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It is of great significance to study and analyze the surface water resources and their change trend in the groundwater overexploitation area of the North China Plain, which is of great significance to solve the shortage of water resources in the groundwater overexploitation area of the North China Plain, promote the exploitation of groundwater, and realize the sustainable development of water resources. This paper takes Minyou Irrigation District of Handan City, a typical overexploitation area in the North China Plain, as an example. Based on the measured rainfall and runoff data from 1957 to 2020, the Mann–Kendall trend test, cumulative anomaly method, double cumulative curve method, and Morlet wavelet transform were used to analyze and predict the trend of water resources in the irrigation area and the individual contribution of climate change and human activities to runoff change. The results show that the annual rainfall and annual runoff in the irrigation area have a significant downward trend and significant cyclical changes throughout the study period. In 1977, the annual runoff showed a sudden change, and the average contribution rates of climate change and human activities to its change were 40.55% and 59.46%, respectively. In the future (2020–2035), runoff will remain stable and rainfall will show an increasing trend. The research results can provide scientific reference for the development, utilization, and rational allocation of surface water resources in the groundwater overexploitation area of the North China Plain.

This study aims to clarify the vulnerability characteristics of the ecological–economic–social system of oasis city clusters in arid zones, promote the deepening of research on the sustainable development of urban clusters, and provide crucial practical reference significance for solving the series of problems brought about by urbanization. This article takes the arid zone oasis city cluster, the Urumqi–Changji–Shihezi urban agglomeration, as the research object and constructs an indicator system from three dimensions of ecological environment, regional economic, and social development, and adopts the comprehensive index method, GeoDetector, the GM(1, 1) gray prediction model, and other methods to study the vulnerability pattern and spatial and temporal changes of the urban cluster from 2009 to 2018. The results show that (1) from 2009 to 2018, the change in the integrated ecological–economic–social system vulnerability index of the Urumqi–Changji–Shihezi urban agglomeration shows a general downward trend, followed by significant differences in the vulnerability of each dimension, with an average vulnerability index of 1.8846, 1.6377, and 0.9831 for the social vulnerability, regional economic, and ecological environment dimensions, respectively; (2) the evolution of the spatial pattern of changes in the vulnerability index of different systems in each region of the Urumqi–Changji–Shihezi urban agglomeration tends to change from large to slight spatial differences, in which the social and ecological environmental vulnerability changes are more prominent in addition to the vulnerability changes of the regional economy; (3) parkland area per capita, arable land area per capita, GDP per capita, social fixed asset investment, population density, and urban road area per capita are the main drivers of decreasing vulnerability of ecological–economic–social systems in urban agglomerations; (4) by predicting and calculating the vulnerability index of each region of the ecological–economic–social system of urban agglomerations, it is found that the vulnerability index of urban agglomerations will show a decreasing trend from 2009 to 2018, and the difference of the vulnerability index between systems will narrow; (5) finally, targeted countermeasures and suggestions to reduce the vulnerability of ecological–economic–social systems are proposed to provide scientific references for the sustainable development of arid oasis cities.

Tourism trend prediction facilitates estimation of tourism investment and revenue. Studies on tourism prediction have primarily relied on linear models and historical visitors; however, relationshi...

Graphical abstract The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented public health, and social and economic challenges. It remains unclear whether seasonal changes in ambient temperature will alter spreading trajectory of the COVID-19 epidemic. The probable mechanism on this is still lacking. This review summarizes the most recent research data on the effect of ambient temperature on the COVID-19 epidemic characteristic. The available data suggest that (i) mesophilic traits of viruses are different due to their molecular composition; (ii) increasing ambient temperature decreases the persistence of some viruses in aquatic media; (iii) a 1°C increase in the average monthly minimum ambient temperatures (AMMAT) was related to a 0.72% fewer mammalian individuals that would be infected by coronavirus; (iv) proportion of zoonotic viruses of mammals including humans is probably related to their body temperature difference; (v) seasonal divergence between the northern and southern hemispheres may be a significant driver in determining a waved trajectory in the next 2 years. Further research is needed to understand its effects and mechanisms of global temperature change so that effective strategies can be adopted to curb its natural effects. This paper mainly explores possible scientific hypothesis and evidences that local communities and authorities should consider to find optimal solutions that can limit the transmission of SARS-CoV-2 virus. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-14625-8.

