Search results

The purpose of this study is to investigate the association between knowledge sharing (KS) and innovative performance (IP) through the mediating effect of employee creativity (EC) in the manufacturing industries operating in Gujrat, Pakistan. KS among employees and its resultant IP is considered as a basic challenge faced by the manufacturing industries in Pakistan. The main motivation of the current study is to research this significant however neglected segment of the economy.

This quantitative study used deductive approach. Social exchange theory and resource-based view framework were used to predict relationships between EC, KS and IP. Data were collected through five-point Likert scale questionnaire from 158 managers including engineers and operational staff selected through convenient sampling. Regression analysis was performed using the Andrew Hayes Process macros in SPSS.

The results showed that there is a positive and significant effect of the KS on IP. Moreover, the mediation analysis showed that EC positively mediate the relationship between KS and IP.

The study target population is limited the single city where most of industries are located. This can be extended to other industrial areas of the country to increase reliability of the results.

The study holds a significant contribution to the understanding of the link between KS and IP by emphasizing the role of stimulating EC in organizations.

It was identified in the literature that there is a lack of studies in this context as current conceptual model has not been studied before in the context of emerging economies at large and very few studies conducted in case of developed countries with totally different institutional setup. To put forward the study by Lee (2018), this is the first study that investigates the mediation of creativity between KS and IP in emerging markets manufacturing industry.

Petri net (PN) and queuing theory are used in performance analysis of a flexible manufacturing system (FMS). They are used to determine the FMS measures of performance. These measures of performance include optimal work‐in‐process (WIP), lead time, production rate, machine utilization, and number of servers at each station. The purpose of this paper is to present a comparison between PN and queuing network tools to determine the optimum values for FMS measures of performance.

PN model with dual kanban and closed queuing network are used for analysis and performance evaluation of FMS. Integrated network analyzer and Lingo softwares are used for performance evaluation of FMS by PN tool. CANQ software is used for performance evaluation of FMS by queuing network. In both the approaches, the throughput is bounded by the utilization of the bottleneck machines.

The comparison shows that for the given number of servers, PN gives better values of performance measures for FMS. The PN optimization gives minimum WIP corresponding to the maximum production rate. Minimum WIP leads to minimum lead time.

The results are concluded based on one case study. In future research, the results may be achieved by doing more case studies with different numbers of system parameters and/or parameter settings.

Using the PN model, the production manager may design, analyze, evaluate, and even optimize the layout of the production system for minimum WIP, maximum throughput, and reduced lead time. The determination of the total WIP, total number of stations in the production system, and the number of servers at each station may be helpful in shop floor management. It may result in more production efficiency along with ease of supervision.

This paper presents a first novel comparison of its kind between PN and queuing network for evaluation of FMS.

This study aims to investigate the comparative importance of factors influencing the customer shift behavior from conventional to Islamic banking for consumer finance in Pakistan.

First, a comprehensive analysis of the existing literature was conducted to identify a broad range of factors related to customer shift behavior. Through an expert sampling, 14 essential factors were chosen for further investigation. Second, a questionnaire was developed using the analytical hierarchy process (AHP). This questionnaire was then distributed among customers who had previously been using conventional banking services but had made a shift toward Islamic banking. The purpose of this questionnaire was to gather data and insights regarding their motivations and decision-making process behind the shift, and a sample 215 customers are taken in the study.

The results of AHP depicts that the religiosity is a most important factor influencing customers to shift from conventional to Islamic banking, and the second most important factor is pricing. The other subsequent important factors are reputation of the bank, marketing and promotion, service quality, behavior of banks staff, Shariah compliance, management, convenience, fastness and charges/fees. Whereas documentation, ambiance and recommendation are found least important factors to patronize Islamic banking.

The study recommends Islamic banks to create awareness, concentrating on religious factor to have a greater impact on growth of Islamic banking and shrinking of conventional banking. Further, it suggests Islamic banks to apply Shariah-recommended approach of doing business, to help community in best possible way and to launch differentiated marketing techniques to attract customers. It also proposes regulatory authorities to provide facilitation to Islamic banking business by providing level playing field similar to conventional banking, tax equality and conversion of public financing from conventional banking to Islamic banking.

