Statistical Series Research Articles

Effect of Stock Market Development on Economic Growth in Peru

This paper aims to investigate the relationship, behavior, and relevance that the main variables of the Peruvian capital market have with the economic growth of Peru in the period 2015 - 2018, for which statistical series and the application of the methodology of a dynamic model have been used in order to validate the hypotheses and answer the problem posed. The results obtained, with each in monthly periods, showed that between Gross Domestic Product (GDP) and the variables of negotiable amounts and stock market capitalization there is a positive relationship during the period 2015 - 2018. However, these variables explain the variation of GDP in a smaller proportion.

The path to scientifically sound biodiversity valuation in the context of the Global Biodiversity Framework

Successful implementation of the Kunming-Montreal Global Biodiversity Framework requires identifying a process for measuring and valuing changes in biodiversity that build on the recognition that economics and valuation must play a key role in “halting and reversing” biodiversity loss. Here, we discuss considerations for a practical path to valuing changes in biodiversity. Framing changes in the value of biodiversity as a summary of changes in certain natural assets enables leveraging existing approaches and international standards associated with environmental-economic accounting. We discuss why an approach that builds from individual species, evolutionary groups, or functional groups into a practical, hierarchical statistical classification system is better than the development of any one biodiversity index. We merge techniques from ecology and other natural sciences, national and environmental-economic accounting, and economics, which are all on the cusp of making measurement of the change in the value of biodiversity possible. The focus should be on scaling and integrating these approaches. The path forward appears to begin with imperfect but useful measures, grounded in robust concepts, while establishing ambition to further scale-up measurements—just like the past evolution of many other official statistical series.

Glass transition temperature as a unified parameter to design self-healable elastomers.

Self-healing ability of materials, particularly polymers, improves their functional stabilities and lifespan. To date, the designs for self-healable polymers have relied on specific intermolecular interactions or chemistries. We report a design methodology for self-healable polymers based on glass transition. Statistical copolymer series of two monomers with different glass transition temperatures (Tg) were synthesized, and their self-healing tendency depends on the Tg of the copolymers and the constituents. Self-healing occurs more efficiently when the difference in Tg between two monomer units is larger, within a narrow Tg range of the copolymers, irrespective of their functional groups. The self-healable copolymers are elastomeric and nonpolar. The strategy to graft glass transition onto self-healing would expand the scope of polymer design.

Personalized context-aware systems for sustainable agriculture development using ubiquitous devices and adaptive learning

Advanced technologies offer a promising answer, especially integrating personalized context-aware systems through ubiquitous devices and adaptive learning. This paper explores the ability of these technologies to transform agricultural practices, improving performance and sustainability. This study aims to analyze the effect of integrating context-aware structures in agriculture using ubiquitous gadgets and adaptive learning fashions. It specializes in assessing the upgrades in helpful resource control, crop yield, and environmental sustainability and explores the farming community's economic, social, and academic advantages. Utilizing a combined-methods approach, the studies combine intensive literature with empirical statistics series, including discipline experiments, surveys, and interviews with key agricultural stakeholders. It examines contemporary farming practices, the capabilities of rising technology, and the conditions for enforcing robust context-aware systems in farming. Implementing context-aware systems improves agricultural practices by optimizing water and chemical usage, enhancing soil health, and increasing crop yields. Ubiquitous devices and adaptive learning models facilitate specific, real-time selection-making, leading to extra sustainable and green farming operations. Feedback from the rural community similarly underscores the positive effect of technology on improving accessibility to facts and collaborative learning. The study demonstrated that integrating personalized, context-aware systems with IoT and adaptive learning significantly improves agricultural efficiency and sustainability, evidenced by enhanced resource management and increased crop yields. This study contributes to the discourse on leveraging advanced technology to reap agricultural sustainability and units the groundwork for destiny research and coverage development in era-better farming.

“Green” business in agricultural sector of Kazakhstan: socio-economic and environmental stability

The goal is to show the advantages and opportunities of “green” business in agriculture. One of the main directions of implementation of the Action Plan for the implementation of the Concept of transition of the Republic of Kazakhstan to “green” economy for 2021-2030 is to solve the problems of ensuring food security, increasing soil fertility, using innovative, resource-saving technologies, combating the destruction of biodiversity, etc. Methods - the article analyzed statistical data series, studied the scientific works of domestic and foreign researchers. Results - the main elements of organizing “green” activities in agricultural sector are identified and factors contributing to the expansion of the scope of ecological farming, “green” technologies, development of research projects, and creation of jobs for development of sustainable agricultural production are considered as condition for the supply of high-quality and healthy food products, maintaining a balance of renewable and non-renewable resources, minimizing harm to ecosystems. Promising directions for functioning of agribusiness during transformation to “green” economy are outlined. Conclusions - regions need to develop strategy that will improve the state of the environment and improve the level and quality of life of the population. The main principle of “green” entrepreneurship is expanding zones of organic agricultural production and increasing export of environmentally friendly products, stimulating the influx of investments, introducing certification, labeling and marketing strategies, cooperation with international organizations and research centers. For these purposes, effective and efficient public policy measures are required. Using integrated approach based on digital economy will reduce costs, increase investment attractiveness, reduce production costs and attract agricultural producers to agribusiness.

