Historical Search Research Articles

Jesus and Therapy in the Gospel of Matthew 8:2-9:35

The article provides a brief panoramic review of the narrative plot of the Gospel of Matthew 8:2-9:35 in the context of the therapeutic ministry of Jesus. The article analyzes both Jesus' motivation for healing and therapy, also the therapeutic methods used by Jesus. As a result, the therapeutic aspects of Mt. 8:2-9:35 are placed in a soteriological key, which is reflected by the analysis of the organic connection between Jesus' therapy and the gift of salvation. The relevance of the article is due to the growing interest in the historical search for the Jesus Christ and the current state of research – the so-called Nest Quest.

A [formula omitted]-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources

With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A Q-Learning-based NSGA-II algorithm (QNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search; and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the QNSGA-II algorithm in solving such problems are validated.

SCIENTIFIC POLICE

This article aims to provide guidance and carrer vision with na emphasis on the technical-scientific police. Based on historical search and academic training to work in technical police bodies.

BioDynGrap: Biomedical event prediction via interpretable learning framework for heterogeneous dynamic graphs

Model dynamic graphs with entity-specific heterogeneity has achieved remarkable success across various domains. By utilizing a novel dynamic graph encoding mechanism that employs rich temporal reasoning, we introduce BioDynGraph — a versatile and innovative framework for biomedical event prediction that bridges the gap between historical search and future reasoning. Existing studies tend to overlook clinical relationships between diseases during a patient’s visit by treating diagnoses as independent illnesses. Many machine learning techniques assume that disease progression within each time period is static and stationary, attenuating the information of all previous time steps using a homogeneous approach. However, in practice, disease progression is non-stationary, necessitating better use of combinational disease information to learn more about the dynamics of diseases. To address this problem, we use event clue searching and event temporal reasoning to infuse entity and relation information into dynamic subgraphs efficiently. BioDynGraph learns a global disease co-occurrence graph through event clue searching and acquires dynamic subgraphs for each patient’s visit using event temporal reasoning. Experimental results demonstrate that BioDynGraph exhibits superior performance compared to state-of-the-art (SOTA) baseline methods on biomedical event prediction using three electronic medical records datasets. Additionally, the clues found by BioDynGraph provide interpretability of the event prediction, showcasing its potential as an effective general-purpose interpretability algorithm for biomedical graph data.

Distributed multi-objective optimization for SNP-SNP interaction detection

The detection of complex interactions between single nucleotide polymorphisms (SNPs) plays a vital role in genome-wide association analysis (GWAS). The multi-objective evolutionary algorithm is a promising technique for SNP-SNP interaction detection. However, as the scale of SNP data further increases, the exponentially growing search space gradually becomes the dominant factor, causing evolutionary algorithm (EA)-based approaches to fall into local optima. In addition, multi-objective genetic operations consume significant amounts of time and computational resources. To this end, this study proposes a distributed multi-objective evolutionary framework (DM-EF) to identify SNP-SNP interactions on large-scale datasets. DM-EF first partitions the entire search space into several subspaces based on a space-partitioning strategy, which is nondestructive because it guarantees that each feasible solution is assigned to a specific subspace. Thereafter, each subspace is optimized using a multi-objective EA optimizer, and all subspaces are optimized in parallel. A decomposition-based multi-objective firework optimizer (DCFWA) with several problem-guided operators was designed. Finally, the final output is selected from the Pareto-optimal solutions in the historical search of each subspace. DM-EF avoids the preference for a single objective function, handles the heavy computational burden, and enhances the diversity of the population to avoid local optima. Notably, DM-EF is load-balanced and scalable because it can flexibly partition the space according to the number of available computational nodes and problem size. Experiments on both artificial and real-world datasets demonstrate that the proposed method significantly improves the search speed and accuracy.

