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In recent years neural networks have been used to achieve all 17 SDGs. This paper is directly related to SDG 9. In particular, the application of neural networks in statistics indicates the creation and development of a scientific research infrastructure (including encouraging innovation, SDG 9.5). Also, this paper shows the possibility of the mass practical application of neural networks for statistics in the context of sustainable development (with the possibilit of increasing the number of researchers, SDG 9.5). The paper aims to test the following two hypotheses in the context of SDG 9.5: (1) The rapid growth of scientific interest in neural networks will lead to a decrease in the number of scientific publications in statistics. (2) It is possible to use neural networks for calculating statistical indicators. Bibliometric analysis, mathematical modeling, the calculation of statistical indicators using the new prompt and Excel table z-statistics were used. The scientific novelty lies in the new knowledge obtained by the author for the first time. This study integrates advanced technologies (neural networks) and a traditional field (statistics), which is a significant contribution to innovation and infrastructure development (Indicator 9.5.1). The practical value lies in the ease of the mass use of neural networks for statistical data processing of more than 100,000 units, which is related to Indicator 9.5.2. Thus, this paper represents an important contribution to the stimulation of innovation, thereby building up technological potential and leading to a significant increase in the number of researchers (SDG 9.5). [ABSTRACT FROM AUTHOR]

The estimation of wind potential is generally assessed using the two parameters of Weibull distribution (k, c). Relative Percentage Error (RPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R), and Index of Agreement (IA) are calculated to comparatively analyze five numerical methods of computing Weibull parameters. The five selected methods are (Empirical Justus (EMJ), Energy Pattern Factor (EPFM), Maximum Likelihood (MLM), Method of Moment (MOM), and L-moment Estimation Method (L-MOM)). With the daily wind speed dataset for the year 2014 measured at 10 m height, the five methods have been applied on the study site Al-Hodeidah city in Yemen. Therefore, compared to other methods, the EPFM method is the most accurate method for estimating the Weibull parameters, while the L-MOM method is the least accurate at 10, 30, and 50 m heights. Moreover, this study reveals that the parameter remains the same with height, while the c parameter increases with height. The values of the skewness and kurtosis of wind speed and power density do not change as height changes.

This paper summarizes the economic loss caused by chemical accidents in China, as well as the outstanding problems that need to be urgently solved. The affected parties of a major chemical accident are personnel, property, resources, the environment, and the social economy from the perspective of an accident target and emergency management. Economic losses of each type of accidental target include property losses, emergency disposal costs, and recovery/reconstruction costs. Based on this idea, a statistical system composed of 12 indicators for economic losses of major chemical accidents is established. The new statistical indicator of economic loss of major chemical accidents is more reasonable than the current statistical method of China's GB 6721‐86 "Standard," and can fully reflect the harm due to major chemical accidents. [ABSTRACT FROM AUTHOR]

Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R 2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R 2 > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer's neurons was less than ten neurons ( R 2 > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer's neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well ( R 2 > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is critical for irrigation design and water management in rainfed and irrigated agriculture. The Penman-Monteith (FAO-56(PM)) equation was demonstrated to be the most reliable and adaptive to a wide range of humid to semi-arid climates. However, it requires several environmental parameters (e.g., wind speed, solar radiation), rarely available in developing countries. Therefore, numerous temperature-based formulas have been designed to address this issue for various environments. Their calibration and validation against the local climate frequently lead to increases in performance. We revised the Hargreaves exponent (EH) and substituted a value of (0.16) for the original value (0.5). The modified Hargreaves formula enhances the ETo predictions with a mean absolute error ranging from (0.791) mm per day for Balakot to (2.36) mm per day in Risalpur, averaging (3.797) mm per day, as compared to the Hargreaves-Samani (16.827) mm per day. In general, all the selected models showed high accuracy. However, the modified Hargreaves equation appeared to give the most promising results. It ranked first in (50%) of the whole area based on the standard error of estimate for estimating ETo in Khyber Pakhtunkhwa. Additional research must be conducted to determine the study's relevance to other regions. HIGHLIGHTS The main objective of this paper is to investigate the possibility of calibrating the Hargreaves equation and comparison for spatial domain Khyber Pakhtunkhwa, Pakistan.; The performance evaluation of three radiation-based methods (Priestly-Taylor, Makkink and Turc) and two temperature based methods (Hargreaves-Samani and modified Hargreaves) are evaluated in this paper. Bayesian Kriging is used for interpolation.;

