Search Results (50)

This study introduces a novel forecasting framework that identifies and predicts recurrently emerging structural patterns in stock trends using rising visibility graphs (RVGs) and the Weisfeiler–Lehman (WL) subtree kernel. The proposed method, RVGWL, addresses a key limitation of traditional visibility graphs, namely the structural indistinguishability between rising and falling trends, by selectively constructing edges only along upward price movements. This approach produces graph representations that capture direction-sensitive market dynamics and facilitate the extraction of meaningful topological features from price data. By applying the WL kernel, RVGWL quantifies structural similarities between graph-transformed time series, enabling the identification of structurally similar preceding patterns and the probabilistic forecasting of their subsequent trajectories based on nine canonical trend templates. Experiments on time series data from four major stock indices and their constituent stocks during the year 2023—characterized by diverse market regimes across the U.S., Japan, the U.K., and China—demonstrate that RVGWL consistently outperforms classical rule-based strategies. These results support the predictive value of recurring topological structures in financial time series and higight the potential of structure-aware forecasting methods in quantitative analysis. Full article

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes. Full article

Computational algorithms are systematically written instructions or steps used to solve logical and mathematical problems with computers. These algorithms are crucial to rapidly and efficiently analyzing complex data, especially in global optimization problems like portfolio investment optimization. Investment portfolios are created because investors seek high average returns from stocks and must also consider the risk of loss, which is measured using the value at risk (VaR). This study aims to develop a computational algorithm based on the metaheuristic particle swarm optimization (PSO) model, which can be used to solve global optimization problems in portfolio investment. The data used in the simulation of the developed computational algorithm consist of daily stock returns from the banking sector traded in the Indonesian capital market. The quantitative research methodology involves formulating an algorithm to solve the global optimization problem in portfolio investment with mathematical calculations and quantitative data analysis. The objective function is to maximize the mean-value-at-risk model for portfolio investment, with constraints on the capital allocation weights in each stock within the portfolio. The results of this study indicate that the adapted PSO algorithm successfully determines the optimal portfolio weight composition, calculates the expected return and VaR in the optimal portfolio, creates an efficient frontier surface graph, and establishes portfolio performance measures. Across 50 trials, the algorithm records an average expected return of 0.000737, a return standard deviation of 0.00934, a value at risk of 0.01463, and a Sharpe ratio of 0.0504. Further evaluation of the PSO algorithm’s performance shows high consistency in generating optimal portfolios with appropriate parameter selection. The novelty of this research lies in developing an accurate computational algorithm for determining the global optima of mean-value-at-risk portfolio investments, yielding precise, consistent results with relatively fast computation times. The contribution to users is an easy-to-use tool for computational analysis that can assist in decision-making for portfolio investment formation. Full article

Background: The temporomandibular joint (TMJ) is a key component of the stomatognathic system, playing a major role in maintaining mandibular stability and function. Temporomandibular disorders (TMDs) are a prevalent disease in the world, with surgical treatment being reserved for complex cases or end-stage TMJ disease. A narrative review was conducted to describe the quantitative and qualitative factors that affect TMJ prosthesis stability. Methods: Studies with a sample size equal to or greater than 10 subjects who underwent surgical procedures for joint replacement using stock or customized ATM prostheses were included. This narrative review examined some variables that may influence in terms of the longevity of the TMJ prosthesis, highlighting issues to be considered in future research. Results: The current development of TMJ prostheses is benefiting from technological advances, offering a suitable adaptation to the patient’s anatomy and superior results in functionality and patient satisfaction. However, the biomechanical complexity of the TMJ shows unique challenges compared to other joints in the body, where anatomical, biomechanical, and functional requirements are high. The stability of the TMJ prosthesis is affected by multiple variables, including the selection of biocompatible materials that resist corrosion and wear, the design of the prosthesis, the diagnosis and indication for its use, and the surgeon’s experience. The success of TMJ replacement can be measured by improving the patient’s quality of life, reducing pain, restoring mandibular functionality, and recovering suitable facial morphology for the patient’s conditions. Conclusion: There is a need for training of maxillofacial surgeons in TMJ surgery and replacement, as well as a greater focus on the research and development of systems to simplify surgical design and procedures and to optimize the results of TMJ replacement. Full article