Understanding the Experiences of Patients with Sickle Cell Disease and Their Caregivers By Social Media Listening in the UK

INTRODUCTION: Sickle cell disease (SCD) is a life-long hereditary condition associated with reduced life expectancy and quality of life. 1 Long-term complications include high infection risk, stroke, severe anemia, organ damage, and vaso-occlusive crises. 1-2 In the United Kingdom (UK) there are ~20,000 patients with SCD. 3 Online conversations involving patients with chronic conditions can provide insight into their health-related concerns, caregiver experiences, and other stakeholder perspectives. 4 Here, we explore online conversations surrounding SCD to gain valuable information regarding real-world experiences and unmet needs. METHODS: A qualitative netnographic social media listening (SML) study was conducted in the UK using a search term syntax containing disease and treatment keywords, hashtags, and relevant website addresses. A 12-month historical search (1 Jun 2022-1 Jun 2023) was conducted using a third-party SML tool (TalkWalker), capturing all relevant SCD-related conversations across publicly accessible social media/digital channels including Twitter, Instagram, TikTok, forums, blogs, YouTube, and Facebook public pages. A sample of randomized data was sifted to isolate posts referencing the patient/caregiver experience for further analysis. Topics were identified from the sifted data using artificial intelligence (AI)-powered theme detection in natural language processing (NLP), manually grouped into common themes and quantified for prevalence and sentiment. Human-led qualitative analyses using keyword filtering and manual trawling were performed to contextualize and validate the findings. RESULTS: A total of 45,864 online posts with 687,302 engagements (i.e., likes, shares, comments, etc.) were captured. The sifted results yielded 513 posts (1.1%). Of these, 93% were from patients with SCD, and the remaining 7% were from caregivers and other sources. The largest number of posts came from Instagram (173 posts), followed by forums (123 posts), TikTok (123 posts), and Twitter (94 posts). NLP and qualitative sentiment analysis identified six overall themes: the experiences with management of SCD within the National Health Service, UK (72.5% [372/513] posts: 8.6% positive, 34.1% negative, 57.3% neutral), living with SCD and understanding the condition (70.8% [363/513] posts:13.2% positive, 41.3% negative, 45.5% neutral), the importance of community, support and family planning (60.6% [311/513] posts: 19.0% positive, 21.9% negative, 59.2% neutral), understanding the burden of SCD through accounts over lifetime (45.4% [233/513] posts: 17.6% positive, 26.6% negative, 55.8% neutral), talking about and raising awareness of SCD (40.9% [210/513] posts: 14.3% positive, 17.1% negative, 68.6% neutral), and access to/experiences with treatment (27.1% [139/513] posts: 6.5% positive, 29.5% negative, 64.0% neutral) ( Figure). The patient experience in accessing healthcare was striking, highlighting a lack of empathy from healthcare professionals (HCPs) and prejudice from those in primary/emergency care in providing care. Patients share their experiences of pain crises, their attempts to understand triggers, and the underestimation by HCPs of the real-life impact, leading to symptom dismissal and stigmatization associated with the need for painkillers. Importantly there is a strong outcry from patients with SCD and their community that current treatments are unsatisfactory, burdensome, or difficult to access. CONCLUSIONS:SML forms a valuable, novel, and quantifiable method of identifying challenges, unmet needs and capturing the patient's digital voice in SCD, adding to the evidence base in an under-researched area. Limited access to emergency care, low levels of empathy from HCPs, racial bias, and stigmatization reported by patients further highlight the presence of health inequity within this population. There is a pressing plea for the development and accessibility of improved treatments for this population. REFERENCES: 1. Lubeck D, et al. JAMA Netw Open 2019;2(11):e1915374. 2. Aljuburi G, et al. J Public Health (Oxf) 2013;35(4):510-7. 3. NHR Monthly Report. (June 2023). Available at: https://nhr.mdsas.com/wp-content/uploads/2023/06/NHR_Public_Report.pdf 4. Healthline Media; The DHC Group. (April 2023). Available at: https://healthlinemedia.com/assets/files/HealthlineMedia_Social_Health_Trends_Report_2023.pdf

Governance of public contracts: the materialization of efficiency and planning principles in law 14,133/2021

This article seeks to elucidate the Governance of public contracts provided for in article 11, sole paragraph, of Law 14,133/2021, as the formalization of the efficiency and planning principles. Thus, it presents the historical search for professionalization, planning and efficiency in the Brazilian Public Administration, from the bureaucratic reform until the advent of the law 14,133/2021, with emphasis on the management reform from the 1990s. Afterwards, the subsequent approximation of private management mechanisms to public management is presented, with emphasis on corporate Governance, which, based on the agency theories, resulted in the concept of the organizational public Governance, based on the notions of leadership, strategy and control, widely present in the 2021 law through various instruments. The methodology used was that of bibliographic research.