The originality of this study lies in its comprehensive analysis of factors influencing consumer shift behavior from conventional to Islamic banking in the context of consumer finance in Pakistan. By using the AHP, the study provides a structured approach to understanding the relative importance of these factors. This is the uniqueness of the paper that it applies the AHP for the analysis. Furthermore, the study offers practical implications for Islamic banks and regulatory authorities to effectively address and capitalize on this consumer shift trend.

The purpose of this paper is to present a new generic Petri net (PN) model based on assembly plan for assembly sequence optimization. The model aims to allow modeling the flexible assembly system (FAS) configuration, determining the optimal work in process, lead time, throughput, and utilization of each station. Moreover, it aims to show assembly features (AFs) as being useful in assembly sequence planning.

Sophisticated knowledge of AFs is used to get very few feasible assembly sequences (ASs) rather than all possible ASs for a product. A PN model is developed to find out the near optimal assembly sequence out of the sequences obtained from the AF knowledge. It is also used for design and performance evaluation of FAS. Multiple optimization criteria are used for assembly sequence optimization, keeping in view the line balancing. The PN is optimized using weighted‐WIP when the throughput is bounded by the utilization of the bottleneck machines.

The results achieved from the example show a considerable reduction in the number of feasible ASs for a product. The PN optimization gives minimum WIP corresponding to the maximum production rate. Moreover, the PN model pushes more inventories to the initial assembly phase.

The proposed PN can be easily extended for inclusion of dual kanban, where the managers may adjust the number of kanban cards as per the requirement.

Managers may use the concept of multiple AFs in order to design and operate robot assembly that will result in more efficient sequence planning. Using the PN model, the assembly manager may design, analyze, evaluate, and even optimize the layout of the FAS for minimum WIP, maximum throughput, and reduced lead time. The determination of total WIP, total number of stations in the assembly line, and the number of servers at each station may be helpful in the factory floor management. Line balancing may result in the highest efficiency and the shortest idling time along with ease of management and supervision.

This paper provides a clear insight into how a large reduction in the number of feasible ASs for a product can be obtained using the knowledge of AFs. It also presents a new PN model used for assembly sequence optimization and design and performance analysis of FAS.

The purpose of this paper is to present a Petri net (PN) model based on assembly plan is presented for modeling a flexible assembly system (FAS) configuration, determining an optimal work‐in‐process (WIP), lead time, throughput, and utilization of each station.

PN model is developed for design, analysis, and performance evaluation of a FAS, keeping in view the assembly line balancing. PN is optimized using weighted WIP. The throughput is bounded by the utilization of the bottleneck machines.

The PN optimization gives minimum WIP corresponding to the maximum production rate. Minimum WIP leads to minimum lead time. The weighted arcs make the model simple and reduce the optimized number of kanbans. Moreover, the PN model pushes more inventory to the initial assembly phase. This reduces the total cost of the WIP.

In the proposed PN model, the transportation times are included in the transitions times. In the future research, the proposed model can be extended for inclusion of transportation times for AGVs.

Using the PN model, the assembly manager may design, analyze, evaluate, and even optimize the layout of the assembly system for minimum WIP, maximum throughput, and reduced lead time. The determination of the total WIP, total number of stations in the assembly system, and the number of servers at each station may be helpful in factory floor management. The same cycle time is managed at each assembly station for the purpose of line balancing. It may result in the highest efficiency and the shortest idling time along with ease of management and supervision.

This paper presents a new PN model for the design and performance evaluation of a dual kanban FAS.

Ohmic heating generates temperature with the help of electrical current and resists the flow of electricity. Also, it generates heat rapidly and uniformly in the liquid matrix. Electrically conducting biofluid flows with Ohmic heating have many biomedical and industrial applications. The purpose of this study is to provide the significance of the effects of Ohmic heating and viscous dissipation on electrically conducting Casson nanofluid flow driven by peristaltic pumping through a vertical porous channel.

In this analysis, the non-Newtonian properties of fluid will be characterized by the Casson fluid model. The long wavelength approach reduces the complexity of the governing system of coupled partial differential equations with non-linear components. Using a regular perturbation approach, the solutions for the flow quantities are established. The fascinating and essential characteristics of flow parameters such as the thermal Grashof number, nanoparticle Grashof number, magnetic parameter, Brinkmann number, permeability parameter, Reynolds number, Casson fluid parameter, thermophoresis parameter and Brownian movement parameter on the convective peristaltic pumping are presented and thoroughly addressed. Furthermore, the phenomenon of trapping is illustrated visually.

