-4.91], luminal B [HR = 2.29, 95 % CI=1.35-3.89], not free margin [HR = 1.98, 95%CI=1.00-3.96] and patients who received only adjuvant chemotherapy [HR= 3.75, 95% CI = 2.00-7.04]. Statistically significant risks of overall cancer recurrence were Her2-enriched [HR = 5.20, 95% CI = 2.75-9.80], triple negative [HR = 3.87, 95% CI = 1.98-7.59], luminal B [HR= 2.59, 95% CI = 1.48-4.54,] and patients who received only adjuvant chemotherapy [HR= 2.59, 95% CI = 1.48-5.66]. Discussion and Implications: Outcomes from this studies have shown that postmenopausal women have been associated with increased risk of recurrence and mortality. As the results, it provides useful information for planning the screening and treatment of early-stage breast cancer in the future.

Keywords: breast cancer, menopause status, recurrence-free survival, overall survival

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Authors: Paula C. Ferreira, Ana Margarida Veiga Simão, Nádia Pereira, Aristides Ferreira, Alexandra Marques Pinto, Alexandra Barros, Vitor Martinho

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Cyberbullying is a phenomenon of worldwide concern regarding children and adolescents’ mental health and risk behavior. Bystanders of this phenomenon can help diminish the incidence of this phenomenon if they engage in pro-social behavior. However, different social-cognitive and affective bystander reactions may surface because of the lack of contextual information and emotional cues in cyberbullying situations. Hence, this study investigated how cyberbullying bystanders’ unpleasant emotions could be related to their personal moral beliefs and their behavioral intentions to cyberbully or defend the victim. It also proposed to investigate how their normative beliefs of perceived severity about cyberbullying behavior could be related to their personal moral beliefs and their behavioral intentions. Three groups of adolescents participated in this study, namely a first of group 402 students (5th – 12th graders; Mage = 13.12; SD = 2.19; 55.7% girls) to compute explorative factorial analyses of the instruments used; a second group of 676 students (5th – 12th graders; Mage = 14.10; SD = 2.74; 55.5% were boys) to run confirmatory factor analyses; and a third group (N = 397; 5th – 12th graders; Mage = 13.88 years; SD = 1.45; 55.5% girls) to perform the main analyses to test the research hypotheses. Self-report measures were used, such as the Personal moral beliefs about cyberbullying behavior questionnaire, the Normative beliefs of perceived severity about cyberbullying behavior questionnaire, the Unpleasant emotions about cyberbullying incidents questionnaires, and the Bystanders’ behavioral intentions in cyberbullying situations questionnaires. Path analysis results revealed that unpleasant emotions were mediators of the relationship between adolescent cyberbullying bystanders’ personal moral beliefs and their intentions to help the victims in cyberbullying situations. Moreover, adolescent cyberbullying bystanders’ normative beliefs of gravity were mediators of the relationship between their personal moral beliefs and their intentions to cyberbully others. These findings provide insights for the development of prevention and intervention programs that promote social and emotional learning strategies as a means to prevent and intervene in cyberbullying.

Keywords: cyberbullying, normative beliefs of perceived severity, personal moral beliefs, unpleasant emotions

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Farzaneh Badinlou, Reza Kormi-Nouri, Monika Knopf, Kamal Kharazi

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Research into long-term memory has demonstrated that the richness of the knowledge base cues in memory tasks improves retrieval process, which in turn influences learning and memory performance. The present research investigated the idea that adding cues connected to knowledge can affect memory performance in the context of action memory in children. In action memory studies, participants are instructed to learn a series of verb–object phrases as verbal learning and experience-based learning (learning by doing and learning by observation). It is well established that executing action phrases is a more memorable way to learn than verbally repeating the phrases, a finding called enactment effect. In the present study, a total of 410 students from four grade groups—2nd, 4th, 6th, and 8th—participated in this study. During the study, participants listened to verbal action phrases (VTs), performed the phrases (SPTs: subject-performed tasks), and observed the experimenter perform the phrases (EPTs: experimenter-performed tasks). During the test phase, cued recall test was administered. Semantic relational cues (i.e., well-integrated vs. poorly integrated items) were manipulated in the present study. In that, the participants were presented two lists of action phrases with high semantic integration between verb and noun, e.g., “write with the pen” and with low semantic integration between verb and noun, e.g., “pick up the glass”. Results revealed that experience-based learning had a better results than verbal learning for both well-integrated and poorly integrated items, though manipulations of semantic relational cues can moderate the enactment effect. In addition, children of different grade groups outperformed for well- than poorly integrated items, in flavour of older children. The results were discussed in relation to the effect of knowledge-based information in facilitating retrieval process in children.

