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This study examines the relationship between education and entrepreneur income in Ecuador, a prototypical small economy grappling with employment challenges. Utilizing quantile regression and neutrosophic statistical methods, the research uncovers varied impacts of education on earnings across different income levels. The findings reveal a decrease in indeterminacy as individuals ascend income quintiles, indicating that higher education levels yield more predictable and substantial economic returns at the upper end of the income spectrum. These insights are vital for policymakers to develop targeted educational interventions. The study's methodological novelty lies in its adoption of neutrosophic statistics, which embraces the indeterminacy intrinsic to economic data, offering a refined lens for understanding the income-education interplay. Future research directions include longitudinal studies to trace the temporal effects of education and cross-comparative analyses across economies. The potential integration of machine learning with neutrosophic statistics promises enhanced predictive models, contributing to data-driven economic policy formulation. [ABSTRACT FROM AUTHOR]

Background: Interprofessional collaborative care such as a split-shared care model involving family physicians and community pharmacists can reduce the economic burden of diabetes management. This study aimed to evaluate the economic outcome of a split-shared care model between family physicians and community pharmacists within a pharmacy chain in managing people with uncontrolled type 2 diabetes and polypharmacy. Method: This was a multi-center, parallel arm, open label, randomized controlled trial comparing the direct and indirect economic outcomes of people who received collaborative care involving community pharmacists (intervention) versus those who received usual care without community pharmacist involvement (control). People with uncontrolled type 2 diabetes, defined as HbA1c > 7.0% and taking ≥ 5 chronic medications were included while people with missing baseline economic data (such as consultation costs, medication costs) were excluded. Direct medical costs were extracted from the institution's financial database while indirect costs were calculated from self-reported gross income and productivity loss, using Work Productivity Activity Impairment Global Health questionnaire. Separate generalized linear models with log link function and gamma distribution were used to analyze changes in direct and indirect medical costs. Results: A total of 175 patients (intervention = 70, control = 105) completed the trial and were included for analysis. The mean age of the participants was 66.9 (9.2) years, with majority being male and Chinese. The direct medical costs were significantly lower in the intervention than the control group over 6 months (intervention: -US$70.51, control: -US$47.66, p < 0.001). Medication cost was the main driver in both groups. There were no significant changes in productivity loss and indirect costs in both groups. Conclusion: Implementation of split-shared visits with frontline community partners may reduce economic burden for patient with uncontrolled type 2 diabetes and polypharmacy. Trial registration: Clinicaltrials.gov Reference Number: NCT03531944 (Date of registration: June 6, 2018). [ABSTRACT FROM AUTHOR]

Background: Dengue is spreading in (sub)tropical areas, and half of the global population is at risk. The macroeconomic impact of dengue extends beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil, from the tourism and workforce perspectives, respectively. Findings: Because the tourism industry is a critical economic sector for Thailand, lost tourism revenues were estimated to analyze the impact of a dengue outbreak. An input-output model estimated that the direct effects (on international tourism) and indirect effects (on suppliers) of dengue on tourism reduced overall GDP by 1.43 billion US dollars (USD) (0.26%) in the outbreak year 2019. The induced effect (reduced employee income/spending) reduced Thailand's GDP by 375 million USD (0.07%). Overall, lost tourism revenues reduced Thailand's GDP by an estimated 1.81 billion USD (0.33%) in 2019 (3% of annual tourism revenue). An inoperability input-output model was used to analyze the effect of workforce absenteeism on GDP due to a dengue outbreak in Brazil. This model calculates the number of lost workdays associated with ambulatory and hospitalized dengue. Input was collected from state-level epidemiological and economic data for 2019. An estimated 22.4 million workdays were lost in the employed population; 39% associated with the informal sector. Lost workdays due to dengue reduced Brazil's GDP by 876 million USD (0.05%). Conclusions: The economic costs of dengue outbreaks far surpass the direct medical costs. Dengue reduces overall GDP and inflicts national economic losses. With a high proportion of the population lacking formal employment in both countries and low income being a barrier to seeking care, dengue also poses an equity challenge. A combination of public health measures, like vector control and vaccination, against dengue is recommended to mitigate the broader economic impact of dengue. Author summary: Dengue is a viral illness spread by mosquitoes in Southeast Asia and South America, and about half of the world's population is at risk. Burden-of-illness studies on the economic impact of dengue typically focus on medical costs. However, the impact of this infectious disease extends far beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil. The analyses estimated that the dengue outbreak in 2019 had a profound impact on Thailand's tourism industry, reducing GDP by an estimated US$1.81 billion, equivalent to 3% of annual tourism revenues or 0.33% of total GDP. Dengue also impacted Brazil's workforce in 2019, leading to an estimated 22.4 million lost workdays, of which 39% were associated with the informal sector, and reducing GDP overall by US$876 million (0.05% of total GDP). Informal workers are vulnerable to dengue-related financial burdens, and a low income can be a barrier to seeking medical care for dengue. This study highlights the broader economic impact of dengue beyond healthcare costs and underscores the importance of a combination of public health measures, such as vector control and vaccination. [ABSTRACT FROM AUTHOR]

