Search Behavior Research Articles

Performance evaluation of logarithmic spiral search and selective mechanism based arithmetic optimizer for parameter extraction of different photovoltaic cell models.

The imperative shift towards renewable energy sources, driven by environmental concerns and climate change, has cast a spotlight on solar energy as a clean, abundant, and cost-effective solution. To harness its potential, accurate modeling of photovoltaic (PV) systems is crucial. However, this relies on estimating elusive parameters concealed within PV models. This study addresses these challenges through innovative parameter estimation by introducing the logarithmic spiral search and selective mechanism-based arithmetic optimization algorithm (Ls-AOA). Ls-AOA is an improved version of the arithmetic optimization algorithm (AOA). It combines logarithmic search behavior and a selective mechanism to improve exploration capabilities. This makes it easier to obtain accurate parameter extraction. The RTC France solar cell is employed as a benchmark case study in order to ensure consistency and impartiality. A standardized experimental framework integrates Ls-AOA into the parameter tuning process for three PV models: single-diode, double-diode, and three-diode models. The choice of RTC France solar cell underscores its significance in the field, providing a robust evaluation platform for Ls-AOA. Statistical and convergence analyses enable rigorous assessment. Ls-AOA consistently attains low RMSE values, indicating accurate current-voltage characteristic estimation. Smooth convergence behavior reinforces its efficacy. Comparing Ls-AOA to other methods strengthens its superiority in optimizing solar PV model parameters, showing that it has the potential to improve the use of solar energy.

Digital epidemiology of high-frequency search listening trends for the surveillance of subjective well-being during COVID-19 pandemic.

The coronavirus disease (COVID-19) pandemic has led to a dramatic increase in online searches related to psychological distress. Governments worldwide have responded with various measures to mitigate the impact of the virus, influencing public behavior and emotional well-being. This study investigated the relationship between government actions and public reactions in terms of online search behaviors, particularly concerning psychological distress during the pandemic. The primary objective of this study was to analyze how changes in government policies during the COVID-19 pandemic influenced public expressions of psychological distress, as reflected in the volume of related online searches in Kuwait. Utilizing Google Trends data, the study analyzed search frequencies for terms associated with psychological distress such as "anxiety" and "lockdown." The analysis correlated these search trends with government actions using the Oxford COVID-19 Government Response Tracker (OxCGRT). The study period covered March 1, 2020, to October 10, 2020, and involved extensive data collection and analysis using custom software in R programming. There was a significant correlation between the stringency of government-imposed restrictions and the volume of online searches related to psychological distress. Increased searches for "lockdown" coincided with heightened government restrictions and were associated with increased searches for "anxiety," suggesting that policy measures significantly impacted public psychological distress. The study concludes that governmental responses to the COVID-19 pandemic, measured through OxCGRT, have a measurable impact on public psychological distress, as evidenced by online search behaviors. This underscores the importance of considering psychological impacts in policymaking and suggests further research to explore this dynamic comprehensively. Future studies should focus on refining the correlation between specific types of policy measures and different expressions of psychological distress to better inform public health strategies and interventions.

Immigrants’ Returns Intentions and Job Search Behavior - When the Home Country is Unsafe

Immigrants’ Returns Intentions and Job Search Behavior - When the Home Country is Unsafe

Ellis's model of student information-seeking behavior on Google Scholar

Information seeking leads to interactions within a system. At the same time, interaction patterns can potentially lead to a behavioral tendency and form a unique behavioral pattern. This research aimed to find out how information search behavior was carried out by students of the Library and Information Science Study Program in conducting literature searches on the Google Scholar platform by applying the stages of the Ellis Model. The research method used quantitative with a survey method. The results showed that at the starting stage, the correlation coefficient was moderate; at the chaining stage, the correlation coefficient was moderate; at the browsing stage, the correlation coefficient was below moderate; at the monitoring stage, the correlation coefficient was below moderate; at the differentiating stage, the correlation coefficient was moderate; and at the extracting stage, the correlation coefficient was below moderate. This study concludes that students know the topic they want to search before using Google Scholar. Chaining, browsing, and monitoring are done alternately at almost the same time. Students have observed the correlation coefficient numbers at the chaining, browsing, and monitoring stages; students pay more attention to the chaining stage, namely obtaining keywords. At the differentiating stage, students have begun to filter incoming information and use their ability to distinguish the sources of information obtained, and at the extracting stage, students work through the selected sources to identify all relevant materials.

