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In this paper, we investigate the relations between the numbers of members of various parties and their results in the elections in Bavaria and in Germany. Deriving from the finding that there is a strong time-delayed correlation between these data-sets for the two largest parties in Bavaria, we show in a simulation based on the Sznajd model that such a correlation leads to very stable majorities, just as in Bavaria.

Understanding trends is helpful to identify future behaviours in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering method. The developed and freely available web application enables users to detect well-defined trending topics by experimenting with various levels of temporal bias — from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

This paper aims to identify global digital trends across industries and to map emerging business areas by co-word analysis. As the industrial landscape has become complex and dynamic due to the rapid pace of technological changes and digital transformation, identifying industrial trends can be critical for strategic planning and investment policy at the firm and regional level. For this purpose, the paper examines industry and technology profiles of top startups across four industries (i.e. education, finance, healthcare, manufacturing) using CrunchBase metadata for the period 2016–2018 and studies in which subsector early-stage firms bring digital technologies on a global level. In particular, we apply word co-occurrence analysis to reveal which subindustry and digital technology keywords/keyphrases appear together in startup company classification. We also use network analysis to visualize industry structure and to identify digitalization trends across sectors. The results obtained from the analysis show that gamification and personalization are emerging trends in the education sector. In the finance industry, digital technologies penetrate in a wide set of services such as financial transactions, payments, insurance, venture capital, stock exchange, asset and risk management. Moreover, the data analyses indicate that health diagnostics and elderly care areas are at the forefront of the healthcare industry digitalization. In the manufacturing sector, startup companies focus on automating industrial processes and creating smart interconnected manufacturing. Finally, we discuss the implications of the study for strategic planning and management.

Empirical mode decomposition (EMD) has been established as a valuable tool in determining nonlinear signal trend. EMD decomposes a one-dimensional (1D) signal into hierarchical components known as intrinsic mode functions (IMFs) and a residue, based on the local properties of the signal. The first IMF depicts the highest local oscillations, while the residue depicts the trend of a signal/data. In each iteration of the EMD process, interpolation is applied to some local maxima and minima points to form upper and lower envelopes, respectively. But, the application of interpolation methods causes huge computation time and other artifacts in the decomposition, which limits the use of EMD for many real life signals. This paper proposes an effective method that replaces the interpolation step by direct envelope estimation using order statistics filters, which results in decreased computation time, following a similar EMD approach that has been recently proposed for two-dimensional data or image analysis. The modified EMD of this paper called pseudo EMD (P-EMD) method is particularly useful in determining, analyzing, and/or modifying the trend of various signals to obtain and/or produce some desired results/outcomes. Several synthetic and real-life signals such as speech signal and sea level pressure and temperature are tested to verify the effectiveness of the P-EMD. From the results, P-EMD has been found as a superior alternative for trend analysis of signal/data, since it results in more accurate trend compared to the other interpolation based EMD methods such as classical EMD (CEMD) and a modified EMD (MEMD), and also facilitates faster computation.

This study attempts to investigate the climatic variability in the southwestern area of Bangladesh focusing on temperature and precipitation extremes. Long-term daily records of temperature and precipitation from six weather stations spread over the study area were analyzed. A total of 20 yearly indices were calculated and testified for the trends with the Mann–Kendal test and Sen’s estimator of slope at a 95% confidence interval. Moreover, one-way ANOVA and Tukey–Kramer post-hoc tests were applied to verify the spatial heterogeneity of the climatic indices among the meteorological stations. Findings revealed that Khulna is the worst affected area with significant changes in 12 climatic indices. Jessore and Faridpur followed Khulna with significant changes in 10 and 9 indices, respectively. Among the indices, significant changes in TN10p (frequencies of daily minimum temperature event that falls below the 10th percentile value), TX10p (frequencies of daily maximum temperature event that falls beyond the 10th percentile value), CSDI (cold spell duration indicator), and CDD (consecutive dry day) are the most common phenomenon in the studied region. Five out of six stations have been experiencing significant increasing trends for TN10p. That indicates an overall increase of extreme cold day frequencies in the area. Khulna station showed shreds of evidence of extreme cold temperature instances with a statistically significant fall in the minimum value of the yearly minimum temperatures (TNn). Besides, Khulna also experienced a statistically significant fall in the minimum value of the yearly maximum temperatures. Consistently, the area showed a significant trend of wider cold spells. Similarly, the extremely warm day frequencies have also been increasing significantly for the Khulna, Jessore, and Faridpur areas. Therefore, it is evident that the studied area has been experiencing temperature extremes. On the other hand, precipitation extremes are apparent for only Khulna and Jessore. Both these stations showed increasing trends in heavy precipitation events and wet spells (consecutive precipitation days). ANOVA outcome suggests that the study area has significant spatial heterogeneity in climatic indices. Precipitation indices show high spatial heterogeneity than those of temperature. Among the meteorological stations, Mongla and Ishwardi show the maximum departure than those of others. This study may help policy-makers, researchers, and academicians to take proper initiatives to reduce the impacts of changing climatic parameters in the coastal areas of Bangladesh.

