Search Coverage Research Articles

COVID-19, media coverage of bats and related Web searches: a turning point for bat conservation?

SARS‐CoV‐2, the virus that caused the COVID‐19 pandemic, is genomically similar to a SARS‐like beta‐coronavirus found in Asian rhinolophid bats. This evolutionary relationship impressed the global media, which then emphasised bats as key actors in the spillover that resulted in the pandemic. In this study, we highlight changes in the traditional and new media coverage of bats and in Internet search volumes that occurred since the beginning of the COVID‐19 pandemic in 2020.We analysed Google and Wikipedia searches for bats and coronaviruses in 21 countries and eight languages, as well as television broadcasts in the USA, some of which have global coverage, between January 2016 and December 2020. In January 2020, the amount of television news about bats boomed, and news associated with the term ‘bat’ shifted to COVID‐19‐related topics. A nearly identical pattern was observed in Google searches during 2020 at the global scale. The daily time series of television coverage and Internet search volumes on bats and coronavirus in the USA covaried in the first quarter of 2020, in line with the existence of a media bubble. Time‐series analysis revealed that both the Google Trends index and visits to Wikipedia pages about bats boomed in early 2020, despite the fact that this time of year is usually characterised by low search volumes.Media coverage emphasised, correctly or not, the role of bats in the COVID‐19 pandemic and amplified public interest in bats worldwide. The public image of these mammals, in many cases threatened and important ecosystem service providers, was seriously compromised. We therefore recommend that policymakers and journalists prioritise scientifically accurate communication campaigns about bats, which would help counteract the surge in bat persecution, and leverage interest towards positive human–bat interactions.

A Systematic Review of the Prevalence of Gestational Diabetes in Norway.

Gestational diabetes mellitus (GDM) is a major public health problem and a threat to maternal and child health. There is a lack of integrated and systematically synthesized knowledge about the prevalence GDM in Norway. Therefore, this systemic review aimed to present the best available peer-reviewed published evidence from the past 20 years about the prevalence of GDM in Norway. A comprehensive literature search was performed on online databases consisting of PubMed (including Medline), Web of Science, and Scopus to retrieve original research articles published on the prevalence of GDM up to August 2020. Also, databases of Norart and SveMed+ in the Norwegian language were searched to enhance the search coverage. Data were extracted using a standardized protocol and data collection form and were presented narratively. A total of 11 studies were selected to include for data analysis and synthesis with the total sample size of 2,314,191 pregnant women. The studies included heterogenous populations and mostly reported the prevalence of GDM in healthy mothers with singleton pregnancies. The prevalence of GDM in population registered-based studies in Norway was reported to be lower than 2%, using the World Health Organization (WHO) 1999 criteria. However, studies on high-risk populations such as the non-European ethnicity reported prevalence rates ranging from 8% to 15%. Given the evidence from available literature that reported trends in the prevalence of GDM, an increase in the prevalence of GDM across most racial/ethnic groups studied in Norway was observed. Overall, the prevalence of GDM in the low risk population of Norway is fairly low, but the available literature supports the perspective that the prevalence of GDM has shown an increasing trend in recent decades. This finding is very important for health service planning and evaluation, policy development, and research in Norway. Large-scale prospective studies, using the national data, are warranted to provide firm evidence over coming years. Our review findings can help policy makers devise appropriate strategies for improving women’s reproductive health.

RAPOT: An Adaptive Multifactor Risk Assessment Framework on Public Opinion for Trial Management

Since public opinion from social media has a growing impact and supervision on trial, risk assessment on public opinion is increasingly important in refined trial management. However, the tremendous amount of public opinion and the insufficient historical logs of trial procedures bring challenges to risk assessment on public opinion. To address this, we propose an adaptive multifactor risk assessment framework on public opinion with fuzzy numbers. Initially, we establish a multilayer indicator model for assessing the risk of public opinion (POR) with multilayer analysis and decision methods. Then, we explore the association rules hidden in the process logs to update the indicator model periodically. Moreover, we design a public opinion analysis module for indicator evaluation, including analysis in public opinion sentiment, hot search, and social media coverage to deal with big data on social media. Especially, the public opinion sentiment is classified by topic‐based BiLSTM (T‐BiLSTM), which is more accurate. Finally, the fuzzy number similarity is employed to determine POR’s level in the nine‐level risk system. Experimental results validate the efficiency of our framework when assessing the POR.

