Historical Information Research Articles

Role of medieval texts in the system of common Turkic historical sources

Source studies, as a science is fundamental to various fields within the humanities and is inherently interdisciplinary. Among the theoretical issues of source studies, historical source analysis holds the primary position. The article utilized works by Mirza Muhammad Haidar Dughlat and Kadyrgali Kosymuly Zhalairi as the primary source. These works contain unique information about the consolidation of Kazakh tribes in the middle of the 15th century and their unification into a large state entity.The fundamental nature of source studies is determined primarily by the distinctive features of historical science: a historian cannot directly study the object of history - the “past of society”. It is only mastered through critical analysis of historical sources, which are the subject of historical science. According to the theory of source studies, the main goal of research is generalization and systematization of historical information, methods of assessment and generalization are shown. Data science is closely intertwined with the practice of factual data analysis. Source studies is a critical assessment of their reliability and validity. Theory of source studies theory should ensure professionalism, quality, and the effectiveness of research.

Crises of a Fractional Birhythmic Van der Pol Oscillator Using the Improved Cell Mapping Method

Birhythmicity oscillators have been extensively applied in fields such as biology, physics, and engineering. Studying their global dynamics is crucial for gaining a comprehensive understanding of the intrinsic mechanisms that govern oscillator behavior. This paper focuses on investigating the influence of memory effects on the global dynamics of the fractional birhythmic van der Pol (BVDP) oscillator. To determine the system’s global properties, we employ an improved cell mapping method. Specifically, the system’s evolution is computed by introducing additional auxiliary variables, creating a space for storing historical information. This method allows us to examine the system’s global properties without memory loss. Through a comparison of the global dynamic behaviors of BVDP oscillators with memory to those without memory, we observe that the presence of memory effects results in the emergence of chaotic attractors in the system. This, in turns, results in system instability and heightened sensitivity to initial conditions. Furthermore, our findings suggest that changes in the fractional-order can induce various crises in the oscillator and may have the potential to suppress chaotic oscillations.

Limited Memory-Based Random-Weighted Kalman Filter.

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation.

Testing the weak-form efficiency of Arab stock markets after the COVID-19 pandemic

Weak-form efficiency means that stock prices should reflect all historical information and follow a random walk. This study examines the effect of the COVID-19 pandemic on the stock market weak-form efficiency of Arab countries, namely, Jordan, Lebanon, Kuwait, Morocco, Oman, Palestine, Bahrain, Egypt, Iraq, Qatar, Saudi Arabia, the United Arab Emirates, Syria, Tunisia, and Sudan. Daily data from July 1st, 2021 to November 12th, 2022 (370 trading days) are used to cover the period after starting the pandemic. The variance ratio and the runs test are used to test return predictability. The results show that the variance ratio values of Boursa Kuwait, the Egyptian Exchange, Tadawul, and the Amman Stock Exchange are statistically significant, indicating that their returns are unpredictable. In specific, the indices of these stock markets follow a random walk, and their price changes are independent. This is evidence that these stock markets are efficient at a weak level. In contrast, the insignificant values of the variance ratio indicate that returns are predictable in other Arab stock exchanges after the pandemic era. The findings of the Egyptian Exchange, Tadawul, and the Amman Stock Exchange are confirmed using the run test of weak-form efficiency. It reveals that the indices of these stock exchanges follow a random walk, while the indices of other Arab stock markets do not.

Scheme for Classification of Sources by Types and Subtypes

Historical sources are material, oral and written monuments of the past that contain information about the past and are involved in the process of scientific research by scientists. It became It is clear that different historical stages of development of society and countries reserve the dominant forms of historical information, in the form of material and oral information, written, documentary sources, or, as is now dominant, digitalized ones. electronic records and more.

A Disability Studies Analysis of Alcohol Use: Understanding Personal Experiences through Dominant Discourses on Addiction

This paper describes my experiences with coming to understand my own relationship with alcohol dependency and addiction. Disability studies has offered me a lens and guide through which I have critically interrogated discourses about addiction, while examining the ways dominant and counter-narratives have impacted my own recovery process. In this paper, I review historical information about the emergence of culturally accepted recovery ideologies in the United States that have led to a dominant disease model perspective. Then, I explain the disability studies-informed theoretical underpinnings of this paper, which include discourse theory and disability studies as applied to alcohol addiction. Using disability studies and autoethnography as a guide, the body of the paper uses examples from my own journals to elucidate salient themes that emerged about my experiences in early recovery. The paper uncovers the ways I came to understand my own identity related to addiction, how I navigated feelings of stigma and shame, the ways I found recovery spaces that embraced empowering frameworks aligned to a disability studies ethos, and how I discovered community and pride through this experience.

