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In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

This study aims to minimize the pressure drop across wavy microchannels using secondary branches without compromising its capacity to transfer the heat. The impact of secondary flows on the pressure drop and heat transfer capabilities at different Reynolds numbers are investigated numerically for different wavy microchannels. Finally, different channels are evaluated using performance evaluation criteria to determine their effectiveness.

To investigate the flow and heat transfer capabilities in wavy microchannels having secondary branches, a 3D conjugate heat transfer model based on finite volume method is used. In conventional wavy microchannel, secondary branches are introduced at crest and trough locations. For the numerical simulation, a single symmetrical channel is used to minimize computational time and resources and the flow within the channels remains single-phase and laminar.

The findings indicate that the suggested secondary channels notably improve heat transfer and decrease pressure drop within the channels. At lower flow rates, the secondary channels demonstrate superior performance in terms of heat transfer. However, the performance declines as the flow rate increased. With the same amplitude and wavelength, the introduction of secondary channels reduces the pressure drop compared with conventional wavy channels. Due to the presence of secondary channels, the flow splits from the main channel, and part of the core flow gets diverted into the secondary channel as the flow takes the path of minimum resistance. Due to this flow split, the core velocity is reduced. An increase in flow area helps in reducing pressure drop.

Many complex and intricate microchannels are proposed by the researchers to augment heat dissipation. There are challenges in the fabrication of microchannels, such as surface finish and achieving the required dimensions. However, due to the recent developments in metal additive manufacturing and microfabrication techniques, the complex shapes proposed in this paper are feasible to fabricate.

Wavy channels are widely used in heat transfer and micro-fluidics applications. The proposed wavy microchannels with secondary channels are different when compared to conventional wavy channels and can be used practically to solve thermal challenges. They help achieve a lower pressure drop in wavy microchannels without compromising heat transfer performance.

Globally, consumer’s inclination towards functional foods had noticed due to their greater health consciousness coupled with enhanced health-care cost. The fact that probiotics could promote a healthier gut microbiome led projection of probiotic foods as functional foods and had emerged as an important dietary strategy for improved human health. It had established that ice cream was a better carrier for probiotics than fermented milked due to greater stability of probiotics in ice cream matrix. Global demand for ice cream boomed and probiotic ice cream could have been one of the most demanded functional foods. The purpose of this paper was to review the technological aspects and factors affecting probiotic viability and to standardize methodology to produce functional probiotic ice cream.

Attempt was made to search the literature (review and researched papers) to identify diverse factors affecting the probiotic viability and major technological challenge faced during formulation of probiotic ice cream. Keywords used for data searched included dairy-based functional foods, ice cream variants, probiotic ice cream, factors affecting probiotic viability and health benefits of probiotic ice cream.

Retention of probiotic viability at a level of >10⁶ cfu/ml is a prerequisite for functional probiotic ice creams. Functional probiotic ice cream could have been produced with the modification of basic mix and modulating technological parameters during processing and freezing. Functionality can be further enhanced with the inclusion of certain nutraceutical components such as prebiotics, antioxidant, phenolic compounds and dietary fibres. Based upon reviewed literature, suggested method for the manufacture of functional probiotic ice cream involved freezing of a probiotic ice cream mix obtained by blending 10% probiotic fermented milk with 90% non-fermented plain ice cream mix for higher probiotic viability. Probiotic ice cream with functional features, comparable with traditional ice cream in terms of technological and sensory properties could be produced and can crop up as a novel functional food.

Probiotic ice cream with functional features may attract food manufacturers to cater health-conscious consumers.

COVID-19 pandemic has accelerated the adoption of home-based teleworking globally. Coupled with this, there are rising concerns about workplace cyberbullying. However, less studies have explored workplace cyberbullying in non-western countries. The purpose of the current study is to examine whether workplace cyberbullying affects employees' intention to stay and to find out the mechanisms underlying the relationship.

Data were collected among Indian home-based teleworkers. Data were analysed using SmartPLS and SPSS-PROCESS macro.

Results show that workplace cyberbullying negatively impacts intention to stay and affective commitment acts as a mediator between this link. The results also reveal that workplace social capital moderates the negative effects of workplace cyberbullying on affective commitment. The results further confirm that workplace social capital moderated the indirect impact of workplace cyberbullying on intention to stay via affective commitment.

This study highlights the potential of leveraging workplace social capital in order to reduce the negative effects of workplace cyberbullying.

These findings can complement the previous studies on the impact of negative work events on affective commitment and intention to stay as well as extend researchers' understanding of the underlying mechanism between workplace cyberbullying and intention to stay. Furthermore, this research explains how employees can utilise social resources from workplace social capital to mitigate the negative outcomes of workplace cyberbullying.

This paper aims to develop a specific type of mobile nonrigid support friction stir welding (FSW) robot, which can adapt to aluminum alloy trucks for rapid online repair.

The friction stir welding robot is designed to complete online repair according to the surface damage of large aluminum alloy trucks. A rotatable telescopic arm unit and a structure for a cutting board in the shape of a petal that was optimized by finite element analysis are designed to give enough top forging force for welding to address the issues of inadequate support and significant deformation in the repair process.