Directed quick search guided evolutionary framework for large-scale multi-objective optimization problems

For large-scale multi-objective evolutionary algorithms (LSMOEAs), obtaining efficient evolutionary directions in an ultrahigh-dimensional decision space to produce high-quality offspring is a major challenge. This study proposes a novel framework called the directed quick search-guided large-scale evolutionary framework (QSLMOF) to address multi-objective optimization problems on a large scale. The framework contains two innovative strategies: the bidirectional vector-based sampling strategy (BDVS) and the quick search-guided directed reproduction strategy (QS-DRS). BDVS is introduced as an approach to swiftly discern promising solutions that can steer the exploration in the large-scale decision space. This is achieved by formulating two distinct types of sampling directions to rapidly reduce the search space and strike a delicate balance between convergence and diversity. In QS-DRS, we introduced the concept of the potential convergence gradient (PCG), incorporating directional information from historical searches and convergence directions indicated byelite solutions in the current population. With this property, inferior solutions can obtain excellent search directions to explore the decision space, which can improve the convergence rate and prevent the search from falling into a local optimum. The proposed large-scale evolutionary framework incorporates an existing environmental selection mechanism. Comprehensive experiments show that the two novel strategies improve the search efficiency and evolutionary quality of LSMOEAs in ultrahigh-dimensional decision spaces. Moreover, the proposed framework outperformed seven state-of-the-art LSMOEAs for nine large-scale multi-objective benchmark problems LSMOP1-LSMOP9 with up to three objectives and 10000 decision variables.

Accelerating Two-Phase Multiobjective Evolutionary Algorithm for Electric Location-Routing Problems

Electric location-routing problem is a challenging problem consisting of the optimization of electric vehicle routing and charging facility location, simultaneously. Existing algorithms generally adopt the two-phase search strategy to alternately optimize the routing and the location. However, they are usually criticized for the inefficiency as the problem scale increases. In order to improve the search efficiency in each phase, we propose an accelerating two-phase multiobjective evolutionary algorithm, where the learning method is used to mine the useful information from the historical search process to generate the high-quality routing and location offspring. To be specific, in the routing optimization phase, an interpolation method is developed to extract the frequent visiting orders existing in the historical best solutions. These frequent visiting orders are used to create potential routing offspring that can accelerate the convergence toward the optimal solutions. In the location optimization phase, a surrogate model is used to approximatively represent the relationship from routing to location, which can directly output a promising location scheme for a given routing offspring and thus reduce the optimization time. Experimental results on different scales of test instances demonstrate the competitiveness of the proposed algorithm in comparison with several state of-the-art algorithms, including four widely used heuristic algorithms and two multiobjective evolutionary algorithms.

Differential Evolution Algorithm with Dual Information Guidance

As an effective tool to solve continuous optimization problems, differential evolution (DE) algorithm has been widely used in numerous fields. To enhance the performance, in recent years, many DE variants have been developed based on the idea of multiple strategies. However, there still exists an issue for them that the strategy selection method relies on the historical search experience. The experience may be suitable for the problems with smooth fitness landscapes, but not for the problems with rugged fitness landscapes. To alleviate this issue, in this work, a new multiple strategies-based DE variant with dual information guidance is proposed, called DIGDE. In the DIGDE, to avoid the unreliable historical search experience, the fitness information and spatial information are utilized simultaneously as a guidance to estimate the evolutionary states for each individual, and then the most appropriate strategy can be chosen correspondingly. To verify the effectiveness of the DIGDE, 52 test functions are included in the experiments, and three well-established DE variants and four other evolutionary algorithms are involved in the performance comparison. The results show that the DIGDE achieves competitive performance on not only the result accuracy but also the convergence rate.