Better Understanding Procedural Search Tasks: Perceptions, Behaviors, and Challenges

People often search for information to acquire procedural knowledge–“how to” knowledge about step-by-step procedures, methods, algorithms, techniques, heuristics, and skills. A procedural search task might involve implementing a solution to a problem, evaluating different approaches to a problem, and brainstorming on the types of problems that can be solved with a specific resource. We report on a study ( N =36) that aimed to better understand how people search for procedural knowledge. Much research has investigated how search task characteristics impact people’s perceptions and behaviors. Along these lines, we manipulated procedural search tasks along two orthogonal dimensions: product and goal. The product dimension relates to the main outcome of the task and the goal dimension relates to task’s success criteria. We manipulated tasks across three product categories and two goal categories. The study investigated four research questions. First, we examined the effects of the product and goal on participants’ (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. Second, regardless of the task product and goal, by analyzing participants’ think-aloud comments and screen activities we closely examined how people search for procedural knowledge. Specifically, we report on (RQ4) important relevance criteria, types of information sought, and challenges.

An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system.

Tracking online searches for gambling activities and operators in the United Kingdom during the COVID-19 pandemic: A Google Trends™ analysis.

Whilst some research has explored the impact of COVID-19 on gambling behaviour, little is yet known about online search behaviours for gambling during this period. The current study explored gambling-related online searches before, during and after the outbreak of the COVID-19 pandemic in the UK. We also assessed whether search trends were related to Gambling Commission behavioural data over the same period. Google Trends™ search data, covering thirty months from January 2020 to June 2022, for five gambling activities and five gambling operators were downloaded. Graphical displays of the weekly relative search values over this period were then produced to visualise trends in search terms, with key dates in COVID-19 policy and sporting events highlighted. Cross-correlations between seasonally adjusted monthly search data and behavioural indices were conducted. Sharp increases in internet searches for poker, slots, and bingo were evident during the first lockdown in the UK, with operator searches sharply decreasing over this period. No changes in gambling activity searches were highlighted during subsequent lockdowns, although small increases in operator-based searches were detected. Strong positive correlations were found between search data and industry data for sports betting and poker but not for slots. Google Trends™ data may act as an indicator of population-level gambling behaviour. Substitution of preferred gambling activities for others may have occurred during the first lockdown when opportunities for sports betting were limited. Further research is needed to assess the effectiveness of internet search data in predicting gambling-related harm.

11. ENHANCING EVIDENCE-BASED MEDICINE COMPETENCE IN THIRD-YEAR STUDENTS THROUGH BASIC SCIENCE RESEARCH TRAINING: OUTCOMES AND FINDINGS

Background: In 2022, for the first time, the Faculty of Medicine, Dai Nam University applies scientific research methods to 3rd year medical students.
 Objective: To evaluate the impact of the Medical Scientific Research Methods module on medical students’ skills and knowledge.
 Methods: This pre-test post-test pilot study used a prospective descriptive exploratory method by non-probability convenience sampling of all people participating in the course “Medical science research methods,” which was compulsory for 3rd-year medical students, Faculty of Medicine, Dai Nam University, from August to December 2022. The study invited a total of 89 students from two classes to participate. At the beginning of the course, 82 students completed the survey, while 66 students responded to the second survey after completing the course.
 Result: This study demonstrated that an undergraduate research methods course had a meaningful impact on medical literature search behaviors, understanding of medical research terminology, and confidence in evidence-based medical practice among students. However, despite these positive results, the knowledge and skills of students in evidence-based medicine were limited.
 Conclusion: The scientific research methods course has significantly improved students’ EBM knowledge and skills, albeit with some limitations. However, this study highlights the need for continued training programs and practical experience in medical research to enhance further and maintain learners’ proficiency in EBM.

