Pattern Search Research Articles

Basal implants and their importance in clinical Dentistry: Literature review

Background: Basal implants, also known as cortical implants, have a history dating back to the mid-20th century. Although they are not as well known or widely used as conventional dental implants, they have been gaining relevance in dentistry in recent years due to their effectiveness in specific clinical situations. Objective: evaluate clinical effectiveness, analyze their indications, contraindications, examine complications and success rate. Methods: the research was structured based on a search tree (PubMed, Google Scholar, ProQuest, Scopus), with keywords such as: Basal Implant. The languages: English, Portuguese and Spanish were used as limiters; open access articles, systematic reviews, meta-analysis, randomized clinical trials, case reports. We established a secondary selection based on the title and summary of the articles according to the objective of the study.Results: The initial search showed 3,099 studies whose title was related to the search patterns, of which 3,029 articles were excluded based on their title and abstract, thus resulting in 70 potentially relevant articles. After reading, 21 articles that did not meet the inclusion criteria were excluded: repeated. In this way, 49 articles were finally selected to carry out this review. Conclusions: Dental implants offer significant benefits in oral functionality, facial aesthetics, self-esteem and long-term health. However, there are important considerations that encompass postoperative care, costs, insurance coverage, and access to these treatments.

European Online Search Patterns of Flu Vaccination During the Covid-19 Pandemic

European Online Search Patterns of Flu Vaccination During the Covid-19 Pandemic

TRGST: An enhanced generalized suffix tree for topological relations between paths

This paper introduces the TRGST data structure, which is designed to handle queries related to topological relations between paths represented as sequences of stops in a network. As an example, these paths could correspond to stops on a public transport network, and a query of interest is to retrieve paths that share at least k consecutive stops. While topological relations among spatial objects have received extensive attention, the efficient processing of these relations in the context of trajectory paths, considering both time and space efficiency, remains a relatively less explored domain. Taking inspiration from pattern matching implementations, the TRGST data structure is constructed on the foundation of the Generalized Suffix Tree. Its purpose is to provide a compact representation of a set of paths and to efficiently handle topological relation queries by leveraging the pattern search capabilities inherent in this structure. The paper provides a detailed account of the structure and algorithms of TRGST, followed by a performance analysis utilizing both real and synthetic data. The results underscore the remarkable scalability of the TRGST in terms of both query time and space utilization.

Expert knowledge data-driven based actor–critic reinforcement learning framework to solve computationally expensive unit commitment problems with uncertain wind energy

With the expansion of power grid, unaffordable computational cost and time will pose serious challenges of time-efficient scheduling in unit commitment problem (UCP). However, existing optimization methods, i.e., mathematical programming methods and meta-heuristic algorithms, are powerless and time-consuming to handle computationally expensive UCP (CEUCP). Thus, reinforcement learning methods with strong inference and time-saving performances are motivated to solve the computationally expensive challenges in tackling CEUCPs. In this paper, a novel expert knowledge data-driven based actor–critic (AC) reinforcement learning methodology is proposed for solving CEUCPs. Specifically, in the proposed AC reinforcement learning methodology, expert knowledge, data-driven surrogate model, and improved meta-heuristic algorithm are integrated for further performance enhancement. Firstly, a novel action selection mechanism (based on the expert knowledge of thermal units characteristic) is integrated into AC to improve the efficiency of network training. Secondly, an improved extreme learning machine (ELM) data-driven surrogate model is proposed to build reward function in AC. In detail, original cost function in reward is replaced by a lightweight ELM model. Shape distance is integrated into ELM for enhancing accuracy. Finally, original marine predators algorithm (MPA) is improved for obtaining optimal dispatching decisions and rewards of AC method quickly and correctly. Original search pattern is replaced by quantum based representation for boosting convergence. The excellent performances of the proposed AC framework are verified by simulations of 10-units, 100-units, and 100-units with wind energy test systems.

Forget Me Not: Incidental Findings on Breast MRI.

With the growing utilization and expanding role of breast MRI, breast imaging radiologists may encounter an increasing number of incidental findings beyond the breast and axilla. Breast MRI encompasses a large area of anatomic coverage extending from the lower neck to the upper abdomen. While most incidental findings on breast MRI are benign, identifying metastatic disease can have a substantial impact on staging, prognosis, and treatment. Breast imaging radiologists should be familiar with common sites, MRI features, and breast cancer subtypes associated with metastatic disease to assist in differentiating malignant from benign findings. Furthermore, detection of malignancies of nonbreast origin as well as nonmalignant, but clinically relevant, incidental findings can significantly impact clinical management and patient outcomes. Breast imaging radiologists should consistently follow a comprehensive search pattern and employ techniques to improve the detection of these important incidental findings.

