Contribution Of Different Factors Research Articles

RUM leads to noise: the significance of finding the sources of variability between experimental runs

Scientists typically run experiments many times to find general patterns over multiple specific runs. The results of those runs vary, and the variance is often simply referred to as “noise”. We claim that it is highly important to separate the components that contribute to noise and to recognize to which degree they contribute to it. Consideration of the relative contributions of R (randomness), U (uncontrolled variables), and M (measurement error) helps to interpret data and can help to improve experimental designs. We explain this using a hypothetical example and point out that assumptions of what causes variability in the results of experiments are often made implicitly. Further, we demonstrate our point by showing how it can change the interpretation of real data. Because of a lack of explicit discussion of underlying assumptions, it is possible that sources of noise are misidentified to be either existent or non-existent. This can happen if, for example, measurement error is assumed when there is none, an assumption that would mask a real effect that could deserve further study. Despite these factors, the contribution of different factors to the overall noise is rarely considered, hampering scientific progress.

Investigating hydrological processes using explainable deep-learning models

This paper examines how meteorological factors influence streamflow. It is crucial for water resources planning and management. It develops three deep learning models (LSTM, RF and GBM) and compares their performance with multiple linear regression (MLR). LSTM is identified as the best model, and SHAP is used to analyze the contributions of different factors to streamflow. The model is applied to the Tang River catchment in China. The results indicate that: 1) DL models outperform MLR in nonlinear regression; 2) the ranking of meteorological factors contributing to streamflow is: precipitation, evaporation, relative humidity, sunshine duration, ground temperature, and wind speed; 3) these factors affect streamflow within a 12-day lead time, with significant contributions closer to the prediction date; 4) during droughts, precipitation has a reduced effect on streamflow, suggesting groundwater recharge is likely recharged by groundwater. The model enhances our understanding of how meteorological factors interact with streamflow dynamics.

Temperature and Ozone Response to Different Forcing in the Lower Troposphere and Stratosphere

To evaluate the contributions of different forcings to the temperature and atmospheric composition changes between 1980 and 2020, we exploited the chemistry-climate model (CCM) SOCOLv3. The study examined ozone content and atmospheric temperature response to (1) ozone-depleting substances; (2) greenhouse gas concentrations, ocean surface temperature, and sea ice coverage; (3) solar irradiance; and (4) stratospheric aerosol loading and, separately, (5) greenhouse gas concentrations, (6) ocean surface temperature and sea ice coverage, and (7) NOx surface emissions. To evaluate the impacts of specific factors, we performed model runs driven by each factor (1–7) variability as well as a reference experiment that accounted for the influence of all factors simultaneously. We identified the relative contribution of different factors to the evolution of the temperature and ozone content of the lower troposphere and stratosphere from 1980 to 2020. The model results were in good agreement with the reanalyses (MERRA2 and ERA5). We showed that stratospheric ozone depletion before the Montreal Protocol introduction and partial recovery after that were chiefly driven by ODS. Stratospheric aerosol from major volcanic eruptions caused only short-term (up to 5 years) ozone decline. Increased greenhouse gas emissions dominate the ongoing long-term stratospheric cooling as well as tropospheric and surface warming. Solar irradiance contributed to short-term fluctuations but had a minimal long-term impact. Furthermore, our analysis of the solar signal in the tropical stratosphere underscores the complex interplay of solar radiation with volcanic, oceanic, and atmospheric factors, revealing significant altitudinal distributions of temperature and ozone responses to solar activity. Our findings advocate further innovative methodologies to take into account the nonlinearity of the atmospheric processes.

Multisource information fusion for safety risk assessment in complex projects considering dependence and uncertainty.