I[formula omitted]U-Net: A dual-path U-Net with rich information interaction for medical image segmentation

Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers. This weakness makes it difficult for the current layer to effectively utilize the historical information of the previous layer, leading to unsatisfactory segmentation results for lesions with blurred boundaries and irregular shapes. To solve this problem, we propose a novel dual-path U-Net, dubbed I2U-Net. The newly proposed network encourages historical information re-usage and re-exploration through rich information interaction among the dual paths, allowing deep layers to learn more comprehensive features that contain both low-level detail description and high-level semantic abstraction. Specifically, we introduce a multi-functional information interaction module (MFII), which can model cross-path, cross-layer, and cross-path-and-layer information interactions via a unified design, making the proposed I2U-Net behave similarly to an unfolded RNN and enjoying its advantage of modeling time sequence information. Besides, to further selectively and sensitively integrate the information extracted by the encoder of the dual paths, we propose a holistic information fusion and augmentation module (HIFA), which can efficiently bridge the encoder and the decoder. Extensive experiments on four challenging tasks, including skin lesion, polyp, brain tumor, and abdominal multi-organ segmentation, consistently show that the proposed I2U-Net has superior performance and generalization ability over other state-of-the-art methods. The code is available at https://github.com/duweidai/I2U-Net.

Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach

Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for similar cases. However, such data are typically stored in multiple systems, making its reachability technical. Utilizing technologies like deep learning and sentiment analysis provides chances to facilitate faster access to court records. Such practice enhances impartial verdicts, minimizing workloads for court employees, and decreases the time used in legal proceedings for claims during maritime contracts such as shipping disputes between parties. This paper seeks to develop a sentiment analysis framework that uses deep learning, distributed learning, and machine learning to improve access to statutes, laws, and cases used by maritime judges in making judgments to back their claims. The suggested approach uses deep learning models, including convolutional neural networks (CNNs), deep neural networks, long short-term memory (LSTM), and recurrent neural networks. It extracts court records having crucial sentiments or statements for maritime court verdicts. The suggested approach has been used successfully during sentiment analysis by emphasizing feature selection from a legal repository. The LSTM + CNN model has shown promising results in obtaining sentiments and records from multiple devices and sufficiently proposing practical guidance to judicial personnel regarding the regulations applicable to various situations.

Security Development Lifecycle-Based Adaptive Reward Mechanism for Reinforcement Learning in Continuous Integration Testing Optimization

Continuous automated testing throughout each cycle can ensure the security of the continuous integration (CI) development lifecycle. Test case prioritization (TCP) is a critical factor in optimizing automated testing, which prioritizes potentially failed test cases and improves the efficiency of automated testing. In CI automated testing, the TCP is a continuous decision-making process that can be solved with reinforcement learning (RL). RL-based CITCP can continuously generate a TCP strategy for each CI development lifecycle, with the reward mechanism as the core. The reward mechanism consists of the reward function and the reward strategy. However, there are new challenges to RL-based CITCP in real-industry CI testing. With high-frequency iteration, the reward function is often calculated with a fixed length of historical information, ignoring the spatial characteristics of the current cycle. Therefore, the dynamic time window (DTW)-based reward function is proposed to perform the reward calculation, which adaptively adjusts the recent historical information range based on the integration cycle. Moreover, with low-failure testing, the reward strategy usually only rewards failure test cases, which creates a sparse reward problem in RL. To address this issue, the similarity-based reward strategy is proposed, which increases the reward objects of some passed test cases, similar to the failure test cases. The DTW-based reward function and the similarity-based reward strategy together constitute the proposed adaptive reward mechanism in RL-based CITCP. To validate the effectiveness of the adaptive reward mechanism, experimental verification is carried out on 13 industrial data sets. The experimental results show that the adaptive reward mechanism can improve the TCP effect, where the average NAPFD is maximally improved by 7.29%, the average Recall is maximally improved by 6.04% and the average TTF is improved by 6.81 positions with a maximum of 63.77.

Location and Distribution Decision of Emergency Logistics Network under Uncertain Demand Distributions

To ensure the supply of materials after urgent events, including recent public health crisis, it is crucial to formulate a reasonable preventive strategy for the location and distribution of emergency materials within the logistics network. In this paper, a class of multi-category emergency material storage distribution models under scenarios with uncertain post-event demand distribution is studied. Based on historical small-sample post-event demand information, the model constructs a two-stage material storage and scheduling distributionally robust optimization model. By applying a dual method, the non-linear distributionally robust optimization is transformed into linear optimization, and an improved Lagrangian L-shaped algorithm is designed to solve the two-stage model. Finally, the robustness of the model and algorithm is verified by implementing the method to a numerical example, and the sensitivity of the location and distribution decisions to material shortage under different levels of urgent events is analyzed.

