--Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaW ANbased data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-T erm Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments. While remote-sensing techniques have proven valuable for water quality monitoring [1], [2], regarding water distribution, efficient distribution is a significant issue in rural regions, especially where infrastructure is poor and digital monitoring is scarce.

The paper is modelling MARL problems under the angle of social dilemma, and tries to tackle the problem of common-pool resource management. The authors do not introduce a novel method, instead this paper is a comparison of a wide range of existing relevant algorithms on a single problem (water management). The experiments are well motivated and in general, the paper is very clear. My understanding is that although the paper focuses on a water management, it is aimed as a more general survey of the quality of current MARL algorithms on common-pool resource management. The authors argue that water management is a good example to study because it is critical and life-supporting, and safety issues are very relevant.

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters ($hm^3$) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

The exponential growth of IoT networks necessitates a paradigm shift towards architectures that offer high flexibility and learning capabilities while maintaining low energy consumption, minimal communication overhead, and low latency. Traditional IoT systems, particularly when integrated with machine learning approaches, often suffer from high communication overhead and significant energy consumption. This work addresses these challenges by proposing a neuromorphic architecture inspired by biological systems. To illustrate the practical application of our proposed architecture, we present a case study focusing on water management in the Carinthian community of Neuhaus. Preliminary results regarding water consumption prediction and anomaly detection in this community are presented. We also introduce a novel neuromorphic IoT architecture that integrates biological principles into the design of IoT systems. This architecture is specifically tailored for edge computing scenarios, where low power and high efficiency are crucial. Our approach leverages the inherent advantages of neuromorphic computing, such as asynchronous processing and event-driven communication, to create an IoT framework that is both energy-efficient and responsive. This case study demonstrates how the neuromorphic IoT architecture can be deployed in a real-world scenario, highlighting its benefits in terms of energy savings, reduced communication overhead, and improved system responsiveness.

Sustainable development is a framework for achieving human development goals. It provides natural systems' ability to deliver natural resources and ecosystem services. Sustainable development is crucial for the economy and society. Artificial intelligence (AI) has attracted increasing attention in recent years, with the potential to have a positive influence across many domains. AI is a commonly employed component in the quest for long-term sustainability. In this study, we explore the impact of AI on three pillars of sustainable development: society, environment, and economy, as well as numerous case studies from which we may deduce the impact of AI in a variety of areas, i.e., agriculture, classifying waste, smart water management, and Heating, Ventilation, and Air Conditioning (HVAC) systems. Furthermore, we present AI-based strategies for achieving Sustainable Development Goals (SDGs) which are effective for developing countries like Bangladesh. The framework that we propose may reduce the negative impact of AI and promote the proactiveness of this technology.

Experts predict that the size of urban areas will rise by around three times between the years 2000 and 2030 [1]. It is well documented that the "structures of our cities" have a major impact on the occurrence of severe or extreme weather in the surrounding regional environment [2]. In rapidly expanding cities, especially in developing nations, these urban morphologies are characterized by impermeable surfaces, rapid loss of green area, and habitat fragmentation [3]. Green areas in cities, known collectively as urban forests, help lessen regional and local storm-related flooding and water pollution [4], improve air and water quality, moderate temperature, and promote nutrient cycling in soil, all while sequestering carbon [5]. So, Massive land transformations in urban areas--from green to concrete – result in an ever-increasing number of impermeable surfaces, resulting in an unnatural environment [6].

The Indian agricultural sector is at the cusp of a breakthrough. The Indian agriculture industry is going through a huge transformation, a revolution as we speak. The Green Revolution was a path breaking initiative that brought agriculture to the forefront of the Indian economy. If the industrialisation of agriculture and initiatives like the Green revolution brought agriculture to the forefront, a new wave of technological advancement and new-age startups are revolutionising agriculture as we know it. At present, agriculture and allied industries contributes to 17-18 per cent of the country's GDP.

We examine the ability of machine learning (ML) and deep learning (DL) algorithms to infer surface/ground exchange flux based on subsurface temperature observations. The observations and fluxes are produced from a high-resolution numerical model representing conditions in the Columbia River near the Department of Energy Hanford site located in southeastern Washington State. Random measurement error, of varying magnitude, is added to the synthetic temperature observations. The results indicate that both ML and DL methods can be used to infer the surface/ground exchange flux. DL methods, especially convolutional neural networks, outperform the ML methods when used to interpret noisy temperature data with a smoothing filter applied. However, the ML methods also performed well and they are can better identify a reduced number of important observations, which could be useful for measurement network optimization. Surprisingly, the ML and DL methods better inferred upward flux than downward flux. This is in direct contrast to previous findings using numerical models to infer flux from temperature observations and it may suggest that combined use of ML or DL inference with numerical inference could improve flux estimation beneath river systems.

Morocco's permanent representative to the United Nations, Ambassador Omar Hilale, highlighted on September 30 that agricultural sciences and new technologies are an important part of the country's new economic projections. Morocco's Green Plan reached a goal of strengthening localized irrigation, one of the three major components of its Irrigation Strategy. The high-level meeting addressed "the role of Artificial Intelligence (AI) in achieving post-Covid food security." "Today, these sciences and technologies are helping to increase the production of small and medium farmers," Ambassador Hilale emphasized during the meeting. He also explained the crucial role that AI plays in "helping to produce more food with less water and energy."