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The purpose of this paper is to determine the effect of adding Aloe vera powder (AVP) in the production of mahewu with the aim of determining its shelf-life and sensory qualities.

Mahewu was produced at home (Sample B) and in the laboratory (Sample C) using a standard home-made procedure with the addition of AVP. A control mahewu (Sample A) was produced without AVP. Shelf-life was determined by following the chemical, microbiological, physical properties at 36 ± 2 °C for 60 days and the sensory properties of the products were also evaluated.

Physicochemical analysis revealed decreases in pH ranging between 3.3 and 2.4 from day 15–60 days of storage in all three samples. There was a significant increase (p < 0.05) in titratable acidity (0.2–1.8%) of all mahewu samples during storage. Total soluble solids were different amongst the samples from day 15 to day 60. The colour of the products was significantly different (p = 0.05) with respect to L*, a* and b* throughout the storage period. Microbiological results revealed an increase in coliforms bacteria, lactic acid bacteria, and yeast during storage. Sensory analysis showed that the control mahewu was more preferred than AVP added mahewu.

The study may help small-scale brewers to increase the shelf-life of mahewu.

Results of this study showed that the addition of AVP extended shelf-life of mahewu up to 15 days at 36 ± 2 °C.

This study aims to use a comparative analysis to examine the channel of deferring cash commitments, which can be seen as a strategic solution to mitigate the impact of COVID-19 on Moldova's service sector.

This paper uses the Oaxaca–Blinder decomposition analysis. The World Bank's post-COVID-19 survey is used. The methodology takes into account heterogeneity among firms.

The results of the Oaxaca–Blinder decomposition analysis show that service firms use deferred cash commitments more than industrial firms, corporate governance and their pandemic-related strategies are also effective in the post-COVID Moldovan economy. The results are robust to different modeling alternatives.

COVID-19 can be considered a key source of uncertainty for firms, especially those operating in economies where financial frictions occasionally occur in a transition economy. Therefore, this study can shed new light on the impact of COVID-19 on financial strategies in a transition economy.

The purpose of this paper is to propose a framework to improve the effectiveness of university–industry collaboration (UIC). This work enhances the existing body of literature and knowledge regarding collaboration and offers concrete steps to be taken for effective collaboration between universities and industries.

A literature review to study the best practices, impediments to collaboration and the various models proposed in the past for successful UIC was conducted. A workshop and focus-group meetings of practitioners and academic researchers was designed and organised to explore the current state of the university–industry engagement within the Australian Capital Territory (ACT) region and gather inputs regarding possible approaches to improve collaboration. The findings from the literature review and the results from this qualitative research regarding the approaches to improve the effectiveness of the collaboration were analysed.

The study discovers that various measures have been proposed in the form of best practices or models to improve the effectiveness of UIC. However, these measures often address a specific concern such as technology transfer, intellectual property (IP), etc. There is a scope for a comprehensive holistic framework to address many aspects of UIC in order to improve effectiveness and achieve success. A framework for improving the effectiveness of collaboration considering a comprehensive list of factors operating in a broad context within the collaboration system was proposed.

The framework builds on previous literature dealing with measures for successful UIC. However, it is the first of its kind, in the researcher's knowledge, in terms of comprehensiveness of the factors contributing to establishing and sustaining successful collaboration. The value of the individual experience of the participants in this qualitative research, which is on average more than 10 years in the software engineering field, validates the importance and quality of the data collected. The addition of these results to the framework increases its validity.The framework can be utilised by universities and industry practitioners to foster successful and effective collaboration. The results have significant relevance, particularly within the Australian context as the government has intensified the adoption of measures to encourage and improve collaboration between universities and the industry.

This study aims to identify the gap between the requirements of the accreditation bodies and the widely used learning management systems (LMSs) in assessing the intended learning outcomes (ILOs). In addition, this study aims to introduce a framework, along with the evaluation of the functionality of the LMS, for measuring the ILO.

A qualitative method was deployed to examine the gap between the requirements of the accreditation standards and the LMS functionalities. The researchers collaborated to design a mechanism, develop a system architecture to measure the ILO in alignment with the accreditation standards and guide the development of the Moodle plugin. The appropriateness and effectiveness of the plugin were evaluated within the scope of assessment mapping and design. Focus group interviews were conducted to collect feedback from the instructors and program leaders regarding its implementation.

The results of this study indicate that there is no standardized mechanism to measure course and program ILO objectively, using the existing LMS. The implementation of the plugin shows the appropriateness and effectiveness of the system in generating ILO achievement reports, which was confirmed by the users.

