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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.

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.

In wireless communication system, use of multiple antennas for different requirements of system will increase the system complexity. However, reconfigurable antenna is maximizing the connectivity to cover different wireless services that operate different frequency range. Pattern reconfigurable antenna can improve security, avoid noise and save energy. Due to their compactness and better performance at different applications, reconfigurable antennas are very popular among the researchers. The purpose of this work, is to propose a novel design of S-shaped antenna with frequency and pattern diversity. The pattern and frequency reconfiguration are controlled via ON/OFF states of the PIN diode.

The geometrical structure of the proposed antenna dimension is 18 × 18 × 0.787 mm³ with εr = 2.2 dielectric constant. Three S-shaped patches are connected to a ring patch through PIN diodes. The approximate circumference of ring patch is 18.84 mm and length of patch is 5 mm, so approximate length of radiating patch is 14.42 mm and effective dielectric constant is 1.93. Conductor backed coplanar waveguide (CPW) is used for feeding. The proposed antenna is designed and simulated on CST microwave studio and fabricated using photolithography process. Measurements have been done in anechoic chamber.

Antenna shows the dual band operation at 2.1 and 3.4 GHz frequency. The first band remains constant at 2.1 GHz resonant frequency and 200–400 MHz impedance bandwidth. Second band is switched at seven different resonant frequencies as 3.14, 3.45, 3.46, 3.68, 3.69, 3.83 and 3.86 GHz with switching of the diodes. The −10 dB bandwidth is more than 1.4 GHz.

Pattern reconfigurability can be achieved using mechanical movement of antenna easily but it is not a reliable approach for planar antennas. Electronic switching method is used in proposed antenna. Antenna size is very small so fabrication is very crucial task. Measured results are deviated from simulation results due to fabrication error and effect of leads of diodes, connecting wires and battery.

The reconfiguration of the proposed antenna is controlled via ON/OFF states of the three PIN diodes. The lower band of 2.1 GHz is fixed, while second band is switched at five different resonant frequencies as 3.27, 3.41, 3.45, 3.55 and 3.88 GHz, with switching of the PIN diodes with all state of diodes and exhibit pattern reconfigurability at 2.1 GHz frequency. At second band center frequency is significantly changed with state of diodes and at 3.4 GHz pattern is also changed with state of diodes, hence antenna exhibits frequency and pattern reconfigurability.

A novel design of pattern and frequency reconfigurable antenna is proposed. Here, work is divided into two parts: first is frequency reconfiguration and second is radiation pattern reconfiguration. PIN diodes as switch are used to select the frequency band and reconfigure the radiation pattern. This proposed antenna design is novel dual band frequency and pattern reconfigurable antenna. It resonates at two distinct frequencies, i.e. 2.1 and 3.4 GHz, and has a pattern tilt from 0° to 355°. The conductor backed CPW feed technique is used for impedance matching.

Our study examines how artificial intelligence (AI) can enhance decision-making processes to promote circular economy practices within the utility sector.

A unique case study of Alia Servizi Ambientali Spa, an Italian multi-utility company using AI for waste management, is analyzed using the Gioia method and semi-structured interviews.

Our study discovers the proactive role of the user in waste management processes, the importance of economic incentives to increase the usefulness of the technology and the role of AI in waste management transformation processes (e.g. glass waste).

The present study enhances the circular economy model (transformation, distribution and recovery), uncovering AI’s role in waste management. Finally, we inspire managers with algorithms used for data-driven decisions.

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.