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The study of continuous natural or industrial phenomena in time and space requires the emergence of new wireless sensor networks. A virtual sensor network (VSN) is a wireless camera network that appears to overcome the limitations of traditional wireless sensor networks in terms of the ability to store, process, and communicate data in a 3D region of interest. In this paper, we proposed a Starlings Model-based virtual Sensor Network for Coverage, Connectivity, and Data Communication (SM-VSN-3C) to ensure 3D coverage of temporally and spatially continuous 3D phenomena. Starlings are a good example of a VSN in nature. We therefore simulate the 3D movement of the stars in the sky, ensure the associated permanent coverage, and communicate with the Renault model. Use the behavioral model proposed by Reynolds to simulate herd movement. We've demonstrated the efficiency of the proposed network (SM-VSN-3C) in terms of communication and continuous coverage in time and space (3D). When simulating large and dense VSNs, there are two challenges in terms of coverage and communication: How to efficiently track a set of VSN-Starlings (VSN-Birds) in terms of coverage? In such a dense environment, how can a single Starling be tracked in terms of communication and data routing? [ABSTRACT FROM AUTHOR]

The significant simplification achieved by Main Path Analysis (MPA) to a network of documents raises concerns about whether the derived main paths (MPs) accurately capture and reflect the overall knowledge development within the network. This study addresses this MP representativeness issue from a network-structural perspective, considering that MPs can only represent the parts of the network that are structure-related to them, and these parts of the network are referred to as constituting the MPs' coverage. The share of documents falling within the MPs' coverage can serve as a quantitative measure to complement the qualitative assessment of MP representativeness. This study introduces a so-called key-node path search to uncover the coverage of MPs. In cases where a significant portion of the network falls outside the coverage of MPs, this study proposes a method to discover auxiliary MPs from the out-of-coverage parts of the network. These auxiliary MPs provide additional insights into the representativeness of the primary MPs, the structural characteristics of the network, and additional knowledge development trajectories. To demonstrate the practical application of these concepts and methodologies, a case study using U.S. utility patents related to chimeric antigen receptor (CAR) T-cell therapy is conducted. [ABSTRACT FROM AUTHOR]

This study reviews the impact of eligibility policies in the early rollout of the COVID-19 vaccine on coverage and probable outcomes, with a focus on New York City. We conducted a retrospective ecological study assessing age 65+, area-level income, vaccination coverage, and COVID-19 mortality rates, using linked Census Bureau data and New York City Health administrative data aggregated at the level of modified zip code tabulation areas (MODZCTA). The population for this study was all individuals in 177 MODZCTA in New York City. Population data were obtained from Census Bureau and New York City Health administrative data. The total mortality rate was examined through an ordinary least squares (OLS) regression model, using area-level wealth, the proportion of the population aged 65 and above, and the vaccination rate among this age group as predictors. Low-income areas with high proportions of older people demonstrated lower coverage rates (mean vaccination rate 52.8%; maximum coverage 67.9%) than wealthier areas (mean vaccination rate 74.6%; maximum coverage 99% in the wealthiest quintile) in the first 3 months of vaccine rollout and higher mortality over the year. Despite vaccine shortages, many younger people accessed vaccines ahead of schedule, particularly in high-income areas (mean coverage rate 60% among those 45–64 years in the wealthiest quintile). A vaccine program that prioritized those at greatest risk of COVID-19-associated morbidity and mortality would have prevented more deaths than the strategy that was implemented. When rolling out a new vaccine, policymakers must account for local contexts and conditions of high-risk population groups. If New York had focused limited vaccine supply on low-income areas with high proportions of residents 65 or older, overall mortality might have been lower. [ABSTRACT FROM AUTHOR]

Objectives: To assess the effect of a single clinic-based educational intervention session on parents of children aged 4.5 to 5.5 years on improving the coverage of a second booster dose of the DPT vaccine. The secondary objective was to assess the coverage of second booster dose of the DPT vaccine among children aged > 6 years and to learn about the reasons behind such dropouts, if any. Methods: The study was conducted in two phases. In the first phase, a cross-sectional study was conducted among children aged > 6 years who were attending the pediatric OPD or IPD to determine the coverage of the second booster dose of DPT vaccine and possible reasons for dropout. This was followed by a clustered randomized trial evaluating the effect of an educational intervention (clinic-based, single session) among parents of children aged 4.5 to 5.5 years for improving second DPT booster coverage. Results: A total of 384 children were enrolled in the first phase, of which 233 (60.68%) were vaccinated. Subgroup analysis showed significant differences in the vaccine coverage between children from tribal-dominant and non-tribal-dominant districts (45.10% vs 63.06%, P = 0.01). Educational intervention resulted in higher vaccination coverage (77.24%) compared to 71.43% in the control arm (P = 0.300) Conclusions: The current study showed low coverage for second booster of DPT vaccine. With educational intervention, the target immunization coverage could be attained early which has implications for reducing childhood morbidity due to vaccine-preventable diseases. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are widely used for detecting objects, collecting, analyzing, and communicating information. Optimizing the estimation of sensor node positions is crucial for precise and reliable operations. The standard Distance Vector Hop (DV-Hop) localization method, while simple and practical, lacks sufficient accuracy. This article introduces an Improved Distance Vector Hop (IDV-Hop) algorithm that enhances positioning accuracy using a Variable Velocity Strategy Human Conception Optimization (VVS-HCO). This method refines the velocity update technique to improve convergence and precision without additional hardware costs. The proposed algorithm allows adjustable hop sizes for anchor nodes. Simulation results demonstrate that the IDV-Hop algorithm significantly reduces localization error and variance, improving accuracy under various conditions, outperforming other advanced algorithms. [ABSTRACT FROM AUTHOR]

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network's physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem's assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics. [ABSTRACT FROM AUTHOR]

We address the strategic deployment of base stations (BSs) or access points (APs) within a smart building to form ultra-dense, small-cell-based 5G or beyond 5G networks. We consider the coverage of an arbitrarily shaped service area (convex or non-convex) in an indoor environment by placing BSs/APs with varying transmit powers at appropriate locations such that every point in the indoor region under consideration will be covered by at least one BS. Towards this goal, we propose a Voronoi diagram-based BS placement framework that can completely cover a given convex/non-convex region without generating any coverage hole. Our placement technique considers the presence of different obstacles, partition walls, etc., of varying attenuation factors inside a building affecting the LOS propagation of signals in the mmWave range. Our proposed strategy simultaneously minimizes the total wastage of transmission powers of all BSs by minimizing the total area of overlapped regions and out-of-region coverages. The unique feature of our approach is that it is applicable for fully covering any convex or non-convex region in contrast to the existing coverage techniques in the literature, which are applicable only for convex regions. We demonstrate that our proposed algorithm outperforms existing techniques for covering convex geometries in terms of the total transmission power requirement of all BSs/APs. Our proposed framework for the placement of access points will also be applicable for upcoming Wi-Fi standards such as the 802.11ad and the 802.11ay standards that can operate in the 60 GHz band. [ABSTRACT FROM AUTHOR]