Lissajous curves as aerial search patterns

Manned and unmanned systems are prevalent in a wide range of aerial searching applications. For aircraft whose trajectory is not or cannot be planned on-the-fly, optimal deterministic search pattern generation is a critical area of research. Lissajous curves have recently caught attention as excellent candidates for all kinds of aerial search applications, but little fundamental research has been done to understand how best to design Lissajous pattern (LP)s for this use. This paper examines the optimization of these search patterns from analytical, numerical, and data-driven perspectives to establish the state of the field in Lissajous curves for aerial search. From an analytical perspective, it was found that the average expected distance between a Lissajous searcher and a random target on a unit square approaches 0.586 as search time increases. Furthermore, an analytical approximation for the average searcher speed was found to guarantee error of no more than 22.1%. Important outcomes from the numerical optimization of Lissajous search patterns include the development of an intuitive evaluation criterion and the conclusion that irrational frequency ratios near 0.8 typically yield highest performance. Finally, while a robust predictive model for fast pattern optimization is yet out of reach, initial results indicate that such an approach shows promise.

Spherical search algorithm with memory-guided population stage-wise control for bound-constrained global optimization problems

The recently proposed Spherical Search (SS) algorithm replaces the traditional square search pattern with a spherical boundary to provide position-diverse solutions. The algorithm balances its exploration and exploitation performance by utilizing 2 exploration and exploitation sub-populations of equal size. SS has been proven to be highly competitive. However, we observed that when it is used to solve a variety of problems as well as during different searching stages, the fixed sub-population size limits its adaptability and flexibility for achieving continuous exploitation–exploration balance. The balance potential of two operators with distinct characteristics is underdeveloped. As a result, SS and its advanced variants are prone to still easily falling into local optima and lacks certain performance advantages over peer algorithms. In this paper, we further develop SS and propose a memory-guided population stage-wise control strategy based SS, called SSM. By our proposed memory-guided stage-wise evaluation mechanism, SS evaluates the exploitation–exploration balance extent in real time and thus adaptively optimizes and predicts better resource allocation ratio values between its 2 sub-populations and thus achieves significant performance advantages over peer algorithms. The experiments are conducted on 120 benchmark functions and 22 real-world problems, and the results show that SSM significantly outperforms other 13 state-of-the-art evolutionary algorithms. Additionally, we conduct analyses based on method characteristics, convergence process, solution quality robustness testing, population diversity, exploitation and exploration balance, and computational complexity.

Semantic mask-based two-step approach: a general framework for X-ray diffraction peak search in high-throughput molecular sieve synthetic system

X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput capacity makes it difficult to analyze timely. While the kernel step of XRD analysis is to search peaks, an automatic way for peak search is needed. Thus, we proposed a novel semantic mask-based two-step framework for peak search in XRD patterns: (1) mask generation, we proposed a multi-resolution net (MRN) to classify the data points of XRD patterns into binary masks (peak/background). (2) Peak search, based on the generated masks, the background points are used to fit an n-order polynomial background curve and estimate the random noises in XRD patterns. Then we proposed three rules named mask, shape, and intensity to screening peaks from initial peak candidates generated by maximum search. Besides, a voting strategy is proposed in peak screening to obtain a precise peak search result. Experiments show that the proposed MRN achieves the state-of-the-art performance compared with other semantic segmentation methods and the proposed peak search method performs better than Jade when using f1 score as the evaluation index.

Dynamin1 long- and short-tail isoforms exploit distinct recruitment and spatial patterns to form endocytic nanoclusters

Endocytosis requires a coordinated framework of molecular interactions that ultimately lead to the fission of nascent endocytic structures. How cytosolic proteins such as dynamin concentrate at discrete sites that are sparsely distributed across the plasma membrane remains poorly understood. Two dynamin-1 major splice variants differ by the length of their C-terminal proline-rich region (short-tail and long-tail). Using sptPALM in PC12 cells, neurons and MEF cells, we demonstrate that short-tail dynamin-1 isoforms ab and bb display an activity-dependent recruitment to the membrane, promptly followed by their concentration into nanoclusters. These nanoclusters are sensitive to both Calcineurin and dynamin GTPase inhibitors, and are larger, denser, and more numerous than that of long-tail isoform aa. Spatiotemporal modelling confirms that dynamin-1 isoforms perform distinct search patterns and undergo dimensional reduction to generate endocytic nanoclusters, with short-tail isoforms more robustly exploiting lateral trapping in the generation of nanoclusters compared to the long-tail isoform.

Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study

Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study

Divergent and Convergent Creativity Are Different Kinds of Foraging

According to accounts of neural reuse and embodied cognition, higher-level cognitive abilities recycle evolutionarily ancient mechanisms for perception and action. Here, building on these accounts, we investigate whether creativity builds on our capacity to forage in space (“creativity as strategic foraging”). We report systematic connections between specific forms of creative thinking—divergent and convergent—and corresponding strategies for searching in space. U.S. American adults completed two tasks designed to measure creativity. Before each creativity trial, participants completed an unrelated search of a city map. Between subjects, we manipulated the search pattern, with some participants seeking multiple, dispersed spatial locations and others repeatedly converging on the same location. Participants who searched divergently in space were better at divergent thinking but worse at convergent thinking; this pattern reversed for participants who had converged repeatedly on a single location. These results demonstrate a targeted link between foraging and creativity, thus advancing our understanding of the origins and mechanisms of high-level cognition.

Seasonal Trends in Hidradenitis Suppurativa: Data Analysis of the United States and Canada Google Search Patterns.

Seasonal Trends in Hidradenitis Suppurativa: Data Analysis of the United States and Canada Google Search Patterns.

Virtual reality gameplay classification illustrates the multidimensionality of visuospatial neglect.

Brain injuries can significantly impact mental processes and lead to hidden disabilities not easily detectable. Traditional methods for assessing these impacts are imprecise, leading to unreliable prevalence estimates and treatments with uncertain effectiveness. Immersive virtual reality has shown promise for assessment, but its use as a standalone tool is rare. Our research focused on developing and validating a standalone immersive virtual reality classification system for unilateral spatial neglect, a condition common following brain injury characterized by inattention to one side of space. Our study involved 51 brain injury inpatients and 30 controls, all engaging with 'The Attention Atlas', an immersive virtual reality game for testing visual search skills. Our classification system aimed to identify patients with neglect, 'minor atypicality' (indicative of inattention not consistent enough to be labelled as neglect) or non-neglect. This categorization was based on a simple mathematical definition, utilizing gameplay to describe spatial orientation (to the left or right side) and attentional challenge (indicative of search inefficiency). These metrics were benchmarked against a normative model to detect atypical visual search, which refers to gameplay beyond the usual bounds. The combination of neglected side, orientation and challenge factors was used to categorize neglect. We discovered a strong correlation between atypical visual search patterns and neglect risk factors, such as middle cerebral artery stroke, parietal injuries and existing neglect diagnoses (Poisson regression incidence rate ratio = 7.18, 95% confidence interval = 4.41-11.90). In our study, immersive virtual reality-identified neglect in one-fourth of the patients (n = 13, 25.5%), minor atypicality in 17.6% (n = 9) and non-neglect in the majority, 56.9% (n = 29). This contrasts with standard assessments, which detected neglect in 17.6% (n = 9) of cases and had no intermediate category. Our analysis determined six categories of neglect, the most common being left hemispace neglect with above-median orientation and challenge scores. Traditional assessments were not significantly more accurate (accuracy = 84.3%, P = 0.06) than a blanket assumption of non-neglect. Traditional assessments were also relatively insensitive in detecting immersive virtual reality-identified neglect (53.8%), particularly in less severe cases and those involving right-side inattention. Our findings underline the effectiveness of immersive virtual reality in revealing various dimensions of neglect, surpassing traditional methods in sensitivity and detail and operating independently from them. To integrate immersive virtual reality into real-world clinical settings, collaboration with healthcare professionals, patients and other stakeholders is crucial to ensure practical applicability and accessibility.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

Carcass traits and morphometry, typification of the Longissimus dorsi muscle and non-carcass components of hair lambs: can biscuit bran completely replace corn? A machine learning approach.

