Search results for: learning and teaching

Authors: Jonathan Turner, Julie Doyle, Laura O’Philbin, Dympna O’Sullivan

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People living with dementia should be at the centre of decision-making regarding goals for daily living. These goals include basic activities (dressing, hygiene, and mobility), advanced activities (finances, transportation, and shopping), and meaningful activities that promote well-being (pastimes and intellectual pursuits). However, there is limited involvement of people living with dementia in the design of technology to support their goals. A project is described that is co-designing intelligent computer-based support for, and with, people affected by dementia and their carers. The technology will support self-management, empower participation in shared decision-making with carers and help people living with dementia remain healthy and independent in their homes for longer. It includes information from the patient’s care plan, which documents medications, contacts, and the patient's wishes on end-of-life care. Importantly for this work, the plan can outline activities that should be maintained or worked towards, such as exercise or social contact. The authors discuss how to integrate care goal information from such a care plan with data collected from passive sensors in the patient’s home in order to deliver individualized planning and interventions for persons with dementia. A number of scientific challenges are addressed: First, to co-design with dementia patients and their carers computerized support for shared decision-making about their care while allowing the patient to share the care plan. Second, to develop a new and open monitoring framework with which to configure sensor technologies to collect data about whether goals and actions specified for a person in their care plan are being achieved. This is developed top-down by associating care quality types and metrics elicited from the co-design activities with types of data that can be collected within the home, from passive and active sensors, and from the patient’s feedback collected through a simple co-designed interface. These activities and data will be mapped to appropriate sensors and technological infrastructure with which to collect the data. Third, the application of machine learning models to analyze data collected via the sensing devices in order to investigate whether and to what extent activities outlined via the care plan are being achieved. The models will capture longitudinal data to track disease progression over time; as the disease progresses and captured data show that activities outlined in the care plan are not being achieved, the care plan may recommend alternative activities. Disease progression may also require care changes, and a data-driven approach can capture changes in a condition more quickly and allow care plans to evolve and be updated.

Keywords: care goals, decision-making, dementia, self-care, sensors

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Authors: Zhijie Li, Wenhui Peng, Zelong Yuan, Jianchun Wang

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Turbulence is a complex phenomenon that plays a crucial role in various fields, such as engineering, atmospheric science, and fluid dynamics. Predicting and understanding its behavior over long time scales have been challenging tasks. Traditional methods, such as large-eddy simulation (LES), have provided valuable insights but are computationally expensive. In the past few years, machine learning methods have experienced rapid development, leading to significant improvements in computational speed. However, ensuring stable and accurate long-term predictions remains a challenging task for these methods. In this study, we introduce the implicit U-net enhanced Fourier neural operator (IU-FNO) as a solution for stable and efficient long-term predictions of the nonlinear dynamics in three-dimensional (3D) turbulence. The IU-FNO model combines implicit re-current Fourier layers to deepen the network and incorporates the U-Net architecture to accurately capture small-scale flow structures. We evaluate the performance of the IU-FNO model through extensive large-eddy simulations of three types of 3D turbulence: forced homogeneous isotropic turbulence (HIT), temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The results demonstrate that the IU-FNO model outperforms other FNO-based models, including vanilla FNO, implicit FNO (IFNO), and U-net enhanced FNO (U-FNO), as well as the dynamic Smagorinsky model (DSM), in predicting various turbulence statistics. Specifically, the IU-FNO model exhibits improved accuracy in predicting the velocity spectrum, probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of the flow field. Furthermore, the IU-FNO model addresses the stability issues encountered in long-term predictions, which were limitations of previous FNO models. In addition to its superior performance, the IU-FNO model offers faster computational speed compared to traditional large-eddy simulations using the DSM model. It also demonstrates generalization capabilities to higher Taylor-Reynolds numbers and unseen flow regimes, such as decaying turbulence. Overall, the IU-FNO model presents a promising approach for long-term dynamics prediction in 3D turbulence, providing improved accuracy, stability, and computational efficiency compared to existing methods.

Keywords: data-driven, Fourier neural operator, large eddy simulation, fluid dynamics

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Authors: Atucungwiire Rwebiita

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Introduction: In Uganda, provision of adolescent sexual reproductive health and rights (SRHR) services for key population is still hindered by negative attitudes, stigma and discrimination (S&D) at both the community and facility levels. To address this barrier, Integrated Community Based Initiatives (ICOBI) with support from SIDA is currently implementing a quality improvement (QI) innovative approach for strengthening the capacity of key population (KP) peer leaders and health workers to deliver friendly SRHR services without S&D. Methods: Our innovative approach involves continuous mentorship and coaching of 8 QI teams at 8 health facilities and their catchment areas. Each of the 8 teams (comprised of 5 health workers and 5 KP peer leaders) are facilitated twice a month by two QI Mentors in a 2-hour mentorship session over a period of 4 months. The QI mentors were provided a 2-weeks training on QI approaches for reducing S&D against young key populations in the delivery of SRHR Services. The mentorship sessions are guided by a manual where teams base to analyse root causes of S&D and develop key performance indicators (KPIs) in the 1st and 2nd second sessions respectively. The teams then develop action plans in the 3rd session and review implementation progress on KPIs at the end of subsequent sessions. The KPIs capture information on the attitude of health workers and peer leaders and the general service delivery setting as well as clients’ experience. A dashboard is developed to routinely track the KPIs for S&D across all the supported health facilities and catchment areas. After 4 months, QI teams share documented QI best practices and tested change packages on S&D in a learning and exchange session involving all the teams. Findings: The implementation of this approach is showing positive results. So far, QI teams have already identified the root causes of S&D against key populations including: poor information among health workers, fear of a perceived risk of infection, perceived links between HIV and disreputable behaviour. Others are perceptions that HIV & STIs are divine punishment, sex work and homosexuality are against religion and cultural values. They have also noted the perception that MSM are mentally sick and a danger to everyone. Eight QI teams have developed action plans to address the root causes of S&D. Conclusion: This approach is promising, offers a novel and scalable means to implement stigma-reduction interventions in facility and community settings.

