Search Time Research Articles

Automatic lift path planning of prefabricated building components using semantic BIM, improved A* and GA

PurposePrefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).Design/methodology/approachFirstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.FindingsThe proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.Originality/valueIn this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery

Among the various challenges facing tropical tuna purse seine fleet are the need to reduce fuel consumption and carbon footprint, as well as minimising bycatch of vulnerable species. Tools designed for forecasting optimum tuna fishing grounds can contribute to adapting to changes in fish distribution due to climate change, by identifying the location of new suitable fishing grounds, and thus reducing the search time. While information about the high probability to find vulnerable species could result in a bycatch reduction. The present study aims at contributing to a more sustainable and cleaner fishing, i.e. catching the same amount of target tuna with less fuel consumption/emissions and lower bycatch. To achieve this, tropical tuna catches as target species, and silky shark accidental catches as bycatch species have been modelled by machine learning models in the Indian Ocean using as inputs historical catch data of these fleets and environmental data. The resulting models show an accuracy of 0.718 and 0.728 for the SKJ and YFT, being the absences (TPR = 0.996 for SKJ and 0.993 for YFT, respectively) better predicted than the high or low catches. In the case of the BET, which is not the main target species of this fleet, the accuracy is lower than that of the previous species. Regarding the silky shark, the presence/absence model provides an accuracy of 0.842. Even though the model's performance has room for improvement, the present work lays the foundations of a process for forecasting fishing grounds avoiding vulnerable species, by only using as input data forecast environmental data provided in near real time by earth observation programs. In the future these models can be improved as more input data and knowledge about the main environmental conditions influencing these species becomes available.

Comparison of efficacy of acupuncture-related therapies in treating Acute Gouty Arthritis: A Network Meta-Analysis of Randomized Controlled Trials

Backgroundand purpose: Acupuncture and moxibustion, as a complementary and alternative therapy, has been widely used in the treatment of acute gouty arthritis (AGA). However, there are various forms of acupuncture and moxibustion, and the curative effect of different forms is different. This study evaluated the clinical efficacy of different acupuncture therapies in treating AGA by network meta-analysis. MethodsComputer searches of English databases (including PubMed, The Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Embase) and Chinese databases (including China National Knowledge Infrastructure (CNKI), VIP Database, Wanfang Database and China Biomedical Literature Database) were conducted for randomized controlled trials (RCTs) of acupuncture therapies in treating AGA. We set the search time from the database establishment to May 2022. Data analysis was performed using Stata14.2 software. ResultsThirty-two RCTs involving 2434 patients with AGA were screened out. The results showed that in terms of the improvement of pain visual analogue scale (VAS) scores, the top ones were acupoint application (100%), electroacupuncture + Western medicine (73.5%) and acupuncture + Western medicine (69.2%); In terms of total effective rate, acupoint application (85.2%), acupuncture (75.2%) and acupuncture + Western medicine (63%) ranked the top; In terms of reducing serum uric acid (SUA) levels, the top ones were acupoint application (95%), acupuncture + Western medicine (87.5%) and acupuncture (66.2%); In terms of the reduction of erythrocyte sedimentation rate (ESR), acupuncture (95%), acupoint application (84.7%), and electroacupuncture + Western medicine (52.8%) were the most prominent. ConclusionThe existing evidence shows that acupoint application has more advantages in improving the total effective rate, improving pain and reduce SUA levels, and acupuncture has an advantage in reducing ESR levels and adverse events. However, due to the low qualities of the original studies, the quality of this evidence is poor. Therefore, it is recommended that more scientific research be performed to confirm the efficacy of acupuncture.

Gaze-Data-Based Probability Inference for Menu Item Position Effect on Information Search

This study examines the effect of menu items placed around a slideshow at the center of a webpage on an information search. Specifically, the study analyzes eye movements of users whose search time is long or short on a mixed-type landing page and considers the cause in relation to “directed search” (which triggers a certain type of mental workload). To this end, a Bayesian network model is developed to elucidate the relation between eye movement measures and search time. This model allows the implementation degree of directed search to be gauged from the levels of the measures that characterize a long or short search time. The model incorporates probabilistic dependencies and interactions among eye movement measures, and hence it enables the association of various combinations of these measure levels with different browsing patterns, helping judge whether directed search is implemented or not. When viewers move their eyes in the direction opposite (identical) to the side on which the target information is located, the search time increases (decreases); this movement is a result of the menu items around the slideshow capturing viewers’ attention. However, viewers’ browsing patterns are not related to the initial eye movement directions, which may be classified into either a series of orderly scans (directed search) to reach the target or long-distance eye movements derived from the desire to promptly reach the target (undirected search). These findings suggest that the menu items of a website should not be basically placed around a slideshow, except in cases where they are intentionally placed in only one direction (e.g., left, right, or below).

