Search results for: automated cad drawing

Authors: Ammaarah Gamieldien

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Depression is a very common and serious mental illness that has a significant impact on both the social and economic aspects of sufferers worldwide. This study aimed to investigate the association between body mass index (BMI), blood pressure, and lipid profiles with mental health symptoms in university students. Secondary objectives included the associations between the variables (BMI, blood pressure, and lipids) with themselves, as they are key factors in cardiometabolic disease. Sixty-three (63) students participated in the study. Thirty-two (32) were assigned to the control group (minimal-mild depressive symptoms), while 31 were assigned to the depressive group (moderate to severe depressive symptoms). Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory (BDI) were used to assess depressive scores. Anthropometric measurements such as weight (kg), height (m), waist circumference (WC), and hip circumference were measured. Body mass index (BMI) and ratios such as waist-to-hip ratio (WHR) and waist-to-height ratio (WtHR) were also calculated. Blood pressure was measured using an automated AfriMedics blood pressure machine, while lipids were measured using a CardioChek plus analyzer machine. Statistics were analyzed via the SPSS statistics program. There were no significant associations between anthropometric measurements and depressive scores (p > 0.05). There were no significant correlations between lipid profiles and depression when running a Spearman’s rho correlation (P > 0.05). However, total cholesterol and LDL-C were negatively associated with depression, and triglycerides were positively associated with depression after running a point-biserial correlation (P < 0.05). Overall, there were no significant associations between blood pressure measurements and depression (P > 0.05). However, there was a significant moderate positive correlation between systolic blood pressure and MADRS scores in males (P < 0.05). Depressive scores positively and strongly correlated to how long it takes participants to fall asleep. There were also significant associations with regard to the secondary objectives. This study indicates the importance of determining the prevalence of depression among university students in South Africa. If the prevalence and factors associated with depression are addressed, depressive symptoms in university students may be improved.

Keywords: depression, blood pressure, body mass index, lipid profiles, mental health symptoms

Procedia PDF Downloads 39

Authors: Abisha Isaac Mohanlal

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Amidst all suspicions and cynicism raised by the legal fraternity, Artificial Intelligence has found its way into the legal system and has revolutionized the conventional forms of legal services delivery. Be it legal argumentation and research or resolution of complex legal disputes; artificial intelligence has crept into all legs of modern day legal services. Its impact has been largely felt by way of big data, legal expert systems, prediction tools, e-lawyering, automated mediation, etc., and lawyers around the world are forced to upgrade themselves and their firms to stay in line with the growth of technology in law. Researchers predict that the future of legal services would belong to artificial intelligence and that the age of human lawyers will soon rust. But as far as the Judiciary is concerned, even in the developed countries, the system has not fully drifted away from the orthodoxy of preferring Natural Intelligence over Artificial Intelligence. Since Judicial decision-making involves a lot of unstructured and rather unprecedented situations which have no single correct answer, and looming questions of legal interpretation arise in most of the cases, discretion and Emotional Intelligence play an unavoidable role. Added to that, there are several ethical, moral and policy issues to be confronted before permitting the intrusion of Artificial Intelligence into the judicial system. As of today, the human judge is the unrivalled master of most of the judicial systems around the globe. Yet, scientists of Artificial Intelligence claim that robot judges can replace human judges irrespective of how daunting the complexity of issues is and how sophisticated the cognitive competence required is. They go on to contend that even if the system is too rigid to allow robot judges to substitute human judges in the recent future, Artificial Intelligence may still aid in other judicial tasks such as drafting judicial documents, intelligent document assembly, case retrieval, etc., and also promote overall flexibility, efficiency, and accuracy in the disposal of cases. By deconstructing the major challenges that Artificial Intelligence has to overcome in order to successfully invade the human- dominated judicial sphere, and critically evaluating the potential differences it would make in the system of justice delivery, the author tries to argue that penetration of Artificial Intelligence into the Judiciary could surely be enhancive and reparative, if not fully transformative.

Keywords: artificial intelligence, judicial decision making, judicial systems, legal services delivery

Procedia PDF Downloads 201

Authors: A. Assad, T. Zayed

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Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

Procedia PDF Downloads 127

Authors: Nihar Bheda

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Protecting digital assets such as networks, systems, and data from advanced cyber threats is the aim of Digital Immunity Systems (DIS), which are a subset of cybersecurity. With features like continuous monitoring, coordinated reactions, and long-term adaptation, DIS seeks to mimic biological immunity. This minimizes downtime by automatically identifying and eliminating threats. Traditional security measures, such as firewalls and antivirus software, are insufficient for enterprises, such as healthcare providers, given the rapid evolution of cyber threats. The number of medical record breaches that have occurred in recent years is proof that attackers are finding healthcare data to be an increasingly valuable target. However, obstacles to enhancing security include outdated systems, financial limitations, and a lack of knowledge. DIS is an advancement in cyber defenses designed specifically for healthcare settings. Protection akin to an "immune system" is produced by core capabilities such as anomaly detection, access controls, and policy enforcement. Coordination of responses across IT infrastructure to contain attacks is made possible by automation and orchestration. Massive amounts of data are analyzed by AI and machine learning to find new threats. After an incident, self-healing enables services to resume quickly. The implementation of DIS is consistent with the healthcare industry's urgent requirement for resilient data security in light of evolving risks and strict guidelines. With resilient systems, it can help organizations lower business risk, minimize the effects of breaches, and preserve patient care continuity. DIS will be essential for protecting a variety of environments, including cloud computing and the Internet of medical devices, as healthcare providers quickly adopt new technologies. DIS lowers traditional security overhead for IT departments and offers automated protection, even though it requires an initial investment. In the near future, DIS may prove to be essential for small clinics, blood banks, imaging centers, large hospitals, and other healthcare organizations. Cyber resilience can become attainable for the whole healthcare ecosystem with customized DIS implementations.