The success of tunneling projects is crucial for infrastructure development. However, the potential leakage risk is particularly challenging due to the inherent uncertainties and fuzziness involved. To address this demanding challenge, a hybrid approach integrating the copula theory, cloud model, and risk matrix, is proposed. The dependence of multiple risk-related influential factors is explored by the construct of the copula-cloud model, and the diverse information is fused by applying the risk matrix to gain a crisp risk result. A case study is performed to test the applicability of the proposed approach, in which a risk index system consisting of nine critical factors is developed and Sobol-enabled global sensitivity analysis (GSA) is incorporated to investigate the contributions of different factors to the risk magnitude. Key findings are as follows: (1) Risk statuses of the studied three tunnel sections are perceived as under grade I (safe), II (low-risk), and III (medium-risk), respectively, and the waterproof material aspect is found prone to deteriorating the tunnel sections. Furthermore, the proposed approach allows for a better understanding of the trends in the risk statuses of the tunnel sections. (2) Strong interactions between influential factors exist and exert impacts on the final risk results, proving the necessity of studying the factor dependence. (3) The developed neutral risk matrix presents a strong robustness and displays a higher recognition capacity in risk assessment. The novelty of this research lies in the consideration of the dependence and uncertainty in multisource information fusion with a hybrid copula-cloud model, enabling to perform a robust risk assessment under different risk matrices with varying degrees of risk tolerance.

Desertification Mitigation in Northern China Was Promoted by Climate Drivers after 2000

Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in Northern China (NC) by employing net primary productivity (NPP) as a proxy. The results reveal that 72.74% of the DPR experienced desertification mitigation and 27.26% experienced exacerbation. Climate drivers acted as primary drivers, contributing to both the mitigation (47.2%) and exacerbation (48.5%) of desertification, while human activity also played a crucial role, with contributions of 39.6% to mitigation and 41.0% to exacerbation of desertification. Furthermore, a shift in desertification dynamics emerged around 2000, with climate drivers promoting the mitigation process (66.8%), and precipitation was a dominant climatic factor for the mitigation of desertification after 2000, which was related to internal atmospheric variability. This study highlights changes in the contributions of different factors to desertification, underscoring the need for policy adjustment to attain sustainable land management in NC.

Data-driven simulation methodology for exploring optimal storage location assignment scheme in warehouses

Storage location assignment effects the efficiency of order picking, and thus it is significant for improving operation management in warehouses. Due to the dynamic nature of order picking, system simulation has been recently used for solving Storage Location Assignment Problem (SLAP). However, most previous studies aimed at using simulation to evaluate and verify the optimum storage schemes obtained from mathematical programming, which belongs to “optimize first and then simulation” in essence. In this context, we proposed a novel data-driven simulation methodology for solving SLAP in this study. A four-quadrant judgment criterion is proposed to guide storage locations assignment based on correlation and turnover frequency of items. Moreover, trajectory and blocked heatmaps are involved for exploring the contribution of different factors to order picking efficiency through scenario experiments. A prototype simulator was then developed and applied in a case study to demonstrate its utility. The results obtained reveals the fact that congestion blocking is another key factor impacting global order picking efficiency, and thus considering the correlation between items in storage schemes designing may not always improve the system performance due to the accompanying increases of blocking intensity during the dynamic order picking processes. The findings also demonstrate our inference that the priority of turnover frequency-based storage policy should be higher than correlation between items in designing storage schemes. This work provides new perspectives and valuable demonstration on the evaluating the contribution of different factors to global order picking efficiency as well as exploring optimal storage schemes through emerging data-driven simulation paradigm.

Inequity in the utilization of the home and community integrated healthcare and daily care services in older adults with limited mobility in China

BackgroundThis study aimed to analyze the needs and utilization of the home and community integrated healthcare and daily care services (“home and community care services” for short) among older adults in China and to investigate the inequity in services utilization.MethodsCross-sectional data were obtained from the 2018 China Health and Retirement Longitudinal Study. Needs and utilization rates of the home and community care services in older adults of 60 years old and above were analyzed. Binary logistic regression analysis was performed to explore the factors associated with services utilization among older adults with limited mobility. Concentration index, horizontal inequity index, and Theil index were used to analyze inequity in services utilization. Decomposition analyses of inequity indices were conducted to explain the contribution of different factors to the observed inequity.ResultsAbout 32.6% of older adults aged 60 years old and above had limited mobility in China in 2018, but only 18.5% of them used the home and community care services. Among the single service utilization, the highest using rate (15.5%) was from regular physical examination. Limited mobility, age group, income level, region, self-assessed health, and depression were statistically significant factors associated with utilization of any one type of the services. Concentration indices of any one type service utilization and regular physical examination utilization were both above 0.1, and the contribution of income to inequity were both over 60%. Intraregional factor contributed to about 90% inequity of utilizing any one type service, regular physical examination and onsite visit.ConclusionsThis current study showed that older adults with needs of home and community care services underused the services. Pro-rich inequities in services utilization were identified and income was the largest source of inequity. The difference of the home and community care service utilization was great among provinces but minor across regions. Policies to optimize resources allocation related to the home and community care services are needed to better satisfy the needs of older adults with limited mobility, especially in the low-income group and the central region.

