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Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.

The purpose is to derive the most effective place in the air for an aerial robot, viz., drone to use as an alternative communication system during disasters.

In this technology-driven era, various concepts are becoming the area of interest for multiple researchers. Drone technology is also one of them. The researchers, with interest in drones, are therefore trying to understand the various uses of employing drones in diverse applications which are mind-boggling, starting from civil applications (viz., an inspection of power lines, counting wildlife, delivering medical supplies to inaccessible regions, forest fire detection, and landslide measurement) to military applications (viz., real-time monitoring, surveillance, patrolling, and demining). However, one area where its usage is still to be exploited in many countries is using drones as a relay when communication lines are disrupted due to natural calamities. This will be particularly helpful in rescuing the affected people as the aerial node will enable them to communicate to the rescue team using mobiles/ordinary landline telephones even when regular communication towers are destroyed due to disastrous natural calamities, for example, tsunamis, earthquakes, and floods. Various algorithms, namely, water filling algorithm, advanced water filling algorithm, equal power distribution algorithm, and particle swarm optimization, were therefore studied and analyzed using simulation in addition to various path loss models to realize the desired place for an aerial robot, viz., drone in the air, which will eventually be used as an alternative communication system for badly hit ground users due to any disaster.

It was found that the effective combination of the water filling algorithm and particle swarm optimization algorithm may be done to place the drone in the air to increase the overall throughput of the affected ground users.

The research is original. None of the parts of this research paper has been published anywhere.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

Faced with the growing need to find new viable water supply models for urban areas, this article studies and maps the strategies and identifies the key criteria of sustainable development present in pioneering water supply systems in the medieval period. The main aim is to determine which of its innovative principles could be applied in present-day cities.

From a methodological perspective, two types of cases were established, such as water supply models for human consumption and pre-industrial hydraulic systems, all of which are located in Italy. For the first group, the cases of Venice and Siena were analysed, while for the second, in the context of the cities along the Aemilian Way, the case of Bologna was selected.

Five key criteria resulted from the analysis of the cases: exploitation, self-sufficiency, maintenance, rationalisation and reuse. The said concepts were defined and contextualised within the framework of the Sustainable Development Goals.

The Middle Ages were a historic moment in technological reinvention, before the development of modern systems of sanitation. With very limited resources, these traditional systems focused on rational use and deep cultural and geographical knowledge. This is why its recognition is of great importance today, in a time full of instabilities, with a view to the work that needs to be done for the development of more sustainable communities.

Limited research exists on the role of information access as a key success factor for rural entrepreneurs. The purpose of this study is to examine the importance of information access, among other inequality forms, in shaping entrepreneurial outcomes.

The authors used a qualitative, inductive research methodology. They conducted interviews with rural entrepreneurs and gained insights into the role of information access and basic tangible resources in shaping their ventures.

The authors identified two critical factors that impact rural entrepreneurs: inequalities in access to information and basic tangible resources. They found that inequalities in rural entrepreneurship are often interrelated, rather than isolated. The authors demonstrated that the relationship between entrepreneurship and inequality is not solely characterized by a positive or negative correlation but is a dynamic interplay where certain inequality forms may create barriers to opportunities for some individuals while creating opportunities for others.

The authors uncovered key barriers in rural entrepreneurship and constructed a roadmap to effectively address these challenges, providing valuable insights to policymakers and support initiatives and enabling the target of high-impact resources. This research supports efforts to provide rural entrepreneurs with equal opportunities to grow and succeed.

This research significantly contributes to the field by examining the complexities of entrepreneurship in emerging economies, with a particular focus on rural areas. The authors introduced a comprehensive conceptual model linking inequality and entrepreneurship, explored the challenges faced by rural entrepreneurs and offered strategic policy recommendations for development initiatives.

A faculty development program (FDP) introduced postsecondary instructors to a module focused on the food–energy–water (FEW) nexus, a socio-hydrologic issue (SHI) and a sustainability challenge. This study aims to examine factors influencing faculty interest in adopting the instructional resources and faculty experience with the FDP, including the gains made during the FDP on their knowledge about SHIs and their self-efficacy to teach about SHIs, and highlighted characteristics of the FDP.

Data from n = 54 participants via pre- and post-surveys and n = 15 interviews were analyzed using mixed methods.

Findings indicate that over three quarters of participants would use the curricular resources to make connections between complex SHIs, enhance place-based learning, data analysis and interpretation and engage in evidence-based decision-making. In addition, participants’ experience with the workshop was positive; their knowledge about SHIs remained relatively constant and their self-efficacy to teach about SHIs improved by the end of the workshop. The results provide evidence of the importance of institutional support to improve instruction about the FEW nexus.

The module, purposefully designed, aids undergraduates in engaging with Hydroviz, a data visualization tool, to understand both human and natural dimensions of the FEW nexus. It facilitates incorporating this understanding into systematic decision-making around an authentic SHI.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.