Historical Information Research Articles

The technology of restoration of the red brick facade on the example of an object of cultural heritage of regional significance “Mansion of the Philistine Galaktionov G.I.”

The article provides brief historical and archival information about the object of cultural heritage of regional significance “Mansion of the philistine Galaktionov G.I.”. A description of the decorative solution of the facades of the building, information about its spatial planning solution is given. The emphasis is placed on an unusual design solution of the main facade of the building – the vertical log cabin is lined with brickwork. Some results of complex engineering surveys are presented, from which it can be seen that the preserved structures of the building are in disrepair, there is a large percentage of masonry losses along the facades. However, it is still possible to preserve the original facing layer of the masonry of the main facade and technological solutions for its restoration are given. The description of the measures after the work on the preservation of the red brick facade is given. An assertion is made about the individuality of the developed technology, since each object of cultural heritage is unique from the point of view of the degree of preservation of structures.

Police specialism in England and Wales: an exploratory review

Purpose There is a surprising lack of underpinning evidence relating to how police specialism is defined, conceived and operationalised nationally. This study aims to shed light on the development of specialism, adding insights towards academic and police knowledge on the topic. Design/methodology/approach Exploratory research was conducted to draw evidence on police specialism in England and Wales according to publications in the literature and insights from high-ranking police officers. A total of 57 documents and 10 officers’ accounts were thematically analysed. Five main themes have been identified, relating to the development, impact and barriers of specialist units, knowledge and training. Findings Socio-cultural, policy-based and historical information that contributed to the development of specialism in its contemporary form were discussed. The conceptual triggers for the institution of most specialist units were disasters and a need for modernisation. In both cases, police forces were faced with the inability to keep up with emerging threats and new criminal techniques. Some exceptions apply, with specialisms of sex offences still being underdeveloped and underfunded compared to other crimes. Overall, although specialism has the potential to positively impact police efficacy, the specialist knowledge of officers working within specialist units is frequently inferred – rather than measured. Practical implications Organisational support and the quality of training impact specialism effectiveness. The substantial lack of resources allocated to specialism means that training for some crime types is either unavailable or difficult to complete. Police officers fear that specialism might lead to elitism and corrupt police practices. There is no evidence, however, that elitist beliefs are the direct result of specialism or rather additional co-occurring organisational, cultural and force-specific or role-dependant factors. Police officers’ wellbeing and specialist capacity take a significant toll. Originality/value To the best of the authors’ knowledge, this was the first modern study of its kind that investigated how police specialism is conceived, instituted and prioritised in England and Wales.

Design of Travel Route Planning Model Based on Deep Learning

Tourism route planning is affected by the factors such as tourism destination and tourists’ preference, which leads to poor automatic matching of routes. A mathematical model of tourism route planning based on deep learning is proposed. Under the condition of total route constraints, according to the historical tourism preference information and prior information of tourists, a big data model for the distribution of personalized characteristics of tourists is established, spatial constraints and time constraints parameters are input, and the feature matching of geographic location information and tourist interest parameter information is carried out by using the deep learning method, and the statistical feature quantity of the parameters of the tourism route planning model is extracted. Under the control of the deep learning and geographic information data set, Carry out multi-constraint and multi-objective hierarchical analysis of tourists and tourism destinations, and realize the optimization design of tourism route planning algorithm. The simulation results show that the accuracy of this method is high and the deviation is small. It improves the satisfaction level of tourists and helps users complete a series of visual analysis tasks such as route mining, route planning and destination analysis.

