Successful Search Research Articles

New AI Tool Shows the Power of Successive Search

New AI Tool Shows the Power of Successive Search

Application of the Gray Wolf Optimization Algorithm and Neural Networks for Solving Discrete Problems

Relevance. In recent decades, metaheuristic optimization methods have become popular for solving complex problems that require searching for global extrema. Algorithms such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), as well as more modern approaches such as cat pack optimization (CSO) and gray wolf pack optimization (GWO) demonstrate high efficiency, but their application is often limited by the conditions of continuity and differentiability of the objective functions. This is a challenge when solving problems with discrete data, where such requirements are not met. In this context, the search for methods that allow adapting metaheuristic algorithms to work with discrete functions is of particular relevance.Aim. The study is aimed at testing the hypothesis about the possibility of using a neural network trained on a limited set of discrete data as an approximation of a function sufficient for the correct execution of the GWO algorithm when searching for a global minimum. The implementation of this hypothesis can significantly expand the scope of GWO, making it available for a wider range of problems where functions are defined on discrete sets.Methods. The study is based on the analysis of existing approaches and experimental verification of the hypothesis on two test functions: a linear function and a Booth function, which are widely used as standards for evaluating the performance of optimization algorithms. Numerical experiments were conducted using neural networks as an approximating model to obtain the results. Solution. During the experiments, an analysis of the applicability of neural networks for approximating discrete functions was carried out, which showed the success of this approach. It was found that neural networks can approximate discrete functions with high accuracy, creating conditions for a successful search for a global minimum using the GWO algorithm.Novelty. For the first time, a hypothesis was proposed and tested on the use of neural networks for approximating objective functions in metaheuristic optimization problems on discrete data. This direction has not previously received due coverage in the scientific literature, which adds significance to the obtained results and confirms the effectiveness of the proposed approach.Practical significance. The results of the study open up new prospects for the application of algorithms such as GWO in optimization problems based on discrete data, expanding the capabilities of metaheuristic methods and facilitating their implementation in a wider class of applied problems, including problems where the use of other methods is limited.

Dynamic switched crowding-based multi-objective particle swarm optimization algorithm for solving multi-objective AC-DC optimal power flow problem

In this paper, the multi-objective AC-DC optimal power flow (MO/AC-DC OPF) problem in the presence of renewable energy sources (RESs), flexible AC transmission system (FACTS) devices and multi-terminal direct current (MTDC) systems is introduced for the first time. Conflicting objective functions and the high complexity of the objective and constraint spaces are the main challenges in finding optimal solutions for MO/AC-DC OPF. To overcome these challenges, twelve different versions of the dynamic switched crowding-based multi-objective particle swarm optimization (DSC-MOPSO) algorithm are introduced in this paper. Studies on multimodal optimization problems have shown that all DSC-MOPSO versions have better performance metrics than the MOPSO algorithm. Using the developed DSC-MOPSO and its strong competitors, the Pareto-optimal solution sets of the MO/AC-DC OPF problem are investigated. In these investigations, the performances of the algorithms are tested for the minimization of dual and triple objectives such as fuel cost, voltage level deviation, emission and power loss in a modified IEEE 30-bus power grid. According to the simulation results, the proposed DSC-MOPSO achieved an improvement in fuel cost between 0.02 % and 5.05 % and a reduction in active power loss between 0.44 % and 30.74 % compared to its competitors. The Hypervolume (HV) performance metric was used to evaluate the Pareto-front coverage performance of DSC-MOPSO and other optimizers. The results from nine case studies of the MO/AC-DC OPF were statistically analyzed by the Friedman test according to the 1/HV metric. According to the Friedman test results, the rankings of DSC-MOPSO and MOMA are 1.984 and 3.079, respectively, ranking first and second among all competitors. Finally, in this study, feasible solutions for MO/AC-DC OPF problem are identified for the first time and the stability of competitive algorithms in finding these solutions is analyzed for the first time. The success rates and search times of DSC-MOPSO and MOMA algorithms in finding feasible solutions for MO/AC-DC OPF are 91.01 % (30.641 s) and 82.01 % (46.038 s), respectively.

