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The relation between the structure of the silver network electrodes and the properties of Cu(In,Ga)Se2 (CIGS) solar cells is systemically investigated. The Ag network electrode is deposited onto an Al:ZnO (AZO) thin film, employing a self‐forming cracked template. Precise control over the cracked template's structure is achieved through careful adjustment of temperature and humidity. The Ag network electrodes with different coverage areas and network densities are systemically applied to the CIGS solar cells. It is revealed that predominant fill factor (FF) is influenced by the figure of merit of transparent conducting electrodes, rather than sheet resistance, particularly when the coverage area falls within the range of 1.3–5%. Furthermore, a higher network density corresponds to an enhanced FF when the coverage areas of the Ag networks are similar. When utilizing a thinner AZO film, CIGS solar cells with a surface area of 1.0609 cm2 exhibit a notable performance improvement, with efficiency increasing from 10.48% to 11.63%. This enhancement is primarily attributed to the increase in FF from 45% to 65%. These findings underscore the considerable potential for reducing the thickness of the transparent conductive oxide (TCO) in CIGS modules with implications for practical applications in photovoltaic technology. [ABSTRACT FROM AUTHOR]

Transient receptor potential vanilloid‐1 (TRPV1) is a capsaicin receptor that employs use‐dependent desensitization to protect highly evolved mammals from noxious heat damage in response to repeated or constant heat stimuli. However, the underlying structural factor or motif has not been precisely resolved. In this computational study, the graph theory‐based grid thermodynamic model was used to reveal how temperature‐dependent noncovalent interactions, as identified in the 3D structures of rat TRPV1, could develop a well‐organized fluidic grid‐like mesh network. This network features various topological grids constrained as thermosensitive rings that range in size from the biggest to the smallest, governing distinct structural and functional traits of the channel in response to different temperature degrees. After discovering that heat unfolding of three specific biggest grids, one in the closed state and two in the open state, respectively, causes the reversible activation at 43°C and thermal inactivation between 56°C and 61°C, a smaller random grid was also found to be responsible for irreversible inactivation and use‐dependent desensitization from the pre‐open closed state within the temperature range of 43°C–61°C. Thus, these two distinct inactivation pathways of TRPV1 may be involved in protecting those mammals against noxious heat damage. Key Points: A perturbation at the protein–water interface was accompanied by partial heat or cold unfolding of the membrane protein.A reversible or irreversible gating transition of an ion channel may result from a specific or random interaction between two active sites, respectively.Kinetically driven protein aggregation was not the cause of thermodynamically trapped irreversible inactivation, but rather a later stage of partial heat‐induced unfolding. [ABSTRACT FROM AUTHOR]

Advancements in biomedical research have spurred the development of stretchable electronic devices. While soft insulators are readily available, soft conductors with metal‐like electrical conductivity are rare. Gallium and its alloys, being nontoxic and intrinsically stretchable, are potentially ideal solutions. However, current additive liquid metal (LM) patterning methods face limitations in achieving high‐throughput, high‐resolution, and high‐density LM wiring. Here, a subtractive LM patterning method is developed to meet all these requirements simultaneously. The innovative method involves parallel filling a single continuous microfluidic mesh network with LM that short‐circuits all the pins and pads of a circuit, followed by parallel cutting of the unwanted short‐circuited interconnections using hydrochloric acid (HCl) vapor. Cutting locations are pre‐defined by designing narrower intersecting channels, leveraging capillary force for precise filling and cutting. The process is characterized using a multidimensional parametric study with varying LM line widths and HCl concentrations, and in situ impedance measurements to assess insulation performance. To showcase its high‐throughput capabilities, a mock circuit is used to successfully generate complex LM interconnects that connected hundreds of electrical pads. Finally, a stretchable LM circuit with a micro‐LED array is fabricated to demonstrate the practical application of this technology for massively parallel wiring in stretchable electronics. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments. [ABSTRACT FROM AUTHOR]

The general connectivity indices (including the general Randić index, the general sum-connectivity index, the general ABC index and the general ABS index) are an important degree-based topological index in chemical informatics. In this paper, the general connectivity entropies of a graph are defined as the Shannon's entropy based on the information functional that associates the general connectivity indices. We compute the general connectivity entropies for certain interconnection networks like butterfly networks, Benes networks, and mesh derived networks, which can be helpful to understand their underlying topologies and structural complexity. [ABSTRACT FROM AUTHOR]