Background: Standardization of procedures for data abstraction by cancer registries is fundamental for cancer surveillance, clinical and policy decision-making, hospital benchmarking, and research efforts. The objective of the current study was to evaluate adherence to the four components (completeness, comparability, timeliness, and validity) defined by Bray and Parkin that determine registries' ability to carry out these activities to the hospital-based National Cancer Database (NCDB). Methods: Tbis study used data from U.S. Cancer Statistics, the official federal cancer statistics and joint effort between the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), which includes data from National Program of Cancer Registries (NPCR) and Surveillance, Epidemiology, and End Results (SEER) to evaluate NCDB completeness between 2016 and 2020. The study evaluated comparability of case identification and coding procedures. It used Commission on Cancer (CoC) standards from 2022 to assess timeliness and validity. Results: Completeness was demonstrated with a total of 6,828,507 cases identified within the NCDB, representing 73.7% of all cancer cases nationwide. Comparability was followed using standardized and international guidelines on coding and classification procedures. For timeliness, hospital compliance with timely data submission was 92.7%. Validity criteria for re-abstracting, recording, and reliability procedures across hospitals demonstrated 94.2% compliance. Additionally, data validity was shown by a 99.1% compliance with histologic verification standards, a 93.6% assessment of pathologic synoptic reporting, and a 99.1% internal consistency of staff credentials. Conclusion: The NCDB is characterized by a high level of case completeness and comparability with uniform standards for data collection, and by hospitals with high compliance, timely data submission, and high rates of compliance with validity standards for registry and data quality evaluation. [ABSTRACT FROM AUTHOR]

WSNs (Wireless sensor networks) are used in multiple applications including IoT (Internet of Things) applications like intelligent control, prediction, tracking, and other communication network services (Internet-of-Things). Due to their limited design for two-dimensional space, high computing costs, or sensitivity to measurement errors, the typical localization frameworks might not perform well in real-world settings. Location information of deployed sensor nodes in their surrounding environments is important for algorithmic three-dimensional localizations. But there are drawbacks to current 3D localization algorithms in many parameters including complexity, positional precisions, and excessive energy consumption. Hence, this work proposes Enhanced 3D-DV-Hop (3D-Distance Vector Hop) localizations based on PSO (Particle Swarm Optimization) and GAs (Genetic algorithms) for the aforementioned issues. To further increase the diversity and accuracy of the DV-Hop outputs, a learning technique is employed. The learning technique leads to an improvement in search effectiveness, convergence speed, and result in quality. The simulation results demonstrate that the suggested strategy can improve positioning coverage while maintaining positioning accuracy. [ABSTRACT FROM AUTHOR]

UAVs in most real-world deployments communicate with a ground control station either in a one-hop manner, which has a limited working range, or relies on infrastructure support such as satellite communication, which is expensive. Although multi-hop transmission is more economic, it is lossy and generally incurs large delays. In this paper, we consider a mixed method, where a few UAVs are equipped with satellite modules, and aggregate and forward traffic of nearby UAVs. The surrounding UAVs and the forwarding UAV form a star topology. Based on this model, we design an UAV formation strategy to cover a given region to minimize both the total number of UAVs and the number of UAVs with satellite modules. Since these two goals may be competing, we propose seven strategies and analyze them theoretically. Simulations show that a good tradeoff between the two goals can be obtained when each star network consists of four UAVs. [ABSTRACT FROM AUTHOR]

In this study, the coverage and overlap of retracted publications, retraction notices and withdrawals are compared across seven significant scholarly databases, with the aim to check for discrepancies, pinpoint the causes of those discrepancies, and choose the best product to produce the most accurate picture of retracted literature. Seven scholarly databases were searched to obtain all the retracted publications, retraction notices and withdrawal from 2000. Only web search interfaces were used, excepting in OpenAlex and Scilit. The findings demonstrate that non-selective databases (Dimensions, OpenAlex, Scilit, and The Lens) index a greater amount of retracted literature than do databases that rely their indexation on venue selection (PubMed, Scopus, and WoS). The key factors explaining these discrepancies are the indexation of withdrawals and proceeding articles. Additionally, the high coverage of OpenAlex and Scilit could be explained by the inaccurate labeling of retracted documents in Scopus, Dimensions, and The Lens. 99% of the sample is jointly covered by OpenAlex, Scilit and WoS. The study suggests that research on retracted literature would require querying more than one source and that it should be advisable to accurately identify and label this literature in academic databases. [ABSTRACT FROM AUTHOR]

Background: This study aimed to conduct a systematic review of the cost-effectiveness studies of interventions to increase cervical cancer screening uptake rates in underserved women in Europe. Methods: A search of Embase, Medline, Global Health, PsychINFO, and NHS Economic Evaluation Database was conducted for studies published between January 2000 and September 2022. Studies were eligible if they analysed the cost-effectiveness of any interventions to improve participation in cervical cancer screening among underserved women of any age eligible to participate in cervical cancer screening in European countries, in any language. Study characteristics and cost-effectiveness results were summarised. Study quality was assessed using the Drummond Checklist, and methodological choices were further compared. Results: The searches yielded 962 unique studies, with 17 of these (from twelve European countries) meeting the eligibility criteria for data extraction. All studies focused on underscreened women as an overarching group, with no identified studies focusing on specific subgroups of underserved women. Generally, self-HPV testing and reminder interventions were shown to be cost-effective to increase the uptake rates. There was also research showing that addressing access issues and adopting different screening modalities could be economically attractive in some settings, but the current evidence is insufficient due to the limited number of studies. Conclusion: This systematic review has revealed a gap in the literature on the cost-effectiveness of interventions to improve uptake rates of cervical cancer screening through tailored provision for specific groups of underserved women. [ABSTRACT FROM AUTHOR]

We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications, including military, environment monitoring, and surveillance and body area sensor networks. A wireless sensor network builds the core part for IoT. Besides this, lifetime maximization is the biggest challenge in the wireless sensor network. Also, In a wireless sensor network, it is difficult to find an optimal node deployment approach that would minimize costs, be robust to node failures, decrease computing overhead and communication, and maintain a high degree of coverage and network connectivity. There is numerous literature addressed this challenge which is discussed in this paper; still there are lot many challenges yet to be addressed. Considering this scenario, in this paper, we propose a scheduled-based node deployment algorithm using Firefly Optimization (FA) to offer a circumstance where we have a group of target points that satisfy p-coverage and sensor nodes that satisfy q-connectivity, with subject to the selection of the optimal number of a sensor node that has the highest energy and minimum distance. The multiple parameters as no. of sensor nodes, distance, survivability factors, coverage, and connectivity of the sensor nodes are considered for designing the fitness function. A comprehensive statistical analysis is done using the simulation results to prove the proposed scheme's efficiency with other existing state-of-the-art methods under various p-coverage and q-connectivity configurations. [ABSTRACT FROM AUTHOR]

