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Despite favourable agro-ecological conditions and being the largest international mango producer, India still struggles to build competence in sustainable mango production and post-harvest. The purpose of this paper is to contribute to the literature on innovation capacity development, and to explore aspects of innovation systems ideas in the analysis of mango production and marketing by small-scale farmers in the South Indian state of Andhra Pradesh.

This paper uses case study research methods to an analysis of the sector ' s recent history combined with an empirical account of systems thinking on integrating technology supply chains and commodity supply chains.

Findings suggest that the case of mango production and post-harvest in the Krishna district is a dismal one and the remedial actions to strengthen mango innovation systems in the district relate to aspects of capacity development to promote upward spiral of learning and innovation, and involve multistakeholder processes to integrate the supply chains of technologyand commodity.

This paper, with its aim to contribute to the literature on innovation capacity development, brings together conventionally distinct bodies of literature on strengthening innovation systems and developing stakeholder capacity. The value of this paper lies on how it addresses technology supply and commodity supply issues in the analysis of competence challenges to strengthening mango innovation systems performance.

This paper aims to propose and analyse a two-prey–one-predator system with mixed functional response.

The predator exhibits Holling type IV functional response to one prey and Holling type I response to other. The occurrence of various positive equilibrium points with feasibility conditions are determined. The local and global stability of interior equilibrium points are examined. The boundedness of system is analysed. The sufficient conditions for persistence of the system is obtained by using Bendixson–Dulac criteria. Numerical simulations are carried out to illustrate the analytical findings.

The authors have derived the local and global stability condition of interior equilibrium of the system.

The authors observe that the critical values of some system parameter undergo Hopf bifurcation around some selective equilibrium. Hence, numerical simulations reveal the condition for the system to be stable and oscillatory.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

This paper aims to study the various developments taking place in the field of gas sensors made from polyaniline (PANI) nanocomposites, which leads to the development of high-performance electrical and gas sensing materials operating at room temperature.

PANI/ferrite nanocomposites exhibit good electrical properties with lower dielectric losses. There are numerous reports on PANI and ferrite nanomaterial-based gas sensors which have good sensing response, feasible to operate at room temperature, requires less power and cost-effective.

This paper provides an overview of electrical and gas sensing properties of PANI/ferrite nanocomposites having improved selectivity, long-term stability and other sensing performance of sensors at room temperature.

The main purpose of this review paper is to focus on PANI/ferrite nanocomposite-based gas sensors operating at room temperature.

International Development Enterprises (iDE), a non-profit organization, won numerous awards for its poverty alleviation efforts through the sale of low-cost irrigation technologies to the Base of Pyramid (BoP) farmers around the world. This case discusses iDE’s entry into Nicaragua and the challenges this global social enterprise faced in bringing drip irrigation and other water technologies to the rural subsistence coffee farmers in Nicaragua. It presents the tough decisions it faced in 2012 regarding the future of its for-profit social business, iDEal Tecnologias, in Nicaragua. This case captures the tension in hybrid social enterprises.

This case was developed through the following primary sources as well as some secondary sources. Primary: discussions with iDE’s CEO Doerksen, Urs Heierli (Coordinator of iDE’s operations in Nicaragua), and Skype conversations with iDEal Tec’s Country Director, Nadja Kränzlin. Secondary: documents provided by the company and other publicly available sources.

This case is intended for use in undergraduate, graduate, and executive courses in: social entrepreneurship, non-profit management, and managing sustainable businesses. It may also be used in the sustainability module of courses in international business/management/marketing, and business strategy and policy. It would be best to position this case toward the middle or latter half of the course as it is an integrative case that challenges students to evaluate the sustainability of a social enterprise from multiple perspectives.

The theoretical bases for this case are: defining and implementing a sustainable strategy in hybrid social enterprises. Serving BoP customers with a vision of enabling prosperity.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

This research is based on a theoretical interest and practical need to examine female actors as entrepreneurs and innovators. The Saudi Vision 2030 emphasizes entrepreneurship, innovation and stronger female participation in the work force. The purpose of this paper is to point to theoretical underpinnings in female entrepreneurial and innovative activity and to examine success factors for female entrepreneurial innovators in the context of the Kingdom of Saudi Arabia (KSA).

The paper is based on secondary data sources. The secondary data in this research were collected from recent academic journals related to female innovation and entrepreneurship. The selected literature represents international and regional (Middle Eastern) research, including research addressing KSA.

Theories are not universal but contextually bound, and Western theories may not directly apply to the KSA context. The late female labor history and gender segregation have not formed an environment similar to the West where innovation has a male connotation and where gender is created or recreated in interaction. The paper suggests gender differences and similarities in innovation as a suitable approach to examine female entrepreneurs and innovators in KSA. While there are similar tendencies between the Saudi and international contexts in regard success factors, KSA inhibits unique factors associated with female entrepreneurs and innovators and their environment.