Digital Content Marketing on Buying Decision: Literature Review

Digital content marketing on buying decisions, the aim is to review existing literature on digital content marketing and buying decisions, namely that through content marketing you can increase interest in visiting a product so that it becomes a decision to purchase goods or services, and understand the stages of buying. decision. By using the literature method, by searching for research data or information through reading scientific journals, reference books and publication materials available in libraries and the internet. and the results of this research are studied in this article so it can be concluded to build a hypothesis for further research, namely: 1) digital content marketing has an influence on improving purchasing decisions. 2) Through content marketing, you can increase interest in visiting a product so that it becomes a decision to purchase goods or services. There are five stages of buying decision including: problem or need recognition, information search, alternative evaluation, purchase, and post purchase behavior

Partial‐depth search and search pattern comparisons

AbstractThis paper compares consumers' optimal search behaviors and firms' strategic reactions in prices under two modes of search: parallel search and sequential search, with the incorporation of partial search depth that measures the amount of information to evaluate a product. Our analysis shows a series of new results: when products are partially evaluated, search depth is greater under sequential search than under parallel search; consumers search for (on average) more products either under parallel search or sequential search, depending on whether partial‐depth search is chosen; the equilibrium price is higher under parallel (sequential) search when search costs are high (low). Interestingly, parallel search might yield higher equilibrium prices with more products searched, compared with sequential search. This challenges the conventional wisdom in the search literature that more products searched lower prices because of the intensified competition.

Forager: a Python package and web interface for modeling mental search.

Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open-source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming. To remove this barrier to studying search patterns in the fluency task, we created forager, a Python package ( https://github.com/thelexiconlab/forager ) and web interface ( https://forager.research.bowdoin.edu/ ). forager provides multiple automated methods to designate clusters and switches within a fluency list, implements a novel set of computational models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search using semantic embeddings, and also enables researchers to evaluate relative model performance at the individual and group level. The package and web interface cater to users with various levels of programming experience. In this work, we introduce forager's basic functionality and use cases that demonstrate its utility with pre-existing behavioral and clinical data sets of the semantic fluency task.

Tahapan Pencarian Informasi Mahasiswa (Studi Kasus Program Magister Kenotariatan Universitas Sumatera Utara)

The purpose of this study was to determine the information search behavior of USU Master of Kenotariatan students. This research uses a descriptive method with a qualitative approach. The data of this research was taken by using interview techniques with informants as many as 4 people. The results showed that: First, at the starting stage the informant will first understand what information he needs and discuss with others about the research being carried out; Second, at the chaining stage the informant will see other references that might be used; Third, at the browsing stage the informant will search for information via the internet or by visiting the library; Fourth, at the monitoring stage the informant wants to stay up to date by visiting an information source regularly, or by asking directly to a lecturer or colleague; Fifth, at the differentiating stage the informant will distinguish the information found based on the relevance to the topic and the age of the information; Sixth, at the extracting stage the informant will take the important points of the information that has been found by reading and summarizing; Seventh, at the verifying stage the informant will double-check the identity and references used by the author; Eighth, at the ending stage the informant will re-evaluate the information to be used, At this stage, informants will also look for other information that might be useful for research. Based on the research results, all eight stages made by David Ellis have been carried out by informants. This research contributes that students who generally utilize libraries and the internet as a source of information and the campus provides information search education to students.