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 M Hz , the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc. [ABSTRACT FROM AUTHOR]

The integration of wireless devices has represented a significant leap forward in Information Technology, substantially enhancing human convenience. A notable subset of these networks is Aerial Mesh Networks (AMNs), akin to Mobile Ad-hoc networks (MANETs) and Vehicular Ad-hoc networks (VANETs), functioning wirelessly with mobility capabilities. AMNs have found increasing utility in remote and challenging environments, particularly in disaster scenarios where swift and effective action is imperative for saving lives. These networks boast traits such as easy deployment, self-configuration, and self-organization, seamlessly integrating with existing networks like cellular networks, WLANs, and WMANs. Despite the myriad benefits of AMNs, there remain unexplored avenues that necessitate further investigation to fully harness their potential. Researchers continue to strive towards optimizing AMNs for diverse application domains, ensuring maximal efficiency and accuracy. A succinct literature review of AMNs encompasses their varied characteristics, addressing issues such as routing, handover, node positioning, security, as well as network considerations like target coverage, robustness, fault tolerance, and load balancing. Additionally, it highlights key application areas including agriculture, civil applications, greenhouse gas monitoring, and disaster management. Finally, it elucidates pertinent open issues and future directions warranting exploration. [ABSTRACT FROM AUTHOR]

The diffusion of small molecular penetrants through polymeric materials represents an important fundamental problem, relevant to the design of materials for applications such as coatings and membranes. Polymer networks hold promise in these applications because dramatic differences in molecular diffusion can result from subtle changes in the network structure. In this paper, we use molecular simulation to understand the role that cross-linked network polymers have in governing the molecular motion of penetrants. By considering the local, activated alpha relaxation time of the penetrant and its long-time diffusive dynamics, we can determine the relative importance of activated glassy dynamics on penetrants at the segmental scale vs entropic mesh confinement on penetrant diffusion. We vary several parameters, such as the cross-linking density, temperature, and penetrant size, to show that cross-links primarily affect molecular diffusion through the modification of the matrix glass transition, with local penetrant hopping at least partially coupled to the segmental relaxation of the polymer network. This coupling is very sensitive to the local activated segmental dynamics of the surrounding matrix, and we also show that penetrant transport is affected by dynamic heterogeneity at low temperatures. To contrast, only at high temperatures and for large penetrants or when the dynamic heterogeneity effect is weak, does the effect of mesh confinement become significant, even though penetrant diffusion more broadly empirically follows similar trends as established models of mesh confinement-based transport. [ABSTRACT FROM AUTHOR]

We generalize a microscopic statistical mechanical theory of the activated dynamics of dilute spherical penetrants in glass-forming liquids to study the influence of crosslinking in polymer networks on the penetrant relaxation time and diffusivity over a wide range of temperature and crosslink fraction (fn). Our calculations are relevant to recent experimental studies of a nm-sized molecule diffusing in poly-(n-butyl methacrylate) networks. The theory predicts the penetrant relaxation time increases exponentially with the glass transition temperature, Tg(fn), which grows roughly linearly with the square root of fn due to the coupling of local hopping to longer-range collective elasticity. Moreover, Tg is also found to be proportional to a geometric confinement parameter defined as the ratio of the penetrant diameter to the mean network mesh size. The decoupling ratio of the penetrant and Kuhn segment alpha times displays a complex non-monotonic dependence on fn and temperature that is well collapsed based on the variable Tg(fn)/T. A model for the penetrant diffusion constant that combines activated relaxation and entropic mesh confinement is proposed, which results in a significantly stronger suppression of mass transport with degree of effective supercooling than predicted for the penetrant alpha time. This behavior corresponds to a new network-based type of "decoupling" of diffusion and relaxation. In contrast to the diffusion of larger nanoparticles in high temperature rubbery networks, our analysis in the supercooled regime suggests that for the penetrants studied the mesh confinement effects are of secondary importance relative to the consequences of crosslink-induced slowing down of activated hopping of glassy physics origin. [ABSTRACT FROM AUTHOR]