Keywords: key populations, sexual reproductive health and rights, stigma and discrimination , quality improvement approach

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Authors: Godian Asami Mabindah

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The convergence of knowledge management (KM) and competitive intelligence (CI) has gained significant attention in recent years as organizations seek to enhance their competitive advantage in an increasingly complex and dynamic business environment. This research study aims to explore and understand the synergistic relationship between KM and CI and its impact on organizational performance. By investigating how the integration of KM and CI practices can contribute to decision-making, innovation, and competitive advantage, this study seeks to unlock the potential benefits and challenges associated with this integration. The research employs a mixed-methods approach to gather comprehensive data. A quantitative analysis is conducted using survey data collected from a diverse sample of organizations across different industries. The survey measures the extent of integration between KM and CI practices and examines the perceived benefits and challenges associated with this integration. Additionally, qualitative interviews are conducted with key organizational stakeholders to gain deeper insights into their experiences, perspectives, and best practices regarding the synergistic relationship. The findings of this study are expected to reveal several significant outcomes. Firstly, it is anticipated that organizations that effectively integrate KM and CI practices will outperform those that treat them as independent functions. The study aims to highlight the positive impact of this integration on decision-making, innovation, organizational learning, and competitive advantage. Furthermore, the research aims to identify critical success factors and enablers for achieving constructive interaction between KM and CI, such as leadership support, culture, technology infrastructure, and knowledge-sharing mechanisms. The implications of this research are far-reaching. Organizations can leverage the findings to develop strategies and practices that facilitate the integration of KM and CI, leading to enhanced competitive intelligence capabilities and improved knowledge management processes. Additionally, the research contributes to the academic literature by providing a comprehensive understanding of the synergistic relationship between KM and CI and proposing a conceptual framework that can guide future research in this area. By exploring the synergies between KM and CI, this study seeks to help organizations harness their collective power to gain a competitive edge in today's dynamic business landscape. The research provides practical insights and guidelines for organizations to effectively integrate KM and CI practices, leading to improved decision-making, innovation, and overall organizational performance.

Keywords: Competitive Intelligence, Knowledge Management, Organizational Performance, Incusivity, Optimum Performance

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Pongthep Bunrueng

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This action research accentuates the outcome of a development in English pronunciation, using principles of phonetics for English major students at Loei Rajabhat University. The research is split into 5 separate modules: 1) Organs of Speech and How to Produce Sounds, 2) Monopthongs, 3) Diphthongs, 4) Consonant sounds, and 5) Suprasegmental Features. Each module followed a 4 step action research process, 1) Planning, 2) Acting, 3) Observing, and 4) Reflecting. The research targeted 2nd year students who were majoring in English Education at Loei Rajabhat University during the academic year of 2011. A mixed methodology employing both quantitative and qualitative research was used, which put theory into action, taking segmental features up to suprasegmental features. Multiple tools were employed which included the following documents: pre-test and post-test papers, evaluation and assessment papers, group work assessment forms, a presentation grading form, an observation of participants form and a participant self-reflection form. All 5 modules for the target group showed that results from the post-tests were higher than those of the pre-tests, with 0.01 statistical significance. All target groups attained results ranging from low to moderate and from moderate to high performance. The participants who attained low to moderate results had to re-sit the second round. During the first development stage, participants attended classes with group participation, in which they addressed planning through mutual co-operation and sharing of responsibility. Analytic induction of strong points for this operation illustrated that learner cognition, comprehension, application, and group practices were all present whereas the participants with weak results could be attributed to biological differences, differences in life and learning, or individual differences in responsiveness and self-discipline. Participants who were required to be re-treated in Spiral 2 received the same treatment again. Results of tests from the 5 modules after the 2nd treatment were that the participants attained higher scores than those attained in the pre-test. Their assessment and development stages also showed improved results. They showed greater confidence at participating in activities, produced higher quality work, and correctly followed instructions for each activity. Analytic induction of strong and weak points for this operation remains the same as for Spiral 1, though there were improvements to problems which existed prior to undertaking the second treatment.

Keywords: action research, English pronunciation, phonetics, segmental features, suprasegmental features

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Paulo O. Motita

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The Philippine Association of State Colleges and University (PASUC) is an association of State owned and operated higher learning institutions in the Philippines, it is the association that spearhead the conduct of the Annual National Athletic competitions for State Colleges and Universities and Arnis is one of the regular sports. In 2009, Republic Act 9850 also known as declared Arnis as the National Sports and Martial arts of the Philippines. Arnis an ancient Filipino Martial Arts is the major sports in the Annual Palarong Pambansa and other school based sports events. The researcher as a Filipino Martial Arts master and a former athlete desired to determine the extent of acceptability of the arnis rules in competition which serves as the basis for the development of arnis rules. The study aimed to assess the conduct of Arnis competition in PASUC Olympics 2015 in Tugegarao City, Cagayan, Philippines.the rules and conduct itself as perceived by Officiating officials, Coaches and Athletes during the competition last February 7-15, 2015. The descriptive method of research was used, the survey questionnaire as the data gathering instrument was validated. The respondents were composed of 12 Officiating officials, 19 coaches and 138 athletes representing the different regions. Their responses were treated using the Mean, Percentage and One-way Analysis of Variance. The study revealed that the conduct of Arnis competition in PASUC Olympics 2015 was at the low extent to moderate extent as perceived by the three groups of respondents in terms of officiating, scoring and giving violations. Furthermore there is no significant difference in the assessment of the three groups of respondents in the assessment of Anyo and Labanan. Considering the findings of the study, the following conclusions were drawn: 1). There is a need to identify the criteria for judging in Anyo and a tedious scrutiny on the rules of the game for labanan. 2) The three groups of respondents have similar views towards the assessment on the overall competitions for anyo that there were no clear technical guidelines for judging the performance of anyo event. 3). The three groups of respondents have similar views towards the assessment on the overall competitions for labanan that there were no clear technical guidelines for majority rule of giving scores in labanan. 4) The Anyo performance should be rated according to effectiveness of techniques and performance of weapon/s that are being used. 5) On other issues and concern towards the rules of competitions, labanan should be addressed in improving rules of competitions, focus on the applications of majority rules for scoring, players shall be given rest interval, a clear guidelines and set a standard qualifications for officiating officials.

Keywords: PASUC Olympics 2015, Arnis rules of competition, Anyo, Labanan, officiating

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Authors: Michael N. O'Sullivan, Con Sheahan

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Researchers have spent decades developing tools and techniques to aid teams in the new product development (NPD) process. Despite this, it is evident that there is a huge gap between their academic prevalence and their industry adoption. For the fuzzy front-end, in particular, there is a wide range of tools to choose from, including the Kano Model, the House of Quality, and many others. In fact, there are so many tools that it can often be difficult for teams to know which ones to use and how they interact with one another. Moreover, while the benefits of using these tools are obvious to industrialists, they are rarely used as they carry a learning curve that is too steep and they become too complex to manage over time. In essence, it is commonly believed that they are simply not worth the effort required to learn and use them. This research explores a streamlined process for the fuzzy front-end, assembling the most effective tools and making them accessible to everyone. The process was developed iteratively over the course of 3 years, following over 80 final year NPD teams from engineering, design, technology, and construction as they carried a product from concept through to production specification. Questionnaires, focus groups, and observations were used to understand the usability issues with the tools involved, and a human-centred design approach was adopted to produce a solution to these issues. The solution takes the form of physical toolkit, similar to a board game, which allows the team to play through an example of a new product development in order to understand the process and the tools, before using it for their own product development efforts. A complimentary website is used to enhance the physical toolkit, and it provides more examples of the tools being used, as well as deeper discussions on each of the topics, allowing teams to adapt the process to their skills, preferences and product type. Teams found the solution very useful and intuitive and experienced significantly less confusion and mistakes with the process than teams who did not use it. Those with a design background found it especially useful for the engineering principles like Quality Function Deployment, while those with an engineering or technology background found it especially useful for design and customer requirements acquisition principles, like Voice of the Customer. Products developed using the toolkit are added to the website as more examples of how it can be used, creating a loop which helps future teams understand how the toolkit can be adapted to their project, whether it be a small consumer product or a large B2B service. The toolkit unlocks the potential of these beneficial tools to those in industry, both for large, experienced teams and for inexperienced start-ups. It allows users to assess the market potential of their product concept faster and more effectively, arriving at the product design stage with technical requirements prioritized according to their customers’ needs and wants.