Spatial Remodeling of Industrial Heritage from the Perspective of Urban Renewal: A Case Study of Coal Mine Site in Jiaozuo City

Resource-oriented cities are faced with the problems of the decline of traditional industries, exhaustion of resources, and wastage of space. Faced with these problems, urban renewal has become an important role and is widely used in the reuse of old and abandoned spaces. As a historical witness of the industrial revolution and urbanization process, coal mine industrial heritage not only has the value of material heritage but also carries rich historical and cultural information. However, with the adjustment of industrial structure, much coal mine industrial heritage has gradually lost its original production function and become neglected idle space in cities, and industrial buildings and equipment in these spaces have been abandoned or dismantled. The study takes the Wangfeng Mine site in Jiaozuo City, Henan Province as an example, combined with the urban development history and current situation of Jiaozuo city, it discusses the remodeling strategy of industrial heritage space from the perspective of urban renewal. Firstly, through case analysis, historical data sorting, and field research, the study integrated the historical development context of the Jiaozuo coal mine site and its impact on the urban spatial pattern, secondly, discussed the practical problems in the reuse process of industrial sites, and finally proposed specific spatial remodeling strategies based on the conjugation theory. This included determining how to deal with the three pairs of conjugated relations between protection and development, function and ecology, and history and modernity so as to make the spatial remodeling strategy of industrial sites more scientific and sustainable. To promote the sustainable and healthy development of urban industrial heritage space.

RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

Burden of Parkinson's disease in Central Asia from 1990 to 2021: findings from the Global Burden of Disease study.

Central Asia is known to face various ecological challenges that constitutes major risk factors for Parkinson's disease (PD). This study examines the burden of PD in Central Asia, a region where data on neurological disorders is notably sparse. Building on the latest Global Burden of Disease Study (GBD 2021), this study investigates the Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and Disability-Adjusted Life Years (DALYs) associated with PD in Central Asia and its countries from 1990 to 2021. The authors calculated average annual percent change (AAPC) to analyze trends, and compared individual country estimates to global figures. Additionally, incorporating data from the World Bank, both Bayesian hierarchical and non-hierarchical frequentist regression models were employed to assess their impact on DALYs. The DALYs varied across the study period, primarily driven by YLLs. While YLLs showed a uniform trend, YLDs were mostly incremental. Kazakhstan had the highest estimates across all metrics and was the only country aligned with global patterns. Age- and sex-specific estimates revealed substantial variations, with notably high figures found in male subjects from Tajikistan. The YLLs, YLDs, and DALYs for Kazakhstan, Uzbekistan, and Turkmenistan saw a significant increase in AAPCs. In contrast, Kyrgyzstan and Tajikistan saw declines, likely attributable to civic conflict and inter-country differences in population structure. Further comparison of DALY trends revealed significant deviations for all countries from the global pattern. This study showed an overall increase in PD burden from 1990 to 2021. These findings underscore the need for targeted strategies to reduce PD burden, with a particular focus on Kazakhstan. Integrating historical information is crucial for discussing the plausible mechanisms in studies sourced from the GBD.

Rasmussen Encephalitis: Clinical Features, Pathophysiology, and Management Strategies—A Comprehensive Literature Review

Rasmussen encephalitis (RE) is a rare and progressive form of chronic encephalitis that typically affects one hemisphere of the brain and primarily occurs in pediatric individuals. The current study aims to narratively review the literature about RE, including historical information, pathophysiology, and management of this condition. RE often occurs in individuals with normal development, and it is estimated that only a few new cases are identified each year in epilepsy centers. Approximately 10% of cases also occur in adolescents and adults. The hallmark feature of RE is drug-resistant focal seizures that can manifest as epilepsia partialis continua. Also, patients with RE usually develop motor and cognitive impairment throughout the years. Neuroimaging studies show progressive damage to the affected hemisphere, while histopathological examination reveals T-cell-dominated encephalitis with activated microglial cells and reactive astrogliosis. The current therapy guidelines suggest cerebral hemispherotomy is the most recommended treatment for seizures in RE, although significant neurological dysfunction can occur. Another option is pharmacological management with antiseizure medications and immunomodulatory agents. No significant progress has been made in understanding the pathophysiology of this condition in the last decades, especially regarding genetics. Notably, RE diagnosis still depends on the criteria established by Bien et al., and the accuracy can be limited and include genetically different individuals, leading to unexpected responses to management.