In recent literature, the issue of sustainability and its measure has been addressed with different approaches that depend on the multidimensional nature of the concept and the specific sector and context to which it applies. The present work focuses on the economic sustainability component and suggests an operative measure at the farm level. The measure of economic sustainability has been applied to Italian family farms using Farm Accountancy Data Network (FADN). Based on this data, an efficiency indicator (EI) and two income indicators [a factor profitability indicator (FPI) and a comparable income indicator (CII)] expressing the ability to remunerate the entrepreneur's production factors at their opportunity cost and the farm's income capacity have been used in a principal component analysis (PCA) to build an economic sustainability index (SI). The index was used to describe Italian farms' economic sustainability levels but was also the cue to discuss problems related to identifying economic sustainability thresholds and the trade-off between efficiency and income components.

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study's data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual. [ABSTRACT FROM AUTHOR]

The tooth root crack fault is a common fault type of the spiral bevel gear pair (SBGP). Affected by the strong bearing capacity, the early crack fault of the SBGP cannot be found in time. In this study, a finite element (FE) model of the SBGP is established and assembled through the tooth contact analysis. The maximum tooth root stress is analyzed considering the variation of assembly errors. Meanwhile, this study simulates the tooth root crack fault of the bevel pinion with different crack degrees. The initial position of the crack is located where the maximum tooth root stress appears. The time-varying mesh stiffness (TVMS) of the SBGP considering different degrees of the pinion tooth root crack fault is obtained. The TVMS and the non-load transmission error are brought into a hybrid FE dynamic model, and steady responses are solved. Based on this, the sensitivities of various statistical indicators for identifying the tooth root crack fault of SBGP under the influence of assembly errors are verified. This paper can provide the necessary theoretical basis for the analysis and diagnosis of tooth root crack faults in the SBGP transmission system. [ABSTRACT FROM AUTHOR]

Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R2 > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons (R2 > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well (R2 > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications.

In recent literature, the issue of sustainability and its measure has been addressed with different approaches that depend on the multidimensional nature of the concept and the specific sector and context to which it applies. The present work focuses on the economic sustainability component and suggests an operative measure at the farm level. The measure of economic sustainability has been applied to Italian family farms using Farm Accountancy Data Network (FADN). Based on this data, an efficiency indicator (EI) and two income indicators [a factor profitability indi)cator (FPI) and a comparable income indicator (CII)] expressing the ability to remunerate the entrepreneur's production factors at their opportunity cost and the farm's income capacity have been used in a principal component analysis (PCA) to build an economic sustainability index (SI). The index was used to describe Italian farms' economic sustainability levels but was also the cue to discuss problems related to identifying economic sustainability thresholds and the trade- -off between efficiency and income components. [ABSTRACT FROM AUTHOR]

The socio-economic evolution of all the states of the world is currently influenced by the "concentration" of crises that affect inthe eaging socio-economic situation. Thus, the pandemic crisis triggered the financial and economic crisis, then the energy crisis, the food crisis and, finally, the European security crisis appeared due to the armed conflict (război) Ukraine - the Russian Federation. In this article, the authors aim to highlight how these crises that we have mentioned, concentrated, have an effect that can become disastrous for the entire population of Europe and, why not, more broadly, of mankind. In order to achieve this goal, we used the methodology that is based on statistical indicators (dynamics indicators, structural indicators, indicators of macroeconomic evolution as well as of the standard of living), logical analysis, the use of statistical-econometric models that highlight how the socio-economic life is influenced by these crises that we have to bear. To highlight the fact that if, based on forecasts, these crises with an effect on the economy continue, some countries, if not the entire economy, may enter into recession. In presenting these aspects, based on the mentioned methodology, we also used graphical representations, synthetic tables, all obtained from the data vases of the National Institute of Statistics, Eurostat, the OECD or other sources, such as the European Union or international statistical bodies. [ABSTRACT FROM AUTHOR]