This paper introduces a novel stability metric specifically developed for IQRF wireless mesh sensor networks, emphasizing flooding routing and data collection methodologies, particularly IQRF's Fast Response Command (FRC) technique. A key feature of this metric is its ability to ensure network resilience against disruptions by effectively utilizing redundant paths in the network. This makes the metric an indispensable tool for field engineers in both the design and deployment of wireless sensor networks. Our findings provide valuable insights, demonstrating the metric's efficacy in achieving robust and reliable network operations, especially in data collection tasks. The inclusion of redundant paths as a factor in the stability metric significantly enhances its practicality and relevance. Furthermore, this research offers practical ideas for enhancing the design and management of wireless mesh sensor networks. The stability metric uniquely assesses the resilience of data collection activities within these networks, with a focus on the benefits of redundant paths, underscoring the significance of stability in network evaluation. [ABSTRACT FROM AUTHOR]

Purpose: We primary aimed to synthesise the available data, assess the effectiveness of different mesh materials in prophylactic mesh placement, and rank these materials according to the incidence of parastomal hernia (PSH) and other stoma complications. Method: This network meta-analysis performed a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Four databases were searched for randomised controlled trials of prophylactic mesh placement. The aggregated results were performed in the STATA routine for Bayesian hierarchical random effects models. Result: Thirteen randomised controlled trials from 1203 articles, met the inclusion criteria, including 681 cases without meshes, 65 cases with mesh material of xenogeneic acellular dermis (porcine/bovine), 27 cases with polypropylene/PG910, 114 cases with polypropylene/polyglecaprone (Monocryl), 117 cases with polypropylene/cellulose (ORC), 233 cases with polypropylene, and 35 cases with polypropylene/PVDF. In network A, compared with no mesh, only polypropylene (RR 0.24, 95% CI 0.04–0.80) were significantly associated with a reduction in the incidence of PSH. In network B, no statistical difference regarding stoma complications was found between mesh and no mesh. Conclusion: Based on the network meta-analysis and ranking results, the polypropylene mesh material exhibited the best performance. However, this conclusion needs to be confirmed with larger sample sizes and high-quality randomised controlled trials. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid modelling approach that combines physics-based electromigration modelling (PEM) and statistical methods to evaluate the electromigration (EM) limits of nano-interconnects in mesh networks. The approach, which is also compatible with standard Place and Route (P&R) tools and practises, takes into account the positive impact of network redundancy on EM current limits. The numerical simulations conducted in this study show that conventional methods underestimate the EM current limits of a power delivery network (PDN) unit-cell by 80% due to their lack of consideration for redundancy. Additionally, the time-to-failure (TTF) distributions of a PDN unit-cell obtained by the developed modelling framework adhered to a lognormal distribution, where the lognormal sigma, σlogn, exhibits a 55% reduction compared to that of the single constituent interconnects. The study also found the negative voltage (i.e., ground or Vss) grid to be more susceptible to EM than the positive voltage, i.e., Vdd grid. In the examined grid unit-cell design, both the number of interconnect sites prone to voiding and also the magnitude of the peak tensile stress within the nano-interconnects were found to be two times as high in the Vss case compared to Vdd. The lognormal sigma of TFF for the grid unit-cells, σ l o g n − t i l e , show a marked reduction compared to the lognormal sigma of the constituent single interconnects, σ l o g n , with a 50% and 66% decrease compared to single interconnects, for downstream (Vss) and upstream (Vdd), respectively. In addition, σ l o g n − t i l e was three times higher for downstream (Vss) compared to upstream (Vdd), whilst, in contrast, this difference was only 2-fold at the single interconnect level. TTF50% was predicted to be 4.13-fold higher at the grid unit-cell level for the upstream compared to downstream operation, which was also more pronounced than in the single interconnect level where the difference was only 2-fold. This research provides valuable insights into the EM ageing of nano-interconnects in mesh networks and could pragmatically enhance the accuracy of EM compliance evaluation methods. [ABSTRACT FROM AUTHOR]

In today's digital landscape, content delivery networks (CDNs) play a pivotal role in ensuring rapid and seamless access to online content across the globe. By strategically deploying a network of edge servers in close proximity to users, CDNs optimize the delivery of digital content. One key mechanism involves caching frequently requested content at these edge servers, which not only alleviates the load on the source CDN server but also enhances the overall user experience. However, the exponential growth in user demands has led to increased network congestion, subsequently reducing the cache hit ratio within CDNs. To address this reduction, this paper presents an innovative approach for efficient cache replacement in a dynamic caching environment while maximizing the cache hit ratio via a cooperative cache replacement policy based on reinforcement learning. This paper presents an innovative approach to enhance the performance of CDNs through an advanced cache replacement policy based on reinforcement learning. The proposed system model depicts a mesh network of CDNs, with edge servers catering to user requests, and a main source CDN server. The cache replacement problem is initially modeled as a Markov decision process, and it is extended to a multi-agent reinforcement learning problem. We propose a cooperative cache replacement algorithm based on a multi-agent deep-Q network (MADQN), where the edge servers cooperatively learn to efficiently replace the cached content to maximize the cache hit ratio. Experimental results are presented to validate the performance of our proposed approach. Notably, our MADQN policy exhibits superior cache hit ratios and lower average delays compared to traditional caching policies. [ABSTRACT FROM AUTHOR]