Keywords: digital immunity system, cybersecurity, healthcare data, emerging technology

Procedia PDF Downloads 38

Authors: Ylva Fältholm, Cathrine Norberg

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One of the major threats identified to successful future mining is that women do not find the industry attractive. Many attempts have been made, for example in Sweden and Australia, to create organizational structures and mining communities attractive to both genders. Despite such initiatives, many mining areas are developing into gender-segregated fly-in/fly out communities dominated by men with both social and economic consequences. One of the challenges facing many mining companies is thus to break traditional gender patterns and structures. To do this increased knowledge about gender in the context of mining is needed. Since language both constitutes and reproduces knowledge, increased knowledge can be gained through an exploration and description of the mining discourse from a gender perspective. The aim of this study is to explore what conceptual ideas are activated in connection to the physical/geographical mining area and to work within the mining industry. We use a combination of critical discourse analysis implying close reading of selected texts, such as policy documents, interview materials, applications and research and innovation agendas, and analyses of linguistic patterns found in large language corpora covering millions of words of contemporary language production. The quantitative corpus data serves as a point of departure for the qualitative analysis of the texts, that is, suggests what patterns to explore further. The study shows that despite technological and organizational development, one of the most persistent discourses about mining is the conception of dangerous and unfriendly areas infused with traditional notions of masculinity ideals and manual hard work. Although some of the texts analyzed highlight gender issues, and describe gender-equalizing initiatives, such as wage-mapping systems, female networks and recruitment efforts for women executives, and thereby render the discourse less straightforward, it is shown that these texts are not unambiguous examples of a counter-discourse. They rather illustrate that discourses are not stable but include opposing discourses, in dialogue with each other. For example, many texts highlight why and how women are important to mining, at the same time as they suggest that gender and diversity are all about women: why mining is a problem for them, how they should be, and what they should do to fit in. Drawing on a constitutive view of discourse, knowledge about such conflicting perceptions of women is a prerequisite for succeeding in attracting women to the mining industry and thereby contributing to the development of future mining.

Keywords: discourse, corpus linguistics, gender, mining

Procedia PDF Downloads 237

Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

Procedia PDF Downloads 443

Authors: Araya Yordchim, Rosjana Chandhasa, Suwaree Yordchim

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This research aims to study guidelines on the development of management process for research journals in order to develop Suan Sunandha Rajabhat University to international standards. This research investigated affecting elements ranging from the format of the article, evaluation form for research article quality, the process of creating a scholarly journal, satisfaction level of those with knowledge and competency to conduct research, arisen problems, and solutions. Drawing upon the sample size of 40 persons who had knowledge and competency in conducting research and creating scholarly journal articles at an international level, the data for this research were collected using questionnaires as a tool. Through the usage of computer software, data were analyzed by using the statistics in the forms of frequency, percentage, mean, standard deviation, and multiple regression analysis. The majority of participants were civil servants with a doctorate degree, followed by civil servants with a master's degree. Among them, the suitability of the article format was rated at a good level while the evaluation form for research articles quality was assessed at a good level. Based on participants' viewpoints, the process of creating scholarly journals was at a good level, while the satisfaction of those who had knowledge and competency in conducting research was at a satisfactory level. The problems encountered were the difficulty in accessing the website. The solution to the problem was to develop a website with user-friendly accessibility, including setting up a Google scholar profile for the purpose of references counting and the articles being used for reference in real-time. Research article format influenced the level of satisfaction of those who had the knowledge and competency to conduct research with statistical significance at the 0.01 level. The research article quality assessment form (preface section, research article writing section, preparation for research article manuscripts section, and the original article evaluation form for the author) affected the satisfaction of those with knowledge and competency to conduct research with the statistical significance at the level of 0.01. The process of establishing journals had an impact on the satisfaction of those with knowledge and ability to conduct research with statistical significance at the level of .05

Keywords: guidelines, development of management, research journals, international standards

Procedia PDF Downloads 107

Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

Procedia PDF Downloads 113

Authors: Dr. Dionysis Panos Dpt. Communication, Internet Studies Cyprus University of Technology

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The widespread use of New Media and particularly Social Media, through fixed or mobile devices, has changed in a staggering way our perception about what is “intimate" and "safe" and what is not, in interpersonal communication and social relationships. The distribution of self and identity-related information in communication now evolves under new and different conditions and contexts. Consequently, this new framework forces us to rethink processes and mechanisms, such as what "exposure" means in interpersonal communication contexts, how the distinction between the "private" and the "public" nature of information is being negotiated online, how the "audiences" we interact with are understood and constructed. Drawing from an interdisciplinary perspective that combines sociology, communication psychology, media theory, new media and social networks research, as well as from the empirical findings of a longitudinal comparative research, this work proposes an integrative model for comprehending mechanisms of personal information management in interpersonal communication, which can be applied to both types of online (Computer-Mediated) and offline (Face-To-Face) communication. The presentation is based on conclusions drawn from a longitudinal qualitative research study with 458 new media users from 24 countries for almost over a decade. Some of these main conclusions include: (1) There is a clear and evidenced shift in users’ perception about the degree of "security" and "familiarity" of the Web, between the pre- and the post- Web 2.0 era. The role of Social Media in this shift was catalytic. (2) Basic Web 2.0 applications changed dramatically the nature of the Internet itself, transforming it from a place reserved for “elite users / technical knowledge keepers" into a place of "open sociability” for anyone. (3) Web 2.0 and Social Media brought about a significant change in the concept of “audience” we address in interpersonal communication. The previous "general and unknown audience" of personal home pages, converted into an "individual & personal" audience chosen by the user under various criteria. (4) The way we negotiate the nature of 'private' and 'public' of the Personal Information, has changed in a fundamental way. (5) The different features of the mediated environment of online communication and the critical changes occurred since the Web 2.0 advance, lead to the need of reconsideration and updating the theoretical models and analysis tools we use in our effort to comprehend the mechanisms of interpersonal communication and personal information management. Therefore, is proposed here a new model for understanding the way interpersonal communication evolves, based on a revision of social penetration theory.

