Search Coverage Research Articles

Comparing cost‐effectiveness of radio and drone telemetry with playback surveys for assessing translocation outcomes

Abstract Post‐release monitoring is critical for assessing translocation outcomes. Yet the quality of information gained from monitoring can vary greatly, and perceived monitoring costs often results in reduced monitoring effort. Selecting cost‐effective monitoring strategies that provide high quality data are therefore important for assessing translocation outcomes and making informed management decisions. To compare how monitoring strategy affects information gained, we monitored a toutouwai/North Island robin (Petroica longipes) reintroduction in Aotearoa New Zealand, based on monitoring objectives of determining survival, site fidelity and whether the extent of management was large enough to protect dispersing individuals. We compared how these objectives were met through four monitoring strategies: (1) comprehensive surveys with ground radio telemetry and playback; (2) aerial drone telemetry; (3) dedicated playback by trained surveyors and (4) opportunistic playback by predator control contractors. We undertook a viewshed analysis to determine search coverage of each strategy and compared detection rates, efficiency and cost. Comprehensive ground telemetry and playback, while costly, covered the largest area and provided the most accurate data on dispersal, survival and the translocation outcome. In comparison, opportunistic playback monitoring detected substantially fewer individuals, giving a false impression of low site fidelity and survival and a failed translocation. Although drone telemetry had considerable site‐specific limitations, which limited its effectiveness during our study, it was the most cost‐effective with a high detection rate and low search effort. Synthesis and applications: Our study shows the value of intensive monitoring in facilitating management decisions for wildlife translocations. Comprehensive telemetry and playback, while costly, were invaluable for gaining high quality information on the translocation outcome. Without suitable monitoring, reintroduction outcomes can be difficult to assess and potentially result in unnecessary, ineffective or overly expensive management actions. We recommend that monitoring intensity and methodology should reflect the site, species and level of uncertainty regarding the translocation outcome. Prioritising monitoring can help reduce long‐term costs, increase quality of information gained and allow for more informed management decisions that can improve subsequent translocation outcomes.

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, and a well-performed routing protocol can effectively achieve this goal. However, the nodes in the network are prone to drift, and the topology is easily changed because of the movement caused by ocean currents, resulting in a routing void. The data cannot be effectively aggregated to the sink terminal on the surface. Thus, it is extremely important to determine how to find an alternative node as a relay node after node drift and how to rebuild a reliable transmission path. Although many relay routing protocols have been proposed to avoid routing voids, few of them consider the relay node selection between the outage probability and the ocean current model. Therefore, we propose an ocean current motion model based routing (OCMR) protocol to avoid the routing void in UASNs. We predicted underwater node movement based on the ocean current motion model and designed a protection radius to construct a limited search coverage based on the optimal outage probability; then, the node with the best fitness value within the protection radius was selected as the alternative relay node using an improved WOA. In OCMR, the problem of the routing void caused by ocean current motion is effectively suppressed. The simulation results show that, compared with VBF, HH-VBF, and QELAR, the proposed OCMR platform performs well in terms of the PDR (packet delivery ratio), average end-to-end delay, and average energy consumption.

The interplay of machine learning-based resonant anomaly detection methods

Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM physics is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of the methods’ complementarity. To this end, we address two questions. First, in the absence of any signal, do different methods pick the same events as signal-like? If not, then we can significantly reduce the false-positive rate by comparing different methods on the same dataset. Second, if there is a signal, are different methods fully correlated? Even if their maximum performance is the same, since we do not know how much signal is present, it may be beneficial to combine approaches. Using the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative answers to these questions. We find that there are significant gains possible by combining multiple methods, which will strengthen the search program at the LHC and beyond.

Effectiveness of cognitive rehabilitation in comparison with routine rehabilitation methods in patients with multiple sclerosis: A systematic review and meta-analysis.

