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The escalating global water usage and the increasing strain on major cities due to water shortages highlights the critical need for efficient water management practices. In water-stressed regions worldwide, significant water wastage is primarily attributed to leakages, inefficient use, and aging infrastructure. Undetected water leakages in buildings' pipelines contribute to the water waste problem. To address this issue, an effective water leak detection method is required. In this paper, we explore the application of edge computing in smart buildings to enhance water management. By integrating sensors and embedded Machine Learning models, known as TinyML, smart water management systems can collect real-time data, analyze it, and make accurate decisions for efficient water utilization. The transition to TinyML enables faster and more cost-effective local decision-making, reducing the dependence on centralized entities. In this work, we propose a solution that can be adapted for effective leakage detection in real-world scenarios with minimum human intervention using TinyML. We follow an approach that is similar to a typical machine learning lifecycle in production, spanning stages including data collection, training, hyperparameter tuning, offline evaluation and model optimization for on-device resource efficiency before deployment. In this work, we considered an existing water leakage acoustic dataset for polyvinyl chloride pipelines. To prepare the acoustic data for analysis, we performed preprocessing to transform it into scalograms. We devised a water leak detection method by applying transfer learning to five distinct Convolutional Neural Network (CNN) variants, which are namely EfficientNet, ResNet, AlexNet, MobileNet V1, and MobileNet V2. The CNN models were found to be able to detect leakages where a maximum testing accuracy, recall, precision, and F1 score of 97.45%, 98.57%, 96.70%, and 97.63%, respectively, were observed using the EfficientNet model. To enable seamless deployment on the Arduino Nano 33 BLE edge device, the EfficientNet model is compressed using quantization resulting in a low inference time of 1932 ms, a peak RAM usage of 255.3 kilobytes, and a flash usage requirement of merely 48.7 kilobytes. [ABSTRACT FROM AUTHOR]

One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%. [ABSTRACT FROM AUTHOR]

In this work we present the rational and design of a methodology to support Integrated Environmental Assessment using the DPSIR (Driving Forces–Pressures–State–Impact–Response) causal-effect framework and non-monotonic Fuzzy Cognitive Maps. The methodology is based on key pillars in environmental management, namely connecting the socioeconomic and the natural environment dimensions into a policy oriented context; integration of stakeholders with inter-sectorial synergies and tradeoffs; handling of ambiguities and uncertainties intrinsic to environmental modeling and representation of complex non-linear cause-effect relationships in the form of Fuzzy Inference Systems, capable of adapting dynamically the influence between indicators. The methodology has the potential to support the development of informed policies and improves reliability through transparent, traceable and reproducible results. The illustrative example assesses the impact of air pollution abatement policies according to expert perceptions using proactive scenarios; the results revealed that, despite some positive changes, air protection activities are missing an overall strategic vision. [ABSTRACT FROM AUTHOR]

Fuzzy Cognitive Maps were missing in the latest guidance by Kelly et al. (2013), which reviewed five common integrated modeling approaches. Through a scoping review, the paper shows how Fuzzy Cognitive Maps satisfy key criteria for suitability in integrated environmental assessment, such as model purpose, types of data available to build a model, requirements in terms of spatial and temporal scales, stakeholder involvement, and treatment of uncertainty. The paper consequently updates the guiding framework that can be used by modelers to select the most appropriate modeling approach by including Fuzzy Cognitive Map techniques. We argue that the method can be the starting point of any integrated environmental assessment research to map out perspectives and potential system sensitivities. They constitute a great addition to the integrated assessment modeling toolbox as they can complement quantitative model approaches that are invariably constructed around a single perspective that relates to available data. • We review FCMs as an integrated environmental assessment modeling approach. • FCMs can be the starting point of any environmental assessment research to map out perspectives and system sensitivities. • FCMs can complement quantitative approaches that are invariably constructed around a single data-related perspective. [ABSTRACT FROM AUTHOR]

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.