Years Of Data Research Articles

10-Year Real-World Data on Acute Myeloid Leukemia:

Introduction: Despite advances in Acute Myeloid Leukemia (AML) diagnosis and treatment, the outcomes in low- and middle-income countries (LMIC) are far apart from those in high-income countries (HIC). Objective: To describe the clinical features and outcomes of AML patients in Brazil's public health system, we conducted a retrospective analysis of all cases of non-promyelocytic AML diagnosed within 10 years (2007- 2017) in northeastern Brazil, Bahia. Methodology: We analyzed the real-life outcomes of 62 patients diagnosed with non-promyelocytic AML between 2007 and 2017 at a university hospital in Northeast Brazil. We classified patients using the European LeukemiaNet 2022 guideline into favorable (n=8), intermediate (n=18), and adverse risk (n=7) groups. Twenty-nine were not otherwise classified because no cytogenetic and/or molecular tests were available at diagnosis. Results: Allogeneic bone marrow transplant (alloBMT) was performed in 16 patients (37%). Median overall survival (mOS) was seven months. Among patients receiving alloBMT, mOS was 49 months, while for the chemotherapy group, it was six months (P = 0.003). For 10-year real-life data, we found complete remission of 53%, 5-year OS of 27%, and a mortality rate during induction therapy of 27%, inferior to HIC. Conclusion: Inferior outcomes found in LMIC result from a multifactorial scenario and an unmet need in the worldwide panorama of AML.

Metal-Free Custom-Made Zirconia Implants-A Prospective 5-Year Follow-Up Single-Arm Clinical Trial.

Dental implants made of zirconia (ZrO2) are a potential alternative for titanium implants in dentistry because of their good biocompatibility, mechanical properties and excellent aesthetic results. However, solid long-term scientific data to prove clinical success of ZrO2 implants are scarce. The aim of this study was to describe and to examine the clinical performance of custom-made two-piece ZrO2 implants, to identify possible influencing factors: a) manipulation of the implant after placement and b) the occlusal scheme on the survival rate, and to evaluate the performance of the implant-supported crown. This follow-up study collected and examined the 5-year data to answer the main question: What are the survival and the success rates of custom-made ZrO2 implants in the maxillary premolar region after 5 years? Of the 31 included patients in this prospective 5-year follow-up single-arm clinical trial, 30 received a custom-made ZrO2 implant to replace a missing single maxillary premolar, which was subsequently restored with a lithium disilicate crown. Parameters regarding clinical performance, marginal bone-level (MBL) changes, and patient-related outcome measures (PROMs) were assessed preoperatively, at the baseline, as well as 1 and 5 years after crown placement. Chances of survival and success of the implant were calculated and displayed using Kaplan-Meier statistics. Kaplan-Meier survival analysis was also performed with stratification based on the variables "manipulation of the implant prior to impression taking" and "occlusal scheme" and compared using log-rank tests. Bone-level moderation in time was compared using a paired samples t-test. Patient's expectations and satisfaction after 5 years were compared as a measure of fulfilled expectations, using a Wilcoxon signed-rank test. Performance of the implant-supported crowns was evaluated using validated criteria. Survival and success probabilities after 5 years were, respectively, 75.8% (95% CI [60.0%; 91.0%]) and 71.0% (95% CI [54.0%; 88.0%]) for the custom-made ZV3 implants. No significant differences in survival rate were found after stratification on "manipulation of the implant" and on "occlusal scheme." Mean bone-level alteration between baseline and the first follow-up was +0.06 mm (95% CI [-0.23 mm; 0.12 mm]; SD = 0.42 mm) and between baseline and the second follow-up was +0.04 mm (95% CI [-0.35 mm; 0.26 mm]; SD = 0.54 mm). Patients' satisfaction for patients with implants still in function after 5 years was 91.7% (IQR = [90.5%-97.3%]), indicating satisfaction with the treatment. Pooled satisfaction in patients with successful implants after 5 years was significantly higher than patients' expressed expectations before treatment. None of the crowns failed, and no interventions were required. Survival rate of these particular ZV3 implants in our study was lower than expected and clinically not acceptable. Hence, ZV3 implant placement as applied in this study cannot be recommended for clinical practice. Further research on the different appearances of mechanical failure in ZrO2 implants would be highly recommended before a larger prospective randomized clinical trial is conducted to evaluate treatment with custom-made ZrO2 dental implants.

Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures

The emergence of deep learning models has revolutionized various industries over the last decade, leading to a surge in connected devices and infrastructures. However, these models can be tricked into making incorrect predictions with high confidence, leading to disastrous failures and security concerns. To this end, we explore the impact of adversarial attacks on multivariate time-series forecasting and investigate methods to counter them. Specifically, we employ untargeted white-box attacks, namely the Fast Gradient Sign Method (FGSM) and the Basic Iterative Method (BIM), to poison the inputs to the training process, effectively misleading the model. We also illustrate the subtle modifications to the inputs after the attack, which makes detecting the attack using the naked eye quite difficult. Having demonstrated the feasibility of these attacks, we develop robust models through adversarial training and model hardening. We are among the first to showcase the transferability of these attacks and defenses by extrapolating our work from the benchmark electricity data to a larger, 10-year real-world data used for predicting the time-to-failure of hard disks. Our experimental results confirm that the attacks and defenses achieve the desired security thresholds, leading to a 72.41% and 94.81% decrease in RMSE for the electricity and hard disk datasets respectively after implementing the adversarial defenses.

Observing the interaction between energy generation carbon emissions and economic conditions could consider generation, intensity, and times of the day.

Management of carbon emissions of regional electricity generation involves resolving much nuanced relationships. However, current studies pertaining to the relationship between energy and the economy utilize only quarterly and annual data and focus on the long-run relationship but are oblivious to temporal associations. This study takes the context of the State of Texas and utilizes per 15min of data between 2007 and 2020 to derive the monthly time series of life-cycle emissions, emissions factor, and energy generation. The study also divides each day into three portions (0:00-8:00, 8:00-16:00, and 16:00-24:00) in order to descend precision down to the hourly level. Impulse-response analysis indicates that (1) employment conditions impact emissions by affecting the amount of electric energy generated, while demographic (population as reflected by property price) tends to impact emissions by affecting the carbon intensity of electricity, and (2) the emissions produced during 0:00-8:00 display different reaction to fluctuation of regional economic conditions compared to emissions produced during other periods, the effect emanates from either energy generation fluctuation or emissions factor shocks. The broader implication of the findings lies in an emphasis on time-interval-specific analysis and concurrent consideration of both energy generation and emissions factors that can lead to optimized policy implementation results.

Implication of Macroeconomic Fundamentals on the Twin-Evil of Macroeconomic Phenomena: “With the Focus on Nigeria”

Purpose of the Study: This study was designed to analyze the implication of Macroeconomic fundamentals on twin-evil of Macroeconomic phenomena: “with the focus on Nigeria.’’ Methodology: The study Utilized annual time-series data on exogenous variables (Government Deficit Spending, GDS; Official exchange rate, OEXCHR; Monetary policy rate, MPR) to capture Macroeconomic fundamentals. While, Unemployment rate, UNEMPLR; Inflation rate, INFLR and Consumer price index, CPI were used as a summation of Macroeconomic phenomena to capture Misery index. The data on variables used in the study were sourced from Nigerian federal ministry of finance; Central Bank of Nigeria; and World Bank data bank between 1991and 2022 respectively. The study employed econometric techniques of ordinary least square and Two-stage least to analyze its data. Findings: The study of Ordinary Least Square revealed that Government deficit spending and official exchange rate significantly eradicate misery index in a positive manner. Meanwhile, monetary policy rate insignificantly eradicate misery index in a negative manner. There was also a further revelation from the findings of two-stage least square that government deficit spending and official exchange rate exert concerted significant strength to eradicate misery from every citizen of Nigeria within the periods under study.. Meanwhile, the result found that it was insignificantly weak for monetary policy rate (MPR) to minimize misery index from every Nigerian citizen within the periods under study. Unique Contribution to Theory, Practice and Policy: The study concluded that the authority should maintain a sound policy that will oversee money borrowed for critical projects and use it to create employment opportunity for Nigerian citizenry, ensure exchange rate is kept within a certain band that will reduce high cost of goods and services in the economy that we are currently facing and jettison the idea of increasing monetary policy rate, since this displayed a negative sign to help reducing misery index of the Nigerian populace during the periods of study.

Using the Nasopalatine Canal for Enhanced Distribution in Severely Atrophic Maxilla With Immediate Loading of Zygomatic Implants: An 8-Year Retrospective Cohort Study.