Safety underreporting during naval operations: Prevalence, associated risk, and several contributing factors

Workplace safety concerns or violations are often underreported, which can result in small issues manifesting into a mishap or accident. Safety underreporting can also be difficult to track since, by definition, workers are knowingly failing to report issues. Unfortunately, underreporting remains pervasive even in high-risk occupations where safety issues may produce exceptionally dangerous and costly outcomes. This study investigated safety underreporting in the context of naval operations using a multi-level, multi-factor approach to evaluate underreporting with three study aims: 1) determine the prevalence of safety underreporting in a naval environment; 2) estimate the link between underreporting and real-world safety incidents; and finally, 3) identify the relative contribution of different factors to underreporting. Analyzes examined a large (N>11,000) sample of active-duty servicemembers who voluntarily participated in a routine online anonymous safety climate survey. Approximately 30% of respondents underreported safety incidents despite the high-risk occupational setting, and Monte Carlo simulations provided estimates for safety underreporting frequency aboard different ship-classes. Additional regression analyses linked safety underreporting aboard ship to actual safety issues (e.g., fire and electrical shock) thereby underscoring the real-world relationship. Finally, hierarchical logistic regression estimated that the most consistent predictor of safety underreporting was making prior reports of safety incidents. For every additional safety event reported, there was a 65 % increase in the likelihood of failing to report safety issues. The next most significant predictors were non-compliance attitudes toward safety regulations at the individual and crew level. Taken together, these results demonstrate the prevalence of underreporting in a naval environment while implicating several key contributing factors, including frequency of safety incidents in the work environment and non-compliance attitudes. This study’s findings highlight the importance of actively leveraging approaches to counter underreporting (e.g., anonymous reporting systems) and promoting positive safety climates to improve safety reporting and reduce negative outcomes (e.g., mishaps and accidents).

Agricultural Economic Water Productivity Differences across Counties in the Colorado River Basin

This study estimates the relative contribution of different factors to the wide variation in agricultural economic water productivity (EWP) across Colorado River Basin counties. It updates EWP measures for Basin counties using more detailed, localized data for the Colorado River mainstem. Using the Schwarz Bayesian Information Criterion for variable selection, regression analysis and productivity accounting methods identified factors contributing to EWP differences. The EWP was USD 1033 (USD 2023)/acre foot (af) for Lower Basin Counties on the U.S.–Mexico Border, USD 729 (USD 2023)/af for other Lower Basin Counties, and USD 168 (USD 2023)/af for Upper Basin Counties. Adoption rates for improved irrigation technologies showed little inter-county variation and so did not have a statistically significant impact on EWP. Counties with the lowest EWP consumed 25% of the Basin’s agricultural water (>2.3 million af) to generate 3% of the Basin’s crop revenue. Low populations/remoteness and more irrigated acreage per farm were negatively associated with EWP. Warmer winter temperatures and greater July humidity were positively associated with EWP. When controlling for other factors, being on the Border increased a county’s EWP by USD 570 (2023 USD)/af. Border Counties have greater access to labor from Mexico, enabling greater production of high-value, labor-intensive specialty crops.

Modeling passenger comfort in turboprop aircraft using objective measures.

A quantitative comfort model will aid in evaluating comfort levels of various target groups before the actual flight of an airplane. However, constructing the model is always a challenge due to the complexity of the phenomenon. In this paper, we present quantitative comfort models to predict the (dis)comfort of passengers flying with turboprops based on objective measures. Ninety-seven participants took part in two experiments conducted during real flights, during which forty of them had environmental and personal factors recorded using (self-developed) measurement tools. The collected data were analyzed to model the relations between objective measures and subjective feelings. Two preliminary models based on gradient boosting regression were developed. The models were able to predict the changes in comfort and discomfort of individual passengers with an accuracy of 0.12±0.01 and 0.21±0.01 regarding normalized comfort and discomfort scores, respectively. Additionally, contributions of different factors were highlighted. The outcomes of the models show that we took a step forward in modeling the human comfort experience using objective measurements. Anthropometry (including age), seat positions, time duration, and row (noise) emerged as leading factors influencing the feeling of (dis)comfort in turboprop planes.

