Pattern Search Research Articles

Pandemic, sanctions and anxiety in Russia’s regions: Business expectations nowcasting

The study develops a methodology of business expectations index nowcasting with testing on data for the Russian economy as a whole and its regions. This methodology differs from the existing solutions in that it introduces a Bayesian averaging approach to define a set of search patterns for nowcasting and solves the issue of aggregation of time series by individual queries. The developed indices have shown a high level of adequacy, serving as effective tools to reflect shock events in the country’s economic and political landscape and also as predictors of fluctuations in survey­based expectation indices. The application of the presented methodology has revealed the factors that affect the volatility of business expectations indices depending on the level of development and sectoral specialization of Russian regions. In particular, financial and economic centers of highly developed regions and developed regions with diversified economy show high volatility, while business expectations indices in less developed agrarian and commodity regions show low volatility. These results can be useful for economic policy decisions and are of interest to researchers concerned with economic stability and forecasting.

A predictive modelling strategy for warpage and shrinkage defects in plastic injection molding using fuzzy logic and pattern search optimization

A predictive modelling strategy for warpage and shrinkage defects in plastic injection molding using fuzzy logic and pattern search optimization

Bio-Inspired Jumping Spider Optimization for Controller Tuning/ Parameter Estimation of an Uncertain Aerodynamic MIMO System

The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS), Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional, Integral and Derivative (PID) controllers. With the k,p,i controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers’ variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers, marginally in relation to the others (optimization methods).

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.

Automated Equations of State Tuning Workflow Using Global Optimization and Physical Constraints

A computational model that can accurately describe the thermodynamics of a hydrocarbon system and its properties under various conditions is a prerequisite for running reservoir and pipeline simulations. Cubic Equations of State (EoS) are mathematical tools used to model the phase and volumetric behavior of reservoir fluids when compositional effects need to be considered. To anticipate uncertainty and enhance the quality of their predictions, EoS models must be adjusted to adequately match the available lab-measured PVT values. This task is challenging given that there are many potential tuning parameters, thus leading to various tuning results of questionable validity. In this paper, we present an automated EoS tuning workflow that employs a Generalized Pattern Search (GPS) optimizer for efficient tuning of a cubic EoS model. Specifically, we focus on the Peng–Robinson (PR) model, which is the oil and gas industry standard, to accurately capture the behavior of diverse multicomponent, complex hydrocarbon mixtures encountered in subsurface reservoirs. This approach surpasses the limitations of conventional gradient-based (GB) methods, which are susceptible to getting trapped in local optima. The proposed technique also allows physical constraints to be imposed on the optimization procedure. A gas condensate and an H2S-rich oil were used to demonstrate the effectiveness of the GPS algorithm in finding an optimized solution for high-dimensional search spaces, and its superiority over conventional gradient-based optimization was confirmed by automatically tracking globally optimal and physically sound solutions.

A spherical evolution algorithm with two-stage search for global optimization and real-world problems

This paper proposes a spherical evolution algorithm with two-stage search. Spherical search and hypercube search are combined to achieve individuals' evolution. A self-adaptive Gaussian scale factor and a variable scale factor are designed to adaptively control individuals' spherical and hypercube search area according to their search situations. Two search stages frequently switch with four search cases to enhance the balance between exploration and exploitation processes. A directed adjacency matrix is devised to analyze the relationship among individuals from the perspective of graph theory. Experiments compare the proposed algorithm with five algorithms with distinctive search patterns on twenty nine CEC2017 benchmark functions. The diversity analysis and graph theory analysis show the good population diversity and effective information spreading of the proposed algorithm. Twenty two real-world problems evaluate the practicality and optimization ability of the proposed algorithm. Finally, the computational time complexity demonstrates that the proposed algorithm is more efficient than the original spherical evolution algorithm.

A fast spatial high utility co-location pattern mining approach based on branch-and-depth-extension

Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. High Utility Co-location Pattern Mining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like pruning technique does not apply. To address this problem, we firstly suggest two novel pruning strategies to trim the pattern search space. Then, a series of optimizing techniques are presented to speed up the pattern utility ratio calculation of each candidate. Based on above techniques, a fast HUCPM algorithm is proposed, which searches for high utility co-locations involved in each pattern branch via a depth-extending manner and equips with a heuristic strategy to enhance the effect of pruning techniques. Moreover, we theoretically prove the completeness and correctness of the proposed algorithm, and discuss its algorithmic complexity. On multiple spatial datasets, we conduct substantial experiments to reveal the superiority of our algorithm in efficiency and scalability, as well as the effectiveness of the proposed technique. Particularly, the proposed algorithm in this paper runs faster than other baselines for several times to several orders of magnitude.

Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field.

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides.

The marine sponge-derived fungus Stachylidium bicolor 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by the presence of the unusual amino acid N-methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V1A receptor, a member of the G protein-coupled receptor superfamily.

Expert Performance in Action Anticipation: Visual Search Behavior in Volleyball Spiking Defense from Different Viewing Perspectives.

Volleyball spiking requires defenders to possess exceptional anticipatory skills. However, most volleyball defense video eye-tracking studies have used fixed or off-court perspectives, failing to replicate real-world environments. This study explored different visual search behaviors between elite and novice volleyball players from various viewing perspectives using video eye tracking. We examined spiking anticipation in 14 competitive elite, 13 semi-elite, and 11 novice players. We captured spiking videos from three on-court perspectives using GoPro cameras mounted on the defenders' heads, closely replicating real game scenarios. For comparison, we recorded baseline videos using a fixed camera. The present study revealed that competitive and semi-elite players demonstrated higher accuracy than novices. Competitive elite players used fewer fixations, indicating that their superior performance was related to stable visual search patterns. All participant groups, regardless of skill level, showed similar visual allocation among areas of interest (AOIs). However, notable differences in visual search patterns and AOI allocation were observed between baseline and on-court perspective videos. From the baseline perspective, the participants primarily utilized global perception and peripheral vision, focusing more on the setter zone or the spiker's trunk. Conversely, from the on-court perspective, they employed more fixations, focusing more intensely on the spiker's detailed movements.

Embodied planning in climbing: how pre-planning informs motor execution.

The aim of the study is to address embodied planning in climbing. Embodied planning was conceptualized as the interaction between perceptual-cognitive pre-planning and motor execution. In an experimental study, 18 climbers were asked to pre-plan a climbing route and to perform the route afterward. During pre-planning, the visual search pattern of climbers was captured using a portable eye tracker. After previewing, they were invited to climb the wall. Results revealed that holds looked at during pre-planning were used twice as much during route execution than those not looked at. The duration of fixations was longer for holds used than those not used during route execution. The experience of climbers (training years) correlated with visual strategies and climbing performance, such that experienced participants climbed faster and fixated at the holds not used for a shorter time. The visual behaviors of climbers were influenced by their past sensorimotor experiences during route previewing, impacting subsequent climbing performance.

DIY google trends indicators in social sciences: A methodological note

Various branches of the social sciences often require high-frequency measures of collective behavior. Examining peoples' Google search patterns enables uncovering otherwise hard-to-measure social trends or constructing different leading indicators. Google Trends (GT) data presents a unique and rich dataset that enables a deeper look at the popularity of peoples’ search interests. This paper delves into the main empirical approaches of constructing GT indicators, conducts a thorough methodological review, and elucidates the nuances and potentials of GT data. Specifically, the paper furnishes a step-by-step methodological guide for constructing GT indicators: from data pre-treatment to composite estimation using an array of machine learning techniques (principal component analysis, XGBoost, and dynamic factor models). We scrutinize the prevalent construction strategies of GT indicators, compare the use of keyword-based vs. category-based estimations, single indicators vs. composite indicators, and evaluate the significance of various data transformations. Illustrating the utility of our methodological framework through a case study, we create GT indicators for US retail trade and examine their forecasting accuracy in comparison to a benchmark autoregressive model. Our findings reveal that XGBoost yields the optimal test sample fit among composite indicators, while category-specific indicators like Shopping and Retail Trade exhibit worse predictive accuracy. As an additional contribution, we provide a detailed and user-friendly R code for replicating our analysis.