This research paper introduces an optimization methodology for the strategic electric sources' placement at multiple positions in a DC islanded microgrid characterized by a mesh network, aiming to minimize line losses while considering minimal cable weight. The DC microgrid studied in this paper is composed of PV panels, batteries, a diesel generator, and 20 residential loads. Employing Dijkstra's algorithm, a graph algorithm used in Google Maps, the study identifies the shortest path (resistance) between potential source nodes and various variable loads within a predefined electric distribution mesh network topology. This study focuses on active power considerations and offers valuable insights into the placement optimization of multiple sources' positions in DC microgrid mesh networks. A key contribution of this paper lies in the ranking of source node positions based on minimal to maximal line losses, taking into consideration optimal cable weights, while using MATPOWER to validate sources' ranking based on Dijkstra's hypothesis. The research further includes a techno-economic study to assess the viability of sources' placement at multiple positions within the mesh network, comparing it with the optimal placement scenario involving a single position for all sources. This methodology serves as a valuable resource for system designers and operators aiming to minimize line losses and optimize energy distribution in DC microgrids in a mesh topology. [ABSTRACT FROM AUTHOR]

Wireless mesh networks (WMNs) are a type of wireless network that can be used for various applications, such as Internet access, disaster response, and military communication. These networks consist of mesh routers that can communicate with each other and form a mesh topology, allowing them to provide connectivity even if some of the routers fail or are out of range. In this study, we used spectral graph theory to analyze the performance of a WMN. For the analysis process, software was developed to calculate the topological characteristics of the graph representing the WMN. The correlation between the values of the parameters of spectral graph theory and the topological characteristics of the observed network is analyzed. First, an analysis of the influence of the change in signal strength, in the observedWMN, on the algebraic connectivity was performed, and then the change in the spectral radius was also analyzed. The analysis was performed using special software, which was developed for that purpose. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs. [ABSTRACT FROM AUTHOR]

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement. [ABSTRACT FROM AUTHOR]

The development of data transmission systems based on wireless radio communication channels allowed the construction of fundamentally new networks – mesh networks, which are used not only in smart technologies, but are the basis for the construction of cyber-physical and socio-cyber-physical systems (objects of critical infrastructure). The object is the process of ensuring reliable and secure data transmission based on the use of wireless radio communication channels. A mathematical model of information resources protection system functioning is proposed to ensure the signs of immunity and security of the automated data transmission system. To identify threats, a unified classifier and flow state estimation technique are used, which take into account the hybridity and synergy of targeted (mixed) attacks on communication channels. The critical points of the infrastructure elements, as well as the information that circulates and/or is stored, are determined. The assessment of compliance with the regulators’ requirements, both international and state regulatory acts, and the presence and ability of the security system elements to ensure the required level of infrastructure elements protection is taken into account. The proposed approach allows to determine: coefficients of information and internal availability of a wireless radio communication channel, the vector potential of the lagging magnetic field as a result of data transmission work. When evaluating the coefficient of a wireless radio communication channel internal availability, it is proposed to take into account coherent reception of the signal. At the same time, the immunity factor of the wireless radio communication channel is much higher than 1, which provides sufficient protection of information. A technical solution is proposed that will allow the level of confidentiality, integrity, authenticity and reliability of a wireless radio communication channel to approach 100 %. [ABSTRACT FROM AUTHOR]

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition. [ABSTRACT FROM AUTHOR]

