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This study aims to estimate Pakistan’s export potential in new and existing export products, as well as their potential destination markets.

This study uses a nonparametric approach based on demand, supply and easiness factors for estimating export potential at disaggregated product and destination levels.

A significant number of new export products (extensive margin) and existing products (intensive margin) are identified that have export potential. The estimated unrealized export potential at extensive margins is $2bn and at intensive margins is $5bn. The range of new products included value-added products, semifinished products and intermediate products. Surprisingly, there is high potential to diversify in China and export existing products more intensively in the EU. Moreover, the potential at extensive margins is regional diverse compared to intensive margins.

The methodology used in this paper only provides export potential for short-to-medium term period because the global demand conditions are varying. Also, the mineral and resource-based products cannot be included in the analysis because their exports are heavily dependent on the availability of natural resources.

The findings have important policy implications in terms of providing guidelines for government policies related to industrial development, international trade and export promotion at the product and destination level. Overall, the study reveals that traditional sectors lack room for product diversification. As the existing export incentives favor major industries. To foster diversification, existing incentives must be redesigned to cover new products or sectors. Moreover, China has the greatest potential for product diversification, while Europe has the greatest potential to export current products more intensively. Further research is needed to simulate trade policy scenarios and estimate demand, supply and ease factors in export potential.

This study provides a unique perspective on export potential assessment at disaggregated product and destination levels, reinforcing the importance of redesigning trade policies and export incentives separately for export diversification.

The evolution of technologies has unleashed a wealth of challenges by generating massive amount of data. Recently, biological data has increased exponentially, which has introduced several computational challenges. DNA short read alignment is an important problem in bioinformatics. The exponential growth in the number of short reads has increased the need for an ideal platform to accelerate the alignment process. Apache Spark is a cluster-computing framework that involves data parallelism and fault tolerance. In this article, we proposed a Spark-based algorithm to accelerate DNA short reads alignment problem, and it is called Spark-DNAligning. Spark-DNAligning exploits Apache Spark ’s performance optimizations such as broadcast variable, join after partitioning, caching, and in-memory computations. Spark-DNAligning is evaluated in term of performance by comparing it with SparkBWA tool and a MapReduce based algorithm called CloudBurst. All the experiments are conducted on Amazon Web Services (AWS). Results demonstrate that Spark-DNAligning outperforms both tools by providing a speedup in the range of 101–702 in aligning gigabytes of short reads to the human genome. Empirical evaluation reveals that Apache Spark offers promising solutions to DNA short reads alignment problem.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

The purpose of this paper is to investigate some cheap and highly stable additives to improve the quality of lubricating oil.

The study was performed using phenol, p‐cresol and pyrogallol as antioxidants. The concentration of each antioxidant was varied between 0 and 1 per cent. Sample (50 ml) blended with the antioxidant was taken in the same trap. The trap was placed on water both maintained at a temperature of 50°C. Air was bubbled for the time duration of 6 h. After 6 h, the contents of the trap were carefully collected and characterized. The oxidation was performed in a specially designed glass made U‐shaped trap in the absence and presence of antioxidants. The trap containing sample was tied with an iron stand. Air was bubbled through the sample. The bubbling was assisted by the suction pump at room temperate (24°C). The sample was aerated for time duration of 6 h. After 6 h, the contents of the trap were carefully collected in a dried bottle for physicochemical tests.

The results indicate that phenol is the best antioxidant in concentration of 0.5 per cent amongst the three antioxidants used at room temperature as well as at 50°C. Amongst the antioxidants used, the order of suitability is phenol > pyrogallol > p‐creosol.

The antioxidants studied will help increase the service time of the lubricant to save money and to avoid environmental problems arising from careless disposal of used lubricating oils.

Finding an efficient way for enhancing brand reputation by using social media among customers and business partners has long been the main purpose of every business. However, due to micro and macro changes in the business field, strategies to find a way to improve brand reputation with business partners and customers have altered from transaction marketing to newer approaches such as employing a social media platform. However, not all brands make use of social media to enhance their reputation. The present study uses Apple as an example of the one of the best known brands which does not benefit from social media platforms as to the degree expected.

The purpose of this paper is to examine the mediating role of work engagement (vigor and dedication) between job resources (job characteristics, supervisor and co-worker support, participation in decision making and job security) and job performance (task and contextual) rated by the supervisor.

A sample of 364 nurses and their supervisors was used. Structural equation modeling with Amos 17 was used to obtain a model fit with path significance of work engagement as the mediator between job resources and job performance.