This study proposed a framework and developed a system architecture for the objective measurement of the ILO through direct assessment. The plugin was tested to generate consistent reports during the measurement of course and program ILO. The plugin has been implemented across Gulf University’s program courses, ensuring appropriate reporting and continuous improvement.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The main purpose of this exploratory study is to investigate the attitude of pharmacists, as small- and medium-sized enterprise (SME) owners, toward new technologies, and more precisely, toward the adoption of mobile apps for mobile health (mHealth). Such apps are generally used to improve customer satisfaction and loyalty. This study measures pharmacists’ subjective experiences of mobile apps for mHealth and aims to understand how these pharmacists make sense of these apps.

The study adopted the narrative inquiry technique combined with critical event analysis. Participants' experiences were categorized based on how they viewed new technology tools. Interpretative inductive analysis identified precise aspects of the sense making illustrative of non-adoption or confused adoption of new technologies by pharmacists.

This study investigates to what extent new technology tools such as mobile apps affect retailers and more precisely the reasons why mobile apps are and are not adopted by retailers, as potential users, in the pharmaceutical industry. We identified four aspects of sense making that illustrated non-adoption or confused adoption of new technologies by pharmacists. These aspects are deeply discussed in the paper and are referred to the dimensions of confusion to confidence; suspicion to trust; frustration to education; mistrust to cooperation.

The main limitation of the present study is the limited number of territories investigated. This limitation arose because of the exploratory nature of the available research, which is generally based on case studies, and the lack of clear operationalization of the research available at the time of data collection. Another limitation is that the sample included only SMEs operating in the Italian pharmacy industry.

Many studies have highlighted the opportunities related to new mobile apps in the business-to-business market. Several have investigated customer interest in such new technology. If some contributions have indirectly investigated the acceptance of information technology tools, to the best of our knowledge, no study has been conducted to investigate directly and precisely the level of pharmacists' acceptance, use, and willingness to adopt information technology (e.g., mobile apps) for customer service in mHealth and mainly the reasons of non-adoption.

Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hierarchy, through which we present the functional similarities between cognitive service and human cognitive functions to illustrate what kind of functions are cognitive in the computing. The results from the comparison of the functional hierarchy of cognitive functions are consistent with cognitive computing literature. Thus, the functional hierarchy allows us to find the type of cognition and reach the comparability between the applications.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

For a number of years, the construction industry has seen an ongoing shift from design-bid-build to design-build contracts. This transition in contract type entails changes for both the organizations and the individuals involved. Consequently, the purpose of this paper is to investigate how the client manages the transition between the different contract types from an organizational change perspective in a project-led organization.

A multiple case study of six infrastructure projects with DB contracts, all managed by the Swedish Transport Administration, was conducted. The major source of data is semi-structured interviews with respondents from both the client and the contractors.

Results suggest that the transition has resulted in a mix of design-bid-build and design-build as contract type owing to issues when changing in a project-led organization. A change in vision also requires a concomitant change in culture, systems and roles.

The study only includes cases from the Swedish transport infrastructure sector, which limits the generalizability. The findings are also indicative owing to the small number of cases.

The findings further our understanding of managing change in complex projects, which might help practitioners to manage change in a more integrated way.

The findings enrich our understanding of the systemic change that a switch in contract types can have in inter-organizational complex projects such as transport infrastructure projects. Furthermore, it emphasizes the intricate task of change management in project-led organizations and its effects on roles and responsibilities.

The purpose of this paper is to propose a minimum set of indicators to be measured by industrial companies to represent the triple bottom line (TBL) approach.

The research is both descriptive and quantitative. Three hypotheses establish associations among the degrees of use of TBL indicators and their different degrees of use in firms. The authors used confirmatory factor analysis (CFA) to validate the scale and structural equation modelling to represent the final measurement model. The survey gathered 149 industrial companies.

The results pointed out that there are positive associations among the degree of use of environmental indicators and social indicators, economic, environmental and social indicators have different degrees of use in firms, a positive association between the degree of use of environmental and social indicators and the use of economic indicators was not confirmed. The findings suggest how to measure sustainable performance for industrial companies and highlight the differences in the degree of use for the three dimensions of TBL.

The limitations refer to the non-probabilistic sample, applied in a specific context, industrial companies.

This set of indicators is intended to be used by industrial companies as a reliable instrument to sustainable performance assessment of the current stage of the TBL deployment and provide alternative approaches to address specific issues related to the environmental, social and economic sustainability.

The results offer tangible results for measuring and reporting firm’s social and environmental performance.

This paper intends to offer an integrated and consistent way of measuring sustainability in industrial companies.