Maritime shipping companies have identified continuous tracking of intermodal containers as a key tool for increasing shipment reliability and generating important economies of scale. Equipping all dry containers with an Internet-connected tracking device is a need in the global shipping market that is still waiting to be met. This paper presents the methods and tools to build and test a prototype of a Container Tracking Device (CTD) that integrates NB-IoT, BLE Mesh telecommunication and low-power consumption technologies for the massive deployment of the IoT. The work was carried out as part of a project to build the so-called "5G Global Tracking System", enabling several different logistic applications relying on massive IoT, M2M standard platforms, as well as satellite networks to collect data from dry containers when the vessel is in open sea. Starting from a preliminary phase, in which state-of-the-art technologies, research approaches, industrial initiatives and developing standards were investigated, a prototype version of the CTD has been designed, verified and developed as the first fundamental step for subsequent industrial engineering. The results of specific tests are shown: after verifying that the firmware is capable of handling the various functions of the device, a special focus is devoted to the power consumption measurements of the CTD to size the battery pack. [ABSTRACT FROM AUTHOR]

Knowing the functions of buildings is valuable in urban planning and management. For example, it can be used for the assessment of urban planning implementation and the fine-tuning of community governance. At large scales, determining building functions can be time-consuming and laborious. While point of interest (POI) data can be used to identify urban building functions, it is prone to missing values. The present study proposes combining POIs with the spatial relationships between geographic entities and geographic information systems (GIS) to improve the accuracy of urban building function identification. First, the POIs are reclassified according to building functions. Second, the spatial relationships among road networks, buildings and POIs are analysed, and the frequency density ratios of POI types are calculated to identify the functions of buildings that contain POIs. Finally, buildings that do not contain POIs are identified by calculating the spatial similarity between unrecognised buildings and recognised buildings within the same road network mesh. The method can identify buildings with singular residential, commercial, office, and public services functions, as well as seven mixed functions, with an accuracy, recall, and F1 value of 90.28%, 97.52%, and 93.76%, respectively. Public service buildings and residential buildings have the highest identification precision, while the identification precision of mixed commercial and public service buildings and mixed residential and public service buildings are the lowest. An experiment demonstrates the effectiveness of the method. The results indicate that the spatial relationships between entities can compensate for missing POI data. [ABSTRACT FROM AUTHOR]

Rural communities struggle to connect to the internet, a phenomenon known as the "digital divide." Mesh networks, with improved access in rural regions, might help to tackle this problem. From a social, economic, and scientific standpoint, this study investigated whether mesh networks may improve rural connectivity. This project developed and implemented methodologies to assess community participation, cost, and network coverage. Five well-known methods were pitted against these ones. Locals are working on a mesh node placement project in a rural location with diverse topography. In terms of network coverage, the Network Coverage Assessment revealed that the proposed approach frequently outperformed the most recent approaches. Finding the ideal locations for mesh nodes helped to tackle challenges in rural regions. After putting the strategy into effect, the Cost-Effectiveness Analysis revealed a positive ROI. Many alternative options seemed unprofitable. On the Community Engagement Index, the recommended method performed better than others. Participating in network activities with individuals from the local community helps to foster ownership and shared accountability. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis. [ABSTRACT FROM AUTHOR]

The rapid proliferation of Internet of Things (IoT) devices and applications has resulted in an increasing demand for Low Power and Wide Area Network (LPWAN) solutions. The adoption of IoT networks still faces several challenges, despite the rapid advancement of low-power communication technology. Homogenizing this sector requires allowing interoperability between many technologies, which is now one of the largest obstacles. In this article, we present the design and implementation of the hybrid LPWAN architecture that can accomplish wide-area communication coverage and low-power consumption for IoT applications by leveraging two LPWAN technologies, Wireless Smart Ubiquitous Network (Wi-SUN) and Long Range (LoRa). In particular, LoRa is used for long-range communication, and Wi-SUN for a low-latency mesh network. Additionally, we implemented smart street light controlling system as a real-world deployment at the university campus to showcase the efficiency of the hybrid network. Our results demonstrate that the hybrid LPWAN architecture provides a better coverage and capacity while consuming less power than that of the LoRa or Wi-SUN network. The results of this study demonstrate the effectiveness of the proposed hybrid LPWAN architecture as a viable solution for next-generation IoT applications. [ABSTRACT FROM AUTHOR]

Maritime shipping companies have identified continuous tracking of intermodal containers as a key tool for increasing shipment reliability and generating important economies of scale. Equipping all dry containers with an Internet-connected tracking device is a need in the global shipping market that is still waiting to be met. This paper presents the methods and tools to build and test a prototype of a Container Tracking Device (CTD) that integrates NB-IoT, BLE Mesh telecommunication and low-power consumption technologies for the massive deployment of the IoT. The work was carried out as part of a project to build the so-called “5G Global Tracking System”, enabling several different logistic applications relying on massive IoT, M2M standard platforms, as well as satellite networks to collect data from dry containers when the vessel is in open sea. Starting from a preliminary phase, in which state-of-the-art technologies, research approaches, industrial initiatives and developing standards were investigated, a prototype version of the CTD has been designed, verified and developed as the first fundamental step for subsequent industrial engineering. The results of specific tests are shown: after verifying that the firmware is capable of handling the various functions of the device, a special focus is devoted to the power consumption measurements of the CTD to size the battery pack.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas. [ABSTRACT FROM AUTHOR]