In the modern era, the empirical analysis of countermeasures against crime has developed rapidly from the perspective of crime causes. The concept of punishment based on preventionism has been vigo...

Time series forecasting covers a wide range of topics, such as predicting stock prices, estimating solar wind, estimating the number of scientific papers to be published, etc. Among the machine learning models, in particular, deep learning algorithms are the most used and successful ones. This is why we only focus on deep learning models. Even though it is a hot topic, there are only a few comprehensive studies, and in many studies, there is not much detail about the tested models, which makes it impossible to constitute a comparison chart. Thus, one of the main motivations for this work is to present comprehensive research by providing details about the tested models. In this study, a corpus of the asked questions and their metadata were extracted from the software development and troubleshooting website. Then, univariate time series data were created from the frequency of the questions that included the word “python” as the tag information. In the experiments, deep learning models were trained on the extracted time series, and their prediction performances are presented. Among the tested models, the model using convolutional neural network (CNN) layers in the form of wavenet architecture achieved the best result.

Disruptive technologies are related to a country’s competitiveness and international status. Accurately identifying and predicting the trends in disruptive technologies through scientific methods can effectively grasp the dynamics of technological development, adjust the national science and technology strategic layout, and better seize the high ground in international competition. Based on patent text data, this paper uses the improved LDA2Vec model combined with relevant indicators to identify the main topics in disruptive technologies, and predicts and analyzes the development trend through the establishment of an ARIMA model. Taking the energy technology field as an example, the main topics and development trends concerning disruptive technologies in this field are obtained. The study found that ten technologies, including energy storage technology, energy internet management technology, and offshore wind energy technology, are disruptive technologies in the energy technology field, and the development speed of energy storage technology is the fastest. To verify the correctness of the conclusion, this paper compares the results with artificial verification methods such as expert interviews and document verification, and finds that the two are basically consistent, thus verifying the effectiveness and feasibility of the proposed method.

The Yellow River has long been known for having low water and abundant sediment. The amount of sediment transported in the upper and middle reaches of the Yellow River (UMRYR) has changed significantly in recent years, resulting in an obvious imbalance in the spatiotemporal distribution of the water resources in the Yellow River Basin (YRB). The changes in the sediment transport in the Yellow River significantly affect ecological security and socioeconomic development in the YRB. In this study, the Google Earth Engine (GEE) platform was used to obtain the potential driving factors influencing the five main gauge stations in the UMRYR: vegetation, soil moisture, population, precipitation, land types, etc. The data on the annual sediment transport (AST) were from the River Sediment Bulletin of China (2001~2020). Linear regression and the Mann–Kendall test were used to study the temporal variation in the AST. The first-order difference was determined from the original data to remove the autocorrelation, and it met the requirement of sample independence. The factors without collinearity were used for the driving force analysis using linear regression (linear model) and random forest regression (nonlinear model). We used the selected driving factors to establish the linear regression, the random forest model for predicting the AST, and cross-validation for verifying the prediction accuracy. Furthermore, the prediction outcomes were compared with the simplest ARIMA time-series model (control model). Our findings showed that the changing trend and the mutation of the AST were different in the UMRYR during the past 20 years. However, after the first-order difference of the AST, the amount of interannual variation in the annual sediment transport (ΔAST) was almost unchanged in the UMRYR. The five driving factors were chosen to establish the prediction models of linear regression and random forest regression, respectively. Compared with the control model, ARIMA, the prediction accuracy of the random forest model was the highest.

As a clean energy with mature technology, hydropower has been widely applied in industry. The hydraulic turbine unit plays an important role in hydropower station. Since the fault of turbine unit will affect the normal operation of the whole hydropower station, this paper proposes a universal, fast and memory-efficient method trend for time-series prediction of hydraulic turbines. The proposed method adopts the expressive power of deep neural networks and the time characteristics of sequence-to-sequence structure (parallel convolution and recurrent neural network) to make time-series prediction. It also uses convolutional quantile loss and memory network to predict future extreme events. The experimental results show that the proposed method is fast, robust and accurate. It can reduce at least 34% in mean square error and 33% in convergence speed compared with the existing methods.

We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall- $\tau $ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.