Cognitive rehabilitation includes a set of programs to train the brain, which leads to the improvement of mental and neuro-psychological functions. This meta-analysis was conducted with the aim of investigating the effectiveness of cognitive rehabilitation in comparison with routine rehabilitation methods in patients with multiple sclerosis (MS). The Cochrane Library, ProQuest, PubMed, PsycINFO, and Web of Science databases were searched from inception to August 2022. Google Scholar was used to find relevant sources and complete the search coverage. Two authors independently selected studies based on predefined inclusion criteria. Data analysis was performed using RevMan (version 5.3). Out of the 578 studies found, 20 studies were finally included in this review. The results of the meta-analysis on four outcomes (Paced Auditory Serial Addition Test (PASAT), Brief Visuospatial Memory Test (BVMT), MS Neuropsychological Screening Questionnaire (MSNQ), and Beck Depression Inventory (BDI)) indicated that the outcome was significant in favor of the cognitive rehabilitation group. However, for five outcomes (Controlled Oral Word Association Test (COWAT), California Verbal Learning Test (CVLT), Everyday Memory Questionnaire (EMQ), Symbol Digit Modalities Test (SDMT), and Expanded Disability Status Scale (EDSS)), the differences between the two groups were not significant. The results of this meta-analysis showed that cognitive rehabilitation has an effect on improving the performance of patients with MS. However, further studies with more accurate methodologies are required to determine which of the outcomes cognitive rehabilitation has a greater effect on.

UAV search coverage under priority of important targets based on multi-location domain decomposition

<abstract> <p>In recent years, the coverage path planning (CPP) of unmanned aerial vehicles (UAVs) has attracted attention in reconnaissance, patrol, and search and rescue efforts, aiming to plan the paths for UAVs to cover a specified area as efficiently as possible. This paper proposes a UAV path fast coverage model to prioritize important targets with domain composition based on the starting point and location of the targets, combined with the domain decomposition strategy of important targets. Considering the constraints of the number of UAVs, the number of operators, and the flight time, the parallel search strategy can plan the coverage scheme with the shortest search time for the search range, and further obtain the coordinate points and path coordinates of the UAV turning. Finally, through multiple simulation experiments in four maps of various islands, the proposed method is verified to have an improved performance compare to the two track path coverage algorithms methods in terms of the coverage efficiency and the time complexity, thus providing a more scientific basis for the path coverage research of multi-target searches.</p> </abstract>

Analysis and Requirement Generation for Defense Intelligence Search: Addressing Data Overload through Human–AI Agent System Design for Ambient Awareness

This research addresses the data overload faced by intelligence searchers in government and defense agencies. The study leverages methods from the Cognitive Systems Engineering (CSE) literature to generate insights into the intelligence search work domain. These insights are applied to a supporting concept and requirements for designing and evaluating a human-AI agent team specifically for intelligence search tasks. Domain analysis reveals the dynamic nature of the ‘value structure’, a term that describes the evolving set of criteria governing the intelligence search process. Additionally, domain insight provides details for search aggregation and conceptual spaces from which the value structure could be efficiently applied for intelligence search. Support system designs that leverage these findings may enable an intelligence searcher to interact with and understand data at more abstract levels to improve task efficiency. Additionally, new system designs can support the searcher by facilitating an ‘Ambient Awareness’ of non-selected objects in a large data field through relevant system cues. Ambient Awareness achieved through the supporting concept and AI teaming has the potential to address the data overload problem while increasing the breadth and depth of search coverage.