Machine learning has been applied by more and more scholars in the field of quantitative investment, but traditional machine learning methods cannot provide high returns and strong stability at the same time. In this paper, a multimodal model based on reinforcement learning (RL) is constructed for the stock investment portfolio management task. Most of the previous methods based on RL have chosen the value-based RL methods. Policy gradient-based RL methods have been proven to be superior to value-based RL methods by a growing number of research. Commonly used policy gradient-based reinforcement learning methods are DDPG, TD3, SAC, and PPO. We conducted comparative experiments to select the most suitable method for the dataset in this paper. The final choice was DDPG. Furthermore, there will rarely be a way to refine the raw data before training the agent. The stock market has a large amount of data, and the data are complex. If the raw stock market data are fed directly to the agent, the agent cannot learn the information in the data efficiently and quickly. We use state representation learning (SRL) to process the raw stock data and then feed the processed data to the agent. It is not enough to train the agent using only stock data; we also added comment text data and image data. The comment text data comes from investors’ comments on stock bars. Image data are derived from pictures that can represent the overall direction of the market. We conducted experiments on three datasets and compared our proposed model with 11 other methods. We set up three evaluation indicators in the paper. Taken together, our proposed model works best. Full article

This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables (IV) two-stage least squares (2SLS) model approach to evaluate the effects of changes in the size of the ECB’s balance sheet on the pricing of key equity market indices in Europe. The results of this study suggest that the ECB’s asset value expansion had the opposite statistically significant effects on the European stock market indices’ prices between the interventions. That is, an increase in the ECB’s balance sheet size was associated with a decrease in the prices of the indices during the sovereign debt crisis and with a rise during the COVID-19 pandemic. This research pinpoints the price sensitivity of each of the European equity indices to the ECB’s UMP and determines the different outcomes of the ECB’s quantitative easing policy between the interventions. Full article

In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection. Full article

In recent years, the development of China’s megacities has entered the stage of stock renewal. Research and practice concerning old city renewal in cities with a long history, represented by Beijing, has also become a hot spot for researchers and designers in the fields of urban planning, architecture and landscape architecture. As one of the main spatial components of the old city, Beijing’s traditional quadrangles are closely related to the spatial perception and emotional experience of citizens and tourists due to their near-human scale characteristics. However, current research focuses more on the evaluation of the historical value of the quadrangles in the early stage of renovation and the specific design and construction methods in the process of renovation, and few studies pay attention to the characteristics of the built environment that promote the emotional experience of users after renovation is completed. Under these circumstances, the study focuses on the emotional attachment between people and the spatial composition and built environment characteristics of the renovated traditional Beijing quadrangles; the avant garde small-scale quadrangle renovation type, which has a wider coverage, more types of user, and pays more attention to people’s emotional experience in the renovated space, was selected as the research object. Four typical quadrangle cases were selected for in-depth discussion. Based on the theory of emotional attachment from a multidisciplinary perspective, this study constructed a series of scales that can measure the degree and dimension of emotional attachment between people and the built environment, coupled with local observation and interviews, to obtain multi-dimensional data reflecting attachment, and used SPSS to conduct correlation analysis and exploratory factor analysis to quantitatively explore the effects of different built environment characteristics on attachment. The results show that: (1) As for the material characteristics, the organic integration of traditional and modern materials, structures and colors can effectively enhance people’s positive emotional experience and promote the establishment of emotional attachment. The combination of these characteristics and the process of people’s three-dimensional spatial experience can further enhance the degree of attachment. In addition, the consistency of materials and technologies, the organic integration of the old and the new, and the carrying capacity of the renovation method for traditional history and culture are the basis for promoting this kind of emotional attachment, which needs to be further explored and considered. (2) As for the non-material characteristics, the diverse, variable, recognizable, unique, and digital spatial function settings that respond to changes in people’s need and current developments can significantly promote the establishment of emotional attachment between people and the environment. This further emphasizes the importance of positioning the space in the early stage of the renovation and the operation of the space in the later stage. (3) The results further support the validity and rationality of the series of scales constructed in this study in quantitatively measuring the attachment characteristics between people and the built environment. As a result, the study provides a reference for emotion-oriented design means, research logic and quantitative evaluation methods in the practice and research of urban renovation and renewal in the future. Full article

Evolutionary economics has been instrumental in explaining the nature of innovation processes and providing valuable heuristics for applied research. However, quantitative tests in this field remain scarce. A significant challenge is accurately estimating the fitness of companies. We propose the estimation of the financial fitness of a company by its market capitalization (MC) time series using Malthusian fitness and the selection equation of evolutionary biology. This definition of fitness implies that all companies, regardless of their industry, compete for investors’ money through their stocks. The resulting fluctuating selection from market capitalization (FSMC) formula allows forecasting companies’ shares of total MC through this selection equation. We validate the method using the daily MC of public-owned Fortune 100 companies over the period 2000–2021. Full article