This study presents a support vector machine (SVM) based structural damage detection approach for a steel frame model. Totally 10 structural scenarios including one undamaged and nine damaged cases (D1–D9) are defined by replacing different column elements to reduce the lateral stiffness between layers. Sensors are laid out at each layer of the frame structure to pick up acceleration response stirred up by the simulated white noise excitations. Statistical analysis is carried out and three damage indictor features are extracted from 800 samples of record duration. Subsequently, the SVM classifiers are developed to conduct a series of indictor fusion schemes to identify multiple damage states. The results indicate that the approach has good identification performance with damage features from three indicators, especially for a high dimensional input eigenvector. Meanwhile, acceptable detection accuracy can be obtained in situations with incomplete measurement; the robustness in terms of the resistance against noise is also discussed. [ABSTRACT FROM AUTHOR]

The study subject of the paper is the directions of development of leasing in Russia in various forms of its manifestation. There is no generally accepted classification of types of leasing today — the system of statistical indicators characterizing the state and development of the financial leasing market is in a state of formation; the factors (external and internal) and cause-and-effect relationships that bring such an uneven development of the leasing services market in a way of changing the legal environment, new challenges of the time (economic, financial, social) have been little studied. Thereby, the study of leasing as a unique tool for investing in assets (including labor force) in various spheres of human activity is a relevant scientific and the applied problem. Within the framework of this paper, being used statistical methods for analyzing the structure and dynamics of the phenomena of public life, the features and trends that are emerging in thebRussian market of leasing services are investigated, a forecast for 2020 is being constructed. and expert agencies and summarizes the results of the comprehensive statistical analysis of one of the segments of the financial intermediation services market. The proposed methodology for analyzing a segment of the financial intermediation services market using the example of leasing can be widely used in the analytical activities of expert agencies, financial analysts, be useful in scientific and applied research conducted by research organizations and universities, as well as in the training of scientific and pedagogical personnel in financial and economic profile universities.

Solar radiation data are essential information for designing and studying various engineering systems such as building thermal performance, photovoltaics, solar thermal systems, and passive solar design. Several empirical models have been developed to simulate the available solar radiation on the earth's surface. This article presents a comprehensive review of cloud cover-based solar radiation models (CSRMs) for estimating the hourly global solar radiation (GSR). Many of these models have been developed according to the Angstrom–Prescott relationship for solar radiation and are mainly used to estimate the monthly average daily total solar radiation. Based on the comprehensive review of CSRMs, it could be stated that the Kasten-Czeplak model and the Lam-Li model are comparatively straightforward and precise for estimating the hourly GSR. Furthermore, the study evaluated the selected model (Lam-Li model) to estimate the hourly GSR on the horizontal surfaces for four different cities in Canada. Results revealed that the original Lam-Li model performs within an acceptable range based on the statistical indices (R2 = 0.77–0.80 and rRMSE = 39.7–44.0%); however, the performance of the newly modified model (M2) from this study performs significantly better (R2 = 0.8–0.82 and rRMSE = 3.65–39.5%) than the original model (M1). [ABSTRACT FROM AUTHOR]

Educational capital is an essential factor in promoting the economic sector and is the most important investment in human capital. And if education is an important factor in promoting the economic sector, the opposite can also be used: economic modernization means new opportunities and stimulation for the additional acquisition of human capital. This is an additional argument to be able to know the science of our book cannot be ignored by these social or economic, sectorial or regional policy proposals. The purpose of the present paper is to prefigure a presentation on the statistical indicators in the field of online dissemination education by the two major official institutions: the National Institute of Statistics in Romania and the National Bureau of Statistics of the Republic of Moldova. In the process of paper elaboration, in order to be in conformity with the methodological requirements existing at the academic level, a variety of research methods were considered, which will be involved in the capitalization of the main directions of the paper.

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study’s data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual.

The tooth root crack fault is a common fault type of the spiral bevel gear pair (SBGP). Affected by the strong bearing capacity, the early crack fault of the SBGP cannot be found in time. In this study, a finite element (FE) model of the SBGP is established and assembled through the tooth contact analysis. The maximum tooth root stress is analyzed considering the variation of assembly errors. Meanwhile, this study simulates the tooth root crack fault of the bevel pinion with different crack degrees. The initial position of the crack is located where the maximum tooth root stress appears. The time-varying mesh stiffness (TVMS) of the SBGP considering different degrees of the pinion tooth root crack fault is obtained. The TVMS and the non-load transmission error are brought into a hybrid FE dynamic model, and steady responses are solved. Based on this, the sensitivities of various statistical indicators for identifying the tooth root crack fault of SBGP under the influence of assembly errors are verified. This paper can provide the necessary theoretical basis for the analysis and diagnosis of tooth root crack faults in the SBGP transmission system.

