Short-term Trends Research Articles

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

Short-term Functional Outcomes and Treatment Trends between Branch Atheromatous Disease and Lacunar Infarction: Retrospective Cohort Study of the Nationwide Multicenter Registry.

Branch atheromatous disease (BAD) is a form of ischemic stroke that presents with imaging findings similar to those of lacunar infarction, but has a different pathogenesis and is known to cause progressive paralysis. Due to regional variations, the epidemiology of BAD is not wellunderstood, and its relationship with functional prognosis remains unclear. Using a comprehensive Japanese stroke database, we investigated its epidemiological characteristics and associations with functional outcomes. In this multicenter cohort study, we retrospectively analyzed data from the Saiseikai Stroke Database (2013-2021) including 27 hospitals. We used multivariable logistic regression to calculate adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of BAD compared with LI for functional outcomes at discharge. Ischemic stroke caused by BAD or LI was included and demographic characteristics and clinical data were evaluated and contrasted between BAD and LI. Of the 5,966 analyzed patients, 1,549 (25.9%) had BAD and 4,434 (74.1%) had LI. BAD was associated with worse functional outcomes (adjusted OR of 2.77, 95%CI: 2.42-3.17, relative to LI) and extended hospital stays (median 19 days for BAD vs. 13 days for LI). Moreover, aggressive treatment strategies, including the use of argatroban and dual antiplatelet therapy, were more common in BAD patients. BAD presented worse functional outcomes and longer hospital stays than LI, necessitating treatment plans that take into account its progression and prognosis.

Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational resources and may not accurately capture local weather patterns. This study explores the potential of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting daily climate variables such as temperature, precipitation, and humidity. Utilizing historical climate data from the city of Delhi, we developed an LSTM model to forecast short-term climate trends. The model consists of two LSTM layers followed by three Dense layers and is compiled with the Adam optimizer, mean squared error loss, and mean absolute error as a metric. Our results demonstrate the model's capability to capture temporal dependencies in climate data, achieving a satisfactory level of accuracy in temperature forecasting. This research underscores the potential of machine learning techniques, particularly LSTM networks, in enhancing climate prediction and contributing to more informed decision-making in weather-sensitive sectors.

Comparative Performance of Neural Network Architectures in Gold Price Prediction: a Study of GRU, N-BEATS, LSTM and Hybrid Models

This study assesses the efficacy of a variety of neural network models in predicting gold prices, with a particular emphasis on GRU, N-BEATS, N-BEATS-GRU, N-BEATS-LSTM, and LSTM. We evaluated the models using a comprehensive dataset and metrics such as MSE, MAE, MAPE, and RMSE. The GRU model demonstrated the highest MAPE of 3.10%, as indicated by the results. N-BEATS was the second-best-performing model. In stark contrast to expectations, hybrid models, notably N-BEATS-LSTM, underperformed. Compared to LSTM-based models, visual analysis demonstrated that GRU and N-BEATS more effectively captured short-term and long-term trends. The results indicate that simplified models, such as GRU, may be more effective than complex hybrids in predicting gold prices.

EFFECTIVENESS OF THE USE OF COMPENSATION BONUSES IN THE CZECH REPUBLIC

Aim: This paper focuses on the evaluation of the impact of the use of compensation bonuses during a pandemic on the economic situation of companies. Methods: In order to achieve this objective, a primary quantitative survey in the form of a questionnaire survey was conducted in Czech enterprises and two research questions were set. The research questions were answered by testing four hypotheses, which focused on testing the relationship between the use of compensation bonuses and the nature of the change in the economic situation of the enterprise in terms of sector, size and prevailing business relationship. The parametric two-sample one-factor ANOVA and Pearson's Chi-squared test were used to test the hypotheses. Main findings: The results showed that the use of compensation bonuses was associated with a negative change in the economic situation of the firms. Differences in the magnitude of the effect were observed in terms of the prevailing business relationship and the size of the enterprises. The business sector did not affect the strength of the association between bonus use and change in economic situation. Limitations of the paper: the paper focuses on observing the short-term trend and is limited to Czech enterprises during the pandemic period.

Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe

Methane (CH4) was measured at El Arenosillo observatory, in an Atlantic coastal area located in Southwestern Europe, throughout a four-year period (September 2019–December 2023). Short-term trend, temporal variations and patterns were explored. A trend of 12.1 ± 1.1 nmol mol−1 year−1 was obtained based on daily averages. CH4 exhibited its highest mole fraction in cold months and its lowest in summer, and a seasonally varying diurnal cycle was also observed, with peaks in the early morning (7:00–8:00 UTC) and minimum values at 18:00–19:00 UTC. Mitigation strategies should be implemented based on an understanding of CH4 patterns. To achieve this, CH4 variations under an atmosphere governed by two synoptic patterns were studied. The atmospheric dynamic was analysed with the wind fields from ERA5 data reanalysis and the air masses pathways with the back-trajectories computed with the HYSPLIT model. CH4 horizontal distribution was explored with the TROPOMI observations. The CH4 baseline was collected under the influence of Atlantic air, while a rise from 40 to 80 nmol mol−1 was found with continental airflows from the inland Guadalquivir Valley. CH4 under sea-land breezes did not contribute to the increase of CH4. However, XCH4 from TROPOMI showed an accumulation in the marine area of the Gulf of Cadiz. Ongoing CH4 observations will be used to identify potential changes in the trend, understand the contribution of transported CH4 from areas such as the Western Mediterranean Basin, or assess its impact on surface ozone, among other objectives.

Analysis of streamflow and rainfall trends and variability over the Lake Kariba catchment, Upper Zambezi Basin

ABSTRACT Hydro-meteorological trend analysis is critical for assessing climate change and variability at basin and regional levels. This study examined the long- and short-term trends from stream discharge and rainfall data in the Lake Kariba catchment. A trend and change point analysis was carried out on the mean, minimum and maximum monthly average time series for 14 gauging stations that are located within the Kariba catchment. The Mann–Kendall and the Pettitt tests were used to determine the trend and any changes in the long-term average of the time series. The magnitude of the trend was determined by Sen's slope method. The results indicate that generally there has been a decreasing trend in river and rainfall long-term mean values across the catchment. A statistically significant trend (p ≤ 0.05) was observed at Zambezi River at Lukulu, Senanga and Victoria Falls, with a positive correlation in Pearsons's coefficient of water levels and rainfall at Lukulu (0.312) and Senanga (0.365). The decrease in the time series trend and the change point observed have been attributed to anthropogenic activities, climate change and variability impact on the catchment. The findings are critical for climate risk management and reduction decisions for near- and long-term timescales.

P112 Real-time estimation of individual long- and short-term lung functions trends in persons with cystic fibrosis within the Swedish cystic fibrosis registry

P112 Real-time estimation of individual long- and short-term lung functions trends in persons with cystic fibrosis within the Swedish cystic fibrosis registry

Air pollution in Białowieża forest: Analysis of short-term trends from 2014 to 2021

Air pollution caused by sulphur dioxide (SO2) and nitrogen oxides (NOx) has negative impacts on forest health and can initiate forest dieback. Long-term monitoring and analysis of these pollution are carried out in Białowieża Forest in NE Poland due to the threats from abiotic, biotic and anthropogenic factors. The main objective of our study was to monitor the levels and trends of air pollutant deposition in Białowieża Forest. During a short-term monitoring period over six years (2014–2021), the concentration of SO2 in the air decreased significantly (from 2.03 μg m−3 in December 2015 to 0.20 μg m−3 in July 2016), while the concentration of NO2 in the air showed a non-significant decrease (from 8.24 μg m−3 in December 2015 to 1.61 μg m−3 May 2016). There was no significant linear trend in the wet deposition of S–SO4 anions. Mean monthly S–SO4 deposition varies between 4.54 and 94.14 mg m−2month−1. Wet nitrogen deposition, including oxidized nitrogen (N–NO3) and reduced nitrogen (N–NH4), showed a non-significant increase. Mean monthly precipitation of N–NO3 and N–N H4 ranged from 1.91 to 451.73 mg m−2month−1. Neither did total sulphur deposition nor total nitrogen deposition exceed the mean deposition values for forests in Europe (below 6 ha−1yr−1 and 3–15 ha−1yr−1, respectively). Our results indicate that air pollutants originate from local sources (households), especially from the village of Białowieża, as demonstrated by the level and spatial distribution of air pollutant deposition. This indicates that air pollutants from the village of Białowieża could spread to other parts of Białowieża Forest in the future and may have a negative impact on forest health and can initiate forest dieback. It is therefore important to continue monitoring air pollution to assess the threats to this valuable forest ecosystem.

