Pattern Search Research Articles

How Patients With Cancer Use the Internet to Search for Health Information: Scenario-Based Think-Aloud Study.

Patients with cancer increasingly use the internet to seek health information. However, thus far, research treats web-based health information seeking (WHIS) behavior in a rather dichotomous manner (ie, approaching or avoiding) and fails to capture the dynamic nature and evolving motivations that patients experience when engaging in WHIS throughout their disease trajectory. Insights can be used to support effective patient-provider communication about WHIS and can lead to better designed web-based health platforms. This study explored patterns of motivations and emotions behind the web-based information seeking of patients with cancer at various stages of their disease trajectory, as well as the cognitive and emotional responses evoked by WHIS via a scenario-based, think-aloud approach. In total, 15 analog patients were recruited, representing patients with cancer, survivors, and informal caregivers. Imagining themselves in 3 scenarios-prediagnosis phase (5/15, 33%), treatment phase (5/15, 33%), and survivor phase (5/15, 33%)-patients were asked to search for web-based health information while being prompted to verbalize their thoughts. In total, 2 researchers independently coded the sessions, categorizing the codes into broader themes to comprehend analog patients' experiences during WHIS. Overarching motives for WHIS included reducing uncertainty, seeking reassurance, and gaining empowerment. At the beginning of the disease trajectory, patients mainly showed cognitive needs, whereas this shifted more toward affective needs in the subsequent disease stages. Analog patients' WHIS approaches varied from exploratory to focused or a combination of both. They adapted their search strategy when faced with challenging cognitive or emotional content. WHIS triggered diverse emotions, fluctuating throughout the search. Complex, confrontational, and unexpected information mainly induced negative emotions. This study provides valuable insights into the motivations of patients with cancer underlying WHIS and the emotions experienced at various stages of the disease trajectory. Understanding patients' search patterns is pivotal in optimizing web-based health platforms to cater to specific needs. In addition, these findings can guide clinicians in accommodating patients' specific needs and directing patients toward reliable sources of web-based health information.

Gain scheduling of backstepping sliding mode controller using optimized type-1 Sugeno fuzzy logic approach for quadcopter

Purpose This paper aims to propose an optimized type-1 Sugeno fuzzy logic backstepping sliding mode controller (T1-SFL-BSMC) to control the altitude and rotational behaviour of a quadcopter. The proposed controller improves the performance of the system by overcoming the limitations of conventional trial-and-error techniques for BSMC gain design. Design/methodology/approach In this study, the type-1 Sugeno fuzzy logic controller (T1-SFLC) technique is proposed which dynamically modifies the BSMC gains. In addition, the parameters of T1-SFLC are further tuned by five different optimization techniques, namely, genetic algorithm, particle swarm optimization (PSO), pattern search (PS), simulated annealing (SA) and adaptive neural network (ANN). Findings The effectiveness of these optimization techniques in tuning the T1-SFLCs for the gain scheduled BSMC is evaluated in terms of time response metrics; rising time, settling time and root mean square error (RMSE) for altitude and rotational behaviour. The results demonstrated that PSO-optimized T1-SFL-BSMC provides better responses in terms of time response metrices with reduced rise and settling times and minimized RMSE when compared to other optimized T1-SFL-BSMC. Originality/value This paper proposes a novel approach for controlling quadcopters by combining fuzzy logic with a BSMC. It is then improved by using many optimization strategies. A more dependable and effective approach to controller design is provided by the suggested method, which is essential for unmanned aerial vehicle applications.

Assessment of drivers' visual search patterns and cognitive load during driving in curved tunnels.

The objective of this study was to assess drivers' visual search patterns and cognitive load during driving in curved tunnels. Specifically, we aimed to investigate how different curved tunnel geometries (tunnel radii, turning directions) and zones (entrance, middle, exit) influence drivers' saccadic eye movements. This understanding can inform the development of safer tunnel designs and driving guidelines. A total of 30 licensed Chinese drivers participated in the study. Eye movement data were collected using the eye tracker while participants drove through four experimental curved tunnels. Each tunnel differed in radius, turning direction, and zone. Eye movement parameters, including saccade duration, frequency, amplitude, and velocity, were analyzed to evaluate drivers' visual search patterns and cognitive load. The results revealed significant differences in drivers' saccadic eye movements across different tunnel conditions. As the tunnel radius decreased, drivers exhibited shorter saccade durations, increased saccade frequencies, decreased saccade amplitudes, and reduced saccade velocities, indicating increased cognitive load and more frequent visual updates. The turning direction also influenced saccadic behavior, with drivers demonstrating shorter saccade durations and higher frequencies when turning left compared to turning right. Furthermore, significant variations were observed across tunnel zones, with drivers showing the shortest saccade durations and highest frequencies in the entrance zone, while the mean saccade amplitude and velocity were largest in the exit zone. This study provides valuable insights into how drivers adapt their visual search strategies and cognitive processes in response to varying curved tunnel conditions. The findings highlight the importance of considering tunnel geometry, turning direction, and tunnel zones in designing safer tunnel infrastructures and driving guidelines. Specifically, the results suggest that drivers experience increased cognitive load in narrower tunnels and during left turns, necessitating a more dynamic and frequent visual search strategy.