Learning from the Wisdom of Crowds: Exploiting Similar Sessions for Session Search

Search engines are essential internet services, enabling users to efficiently find the information they need. Session search employs users’ session logs of queries to solve complex retrieval tasks, in which users search multiple times until interested documents are found. Most existing session search models focus on the contextual information within the current search, ignoring the evidence from historical search sessions. Considering the fact that many ongoing retrieval tasks should have already been carried out by other users with a similar intent, we argue that historical sessions with similar intents can help improve the accuracy of the current search task. We propose a novel Similar Session-enhanced Ranking (SSR) model to improve the session search performance using historical sessions with similar intents. Specifically, the candidate historical sessions are matched by query-level and session-level semantic similarity, and then query-level neighbor behaviors are aggregated by a Query-guided GNN (QGNN) while session-level neighbor behaviors are aggregated using the attention mechanism. Finally, we integrate the refined and aggregated historical neighbor information into the current search session. Experimental results on AOL and Tiangong-ST datasets show that our SSR model significantly outperforms the state-of-the-art models.

A Surrogate-Assisted Two-Stage Differential Evolution for Expensive Constrained Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully employed for expensive optimization. However, most SAEAs are designed for expensive unconstrained optimization, and less attention has been paid to expensive optimization with inequality constraints. Therefore, this work proposes a novel SAEA, called surrogate-assisted two-stage differential evolution (SA-TSDE), for expensive constrained optimization. In the first search stage, surrogate-assisted hybrid differential evolution is adopted for prescreening promising solutions in the decision space for exploration. Moreover, an effective repair strategy, named surrogate based repair strategy, is introduced to move the infeasible solutions closer to the feasible region. In the second search stage, a clustering strategy of feasible solutions is proposed based on the information provided by the first search stage and historical search. The clustering strategy adaptively generates a number of clusters, each of which can form a promising local region for the local search. Afterwards, local surrogates are built for finding the predicted optima in each local region. During the search process, a good balance between exploration and exploitation can be obtained by interleaving the two search stages. Experimental results indicate that SA-TSDE is highly competitive compared with some state-of-the-art methods.

The CORALIE survey for southern extrasolar planets

Context. A historical search for exoplanets among a sample of 1647 nearby southern main sequence stars with the CORALIE spectrograph at La Silla Observatory has been underway since 1998, with a backup subprogram dedicated to the monitoring of binary stars. Aims. We reviewed 25 years of CORALIE measurements and search for Doppler signals consistent with stellar or brown dwarf companions to produce an updated catalog of both known and previously unpublished binary stars in the planet-search sample. We assessed the binarity fraction of the stellar population and survey the prospects for more precise searches for planets in the binary sample. Methods. We performed a new analysis on the CORALIE planet-search sample’s radial velocity measurements, searching for stellar companions and obtaining orbital solutions for both known and new binary systems. We performed simultaneous radial velocity and proper motion anomaly fits on the subset of these systems for which HIPPARCOS and Gaia astrometry measurements are available, obtaining accurate estimates of true mass for the companions. Results. We found 218 stars in the CORALIE sample to have at least one stellar companion, 130 of which are not yet published in the literature and for which we present orbital solutions. The use of the proper motion anomaly allowed us to derive true masses for the stellar companions in 132 systems, which we additionally used to estimate stability regions for possible planetary companions on circumprimary or circumbinary orbits. Finally, we produced detection-limit maps for each star in the sample and obtained occurrence rates of 0.43−0.11+0.23% and 12.69−0.77+0.87% for brown dwarf and stellar companions, respectively, in the CORALIE sample.

Text Matching in Insurance Question-Answering Community Based on an Integrated BiLSTM-TextCNN Model Fusing Multi-Feature