Simulating urban evolution by H-GIS and H-BIM: a temporal and spatial study of Pingjiang map

AbstractHistorical maps provide insights into the morphology, cultural, and political shifts of ancient societies. Pingjiang Map is one of the earliest comprehensive ancient Chinese maps, depicting the heritage water city of Suzhou. This study takes it as the subject to examine the temporal and spatial reconstructions of Suzhou city after its devastation in the Song-Jin wars during the twelfth–thirteenth centuries. It uses historical geographical information system (H-GIS) and historical building information management (H-BIM) to simulate Suzhou city in the Southern Song dynasty (1127–1279) and the city’s subsequent modernization to the present. By comparing and contrasting Pingjiang Map, the Song dynasty Suzhou, and the current city in the configuration of clusters such as urban wall, canal, street, government and citadel, temple, and residential ward, we establish evolutionary links between Pingjiang Map and the city, providing insights to the mechanism underlying urban transformation. Through H-BIM, we conducted a temporal and spatial analysis of the twelfth–thirteenth centuries’ citadel of Suzhou, simulating the construction sequence of palatial artefacts. Our research argues that Pingjiang Map stele forged an imperial vision, serving both for documentation and planning. It encoded building information for the central government to manage post-war reconstruction. The research establishes the workflow of ‘SQS’—spatialization, quantitative analysis, and simulation—to investigate the evolution of imperial cities and their cartography.

Reinforcement of Oracle Bones Using a Novel Silicone Coupling Reagent for Preservation

Oracle bones are artifacts of great significance and value in the study of Chinese history and culture. Because of soil and bacterial erosion, bones become fragile, and the inscriptions on the surface become blurred, resulting in the loss of historical information on the bones. In early times, scholars often used adhesives for bonding and reinforcement, whereas in modern times, organic and inorganic materials have been used as reinforcement for oracle bones. In this study, the surface of oracle bone was protected and reinforced by a new silicone coupling reagent that could self-polymerize in the format of colorless solution with good antimicrobial properties. The DESPMA was applied to the surface of oracle bone by dropwise addition and curing, effectively protecting it from bacteria and slowing down the yellowing process. The results showed that the reagent could significantly improve the antimicrobial properties of bone samples and reduce the yellowing and discoloration caused by bacterial attack. The reagent barely affected the appearance of the bone samples. These findings are promising and valuable for effective application in bone protection and utilization.

Combined query embroidery image retrieval based on enhanced CNN and blend transformer.

Embroidery images carry rich historical information and are an important form of embroidery art. In the field of combination query image retrieval, how to efficiently retrieve the embroidery image information required by users has become a current research challenge. In recent years, convolutional neural networks (CNNs) have achieved significant success in image feature extraction, but they tend to focus on local information, making it easy to ignore global context information when processing such textured embroidery images. Therefore, we propose a combination query retrieval method for embroidery images. First, we propose Blend-Transformer, which introduces Group External Attention (GEA). GEA can integrate feature information from three different dimensions, effectively capturing the local and global context information of embroidery images. Second, we propose Enhanced CNN, which introduces Shuffle Attention (SA), regrouping the reference image features extracted by CNN and reaggregating them by channel to enhance the richness of embroidery image feature information. Through experiments on the TCE-S and ICR2020 standard datasets, we verify the excellent performance of the proposed algorithm in embroidery image retrieval. Our method fills the gap in embroidery image retrieval research and provides a new perspective for the protection of embroidery art.

Predictive Modeling for Bike Rental Demand: Enhancing Operational Efficiency and User Satisfaction in Urban Mobility

This project focuses on developing a predictive model to forecast bike rental demand by analyzing historical rental data, weather information, and temporal variables. The model will employ machine learning techniques, such as regression analysis, time series forecasting, and ensemble methods, to capture the complex relationships between these factors and rental demand.

Continuous evolution of user-defined genes at 1 million times the genomic mutation rate.