Keywords: action memory, enactment effect, knowledge-based cues, school-aged children, semantic relational cues

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Authors: Israel Ropo Orimoloye, Johanes A. Belle, Olusola Adeyemi, Olusola O. Ololade

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With more than 100,000 orbits during the past 20 years, Terra has significantly improved our knowledge of the Earth's climate and its implications on societies and ecosystems of human activity and natural disasters, including drought events. With Terra instrument's performance and the free distribution of its products, this study utilised Terra MOD13Q1 satellite data to assess drought disaster events and its spatiotemporal patterns over the Free State Province of South Africa between 2001 and 2019 for summer, autumn, winter, and spring seasons. The study also used high-resolution downscaled climate change projections under three representative concentration pathways (RCP). Three future periods comprising the short (the 2030s), medium (2040s), and long term (2050s) compared to the current period are analysed to understand the potential magnitude of projected climate change-related drought. The study revealed that the year 2001 and 2016 witnessed extreme drought conditions where the drought index is between 0 and 20% across the entire province during summer, while the year 2003, 2004, 2007, and 2015 observed severe drought conditions across the region with variation from one part to the another. The result shows that from -24.5 to -25.5 latitude, the area witnessed a decrease in precipitation (80 to 120mm) across the time slice and an increase in the latitude -26° to -28° S for summer seasons, which is more prominent in the year 2041 to 2050. This study emphasizes the strong spatio-environmental impacts within the province and highlights the associated factors that characterise high drought stress risk, especially on the environment and ecosystems. This study contributes to a disaster risk framework to identify areas for specific research and adaptation activities on drought disaster risk and for environmental planning in the study area, which is characterised by both rural and urban contexts, to address climate change-related drought impacts.

Keywords: remote sensing, drought disaster, climate scenario, assessment

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Authors: Dorottya Horváth, Tímea Harmath-Tánczos

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Attention deficit hyperactivity disorder (ADHD) and learning disabilities are common neurocognitive disorders that can have a significant impact on a child's academic performance. ADHD is characterized by inattention, hyperactivity, and impulsivity, while learning disabilities are characterized by difficulty with specific academic skills, such as reading, writing, or math. The aim of this study was to investigate the working memory, executive, and attention functions of neurotypical children and children with ADHD and learning disabilities in order to fill the gaps in the Hungarian mean test scores of these cognitive functions in children with neurocognitive disorders. Another aim was to specify the neuropsychological differential diagnostic toolkit in terms of the relationships and peculiarities between these cognitive functions. The research question addressed in this study was: How do the working memory, executive, and attention functions of neurotypical children compare to those of children with ADHD and learning disabilities? A self-administered test battery was used as a research tool. Working memory was measured with the Non-Word Repetition Test, the Listening Span Test, the Digit Span Test, and the Reverse Digit Span Test; executive function with the Letter Fluency, Semantic Fluency, and Verb Fluency Tests; and attentional concentration with the d2-R Test. The data for this study was collected from 115 children aged 9-14 years. The children were divided into three groups: neurotypical children (n = 44), children with ADHD without learning disabilities (n = 23), and children with ADHD with learning disabilities (n = 48). The data was analyzed using a variety of statistical methods, including t-tests, ANOVAs, and correlational analyses. The results showed that the performance of children with neurocognitive involvement in working memory, executive functions, and attention was significantly lower than the performance of neurotypical children. However, the results of children with ADHD and ADHD with learning disabilities did not show a significant difference. The findings of this study are important because they provide new insights into the cognitive profiles of children with ADHD and learning disabilities and suggest that working memory, executive functions, and attention are all impaired in children with neurocognitive involvement, regardless of whether they have ADHD or learning disabilities. This information can be used to develop more effective diagnostic and treatment strategies for these disorders.