Keywords: new product development, fuzzy front-end, usability, Kano model, quality function deployment, voice of customer

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Authors: Babulayi B. Wilson

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Unsatisfactory performance of construction projects in both the industrialised and developing countries indicate that there could be several defects in construction projects phases. Notwithstanding the fact that some project defects are often conceived at the initiation phase of construction projects, insufficient knowledge of contract procedures has been identified as one of the major sources of construction disputes. Contract procedures are a set of rules that outlines the primary obligations and liabilities of parties involved in the implementation of a construction project. Engineering professional bodies often codify contract procedures into standard forms of contract such as the Institution of Civil Engineers (ICE, UK) and Association of Consulting Engineers (ACE, UK) and keep them under constant review by updating any clause to reflect any change in case law or relevant piece of legislation. Even so, it is the responsibility of a professional body or conditions of contract draftsperson to introduce contract-specific clauses that may be necessary for business efficacy but not covered in the chosen standard conditions of contract. In Botswana, the use of clients’ drafted and/or un-adapted for environment of use international forms of contract in conjunction with client-drafted pricing schedules is common. The product of the latter often impact negatively upon contractors’ claims and payments, in that, tender rates and prices can only be deemed to be sufficient if the chosen conditions of contract compliment the pricing schedule (use of standardised procurement documents). In addition, client drafted and the use of borrowed forms of contract such as FIDIC often conflict with domicile law resulting in costly disputes on the part of the client. It is upon the preceding text that the object of the research is to measure the level of knowledge of contract procedures amongst key stakeholders in the Botswana construction industry by requesting a representative sample from the industry and academia to respond to tutorial questions prepared from two commonly used forms of contract for civil works, that is, FIDIC (International Form of Contract) and ICE (UK). The questions were prepared under the following captions: (a) preparation of tender documents (b) obligations of the parties (c) contract administration; and (d) claims, variations, and valuation of variations. After ascertaining that the level of knowledge of contract procedures is insufficient among most practitioners in the Botswana construction industry, major procurement entities, and engineering institutions of learning; a guide to drafting a condition of a construction contract was developed and then validated through seminars and workshops. In the present, the effectiveness of the guide is not yet measured but feedback from seminars and workshops conducted indicates an appreciation of the guide by the majority of major construction industry stakeholders.

Keywords: contract procedures, conditions of contract, professional practice, construction law, forms of contract

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Christine Lassen, Elizabeth Leonard, Matthew Oliver

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The management of trauma is often complex and outcomes dependent on clinical expertise, effective teamwork, and a supported trauma system. The implementation of a statewide trauma education program should be accessible to all clinicians who manage trauma, but this can be challenging due to diverse individual needs, trauma service needs and geography. The NSW Institute of Trauma and Injury Management (ITIM) is a government funded body, responsible for coordinating and supporting the NSW Trauma System. The aim of this presentation is to describe how education initiatives have been implemented across the state. Simulation: In 2006, ITIM developed a Trauma Team Training Course - aimed to educate clinicians on the technical and non-technical skills required to manage trauma. The course is now independently coordinated by trauma services across the state at major trauma centres as well as in regional and rural hospitals. ITIM is currently in the process of re-evaluating and updating the Trauma Team Training Course to allow for the development of new resources and simulation scenarios. Trauma Education Evenings: In 2013, ITIM supported major trauma services to develop trauma education evenings which allowed the provision of free education to staff within the area health service and local area. The success of these local events expanded to regional hospitals. A total of 75 trauma education evenings have been conducted within NSW, with over 10,000 attendees. Wed-Based Resources: Recently, ITIM commenced free live streaming of the trauma education evenings which have now had over 3000 live views. The Trauma App developed in 2015 provides trauma clinicians with a centralised portal for trauma information and works on smartphones and tablets that integrate with the ITIM website. This supports pre-hospital and bedside clinical decisions and allows for trauma care to be more standardised, evidence-based, timely, and appropriate. Online e-Learning modules have been developed to assist clinicians, reduce unwarranted clinical variation and provide up to date evidence based education. The modules incorporate clinically focused education content with summative and formative assessments. Conclusion: Since 2005, ITIM has helped to facilitate the development of trauma education programs for doctors, nurses, pre-hospital and allied health clinicians. ITIM has been actively involved in more than 100 specialized trauma education programs, seminars and clinical workshops - attended by over 12,000 staff. The provision of state-wide trauma education is a challenging task requiring collaboration amongst numerous agencies working towards a common goal – to provide easily accessible trauma education.

Keywords: education, simulation, team-training, trauma

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Authors: Fredrick Forster, Gareth Ward, Matthew Tubby, Pamela Lithgow, Anne Nortcliffe

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This paper presents the results from a research study of random adult participants accessing one of four different commercially available Virtual Reality (VR) Head Mounted Displays (HMDs) and completing a post user experience reflection questionnaire. The research sort to understand how inclusive commercially available VR HMDs are and identify any associated barriers that could impact the widespread adoption of the devices, specifically in Higher Education (HE). In the UK, education providers are legally required under the Equality Act 2010 to ensure all education facilities are inclusive and reasonable adjustments can be applied appropriately. The research specifically aimed to identify the considerations that academics and learning technologists need to make when adopting the use of commercial VR HMDs in HE classrooms, namely cybersickness, user comfort, Interpupillary Distance, inclusiveness, and user perceptions of VR. The research approach was designed to build upon previously published research on user reflections on presence, usability, and overall HMD comfort, using quantitative and qualitative research methods by way of a questionnaire. The quantitative data included the recording of physical characteristics such as the distance between eye pupils, known as Interpupillary Distance (IPD). VR HMDs require each user’s IPD measurement to enable the focusing of the VR HMDs virtual camera output to the right position in front of the eyes of the user. In addition, the questionnaire captured users’ qualitative reflections and evaluations of the broader accessibility characteristics of the VR HMDs. The initial research activity was accomplished by enabling a random sample of visitors, staff, and students at Canterbury Christ Church University, Kent to use a VR HMD for a set period of time and asking them to complete the post user experience questionnaire. The study identified that there is little correlation between users who experience cyber sickness and car sickness. Also, users with a smaller IPD than average (typically associated with females) were able to use the VR HMDs successfully; however, users with a larger than average IPD reported an impeded experience. This indicates that there is reduced inclusiveness for the tested VR HMDs for users with a higher-than-average IPD which is typically associated with males of certain ethnicities. As action education research, these initial findings will be used to refine the research method and conduct further investigations with the aim to provide verification and validation of the accessibility of current commercial VR HMDs. The conference presentation will report on the research results of the initial study and subsequent follow up studies with a larger variety of adult volunteers.

