Search results for: engagement prediction

Authors: Hamed Afrasiab, Saeed Khodaygan

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Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis

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Authors: Lynn Sheridan, Dennis Alonzo, Hoa Nguyen, Andy Gao, Tracy Durksen

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Disruptions to teaching (e.g., COVID-related) have increased work demands for teachers. There is an opportunity for research to explore evidence-informed steps to support teachers. Collective evidence informs data on teachers’ personal attributes (e.g., self-efficacy beliefs) in the workplace are seen to promote success in teaching and support teacher engagement. Teacher engagement plays a role in students’ learning and teachers’ effectiveness. Engaged teachers are better at overcoming work-related stress, burnout and are more likely to take on active roles. Teachers’ commitment is influenced by a host of personal (e.g., teacher well-being) and environmental factors (e.g., job stresses). The job demands-resources model provided a conceptual basis for examining how teachers’ well-being, and is influenced by job demands and job resources. Job demands potentially evoke strain and exceed the employee’s capability to adapt. Job resources entail what the job offers to individual teachers (e.g., organisational support), helping to reduce job demands. The application of the job demands-resources model involves gathering an evidence-base of and connection to personal attributes (job resources). The study explored the association between constructs (resilience, adaptability, commitment, self/collective efficacy) and a teacher’s engagement with the job. The paper sought to elaborate on the model and determine the associations between key constructs of well-being (resilience, adaptability), commitment, and motivation (self and collective-efficacy beliefs) to teachers’ engagement in teaching. Data collection involved online a multi-dimensional instrument using validated items distributed from 2020-2022. The instrument was designed to identify construct relationships. The participant number was 170. Data Analysis: The reliability coefficients, means, standard deviations, skewness, and kurtosis statistics for the six variables were completed. All scales have good reliability coefficients (.72-.96). A confirmatory factor analysis (CFA) and structural equation model (SEM) were performed to provide measurement support and to obtain latent correlations among factors. The final analysis was performed using structural equation modelling. Several fit indices were used to evaluate the model fit, including chi-square statistics and root mean square error of approximation. The CFA and SEM analysis was performed. The correlations of constructs indicated positive correlations exist, with the highest found between teacher engagement and resilience (r=.80) and the lowest between teacher adaptability and collective teacher efficacy (r=.22). Given the associations; we proceeded with CFA. The CFA yielded adequate fit: CFA fit: X (270, 1019) = 1836.79, p < .001, RMSEA = .04, and CFI = .94, TLI = .93 and SRMR = .04. All values were within the threshold values, indicating a good model fit. Results indicate that increasing teacher self-efficacy beliefs will increase a teacher’s level of engagement; that teacher ‘adaptability and resilience are positively associated with self-efficacy beliefs, as are collective teacher efficacy beliefs. Implications for school leaders and school systems: 1. investing in increasing teachers’ sense of efficacy beliefs to manage work demands; 2. leadership approaches can enhance teachers' adaptability and resilience; and 3. a culture of collective efficacy support. Preparing teachers for now and in the future offers an important reminder to policymakers and school leaders on the importance of supporting teachers’ personal attributes when faced with the challenging demands of the job.

Keywords: collective teacher efficacy, teacher self-efficacy, job demands, teacher engagement

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Authors: Chaluntorn Vichasilp, Sutee Wangtueai

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This study aims to develop a new method to detect active compounds in Thai herbs (1-deoxynojirimycin (DNJ) in mulberry leave, anthocyanin in Mao and curcumin in turmeric) using near infrared spectroscopy (NIRs). NIRs is non-destructive technique that rapid, non-chemical involved and low-cost determination. By NIRs and chemometrics technique, it was found that the DNJ prediction equation conducted with partial least square regression with cross-validation had low accuracy R2 (0.42) and SEP (31.87 mg/100g). On the other hand, the anthocyanin prediction equation showed moderate good results (R2 and SEP of 0.78 and 0.51 mg/g) with Multiplication scattering correction at wavelength of 2000-2200 nm. The high absorption could be observed at wavelength of 2047 nm and this model could be used as screening level. For curcumin prediction, the good result was obtained when applied original spectra with smoothing technique. The wavelength of 1400-2500 nm was created regression model with R2 (0.68) and SEP (0.17 mg/g). This model had high NIRs absorption at a wavelength of 1476, 1665, 1986 and 2395 nm, respectively. NIRs showed prospective technique for detection of some active compounds in Thai herbs.