When nature evolves a gene over eons at scale, it produces a diversity of homologous sequences with patterns of conservation and change that contain rich structural, functional, and historical information about the gene. However, natural gene diversity accumulates slowly and likely excludes large regions of functional sequence space, limiting the information that is encoded and extractable. We introduce upgraded orthogonal DNA replication (OrthoRep) systems that radically accelerate the evolution of chosen genes under selection in yeast. When applied to a maladapted biosynthetic enzyme, we obtained collections of extensively diverged sequences with patterns that revealed structural and environmental constraints shaping the enzyme's activity. Our upgraded OrthoRep systems should support the discovery of factors influencing gene evolution, uncover previously unknown regions of fitness landscapes, and find broad applications in biomolecular engineering.

HANZE v2.1: an improved database of flood impacts in Europe from 1870 to 2020

Abstract. Assessing long-term trends in flood losses and attributing them to climatic and socioeconomic changes requires comprehensive and systematic collection of historical information. Here, we present flood impact data for Europe that are part of the HANZE (Historical Analysis of Natural HaZards) database. The dataset covers riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The data were obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected. or economic loss. Economic losses are presented both in the original currencies and price levels and with the inflation and exchange rate adjusted to the 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union's Nomenclature of Territorial Units for Statistics (NUTS) level 3. Daily start and end dates, information on causes of the events, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility of downloading the data (https://doi.org/10.5281/zenodo.8410025; Paprotny, 2023a), the database can be viewed, filtered, and visualized online at https://naturalhazards.eu (last access: 4 November 2024). The dataset is designed to be complementary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset values) and be easily usable in statistical and spatial analyses, including multi-hazard studies.

Can Historical Accident Data Improve Sustainable Urban Traffic Safety? A Predictive Modeling Study

Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Modelling and Analysis of the Pisa Tower Based on ANSYS

Abstract. This study looks at why the Leaning Tower of Pisa leans by using historical information and computer simulations. Using the "Spaceclaim" function in ANSYS, we made a full-size model of the tower and the ground it sits on. We did two experiments: first, we simulated how the tower was built to understand why it started leaning, and second, we changed the soil's stiffness (Youngs Modulus) to see how this affects the tower's tilt and vibration. Our results show that softer soil makes the tower lean more, giving important information for preserving the tower and other similar structures around the world.

An energy management method for combined load forecasting of fuel cell ships

Abstract The article proposes an energy management method (EMM) for fuel cell ships, which consists of two parts: load forecasting (LF) and real-time optimized scheduling (RTOS). LF is composed of Long Short-Term Memory neural networks, which are capable of being trained to predict future loads based on historical ship load information. RTOS consists of model predictive control and can allocate energy in real time. Initially, shiploads and battery status are collected in real time, and then LF passes the predicted load sequence to RTOS. RTOS optimizes real-time energy allocation based on the predicted load and the current operating status of two sets of batteries, while meeting system constraints and maintaining the battery SOC at a healthy level, to minimize operational cost consumption. The simulations demonstrate that the EMM can consistently maintain SOC at a healthy level and reduce fuel cell ship operating costs to some extent, thereby improving economic efficiency.

TDOcc: Exploit machine learning and big data in multi-view 3D occupancy prediction

With the advancement of machine learning and big data technologies, BEV(Bird’s Eye View)-based methodologies have recently achieved significant breakthroughs in multi-view 3D occupancy prediction tasks. However, BEV(Bird’s Eye View)-centric 3D occupancy prediction continues to grapple with feature representation and annotation costs when applied to complex open environments. In order to surmount these issues and further propel the evolution of 3D occupancy tasks, this study introduces a novel framework termed TDOcc. By leveraging multi-camera imagery, TDOcc executes 3D semantic occupancy prediction by directly learning from unprocessed 3D spaces, thereby maximizing information retention. TDOcc presents two notable advantages: firstly, it utilizes dense occupancy labels, which not only facilitate robust dense occupancy inference but also enable comprehensive object estimation within the scene. Secondly, the framework synthesizes historical feature information by adeptly aligning past and present features through temporal cues, thereby bolstering the efficacy of the feature fusion module. Additionally, with a view to address the ill-posed nature inherent in camera-based 3D occupancy prediction, we incorporate an enhancement module that operates within the 3D feature space. This module has been meticulously crafted for the training phase to amplify the model’s learning potential. Extensive experiments conducted on the widely recognized nuScenes dataset underscore the efficacy of our proposed approach. Compared to the most recent TPVFormer and OccFormer, our approach has achieved a significant improvement in mean Intersection over Union (mIoU) by 2.0 and 0.8 respectively, and has reached performance comparable to the state-of-the-art LiDAR-based methods.