The potentially catastrophic loss of biodiversity happening around the globe is largely caused by economic activity that is not connected to its environmental impacts. To improve positive and reduce negative outcomes for nature, investment decision-makers in companies, governments, and the finance sector need data on the impacts of economic activity, especially production of food and other commodities, on biodiversity, at fine geographical scales. This Essay argues that the data allowing us to identify the most important factors causing biodiversity loss are already available. However, we need more data to track impacts on biodiversity up value chains into pathways, toolkits, and approaches that will facilitate verified, concrete actions by companies and consumers to reduce threats to biodiversity in particular places. Our current knowledge is insufficient to deliver complete responses to the biodiversity crisis, but this is no excuse for delaying action. Biodiversity is being lost because of economic activity that has no connection its impacts on biodiversity. This Essay argues that we need better spatially explicit and actionable data on the causes of biodiversity loss that can be conveyed to company decision-makers in a format that they can use easily and efficiently. [ABSTRACT FROM AUTHOR]

Environmental markets are a rapidly emerging tool to mobilize private funding to incentivize landholders to undertake more sustainable land management. How units of biodiversity in these markets are measured and subsequently traded creates key challenges ecologically and economically because it determines whether environmental markets can deliver net gains in biodiversity and efficiently lower the costs of conservation. We developed and tested a metric for such markets based on the well‐established principle of irreplaceability from systematic conservation planning. Irreplaceability as a metric avoids the limitations of like‐for‐like trading and allows one to capture the multidimensional nature of ecosystems (e.g., habitats, species, ecosystem functioning) and simultaneously achieve cost‐effective, land‐manager‐led investments in conservation. Using an integrated ecological modeling approach, we tested whether using irreplaceability as a metric is more ecologically and economically beneficial than the simpler biodiversity offset metrics typically used in net gain and no‐net‐loss policies. Using irreplaceability ensured no net loss, or even net gain, of biodiversity depending on the targets chosen. Other metrics did not provide the same assurances and, depending on the flexibility with which biodiversity targets can be achieved, and how they overlap with development pressure, were less efficient. Irreplaceability reduced the costs of offsetting to developers and the costs of ecological restoration to society. Integrating economic data and systematic conservation planning approaches would therefore assure land managers they were being fairly rewarded for the opportunity costs of conservation and transparently incentivize the most ecologically and economically efficient investments in nature recovery. [ABSTRACT FROM AUTHOR]

In this paper, we study asymptotic model change detection in multivariate linear regression by using the Kolmogorov–Smirnov function of the partial sum process of recursive residuals. We approximate the rejection region and also the power function of the test by establishing a functional central limit theorem for the sequence of the partial sum processes of the recursive residuals of the observations. When the assumed model is true, the limit process is given by the standard multivariate Brownian motion which does not depend on the regression functions. However, when the assumed model is not true (some models change), the limit process is represented by a vector of deterministic trend plus the standard multivariate Brownian motion. The finite sample size rejection region and the power of the test are investigated by means of Monte Carlo simulation. The simulation study shows evidence that the proposed test is consistent in the sense that it attains the power larger than the size of the test when the hypothesis is not true. We also demonstrate the application of the proposed test method to Indonesian economic growth data in which we test the adequacy of three-variate low-order polynomial model. The test result shows that the growth of the Indonesian economy is neither simultaneously constant nor linear. The test has successfully detect the appearance of a change in the model which is mainly caused by the COVID-19 pandemic in 2020. [ABSTRACT FROM AUTHOR]

Objective: To examine the relationship between the level of state funding for Home‐ and Community‐Based Services (HCBS) and state overall and dimension‐specific performances in Long‐Term Services and Supports (LTSS). Data Sources and Study Setting: We employed state‐level secondary data from the Medicaid LTSS Annual Expenditures Reports, the American Association of Retired Persons (AARP) State Scorecards, the U.S. Census, and Federal Reserve Economic data, spanning the timeframe of 2010–2020. Study Design: Overall state LTSS rankings, along with dimension‐specific rankings, were modeled separately against state Medicaid spending on HCBS relative to total Medicaid spending on LTSS. All models were adjusted for state covariates, secular trend, and state fixed effects. Data Collection/Extraction Methods: The study sample included all 50 states and the District of Columbia. However, California, Delaware, Illinois, and Virginia were excluded from FY2019 due to missing data on Medicaid HCBS expenditures. Principal Findings: Every 10 percentage‐point increase in the proportion of Medicaid LTSS spending to HCBS demonstrated 2.05 points improvement (95% confidence interval [CI]: −3.88 to 0.22, p = 0.03) in rankings for state overall LTSS system performance, 2.92 points improvement (95% CI: −4.87 to 0.98, p < 0.01) in rankings for the Choice of Setting and Provider dimension, as well as 1.73 points (95% CI: −3.14 to 0.32, p = 0.02) ranking improvement in the dimension of Effective Transitions. Conclusions: Our study suggested promising effects of increased state funding for HCBS on LTSS performance. [ABSTRACT FROM AUTHOR]

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenario-based ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM. [ABSTRACT FROM AUTHOR]

In the context of the debate on cryptocurrencies as the 'digital gold', this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments. [ABSTRACT FROM AUTHOR]

We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of "sweet spots", that is, specific areas within shale-gas plays that tend to yield more profitable wells and where many operators concentrate their production. We shed light on the rational origin of this phenomenon: while shale formations are abundant, areas prone to having a multi-parameter combination that renders the well profitable are less common. [ABSTRACT FROM AUTHOR]