The results found support for the proposed conceptual claim and confirm that work engagement with a two-factor model (vigor and dedication) mediates the relationship between job resources (job characteristics, supervisor and co-worker support, participation in decision making and job security) and with a multidimensional construct of job performance (task and contextual performance) rated by the supervisor.

The findings of this research will help human resource managers and professionals to further develop the working environment, provide job security and opportunities for employees to participate in decision making in a way that enhances employee work engagement, which, ultimately, improves employee job performance.

Past studies have not previously tested the two-factor model of work engagement (vigor and dedication) as the mediating variable between job resources (job characteristics, supervisor and co-worker support, participation in decision making and job security) and job performance rated by the supervisor.

Reports the results of a study into the urban health issues of Muslim minorities, in the city of Aligarh, India, in 1991‐1993. Outlines the conceptual framework and presents the methodology before providing a brief portrait of the Muslim minority status and urbanization. Concludes that basic health information is still lacking. Comments that infectious diseases, malnutrition and occupational diseases are the most common, with women, children and elderly being the most vulnerable.

The purpose of this paper is to identify the influence of the company’s reputation and individual consumer involvement in the relationship between satisfaction, loyalty and willingness to pay more for a product.

The method used is quantitative, by means of a survey with real consumers of automotive services of two vehicle dealerships, whose data were analyzed through linear regression analysis and conditional analysis of moderation.

The authors have identified that the relationship between satisfaction and loyalty and between loyalty and willingness to pay more for a product is entirely moderated by the (high) reputation of the brand and the (high) individual involvement of the consumer.

The study contributes to marketing managers as it demonstrates effect of brand reputation and involvement. Therefore, it is understood that these variables need to be considered in satisfaction surveys, as it has been proven that satisfaction alone cannot explain the variables of business performance (loyalty and willingness to pay).

The greatest innovation of this study is the identification of the total moderation between stated satisfaction and loyalty and between satisfaction and willingness to pay more. It has been demonstrated that high levels of brand reputation coupled with high levels of consumer involvement account for the fully dependent variables.

The purpose of this paper is to examine whether management supports police officers adequately, or whether police have to rely on their individual attributes, specifically psychological capital (PsyCap), to cope with red tape and stress. Work outcomes/consequences examined were discretionary power, affective commitment and turnover intentions.

A cross-sectional design using a survey-based, self-report strategy was used to collect data from 588 police officers from USA, who are most engaged with the public. The data were analysed using AMOS and a structural model to undertake structural equation modelling.

Two significant paths were identified Path 1: management support to red tape to discretionary power to affective commitment and turnover intentions; and Path 2: supervisor relationships to PsyCap to stress to affective commitment and turnover intentions. Further, management support predicted PsyCap, red tape and police stressors. Red tape increased police stressors and turnover intentions.

The use of self-report surveys is a limitation, causing common methods bias. Using Harmon’s one-factor post hoc test, the authors were able to provide some assurance that common method bias was of no major concern.

As far as is known, this study is the first to examine, for police officers, how PsyCap impacts upon negative factors (stress and red tape) and enhances positive drivers for employees. Examining the impact of an individual attribute – PsyCap – provides an important piece of the organizational puzzle in explaining the commitment and turnover intentions of police officers. By examining the impact of both organizational and individual factors, there is now more knowledge about the antecedents of police outcomes.

The prime objective of this study is to examine the impact of sight cues and corporate social responsibility (CSR) activities of the Islamic banks on loyalty by improving the corporate image (CI) of the Islamic banks. This study also examines the mediating effect of a CI along with moderating impact of religion and collectivism on loyalty.

The data were obtained through the purposive sampling method by distributing questionnaires among the customers of Islamic banks. A total of 550 questionnaires were surveyed from six full-fledged commercial Islamic banks and eight Islamic windows of the commercial bank in Pakistan using variance-based structural equation modelling (partial least squares) to test the hypothesis.

The results of the study reflected that sight cues and CSR activities have a significant impact on CI and keeping the customer’s loyalty. Additionally, religion and collectivism also moderate the relationship between CI and loyalty.

From a managerial perspective, the study finding exhibits the significant effect, both directly and indirectly, of religiosity on the loyalty intention of the customers of Islamic banks. Hence, persuading the client that the bank is in complete compliance with (Shariah) will forestall their clients switching to another bank.

This study is unique in its nature because it revealed the factors that lead to customer loyalty towards Islamic banks. The study found sight cues and CSR are the new indicators used to build CIs of Islamic banks.