Aircraft Ground Taxiing Deduction and Conflict Early Warning Method Based on Control Command Information

Aircraft taxiing conflict is a threat to the safety of airport operations, mainly owing to human error in control command information. To solve this problem, an aircraft taxiing deduction and conflict early warning method based on control order information is proposed. Our method does not require additional equipment or operating costs, and is completely based on historical data and control information. To detect conflicts with other taxiing aircraft when an aircraft taxis according to commands, it can infer the future route and calculate the probability of conflict with other taxiing aircraft. Validation of the method was conducted using real-world airport taxi data. The results show that the method can effectively predict the aircraft taxiing process and provide an early warning of possible conflicts. Decision support systems for airport operations can benefit from this method.

Short-Term Flood Prediction Model Based on Pre-Training Enhancement

With the rapid advancement of deep learning techniques, deep learning-based flood prediction models have drawn significant attention. However, for short-term prediction in small- and medium-sized river basins, models typically rely on hydrological data spanning from the past several hours to one day, utilizing a fixed-length input window. Such input limits the models’ adaptability to diverse rainfall events and restricts their capability to comprehend historical temporal patterns. To address the underutilization of historical information by existing models, we introduce a Pre-training Enhanced Short-term Flood Prediction Method (PE-SFPM) to enrich the model’s temporal understanding without necessitating changes to the input window size. In the pre-training stage, the model uses a random masking and prediction strategy to learn segment features, capturing a more comprehensive evolutionary trend of historical floods. In the flow forecasting stage, temporal features and spatial features are captured and fused using the temporal attention, spatial attention, and gated fusion. Features are further enhanced by integrating segment features using a feed-forward network. Experimental results demonstrate that the proposed PE-SFPM model achieves excellent performance in short-term flood prediction tasks.

A Knowledge-Guided Multi-Objective Shuffled Frog Leaping Algorithm for Dynamic Multi-Depot Multi-Trip Vehicle Routing Problem

Optimization algorithms have a wide range of applications in symmetry problems, such as graphs, networks, and pattern recognition. In this paper, a dynamic periodic multi-depot multi-trip vehicle routing model for scheduling test samples is constructed, which considers the differences in testing unit price and testing capacity of various agencies and introduces a cross-depot collaborative transport method. Both the cost and the testing time are minimized by determining the optimal sampling routes and testing agencies, subjecting to the constraints of vehicle capacity, number of vehicles, and delivery time. To solve the model, a knowledge-guided multi-objective shuffled frog leaping algorithm (KMOSFLA) is proposed. KMOSFLA adopts a convertible encoding mechanism to realize the diversified search in different search spaces. Three novel strategies are designed: the population initialization with historical information reuse, the leaping rule based on the greedy crossover and genetic recombination, and the objective-driven enhanced search. Systematic experimental studies are implemented. First, feasibility analyses of the model are carried out, where effectiveness of the cross-depot collaborative transport is validated and sensitivity analyses on two parameters (vehicle capacity and proportion of the third-party testing agencies) are performed. Then, the proposed algorithm KMOSFLA is compared with five state-of-the-art algorithms. Experimental results indicate that KMOSFLA can provide a set of non-dominated schedules with lower cost and shorter testing time in each scheduling period, which provides a reference for the dispatcher to make a final decision.

Изучение краниологических серий из некрополей русских городов XI–XVIII вв. методом геометрической морфометрии

Anthropological features of the Russian city population in the 11th – 18th centuries were studied using classical methods of craniometry as well as cranio- and osteoscopy. However, the method of geometric morphometry, which has been actively used since the 1990s and is a recent morphometric tool, has not yet been applied to Russian urban craniological series. We obtained and analyzed by the GM method three-dimensional copies of 225 skulls from thirteen series of the Middle Ages and the Early Modern period from the excavations of the Institute of Archaeology of the Russian Academy of Sciences. Despite the small number of studied samples from Tver and Torzhok, we were able to trace their features and originality relative to other comparable groups and among themselves. Based on historical information, these variations can be explained by political factors. The differences between the political systems of Ancient Rus’ and Russia in the Early Modern period also explain the greater morphological homogeneity of the urban population in the 15th – 18th centuries compared to the 11th –13th centuries. The study of variability in the late urban series and comparison of diachronic samples from Yaroslavl and Pereslavl-Zalessky showed morphological similarity between the inhabitants of the Middle Ages and the Early Modern period, which may indicate the constancy of administrative, trade, and economic ties, as well as the significant contribution of the local rural population to the formation of the anthropological appearance of the townspeople. The revealed greater variability in the female samples compared to the male ones may indicate significant irregularities in the facial skeleton shape of women, which cannot be disclosed using classical craniology data. In general, the obtained results not only confirm many of the conclusions of previous craniological studies of the urban population from Eastern Europe but also make it possible to obtain new data on the degree of homogeneity of the townspeople’s anthropological appearance in the Middle Ages and Early Modern periods.