Background: Economic incentives can improve clinical outcomes among in-care people living with HIV (PLHIV), but evidence is limited for their effectiveness among out-of-care PLHIV or those at risk of disengagement. We propose a type 1 hybrid effectiveness-implementation study to advance global knowledge about the use of economic incentives to strengthen the continuity of HIV care and accelerate global goals for HIV epidemic control. Methods: The Rudi Kundini, Pamoja Kundini study will evaluate two implementation models of an economic incentive strategy for supporting two groups of PLHIV in Tanzania. Phase 1 of the study consists of a two-arm, cluster randomized trial across 32 health facilities to assess the effectiveness of a home visit plus one-time economic incentive on the proportion of out-of-care PLHIV with viral load suppression (< 1000 copies/ml) 6 months after enrollment (n = 640). Phase 2 is an individual 1:1 randomized controlled trial designed to determine the effectiveness of a short-term counseling and economic incentive program offered to in-care PLHIV who are predicted through machine learning to be at risk of disengaging from care on the outcome of viral load suppression at 12 months (n = 692). The program includes up to three incentives conditional upon visit attendance coupled with adapted counselling sessions for this population of PLHIV. Consistent with a hybrid effectiveness-implementation study design, phase 3 is a mixed methods evaluation to explore barriers and facilitators to strategy implementation in phases 1 and 2. Results will be used to guide optimization and scale-up of the incentive strategies, if effective, to the larger population of Tanzanian PLHIV who struggle with continuity of HIV care. Discussion: Innovative strategies that recognize the dynamic process of lifelong retention in HIV care are urgently needed. Strategies such as conditional economic incentives are a simple and effective method for improving many health outcomes, including those on the HIV continuum. If coupled with other supportive services such as home visits (phase 1) or with tailored counselling (phase 2), economic incentives have the potential to strengthen engagement among the subpopulation of PLHIV who struggle with retention in care and could help to close the gap towards reaching global "95–95-95" goals for ending the AIDS epidemic. Trial registration: Phase 1: ClinicalTrials.gov, NCT05248100, registered 2/21/2022. Phase 2: ClinicalTrials.gov, NCT05373095, registered 5/13/2022. [ABSTRACT FROM AUTHOR]

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions. [ABSTRACT FROM AUTHOR]

With the ongoing sovereign debt and biodiversity crises in many emerging economies, applications of debt-for-nature swaps as a dual solution for sovereign debt and nature conservation have been re-emerging. We analyze how debt-for-nature swaps (DNS) can be scaled to protect biodiversity priority areas and reduce debt burden. We build a dataset for biodiversity conservation and debt restructuring in 67 countries at risk of sovereign debt distress and show that they hold over 22% of global biodiversity priority areas, 82.96% of which are unprotected. Furthermore, we show that for 35 of the 67 countries, using conservative cost estimates, 100% of unprotected biodiversity priority areas could be protected for a fraction of debt; for the remaining countries, applying DNS would allow the protection of 11–13% of currently unprotected biodiversity priority areas. By applying interdisciplinary research combining fundamental biodiversity and economic data and methods merging, the research contributes methodologically and practically to the understanding of debt-for-nature swaps for emerging economies. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is one of the most common cancers worldwide. Comprehensive data on the economic burden of CRC at a populationlevel is critical in informing policymaking, but such data are currently limited in China. Methods: From a societal perspective, the economic burden of CRC in 2019 was estimated, including direct medical and nonmedical expenditure, disability, and premature-death-related indirect expenditure. Data on disease burden was taken from the GBD 2019 and analyzed using a prevalence-based approach. The perperson direct expenditure and work loss days were from a multicenter study; the premature-death-related expenditure was estimated using a human capital approach. Projections were conducted in different simulated scenarios. All expenditure data were in Chinese Yuan (CNY) and discounted to 2019. Results: In 2019, the estimated overall economic burden of CRC in China was CNY170.5 billion (0.189% of the local GDP). The direct expenditure was CNY106.4 billion (62.4% of the total economic burden), 91.4% of which was a direct medical expenditure. The indirect expenditure was CNY64.1 billion, of which 63.7% was related to premature death. The predicted burden would reach CNY560.0 billion in 2030 given constant trends for disease burden; however, it would be alternatively reduced to

With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy. [ABSTRACT FROM AUTHOR]

Soil acidity is one of the major crop production constraints in the highlands of Ethiopia. Liming is becoming a common practice to amend soil acidity, but its effects on soil properties, crop yield, and farm income are not well studied. In this study, an on-farm liming experiment was conducted for two consecutive years (2020-2021) on acidic Nitisols (pH < 5.5) in Southern Ethiopia. The experiment consisted of six liming rates (control, 2.74, 4.11, 5.48, 6.85, and 8.22 t·ha−1) laid out in a randomized complete block design with three replications. Soil, agronomic, and economic data were collected in 2020 and 2021 cropping seasons and analyzed. The application of lime in the ranges of 2.74–8.22 t·ha−1 increased soil pH by 0.46–1.25 units and reduced exchangeable acidity by 2.02–3.17 units. Higher lime rates of 6.85–8.22 t·ha−1 increased soil pH sharply from 5.22 to 5.99 and 6.46, respectively, but such a rise in soil pH was not proportionally reflected in the yield increment. Higher available phosphorus contents of 7.16 and 6.01 mg·kg−1 were measured at the liming rates of 4.11 and 5.48 t·ha−1, respectively. Combined over the two years, 5.45 t·ha−1 lime application yielded the highest barley total biomass of 19,199 kg·ha−1 and a grain yield of 4,328 kg·ha−1, which are 46% and 30% higher than those of the control, respectively. It also yielded the highest marginal rate of return of 477% and a gross margin of 192,857.3 ETB1·ha−1, which is 53% higher than the control. Based on our results, 5.45 t·ha−1 of lime appears to have the optimal rate for economically viable barley production in the study area or similar environments. [ABSTRACT FROM AUTHOR]