Optimal Search Facilities Selection Model for Joint Aeronautical and Maritime Search

Joint aeronautical and maritime search and rescue is the most effective way of performing rescues at sea. The value and effectiveness of a search and rescue (SAR) are far greater when using a coordinated air-maritime search than when using only vessels or aircraft. However, the harmonization of aeronautical and maritime SAR is complex and potentially life-threatening. When the location of the target in distress is unknown, the search process must be carried out. As the sole way to locate and rescue survivors, the search process is the most costly, hazardous, and complicated part of the whole SAR operation. This article focuses on the key problem of the optimal selection of search facilities, that is often encountered in large-area maritime search practice and urgently needs to be solved in joint aeronautical and maritime search operations. The problem may be abstracted into an optimization model with vessel and aircraft quantitative constraints that fully considers the area of the sea region to be searched, maximum speeds, search capabilities, initial distances of vessels and aircraft from the search area, and maximum endurance of aircraft. By introducing 0-1 decision variables, the search facility selection can be judged and optimized directly and effectively. By analyzing the results with different vessel and aircraft quantities, and taking the relationship between search coverage time and the number of search facilities (cost) into account, the optimal (most economic and feasible) search facility selection scheme can be produced.

Dimension decided Harris hawks optimization with Gaussian mutation: Balance analysis and diversity patterns

Harris hawks optimization (HHO) is a newly developed swarm-based algorithm and the most popular optimizer in the recent year, which mimics the cooperation behavior of Harris hawks. Although the original HHO has a specific weight compared to other state-of-the-art methods when working on the local exploitation of feasible solutions, it still may fail to achieve an excellent set of scales between locally accurate exploitation and globally exploratory search. This imbalanced behavior forms an overall perspective, which can be experienced by slow convergence, inaccuracy or inadequate search coverage, and quickly dropping into local solutions. In order to strengthen the performance of the HHO, two strategies of Gaussian mutation and a dimension decision strategy observed in the cuckoo search method are introduced into this optimizer. The mechanism of cuckoo search is very useful in improving the convergence speed of the search agents as well as sufficient excavation of the solutions in the search area, while the Gaussian mutation strategy performs well in increasing the accuracy and jumping out of the local optimum. In order to verify the remarkable performance of the proposed method, the enhanced paradigm is compared with other mate-heuristic algorithms on 30 IEEE CEC2017 benchmark functions and three typical engineering problems. The experimental results illustrate that the novel developed GCHHO has an excellent ability to achieve superior performance in competition with the original HHO as well as other well-established optimizers. Open source codes and information on original HHO algorithm is available at: https://aliasgharheidari.com/HHO.html. Supportive data and info for this research will be publicly provided in http://aliasgharheidari.com.

Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider

Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton–proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments—as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER—to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity ‘dark showers’, highlighting opportunities for expanding the LHC reach for these signals.

Depression, anxiety, stress, and dysmenorrhea: a protocol for a systematic review