Systematic literature reviews in educational research have become a popular research method. A key point hereby is the choice of bibliographic databases to reach a maximum probability of finding all potentially relevant literature that deals with the research question analyzed in a systematic literature review. Guidelines and handbooks on review recommend proper databases and information sources for education, along with specific search strategies. However, in many disciplines, among them educational research, there is a lack of evidence on the relevance of databases that need to be considered to find relevant literature and lessen the risk of missing relevant publications. Educational research is an interdisciplinary field and has no core database. Instead, the field is covered by multiple disciplinary and multidisciplinary information sources that have either a national or international focus. In this article, we discuss the relevance of seven databases in systematic literature reviews in education, based on results of an empirical data analysis of three recently published reviews. To evaluate the relevance of a database, the relevant literature of those reviews served as the gold standard. Results indicate that discipline-specific databases outperform international multidisciplinary sources, and a combination of discipline-specific international and national sources is most efficient in finding a high proportion of relevant literature. The article discusses the relevance of the databases in relation to their coverage of relevant literature, while considering practical implications for researchers performing a systematic literature search. We, thus, present evidence for proper database choices for educational and discipline-related systematic literature reviews. [ABSTRACT FROM AUTHOR]

Introduction: Knowing how metabolic and bariatric surgery (MBS) is indicated in different countries is essential information for the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO). Aim: To analyze the indications for MBS recommended by each of the national societies that comprise the IFSO and how MBS is financed in their countries. Methods: All IFSO societies were asked to fill out a survey asking whether they have, and which are their national guidelines, and if MBS is covered by their public health service. Results: Sixty-three out of the 72 IFSO national societies answered the form (87.5%). Among them, 74.6% have some kind of guidelines regarding indications for MBS. Twenty-two percent are still based on the US National Institute of Health (NIH) 1991 recommendations, 43.5% possess guidelines midway the 1991s and ASMBS/IFSO 2022 ones, and 34% have already adopted the latest ASMBS/IFSO 2022 guidelines. MBS was financially covered in 65% of the countries. Conclusions: Most of the IFSO member societies have MBS guidelines. While more than a third of them have already shifted to the most updated ASMBS/IFSO 2022 ones, another significant number of countries are still following the NIH 1991 guidelines or even do not have any at all. Besides, there is a significant number of countries in which surgical treatment is not yet financially covered. More effort is needed to standardize indications worldwide and to influence insurers and health policymakers to increase the coverage of MBS. [ABSTRACT FROM AUTHOR]

The structural characteristics of plant communities in urban green spaces have a significant impact on their carbon sequestration function. In this study, comprehensive data were collected from 106 plant communities (each 20 m × 20 m) in Zhengzhou Green Expo Park. We assessed aboveground and soil carbon storage, alongside maintenance carbon emissions, to quantify carbon dynamics. Our primary objective was to establish a statistical model that correlates the structural attributes of plant communities with their total annual carbon sequestration. This model aims to provide a quantitative framework for optimizing community structures to maximize carbon sequestration in urban green spaces. The results showed that density and coverage were significantly and positively correlated with aboveground and soil carbon stocks. Density and mean height were significantly and positively correlated with maintenance carbon emissions. Density played a key structural role in regulating the total carbon sequestration of the plant communities, being 27.24 times more effective than coverage. The total annual carbon sequestration of the plant community reached an optimal value of 327.67 kg CO2-eq/y−1 at a density and cover of 0.15 and 1, respectively. This study provides valuable data for increasing the carbon sink ability of urban green spaces through plant structure regulation and supporting low-carbon development strategies in urban management. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been extensively explored due to their incredible capabilities and ever-growing field of applications. In cluster-based WSNs, cluster heads (CHs) deplete their energy quickly due to the extra workload of data aggregation and data forwarding as relay nodes (RNs) from the member sensor nodes (SNs). While placing the RNs in the application areas to form the clusters, coverage of all the SNs and connectivity amongst the RNs are essential for the proper function of the networks. Moreover, reducing the intra-cluster distances between the RNs and SNs is important to save the transmission energy of the SNs. Moreover, the problem of placement of RNs in cluster-based WSNs is known as NP-Hard. To address the challenge of deploying RNs while managing multiple conflicting objectives, we present a range of evolutionary algorithms (EAs) as potential solutions. These include techniques such as differential evolution (DE), particle swarm optimization (PSO), multi-objective DE (MODE), and multi-objective PSO (MOPSO). First, a mathematical formulation of the problem is given. The solution vectors are efficiently encoded. All the objective functions are efficiently derived to evaluate the solution vectors. An extensive simulation is performed over the proposed algorithms. The results are analyzed to determine the robust algorithm to be recommended for the studied problem. The simulated results claimed that the MODE is comparably better than others for the studied problem. The analysis of variance (ANOVA) is also performed, followed by a post-hoc analysis using the least significant difference (LSD) method. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for relocating sensors in a Wireless Sensor and Robots Network (WSRN) using a mobile robot. The goal is to repair coverage holes using redundant sensors that are caused by an initial random deployment. The holes are repaired without prior knowledge of their positions or that of the redundant sensors. The existing solutions mainly focus either on how to optimally repair holes by determining to where relocate redundant sensors, or how to build a repair path with assumption that the positions of holes and redundant sensors are known. In both scenarios, the literature lacked the optimization of the robot’s path for its initial exploration to identify both the holes and redundant sensors. Our proposed solution introduces an efficient robot trajectory that utilizes stochastic paths that adhere to the principles of light reflection. This trajectory serves the dual purpose of identifying redundant sensors and detecting as well as repairing coverage holes. We achieve this by incorporating the law of large numbers into the light reflection principal, enabling the robot to move randomly while adhering to the pathways of light reflection to efficiently relocate the redundant sensors. This approach results in a highly efficient and effective sensor relocation process. The effectiveness of the proposed solution is assessed across multiple parameters, including relocation time, the length of the relocation path, the robot average moves, and the total energy consumption required to cover holes with varying carrying capacity, dimensions of region of interest, coverage ratio and exit angles of reflection. Through a series of extensive simulations, we provide compelling evidence that our proposed solution distinctly surpasses the existing state-of-the-art approaches. This notable advantage becomes evident in multiple aspects: from reduced relocation time and shorter relocation path length to minimized total energy consumption. These combined enhancements underscore the effectiveness of our solution in tackling the challenge of sensor relocation. [ABSTRACT FROM AUTHOR]

