Results of future JIF prediction.

IScience Impact Factor 2024-2025 - ResearchHelpDesk - Science has many big remaining questions. To address them, we will need to work collaboratively and across disciplines. The goal of iScience is to help fuel that type of interdisciplinary thinking. iScience is a new open-access journal from Cell Press that provides a platform for original research in the life, physical, earth, and health sciences. The primary criterion for publication in iScience is a significant contribution to a relevant field combined with robust results and underlying methodology. The advances appearing in iScience include both fundamental and applied investigations across this interdisciplinary range of topic areas. To support transparency in scientific investigation, we are happy to consider replication studies and papers that describe negative results. We know you want your work to be published quickly and to be widely visible within your community and beyond. With the strong international reputation of Cell Press behind it, publication in iScience will help your work garner the attention and recognition it merits. Like all Cell Press journals, iScience prioritizes rapid publication. Our editorial team pays special attention to high-quality author service and to efficient, clear-cut decisions based on the information available within the manuscript. iScience taps into the expertise across Cell Press journals and selected partners to inform our editorial decisions and help publish your science in a timely and seamless way. iScience is a fully gold open access journal. To provide open access, expenses are offset by an author publication fee that allows the journal to support itself and the research community in a fully sustainable way. iScience has a small budget for reducing open access charges for authors in developing countries and others in genuine financial hardship. Please note that funds for other reductions are limited.

Cell Reports Impact Factor 2024-2025 - ResearchHelpDesk - The Cell Reports Portfolio includes gold open-access journals that span life, medical, and physical science. Our mission is to make cutting-edge research and methodologies across disciplines available to a wide readership. Cell Reports publishes high-quality papers across the entire life sciences spectrum. The primary criterion for publication in Cell Reports is new biological insight. Cell Reports features cutting-edge research, with a focus on a shorter, single-point story, called a report, in addition to a longer research article format and resources. Resources highlight significant technical advances or major informational data sets that provide new biological insight. Reviews covering recent literature in emerging and active fields are also welcome. The professional in-house editors of Cell Reports work closely with authors, reviewers, and the journal's scientific advisory board. The advisory board comprises current and future leaders in their respective fields who guide the journal with regard to its scope, its content, and the quality of the papers it publishes. However, editorial decisions at Cell Reports are made independently by its in-house editors.

Background The impact factors of scientific journals are interesting but not unproblematic. It is speculated that the number of journals in which citations can be made correlates with the impact factors in any given speciality. Methods Using the Journal Citation Report (JCR) for 1997, a bibliometric analysis was made to assess the correlation between the number of journals available in different fields of clinical medicine and the top impact factor. A detailed study was made of dermatological journals listed in the JCR 1991–2000, to assess the relevance of this general survey. Results Using the 1997 JCR definitions of speciality journals, a significant linear correlation was found between the number of journals in a given field and the top impact factor of that field (rs = 0.612, p < 0.05). Studying the trend for dermatological journals 1991 to 2000 a similar pattern was found. Significant correlations were also found between total number of journals and mean impact factor (rs = 0.793, p = 0.006), between the total number of journals and the top impact factor (rs = 0.759, p = 0.011) and between the mean and the top impact factor (rs = 0.827, p = 0.003). Conclusions The observations suggest that the number of journals available predict the top impact factor. For dermatology journals the top and the mean impact factor are predicted. This is in good agreement with theoretical expectations as more journals make more print-space available for more papers containing citations. It is suggested that new journals in dermatology should be encouraged, as this will most likely increase the impact factor of dermatological journals generally.

The rapid growth of online tools to communicate scientific research raises the important question of whether online attention is associated with citations in the scholarly literature. The Altmetric Attention Score (AAS) quantifies the attention received by a scientific publication on various online platforms including news, blogs and social media. It has been advanced as a rapid way of gauging the impact of a piece of research, both in terms of potential future scholarly citations and wider online engagement. Here, we explore variation in the AAS of 2677 research articles published in 10 ornithological journals between 2012 and 2016. On average, AAS increased sevenfold in just five years, primarily due to increased activity on Twitter which contributed 75% of the total score. For a subset of 878 articles published in 2014, including an additional 323 ornithology articles from non-specialist journals, an increase in AAS from 1 to 20 resulted in a predicted 112% increase in citation count...

