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Entropy generation in nanofluids with peristaltic scheme occupies a primary consideration in the sense of its application in clinical, as well as the industrial field in terms of improved thermal conductivity of the original fluid. Three-dimensional cylindrical configurations are the most realistic and commonly used geometries which incorporate most of the experimental equipment. In the current study, three-dimensional cylindrical enclosures have been assumed to receive the results of entropy generation occurring due to viscous dissipation, heat transfer of nanofluid and mass concentration of nanoparticles through peristaltic pumping. Applications of the study can be found in peristaltic micro-pumps and novel drug delivery mechanism in pharmacological engineering.

The equations of interest have been structured under physical constraints of lubrication theory and dimensionless strategy. Finalized relations involve highly complicated partial differential equations whose solutions are tabulated through some perturbation procedure and expression of pressure rise is manipulated by a numerical technique through built-in command NIntegrate on Mathematical tool “Mathematica.”

It is evaluated that entropy production goes linear with the greater magnitudes of Brownian motion but inverse characteristics have been sorted against thermophoresis factor.

To the best of authors’ knowledge, this study does not exist in literature yet and it contains a new innovative idea.

This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy flow by merging the firefly algorithm (FA) and the water cycle algorithm (WCA).

Nonlinear Hall currents and EDL effects in multiphase wavy flow are originally described by partial differential equations, which are then translated into an ordinary differential equation model. The hybrid FA-WCA technique is used to take on the optimization challenge and find the best possible design weights for artificial neural networks. The fitness function is efficiently optimized by this hybrid approach, allowing the optimal design weights to be determined.

The proposed strategy is shown to be effective by taking into account multiple variables to arrive at a single answer. The numerical results obtained from the proposed method exhibit good agreement with the reference solution within finite intervals, showcasing the accuracy of the approach used in this study. Furthermore, a comparison is made between the presented results and the reference numerical solutions of the Hall Currents and electroosmotic effects in multiphase wavy flow problem.

This comparative analysis includes various performance indices, providing a statistical assessment of the precision, efficiency and reliability of the proposed approach. Moreover, to the best of the authors’ knowledge, this is a new work which has not been explored in existing literature and will add new directions to the field of fluid flows to predict most accurate results.

This study aims to investigate the impact of family management and ownership structure, including foreign ownership and business group ownership, on corporate performance.

Using an agency perspective and a quantitative research methodology, this study examines listed firms in Pakistan from 2009 to 2018.

The results suggest that family management and concentrated leadership constrain, whereas family leadership, foreign ownership and group ownership strengthen monitoring effectiveness and corporate performance. These findings imply that the shareholder governance logic offers optimal solutions in an emerging economy, as relational governance may activate agency problems.

The findings are consistent with the relevance of relational governance mechanisms in the form of family leadership. However, the results suggest that emerging economies require a hybrid governance model to address their unique agency problems, thereby underlining context relevance in corporate governance scholarship. Furthermore, this research adopts a thick view of institutions to clarify institutional embeddedness and corporate governance contextuality in an emerging economy.

The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.

Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.

Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.

Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.

This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.

This paper aims to present a technique that has been developed to study the wear in the camshaft and tappet. The engine manufacturers use the most suitable materials, lubrication additives and surface coatings to minimize friction in all the components of the engine. Reduced friction results in less wear of critical engine components. The researchers are constantly trying to find an improved lubrication formula which reduces the wear and friction coefficient at a considerably low price. In this regard, the cam follower interface is of much importance because most of the wear occurs in this interface.

The tappets and the cam lobes are analyzed to determine wear. A two-dimensional optical surface profilometer is used to measure the tappet wear, and a high-resolution linear variable differential transformer is used for the measurement of cam lobes. Tests are conducted on Mercedes Benz engine OM 646 under constant camshaft speed, constant inlet lubricant temperature and constant lubricant pressure to study the oil rheology on cam tappet wear.

The results show that the wear occurs on the cam tappet interface, which is almost a linear phenomenon, and it increases with use.