World is now experiencing the new pandemic caused by COVID-19 virus and all countries are affected by this disease specially Iran. From the beginning of the outbreak until April 30, 2020, over 90,000 confirmed cases of COVID-19 have been reported in Iran. Due to socio-economic problems of this disease, it is required to predict the trend of the outbreak and propose a beneficial method to find out the correct trend. In this paper, we compiled a dataset including the number of confirmed cases, the daily number of death cases and the number of recovered cases. Furthermore, by combining case number variables like behavior and policies that are changing over time and machine-learning (ML) algorithms such as logistic function using inflection point, we created new rates such as weekly death rate, life rate and new approaches to mortality rate and recovery rate. Gaussian functions show superior performance which is helpful for government to improve its awareness about important factors that have significant impacts on future trends of this virus.

The coronavirus disease 2019 (COVID-19) is an infectious disease identified at Wuhan, China, in December 2019 caused by new Coronavirus. The Indian government has taken many initiatives to mitigate the effect of COVID by encouraging the standard mechanisms of social distancing, the use of masks, and various safety parameters. COVID-19 hotspot identifies regions in India where COVID-19 severity is very high. We propose a novel hybrid cellular automata classifier for predicting the trend of various Hotspots in India, processing different parameters including infection control, virus reproduction rate, critical correlation, safety parameters, and social distancing. The proposed classifier was named Hybrid Cellular Automata-Hotspot (HCA-HS), predicts the number of hotspots in various districts of states, and also gives the status of each city marked either as Totally Safe or Marginally Safe or Unsafe. This will alert the state authorities to take necessary action to mitigate the COVID effect and help the people for possibly refraining from going to the infected areas, i.e., hotspots. The data sets were collected from Kaggle and the local Indian database for more adaptability. The accuracy of the predictions of Hotspots is reported as 91.58%, which is considerable at this moment. The developed classifier is compared with Support Vector Mechanism (SVM), K-Means, Decision Tree, and HCA-HS has reported an accuracy of 10.69% higher than the existing literature.

In rainy weather, the accurate prediction of traffic status not only helps road traffic managers to formulate traffic management methods but also helps travelers design travel routes and even adjust travel time. In this paper, based on six-dimensional data (e.g., past and present spatiotemporal traffic status, road network structure, pavement type, water accumulation, and rainfall level), a fuzzy neural network (FNN) prediction system is proposed to predict traffic status. The traffic status evolution trend is related not only to the existing traffic but also to the new traffic demand. Therefore, the FNN prediction system designed includes offline and online parts using the data of the past and the day separately and avoids the forecast of new traffic demand. The fuzzy C-means clustering algorithm is applied to cluster traffic status data under similar rainy weather in the past to form an offline initial dataset, which is used to train FNN weight parameters. The online part uses real-time detection data and the parameters trained by the offline part to further predict the traffic status and returns the prediction errors to the offline part to correct the weight parameters to further improve prediction accuracy. Finally, the FNN prediction system is verified using real Beijing expressway network data. The verification results show that the prediction system can guarantee prediction accuracy and can be used to effectively identify traffic status.

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach.

Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.

Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces the performance of temporal patterns mining. Furthermore, these approaches overlook the influence of the social context surrounding the Web events. In this paper, we propose a novel method to predict future trends of Web events, based on their social contexts rather than temporal patterns. More specially, in the proposed method, a computational model for the social context is first built as a two-layer Association Linked Network considering its properties, such as the associative network property and the small world property. Then, the interaction between a Web event and the social context is simulated, based on the anchoring theory. Finally, an external force is defined and evaluated to quantify the influence of the social context on the evolution of Web events, which is used to predict future trends of Web events. Experiments show that the performance of the proposed method is better than that of the traditional time series-based approaches.