Population displacement is one of the most common consequences of disasters, and it can profoundly affect communities and territories. However, gaining an accurate measure of the size of displacement in the days and weeks following a major disaster can be extremely difficult. This study uses aggregated Call Detail Records as an inexpensive and efficient technique to measure post-disaster displacement in four Italian regions affected by repeated earthquakes in 2016–2017. By comparing post-disaster mobile phone count with a forecast computed before the earthquake hit, we can compute an index of change in the presence of mobile phones (MPE). This measure, obtained thanks to advanced analytical techniques, provides a reliable indication of the effect of the earthquake in terms of immediate and medium-term displacement. We test this measure against census data and in combination with other datasets. Looking into available data on economic activities and requests for financial support to rebuild damaged buildings, we can explain MPE and identify significant factors affecting population displacement. It is possible to apply this innovative methodology to other disaster scenarios and use it by policymakers who want to understand the determinants of population displacement. [ABSTRACT FROM AUTHOR]

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market. [ABSTRACT FROM AUTHOR]

To use different models for forecasting economic data suitably, three main basic models (the grey system model, time series analysis model, and artificial neural network (ANN) model) are analyzed and compared comprehensively. Based on the analysis results of forecasting models, one new hybrid intelligent model based on the ANN model and Harris hawks optimization (HHO) has been proposed. In this hybrid model, HHO is used to select the hyperparameters of the ANN and also to optimize the linking weights and thresholds of the ANN. At last, by using four economic data cases including two simple data sets and two complex ones, the analysis of the basic models and the proposed hybrid model have been verified comprehensively. The results show that the grey system model can suitably analyze exponential data sequences, the time series analysis model can analyze random sequences, and the ANN model can be applied to any kind of data sequence. Moreover, when compared with the basic models, the new hybrid model can be suitably applied for both simple data sets and complex ones, and its forecasting performance is always very suitable. In comparison with other hybrid models, not only for computing accuracy but also for computing efficiency, the performance of the new hybrid model is the best. For the least initial parameters used in the new hybrid model, which can be determined easily and simply, the application of the new hybrid model is the most convenient too. [ABSTRACT FROM AUTHOR]

Background: IMPACT-AF is a prospective, randomized, cluster design trial comparing atrial fibrillation (AF) management with a computerized decision support system (CDS) to usual care (control) in the primary care setting of Nova Scotia, Canada. The objective of this analysis was to compare the resource use and costs between CDS and usual care groups. Methods: Case costing data, 12-month self-administered questionnaires, and monthly diaries from IMPACT-AF were used in this analysis. Descriptive statistics were used to compare costs and resource use between groups. All costs are presented in 2021 Canadian dollars and cover the 12-month period of participation in the study. Results: A total of 1,145 patients enrolled in the trial. Case costing data were available for 466 participants (41.1%), 12-month self-administered questionnaire data for 635 participants (56.0%) and monthly diary data for 223 participants (19.7%). Emergency department visits and hospitalizations comprised the most expensive component of AF care. Across all three datasets, there were no statistically significant differences in costs or resource use between CDS and usual care groups. Conclusions: Although there were no significant differences in resource use or costs among CDS and usual care groups in the IMPACT-AF trial, this study provides insight into the methodology and practical challenges of collecting economic data alongside a trial. Registration: Clinicaltrials.gov (registration number: NCT01927367, date of registration: 2013-08-20). [ABSTRACT FROM AUTHOR]

Rice is one of the most important cereal foods not only for India but also for the world. The production of crop depends upon the favorable climatic conditions. Farmers' access to more accurate data on crop yields in various climate conditions can aid in crucial agronomic and crop selection decisions. Taking this into account, the motive of the present research was to find the best method of predicting rice crop yield in seven important rice producing districts of Uttarakhand, namely Udham Singh Nagar, Nainital, Haridwar, Dehradun, Champawat, Tehri-Garhwal, and Pauri Garhwal. Data on the weather variables for the crop-growing season (27th to 44th SMW) for 19 years was gathered from the respective district and the NASA power website, while rice production data for the research period was gathered from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare. Stepwise multiple linear regression (SMLR), least absolute shrinkage and selection operator (LASSO), ridge regression, elastic net (ELNET), and artificial neural network (ANN) were employed for the model's development. The 80% data of the total datasets was utilized to calibrate the models, while the remaining 20% data was allocated for the model validation. On examining these models, LASSO was found to be the finest performing model followed by ELNET, while SMLR was the worst performing model during calibration stage. During validation stage, ANN performed better for Champawat, Dehradun, Haridwar, Pauri Garhwal, and Udham Singh Nagar. The performance of ELENT and LASSO was found to be best for Nainital and Tehri Garhwal, respectively. The performance of ridge regression and SMLR were found to be poor as compared to the other models for the rice yield forecasting. [ABSTRACT FROM AUTHOR]

