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The study of novel exponential heat source phenomena across a flowing fluid with a suspension of microparticles and nanoparticles towards a convectively heated plate has been an open question. Therefore, the impact of the exponential heat source in the transport of nano micropolar fluid in the existence of magnetic dipole, Joule heating, viscous heating and convective condition effects has been analytically investigated. Influence of chemical reaction has also been exhibited in this discussion.

The leading equations are constructed via conservation equations of transport, micro-rotation, energy and solute under the non-transient state situation. Suitable stretching transformations are used to transform the system of partial differential equations to ordinary. The transformed ODEs admit numerical solution via Runge–Kutta fourth order method along with shooting technique.

The effects of pertinent physical parameters characterizing the flow phenomena are presented through graphs and discussed. The inclusion of microparticles and nanoparticles greatly affects the flow phenomena. The impact of the exponential heat source (EHS) advances the heat transfer characteristics significantly compared to usual thermal-based heat source (THS). The thermal performance can be improved through the effects of a magnetic dipole, viscous heating, Joule heating and convective condition.

The effectiveness of EHS phenomena in the dynamics of nano micropolar fluid past an elongated plate which is convectively heated with regression analysis is for the first time investigated.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

The purpose of this paper is to statistically explore the relationship between web usability and web presence of the universities. As a case study, five Turkish universities in different rankings which were selected from Webometrics rankings were evaluated and compared.

Two different methods were employed for performing usability evaluation of the selected universities: a user testing was used to measure the user performance on the selected tasks and a questionnaire to assess the user satisfaction on the website use. Both usability evaluation methods were applied on the pre-determined tasks for each university by participation of 20 subjects. After the usability evaluation, the universities were ranked in terms of usability results and finally, the relationship between web usability and web presence of universities was statistically investigated by using Kendall’s rank correlation.

Several common usability problems which were asserted by related previous studies were identified at the end of usability evaluation of university websites. The usability results also revealed that selected Turkish university websites suffer from numerous usability problems. Further, a strong positive correlation (p < 0.05) between the usability of the university websites and their web presences were identified. Hence, the participants showed a higher success and satisfaction while performing the tasks on the university websites which have strong web presences.

The findings from this study have practical implications for universities. Correlation results showed that universities can improve their web usability by giving importance to their web presence volumes. Universities can estimate their web usability levels by investigating their web presence rankings and they can also raise their rankings in Webometrics ranking system by improving the usability of their websites. Moreover, university web developers can design more usable and more user-friendly websites by avoiding usability and design problems identified through usability evaluation.

Different from the prior research efforts focussing on usability of educational web pages, this study contributes to the growing literature by statistically exploring the relationship between web presence and web usability of universities. This study is also precious from the point of view that it is one of the first attempts to evaluate and compare usability levels of a set of universities’ websites from Turkey.

Nowadays, the most important challenge in mechanical engineering, power engineering and electronics is a development of effective cooling systems for heat-generating units. Taking into account this challenge, this study aims to deal with computational investigation of thermogravitational energy transport of pseudoplastic nanoliquid in an electronic chamber with a periodic thermally producing unit placed on the bottom heat-conducting wall of finite thickness under an influence of isothermal cooling from vertical side walls.

The control equations formulated using the Boussinesq approach, Ostwald–de Waele power law and single-phase nanofluid model with experimentally based correlations of Guo et al. for nanofluid dynamic viscosity and Jang and Choi for nanofluid thermal conductivity have been worked out by the in-house computational procedure using the finite difference technique. The impact of the Rayleigh number, nanoadditives concentration, frequency of the periodic heat generation from the local element and thickness of the bottom solid substrate on nanoliquid circulation and energy transport has been studied.

It has been found that a raise of the nanoadditives concentration intensifies the cooling of the heat-generating element, while a growth of the heat-generation frequency allows reducing the amplitude of the heater temperature.

Mathematical modeling of a pseudoplastic nanomaterial thermogravitational energy transport in an electronic cabinet with a periodic thermally generating unit, a heat-conducting substrate and isothermal cooling vertical surfaces to identify the possibility of intensifying heat removal from a heated surface.

This paper aims to present the fabrication and testing of a pressure sensor integrated with Hall effect sensors and permanent magnets arranged in two configurations to measure pressure in the range of 0–1 bar. The sensor is fabricated using stainless steel (SS) and can be used in high-temperature and highly corrosive environments. The fabricated sensor is of low cost, self-packaged and the differential arrangement helps in compensating for any ambient temperature variations.

The sensor deflects of a circular diaphragm with a simple rigid mechanical structure to convert the applied pressure to a Hall voltage output. Two sensor designs are proposed with a single pair of Hall sensors and magnets and a differential configuration with two Hall sensors and magnets. Two sensor designs are designed, fabricated and tested for their input–output characteristics and the results are compared.

The fabricated sensors are calibrated for 25 cycles of ascending and descending pressure in steps of 0.1 bar. Various static characteristics like nonlinearity, hysteresis and % error are estimated for both the sensor designs and compared with the existing Hall effect based pressure sensors. The differential arrangement design was found to have better characteristics as compared to the other design from the experimental data.

This paper focuses on fabricating and testing a novel differential Hall effect based pressure sensor. The differential arrangement of the sensor aids in the compensation of ambient temperature variations and the use of SS enables the sensor in high-temperature and highly corrosive applications. The proposed sensor is low cost, simple and self-packaged, and found to have high repeatability and good linearity compared to other similar Hall effect based pressure sensors available in the literature.

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

The study aims to explore the themes responsible for employee orientation and further their role in building strong customer relationships in telecom and organized retail outlets in emerging markets.

Employing the qualitative approach, a total of 31 in-depth interviews with customer relationship management (CRM) officers of telecom and organized outlets were carried out. Data were analysed using NVIVO-12 plus and ATLAS tools.

The analysis extracted 13 unique themes namely training and development, communication skills, convincing skills, interpersonal skills, knowledge management, grievance handling, organisation's culture development, service climate, adaptive behaviour, customer retention skills, customer profiling, trust and commitment, and suitability of employees. The results claimed that these themes can assist in building strong customer relationships. However, the extent of the significance of themes may vary for telecom and organized retail outlets.

The findings have vital implications for frontline employees to drive customer value as they directly involve with customers. Findings further suggest that hiring of employees according to their location and catchment area of outlets are utmost important.

Employee orientation is an indispensable determinant in building a sustainable relationship with customers through uniqueness and consistency in meeting customers' expectations. This is a first attempt to explore the various unique themes responsible for employee orientation and the imperative role of such themes in maintaining long-lasting customer relationships.

Finding a rational approach to maintain a freshness of foods and perishable goods and saving their intrinsic attributes during a distribution of these products is one of the main issues for distribution and logistics companies. This paper aims to provide a framework for distribution of perishable goods which can be applied for real life situations.

This paper proposes a novel mathematical model for transportation inventory location routing problem. In addition, the paper addresses the impact of perishable goods age on the demand of final customers. The model is optimally solved for small- and medium-scale problems. Moreover, regarding to NP-hard nature of the proposed model, two simple and one hybrid metaheuristic algorithms are developed to cope with the complexity of problem in large scale problems.

Numerical examples with different scenarios and sensitivity analysis are conducted to investigate the performance of proposed algorithms and impacts of important parameters on optimal solutions. The results show the acceptable performance of proposed algorithms.

The authors formulate a novel mathematical model which can be applicable in perishable goods distribution systems In this regard, the authors consider lost sale which is proportional to age of products. A new hybrid approach is applied to tackle the problem and the results show the rational performance of the algorithm.

This paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.

It is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.

DTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.

Verification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.