This study presents retrospective 8-year clinical performance data from a patient cohort treated with immediately loaded maxillary full prostheses supported by zygomatic implants combined with implants placed in the nasopalatine canal, as a treatment for severely resorbed edentulous ridges. A retrospective analysis was conducted on data of maxillary edentulous patients with severe bone atrophy. All of them received zygomatic implants in combination with an implant placed in the nasopalatine foramen and an immediately loaded fixed temporary prosthesis in the first 24 hours. The frequency of major and minor adverse events was presented according to the time of occurrence. Fifteen patients were included in the study, without cases of implant loss or sensitivity in the nasopalatine region were observed up to 8 years. Only 1 major complication was observed, whereas minor complications were more frequent. In conclusion, this 8-year follow-up study demonstrated that the nasopalatine canal can be considered a viable implant site, with no major risks, to improve biomechanics by enabling a polygonal distribution in severely atrophic maxillae treated with zygomatic implants and immediate loading.

PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5.

Prevalence of inhaled allergen sensitization among patients with suspected allergic diseases in Sichuan province: 3-year data from a single center

Allergic diseases are becoming increasingly common, and they are a threat to people’s health. Exploring the distribution characteristics of allergen-specific immunoglobulin E (sIgE) in people from Sichuan province, southwest China, can provide clinical epidemiological data. This study enrolled 12,204 consecutive patients with suspected allergies from May 2018 to May 2021. Among the patients, 4206 were diagnosed with allergic rhinitis or asthma. The Rayto Lumiray 1600 detection system and sIgE and total IgE (tIgE) detection kits were used to measure the levels of nine common sIgE and tIgE. sIgE ≥ 0.35 IU/mL was considered positive. The sensitization rate of D1 (Dermatophagoides pteronyssinus) is the highest (22.97%), followed by D2 (Dermatophagoides farinae) (23.33%); M3 (Aspergillus fumigatus) has the lowest positive rate (1.5%). E5 (Canis familiaris) and I6 (Blatella germanica) exhibited relatively high sensitization rates (8.64% and 12.35%, respectively). Allergen sensitization was significantly higher in men than in women. Moreover, 90.3% of sensitization samples from D1 were combined with at least one of the other eight allergens. Similarly, a positive correlation was demonstrated between D1, D2, and E1 (Felis domesticus). In addition, patients with allergic rhinitis (91.88% and 91.54%, respectively), allergic asthma (72.44% and 74.01%, respectively), and allergic rhinitis with asthma (92.12% and 93.22%, respectively) were mainly sensitized to D1 and D2. D1, D2, I6, and E5 are the main allergens. Moreover, D1 and D2 are the main allergens in patients with respiratory allergic diseases. Research focusing on the distribution of allergens and the characteristics of hypersensitivity in different people helps prevent, diagnose, and treat allergic diseases.

Trends in Driving Under the Influence of Alcohol and Cannabis Among Young Adults in Washington State From Before to During the COVID-19 Pandemic.

Objectives. To examine trends in young adult self-reported driving under the influence of alcohol (DUI-A), cannabis (DUI-C), and simultaneous alcohol and cannabis use (DUI-AC) in a state with legalized nonmedical cannabis use from before to during the COVID-19 pandemic. Methods. We used logistic regression and annual statewide data from the Washington Young Adult Health Survey to assess DUI behaviors from 2016 to 2021. Results. Both prepandemic yearly changes in prevalence and deviations from those trends during the pandemic years were small and not statistically significant. However, prevalence estimates were alarming: 12.0% of participants reported DUI-A, 12.5% reported DUI-C, and 2.7% reported DUI-AC. Exploratory moderation analyses indicated a relative increase in DUI-A during 2020 among 4-year college students relative to young adults not attending 4-year colleges. Conclusions. Young adults in Washington State continued to engage in risky DUI behaviors during the pandemic. College students may have increased their likelihood of DUI-A during COVID-19. Public Health Implications. Young adults, for whom vehicle crashes remain a leading cause of death, showed little change in DUI behaviors during the COVID-19 pandemic. There is continued need for young adult DUI prevention efforts. (Am J Public Health. 2024;114(S8):S698-S701. https://doi.org/10.2105/AJPH.2024.307767).