Job search intensity and wage rigidity in business cycles

Search intensity, as well as the aggregate economic condition, is a crucial factor that determines unemployed workers’ success in job searches. Their search intensity is predicted to be procyclical in standard search and matching models. However, many empirical studies show that search intensity is countercyclical. This study examines the job search behavior of unemployed workers over business cycles using a search and matching model that incorporates wage rigidity and a generalized matching function. Unlike previous studies, the proposed model can generate both procyclical and countercyclical search intensities by introducing wage rigidity. This study calibrates the model to the U.S. economy and provides various impulse response analyses. The numerical exercises show that the model successfully and simultaneously reproduces countercyclical search efforts and sizable labor market fluctuations.

Post-Processing Maritime Wind Forecasts from the European Centre for Medium-Range Weather Forecasts around the Korean Peninsula Using Support Vector Regression and Principal Component Analysis

Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model.

Where are they now? An analysis of integrated cardiothoracic surgery residency applicants

Changes in cardiothoracic surgery (CTS) workforce trends have affected training paradigms to include the establishment of integrated six-year CTS residency (I6) programs. This study aimed to determine commitment of selected I6 program applicants to the specialty.Internal archives at a single institution were accessed to identify applicants interviewed for an I6 position from 2014 to 2016. A systematic internet search of publicly available information was performed to identify the trainee's current specialty. Descriptive analysis was performed.Forty-seven applicants were identified. Successful search results were achieved for 97.9 % of the applicants. One applicant was excluded, resulting in a cohort of 45. 48.9 % of the applicants successfully matched into I6 programs. Of the 23 who did not match into an I6 program, 91.3 % began a General Surgery (GS) residency. When looking solely at the GS trainees, 71.4 % sought and matched into a traditional or 4/3 CTS residency. In total, 77.8 % of the cohort are currently pursuing careers in cardiothoracic surgery.The study identified a strong continued interest in the field of CT Surgery among those interviewed for integrated residency. The methodology used in this study provided an effective way to follow career choice of applicants interviewed and could be applied by additional programs to further elucidate career choice and levels of commitment.

UAV path planning algorithm based on Deep Q-Learning to search for a floating lost target in the ocean

In the context of real world application, Search and Rescue Missions on the ocean surface remain a complex task due to the large-scale area and the forces of the ocean currents, spreading lost targets and debris in an unpredictable way. In this work, we present a Path Planning Approach to search for a lost target on ocean surface using a swarm of UAVs. The combination of GlobCurrent dataset and a Lagrangian simulator is used to determine where the particles are moved by the ocean currents forces while Deep Q-learning algorithm is applied to learn from their dynamics. The evaluation results of the trained models show that our search strategy is effective and efficient. Over a total search area (red Sea zone), surface of 453422 Km2, we have shown that our strategy Search Success Rate is 98.61%, the maximum Search Time to detection is 15 days and the average Search Time to detection is almost 15 h.

Living together, feeding apart: the comparative foraging ecology of two African flycatcher species

This study examined how habitat and resource conditions influence the foraging behaviour and ecology of the Pale (Pallid) Flycatcher Agricola pallidus and Marico Flycatcher Bradornis mariquensis, two perch-and-pounce insectivores that swoop on prey from elevated perches. These species were studied in the early 1980s at the Nylsvley Nature Reserve in Limpopo Province, South Africa, where Marico Flycatchers occupied fine-leafed, spinescent Vachellia (Acacia) tortilis-dominated woodland, while Pale Flycatchers were confined to Burkea africana-dominated mesophyll woodland. These two woodland types correspond to two contrasting savanna types across Africa more broadly: arid-eutrophic, fine-leafed savannas and mesic/moist-dystrophic, broad-leafed woodlands. Their juxtaposition at the Nylsvley Reserve provided an opportunity to explore the factors affecting the habitat preferences and foraging behaviour of these two species. Acacia woodlands had higher invertebrate abundance, at least on the ground, and lower groundcover than Burkea woodlands in both seasons, although the difference was less during the dry season. Throughout the year, Marico Flycatchers consistently foraged at a faster rate, pouncing more frequently and more successfully on prey than did Pale Flycatchers. They also spent less time searching from each perch and gave up sooner if they found nothing. The average time difference between successful and unsuccessful searches was only a few seconds for both species, however. Presumably because they occupied a less productive woodland, Pale Flycatchers had considerably larger home ranges and occurred at lower densities overall, and so experienced fewer intraspecific and interspecific interactions. In contrast, Marico Flycatchers faced more competition both intra- and interspecifically, especially in the dry season when prey was scarce; they lost most of their conflicts, all with larger birds. Habitat separation between these two flycatcher species is apparently maintained by the differences in habitat structure, resource availability and occasional interspecific interactions, in which Marico Flycatchers dominated Pale Flycatchers.