Hyaluronidase from Paenibacillus aquistagni SH-7-A: Identification and characterization

The research identified a hyaluronidase produced by Paenibacillus aquistagni SH-7-A, the highest hyaluronidase activity with 830 U/mL obtained under 24 h fermentation. Based on genomic analysis, a gene hysa1 encoding hyaluronidase hysA1 was mined. Hyaluronidase H1 was purified by ammonium sulfate precipitation, anion exchange chromatography, and gel permeation chromatography. The molecular weight of purified H1 was determined to be about 110 kDa by SDS-PAGE, and the sequence coverage of the protein search with hysA1 resulted in 77% by HPLC-MS/MS. Therefore, it was proven that hyaluronidase H1 was hysA1. The optimum temperature and pH for H1 activity were 40°C and pH 6, and it was stable at 30–40°C and pH 5–7, respectively. Ca2+, Mg2+, and Ni2+ enhanced the enzymatic activity, and it was strongly inhibited by Zn2+, Cu2+, EDTA, tween 80, Triton X-100, CTAB, DTT, and SDS. Using HA as a substrate, the Km and Vmax of H1 were found to be 0.1124 mg/mL and 0.0184 mol/min/mL, respectively. This is the first hyaluronidase production, purification, and characterization report from P.aquistagni. These characteristics showed that the hyaluronidase H1 would provide potential possibilities for commercial applications.

An Effective Obstacle Avoidance and Motion Planning Design for Underwater Telescopic Arm Robots Based on a Tent Chaotic Dung Beetle Algorithm

As the underwater environment is complex, the existence of obstacles will produce a certain collision interference to underwater robot operations, which causes the overall path planning and time costs to increase. In this paper, we propose a Tent chaotic mapping and dung beetle hybrid algorithm (MDBO) application for trajectory optimal planning and effective obstacle avoidance for an underwater telescopic arm robot. The method invokes the unique obstacle avoidance habit and foraging optimization idea of the dung beetle algorithm. Introducing it into the chaotic Tent mapping idea prevents the dung beetle algorithm (DBO) from falling into local optimality and increases the coverage of a global search. Simulation results show that the MDBO algorithm exhibits strong optimization ability and stability when multiple algorithms are verified using eight test functions. The MATLAB test reflects the performance indexes of the six joints of the underwater telescopic arm, and compared with various algorithms, the MDBO algorithm has an obvious convergence trend and strong global search ability. The algorithm is applied to real underwater experiments to verify that the improved dung beetle algorithm has better obstacle avoidance ability and reduces trajectory planning time by 30%, which helps the underwater robot to complete motion planning.

Design of compound data acquisition gateway based on 5G network

With the wide application of industrial Internet of Things, the increasing amount of data and the complexity of data types, higher requirements are put forward for the performance of data acquisition gateway. In order to reduce the data acquisition time of the gateway and improve the data retrieval coverage of the gateway, a novel design method of composite data acquisition gateway based on 5G network is proposed. Based on the analysis of related technologies, the functional requirements of the composite data acquisition gateway are summarized, and the overall design of the gateway is completed. On this basis, the gateway hardware environment is constructed by designing the main control module, 5G module and FPGA program, and then the software program is designed by designing the data acquisition driver, 5G module driver, embedded software and protocol conversion process. The experimental results show that the data retrieval coverage of the gateway designed by this method is always above 92%, which is 6% higher than that of method 1. This shows that the method significantly improves the coverage of data search, speeds up the efficiency of data collection, and improves the performance of the data collection gateway, which proves the effectiveness and feasibility of the method and is conducive to promoting the intelligent development of the data collection gateway technology.

ФАКТОРЫ РИСКА КОРОНАРНОГО РЕСТЕНОЗА

Introduction. Cardiovascular disease has been the leading cause of death and disability worldwide for many years. The widespread use of percutaneous coronary intervention has significantly improved clinical outcomes in patients with cardiovascular diseases. However, inflammatory reactions that occur in response to mechanical stretch, endothelial exposure and subintimal hemorrhage play a key role, triggering a cascade of proliferative processes leading to neointimal hyperplasia and restenosis. Aim. Aim of this review was to study the known mechanisms of coronary restenosis and to search possible new risk factors for in-stent restenosis. Material and methods. A review of current medical publications in PubMed, MedLine, Google Scholar, ScienceDirect was carried out. Search coverage included articles published from 1993 to 2021. Results and discussion. Risk factors for restenosis have been shown, such as genetic, mechanical (under exploitation, fracture, excessive dilatation, polymer damage), high level of systemic inflammation. In addition, it has been shown that vitamin D deficiency can also be a risk factor for atherosclerosis and immune inflammation. Conclusion. The in-stent restenosis is an actual problem. Incidence of in-stent restenosis has been reported in up to 10% after percutaneous coronary intervention. Multiple mechanical, genetic and inflammatory factors might contribute to in-stent restenosis. In recent years, serum levels of NT-proBNP, pentraxin-3, vitamin D have been studied as restenosis risk factors. Further research of this subject may allowed to develop methods for preventing this complication.