Keywords: ADHD, attention functions, executive functions, learning disabilities, working memory

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Authors: Melissa R. Quispe-Zuniga, Daniel Callo-Concha, Christian Borgemeister, Klaus Greve

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The Peruvian Andes have a high potential for mining, but many of the mining areas overlay with campesino community lands, being these key actors for agriculture and livestock production. Lead by economic incentives, some communities are renting their lands to mining companies for exploration or exploitation. However, a growing number of campesino communities, usually social and economically marginalized, have developed resistance, alluding consequences, such as water pollution, land-use change, insufficient economic compensation, etc. what eventually end up in Socio-Environmental Conflicts (SEC). It is hypothesized that disclosing the information on environmental pollution and enhance the involvement of communities in the decision-making process may contribute to prevent SEC. To assess whether such complains are grounded on the environmental impact of mining activities, we measured the heavy metals concentration in 24 indicative samples from rivers that run across mining exploitations and farming community lands. Samples were taken during the 2016 dry season and analyzed by inductively-coupled-plasma-atomic-emission-spectroscopy. The results were contrasted against the standards of monitoring government institutions (i.e., OEFA). Furthermore, we investigated the water/environmental complains related to mining in the neighboring 14 communities. We explored the relationship between communities and mining companies, via open-ended interviews with community authorities and non-participatory observations of community assemblies. We found that the concentrations of cadmium (0.023 mg/L), arsenic (0.562 mg/L) and copper (0.07 mg/L), surpass the national water quality standards for Andean rivers (0.00025 mg/L of cadmium, 0.15 mg/L of arsenic and 0.01 mg/L of copper). 57% of communities have posed environmental complains, but 21% of the total number of communities were receiving an annual economic benefit from mining projects. However, 87.5% of the communities who had posed complains have high concentration of heavy metals in their water streams. The evidence shows that mining activities tend to relate to the affectation and vulnerability of campesino community water streams, what justify the environmental complains and eventually the occurrence of a SEC.

Keywords: mining companies, campesino community, water, socio-environmental conflict

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Authors: Zeina Bourhane, Anders Lanzen, Christine Cagnon, Olfa Ben Said, Cristiana Cravo-Laureau, Robert Duran

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The anthropogenic pressure in coastal areas increases dramatically with the exploitation of environmental resources. Biomonitoring coastal areas are crucial to determine the impact of pollutants on bacterial communities in soils and sediments since they provide important ecosystem services. However, relevant biomonitoring tools allowing fast determination of the ecological status are yet to be defined. Microbial ecology approaches provide useful information for developing such microbial monitoring tools reporting on the effect of environmental stressors. Chemical and microbial molecular approaches were combined in order to determine microbial bioindicators for assessing the ecological status of soil and river ecosystems around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected along soil/river/lake continuums in three stations around the Ichkeul Lake influenced by different human activities at two seasons (summer and winter). Contaminant pressure indexes (PI), including PAHs (Polycyclic aromatic hydrocarbons), alkanes, and OCPs (Organochlorine pesticides) contents, showed significant differences in the contamination level between the stations with seasonal variation. Bacterial communities were characterized by 16S ribosomal RNAs (rRNA) gene metabarcoding. Although microgAMBI indexes, determined from the sequencing data, were in accordance with contaminant contents, they were not sufficient to fully explain the PI. Therefore, further microbial indicators are still to be defined. The comparison of bacterial communities revealed the specific microbial assemblage for soil, river, and lake sediments, which were significantly correlated with contaminant contents and PI. Such observation offers the possibility to define a relevant set of bioindicators for reporting the effects of human activities on the microbial community structure. Such bioindicators might constitute useful monitoring tools for the management of microbial communities in coastal areas.