Search frictions in rental markets: Evidence from urban China

In this paper, we study consumer search in a housing market subject to objective frictions induced by intermediaries and psychological frictions intrinsic to tenants themselves. Using rental data from a leading real estate brokerage company during 2016 and 2018, we find a unimodal distribution of objective search frictions and a bimodal distribution of psychological search frictions revealed by tenants' search behaviors. This bimodal distribution originates from divergent search strategies of tenants with different search criteria. Furthermore, psychological search frictions explain the deviation between a tenant's actual choice and stated preference, and enhance the degree of overpay/mark-up in the deal. These effects of psychological frictions led by divergent search strategies hold when we consider the expertise/incentive of agents and the rigidity/opportunity cost of tenants' search. Aggregately, psychological search frictions result in a dispersed and asymmetric distribution of rent residuals.

Optimizing renewable energy utilization with high gain converters

This research proposes a novel control technique for optimizing hybrid renewable energy sources (HRES) by using a high-gain DC-to-DC converter topology. The Hybrid RES system comprises a battery components, wind turbine (WT), and photovoltaic (PV). The proposed control methodology utilizes an enhanced jellyfish search (EJS) controller, which enhances the search behavior of the jellyfish search algorithm through the incorporation of mutation and crossover operators. The converter is designed to maximize efficiency, minimize switching losses, and effectively utilize renewable energy. To achieve optimal performance, a dataset with optimum gain values is constructed using a minimal error target function and proportional-integral (PI) controller gain parameters. The EJS algorithm determines the PI controller gain based on the error in reactive and active power. Randomly generated gain values are used as inputs to the system, and the algorithm adjusts the PI settings by minimizing system error and optimizing power flow management. The inclusion of batteries helps balance renewable power and enhances output consistency. The proposed system is implemented in MATLAB and is compared with existing systems. The novelty of this research lies in the combination of the EJS algorithm with converter efficiency, switching losses, and hybrid renewable energy sources. By utilizing the EJS algorithm, the proposed strategy has the potential to achieve superior outcomes based on cost savings, energy efficiency, and grid stability compared to conventional methods. Furthermore, the integration of this method contributes to the advancement of optimization algorithms and neural network applications.

Just say ‘I don’t know’: Understanding information stagnation during a highly ambiguous visual search task

Visual search experiments typically involve participants searching simple displays with two potential response options: ‘present’ or ‘absent’. Here we examined search behavior and decision-making when participants were tasked with searching ambiguous displays whilst also being given a third response option: ‘I don’t know’. Participants searched for a simple target (the letter ‘o’) amongst other letters in the displays. We made the target difficult to detect by increasing the degree to which letters overlapped in the displays. The results showed that as overlap increased, participants were more likely to respond ‘I don’t know’, as expected. RT analyses demonstrated that ‘I don’t know’ responses occurred at a later time than ‘present’ responses (but before ‘absent’ responses) when the overlap was low. By contrast, when the overlap was high, ‘I don’t know’ responses occurred very rapidly. We discuss the implications of our findings for current models and theories in terms of what we refer to as ‘information stagnation’ during visual search.

Mutually exclusive feeding yields Holling type III functional response

Abstract Many generalist predators, including fish, insects and mammals, temporarily focus on their search behaviour and can switch between prey species. Flexible and rapid behavioural plasticity enables a predator to forage optimally, as guided by search image formation and prey switching in response to changing prey abundances. More than half a century of studies have used Holling type III functional response to model the impact of this feeding behaviour on population dynamics, yet a mechanistic understanding for the pathway between individual behaviour and feeding rates remains poorly understood. To understand this common feeding behaviour, we developed three mechanistic models using first‐principles to provide direct derivations for the relationship between observed individual behaviour and feeding rates. The derived models yield three novel, generalised functional responses for predators characterised by prey switching and mutually exclusive feeding (i.e. feeding is limited to one prey species at a time). We show how these functional responses act as Holling type III response, and how they can be used to predict predators' diet compositions. Our results demonstrate that several behavioural forms of prey switching, as displayed in a wide range of predator species, consistently produce type III conforming functional responses. Read the free Plain Language Summary for this article on the Journal blog.