Developing Predictive Models for Financial Stability: Integrating Behavioral Analytics into Credit Risk Management

In this article, its author takes an attempt at investigating the use of behavioral analytics to complement credit risk management in the world of finance. Current mainstream credit risk models very much depend on conventional methods that incorporate historical financial information like credit scores and income while ignoring client behavioral contemporaneous patterns essential for modeling their risk levels. By this, the transactional patterns; payment behaviour and digital activities included besides the traditional financial parameters allows the financial institution to make superior models that gives better results on credit worthiness and risk identification earlier. It is crucial for both better risk assessment and for embracing more inclusion within lending networks and financial creativity. Analyzing the informational component of behavioral data as well as its applicability in credit risk modeling and considering the difficulties of implementing it in practice, the article provides an outline of the approaches to telemetry integration.

Silk road heritage: The artistic representation of port trading culture in the images of characters in Qing Dynasty Guangzhou export paintings.

As customized artistic commodities, export paintings mirrored Guangzhou's unique overseas trade culture and social power in a microscopic view, nevertheless, few researchers have delved into the cultural relationship between the use of the painting techniques and the characters of the export paintings. Based on an iconographic approach, this article aims to analyze the historical iconography information of the Qing dynasty export paintings from the perspective of the trade development and cultural exchange between the East and West during the 18th century by the study of the export paintings' characters expressions and costumes colors, providing insights into the exploration of the value of tangible cultural heritage. In the process of our research, shows that: 1) the dominant laboring characters in the export paintings present standing postures, and most of them demonstrate negative emotions; 2) many of the characters of the export paintings show high similarities due to the assembly-line mode of painting during manufacture; 3) the portrayal of the characters in the export paintings reflects a return of a sense of humanity. This is evident in the fact that the subject of the paintings abounds in the scenes showing local folklore, working views, and daily life in Guangzhou. Meanwhile, the characters in the painting are vividly portrayed in a realistic style. The use of mixed colors on the costumes of different characters reflects a universal value of having regard for the equality of all men; 4) Guangzhou export painting decorative art evolution of the development of the dual development of Chinese and Western fusion of veins. Guangzhou export paintings of the Qing dynasty satisfied the customers' reverie for 'Oriental Civilization' and their purpose of seeking novelty of foreign cultures. It also revealed the subtle dynamics of the interplay between Guangzhou's social power, trading capital, and blending art.

Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty

This study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; these predictions are utilized as BL views. While constructing the BL portfolio, the Hurst exponent obtained from the asymmetric multifractal detrended fluctuation analysis is employed to determine the uncertainty associated with the views. The Hurst exponent describes the long-range persistence in time-series data, which can also be interpreted as the uncertainty in time-series predictions. Additionally, uncertainty is measured using asymmetric fractality to account for the financial time series’ asymmetric characteristics. Then, backtesting is conducted on portfolios comprising 10 countries’ ETFs, rebalanced on a 10-day basis. While benchmarking to a Markowitz portfolio and the MSCI world index, profitability is assessed using the Sharpe ratio, maximum drawdown, and sub-period analysis. The results reveal that the proposed model enhances the overall portfolio return and demonstrates particularly strong performance during negative trends. Moreover, it identifies ongoing investment opportunities, even in recent periods. These findings underscore the potential of fractality in adjusting uncertainty for diverse portfolio optimization applications.