Modeling and optimization of a passive building HVAC system based on improved PSO and H-J Algorithm

Passive buildings employ the principles of active optimization and passive priority to demonstrate their energy-saving, comfortable, and cost-effective qualities, thereby addressing energy consumption in an efficient manner. However, it cannot be ignored that the Heating Ventilation Air Conditioning (HVAC) system is still the largest energy consumer, especially when it is necessary to maintain the comfort of the indoor environment. Consequently, the control and optimization of the HVAC system is a research hotspot and a challenge in the field of building energy conservation, as it is the key to achieving ultra-low energy consumption operation in passive buildings while ensuring the thermal comfort of the indoor environment. This paper presents a comprehensive modeling, control, and optimization framework for the efficient operation of HVAC systems in a passive building located in Jinan, China. Firstly, the HVAC system is modeled using the transient system simulation tool (TRNSYS), GenOpt, Java, and other software. Secondly, the HVAC system’s year-round operation is simulated using specific configurations. In order to enhance the energy-saving effect, a strategy of increasing heat storage and return air ducts was proposed, and a significant energy-saving effect (approximately 17.31%) was realized. Lastly, the data communication interface with GenOpt is established utilizing the TRNOPT module, a method for actively optimizing the refrigerant temperature is proposed, and recalculation and optimization are carried out utilizing the swarm intelligence algorithm and the pattern search algorithm (Hooker-Jeeves algorithm, H-J algorithm). The final optimization results demonstrate a 19% reduction in energy consumption relative to the baseline heat recovery model, validating the applicability of the optimization method under this strategy. The control and optimization method of the HVAC system proposed in this paper can further exploit the energy-saving potential of the passive building HVAC system and will contribute to the advancement of building energy conservation.

Comparative Analysis of Metaheuristic Optimization Methods for Trajectory Generation of Automated Guided Vehicles

This paper presents a comparative analysis of several metaheuristic optimization methods for generating trajectories of automated guided vehicles, which commonly operate in industrial environments. The goal is to address the challenge of efficient path planning for mobile robots, taking into account the specific capabilities and mobility limitations inherent to automated guided vehicles. To do this, three optimization techniques are compared: genetic algorithms, particle swarm optimization and pattern search. The findings of this study reveal the different efficiency of these trajectory optimization approaches. This comprehensive research shows the strengths and weaknesses of various optimization methods and offers valuable information for optimizing the trajectories of industrial vehicles using geometric occupancy maps.

WeStore or AppStore: How Customers Shop Differently in Mobile Apps vs. Social Commerce

In the dynamic e-commerce environment, social commerce has emerged as a revolutionary force, transforming how consumers interact and transact online. This paper investigates the differences in customers’ search and purchase patterns between a prominent online retailer’s burgeoning social commerce channel, the WeChat mini-program, and its native mobile app. We analyze the customers’ entire journey through a sequential search model that encapsulates decisions from channel selection to product search, search termination, and the final purchase. This study contributes to the search model literature by being the first to estimate both fixed and marginal search costs in a sequential search model in an omnichannel retail environment. We calculate fixed search costs, marginal search costs, and preferences for each channel, revealing differences in customers’ behaviors across channels. Our analysis shows that customers’ fixed search costs are higher, but marginal costs are lower on WeChat channel compared to the App channel. Also, customer characteristics like historical spending levels and search timing influence their search costs. From these insights, we suggest strategies tailored to each channel capitalizing on the differences in customers’ search costs. The first strategy encourages search initiation by lowering fixed search costs through peer-to-peer link sharing in the WeChat channel. The second strategy aims to minimize marginal search costs using search-triggering coupons in the App channel. Implementing these strategies significantly boosts conversion rates and profits for the online retailer. This research is one of the first to explore the differences between traditional retail channels and emerging social commerce channels.

Predicting horizontal gene transfers with perfect transfer networks

BackgroundHorizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity.Our contributionsWe introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.

Visual search patterns during exploration of naturalistic scenes are driven by saliency cues in individuals with cerebral visual impairment

We investigated the relative influence of image salience and image semantics during the visual search of naturalistic scenes, comparing performance in individuals with cerebral visual impairment (CVI) and controls with neurotypical development. Participants searched for a prompted target presented as either an image or text cue. Success rate and reaction time were collected, and gaze behavior was recorded with an eye tracker. A receiver operating characteristic (ROC) analysis compared the distribution of individual gaze landings based on predictions of image salience (using Graph-Based Visual Saliency) and image semantics (using Global Vectors for Word Representations combined with Linguistic Analysis of Semantic Salience) models. CVI participants were less likely and were slower in finding the target. Their visual search behavior was also associated with a larger visual search area and greater number of fixations. ROC scores were also lower in CVI compared to controls for both model predictions. Furthermore, search strategies in the CVI group were not affected by cue type, although search times and accuracy showed a significant correlation with verbal IQ scores for text-cued searches. These results suggest that visual search patterns in CVI are driven mainly by image salience and provide further characterization of higher-order processing deficits observed in this population.