Predicted citations from the Linear model, with confidence and prediction intervals.

Due to the increasing amount of scientific work and the typical delays in publication, promptly assessing the impact of scholarly work is a huge challenge. To meet this challenge, one solution may be to create and discover innovative indicators. The goal of this paper is to investigate whether Facebook likes for unpublished manuscripts that are uploaded to the Internet could be used as an early indicator of the future impact of the scientific work. To address our research question, we compared Facebook likes for manuscripts uploaded to the Harvard Business School website (Study 1) and the bioRxiv website (Study 2) with traditional impact indicators (journal article citations, Impact Factor, Immediacy Index) for those manuscripts that have been published as a journal article. Although based on our full sample of Study 1 (N = 170), Facebook likes do not predict traditional impact indicators, for manuscripts with one or more Facebook likes (n = 95), our results indicate that the more Facebook likes a manuscript receives, the more journal article citations the manuscript receives. In additional analyses (for which we categorized the manuscripts as psychological and non-psychological manuscripts), we found that the significant prediction of citations stems from the psychological and not the non-psychological manuscripts. In study 2, we observed that Facebook likes (N = 270) and non-zero Facebook likes (n = 84) do not predict traditional impact indicators. Taken together, our findings indicate an interdisciplinary difference in the predictive value of Facebook likes, according to which Facebook likes only predict citations in the psychological area but not in the non-psychological area of business or in the field of life sciences. Our paper contributes to understanding the possibilities and limits of the use of social media indicators as potential early indicators of the impact of scientific work.

To study the mining-induced failure depth of floor rocks in a fully mechanized mining caving field affected by different coal seam pitches, mining face lengths, burial depths and aquifer water pressures, multifactor-coupled orthogonal numerical tests on the failure depth of floor rocks were conducted. The numerical results show that the failure depth of floor rocks increases with increasing mining face length, coal seam pitch and burial depth. According to the relationship between failure depth and these impact factors, a multifactor-coupled prediction model for the failure depth of floor rocks was established. In addition, the in situ measurement of the failure depth of floor rocks in the Yitang Coal Mine in Huoxi coal field in Shanxi Province, China, was performed, and the in situ failure depths of floor rocks in the 100 502 (80 m) and 100 502 (180 m) mining faces were approximately 12.50–14.65 m and 17.50–19.20 m, in good agreement with the results of the multifactor prediction model. Furthermore, the sensitivity of each impact factor in the prediction model of the floor failure depth was further analysed by F-test and range analysis, and the impact order of studied factors on the floor failure depth is coal seam pitch > mining face length > burial depth > aquifer water pressure.

Variables include journal identity, 5-year journal impact factor, publication information (year published, volume, issue, and authors), collection date and publication date (used to calculate time since publication), number of tweets, number of users, Twitter reach, and number of Web of Science citations. (CSV)

Regression Coefficients from Predicting Open Data and Code Policies by Publisher and Impact Factor.

Predicted Rank Correlations.

In a survey conducted in December 2024 among Chinese travel agents, ** percent of respondents believed that China's economic environment will have a major positive impact on the Chinese outbound travel market in 2025. In contrast, around ** percent thought that the international/geopolitical situation will likely influence the outbound market negatively.

In this study, our goal is to test the hypothesis that adding a fourth factor (Impact) to current measures of Meaning In Life will improve our assessment of Meaning In Life. More precisely, we hypothesize that (i) Impact will significantly predict Meaning in Life even when controlling for the three traditional factors, that (ii) adding Impact to the predictors of meaning in life will significantly improve prediction of Meaning in Life, that (iii) a four-factor structure will be a better fit of the data than a 1-factor, 2-factor, and 3-factor structure, and (iv), there will be a positive correlation between the facet Impact and the intrinsic aspiration Community contributions.

