This paper discusses the dawn of cognitive neuroscience in management and organizational research. The study does that in two tiers: first, it reviews the interdisciplinary field of organizational cognitive neuroscience, and second, it analyzes the role organizational cognitive neuroscience (OCN) could play in reducing counterproductive workplace behaviors (CWB). Theoretically, the literature has established the benefits of a neuro-scientific approach to understanding various organizational behaviors, but no research has been done on using organizational neuroscience techniques to study counterproductive work behaviors. This paper, however, has taken the first step towards this research avenue. The study will shed light on this interdisciplinary field of organizational cognitive neuroscience (OCN) and the benefits that organizations can reap from it with respect to understanding employee behavior. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of cognition in counterproductive work behaviors, also by using neuroscience techniques. The study concludes by providing evidence drawn from the literature in favor of adopting an OCN approach in organizations.

This article aims to explain the principles of the leadership styles of madrasah heads in enhancing the quality of Islamic education in Lhokseumawe City. It turns out that the leadership of madrasah heads has a significant impact on the functioning of the madrasah leadership. The madrasah head plays a direct role in developing Islamic educational institutions through leadership characteristics, leader types, leader functions, and leader activities during their tenure as madrasah heads. A quantitative research method with a phenomenological approach is considered capable of addressing various issues in the research problem formulation. It simultaneously analyzes data obtained from observations, interviews, and in-depth documentation to find answers to the research problem. The research findings reveal that the leadership styles of madrasah heads can be divided into two categories: democratic leadership style and autocratic leadership style. The first principle of leadership style involves giving responsibilities and authority to all parties, encouraging active involvement in the organization. Members are given opportunities to provide suggestions, recommendations, and criticisms for the progress of the organization. The second principle, the autocratic leadership style, positions a leader as the source of policies.

Sustainable leadership matters, spreads and lasts. It is shared responsibility, that does not unduly deplete human or financial resources, and that cares for avoids exerting negative damage on the surrounding educational and community environment. In fact, sustainability does not simply mean whether something can last. It addresses how particular initiatives can be developed without compromising the development of others in the surrounding environment, now and in the future. Sustainable leadership is one of the most valued characteristics that employers and stakeholders are looking for, especially in this new fast changing market. Having a good Leadership sense is essential for the success of a company and for its collaborative working environment. Leadership is usually linked with sustained learning, success, diversity, capacity and maintaining a continuous development of other team members and coworkers. However, various authors are debating and discussing how we define good and sustainable leadership and what we expect from a leader to do, but few of them are raising the question how we can build and create leaders?. We present in this paper an overview of leadership definition and the challenges set by this important and complex concept. We propose a vision based on four pillars to create an adequate environment to build leaders. We present and describe our perspective on what we need to provide and establish as a company or a community in order to create an effective team of leaders in the perspective of the new requirements of the market and the expectations of companies.

In the new century, the traditional model of enterprise human resource management is facing the challenge of the times, improving the human resource management of enterprises, and must innovate the concept of enterprise human resource management. After the 1950s, some economists established the theory of human capital, not only can more effectively explain the problems of modern social economic growth, but also on the enterprise's human resources management contribution to a positive impact. This paper introduces the concept of human capital and human capital investment into enterprise human resource management, which opens up a new perspective for enterprise human resource management. In this paper, we will first define the characteristics of human capital and the main body of human capital investment, and then analyze the meaning of various human resource management behaviors from the perspective of capital investment, estimate their benefits, costs and risks, and finally use scientific means to establish investment decision model and risk control mechanism, to maximize the effectiveness of human resources, so that the management behavior of enterprise's human can bring more revenue for the enterprises, thereby enhancing the competitiveness of enterprises. At present, the scientific operation of human resources is the key to the healthy development of enterprises.

Fungi can be used to remove or degrade polluting compounds through a mycoremediation process. Sometimes even more efficiently than prokaryotes, they can therefore be used to combat pollution from non-biodegradable polymers. Cellulose acetate is a commonly used material in the manufacture of cigarette butts, so when discarded, it generates pollution. The fungus Pleurotus ostreatus has the ability to degrade cellulose acetate through the enzymes it secretes. The enzyme hydrolyzes the acetyl group of cellulose acetate, while cellulolytic enzymes degrade the cellulose backbone into sugars, polysaccharides, or cellobiose. In addition to cellulose acetate, this fungus is capable of degrading other conventionally non-biodegradable polymers, so it has the potential to be used to reduce pollution. Large-scale cultivation of the fungus has proven to be more economically viable than conventional methods for treating non-biodegradable polymers, which is an additional advantage.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.