The purpose of the article is the possible reasons for the violation of the reliability of statistical indicators in the “digital economy”, ultimately violating the effectiveness of management decisions, leading to their inefficiency in conditions of intensive information flows. So far, the errors associated with the construction of indicators in the economy have not been sufficiently investigated. The main results of the study are theoretical descriptions of the methodological foundations of errors that violate the reliability of statistical indicators in the “digital economy”. By the nature of the action, errors are divided into systematic, random, and, if possible, predictions - predictable in magnitude and probability of occurrence and unpredictable (uncertain) errors. Systematic errors are the most dangerous. Their source is incorrect principles and methods of calculation. Therefore, such errors are difficult to eliminate. Random errors are in the nature of random variables or random functions, i.e., the limits within which their size can be found, can be argued only with some probability. Often the method of calculation does not take into account the inevitability of variation of indicators, limited to a deterministic description of economic phenomena. If the objective variation is studied, in some cases its estimation is made approximately. This is due to the lack of mass implementation of processes, schematization of random processes on the basis of hypotheses about their stationarity, linearity; conditional choice of the type of distribution of random variables, etc. Any calculation takes into account only the essential factors of the phenomena, keeping the possibility of residual variation due to unaccounted factors. The actual data are formed as a result of the influence of all the factors that determined this or that phenomenon, so the probabilistic nature of the implementation and regulatory calculations should be taken into account. Random error occurs when aggregating and aggregating indicators due to the random nature of the shifts in the population. Probabilistic characteristics determined on average for the whole unit by mass of cases and over a long period are unreliable indicators for the calculation of individual parts of the unit (for example, for individual enterprises, activities). The study made it possible to draw the following conclusions. Any deviation from the required reliability of indicators associated with economic damage. Insufficient accuracy of calculations can lead to imbalances in the economy of organizations, regions, States, economic communities (with the globalization of the economy). In these circumstances, the creation of reliable statistical indicators is necessary to improve the practice of calculations at various levels of the “digital economy”, as well as for the further development of statistical science.

Abstract Background Conclusive association entities (CAEs) in a biomedical article a are those biomedical entities (e.g., genes, diseases, and chemicals) that are specifically involved in the associations concluded in a. Identification of CAEs among candidate entities in the title and the abstract of an article is essential for curation and exploration of conclusive findings in biomedical literature. However, the identification is challenging, as it is difficult to conduct semantic analysis to determine whether an entity is a specific target on which the reported findings are conclusive enough. Results We investigate how five types of statistical indicators can contribute to prioritizing the candidate entities so that CAEs can be ranked on the top for exploratory analysis. The indicators work on titles and abstracts of articles. They are evaluated by the CAEs designated by biomedical experts to curate entity associations concluded in articles. The indicators have significantly different performance in ranking the CAEs identified by the biomedical experts. Some indicators do not perform well in CAE identification, even though they were used in many techniques for article retrieval and keyword extraction. Learning-based fusion of certain indicators can further improve performance. Most of the articles have at least one of their CAEs successfully ranked at top-2 positions. The CAEs can be visualized to support exploratory analysis of conclusive results on the CAEs. Conclusion With proper fusion of the statistical indicators, CAEs in biomedical articles can be identified for exploratory analysis. The results are essential for the indexing of biomedical articles to support validation of highly related conclusive findings in biomedical literature.