Development of an older adult Nutrition Equity Index (NEI) and association with the Healthy Eating Index (HEI) in older Black and White U.S. adults

ObjectivesWe developed a novel measure for older U.S. adults called the Nutrition Equity Index (NEI), which includes functional ability. We compared both the NEI and food insufficiency with Healthy Eating Index (HEI) scores. DesignThe Health, Aging, and Body Composition Study is a prospective, longitudinal cohort of Medicare-eligible, community-dwelling Black and White men and women. SettingPittsburgh, PA and Memphis, TN. ParticipantsParticipants (N = 2468, aged 74.7 ± 2.9 years) had baseline (1997−98) and 1-year follow-up NEI data; and 1 year follow-up for food insufficiency, key covariates, and the HEI (diet quality measure) from a 108-item interviewer-administered food frequency questionnaire. MeasurementsFood insufficiency was defined from a modified validated question on ample food amount/variety from the U.S. Department of Agriculture. NEI was constructed from 8 questions in three subdomains: food insecurity (food insufficiency, hunger, anxiety about limited food, limited money for food), food access (groceries), and food acquisition (difficulty shopping, preparing meals, carrying groceries). Final NEI categories were low nutrition equity (0), moderate nutrition equity (1), and high nutrition equity (2). Multivariable linear regression compared both NEI and food insufficiency with HEI, adjusted for sociodemographic, lifestyle and comorbidity factors. ResultsOf participants, 13.5% had food insufficiency, 12.1% had low NEI, and 32.5% had moderate NEI. Participants with food insufficiency (vs. food sufficiency) or low NEI (vs. high NEI) were more likely to be Black and have < high school education (both p < 0.0001). In minimally adjusted models, food insufficiency was associated with 2.2 point lower HEI score (p = 0.001), low NEI with 2.6 point lower HEI score (p = 0.001), and moderate NEI with 1.0 point lower HEI score (p < 0.05). Adjusting for race attenuated NEI associations with HEI to non-significance. After multivariable adjustment, food insufficiency was associated with 1.5 point lower HEI score (p = 0.03). ConclusionFood insufficiency was associated with lower dietary quality in this sample of older Black and White U.S. adults. NEI performed similarly to food insufficiency for HEI associations prior to multivariable adjustment and identified more older adults with nutrition inequity, who may be at risk for poor health outcomes.

Can Oil Prices Volatility Explain Economic Growth?

The research focuses on the relationship between oil price volatility and economic activity in the context of a sample of six GCC countries. The study derives the results from an annual data set (1998-2021) and involves correlations, linear regression, curvilinear regression, panel data regression and causality tests to draw conclusions. It was observed that panel data analysis with random effects was best able to explain the positive relationship between oil price volatility and economic activity. Kuwait was observed as an exception country in the study as it demonstrated insignificant relationships and differentiating behavior. The global financial crisis was observed as a significant event on global oil volatility.

The ETNA-AF Europe registry: 4-year data of edoxaban use in atrial fibrillation in the Italian real world compared to the European cohort

The prospective, single-arm, observational, phase 4 ETNA-AF Europe study collected real-world data about safety, effectiveness and therapeutic adherence in European patients with non-valvular atrial fibrillation newly prescribed with edoxaban and followed up for 4 years. Overall, 13 164 patients were included in the full-analysis set, which means that they had at least one documentation after baseline at 4 years. The current paper reports about the 3329 Italian patients out of the whole European population. In the Italian cohort, median age was 76.0 (69.0-82.0) years, with 57.4% of the patients being ≥75 years old. The CHA2DS2-VASc score was >4 in 586 (18.1%) patients. At baseline, 670 (20.8%) patients were classified as frail by the investigators. Edoxaban 30 mg/day was prescribed to 1013 (31.8%) patients: these were older, with more comorbidities and a lower estimated creatinine clearance compared with those receiving 60 mg/day. All-cause mortality was 4.1%/year and there were very low yearly rates of bleeding and thromboembolic events: major bleeding, 0.9%; intracranial hemorrhage, 0.2%; ischemic stroke, 0.3%; systemic embolism, <0.1%. These events were more frequent in patients ≥75 years or in patients with renal impairment or treated with edoxaban 30 mg/day. Advancing age was not associated with an increased incidence of intracranial bleeding. These findings confirm the favorable long-term safety and effectiveness profile of edoxaban in non-valvular atrial fibrillation patients treated in routine clinical care in Italy.

Impact of greenhouse gas emission, renewable energy, and economic growth on health expenditure in Southeast Asia: A comparative analysis of econometric models

Purpose — The study explores the effects of greenhouse gas emissions, renewable energy, and economic growth on health expenditures across Southeast Asia while comparing the performance of different econometric models for accuracy in analysis.Method — To analyze the relationships among variables, the study employs three econometric models, the Autoregressive Distributed Lag Model, the co-integration Model, and the Quantile Regression Model, using annual data from 2000 to 2020.Findings — The results reveal that greenhouse gas emissions and GDP significantly influence health expenditure in all three models. However, the significance of renewable energy consumption varies, with only the quantile regression model indicating a significant relationship with health expenditure. Model comparison based on Mean Squared Error (MSE) suggests that the autoregressive distributed lag (ARDL) model provides the most accurate predictions. Also, it found that there is a short-run and long-run causal effect of GHG and GDP on health expenditure, as well as health spending on GDP.Implication — This study helps to understand how economic growth, environmental factors, and healthcare spending interact to develop sustainable policies to address health and environmental problems in Southeast Asia.Originality — This research contributes to the body of knowledge examining the impact of economic and environmental factors on health expenditures in Southeast Asia through a comparative analysis of different econometric models.

