Explore the vast range of titles available.

The aim of this study is to detect changes in flow regime of rivers in Poland. On the basis of daily discharges recorded in 1951-2010 at 159 gauging stations located on 94 rivers regularities in the variability of the river flow characteristics in the multi-year period and in the annual cycle were identified and also their spatial uniformity was examined. In order to identify changes in the characteristics of river regime, similarities of empirical distribution functions of the 5-day sets (pentads) of discharges were analyzed and the percent shares of similar and dissimilar distributions of the 5-day discharge frequencies in the successive 20-year sub-periods were compared with the average values of discharges recorded in 1951-2010. Three alternative methods of river classification were employed and in the classification procedure use was made of the Ward’s hierarchical clustering method. This resulted in identification of groups of rivers different in terms of the degree of transformation of their hydrological regimes in the multi-year and annual patterns.

A gully system is an important indicator that reflects the development of regional topography and landforms, and topography is one of the most important factors affecting the development of gullies. However, at present, research on the impact of topography on the development of gully systems in the mountainous area of Ningnan dry-hot valley still needs to be strengthened. In order to study the characteristics of gullies and the influence of topography on the development of gully systems, based on both the visual interpretation of remote sensing images and field investigations, five topographic factors (elevation, slope gradient, aspect, relief, and dissection) were employed and three gully erosion indexes (gully length, density, and frequency) were calculated. The geographical information system was used in this study to carry out the spatial analysis, Ward’s hierarchical clustering and correlation analysis. Results showed that the development of gully systems is greatly affected by the degree of relief and dissection, and there is a significant positive correlation (p < 0.01; p < 0.05), while elevation, slope gradient and aspect have little influence on it. Analysis of the gully systems showed that the gully erosion is the most intense in the area with an elevation of 2800–3200 m and slope gradients ≥ 38°. Furthermore, the degree of erosion on shady slopes was greater than that on sunny slopes. These results will help us to understand the spatial distribution and formation of gully systems in mountainous areas.

Effective real-time structural health monitoring in concrete structures is paramount to evaluating safety conditions and the timely maintenance of concrete structures. Especially, the presence of discrete fibers in fiber-reinforced concrete restrains crack propagation into small and thin cracks, which increases the difficulty in detecting damage. In this study, an array of piezoelectric lead zirconate titanate (PZT) transducers was applied to study the effects of external load-induced flexural stress and damage in fiber-reinforced concrete beams using the electromechanical impedance (EMI) or electromechanical admittance (EMA) methods. Beams were subjected to a four-point bending test under repeatable loading, while PZTs evaluated corresponding flexural stress and induced damage simultaneously. Due to the influence of the medium’s stress fields in the different types of wave propagation in structural elements, PZT transducers measurements are accordingly affected under variable stress fields, in addition to the effect of the higher level of damage that occurred in the medium. According to the results of the tests, variation in EMA signatures, following flexural stress and gradual damage changes, provided convincing evidence for predicting stress and damage development.

World experience in the creating of clusters in different industries has shown their effectiveness. This paper investigated the resource potential for creating a cluster designed for wood processing and to process wood waste from the timber industry of the Krasnoyarsk Territory of Russia. Static indicators were assessed, representing a quantitative characteristic of forest raw material resources: total and operational reserves of wood available in the region. While studying the state and use of forest resources, significant reserves of forest resources and secondary raw materials were revealed. Main indicators of the forest industry of the region over recent years were analyzed. The main systemic issues hindering the development of the timber industry were exposed. It was concluded that the region has raw material potential and industrial infrastructure necessary for the formation and sustainable development of a cluster for processing waste from the timber industry. Analysis of the producers and harvesters of forest products’ locations revealed potential wood industry clusters, and areas suitable for cluster economic development were proposed. The average figures of the nearest neighbor were used and analyzed to examine the spatial distribution of raw material harvesters and enterprises that produce finished products with respect to transport infrastructure, staffing, and raw material availability.

Mapping crop patterns with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. In this paper, a hierarchical clustering method was proposed to map cropping frequency from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Indices (EVI) data and the spatial and temporal patterns of cropping frequency from 2001 to 2015 in Hubei Province of China were analyzed. The results are as follows: (1) The total double crop areas decreased slightly, while total single crop areas decreased significantly during 2001 and 2015; (2) The transfer between double crop and single crop was frequent in Hubei with about 11~15% croplands changed their cropping frequency every 5 years; (3) The crop system has obvious regional differentiation for their change trend at the county level.