This paper focuses on the spatio-statistical analysis of rainfall fluctuation, anomaly and trend in the Hindu Kush region using auto-regressive integrated moving averages (ARIMA) approach. In the study area, trend in rainfall has significant impact on fluctuations in river discharge, which ultimately led to floods and hydrological drought. In this study, rainfall has been used as a climatic parameter. For this study, average annual and mean monthly rainfall data for Dir, Timergara, Saidu, Chitral, Drosh, Malam Jabba and Kalam meteorological stations located in the study region were gathered from Regional Meteorological Center Peshawar. In the study area, the rainfall is mostly received during two prominent periods, i.e., summer rainfall from monsoon, whereas winter and spring rainfall from western depressions. In the study area, Malam Jabba has recorded the heavy mean annual rainfall (1647 mm) and is considered as the humid station followed by met station Dir with a 1362 mm mean annual rainfall. Similarly, Saidu met station received 1050 mm mean annual rainfall and Kalam 1038 mm, whereas Timergara, Drosh and Chitral recorded 796 mm, 568 mm and 458 mm, respectively. The temporal data regarding rainfall were calculated and simulated in Addinsoft Excel state 2014 by applying ARIMA statistical model for trend prediction, fluctuations and anomaly. The analysis indicates that in terms of rainfall, an increasing trend has been detected at Dir, Chitral, Saidu and Kalam meteorological stations, whereas a declining trend has been recorded at Timergara, Drosh and Malam Jabba meteorological stations. In terms of rainfall anomaly, the met station Dir has indicated comparatively high positive anomaly. Contrary to this, the met stations of Saidu and Drosh have experienced negative rainfall anomaly.

The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. In this paper, the features are selected by various feature selection algorithms, and the parameters of the machine learning-based stock price trend prediction models are set through time-sliding window cross-validation based on 8-year data of Chinese A-share market. Through the analysis of different integrated models, the model performs best when the random forest algorithm is used for both feature selection and stock price trend prediction. Based on the random forest algorithm, a long-short portfolio is constructed to validate the effectiveness of the best model.

[EN] In scientific literature, museum cultural marketing is usually studied from the perspective of the case, analyzing specific projects; or, on the contrary, in a generic way, without basing the study on data, sometimes difficult to capture. In this case, the joint activity of a sample of the museums of the REMED network on Twitter is analyzed, extracting cybermetric data from common and specific hashtags used by the members of the network. On these data, the predictive capacity (for certain parameters of success in terms of digital cultural marketing) of the negative binomial regression or Poisson regression, generalized linear model types used to work with count data (number of times that certain phenomenon occurs). As a result, we will obtain a map of certain communication characteristics used by this subset of social accounts, through this social network, whose future performance may be approximated based on the prediction capacity of different variables, which would allow those accounts to achieve better performance, in terms of digital success., [ES] En la literatura científica se suele estudiar el marketing cultural de museos desde la perspectiva del caso, analizando proyectos específicos; o, por el contrario, de forma genérica, sin basar el estudio en datos, en ocasiones difíciles de capturar. En este caso, se analiza la actividad conjunta de una muestra de los museos de la red REMED en Twitter, extrayendo datos cibermétricos de hashtags comunes y específicos utilizados por los miembros de la red. Sobre esos datos, se estudiará y aplicará la capacidad predictiva (para ciertos parámetros de éxito en términos de marketing cultural digital) de la regresión binomial negativa o la regresión de Poisson, unos tipos de modelo lineal generalizados que se usan para trabajar con datos de conteo (número de veces que ocurre cierto fenómeno). Como resultado obtendremos un mapa de ciertas características de comunicación empleadas por este subconjunto de cuentas sociales, a través de esta red social, cuyo rendimiento futuro podrá ser aproximado en base a la capacidad de predicción de distintas variables, que permitirían a esas cuentas conseguir mejor rendimiento, en términos de éxito digital.

Landslides along the Three Gorges Reservoir in China pose a threat to coastal residents and waterway safety. To reduce false positive misjudgments caused by a sudden local change in the landslide deformation curve, in this paper, we propose an effective method for predicting the deformation trend of reservoir bank landslides. We take reservoir bank landslides in the Wanzhou District of the Three Gorges Reservoir area as the research object. The Time-Series Interferometric Synthetic Aperture Radar (InSAR) method and 62 Sentinel-1A images from 2018 to 2022 were selected for landslide deformation monitoring, and the Hurst index was calculated to characterize the deformation trend. Furthermore, we propose a method for predicting the deformation trend based on the statistical distribution of deformation rates and the physical significance of the Hurst index. After the field survey and Global Positioning System (GPS) verification, the Time-Series InSAR results are shown to be reliable. We take the Sifangbei landslide as a representative case to analyze the validation results. It is found that the determined Sifangbei landslide deformation trend is consistent with the conclusions for the region. In addition, the deformation trend of a reservoir bank slope has obvious spatial and temporal differences. Changes in the reservoir water level and concentrated rainfall play roles similar to those of catalysts. The proposed method, involving the combination of Time-Series InSAR and the Hurst index, can effectively monitor deformation and predict the stability trend of reservoir bank landslides. The presented research results provide new ideas and solutions for landslide prevention and risk mitigation in reservoir areas.

