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The purpose of this experimental research was to examine a novel axial heat exchanger featuring swirling air movement over heated tubes. This apparatus is designed with perforated circular baffle plates complemented by rectangular air deflectors operating at different inclination angles. The tubes were arranged in a consistent layout parallel to the longitudinal airflow. The deflector’s heightened air-side turbulence initiates the frenzied motion, escalating the surface heat transfer rate.

The tubes maintained a constant heat flux condition over the surface. In each baffle plate, eight deflectors with identical inclination angles were devised in a reverse position, forming a rotation of air inside a circular duct that held tubes (carrying hot water) which elevated air-side turbulence, thereby enhancing the rate of heat transference on the surface. The baffle plates were equally situated from each other at changing pitch ratios. The Reynolds quantity was preserved in the scope of 16,000–30,000. The performance of the heat exchanger considering pitch ratios and inclination angles was examined.

The research indicates that when examined under similar conditions, an exchanger with a deflector baffle plate shows a strong dependence on the pitch ratio and inclination angle with a mean rise of 0.19 times in thermal enhancement factor at an inclination angle of 30° and a pitch ratio of 1.2 contrasted with an exchanger with segmental baffle plates.

The result shows the dependence of pitch ratio, Reynolds number and inclination on the heat transfer and friction factor rate.

This paper aims to apply the novel numerical model to analyze the effect of pillar material on the response of compound quartz crystal resonator (QCR) with an array of pillars. The performance of the proposed device compared to conventional QCR method was also investigated.

A finite element method model was developed to analyze the behavior of QCR coupled with an array of pillars. The model was composed of an elastic pillar, a solution and a perfectly matched layer. The validation of the model was performed through a comparison between its predictions and previous experimental measurements. Notably, a good agreement was observed between the predicted results and the experimental data.

The effect of pillar Young’s modulus on the coupled QCR and pillars with a diameter of 20 µm, a center-to-center spacing of 40 µm and a density of 2,500 kg/m³ was investigated. The results indicate that multiple vibration modes can be obtained based on Young’s modulus. Notably, in the case of the QCR–pillar in air, the second vibration mode occurred at a critical Young’s modulus of 0.2 MPa, whereas the first mode was observed at 3.75 Mpa. The vibration phase analysis revealed phase-veering behavior at the critical Young’s modulus, which resulted in a sudden jump-and-drop frequency shift. In addition, the results show that the critical Young’s modulus is dependent on the surrounding environment of the pillar. For instance, the critical Young’s modulus for the first mode of the pillar is approximately 3.75 Mpa in air, whereas it increases to 6.5 Mpa in water.

It was concluded that the performance of coupled QCR–pillar devices significantly depends on the pillar material. Therefore, choosing pillar material at critical Young’s modulus can lead to the maximum frequency shift of coupled QCR–pillar devices. The model developed in this work helps the researchers design pillars to achieve maximum frequency shift in their measurements using coupled QCR–pillar.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

Supply chain agility (SCA) is the primary strategy for reducing impacts and quick recovery when supply chains experience a disruption risk, such as the COVID-19 pandemic. This study will investigate how SCA can be achieved through supply chain information sharing (SCIS) under the different dependence relationships (DR) with suppliers or customers. The purpose of this paper is to investigate this issue.

Based on information process and resource dependency theories, this study constructs and empirically tests a proposed model of the relationships amongst the three dimensions of SCIS and the two areas of SCA and the contingency effects of two types of DR on those relationships. Using a dataset collected from 400 manufacturers in China, the authors tested this theoretical model using multi-group and structural path analysis.

The results of the structural path and multi-group analyses show that (1) all dimensions of SCIS are positively correlated with both areas of SCA and (2) dependence on the supplier and dependence on the customer have completely different impacts on the relationship between SCIS and SCA.

This study can improve the understanding of the multidimensional concepts of SCIS and SCA and relationships between them under two different DR conditions in the Chinese manufacturing setting. It contributes to IS and the SCA literature and provides theoretically driven and empirical explanations for the diverse dynamics between the dependence on the supplier and customer.

This work analyses how the adoption of technological innovations correlates with workers' perceived levels of job insecurity, and what factors moderate such relationship.

The study makes use of the 2018 wave of the Participation, Labour, Unemployment Survey (PLUS) from Inapp. The richness of the survey and the representativeness of the underlying sample (including 13,837 employed workers) allow employing various empirical specifications where it is possible to control and moderate for many socio-demographic features of the worker, including her occupation and industry of employment, thereby accounting for various potential confounding factors.