Keywords: virtual reality, education technology, inclusive technology, higher education

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Authors: Kevin De Castro Cogle, Mirian Kubo, Maria Anastasiadi, Fady Mohareb, Claire Rossi

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Background: The bioaccessibility of bioactive compounds is a critical determinant of the nutritional quality of various food products. Despite its importance, there is a limited number of comprehensive studies aimed at assessing how the composition of a food matrix influences the bioaccessibility of a compound of interest. This knowledge gap has prompted a growing need to investigate the intricate relationship between food matrix formulations and the bioaccessibility of bioactive compounds. One such class of bioactive compounds that has attracted considerable attention is curcuminoids. These naturally occurring phytochemicals, extracted from the roots of Curcuma longa, have gained popularity owing to their purported health benefits and also well known for their poor bioaccessibility Project aim: The primary objective of this research project is to systematically assess the influence of matrix composition on the bioaccessibility of curcuminoids. Additionally, this study aimed to develop a series of predictive models for bioaccessibility, providing valuable insights for optimising the formula for functional foods and provide more descriptive nutritional information to potential consumers. Methods: Food formulations enriched with curcuminoids were subjected to in vitro digestion simulation, and their bioaccessibility was characterized with chromatographic and spectrophotometric techniques. The resulting data served as the foundation for the development of predictive models capable of estimating bioaccessibility based on specific physicochemical properties of the food matrices. Results: One striking finding of this study was the strong correlation observed between the concentration of macronutrients within the food formulations and the bioaccessibility of curcuminoids. In fact, macronutrient content emerged as a very informative explanatory variable of bioaccessibility and was used, alongside other variables, as predictors in a Bayesian hierarchical model that predicted curcuminoid bioaccessibility accurately (optimisation performance of 0.97 R2) for the majority of cross-validated test formulations (LOOCV of 0.92 R2). These preliminary results open the door to further exploration, enabling researchers to investigate a broader spectrum of food matrix types and additional properties that may influence bioaccessibility. Conclusions: This research sheds light on the intricate interplay between food matrix composition and the bioaccessibility of curcuminoids. This study lays a foundation for future investigations, offering a promising avenue for advancing our understanding of bioactive compound bioaccessibility and its implications for the food industry and informed consumer choices.

Keywords: bioactive bioaccessibility, food formulation, food matrix, machine learning, probabilistic modelling

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Authors: Piret Pernik

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Based on strategic, operational, and technical analysis of the ongoing armed conflict in Ukraine, this paper will examine the opportunities and risks of using small commercial drones (dual-use unmanned aerial vehicles, UAV) for military purposes. The paper discusses the opportunities and risks in the information domain, encompassing both cyber and electromagnetic interference and attacks. The paper will draw conclusions on a possible strategic impact to the battlefield outcomes in the modern armed conflicts by the widespread use of dual-use UAVs. This article will contribute to filling the gap in the literature by examining based on empirical data cyberattacks and electromagnetic interference. Today, more than one hundred states and non-state actors possess UAVs ranging from low cost commodity models, widely are dual-use, available and affordable to anyone, to high-cost combat UAVs (UCAV) with lethal kinetic strike capabilities, which can be enhanced with Artificial Intelligence (AI) and Machine Learning (ML). Dual-use UAVs have been used by various actors for intelligence, reconnaissance, surveillance, situational awareness, geolocation, and kinetic targeting. Thus they function as force multipliers enabling kinetic and electronic warfare attacks and provide comparative and asymmetric operational and tactical advances. Some go as far as argue that automated (or semi-automated) systems can change the character of warfare, while others observe that the use of small drones has not changed the balance of power or battlefield outcomes. UAVs give considerable opportunities for commanders, for example, because they can be operated without GPS navigation, makes them less vulnerable and dependent on satellite communications. They can and have been used to conduct cyberattacks, electromagnetic interference, and kinetic attacks. However, they are highly vulnerable to those attacks themselves. So far, strategic studies, literature, and expert commentary have overlooked cybersecurity and electronic interference dimension of the use of dual use UAVs. The studies that link technical analysis of opportunities and risks with strategic battlefield outcomes is missing. It is expected that dual use commercial UAV proliferation in armed and hybrid conflicts will continue and accelerate in the future. Therefore, it is important to understand specific opportunities and risks related to the crowdsourced use of dual-use UAVs, which can have kinetic effects. Technical countermeasures to protect UAVs differ depending on a type of UAV (small, midsize, large, stealth combat), and this paper will offer a unique analysis of small UAVs both from the view of opportunities and risks for commanders and other actors in armed conflict.

Keywords: dual-use technology, cyber attacks, electromagnetic warfare, case studies of cyberattacks in armed conflicts

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Authors: Olusegun F. Adebowale

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Research has confirmed that the emergence of information technology is significantly reflected in the field of psychology and its related disciplines due to its widespread use at reasonable price and its user-friendliness. It is consequently affecting ordinary life in many areas like shopping, advertising, corresponding and educating. Specifically the innovations of computer technology led to several new forms of communication, all with implications and applicability for counselling and psychotherapy practices. This is premise on which online counselling is based. Most institutions of higher learning in Nigeria have established their presence on the Internet and have deployed a variety of applications through ICT. Some are currently attempting to include counselling services in such applications with the belief that many counselling needs of students are likely to be met. This study therefore explored different challenges and preferences students present in online counselling interaction in a given Nigerian university with the view to guide new universities that may want to invest into these areas as to necessary preparations and referral requirements. The study is a mixed method research incorporating qualitative and quantitative methodologies to sample the preferences and concerns students express in online interaction. The sample comprised all the 876 students who visited the university online counselling platform either voluntarily, by invitation or by referral. The instrument for data collection was the online counselling platform of the university 'OAU Online counsellors'. The period of data collection spanned between January 2011 and October 2012. Data were analysed quantitatively (using percentages and Mann-Whitney U test) and qualitatively (using Interpretative Phenomenological Analysis (IPA)). The results showed that the students seem to prefer real-time chatting as their online medium of communicating with the online counsellor. The majority of students resorted to e-mail when their effort to use real-time chatting were becoming thwarted. Also, students preferred to enter into online counselling relationships voluntarily to other modes of entry. The results further showed that the prevalent counselling needs presented by students during online counselling sessions were mainly in the areas of social interaction and academic/educational concerns. Academic concerns were found to be prevalent, in form of course offerings, studentship matters and academic finance matters. The personal/social concerns were in form of students’ welfare, career related concerns and relationship matters. The study concludes students’ preferences include voluntary entry into online counselling, communication by real-time chatting and a specific focus on their academic concerns. It also recommends that all efforts should be made to encourage students’ voluntary entry into online counselling through reliable and stable internet infrastructure that will be able to support real-time chatting.