Human operators always have the possibility to commit human errors, and in safety-critical infrastructures such as a nuclear power plant, human error could cause serious consequences. Since nuclear...

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

This paper presents a heterogeneous ensemble classifier for price trend prediction of a stock, in which the prediction results are subsequently used in trading recommendation. The proposed ensemble...

[EN] The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in an online media and the possible prediction of the selected success variables. Framed in the field of digital journalism, it responds to the need to analyze the success of web content so that it can help in the decision-making of the editorial team of a digital medium. A line of research focused on the content itself, providing an innovative vision to that of previous research, and a methodology that serves as a basis for future scientific advances. It is about the contribution of valuable information, either from the statistical analysis of the data or from the possible prediction of the success indicators of greatest interest to the environment. In this way, it could be integrated as a feedback into the content strategy and thus favor its iterative optimization. The main objective, therefore, is the design of a cybermetric methodology for calculating the success of an online publication, having as specific objectives: to research the concept of success in digital journalism, the social network Twitter, web analytics and web advertising; design the methodology and determine what tools and reports are needed; extract and process data for statistical analysis; perform regressions that allow to obtain prediction equations of the selected success variables; and validate the prediction equations with test data and obtain their precision, serving this as a degree of confidence in the prediction. The design of the methodology has served to observe a significant over-dispersion in the data, as well as to demonstrate that the success of a web content has a strongly multifactorial nature, which causes a decrease in the variability calculated using the indicators proposed by previous research. This thesis serves, then, as the basis for a very interesting research framework both at an academic and business level: the prediction of the success of digital content., [ES] El objetivo de estudio es el diseño de una metodología cibermétrica para medir el éxito de los contenidos publicados en un medio de comunicación online y su posible predicción, de manera que se pueda orientar la optimización de los futuros contenidos publicados por el medio. Enmarcada en el ámbito del periodismo digital, responde a la necesidad de analizar el éxito de los contenidos web de manera que se pueda ayudar en la toma de decisiones del equipo editorial. Para ello, se ha realizado un extenso estudio de las publicaciones académicas versadas en las diferentes disciplinas que tienen lugar en esta tesis: la comunicación de contenidos digitales, Twitter, la difusión de las noticias en Twitter, la analítica web, la cibermetría, la analítica en Twitter, el análisis de tendencias en Twitter y la publicidad web. Con dicho marco, se ha obtenido información valiosa para la optimización futura de los contenidos digitales, ya sea procedente del análisis estadístico de los datos o de la posible predicción de los indicadores de éxito de mayor interés para el medio. De esta manera, se podría integrar de manera retroalimentada en la estrategia de contenidos y favorecer así su optimización iterativamente. Para ello, se han tenido en cuenta los siguientes objetivos específicos: investigar el concepto de éxito en el periodismo digital, la red social Twitter, la analítica web y la publicidad en la web; diseñar la metodología y determinar qué herramientas y reportes son necesarios; extraer y procesar los datos para su análisis estadístico; realizar regresiones que permitan obtener ecuaciones de predicción de las variables de éxito seleccionadas; y validar las ecuaciones de predicción con datos de test y obtener su precisión, sirviendo esta como grado de confianza en la predicción. El diseño de la metodología ha servido para observar una sobre dispersión significativa en los datos, así como demostrar que el éxito de un contenido web tiene un carácter fuertemente multifactorial, lo cual provoca una disminución en la variabilidad calculada mediante los indicadores propuestos por investigaciones previas. Esta tesis sirve, entonces, como base para una línea de investigación sobre la optimización de contenido digital basándose en la predicción estadística de su éxito., [CAT] L'objectiu d'estudi és el disseny d'una metodologia cibermètrica per a mesurar l'èxit dels continguts publicats en un mitjà de comunicació en línia i la seua possible predicció, de manera que es puga orientar l'optimització dels futurs continguts publicats pel mitjà. Emmarcada en l'àmbit del periodisme digital, respon a la necessitat d'analitzar l'èxit dels continguts web de manera que es puga ajudar en la presa de decisions de l'equip editorial. Per a això, s'ha realitzat un extens estudi de les publicacions acadèmiques versades en les diferents disciplines que tenen lloc en aquesta tesi: la comunicació de continguts digitals, Twitter, la difusió de les notícies en Twitter, l'analítica web, la cibermetría, l'analítica en Twitter, l'anàlisi de tendències en Twitter i la publicitat web. Amb aquest marc, s'ha obtingut informació valuosa per a l'optimització futura dels continguts digitals, ja siga procedent de l'anàlisi estadística de les dades o de la possible predicció dels indicadors d'èxit de major interés per al mitjà. D'aquesta manera, es podria integrar de manera retroalimentada en l'estratègia de continguts i afavorir així la seua optimització iterativament. Per a això, s'han tingut en compte els següents objectius específics: investigar el concepte d'èxit en el periodisme digital, la xarxa social Twitter, l'analítica web i la publicitat en la web; dissenyar la metodologia i determinar quines eines i reportes són necessaris; extraure i processar les dades per a la seua anàlisi estadística; realitzar regressions que permeten obtindre equacions de predicció de les variables d'èxit seleccionades; i validar les equacions de predicció amb dades de test i obtindre la seua precisió, servint aquesta com a grau de confiança en la predicció. El disseny de la metodologia ha servit per a observar una sobre dispersió significativa en les dades, així com demostrar que l'èxit d'un contingut web té un caràcter fortament multifactorial, la qual cosa provoca una disminució en la variabilitat calculada mitjançant els indicadors proposats per investigacions prèvies. Aquesta tesi serveix, llavors, com a base per a una línia d'investigació sobre l'optimització de contingut digital basant-se en la predicció estadística del seu èxit.