In this study, we present a novel interpolation scheme for chimera simulations in CFD that treats flows with discontinuities. This scheme is suitable for interpolation over polyhedral meshes using only scattered data and without the use of stencils. During the interpolation data transfer among overset meshes, both solution variables and their gradients are communicated and then used for the interpolation process. First, the overset topology problem in partitioned polyhedral meshes is addressed, and then a new interpolation algorithm for generally discontinuous fields is introduced. Then, we describe how the gradient approximation of the Finite Volume (FV) method is utilized in order to construct bounded approximations on values on the direction of discontinuities and enhance the accuracy of the low order Nearest Neighbor value (NNV) algorithm. The performance of the proposed algorithm is quantified and validated in various test cases, together with a comparison with NNV. Finally, scalability tests are presented to prove computational efficiency. The method is proved to be highly accurate in propagation cases and performs well in unsteady two-phase problems executed using parallel architectures. [ABSTRACT FROM AUTHOR]

Given a graph G and an edge $ e\in E(G) $ e ∈ E (G) , let S be a vertex set of G. For any two vertices x, $ y\in S $ y ∈ S , if e belongs to all the shortest paths between x and y, then x and y can monitor the edge e. For each edge e of G, if there exists x and y in S such that x and y can monitor e, then the set S can be called a monitoring-edge-geodetic ( $ \operatorname {MEG} $ MEG for short) set of G. The $ \operatorname {MEG} $ MEG number, denoted by $ \operatorname {meg}(G) $ meg ⁡ (G) , is the size of the smallest $ \operatorname {MEG} $ MEG set of G. In this paper, we obtain the exact values of the $ \operatorname {MEG} $ MEG numbers for radix triangular mesh networks, Sierpiński graphs, Sierpiński gasket graphs and Sierpiński generalized graphs. [ABSTRACT FROM AUTHOR]

Resin-bonded bridges (RBBs) are a minimally invasive and aesthetically pleasing treatment modality. However, their frequent failure has posed challenges for both dental professionals and patients. This necessitates the exploration of innovative strategies to enhance the longevity of RBBs. This study aimed to assess the bond strength of a mesh bridge fabricated using computer-aided design and three-dimensional (3D) printing technology in comparison to the traditional aluminum oxide sandblasting method. A total of 48 lower incisors were embedded in acrylic bases according to a standardized computer-generated model to receive 24 metal RBBs. The two groups underwent distinct metal surface treatments: the 3D mesh novel design and sandblasting with aluminum oxide particles sized at 250.00 µm. The bond strength of the bridges was evaluated, and statistical analysis was performed using the independent samples t-test with a significance level set at α = 0.05. The findings revealed a significant difference between the two methods (p < 0.001). The 3D mesh design exhibited a mean bond strength of 387.89 ± 24.15 N, while the sandblasting technique yielded a mean value of 161.46 ± 31.25 N. In summary, the 3D mesh design substantially enhanced the bond strength of RBBs compared to the traditional sandblasting technique. [ABSTRACT FROM AUTHOR]

This study introduces QMNN, a quantum mesh neural network model incorporating a Quantum Neural Network Expectation (QNNE) circuit, and evaluates its performance on MNIST and CIFAR-10 datasets. QMNN employs innovative quantum mesh architecture with increased random layers, while the QNNE circuit, inspired by Circuit 15, integrates extra Hadamard gates for enhanced feature extraction. Implemented using PennyLane libraries, the model demonstrates superior accuracy in training and testing on both datasets compared to classical neural networks, emphasizing its effectiveness in image classification. While classic methods like CNNs may achieve higher accuracy, the significance lies in QMNN's ability, along with the QNNE circuit, to improve accuracy without introducing additional optimization parameters. The observed quantum advantage suggests that strong entanglement between qubits may enhance pixel correlations, highlighting the potential of quantum-inspired models in advancing image diagnostics. Conclusively, this study provides a stepping stone for further research into the synergy between quantum computing and neural networks, showcasing the promising role of QMNN in augmenting image classification accuracy. The results indicate notable improvements, with MNIST testing accuracy reaching 92.80% and CIFAR-10 testing accuracy achieving 36.90%, demonstrating the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification. [ABSTRACT FROM AUTHOR]