Keywords: anthocyanin, curcumin, 1-deoxynojirimycin (DNJ), near infrared spectroscopy (NIRs)

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Authors: L. Tirabeni, K. E. Soderquist, P. Pisano

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Our purpose is to investigate how the relationship between employees and innovation management processes can drive organizations to successful innovations. This research is deeply related to a new way of thinking about human resources management practices. It’s not simply about improving the employees’ engagement, but rather about a different and more radical commitment: the employee can take on the role traditionally played by the customer, namely to become the first tester of an innovative product or service, the first user/customer and eventually the first investor in the innovation. This new perception of employees could create the basis of a novelty in the innovation process where innovation is taken to a next level when the problems with customer driven innovation on the one hand, and employees driven innovation on the other can be balanced. This research identifies an effective approach to innovation where the employees will participate throughout the whole innovation process, not only in the idea creation but also in the idea definition and development by giving feedback in parallel to that provided by customers and lead-users.

Keywords: employee-driven innovation, engagement, human resource management, innovative companies

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Authors: Holly A. Rick, Melissa McCartney

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The role of faculty has changed from the impact of Covid-19. Universities are changing faculty expectations. There is a changes in faculty workloads, and shift in how faculty work within a university. The research will identify areas where faculty are satisfied with their work, areas they would like their organizations to change, and how the faculty life is impacted by outside university obligations. A survey to obtain work-life balance, teaching responsibilities, and how a faculty’s personal life impacts their ability to work at their organization was conducted. The results of this research will identify areas where faculty have opportunities to engage in teaching, to balance their work life, and where organizations can change to support their faculty. Different ways of teaching including hyflex and other multimodal models will allow for faculty to engage in their teaching practice, professional development, and begin to establish work-life balance activities.

Keywords: faculty engagement, faculty responsibilities, HyFlex, teaching, work-life balance

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Authors: Eze R. Onukwugha

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The presence of cyanide in wastewater is known to inhibit the normal functioning of bio-reactors since it has the tendency to poison reactor micro-organisms. Bench scale models of activated sludge reactors with varying aspect ratios were operated for the treatment of cassava wastewater at several values of hydraulic retention time (HRT). The different values of HRT were achieved by the use of a peristaltic pump to vary the rate of introduction of the wastewater into the reactor. The main parameters monitored are the cyanide concentration and respective COD values of the influent and effluent. These observed values were then transformed into a mathematical model for the prediction of treatment efficiency.

Keywords: wastewater, aspect ratio, cyanide-inhibited wastewater, modeling

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Authors: Lela Mirtskhulava, Mariam Khunjgurua, Nino Lomineishvili, Koba Bakuria

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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability

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Authors: Adam Patterson

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This study analyzes the self-reported experiences of virtual reality users to develop insight into an optimal learning path for education within virtual reality. This research uses a sample of 1000 observations to statistically define factors influencing (i) immersion level and (ii) motion sickness rating for virtual reality experience respondents of college age. This paper recommends an efficient duration for each virtual reality session, to minimize sickness and maximize engagement, utilizing modern machine learning methods such as association rules. The goal of this research, in augmentation with previous literature, is to inform logistical decisions relating to implementation of pilot instruction for virtual reality at the collegiate level. Future research will include a Randomized Control Trial (RCT) to quantify the effect of virtual reality education on student learning outcomes and engagement measures. Current research aims to maximize the treatment effect within the RCT by optimizing the learning benefits of virtual reality. Results suggest significant gender heterogeneity amongst likelihood of reporting motion sickness. Females are 1.7 times more likely, than males, to report high levels of motion sickness resulting from a virtual reality experience. Regarding duration, respondents were 1.29 times more likely to select the lowest level of motion sickness after an engagement lasting between 24.3 and 42 minutes. Conversely, respondents between 42 to 60 minutes were 1.2 times more likely to select the higher levels of motion sickness.