Energy Transition: The Impact of Increasing Energy Production from Renewable Sources on the Commercial Optimization Process of Conventional Power Generation Assets

Abstract Changing the way electricity is produced, as part of the energy transition process, brings new challenges to the energy industry and in particular to the power generation practices. The switch from energy systems based on fossil fuels to generation technologies based on renewable sources is proving to be much more complex than expected. Multiple recent studies have shown that the intermittency in renewable production processes is putting pressure on the system and increasing balancing and maintenance costs of the grid, calling for an efficient management of the electricity produced. Therefore, a complete phase-out of conventional power generation units (e.g. coal, gas and nuclear) is still difficult to imagine in practice, mainly because of their role in balancing the system during the hours when renewables are not available (e.g. no wind, no sun or reduced hydraulicity). From the perspective of technical and commercial management aspects, the operation of conventional assets involves the consideration of multiple constraints (such as a longer operating time before they are shut down, or a longer preparation time before they can be restarted) in order to avoid financial loss and further damages. Therefore, the aim of this is to: 1. study the impact brought by the large installed capacity of renewable assets on the energy system; 2. analyse the options for gradual phase-out of conventional assets, from the perspective of system stability; 3. draw conclusions on how the commercial optimization of the production assets should develop as the energy transition advances. The novelty of this paper lies in the fact that it makes a business study of this transition process, which does not really exist yet, and case and simulation studies, along with statistical and time series analysis were used as a methodological approach for the research.

An Implicit Factorized Transformer with Applications to Fast Prediction of Three-dimensional Turbulence

Transformer has achieved remarkable results in various fields, including its application in modeling dynamic systems governed by partial differential equations. However, transformer still face challenges in achieving long-term stable predictions for three-dimensional turbulence. In this paper, we propose an implicit factorized transformer (IFactFormer) model, which enables stable training at greater depths through implicit iteration over factorized attention. IFactFormer is applied to large eddy simulation of three-dimensional homogeneous isotropic turbulence (HIT), and is shown to be more accurate than the FactFormer, Fourier neural operator (FNO), and dynamic Smagorinsky model (DSM) in the prediction of the velocity spectra, probability density functions of velocity increments and vorticity, temporal evolutions of velocity and vorticity root-mean-square value and isosurface of the normalized vorticity. IFactFormer can achieve long-term stable predictions of a series of turbulence statistics in HIT. Furthermore, IFactFormer showcases superior computational efficiency compared to the conventional DSM in large eddy simulation.

ASSESSING THE VIABILITY OF ROMANIA'S NEWLY ESTABLISHED METROPOLITAN AREAS

The legislation of new metropolitan of Romania areas follows the complicated and confusing experience of previous metropolitan areas, instilling a sense of excessive caution among both public authorities and researchers. A review of the literature demonstrates the prevalence of theoretical, methodological and sectoral approaches when dealing with this functional frame of territorial delineation. Our research proposes an exploratory analysis of the major metropolitan areas (MMAs) developed around county seats, in order to understand the medium and long-term dynamics affecting the development of these territorial structures contingent on local governance. By using a methodology based on the harmonisation of statistical series according to the current administrative structure, as well as on the cartographic analysis of demographic trends between 1992 and 2021, the study demonstrates the uneven development trends of major metropolitan areas, increasing the risk of widening territorial gaps.