Impacts of Weather Variability on the International Tourism Receipts—Evidence from Ethiopia (1995–2019)

Every economic sector is susceptible to the direct or indirect effects of weather variability, and the tourism sector is no exception. In fact, the tourism industry is considered to be more vulnerable to the effects of weather variability than the general economy, with changes in weather patterns, extreme events, and environmental degradation offering substantial obstacles. Ethiopia’s tourism industry, like many others, faces challenges from weather variability. This study investigates the short- and long-term effects of weather variability on Ethiopia’s international tourism receipts. Utilizing data from 1995 to 2019, the research employs a vector error correction model to analyze the relationships between weather variables (temperature, rainfall), economic factors (GDP growth, inflation), political stability, and tourist arrivals. The findings reveal that in the long run, higher temperatures, rainfall, and inflation have negative impacts on tourism receipts, while political stability and past tourist arrivals have positive effects. Short-term trends mirror these, with the addition of GDP growth not showing a significant impact. To ensure the sustainability of tourism in Ethiopia, the study emphasizes the importance of understanding weather’s influence, developing adaptation strategies, and promoting sustainable tourism practices.

Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics

In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to the global efforts to address climate change.

Analysis of Spatial Differentiation of NDVI and Climate Factors on the Upper Limit of Montane Deciduous Broad-Leaved Forests in the East Monsoon Region of China

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI’s lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation’s vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation’s diverse adaptability to climatic variations.

Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting

This study aims to forecast crude oil and natural gas volatility across various forecasting horizons, from daily to quarterly, using diverse machine learning models. It critically analyzes eleven models, including Linear Regression, Elastic Regression, Ridge Regression, Lasso Regression, Huber Regression, Random Forest Regression, SVM, LSTM, GRU, ANN, and XGBoost, using RMSE for accuracy. The study reveals that model performance significantly varies with forecasting horizons; a phenomenon attributed to each model's inherent capabilities in processing short-term versus long-term market trends. This detailed understanding aids in selecting the most appropriate models for specific forecasting needs, essential for policymakers and practitioners in managing volatility effectively. The study concludes with a recommendation for using Random Forest Regression and XGBoost for natural gas volatility forecasting, providing key insights for enhancing economic resilience and stability.

Long-Term Evolution of Significant Wave Height in the Eastern Tropical Atlantic between 1940 and 2022 Using the ERA5 Dataset

Studies on the variability in ocean wave climate provide engineers and policy makers with information to plan, develop, and control coastal and offshore activities. Ocean waves bear climatic imprints through which the global climate system can be better understood. Using the recently updated ERA5 dataset, this study evaluated the spatiotemporal distribution and variability in significant wave height (SWH) in the Eastern Tropical Atlantic (ETA). The short-term trends and rates of change were obtained using the Mann–Kendall trend test and the Theil–Sen slope estimator, respectively, and decadal trends were assessed using wavelet transformation. Significant, positive monthly and yearly trends and a prevailing decadal trend were observed across the domain. Observed trends suggest that stronger waves are getting closer to the coast and are modulated by the Southern and Northern Atlantic mid-latitude storm fields. These observations have implications for the increasing coastal erosion rates on the eastern coast of the Tropical Atlantic.

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Sustaining Community-Based Research in Computing: Lessons from Two Tech Capacity Building Initiatives for Local Businesses

The field of human-computer interaction (HCI) has traditionally focused on the design of novel technology artifacts. However, ensuring considerations for artifact maintenance and repair is crucial to sustainably supporting the populations they aim to serve over the long term. Drawing on two multi-year programs for tech capacity building in post-industrial U.S. cities, this article presents a comparative analysis to investigate the challenges and strategies for sustained community-based research in computing. In particular, our work detailed three considerations for academic-community partnerships. First, long-term partnerships prioritized transferring trust across academic and community personnel and continually set expectations that responded to evolving community initiatives (i.e., relational sustainability). Second, partnerships used academic support as a way to kickstart community initiatives, and flexibly reframed interventions to stay aligned with evolving community goals (i.e., economic sustainability). Third, partnerships trained personnel to provide technical support alongside interventions and prioritized advice that resisted short-term trends (i.e., technical sustainability). We provide concrete examples of how our two academic-community partnerships carried out such suggestions-such details go unreported in scholarly articles yet are essential for sustainability considerations. We discuss ongoing challenges, such as rethinking when longevity should and should not be the end goal.