Enteric fermentation from dairy cows is a major source of methane. Significantly and rapidly reducing those emissions would be a powerful lever to mitigate climate change. For a given productivity level, introducing fodder with high sources of n-3 content, such as grass or linseed, in the feed ration of dairy cows both improves the milk nutritional profile and reduces enteric methane emissions per liter. Changing cows' diet may represent additional costs for dairy farmers and calls for the implementation of payments for environmental services to support the transition. This paper analyzes 2 design elements influencing the effectiveness of a payment conditioned toward the reduction of enteric methane emissions: (1) the choice of emission indicator capturing the effect of farmers' practices, and (2) the payment amount relative to the additional milk production costs incurred. Using representative farm-level economic data from the French farm accountancy data network, we compare enteric methane emissions per liter of milk calculated with an Intergovernmental Panel on Climate Change Tier 2 method, to baseline emissions from a Tier 3 method accounting for diet effects. We also quantify the additional milk production costs of integrating more grass in the fodder systems by estimating variable cost functions for different dairy systems in France. Our results show the relevance of using an emission indicator sensitive to diet effects, and that the significance and direction of the additional costs for producing milk with a diet containing more grass differ according to the production basin and the current share of grasslands in the fodder crop rotation. We emphasize the importance of developing payments for environmental services with well-defined environmental indicators accounting for the technical problems addressed, and the need to better characterize heterogeneous funding requirements for supporting a large-scale adoption of more environment-friendly practices by farmers. [ABSTRACT FROM AUTHOR]

The rapid growth of artificial intelligence (AI) to ensure the competitiveness of corporations, driven by "data-hungry" companies, is raising ethical concerns about privacy governance and cybersecurity of datasets. It is key to avoid the abuse of users not aware of the economic value of their data, as well as their manipulation through advertising in digital markets, and by predicting their behavior, also to avoid the abuse of consumers' ignorance about the ethics of the products/services they consume. The European Union, through the Artificial Intelligence Act, developed a set of rules to safeguard the impact of AI on society and mitigate the adverse consequences of unethical behaviors, but these unethical AI practices are still occurring. This study uses an event model to calculate cumulative abnormal returns (CARs) with registered data on fines from AI ethical violations to measure the impact on financial markets of news about the unethical behavior of offending firms and to determine whether shareholders are rewarding these activities. Results help explain how some firms and their shareholders become immune to financial economic sanctions and turn into multi-violators who are not affected by the amount of the fines imposed on them, developing a "fine is a price" attitude. In summary, the findings of this study advise companies against engaging in this type of malpractice and make a great contribution in helping governments prevent the misuse of AI and its potentially disastrous consequences at both the individual and societal levels. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess the development of business activities in the Czech countryside in the period from 2008 to the present and to show possible limitations and perspectives of this area. The research was conducted by analysing economic entities located in rural municipalities in the peripheral areas of Western Bohemia. The assessment is based on the use of the Public Database of the Czech Statistical Office (statistics on economic entities, number of economic entities, number of economic entities created in 2008–2020) and a dataset on economic entities defunct in 2008–2021 provided by the Czech Statistical Office. We have analysed the economic entities by geographical location, size of municipality and industry specification. We use analysis of variance to assess whether the population size of a municipality has a statistically significant effect on the number of economic entities per 100 residents of the municipality, and we use visualization cartographic methods, density maps and cartograms to assess spatial differentiation. We have found that the nature of business activities in the study area is changing along the lines of urban locations, but, due to a number of factors, many rural economic entities are in a more economically precarious position. [ABSTRACT FROM AUTHOR]

We introduce a new approach for nonparametric spectral density estimation based on the subsampling technique, which we apply to the important class of nonstationary time series. These are almost periodically correlated sequences. In contrary to existing methods, our technique does not require demeaning of the data. On the simulated data examples, we compare our estimator of spectral density function with the classical one. Additionally, we propose a modified estimator, which allows to reduce the leakage effect. Moreover, in the , we provide a simulation study and two real data economic applications. for this article are available online. [ABSTRACT FROM AUTHOR]

To study the heating performance of a solar-assisted cold-water phase-change-energy heat pump system, its heating performance under series and parallel connections is simulated for a community in Harbin, the influence of ice thickness on the different operation modes is analyzed, and the economy of the system is calculated for series and parallel connections in this paper. The results show that the water supply temperature is higher and more uniform in the parallel operation, and more terminal heat is supplied; the ice thickness has more of an influence on the series connection compared to the parallel connection; and the dynamic payback period is 6.72 years for the series connection and 7.28 years for the parallel connection. This case study can serve as a guide for practical engineering application projects and act as a reference for heating and economic data for the promotion of this heat pump system. [ABSTRACT FROM AUTHOR]

The article sought to detect the impact of the value-added tax (VAT) policy on the enterprises' asset allocation from the dual perspectives of the VAT input refund and the VAT rate. Based on the influenced mechanism of the VAT input refund and the tax burden effect (and the price effect) caused by the VAT rate, enterprises' intertemporal optimal asset allocation models are constructed under the states of adopting the VAT input refund and maintaining the theoretical tax (non-)neutrality of VAT. When VAT rates of the general taxpayers are predicted to be reduced, we also use China's manufacturing and economic data to simulate specific cases to verify propositions under different states. The results show that: (1) When the VAT output tax rate decreases: if returns to scale are diminishing, enterprises will increase the number of productive material assets and labor and reduce financial assets. (2) When the VAT input tax rate reduces: under the state of adopting the VAT input refund and maintaining the theoretical tax (non-)neutrality of VAT, if returns to scale are decreasing, enterprises will reduce the number of productive material assets and labor and increase financial assets. Under the state of adopting the VAT input refund and maintaining the theoretical tax neutrality of VAT, if returns to scale are increasing and the expected rate of return of financial assets is lower than the additional tax rate, or the enterprise has diminishing returns to scale and the expected rate of return of financial assets is higher than the additional tax rate, enterprises will increase the number of productive material assets and labor. (3) When VAT output and input tax rates reduce simultaneously: under the state of adopting the VAT input refund and maintaining the theoretical tax neutrality of VAT, if returns to scale are increasing and the expected return rate of financial assets is higher than the additional tax rate, the enterprise will reduce the number of productive material assets and labor and increase financial assets. Under the diminishing returns to scale in China's national economy, the research conclusions endorse the rational necessity of the VAT policy change—VAT rate reduction to develop the entity economy and provide a reference for enterprises to make asset allocation decisions. The conclusions also provide possible changes in VAT policy for different countries according to their actual economic conditions. [ABSTRACT FROM AUTHOR]

