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The purpose of this manuscript is to explore an assignment given to students in an online gender and leadership graduate course as a tool to help them think critically about how music influences perceptions of gender roles in both society and leadership.

The assignment directs students to review the current Billboard “Hot 100” chart, which lists the top 100 songs in the United States each week based on sales and streams. Students are prompted to identify a song with gendered themes and discuss how the song portrays women and/or men, what gender stereotypes the song supports or refutes, and whether the messaging is positive or negative in nature. Finally, the students discuss ways that the message in the song could influence the listener’s opinion about gender stereotypes and what effect that could have on gendered leadership issues.

Students use this assignment as an opportunity to apply the course material that relates to the importance of gender representation and the influence of media on gender issues in leadership.

Recommendations are provided to inspire creative ideas for leadership educators who seek to prepare students to understand organizational challenges related to gender issues in leadership.

The issue of Education for Sustainable Development (ESD) has been on the rise in recent years, with concerns being raised by various stakeholders about its potential effects on education and the environment. However, little research has been done into school leaders' fundamental challenges in addressing ESD.

A qualitative, single-case study was embarked on to examine the experiences of Malaysian school leaders who actively engaged ESD in their schools. The data were collected by using semi-structured interviews among four school leaders in schools involved in Johor Sustainable Education Action Plan (JSEAP). A thematic analysis was used to understand the challenges and later drive the strategy used to overcome those challenges.

This preliminary study revealed that the principals perceived the ESD's four main challenges: encouraging positive thinking and passion, acquiring ESD knowledge, developing system thinking, and curriculum adaptation. Based on this study's findings, school leaders need to make concerted efforts to overcome these challenges, such as finding best practices, encompassing support systems, and exploring innovative partnerships to address ESD effectively in their schools.

This paper is limited to a case analysis of the selected schools and cannot be generalized to a larger population.

The results of the study may be of interest to other school leaders and educators who are concerned about ESD and its role in their schools, as well as to other academics who are interested in the topic of ESD and the challenges faced by school leaders in implementing sustainable practices.

To the authors' knowledge, this is the first study investigating ESD challenges in the Malaysian context. The novel finding helps the readers understand the recent phenomena of ESD implementation better and, at the same time, compare it to other settings.

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.

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.

Education for sustainable development (ESD) has gained significant attention, but integrating ESD into existing education systems is challenging. The study aims to explore the challenges of ESD experienced by school leaders, focusing on the context of Malaysian schools.

The study uses a qualitative approach with a single-case study design. Eight school leaders involved in the Johor sustainable education action plan (JSEAP) were interviewed and analyzed. The study uses thematic analysis to identify the challenges and other causes associated with the implementation of ESD.

This study revealed that the school leaders perceived the ESD challenges at three levels. First, restriction to the standardized curriculum (systemic); second, resistance to change (organization) and third, awareness and readiness (individual). These themes stemmed from seven primary codes that school leaders encountered throughout the JSEAP program.

This paper is limited to a case study of the chosen schools and cannot be extrapolated to a larger population.

The study benefits school leaders and educators concerned about ESD and its role in their schools and other academics interested in ESD.

To the authors' knowledge, this is the first study to investigate ESD challenges in Malaysia. The novel discovery of the three levels of ESD challenges helps readers better understand the recent phenomenon of ESD implementation and compare it to other settings.

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.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.