Do governance indicators have a role in remittances-growth nexus in Egypt?

Purpose ― Drawing on annual data from 1996 to 2022, this study aims to examine the effect of Egypt’s real GDP growth. Methods ― Autoregressive Distributed Lag (ARDL) and Cointegration technique are applied by first examining the stationarity of the series by utilizing the Augmented-Dicky-Fuller (ADF) unit root test. The bound cointegration test is then implemented to evaluate the existence of cointegration among the variables. Findings ― The results indicate that the GDP growth rate has a long-run positive relationship with remittance inflows in Egypt. This paper also finds that the average governance indicators in Egypt, which include government effectiveness, political stability, control of corruption and regulatory quality, voice and accountability, and the rule of law, could help facilitate the long-run effect of remittances on GDP growth. Implication ― The findings imply that maintaining high levels of governance indicators is essential for helping Egypt benefit from remittances and enhancing its economic growth.Originality/value ― The study is a pioneer in including the average governance indicators in the remittance-growth nexus study for Egypt. Its purpose is to assess whether governance quality affects the nexus and whether there exists a threshold for the average governance indicators below which the flow of remittances does not encourage economic growth.

Unravelling self-regulation in early childhood: protocol for the longitudinal SPROUTS study

BackgroundEnough is known about self-regulation to establish it as a priority target for education and intervention efforts beginning in early childhood, yet not enough to meaningfully and reliably alter developmental trajectories. Rather than resigning our aspirations, we need more nuanced and integrative understanding of self-regulation abilities and change.MethodsLaunching in 2024, SPROUTS is a 3-year longitudinal study of early self-regulation, beginning in the pre-school period (3–5 years old at Wave 1) with retrospective data back to birth and annual data collection across the transition to school period (ages 5–7 years at Wave 3). Data will be collected on children’s self-regulation, related abilities, outcomes, as well as prior and current contexts. One nested study within each Wave–that contributes complementary insights via supplementary and in-depth methods and data–will enable further exploration of contemporary issues related to self-regulation.DiscussionInsights generated can potentiate more effective intervention and education efforts by: improving intervention cost-benefit ratios; identifying likely mechanisms of change; easing burdens of unhealthy and antisocial behaviours associated with low self-regulation; and, most importantly, contributing to giving children the best early start to life. These benefits are timely in the context of intense policy and educational interest in fostering children’s self-regulation.Trial RegistrationOpen Science Framework: osf.io/maqdg. Date of registration: 26 Sep 2024.

Citizen science observations shed light on how anthropogenic food sources influence wildlife disease.

Research Highlight: Knutie, S., Bahouth, R., Bertone, M., Webb, C., Mehta, M., Nahom, M., Barta, R., Ghai, S., Love, A., Horan, S., Soldo, A., Cochrane, E., Bartholomew, J., Cowan, E., Bjerke, H., Balenger, S., Butler, M., Cornell, A., Kennedy, A., Rolland, V., Schultz, E., Stanback, M., Taff, C., Albery, G. (2024). Understanding spatiotemporal effects of food supplementation on host-parasite interactions using community-based science. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14155. Wildlife have become increasingly dependent on anthropogenic food supplementation, resulting in altered nutritional intake and inter- and intraspecific interactions. Subsequently, supplemental feeding can affect both the immunological function of individuals and transmission dynamics among individuals and species. The magnitude of the effect supplemental feeding has on disease is likely to vary across time and space with the nutritional demands of hosts. However, the broad temporal or spatial scale effects of supplementation are poorly understood. Recently, Knutie etal. (2024) introduced their citizen science program, the Nest Parasite Community Project, a broadscale coordinated effort by scientists and the public to monitor box nesting wild birds and their ectoparasites across the eastern United States. The authors amassed an impressive 4-year data set with hundreds of nests spanning the entire US breeding range of Eastern bluebirds (Sialia sialis). In the first study to come from the project, the authors demonstrate that the effects of food supplementation on host-parasite interactions vary across time and space and do not consistently influence host-parasite outcomes, highlighting that host-parasite interactions are often context dependent and influenced by many environmental factors (e.g. weather and habitat quality). The authors also found that supplemental feeding increases host fitness, regardless of parasitism. The study provides strong evidence that citizen science projects can help broaden our understanding of how human food sources influence wildlife disease in various environmental contexts.