This research is aimed at academia and offers points to consider for theoretical examination, focusing on the fitness of theory from an economic, social and cultural perspectives. The research may offer knowledge to policymakers and practitioners in the Middle East region and KSA when applying new policies on entrepreneurship and innovation.

The value of the research lies in its timeliness. While there are economic rationales for research on female entrepreneurial innovators, there are theory-related reasons to examine the topic. With the increased interest in research on Saudi female entrepreneurs, academic discussion needs to take a closer look at the context to gain an understanding of how theoretical approaches may or may not transfer across regions.

Diagnosing genetic neuromuscular disorder such as muscular dystrophy is complicated when the imperfection occurs while splicing. This paper aims in predicting the type of muscular dystrophy from the gene sequences by extracting the well-defined descriptors related to splicing mutations. An automatic model is built to classify the disease through pattern recognition techniques coded in python using scikit-learn framework.

In this paper, the cloned gene sequences are synthesized based on the mutation position and its location on the chromosome by using the positional cloning approach. For instance, in the human gene mutational database (HGMD), the mutational information for splicing mutation is specified as IVS1-5 T > G indicates (IVS - intervening sequence or introns), first intron and five nucleotides before the consensus intron site AG, where the variant occurs in nucleotide G altered to T. IVS (+ve) denotes forward strand 3′– positive numbers from G of donor site invariant and IVS (−ve) denotes backward strand 5′ – negative numbers starting from G of acceptor site. The key idea in this paper is to spot out discriminative descriptors from diseased gene sequences based on splicing variants and to provide an effective machine learning solution for predicting the type of muscular dystrophy disease with the splicing mutations. Multi-class classification is worked out through data modeling of gene sequences. The synthetic mutational gene sequences are created, as the diseased gene sequences are not readily obtainable for this intricate disease. Positional cloning approach supports in generating disease gene sequences based on mutational information acquired from HGMD. SNP-, gene- and exon-based discriminative features are identified and used to train the model. An eminent muscular dystrophy disease prediction model is built using supervised learning techniques in scikit-learn environment. The data frame is built with the extracted features as numpy array. The data are normalized by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations. Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. This paper also deliberates the results of statistical learning carried out with the same set of gene sequences with synonymous and non-synonymous mutational descriptors.

The data frame is built with the Numpy array. Normalizing the data by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn. While learning the SVM model, the cost, gamma and kernel parameters are tuned to attain good results. Scoring parameters of the classifiers are evaluated using tenfold cross-validation using metric functions of scikit-learn library. Results of the disease identification model based on non-synonymous, synonymous and splicing mutations were analyzed.

Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. The performance of the classifiers are increased by using different estimators from the scikit-learn library. Several types of mutations such as missense, non-sense and silent mutations are also considered to build models through statistical learning technique and their results are analyzed.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations.

The topic of Lean Six Sigma (LSS) implementation in a supply chain context is an emerging research stream comprising of diverse objectives and complex processes thereby presenting opportunities for further exploration and organizational process improvement. Thus, this study proposes an integrated multi-criteria decision-making methodology to determine what can facilitate the successful implementation of LSS as an organizational change strategy in the manufacturing supply chain.

The proposed methodology based on Decision-Making Trial and Evaluation Laboratory and hierarchical Evaluation Based on Distance to Average Solution is employed to ascertain the relative importance and priorities of an identified framework of factors with the aid of opinions of managers in the Nigerian plastics industry.

The results show a high significance of institution-based factors (e.g. government regulations) and present relevant implications to the policymakers as well as the managers and practitioners of the plastics manufacturing industry.

This study indicates a possible pathway to accurately evaluate a framework of critical factors to integrate and institutionalize LSS in the manufacturing supply chain for organizational performance improvement.

The purpose of this paper is to pay tribute to several of the ideas of Jan‐Erik Gröjer by reviewing and critiquing the field of Human Competence Accounting (HCA) since his Accounting, Auditing & Accountability Journal review article in 1998.Three research questions he posed in that article are now addressed in the current paper: What could be done, as envisaged in the commentary of Gröjer and Johanson; What has been published in HCA research in the ten year period (1999‐2008); What more could/should be done in HCA as a research agenda.

A literature based analysis and critique of HCA accounting articles published in the selected journals from 1999 to 2008 is employed. A purpose built coding and classification scheme is built around several categories for the purpose of analysing the HCA literature.

The paper seeks to demonstrate the importance of Gröjer and Johanson's review and indicates that questions they raised and possible research directions have been acted upon by a number of authors. The findings of the analysis indicate that, a decade on from the original review, HCA is a legitimate area for accounting research and is multi‐disciplinary and multi‐focused in nature.

The paper only considers selected HCA articles within the ten journals used over the period 1999‐2008.American mainstream positivist research has not been reviewed. Also the categories chosen to represent HCA have several limitations which are addressed in the paper.

HCA research has a strong tradition and the Gröjer and Johanson review is an important beacon that provided insights to accounting researchers. The current analysis extends Jan‐Erik's work by providing several insights into what has happened and also provides several ideas for future research and policy work in accounting for HCA.