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Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

The purpose of this study is the influence of various combinations of metal sulfides on the tribological performance of brake pads.

Three brake pads were prepared using the possible combination of any two of the solid lubricants from Bismuth trisulfide (Bi₂S₃); Tin disulfide (SnS₂) and Antimony trisulfide (Sb₂S₃) are chosen and blended with molybdenum disulfide and graphite. The tribological performance was compared with the brake pad containing aftermarket sulfide mixture. The tribological performance parameters such as performance coefficient of friction, fade percent, recovery percent, wear thickness loss, time is taken to reach the maximum temperature and fluctuation of friction were investigated using Chase tribometer adopting IS 2742 Part-4 (1994) test procedure.

The friction stability of the brake pad with 4Wt% of MoS₂, Bi₂S₃ and SnS₂ was observed to be better, but it showed poor wear performance and aggressive towards the rotor, whereas the brake pad contained 4Wt% of MoS₂, Bi₂S₃ and Sb₂S₃ exhibited improved wear performance.

This paper explains the influence of the combination of multiple metal sulfide in the tribological performance of the copper-free brake friction composite.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0249/

The purpose of this paper is to design an efficient on-demand routing protocol for mobile ad hoc networks (MANETs) which combines greedy forwarding and backtracking.

It uses a hybrid approach. It uses greedy approach to determine a route from source to the destination. If the greedy approach fails, then it uses a backtracking approach. Combining greedy and backtracking approaches results in a more efficient protocol.

A new routing algorithm for MANETs which performs better than some of the existing algorithms.

A major limitation of this work is that the authors did not implement the protocol on a test bed and evaluated the performance. They neither had the infrastructure nor the resources to actually implement the protocol. Instead, like most of the researchers, they evaluated the performance of their protocol based on extensive simulation. The mobility model, the area of deployment and the density of nodes chosen to simulate the protocol are consistent with what many of the other researchers have done. Intuitively, the authors’ protocol, as the performance evaluation indicates, is likely to perform well in small networks.

The authors did not implement the protocol on a test bed to evaluate the performance of the protocol. The authors used simulation to study the performance of their protocol. Their simulation model is similar to many of the research works published in the literature.

To the authors’ knowledge, no other paper has combined the greedy and backtracking approach to design a routing protocols for MANETs.

Digital transformation (DT) is a priority for the healthcare sector. In many countries, it is still considered in the early stages with an underestimation of its benefits and potentiality. Especially in Italy, little is known about the impact of digitalization – particularly of the Internet of Things (IoT) – on the healthcare sector, for example, in terms of clinician's jobs and patient's experience. Drawing from such premises, the paper aims to focus on an overlooked healthcare area related to the chronic heart diseases field and its relationship with DT. The authors aim at exploring and framing the main variables of remote Monitoring (RM) adoption as a specific archetype of healthcare digitalization, both on patients and medical staff level, by shedding some lights on its overall implementation.

The authors empirically inquiry the RM adoption within the context of the Cardiology Department of the Casilino General Hospital of Rome. To answer our research question, the authors reconstruct the salient information by using induction-type reasoning, direct observation and interviewees with 12 key informants, as well as secondary sources analysis related to the hospital (internal documentation, presentations and technical reports).

According to a socio-technical framework, the authors build a model composed of five main variables related to medical staff and patients. The authors classify such variables into an input-process-output (I-P-O) model. RM adoption driver represents the input; cultural digital divide, structure flexibility and reaction to change serve the process and finally, RM outcome stands for the output. All these factors, interacting together, contribute to understanding the RM adoption process for chronic disease management.

The authors' research presents two main limitations. The first one is related to using a qualitative method, which is less reliable in terms of replication and the interpretive role of researchers. The second limitation, connected to the first one, is related to the study's scale level, which focuses on a mono-centric consistent level of analysis.

The paper offers a clear understanding of the RM attributes and a comprehensive view for improving the overall quality management of chronic diseases by suggesting that clinicians carefully evaluate both hard and soft variables when undertaking RM adoption decisions.

RM technologies could impact on society both in ordinary situations, by preventing patient mobility issues and transport costs, and in extraordinary times (such as a pandemic), where telemedicine contributes to supporting hospitals in swapping in-person visits with remote controls, in order to minimize the risk of coronavirus disease (COVID-19) contagion or the spread of the virus.

The study enriches the knowledge and understanding of RM adoption within the healthcare sector. From a theoretical perspective, the authors contribute to the healthcare DT adoption debate by focusing on the main variables contributing to the DT process by considering both medical staff and patient's role. From a managerial perspective, the authors highlight the main issues for RM of chronic disease management to enable the transition toward its adoption. Such issues range from the need for awareness of the medical staff about RM advantages to the need for adapting the organizational structure and the training and education process of the patients.

Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

This paper aims to investigate numerically the free convective heat transfer efficiency inside a rectotrapezoidal enclosure filled with Al₂O₃–Cu/water hybrid fluid. The bottom wall of the cavity is uniformly heated, the upper horizontal wall is insulated, and the remaining walls are considered cold. A new thermophysical relation determining the thermal conductivity of the hybrid nanofluid has been established, which produced results those match with experimental ones.

The governing partial differential equations are solved using the finite element method of Galerkin type. The simulated results in terms of streamlines, heat lines and isotherms are displayed for various values of the model parameters, which govern the flow.

The Nusselt number, friction factor and the thermal efficiency index are also determined for the pertinent parameters varying different ratios of the hybrid nanoparticles. The simulated results showed that thermal buoyancy significantly controls the heat transfer, friction factor and thermal efficiency index. The highest thermal efficiency is obtained for the lowest Rayleigh number.

This theoretical study is significantly relevant to the applications of the hybrid nanofluids electronic devices cooled by fans, manufacturing process, renewable energies, nuclear reactors, electronic cooling, lubrication, refrigeration, combustion, medicine, thermal storage, etc.

The results showed that nanoparticle loading intensified the rate of heat transfer and thermal efficiency index at the expense of the higher friction factor or higher pumping power. The results further show that the heat transmission in Al₂O₃–Cu/water hybrid nanofluid at a fixed value of intensified $\phi_{hnf}$ compared to the Al₂O₃/water nanofluid when an amount of higher conductivity nanoparticles (Cu) added to it. Besides, the rate of heat transfer in Cu/water nanofluid declines when the lower thermal conductivity Al₂O₃ nanoparticles are added to the mixture.

The analysis of boundary layers is needed to reflect the behaviour of fluid flows in current industrial processes and to improve the efficacy of products. Hence, this study aims to analyse the flow and heat transfer performance of hybrid alumina-copper/water (Al₂O₃-Cu/H₂O) nanofluid with the inclusion of activation energy and binary chemical reaction effect towards a moving wedge.

The multivariable differential equations with partial derivatives are converted into a specific type of ordinary differential equations by using valid similarity transformations. The reduced mathematical model is elucidated in the MATLAB system by using the bvp4c procedure. This solution method is competent in delivering multiple solutions once appropriate assumptions are supplied.

The results of multiple control parameters have been studied, and the findings are verified to provide more than one solution. The coefficient of skin friction was discovered to be increased by adding nanoparticles volume fraction from 0% to 0.5% and 1%, by almost 1.6% and 3.2%. Besides, increasing the nanoparticles volume fraction improves heat transfer efficiency gradually. The inclusion of the activation energy factor displays a downward trend in the mass transfer rates, consequently reducing the concentration profile. In contrast, the increment of the binary reaction rate greatly facilitates the augmentation of mass transfer rates. There is a significant enhancement in the heat transfer rate, approximately 13.2%, when the suction effect dominates about 10% in the boundary layer flow. Additionally, the results revealed that as the activation energy rises, the temperature and concentration profiles rise as well. It is proved that the activation energy parameter boosts the concentration of chemical species in the boundary layer. A similar pattern emerges as the wedge angle parameter increases. The current effort aims to improve the thermal analysis process, particularly in real-world applications such as geothermal reservoirs, chemical engineering and food processing, which often encountered mass transfer phenomenon followed by chemical reactions with activation energy.

The present results are original and new for the study of flow and heat transfer over a permeable moving wedge in a hybrid nanofluid with activation energy and binary chemical reaction.

This study aims to analyze simplified methods for modelling the flow through perforated elements (i.e. porous baffle interface and porous region), searching for a faster and easier way to simulate these components. The numerical simulations refer to a muffler geometry available in literature as a case study.

The installation of scrubber onboard ships to satisfy the International Maritime Organization emissions regulations is a reliable and efficient solution. However, scrubbers have considerable dimensions, interfering with other exhaust line components. Therefore, scrubber installation in the funnels requires integration with other elements, for example, silencers. Perforated pipes and plates represent the main elements of scrubber and silencers. The study of their layout is, therefore, necessary to reduce emissions and noise. Numerical simulations allow evaluating the efficiency of integrated components.

The study highlights that velocity and pressure predicted by the simplified models have a strong correlation with the resistance coefficients. Even though the simplified models do not accurately reproduce the flow through the holes, the use of such models allows a fast and easy comparison between concurrent muffler geometries, giving aid in the early design phases.

The lack of general guidelines and comparisons in the literature between different modelling strategies of perforated elements supports the novelty of the present work and its impact on design applications. Study the flow inside scrubbers and mufflers is fundamental to evaluate their performances. Therefore, having a simple numerical method is suited for industrial applications during the design process.