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Many industries use non-Newtonian ternary hybrid nanofluids (THNF) because of how well they control rheological and heat transport. This being the case, this paper aims to numerically study the Casson-Williamson THNF flow over a yawed cylinder, considering the effects of several slips and an inclined magnetic field. The THNF comprises Al₂O₃-TiO₂-SiO₂ nanoparticles because they improve heat transmission due to large thermal conductivity.

Applying suitable nonsimilarity variables transforms the coupled highly dimensional nonlinear partial differential equations (PDEs) into a system of nondimensional PDEs. To accomplish the goal of achieving the solution, an implicit finite difference approach is used in conjunction with Quasilinearization. With the assistance of a script written in MATLAB, the numerical results and the graphical representation of those solutions were ascertained.

As the Casson parameter β increases, there is an improvement in the velocity profiles in both chord and span orientations, while the gradients Re1/2Cf, Re1/2C¯f reduce for the same variations of β. The velocities of Casson THNF are greater than those of Casson-Williamson THNF. Approximately, a 202% and a 32% ascension are remarked in the magnitudes of Re1/2Cf and Re1/2C¯f for Casson-Williamson THNF than the Casson THNF only. When velocity slip attribute S1 jumps to 1 from 0.5, magnitude of both F(ξ,η) and Re1/2Cf fell down and it is reflected to be 396% at ξ=1, Wi=1 and β=1. An augmentation in thermal jump results in advanced fluid temperature and lower Re−1/2Nu. In particular, about 159% of down drift is detected when S2 taking 1.

There is no existing research on the effects of Casson-Williamson THNF flow over a yawed cylinder with multiple slips and an angled magnetic field, according to the literature.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

Menstruation is a part of the female reproductive cycle that begins with adolescence. Menstruation is a natural change; it relates to several malpractices and misconceptions that may contribute to adverse health outcomes.

The authors have searched relevant papers using Google Scholar and PubMed to write this mini review.

During menstruation, poor hygiene maintenance can cause serious illness, which includes the urinary tract and reproductive tract infection. Menstruation management is a hygienic system, and it is essential for females because poor hygiene maintenance during menstruation can cause some infections and numerous sexually transmitted diseases. There are a few nanotechnology-based products that have come into the market to offer some relief to females during their periods.

This mini review will help researchers to design innovative female hygiene products that can relieve the discomfort caused to women during their reproductive age.

Based on social exchange theory and social identification theory, I investigated how employee organizational identification affects the effectiveness of commitment-based human resource (HR) practices. I focused on employee attitudes (job satisfaction) and behaviors (proactive knowledge seeking) as HR practices’ outcomes.

Using a structural equation modeling analytical approach, I tested the hypotheses with data from a web-based cross-sectional survey of 208 specialists and engineers of manufacturing subsidiaries in Poland.

Results showed that the positive relationship between commitment-based-HR practices and job satisfaction is weakened for employees strongly identified with the organization. Simultaneously, the connection between seeking knowledge and job satisfaction is stronger and more important for people who identify moderately to strongly.

The study limitations regard mainly its cross-sectional design and single cultural and industrial context.

From the managerial perspective, the study suggests that to enhance proactive employee behavior, companies need to increase employee organizational identification and ensure that employees have a positive perception of the implemented HR practices.

The study contributes to the ongoing discussion on whether individual contingencies affect the effectiveness of commitment-based HR practices in the form of individual attitudinal and behavioral outcomes. The findings revealed that the contingent effect of organizational identification depends on the type of individual outcomes, suggesting that the strength of organizational identification affects how employees decide to reciprocate the organization’s attention and investment.

This paper outlines how Design for Lean Six Sigma methods aided a medical device manufacturing company in developing a new strategic space management and approval process for its manufacturing site.

The project demonstrates the application of the Design for Lean Six Sigma and structured Define, Measure, Analyse, Design, and Verify methodology in designing and implementing a process that enables the case study manufacturing site to improve its space utilisation and free up space.

The project was validated in one manufacturing department, and the Design for Lean Six Sigma methodology resulted in creating 15% new space for that area, with opportunities identified to free up 44.7% of the total manufacturing floor space and realise over €2.2 million cost savings as well as start to manufacture new products launched.