This study is similar to the one preregistered to the one preregistered under the name "Meaning in life and positive impact - Testing a fourth factor to the measure of Meaning In Life". However, due to the very high correlations between the different facets of Meaning in Life we observed in this study, we feared the presence of a response bias, and increased the number of attention checks (including more discrete attention checks) in the present study.

MSCI World index is predicted to experience a moderate increase. The predicted range for the index is between a slight increase and a significant increase. The risk associated with this prediction is moderate, as there are some factors that could potentially impact the index's performance.

Context

The data is of a city and its zones. The data is an attempt to collect no. of hospital beds and schools in each zone and establish an impact of these features on flat circle rate

Content

It represents 2017-2020 data period and involves the latest circle rate.

Acknowledgements

https://smartcities.data.gov.in/ - open data source from where individual data is collected, cleaned, aggregated and merged.

Inspiration

The data is an attempt to collect no. of hospital beds and schools in each zone and establish an impact of these features on flat circle rate

Predicted citation advantages relative to paid-access articles, from Table 13.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Which of the four factors directly impact your total cost of using the credit card?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Weather Forecast System Market Outlook

The global weather forecast system market size, which was valued at approximately $3.5 billion in 2023, is anticipated to reach around $6.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 8% during this forecast period. This significant growth is driven by the increasing demand for accurate and timely weather forecasting services across various sectors. The rising adoption of advanced technologies such as artificial intelligence and machine learning to enhance the precision of weather prediction models is a major factor propelling the market forward. Furthermore, the growing need for sophisticated weather forecasting systems to mitigate the adverse impacts of climate change is further fueling market expansion.

One key growth factor in the weather forecast system market is the increasing frequency and severity of extreme weather events worldwide. These events, such as hurricanes, floods, and heatwaves, are driving governments and organizations to invest heavily in advanced forecasting systems to improve preparation and response efforts. The ability to predict such events with greater accuracy and lead time can significantly reduce their impact on human lives, infrastructure, and the economy. As climate change continues to influence weather patterns, the role of weather forecast systems becomes even more crucial, leading to increased investment and development in this sector.

Technological advancements play a pivotal role in the expansion of the weather forecast system market. The integration of cutting-edge technologies like big data analytics, machine learning, and the Internet of Things (IoT) has revolutionized the way weather data is collected, analyzed, and disseminated. These technologies enable the processing of vast datasets in real-time, resulting in more accurate and reliable forecasts. Additionally, the use of satellite technology and high-performance computing has enhanced the ability to monitor and predict weather conditions with unprecedented precision, driving the demand for advanced forecasting systems across various industries.

Another significant growth driver is the increasing demand for weather forecast systems from sectors such as agriculture, energy, and transportation. In agriculture, accurate weather forecasts are essential for optimizing planting and harvesting schedules, managing water resources, and reducing crop losses due to adverse weather conditions. Similarly, the energy sector relies on weather forecasts to manage the supply and demand of energy efficiently, especially for renewable energy sources like wind and solar power. In transportation, accurate weather forecasts are crucial for ensuring the safety and efficiency of operations, particularly in aviation and maritime industries. As these sectors continue to grow, the demand for advanced weather prediction systems is expected to rise, contributing to the market's expansion.

Regionally, the weather forecast system market exhibits varying growth patterns across different geographies. North America holds a significant share of the market due to the presence of established infrastructure and the early adoption of advanced technologies. The region's proactive approach to disaster management and climate change mitigation further facilitates market growth. Europe follows closely, with significant investments being made in upgrading weather forecasting capabilities to meet the region's environmental and economic challenges. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in infrastructure development, and heightened awareness of climate-related risks. Meanwhile, Latin America and the Middle East & Africa are also anticipated to experience noteworthy growth, albeit at a relatively slower pace.

Component Analysis

The weather forecast system market can be segmented by component into software, hardware, and services, each playing a crucial role in the overall system's functionality and efficacy. The software component is integral to the market, comprising advanced algorithms and models used for data analysis and prediction. As weather forecast systems become more sophisticated, the demand for customized software solutions that can provide accurate and reliable forecasts increases. Software development in this segment focuses on enhancing user interfaces, improving data visualization, and integrating real-time data processing capabilities to offer more actionable insights to end-users across various sectors.