Econometric analysis of the development of labor potential of agro-industrial complex of Kazakhstan

The goal is to reveal the basis for the formation of labor potential in agriculture, its condition and role in socio-economic development of rural areas. Consider theoretical foundations of economic and mathematical modeling of dynamics of the formation of human resources in agro-industrial complex of Kazakhstan. Methods - application of methodology for carrying out forecast calculations based on econometric model in the form of a system of joint equations, tracing the connection of labor market with changes in demographic structure of the country's rural population. The study used statistical data series provided by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. An overview of government support measures aimed at increasing labor reserves in rural areas is presented. As a result of scientific research, calculation forecast models were obtained, on the basis of which the values of main statistical indicators characterizing the dynamics of labor activity in rural areas of the republic for the long-term period were determined. Results - ways to overcome negative trends that slow down labor activity and form effective labor potential in rural areas are proposed: ensuring real growth in rural employment in all areas of activity; creation of additional high-performance jobs; improvement of social infrastructure. Conclusions - based on forecast calculations of indicators reflecting the directions of development of human capital in agricultural sector, opportunities for increasing GDP of products (services) of agriculture, forestry and fisheries have been identified. Studying the problems of rural labor economics makes it possible to note imbalances in the supply and demand of qualified specialists. In this regard, there is a need for active labor policy, stimulation of job search, training in professional skills, acquisition of a new profession.

Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts

Abstract. The availability of coherent time series of crop statistics is essential for better analyzing the past, understanding the present, and predicting future trends in yield, area, and production. Importantly, such data also underpin assessments and subsequent policy actions that can shape future food systems that are more resilient in the face of climate change and that are sustainable in terms of resource use efficiency. In the European Union (EU), there currently is no legal obligation for EU countries to provide subnational crop statistics. However, such data could improve in-season crop forecasts, climate change impacts and adaption need assessments, and evaluation of agri-environmental schemes. The dataset described in this paper includes a harmonized collection of subnational crop statistics on area, production, and yield, collected for the EU from National Statistical Institutes (NSIs) and the EUROSTAT REGIO database – subnational crop statistics voluntarily contributed by EU countries. The crops considered are wheat (including soft and durum wheat), barley (including winter and spring barley), grain maize, sunflower, and sugar beet. All data are harmonized towards the hierarchical structure of the EUROSTAT legend and the regional classification of NUTS (Nomenclature of Territorial Units for Statistics) version 2016, to provide coherent time series. A total of 344 282 records is presented here (115 974 for wheat, 122 705 for barley, 35 274 for grain maize, 34 916 for sugar beet, and 35 413 for sunflower) for a total of 961 regions in 27 EU countries. Statistics are reported from 1975 to 2020, with a median time-span range of 21 years. A flagging system details, for each data record, information on data sources, processing steps, and quality-checking results. This includes consistency checks between reported values for area, yield and production, identification of null values, missing and calculated data, information on crop legend matching, and NUTS versioning. We illustrate the value of this dataset by analyzing impacts on crops and production zone shifts in Europe due to climatic and economic factors. Recommendations and future developments of collecting subnational statistics at the EU level are briefly discussed. The dataset is accessible with European Commission Authentication Service (ECAS) login at https://doi.org/10.2905/685949ff-56de-4646-a8df-844b5bb5f835 (Ronchetti et al., 2023b).

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

The seamless integration of indoor and outdoor positioning has gained considerable attention due to its practical implications in various fields. This paper presents an innovative approach aimed at detecting and delineating outdoor, indoor, and transition areas using a time series analysis of Global Navigation Satellite System (GNSS) error statistics. By leveraging this contextual understanding, the decision-making process between GNSS-based and Visual-Inertial Odometry (VIO) for trajectory estimation is refined, enabling a more robust and accurate positioning. The methodology involves three key steps: proposing the division of our context environment into a set of areas (indoor, outdoor, and transition), exploring two methodologies for the classification of space based on a time series of GNSS error statistics, and refining the trajectory estimation strategy based on contextual knowledge. Real data across diverse scenarios validate the approach, yielding trajectory estimations with accuracy consistently below 10 m.

Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment

BackgroundThe breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment. MethodsWe extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms. ResultsThe results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model. ConclusionsThe two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.

Algorithm for identifying parameters of production function models using a neural network

The purpose of the work is to ensure the monitoring and reliability of statistical data used for the analysis and forecasting of the production development on the basis of generalized indicators that take into account the volume of resources used and the ways of their use. An algorithm for parametric identification of production functions models from time series of statistical data using generalized regression, which provides the best estimation of the parametric optimization error by the method of least squares, is proposed. In contrast to existing assessment methods, neural networks are used to build models, which significantly expand the technical capabilities of modeling and contribute to improving the accuracy of calculations through the use of neural network technologies. It is shown that in order to solve the problems of this class, it is advisable to use generalized regression neural networks with simple training modes and high modeling accuracy. As a result, it is possible to propose an algorithm for quantitative assessment of the production functions parameters, which consists in the construction of a neural model with its subsequent use to fit the trajectory of the production model of a given structure to the obtained data by means of recurrent estimates of the vector of the desired coefficients at a given initial approximation. The proposed algorithm is demonstrated by estimating the parameters of the Cobb-Douglas production function and the discrete-dynamic model of the consumption function according to the corresponding statistical series. The calculations are performed using the functions of the Neural Networks package of the MATLAB software environment. The algorithm is applicable for quantitative estimation of the production models parameters with complex logical-probabilistic relationships, as well as for obtaining numerical values of target indicators and indicators for assessing the inland water transport development by statistical series and monitoring.