Search task oriented path planning method of airborne radar network

Reasonable planning the trajectory of the airborne radar network can decrease its repeated surveillance region, and improve the search efficiency. To complete a wider range of reconnaissance searches within a certain time, a search task oriented path optimization method of airborne radar network is proposed. Firstly, the single-platform search range by combing the radar equation is characterized, and the multi-radar area coverage function based on the rasterization idea is calculated. A trajectory optimization model with the goal of maximizing the search coverage function is constructed, by combining with the motion constraints of each node and adopting an intelligent method. Simulation results show that the present method can obtain higher search coverage within specified time.

Do “Ten simple rules for neuroimaging meta-analysis” receive equal attention and accurate quotation? An examination on the quotations to an influential neuroimaging meta-analysis guideline

The collection of recommendations and guidelines for conducting and reporting neuroimaging meta-analyses, called “Ten simple rules for neuroimaging meta-analysis” by Müller et al., has been published for a few years. Here, the papers citing this reference were examined to evaluate the rationale of the quotations and what quotation errors existed. In May 2023, an online query via Scopus identified 386 papers citing this reference, 2 of which were inaccessible. The resultant 384 papers were checked to identify the total number of quotations to the reference, the exact quotations, which of the ten recommendations/rules was concerned by each quotation, and if any quotation error existed. Results found that the reference by Müller et al. were quoted 804 times by the 384 papers, meaning an average of 2.1 quotations per paper. Out of the 804 quotations, the three rules that the researchers most frequently referred were the power of the meta-analysis (Rule #2, 14.1%), the consistency of the search coverage and reference space (Rule #4, 13.8%), and the statistical threshold (Rule #8, 10.2%). Overall, 63 quotations from 51 papers contained some errors. In other words, 7.8% (63/804) of the quotations contained errors and they involved 13.3% (51/384) of the papers. The commonest quotation errors were dealing with a failure to substantiate the assertion, unrelated to the assertion, and oversimplification of the original notion. Some notable quotation error examples were to quote Müller et al. to substantiate the assertion of having at least 10 datasets to be considered to have adequate power for ES-SDM meta-analysis (no such recommendation), and having a misquoted primary cluster-forming threshold of p < 0.05 or p < 0.005 (should be p < 0.001). The neuroscience community should be cautious and double-check the accuracy of assertions, even with a quotation.

Cooperative Path Planning of Multiple Unmanned Surface Vehicles for Search and Coverage Task

This paper solves the problem of cooperative path planning of multiple unmanned surface vehicles (USVs) for search and coverage tasks in water environments. Firstly, taking the search coverage problem of water surface pollutants as an example, the information concentration map is built to predict the diffusion of water surface pollutants. Secondly, we propose a region division method based on a Voronoi diagram, which divides the region and assigns it to each unmanned surface vehicle (USV). Then, on the basis of the traditional Model Predictive Control (MPC), the future reward index based on the regional centroid is introduced, and the Improved Salp Swarm Algorithm (ISSA) is used to solve MPC. Simulation results show the effectiveness of the proposed method.