The research aims to resolve the challenges faced in traditional Test Case Prioritization(TCP) techniques and the need to enhance the efficiency of the software testing process.Software development involves test case execution, which tests the changes to the system that requires more resources and time. TCP is the indispensable method, which is established to obtain the goals, such as attaining fast fault detection and a high convergence rate. The code-coverage- methods are the advanced approach employed in the TCP, which is utilized in various prioritization methods to enhance the efficiencyof the method. In this research, the multi-objective- hybrid Chaotic Flower fruit-fly optimization algorithm (Hybrid CFFO) is proposed for the TCP. Instead of using rigid rules or deterministic algorithms, metaheuristic algorithms were created to make intelligent decisions on heuristics and approximations. The foraging behavior of the flower fly and the reproduction characters of the flower are merged which covers a large exploration phase and overcome the local optima. In TCP, being able to quickly explore interesting search space regions and avoid local optima the proposed hybrid Chaotic Flower fruit-fly optimization is essential for producing high-quality solutions. The selection measures used by the optimization algorithm are on the average percentage of combinatorial coverage (APCC) and the Normalized average of the percentage of faults detected (NAPFD), which is designed to develop the multi-objective function. Based on the algorithm, the significance of the test cases is ordered and prioritized. The effectiveness of the proposed methodology is revealed by comparing the proposed multi-objective-based hybrid CFFO method with the conventional methods. From the results obtained, it is proved that the multi-objective-hybrid CFFO method outperforms all the conventional methods in terms of fitness, NAPFD, and APCC. The maximum fitness, NAPFD,and APCC achieved by the proposed methods are 93.1%,92.82%, and 91.1%, respectively for the big fault matrix data. [ABSTRACT FROM AUTHOR]

The quantity and quality of spray deposition are fundamental for improving pesticide performance in plant pests. This study evaluated how spray droplet size (1) and spray nozzle type (2) affect the quantity and quality of spray deposition on peanut plants at the vegetative and reproductive growth stages. Two greenhouse experiments were performed on peanut plants at the V1 and R1 growth stages. Experiment 1 treatments included three droplet sizes: medium (flat fan XR 11002), very coarse (TT 11002), and extremely coarse (AIXR 11002). Experiment 2 treatments were five spray nozzles: hollow cone (TXA 8002 VK), twin flat fan (TTJ60 11002), air induction flat fan (AIXR 110025), drift guard twin flat fan (DGTJ60 11002), and extended range flat fan (XR 11003). Spray deposition values were determined using a solution of brilliant blue dye sprayed by a CO2-pressurized backpack sprayer. The medium droplet size provided higher spray deposition on peanut plants at the V1 and R1 growth stages. Increasing the droplet size from medium to extremely coarse reduces spray deposition at the V1 growth stage. Spray deposition uniformity increased as droplet size increased at the V1 growth stage. The spray uniformity decreased at the R1 growth stage as the droplet size increased. The flat fan XR 11003 (medium droplet) and hollow cone nozzle TXA 8002 VK (fine droplet) provided a higher spray deposition on peanut plants at both growth stages. The spray nozzles that provide a higher spray deposition did not perform well on spray uniformity. [ABSTRACT FROM AUTHOR]

Better road network connection and coverage result in better transport, which is the fundamental foundation of urban growth. This paper is focussed on the assessment of connectivity, coverage and spatial pattern of the road network of Varanasi city. Connectivity has been analysed with the help of nine indices—alpha index, beta index, gamma index, cyclomatic number, aggregate transportation score, eta index, grid-tree pattern, connected node ratio and completeness of network. Coverage has been measured in terms of network density and intersection density. Fractal dimension has been utilised to reveal the spatial pattern of road network. The road network shows tree or spinal pattern, indicating very poor connectivity in Varanasi city. The wards near the core of the city show better connectivity and coverage while the peripheral wards have poorer connectivity. Fractal dimension values show opposite pattern as the wards at the edge of the city have more regular network compared to the wards near central business district (CBD). The correlation analysis shows statistically significant correlation between the connectivity, coverage indices and fractal dimension. Two regression models- ordinary least square (OLS) and geographically weighted regression (GWR) have been applied to show interaction between connectivity and spatial pattern. GWR with adaptive kernel type is the most suitable model with R2 value 0.50. Cluster analysis including all the indices shows four types of clusters in the city. The highest network development is seen in the cluster acquiring the city core and it gradually lowers down towards the edge. [ABSTRACT FROM AUTHOR]

The general problem of constructing regions that have a guaranteed coverage probability for an arbitrary parameter of interest ψ ∈ Ψ is considered. The regions developed are Bayesian in nature and the coverage probabilities can be considered as Bayesian confidences with respect to the model obtained by integrating out the nuisance parameters using the conditional prior given ψ. Both the prior coverage probability and the prior probability of covering a false value (the accuracy) can be controlled by setting the sample size. These coverage probabilities are considered as a priori figures of merit concerning the reliability of a study while the inferences quoted are Bayesian. Several problems are considered where obtaining confidence regions with desirable properties have proven difficult to obtain. For example, it is shown that the approach discussed never leads to improper regions which has proven to be an issue for some confidence regions. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is widely used in a variety of practical applications. WSN may be used to sense objects, gather information, analyze it, and then transmit it again. The significance of optimization techniques is crucial for the accurate and reliable estimation of the sensor nodes' location. The positioning accuracy of traditional distance vector hop (DV-Hop) localization algorithm is not entirely satisfactory instead of it is quite simple, stabilized, feasible, and requires less hardware. Thus to enhance the positioning accuracy without increasing the hardware cost of a sensor node, this article provides an improved distance vector hop (IDV-Hop) localization algorithm using human conception optimization. The proposed method adds a parameter to alter the anchor nodes' hop size. Furthermore, it is analyzed with traditional DV-Hop, IDV-Hop algorithm, DV-Hop based particle swarm optimization, and DV-Hop based class topper optimization. The simulation results support the conclusion that, the proposed algorithm performs better than the competing algorithms by minimizing the localization error, localization error variance, and the localization accuracy with varying the number of anchor nodes, total number of nodes, and the communication range. [ABSTRACT FROM AUTHOR]