Introduction. Logistics as a type of economic activity plays one of the main roles in the formation and development of market relations, so its study is especially relevant in a market economy. Information resources are one of the most important subsystems of the resource potential of the firm, and information is a key element of logistics operations. The information specifies the needs of the objects of logistics systems and supply chain links. The article investigates that logistics is one of the important components of the state economy and now this component is at the stage of its formation and requires a scientific approach to study; the place of the information and analytical center as an infrastructural component of logistics systems is determined; the information and analytical support of the market research of logistics services is substantiated and the system of statistical indicators of the research of this market is formed. It is determined that the prospects for the development of the logistics services market in Ukraine directly depend on the state of the country's economy. Methods. The following scientific methods were used in the research process: inductions and deductions - in the process of studying the general tendencies of logistics development in Ukraine; analysis and synthesis - during the theoretical generalizations, the formation of appropriate conclusions; bibliographic - research of literary sources to obtain the necessary information on the research topic; method of associations and analogies - for the proposed new ideas and suggestions that arise from comparison with other more or less similar objects. Results. Logistics in Ukraine is developing in accordance with world trends, has a significant impact on the economic potential of the country, stimulates the development of related, interconnected and complementary industries, is a reflection of the competitiveness of our country in the world. Logistics efficiency is now at the forefront of the agenda for professionals, private firms, and international organizations. Thus, the logistics of information flow is relevant in the field of logistics. Information resources are one of the most important subsystems of the resource potential of the firm, and information is a key element of logistics operations. The current level of logistics development requires constant improvement and introduction of new information systems. Discussion. The main task set during the writing of the article is to substantiate the feasibility of using logistics services in Ukraine, taking into account the peculiarities of business globalization. [ABSTRACT FROM AUTHOR]

Data-driven approaches, when tasked with situation awareness, are suitable for complex grids with massive datasets. It is a challenge, however, to efficiently turn these massive datasets into useful big data analytics. To address such a challenge, this paper, based on random matrix theory, proposes a data-driven approach. The approach models massive datasets as large random matrices; it is model-free and requires no knowledge about physical model parameters. In particular, the large data dimension N and the large time span T, from the spatial aspect and the temporal aspect, respectively, lead to favorable results. The beautiful thing lies in that these linear eigenvalue statistics (LESs) are built from data matrices to follow Gaussian distributions for very general conditions, due to the latest breakthroughs in probability on the central limit theorems of those LESs. Numerous case studies, with both simulated data and field data, are given to validate the proposed new algorithms.

Prediction of solar radiation has drawn increasing attention in the recent years. This is because of the lack of solar radiation measurement stations. In the present work, 14 solar radiation models have been used to assess monthly global solar radiation on a horizontal surface as function of three parameters: extraterrestrial solar irradiance (G0), duration sunshine (S) and daylight hours (S0). Since it has been observed that each model is adequate for some months of the year, one model cannot be used for the prediction of the whole year. Therefore, a smart hybrid system is proposed which selects, based on the intelligent rules, the most suitable prediction model of the 14 models listed in this study. For the test and evaluation of the proposed models, Tamanrasset city, which is located in the south of Algeria, is selected for this study. The meteorological data sets of five years (2000–2004) have been collected from the Algerian National Office of Meteorology (NOM), and two spatial databases. The results indicate that the new hybrid model is capable of predicting the monthly global solar radiation, which offers an excellent measuring accuracy of R2 values ranging from 93% to 97% in this location. [ABSTRACT FROM AUTHOR]

Abstract The diffuse solar radiation is required for estimating global solar radiation on inclined surface or for estimating beam solar radiation for concentrating photovoltaic applications. In the present study, the accuracy and suitability of 72 existing and developed empirical models in nine categories are evaluated for estimating diffuse horizontal solar radiation in different climatic regions of China, i.e. the arid desert of northwest China (NWC), the Qinghai-Tibetan Plateau (QTP), the (semi-)humid cold-temperate northeast China (NEC), the semi-humid warm-temperate north China (NC) and the humid (sub-)tropical central and south China (CSC). For this purpose, diffuse solar radiation and other meteorological data during 1966–2000 and 2001–2015 from 10 weather stations were used for model calibration and validation, respectively. The results show that the sunshine duration-based models performed similarly to the clearness index-correlated ones on a daily basis, while they were more accurate on a monthly mean daily basis. The diffuse fraction-correlated models performed similarly to models based on the diffuse coefficient in NWC and QTP but better in NEC, NC and CSC. Models with both sunshine duration and global solar radiation as input parameters outperformed single variable-based models. The incorporation of average temperature and relative humidity further improved the prediction accuracy of diffuse solar radiation. The best performing model differed among the five studied climatic regions and also at the two time scales. The proposed models Ⅷ-3, Ⅳ-3, Ⅳ-3, Ⅳ-3 and the existing model Ⅲ-2 were the best-performing models in NWC, QTP, NEC, NC and CSC on a daily basis, respectively. The best performance on a monthly mean daily basis was obtained by the proposed models Ⅳ-3, Ⅷ-3, Ⅶ-7, Ⅷ-5 and Ⅳ-5, respectively. The different categories of models reviewed and developed in the study can be of great interest to researchers and managers in the design and applications of solar thermal/photovoltaic systems. Highlights • Seventy-two existing and developed empirical models for estimating diffuse solar radiation estimation in China. • Sunshine-based models performing similarly to clearness index models on a daily basis but outperforming on a monthly basis. • Diffuse fraction-based models performing similarly to diffuse coefficient ones in NWC and QTP but better in NEC, NC and CSC. • Combination of sunshine duration and global radiation along with temperature and relative humidity for improving accuracy. • Different best-performing model for each of the five climatic regions on both daily and monthly basis. [ABSTRACT FROM AUTHOR]