Guards: Benchmarks for weighted grid-based pathfinding

The primary objective of this paper is to aid game developers in finding the most suitable pathfinding algorithm for their games. Despite recent advancements in this field, there are few available studies that can be compared due to the absence of a standard benchmark set for weighted environments. This paper presents a new dataset for pathfinding in weighted environments. Furthermore, an investigation was conducted into the impact of node weights on pathfinding speed, and a correlation between them was identified. The complexity added to the maps due to node weights was defined as weight complexity, and two metrics were introduced to estimate it. The weight correlation factor has been identified as the most effective metric for estimating the weight complexity of the map. Another contribution of this paper pertains to the development of a model for estimating the pathfinding speed of algorithms based on weight complexity. This was accomplished through the utilization of the non-linear least squares method, which was applied to create a model for each algorithm, considering both its average search time and weight correlation factor values associated with the map. Finally an overall score metric was developed by using the integral of the models, enabling the evaluation of different algorithms in various scenarios.

Deep code search efficiency based on clustering

AbstractThe deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development.

STile: Searching Hybrid Sparse Formats for Sparse Deep Learning Operators Automatically

Sparse operators, i.e., operators that take sparse tensors as input, are of great importance in deep learning models. Due to the diverse sparsity patterns in different sparse tensors, it is challenging to optimize sparse operators by seeking an optimal sparse format, i.e., leading to the lowest operator latency. Existing works propose to decompose a sparse tensor into several parts and search for a hybrid of sparse formats to handle diverse sparse patterns. However, they often make a trade-off between search space and search time: their search spaces are limited in some cases, resulting in limited operator running efficiency they can achieve. In this paper, we try to extend the search space in its breadth (by doing flexible sparse tensor transformations) and depth (by enabling multi-level decomposition). We formally define the multi-level sparse format decomposition problem, which is NP-hard, and we propose a framework STile for it. To search efficiently, a greedy algorithm is used, which is guided by a cost model about the latency of computing a sub-task of the original operator after decomposing the sparse tensor. Experiments of two common kinds of sparse operators, SpMM and SDDMM, are conducted on various sparsity patterns, and we achieve 2.1-18.0× speedup against cuSPARSE on SpMMs and 1.5 - 6.9× speedup against DGL on SDDMM. The search time is less than one hour for any tested sparse operator, which can be amortized.

Triple Time Interval Hybridization Strategy for Rapidly Calculating Regional Target–Visible Time Window of Earth Observation Payloads on Space Station

Earth observation payloads deployed outside the space station play a pivotal role in remote sensing and atmospheric monitoring. Calculating the visible time window (VTW) for regional targets is a fundamental aspect of payload observation and operational mission planning. Aiming to address the challenge of rapidly calculating the VTW for regional targets, this paper introduces the Triple Time Interval Hybridization Strategy (TTIHS). By integrating orbit filtering, large-step angle calculation, and a short interval binary search, the TTIHS adapts different strategies within global, coarse search, and precise search time intervals, effectively reducing computational workload and improving speed. Simultaneously, optimization analysis is conducted on the time step in the coarse search, achieving a balance between computation speed and accuracy. Simulation results, based on real data from the China Space Station, highlight a significant improvement in computational speed while ensuring accuracy. Specifically, the proposed rapid algorithm exhibits a 2.73-fold enhancement in speed compared to the existing rapid algorithm.

DARTS-PT-CORE: Collaborative and Regularized Perturbation-based Architecture Selection for differentiable NAS

DARTS-PT is a well-known differentiable NAS method that measures the operation strength through its contribution to the supernet performance, extracting architecture from the underlying supernet. However, persistent issues of degraded architecture in DARTS-PT have been identified in recent studies. In response, we undertake a comprehensive analysis of this performance degradation issue and identify two primary contributing factors: the unfavorable competition among correlated operations during the operation selection process and the unfair advantage of parameter-free operations within DARTS-PT supernet. Building upon these findings, we propose DARTS-PT-CORE, a novel architecture selection algorithm that incorporates a collaborative operation competition mechanism and a regularization technique in the perturbation-based architecture selection approach. Our method aims to mitigate the negative effects of competition among correlated operations, yielding more reliable operation contribution scores. Furthermore, our regularization technique addresses the unfair advantage of parameter-free operations, facilitating a more balanced architecture selection process. Extensive experiments conducted on various datasets and search spaces indicate that DARTS-PT-CORE outperforms other state of-the-art methods. Specifically, in the DARTS search space, DARTS-PT-CORE achieves 2.43% test error on CIFAR10 and 16.23% test error on CIFAR100, while the search time is less than 0.8 GPU days. When transferring to ImageNet, DARTS-PT-CORE achieves 24.97% top-1 error. Such results underscore the effectiveness of our method in enhancing the reliability and balance of architecture selection in differentiable NAS.