Keywords: online, counselling, needs, preferences

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Authors: Mathieu Audet, Monica Soliman, Emilie Eve Gravel, Rebecca Friesdorf

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Access to higher education is critical for reducing social inequalities. The Canada Learning Bond (CLB) is a government savings incentive aimed at increasing higher education access for children of low income families by providing money toward a Registered Education Savings Plan. To better understand the educational and financial decision-making of low income families, Employment Social Development Canada conducted qualitative fieldwork with eligible parents and children, teachers, and community organizations promoting the Bond. Insights from this fieldwork were then used to develop letters to better target the needs and experiences of eligible families. In the present study, we conducted a randomized controlled trial with children ages 12 to 13, the oldest cohort of eligible children, to test the effectiveness of the new letters. Parents or caregivers of 150,088 eligible children were assigned to one of five letter conditions promoting the Bond or to a control condition that did not receive a letter. The letter conditions were: (a) the standard letter from past outreach, (b) a letter presenting the exact amount the child was eligible to receive, enhancing the salience of benefits, (c) a letter with a social norm, (d) a letter with an image emphasizing the feasibility of higher education by presenting the diversity of options (i.e., college, trade schools, apprenticeships) – many participants interviewed viewed that university was unfeasible, and (e) a letter minimizing references to 'saving' (i.e., not framing the Bond explicitly as a savings incentive) – a concept that did not resonate with low income families who felt they could not afford to save. The exact amount was also presented in letters (c) through (e). The letter minimizing references to 'saving' and presenting the exact amount had the highest net take-up rate at 6.6%, compared to 3.5% for the standard letter group. Furthermore, this trial’s BI-informed letters showed the largest impact on take-up so far, with a net take-up of 5.7% compared to 3.0% and 3.9% in the first two trials. This research highlights the value of mixed-method approaches combining qualitative and behavioural insights methods for developing context-sensitive interventions for social programs. By gaining a deeper understanding of the needs and experiences of program users through qualitative fieldwork, and then integrating these insights into behaviourally informed communications, we were able to increase take-up of an education savings program, which may ultimately improve access to higher education in children of low income families.

Keywords: access to higher education, behavioral insights, government, innovation, mixed-methods, social programs

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Authors: Vincent Andrew Cappellano

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In the early phases of critical infrastructure system design, translating distributed computing requirements to an architecture has risk given the multitude of approaches (e.g., cloud, edge, fog). In many systems, a single requirement for system uptime / availability is used to encompass the system’s intended operations. However, when architected systems may perform to those availability requirements only during normal operations and not during component failure, or during outages caused by adversary attacks on critical infrastructure (e.g., physical, cyber). System designers lack a structured method to evaluate availability requirements against candidate system architectures through deep degradation scenarios (i.e., normal ops all the way down to significant damage of communications or physical nodes). This increases risk of poor selection of a candidate architecture due to the absence of insight into true performance for systems that must operate as a piece of critical infrastructure. This research effort proposes a process to analyze critical infrastructure system availability requirements and a candidate set of systems architectures, producing a metric assessing these architectures over a spectrum of degradations to aid in selecting appropriate resilient architectures. To accomplish this effort, a set of simulation and evaluation efforts are undertaken that will process, in an automated way, a set of sample requirements into a set of potential architectures where system functions and capabilities are distributed across nodes. Nodes and links will have specific characteristics and based on sampled requirements, contribute to the overall system functionality, such that as they are impacted/degraded, the impacted functional availability of a system can be determined. A machine learning reinforcement-based agent will structurally impact the nodes, links, and characteristics (e.g., bandwidth, latency) of a given architecture to provide an assessment of system functional uptime/availability under these scenarios. By varying the intensity of the attack and related aspects, we can create a structured method of evaluating the performance of candidate architectures against each other to create a metric rating its resilience to these attack types/strategies. Through multiple simulation iterations, sufficient data will exist to compare this availability metric, and an architectural recommendation against the baseline requirements, in comparison to existing multi-factor computing architectural selection processes. It is intended that this additional data will create an improvement in the matching of resilient critical infrastructure system requirements to the correct architectures and implementations that will support improved operation during times of system degradation due to failures and infrastructure attacks.

Keywords: architecture, resiliency, availability, cyber-attack

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Asikhia Olubusayo Aduke

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Students’ inability to set data-based (specific, measurable, attainable, reliable, and time-bound) personal improvement goals threatens their academic success. Hence, the study aimed to investigate year-one students’ academic goal-setting practices at Lagos State University of Education, Nigeria. Descriptive survey research was used in carrying out this study. The study population consisted of 3,101 year-one students of the University. A sample size of five hundred (501) participants was selected through a proportional and simple random sampling technique. The Formative Goal Setting Questionnaire (FGSQ) developed by Research Collaboration (2015) was adapted and used as an instrument for the study. Two main research questions were answered, while two null hypotheses were formulated and tested for the study. The study revealed higher data-based goals for all students than personal improvement goals. Nevertheless, data-based and personal improvement goal-setting for female students was higher than for male students. One sample test statistic and Anova used to analyse data for the two hypotheses also revealed that the mean difference between male and female year one students’ data-based and personal improvement goal-setting formation was statistically significant (p < 0.05). This means year one students’ data-based and personal improvement goals showed significant gender differences. Based on the findings of this study, it was recommended, among others, that therapeutic techniques that can help to change students’ faulty thinking and challenge their lack of desire for personal improvement should be sought to treat students who have problems with setting high personal improvement goals. Counsellors also need to advocate continued research into how to increase the goal-setting ability of male students and should focus more on counselling male students’ goal-setting ability. The main contributions of the study are higher institutions must prioritize early intervention in first-year students' academic goal setting. Researching gender differences in this practice reveals a crucial insight: male students often lag behind in setting meaningful goals, impacting their motivation and performance. Focusing on this demographic with data-driven personal improvement goals can be transformative. By promoting goal setting that is specific, measurable, and focused on self-growth (rather than competition), male students can unlock their full potential. Researchers and counselors play a vital role in detecting and supporting students with lower goal-setting tendencies. By prioritizing this intervention, we can empower all students to set ambitious, personalized goals that ignite their passion for learning and pave the way for academic success.