The decreasing importance of the state as a prominent actor in international politics has opened a debate about the rising importance of transnational relations. The research focuses on other actors, including epistemic communities. The paper traces previous research using the concept of epistemic communities and their influence in international politics and responds to its limitations. It uses the social constructivist approach for the elaboration of criteria of the professionalization process needed for recognition of epistemic communities. The paper also elaborates the concept of their influence in international regulation, including defining conditions of demand for epistemic communities' activities. The conditions include the introduction of complex problems with an element of uncertainty, acquisition of direct or indirect access to the political decision-making process in the arenas of international politics, and the ability of these arenas to execute international regulation. The paper also explains and elaborates the diffusion process of shared beliefs created by epistemic communities. The ability to diffuse shared beliefs supports modes of persuasion including statistical indicators, focusing events and narratives. Shared beliefs transfer via a process of institutional learning. Successful transfer means that shared beliefs become part of a political agenda. Utilization of the shared beliefs for creation, execution and enforcement of political decisions manifests the influence of epistemic communities in international politics. [ABSTRACT FROM AUTHOR]

Leaf area (LA) is an important parameter for physiological and phytotechnical studies and its measurement in a fast, accurate, and inexpensive way is essential and desirable. In this context, mathematical modeling is used as a tool to estimate leaf area from its relation with biometrical parameters and biomass. This study aimed to generate, validate, and determine the best mathematical estimation models of leaf area using the linear variables length (with and without petiole) and width of leaves and leaflets, in addition to dry mass of the native species Tabebuia roseoalba, Tabebuia impetiginosa and Handroanthus chrysotrichus collected in Sinop, Mato Grosso State (Brazil), between January and March 2014. The model assessment was performed by the method of weighted values of statistical indicators. The models based on linear measurements as independent variable that provided best performance of LA estimation for T. impetiginosa and T. roseoalba use the average leaflet width (Wla) measurements: LA=10.919×Wla1.854 and LA=6.196×Wla1.684, respectively. For H. chrysotrichus, the model was based on the length and width of leaves (L and W): LA=(0.383×L×W)+16.586. The best models of leaf area estimation considering dry mass (DM) were LA=119.510×DM−32.044×DM2 for H. chrysotrichus, LA=143.610×DM−6.383×DM2 for T. impetiginosa, and LA=90.623×DM for T. roseoalba.

Planets bearings of planetary gear sets exhibit high rate of failure; detection of these faults which may result in catastrophic breakdowns have always been challenging. The objective of this paper is to investigate the planetary gears vibration properties in healthy and faulty conditions. To seek this goal a previously proposed lumped parameter model (LPM) of planetary gear trains is integrated with a more comprehensive bearing model. This modified LPM includes time varying gear mesh and bearing stiffness and also nonlinear bearing stiffness due to the assumption of Hertzian contact between the rollers/balls and races. The proposed model is completely general and accepts any inner/outer race bearing defect location and profile in addition to its original capacity of modelling cracks and spalls of gears; therefore, various combinations of gears and bearing defects are also applicable. The model is exploited to attain the dynamic response of the system in order to identify and analyze localized faults signatures for inner and outer races as well as rolling elements of planets bearings. Moreover, bearing defect frequencies of inner/outer race and ball/roller and also their sidebands are discussed thoroughly. Finally, frequency response of the system for different sizes of planets bearing faults are compared and statistical diagnostic algorithms are tested to investigate faults presence and growth. [ABSTRACT FROM AUTHOR]