Plagiarism has been among the top forms of academic misconduct. Detective, reactive and proactive measures are taken to mitigate plagiarism in scholarly works. Text-matching tools play a significant role in the detection of plagiarism. Many studies have tested the performance of text-matching tools in detecting plagiarism from various perspectives. However, no study addressed the performance of such tools in ideographic languages, particularly Japanese. Considering the sharp increase in the number of academic Japanese text and plagiarism incidents in the Japanese context, it is essential to explore to what extent text-matching tools catch similarities in Japanese texts and respond to the needs of Japanese users. Within this scope, this study set out to explore the coverage and usability performance of text-matching tools in the Japanese language. We tested the coverage performance of 10 text-matching tools with five types of intentionally plagiarized documents. Also, we tested the usability performance via a feature checklist. The testing results suggested that the tools generally give a relatively higher performance on the usability side rather than the coverage aspect. Most tools have minimal coverage performance in the Japanese language. In the end, we provided takeaways for vendors, policymakers and educators. [ABSTRACT FROM AUTHOR]

Digital transformation and technological advances are causing a radical change in communication structures and in the way information is consumed. With rapid development of computing and the Internet, data is generated, recorded, stored and accumulated on a large scale, making it necessary for economic sectors to act quickly in order to adapt their businesses to the online environment and thus, ensure their own survival. The application of Big Data in tourism enables to transform all this data into useful information, so that companies in the sector can define and optimize their strategies in order to increase their profits. This article performs a comparative bibliometric analysis of the presence and impact of scientific production related to Big Data within the area of tourism research indexed in the WoS and Scopus databases. The aim is to know key aspects such as its growth, correlation, citation, coverage, overlap, dispersion or concentration that will support future researchers when they start their work in this emerging field. From the analysis of the 113 articles selected between the two bases through an advanced search for terms with a time limit set in 2019, it can be concluded that this is a new field of knowledge, which has aroused great interest since 2017, publishing about two thirds of the articles during the period 2017–2019. Although WoS and Scopus differ in general terms in scope and coverage policies, both systems are complementary and not exclusive. In the specific area of Big Data and Tourism Research, Scopus is the base that provides better coverage by collecting a higher number of articles and receiving more citations. [ABSTRACT FROM AUTHOR]

Maintaining coverage, power consumption, and network lifetime are the most fundamental challenges for wireless sensor networks. Since it is impossible to replace or recharge the battery of the sensor nodes, discharging the battery will end the sensor node's life. With the death of some sensor nodes and disconnecting, the network coverage is also violated. This paper presents a method for detecting and recovering coverage holes in the wireless sensor network. In the proposed method, the network is cellulated first, and a node is selected as an agent for each cell. Then, the degree of overlap of each node's sensing area by its neighbors is calculated to schedule sensor nodes. Based on the node overlap information, the cell agent determines cell coverage and detects holes. Finally, mobile nodes and the grasshopper optimization algorithm are used to recover the holes. The simulation results reveal that the proposed method leads to a decrease in the network's energy consumption, an increase in network lifetime, and improved coverage in the network. [ABSTRACT FROM AUTHOR]

Wireless ad hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential and ability to transform our lives and pose many new system building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Most of researches in wireless sensor networks are focused in obtaining better target coverage in order to reduce energy and cost of the network. The problem of planar target analysis is one of the crucial problems that should be considered while studying coverage problem of sensor networks. By combining computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, this paper introduces a novel sensor network coverage model that deals with plane target problem based on Clifford algebra which is a powerful tool that is coordinate free. Also, the calculations of the node coverage rate for the plane target in the sensor network using Clifford algebra are presented. Then, the maximum clearance path (worst-case coverage) of the sensor network for a plane target is proposed. The optimality and reliability of the proposed algorithm have been proved using simulation. Also, a comparison between the breach weight of the point target and the plane target is provided. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of coverage of an environment with an energy-constrained robot in the presence of multiple charging stations. As the robot's on-board power supply is limited, it might not have enough energy to cover all the points in the environment with a single charge. Instead, it will need to stop at one or more charging stations to recharge its battery intermittently. The robot cannot violate the energy constraint, i.e., visit a location with negative available energy. To solve this problem, we propose a deep Q-learning framework that produces a policy to maximize the coverage and minimize the budget violations. Our proposed framework also leverages the memory of a recurrent neural network (RNN) to better suit this multi-objective optimization problem. We have tested the presented framework within a 16 × 16 grid environment having different charging station layouts and various obstacle configurations. Results show that our proposed method finds feasible solutions while performing favorably against two comparable techniques. [ABSTRACT FROM AUTHOR]

Introduction: During the last years, main attention while performing total knee replacement was paid to femoral component alignment; however, there is still lack of studies concerning tibial baseplate rotational alignment, especially in terms of anatomical designs of knee prosthesis. Some recent studies proved that tibial baseplate malrotation might be a cause of knee pain and patients' dissatisfaction. The aim of this study was to compare tibial component rotation and its coverage on the tibial plateau achieved with curve-on-curve and tibial tuberosity techniques (t-t technique) with use of anatomic knee designs with asymmetric tibial baseplate. Materials and methods: A total of 88 patients were randomly assigned in a 1:1 ratio to undergo total knee arthroplasty with use of the PERSONA PS (Zimmer Biomet) knee design with an asymmetric baseplate. The rotation of the tibial component was assessed and performed with two different techniques: curve-on-curve technique and tibial tuberosity technique. Tibial component rotation was measured on computed tomography (CT) scans using the method suggested by Benazzo et al. and designed for asymmetrical implants. For the measurement of the tibial bone coverage, the component surface area was outlined and measured on a proper CT section, then the tibial cut surface area was outlined and measured on a section just below the cement level. Pre- and post-operative range of motion was measured by another independent researcher 12 months post-operatively during follow-up visit. Results: There was a statistically significant difference between both groups in median value of tibial rotation angle: 7° (interquartile range (IQR) = 0–12) in curve-on-curve technique group vs 2° (IQR-1–7) in tibial tuberosity technique group, probability value (p) = 0.0041, with values above 0 meaning external rotation of the component. There was no statistically significant difference between both groups in terms of range of motion (ROM) with average values of 124.3° ± 13.0° for curve-on-curve technique and 125.6° ± 12.8° for t-t technique with p = 0.45. There was a statistically insignificant difference between both groups in terms of coverage percentage in slight favor for curve-on-curve technique (85.9 ± 4.2 vs 84.5 ± 4.8, p = 0.17). Conclusion: In this study, no difference between the groups in terms of tibial bone coverage and range of motion was proved, even though both techniques differed significantly with values of tibial rotation. Future studies should be focused on influence of specific values of tibial rotation on patient-reported outcomes and survivorship of anatomic knee implants. [ABSTRACT FROM AUTHOR]

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p ( Δ P ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems. [ABSTRACT FROM AUTHOR]