Purpose This study proposes the best clustering method(s) for different distance measures under two different conditions using the cophenetic correlation coefficient. Methods In the first one, the data has multivariate standard normal distribution without outliers for n = 10 , 50 , 100 and the second one is with outliers (5%) for n = 10 , 50 , 100 . The proposed method is applied to simulated multivariate normal data via MATLAB software. Results According the results of simulation the Average (especially for n = 10 ) and Centroid (especially for n = 50 and n = 100 ) methods are recommended at both conditions. Conclusions This study hopes to contribute to literature for making better decisions on selection of appropriate cluster methods by using subgroup sizes, variable numbers, subgroup means and variances.

World experience in the creating of clusters in different industries has shown their effectiveness. This paper investigated the resource potential for creating a cluster designed for wood processing and to process wood waste from the timber industry of the Krasnoyarsk Territory of Russia. Static indicators were assessed, representing a quantitative characteristic of forest raw material resources: total and operational reserves of wood available in the region. While studying the state and use of forest resources, significant reserves of forest resources and secondary raw materials were revealed. Main indicators of the forest industry of the region over recent years were analyzed. The main systemic issues hindering the development of the timber industry were exposed. It was concluded that the region has raw material potential and industrial infrastructure necessary for the formation and sustainable development of a cluster for processing waste from the timber industry. Analysis of the producers and harvesters of forest products’ locations revealed potential wood industry clusters, and areas suitable for cluster economic development were proposed. The average figures of the nearest neighbor were used and analyzed to examine the spatial distribution of raw material harvesters and enterprises that produce finished products with respect to transport infrastructure, staffing, and raw material availability.

Background To enhance teleconsultation management, demands can be classified into different patterns, and the service of each pattern demand can be improved. Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity, and network graph representation. In the built network graph about the high cosine similarity, the connected demand series can be categorized into one pattern. For verification, 43 teleconsultation demand series are used as sample data, and the efficiency and quality of teleconsultation services are respectively analyzed before and after the demand classification. Results The teleconsultation demands are classified into three categories, erratic, lumpy, and slow. Under the fixed strategies, the service analysis after demand classification reveals the deficiencies of teleconsultation services, but analysis before demand classification can’t. Conclusion The proposed ensemble hierarchical clustering method can effectively category teleconsultation demands, and the effective demand categorization can enhance teleconsultation management.

The classification of erosion-prone areas is a pre-requisite for providing an efficient watershed management as well as soil conservation. Thus, a practical decision support tool is required to assign priority weights to different related criteria. In this article, 13 effective soil erosion criteria have been used to prioritize different sub-watersheds in Arangeh watershed, Iran. The criteria have been classified in four groups of soil, geomorphology, topography and climate categories. The ranking outcomes of sub-watersheds via Modified Pacific Southwest Inter-Agency Committee method were compared with induced ordered weighted averaging (IOWA) model which considered hierarchy-based attitude and risk-based attitude (RBA) criteria. Copeland aggregation method was also applied as a benchmark for the comparison. Kendall’s Tau, a nonparametric stochastic test, investigated that RBA-based IOWA with fairly optimistic coefficient ( α  = 0.5) is the most conclusive ranking model because of its highest correlation coefficient which was about 0.9. Finally, the hierarchical clustering method classified sub-catchments. According to the results, four priority groups were identified based on vulnerability of sub-watersheds to soil erosion.

Purpose When a clustering problem is encountered, the researcher must be aware that choosing an incorrect clustering method and distance measure may significantly affect the results of the analysis. The purpose of this study is to determine the best clustering method and distance measure in cluster analysis and to cluster the regions of Turkey on the basis of this result. Methods In hierarchical clustering, there are several clustering methods and distance measures. For comparison of the clustering methods and distance measures, Rand’s C statistic is one of the best methods. Rand’s comparative statistic C takes on values from 0.0 to 1.0 inclusive that may be used to compare two resultant clusterings produced by applying clustering methods to a data set with unknown structure or to assess the performance of a clustering method on a data set with known structure. Results In this study, the seven regions of Turkey are clustered by all the clustering methods and distance measures. Related with the social and economic indicators, the final cluster number is taken as three. Then, according to Rand’s C statistics, all possible pairs of distance measures for all clustering methods in hierarchical clustering are compared, and the results are given in the related tables. Conclusions According to the results of all possible comparisons, Ward’s method is found to be the best among others, and final clustering of the regions is applied according to Ward’s clustering measure.