Keywords: new media, interpersonal communication, social penetration theory, communication exposure, private information, public information

Procedia PDF Downloads 343

Authors: Veliswa Nonfundo Hoho, Jabulani Gilford Kheswa

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This paper reports on teenage motherhood and its adverse outcomes on the academic performance, emotional well-being and sexual relationships that adolescent females encounter. Drawing from Ryff’s six dimensions of psychological well-being and Bronfenbrenner’s ecological model which underpinned this study, teenage motherhood has been found to link with multiple factors such as poverty, negative self-esteem, substance abuse, cohabitation, intimate partner violence and ill-health. Furthermore, research indicates that in schools where educators fail to perform their duties as loco-parentis to motivate adolescent females learners who are mothers, absenteeism, poor academic performance and learned helplessness, are likely. The aim of this research was two-fold, namely; (i) to determine the impact of teenage motherhood on the psychological well-being of the teenage mothers and (ii) to investigate the policies which protect the human rights of teenage mothers attending secondary schools. In a qualitative study conducted in one secondary school, Fort Beaufort, Eastern Cape, South Africa, fifteen Xhosa-speaking teenage mothers, aged 15-18 years old, were interviewed. The sample was recruited by means of snow-ball sampling. To safeguard the human dignity of the respondents, informed consent, confidentiality, anonymity and privacy of the respondents were assured. For trustworthiness, this research ensured that credibility, neutrality, and transferability, are met. Following an axial and open coding of responses, five themes were identified; Health issues of teenage mothers, lack of support, violation of human rights, impaired sense of purpose in life and intimate partner-violence. From these findings, it is clear that teenage mothers lack resilience and are susceptible to contract sexually transmitted infections and HIV/AIDS because they are submissive and hopeless. Furthermore, owing to stigma that the teenage mothers' experience from family members, they resort to alcohol and drug abuse, and feel demotivated to bond with their babies. In conclusion, the recommendations are that the Health and Social Development departments collaborate to empower the psychological well-being of teenage mothers. Furthermore, school policies on discrimination should be enacted and consistently implemented.

Keywords: depression, discrimination, self-esteem, teenage mothers

Procedia PDF Downloads 253

Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

Procedia PDF Downloads 106

Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Lottie Lane

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It is habitually assumed and stated that the World Bank should engage and comply with international human rights standards. However, the basis for holding the Bank to such standards is unclear. Most advocates of the idea invoke aspects of international law to argue that the Bank has existing obligations to act in compliance with human rights standards. The Bank itself, however, does not appear to accept such arguments, despite having endorsed the importance of human rights for a considerable length of time. A substantial challenge is that under the current international human rights law framework, the World Bank is considered a non-state actor, and as such, has no direct human rights obligations. In the absence of clear legal duties for the Bank, it is necessary to look at the tools available beyond the international human rights framework to encourage the Bank to comply with human rights standards. This article critically examines several bases for arguing that the Bank should comply and engage with human rights through its policies and practices. Drawing on the Bank’s own ‘good governance’ approach as well as the United Nations’ ‘human rights-based-approach’ to development, a new basis is suggested. First, the relationship between the World Bank and human rights is examined. Three perspectives are considered: (1) the legal position – what the status of the World Bank is under international human rights law, and whether it can be said to have existing legal human rights obligations; (2) the Bank’s own official position – how the Bank envisages its relationship with and role in the protection of human rights; and (3) the relationship between the Bank’s policies and practices and human rights (including how its attitudes are reflected in its policies and how the Bank’s operations impact human rights enjoyment in practice). Here, the article focuses on two examples – the (revised) 2016 Environmental and Social Safeguard Policies and the 2012 case-study regarding Gambella, Ethiopia. Both examples are widely considered missed opportunities for the Bank to actively engage with human rights. The analysis shows that however much pressure is placed on the Bank to improve its human rights footprint, it is extremely reluctant to do so explicitly, and the legal bases available are insufficient for requiring concrete, ex ante action by the Bank. Instead, the Bank’s own ‘good governance’ approach to development – which it has been advocating since the 1990s – can be relied upon. ‘Good governance’ has been used and applied by many actors in many contexts, receiving numerous different definitions. This article argues that human rights protection can now be considered a crucial component of good governance, at least in the context of development. In doing so, the article explains the relationship and interdependence between the two concepts, and provides three rationales for the Bank to take a ‘human rights-based approach’ to good governance. Ultimately, this article seeks to look beyond international human rights law and take a governance approach to provide a convincing basis upon which to argue that the World Bank should comply with human rights standards.