Competing for Jobs: How COVID-19 Changes Search Behaviour in the Labour Market

Abstract We provide insights on how job search changed in the Covid-19-crisis by analysing data from the LinkedIn professional network for Germany. We find that competition among workers for jobs strongly increased – which is due to additional job seekers rather than higher search intensity. Furthermore, the LinkedIn data show that people from industries particularly affected by the crisis applied much more frequently and there had been a substantial shift in the target industries for applications. Finally, we find that at the onset of the Covid-19-crises applications were made significantly more often below and significantly less often above a person’s level of seniority.

Influência da supervisão clínica na integração dos enfermeiros em serviços de cirurgia: protocolo de scoping review

Introduction: Nurse integration is a process of incorporating new nurses into a healthcare institution or a healthcare team. This process ensures that new nurses are properly trained, supported, and have a good understanding of their role. Integration programs are designed to support individuals or groups as they navigate a new environment. Aim: Map the literature regarding the elements of clinical supervision that influence the nurse’s integration in the surgical department. Material and Methods: Scoping review according to the Joanna Briggs Institute methodology. The databases will be MEDLINE (via PubMed), CINAHL complete (via EBSCOhost), Academic search complete (via EBSCOhost), Medic Latina (via EBSCOhost), Psychology and Behaviour Sciences Collection (via EBSCOhost), Cochrane Central Register of Controlled Trials (Via Cochrane Library), Cochrane Database of Systematic Reviews (Cochrane Library), SciELO, and Scopus. For grey literature research, the Open Access Scientific Repository of Portugal (RCAAP), DART - Europe E-theses, Dialnet - Thesis Doctorates and Open Access Theses and Dissertations will be used. Studies in Portuguese, English and Spanish will be included. The analysis of the study relevance, data extraction and synthesis will be carried out by two independent researchers considering the inclusion criteria. In case of disagreement, a third researcher will be involved. Results: With the collected data, it is intended to list the elements of clinical supervision that influence the integration of nurses in the surgical department. Conclusion: This review is expected to gather evidence on elements of clinical supervision that influence the integration of nurses in the surgical department and to identify gaps in knowledge.

Decreased Visual Search Behavior in Elderly Drivers during the Early Phase of Reverse Parking, But an Increase during the Late Phase.

The aim of this study was to assess the characteristics of visual search behavior in elderly drivers in reverse parking. Fourteen healthy elderly and fourteen expert drivers performed a perpendicular parking task. The parking process was divided into three consecutive phases (Forward, Reverse, and Straighten the wheel) and the visual search behavior was monitored using an eye tracker (Tobii Pro Glasses 2). In addition, driving-related tests and quality of life were evaluated in elderly drivers. As a result, elderly drivers had a shorter time of gaze at the vertex of the parking space both in direct vision and reflected in the driver-side mirror during the Forward and the Reverse phases. In contrast, they had increased gaze time in the passenger-side mirror in the Straighten the wheel phase. Multiple regression analysis revealed that quality of life could be predicted by the total gaze time in the Straighten the wheel phase (β = -0.45), driving attitude (β = 0.62), and driving performance (β = 0.58); the adjusted R2 value was 0.87. These observations could improve our understanding of the characteristics of visual search behavior in parking performance and how this behavior is related to quality of life in elderly drivers.

Tasks, Copilots, and the Future of Search: A Keynote at SIGIR 2023

Search is far from being a solved problem. While search engines may cope well with simple tasks, searchers and systems struggle as task complexity increases. Task is central to the search process, motivating the search and driving search behavior. Complex search tasks require more than support for rudimentary fact finding or re-finding. Various support options have been offered by search systems over time (e.g., query suggestions, contextual search) to help search engine users more effectively tackle complex tasks. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots , based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. The implications from these advances for the design of intelligent systems and for the future of search itself are significant. This overview of the keynote that I gave at the 2023 ACM SIGIR Conference introduces AI copilots and briefly presents some of the challenges and opportunities for researching, developing, and deploying search copilots. Date : 26 July 2023.