The history of the Church of St. Nicholas the Wonderworker in the village of Batyushkovo, Dmitrovsky district (XVII-XIX centuries)

This article provides historical information about the architectural monument characteristic of the Moscow patrimonial (Posadsky) architecture – the Church of St. Nicholas the Wonderworker in the village of Batyushkovo, Dmitrovsky district. The author has studied and systematized data on the owners, construction and repair of the church since the XVII century, collected data on local clergy. The research is based on materials from the archive of the Dmitrov Kremlin State Museum-Reserve, the archive of the Shchusev State Museum of Architecture, the archive of the New Jerusalem State Historical and Art Museum, the Russian State Archive of Ancient Acts and the Central State Archive of Moscow. The work contains research on the history of the Dmitrov region, studied archival sources: metric books and church records. There is practically no historical information about the state of the church for the period from the XVII to the beginning of the XIX century, and information about the landowners of the village in the second half of the XVIII – early XIX century is sketchy. A valuable contribution to the study is information about the biographies of each of the owners of the village of Batyushkovo: their kind of activity, position in society. The most important aspect is the publication of previously unknown sources: drawings of the Church of St. Nicholas the Wonderworker in the middle of the XIX century by architect Vasily Dregalov.

A Lambda Layer-Based Convolutional Sequence Embedding Model for Click-Through Rate Prediction

In the era of intelligent economy, the click-through rate (CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors (point-level), while ignoring the relationships between elements in item embedding vectors (union-level). The Lambda layer-based Convolutional Sequence Embedding (LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC (Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.

On the role of toponymic commemorative practices in the research and preservation of disappeared names of geographical objects

INTRODUCTION. The problem of research and preservation of disappeared names of geographical objects through toponymic commemorative practices is considered. An analysis is given of the activities of mass communications, thanks to which the memory of bygone toponyms is passed on to future generations of residents of the country as a whole and individual regions, and the local toponymic martyrology is regularly updated with new names. The purpose of the study is to identify the role of toponymic commemorative practices in the study and preservation of disappeared names of geographical objects located on the territory of the Russian Federation.MATERIALS AND METHODS. Using specific examples from the experience of the Voronezh Region on the successful implementation of toponymic commemorative practices, their main types are considered, recommendations are given to public authorities and local governments on how to work in this direction. The empirical base of the study consists of alphabetical lists of geographical names of previously existing settlements registered in the automated state catalog of geographical names, publications in the media and in scientific publications.RESULTS AND DISCUSSION. It is shown that the main types of toponymic commemorative practices can be the installation of commemorative signs, information plaques, chapels, monuments on the site of disappeared settlements, the publication of relevant local history materials, works of art and memoirs of former residents of these settlements in regional and district media, the holding of mass cultural and historical events.CONCLUSION. Communication practices are a reliable channel for preserving the memory of disappeared settlements and their names and transmitting relevant historical and toponymic information to subsequent generations of Russians. To this end, the state authorities and local governments of the subjects of the Russian Federation are recommended to apply toponymic commemorative practices in their activities.

Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

Enhancing Document Information Selection Through Multi-Granularity Responses for Dialogue Generation

Document information selection is an essential part of document-grounded dialogue tasks, and more accurate information selection results can provide more appropriate dialogue responses. Existing works have achieved excellent results by employing multi-granularity of dialogue history information, indicating the effectiveness of multi-level historical information. However, these works often focus on exploring the hierarchical information of dialogue history, while neglecting the multi-granularity utilization in response, important information that holds an impact on the decoding process. Therefore, this paper proposes a model for document information selection based on multi-granularity responses. By integrating the document selection results at the response word level and semantic unit level, the model enhances its capability in knowledge selection and produces better responses. For the division at the semantic unit level of the response, we propose two semantic unit division methods, static and dynamic. Experiments on two public datasets show that our models combining static or dynamic semantic unit levels significantly outperform baseline models.