Keywords: World Bank, international human rights law, good governance, human rights-based approach

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Authors: Samuel Owoade, Zainab Idowu, Idris Ajibade, Abel Uzoka

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Cloud computing adoption continues to soar, with 83% of enterprise workloads estimated to be in the cloud by 2022. However, this rapid migration raises security concerns, needing strong security management solutions to safeguard sensitive data and essential applications. This paper investigates the critical role of technical project managers in orchestrating security management initiatives for cloud environments, evaluating their responsibilities, challenges, and best practices for assuring the resilience and integrity of cloud infrastructures. Drawing from a comprehensive review of industry reports and interviews with cloud security experts, this research highlights the multifaceted landscape of security management in cloud environments. Despite the rapid adoption of cloud services, only 25% of organizations have matured their cloud security practices, indicating a pressing need for effective management strategies. This paper proposes a strategy framework adapted to the demands of technical project managers, outlining the important components of effective cloud security management. Notably, 76% of firms identify misconfiguration as a major source of cloud security incidents, underlining the significance of proactive risk assessment and constant monitoring. Furthermore, the study emphasizes the importance of technical project managers in facilitating cross-functional collaboration, bridging the gap between cybersecurity professionals, cloud architects, compliance officers, and IT operations teams. With 68% of firms seeing difficulties integrating security policies into their cloud systems, effective communication and collaboration are critical to success. Case studies from industry leaders illustrate the practical use of security management projects in cloud settings. These examples demonstrate the importance of technical project managers in using their expertise to address obstacles and generate meaningful outcomes, with 92% of firms reporting improved security practices after implementing proactive security management tactics. In conclusion, this research underscores the critical role of technical project managers in safeguarding cloud environments against evolving threats. By embracing their role as guardians of the cloud realm, project managers can mitigate risks, optimize resource utilization, and uphold the trust and integrity of cloud infrastructures in an era of digital transformation.

Keywords: cloud security, security management, technical project management, cybersecurity, cloud infrastructure, risk management, compliance

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Authors: T. Ewetumo, K. D. Adedayo, Festus Ben

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Knowledge of the thermal properties of foods is of fundamental importance in the food industry to establish the design of processing equipment. However, for tropical fruit juice, there is very little information in literature, seriously hampering processing procedures. This research work describes the development of an instrument for automated thermal conductivity and thermal diffusivity measurement of tropical fruit juice using a transient thermal probe technique based on line heat principle. The system consists of two thermocouple sensors, constant current source, heater, thermocouple amplifier, microcontroller, microSD card shield and intelligent liquid crystal. A fixed distance of 6.50mm was maintained between the two probes. When heat is applied, the temperature rise at the heater probe measured with time at time interval of 4s for 240s. The measuring element conforms as closely as possible to an infinite line source of heat in an infinite fluid. Under these conditions, thermal conductivity and thermal diffusivity are simultaneously measured, with thermal conductivity determined from the slope of a plot of the temperature rise of the heating element against the logarithm of time while thermal diffusivity was determined from the time it took the sample to attain a peak temperature and the time duration over a fixed diffusivity distance. A constant current source was designed to apply a power input of 16.33W/m to the probe throughout the experiment. The thermal probe was interfaced with a digital display and data logger by using an application program written in C++. Calibration of the instrument was done by determining the thermal properties of distilled water. Error due to convection was avoided by adding 1.5% agar to the water. The instrument has been used for measurement of thermal properties of banana, orange and watermelon. Thermal conductivity values of 0.593, 0.598, 0.586 W/m^o C and thermal diffusivity values of 1.053 ×〖10〗^(-7), 1.086 ×〖10〗^(-7), and 0.959 ×〖10〗^(-7) 〖m/s〗^2 were obtained for banana, orange and water melon respectively. Measured values were stored in a microSD card. The instrument performed very well as it measured the thermal conductivity and thermal diffusivity of the tropical fruit juice samples with statistical analysis (ANOVA) showing no significant difference (p>0.05) between the literature standards and estimated averages of each sample investigated with the developed instrument.

Keywords: thermal conductivity, thermal diffusivity, tropical fruit juice, diffusion equation

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Authors: Estelle Ardanouy, Helene Delage, Pascal Zesiger

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Morphological awareness contributes to the acquisition of reading and spelling in typical learners as well as in children with learning disorders. Indeed, the acquisition of phoneme-grapheme correspondences is not sufficient to master spelling, especially in inconsistent orthographic systems such as English or French. Several meta-analyses show the benefit of explicit training in derivational morphology on reading and spelling in old children (who have already learned the main grapheme-phoneme correspondences), but highlight the lack of studies with younger children, particularly in French. In this study, we chose to focus on the efficiency of an intensive training in derivational morphology on spelling skills in French-speaking four-graders (9-10 years of age). The training consisted of 1) learning how to divide words into morphemes (ex: para/pente in French, paraglider in English), as well as 2) working on the meaning of affixes in relation to existing words (ex: para/pente: to protect against – para - the slope -pente). One group of pupils (N = 37, M age = 9.5) received this experimental group training in morphology while an alternative training group (N = 34, M age = 9.6) received a visuo-semantic training based on visual cues to memorize the spelling difficulties of complex words (such as the doubling of “r” in “verre” in French -or "glass" in English-which are represented by the drawing of two glasses). Both trainings lasted a total of 15 hours at a rate of four 45 minutes sessions per week, resulting in five weeks of training in the school setting. Our preliminary results show a significant improvement in the experimental group in the spelling of affixes on the trained (p < 0.001) and untrained word lists (p <0.001), but also in the root of words on the trained (p <0.001) and untrained word lists group (p <0.001). The training effect is also present on both trained and untrained morphologically composed words. By contrast, the alternative training group shows no progress on these previous measures (p >0.15). Further analyses testing the effects of both trainings on other measures such as morphological awareness and reading of morphologically compose words are in progress. These first results support the effectiveness of explicitly teaching derivational morphology to improve spelling in school-aged children. The study is currently extended to a group of children with developmental dyslexia because these children are known for their severe and persistent spelling difficulties.