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China.

In this paper, a daily gridded observation data across China from 1961 to 2022 were used to calculate daily potential evapotranspiration (PET). The observed variables included daily temperature, sunshine hours, average wind speed, and average relative humidity. PET was determined using the Penman-Monteith method recommended by the Food and Agriculture Organization (FAO). The long-term trend of PET was investigated in six regions of China during different seasons. To further compressed the influence of various meteorological factors on the PET trend, the contribution of each meteorological element to the long-term trend of PET was analyzed. The results indicate the following: (1) PET reaches its peak during summer which values from 145 to 640 mm, while it is lowest during winter from 21 to 244 mm. (2) The spatial patterns of PET trend changes are relatively similar across the four seasons, characterized by a decrease in the eastern regions and an increase in the western regions. The reduction is most significant during the summer and the range of trend is from -2.04 to 1.48 mm/day, while the increase becomes more pronounced in the winter which trend is from -0.34 to 0.53 mm/day. (3) The contribution of factors varies significantly across different regions. In spring and autumn, RH and U have little difference in contribution from other factors. But tsun is varies different from regions, the contribution value is largest in the northwest and smallest in the northeast. However, during summer, tsun become the most significant contributor in the YZ and SE regions, while in winter, Tm emerges as the most significant contributor to the PET trend in all six subregions. In SW, the contribution from U2 is the smallest in all seasons, with RH and Tm being the two crucial factors determining the PET trend in this region.

Advanced ceramics with integrated structures and functions: Machine learning prediction and experimental verification

Improving the thermal conductivities and bending strengths of ceramic substrates is crucial for their applicability as key components of integrated chips. Herein, we report the latest study in which the thermal conductivity and bending strength of aluminum nitride (AlN) ceramics were systematically predicted using high-precision prediction model approached with a machine learning (ML) method based on extreme gradient enhancement method (XGBoost). The ML model was used to rank the effects of the process parameters, and SHapley Additive exPlanations (SHAP) was employed to quantify the contributions of different factors. Sintering additives were found to have the predominant influence on the thermal conductivity and bending strength; particularly, those with cationic radii within 0.085–0.105 nm enhance both properties. By using Pr2O3 as the sintering additive and adopting the preparation conditions recommended by the ML model, we prepared AlN ceramics with thermal conductivity as high as 195.63 W m−1 K−1 and bending strength of 371.60 ± 9.31 MPa, thus satisfying the application requirement of high thermal conductivity. The proposed model is also applicable to alumina and silicon nitride ceramics. This study provides a practicable and once-for-all strategy to realize the entire process from the ML prediction design to the preparation of ceramic materials exhibiting superior thermal conductivity and high strength.

Factors affecting the place of death in patients with liver cancer in China, 2013–2020: A population-based study

BackgroundDespite the country's substantial liver cancer burden, there is limited research on the factors influencing the place of death (POD) of patients with liver cancer in China. This study aimed to delineate POD distribution among patients with liver cancer, identify the factors associated with hospital deaths, and offer valuable insights for the government to develop healthcare policies. MethodsData from 2013 to 2020 were obtained from the National Mortality Surveillance System (NMSS) of China. This analysis focused on the distribution of POD among individuals who succumbed to liver cancer. Variations in characteristic distributions across different categories were evaluated using a chi-squared test. We also applied a multilevel logistic regression analysis to identify the factors associated with hospital liver cancer deaths. The proportional change in variance was computed to evaluate the contributions of different factors in the model. ResultsFrom 2013 to 2020, the NMSS reported a total of 608,789 liver cancer-related deaths, of which 440,079 (72.29%) died at home, and 158,291 (26.00%) died in the hospital. Home remained the preferred POD among patients with liver cancer. The results demonstrated that female patients, aged between 0 and 14 years, of Han ethnicity, living in urban areas, unmarried, highly educated, and either employed in a professional, staff, or civil servant capacity, or retired patients tended to end their lives in the hospital. ConclusionsIn China, home continues to be the predominant POD for patients with liver cancer, with demographic and socioeconomic factors significantly influencing whether a hospital is their POD. Enhancing healthcare policymakers' understanding of the factors influencing the place of death for patients with liver cancer may assist in creating a more equitable distribution of healthcare resources and providing a variety of choices for minorities with distinct preferences for end-of-life care.