The results of this ordered logit estimations show that workers' perception of job insecurity is affected by many subjective, firm-related and even macroeconomic factors. This study demonstrates that the adoption of technological innovations by companies is associated with lower levels of job insecurity perceived by their workers. In fact, the adoption of technological innovations by a company is perceived by surviving workers (those who remain in the same firm even after the introduction of such innovations) as a signal of the firm's health and its commitment to preserving the activity. Individual- and occupation-specific moderating factors play a limited role.

This study estimates how perceived job insecurity relates to the technological innovations adopted by the firms in which the interviewees are employed rather than analyzing their general concerns about job insecurity. In addition, this study identifies different types of innovations, such as product and process innovation, automation and other types of innovations.

Higher economic growth accompanied by rising energy demand poses severe challenges to the long-term environmental sustainability of E7 economies, including Brazil, China, India, Indonesia, Mexico, Russia and Turkey. Thus, this paper explores the influence of foreign direct investment (FDI) inflows on energy diversification for E7 economies.

The dataset is panel data for emerging seven (E7) economies, covering the period 1992–2017. The empirical investigation relies on econometric techniques: panel cointegration test and panel autoregressive distributed lag model.

The findings reveal that energy diversification and FDI inflows are cointegrated. In the long run, higher FDI inflows encourage energy diversification, but energy efficiency improvements discourage energy diversification. In the short run, the effects of FDI inflows on energy diversification vary across E7 economies, highlighting the role of country-specific factors in determining the short-run influence of FDI inflows on energy diversification.

The findings suggested that FDI policies should encourage the adoption of nonconventional energy resources to stimulate energy diversification in E7 economies. Besides, better coordination between energy diversification and energy efficiency policies is required in the long run for a successful transition towards low-carbon economy goals.

This study is a unique empirical exercise that uncovers a cointegrating relationship between energy diversification and FDI inflows for E7 economies. Moreover, the analysis provides homogenous long-run and heterogeneous (country-specific) short-run coefficient estimates for the effect of FDI inflows on energy diversification.

Prosocial job characteristics have received the least scholarly attention among the antecedents of employee innovative work behavior (IWB). Hence, antecedents' role remains largely unknown, especially in public sector organizations. Based on the relational job design theory, job characteristics theory (JCT) and public sector innovation literature, the authors examine whether job impact (JI) promotes public sector employees' IWB mediated by work meaningfulness (WM). This study also assesses whether servant leadership (SL) strengthens the influence of JI on IWB (via WM).

The model was examined on a sample of 180 employee-supervisor dyads (90 supervisors and 180 employees) recruited from various government departments in the United Arab Emirates (UAE).

The finding supports the proposed moderated mediation model in which JI enhances IWB. The association between JI and innovative behavior via WM is found to be stronger under high SL behavior.

The authors' findings offer prescriptive insights into SL's crucial role in illustrating when and how JI promotes IWB and offer relevant managerial recommendations for encouraging public sector employees to demonstrate innovative behavior. The authors also provide empirical evidence on the significant contribution of a prosocial job characteristic, i.e. JI, to IWB. In addition to having theoretical and practical significance, the authors' study is aligned with the UAE National Innovation Strategy 2015.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

The purpose of this paper is to examine the effects of several structural shocks in oil prices on the Vietnamese economy and answer three key research questions: Is there a relationship between oil price shocks and macroeconomic indicators in Vietnam? How do different types of oil price impulses affect Vietnamese inflation and economic performance? To what extent do structural shocks in oil prices explain variations in Vietnam’s macroeconomic indicators?

Lower triangular Cholesky decomposition is performed on a short-term impact matrix in a two-block structural vector autoregressive model. The data set is defined monthly, from January 2000 to December 2021. The contributions of structural shocks in oil prices to the domestic variances are analysed using variance decomposition methods. In this study, both forecast error variance decomposition and historical decomposition are used.

The consequences of oil price fluctuations on Vietnamese output and inflation depend on different sources of oil price shocks. In comparison, oil supply shocks have an insignificant effect on both domestic industrial output and consumer price index inflation; however, positive shocks in aggregate and precautionary oil demands increase these domestic indicators substantially and sustainably. An analysis of variance decompositions reveals that supply-side oil shocks have very limited explanatory power for variations in domestic variables. Nevertheless, the contributions of unanticipated demand-side booms to domestic variations in the past and projected forecasts are considerable.

The findings from this research uncover potential risks for Vietnam’s economic prospects if the consequences of oil price shocks are not managed effectively.

Given the lack of economic sensitivity to supply-side oil shocks and the strong response to shifts in oil demands, greater pressure on the domestic economy is likely when Vietnam increases its dependence on oil imports.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.