Along with the explosion of ChatGPT, the artificial intelligence question-answering system has been pushed to a climax. Intelligent question-answering enables computers to simulate people's behavior habits of understanding a corpus through machine learning, so as to answer questions in professional fields. How to obtain more accurate answers to personalized questions in professional fields is the core content of intelligent question-answering research. As one of the key technologies of intelligent question-answering, the accuracy of text matching is related to the development of the intelligent question-answering community. Aiming to solve the problem of polysemy of text, the Enhanced Representation through Knowledge Integration (ERNIE) model is used to obtain the word vector representation of text, which makes up for the lack of prior knowledge in the traditional word vector representation model. Additionally, there are also problems of homophones and polyphones in Chinese, so this paper introduces the phonetic character sequence of the text to distinguish them. In addition, aiming at the problem that there are many proper nouns in the insurance field that are difficult to identify, after conventional part-of-speech tagging, proper nouns are distinguished by especially defining their parts of speech. After the above three types of text-based semantic feature extensions, this paper also uses the Bi-directional Long Short-Term Memory (BiLSTM) and TextCNN models to extract the global features and local features of the text, respectively. It can obtain the feature representation of the text more comprehensively. Thus, the text matching model integrating BiLSTM and TextCNN fusing Multi-Feature (namely MFBT) is proposed for the insurance question-answering community. The MFBT model aims to solve the problems that affect the answer selection in the insurance question-answering community, such as proper nouns, nonstandard sentences and sparse features. Taking the question-and-answer data of the insurance library as the sample, the MFBT text-matching model is compared and evaluated with other models. The experimental results show that the MFBT text-matching model has higher evaluation index values, including accuracy, recall and F1, than other models. The model trained by historical search data can better help users in the insurance question-and-answer community obtain the answers they need and improve their satisfaction.

Cisheteronormativity and its influence on the psychosocial experience of LGBTQ+ people with cancer: A qualitative systematic review.

Cisheteronormativity refers to the relationship of heterosexual and cisgender privilege stemming from patriarchy. Although studies have shown that cisheteronormativity can impact health outcomes for lesbian, gay, bisexual, transgender, queer and other sexual, gender diverse, and gender nonconforming (LGBTQ+) people, the specific impact on cancer care has not been described. We synthesized the qualitative evidence on how cisheteronormativity impacts the psychosocial experience of LGBTQ+people with cancer. We conducted a historic search in the CINAHL, LGBT+Health, PsycInfo, and PubMed databases. Qualitative studies that described the psychosocial experience of LGBTQ+people with cancer were included. After appraising the quality of the publications, 11 articles were included. Then, we conducted inductive nominal coding, taxonomic analysis, and thematic synthesis. Two main themes emerged, (1) Cisheteronormativity as a social determinant of health, and (2) Cancer, sexual orientation, and gender: Associations and introjections. The themes comprise four categories and 13 subcategories that describe the impact of cisheteronormativity on the cancer experience of LGBTQ+people. Cisheteronormativity within the healthcare system impacts the psychosocial experience of LGBTQ+people with cancer. Understanding how these gender biases, norms, and social expectations impact the cancer experience is necessary to transform social norms and promote health equity.

Unity or Anarchy? A Historical Search for the Psychological Consequences of Psychotrauma

The field of traumatic stress is often referred to as being in a state of controversy and lack of continuity. Throughout history, disputes repeatedly centered on defining the psychological consequences of severe adverse events and on their causes. Even to this day this is current. To understand these controversies, an extensive historical literature review is presented of how mental consequences of trauma have been described in history, of the circumstances in which this took place, and of the disputes that have influenced the conceptualization of these mental responses. We found psychotrauma always being surrounded by controversy. Significant heterogeneity in symptom expression has been described over the centuries to this day. Some symptoms appeared steadily over many decades, but often each time period showed its own core symptoms. At syndrome level, we found an acute condition, one with longer duration, and a complex condition. Also here, definitions varied over the decades. Finally, causes have always been debated, such as biological, psychological, socio-economic, cultural, political, or legal. To better reflect the described ongoing variation in symptomatology, a more flexible diagnostic approach is proposed with a combination of both staging and subtyping that offers room for a more flexible, symptom-oriented, and personalized perspective.

Quantile-guided multi-strategy algorithm for dynamic multiobjective optimization

Dynamic multiobjective optimization problems (DMOPs) require that the algorithm is capable of tracking the position of Pareto-optimal solution (PS) in a rapidly changing environment. Prediction-based and memory-based methods currently gain much attention for their good tracking ability. However, an elaborate predictor may not be suitable for different problems and the improper reuse of the memory may lower the quality of solution. To overcome these limitations, a quantile-guided multi-strategy algorithm (QMA) for dynamic multiobjective optimization is proposed in this paper. Quantile is always employed to represent characteristics of data because of its robustness to the outliers. In QMA, historical quantile information of decision space is used to find the new position of quantile. Then, the solution set is expanded based on the new quantile. Moreover, historical search information is used to assist in the evolution of population, which is achieved through selecting the mapping matrix as a judgment of retrieval. Meanwhile, the quantile-guided environmental change degree detector is employed to determine the number of retained and the randomly generated individuals when there is no similarity. Experimental results carried out on various DMOPs demonstrate that QMA is highly competitive compared with some state-of-the-art algorithms.