Visual scanning patterns of a talking face when evaluating phonetic information in a native and non-native language.

When comprehending speech, listeners can use information encoded in visual cues from a face to enhance auditory speech comprehension. For example, prior work has shown that the mouth movements reflect articulatory features of speech segments and durational information, while pitch and speech amplitude are primarily cued by eyebrow and head movements. Little is known about how the visual perception of segmental and prosodic speech information is influenced by linguistic experience. Using eye-tracking, we studied how perceivers' visual scanning of different regions on a talking face predicts accuracy in a task targeting both segmental versus prosodic information, and also asked how this was influenced by language familiarity. Twenty-four native English perceivers heard two audio sentences in either English or Mandarin (an unfamiliar, non-native language), which sometimes differed in segmental or prosodic information (or both). Perceivers then saw a silent video of a talking face, and judged whether that video matched either the first or second audio sentence (or whether both sentences were the same). First, increased looking to the mouth predicted correct responses only for non-native language trials. Second, the start of a successful search for speech information in the mouth area was significantly delayed in non-native versus native trials, but just when there were only prosodic differences in the auditory sentences, and not when there were segmental differences. Third, (in correct trials) the saccade amplitude in native language trials was significantly greater than in non-native trials, indicating more intensely focused fixations in the latter. Taken together, these results suggest that mouth-looking was generally more evident when processing a non-native versus native language in all analyses, but fascinatingly, when measuring perceivers' latency to fixate the mouth, this language effect was largest in trials where only prosodic information was useful for the task.

The R-Process Alliance: Analysis of limited-r stars

In recent years, the $R$-Process Alliance (RPA) has conducted a successful search for stars that are enhanced in elements produced by the rapid neutron-capture ($r$-)process. In particular, the RPA has uncovered a number of stars that are strongly enriched in light $r$-process elements, such as Sr, Y, and Zr. These so-called limited-$r$ stars were investigated to explore the astrophysical production site(s) of these elements. We investigate the possible formation sites for light neutron-capture elements by deriving detailed abundances for neutron-capture elements from high-resolution spectra with a high signal-to-noise ratio of three limited-$r$ stars. We conducted a kinematic analysis and a 1D local thermodynamic equilibrium spectroscopic abundance analysis of three stars. Furthermore, we calculated the lanthanide mass fraction ($ X_ La $) of our stars and of limited-$r$ stars from the literature. We found that the abundance pattern of neutron-capture elements of limited-$r$ stars behaves differently depending on their $ Ba/Eu $ ratios, and we suggest that this should be taken into account in future investigations of their abundances. Furthermore, we found that the $ X_ La $ of limited-$r$ stars is lower than that of the kilonova AT2017gfo. The latter seems to be in the transition zone between limited-$r$ $ X_ La $ and that of $r$-I and $r$-II stars. Finally, we found that unlike $r$-I and $r$-II stars, the current sample of limited-$r$ stars is largely born in the Galaxy and is not accreted.

GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation.

Exploring the potential energy surface (PES) of molecular systems is important for comprehending their complex behaviors, particularly through the identification of various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational resources. Our study introduces the gradient-based navigation (GradNav) algorithm, which accelerates the exploration of the energy surface and enables proper reconstruction of the PES. This algorithm employs a strategy of initiating short simulation runs from updated starting points derived from prior observations to effectively navigate across potential barriers and explore new regions. To evaluate GradNav's performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Müller-type PESs and molecular dynamics simulations of the Fs-peptide protein, these metrics demonstrate GradNav's enhanced ability to escape deep energy wells and its reduced reliance on initial conditions, as denoted by the reduced DWEF values and increased SSIR values, respectively. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

Tourism Development and Urban Landscape Conservation in Rural Areas: Opportunities and Ambivalences in Local Regulations—The Case of Spain

This article analyzes local-level normative regulations aimed at directly or indirectly conserving the urban landscape in rural areas. Using a discursive analysis methodology on regulatory documents being enforced in a series of localities assigned to a tourism promotion program, the evidence suggests that promotional activity retroactively influences the phraseology of these municipal regulations. The results obtained point to the existence of perpetuating historicist approaches within the current regulations, which appear to largely derive from the search for success in the tourism market and the resulting benefits to the local economy.