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(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. [ABSTRACT FROM AUTHOR]

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic's dynamic mesh networking capabilities and LoRa's low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices' measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges. [ABSTRACT FROM AUTHOR]

We summarize the fabrication strategies of metallic meshes (MMs) with hard or soft templates, which produce regular-patterned or random-patterned meshes, emphasizing large scale production technology free of templates would promote the advancement of MMs. Mechanisms and strategies are highlighted in terms of enhancement in optoelectronic properties, thermal, chemical, and mechanical stability that are crucial for improving environment adaptivity of MMs. [Display omitted] Metallic meshes (MMs) are promising for replacement of traditional brittle metal oxide conductors to serve as flexible transparent devices for applications such as environment, energy, display, and human–machine-environment interface. The diversities of MMs in materials, fabrication mechanisms, geometrics and performances extend their applications, however the systematization of this field is rarely concerned. Here, this article summarizes fabrication strategies of MMs in terms of using hard or soft templates, which produce regular-patterned meshes or random-patterned meshes, emphasizing large scale production technology free of templates would lead the development of MMs. Secondly, mechanisms and strategies are highlighted in terms of enhancement of MMs in optoelectronic properties, thermal, chemical, and mechanical stability that are crucial for adapting different application scenarios. Accordingly, the representative applications of MMs in filters, shielding materials, nanogenerators, supercapacitors, solar cells, light-emitting devices, electroluminescent devices, smart windows, thermal management equipment, sensors, and touch panels are introduced, reflecting their respective advantages in diverse applications. Finally, promising solutions to balance the optoelectronic properties and the stability and durability of MMs are proposed to adapt to complex and extreme applications. It is convinced that this review can provide a comprehensive understanding to guide and promote the future development of flexible optoelectronic devices. [ABSTRACT FROM AUTHOR]

Polynomial interpolation is an important component of many computational problems. In several of these computational problems, failure to preserve positivity when using polynomials to approximate or map data values between meshes can lead to negative unphysical quantities. Currently, most polynomial-based methods for enforcing positivity are based on splines and polynomial rescaling. The spline-based approaches build interpolants that are positive over the intervals in which they are defined and may require solving a minimization problem and/or system of equations. The linear polynomial rescaling methods allow for high-degree polynomials but enforce positivity only at limited locations (e.g., quadrature nodes). This work introduces open-source software (HiPPIS) for high-order data-bounded interpolation (DBI) and positivity-preserving interpolation (PPI) that addresses the limitations of both the spline and polynomial rescaling methods. HiPPIS is suitable for approximating and mapping physical quantities such as mass, density, and concentration between meshes while preserving positivity. This work provides Fortran and Matlab implementations of the DBI and PPI methods, presents an analysis of the mapping error in the context of PDEs, and uses several 1D and 2D numerical examples to demonstrate the benefits and limitations of HiPPIS. [ABSTRACT FROM AUTHOR]

This study presents a unique method for network reconfiguration for Searching Maximum Loading Capacity Radial Network from an interconnected meshed network using continuation power flow. The network reconfiguration outcomes are also evaluated for various graph theory approaches presented in the literature. The combined graph theory and machine learning methods are used to determine the best sectional switch position. The system's radiality is verified using graph theory. The recommended technique is tested on a 30-node mesh network with 41 sectional switches. The results demonstrate that the distribution system can manage a higher connected load using the suggested Bayesian-Ridge approach while lowering total active and reactive power losses. [ABSTRACT FROM AUTHOR]