Novel evidence for cue-based retrieval of top-down sets in spatial cueing

Task cues that correctly (vs.incorrectly) inform humans about their upcoming tasks, benefit (vs. interfere with) performance because participants can use task cues to retrieve the corresponding task set, so that targets can (vs. cannot) be processed according to the currently applying task set from target onset onwards. Here, we tested if task-associated features of peripheral cues have a similar effect. Typically, peripheral cues with a task-associated, searched-for color (i.e., top-down matching cues) capture attention: Search for targets presented at the cued position (valid condition) is faster than for targets presented away from the cue (invalid condition), even if cues do not predict the likely target location. For example, when searching for red and green targets, a red cue captures attention even if presented prior to a green target, but a blue cue does not. We know that cue-target color congruence—whether the cue has a target-similar color (congruent case) vs. a target-dissimilar color (incongruent case)—additionally expedites vs. delays search times. However, it is unclear if this congruence effect reflects feed-forward color priming of the target only; or if cue-elicited retrieval of color-specific task sets is involved. Crucially, we hypothesized that cue-based task-set retrieval should incur additional costs if the task sets for the two target colors differ more. In contrast, mere feed-forward priming should not be affected by task-set similarity between color-associated task sets. Congruence effects were indeed larger when color-associated task sets were more different. This finding indicates cue-elicited retrieval of color-associated task sets can contribute to effects of cue-target color congruence. Results are discussed in light of recent theories.

Impact of social media “challenges” on poison center case volume for intentional ingestions among school-aged children: an observational study

Introduction Mental health problems among youth have escalated over the past decade, with increased rates of self-harm, including suicide attempts by ingestion. Social media use has been linked to youth mental health, including “challenges” urging youth to ingest substances for recreational and other purposes. We hypothesized that social media challenges for particular substances would temporally correspond with increased ingestions of these substances. Methods We identified peak Google Trends search times for social media ingestion challenges involving diphenhydramine, laundry pods, nutmeg, and cinnamon, and used data from America’s Poison Centers National Poison Data System to plot reported ingestions 3 months before and after peak searches in school-aged children. Results There were 2,169 individuals in the analysis. Diphenhydramine was the most frequently reported ingestion for misuse/abuse and suicidal purposes (n = 266 and 1,609, respectively). For all ingestions together, 45 percent (n = 979) had a moderate health effect, and 6.35 percent (n = 137) had a major health effect. Time of peak searches corresponded with increased ingestions for each substance. Discussion We found a temporal relationship between peak Google Trends searches for ingestion challenges and ingestions of that substance reported to United States poison centers. Compared to misuse/abuse ingestions, most suicidal ingestions peaked 1–2 months later, suggesting a public health opportunity for intervention. Limitations This retrospective observational study does not establish causal effect. All data are a result of self-reporting of the exposures, which may lead to a reporting bias. Google Trends is not the only search engine and likely underestimates the true incidence of social media posts. Conclusions Additional research is needed on the relationship between social media and youth mental health, particularly around “challenges” that place youths’ health at risk. There may be opportunities for intervention to decrease medical and mental health sequelae of these challenges.

Dense and uniform displays facilitate the detection of salient targets

ABSTRACT Increasing the density or uniformity of nontarget stimuli appears to increase the saliency of singleton stimuli. Consequently, search times should be shorter. Surprisingly, however, effects of density or uniformity on search times were not always observed in detection tasks. We re-examined this finding with stimuli having two features, color and shape. Half of the participants indicated the presence or absence of a color singleton, and the other half indicated the presence or absence of a shape singleton. Density was changed by increasing the number of stimuli from 4 to 10. We found that the effects of density were either limited to target-absent trials or to target-present trials, which may explain previous failures to observe these effects. When color was the target feature, we found shorter RTs to dense than sparse displays on target-absent trials, but no difference on target-present trials. When shape was the target feature, it was the opposite. Concerning the uniformity of the nontargets, we found shorter RTs with uniform than mixed displays and this difference was larger on target-absent than target-present trials. These results are mostly consistent with the Guided Search Model.