Keywords: applications and integration of e-education, practices and cases in e-education, systems and technologies in e-education, technology adoption and diffusion of e-learning

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Authors: Victor Milanes, Martha Hubertz

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In the wake of COVID-19, all aspects of life have been estranged, and humanity has been forced to shift toward a more technologically integrated mode of operation. Essential work such as Healthcare, business, and public policy are a few notable industries that were initially dependent upon face-to-face modality but have completely reimagined their operation style. Unique to these fields, education was particularly strained because academics, teachers, and professors alike were obligated to shift their curriculums online over the course of a few weeks while also maintaining the expectation that they were educating their students to a similar level accomplished pre-pandemic. This was notable as research indicates two key concepts: Students prefer face-to-face modality, and due to the disruption in academic continuity/style, there was a negative impact on student's overall education and performance. With these two principles in mind, this study aims to inquire what online strategies could be best employed by teachers to educate their students, as well as what strategies could be adopted in a multimodal setting if deemed necessary by the instructor or outside convoluting factors (Such as the case of COVID-19, or a personal matter that demands the teacher's attention away from the classroom). Strategies and methods will be cross-analyzed via a ranking system derived from various recognized teaching assessments, in which engagement, retention, flexibility, interest, and performance are specifically accounted for. We expect to see an emphasis on positive social pressure as a dominant factor in the improved propensity for education, as well as a preference for visual aids across platforms, as research indicates most individuals are visual learners.

Keywords: technological integration, multimodal teaching, education, student engagement

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Authors: Hao Yan, Xiaobing Zhang

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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

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Authors: A. Daftari, W. Kudla

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Liquefaction is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid cyclic loading. Liquefaction and related phenomena have been responsible for huge amounts of damage in historical earthquakes around the world. Modelling of soil behaviour is the main step in soil liquefaction prediction process. Nowadays, several constitutive models for sand have been presented. Nevertheless, only some of them can satisfy this mechanism. One of the most useful models in this term is UBCSAND model. In this research, the capability of this model is considered by using PLAXIS software. The real data of superstition hills earthquake 1987 in the Imperial Valley was used. The results of the simulation have shown resembling trend of the UBC3D-PLM model.