Keywords: bacterial communities, biomonitoring, contamination, human impacts, microbial bioindicators

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Authors: Shehu Isyaku

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The general objective of the study was to investigate ‘the relationship between the external business environment and the sustainability of micro, small and medium enterprises (MSMEs) in Jigawa state’, Nigeria. Specifically, the study was to examine the relationship between 1) the economic environment, 2) the social environment, 3) the technological environment, and 4) the political environment and the sustainability of MSMEs in Jigawa state, Nigeria. The study was drawn on Resource-Based View (RBV) Theory and Knowledge-Based View (KBV). The study employed a descriptive cross-sectional survey design. A researcher-made questionnaire was used to collect data from the 350 managers/owners who were selected using stratified, purposive and simple random sampling techniques. Data analysis was done using means and standard deviations, factor analysis, Correlation Coefficient, and Pearson Linear Regression analysis. The findings of the study revealed that the sustainability potentials of the managers/owners were rated as high potential (economic, environmental, and social sustainability using 5 5-point Likert scale. Mean ratings of effectiveness of the external business environment were; as highly effective. The results from the Pearson Linear Regression Analysis rejected the hypothesized non-significant effect of the external business environment on the sustainability of MSMEs. Specifically, there is a positive significant relationship between 1) economic environment and sustainability; 2) social environment and sustainability; 3) technological environment and sustainability and political environment and sustainability. The researcher concluded that MSME managers/owners have a high potential for economic, social and environmental sustainability and that all the constructs of the external business environment (economic environment, social environment, technological environment and political environment) have a positive significant relationship with the sustainability of MSMEs. Finally, the researcher recommended that 1) MSME managers/owners need to develop marketing strategies and intelligence systems to accumulate information about the competitors and customers' demands, 2) managers/owners should utilize the customers’ cultural and religious beliefs as an opportunity that should be utilized while formulating business strategies.

Keywords: business environment, sustainability, small and medium enterprises, external business environment

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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Authors: Ahmed Sami Hammami, Mohamed Jellazi

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Background: The aim of the study is to evaluate the magnitude of different psychological outcomes among Tunisian health care professionals (HCP) during the COVID-19 pandemic and to identify the associated factors. Methods: HCP completed a cross-sectional questionnaire from April 4th to April, 28th 2020. The survey collected demographic information, factors that may interfere with the psychological outcomes, behavior changes and mental health measurements. The latter was assessed through 3 scales; the 7-item questions Insomnia Severity Index, the 2-item Patient Health Questionnaire and the 2-item Generalized Anxiety Disorder. Multivariable logistic regression was conducted to identify factors associated with psychological outcomes. Results: A total of 503 HCP successfully completed the survey; among those, n=493 consented to enroll in the study, 411 [83.4%] were physicians, 323 [64.2%] were women and 271 [55%] had a second-line working position. A significant proportion of HCP had anxiety 35.7%, depression 35.1% and insomnia 23.7%. Females, those with psychiatric history and those using public transport exhibited the highest proportions for overall symptoms compared to other groups e.g., depression among females vs. males: 44,9% vs. 18,2%, P=0.00. Those with a previous medical history and nurses, had more anxiety and insomnia compared to other groups e.g. anxiety among nurses vs. interns/residents vs. attending 45,1% vs 36,1% vs 27,5%; p=0.04. Multivariable logistic regression showed that female gender was a risk factor for all psychological outcomes e.g. female sex increased the odds of anxiety by 2.86; 95% confidence interval [CI], 1, 78-4, 60; P=0.00, whereas having a psychiatric history was a risk factor for both anxiety and insomnia. (e.g. for insomnia OR=2,86; 95% [CI], 1,78-4,60; P=0.00), Having protective equipment was associated with lower risk for depression (OR=0,41; 95% CI, 0,27-0,62; P=0.00) and anxiety. Physical activity was also protective against depression and anxiety (OR=0,41, 95% CI, 0,25-0,67, P=0.00). Conclusion: Psychological symptoms are usually undervalued among HCP, though the COVID-19 pandemic played a major role in exacerbating this burden. Prompt psychological support should be endorsed and simple measures such as physical activity and ensuring the necessary protection are paramount to improve mental health outcomes and the quality of care provided to patients.