The global economy operates as a complex and interconnected system, necessitating the application of sophisticated network methods for analysis. This study examines economic data from all countries across the globe, representing each country as a node and its exports as links, covering the period from 2008 to 2019. Through the computation of relevant indices, we can discern shifts in countries' positions within the world trade network. By interpreting these changes through geopolitical perspectives, we can gain a deeper understanding of their root causes. The analysis reveals a notable trend of slow growth in the world trade network. Additionally, an intriguing observation emerges: countries naturally form stable groups, shedding light on the underlying structure of global trade relations. Furthermore, this research highlights the trade balance as a reflection of geopolitical strength, making it a valuable contribution to the study of the evolution of global geopolitical stability. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalization, population, capital and information are rapidly flowing and clustering between regions. As the most important transportation mode in the high-speed transportation systems, airports are playing an increasingly important role in promoting regional economic development, yielding a number of airport economic promotion areas. To boost effective development management of these areas, accurate risk assessment through data analysis is quite important. Thus in this paper, the idea of ensemble learning is utilized to propose a big data-driven assessment model for supply chains in airport economic promotion areas. In particular, we combine two aspects of data from different sources: (1) national economic statistics and enterprise registration data from the Bureau of Industry and Commerce; (2) data from the Civil Aviation Administration of China and other multi-source data. On this basis, an integrated ensemble learning method is constructed to quantitatively analyze the supply chain security characteristics in domestic airport economic area, providing important support for the security of supply chains in airport economic area. Finally, some experiments are conducted on synthetic data to evaluate the method investigated in this paper, which has proved its efficiency and practice. [ABSTRACT FROM AUTHOR]

Visualization techniques can aid in the comprehension and appreciation of the inherent connections between ecological and economic data. However, traditional visualization techniques still suffer from the problems of information overload and missing information. To address this problem, the study proposed a technique to improve the clustered data based on force-directed edge binding of segmented skeletons and constructed a visual analysis model for ecological and economic data based on the improved technique. The study used the parallel coordinates technique, the technique of presenting complex data relationships in parallel coordinates, as the control experimental group in the technical performance comparison test. The results showed that the data relevance indexes of the improved technique and all the control experimental groups were 0.145 and 0.138, respectively. The maximum positive value of the accuracy error of the number of clusters in the improved model was 0.012, while the minimum negative value was -0.003. The control experimental groups were 0.022, -0.016 and 0.022, -0.018, respectively. The study proposed a superior visual analysis model for ecological and economic research. The model utilized a force-directed edge-bound clustering technique based on segmented skeleton data, providing accurate and comprehensive insights. The model could provide more accurate and comprehensive data analysis and decision-making basis to promote the development and sustainability of ecological economy. [ABSTRACT FROM AUTHOR]

South Africa's historical reliance on mineral resources has established thriving towns and settlements, sustaining households for decades. However, the decline in demand for these resources and mine closures pose significant threats to the future of these areas, creating vulnerabilities in towns and their dependent regions. This article addresses concerns about the vulnerability of certain South African municipalities, particularly those heavily dependent on mining. Employing an Economic Complexity Index (ECI), the study identifies and classifies vulnerable mining towns and regions. The methodology involves applying the concept of economic vulnerability, utilising sub-national economic and trade data to determine the complexity of municipal economies. In addition, geospatially linked data is employed to identify miningdependent areas at risk. The results highlight municipalities with low economic complexity, signalling the need for targeted interventions and emphasising the importance of economic diversification in mitigating risks. The study not only serves as a warning for current planning, but also lays the groundwork for future research and evidence-based policymaking to ensure the sustainability and resilience of regional economies in South Africa. [ABSTRACT FROM AUTHOR]

Smart cities are an emerging phenomenon, which is not only called for by their inhabitant's needs, but also by the more and more pressuring external factors, such as global economic slowdown, the increasing scarcity of resources and climate change. While smart cities are to increase the quality of life of the people, often the people themselves are not ready, not smart enough for them. Technology readiness is an important factor of technology adaptation; hence it is the basis of whether people are looking forward to smart cities or are rather afraid of them. In the current article, the readiness of people in an international sample is assessed with the help of a questionnaire, but the understanding of their choices is enriched by cultural and economic background data stemming from two representative international surveys, namely the Cultural Dimensions research of Geert Hofstede and the Global Entrepreneurship Monitor data. The results presented in the article are in line with the international literature but are enriching the existing body by combining the socioeconomic and cultural aspects in the interpretation of individual choices. [ABSTRACT FROM AUTHOR]

