The proposed scientific article aims to analyze the application of Lean Six Sigma in the food industry. To this end, a detailed methodology has been designed that ranges from the selection of the works to the synthesis and presentation of the results obtained. The methodology is based on rigorous inclusion criteria to ensure the relevance and quality of the selected sources, including books, academic articles, theses, and other relevant documents. Through extensive searches of academic databases and other reliable sources, key works were identified that specifically address the implementation of Lean Six Sigma in the context of food production. Once the relevant papers were collected, a critical analysis was conducted to identify common themes, trends, and key findings. The works were classified according to their main focus, such as process improvement, waste reduction, supply chain optimization and food safety assurance. This categorization allowed the information to be organized in a coherent way and to facilitate the synthesis of the results. The results obtained were presented in a table that included details about each selected work, such as title, author, year of publication, abstract and links to the original source. This structured and rigorous approach provides a clear and comprehensive view of the topic, contributing to the advancement of knowledge in this area and offering practical guidance for practitioners and researchers interested in the application of Lean Six Sigma in the food industry. The literature on Lean Six Sigma in the food industry highlights its importance in improving efficiency, quality, and safety. Key recommendations include gradual implementation, appropriate training, focus on quality, and continuous improvement.

The connection between the gendered division of housework and intimate partner violence (IPV) is a complex reality and context-dependent. In this article, I explore the perceptions of gender norms among African men and how these perceptions intersect with their experiences of housework and IPV. Employing a qualitative approach, the article examines the viewpoints of 25 African men who have encountered IPV in Johannesburg, South Africa. The findings reveal a spectrum of attitudes towards gender norms among these men, ranging from more traditional patriarchal views to less patriarchal and egalitarian perspectives. The analysis indicates that men who adhere to both more and less patriarchal expressions of gender norms tend to view being forced to perform housework as a form of abuse within the context of controlling behaviour in intimate partner relationships. Conversely, men who lean towards egalitarianism perceive the expectation of women to solely manage housework as a form of abuse. However, many of the men express resistance towards gender equality discourses in South Africa, perceiving them as disruptors of traditional gender roles and enablers of women’s refusal to solely perform domestic housework. These findings deepen our understanding of the complexities and tensions within intimate relationships amidst evolving gender norms in South Africa.

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.

Conversion of the ocean’s vertical thermal energy gradient to electricity via OTEC has been demonstrated at small scales over the past century. It represents one of the planet’s most significant (and growing) potential energy sources. As described here, all living organisms need to derive energy from their environment, which heretofore has been given scant serious consideration. A 7th Law of Thermodynamics would complete the suite of thermodynamic laws, unifying them into a universal solution for climate change. 90% of the warming heat going into the oceans is a reasonably recoverable reserve accessible with existing technology and existing economic circumstances. The stratified heat of the ocean’s tropical surface invites work production in accordance with the second law of thermodynamics with minimal environmental disruption. TG is the OTEC improvement that allows for producing two and a half times more energy. It is an endothermic energy reserve that obtains energy from the environment, thereby negating the production of waste heat. This likewise reduces the cost of energy and everything that relies on its consumption. The oceans have a wealth of dissolved minerals and metals that can be sourced for a renewable energy transition and for energy carriers that can deliver ocean-derived power to the land. At scale, 31,000 one-gigawatt (1-GW) TG plants are estimated to displace about 0.9 W/m² of average global surface heat into deep water, from where, at a depth of 1000 m, unconverted heat diffuses back to the surface and is available for recycling.

Renewable energy is gaining momentum in developing countries as an alternative to non-renewable sources, with rooftop solar power systems emerging as a noteworthy option. These systems have been implemented across various provinces and cities in Vietnam, accompanied by government policies aimed at fostering their adoption. This study, conducted in Ho Chi Minh City, Vietnam investigates the factors influencing the utilization of rooftop solar power systems by 309 individuals. The research findings, analyzed through the Partial least squares structural equation modeling (PLS-SEM) model, reveal that policies encouragement and support, strategic investment costs, product knowledge and experience, perceived benefits assessment, and environmental attitudes collectively serve as predictors for the decision to use rooftop solar power systems. Furthermore, the study delves into mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also furnishes policymakers with evidence to chart new directions for encouraging the widespread adoption of solar power systems.

Fiscal decentralization is one of the policy implementations of regional autonomy, which authorizes local governments to manage their local finances independently. However, with the evolution of the times and the dynamics that are taking place, the application of fiscal decentralization worldwide is changing at each time of year. Therefore, it is necessary to investigate fiscal decentralization research temporarily over the course of four decades. The study aims to explain the development of research on fiscal decentralization over a period of four decades. This research integrates Scopus database to offer a thorough conceptual and structural overview of the field by integrating bibliometric approaches and content analysis. The research procedure begins with the determination of the scope of the research, the inclusion and exclusion criteria for the selection process, the collection of data on Publish or Perish (PoP), and the execution of bibliometric analysis on VosViewer. The research shows that the type of journal with the highest productivity has sub-topics of economy, public service, development, and environmental. The development of fiscal decentralization research has a positive upward trend and most of the top-ranked journals indicate that fiscal decentralization has links and influences with other variables. It is apparent that the most often keywords emerged and studied in the research on fiscal decentralization are related to efficiency, measure, role, degree, growth, and fiscal federalism. Meanwhile, the least frequent keywords are related to poverty and inequality, health outcome, environmental pollution, Latin America, South Africa, fiscal autonomy, corruption, OECD country, determinant, and public sector. These keywords are the future lines of research that may be used for future research on the topic of fiscal decentralization.