Biscuit bran (BB) is a co-product with worldwide distribution, with Brazil as the second largest cookie producer in the world with 1,157,051 tons. We evaluate the impact of completely replacing corn with BB on the characteristics and morphometry of carcass of purebred and crossbred Morada Nova lambs using machine learning techniques as an auxiliary method. Twenty male lambs from two genetic groups (GG) were used: purebred red-coated Morada Nova (MNR) and crossbred MNR × white-coated Morada Nova (MNF1). Supervised and unsupervised machine learning techniques were used. No interaction (P > 0.05) was observed between diets (D) and genetic groups (GG) and no simple isolated effect was observed for carcass characteristics, qualitative-quantitative typification of the Longissimus dorsi muscle, weight of non-carcass components, weight and yield of commercial cuts and carcass morphometric measurements. The formation of two horizontal clusters was verified: (i) crossed lambs with corn and BB and (ii) purebred lambs fed corn and BB. Vertically, three clusters were formed based on carcass and meat characteristics of native lambs: (i) thermal insulation, body capacity, true yield, and commercial cuts; (ii) choice, performance, physical carcass traits, and palatability; and (iii) yield cuts and non-carcass components. The heatmap also allowed us to observe that pure MN lambs had a greater body capacity when fed BB, while those fed corn showed superiority in commercial cuts, true yields, and non-carcass components. Crossbred lambs, regardless of diet, showed a greater association of physical characteristics of the carcass, performance, palatability, and less noble cuts. Crossbred lambs, regardless of diet, showed a greater association of physical characteristics of the carcass, performance, palatability, and less noble cuts. BB can be considered an alternative energy source in total replacement of corn. Integrating of machine learning techniques is a useful statistical tool for studies with large numbers of variables, especially when it comes to analyzing complex data with multiple effects in the search for data patterns and insights in decision-making on the farm.

Implementation and testing of a 100 mK ADR backed by a 4He/3He sorption fridge for CMB-S4 project

CMB-S4 is a ground-based experiment that will use large-format bolometer arrays to map Cosmic Microwave Background (CMB) polarization with unprecedented sensitivity, including the search for a B-mode polarization pattern associated with cosmic inflation. The focal planes on which the superconducting detectors are mounted weigh about 20 kg each and must be cooled at 100 mK. To reach this temperature for a first receiver fielding prototype hardware, we have developed an Adiabatic Demagnetization Refrigerator (ADR) used in conjunction with an existing 4He/3He sorption refrigerator.The ADR unit described here is based on a Chromium Potassium Alum (CPA) salt pill. It is backed by a 3-stage (4He/3He/3He) sorption fridge, coupled through a 3He gas-gap heat switch. This heat switch is connected to a 3He stage of the sorption fridge to dissipate the heat generated by magnetization. The ADR is cycled with a 1.3 T magnetic field. The parasitic field at the detectors’ location during observations is kept below 2.5 µT thanks to a ferromagnetic shield. The pill is supported by Kevlar lines, which are doubly intercepted by the 4He and 3He stages of the sorption fridge to reduce conductive heat losses. With a total recycling time of 12 hours, this cryogenic assembly provides 2 µW of cooling power during 48 hours at 100 mK. The ADR has been designed by CEA/DSBT in France and is now implemented in a cryostat at Caltech.

What Canadians search about asthma: A 10-year Google Trends study on asthma and related topics

BackgroundGoogle Trends (GTr) is now becoming an important tool in epidemiological studies to assess the trend of online searches by people of any geographical location. While previous studies assessed trends in searches for some allergic conditions, very few studies reported search patterns for asthma and asthma-related topics. MethodsWe took monthly Canadian GTr data for asthma-related keywords such as ‘asthma’, ‘shortness of breath’, ‘wheeze’, ‘asthma emergency’, ‘asthma hospital’, and ‘asthma medication’ between January 1, 2014, and December 31, 2023. We assessed the trend of yearly relative search volume (RSV) for the past 10 years. We also assessed the trend by influenza and wildfire seasons. ResultsWe observed a marginal increase in searches for some asthma-related topics such as ‘asthma’ ‘shortness of breath’, and ‘wheeze’ in Canada between 2014 and 2023 but not other topics such as ‘asthma emergency’, ‘asthma hospital’, and ‘asthma medication’. The pattern of searches for ‘asthma’, ‘shortness of breath’, and ‘wheeze’ did not change by influenza of wildfire seasons across the years. ConclusionOur study is the first to demonstrate searches for asthma and related topics by Canadians in the past 10 years and indicates GTr as an emerging tool in epidemiology that can be used in planning the implementation of public health policies.

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.

Research on Settlement and Section Optimization of Cemented Sand and Gravel (CSG) Dam Based on BP Neural Network

In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R2 reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams.

Fault detection in analog electronic circuits using fuzzy inference systems and particle swarm optimization

Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study.