Keywords: academic goal setting, counselling, practice, university, year one students

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Authors: P. Kavuma, J. Mukasa, M. Lusunku

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Background and Problem: The widespread poverty in Africa- together with the negative impacts of climate change-are two great global challenges that call for everyone’s involvement including Architects. This in particular places serious challenges on architects to have additional skills in both Entrepreneurship and Environmental Design (SEED). Regrettably, while Architectural Training in most African Universities including those from Uganda lack comprehensive implementation of SEED in their curricula, regulatory bodies have not contributed towards the effective integration of SEED in their professional practice. In response to these challenges, Nkumba University (NU) under Architect Kavuma Paul supported by the Uganda Chambers of Architects– initiated the SEED integration in the undergraduate Architectural curricula to cultivate SEED know-how and examples of best practices. Main activities: Initiated in 2007, going beyond the traditional Architectural degree curriculum, the NU Architect department offers SEED courses including provoking passions for creating desirable positive changes in communities. Learning outcomes are assessed theoretically and practically through field projects. The first set of SEED graduates came out in 2012. As part of the NU post-graduation and alumni survey, in October 2014, the pioneer SEED graduates were contacted through automated reminder emails followed by individual, repeated personal follow-ups via email and phone. Out of the 36 graduates who responded to the survey, 24 have formed four (4) private consortium agencies of 5-7 graduates all of whom have pioneered Ugandan-own-cultivated Architectural social projects that include: fishing farming in shipping containers; solar powered mobile homes in shipping containers, solar powered retail kiosks in rural and fishing communities, and floating homes in the flood-prone areas. Primary outcomes: include being business self –reliant in creating the social change the architects desired in the communities. Examples of the SEED project returns to communities reported by the graduates include; employment creation via fabrication, retail business, marketing, improved diets, safety of life and property, decent shelter in the remote mining and oil exploration areas. Negative outcomes-though not yet evaluated include the disposal of used-up materials. Conclusion: The integration of SEED in Architectural Training has established a baseline benchmark and a replicable model based on best practice projects.

Keywords: architectural training, entrepreneurship, environment, integration

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Authors: JaeYoon Park

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The ability to communicate effectively across different cultures is a necessary skill in today’s increasingly globalized world. Intercultural communication competency (ICC) is a way of being that benefits all members of a society in their living, learning, and working environments as well as in the context of mediated communications. This study examines the effects of self-reflection processes on the improvement of intercultural communication skills focusing on college students at the University of Arkansas-Fort Smith. A total of sixty-nine students’ works were analyzed based on the data collected in the past three years (2016, 2017 and 2018). The students in the ‘Culture and Communication’ class, each spring, completed the Diversity Awareness Profile (DAP) survey as a pre- and post-test for the course. DAP is a self-assessment tool designed by Karen Stinson and widely used in college classes, companies, and organizations to evaluate an individual’s behaviors in various intercultural settings. It can assist individuals in becoming more aware of diversity issues and also provide a foundation for developing strategies for modifying any undesirable behavior they may discover in the assessment. In addition to the DAP surveys, the students also submitted self-reflection essays that discussed their own scores. The University of Arkansas-Fort Smith is a small regional university located in the Bible Belt of the United States. White, Christian, working-class students dominate its student population. The students, whose data were collected, were predominantly seniors in college majoring in either Media Communication or International Business. Approximately, 80% of the students increased their scores, and 42% of them moved forward to a new category. The findings also indicate that the students in the underrepresented groups (i.e., women, minority, and international students) show less change in their scores and behaviors than the rest of the students (i.e., white heterosexual male students). These findings, in most part, result from the fact that the underrepresented students were already aware of diversity and intercultural issues through their personal experiences before taking the class. The white heterosexual male students demonstrated the greatest improvements, judging from their DAP scores (pre- and post-tests) and self-reflection essays. Through the class assignments and discussions, which emphasized critical thinking and self-reflection, the latter group of students not only became more aware of the meaning of their own words and behaviors, but they were also able to develop greater proficiency in intercultural communication. This e-poster presentation will analyze the findings of this research data, and also discuss the pedagogical implications of such results.

Keywords: cross-cultural communication, diversity awareness survey, self-reflection, underrepresented students

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Authors: Beatriz Garcia-Juan, Ana B. Escrig-Tena, Vicente Roca-Puig

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The aim of this article is to conduct a theoretical and empirical study in order to examine how the goal orientation (GO) of public employees affects the relationship between the structural and psychological empowerment that they experience at their workplaces. In doing so, we follow structural empowerment (SE) and psychological empowerment (PE) conceptualizations, and relate them to the public administration framework. Moreover, we review arguments from GO theories, and previous related contributions. Empowerment has emerged as an important issue in the public sector organization setting in the wake of mainstream New Public Management (NPM), the new orientation in the public sector that aims to provide a better service for citizens. It is closely linked to the drive to improve organizational effectiveness through the wise use of human resources. Nevertheless, it is necessary to combine structural (managerial) and psychological (individual) approaches in an integrative study of empowerment. SE refers to a set of initiatives that aim the transference of power from managerial positions to the rest of employees. PE is defined as psychological state of competence, self-determination, impact, and meaning that an employee feels at work. Linking these two perspectives will lead to arrive at a broader understanding of the empowerment process. Specifically in the public sector, empirical contributions on this relationship are therefore important, particularly as empowerment is a very useful tool with which to face the challenges of the new public context. There is also a need to examine the moderating variables involved in this relationship, as well as to extend research on work motivation in public management. It is proposed the study of the effect of individual orientations, such as GO. GO concept refers to the individual disposition toward developing or confirming one’s capacity in achievement situations. Employees’ GO may be a key factor at work and in workforce selection processes, since it explains the differences in personal work interests, and in receptiveness to and interpretations of professional development activities. SE practices could affect PE feelings in different ways, depending on employees’ GO, since they perceive and respond differently to such practices, which is likely to yield distinct PE results. The model is tested on a sample of 521 Spanish local authority employees. Hierarchical regression analysis was conducted to test the research hypotheses using SPSS 22 computer software. The results do not confirm the direct link between SE and PE, but show that learning goal orientation has considerable moderating power in this relationship, and its interaction with SE affects employees’ PE levels. Therefore, the combination of SE practices and employees’ high levels of LGO are important factors for creating psychologically empowered staff in public organizations.