Keywords: liquefaction, plaxis, pore-water pressure, UBC3D-PLM

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Authors: Pauliina Haustein

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Improvisation was part of Western classical concert culture and performers’ skill sets until early 20th century. Historical accounts, as well as recent studies, indicate that improvisatory elements in the programme may contribute specifically towards the audiences’ experience of enhanced emotional engagement during the concert. This paper presents findings from the author’s artistic practice research, which explored re-introducing improvisation to Western classical performance practice as a musician (cellist and ensemble partner/leader). In an investigation of four concert cycles, the performer-researcher sought to gain solo and chamber music improvisation techniques (both related to and independent of repertoire), conduct ensemble improvisation rehearsals, design concerts with an improvisatory approach, and reflect on interactions with audiences after each concert. Data was collected through use of reflective diary, video recordings, measurement of sound parameters, questionnaires, a focus group, and interviews. The performer’s empirical experiences and findings from audience research components were juxtaposed and interrogated to better understand the (1) rehearsal and planning processes that enable improvisatory elements to return to Western classical concert experience and (2) the emotional experience and type of engagement that occur throughout the concert experience for both performer and audience members. This informed the development of a concert model, in which a programme of solo and chamber music repertoire and improvisations were combined according to historically evidenced performance practice (including free formal solo and ensemble improvisations based on audience suggestions). Inspired by historical concert culture, where elements of risk-taking, spontaneity, and audience involvement (such as proposing themes for fantasies) were customary, this concert model invited musicians to contribute to the process personally and creatively at all stages, from programme planning, and throughout the live concert. The type of democratic, personal, creative, and empathetic collaboration that emerged, as a result, appears unique in Western classical contexts, rather finding resonance in jazz ensemble, drama, or interdisciplinary settings. The research identified features of ensemble improvisation, such as empathy, emergence, mutual engagement, and collaborative creativity, that became mirrored in audience’s responses, generating higher levels of emotional engagement, empathy, inclusivity, and a participatory, co-creative experience. It appears that duringimprovisatory moments in the concert programme, audience members started feeling more like active participants in za\\a creative, collaborative exchange and became stakeholders in a deeper phenomenon of meaning-making and narrativization. Examining interactions between all involved during the concert revealed that performer-audience impulse exchange occurred on multiple levels of awareness and seemed to build upon each other, resulting in particularly strong experiences of both performer and audience’s engagement. This impact appeared especially meaningful for audience members who were seldom concertgoers and reported little familiarity with classical music. The study found that re-introducing improvisatory elements to Western classical concert programmes has strong potential in increasing audience’s emotional engagement with the musical performance, enabling audience members to connect more personally with the individual performers, and in reaching new-to-classical-music audiences.

Keywords: artistic research, audience engagement, audience experience, classical improvisation, ensemble improvisation, emotional engagement, improvisation, improvisatory approach, musical performance, practice research

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: Yuichi Kida, Takuro Kida

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In a new smart society supported by the recent development of 5G and 6G Communication systems, the im- portance of wireless power transmission is increasing. These systems contain discrete sampling systems in the middle of the transmission path and equivalent circuit representation of lossless or lossy power transmission through these systems is an important issue in circuit theory. In this paper, for the given weight function, we show that a lossless power transmission system with the given weight is expressed by an equivalent circuit representation of the Kida’s optimal signal prediction system followed by a reactance multi-port circuit behind it. Further, it is shown that, when the system is lossy, the system has an equivalent circuit in the form of connecting a multi-port positive-real circuit behind the Kida’s optimal signal prediction system. Also, for the convenience of the reader, in this paper, the equivalent circuit expression of the reactance multi-port circuit and the positive- real multi-port circuit by Cauer and Ohno, whose information is currently being lost even in the world of the Internet.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, power transmission

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Authors: Irene Kalemaki, Ioanna Garefi

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Social Innovation Education (SIE) is a new educational approach that aims to empower students to take action for a more democratic and sustainable society. Conceptually and pedagogically wise, it is situated at the intersection of Enterprise Education and Citizenship Education as it aspires to i) combine action with activism, ii) personal development with collective efficacy, iii) entrepreneurial mindsets with democratic values and iv) individual competences with collective competences. This paper abstract presents the work of the NEMESIS project, funded by H2020, that aims to design, test and validate the first consolidated approach for embedding Social Innovation Education in schools of primary and secondary education. During the academic year 2018-2019, eight schools from five European countries experimented with different approaches and methodologies to incorporate SIE in their settings. This paper reports briefly on these attempts and discusses the wider educational philosophy underlying these interventions with a particular focus on analyzing the learning outcomes and impact on students. That said, this paper doesn’t only report on the theoretical and practical underpinnings of SIE, but most importantly, it provides evidence on the impact of SIE on students. In terms of methodology, the study took place from September 2018 to July 2019 in eight schools from Greece, Spain, Portugal, France, and the UK involving directly 56 teachers, 1030 students and 69 community stakeholders. Focus groups, semi-structured interviews, classroom observations as well as students' written narratives were used to extract data on the impact of SIE on students. The overall design of the evaluation activities was informed by a realist approach, which enabled us to go beyond “what happened” and towards understanding “why it happened”. Research findings suggested that SIE can benefit students in terms of their emotional, cognitive, behavioral and agentic engagement. Specifically, the emotional engagement of students was increased because through SIE interventions; students voice was heard, valued, and acted upon. This made students feel important to their school, increasing their sense of belonging, confidence and level of autonomy. As regards cognitive engagement, both students and teachers reported positive outcomes as SIE enabled students to take ownership of their ideas to drive their projects forward and thus felt more motivated to perform in class because it felt personal, important and relevant to them. In terms of behavioral engagement, the inclusive environment and the collective relationships that were reinforced through the SIE interventions had a direct positive impact on behaviors among peers. Finally, with regard to agentic engagement, it has been observed that students became very proactive which was connected to the strong sense of ownership and enthusiasm developed during collective efforts to deliver real-life social innovations. Concluding, from a practical and policy point of view these research findings could encourage the inclusion of SIE in schools, while from a research point of view, they could contribute to the scientific discourse providing evidence and clarity on the emergent field of SIE.

