This study evaluates the sustainability and ethical practices of Kerry Logistics Network Limited (KLN), a prominent logistics service provider headquartered in Hong Kong. Using normative ethical theories, stakeholder analysis, and the Circle of Sustainability framework, this research examines KLN’s alignment with global sustainability standards, particularly the United Nations Sustainable Development Goals (SDGs). The findings reveal that KLN has achieved significant milestones in environmental management, such as reducing greenhouse gas emissions by 11% from 2021 to 2022 through the deployment of electric trucks and incorporating renewable energy in warehouse operations. KLN has also enhanced social responsibility and governance practices by implementing fair labor policies and establishing a rigorous code of conduct, ensuring compliance with ethical guidelines across its supply chain. However, the study identifies areas for improvement, including biodiversity actions, battery recycling processes, and transparency in stakeholder engagement. Emphasizing the importance of third-party validation, this paper underscores KLN’s leadership in the logistics industry and provides insights for other companies aiming to improve sustainability performance through comprehensive, verifiable practices.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

Aims of this study clarify the intrinsic value of Galileo’s law of inertia, which holds significance in the history of science, and the process through which such law of inertia was formed, for educational purposes, and explores a possible conversion of this intrinsic value into an environmental ethical value. The research methodology is to establish a value schema and, through its application, to explore the changes in the active intrinsic value principle of Galileo’s law of inertia based on the history of science. This study derived the following results: First, Galileo professed the value he assigned and discovered as a complete experience to support heliocentrism. Second, he realized his personal religious ideal, or in other words, the ideal of life as a whole. Third, the overall process is to feel a comprehensive and integral expansion of the self. Above all, it shows that the principle of active intrinsic value based on Galileo’s experimental activities has changed and expanded throughout the history of science. One internalizes one’s faith in accordance with the activity-centered value. Only when combined with aesthetic experience does education make one ethical. As general school education does not necessarily guarantee ethics, we must lead our values education toward ecocentric ethics education, which highlights beauty. It shows that these active intrinsic values also extend to ethical values.

Localization is globally accepted as the strategy towards attaining the Sustainable Development Goals (SDGs). In this article, we put forth the South Indian state of Kerala as a true executor of the localization of SDGs owing to her foundational framework of decentralized governance. We attempt to understand how the course of decentralization acts as a development trajectory and how it has paved the way for the effective assimilation of localization principles post-2015 by reviewing the state documents based on the framework propounded by the United Nations. We theorize that the well-established decentralization mechanism, with delegated institutions and functions thereof, encompasses overlapping mandates with the SDGs. Further, through the tools of development plan formulation, good governance, and community participation at decentralized levels, Kerala could easily adapt to localization, concocting output through innovative measures of convergence, monitoring, and incentivization carried out through the pre-existing platforms and processes. The article proves that constant and concerted efforts undertaken by Kerala through her meticulous and action-oriented decentralized system aided the localization of SDGs and provides an answer to the remarkable feat that the state has achieved through the consecutive four times achievements in the state scores of SDG India Index.

The Oued Kert watershed in Morocco is essential for local biodiversity and agriculture, yet it faces significant challenges due to meteorological drought. This research addresses an urgent issue by aiming to understand the impacts of drought on vegetation, which is crucial for food security and water resource management. Despite previous studies on drought, there are significant gaps, including a lack of specific analyses on the seasonal effects of drought on vegetation in this under-researched region, as well as insufficient use of appropriate analytical tools to evaluate these relationships. We utilized the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) to analyze the relationship between precipitation and vegetation health. Our results reveal a very strong correlation between SPI and NDVI in spring (98%) and summer (97%), while correlations in winter and autumn are weaker (66% and 55%). These findings can guide policymakers in developing appropriate strategies and contribute to crop planning and land management. Furthermore, this study could serve as a foundation for awareness and education initiatives on the sustainable management of water and land resources, thereby enhancing the resilience of local ecosystems in the face of environmental challenges.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.