Random-sampling-based multi-UAV cooperative search planning for high-rise firefighting

This paper proposes a multi-UAV cooperative search planning method based on random sampling for high-rise fire rescue. First, an optimal distribution method of search points inspired by the random tree sampling search algorithm is proposed, which uses its repeated search feature in a limited complex space to solve the problems of waypoint distribution and task assignment for multiple UAVs simultaneously. Second, a waypoint optimization method based on transaction rules is designed to further improve search coverage. Third, the optimal traversal order of waypoints for each UAV is obtained by using the improved harmony search algorithm to acquire the planned path of each UAV. Finally, the multithreading method is used to simulate the distributed operation in the actual task to verify the feasibility of the proposed method.

A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization

Whale optimization algorithm (WOA) has received increasing attention in engineering optimization owing to its high computation efficiency, whereas, it has exhibited the drawback of premature convergence in solving multi-dimensional engineering global optimization problems. In this research, a niching hybrid heuristic whale optimization algorithm (NHWOA) is proposed to enhance convergence speed and search coverage in solving global optimization problems. In the algorithm, the niching technique is introduced to promote the diversity of population and restrain premature convergence in search of a global best solution. A heuristic adjustment to the parameters of the hybrid WOA algorithm is made to promote the exploration potential of search agents in the evolution. A designed perturbation to all the search agents’ positions is executed to avoid their falling into a local optimum. Optimization to the CEC2014 benchmark functions as validation cases are conducted along with comparisons to both conventional intelligent optimization algorithms and other state-of-the-art modified WOAs. Results indicate the effectiveness and superiority of NHWOA in solving the problems. Five practical engineering problems for global optimization with multiply variables are introduced to validate the performance of the presented algorithm with good performance results in the global computations.

Enhanced PSO Based on Population Initialization and Exploration for the Permutation Flow Shop Scheduling Problem

In this paper, a hybrid PSO (NLPSO*) is adapted to improve the obtained local optimal solution for the Permutation Flow Shop Scheduling Problem (PFSP) with minimizing the makespan. In this method, an improved NEH heuristic called -NEH+ is used to generate a good initial population. Then the PSO is triggered, followed by Iterated Local Search (ILS) to increase the coverage of exploration and exploitation search in the solution space. Both of the -NEH+ and ILS are simple and efficient algorithms. A computational study is performed to show the efficiency of the proposed technique. Several of well-known PFSP instances of small, medium, and large sizes were used in this study. The experimental study shows that the NLPSO* algorithm is significantly efficient in reaching better local optimal solutions.

Covering the Rooney Rule: An Exploratory Study of Print Coverage of NFL Head Coaching Searches

This content analysis uses media framing theory to explore and compare the rate at which NFL beat writers discussed race in their coverage of the 2020 and 2021 NFL head coach hiring cycles, a perennial process that has historically maintained the statistical overrepresentation of white men among the league’s head coaches. The study of the articles (N = 374) found significant year over year increases in 2021 in both the percentage of all sampled articles overall that mentioned race and in the percentage of stories that mentioned race after a team’s head coach was hired. This study’s findings suggest that, while NFL beat writers are unsurprisingly likely to avoid using race to frame their coaching search stories, their willingness to include race in their reporting may be increasing. Given increased calls for the NFL to address its lack of Black head coaches, sportswriters’ (un)willingness to include race in their reporting of coaching searches has become increasingly relevant. Given this study’s results, we therefore call for further qualitative and longitudinal quantitative studies to more definitively investigate these results as a sustained (and sustainable) phenomenon.

