Trend Analysis Research Articles

Accelerated reduction in China's cropland fires against the background of policy enhancement

Cropland fires contribute significantly to the emission of air pollutants, with implications for the atmospheric environment, public health, and climate change. However, the development of cropland fires in response to straw burning policy interventions has not been fully explored. With the Moderate Resolution Imaging Spectroradiometer Collection 6 (MODIS C6) active fire products and China land-cover data (CLCD), counts and fire radiative power (FRP) of cropland fire spots were extracted in China from 2003 to 2022, followed by analyses of their spatiotemporal patterns and evolutionary characteristics at the provincial, regional, national, and grid (0.25° × 0.25°) levels and at monthly to annual scales via trend analysis, kernel density analysis, and geographic distribution analysis. The key findings include: (1) National policies banning straw burning reversed the changes in the quantity and intensity of China's cropland fires in 2013, with the frequency and FRP in 2022 declining by 58% and 62% compared with the 2014 peak. (2) Cropland fires were highly prevalent in Northeast and North China (NEC and NC), with significant regional differences, particularly in spring, summer and autumn. (3) A notable lag effect of burning ban policies was seen in Northeast China (NEC), where the increasing trends of the frequency and intensity of cropland fires were not reversed until 2017, followed by declines in frequency and FRP of 67% and 62%, respectively during 2017–2022. (4) Cropland fires showed an increasing concentration trend but with enormous regional differences, with an increasing trend of a “high-high” concentration pattern in NEC and a decreasing trend in NC. This study contributes to understanding the effectiveness of national policies that ban straw burning and support cropland fire management and policy-making.

Wetting or greening? Probing the global trends in Vegetation Condition Index (VCI)

Vegetation Condition Index (VCI), as a widely used drought index for monitoring vegetation drought stress and estimating drought trends, is constructed by normalizing the long-term satellite-based Normalized Difference Vegetation Index (NDVI) data. However, under global greening, vegetation across different regions has shown an increasing trend in greenness, which may cause VCI to inherit the greening trend in NDVI and further hamper its ability in drought trend analysis. Therefore, this study quantitatively explored the underlying relationship among VCI, global greening, and drought from 2001 to 2021 to examine the utility of VCI in a changing environment. Multi-source indicators were employed as surrogates for vegetation greenness and drought, respectively. Particularly, the Leaf Area Index (LAI) and Net Primary Production (NPP) were used as proxies for vegetation greenness while the Root-Zone Soil Moisture (RZSM), Palmer Drought Severity Index (PDSI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were used for drought indices. Based on Sen’s slope estimator, Mann-Kendall (MK) test, and partial correlation analysis, our results show that the proportion of pixels with an increasing trend in VCI is approximately 38.5%, which is similar to the greenness-related indices (i.e., NPP [26.47%] and LAI [59.14%]), and is significantly higher than the other drought indices (i.e., RZSM [19.83%], PDSI [10.38%], and SPEI [9.32%]). Furthermore, our results demonstrate that the VCI, as a drought index, exhibits a closer correlation with greenness-related indicators (LAI and NPP) than other drought indices (RZSM, PDSI, and SPEI). These results reveal the potential limitations in the practical applications of VCI and could enhance our understanding of vegetation dynamics and drought trends, especially in the current greening globe. Additionally, this study serves as a cautionary note for the scientific community involved in drought monitoring, emphasizing that the VCI may not be a suitable tool for evaluating long-term trends in drought impacts on vegetation.

Survey and Analysis of Current Manufacturing Trends in the Global Market: A Practical Investigation

bstract: Manufacturing has undergone a significant digital transformation in recent years, driven by technological advancements and evolving consumer demands. To remain competitive and overcome today's challenges, manufacturers have prioritized agility and adaptability. The ongoing march of technology, connectivity, and automation enhancements is poised to continue reshaping the industry

Financial Analysis and Future Outlook of Ford Motor Company

Ford Motor Company is an American multinational automaker founded in 1903. It is one of the largest automobile manufacturers in the world. This research delves into the financial condition of Ford Motor Company, an automotive industry leader, to provide a comprehensive analysis of its financial performance and prospects. Against the backdrop of an evolving automotive landscape and increasing consumer demand for sustainable and technologically advanced vehicles, this study is timely and relevant. The primary objective of this paper is to scrutinize Ford's financial health and prospects in-depth. The research combines quantitative data analysis of Ford's financial statements, historical performance, and market trends with a qualitative assessment of its strategic positioning and competitive environment. The findings indicate that while Ford faces certain financial challenges, it possesses the potential for growth and innovation. Recommendations include leveraging 5G technology to enhance product quality and cautiously exploring opportunities in the electric vehicle sector.