BackgroundDysmenorrhea is one of the most common menstrual disorders and is influenced by various factors. Psychological disorders including anxiety, depression, and stress have been suggested as influencing dysmenorrhea, but previous findings are inconsistent. This study will investigate the relationship between depression/anxiety/stress and dysmenorrhea using a systematic review and meta-analysis.MethodsOnline databases including PsycINFO, Scopus, PubMed, Science Direct, ProQuest, ISI Web of Knowledge, and Embase will be searched. Appropriate keywords and MeSH terms will be used to retrieve the journal papers published from 1990 until the end of December 2019. To improve search coverage, the reference lists of all included studies will be reviewed to find eligible papers. Inclusion criteria include the following: descriptive, cohort, case-control, and cross-sectional studies; the relationship between depression/anxiety/stress and dysmenorrhea being an objective of the study; and published in peer-reviewed journals. The paper selection, data extraction, and quality assessment of selected studies will be performed independently by two researchers, and disagreements will be resolved through discussions. The Newcastle-Ottawa Quality Assessment Scale will be used to assess the quality of selected studies. A quantitative synthesis will be performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) via the STATA software, if retrieving enough number of studies with no severe methodological heterogeneities. Otherwise, qualitative synthesis will be used to report the findings.DiscussionTo the best of our knowledge, this will be the first systematic review on this topic. Performing an inclusive search in major databases over a wide timescale is one key strength of the proposed study and will maximize the coverage of the original research studies on this topic. Results of present study are expected to lead to deeper understanding the relationship between common mental health conditions and dysmenorrhea.Systematic review registrationPROSPERO CRD42018102199

Enlarging the scope of resonant di-Higgs searches: hunting for Higgs-to-Higgs cascades in 4b final states at the LHC and future colliders

We extend the coverage of resonant di-Higgs searches in the b overline{b} b overline{b} final state to the process pp → H1→ H2H2→ b overline{b} b overline{b} , where both H1,2 are spin-0 states beyond the Standard Model. Such a process constitutes a joint discovery mode for the new states H1 and H2. We present the first sensitivity study of this channel, using public LHC data to validate our analysis. We also provide a first estimate of the sensitivity of the search for the HL-LHC and future facilities like the HE-LHC and FCC-hh. We analyze the discovery potential of this search for several non-minimal scalar sector scenarios: an extension of the SM with two extra singlet scalar fields, the two-Higgs-doublet model and a two-Higgs doublet model plus a singlet, which captures the scalar potential features of the NMSSM. We find that this channel represents a novel, very powerful probe for extended Higgs sectors, offering complementary sensitivity to existing analyses.

Area Decomposition Algorithm for Large Region Maritime Search

In this paper, an area decomposition algorithm is presented that is suitable for multiple vessels and multiple aircraft participating in maritime searches over a large region. The algorithm can decompose the entire sea region to be searched (deemed as polygonal area) into nonoverlapping subpolygons (search subareas) according to the sizes of the areas covered by various search facilities while considering their search capabilities as well as their corresponding commence search points. The algorithm draws on the concept of a polygon division algorithm in computational geometry. The main novelty in this study is the optimization of the classic polygon division algorithm by introducing a “maximizing-minimum-angle” strategy, which can effectively compensate for the deficiency of the traditional algorithm, as reflected in the area decomposition result. This improved algorithm can produce rectangle-like subareas, especially for a rectangular search region, which is commonly used in maritime search operations. For nonrectangular search regions, a right-angle division can be achieved so that the shapes of the search subareas are more conducive to planning specific search routes for search facilities. Fast maritime search coverage over a large region can be achieved. The effectiveness of the algorithm is validated by comparing decomposition results before and after the improvement.

Coverage uniformity with improved genetic simulated annealing algorithm for indoor Visible Light Communications

Indoor visible light communication (VLC) based on light-emitting diodes (LED) can meet needs of illuminance and high-rate communication. However, the distributions of optical power and illuminance intensity on the receiving plane are nonuniform owing to the Lambertian emission pattern and inherent multipath diffuse channel characteristics. To reduce the power fluctuation and improve the fairness, an improved genetic simulated annealing algorithm (IGSAA) is proposed to search a set of optimal power factors (PF) quickly without changing LED layouts and LED location in this paper. In IGSAA, fitness function is designed in the view of the differences of received power among different positions on receiving plane. Based on the information of optimal chromosome and evolutional generation, two-point crossover operation and dynamic mutation probability are designed for speeding up search coverage. Besides, to maintain the diversity of population, Metropolis criterion is introduced to accept chromosomes in each generation of IGSAA. Numerical results show that the proposed IGSAA can effectively improve the coverage uniformity of indoor VLC communications within limited generations.