Keywords: goal orientation, moderating effect, psychological empowerment, structural empowerment

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Authors: Mariana L. de Araújo, Pedro P. M. Menezes

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Research on strategic human resource management (SHRM) has made progress in the last few decades, showing a relationship between policies and practices of human resource management (HRM) and improving organizational results. That's because demonstrating the effectiveness of any HRM or other organizational practice, which means the extent that this can operate as a tool to achieve organizational performance, is a complex and arduous task to execute. Even today, there isn't consensus about "effectiveness," and the tools to measure the effectiveness are disconnected and not convincing. It is not different from the performance management system (PMS) effectiveness. A disproportionate focus on specific criteria adopted and an accumulation of studies that don't relate to the others, which damages the development of the field. Therefore, it aimed to evaluate the effectiveness of the PMS through models, dimensions, criteria, and measures. The objective of this study is to propose a theoretical-integrative model for evaluating PMS based on the literature in the PMS field. So, the PRISMA protocol was applied to carry out a systematic review, resulting in 57 studies. After performing the content analysis, we identified six dimensions: learning, societal impact, reaction, financial results, operational results and transfer, and 22 categories. In this way, a theoretical-integrative model for assessing the effectiveness of PMS was proposed based on the findings of this study, in which it was possible to confirm that the effectiveness construct is somewhat complex when viewing that most of the reviewed studies considered multiple dimensions in their assessment. In addition, we identified that the most immediate and proximal results of PMS are the most adopted by the studies; conversely, the studies adopted less distal outcomes to assess the effectiveness of PMS. Another finding of this research is that the reviewed studies predominantly analyze from the individual or psychological perspective, even when it comes to criteria whose phenomena are at an organizational level. Therefore, this study converges with a trend recently identified when referring to a process of "psychologization" in which GP studies, in general, have demonstrated macro results of the GP system from an individual perspective. Therefore, given the identification of a methodological pattern, the predominant influence of individual and psychological aspects in studies on HRM in administration is highlighted, demonstrated by the reflection on the practically absolute way of measuring the effectiveness of PMS from perceptual and subjective measures. Therefore, based on the recognition of the patterns identified, the model proposed to promote studies on the subject more broadly and profoundly to broaden and deepen the perspective of the field of management's interests so that the evaluation of the effectiveness of PMS can promote inputs on the impact of the PMS system in organizational performance. Finally, the findings encourage reflections on assessing the effectiveness of PMS through the theoretical-integrative model developed so that the field can promote new theoretical and practical perspectives.

Keywords: performance management, strategic human resource management, effectiveness, organizational performance

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Authors: África Vicario, Fernando J. Álvarez, Felipe Parralejo, Fernando Aranda

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The irrigation systems of traditional agriculture, such as gravity-fed irrigation, produce a great waste of water because, generally, there is no control over the amount of water supplied in relation to the water needed. The AIRON Project tries to solve this problem by implementing an IoT-based system to sensor the irrigation plots so that the state of the crops and the amount of water used for irrigation can be known remotely. The IoT system consists of a sensor network that measures the humidity of the soil, the weather conditions (temperature, relative humidity, wind and solar radiation) and the irrigation water flow. The communication between this network and a central gateway is conducted by means of long-range wireless communication that depends on the characteristics of the irrigation plot. The main objective of the AIRON project is to deploy an IoT sensor network in two different plots of the irrigation community of Aranjuez in the Spanish region of Madrid. The first plot is 2 km away from the central gateway, so LoRa has been used as the base communication technology. The problem with this plot is the absence of mains electric power, so devices with energy-saving modes have had to be used to maximize the external batteries' use time. An ESP32 SOC board with a LoRa module is employed in this case to gather data from the sensor network and send them to a gateway consisting of a Raspberry Pi with a LoRa hat. The second plot is located 18 km away from the gateway, a range that hampers the use of LoRa technology. In order to establish reliable communication in this case, the long-term evolution (LTE) standard is used, which makes it possible to reach much greater distances by using the cellular network. As mains electric power is available in this plot, a Raspberry Pi has been used instead of the ESP32 board to collect sensor data. All data received from the two plots are stored on a proprietary server located at the irrigation management company's headquarters. The analysis of these data by means of machine learning algorithms that are currently under development should allow a short-term prediction of the irrigation water demand that would significantly reduce the waste of this increasingly valuable natural resource. The major finding of this work is the real possibility of deploying a remote sensing system for irrigated plots by using Commercial-Off-The-Shelf (COTS) devices, easily scalable and adaptable to design requirements such as the distance to the control center or the availability of mains electrical power at the site.

Keywords: internet of things, irrigation water control, LoRa, LTE, smart farming

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Authors: Amir Nazemi, Milad Ramezankhani, Marian Kӧrber, Abbas S. Milani

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The excellent strength to weight ratio of woven fabric composites, along with their high formability, is one of the primary design parameters defining their increased use in modern manufacturing processes, including those in aerospace and automotive. However, for emerging automated preform processes under the smart manufacturing paradigm, complex geometries of finished components continue to bring several challenges to the designers to cope with manufacturing defects on site. Wrinklinge. g. is a common defectoccurring during the forming process and handling of semi-finished textile composites. One of the main reasons for this defect is the weak bending stiffness of fibers in unconsolidated state, causing excessive relative motion between them. Further challenges are represented by the automated handling of large-area fiber blanks with specialized gripper systems. For fabric composites forming simulations, the finite element (FE)method is a longstanding tool usedfor prediction and mitigation of manufacturing defects. Such simulations are predominately meant, not only to predict the onset, growth, and shape of wrinkles but also to determine the best processing condition that can yield optimized positioning of the fibers upon forming (or robot handling in the automated processes case). However, the need for use of small-time steps via explicit FE codes, facing numerical instabilities, as well as large computational time, are among notable drawbacks of the current FEtools, hindering their extensive use as fast and yet efficient digital twins in industry. This paper presents a novel woven fabric simulation technique through the application of the material point method (MPM), which enables the use of much larger time steps, facing less numerical instabilities, hence the ability to run significantly faster and efficient simulationsfor fabric materials handling and forming processes. Therefore, this method has the ability to enhance the development of automated fiber handling and preform processes by calculating the physical interactions with the MPM fiber models and rigid tool components. This enables the designers to virtually develop, test, and optimize their processes based on either algorithmicor Machine Learning applications. As a preliminary case study, forming of a hemispherical plain weave is shown, and the results are compared to theFE simulations, as well as experiments.