Adaptation of trend analysis over a small-scale region for assessing climate change parameters

Abstract Several natural and anthropogenic activities mainly escalate the demand for water and food and develop implications for their availability. Hence, it is necessary to analyse the natural and man-made changes for managing resources precisely. This study focuses on assessing the trends of climatic factors such as rainfall and temperature using the Mann–Kendall (MK) test and innovative trend analysis (ITA) for the agricultural cropping seasons. The findings revealed that a statistically insignificant increasing trend in rainfall is observed for all three cropping seasons (i.e.) Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May), while the mean temperature shows a significantly increasing trend for all three cropping seasons. The results obtained using the MK test are compared with those of the ITA test to identify the reliable trend technique for climatic factor analysis of small regions. To manage the impacts of climate change and develop sustainable conditions in agriculture, this micro-level study assists the decision makers to prefer suitable trend analysis techniques and helps in identifying the climate change adaptation strategies for small regions. Also, this study is helpful for rural farmers to increase their adaptability conditions with regard to the climate change impacts as they are the frontline victims of climate change.

Using machine learning to predict artistic styles: an analysis of trends and the research agenda

AbstractIn the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles.

Modeling of ionic liquids viscosity via advanced white-box machine learning.

Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9MPa) and temperatures (253.15-573K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDHmodel with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Abstract P41: Ex vivo Drug Testing Identifies a Novel Drug Pair in Targeting STS

Abstract Soft tissue sarcomas (STS) are a highly heterogeneous collection of tumors that arise from primitive mesenchymal cells. Due to a reliance on cytotoxic chemotherapy as standard of care, survival outcomes for advanced STS patients remain poor. While efforts to understand sarcomagenesis have revealed potential biomarkers, the diversity of STS subtypes and interpatient tumour heterogeneity has led to the lack of effective molecular targeted therapies available. Given the uniquely individualized nature of STS, we hypothesize that the application of an experimental-analytics method, Quadratic Phenotypic Optimization Platform (QPOP), towards primary STS patient samples can improve identification of patient-specific drug combinations. Across a total of 45 patient samples collected, QPOP identified a novel drug pair as most frequently top-ranked, outperforming standard of care ifosfamide and doxorubicin. Interestingly, analysis of QPOP ranking trends revealed this combination to be broadly effective, implying common targetable pathways independent of STS subtype. Single-drug and combination dose-response assays performed in a panel of established patient lines supported its synergistic interaction, accompanied by increased PARP and caspase-3 cleavage with immunoblotting, and increased apoptotic response with Annexin V assay. Mechanistically, this synergy results in cell cycle arrest and induction of programmed cell death pathways, as revealed by molecular profiling on representative patient and cell lines. We further showed that co-treatment synergistically repressed oncogenic MYC and activated the p27kip1 tumor suppressor. Finally, this combination was more effective at impairing tumor progression than single agents alone in an STS xenograft mouse model. Overall, work here provides preliminary clinical evidence for the prioritization of a novel drug pair in addressing STS beyond known clinicopathologic parameters. Citation Format: Sharon Pei Yi Chan, Wai Yee Wong, Nur Nadiah Ismail, Baiwen Luo, Benjamin Jieming Chen, Brandon Xuan Ming Phua, Xing Yi Woo, Tan Boon Toh, Edward Kai-Hua Chow, Valerie Shiwen Yang. Ex vivo Drug Testing Identifies a Novel Drug Pair in Targeting STS [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P41.

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

Analysis of Hotspots and Trends in Domestic and International Data Asset Research: A Knowledge Graph Analysis Based on CiteSpace

The article summarized 363 literature on data assets in the CNKI and WOS databases and conducted a knowledge map analysis using CiteSpace. The research found that the study of data assets at home and abroad is continuously growing, but there are differences in origins, content, and hotspots. Foreign research focuses on areas such as big data, data governance, machine learning, and the Internet, while domestic research focuses on data assets, the digital economy, data transactions, and accounting. Domestic research is gradually expanding to include data middle platforms and asset confirmation, while foreign research focuses on the Internet and artificial intelligence. This analysis helps grasp the research trends and hotspots in the field of data assets, promoting cooperation and knowledge sharing.

Four Generations of Volcano Plots for the Oxygen Evolution Reaction: Beyond Proton-Coupled Electron Transfer Steps?