Damage location in mechanical structures by multi-objective pattern search

Damage location in mechanical structures by multi-objective pattern search

Machine Learning to Detect Cervical Spine Fractures Missed by Radiologists on CT: Analysis Using Seven Award-Winning Models from the RSNA 2022 Cervical Spine Fracture AI Challenge.

Background: Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on re-evaluation; such studies do not capture the full extent of missed fractures. Objective: To use machine-learning (ML) models to identify cervical spine fractures on CT missed by interpreting radiologists, characterize the nature of these fractures, and assess their clinical significance. Methods: This retrospective study included all cervical spine CT examinations performed in adult patients in the emergency department between January 1, 2018 and December 31, 2022. Examinations reported as negative for cervical spine fracture were processed by seven award-winning ML models from the 2022 RSNA Cervical Spine Fracture AI Challenge; examinations classified as positive by at least four of seven models were considered to have ML-detected fractures. Two neuroradiologists independently reviewed examinations with ML-detected fractures, using ML-derived heat maps, to identify those representing true missed fractures. The neuroradiologists further assessed fractures' extent. Two spine surgeons independently assessed whether missed fractures were clinically significant (i.e., warranting at least one of surgical consultation, MRI, CTA, or collar immobilization). Results: The study included 6671 patients (2414 female, 4257 male; mean age, 54.6±22.1 years) who underwent a total of 6979 cervical spine CT examinations. Interpreting radiologists reported 6378 examinations as negative for fracture. Of these, 356 had ML-detected fractures (i.e., positive by ≥4 of 7 models). The neuroradiologists classified 40 of these examinations, in 39 unique patients, as having true fractures. ML-detected missed true fractures involved 51 unique sites, most commonly the C7 transverse process (n=12), C5 spinous process (n=12), and C6 spinous process (n=8). The surgeons considered missed fractures clinically significant in 15/40 examinations [MRI and collar immobilization (n=7), MRI and surgical evaluation (n=1), CTA (n=9)]. Interobserver agreement, expressed as kappa, was 0.88 between neuroradiologists for true fracture classification and 0.94 between surgeons for clinical significance classification. Conclusion: ML models identified cervical spine fractures missed by radiologists. These fractures were further characterized to systematically highlight radiologists' common misses. Clinical Impact: This ML-based framework can be applied in quality improvement efforts, to help refine radiologists' search patterns based on prone-to-miss findings.

Transient cognitive impacts of oxygen deprivation caused by catch-and-release angling.

Vertebrate brain function is particularly sensitive to the effects of hypoxia, with even brief periods of oxygen deprivation causing significant brain damage and impaired cognitive abilities. This study is the first to investigate the cognitive consequences of hypoxia in fish, specifically induced by exhaustive exercise and air exposure, conditions commonly encountered during catch-and-release (C&R) practices in recreational fishing. Angling exerts substantial pressure on inland fish populations, underscoring the need for sustainable practices like C&R. While C&R survival rates are generally high, understanding its sublethal impacts is crucial for evaluating the practice's ethical and ecological sustainability. We examined the effects of these stressors on the cognitive function of 238 rainbow trout, using the free movement pattern Y-maze method to assess working memory through navigational search patterns during free exploration sessions. Our results showed that air exposure led to short-term (3-4 h post-treatment), but transient impairments in working memory, with no long-term cognitive deficits observed at one week and one month post-treatment. These findings emphasize the high tolerance of fish to hypoxia and support the sustainability of C&R as a tool in fisheries management.

Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm

Optimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality of initial solutions. To overcome this limitation, initial solutions must be optimally tuned to achieve good outcomes with faster convergence. This paper proposes an optimally tuned pattern search (OPS) algorithm to solve OPF problems in medium and large power systems. The tuning process, performed using the classical interior point method (IPM), provides optimal initial control variable values for the standard pattern search (PS) algorithm. The proposed technique is applied to three test systems: IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. The OPF problem is formulated to minimize four objectives: total active power loss, total generator fuel cost, total generator emission, and total deviation in load bus voltage magnitude. The performance of the OPS algorithm is evaluated based on objective function values and computation times and is compared with IPM and two popular metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA). Results indicate that the OPS algorithm's performance varies across test systems but generally balances optimization performance with computational efficiency.

Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs

The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time–frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training.

Health Information Search During COVID-19: Does Culture Matter?

ABSTRACT This paper focuses on the aspects of intersection between the COVID-19 pandemic, Google search queries, and cultural dimensions of countries. Google Trend indices of 138 countries were collected for the topic “coronavirus” and analyzed to identify the determinants of abnormal health search intensity. Registered cases of COVID-19 in a country did not predict search patterns, yet deaths registered within a country did increase health search intensity. Abnormal health search behaviours were best explained using Hofstede’s cultural dimensions of power distance, masculinity, and long-term orientation.

Efficient Verifiable Searchable Encryption with Search and Access Pattern Privacy

Searchable encryption (SE) enables data users to securely search encrypted data stored in untrusted cloud servers. However, most SE schemes allow for leakages of access and search patterns to maximize efficiency and functionality. Recent attacks have shown that adversaries can recover query keywords with prior knowledge of the database by exploiting these leakages. Unfortunately, the existing schemes that protect access and search patterns result in frequent communications and high computational costs. Furthermore, complex calculation processes also raise challenges for verifying search results. To address these concerns, we first design an efficient conjunctive SE scheme with search and access pattern privacy using private set intersection. In the proposed scheme, we utilize random numbers to obfuscate the values of polynomials and randomly divide the results into two parts, which simplifies the search process, improves search efficiency, and eliminates the need for time-consuming ciphertext multiplication operations. We also extend this scheme to support search result verifiability. Specifically, by embedding a random number as the root of the return polynomial, we achieve verifiability of search results. Furthermore, we prove the security of both schemes employing the simulation-based method. Finally, we implement the schemes in a real database and thorough performance analyses demonstrate their efficiency.

Determining the most suitable empirical model for global solar radiation prediction in the lakes region

In this study, it was aimed to determine the most suitable model for predicting global solar radiation in the Lakes Region (Isparta, Burdur, Antalya). Through ATATEK-Solar software, a total of 15 models were tested, including 14 empirical models from the literature and a new artificial intelligence-supported model. Each model was analyzed with three different optimization algorithms (Nelder-Mead Simplex, Pattern Search, Simulated Annealing). In province-based evaluations, the Model 9 (RMSE: 0.1507, R²: 0.9990) for Isparta, and the Model 14 for Burdur and Antalya (RMSE: 0.1940, R²: 0.9992 and RMSE: 0.2218, R²: 0.9987, respectively) provided the most successful results. In regional analysis results, while the Model 5 (RMSE: 0.2626, R²: 0.9980) gave the lowest average error, the Model 13 (RMSE: 0.2649, R²: 0.9979, standard deviation: 0.0122) showed the highest consistency. These models were followed by the Model 6 (RMSE: 0.2646, R²: 0.9979, standard deviation: 0.0444). Although the Model 15 gave the best results in Burdur and Antalya, it had a high standard deviation value (0.2201) due to its low performance in Isparta. The characteristic features of the Lakes Region, including the presence of lake ecosystems, elevation differences, and the resulting microclimatic diversity, necessitate a regional approach in predicting global solar radiation. In this context, the Model 13 has been determined as the most suitable model that can be used throughout the region with its low error rate and high consistency. The obtained results can provide reliable predictions in evaluating the solar energy potential of the region and designing solar energy systems.

A new approach for predicting solar radiation based on a pattern search algorithm

A new approach for predicting solar radiation based on a pattern search algorithm

A Geometrical Speculation. The Twelfth Century Roof and some Peculiar Aspects of the Ground Plan of Værnes Church, Norway

The following is an exercise in what may seem an old-fashioned art, popular among architects in the nineteenth and early twentieth centuries, but later somewhat discredited – the search for geometrical patterns underlying medieval church design. However, the interest in mathematically based tools for design of historic buildings has in recent years been revived, proof of which is a steadily increasing flow of publications reporting scientifically rigorous studies. My contribution concerns the church at Værnes, near Trondhei, Norway, and the impressive open truss timber roof of the nave. Not being content with previous suggestions on how the roof design might have been determined, I compared it to similar structures in the region and found several cases of the same ratio of width to height. I also noted that this ratio corresponded almost exactly to a simple geometrical diagram, which further led me to make some assumptions on how Værnes church was originally planned.