Spatiotemporal Variability of Convective Events in Romania Based on METAR Data

Convective weather, through its heavy showers, strong winds and hail, significantly impacts human activities, having the potential to inflict serious damage on social and environmental sectors. Limited research has been conducted on this phenomenon within Romanian territory, and currently there is no referenced climatological study primarily aimed at air traffic management users in this context. This study aims to assess the climatological aspects related to convective events based on sub-hourly observation data recorded at 17 airport weather stations throughout Romania during an 11-year period (2012–2022). The spatiotemporal distribution of convective events was analyzed based on occurrences of Cumulus Congestus (TCU) clouds, Cumulonimbus (CB) clouds, thunderstorms (TSs), heavy showers (+SHs), and hail (GR). With the data being extracted from meteorological aerodrome reports (METARs) and special meteorological aerodrome reports (SPECIs). Short-term trends were determined using Sen’s slope estimator, and statistical significance was assessed through the Mann–Kendall test. The main findings indicated that the highest occurrence of convective events is located over central and western Romania, with June emerging as the extreme month in terms of convective events, while the hourly distribution emphasizes that the highest frequency of convective events occurred in the afternoon. Trend analysis in TCU, CB, and TS show tendencies toward higher frequency of convective events while the results related to +SH and GR indicate a high variability across Romanian territory. Trend analysis disclosed more substantial changes in the TS variable. The results of this study bear potential significance for a broad spectrum of human activities and the management of natural environments.

Analysis of short-term wind speed variation, trends and prediction: A case study of Tamil Nadu, India

Abstract Purpose The purpose of this research article is to analyze the short-term wind speed and develop a framework model to overcome the challenges in the wind power industry. Design/Methodology/Approach Real data with a case study of wind speed is presented to illustrate the advantages of this new wind speed analytical framework. Hourly measurements of wind speed are observed, and the experiments are conducted using tools such as ANOVA, control charts, trend analysis, and predictive models. The August month data for over 13 years from modern era retrospective-analysis for research and applications (MERRA) National aeronautics and space administration (NASA) for Coimbatore and Erode locations in Tamil Nadu, India, have been used. The results were considered for the study to understand the wind speed data and the implementation of new wind power projects in India. Findings The essence of the proposed wind speed analytical framework is its flexible approach, which enables the effective integration of wind firms’ individual requirements by developing tailor-made analytical evaluations. Originality/Value This article derives the wind speed analytical framework with the application of statistical tools and machine learning algorithms.

Research on Machine Learning-based Prediction of Coffee Futures Prices

Coffee is one of the major agricultural commodities in world trade, and its futures are vital tools in the global capital market. The continued vitality of the coffee market and substantial price fluctuations have increased hedging demands among market participants. Consequently, predicting future price trends of coffee futures to yield excess returns has become a focal point in the field of quantitative investment. Machine learning methods are increasingly being applied in the field of quantitative investment due to their performance advantages in complex data classification and regression. This paper analyzes the current state of the coffee futures market and the factors influencing its prices. In this study, five market indicators and one technical indicator, the bias rate, were selected as inputs. The closing price for the subsequent day, along with short-term (50 days) and long-term (200 days) price trends, were forecasted using two machine learning techniques: the linear regression model and the random forest model. The results demonstrated that, of the two predictive models utilized in this study, the random forest model performed better concerning regression prediction evaluation indices. When predicting short-term (50-day) price trends, the linear regression model exhibited superior performance. However, both models revealed significant errors in predicting long-term (200-day) price trends.

Short-term trends of air quality and pollutant concentrations in Nigeria from 2018–2022 using tropospheric sentinel-5P and 3A/B satellite data

This study presents a comprehensive analysis of atmospheric parameters in Nigeria, utilizing Sentinel-5P and 3A/B offline datasets from April 2018 to December 2022. The research evaluates the concentrations of pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), Methane (CH4), Carbon Monoxide (CO), and Aerosol Indices (AI) across different periods. The data analysis employs multivariate techniques, including Factor Analysis, Pearson Correlation Coefficient, Scatter Plots, and Dendrogram Clustering, providing insights into the interrelationships and potential sources of pollution. Additionally, the study examines monthly trends, temperature variations, and their impacts on human health and climate. The findings reveal stable levels of NO2, SO2, O3, and HCHO with minimal fluctuations, while CH4 and CO exhibit slight variations, possibly influenced by emission reductions or atmospheric changes. Aerosol optical indices consistently indicate aerosol presence, influenced by local emissions and atmospheric processes. Multivariate analyses highlight shared patterns among atmospheric constituents, shedding light on potential common sources and chemical relationships. The Pearson correlation coefficients illustrate the strength and direction of linear relationships, offering valuable information on pollution dynamics. The study's visualizations, including scatter plots and dendrogram clustering, enhance the understanding of complex interactions. Overall, the research underscores the significance of continuous monitoring and research efforts for informed decision-making and environmental management in Nigeria.