Modeling and forecasting mortality with economic, environmental and lifestyle variables

AbstractTraditional stochastic mortality models tend to extrapolate, to focus on identifying trends in mortality without explaining them. Those that do link mortality with other variables usually limit themselves to GDP. This article presents a novel stochastic mortality model that incorporates a wide range of variables related to economic, environmental and lifestyle factors to predict mortality. The model uses principal components derived from these variables, extending the Niu and Melenberg (Demography 51(5):1755–1773, 2014) model to variables other than GDP, and is applied to 37 countries from the Human Mortality Database. Model fit is superior to the Lee–Carter model for 18 countries. The forecasting accuracy of the proposed model is better than that of the Niu–Melenberg model for half of the countries analyzed under various jump-off years. The model highlights the importance of economic prosperity and healthy lifestyle choices in improving lifespan, while the effect of environmental variables is mixed. By clarifying the specific contributions of different factors and thus making trade-offs explicit, the model is designed to facilitate scenario building and policy planning.

Future projections of compound temperature and precipitation extremes and corresponding population exposure over global land

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health.

Spatiotemporal variability and drivers of water microchemistry in the upper Nu-Salween river: With implications for fish habitat conservation

Microchemical maps, also known as “chemoscapes”, hold immense potential for reconstructing fish habitat utilization and guiding conservation efforts. This approach relies on matching the microchemical composition of fish calcified structures (e.g., otoliths) with the surrounding water's microchemistry. However, applying this method faces a major challenge: a clear understanding of the spatiotemporal variability and drivers of water microchemistry, particularly in vast, free-flowing river ecosystems like the Nu-Salween River, Southeast Asia's longest free-flowing river. We analyzed the spatiotemporal variability and influencing factors of water microchemistry (i.e., Na:Ca, Mg:Ca, Mn:Ca, Cu:Ca, Zn:Ca, Se:Ca, Sr:Ca, and Ba:Ca) in the upper Nu-Salween River, based on a two-year sampling. Five elemental ratios (excluding Na:Ca, Mg:Ca, and Zn:Ca) in water demonstrated significant spatiotemporal variability, with Cu:Ca having the largest spatial variation, and Mn:Ca and Sr:Ca showing the greatest temporal variation. More specifically, four elemental ratios (Cu:Ca, Se:Ca, Sr:Ca, and Ba:Ca), exhibited significant variations along the longitudinal gradient, and Mn:Ca, Cu:Ca, Sr:Ca, and Ba:Ca, showed significant differences between mainstreams and tributaries. Temporally, Mn:Ca, Cu:Ca, and Ba:Ca displayed higher values and variations during the wet season, opposing the seasonal patterns of Na:Ca, Mg:Ca, and Sr:Ca. The four-element (Ba:Ca, Sr:Ca, Mg:Ca, and Mn:Ca) forest model showed the highest classification accuracy of 93%. Linear mixed-effects models showed that spatial factors have the largest influence on the variances in water microchemistry (56.36 ± 28.64%). Our study highlights the feasibility and reliability of utilizing microchemistry to reconstruct fish habitat utilization, thereby unveiling promising avenues for a more accurate understanding of fish life history in large rivers characterized by high heterogeneity in water microchemistry. By proportionally accounting for contribution of different factors to water microchemistry variability and relating them to microchemical composition of fish calcified structures, key fish habitats (e.g., spawning grounds) and migratory routes can be more precisely identified and thus protected.

New Model for Predicting Production Capacity of Horizontal Well Volume Fracturing in Tight Reservoirs.