A Surrogate-Assisted Evolutionary Feature Selection Algorithm With Parallel Random Grouping for High-Dimensional Classification

Various evolutionary algorithms (EAs) have been proposed to address feature selection (FS) problems, in which a large number of fitness evaluations are needed. With the rapid growth of data scales, the fitness evaluation becomes time consuming, which makes FS problems expensive optimization problems. Surrogate-assisted EAs (SAEAs) have been widely used to solve expensive optimization problems. However, the SAEAs still face difficulties in solving expensive FS problems due to their high-dimensional discrete decision variables. To address this issue, we propose an SAEA with parallel random grouping for expensive FS problems, in which three main components consist. First, a constraint-based sampling strategy is proposed, which considers the influence of the constraint boundary and the number of selected features. Second, a high-dimensional FS problem is randomly divided into several low-dimensional subproblems. Surrogate models are then constructed in these low-dimensional decision spaces. After that, all the subproblems are optimized in parallel. The process of random grouping and parallel optimization continues until the termination condition is met. Finally, a final solution is chosen from the best solution in the historical search and the best solution in the last population using a random, distance-, or voting-based method. Experimental results show that the proposed algorithm generally outperforms traditional, ensemble, and evolutionary FS methods on 14 datasets with up to 10 000 features, especially when the required number of real fitness evaluations is limited.

Patients are Most Interested in Which Hip Arthroplasty Approach? A 15-year Google Trends Analysis

BackgroundThe purpose of this analysis was to assess the public interest in total hip arthroplasty (THA) based on approach by analyzing Google Trends online search volume trends between 2007 and 2021. MethodsData were obtained by querying the Google Trends online search tool for key terms and phrases relating to anterior, posterior, lateral, and minimally invasive approaches to THA. Data from January 2007 to December 2021 were utilized. Relative search volume (RSV) was generated for each THA approach group based on historical search volume trends in the United States. ResultsOver the 15-year period, Google Trends Search Data demonstrated a statistically significant increase (P < .001) in the RSV for all 4 major hip arthroplasty approaches. The growth in public interest for anterior hip arthroplasty was significantly greater than the growth for posterior (P = .02) and minimally invasive hip arthroplasty (P = .02). The difference in RSV growth between lateral and anterior approaches was not significant (P = .88). The average RSV for anterior hip arthroplasty was 59.0, which was significantly greater than the average RSV of all other groups. ConclusionsThe anterior approach to hip arthroplasty has demonstrated a consistent and statistically significant increase in RSV over the past 15 years that has outpaced the increases observed in the posterior and minimally invasive approaches. Despite the increase in public awareness and interest for anterior approach hip arthroplasty, it is yet to demonstrate any long-term clinical benefits over other approaches.

Understanding Church History in monographs and thematic collections in the Ukrainian SSR in the second half of the 1980s.

The subject of the study is the main directions of intellectual search in the field of church history among Ukrainian scientists of the second half of the 1980s, which were reflected in monographs and thematic collections. The Ukrainian national intelligentsia of the era of "perestroika" forms images of the Church, which will be actively interpreted in different approaches of one or another part of society to the construction of church life in Ukraine as an independent state. With the help of historical-genetic and ideographic methods, the publications of Ukrainian scientists of this period concerning the issues of understanding the role and place of the Church in the past and present of the country are analyzed. The analysis undertaken allows us to identify the ideological foundations of various trends in modern Ukrainian historiography concerning church history. The conclusions about the formation of several directions for understanding the church past of the country are substantiated. The first direction should be characterized by the desire to remove church-historical issues from the periphery of scientific research into a full-fledged sphere of scientific research. A separate group includes works whose authors, when considering the issues of the history of the Orthodox Church, largely relied on Marxist methodology and used characteristic scientific tools. Finally, representatives of the third direction are characterized by a focus on the ideological side of historical search and personal experience, which often prevailed over an objective scientific approach.