Due to the lack of solar radiation measurement stations, prediction of solar radiation had a great interest in the recent years. In the present work, fourteen solar radiation models had been used to assess monthly mean global solar radiation on a horizontal surface. Since we observed that each model was adequate for some months of the year, one model can't be sufficient for the prediction of the whole year, that is why we proposed a smart hybrid system, based on intelligent rules, which could select the most suitable prediction model from the fourteen models listed herein. In order to test and evaluate the proposed models, the southern Algerian city; Tamanrasset was selected for this study. Five years (2000–2004) of meteorological data sets were collected from the Algerian National Office of Meteorology (NOM), and from the two spatial databases. Thus, the reached results indicated that the new hybrid model could be able to predict global solar radiation with an excellent accuracy in this location. [ABSTRACT FROM AUTHOR]

The article presents summarized results of the research project; its aim was the development of methodological; methodical; and organizational basis of sociological assessment of social policy effectiveness on regional and municipal levels . Relying on systematic approach; the authors gave definition of the social policy; regional and municipal social policies and some other terms; proposed the own variant of social policy structuring; list of guidelines according to which the relevant object was studied empirically. Complex approach of studying of social policy effectiveness is based on the system of statistical indicators and sociological methods that allow not only to characterize every social policy guideline but also to obtain integrated indices of these guidelines and of the social policy as a whole. Statistical material gathered in Russia; the Kemerovo region; municipal entities; and the results of the conducted sociological study gave opportunity to draw conclusions about positive and negative trends in implementation of social policy in the Kemerovo region and in one of the towns; as well as to prepare recommendations on social policy development.

The recent decline in year-on-year (y/y) growth rate has sparked fear of a China ‘hard landing’. However, the literature found that y/y measures can be inadequate in catching cyclical turning points, especially when, as now in China, a short, sharp deceleration has occurred but bottomed out within the past 12 months. We regard three-month/three-month seasonally adjusted annualized growth rates (QSAA) as a better leading indicator for identifying patterns than y/y growth rates given that QSAA not only irons out very short-term volatility by taking a three-month moving average, it summarizes information from the previous six months (y/y indicators only exhibit the latest data point and information 12 months ago). Based on a sample of six important Chinese economic indicators, we find the new measure shows a different growth trajectory relative to that measured by the y/y growth rate. Thus, policy makers may find the new measures complementary to the widely used y/y growth rates in decision making. [ABSTRACT FROM PUBLISHER]

An empirical model for the estimation of solar energy on the basis of Angstrom's model is proposed in this work. Seven regression equations are developed by using different meteorological parameters such as mean sunshine duration per hour, temperature, relative humidity, wind speed, and rainfall. The performance of the model is determined on the basis of statistical indicators like correlation coefficient(r), coefficient of determination (R2), root mean square error (RMSE), mean percentage error (MPE), and mean bias error (MBE). The results show that the equation with the highest value ofr, R2and the least value of RMSE, MPE, and MBE provides better results. [ABSTRACT FROM AUTHOR]

In spite of many research efforts on fault diagnosis using frequency response analysis (FRA) method, there is still no universally accepted and systematic interpretation technique for these tests and 'expert opinion' is often sought when any damaging trend is observed as a result of fault occurrence. This study deals with statistical criteria for interpretation of the FRA results and presents a number of statistical and mathematical indicators that have not been used so far. A measurement setup accompanied by a transformer as a test object, on which winding faults are imposed, completes the test setup for performing the FRA measurements. The parameters are applied to the experimental FRA measurements and the results are compared by a simple normalising equation. Finally, the indicators which are adding more objectivity and transparency to interpretation of the FRA results are presented. Sweep frequency response analysis (SFRA) method is used in this study because of its efficiency and simplicity. It was found through this research work that F‐test, which introduced in this study for the first time, has higher sensitivity, and the differences between the normal and faulty responses of the transformers' windings is more reflected in this parameter compared with the other ones. [ABSTRACT FROM AUTHOR]