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.

The evaluation and trend prediction of tourism economic vulnerability (TEV) in major tourist cities are necessary for formulating tourism economic strategies scientifically and promoting the sustainable development of regional tourism. In this study, 58 major tourist cities in China were taken as the research object, and an evaluation index system of TEV was constructed from two aspects of sensitivity and adaptive capacity. On the basis of the entropy weight method, TOPSIS model, obstacle diagnosis model, and BP neural network model, this study analyzed the spatiotemporal patterns, obstacle factors, and future trends of TEV in major tourist cities in China from 2004 to 2019. The results show three key findings: (1) In terms of spatiotemporal patterns, the TEV index of most of China’s tourist cities has been on the rise from 2004 to 2019. Cities throughout the coast of China’s Yangtze River Delta and the Pearl River Delta urban agglomeration show high vulnerability, whereas low vulnerability has a scattered distribution in China’s northeast, central, and western regions. (2) The proportion of international tourists out of total tourists, tourism output density, urban industrial sulfur dioxide emissions per unit area, urban industrial smoke and dust emission per unit area, and discharge of urban industrial wastewater per unit area are the five major obstacles affecting the vulnerability degree of the tourism economy. (3) According to the prediction results of TEV from 2021 to 2030, although the TEV of many tourist cities in China is increasing year by year, cities with low TEV levels occupy the dominant position. Research results can provide reference for tourist cities to prevent tourism crises from occurring and to reasonably improve the resilience of the tourism economic system.

The polynomial regression algorithm can fit almost all data sequences by adding high-order terms of independent variables, but on long sequences, it can only reflect the development trends of the sequences, and the predicted value is not accurate enough. RNN has great advantages in predicting and fitting time sequence data, especially Long Short-Term Memory (LSTM) network has good performance in predicting long time sequence. This paper respectively adopts polynomial regression and LSTM analysis and predict the effect of epidemic prevention and control policies in various countries to control the epidemic and the economic recession. The experiment results show the Covid-19 epidemics trend prediction algorithm based on LSTM has better prediction effects, and is helpful in practical life.