Research Status and Trends of Traditional Chinese Medicine Therapeutic Formulae for Coronary Heart Disease Scientometrics Research.

This study analyzes research on TCM formulae in CHD over the past 30 years, using VOSviewer and CiteSpace. It aims to highlight key trends and hotspots in the field. The core database of Web of Science was collected, and the search time range was from the establishment of the database to the present (August 2023) for the literature related to the study of TCM prescriptions in CHD, and the information on the number of literature, countries, journals, authors, institutions, keywords were summarized by applying the software VOSviewer and CiteSpace. A total of 135 kinds of literature were included. The number of published journal papers on research on TCM therapeutic formulae for CHD showed an upward trend; China was the most prolific country in this field; the largest number of papers were published in Evid Based Complement Alternat Med, MEDICINE; the average number of citations for authors and institutional analysis revealed that Xu Hao of China Academy of Traditional Chinese Medicine, Mao Jingyuan of Tianjin University of Traditional Chinese Medicine, and Shang Hongcai of Beijing University of Traditional Chinese Medicine constituted the core team of researchers studying the study of TCM formulae for CHD; the keyword analysis suggests that there are mainly 42 specifically named TCM formulae for the treatment of CHD, which are classified into a total of 7 major categories, and the research direction is mainly in the clinical efficacy study of different TCM therapeutic formulae and other aspects. This study shows that there are more types of TCM therapeutic formulae for CHD, and the related research has a good prospect. It is foreseeable that more relevant research results will rely on the study of network pharmacology, signalling pathways, and action targets of TCM therapeutic formulae.

Assessment of a Clinical Test for Detection of Alteration in Visual Perception Due to Astigmatism.

Astigmatism blurs the retinal image of a circular spot along a particular orientation rendering it an elliptical shape. Astigmatic patients demonstrate adaptation to residual astigmatic blur that may affect their discrimination between oval and circular targets. The Wilkins Egg and Ball Test (WEBT) was created to detect altered visual perception due to residual astigmatic blur by discriminating a circle within a row of oval elements. This prospective, cross-sectional study examined the utility of WEBT in detecting uncorrected residual astigmatism on the perception of form symmetry in astigmatic and keratoconic participants as well as normal participants with induced astigmatism at four primary meridians. The mean search time (sT) and number of errors (noE) of 33 non-astigmatic controls (mean age: 24±5, range: 18-43, 6 males), 23 astigmatic participants (mean age: 36±12, range: 18-43, 6 males) and 13 keratoconic participants (N=22 eyes, mean age: 36±12, range: 18-58, 6 males) were measured under baseline, and 2.00 DC induced cylinder at four primary meridians, and for uncorrected, spherical-correction only, and fully corrected conditions, respectively. Mean sT and noE were converted to Z-scores, combined for each condition, and compared using repeated measures ANOVA with post-hoc analysis. Combined Z-scores for the controls were significantly worse (p<0.001) for all induced cylinder conditions. The induced 180° condition was significantly better than 45° and 90° conditions (p=0.04), but not the 135° condition. For both astigmatic and keratoconic cohorts, Z-scores of the uncorrected condition were significantly worse than the fully corrected condition (both p<0.01), but the fully corrected and spherical-only conditions did not differ significantly (p=0.06 and p=0.05, respectively). In accommodating young adults, WEBT detected altered visual perception due to overall blur, and moderate-high amounts of uncorrected induced astigmatism and keratoconus, but is not useful as a tool for detection of altered visual perception due to small residual astigmatic blur.

A heuristic autonomous exploration method based on environmental information gain during quadrotor flight

Autonomous exploration is a widely studied fundamental application in the field of quadrotor, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration, and how to improve the efficiency of autonomous exploration is the focus of research nowadays. Slow frontier generation affecting real-time viewpoint determination and insufficient determination methods affecting the quality of viewpoints are typical of these problems. Therefore, to overcome the aforementioned problems, this article proposes a two-level viewpoint determination method for frontier-based autonomous exploration. First, a sampling-based frontier detection method is presented for faster frontier generation, improving the immediacy of environmental representation compared to traditional traversal-based methods. Second, the access to environmental information during flight is considered for the first time, and an innovative heuristic evaluation function is designed to decide on high-quality viewpoint as the next local navigation target in each exploration iteration. Extensive benchmark and real-world tests are conducted to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20%–30%, and the exploration path by 25%–35%.