Dengue is a major international public health concern and one of the most important vector-borne diseases. The purpose of this article is to investigate the association among temperature, rainfall, relative humidity, and dengue fever by incorporating the lag effect and examining the dominant interannual model of the modern climate, the El Niño Southern Oscillation (ENSO), in the southern region of Taiwan. We built a linear Poisson regression model by including linear time treads and statistical indicators, verified with disease data in the 2004-2013 period. Here we showed that regional climatic factors in association with the interannual climate variability expressed by the ENSO phenomenon had a significant influence on the dynamics of urban dengue fever in southern Taiwan. The 2-4-month lag of statistical indicators of regional climate factors together with the 4-month lagged Pacific surface temperature (SST) anomaly in the proposed Poisson regression model could capture the regional dengue incidence patterns well. The statistical indicators of mean and coefficient of variation of temperature showed the greatest impact on the dengue incidence rate. We also found that the dengue incidence rate increased significantly with the lag effect of the warmer SST. The ability to forecast regional dengue incidence in southern Taiwan could permit pretreatment of mosquito habitats adjacent to human habitations with highly effective insecticides that would be released at the time of the high-temperature season. [ABSTRACT FROM AUTHOR]

Tourism activity is often analysed using data about arrivals and accommodation capacities in hotels and similar establishments. We contend that these statistics are not sufficient to analyse the tourism phenomena, particularly place attractiveness. In order to fill this gap, we suggest an original indicator of touristic attractiveness at the municipal level, using textual analysis of travel guidebooks. After a review of the literature devoted to the role of guidebooks in shaping tourist destinations, we apply our methodology to the Corsican case (France). Due to linguistic and statistical issues, notably in Corsica, it is crucial to make someone supervise the indicator building process. Moreover, testing our indicator through several guidebooks yields very similar results, suggesting that our indicator is robust. Finally, using multivariate regression and decorrelation technics, we develop an attractiveness indicator that offers a better understanding of tourism activity, compared to more traditional communal statistics and touristic indicators.

In response to the three influential doubts about government statistics, this article expounds the differences between relevant common economic statistical indicators and the national account indicators that reflect demand structure and national income distribution structure. The comparisons include the following aspects: the differences between household consumption expenditure from household survey and from expenditure-based GDP; the differences between total retail sales of consumer goods and final consumption expenditure from expenditure-based GDP; the differences between government expenditure from government fiscal statistics and government consumption expenditure from expenditure-based GDP; the differences between total investment in fixed assets from investment statistics and gross fixed capital formation from expenditure-based GDP; the differences between inventories and change in inventories from expenditure-based GDP; the differences between foreign trade balance from customs statistics and net exports of goods and services from expenditure-based GDP; and the differences between household disposable income from the household survey and from the Flow of Funds Table, etc. In addition, this article answers the questions proposed by the three doubts correspondingly. [ABSTRACT FROM PUBLISHER]

Early warning systems of extinction thresholds have been developed for and tested in microcosm experiments, but have not been applied to populations of wild animals. We used state-space population models and a statistical indicator to detect a transcritical bifurcation extinction threshold in a population of bobwhite quail ( Colinus virginianus) located in an agricultural region experiencing habitat deterioration and loss. The extinction threshold was detectible using two independent data sets. We compared predictions from state-space population models to predictions from a statistical indicator and found that predictions were corroborated. Using state-space population models, we estimated that our study population crossed the extinction threshold in 2010 (2002-2036; 95 % confidence intervals [CI]) using the whistle count (WC) data set and in 2008 (1999-2064; 95 % CI) using the Breeding Bird Survey (BBS) data. With the statistical indicator, we estimated that the extinction threshold will be crossed in 2018 (2004-2031; 95 % CI) using the WC data and will be crossed in 2012 (2006-2018; 95 % CI) using the BBS data. We expect extinction in our study population soon after crossing the extinction threshold, but the time to extinction and potential reversibility of the threshold are unknown. Our results suggest that neither small nor decreasing population size will warn of the transcritical bifurcation extinction threshold. We suggest that managers of wildlife populations in regions experiencing land use change should try to predict extinction thresholds and make management decisions to ensure the persistence of the species. [ABSTRACT FROM AUTHOR]

The analysis of economic phenomena, including those pertaining to the specific activity of the stock market, can be started by using a set of specific indicators, by which can be determined the overall trend of the data subject to present research. In this sense, in the literature of our country and from abroad, there have been defined indicators that define both the variation of the terms of the series, as well as the values of the average that characterize it. [ABSTRACT FROM AUTHOR]

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena. [ABSTRACT FROM AUTHOR]