Keywords: COVID-19 pandemic, health care professionals, mental health, protective factors, psychological symptoms, risk factors

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Authors: Tammeen Siraj, Wenxing Zhou, Terry Huang, Mohammad Al-Amin

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The high resolution inline inspection (ILI) tool is used extensively in the pipeline industry to identify, locate, and measure metal-loss corrosion anomalies on buried oil and gas steel pipelines. Corrosion anomalies may occur singly (i.e. individual anomalies) or as clusters (i.e. a colony of corrosion anomalies). Although the ILI technology has advanced immensely, there are measurement errors associated with the sizes of corrosion anomalies reported by ILI tools due limitations of the tools and associated sizing algorithms, and detection threshold of the tools (i.e. the minimum detectable feature dimension). Quantifying the measurement error in the ILI data is crucial for corrosion management and developing maintenance strategies that satisfy the safety and economic constraints. Studies on the measurement error associated with the length of the corrosion anomalies (in the longitudinal direction of the pipeline) has been scarcely reported in the literature and will be investigated in the present study. Limitations in the ILI tool and clustering process can sometimes cause clustering error, which is defined as the error introduced during the clustering process by including or excluding a single or group of anomalies in or from a cluster. Clustering error has been found to be one of the biggest contributory factors for relatively high uncertainties associated with ILI reported anomaly length. As such, this study focuses on developing a consistent and comprehensive framework to quantify the measurement errors in the ILI-reported anomaly length by comparing the ILI data and corresponding field measurements for individual and clustered corrosion anomalies. The analysis carried out in this study is based on the ILI and field measurement data for a set of anomalies collected from two segments of a buried natural gas pipeline currently in service in Alberta, Canada. Data analyses showed that the measurement error associated with the ILI-reported length of the anomalies without clustering error, denoted as Type I anomalies is markedly less than that for anomalies with clustering error, denoted as Type II anomalies. A methodology employing data mining techniques is further proposed to classify the Type I and Type II anomalies based on the ILI-reported corrosion anomaly information.

Keywords: clustered corrosion anomaly, corrosion anomaly assessment, corrosion anomaly length, individual corrosion anomaly, metal-loss corrosion, oil and gas steel pipeline

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Authors: Florence Awde

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In 2023, it was expected 350 parents in Arizona would have a child receive a cancer diagnosis (Welcome Arizona Cancer Foundation For Children, n.d.). The news of a child’s diagnosis with cancer can be overwhelming and confusing, especially for those lucky enough to lack a personal tie to the disease that takes approximately 1800 children’s lives each year in the United States (Deegan et al., n.d.). A parent’s beliefs, attitudes, and understandings surrounding cancer are vital for medical staff to provide adequate and culturally competent care for each patient, especially across cultural and ethnic lines in regions housing multicultural populations. Arizona's cultural/linguistic mosaic houses many White and Latino populations and English and Spanish speakers. Variations in insurance coverage, from those insured through public insurance programs (e.g., Medicaid) or private insurance plans (e.g., employee-sponsored insurance) versus those uninsured, also factor into health-seeking attitudes and behaviors. To further understand parental attitudes, understandings, and beliefs towards childhood cancer, 22 parents (11 of Latino ethnicity, 11 of White ethnicity) were interviewed on these facets of childhood cancer, despite 21 of the 22 never having a child receive a cancer diagnosis. The exploration of these perceptions across ethnic lines revealed a higher report of fear-orientated beliefs amongst Latino parents--hypothesized to be rooted in the starkly contrasting lack of belief in the possibility of recovering for children with cancer, compared to their white counterparts who displayed more optimism in the recovery process. Further, this study’s results lay the foundation for future scholarship to explore avenues of information dispersal to Latino parents that correct misconceptions of health outcomes and enable earlier intervention to be possible, ultimately correlating to better health and treatment outcomes by increasing parental health literacy rates for childhood cancer in the Phoenix Metropolitan.