The foundations of today's societies are provided by manufactured capital accumulation driven by investment decisions through time. Reconceiving how the manufactured assets are harnessed in the production-consumption system is at the heart of the paradigm shifts necessary for long-term sustainability. Our research integrates 50 years of economic and environmental data to provide the global legacy environmental footprint (LEF) and unveil the historical material extractions, greenhouse gas emissions, and health impacts accrued in today's manufactured capital. We show that between 1995 and 2019, global LEF growth outpaced GDP and population growth, and the current high level of national capital stocks has been heavily relying on global supply chains in metals. The LEF shows a larger or growing gap between developed economies (DEs) and less-developed economies (LDEs) while economic returns from global asset supply chains disproportionately flow to DEs, resulting in a double burden for LDEs. Our results show that ensuring best practice in asset production while prioritizing well-being outcomes is essential in addressing global inequalities and protecting the environment. Achieving this requires a paradigm shift in sustainability science and policy, as well as in green finance decision-making, to move beyond the focus on the resource use and emissions of daily operations of the assets and instead take into account the long-term environmental footprints of capital accumulation. [ABSTRACT FROM AUTHOR]

Introduction: Herpes zoster (HZ; i.e., shingles) is caused by the reactivation of varicella zoster virus leading to a painful dermatomal rash. An increasing trend in cases of HZ is evident worldwide; however, there is a lack of comprehensive reviews for Southeast Asian countries. Methods: We performed a systematic literature review of articles published until May 2022 that reported HZ epidemiology, clinical management, and health economic data in six Southeast Asian countries: Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Literature searches were conducted in Medline, Scopus, Embase, and gray literature. Articles written in English or local languages were considered for inclusion. Results: In total, 72 publications were included in the study; 22 were case studies and over 60% originated in Singapore and Thailand. Only two studies (data from Thailand) reported incidence of HZ. The proportion of patients reported with HZ was 0.68–0.7% among dermatology clinics, 0.14% at one emergency department (5.3% of dermatology cases) in Singapore, and 3% of admissions at another hospital in Singapore. Pain was the most common symptom associated with HZ, reported in 74.21–100% of patients. HZ complications were reported in 10.2–21.2% of patients, and the proportions with postherpetic neuralgia and HZ ophthalmicus were 6.3–50% and 4.98–28.57%, respectively. Additionally, there is a lack of comprehensive, up-to-date HZ economic data, with only six studies identified for the Philippines, Singapore, and Thailand. Conclusions: Overall, there are limited data reporting incidence and prevalence of HZ in Southeast Asia at a national level. High rates of complications, symptoms, and abundance of case reports suggest substantial healthcare resource utilization for patients with HZ and highlight the need for further research in Southeast Asia assessing the societal impact. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). [ABSTRACT FROM AUTHOR]

It is important to quantify the differences in returns to skills using the online job advertisements data, which have attracted great interest in both labor economics and statistics fields. In this article, we study the relationship between the posted salary and the job requirements in online labor markets. There are two challenges to deal with. First, the posted salary is always presented in an interval-valued form, for example, 5k–10k yuan per month. Simply taking the mid-point or the lower bound as the alternative for salary may result in biased estimators. Second, the number of the potential skill words as predictors generated from the job advertisements by word segmentation is very large and many of them may not contribute to the salary. To this end, we propose a new feature screening method, Absolute Distribution Difference Sure Independence Screening (ADD-SIS), to select important skill words for the interval-valued response. The marginal utility for feature screening is based on the difference of estimated distribution functions via nonparametric maximum likelihood estimation, which sufficiently uses the interval information. It is model-free and robust to outliers. Numerical simulations show that the new method using the interval information is more efficient to select important predictors than the methods only based on the single points of the intervals. In the real data application, we study the text data of job advertisements for data scientists and data analysts in a major China's online job posting website, and explore the important skill words for the salary. We find that the skill words like optimization, long short-term memory (LSTM), convolutional neural networks (CNN), collaborative filtering, are positively correlated with the salary while the words like Excel, Office, data collection, may negatively contribute to the salary. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper investigates the forecasting accuracy of alternative time series models when augmented with partial least-squares (PLS) components extracted from economic data, such as Federal Reserve Economic Data, as well as Monthly Database (FRED-MD). Our results indicate that PLS components extracted from FRED-MD data reduce the forecasting error of linear models, such as ARIMA and SARIMA, but produce poor forecasts during high-volatility periods. In contrast, conditional variance models, such as ARCH and GARCH, produce more accurate forecasts regardless of whether or not the PLS components extracted from FRED-MD data are used. [ABSTRACT FROM AUTHOR]