The positions of sensor nodes in wireless sensor networks (WSNs) plays important role in order to achieve optimum values of various parameters, such as coverage, localization, etc. WSNs are used in every field such as in agriculture, military etc. and to achieve optimum performance of WSNs in these fields the sensor nodes are required to deploy at optimized positions. In this paper the jaya-sine cosine optimization algorithm (Jaya-SCOA) is proposed to deploy the sensor nodes at optimum positions and to increase the coverage of sensor nodes. Further the sensor nodes are deployed in three different topologies such as C shape, outer boundary and random topology and coverage rate is computed for all topologies. The major goal of the paper is to find the best topology for deploying the sensor nodes in WSNs and to enhance the coverage of nodes. The simulation results of proposed Jaya-SCOA is compared with fruit fly optimization algorithm (FFOA) and bat optimization algorithm (BOA) for all topologies in terms of maximum and minimum coverage, standard deviation and mean coverage rate. The simulation results demonstrate that for all topologies the coverage rate of Jaya-SCOA is higher than that of FFOA and BOA and average coverage rate of Jaya-SCOA for random topology is increased by 14.55% and 27.04% as compared to outer boundary and C shape topology, respectively. Therefore, the random topology is the best topology as compared to other topologies and Jaya-SCOA is effective than BOA and FFOA for all topologies in terms of coverage and stability. [ABSTRACT FROM AUTHOR]

Metrics such as diversity and novelty have become important, beside accuracy, in the design of Recommender Systems (RSs), in response the increasing users' heterogeneity. Therefore, the design of RSs is now increasingly modelled as a multi-objective optimization problem (MOP) for whose solution Multi-objective evolutionary algorithms (MOEAs) have been increasingly considered. In this paper we focus on the k-top recommendation problem in which a solution is encoded as a matrix whose rows correspond to customers and column to items. The value of accuracy, novelty, and coverage for each candidate list, is evaluated as a sample and can be represented as a 3-d histogram which encodes the knowledge obtained from function evaluations. This enables to map the solution space into a space, whose elements are histograms, structured by the Wasserstein (WST) distance between histograms. The similarity between 2 users in this probabilistic space is given by the Wasserstein distance between their histograms. This enables the construction of the WST graph whose nodes are the users and the weights of the edges are the WST distance between users. The clustering of users takes then place in the WST-graph. In the optimization phase the difference between two top-k lists can be encoded as the WST distance between their 3-dimensional histograms. This enables to derive new selection operators which provide a better diversification (exploration). The new algorithm Multi-objective evolutionary optimization/Wasserstein (MOEA/WST), compared with the benchmark NSGA-II, yields better hypervolume and coverage, in particular at low generation counts. [ABSTRACT FROM AUTHOR]

The number of connected devices is increasing exponentially, calling for more and more dynamic networks that need little maintenance and are able to optimize end users experience in the process. Wireless mesh networks (WMNs) are viewed as a solution to keep end users satisfied with reliable connectivity while minimizing the maintenance and upfront costs. However, designing a robust WMN at low cost requires the use of the least possible mesh routers but still interconnected and able to offer full coverage. Therefore, the placement of mesh routers over the area of interest is a problem that entails thorough planning. To deal with this problem, we have previously implemented a simulation system that combines particle swarm optimization (PSO) and distributed genetic algorithm (DGA), in a hybrid intelligent system called WMN-PSODGA. In this work, we implement three distributions of mesh clients: Weibull, normal and Boulevard, and evaluate the performance of the network for different router replacement methods. The router replacement methods considered are constriction method, random inertia weight method, linearly decreasing inertia weight method (LDIWM), linearly decreasing Vmax method and rational decrement of Vmax method. By comparing all the simulated scenarios, we conclude that the best scenario in terms of both coverage and load balancing is normal distribution with LDIWM as a router replacement method. [ABSTRACT FROM AUTHOR]

Although researchers have investigated multiple facets of fault tolerance, majority of them have overlooked fault tolerance in face structured WSNs. Motivated by this, we propose a Fault-Tolerant Coverage Preserving Strategy for Face Topology-based WSNs (FtCFt). Unlike existing methods of recovering failures by merging the adjacent faces, we propose a coverage-aware node replacement method to replace the failing node with a suitable alternate node. This is significant because a mobile target will go undetected, and no evidence of it can be acquired until it leaves the hole region and is sensed by a node. FtCFt offers fault tolerance by incorporating node self-check and link-check strategies that works in conjunction with one of its mobile target tracking applications. Unlike existing works, the proposed restoration algorithm effectively repairs and restores the face structure to ensure network coverage and connectivity. Simulation results reveal that FtCFt improves coverage, quality of service and WSN liferime. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC's data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an effective MC trajectory. Much attention has been received to MC's path planning through SPs in a static environment where the path is determined during the initial phase, but they do not emphasize the nodes' coverage rate and cannot be adapted to network topology changes. In this context, we propose an Efficient Trajectory Planning method for Coverage Enhanced Data collection in WSN (ETP-CED). We introduce an enhanced method based on integrated Particle Swarm Optimization and Ant Colony Optimization for selecting the best set of SPs and planning efficient MC trajectory. ETP-CED is adaptive to node failures, allowing the nodes to reposition themselves to patch up coverage holes in the network. MC readjusts its planned path when there are less nodes in the network due to node failures, thereby shortening the trajectory length and speeding up data delivery. The results show that ETP-CED outperforms existing methods in the aspects of nodes' coverage and data collection efficiency. [ABSTRACT FROM AUTHOR]

Connectivity and area coverage are two essential criteria for improving the Quality of Service (QoS) in Wireless Sensor Networks (WSNs). These criteria strongly affect lifetime and performance of the WSNs. Furthermore, connectivity and full area coverage between the sensors ensure that the collected data will be transferred to the base station. In this paper, a new algorithm is proposed to improve the connectivity and full area coverage by utilizing some of the mobile robots to change the initial topology and the position of the sensors. Therefore, a grid-based model is first used to divide the environment into several cells and megacells. In order to change the position of the sensors by mobile robots, the robot path planning algorithm is also introduced in two steps. In the local phase, the Analytic Hierarchy Process (AHP) is utilized to determine the source point and destination point. An improved evolutionary programming algorithm is then used to find the optimal path between the source point and destination point, thereby changing the position of the sensors which cover the environment. In the global phase, the A* algorithm is used to cover the part of the environment which is not covered in the local phase. Finally, we perform comprehensive experiments to validate the performance of the proposed method. Compared with existing approaches, the proposed algorithm demonstrates clear improvements in the connectivity and area coverage, showing the superiority of our model. [ABSTRACT FROM AUTHOR]