First-Order Difference Bare Bones Particle Swarm Optimizer

The Bare Bones Particle Swarm Optimization (BBPSO), because of its implementation simplicity, has been a popular swarm-based metaheuristic algorithm for solving optimization problems. However, as found in its many variants, their search behaviors were not considered in the design. Instead of employing heuristics, we formulate a low complexity particle swarm optimizer, called the First-Order Bare Bones Particle Swarm Optimizer (FODBB), whose behavior obeys the principle of first-order difference equations. The search trajectory can be constructed in a prescribed manner together with decreasing random searches that enable particles to explore the search space more completely. This characteristic thus allows for a wider search coverage at initial iterations and consequently improves the search performance. A comparative evaluation with recently reported BBPSO algorithms was conducted and experimental results indicate that the proposed optimizer outperforms others in a majority of benchmark optimization functions.

Habitat complexity and predictability effects on finding and collecting food when ants search as cooperative groups

Cooperatively foraging groups have two sequential goals: to find food and thereafter efficiently exploit or retrieve it. Previous research has largely focused on searching behaviours of individuals or organization of food retrieval processes, rather than on how groups initially distribute themselves to find ephemeral food items that are unpredictable in time and space. In the present study, we examined how Argentine ants, Linepithema humile, search environments in anticipation of food appearing briefly in areas with differing spatial complexity. Nests were connected to three foraging arenas containing 1, 9 or 25 cells. Food appeared briefly in one cell each day, either randomly or more predictably in distant cells (but equally often in each arena). We recorded the number of ants in cells when food had not been recently present, and thereafter whether ants successfully located the food when presented. Surprisingly, as food location became more predictable, ants found it less frequently. Foragers were located more often in cells closer to the nest (i.e. at information ‘choke points’ that returning foragers needed to traverse), and in cells with higher connectivity and greater centralness within foraging arenas. Such distributions reduce search coverage area but likely increase information transmission. Thus, it appears that L. humile foragers distribute themselves to favour rapid recruitment when food is found rather than maximizing food encounter rates. Although the reduced foraging success with more predictably located food suggests that ants did not adjust expectations in a Bayesian manner within arenas towards individual cells, they did appear Bayesian across arenas. Because foragers missed food more often in higher-complexity arenas than in lower-complexity arenas, this could increase perceptions that the latter are more rewarding. Shifts in distributions were consistent with such biased perceptions. Future studies to determine whether other group-foraging species use analogous solutions would be highly useful.

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles.

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

Inverse identification of elastic properties of composite materials using hybrid GA-ACO-PSO algorithm

The main emphasis of this paper is placed on the effectiveness of the proposed optimization method in material identification. The primary motivation of integrating GA, ACO and PSO is to minimize each other’s weaknesses and to promote respective strengths. In the proposed algorithm, the effect of random initialization of GA is subdued by passing the products of GA through the ACO and PSO operators to well organize the exploitative and exploratory search coverage. In return, GA improves the convergence rate and alleviates the strong dependency on the pheromone array in ACO as well as resolves the conflict arisen in identifying the trade-off parameter and further refine the exploitative search of PSO with the introduction of two-point standard mutation and one-point refined mutation. The proposed algorithm has been verified and applied in composite material identification with absolute percentage errors between measured and evaluated natural frequencies not more than 2%.