Keywords: Childhood Cancer, Parental Beliefs, Parental Attitudes, Parental Understandings, Phoenix Metropolitan, Culturally Competent Care, Health Disparities, Health Inequities

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: Seulki Lee, Seoung Bum Kim

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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Oluwasina Folajinmi, Kate Ssamulla, Penninah Lutung, Daniel Reijer

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Background: Despite of well-verified benefits of anti-retroviral therapy (ART) in prolonging life expectancy being lost to follow-up (LTFU) presents a challenge to the success of ART programs in resource limited countries like Nigeria. In several studies of ART programs in developing countries, researchers have reported that there has been a high rate of LTFU among patients receiving care and treatment at ART treatment centers. This study seeks to determine the cause of LTFU among HIV clients. Method: A descriptive cross sectional study focused on a population of 9,280 persons living with HIV/AIDS who were enrolled in nine treatment centers in Nigeria (both pre-ART and ART patients were included). Out of the total population, 1752 (18.9%) were found to be LTFU. Of this group we randomly selected 1200 clients (68.5%) their d patients’ information was generated through a database. Data on demographics and CD4 counts, causes of LTFU were analyzed and summarized. Results: Out of 1200 LTFU clients selected, 462 (38.5%) were on ART; 341 clients (73.8%) had CD4 level < 500cell/µL and 738 (61.5%) on pre-ART had CD4 level >500/µL. In our records we found telephone number for 675 (56.1%) of these clients. 675 (56.1%) were owners of a phone. The majority of the client’s 731 (60.9%) were living at not more than 25km away from the ART center. A majority were females (926 or 77.2%) while 274 (22.8%) were male. 675 (56.1%) clients were reported traced via telephone and home address. 326 (27.2%) of clients phone numbers were not reachable; 173 (14.4%) of telephone numbers were incomplete. 71 (5.9%) had relocated due to communal crises and expert client trackers reported that some patient could not afford transportation to ART centers. Conclusion: This study shows that, low health education levels, poverty, relocations and lack of reliable phone contact were major predictors of LTFU. Periodic updates of home addresses, telephone contacts including at least two next of kin, phone text messages and home visits may improve follow up. Early and consistent tracking of missed appointments is crucial. Creation of more ART decentralized centres are needed to avoid long distances.

Keywords: anti-retroviral therapy, HIV/AIDS, predictors, lost to follow up

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Authors: David Mafullul, Jiang Lei, Edward Goacher, Jibin Francis

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PURPOSE: Timely decompressive surgery for cauda equina syndrome (CES) is dependent on efficient referral pathways for patients presenting at local primary or secondary centres to tertiary spinal centres in the United Kingdom (UK). Identifying modifiable points of delay within this process is important as minimising time between presentation and surgery may improve patient outcomes. This study aims to analyse whether adequacy of referral impacts on time to surgery in CES. MATERIALS AND METHODS: Data from all cases of confirmed CES referred to a single tertiary UK hospital between August 2017 to December 2019, via a suspected CES e-referral pathway, were obtained retrospectively. Referral adequacy was defined by the inclusion of sufficient information to determine the presence or absence of several NICE ‘red flags’. Correlation between referral adequacy and delay from referral-to-surgery was then analysed. RESULTS: In total, 118 confirmed CES cases were included. Adequate documentation for saddle anaesthesia was associated with reduced delays of more than 48 hours from referral-to-surgery [X2(1, N=116)=7.12, p=.024], an effect partly attributable to these referrals being accepted sooner [U=16.5; n1=27, n2=4, p=.029, r=.39]. Other red flags had poor association with delay. Referral adequacy was better for somatic red flags [bilateral sciatica (97.5%); severe or progressive bilateral neurological deficit of the legs (95.8%); saddle anaesthesia (91.5%)] compared to autonomic red flags [loss of anal tone (80.5%); urinary retention (79.7%); faecal incontinence or lost sensation of rectal fullness (57.6%)]. Although referral adequacy for urinary retention was 79.7%, only 47.5% of referrals documented a post-void residual numerical value. CONCLUSIONS: Adequate documentation of saddle anaesthesia in e-referrals is associated with reduced delay-to-surgery for confirmed CES, partly attributable to these referrals being accepted sooner. Other red flags had poor association with delay to surgery. Referral adequacy for autonomic red flags, including documentation for post-void residuals, has significant room for improvement.

