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The purpose of this paper is to highlight the biological activities of low-calorie natural sweetener, i.e. monk fruit (Siraitia grosvenorii), which are associated with its bioactive constituents.

Recent investigations focused on biochemical characterization and nutraceutical potential of monk fruit (traditional Chinese perennial vine) have been critically reviewed. Also, the safety and influence of monk fruit on organoleptic characteristics of prepared food products have been documented.

Biochemistry of monk fruit revealed that mogrosides are the principal compounds responsible for the high-intensity sweetness in the monk fruit. The fruit induces several biological activities including anti-oxidative effect, hypoglycemic response, anti-allergic properties, anti-carcinogenic and anti-tissue damage activities. Attributing to great potential as a bio-functional sweetener in food products, monk fruit extract has been approved as Generally Regarded as Safe.

This paper highlights the biological potential of monk fruit opening the doors to future investigations for its utilization in products of commercial importance including food and pharmaceutical preparations.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

In the pharmaceutical industry, marketing and sales managers often deal with massive amounts of marketing and sales data. One of their biggest concerns is to recognize the impact of actions taken on sold-out products. Data mining discovers and extracts useful patterns from such large data sets to find hidden and worthy patterns for the decision-making. This paper, too, aims to demonstrate the ability of data-mining process in improving the decision-making quality in the pharmaceutical industry.

This research is descriptive in terms of the method applied, as well as the investigation of the existing situation and the use of real data and their description. In fact, the study is quantitative and descriptive, from the point of view of its data type and method. This research is also applicable in terms of purpose. The target population of this research is the data of a pharmaceutical company in Iran. Here, the cross-industry standard process for data mining methodology was used for data mining and data modeling.

With the help of different data-mining techniques, the authors could examine the effect of the visit of doctors overlooking the pharmacies and the target was set for medical representatives on the pharmaceutical sales. For that matter, the authors used two types of classification rules: decision tree and neural network. After the modeling of algorithms, it was determined that the two aforementioned rules can perform the classification with high precision. The results of the tree ID3 were analyzed to identify the variables and path of this relationship.

To the best of the authors’ knowledge, this is one of the first studies to provide the real-world direct empirical evidence of “Analytics of Physicians Prescription and Pharmacies Sales Correlation Using Data Mining.” The results showed that the most influential variables of “the relationship between doctors and their visits to pharmacies,” “the length of customer relationship” and “the relationship between the sale of pharmacies and the target set for medical representatives” were “deviation from the implementation plan.” Therefore, marketing and sales managers must pay special attention to these factors while planning and targeting for representatives. The authors could focus only on a small part of this study.

An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive capabilities, bringing significant efficiency gains in an increasingly complex and uncertain environment. This paper aims to both bridge the gap between the practitioner and academic literature on these topics and contribute to both practice and theory by seeking to understand how such developments will help to address key supply chain challenges and opportunities.

A multi-disciplinary, systematic literature review was conducted on relevant concepts and capabilities. A total of 126 articles were reviewed covering the time period 1950-2018.

The results show that both IoT and AI are the technologies most frequently associated with the anticipated autonomous and predictive capabilities of future supply chains. In addition, the review highlights a lacuna in how such technologies and capabilities help address key supply chain challenges and opportunities. A new supply chain model is, thus, proposed, one with autonomous and predictive capabilities: the self-thinking supply chain.

It is our hope that this novel concept, presented here for the first time in the academic literature, will help both practitioners to craft appropriate future-proofed supply chain strategies and provide the research community with a model (built upon multidisciplinary insights) for elucidating the application of new digital technologies in the supply chain of the future. The self-thinking supply chain has the potential in particular to help address some of today’s key supply chain challenges and opportunities.

This paper aims to investigate hypothesized relationships between the Internet of things (IoT) and big data analytics (BDA) with supply chain visibility (SCV) and operational performance (OP) in the pharmaceutical manufacturing sector in Jordan. The paper also aims to test the conceptual model related to the indirect effects of SCV.

To achieve the objectives of this paper, a conceptual model was developed through a review of the current literature. Data analysis was performed by covariance-based structural equation modelling using Amos 25 software, and the convergent validity, discriminant validity, reliability and confirmatory factor analysis were verified. Then the hypotheses were tested.