Keywords: developmental dyslexia, derivational morphology, reading, school-aged children, spelling, training

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Authors: Armen Grigoryan, Milena Arakelyan

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The idea of smart logistics is still quite a complicated one. It can be used to market products to a large number of customers or to acquire raw materials of the highest quality at the lowest cost in geographically dispersed areas. The use of smart contracts in logistics and supply chain management has the potential to revolutionize the way that goods are tracked, transported, and managed. Smart contracts are simply computer programs written in one of the blockchain programming languages (Solidity, Rust, Vyper), which are capable of self-execution once the predetermined conditions are met. They can be used to automate and streamline many of the traditional manual processes that are currently used in logistics and supply chain management, including the tracking and movement of goods, the management of inventory, and the facilitation of payments and settlements between different parties in the supply chain. Currently, logistics is a core area for companies which is concerned with transporting products between parties. Still, the problem of this sector is that its scale may lead to detainments and defaults in the delivery of goods, as well as other issues. Moreover, large distributors require a large number of workers to meet all the needs of their stores. All this may contribute to big detainments in order processing and increases the potentiality of losing orders. In an attempt to break this problem, companies have automated all their procedures, contributing to a significant augmentation in the number of businesses and distributors in the logistics sector. Hence, blockchain technology and smart contracted legal agreements seem to be suitable concepts to redesign and optimize collaborative business processes and supply chains. The main purpose of this paper is to examine the scope of blockchain technology and smart contracts in the field of logistics and supply chain management. This study discusses the research question of how and to which extent smart contracts and blockchain technology can facilitate and improve the implementation of collaborative business structures for sustainable entrepreneurial activities in smart supply chains. The intention is to provide a comprehensive overview of the existing research on the use of smart contracts in logistics and supply chain management and to identify any gaps or limitations in the current knowledge on this topic. This review aims to provide a summary and evaluation of the key findings and themes that emerge from the research, as well as to suggest potential directions for future research on the use of smart contracts in logistics and supply chain management.

Keywords: smart contracts, smart logistics, smart supply chain management, blockchain and smart contracts in logistics, smart contracts for controlling supply chain management

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Authors: Sora Suh, Catherine Michener

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This paper examines the collaborative forms of advocacy that a group of four public school teachers took for their emergent bilingual students in one public school district. While teacher advocacy takes many forms in and out of the classroom, much advocacy work is done by individuals and less by collective action. As a result, individual teachers risk isolation or marginalization in their school contexts when they advocate for immigrant youth. This paper is intended to contribute to the documentation and understanding of teachers’ advocacy work as a collaborative act in teacher education research. The increase of ELs in US classrooms and a corresponding lack of teacher preparation to meet the needs of ELs has motivated the training of educators in linguistically responsive education (e.g., ESL, sheltered English instruction [SEI], bilingual education). Drawing from educational theories of linguistically responsive teaching for preparing educators, we trace the linguistically responsive advocacy work of the teachers. The paper is a multiple case study that tracks how teachers’ discussions on advocacy during a teacher preparation program leading to collaborative actions in their daily teaching lives in and out of school. Data collected includes online discussion forums on the topic of advocacy, course assignments on the topic of advocacy, video-audio recordings of classroom teaching observations, and video-audio recordings of individual and focus group interviews. The findings demonstrate that the teachers’ understanding of advocacy developed through collaborative partnerships formed in the teacher preparation program and grew into active forms of collaborative advocacy in their teaching practice in and out of school. The teachers formed multi-level and collaborative partnerships with teachers, families, community members, policymakers from the local government, and educational researchers to advocate for their emergent bilingual students by planning advocacy events such as new family orientations for emergent bilinguals, professional development for general education teachers on the topic of linguistically responsive instruction, and family nights hosted by the district. The paper’s findings present types of advocacy work in which teachers engage (pedagogical, curricular, out-of-school work) and provide evidence of collaborative advocacy work by a group of engaged educators. The paper highlights the increased agency and effective advocacy of teachers through teacher education and collaborative partnerships and suggests a need for more research on collaborative forms of teacher advocacy for emergent bilinguals.

Keywords: language education, teacher advocacy, language instruction, teacher education

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Authors: Francesca Perri

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

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

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Authors: Mrinal Kanti Bhowmik, Shawli Bardhan Jr., Debotosh Bhattacharjee

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Human skin containing temperature value more than absolute zero, discharges infrared radiation related to the frequency of the body temperature. The difference in infrared radiation from the skin surface reflects the abnormality present in human body. Considering the difference, detection and forecasting the temperature variation of the skin surface is the main objective of using Medical Infrared Thermography(MIT) as a diagnostic tool for pain detection. Medical Infrared Thermography(MIT) is a non-invasive imaging technique that records and monitors the temperature flow in the body by receiving the infrared radiated from the skin and represent it through thermogram. The intensity of the thermogram measures the inflammation from the skin surface related to pain in human body. Analysis of thermograms provides automated anomaly detection associated with suspicious pain regions by following several image processing steps. The paper represents a rigorous study based survey related to the processing and analysis of thermograms based on the previous works published in the area of infrared thermal imaging for detecting inflammatory pain diseases like arthritis, spondylosis, shoulder impingement, etc. The study also explores the performance analysis of thermogram processing accompanied by thermogram acquisition protocols, thermography camera specification and the types of pain detected by thermography in summarized tabular format. The tabular format provides a clear structural vision of the past works. The major contribution of the paper introduces a new thermogram acquisition standard associated with inflammatory pain detection in human body to enhance the performance rate. The FLIR T650sc infrared camera with high sensitivity and resolution is adopted to increase the accuracy of thermogram acquisition and analysis. The survey of previous research work highlights that intensity distribution based comparison of comparable and symmetric region of interest and their statistical analysis assigns adequate result in case of identifying and detecting physiological disorder related to inflammatory diseases.