Mesh adaptation is crucial in numerical simulation, providing optimal resource allocation for accurately capturing physical phenomena. However, when applied to Computational Fluid Dynamics (CFD) problems with complex multi-scale properties, existing adaptation methods face huge challenges due to the high computational cost of solving auxiliary partial differential equations (PDEs) and the difficulty in aligning the flow features with mesh geometric features. In this work, an end-to-end data-driven mesh adaptation framework, Flow2Mesh, is proposed to address these challenges by adopting a hybrid modeling strategy to construct the mapping from pixelated flow-fields to graph-based meshes. It achieves a rapid and accurate one-step mesh adaptation via a perceptual feature network (PFN) and a mesh movement network (MMN). PFN extracts the global perceptual features from flow-fields to enhance flow feature representation and mesh resolution independence. In MMN, these features are utilized to deform the initial mesh to a topology-invariant adaptive mesh by a proposed physically driven mesh convolutional network. It considers the inherent mesh geometric information for efficient node feature aggregation and alignment of mesh density with a flow-field structure. To generate high-quality adaptive meshes, various mesh-related losses are designed to regularize the mesh movement and alleviate the mesh tangling. Experiments in CFD scenarios demonstrate the generalization of our model to different design parameters and mesh configurations. It takes three orders of magnitude less time to generate similar meshes than the PDE-based method. The results exhibit the potential of Flow2Mesh to be a flexible and reliable tool for rapid mesh adaptation in scientific and industrial fields. [ABSTRACT FROM AUTHOR]

The armature and rail sizes of electromagnetic rail launcher vary greatly, and the refined 3D finite element computation occupies a large amount of physical memory. In order to enhance the economy of dynamic computation, this paper proposes an adaptive hexahedral mesh method based on mesh expansion, compression, and translation. In addition, split nodes are used on both sides of the contact surface, and interface conditions and frictional heat sources are constrained through point penalty function method to solve non-ideal sliding electrical contact problems. Comparative calculations with the same type of software and the same model are carried out, and the results calculated in this paper are consistent with the relevant results of MEAP3D. This paper also compares the EMRL calculation results of adaptive mesh model and constant mesh model to verify the reliability of the method. In addition, the C-type EMRLs are compared and analyzed. The results show that due to the influence of velocity skin effect, the dynamic inductance gradient of the rail gradually increases over time and is greater than the static value. The maximum difference between the two is 5.65% of the dynamic inductance gradient. The steel shell generates eddy currents, causing a decrease in armature velocity of 4.7 m/s under the small caliber launcher. The maximum eddy current density waveform of the shell exhibits two peaks. In the frictionless heat, the temperature of the armature is underestimated, and under the action of frictional heat, the trailing edge of the armature is ablated and melted. [ABSTRACT FROM AUTHOR]

The escalating global demand for electricity is driving a significant expansion in the size and complexity of electric power systems. Explosive growth strains power distribution, creating challenges for operators. Among these challenges, the imperative to minimise power losses is crucial. To enhance power efficiency and quality, Distributed Generation (DG) technology, particularly wind energy, is being integrated into distribution systems. However, the variable nature of wind speed poses a significant challenge to the seamless integration of wind energy into grids. To address this challenge, wind speed prediction methods are explored. In this work, a Machine Learning framework is employed for multivariate wind speed forecasting in Tamil Nadu, India $\left[{{{8.0883}^ \circ }N,{{77.5385}^ \circ }E} \right]$ 8.0883 ∘ N , 77.5385 ∘ E . Key performance metrics, such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error values (MAPE), are used to assess the accuracy of the wind speed predictions. The predicted wind speed data is then utilised to estimate wind farm power output which is seamlessly integrated into the distribution system. Load Flow analysis is conducted using the robust and straightforward current injection method, focusing on IEEE-33 and IEEE-69 bus-balanced Mesh distribution systems. The aim is to determine the impact of wind-powered Distributed Generation on power losses within these systems. [ABSTRACT FROM AUTHOR]

Mesh networks are self-managing wireless systems with dynamic topology. These networks differ from broadcast and mobile networks because their mesh nodes can directly exchange information without the intervention of any other infrastructure. However, the radio propagation environment in urban regions, characterized by dense building clusters and human-made structures, influences signal attenuation and path loss. Therefore, deploying these networks brings distinct challenges from the more intensively studied indoor or rural scenarios. In line with this, predicting radio signal propagation attenuation is crucial for planning and deploying reliable networks. The literature on received signal strength indicator (RSSI) prediction for mesh networks in urban areas is scarce. This paper proposes machine learning-based RSSI prediction models for highly urbanized areas. We highlight the most influential features, including the distance between the transmitter and receiver, obstruction details in the first Fresnel zone, and terrain variability measures. Considering data from two mesh networks in the Metropolitan Region of São Paulo, Brazil, owned by a power utility company, we trained a Random Forest and a Support Vector Regression model for the RSSI prediction task. Comparative analysis indicates an improvement of up to 66% in the RSSI prediction error using the Random Forest approach in comparison with classical and empirical models.