Effects of Agricultural Insurance on Water Pollution in China

Water pollution is the most serious environmental problems in China now, water pollution directly affects people's quality of life and the sustainable development of agriculture, and the biggest source of water pollution is agricultural non-point source pollution, so we need to pay more attention to the environmental impact of agricultural production and reduce agricultural non-point source pollution. This paper examines the environmental impact of agricultural insurance on water pollution. Using annual data from 30 provinces across China between 2011 and 2019, this paper employs a two-way fixed-effects model to examine the correlation between agricultural insurance development and agricultural pollutant emissions. Additionally, it takes agricultural carbon emissions and agricultural output value as dependent variables to comprehensively investigate the environmental and economic impacts of agricultural insurance. The research results indicate that the development of agricultural insurance has a significant effect on reducing agricultural water pollution and carbon emissions. The pollutant reduction effect is more significant in the major grain-producing areas. The positive environmental effect of agricultural insurance is based on the fact that the development of agricultural insurance is conducive to reducing the use of pesticides and chemical fertilisers and does not come at the expense of increased agricultural output.

Analysing Real Exchange Rate Effects on the Trade Balance in South Africa

For years, policymakers and economists have been researching the significance of the native currency value versus other currencies in favouring the country’s trade balance and total. Therefore, this study investigates the impact of the real exchange rate change on the trade balance in South Africa. The study used annual time series data from 1980 through 2022. This study used the ARDL technique to analyse data. Variables used in this study include the trade balance, real exchange rate, terms of trade, money supply, South Africa’s gross domestic product, and the Chinese gross domestic product. Bounds tests showed that the variables are cointegrated. According to the empirical findings, the real exchange rates have significantly influenced the trade balance positively in the short run but in the long run, have a negative significant influence on the trade balance. The terms of trade have a positive significant influence on the trade balance in the short run, but it appears to be positively significant in the long run. The money supply in the short run has an insignificant negative relationship with the trade balance but in the long run, has a significant positive relationship with the trade balance. The SA gross domestic product has a significant negative impact on the trade balance both in the short and long run. Lastly, the Chinese gross domestic product has a negative insignificant relationship with the trade balance in the short. However, in the long run, it has a positive significant relationship with the trade balance. This proves that a rise in Chinese GDP, as a proxy for foreign economic activity, boosts South African exports to the rest of the world in the long term. As a result, the trade balance improves as China imports minerals from South Africa for production purposes. The dependence on currency depreciation to enhance trade in South Africa can improve its trade balance, but it may also damage the economy due to the country’s reliance on imports. However, the advice to weaken the South African currency should not be so harsh as to have a negative impact on the importation of key capital goods critical to the growth and development of the South African economy. It is recommended that the government reduce demand for imported products and boost domestic manufacturing, which might lead to domestic products being exported. The central bank must also stabilize the currency to increase competitiveness by decreasing the overseas price of exports and increasing the cost of imports.

The Impact of Asset-Liability Management on the Profitability of Listed Commercial Banks in Vietnam

This paper investigates the effect of asset-liability management on the profitability of listed commercial banks in Vietnam, analyzing annual data from 2013 to 2023. The study uses VIF tests, Heteroskedasticity tests, Model Specification tests, and the Generalized Least Squares (GLS) model with the Modified Wald test to examine the data. The results reveal a positive correlation between bank asset management and annual GDP growth with profitability. Conversely, liability management and the year-on-year growth rate of total assets negatively impact profitability. Specifically, factors such as loans and advances, investments in securities on the asset side, and deposits and borrowings from other credit institutions, along with customer deposits on the liability side, are significant. Lending activities, in particular, are identified as key drivers of financial performance in the Vietnamese banking sector. However, the minimal impact of investment securities on profitability raises questions, given their traditionally significant role in banking profitability strategies globally. The study also highlights the negative effects of certain liabilities on bank profitability, stressing the need for careful liability management for banks in Vietnam. While GDP growth is generally linked with economic prosperity and financial sector growth, its impact on bank profitability in Vietnam appears limited, suggesting that other factors might play a more significant role.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.