Keywords: acquisition protocol, inflammatory pain detection, medical infrared thermography (MIT), statistical analysis

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Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

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Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

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Authors: Lyka Selene Magnayi, Marcos Vinas, Roseanne Ramos

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As the real estate industry continually grows in the Philippines, Geographic Information Systems (GIS) provide advantages in generating spatial databases for efficient delivery of information and services. The real estate sector is not only providing qualitative data about real estate properties but also utilizes various spatial aspects of these properties for different applications such as hazard mapping and assessment. In this study, a three-dimensional (3D) model and a spatial database of real estate properties in Fort Bonifacio, Taguig City are developed using GIS and SketchUp. Spatial datasets include political boundaries, buildings, road network, digital terrain model (DTM) derived from Interferometric Synthetic Aperture Radar (IFSAR) image, Google Earth satellite imageries, and hazard maps. Multiple model layers were created based on property listings by a partner real estate company, including existing and future property buildings. Actual building dimensions, building facade, and building floorplans are incorporated in these 3D models for geovisualization. Hazard model layers are determined through spatial overlays, and different scenarios of hazards are also presented in the models. Animated maps and walkthrough videos were created for company presentation and evaluation. Model evaluation is conducted through client surveys requiring scores in terms of the appropriateness, information content, and design of the 3D models. Survey results show very satisfactory ratings, with the highest average evaluation score equivalent to 9.21 out of 10. The output maps and videos obtained passing rates based on the criteria and standards set by the intended users of the partner real estate company. The methodologies presented in this study were found useful and have remarkable advantages in the real estate industry. This work may be extended to automated mapping and creation of online spatial databases for better storage, access of real property listings and interactive platform using web-based GIS.

Keywords: geovisualization, geographic information systems, GIS, real estate, spatial database, three-dimensional model

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Authors: Paul R Armstrong

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Doubled haploids (DHs) have become an important breeding tool for creating maize inbred lines, although several bottlenecks in the DH production process limit wider development, application, and adoption of the technique. DH kernels are typically sorted manually and represent about 10% of the seeds in a much larger pool where the remaining 90% are hybrid siblings. This introduces time constraints on DH production and manual sorting is often not accurate. Automated sorting based on the chemical composition of the kernel can be effective, but devices, namely NMR, have not achieved the sorting speed to be a cost-effective replacement to manual sorting. This study evaluated a single kernel near-infrared reflectance spectroscopy (skNIR) platform to accurately identify DH kernels based on oil content. The skNIR platform is a higher-throughput device, approximately 3 seeds/s, that uses spectra to predict oil content of each kernel from maize crosses intentionally developed to create larger than normal oil differences, 1.5%-2%, between DH and hybrid kernels. Spectra from the skNIR were used to construct a partial least squares regression (PLS) model for oil and for a categorical reference model of 1 (DH kernel) or 2 (hybrid kernel) and then used to sort several crosses to evaluate performance. Two approaches were used for sorting. The first used a general PLS model developed from all crosses to predict oil content and then used for sorting each induction cross, the second was the development of a specific model from a single induction cross where approximately fifty DH and one hundred hybrid kernels used. This second approach used a categorical reference value of 1 and 2, instead of oil content, for the PLS model and kernels selected for the calibration set were manually referenced based on traditional commercial methods using coloration of the tip cap and germ areas. The generalized PLS oil model statistics were R2 = 0.94 and RMSE = .93% for kernels spanning an oil content of 2.7% to 19.3%. Sorting by this model resulted in extracting 55% to 85% of haploid kernels from the four induction crosses. Using the second method of generating a model for each cross yielded model statistics ranging from R2s = 0.96 to 0.98 and RMSEs from 0.08 to 0.10. Sorting in this case resulted in 100% correct classification but required models that were cross. In summary, the first generalized model oil method could be used to sort a significant number of kernels from a kernel pool but was not close to the accuracy of developing a sorting model from a single cross. The penalty for the second method is that a PLS model would need to be developed for each individual cross. In conclusion both methods could find useful application in the sorting of DH from hybrid kernels.

Keywords: NIR, haploids, maize, sorting

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Authors: Cristóbal Moënne-Loccoz, Rodrigo C. Vergara, Vladimir López, Domingo Mery, Diego Cosmelli

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Our daily interaction with computational interfaces is plagued of situations in which we go from inexperienced users to experts through self-motivated exploration of the same task. In many of these interactions, we must learn to find our way through a sequence of decisions and actions before obtaining the desired result. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion so that a specific sequence of actions must be performed in order to produce the expected outcome. But, as they become experts in the use of such interfaces, do users adopt specific search and learning strategies? Moreover, if so, can we use this information to follow the process of expertise development and, eventually, predict future actions? This would be a critical step towards building truly adaptive interfaces that can facilitate interaction at different moments of the learning curve. Furthermore, it could provide a window into potential mechanisms underlying decision-making behavior in real world scenarios. Here we tackle this question using a simple game interface that instantiates a 4-level binary decision tree (BDT) sequential decision-making task. Participants have to explore the interface and discover an underlying concept-icon mapping in order to complete the game. We develop a Hidden Markov Model (HMM)-based approach whereby a set of stereotyped, hierarchically related search behaviors act as hidden states. Using this model, we are able to track the decision-making process as participants explore, learn and develop expertise in the use of the interface. Our results show that partitioning the problem space into such stereotyped strategies is sufficient to capture a host of exploratory and learning behaviors. Moreover, using the modular architecture of stereotyped strategies as a Mixture of Experts, we are able to simultaneously ask the experts about the user's most probable future actions. We show that for those participants that learn the task, it becomes possible to predict their next decision, above chance, approximately halfway through the game. Our long-term goal is, on the basis of a better understanding of real-world decision-making processes, to inform the construction of interfaces that can establish dynamic conversations with their users in order to facilitate the development of expertise.