Keywords: education, engagement, social innovation, students

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Authors: Omar M. Elmabrouk

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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

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Authors: Faraz Ali Memon

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Stakeholders are a vital part of any organisation. It includes working on the demands of different stakeholders within the projects. However, the reality of construction projects is slightly different when it comes to practice. The UK construction projects have a history of failing to complete projects on time and within the budget. The purpose of this qualitative study is to enhance knowledge of stakeholder engagement. Semi-structured interviews will be carried out using a purposive sampling technique. It includes interviewing and getting knowledge from industry practitioners from top UK construction firms on how to manage stakeholders effectively. The findings from this study will help in understanding stakeholders' impact and how the engagement of stakeholders can affect construction projects. The conclusions of this study add value to the existing body of knowledge on stakeholder management, especially in the UK, where academic studies on construction projects are few. As a contribution, this study will provide a practical guide for the practitioners to engage stakeholders within the scope of the project. In addition, this study is limited to UK construction projects. Therefore, the outcome may not be generalised to other developing and underdeveloped countries.

Keywords: stakeholders, UK construction, project management, cost and time

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Authors: D. Szinay, O. Perski, A. Jones, T. Chadborn, J. Brown, F. Naughton

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Background: The uptake of health and wellbeing smartphone apps is largely influenced by popularity indicators (e.g., rankings), rather than evidence-based content. Rapid disengagement is common. This study aims to explore how and why potential users 1) select and 2) engage with such apps, and 3) how increased engagement could be promoted. Methods: Semi-structured interviews and a think-aloud approach were used to allow participants to verbalise their thoughts whilst searching for a health or wellbeing app online, followed by a guided search in the UK National Health Service (NHS) 'Apps Library' and Public Health England’s (PHE) 'One You' website. Recruitment took place between June and August 2019. Adults interested in using an app for behaviour change were recruited through social media. Data were analysed using the framework approach. The analysis is both inductive and deductive, with the coding framework being informed by the Theoretical Domains Framework. The results are further mapped onto the COM-B (Capability, Opportunity, Motivation - Behaviour) model. The study protocol is registered on the Open Science Framework (https://osf.io/jrkd3/). Results: The following targets were identified as playing a key role in increasing the uptake of and engagement with health and wellbeing apps: 1) psychological capability (e.g., reduced cognitive load); 2) physical opportunity (e.g., low financial cost); 3) social opportunity (e.g., embedded social media); 4) automatic motivation (e.g., positive feedback). Participants believed that the promotion of evidence-based apps on NHS-related websites could be enhanced through active promotion on social media, adverts on the internet, and in general practitioner practices. Future Implications: These results can inform the development of interventions aiming to promote the uptake of and engagement with evidence-based health and wellbeing apps, a priority within the UK NHS Long Term Plan ('digital first'). The targets identified across the COM-B domains could help organisations that provide platforms for such apps to increase impact through better selection of apps.