ConspectusDue to its importance for electrolyzers or metal-air batteries for energy conversion or storage, there is huge interest in the development of high-performance materials for the oxygen evolution reaction (OER). Theoretical investigations have aided the search for active material motifs through the construction of volcano plots for the kinetically sluggish OER, which involves the transfer of four proton-electron pairs to form a single oxygen molecule. The theory-driven volcano approach has gained unprecedented popularity in the catalysis and energy communities, largely due to its simplicity, as adsorption free energies can be used to approximate the electrocatalytic activity by heuristic descriptors.In the last two decades, the binding-energy-based volcano method has witnessed a renaissance with special concepts being developed to incorporate missing factors into the analysis. To this end, this Account summarizes and discusses the different generations of volcano plots for the example of the OER. While first-generation methods relied on the assessment of the thermodynamic information for the OER reaction intermediates by means of scaling relations, the second and third generations developed strategies to include overpotential and kinetic effects into the analysis of activity trends. Finally, the fourth generation of volcano approaches allowed the incorporation of various mechanistic pathways into the volcano methodology, thus paving the path toward data- and mechanistic-driven volcano plots in electrocatalysis.Although the concept of volcano plots has been significantly expanded in recent years, further research activities are discussed by challenging one of the main paradigms of the volcano concept. To date, the evaluation of activity trends relies on the assumption of proton-coupled electron transfer steps (CPET), even though there is experimental evidence of sequential proton-electron transfer (SPET) steps. While the computational assessment of SPET for solid-state electrodes is ambitious, it is strongly suggested to comprehend their importance in energy conversion and storage processes, including the OER. This can be achieved by knowledge transfer from homogeneous to heterogeneous electrocatalysis and by focusing on the material class of single-atom catalysts in which the active center is well defined. The derived concept of how to analyze the importance of SPET for mechanistic pathways in the OER over solid-state electrodes could further shape our understanding of the proton-electron transfer steps at electrified solid/liquid interfaces, which is crucial for further progress toward sustainable energy and climate neutrality.

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Female genital mutilation: trends, economic burden of delay and basis for public health interventions.

The practice of female genital mutilation (FGM) is a health and social problem. Millions of girls and women have undergone FGM or will soon, and more information is needed to effectively reduce the practice. The aim of this research is to provide an overview of the FGM trendlines, the inequality of its prevalence, and the economic burden. The findings shed light on 30-year trends and the impact of the pandemic on planned efforts to reduce FGM which helps with public health interventions. Temporal trend analysis, and graphical analysis were used to assess the change and inequality over the last 30 years. We included 27 countries in which FGM is prevalent. We calculated the extra economic burden of delayed interventions to reduce FGM like COVID-19. For the 27 countries analyzed for temporal trendlines, 13 countries showed no change over time while 14 had decreasing trends. Among the 14, nine countries, Uganda, Togo, Ghana, Benin, Kenya, Nigeria, Central African Republic, Chad, and Ethiopia had high year-decrease (CAGR - 1.01 and - 10.26) while five, Côte d'Ivoire, Egypt, Gambia, Djibouti, and Mali had low year-decrease (CAGR>-1 and < 0). Among these five are the highest FGM prevalence similar distribution regardless the wealth quintiles or residence. There is an economic burden of delay or non-decline of FGM that could be averted. Findings indicate that some countries show a declining trend over time while others not. It can be observed that there is heterogeneity and homogeneity in the FGM prevalence within and between countries which may indicate inequality that deserves further investigation. There is considerable economic burden due to delays in the implementation of interventions to reduce or eliminate FGM. These insights can help in the preparation of public health interventions.

Trend analysis of machine learning application in the study of soil and sediment contamination

Trend analysis of machine learning application in the study of soil and sediment contamination

Google Trends of political parties in Europe: a fractal exploration

Abstract Google Trends, despite its controversial nature for some authors, can be considered an illustrative tool in exploring the political inclinations of a given audience. In the current European Union context, understanding the views and opinions of the public is of paramount importance. Through the analysis of search trends, Google Trends can provide valuable insights into the popularity of political parties in the context of the European Union along with other jurisdictions and how these trends change over time. Furthermore, by incorporating fractal dimensions and ARFIMA (Autoregressive Fractionally Integrated Moving Average) analysis into the data obtained, it is possible to reveal previously non-evident relationships, thereby providing a more comprehensive understanding of the audience‘s political leanings and their interest in specific political parties. The aim of this exploratory study is to assess the potential of ARFIMA, applied to Google Trends data, in characterizing political parties. Preliminary results indicate that this apparatus can be useful for that purpose.

