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Pro-environmental education incorporates digital technologies to enhance the level of pro-environmental behaviours (PEB) in young adults. Guided by gamified learning and self-directed informal learning theories, this study aims to examine the potential for the use of a gamified mobile app to increase students’ PEB. Also, it explores whether various pre-existing environmental attitudes and beliefs can moderate the effects.

This quasi-experimental study proposed an eight-weeks intervention for undergraduate students based on using a mobile app. The authors evaluated the post-intervention differences in PEB between the intervention group and a control group. The effects of multiple moderators were also tested.

Using the mobile app for eight weeks significantly improves the levels of PEB in the intervention group compared to the control group. None of the proposed interactions showed significant moderator effects.

To the best of the authors’ knowledge, this study is the first one to verify the potential moderators accounting for the success of an environmental education approach using a mobile app. Also, it provides strong empirical support for the use of such educational strategy. Based on these findings, the authors suggest the use of gamified mobile apps as suitable tools for pro-environmental education, especially when targeting young adult or student populations. Moreover, using mobile apps providing self-directed informal learning, combined with gamification, can be used to enhance other desirable behaviours.

This paper explores the reporting of the Russian American Company (RAC), from 1840 to 1863. Trading in fur, company fears of animal extinctions viewed from a monetary perspective led to early extinction reporting practice. These were not altruistic reports; they were generated by a wish to use natural resources. Despite the motivations, these reports present an example of successful extinction management by a for-profit company and a workable example of emancipatory extinction accounting.

Using thematic analysis, this study demonstrates how moving from transparency to accountability driven accounting can assist in biodiversity reporting, by exploring this historical business case of extinction management through the lens of Atkins and Maroun's (2018) extinction framework.

The application of the framework to the RAC's set of reports indicates that this offers a viable proposal for development of extinction management, providing a reporting tool for a for-profit company.

Exploring RAC's reports focusing on their extinction management processes and reporting, the paper contributes to the contemporary debate on the development of extinction reporting frameworks. These historical examples of extinction accounting, show extinction management and reporting is not a unique contemporary development in accounting. The research uses historical data as the empirical foundation for exploring applicability and further development of this extinction framework.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

The purpose of this paper is to provide a review of literatures and previous studies on the relationship between strategic planning and performance and propose conceptual designs and hypotheses using multidimensional constructs to advance the understanding of this relationship, contribute to existing debates in the extant literature and make recommendations.

A semi-systematic literature and previous studies (studied by various groups of researchers within diverse disciplines) review approach has been used in this paper to contribute to the debate on whether strategic planning affects performance and how. Using more recent knowledge about the strategic planning concept, the semi-systematic review looked at how research within strategic planning has progressed over the past five decades and its relationship with performance.

In the past, the strategic planning performance relationship has been treated as a black box and this paper proposes that the conceptualisation of a number of constructs and the inclusion of strategy implementation will help converting the black box into a white box. To strengthen support for the debate regarding the relationship between strategic planning and performance this paper proposes a further conceptual/operational design, mathematical expressions and hypotheses to be tested empirically in further studies. The proposal provides a conceptualisation of the major constructs (strategy development; strategy implementation; and performance), and the use of strategy implementation as a mediator and/or as a moderator in the planning performance relationship.

This study is limited due to fact that the findings have not been tested empirically, it is not a cross-sectional and/or a longitudinal research and only a limited number of dimensions of strategy development and strategy implementation have been used. In addition, the approach used is a semi-systematic review followed by quantitative thinking, which, in turn, typically assumes the relevance of and a warrant mainly from a positivist epistemology.

The proposed design developed in this paper ensures that core issues in planning performance relationships research are addressed. Furthermore, the inclusion of strategy implementation in planning performance relationship studies means that the whole chain of activities in the strategy process is being considered, drawing a complete and comprehensive conclusion on how strategic planning affects an organisation’s performance. In addition, by separating strategy implementation and by not combining it with formulation/formation activities will theoretically and analytically help to evaluate the importance or role of each stage of the strategy process. Moreover, the conceptualisation and operationalisation of the key concepts as multidimensional constructs contribute to past research gaps. Finally, this paper provides some clarity to many contradictory findings concerning the strategic planning and performance relationship.

Purpose: The main goal of this discussion is to explain how competitiveness could be an important source of knowledge and economic power in a society, especially in the period of higher demands on knowledge, innovation and organisational base growth. Our focus of the discussion will be tourism as an important service sector economic activity in countries all over the world.

Methodology: The chapter will be conceptually based on its goal to develop the theories of competitiveness and to discuss how competitiveness influences knowledge, organisational processes and forms with a focus on tourism services.

Findings: Competitiveness in tourism depends on many factors. As an intangible source of knowledge, organisational culture processes and organisational forms generally influences tourism activity. For this reason, not only is comparative advantage important in the competitiveness concept, but also competitive advantage and the way of deploying resources play an important role.

Significance: Resources are not only based on labour, capital and land (neoclassical theory approach), but resource-advantage theory underlines the importance of financial, physical, legal, human, organisational, informational and relational capital. In this process, new processes and organisational forms must be created, as well as innovative approaches to processes and the importance of knowledge capital.

Practical Implications: New ideas about this process could be helpful for researchers and practitioners to recognise the importance of competitiveness for their work and research.