The experimental results indicate that the welding robot is capable of performing online surface repairs for large aluminum alloy trucks without rigid support on the backside, and the welding joint exhibits satisfactory performance.

Compared with other heavy-duty robotic arms and gantry-type friction stir welding robots, this robot can achieve online welding without disassembling the vehicle body, and it requires less axial force. This lays the foundation for the future promotion of lightweight equipment.

The designed friction stir welding robot is capable of performing online repairs without dismantling the aluminum alloy truck body, even in situations where sufficient upset force is unavailable. It ensures welding quality and exhibits high efficiency. This approach is considered novel in the field of lightweight online welding repairs, both domestically and internationally.

The purpose of the study is to optimize the blending ratio of Arecanut and cotton fibers to create yarn with the best quality for various applications, particularly home furnishings. The study aims to determine the effect of different blend ratios on the physical and mechanical properties of the yarn.

The study involves blending Arecanut and cotton fibers in various ratios (90:10, 75:25, 50:50, 25:75 and 10:90) at two different yarn counts (10/1 and 5/1). Various physical and mechanical properties of the blended yarn are analyzed, including unevenness, coefficient of mass variation (cvm%), imperfection, hairiness, breaking strength, elongation, tenacity and breaking work.

The research findings suggest that the blend ratio of 10:90 (10% cotton and 90% Arecanut fiber) produced the best results in terms of physical and mechanical properties for both yarn counts. This blend ratio resulted in reduced unevenness, cvm% and imperfection, while also exhibiting good mechanical properties such as breaking strength, elongation, tenacity and breaking work. The blend with a higher concentration of cotton generally showed better properties due to the coarseness of Arecanut fiber. As the goal of the study was to determine the best blend ratio that included the most Arecanut fiber based on its physical and mechanical properties, which is suitable for home furnishing applications, 75:25 Areca cotton blend ratio of yarn count 5/1 proved to be the best.

The study acknowledges that Arecanut fiber must be blended with other commercially used fibers like cotton due to its coarseness. While the study provides insights into optimizing blend ratios for home furnishings and packaging, further research may be needed to make the material suitable for clothing applications.

The research has practical implications for industries interested in utilizing Arecanut and cotton blends for various applications, such as home furnishings and packaging materials. It suggests that specific blend ratios can result in yarn with desirable properties for these purposes.

The study mentions that the increased use of Arecanut fibers can benefit the growers of Arecanut, potentially providing economic opportunities for communities engaged in Arecanut farming.

The research explores the utilization of Arecanut fibers, an underutilized resource, in combination with cotton to create sustainable yarn. It assesses various blend ratios and their impact on yarn properties, contributing to the understanding of eco-friendly textile materials.

This paper aims to define a hierarchical and multi-criteria framework based on pillars of the Modernization of Higher Education to evaluate European Accounting Doctoral Programmes (EADE-Model).

The authors applied a quali-quantitative methodology based on the analytic hierarchy process and the survey approach. The authors conducted an extensive literature and regulation review to identify the dimensions affecting the quality of Doctoral Programmes, choosing accounting as the relevant and pivotal field. The authors also used the survey to select the most critical quality dimensions and derive their weight to build EADE Model. The validity of the proposed model has been tested through the application to the Italian scenario.

The findings provide a critical extension of accounting ranking studies constructing a multi-criteria, hierarchical and updated evaluation model recognizing the role of doctoral training in the knowledge-based society. The results shed new light on weak areas apt to be improved and propose potential amendments to enhance the quality standard of ADE.

Theoretical and practical implications of this paper are directed to academics, policymakers and PhD programmes administrators.

The research is original in drafting a hierarchical multi-criteria framework for evaluating ADE in the Higher Education System. This model may be extended to other fields.

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.

This systematic review aims to assess the studies collected by identifying factors influencing the acceptance of e-learning systems before and during the current propagation of the COVID-19 pandemic.

This study undertook a literature review on the in-depth revision of studies published before 2021. The reviewed research papers meet the inclusion and exclusion criteria. A total of 97 out of 214 articles met the inclusion criteria and were subsequently used in this review.

The findings revealed that the survey questionnaire is the most common data collection instrument used regardless of the research objectives. 2019 was a remarkable year because of the emergence of the COVID-19 pandemic.

This systematic review relied on specific databases (ScienceDirect, Emerald, IEEE and Google Scholar) to search for the articles included in this paper. However, these databases may not comprehensively represent all papers published on e-learning using the technology acceptance model (TAM).

This paper suggests a guide for managers and scholars in educational institutions and acts as a roadmap for practitioners and academics in the educational field and policymakers. This research spotlights the significant factors influencing the acceptance and adoption of e-learning.

This research assessed articles that examined the TAM in e-learning and classified them according to their methodology, country of dissemination, context and distribution within the year of publication. This paper contributes to the body of knowledge in a way that will benefit stakeholders in an educational setting.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).