Physical properties of odorants affect behavior of trained detection dogs during close-quarters searches

Trained detection dogs have a unique ability to find the sources of target odors in complex fluid environments. How dogs derive information about the source of an odor from an odor plume comprised of odorants with different physical properties, such as diffusivity, is currently unknown. Two volatile chemicals associated with explosive detection, ammonia (NH3, derived from ammonium nitrate-based explosives) and 2-ethyl-1-hexanol (2E1H, associated with composition C4 plastic explosives) were used to ascertain the effects of the physical properties of odorants on the search behavior and motion of trained dogs. NH3 has a diffusivity 3.6 times that of 2E1H. Fourteen civilian detection dogs were recruited to train on each target odorant using controlled odor mimic permeation systems as training aids over 6 weeks and then tested in a controlled-environment search trial where behavior, motion, and search success were analyzed. Our results indicate the target-odorant influences search motion and time spent in the stages of searching, with dogs spending more time in larger areas while localizing NH3. This aligns with the greater diffusivity of NH3 driving diffusion-dominated odor transport when dogs are close to the odor source in contrast to the advection-driven transport of 2E1H at the same distances.

Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA.

Public sequencing databases contain vast amounts of biological information, yet they are largely underutilized as it is challenging to efficiently search them for any sequence(s) of interest. We present kmindex, an approach that can index thousands of metagenomes and perform sequence searches in a fraction of a second. The index construction is an order of magnitude faster than previous methods, while search times are two orders of magnitude faster. With negligible false positive rates below 0.01%, kmindex outperforms the precision of existing approaches by four orders of magnitude. Here we demonstrate the scalability of kmindex by successfully indexing 1,393 marine seawater metagenome samples from the Tara Oceans project. Additionally, we introduce the publicly accessible web server Ocean Read Atlas, which enables real-time queries on the Tara Oceans dataset.

Causal association between lipoproteins and risk of coronary artery disease-a systematic review and meta-analysis of Mendelian randomization studies.

To systematically evaluate the causal effect of lipoproteins to the risk of coronary artery disease (CAD) by systematic review and meta-analysis of the associated Mendelian randomization (MR) studies. This systematic review was registered in PROSPERO (ID CRD42023465430). Searches from the databases (e.g., PubMed, Embase, Cochrane, Web of Science) and non-database sources to collect MR studies. The search time frame was from the database inception to August 2023. After data extraction, quality evaluation was performed, and the meta-analysis with bias evaluation was carried out with RevMan software. A total of 5,828,409 participants from 21 records were included. Quality and bias assessment was performed by evaluating the internal three assumptions of MR studies. Meta-analysis for the causal association between non-HDL lipoproteins and CAD showed a significantly positive association between LDL and CAD (OR 1.37, 95% CI 1.26-1.49; P < 0.001, I2 = 95%), apoB and CAD (OR 1.38, 95% CI 1.11-1.71; P = 0.003, I2 = 98%), and Lp(a) and CAD (OR 1.21, 95% CI 1.12-1.31; P < 0.001, I2 = 99%). Interestingly, although there was no statistical significance in the association between VLDL/apoA1 and CAD (both P > 0.05), the pooled non-HDL lipoproteins showed a significantly positive association with CAD (OR 1.28, 95% CI 1.22-1.34; P < 0.001, I2 = 99%). For the HDL lipoproteins, the pooled OR showed a significantly negative association with CAD (OR 0.84, 95% CI 0.72-0.98; P = 0.002, I2 = 72%). However, the protective effect of HDL on CAD diminished when analyzed together with apoA1 and/or apoB (both P > 0.05). The funnel plot did not show serious publication bias, and sensitivity analysis performed relatively well robustness of the causal association of LDL, apoB, Lp(a), and total cholesterol with CAD. The present meta-analysis suggests an overall effect of causal association between lipoproteins and CAD. Most of the non-HDL lipoproteins (LDL, apoB, Lp(a)) promote CAD, while the protective effect of HDL in CAD still needs to be verified in the future.

Diffusion with two resetting points.

We study the problem of a target search by a Brownian particle subject to stochastic resetting to a pair of sites. The mean search time is minimized by an optimal resetting rate which does not vary smoothly, in contrast with the well-known single site case, but exhibits a discontinuous transition as the position of one resetting site is varied while keeping the initial position of the particle fixed, or vice versa. The discontinuity vanishes at a "liquid-gas" critical point in position space. This critical point exists provided that the relative weight m of the further site is comprised in the interval [2.9028...,8.5603...]. When the initial position is a random variable that follows the resetting point distribution, a discontinuous transition also exists for the optimal rate as the distance between the resetting points is varied, provided that m exceeds the critical value m_{c}=6.6008.... This setup can be mapped onto an intermittent search problem with switching diffusion coefficients and represents a minimal model for the study of distributed resetting.

Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.