H

Weather Forecasting Systems Market Outlook

The global weather forecasting systems market size stood at approximately $4.5 billion in 2023, and it is projected to reach around $8.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.7% during the forecast period. This growth is driven by several factors, including technological advancements, increased demand from various industries, and the rising impact of climate change necessitating accurate weather predictions.

As climate change continues to reshape weather patterns globally, the importance of accurate weather forecasting systems has seen an unprecedented rise. The ability to predict weather phenomena with greater precision is vital for mitigating the adverse impacts of climate change on communities and industries. Industries such as agriculture, aviation, and energy sectors are increasingly reliant on precise weather forecasts to plan and optimize their operations, thereby driving demand for advanced weather forecasting systems. Moreover, governments around the world are investing in meteorological infrastructure and technology to prevent and manage natural disasters, which further fuels market growth.

Technological advancements play a critical role in the expansion of the weather forecasting systems market. The integration of artificial intelligence (AI) and machine learning (ML) into weather prediction models has significantly enhanced the accuracy and reliability of forecasts. These technologies enable the processing of vast amounts of meteorological data, allowing for more precise and timely weather predictions. Additionally, improvements in satellite technology and radar systems have provided meteorologists with more detailed and comprehensive data than ever before, further boosting the market's growth prospects.

Increased awareness and understanding of the economic impact of weather events underscore the market's growth trajectory. Weather-related disruptions can lead to significant financial losses, particularly within sectors like transportation, agriculture, and energy. As such, companies are increasingly prioritizing investments in weather forecasting technologies to minimize these disruptions and safeguard their operations. The growing emphasis on sustainability also demands accurate weather data to optimize resource usage and reduce environmental footprints, creating additional market opportunities.

The integration of a Weather Monitoring System within these forecasting frameworks has become increasingly vital. These systems are designed to collect real-time data on various atmospheric conditions, including temperature, humidity, wind speed, and precipitation. By providing accurate and timely data, Weather Monitoring Systems enhance the precision of weather forecasts, enabling industries to make informed decisions. For instance, in agriculture, real-time weather data can help farmers optimize irrigation schedules and protect crops from adverse weather conditions. Similarly, in aviation, these systems contribute to flight safety by providing crucial information about weather patterns that could affect flight paths. As the demand for precise weather predictions continues to grow, the role of Weather Monitoring Systems becomes ever more critical in supporting the functionality and reliability of weather forecasting systems.

Regionally, North America dominates the weather forecasting systems market, primarily due to the presence of advanced technological infrastructure and significant investments in meteorological research. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, driven by increasing governmental initiatives and investments in weather forecasting capabilities to combat natural disasters and agricultural dependency on climate patterns.

Component Analysis

In terms of components, the weather forecasting systems market is segmented into hardware, software, and services. The hardware segment includes meteorological instruments such as satellites, radars, and sensors, which are fundamental to collecting weather data. These instruments have seen significant advancements in recent years, enabling the collection of more precise and detailed data. The increasing use of satellite and radar technologies has expanded the capabilities of weather forecasting systems, allowing for real-time data analysis and improved forecast accuracy. As a result, the hardware segment is expected to maintain a sub