Head and neck cancer (HNC) is third highest prevalent cancer among Indian which constitutes about 25–30% of all the cancer in India. Further, out-of-pocket expenditure (OOPE) covers around 67% of total healthcare expenditure and direct medical cost is key factor responsible for raised OOPE in India. Thus, we aimed to quantify total direct medical cost and OOPE associated with HNC management among HNC patients using "Ayushman Bharat Arogya Karnataka scheme" (ABArK scheme). A retrospective study was conducted for the duration of 6 months to collect the data related to total direct medical cost, coverage of "ABArK Scheme" and OOPE of past 2 years of HNC patients. The data of HNC patients above 18 years of age utilizing "ABArK scheme" were included in the study whereas data of patients utilizing other healthcare schemes and incomplete data on target schemes were excluded. A total of 196 patients (54.1%) utilized the "ABArK Scheme" out of 362 HNC patients treated in past 2 years. Among 196 patients, males (76.5%) were predominant over females (23.5%) with the mean age of 53.60 ± 11.58 years. We found that INR 17,370,279 as the total direct medical expenditure for the management of HNC patients of which around 87.465% was covered by "ABArK Scheme" minimizing the OOPE up to INR 3,297,970. Thus, Introduction and implementation of novel healthcare policies like "ABArK Scheme" can counteract financial burden of cancer management by significantly reducing OOPE which could be milestone achievement for the low-middle income countries like India. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage, k-multisection neuron coverage, top-k neuron coverage, neuron boundary coverage, strong neuron activation coverage and modified condition/decision coverage. We evaluate the metrics on realistic DNN models used for perception tasks (LeNet-1, LeNet-4, LeNet-5, ResNet20) including networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, to compare different coverage measures, and to more conveniently inspect the model's internals during testing. [ABSTRACT FROM AUTHOR]

Scheduling of sensor nodes in an energy-efficient manner is one of the most effective ways for extending the lifetime of wireless sensor networks (WSNs). In energy-efficient scheduling, only a subset of the deployed sensor nodes is enabled to monitor the targets. As sensor nodes have restricted communication and sensing range, coverage and network connectivity should be considered while scheduling with fewer sensor nodes. For guaranteed transmission of sensing data from every target point to the base station, connectivity and coverage are the most pivotal issues in the scheduling of sensor nodes. In this research article, we have presented the energy-efficient active/sleep scheduling of the sensor nodes using a genetic algorithm (GA) with Dither Creeping mutation in which only few sensors are activated that ensures the coverage to all targets as well as communication with the sensor nodes and base station (BS). The novelty of the proposed GA with crossover and Dither Creeping mutation (GACDCM) is that the mutation probability is generated randomly rather than the fixed value for each string. As a result, for the same generation, the various strings of the proposed algorithm will be subjected to various creeping mutation probabilities and the same string is subjected to various creeping mutation probabilities at successive generations. The proposed algorithm replaces the traditional bitwise mutation. For exploring the search space in case of extremely constrained problems, Dither Creeping Mutation is more efficient than bitwise mutation. We have simulated the proposed algorithm extensively with several WSN scenarios. the simulation results are analyzed with the existent algorithms to validate the efficiency of the presented algorithm. The experimental result showed that the lifetime of the suggested GACDCM is increased by 53.27% than traditional GA, 27.93% than GANCDCM, 13.23% than NSGA-II, and 4% than algorithm proposed by Harizan and Kuila (Wireless Netw 25(4):1995–2011, 2019). [ABSTRACT FROM AUTHOR]

Introduction: For pregnant women, vaccination with inactivated influenza vaccine (IIV) and tetanus, diphtheria, acellular pertussis vaccine (Tdap) is recommended. In Peru, uptake is nonetheless low. A study was conducted to identify factors affecting maternal vaccination coverage. The study's primary objectives were to describe the knowledge, attitudes, and practices regarding maternal immunization among pregnant/postpartum women and health care professionals (HCPs). The secondary objective was to determine the vaccination coverage and the impact of Ministry of Health (MOH) recommendations. Methods: An observational multicenter, cross-sectional survey study was conducted from February 1, 2021 to June 30, 2021 in five cities in Peru. Two surveys were conducted to assess knowledge, attitudes, and practices concerning maternal immunization: one among pregnant/postpartum women and one among HCPs. Results: Participants were 668 pregnant/postpartum women with a mean age of 29.6 years and 219 HCPs—mostly midwives (46.6%) and obstetricians/gynecologists (44.7%). Of the pregnant/postpartum women, 66.9% knew that, in general, vaccinations are given for prevention, and 98.5% believed vaccines are important. Nonetheless, 69.6% of pregnant/postpartum women had poor or moderate knowledge of maternal vaccination. Disease knowledge of influenza (89.1%) and tetanus (87.0%) was high, while knowledge of pertussis (37.7%) was low. Women agreed/strongly agreed that they would get vaccinated with Tdap if a doctor (96.3%), midwife (88.9%), or nurse (72.0%) recommended it. Of the HCPs, 81.3% routinely recommended Tdap vaccination for pregnant women. Conclusions: To enhance vaccine acceptance in pregnant women in Peru, we must improve knowledge of the diseases, MOH recommendations, and benefits of the offered vaccinations. HCPs could provide this vaccination knowledge and information along with their vaccination recommendation as the pregnant/postpartum women indicated they would take the vaccines if recommended by their HCPs. Our findings are important for the successful implementation of maternal immunization programs in Peru. Plain Language Summary: [ABSTRACT FROM AUTHOR]

Data collection is an important task in many mobile wireless sensor network (MWSN) applications. The energy of sensor nodes around the sink depletes rapidly due to transmitting large amounts of data from neighboring nodes. This problem can be mitigated through the use of intelligent mobile vehicles to collect the data. While traditional data collection methods focus on maximizing data acquisition or reducing network energy consumption, they do not take into account the actual sensor nodes' coverage of the region of interest (ROI). To the best of our knowledge, most research on data collection focuses on path planning for the mobile collector in a static environment. During the lifetime of the network, coverage holes may appear due to node energy depletion. We propose a coverage aware topology adaptive path planning algorithm (CAPP) for path planning for WSNs where all sensor nodes are coverage aware and respond by moving to better locations to improve coverage of the network and compensate for the failed nodes. First, the path planning algorithm determines the number of Stop Points (SPs) where it will stop to gather data. Then, Particle Swarm Optimization is used to find the best location for these SPs. Finally, the shortest path through these SPs is determined by Ant Colony Optimization. Through extensive simulation, we show that CAPP performs efficiently in data collection while also allowing the nodes to move for coverage hole repair. The result shows improvement in area coverage and reduced delay in data collection, with no increase in energy consumption. [ABSTRACT FROM AUTHOR]