A Phenomenological Exploration of Individual and University Supports Affecting U.S. International Students’ Job Searches

The present study explores the individual and university supports of U.S. international students during their job searches. Twenty-eight international students (93% Asian, 7% African; 57% female) from a U.S. Midwest public university participated in one-on-one semi-structured interviews in Spring 2021 regarding their U.S. job search experiences. All 28 participants (64% graduate students) were within one year of their graduation date. A transcendental phenomenological approach was employed to conduct a thematic analysis. Lower-order themes that significantly contributed to international students’ successful job searches in the US were organized into individual and institutional-level supports and collated into six key themes: (a) early planning for post-graduation; (b) networking; (c) in-demand majors and degree levels; (d) university-industry connections; (e) personalized department support; and (f) campus career services. Based on these findings, several implications for career development research, theory, and practice are discussed.

Filling-enforced obstructed atomic insulators

Topological band theory has achieved great success in the high-throughput search for topological band structures both in paramagnetic and magnetic crystal materials. However, a significant proportion of materials are topologically trivial insulators at the Fermi level. In this paper, we show that, remarkably, for a subset of the topologically trivial insulators, knowing only their electron number and the Wyckoff positions of the atoms we can separate them into two groups: the obstructed atomic insulator (OAI) and the atomic insulator (AI). The interesting group, the OAI, have a center of charge not localized on the atoms. Using the theory of topological quantum chemistry, in this work we first derive the necessary and sufficient conditions for a topologically trivial insulator to be a filling enforced obstructed atomic insulator (feOAI) in the 1651 Shubnikov space groups. Remarkably, the filling enforced criteria enable the identification of obstructed atomic bands without knowing the representations of the band structures. Hence, no calculations are needed for the filling enforced criteria, although they are needed to obtain the band gaps. With the help of the Topological Quantum Chemistry website, we have performed a high-throughput search for feOAIs and have found that 957 ICSD entries (638 unique materials) are paramagnetic feOAIs, among which 738 (475) materials have an indirect gap. The metallic obstructed surface states of feOAIs are also showcased by several material examples. Published by the American Physical Society 2024

Search engine optimization and its association with readability and accessibility of diabetic retinopathy websites

PurposeThis study investigated whether websites regarding diabetic retinopathy are readable for patients, and adequately designed to be found by search engines.MethodsThe term “diabetic retinopathy” was queried in the Google search engine. Patient-oriented websites from the first 10 pages were categorized by search result page number and website organization type. Metrics of search engine optimization (SEO) and readability were then calculated.ResultsAmong the 71 sites meeting inclusion criteria, informational and organizational sites were best optimized for search engines, and informational sites were the most visited. Better optimization as measured by authority score was correlated with lower Flesch Kincaid Grade Level (r = 0.267, P = 0.024). There was a significant increase in Flesch Kincaid Grade Level with successive search result pages (r = 0.275, P = 0.020). Only 2 sites met the 6th grade reading level AMA recommendation by Flesch Kincaid Grade Level; the average reading level was 10.5. There was no significant difference in readability between website categories.ConclusionWhile the readability of diabetic retinopathy patient information was poor, better readability was correlated to better SEO metrics. While we cannot assess causality, we recommend websites improve their readability, which may increase uptake of their resources.

Multi-robot Source Navigation Method Based on Coordination Graph Monte Carlo Tree Search

To address the dynamic cooperative multi-robot sequential decision problem, we propose a multi-robot source navigation method based on coordination graph Monte Carlo tree search. The Monte Carlo tree search online planning is designed with the algorithm system's structure in mind. We combine the upper confidence bound strategy with the coordination graph, which represents multi-robot cooperative communication, and solve the coordination graph using the max-sum algorithm to select joint actions. This paper proposes a time series prediction model for optimizing multi-robot movement trends using historical data, which is then used to solve the exploration and exploitation problem in Monte Carlo tree search. The experimental analysis and comparison show that our proposed method outperforms the comparison algorithms in terms of source search steps, task execution time, and source search success rate, as well as robustness and efficiency for multi-robot source search.