Keywords: cauda equina, cauda equina syndrome, neurosurgery, spinal surgery, decompression, delay, referral, referral adequacy

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Authors: Ida Zubaidah

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With the advancement of information and communication technologies, in many cases, geographical distance is no longer considered as a main barrier in distance education. Geographical distance, even from a continent to another, between students and their instructor or students and their campus can be connected by the Internet, telephone or any other means of communication technology. Managing distance learning in an archipelagic country like Indonesia, however, has some different stories. Comprising more than 17,000 islands and 6.000 of them inhabited, Indonesia is considered as one of the most archipelagic countries in the world. In some areas or islands that have adequate public transportation and communication facilities the courses can be delivered quite well. In other areas that geographically very remote and dispersed islander, Universitas Terbuka, an open university in Indonesia, has to have very different strategies in overcoming the specific and even emergency situations in learning delivery. This ongoing research paper aims to share experiences of how Universitas Terbuka makes serious and unique efforts in overcoming the barriers and obstacles in providing educational service in part of difficult areas, especially in eastern areas of Indonesia. The data collection methods are observation of sample areas and in-depth interview with the head of regional offices of Universitas Terbuka in eastern Indonesia, staff, and tutors. Conducting educational deliveries in in difficult areas with no regular and adequate transportation has made the regional office have specific strategies in making the learning process run as smooth as possible. Sending a tutor to an area to meet some students and conducting a series of tutorial, which are supposed to be weekly, in several days is one of the strategies. Recruiting local people to manage the students in the area is another strategy. The absence of regular transportation from island to island, high tides, hurricanes, are among the obstacles faced by the regional offices in doing their job. Non geographical barriers such as unavailability of qualified tutor, inadequate tutor payment, are problems as well. The learning process, however, has to be done in any way, otherwise the distance education mission to reach unreachable cannot be achieved.

Keywords: distance education, Terbuka University, difficult area, geographical barrier, learning services

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Authors: Adepeju Simon-Oke, Olatunji Odeyemi, Mobolanle Oniya

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Urinary tract infection has become the most common bacterial infections in humans, both at the community and hospital settings; it has been reported in all age groups and in both sexes. This study was carried out in order to determine and evaluate the prevalence, current drug susceptibility pattern of the isolated organisms and identify the associated risk factors of UTIs among the pregnant women in Akure, Ondo State, Nigeria. A cross-sectional study was conducted on the urine of pregnant women, and socio-demographic information of the women was collected. A total of 300 clean midstream urine samples were collected, and a general urine microscopic examination and culture were carried out, the Microbact identification system was used to identify gram-negative bacteria. Out of the 300 urine samples cultured, 183(61.0%) yielded significant growth of urinary pathogens while 117(39.0%) yielded either insignificant growth or no growth of any urinary pathogen. Prevalence of UTI was significantly associated with the type of toilet used, symptoms of UTI, and previous history of urinary tract infection (p<0.05). Escherichia coli 58(31.7%) was the dominant pathogen isolated, and the least isolated uropathogens were Citrobacter freudii and Providencia retgerri 2(1.1%) respectively. Gram-negative bacteria showed 77.6%, 67.9%, and 61.2% susceptibility to ciprofloxacin, augmentin, and chloramphenicol, respectively. Resistance against septrin, chloramphenicol, sparfloxacin, amoxicillin, augmentin, gentamycin, pefloxacin, trivid, and streptomycin was observed in the range of 23.1 to 70.1%. Gram-positive uropathogens isolated showed high resistance to amoxicillin (68.4%) and high susceptibility to the remaining nine antibiotics in the range 65.8% to 89.5%. This study justifies that pregnant women are at high risk of UTI. Therefore screening of pregnant women during antenatal clinics should be considered very important to avoid complications. Health education with regular antenatal and personal hygiene is recommended as precautionary measures to UTI.

