The current research note is written for personnel managers and MBA students, aiming to raise awareness of the importance of work-life balance in employee management policies. In the intersection of work and personal life, the work-life balance is the equilibrium between the two; more specifically, the work-life balance explains the relationship and interaction between an individual's job and their private life. In the research note, we first introduce the concept and characteristics of work-life balance through relevant literature. We then argue the significance of incorporating work-life balance into employee management practices, as the concept of work-life balance helps managers appreciate individual differences and develop more human-oriented awareness in management. We encourage managers to adopt transformational leadership in their management, in which the concept of work-life balance should be embedded in the design and implementation of employee management policies. By giving more autonomy to the employees through work-life balance policies and practices, employees are more likely to appreciate the work and make more contributions accordingly. Practitioner points are also recommended.

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.

This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.

The COVID-19 pandemic has brought life changing conditions to families that require coping strategies in order to survive and achieve family well-being. This study aims to analyze differences between single earner and dual earner families during the COVID-19 pandemic and to analyze the factors that influence subjective family well-being. The research design used was a cross sectional study with sample collection through non-probability sampling. Data collection was carried out by filling out questionnaires online. The number of respondents involved in the study was 2084 intact families with children residing in DKI Jakarta, West Java, and Banten Provinces. Reliability and validity tests were conducted. The results of the independent t-test showed that dual-earner families experienced better life changes and a higher level of subjective family well-being than single-earner families and had lower economic pressure and lower economic coping than single earner families. The SEM analysis found that life changes affected economic coping negatively and subjective family well-being positively. Family income influenced economic coping negatively and subjective family well-being positively. Finally, it was found that economic coping had no effect on subjective family well-being.

In the dynamic landscape of modern education, the integration of vocational education and intelligent technologies has emerged as a pivotal strategy for fostering lifelong learning. This essay delves into the synergistic relationship between vocational education and the era of intelligent education, highlighting their collective potential to empower individuals with skills that transcend traditional boundaries.

There are numerous studies reported on the usage of the sapindus emarginatus (SE) fruit in cancer and other treatments in the past few years. In this study, crude SE fruit extract was prepared and it was further used to synthesis gold nanoparticles (Au Nps). The synthesized Au Nps were left embedded in the SE fruit extract. The Au Nps embedded in the SE fruit extract (SE-Au Nps) were characterized using UV-Visiable Spectroscopy, Centrifugal Particle Size analyzer (CPS), Scanning Electron Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). MTT assay was carried out for both SE fruit extract and SE-Au Nps on MCF7 breast cancer cell line and thus compared. The UV-Visible Absorbance for the SE-Au Nps was obtained at 543 nm. The centrifugal particle size analysis of the Au Nps embedded in SE fruit extract showed the size of the nanoparticles to be widely varying with higher fraction of particles between the size ranges of 15 to 20 nm. The morphology of the Au Nps embedded in SE fruit extract was observed using SEM. The presence of Au Nps in SE fruit extract was confirmed using FTIR. The results of the MTT assay on MCF7 breast cancer cell line proved that the % cell viability was less for SE-Au Nps than that of the SE fruit extract alone. Thus, the antiproliferative activity of the SE fruit extract was significantly enhanced by embedding it with Au Nps and it can be effectively used in therapeutic applications after further studies.