Production prediction is the most important and comprehensive index to measure the effect of oilfield development, and it is also one of the most fundamental problems in oilfield dynamic analysis. However, the recovery prediction is often affected by many factors. Usually, the recovery is predicted by core experiments, numerical simulations, and mathematical models. The main problem is accurately predicting reservoir recovery based on existing data. This paper proposes a comprehensive prediction model for the problem of recovery. First, the correlation coefficients between 14 factors and recovery were calculated based on Pearson, Spearman, gray correlation, variance selection, univariate selection method, and tree model. Second, the weights of the factors were determined using entropy weighting, CRITIC, and hierarchical analysis to clarify the degree of contribution of different factors to the recovery. Finally, a comprehensive evaluation model was established based on the results of the weighting analysis. The results indicate that the correlation coefficient and weight of porosity, permeability, oil saturation, well spacing, cluster spacing, total fluid volume, and horizontal section length are the most relevant to the recovery. The error between the comprehensive evaluation model and the actual results is less than 3%. Therefore, the method can predict the production capacity of the tight reservoirs. The research results of this paper are of guiding significance for improving the recovery of tight reservoirs.

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

Response of the mid-latitude ionosphere to the solar eclipse on 25 October 2022: Results of F2–layer vertical sounding

The results of observations of the critical frequency (foF2), ionospheric F2 peak electron density (NmF2) and peak height (hmF2) of the ionosphere over Kharkiv (Ukraine, 49.6° N, 36.3° E) during a partial solar eclipse (SE) on 25 October 2022 are presented. The data analysis showed a clear effect in the variations of foF2 (and, accordingly, NmF2): during the SE, foF2 decreased, and 5 min after the time of maximum eclipse, began to increase to its usual level. From the beginning of the SE, hmF2 decreased from 285 to 255 km, and then it began to grow with a maximum near the time of maximum obscuration. To study longitudinal effects and clarify contributions of different factors to the observed temporal variations of the ionospheric parameters, the results of observations of the ionosphere over Kharkiv were compared with the data obtained by the ionosonde in Pruhonice (Czech Republic, 50.0° N, 14.6° E). The NmF2 electron density decreased due to SE by the factor of 1.46 in Kharkiv, in contrast to 1.33 times in Pruhonice. Comparison of data in universal time (UT) showed a coincidence in the nature and time of fluctuations in both foF2 and hmF2 observed in Kharkiv and Pruhonice on 25 October. Variations in hmF2 caused by SE in Kharkiv and Pruhonice were identical. The SE was accompanied by a plasma flow from the plasmasphere to the ionosphere. The flux density change was about –2 × 1013 m–2s−1 and about 0 for Kharkiv and Pruhonice, respectively. The obtained value of the vertical velocity (tens of meters per second) for Kharkiv is the typical velocity for the mid-latitude F2 layer of the ionosphere. It was confirmed that in addition to typical SE effects, results of observations of the ionosphere over Kharkiv on October 25, 2022 had individual features.

Vector Autoregressive Model of Maize Production in Northern Region of Ghana

Agricultural growth plays a crucial role in the Comprehensive African Agriculture Development Programme (CAADP, 2009) agenda. The program recognizes that increasing agricultural productivity is essential for reducing poverty, meeting food production targets, and lowering production costs and food prices for the impoverished. This study aimed to develop two types of models. The first model employed a vector autoregressive (VAR) approach, which involved regressing the production of maize in one district against the production of maize in other districts at various lags. The second model utilized a VAR framework where the maize production in each district was regressed against the production of maize in other districts and the corresponding climate conditions at different lag periods. The available data spanned from 1968 to 2018 and were recorded on an annual basis. Six climatic variables were included in the analysis. A lag order of 3 was selected for the models. The results of the autocorrelation test using the portmanteau test indicated no serial autocorrelation across all lag periods. The test for normality revealed that the residuals followed a normal distribution. Additionally, there was no evidence of heteroscedasticity in the data. Furthermore, a Granger causality test was conducted on the selected districts to explore causal relationships. Variance decomposition analysis was performed to assess the variance relation in the data and understand the contribution of different factors. Based on adjusted R-squared, mean absolute error (MAE), and root mean squared error (RMSE) values, the models that incorporated climatic variables were found to be the most suitable for forecasting maize production in the selected districts. The VAR model, which captures the interdependencies between the variables, was utilized in this analysis. All variables in the VAR model were treated symmetrically, meaning that their relationships were considered equally important.