The results of the study indicate that there is a positive and statistically significant relationship between the IoT and BDA on SCV and OP. The relationship was positive and statistically significant between SCV and OP. In addition, support for the mediation hypothesis that SCV mediates the relationship among IoT, BDA and OP was confirmed.

This paper provides new theoretical and managerial contributions that have not been covered in previous studies, and it is considered the first study that uses this conceptual model with this mechanism in terms of the theoretical lens and empirical application. This paper contributes to understanding the dynamic mechanisms of the IoT and BDA in enhancing OP, which contributes to creating a supply chain capable of facing various environmental fluctuations and pressures. This study presents new implications that can be used in the supply chain literature.

This paper aims to present a hybrid approach based on classification algorithms that was capable of identifying different types of phishing pages. In this approach, after eliminating features that do not play an important role in identifying phishing attacks and also after adding the technique of searching page title in the search engine, the capability of identifying journal phishing and phishing pages embedded in legal sites was added to the presented approach in this paper.

The hybrid approach of this paper for identifying phishing web sites is presented. This approach consists of four basic sections. The action of identifying phishing web sites and journal phishing attacks is performed via selecting two classification algorithms separately. To identify phishing attacks embedded in legal web sites also the method of page title searching is used and then the result is returned. To facilitate identifying phishing pages the black list approach is used along with the proposed approach so that the operation of identifying phishing web sites can be performed more accurately, and, finally, by using a decision table, it is judged that the intended web site is phishing or legal.

In this paper, a hybrid approach based on classification algorithms to identify phishing web sites is presented that has the ability to identify a new type of phishing attack known as journal phishing. The presented approach considers the most used features and adds new features to identify these attacks and to eliminate unused features in the identifying process of these attacks, does not have the problems of previous techniques and can identify journal phishing too.

The major advantage of this technique was considering all of the possible and effective features in identifying phishing attacks and eliminating unused features of previous techniques; also, this technique in comparison with other similar techniques has the ability of identifying journal phishing attacks and phishing pages embedded in legal sites.

The purpose of this paper is to unravel the underpinnings of the phenomenon of shared leadership. The study was carried out with the objective of answering questions such as what is shared in shared leadership, what are individual and team level factors which lead to sharing and what are the outcomes of shared leadership.

The study adopted a qualitative approach. Eighteen in-depth interviews were conducted. The data were analysed using Nvivo 11 software.

The study found that in entrepreneurial teams while sharing leadership, cofounders share competencies, roles, vision, stress and decision-making. The study also reveals various individual and team level factors which facilitate shared leadership and its outcomes.

The study offers critical insights regarding the characteristics of individuals and team where shared leadership would work and hence can be used to understand the factors to be considered while forming teams. The study also has important insights for the investors regarding what dynamics to look for in individuals and teams before making investment decisions.

The inductive approach adopted in the study helps in understanding some of the basic underpinnings of the phenomenon of shared leadership which were not adequately answered by previous studies.

The purpose of this paper is to assess the influence of blends of Jatropha methyl ester (JME) and its nano Al₂O₃ emulsion on variable compression ratio diesel engine. The oxygen in alumina contributed for the smooth burning and resulted in improved performance and emissions.

The biodiesel (methyl ester) is prepared from the raw Jatropha oil. The B10, B20 and B30 blends of and their nanoemulsions are prepared with the 25, 50, 75 and 100 ppm of nano Al₂O₃. The prepared JME blends and its nanoemulsions are tested in a variable compression ratio (VCR) diesel engine to evaluate the engine performance and emission characteristics.

The nanoemulsion B20 + 50 ppm has given maximum brake thermal efficiency (BTE), and with the increased proportion of nanoparticle, the BTE was reduced. Also, the specific fuel consumption is lowest (0.2826 kg/kWh) for B20 + 50 ppm at the compression ratio 16.5 and full load which is 4.10% lower than the diesel and 5.8% lower than the B20 blend. As the load increases, NOx emission increases owing to higher peak temperatures in the combustion chamber. The JME-nano Al₂O₃ emulsion reduces the HC and CO emission compared with all other fuels.

Novel nano emulsions are prepared, characterized and tested on VCR engine.