An equity and efficiency integrated grid-to-level 2SFCA approach: spatial accessibility of multilevel healthcare

BackgroundEquity of healthcare spatial access is essential for the health outcomes of medical investments and the welfare of populations, and efficiency of medical resource allocation is important for obtaining a supply-demand equilibrium with lower cost and higher outputs with limited inputs. However, the literature that involves both equity and efficiency in its analysis of healthcare spatial allocation is rare, and the spatial accessibility of multilevel healthcare is difficult to measure by traditional methods in a large region with diversified population distribution.MethodsTo assist in solving these issues, this paper aims to build an equity and efficiency integrated analytical framework by proposing a new “GTL-2SFCA” approach to analyze the spatial accessibility of multilevel healthcare; maximum and minimum floating catchments of different levels of healthcare were assigned to ensure a combination of universal search coverage and efficient hospitalization behavior simulation.ResultsThe analytical framework was applied and tested in Hubei, China. Almost half of the residents (47.95%) and townships (44.98%) have access to both public general hospitals (PGHs) and primary healthcare centers (PHCs) services, 36.89% of the residents enjoy only one sufficient service, either PGHs or PHCs, and the remaining residents (15.16%) are faced with the risk of lacking access to both services. The results reveal that there are core-periphery effects of multilevel healthcare throughout Hubei and isolate clusters that have adequate access in the western region. The polarization effect of higher-level healthcare and the polycentric pattern of lower-level healthcare coexist. The multilevel healthcare shortage was identified in some areas in boundary and peripheral regions.ConclusionsThis study integrates equity and efficiency into the GTL-2SFCA framework, enriches the FCA series methodologies and provides a more operational solution for evaluating the access of residents in more sophisticated spatial units to each level of healthcare. By more significantly differing and quantifying the catchment area and distance decay effect, this methodology avoids overestimating or underestimating accessibility and discovers some imperceptible spatial inequities. This study has application value for researchers and decision-makers in other scenarios and regions with significant heterogeneity in medical resources and where the population has greater mobility.

Detection probabilities and surveillance sensitivities for managing an invasive mammalian herbivore

AbstractInvasive mammalian herbivores can have significant unwanted impacts on indigenous ecosystems and production landscapes. Their impacts are often mitigated using sustained lethal control or eradication. If eradication is the chosen option, it is critical to confirm where the pest population is distributed and prove that eradication has been achieved following the removal intervention. This form of pest management requires robust and quantitative estimates of surveillance. We compared the detection probabilities, derived surveillance system sensitivities (under standardized conditions), and cost of surveillance for four survey methods for invasive Bennett’s wallabies (Notamacropus rufogriseus) in South Island, New Zealand: helicopter surveys with observers, helicopter surveys using a thermal imaging camera, a ground hunter with detector dogs, and camera traps. We found camera traps and ground hunters with dogs were the most sensitive surveillance methods (82% and 45% chance of detecting a wallaby assuming full search coverage of a 100‐ha area, respectively) and were most cost‐effective (62% and 85% cheaper than helicopter surveys with observers, respectively). However, although these methods are useful for surveying small areas (~1000 ha or less), capacity and time issues make them impractical for surveying large areas. The helicopter‐based methods will likely be more suitable for monitoring large areas (e.g., 10,000–50,000 ha) that need to be completed more rapidly than could be achieved using ground‐based methods. The model that we used for evaluating detection probability and surveillance sensitivity for different survey methods is highly applicable for managing a wide range of mammalian pests and confidently declaring their absence from an area.

Collaborative exploration of multiple unmanned surface vessels in complex areas based on PPO algorithm

The increasing congestion of maritime traffic leads to frequent maritime traffic accidents. Unmanned search and rescue equipment provides a guarantee for rapid rescue operations. In marine accidents, the waiting time is short and the rescue risk factor is high. Unmanned surface vessel (USV) can transport rescue equipment to the accident site in the first time and carry out searching and rescuing, which will greatly improve rescue efficiency. With the development of technology, USV can autonomously conduct area exploration to assist in the discovery of survivors. This paper proposes a complex area exploration of multiple unmanned surface vessels based on the Proximal Policy Optimization algorithm (PPO). The PPO algorithm is used to improve training efficiency, achieve 100% search coverage in complex water surface areas, and make searching and rescuing operations safe, simple and efficient. Experimental simulations show that the algorithm can realize 100% area exploration, have high search efficiency, and consumes less energy. This is a new intelligent ship application that can complete 100% exploration without human involvement.