In this paper, we investigated the impact of foreign direct investment (FDI) and research as well as development (R&D) on industrial carbon dioxide (CO2) emissions reduction. Firstly, a Panel-Corrected Standard Error (PCSE) model was established based on Chinese provincial panel data during period of 2000–2017. Secondly, a Grey Model (GM) was constructed to predict the growth trend of CO2 emissions, FDI and R&D in China from 2018 to 2030. It was deduced that: (1) FDI do bring negative effects on industrial CO2 emissions at the national level. From the perspective of regions, the coefficients of the eastern, central and western regions are all negatively correlated with CO2 emissions and pass the test of 1% significance level except the northeastern China. (2) In terms of R&D, it showed significantly improvement on CO2 emissions at the national and regional levels. Moreover, the efficiency of industrial CO2 emissions was improved by R&D with a rate of 27.2%, while the related value was increased by FDI with a rate of merely 4.3% at the national level. (3) According to the results of GM (1,1), China is expected to achieve zero growth of CO2 emissions by 2030. The growth rate of FDI gradually decreased to less than 5%, while the R&D input will always be at an annual growth rate of more than 5% for a long period of time in the future.

Thanks to advanced technologies, ultra-high-frequency limit order book (LOB) data are now available to data analysts. An LOB contains comprehensive information on all transactions in a market. We u...

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. The small and fluctuating samples of lubricating oil data render the wear trend prediction a challenging task in operation and maintenance management of wind turbine gearboxes. To deal with this problem, this paper puts forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multi-source information into traditional grey models. Multi-source information is applied by creating a mapping sequence according to the sequence to be predicted. The significance of the key parameters in the proposed model was investigated by numerical experiments. Based on the results from the numerical experiments, the effectiveness of the proposed method was demonstrated using lubricating oil data captured from industrial wind turbine gearboxes. A comparative analysis was also conducted with a number of selected other models to illustrate the superiority of the proposed model in dealing with small and fluctuating data. Prediction results show that the proposed model is able to relax the quasi-smooth requirement of data sequence and is much more robust in comparison to exponential regression, linear regression and non-equidistance GM(1,1) models.

In this paper, the convolutional recurrent neural network (ConvLSTM) architecture is proposed to predict individual stock prices. The characteristics of stock data are automatically extracted throu...

Under the background of the big data era, the concept and technology of civil aircraft health management have been continuously innovated. This paper investigates the progress of health management technologies of mainstream manufacturers and airlines. Based on the analysis of operational data, this paper proposes a big data platform architecture for civil health management applications. Besides, the case of applying QAR data for prediction is introduced in the typical fault of air conditioning system, and the effectiveness of AR model in short-term trend prediction is verified.

It is quite extraordinary that trend and colour forecasting for the fashion industry can be traced back to an atelier in Paris in the early nineteenth century. Frenchman Victor Jean-Claude wandered...

The Stock market trend prediction is an efficient medium for investors, public companies and government to invest money by taking into account the profit and risk. The existing studies on the development of stock-based prediction systems rely on data acquired from social media sources (sentiment-based) and secondary data sources (financial-sites). However, the data acquired from such sources is usually sparse in nature. Moreover, the selection of predictor variables is also poor, which ultimately degrades the performance of prediction model. The problems associated with existing approaches can be overcome by proposing an effective prediction model with improved quality of input data and enhanced selection/inclusion of predictor variables. This work presents the results of stock prediction by applying a multiple regression model using R software. The results obtained show that the proposed system achieved a prediction accuracy of 95% on KSE 100-index dataset, 89% on Lucky Cement, 97% on Abbot Company dataset. Furthermore, user-friendly interface is provided to assist individuals and companies to invest or not in a specific stock.

Given a financial time series such as S&P 500, or any historical data in stock markets, how can we obtain useful information from recent transaction data to predict the ups and downs at the next moment? Recent work on this issue shows initial evidence that machine learning techniques are capable of identifying (non-linear) dependency in the stock market price sequences. However, due to the high volatility and non-stationary nature of the stock market, forecasting the trend of a financial time series remains a big challenge. In this paper, we introduced a new method to simplify noisy-filled financial temporal series via sequence reconstruction by leveraging motifs (frequent patterns), and then utilize a convolutional neural network to capture spatial structure of time series. The experimental results show the efficiency of our proposed method in feature learning and outperformance with 4%-7% accuracy improvement compared with the traditional signal process methods and frequency trading patterns modeling approach with deep learning in stock trend prediction.