An Asynchronous Gated Recurrent Network for Estimating Critical Transition of Bearing Deterioration

The service performance of an industrial bearing may deteriorate sharply after a sudden transition which is very difficult to be captured prior to its appearance. It is accordingly valuable to estimate this critical transition for avoiding serious catastrophes caused by the faulty bearing. To address this issue, an asynchronous gated recurrent network (AGRN) is proposed to estimate the critical transition of the bearing deterioration. A gated recurrent unit (GRU) is first developed to extract prognostic data as features using few early samples. Chi-square distributions of the squared prediction error of the residuals and the variation of principal components calculated from the features are fused as a statistic series, where the critical threshold is calculated adaptively. Another GRU is proposed to forecast deterioration process using fused statistic series. The forthcoming transition can be estimated by the process with the critical threshold at the initial operation. The present AGRN is evaluated by lifecycle experiments of three bearings, all from public benchmark datasets. Results show that the proposed method is robust in offering maintenance response time for bearings before transiting to critical failures.

A Novel Radiological Scoring System for Anterior Longitudinal Ligament Injuries.

Cervical hyperextension injury is very frequent with anterior longitudinal ligament (ALL) injury, and the ligament damage has a remarkable effect on whether and what type of operation should be performed. This study aims to establish a new scoring system for the accurate diagnosis of ALL damage. The imaging data of the consecutive patients was measured and scored by four radiologists. Intraoperative exploration was performed by three surgeons. The crude and adjusted odds ratios (cOR and aOR) and receiver operating characteristic curve (ROC) were constructed to assess the diagnostic accuracy of the scoring system. A total of 255 patients with cervical spine trauma were included in this study. There was no statistical difference in the relationship between demographics and ALL injuries (P > 0.05). Thickness of prevertebral soft tissue (aOR = 11.922, P = 0.004), intervertebral disk angle (aOR = 13.21, P = 0.002), avulsion fracture of the anterior edge of the vertebral body (aOR = 13.844, P = 0.029), ALL disrupted in T1-weighted sequence (aOR = 18.349, P < 0.001), and high signal area in T2-weighted sequence (aOR = 20.898, P = 0.002) had significantly higher diagnostic accuracy. The scoring system's sensitivity and specificity were 94.0% and 88.1%, respectively, and the accuracy was 90.8%. The study established a new scoring system for ALL injuries based on the analysis of a series of clinical data and statistics. A total of five scoring items, a total score of 7 points, and an ALL injury may be diagnosed when the score is not less than 3 points. This scoring system enables an efficient and accurate diagnosis of all injuries.

Boosting entrepreneurial competences beyond business and management–related disciplines. The case of engineering programs

PurposeThis study addresses the challenge of integrating entrepreneurial competences development into the traditionally structured engineering curriculum, recognizing its potential contribution to job creation. Specifically, this study proposes a course design that intersects project management and entrepreneurial disciplines, adopting a challenge-based learning approach.Design/methodology/approachConsidering a list of common features that entrepreneurs and engineers—in the role of project managers—should excel at, and building upon the principles of experiential learning, this study proposes and describes a course design that is expected to help engineering students develop entrepreneurial competences. Through a series of assessment instruments and descriptive statistics, the study evaluates its implementation in a pilot test applied in a Project Management course at a Spanish university.FindingsThe results demonstrate a significant improvement in students' entrepreneurial competences after completing the course. Noteworthy variations in receptiveness to skill development among different personality profiles are observed. Gender differences are minimal, with the exception of women exhibiting heightened self-perception in the autonomy dimension.Originality/valueThis study explores the common features shared by two often-disconnected disciplines, namely engineering and entrepreneurship. It suggests that integrating both perspectives through a challenge-based course design can enhance entrepreneurial competences among engineering students without compromising the specific knowledge gained from engineering programs. Engaging students in such pedagogical experiences not only fosters entrepreneurial competences but also contributes to their professional and personal growth.

The measurement of China's provincial physical capital stock based on the improved method and indicators.