Customized jigs were made to measure wear of camshaft and tappet.

The purpose of this study is to uncover how peer ostracism (POS) elicits knowledge hiding directed towards ostracizing peers through the intervening role of peer contact quality (PCQ). Moreover, the authors aim to highlight the role of the need to belong (NTB) as a first-order boundary condition in direct and indirect hypothesized paths.

The research opted for a three-wave time-lagged survey design. The data were obtained from the 234 teaching and non-teaching employees working in Higher Educational Sector in Pakistan through random sampling. Mediation and moderated mediation analysis was done by using PROCESS Models 4 and 7.

The results embraced the mediation, moderation and moderated mediation hypotheses. It was noted that POS creates negative exchange relationships. As a result, the ostracized employees withhold knowledge from the predating peer. NTB served as a buffering agent between POS and PCQ, as well as, in the indirect POS, PCQ and peer-directed knowledge hiding relationship.

This research serves as a guideline for management and faculty of Higher Educational Institutions for minimization of POS to promote effective collegial contact quality and curb knowledge hiding.

Although the research in workplace ostracism and knowledge hiding is not new, yet how this association emerges from the viewpoint of peers is not known. This study has added to the literature by answering who is more likely to reciprocate ostracism from peers by having poor quality contact and directing knowledge hiding towards the predator. By this, the authors have added to the limited stream of moderated mediation mechanisms underlying ostracism and knowledge hiding behaviour. In addition, the authors have drawn attention to the importance of peer relationships in higher educational settings.

This study aims to evaluate the usability of Google Drive (GD) in addition to measuring the effectiveness of GD and the challenges faced while using this cloud storage application among public library users of Lahore.

The study adopted a sequential explanatory mixed method design comprising the quantitative (QUAN) and qualitative (QUAL) parts. For the QUAN phase, a questionnaire survey was conducted among 384 users of public libraries through purposive sampling. For the QUAL phase, the think aloud technique was performed on several tasks during experiments and verbalized their thoughts and experiences while interacting with the system. The cloud usability model (CUM) was used to measure the “usability” of GD.

Findings indicated that most users were satisfied with GD's obvious features and inherent functions. QUAL results indicated that respondents did most tasks easily, whereas only some could complete the task.

The study is valuable as it is the first time used CUM to measure the perceived usability of GD. Second, the study used a mixed method study to get insights into perceived usability, effectiveness and challenges while using GD. The findings might be helpful for cloud support teams, including GD, as they can enhance certain features of usability, which lead to increased usage among users.

This research work is based on MPhil thesis.

This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.

A compact low-cost antenna structure is proposed to augment the impedance-bandwidth in mm-wave range. Beside it, the paper also aimed to enhance high gain for n260 and n261-bands, suitable for futuristic communication systems.

Design consists of radiating patch and a partial ground plane with semi-circle arc for smooth flow of current. The lower corners of patch are gradually clipped away to make the patch nearly elliptical. Further, two tilted slots at an angle α = 15° are etched at the edges of the patch to augment bandwidth for mm-wave range. These slots divert the periphery current of semi elliptical patch towards center portion of antenna which ensures the participation in radiation of central portion of patch. The upper corners are also clipped away to limit the copper losses and smoothly flow of current. The proposed antenna is designed using HFSS and it is structured on inexpensive FR4 substrate of size 27.5 × 20 mm².

It supports enormous −10 dB bandwidth of 5.86–40GHz (148.89%) even though use of high loss-tangent material and high gain for 28 GHz (27.50–28.35 GHz) n261–band and 37 GHz (37–38.6 GHz) and 39 GHz (38.6–40GHz) n260–bands with a peak-gain of 8.76 dBi, 10.8 dBi and 9.92 dBi, respectively.

The proposed methodology of design is very useful to enhance impedance bandwidth to cover all C–, X–, Ku–, K– and Ka–band even though use of low cost material with high loss tangent. In recent literature, the designs were implemented with a costly material and having very low loss tangent and covers partial suggest bands.