The Shortage of School Psychology Faculty in the Aftermath of the COVID-19 Pandemic

A shortage of school psychology trainers hinders efforts to increase the supply of school psychologists entering the field, and the effects of the COVID-19 pandemic on employment trends in general have the potential to worsen the shortage of school psychology faculty. The goal of this research brief is to report on the results of a survey designed to understand the factors associated with successful school psychology faculty searches in the 2021–2022 academic year. A survey was distributed to representatives from 86 programs looking for new faculty, with a total of 31 responses received. Overall, 74% of programs searching for new faculty in 2021–2022 were successful, but this overall rate varied greatly based on the type of program and position being searched for, with positions placing a greater emphasis on teaching and university service responsibilities than on research being filled at a much lower rate. Impact Statement The school psychology trainer shortage presents a challenge in helping the field of school psychology to meet personnel shortages. The present survey was designed to better understand the shortage of trainers and the large number of open positions in the 2021–2022 academic year. Understanding the program and position characteristics associated with successful job searches can help to structure future research and policy discussions about the shortage of school psychology trainers.

The effects of question previewing on response accuracy and text processing: An eye-movement study

The present study investigated the impact of manipulating reading strategies (i.e., reading the questions first [QF] or reading the passage first [PF]) during a reading comprehension test where we explored how reading strategy was related to student characteristics (i.e., reading achievement and working memory capacity). Participants' eye movements were monitored as they read 12 passages and answered multiple-choice questions. We examined differences in (a) response accuracy, (b) average total time on words in the text, (c) total task reading time, and (d) time reading text relevant to questions as a function of PF and QF strategies. Analyses were conducted to examine whether findings varied as a function of student characteristics (i.e., reading achievement and working memory capacity) and grade level (Grades 3, 5, and 8). Several interesting findings emerged from our study, including a limited effect of reading strategy use on response accuracy, with only eighth graders demonstrating better accuracy in the QF condition, and several demonstrations of PF leading to more efficient test-taking processes, including (a) longer average total reading times on words in the passage in the PF condition that could be associated with creating a better mental model of the text, (b) often being associated with less total-task time, and (c) being associated with more successful search strategies. Implications for providing teachers and students with strategies are discussed.

Between Kochel and Kalmunz. Early plein airs by Gabriele Munter at the school "Phalanx" by Vasily Kandinsky.

The subject of the study of the proposed article is the early plein-air works of the German expressionist artist Gabriele Munter, later a member of the Blue Horseman art association, a student and friend of Vasily Kandinsky. The authors of the article aim to review and introduce into scientific circulation a number of Munter's sketches created by her during summer trips to the plein air in 1902-1903 to the Alpine towns of Kochel and Kalmunz with the Munich art school "Phalanx", headed by Kandinsky. The article also examines the picturesque plein-air works by Kandinsky, created by him in the same years there, in Bavaria - mainly landscapes and sketches of figures in the landscape. The authors of the study see as their task to trace the influence of Kandinsky's artistic manner and his role as a teacher on the formation of a Munter painter. The research methodology includes formal stylistic, comparative methods of art criticism analysis, as well as a comprehensive historical and biographical approach. The relevance of the study is due to its scientific novelty: the authors of the article for the first time in Russian-language art history literature describe the pre-expressionist works of Munter during the period of joint plein-airs with Kandinsky, trace the formation of her artistic vision and picturesque manner, the influence of Kandinsky on her formation. The development of the impressionistic technique common to Munter and Kandinsky in the early 1900s, according to the authors of the article, is combined with individual searches for expressiveness of form and color. The first experiments of plein-air oil painting in a special "spatula technique" under the guidance of Kandinsky were accompanied by the successful search for Munter in the field of graphics, mastering the technique of printing color woodcuts. The authors of the article come to the conclusion that the formation of the original manner of Munter was equally influenced by both pictorial emancipation through sketching practice in oil, as well as their own experiences in drawing and engraving.

Enhancing Rice Production Prediction: A Comparative Machine Learning Analysis of Climate Variables

This study aims to enhance rice production prediction through a comparative analysis of machine learning models utilizing climate variables. Eight models were assessed on a predetermined dataset, with Support Vector Regression (SVR) emerging as the top performer. Following the identification of significant climate variables influencing rice production, the models underwent evaluation using two hyperparameter approaches: random search and manual tuning. SVR outperformed other models, achieving impressive metrics with MAE 0.180, MSE 0.186, RMSE 0.431, and an exceptionally low MAPE of 0.020. Key factors influencing rice production included productivity and area, along with humidity, rainfall, temperature, wind velocity, and sunshine duration. Favorable conditions for rice output encompassed low humidity, moderate rainfall, increased wind speed, and prolonged sunshine, while rainfall and temperature exhibited minimal impact. The success of random search emphasizes the importance of effective hyperparameter tuning. This research provides valuable insights for enhancing rice production prediction.