Maintaining prolonged service lifetime and adequate quality of sensing coverage are the key challenges in constructing Wireless Sensor Network (WSN) based applications. As such networks usually operate in inhospitable and hostile environment, failures are ineludible and providing resilience is a necessity. However, it is challenging to satisfy the conflicting problems of enhancing energy efficiency and fault tolerance simultaneously. Fault-tolerance is a significant requirement while designing WSN. It is crucial to detect the failures in advance and take necessary measures to maintain durable and efficient functioning of the network. Generally, in the existing face structured WSNs, node faults and failures can induce the formation of coverage holes, disrupt the face structure and consequently curtail the application performance. The coverage quality will affect the monitoring effectiveness of tracking applications, e.g., a moving target tracking. Moreover, node failures can cause the network to be partitioned, further reducing the accuracy in tracking. In this paper, we propose a robust fault-tolerance scheme with coverage preservation using a face structured WSN topology ( F CAFT ). The key objective of the proposed F CAFT scheme is to sustain the performance of the network by timely healing the faults in the network, to enhance the durability and reliability of the WSN. The results of simulation and comparison with existing methods reveal that F CAFT is efficacious in enhancing the service lifetime of WSN by about 14% and sustains about 96% of coverage even when the failure rate is more than 20%, which is a necessity for critical monitoring and tracking applications of WSNs. [ABSTRACT FROM AUTHOR]

Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure. [ABSTRACT FROM AUTHOR]

Wireless sensor network found immense uses in the daily life. Also, the random deployment of nodes is a preferable option in many applications such as earthquake observation, military applications, and forest fire detection. It is expected that deployed nodes should be able to monitor the field of interest (FoI) with the optimum capacity. In order to maximize the coverage of area, each node should be repositioned to an optimal position inside the FoI. A modified particle swarm optimization (PSO) algorithm has been proposed to achieve optimize coverage while keeping the number of nodes minimum. It introduces the concept of negative velocity in order to avoid premature convergence of the algorithm. This paper describes a way to tackle the two dimensional obstacles present inside the FoI. The simulated results show a significant improvement in the performance with compared to the standard PSO in presence of obstacles. [ABSTRACT FROM AUTHOR]

The loss of important contact herbicides like paraquat opens opportunities for more potentially sustainable solutions demanded by consumers and organizations. Frequently, for adequate weed control, the alternatives to classical synthetic products need well-defined and executed labels and even more detailed use descriptions. One novel candidate with rare contact activity is a pelargonic acid ester of methyl polyethylene glycol (PA-MPEG) with advantages over free pelargonic acid (PA), such as reduced volatility and ease of formulation. Here, we report on the role of the application parameters such as spray volume, rate, sprayer set-up, and climate conditions for weed control with PA-MPEG. At a dose rate of 12.8 kg ae ha−1 in a spray volume of 500 L ha−1, control of Digitaria sanguinalis (L.) Scop. and Solanum nigrum L. was excellent. These values for product rate and spray volume are lower than applications with commercial PA herbicides, at equal or better efficacy. Coverage was too low at spray volumes of 100 to 200 L ha−1, for adequate contact activity of both PA-MPEG and PA. Weed control was significantly increased when PA-MPEG application was made at lower boom height with reduced distance to weed canopy, or under warm and dry climate conditions. The results indicate the potential of PA-MPEG under optimal use conditions as a new contact herbicide in integrated weed management. [ABSTRACT FROM AUTHOR]

This paper presents a novel, low-cost, user-friendly Precision Agriculture platform that attempts to alleviate the drawbacks of limited battery life by carefully designing missions tailored to each field’s specific, time-changing characteristics. The proposed system is capable of designing coverage missions for any type of UAV, integrating field characteristics into the resulting trajectory, such as irregular field shape and obstacles. The collected images are automatically processed to create detailed orthomosaics of the field and extract the corresponding vegetation indices. A novel mechanism is then introduced that automatically extracts possible problematic areas of the field and subsequently designs a follow-up UAV mission to acquire extra information on these regions. The toolchain is finished by using a deep learning module that was made just for finding weeds in the close-examination flight. For the development of such a deep-learning module, a new weed dataset from the UAV’s perspective, which is publicly available for download, was collected and annotated. All the above functionalities are enclosed in an open-source, end-to-end platform, named Cognitional Operations of micro Flying vehicles (CoFly). The effectiveness of the proposed system was tested and validated with extensive experimentation in agricultural fields with cotton in Larissa, Greece during two different crop sessions. [ABSTRACT FROM AUTHOR]

The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations. [ABSTRACT FROM AUTHOR]

Despite the broad study of surface integrity in shot peening, the evolution mechanism of surface morphology considering the initial processing feature is still unclear, which hinders the optimization of peened surface quality. In this study, two typical surfaces (grinding and polishing) were selected to be the initial surface morphology for shot peening. Then the effects of initial surface morphology on peened surface integrity were investigated, including 3D surface morphology, surface roughness, residual stress distribution, and microhardness. It could be found that the processing features of the initial surface still exist but are flattened, even at a relatively high coverage. The shot peening simulation also revealed this flattening effect. Besides, the effect of coverage on surface integrity was investigated. The 12Cr2Ni4A sample with 200% coverage has smaller surface roughness and similar residual stress distribution than 100% coverage, which is different from previous studies on medium-strength steels and lightweight alloys. These results provide a reference for understanding the surface integrity evolution in shot peening. [ABSTRACT FROM AUTHOR]

Due to the massive amount of information accessible on the internet, it has become a challenging task for users to discover the desired information. Automatic document summarization has become an emerging technology to address these issues. This allows the users to get the relevant information in a shortened version. However, the summary should have high content coverage and low redundancy to generate a good quality summary. Therefore, an enriched Dragonfly-Fuzzy Logic (FL) Single Document summarization is presented in this paper. Initially, the web document is preprocessed in which some functions such as segmentation, stop word removal, URL removal, stemming, etc. are performed. After preprocessing, the important features such as sentence location, proper nouns, numeric data, cue phrases, etc. are extracted from the web document. Here, the significance of the extracted features is decided by providing weights to each of the features using the Enriched Dragonfly Optimization Algorithm (EDOA). Once the weights are allotted for the features, the importance of the sentence is determined by using the FL system to form a summary. Finally, the sentence similarity in the generated summary is calculated, and then the similar sentences are eliminated from the summary to avoid redundancy issues. The performance of the proposed Dragonfly-FL summarization is tested in the CNN/Daily Mail dataset, and finally, the results are compared with the existing techniques such as MAMHOA, ExDoS, Karci summarization, and regression-based technique, DSN, Semantic approach, and BERTSUMEXT in terms of ROUGE-1, ROUGE-2, and ROUGE-L measures. The observation demonstrates that the proposed technique performs better than the existing techniques with precision, recall, and an F-score of 0.11, 0.05, and 0.01 respectively. [ABSTRACT FROM AUTHOR]