(180) Unveiling Consumer Interest and Regional Disparities: Comparative Analysis of Online Search Trends for Penile Aesthetic Procedures

Abstract Introduction In recent years, the introduction of Penuma, an implantable silicone penile sleeve, offers urologists a tool in the cosmetic enhancement of the penis, specifically for individuals who are concerned regarding the perceived size of their penis, with buried penis, and with mild to moderate penile curvature. With the increasingly widespread adoption of Penuma, it is crucial to understand how public interest in Penuma compares to other related interventions such as hyaluronic acid injections. Objective To assess the online search interest over time for Penuma and other penile enhancement terms on a national and regional scale. Methods Google Trends data from 2018 to March 2021 were utilized to analyze online traffic for the search terms “Penuma”, “penile implant”, “penile girth”, “HLA injection”, and “penis injection”. Trendlines were generated to assess the changes in search interest over time. The highest search interest locations were identified for each term. Results While online searches for Penuma increased between 2018 through 2021, interest in Penuma has since been in decline (Figure 1). While interest in penile implants remained higher than Penuma throughout the study period, Penuma remained a more popular term compared to other cosmetic treatments including penile injections and HLA injections. In terms of regional disparities, the highest search interest for Penuma was observed in San Antonio, TX, while penile girth searches were highest in OKC, OK. Penile implant searches were prominent in Mobile, AL, and Birmingham, AL. Notably, HA injection searches peaked in New York, NY, and penis injection searches were most prevalent in Los Angeles, CA. Conclusions This study highlights the dynamics of online search interest of Penuma in relation to other penile enhancement terms. While general interest in penile implants and penile girth surpasses that of Penuma, regional disparities in search patterns are evident. The findings emphasize the need for further research to understand the factors driving these disparities and the implications for consumer decision-making in different regions. Such insights can assist healthcare professionals and policymakers in tailoring educational initiatives and resource allocation to meet the diverse needs and preferences of individuals seeking penile enhancement procedures. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Advisory board and speaker for Coloplast; consultant for Cynosure; advisory board and speaker for Halozyme; intellectual property with Masimo; advisory board for Promescent; consultant for Sprout; advisory board for Xialla.

What is a mathematician doing…in a chemistry class?

The way of thinking of mathematicians and chemists in their respective disciplines seems to have very different levels of abstractions. While the firsts are involved in the most abstract of all sciences, the seconds are engaged in a practical, mainly experimental discipline. Therefore, it is surprising that many luminaries of the mathematics universe have studied chemistry as their main subject. Others have started studying chemistry before swapping to mathematics or have declared some admiration and even love for this discipline. Here I reveal some of these mathematicians who were involved in chemistry from a biographical perspective. Then, I analyze what these remarkable mathematicians and statisticians could have learned while studying chemical subjects. I found analogies between code-breaking and molecular structure elucidation, inspiration for statistics in quantitative analytical chemistry, and on the role of topology in the study of some organic molecules. I also analyze some parallelisms between the way of thinking of organic chemists and mathematicians in terms of the use of backward analysis, search for patterns, and use of pictures in their respective researches.

Incorporation of Eye Tracking and Gaze Feedback to Characterize and Improve Radiologist Search Patterns of Chest X-Rays: A Randomized Controlled Clinical Trial

Diagnostic errors in radiology often result from incomplete visual assessments attributed to insufficient training in search patterns. Radiologists lack consistent feedback, relying on ad-hoc strategies, leading to suboptimal patterns and potential false negatives. This study used eye tracking to analyze search patterns, quantify performance metrics, and evaluate the impact of an automated continuous improvement feedback-driven framework on detection accuracy. In a controlled trial with ten residents, the intervention group (received feedback) demonstrated a significant 38.9% absolute improvement in detecting suspicious pulmonary nodules compared to the control group (5.6%, p=0.006). While improvements were more rapid over sessions (p=0.0001), other metrics showed no significant changes. The findings highlight the potential of automated feedback systems to enhance radiologist accuracy and reduce errors, emphasizing the need for further research and broader implementation in radiology training.