Election Interference and Online Propaganda Campaigns: Dynamic Interdependencies on Facebook, Google Trends, and the New York Times

The relationship between propaganda campaigns, news outlets, and search patterns is of significant interest to political authorities and academic scholars from various disciplines. We explore these dynamic relationships using 3,500 Facebook propaganda advertisements, 167,000 New York Times stories, and hundreds of Google Trends searches for terms from the advertisements and articles in the two years preceding the 2016 US presidential election. The data indicate that propaganda campaigns utilize random content infrequently and instead follow specific Google search patterns. Depending on the subject matter, Facebook advertisements can anticipate the New York Times. In the contexts of immigration, racism, and the LGBT community, such patterns of content adaptation are more prominent. We use the results to provide policy and research recommendations.

Unveiling the dynamics of climate change narratives: A Google Trends analysis

This study explores public interest and misinformation surrounding climate change through an analysis of Google Trends data. It focuses on online search behaviors related to “climate”, “climate change”, “climate hoax”, and “HAARP” throughout 2023. By tracking search patterns, we aim to understand public perceptions, concerns, and the influence of misinformation on climate change discourse. Our findings indicate a strong temporal and regional variability in search interest, which spikes notably during significant events like Earth Day and in response to public statements by influential figures. The results demonstrate high engagement in regions with direct climate impact, such as Africa and parts of Asia, contrasted with varied engagement in Western nations. Notably, the data reveals significant public intrigue following conspiracy narratives during specific periods, which emphasizes the role of misinformation in shaping public discourse. This analysis underscores the need for targeted communication strategies to effectively address public misconceptions and enhance the understanding of climate-related issues. This research highlights the utility of Google Trends as a tool for real-time monitoring of public interest and misinformation and offers valuable insights for policymakers, educators, and activists to tailor their approaches to combat climate change more effectively. Through its comprehensive analysis, the study contributes to the broader understanding of how digital tools can aid in the ongoing efforts to engage the public in meaningful climate action discussions.

The Impact of the Size of Funds on the Use of Selectivity and Market Timing by Investment Funds

Abstract Research background Investment funds are an important part of the capital market. Household savings in this type of asset are increasing every year. From an investor’s point of view, the performance of funds is important. These, along with the risks, are what most often determine the choice of fund type and finally the specific fund. The performance of a fund is often determined by both the size of the fund and the application of the managers’ selectivity and market sense. Purpose The aim of this research is to investigate the impact of fund size on performance through the use of selectivity and market sense by managers. Dividing funds into size groups will allow the search for patterns in terms of managers’ use of market timing. Research methodology The research used a Treynor-Mazuy model as a market timing model and Ward’s method as the cluster analysis methods. The performance of the funds was determined using an investment performance indicator, i.e., Omega. Results The obtained results indicate that the funds are similar within the groups. However, there were differences in the results between the groups. During periods of high volatility, it is recommended to invest in units of funds with an average size below the median. Novelty The added value is the study of market timing in groups of funds similar in size. In each group, the results of the Treynor-Mazuy model estimation, and the Omega investment efficiency index were determined, and the differences in the obtained results between the groups were examined. Finally, all funds were clustered using the Ward method.

Frequency regulation of PV-reheat thermal power system via a novel hybrid educational competition optimizer with pattern search and cascaded PDN-PI controller

Maintaining a stable balance between generated power and load demand is a critical challenge in modern power systems, especially with the increasing integration of renewable energy sources like photovoltaic (PV) systems. This study introduces a novel hybrid educational competition optimizer with pattern search (hECO-PS) algorithm to optimally tune a cascaded proportional-derivative with filter and proportional-integral (PDN-PI) controller for load frequency control (LFC) in a two-area power system comprising a PV system and a reheat thermal power system. The proposed hECO-PS algorithm enhances both global exploration and local exploitation capabilities, resulting in superior convergence rates and solution accuracy. The controller's performance was evaluated under various scenarios, including a 10% step load change and solar radiation variations, demonstrating significant improvements in frequency regulation. The hECO-PS tuned PDN-PI controller achieved a minimum integral of time-weighted absolute error (ITAE) value of 0.4464, outperforming conventional methods like the modified whale optimization algorithm and sea horse algorithm, which yielded ITAE values of 2.6198 and 0.8598, respectively. Furthermore, the proposed controller reduced settling time by up to 46% and minimized overshoot by up to 40%. These results confirm the efficacy of the proposed approach in enhancing system stability and reliability under dynamic operating conditions, suggesting it as a promising solution for LFC in modern power systems with high renewable energy penetration.