Physical capital stock is the basic indicator of macroeconomic empirical studies and also an important manifestation of macroeconomic activity capacity. When measuring China's provincial physical capital stock by the perpetual inventory method, the treatment for the depreciation rate and the initial physical capital stock in the existing literature has some defects in reflecting the economic performance. The purpose of this paper is to build the most reliable and reasonable statistical series of China's provincial physical capital stock. We establish a measurement formula in form of the variable depreciation rate and improve the treatment for the depreciation rate and the initial physical capital stock. For the depreciation rate, we measure the benchmark depreciation rate of physical capital and determine the variable depreciation rate according to the economic growth rate, which makes the variable depreciation rate reflect the economic growth. For the initial physical capital stock, we measure the initial physical capital stock according to the capital output ratio, which makes the initial physical capital stock reflect the economic aggregate. Based on these improvements, we measure China's provincial physical capital stock from 1952 to 2022 by the perpetual inventory method. Through analyzing the measurement results, we have a more distinct insight into the temporal and spatial characteristics of China's provincial physical capital stock: The growth rate of physical capital stock at the provincial level has slowed in spite of remaining high; The eastern region is much higher than the central and western regions. According to these two characteristics, it is suggested that policymakers need to improve market mechanisms and adjust investment policies to promote a benign profile of economic growth and a regional optimization of capital formation.

DEVELOPMENT OF ECO-SUSTAINABLE AGRICULTURE THROUGH A SPATIO-TEMPORAL GIS ANALYSIS OF AGRICULTURAL CROPS. CASE STUDY: SOUTH-WEST OLTENIA REGION

This research paper investigates the state of agricultural crops in the Oltenia Region.We analyzed the main major agricultural crops (wheat, corn, sunflower and rapeseed) for the period 1990-2018, in Oltenia.The study aims to answer the following research questions, such as: What are the counties with the largest agricultural cultivated area?, Can the cartographic method be useful in the comparative analysis of the areas of agricultural crops in Oltenia?, and What factors negatively affect the development of sustainable (ecological) agriculture?.Using several research methods, such as document analysis, synthesis, comparison, statistical methods and referring to the available studies and statistical data series on the agriculture in Oltenia, this research paper highlights the position of the South-West Oltenia Region in terms of spatial-temporal dynamics and at the same time, the level of development of agricultural crops.

Racial disparities without racism: Some conceptual &amp; analytical considerations

AbstractMost studies on racial inequality begin with a series of statistics highlighting racial variations in an outcome of interest to illustrate how wide (or narrow) the gaps between racialized groups are. This approach is standard in racial inequality research because emphasizing racial differentials between racialized groups helps researchers frame inequality as a social problem. Scholars across academic disciplines and across sub‐areas within sociology report racial statistics to pay attention to what social scientists refer to as racial disparities. Presenting racial disparities is extremely important for documenting inequality; however, family scholars tend to provide descriptive statistical portraits along ethno‐racial lines (disparities) in the absence of racism, which, in turn, conceals the United States' racialized historical context. In other words, reporting racial inequality as disparities without addressing racism is a critical omission in family science research. Emphasizing racism is important because biological explanation still permeates the American imagination about racial inequality. The purpose of this paper is to provide conceptual and analytical considerations for future racial inequality and family research by recasting disparities as manifestations of racism instead of mere statistical differences. To illustrate the conceptual considerations, I first build on Williams' theoretical model focusing on structural racism and Black family life. I expand on how racism not only makes the idea of race possible but also manifests in observable, measurable outcomes. In the second section, I present an analytical consideration for understanding Black families' inequality by focusing on within‐group analyses. These conceptual and analytical considerations serve as ways to adequately represent Black families and children in the US.

Transformation of the Employment Structure of the Population in the Regions of the Russian Federation

The subject of the work is the regional development of the subjects of the Russian Federation, the topic is the classification and typology of regions as a method of analysis, forecasting and development of measures of state regulation of the economy, the purpose is the classification of regions by indicators of economic efficiency and characteristics of the structure of employment in the economy. The theoretical basis for the article is the domestic literature on spatial analysis and typology of regions, the author's methodology of the quality index of structural shifts– ICS, the practical basis is the methods of analyzing statistical series, forming tables, diagrams and cartograms in Excel, statistical data of the Federal State Statistics Service (Rosstat). The classification of the subjects of the Russian Federation according to the indicator of gross regional product (GRP) per 1 employed in the economy, which is interpreted as the level of labor productivity (production efficiency). A description of the classification results is proposed, including brief characteristics of the employment structure of regions typical for each of the selected groups. The results obtained can be used in scientific work, as well as for teaching courses in population geography, regional economics, labor economics in higher educational institutions. In the future, the research program on the socio-economic typology of regions can be continued as a method of monitoring the situation and a basis for developing recommendations on state regulation of the economy. The goal of such regulation should be to equalize the conditions of economic activity and social development of the population across the country.