Text mining of hypertension researches in the west Asia region: a 12-year trend analysis

More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) methods can analyze articles and extract top trends in each country. Present analysis uses Latent Dirichlet allocation (LDA) as an algorithm of topic modeling (TM) in text mining, to obtain subjective topic-word distribution from the 2790 studies over the EMRO. The period of checked studied is last 12 years and results of LDA analyses show that HTN researches published in EMRO discuss on changes in BP and the factors affecting it. Among the countries in the region, most of these articles are related to I.R Iran and Egypt, which have an increasing trend from 2017 to 2018 and reached the highest level in 2021. Meanwhile, Iraq and Lebanon have been conducting research since 2010. The EMRO word cloud illustrates ‘BMI’, ‘mortality’, ‘age’, and ‘meal’, which represent important indicators, dangerous outcomes of high BP, and gender of HTN patients in EMRO, respectively.

Pancreatic ductal adenocarcinoma: exploring clinicopathological trends and racial disparities in a comprehensive U.S. population-based study.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy about 50% of PDAC are metastatic at presentation. In this study, we evaluated PDAC demographics, annual trend analysis, racial disparities, survival rate, and the role of different treatment modalities in localized and metastatic disease. A total of 144,824 cases of PDAC were obtained from the SEER database from 2000 to 2018. The median age was 69years, with a slightly higher incidence in males (52%) and 80% of all cases were white. Among cases with available data, 43% were grade III tumors and 57% were metastatic. The most common site of metastasis was the liver (15.7%). The annual incidence has increased steadily from 2000 to 2018. The overall observed (OS) 5-year survival rate was 4.4% (95% CI 4.3-4.6%), and 5years cause-specific survival (CSS) was 5% (95% CI 5.1-5.4%). The 5-year survival with multimodal therapy (chemotherapy, surgery, and radiation) was 22% (95% CI 20.5-22.8%). 5-year CSS for the blacks was lower at 4.7% (95% CI 4.2-5.1%) compared to the whites at 5.3% (95% CI 5.1-5.4%). Multivariate analysis found male gender and black race associated with worse prognosis. Kaplan-Meier survival analysis found multimodal therapy to have the best outcomes in all three stages. PDAC is an aggressive malignancy with male gender and black race are associated with a poor prognosis. Surgery with chemoradiation was associated with the best overall survival. With steadily increasing rates of PDAC, improved treatment modalities are paramount to improving survival in these patients.

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

Time trend analysis of rare cancer incidence 2011-2018: Nationwide population-based cancer registries in Japan.

Rare cancers collectively account for a significant proportion of the overall cancer burden in Japan. We aimed to describe and examine the incidence of each rare cancer and the temporal changes using the internationally agreed rare cancer classification. Cancer cases registered in regional population-based cancer registries from 2011 to 2015 and the National Cancer Registry (NCR) from 2016 to 2018 were classified into 18 families, 68 Tier-1 cancer groupings, and 216 single cancer entities based on the RARECAREnet list. Crude incidence rates and age-standardized incidence rates (ASR) were calculated for Tier-1 and Tier-2 cancers. The annual percent change and the 95% and 99% confidence limits for annual ASR for each of the 68 Tier-1 cancers were estimated using the log-linear regression of the weighted least squares method. The differences in ASRs between 2011 and 2018 were evaluated as an absolute change. A total of 5,640,879 cases were classified into Tier-1 and Tier-2 cancers. The ASRs of 18 out of 52 Tier-1 cancers in the rare cancer families increased, whereas the ASR for epithelial tumors of gallbladder decreased. The ASRs of 6 out of the 16 Tier-1 cancers in the common cancer families increased, whereas those of epithelial tumors of stomach and liver decreased. There was no significant change in the incidence of the other 40 Tier-1 cancers. The incidence of several cancers increased due to the dissemination of diagnostic concepts, improved diagnostic techniques, changes in coding practice, and the initiation of the NCR.

Угрозы экономической безопасности в деятельности предприятий производства пластмассовых изделий Рязанской области

The analysis of trends and regularities in evaluation criteria behavior characterizing financial and economic activities of the organizations producing plastic products in Ryazan region is carried out. Against the background of positive dynamics of performance indicators, identical problem areas were identified that can provoke threats to economic security not only for business entities, but also for the entire industry. The revealed threats to economic security of the organizations under study were identified with the threats previously proven in the scientific literature and related to financial losses from inefficient use of resources, which was the evidence base for the necessity to improve and update analytical tools to control negative phenomena in resource management. The purpose of the study is to update and systematize analytical tools for resource control in the organizations producing plastic products in Ryazan region. The set of methods used includes: general scientific research methods, that is, monographic, generalization of materials, system research and special methods of analysis and control: comparisons, comparison of indicators with acceptable values, and platform analysis. The article proposes analytical tools to control resources in the assessment and forecasting of threats to economic security, integrating the relationship of systematized indicators of various forms of accounting (financial) reporting.