Spline based search method for unmodeled transient gravitational wave chirps

A method is described for the detection and estimation of transient chirp signals that are characterized by smoothly evolving, but otherwise unmodeled, amplitude envelopes and instantaneous frequencies. Such signals are particularly relevant for gravitational wave searches, where they may arise in a wide range of astrophysical scenarios. The method uses splines with continuously adjustable breakpoints to represent the amplitude envelope and instantaneous frequency of a signal, and estimates them from noisy data using penalized least squares and model selection. Simulations based on waveforms spanning a wide morphological range show that the method performs well in a signal-to-noise ratio regime where the time-frequency signature of a signal is highly degraded, thereby extending the coverage of current unmodeled gravitational wave searches to a wider class of signals.

Prediction of Wave-induced Liquefaction using Artificial Neural Network and Wide Genetic Algorithm


 The hassle of analytical and numerical solution for liquefaction modeling, repetitive laboratory testing and expensive field observations, have opened opportunities to develop simple, practical, inexpensive and valid prediction of wave-induced liquefaction. In this study, Artificial Neural Network (ANN) regression modeling is used to predict the depth of liquefaction. Despite of using Back Propagation (BP) to train ANN, a modified Genetic Algorithm (called as Wide GA, WGA) is used as ANN training method to improve ANN prediction accuracy and to overcome BP weaknesses such as premature convergence and local optimum. WGA also aim to avoid conventional GA weaknesses such as low population diversity and narrow search coverage. Key WGA operations are Wide Tournament Selection, Multi-Parent BLX-? Crossover, Aggregate Mate Pool Mutation and Direct Fresh Mutation-Crossover. ANN prediction accuracy measured by Median APE (MdAPE). Global optimum solution of WGA is best ANN connections weights configuration with smallest MdAPE.

An Improved RNA Genetic Algorithm for the Parameter Estimation Multiple Solutions of Ordinary Differential Equations

In fluid mechanics, how to solve multiple solutions in ordinary differential equations is always a concerned and difficult problem. An improved RNA genetic algorithm inspired by RNA molecular structure and operators is proposed for solving the parameter estimation problems of the multiple solutions that described as the flow of the fluid through a porous channel with expanding or contracting walls. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. At last, the ordinary differential equations is calculated, and the result has great accuracy and this method is practical.

Integrated Search System using Semantic Analysis

Objectives: With the rapid growth of technology drastically there is an increase in users of computer system. Search system plays a vibrant role in optimizing the search time. Methods/Statistical Analysis: The integrated search system has the need of improving search accuracy and expanding their coverage of search. This can be done using concepts of semantic analysis. Findings: In this paper an integrated system is formulated by accumulating multiple sources such as local storage, secondary storage and online repositories. The keyword is contextually interpreted by making use of ontologies, using this interpretation of the keyword the required multimedia or/and textual data is searched as intended by the user. Applications: This integrated search system analyzes the meaning of the query and provides the search result according to the intention of the user through is proper expansion of keyword.

Collaborative Autonomy for Mapping, Search, and Pursuit

This paper describes the development and flight tests of a multi-aircraft collaborative architecture, focused on decentralized autonomous decision making to solve a scenario-driven challenge problem. The architecture includes two search coverage algorithms, a hide location detection technique, behavior estimation, and a target manipulation algorithm. The architecture was implemented on a pair of Yamaha RMAX helicopters outfitted with modular avionics, as well as an associated set of simulation tools. Simulation and flight test results for single- and multiple aircraft scenarios are presented. Further work suggested includes identification and development of more sophisticated evader models and pursuit algorithms.

Network Traffic Prediction Based on SVR Improved By Chaos Theory and Ant Colony Optimization

Network traffic prediction is one of the significant issues. The model for network traffic prediction should meet the following requirements. First, the model should be taken into consideration the characteristics of the network flow such as burstiness, long-range dependence, periodicity and self-similarity. To achieve this, we decompose the original flow in a multi-scale manner into a set of linear and stable representations, and introduce chaos theory to improve the diversity and search coverage. Second, the model should be efficient and accurate. To this end, we propose a prediction model based on SVR, and utilize Ant Colony Optimization (ACO) algorithm for parameter selection of SVR. Besides, we conduct experiments to evaluate the proposed model.