The manuscript highlights for the first time how the Design for Lean Six Sigma methodology can be utilised for space utilisation and can be leveraged by other manufacturers. The current study's limitations are that it is a single-site case study application. Future longitudinal case studies on Design for Lean Six Sigma application in more manufacturing space utilisation projects would be useful. This study has implications for identifying best practices for Design for Lean Six Sigma methodology application in the device industry, thus improving the state of the art for introducing new manufacturing lines.

This is the first published work to utilise Design for Lean Six Sigma methodology for space utilisation in a medical device company. This review will provide medical devices and other manufacturing organisations with recommendations on utilising Design for Lean Six Sigma and design for improved space utilisation to reduce costs.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

In a move characterized by ambiguity, Facebook changed its name to Meta in October 2021, announcing a new era of social interaction, enabled by the metaverse technology that appears poised to become the future center of gravity for online social interactions. At first glance, the communicated change signals a radically new business model (BM) based on an unprecedented configuration of the three following components: value creation, value proposition and value capture. The purpose of this paper is to analyze Facebook’s announced changes in its BM to clarify whether the change is as radical as communicated or rather represents an incremental transformation of the current BM.

This investigation adopted an in-depth case study research method. The process included using a structured approach to collect 153 data points, including academic studies and publicly available information, followed by qualitative content analysis.

The results of our analysis of Facebook’s entrepreneurial journey indicate that the communicated strategic refocusing does not correspond to a radical BM innovation pattern. Even though Facebook’s BM might evolve into the innovation phase, as the current changes appear very futuristic, the authors estimate that the core elements of the BM will change incrementally. The investigation indicates that the underlying logic of the straightforward communicative efforts primarily serves two purposes: to improve the external perception of the company and to disseminate an internal change signal within the organization.

This paper is the first study that takes an entrepreneurship and BM perspective in analyzing Facebook’s approach in rebranding to Meta and refocusing its strategy on building the metaverse. The academic and practical relevance, as well as the potential future impact on business and society, makes the investigation of this case an intriguing prospect. Additionally, the study illuminates the difference between the communicated vision and the real impact on the business, suggesting critical questions about future large-scale rebranding efforts and their effects.

This study aims to explore the effect of mental health in terms of depression, anxiety, stress, fear of COVID-19 and quality of life (QoL) on the reluctance to be vaccinated in a population of Palestinian adults living in occupied Palestinian territories and Israel.

The authors recruited 1,122 Palestinian adults who consented to participate in the study; 722 were females, and the mean age of the sample was 40.83 (SD 8.8). Depression, anxiety, and stress scale (DASS), World Health Organization QoL-BREF, FCov-19 and reluctance to the vaccine scale were administered; hierarchical regression analysis was applied to test vaccine reluctance as a dependent variable, and mental health, fear of COVID-19 and QoL as independent variables. This study hypothesized influence of such variables on the vaccine choice with differences due to the participants’ geographical locations.

Findings showed an effect of mental health, particularly depression, QoL and fear of COVID on vaccine reluctance, with depression and fear of COVID in the West Bank and Gaza, while in Israel, QoL played a role in vaccination choices.

The future needs to be comprehended more thoroughly to discover mutations and fluctuations over time in vaccine hesitancy and the increasing role of psychological distress, diminished QoL and fear of Covid-19. Online recruitment might not have allowed the study to include the most disadvantaged strips of the Palestinian population.

Human rights perspectives must be considered in public health and public mental health policies to ensure the QoL and well-being for the Palestinian population during and following the pandemic.

The crumbling of the Palestinian health-care system exacerbated the sense of dread among the population and made them less likely to vaccinate. The pandemic-like spread of Covid-19 prompts a plea for the global community to actively advocate for the urgent re-establishment of equity, autonomy and durability of the medical infrastructure in the occupied territories and equal entitlements for the Palestinians in Israel.

The results demonstrated the importance for public mental health to consider the multiple levels implied in the vaccine refusal in Palestine and Israel among the Palestinian population.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.