Keywords: behaviour change, COM-B model, digital health, mhealth

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Vichai Tienthavorn

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Background: Recently, the patients of non-communicable diseases (NCDs) are increasing in Thailand; especially hypertension and diabetes. Hypertension and Diabetes patients were found to be of 3.7 million in 2008. The varieties of human behaviors have been extensively changed in health. Hence, Thai Government has a policy to reduce NCDs. Generally, primary care plays an important role in treatment using medical process. However, NCDs patients have not been decreased. Objectives: This study not only reduce the patient and mortality rate but also increase the quality of life, could apply in different areas and propose to be the national policy, effectively for a long term operation. Methods: Here we report that primary health care (PHC), which is a primary process to screening, rapidly seek the person's risk. The screening tool of the study was Vichai's 7 color balls model, the medical education tool to transfer knowledge from student health team to community through health volunteers, creating community engagement in terms of social participation. It was found that people in community were realized in their health and they can evaluate the level of risk using this model. Results: Projects implementation (2015) in Nong Lom Health Center in Phayao (target group 15-65 years, 2529); screening hypertension coveraged 99.01%, risk group (light green) was decreased to normal group (white) from 1806 to 1893, significant severe patient (red) was decreased to moderate (orange) from 10 to 5. Health Program in behaving change with best practice of 3Es (Eating, Exercise, Emotion) and 3Rs (Reducing tobacco, alcohol, obesity) were applied in risk group; and encourage strictly medication, investigation in severe patient (red). Conclusion: This is the first demonstration of knowledge transfer to community engagement by student, which is the sustainable education in PHC.

Keywords: non-communicable disease, surveillance control and prevention systems, community engagement, primary health care

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Authors: Tony Mann

Abstract:

This paper sets out the case for involving and engaging employees/workers/stakeholders/staff in any significant change that is being considered by the senior executives of the organization. It establishes the rationale, the approach, the methodology of engagement and the benefits of a participative approach. It challenges the new norm of imposing change for fear of resistance and instead suggests that involving people has better outcomes and a longer-lasting impact. Various strategic models are introduced and illustrated to explain how the process can be most effective. The paper highlights one model in particular (the Process Iceberg® Organizational Change model) that has proven to be instrumental in developing effective change. Its use is demonstrated in its various forms and explains why so much change fails to address the key elements and how we can be more productive in managing change. ‘Participation’ in change is too often seen as negative, expensive and unwieldy. The paper aims to show that another model: UIA=O+E, can offset the difficulties and, in fact, produce much more positive and effective change.

Keywords: facilitation, stakeholders, buy-in, digital workshops

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

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Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the work piece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: dexel, process stability, material removal, milling

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Authors: Hannele Kuusisto, Paivi Makila, Ursula Hyrkkanen