Keywords: material point method, woven fabric composites, forming, material handling

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Authors: Fatima Saeed, Abdullah Mudassar

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Introduction: Patient safety remains a global priority in the ever-evolving healthcare landscape. At the forefront of this endeavor are the International Patient Safety Goals (IPSGs), a standardized framework designed to mitigate risks and elevate the quality of care. Doctors, positioned as primary caregivers, wield a pivotal role in upholding and adhering to IPSGs, underscoring the critical significance of their knowledge and understanding of these goals. This research embarks on a comprehensive exploration into the depth of Doctors ' comprehension of IPSGs, aiming to unearth potential gaps and provide insights for targeted educational interventions. Established by influential healthcare bodies, including the World Health Organization (WHO), IPSGs represent a universally applicable set of objectives spanning crucial domains such as medication safety, infection control, surgical site safety, and patient identification. Adherence to these goals has exhibited substantial reductions in adverse events, fostering an overall enhancement in the quality of care. This study operates on the fundamental premise that an informed Doctors workforce is indispensable for effectively implementing IPSGs. A nuanced understanding of these goals empowers Doctors to identify potential risks, advocate for necessary changes, and actively contribute to a safety-centric culture within healthcare institutions. Despite the acknowledged importance of IPSGs, there is a growing concern that nurses may need more knowledge to integrate these goals into their practice seamlessly. Methodology: A Comprehensive research methodology covering study design, setting, duration, sample size determination, sampling technique, and data analysis. It introduces the philosophical framework guiding the research and details material, methods, and the analysis framework. The descriptive quantitative cross-sectional study in teaching care hospitals utilized convenient sampling over six months. Data collection involved written informed consent and questionnaires, analyzed with SPSS version 23, presenting results graphically and descriptively. The chapter ensures a clear understanding of the study's design, execution, and analytical processes. Result: The survey results reveal a substantial distribution across hospitals, with 34.52% in MTIKTH and 65.48% in HMC MTI. There is a notable prevalence of patient safety incidents, emphasizing the significance of adherence to IPSGs. Positive trends are observed, including 77.0% affirming the "time-out" procedure, 81.6% acknowledging effective healthcare provider communication, and high recognition (82.7%) of the purpose of IPSGs to improve patient safety. While the survey reflects a good understanding of IPSGs, areas for improvement are identified, suggesting opportunities for targeted interventions. Discussion: The study underscores the need for tailored care approaches and highlights the bio-socio-cultural context of 'contagion,' suggesting areas for further research amid antimicrobial resistance. Shifting the focus to patient safety practices, the survey chapter provides a detailed overview of results, emphasizing workplace distribution, patient safety incidents, and positive reflections on IPSGs. The findings indicate a positive trend in patient safety practices with areas for improvement, emphasizing the ongoing need for reinforcing safety protocols and cultivating a safety-centric culture in healthcare. Conclusion: In summary, the survey indicates a positive trend in patient safety practices with a good understanding of IPSGs among participants. However, identifying areas for potential improvement suggests opportunities for targeted interventions to enhance patient safety further. Ongoing efforts to reinforce adherence to safety protocols, address identified gaps, and foster a safety culture will contribute to continuous improvements in patient care and outcomes.

Keywords: infection control, international patient safety, patient safety practices, proper medication

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Authors: Mo'ath F. Bataineh

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Exercise is highly effective against obesity. Milk contains several components that support exercising and physical performance. However, there is a lack of published studies on the relationship between camel milk and ability to exercise. A pilot study was conducted with the purpose of comparing the impact of milk type (Cow vs Camel) compared with water on physical performance. Seven male obese participants (age: 20.3 ± 1.5 years; BMI: 35.7 ± 2.7 kg/m2; resting heart rate: 92.7 ± 4.7 beats per minute; training frequency: 4.4 ± 0.8 days/week) were recruited for this pilot study. In a randomized counterbalanced crossover design, participants took part in 3 trials that included ingesting 3 different pre workout drinks in a random order. The pre workout drinks were water (W), whole cow milk (CW), and whole camel milk (CM). On each trial day, participants were asked to report to the laboratory after an overnight fasting. Following a 15-minute short recovery period after their arrival to the laboratory, each participant was presented with a 500 ml of the assigned experimental drink and were asked to ingest it in one minute and at least 120 minutes prior to performing the 6-minute walk test. All drinks were presented at room temperature. Trials with different experimental drinks were performed on separate days. Participants were given at least 4 days of washout period between trials. The trial order was randomized to avoid bias due to learning effect. The 6-minute walk test was performed by all participants and immediately at the conclusion of the test, the covered distance in meters and the rating of perceived exertion (RPE) were recorded. All data were analysed using SPSS software (Version 29.0). The repeated measures ANOVA testing of collected data showed a significant main effect for treatment on covered distance in meters, F (2, 8) = 5.794, p=0.028 with a large effect size (partial eta squared (ηp2) =0.592). Also, LSD post hoc pairwise comparison analysis revealed that Camel milk and Cow milk were significantly (p = 0.044 and p = 0.020 respectively) superior to water in improving the covered distance during the test and that Camel milk tended to be better than Cow’s milk. The RPE values were not significantly different between experimental drinks (p>0.05). In conclusion, milk is superior to water as a pre workout drink, and camel milk is comparable to cow’s milk in enhancing ability to support a higher level of performance compared with water, therefore, camel milk could be used to replace cow’s milk as a suitable pre-exercise drink without expecting any negative consequences on physical performance. The fact that these positive results were obtained with obese individuals should encourage using camel milk without the fear of disturbing physical performance in other weight categories.

Keywords: camel milk, cow milk, obesity, physical performance, pre-workout drink

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Authors: Joan Casas-Roma

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Technology has become one of the main channels through which people engage in most of their everyday activities; from working to learning, or even when socializing, technology often acts as both an enabler and a mediator of such activities. Moreover, the affordances and interactions created by those technological tools determine the way in which the users interact with one another, as well as how they relate to the relevant environment, thus favoring certain kinds of actions and behaviors while discouraging others. In this regard, virtue ethics theories place a strong focus on a person's daily practice (understood as their decisions, actions, and behaviors) as the means to develop and enhance their habits and ethical competences --such as their awareness and sensitivity towards certain ethically-desirable principles. Under this understanding of ethics, this set of technologically-enabled affordances and interactions can be seen as the possibility space where the daily practice of their users takes place in a wide plethora of contexts and situations. At this point, the following question pops into mind: could these affordances and interactions be shaped in a way that would promote behaviors and habits basedonethically-desirable principles into their users? In the field of game design, the MDA framework (which stands for Mechanics, Dynamics, Aesthetics) explores how the interactions enabled within the possibility space of a game can lead to creating certain experiences and provoking specific reactions to the players. In this sense, these interactions can be shaped in ways thatcreate experiences to raise the players' awareness and sensitivity towards certain topics or principles. This research brings together the notions of technological affordances, the notions of practice and practical wisdom from virtue ethics, and the MDA framework from game design in order to explore how the possibility space created by technological interactions can be shaped in ways that enable and promote actions and behaviors supporting certain ethically-desirable principles. When shaped accordingly, interactions supporting certain ethically-desirable principlescould allow their users to carry out the kind of practice that, according to virtue ethics theories, provides the grounds to develop and enhance their awareness, sensitivity, and ethical reasoning capabilities. Moreover, and because ethical practice can happen collaterally in almost every context, decision, and action, this additional layer could potentially be applied in a wide variety of technological tools, contexts, and functionalities. This work explores the theoretical background, as well as the initial considerations and steps that would be needed in order to harness the potential ethically-desirable benefits that technology can bring, once it is understood as the space where most of their users' daily practice takes place.

Keywords: ethics, design methodology, human-computer interaction, philosophy of technology

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

Abstract:

Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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