In this study, a statistical analysis of historical and projected values of the annual, seasonal, and supra-seasonal precipitation concentration index (PCI) was conducted for different stations in the Cheliff watershed. Statistical methods such as breakpoint and trend analysis using Pettitt and Mann–Kendall tests were used for the PCI values of annual, monthly, and supra-seasonal precipitation data between 1950 and 2014. The PCI values varied between 14.24% and 24.86% annually, between 10.75% and 18.74% seasonally, and between 11.23% and 26.98% supra-seasonally. According to the Pettitt and Mann–Kendall tests, there was an insignificant change in precipitation distribution during the study period. Spatial analysis using the inverse distance weighting (IDW) method confirmed the minor variability in precipitation distribution in the study area. The Pearson correlation coefficient between oscillation indices and PCI values at different scales showed significant values with the Mediterranean Oscillation index (MOi), North Atlantic Oscillation (NAO), and Western Mediterranean Oscillation index (WeMOi), highlighting their potential influence on annual PCI values. For the climate projection scenarios, projected PCI values align with historical PCI values. Application of Pettitt and Mann-Kendall tests to the projected PCI series shows that there will not be a significant change in future precipitation distribution.

Based on temperature and rainfall recorded at 34 meteorological stations in Bangladesh during 1989–2018, the trends of yearly average maximum and minimum temperatures have been found to be increasing at the rates of 0.025^∘C and 0.018^∘C per year. Analysis of seasonal average maximum temperature showed increasing trend for all seasons except the late autumn season. The increasing trend was particularly significant for summer, rainy and autumn seasons. Seasonal average minimum temperature data also showed increasing trends for all seasons. The trend of yearly average rainfall has been found to be decreasing at a rate of 0.014mm per year in the same period; especially, for most of the meteorological stations the rainfall demonstrates an increasing trend for rainy season and a decreasing trend in the winter season. It means that in Bangladesh dry periods became drier and wet periods became wetter.

This study examined both long-term and short-term trends and fluctuations in rainfall and temperature over 34 meteorological stations located at seven regions in Bangladesh. Descriptive statistical analysis and Mann–Kendall trend test were utilized to investigate the variability of the rainfall and temperature of all stations in Bangladesh. Our research gave some insights into Bangladesh’s rainfall and temperature trends and variability.

One of the most serious challenges in the world is climate change. This study examines variations in seasonal and yearly fluctuations in extreme temperatures and precipitation in Ethiopia from 1987 to 2020 using 36 meteorological stations located in various climatic zones. The outcomes of the study demonstrated increased trends in the average annual maximum and minimum temperatures, with values of 0.094^∘C and 0.052^∘C, respectively. Despite falling trends in the summer, most stations experienced higher winter temperatures than summer. Furthermore, the analysis revealed a trend toward an increase in the number of rain stations over the summer season, while a trend toward a decrease in the amount of precipitation for a number of stations in the winter season; otherwise, large changes expected in the dega (cold), woina dega (cool), and kola (warm) climatic zones during the summer season did not occur. Precipitation activity has been near normal in most dega (cold), woina dega (cool), and kola (warm) climatic zones over the preceding 33 years. However, precipitation in the berha (hot) climatic zone was lower than average, with fluctuating levels noted throughout the year. The average annual precipitation has been decreasing at a rate of 0.146mm per year over the past 33 years, indicating that only the winter season has shown a downward (decreasing) trend, while the other three seasons, such as summer, spring, and autumn, have shown growing trends. Furthermore, Ethiopia’s average annual rainfall continues to be on a downward trend (decreasing).

China's venture capital industry is merely 20 years old. Compared to its foreign counterparts, it is still in its infancy. First of all, this article discerns key features of China's venture capital industry in recent years, including the different types of venture capital firms, venture capital investment scale, industry and regional distribution, the choice of investment project stages, and exit channels. In addition, it undertakes a comparative analysis with foreign joint venture capital companies. Secondly, it describes new developments in the legal and policy environment of China's venture capital industry.