Platycodon grandiflorus, the sole species from the genus Platycodon and a member of the Campanulaceae family, has been known for its medicinal, culinary, and ornamental uses for approximately 2000 years. Its distribution is primarily in eastern Asia, including China, Korea, Japan, and Russia. With increasing market demand and the depletion of wild resources, understanding the distribution and factors affecting its habitat suitability is crucial for its conservation and sustainable utilization. This study aims to predict the suitable habitat for P. grandiflorus in China considering current and future climate scenarios. The MaxEnt model, with an AUC value of 0.846, demonstrated good predictive ability for the current and future distribution of P. grandiflorus. It also identified the central, eastern, and southern regions as suitable habitats, with the critical environmental factors being precipitation, temperature, and elevation. Future scenarios under both SSP126 and SSP585 projections indicate an increase in suitable habitats, particularly in northeastern and central China, albeit with a shift in the distribution center towards the northeast by 2041-2060 and 2081-2100 under different scenarios. As a result, P. grandiflorus’ distribution is significantly influenced by environmental factors, with precipitation and temperature being pivotal. In summary, this study predicts an expansion of suitable habitats under future climate scenarios, suggesting that climate change may facilitate the growth and distribution of P. grandiflorus in new areas. The northward shift in the distribution center underlines the impact of global warming on plant distribution. These findings are crucial for the conservation, effective utilization, and strategic planning for the cultivation of P. grandiflorus in the face of climate change. Methods Distribution point collection According to the Global Biodiversity Information Facility (GBIF, http://www.gbif.org/) as well as Chinese Virtual Herbarium (CVH), accessed on 7 January 2024, the distribution points of P. grandiflorus in China were screened and recorded. Repeated locations were removed and the remaining locations were screened manually so that only one location appeared in every 10 km × 10 km grid cell. 405 known distribution locations of P. grandiflorus were documented through the use of ArcGIS 10.8 (Esri, Redlands, CA, USA) (Fig. 1). The data points were saved in CSV format for subsequent analysis by ArcGIS. Environmental parameters Fifty-two environmental parameters which could influence the distribution of P. grandiflorus were selected. This study used the latest WorldClim version 2.1 (https://worldclim.org/, accessed on 13 January 2024) to obtain current, future climate projection data as well as elevation data at a spatial resolution of 2.5 min. Climate type data used include January to December precipitation and average temperatures, as well as 19 bioclimatic factors. The Shared Socioeconomic Pathways (SSPs) are based on five narratives of socioeconomic development (Riahi et al. 2017). For this study, we selected the low forcing scenario SSP126 and the high forcing scenario SSP585 to predict the impacts on P. grandiflorus. SSP126 represents a low-material, low-resource, and low-energy green development pathway, while the SSP585 scenario depicts a future socioeconomic pathway with high emissions and high carbon use (O’Neill et al. 2016). In this study, we adopted BCC-CSM2-MR climate model which was reported to accurately simulates temperature and precipitation in China (Wu et al. 2019). The BCC-CSM2-MR model was developed by the National Climate Centre (Beijing, China) and participated in the International Coupled Model Comparison Program (ICMCP) and enhanced its climate simulation capability in Eastern Asia, especially for the China region. We projected the potential distribution areas of P. grandiflorus for 2041-2060 and 2081-2100 under the SSP126 and SSP585 scenarios. In addition to the above-mentioned variables, we considered two topographic factors and six soil factors. The topographic factors included slope and aspect. Soil factors included topsoil clay fraction, topsoil sand fraction, topsoil organic carbon, the acidity and alkalinity of the soil, the total nutrient fixing capacity of the soil, available water storage capacity of the soil unit. We obtained the topographical data from the Geospatial Data Cloud (http://www.gscloud.cn/, downloaded on 15 January 2024)，the soil data through the Harmonized World Soil Database (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmon- ized-world-soil-database-v12/en/, downloaded on 15 January 2024). These environment variables were converted to ASCII format by the ArcGIS Conversion Tools for downstream analysis. High correlations and covariances between the extracted environmental variables can easily lead to overfitting of the model and affect the prediction results, so not all variables were needed (Hu and Hua 2014). Therefore, for every environmental factor, its contribution to the model predictions was first evaluated using the jackknife test (a module in MaxEnt (version 3.4.1)), and the environmental factors that contributed less (<1 %) were eliminated. The correlation between the remaining variables was then calculated in SPSS 20.0 adopting the Pearson correlation coefficient method. We considered two variables with |r|≥0.8 to be significantly correlated, and excluded one of the variables with relatively low biological significance to minimize model overfitting (Xu et al. 2019). After screening, only 17 of the initial 52 environment variables were included for evaluation (Table 1).