Keywords: pregnant women, prevalence, risk factor, UTIs

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Authors: Thomas Ruppert, Shana Jones, J. Scott Pippin

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State and federal government agencies within the United States have recently invested substantial resources into studies of future flood risk conditions associated with climate change and sea-level rise. A review of numerous case studies has uncovered several key themes that speak to an overall incoherence within current flood risk assessment procedures in the U.S. context. First, there are substantial local differences in the quality of available information about basic infrastructure, particularly with regard to local stormwater features and essential facilities that are fundamental components of effective flood hazard planning and mitigation. Second, there can be substantial mismatch between regulatory Flood Insurance Rate Maps (FIRMs) as produced by the National Flood Insurance Program (NFIP) and other 'current condition' flood assessment approaches. This is of particular concern in areas where FIRMs already seem to underestimate extant flood risk, which can only be expected to become a greater concern if future FIRMs do not appropriately account for changing climate conditions. Moreover, while there are incentives within the NFIP’s Community Rating System (CRS) to develop enhanced assessments that include future flood risk projections from climate change, the incentive structures seem to have counterintuitive implications that would tend to promote moral hazard. In particular, a technical finding of higher future risk seems to make it easier for a community to qualify for flood insurance savings, with much of these prospective savings applied to individual properties that have the most physical risk of flooding. However, there is at least some case study evidence to indicate that recognition of these issues is prompting broader discussion about the need to move beyond FIRMs as a standalone local flood planning standard. The paper concludes with approaches for developing climate adaptation and flood resilience strategies in the U.S. that move away from the social welfare model being applied through NFIP and toward more of an informed risk approach that transfers much of the investment responsibility over to individual private property owners.

Keywords: climate change adaptation, flood risk, moral hazard, sea-level rise

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Authors: Jae Ni Jang, Young Uk Kim

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Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

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Authors: Nusrat Praween, Krishna Thej Pammi Guru, Palash Kumar Basu

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Designing non-invasive biosensors for cancer diagnosis is essential for developing an affordable and specific tool to measure cancer-related exosome biomarkers. Exosomes, released by healthy as well as cancer cells, contain valuable information about the biomarkers of various diseases, including cancer. Despite the availability of various isolation techniques, ultracentrifugation is the standard technique that is being employed. Post isolation, exosomes are traditionally exposed to detergents for extracting their proteins, which can often lead to protein degradation. Further to this, it is very essential to develop a sensing platform for the quantification of clinically relevant proteins in a wider range to ensure practicality. In this study, exosomes were immobilized on the Au Screen Printed Electrode (SPE) using EDC/NHS chemistry to facilitate binding. After immobilizing the exosomes on the screen-printed electrode (SPE), we investigated the impact of the electric field by applying various voltages to induce exosome lysis and release their contents. The lysed solution was used for sensing TSG101, a crucial biomarker associated with various cancers, using both faradaic and non-faradaic electrochemical impedance spectroscopy (EIS) methods. The results of non-faradaic and faradaic EIS were comparable and showed good consistency, indicating that non-faradaic sensing can be a reliable alternative. Hence, the non-faradaic sensing technique was used for label-free quantification of the TSG101 biomarker. The results were validated using ELISA. Our electrochemical immunosensor demonstrated a consistent response of TSG101 from 125 pg/mL to 8000 pg/mL, with a detection limit of 0.125 pg/mL at room temperature. Additionally, since non-faradic sensing is label-free, the ease of usage and cost of the final sensor developed can be reduced. The proposed immunosensor is capable of detecting the TSG101 protein at low levels in healthy serum with good sensitivity and specificity, making it a promising platform for biomarker detection.

Keywords: biosensor, exosomes isolation on SPE, electric field lysis of exosome, EIS sensing of TSG101

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