The findings indicate that intensifying the permeability and Casson fluid parameters boosts the temperature distribution. It is observed that the velocity profile is elevated by enhancing the thermal Grashof number and perturbation parameter, whereas it reduces as a function of the magnetic parameter and Reynolds number. Moreover, trapped bolus size upsurges for greater values of nanoparticle Grashof number and magnetic parameter.

There are some interesting studies in the literature to explain the nature of the peristaltic flow of non-Newtonian nanofluids under various assumptions. It is observed that there is no study in the literature as investigated in this paper.

To gain a better and more comprehensive understanding, this study aims to investigate the literature to explore the two popular diets’ health benefits and concerns. Google Scholar and PubMed were used to search for available and relevant nutrition and health articles that pertain to the benefits and concerns of plantogenic and ketogenic diets. Search terms like low carbohydrate, diet, ketogenic, vegetarian and chronic diseases were used. Information was obtained from review articles and original research articles and checked for accuracy. Ketogenic diets have been used for a long time for convulsion in children and now reappeared for weight loss purposes.

Ketogenic and plantogenic (plant-based) diets have been adopted today by many professionals and the public.

Ketogenic diets have been used for a long time for convulsion in children and now reappeared for weight loss purposes. Plantogenic diets also have been practiced for many years for religious, health and environmental reasons. Compared to plantogenic diets, ketogenic diets lack long-term evidence of its potential benefits and harm.

Maybe Lacto-ovo vegetarian and pesco-vegetarian (eat fish but not meats) diets are OK. However, for strict plantogenic diets (total plantogenic/vegan diet), the risk of mineral or vitamin deficiency is present (Melina et al., 2016). Of particular concern is dietary vitamin B12, which is obtained mostly from animal sources (Melina et al., 2016). A long-term deficiency of vitamin B12 can lead to macrocytic anemia and cause neuro and psychological effects (Obeid et al., 2019). Also, omega-3 fatty acids may be deficient in such a diet and probably need to be supplemented on those who follow the total plantogenic diet (Melina et al., 2016). Other deficiencies of concern would be zinc, iron, calcium, vitamin D and iodine (Melina et al., 2016). Another disadvantage is that many junk foods could be easily classified within the plantogenic diet, such as sugar, cakes, French fries, white bread and rice, sugar-sweetened beverages and sweets in general. These items are related to higher weight gain and, consequently, to a higher incidence of diabetes and other chronic diseases (Schulze et al., 2004; Malik et al., 2006; Fung et al., 2009).

Plantogenic diets were concluded to have sustainable health benefits for humans and the environment over ketogenic diets, which could be used but under professional follow-up only.

The purpose of this paper is to design a hybrid robust tracking controller based on an improved radial basis function artificial neural network (IRBFANN) and a novel extended-state observer for a quadrotor system with various model and parametric uncertainties and external disturbances to enhance the resiliency of the control system.

An IRBFANN is introduced as an adaptive compensator tool for model and parametric uncertainties in the control algorithm of non-singular rapid terminal sliding-mode control (NRTSMC). An exact-time extended state observer (ETESO) augmented with NRTSMC is designed to estimate the unknown exogenous disturbances and ensure fast states convergence while overcoming the singularity issue. The novelty of this work lies in the online updating of weight parameters of the RBFANN algorithm by using a new idea of incorporating an exponential sliding-mode effect, which makes a remarkable effort to make the control protocol adaptive to uncertain model parameters. A comparison of the proposed scheme with other conventional schemes shows its much better performance in the presence of parametric uncertainties and exogenous disturbances.

The investigated control strategy presents a robust adaptive law based on IRBFANN with a fast convergence rate and improved estimation accuracy via a novel ETESO.

To enhance the safety level and ensure stable flight operations by the quadrotor in the presence of high-order complex disturbances and uncertain environments, it is imperative to devise a robust control law.

A new idea of incorporating an exponential sliding-mode effect instead of conventional approaches in the algorithm of the RBFANN is used, which makes the control law resistant to model and parametric uncertainties. The ETESO provides rapid and accurate disturbance estimation results and updates the control law to overcome the performance degradation caused by the disturbances. Simulation results depict the effectiveness of the proposed control strategy.

This paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.

The proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.

The performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.

For the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.

The proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.