Keywords: behavioral modeling, expertise acquisition, hidden markov models, sequential decision-making

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Authors: Vishwajit Ghatge, David Looper

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Over the last few decades, oilfield service rolling equipment has significantly increased in weight, primarily because of emissions regulations, which require larger/heavier engines, larger cooling systems, and emissions after-treatment systems, in some cases, etc. Larger engines cause more vibration and shock loads, leading to failure of electronics and control systems. If the vibrating frequency of the engine matches the system frequency, high resonance is observed on structural parts and mounts. One such existing automated control equipment system comprising wire rope mounts used for mounting computers was designed approximately 12 years ago. This includes the use of an industrial- grade computer to control the system operation. The original computer had a smaller, lighter enclosure. After a few years, a newer computer version was introduced, which was 10 lbm heavier. Some failures of internal computer parts have been documented for cases in which the old mounts were used. Because of the added weight, there is a possibility of having the two brackets impact each other under off-road conditions, which causes a high shock input to the computer parts. This added failure mode requires validating the existing mount design to suit the new heavy-weight computer. This paper discusses the modal finite element method (FEM) analysis and experimental modal analysis conducted to study the effects of vibration on the wire rope mounts and the computer. The existing mount was modelled in ANSYS software, and resultant mode shapes and frequencies were obtained. The experimental modal analysis was conducted, and actual frequency responses were observed and recorded. Results clearly revealed that at resonance frequency, the brackets were colliding and potentially causing damage to computer parts. To solve this issue, spring mounts of different stiffness were modeled in ANSYS software, and the resonant frequency was determined. Increasing the stiffness of the system increased the resonant frequency zone away from the frequency window at which the engine showed heavy vibrations or resonance. After multiple iterations in ANSYS software, the stiffness of the spring mount was finalized, which was again experimentally validated.

Keywords: experimental modal analysis, FEM Modal Analysis, frequency, modal analysis, resonance, vibration

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Authors: Mazoun Nasser Rashid Al Kharusi, Nada Al Siyabi

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Objectives: The main aim of this study is to Find the rate of susceptibility of ESBL E. coli causing UTI to oral antibiotics. Secondary objectives: Prevalence of ESBL E. coli from community urine samples, identify the best empirical oral antibiotics with the least resistance rate for UTI and identify alternative oral antibiotics for testing and utilization. Methods: This study is a retrospective descriptive study of the last three years in five major hospitals in Oman (Khowla Hospital, AN’Nahdha Hospital, Rustaq Hospital, Nizwa Hospital, and Ibri Hospital) equipped with a microbiologist. Inclusion criteria include all eligible outpatient urine culture isolates, excluding isolates from admitted patients with hospital-acquired urinary tract infections. Data was collected through the MOH database. The MOH hospitals are using different types of testing, automated methods like Vitek2 and manual methods. Vitek2 machine uses the principle of the fluorogenic method for organism identification and a turbidimetric method for susceptibility testing. The manual method is done by double disc diffusion for identifying ESBL and the disc diffusion method is for antibiotic susceptibility. All laboratories follow the clinical laboratory science institute (CLSI) guidelines. Analysis was done by SPSS statistical package. Results: Total urine cultures were (23048). E. coli grew in (11637) 49.6% of the urine, whereas (2199) 18.8% of those were confirmed as ESBL. As expected, the resistance rate to amoxicillin and cefuroxime is 100%. Moreover, the susceptibility of those ESBL-producing E. coli to nitrofurantoin, trimethoprim+sulfamethoxazole, ciprofloxacin and amoxicillin-clavulanate is progressing over the years; however, still low. ESBL E. coli was predominating in the female gender and those aged 66-74 years old throughout all the years. Other oral antibiotic options need to be explored and tested so that we add to the pool of oral antibiotics for ESBL E. coli causing UTI in the community. Conclusion: High rate of ESBL E. coli in urine from the community. The high resistance rates to oral antibiotics highlight the need for alternative treatment options for UTIs caused by these bacteria. Further research is needed to identify new and effective treatments for UTIs caused by ESBL-E. Coli.