The circular economy is one of the main strategies for mitigating the environmental impacts of civil construction due to the generation of construction and demolition waste (CDW). In this transition, evaluating alternatives for using buildings as material banks is a way to make the process of reusing construction components more efficient. Thus, the article aimed to evaluate the state of the art of publications on the relationship between the circular economy in civil construction and the conceptual model of buildings as material banks to mitigate the environmental impacts of CDW. The authors chose the methodological design of Systematic Literature Review, using the Scopus and Web of Science databases for research, with the following search strings: (“construction” or “civil construction” or “built environment” or “construction industry”) and (“circular economy” or “circular construction”) and (“material banks” or “BAMB” or “buildings as material banks” or “building stocks” or “building materials”) and (“construction waste” or “demolition waste” or “CDW” or “construction and demolition waste” or “environmental impacts”). After a screening in which only articles published in journals were selected, from 2013 to 2023, inclusion and exclusion criteria were applied, to evaluate only those that had a direct relationship with CDW management through circular economy strategies and buildings such as banks of material. As a result, 93 articles remained, which were analyzed using a quantitative and qualitative approach. The predominance of applied studies was also noted through case studies that evaluate the management of materials and waste in the urban environment. The qualitative analysis, carried out using a SWOT matrix, highlighted the strengths of the buildings, such as material banks, the potential reduction of resource extraction and urban mining, and promoting the circulation of construction products. However, the recycling of waste, such as aggregates, still stands out as the main end-of-life strategy adopted, even without occupying the top of the waste hierarchy. Full article

The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (B_a), belowground biomass (B_b), and total biomass (B_t). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the B_a, B_t, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the B_b and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the B_t models and B_a models was higher, while the accuracy of the B_b models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation. Full article

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R² = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R² = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R² = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R² = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. Full article

Quantitatively revealing the response of carbon stocks to land use change (LUCC) and analyzing the vulnerability of ecosystem carbon stock (ECS) services are of great significance for maintaining the carbon cycle and ecological security. For this study, China’s Guizhou Province was the study area. Land use data in 2000, 2010, and 2020 were selected to explore the impacts of LUCC on carbon stocks in multiple scenarios by combining the PLUS and InVEST models and then analyzing the vulnerability of ECS services. The results show that forest land plays an important role in improving ECS services in karst plateau mountainous areas. In 2000–2020, forest land expansion offset the carbon stock reduced by the expansion of built-up land, greatly improving the regional ECS function. Following the natural trend (NT), the total carbon stock in Guizhou Province will decrease by 1.86 Tg; however, under ecological protection (EP) measures, the ECS service performs a positive function for LUCC. Focusing on socioeconomic development (ED) will increase the vulnerability of the regional ECS service. In the future, the forest land area size should be increased, and built-up land should be restricted to better improve the service function of ECS in karst plateau mountainous areas. Full article

This article analyzes the dynamics of the changes in indicators of socio-economic development under conditions of financial and economic crises and their negative consequences. The study proves that financial crises are associated with severe and prolonged downturns in economic activity. The socio-economic development of European countries in times of crises was analyzed. The cyclical nature of the onset of crises was confirmed via the study of the dynamics of socio-economic development indicators. The main emphasis was on the financial crisis of 2008–2009 and the COVID-19 crisis (2020–2021). The main indicators characterizing the crises were identified based on an analysis of literary sources. Their classification was developed according to the following groups: leading indicators, lagging indicators, and client leading indicators of expansion. Based on the correlation analysis, indicators that have a significant impact on socio-economic development and are predictors of crisis onset were identified. The authors suggest considering such leading indicators as increases in the private credit in the GDP, budget deficit, balance of payment deficit, and real interest rate. The major lagging indicators that have strong correlations with the GDP, such as the employment rate, general government debt, stock price volatility, and investment, were identified. Client leading indicators of expansion include unemployment, an increase in the number of new enterprises, an increase in purchasing power, etc. Some indicators, such as unemployment, can be both lagging indicators and client leading indicators of expansion. The negative consequences of the crisis are caused by the crisis itself as well as by the imbalances preceding the crisis. Therefore, the study of the predictors of crisis onset is relevant for timely decision making in order to prevent the negative consequences of the crisis. Based on the identified lagging indicators, the 2008–2009 crisis and the COVID-19 crisis were studied. To study the development processes of these crises, the authors analyzed by quarters the dynamics of the development of the following macroeconomic indicators: the GDP, employment, and investment levels. The similarities and discrepancies were identified in the natures of the emergences and courses of the 2008–2009 crisis and the COVID-19 crisis using the comparison method. The case study of the Eurozone and individual EU countries (Germany, France, Italy, and Spain) was used. Considering the similar courses of the crises, the forecast of the socio-economic development was made using the analyzed indicators during the COVID-19 crisis based on the 2008–2009 crisis data. The forecast approximation indicators were calculated, and a method for constructing further forecasts was selected. Based on retrospective data, the GDP forecast was developed via the use of the extrapolation method for 2023–2024. It is necessary to consider that while forecasting crises caused by unforeseen events and external influences, it is advisable to use qualitative analysis along with quantitative analysis. This article will be useful to researchers, political elites, experts, and financial analysts when developing programs for the socio-economic development of countries. Full article