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Introduction: There have been arguments about the skills university students should have when graduated. Current trends argue that as well as the specific job-related skills the graduated students need problem-solving, interaction and networking skills as well as self-management skills. Skills required in working life are also considered in the Finnish national project called VALTE (short for 'prepared for working life'). The project involves 11 Finnish school organizations. As one result of this project, a five-credit independent online course in study and job engagement as well as in study and job crafting was developed at Turku University of Applied Sciences. The aim of the oral or e-poster presentation is to present the online course developed in the project. The purpose of this abstract is to present the development work of the online course and the feedback received from the pilots. Method: As the University of Turku is the leading partner of the VALTE project, the collaborative education platform ViLLE (https://ville.utu.fi, developed by the University of Turku) was chosen as the online platform for the course. Various exercise types with automatic assessment were used; for example, quizzes, multiple-choice questions, classification exercises, gap filling exercises, model answer questions, self-assessment tasks, case tasks, and collaboration in Padlet. In addition, the free material and free platforms on the Internet were used (Youtube, Padlet, Todaysmeet, and Prezi) as well as the net-based questionnaires about the study engagement and study crafting (made with Webropol). Three teachers with long teaching experience (also with job crafting and online pedagogy) and three students working as trainees in the project developed the content of the course. The online course was piloted twice in 2017 as an elective course for the students at Turku University of Applied Sciences, a higher education institution of about 10 000 students. After both pilots, feedback from the students was gathered and the online course was developed. Results: As the result, the functional five-credit independent online course suitable for students of different educational institutions was developed. The student feedback shows that students themselves think that the developed online course really enhanced their job and study crafting skills. After the course, 91% of the students considered their knowledge in job and study engagement as well as in job and study crafting to be at a good or excellent level. About two-thirds of the students were going to exploit their knowledge significantly in the future. Students appreciated the variability and the game-like feeling of the exercises as well as the opportunity to study online at the time and place they chose themselves. On a five-point scale (1 being poor and 5 being excellent), the students graded the clarity of the ViLLE platform as 4.2, the functionality of the platform as 4.0 and the easiness of operating as 3.9.

Keywords: job crafting, job engagement, online course, study crafting, study engagement

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Authors: Diqin Qi, Jiaming Li, Siman Li

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Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

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Authors: Aagesson, Ekstrand, Persson, Sallander

Abstract:

A description of how a team of librarians at Linnaeus University Library in Sweden utilizes a pedagogical approach to deliver engaging digital workshops on information retrieval. The team consists of four librarians supporting three different faculties. The paper discusses the challenges faced in engaging students who may perceive information retrieval as a boring and difficult subject. The paper emphasizes the importance of motivation, inclusivity, constructive feedback, and collaborative learning in enhancing student engagement. By employing a two-librarian teaching model, maintaining a lighthearted approach, and relating information retrieval to everyday experiences, the team aimed to create an enjoyable and meaningful learning experience. The authors describe their approach to increase student engagement and learning outcomes through a three-phase workshop structure: before, during, and after the workshops. The "flipped classroom" method was used, where students were provided with pre-workshop materials, including a short film on information search and encouraged to reflect on the topic using a digital collaboration tool. During the workshops, interactive elements such as quizzes, live demonstrations, and practical training were incorporated, along with opportunities for students to ask questions and provide feedback. The paper concludes by highlighting the benefits of the flipped classroom approach and the extended learning opportunities provided by the before and after workshop phases. The authors believe that their approach offers a sustainable alternative for enhancing information retrieval knowledge among students at Linnaeus University.

Keywords: digital workshop, flipped classroom, information retrieval, interactivity, LIS practitioner, student engagement

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Authors: Aidan J. Bowes, Reaz Hasan

Abstract:

The automotive industry is continually aiming to develop the aerodynamics of car body design. This may be for a variety of beneficial reasons such as to increase speed or fuel efficiency by reducing drag. However recently there has been a greater amount of focus on wind noise produced while driving. Designers in this industry seek a combination of both simplicity of approach and overall effectiveness. This combined with the growing availability of commercial CFD (Computational Fluid Dynamics) packages is likely to lead to an increase in the use of RANS (Reynolds Averaged Navier-Stokes) based CFD methods. This is due to these methods often being simpler than other CFD methods, having a lower demand on time and computing power. In this investigation the effectiveness of turbulent flow and acoustic noise prediction using RANS based methods has been assessed for different wing mirror geometries. Three different RANS based models were used, standard k-ε, realizable k-ε and k-ω SST. The merits and limitations of these methods are then discussed, by comparing with both experimental and numerical results found in literature. In general, flow prediction is fairly comparable to more complex LES (Large Eddy Simulation) based methods; in particular for the k-ω SST model. However acoustic noise prediction still leaves opportunities for more improvement using RANS based methods.

Keywords: acoustics, aerodynamics, RANS models, turbulent flow

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