Keywords: UTI, ESBL, oral antibiotics, E. coli, susceptibility

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Authors: Samuel Holder

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Mass incarceration in the United States is a human rights issue, not merely a civil rights problem. It is a human rights problem not only because the United States has a high rate of incarceration, but more importantly because of who is jailed, for what purpose they are jailed and, ultimately, the manner in which they are jailed. To sustain the scale of the criminal justice system, one of the darker policies involves a multi-tiered strategy of fee- and fine-collection, targeting, usually, the most vulnerable and poor, many of whom run into the law via small offenses that do not rise to the level of felonies. This paper advances the notion that this debt collection-to-incarceration pipeline is tantamount to a modern-day debtors’ prison system. This article seeks to confront the thorny issue of incarceration via criminal justice debt from a human rights and cause-lawyering position. It will argue that a two-pronged cause-lawyering strategy: the first focused on traditional litigation along constitutional grounds, and the second, an advocacy approach rooted in grassroots campaigns, designed to shift the normative operation and understanding of the rights of marginalized and racialized offenders. Ultimately, the argument suggests that this approach will be effective in combatting the (often highly privatized) criminal justice debt system and bring the roles of 'incapacitation, rehabilitation, deterrence, and retribution' back into the criminal justice legal conversation. Part I contextualizes and historicizes the role of fees, penalties, and fines in American criminal justice. Part II examines the emergence of private industry in the criminal justice system, and its role in the acceleration of profit-driven criminal justice debt collection and incarceration. Part III addresses the failures of the federal and state law and legislation in combatting predatory incarceration and debt collection in the criminal justice system, particularly as waged against the indigent and/or ethnically or racially marginalized. Part IV examines the potential for traditional cause-lawyering litigation along constitutional grounds, using case studies across contexts for illustration. Finally, Part V will review the radical cause-lawyer’s role in the normative struggle in redefining prisoners’ rights and the rights of the marginalized (and racialized) as they intersect at the crossroads of criminal justice debt. This paper will conclude with recommendations for litigation and advocacy, drawing on hypotheses advanced, and informed by case studies from a variety of both national and international jurisdictions.

Keywords: cause-lawyering, criminal justice debt, human rights, judicial fees

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Authors: Aroge Stephen Talabi

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Work is a term that appears to be very common in usage and occurrence the world over. The meanings attached to it and what it implies equally appears to be that common and somewhat similar in description by individuals and groups as derivatives of their contexts. Work is generally seen as the exertion of efforts and the application of knowledge and skills to achieve different purposes comprising of earning a living, making money, prestige, achievement, recognition, companionship and other satisfactions. The paper examined the general meanings of work from the perspectives of various religions. It situated these meanings by drawing on the sociological significance of work among the Yoruba. It established work as social control for a reorientation in peoples approach to work. The Yoruba people of the Western Nigeria share, to a great extent, in common conceptualization and application of work as they believe and understand that their individual and community existence and living are contingent on work participation. The contextual meaning and sociological significance of work as investigated in this paper show that the Yorubas concept of work is daily applied variously in both their material and non-material cultural undertakings to influence individual and group for effective participation in productive ventures for overall social well-being. The Yoruba use all forms of training method which could be adopted by adult educators as pathways to increase individual’s work participation and to improve productivity in work organizations.The paper found out that in the Yoruba socio cultural milieu, the meanings, conceptions and the importance attached to work are used as method of inculcating in members of society the spirit of commitment and hard-work and the advantages thereof. Yoruba contexts of work are geared towards enhancement of commitment, diligence and improved productivity on-the-job behaviour. The paper, therefore, submits that using the Yoruba’s conceptions of work could enhance commitment on the parts of all those engaged in production of goods and services. The paper also suggests that the Yoruba principle and perception and application of work could be used as one of the training techniques in industrial education, which is a major aspect of adult education programmes for inculcating ethics in the workplace. Thus, effort should be made to embrace the Yoruba conception and tenet of work by all stakeholders such as the workers, group (Union), managers and the society at large. Such principles and tenet of work should be included in industrial education curriculum.

Keywords: work, contextual meaning, sociological significance, Yoruba-people, social milieu, productivity

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Authors: Pedro Augusto Da Silva E Souza Miranda, Niloofar Jalali, Shweta Mistry

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BACKGROUND: Software developers utilize automated solutions to scrape users’ reviews to extract meaningful knowledge to identify problems (e.g., bugs, compatibility issues) and possible enhancements (e.g., users’ requests) to their solutions. However, most of these solutions do not consider the health risk aspects to users. Recent works have shed light on the importance of including health risk considerations in the development cycle of mHealth apps to prevent harm to its users. PROBLEM: The COVID-19 Pandemic in Canada (and World) is currently forcing physical distancing upon the general population. This new lifestyle made the usage of mHealth applications more essential than ever, with a projected market forecast of 332 billion dollars by 2025. However, this new insurgency in mHealth usage comes with possible risks to users’ health due to mHealth apps problems (e.g., wrong insulin dosage indication due to a UI error). OBJECTIVE: These works aim to raise awareness amongst mHealth developers of the importance of considering risks to users’ health within their development lifecycle. Moreover, this work also aims to help mHealth developers with a Proof-of-Concept (POC) solution to understand, process, and identify possible health risks to users of mHealth apps based on users’ reviews. METHODS: We conducted a mixed-method study design. We developed a crawler to mine the negative reviews from two samples of mHealth apps (my fitness, medisafe) from the Google Play store users. For each mHealth app, we performed the following steps: • The reviews are divided into two groups, before starting the COVID-19 (reviews’ submission date before 15 Feb 2019) and during the COVID-19 (reviews’ submission date starts from 16 Feb 2019 till Dec 2020). For each period, the Latent Dirichlet Allocation (LDA) topic model was used to identify the different clusters of reviews based on similar topics of review The topics before and during COVID-19 are compared, and the significant difference in frequency and severity of similar topics are identified. RESULTS: We successfully scraped, filtered, processed, and identified health-related topics in both qualitative and quantitative approaches. The results demonstrated the similarity between topics before and during the COVID-19.

Keywords: natural language processing (NLP), topic modeling, mHealth, COVID-19, software engineering, telemedicine, health risks

Procedia PDF Downloads 109