고령자의 구직 여정: 질적연구 접근

Despite the significant number of older adults in South Korea seeking employment for livelihood maintenance, researchers have not given sufficient attention to elderly workers, and studies on their job search experiences remain scarce. This study draws on a self-regulation perspective on job search models to explore the job search patterns and internal experiences of older job seekers, aiming to gain a deeper understanding of their experiences during the job search process. We conducted a qualitative study based on in-depth interviews with 11 older job seekers in South Korea. The results indicate that older job seekers face disadvantageous job search environments due to age-discriminatory employment practices, intergenerational job competition, and limited access to information. These factors seem to restrict their employment options both qualitatively and quantitatively. Older job seekers, desperate for employment to maintain their livelihood, experience anxiety during the job search process but persist through self-regulation strategies. Even after gaining employment, they struggle with job insecurity and vulnerability. In response, they employ various emotional and cognitive regulation strategies, including self-blame, cognitive reappraisal, and downward comparison. However, upon job termination, they repeatedly return to the disadvantageous job search environment, creating a cyclical process.

Optimal properties of hysteretic dampers to minimize peak floor accelerations in multi-story buildings subjected to earthquakes

In this study, the optimal reduction of seismic peak floor accelerations in buildings equipped with hysteretic dampers is investigated. The structures are modeled as linear elastic shear buildings, and the seismic demand is modeled as a stochastic process. The optimal properties and height-wise distribution of the hysteretic dampers are found using a pattern search optimization algorithm. The objective function is defined in terms of the stochastic non-stationary response of the structure, which is obtained using the Explicit Time Domain Method in combination with statistical linearization. Possible influence of the fundamental period of the structure, the frequency content of the excitation, and the number of stories, on the optimal properties of the hysteretic dampers are analyzed. The effects of the optimal solutions on other response quantities, such as inter-story drifts, ductility demand on the hysteretic dampers, energy dissipation, and floor response spectra, are also assessed. Findings of this study are validated by a case study that considers a real building model subjected to actual ground motion acceleration records. The main findings are: a) optimally designed hysteretic dampers are indeed effective in reducing the seismic peak floor acceleration response; and b) optimal non-uniform height-wise distributions of hysteretic dampers are essentially equal to or at most somewhat better (but not by a wide margin) than optimal uniform height-wise distributions.

Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

The Gazelle Optimization Algorithm (GOA) is an innovative metaheuristic inspired by the survival tactics of gazelles in predator-rich environments. While GOA demonstrates notable advantages in solving unimodal, multimodal, and engineering optimization problems, it struggles with local optima and slow convergence in high-dimensional and non-convex scenarios. This paper proposes the Hybrid Gazelle Optimization Algorithm with Differential Evolution (HGOADE), which combines Differential Evolution (DE) with GOA to leverage their complementary strengths for addressing limitations. HGOADE initializes a population of candidate solutions using GOA, then enhances these solutions through DE’s mutation and crossover operations. The algorithm subsequently employs GOA’s exploration and exploitation phases to refine the solutions. By integrating DE’s robust exploration capabilities with GOA’s dynamic search patterns, HGOADE aims to improve global and local search performance. The effectiveness of HGOADE is validated through experiments on benchmark functions from the CEC 2017, CEC 2020, CEC 2022 suite, comparing with ten established optimization techniques, including classical GOA, Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Arithmetic Optimization Algorithm (AOA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), and DE. Additionally, the performance of HGOADE is assessed against prominent winners from CEC competitions, specifically CMA-ES, LSHADEcnEpSin, and LSHADESPACMA, using the CEC-2017 test suite. Statistical analyses using the Wilcoxon Rank Sum Test and Wilcoxon Signed-Rank Test, along with the Weighted Aggregated Sum Product Assessment (WASPAS) method, confirm that HGOADE significantly outperforms existing algorithms in terms of solution quality and convergence speed. HGOADE’s superiority is validated through six complex engineering design problems, demonstrating its higher feasibility and effectiveness than GOA and other methods. This paper addresses GOA’s shortcomings and advances metaheuristic optimization by integrating DE strategies, offering valuable insights and practical applications for global optimization and engineering design.