Early access programs (EAPs) generally refer to patient access to medicines/indications before marketing authorization, possibly extended to price and reimbursement approval. These programs include compassionate use, which is usually covered by pharmaceutical companies, and EAPs reimbursed by third-party payers. This paper aims at comparing EAPs in four European countries (France, Italy, Spain, UK) and providing empirical evidence on EAPs in Italy. The comparative analysis was conducted through a literature review (including scientific and grey literature), complemented by 30-min semi-structured interviews with local experts. The Italian empirical analysis employed data available on the National Medicines Agency website. Although EAPs are very different across countries, they exhibit some common features: (i) eligibility criteria refer to the absence of valid therapeutic alternatives and a presumed favourable risk–benefit profile; (ii) payers do not allocate a pre-determined budget to these programs; (iii) total spending on EAPs is unknown. The French EAPs seem to be the most structured, financed through social insurance, covering pre-marketing, post-marketing and pre-reimbursement phases and providing for data collection. Italy's approach to EAPs has been varied, with several programs covered by different payers, including the cohort-based 648 List (for both early access and off-label use), the nominal-based 5% Fund, and Compassionate Use. Most applications to EAPs are from the Antineoplastic and immunomodulating drug class (ATC L). Some 62% of indications in the 648 List are either not under clinical development or have never been approved (pure off-label use). For those subsequently approved, most approved indications coincide with those covered through EAPs. Only the 5% Fund provides data on economic impact (€ 81.2 million in 2021; average cost per patient € 61.5K). Diverse EAPs are a possible source of inequalities in access to medicines across Europe. A harmonization of these programs, though difficult to achieve, could be modelled on the French EAPs and provide key advantages, not least of which a common effort to collect real-world data in parallel with clinical trials and clear separation between EAPs and off-label use programs. [ABSTRACT FROM AUTHOR]

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure. [ABSTRACT FROM AUTHOR]

Background: Data on the economic burden of RSV-associated illness will inform decisions on the programmatic implementation of maternal vaccines and monoclonal antibodies. We estimated the cost of RSV-associated illness in fine age bands to allow more accurate cost-effectiveness models to account for a limited duration of protection conferred by short- or long-acting interventions. Methods: We conducted a costing study at sentinel sites across South Africa to estimate out-of-pocket and indirect costs for RSV-associated mild and severe illness. We collected facility-specific costs for staffing, equipment, services, diagnostic tests, and treatment. Using case-based data we calculated a patient day equivalent (PDE) for RSV-associated hospitalizations or clinic visits; the PDE was multiplied by the number of days of care to provide a case cost to the healthcare system. We estimated the costs in 3-month age intervals in children aged < 1 year and as a single group for children aged 1–4 years. We then applied our data to a modified version of the World Health Organization tool for estimating the mean annual national cost burden, including medically and non-medically attended RSV-associated illness. Results: The estimated mean annual cost of RSV-associated illness in children aged < 5 years was US dollars ($)137,204,393, of which 76% ($111,742,713) were healthcare system incurred, 6% ($8,881,612) were out-of-pocket expenses and 13% ($28,225,.801) were indirect costs. Thirty-three percent ($45,652,677/$137,204,393) of the total cost in children aged < 5 years was in the < 3-month age group, of which 52% ($71,654,002/$137,204,393) were healthcare system incurred. The costs of non-medically attended cases increased with age from $3,307,218 in the < 3-month age group to $8,603,377 in the 9–11-month age group. Conclusions: Among children < 5 years of age with RSV in South Africa, the highest cost burden was in the youngest infants; therefore, interventions against RSV targeting this age group are important to reduce the health and cost burden of RSV-associated illness. [ABSTRACT FROM AUTHOR]

Simple Summary: Including feed efficiency in breeding objectives for dairy cattle is desirable due to the potential benefit for increased sustainability and herd profitability. An investigation of selection indices that included the direct and indirect selection of feed efficiency traits showed that the best total response was observed when residual feed intake was directly included as a selection criterion. Using indicator traits (e.g., type) could be a useful proxy in the absence of direct genetic evaluations for feed efficiency. Breeding objectives of the current Iranian selection index, which currently focuses on future production and marketing environment, could include feed efficiency in future evaluations. This study aimed to evaluate the economic impact of improving feed efficiency on breeding objectives for Iranian Holsteins. Production and economic data from seven dairy herds were used to estimate the economic values of different traits, and a meta-analysis was conducted to analyze the genetic relationships between feed efficiency and other traits. Economic weights were calculated for various traits, with mean values per cow and per year across herds estimated at USD 0.34/kg for milk yield, USD 6.93/kg for fat yield, USD 5.53/kg for protein yield, USD −1.68/kg for dry matter intake, USD −1.70/kg for residual feed intake, USD 0.47/month for productive life, and USD −2.71/day for days open. The Iranian selection index was revised to improve feed efficiency, and the feed efficiency sub-index (FE$) introduced by the Holstein Association of the United States of America was adopted to reflect Iran's economic and production systems. However, there were discrepancies between Iranian and US genetic coefficients in the sub-index, which could be attributed to differences in genetic and phenotypic parameters, as well as the economic value of each trait. More accurate estimates of economic values for each trait in FE$ could be obtained by collecting dry matter intake from Iranian herds and conducting genetic evaluations for residual feed intake. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the association between gene polymorphisms (SNPs) and mastitis indicators and their relationship with milk production profitability in dairy herd. A functional analysis was also performed of five genes containing the studied SNPs and those located close by. DNA was isolated from the hair bulb of 320 dairy cows kept in three herds and SNP-microarray analysis was performed. The data on 299 cows was subjected to final statistical analysis using AI-REML method with one-trait repeatability test-day animal model and pedigree information using the DMU4 package. Five from 35 SNPs significantly associated with mastitis indicators or production traits and located within a gene or no more than 500,000 nucleotides from the gene were selected for the functional and economic analysis. A questionnaire was also developed to collect associated economic data of 219 cows from three herds, such as the value of milk production and direct costs incurred over three years; this allowed the gross margin, direct profitability index and direct costs incurred to produce one liter of milk to be determined, among others. None of the five studied SNPs were related to protein content. The rs110785912(T/A), found near CXCR4, and rs136813430(T/C), located in the TLR4 gene exon, were associated with lnSCC, while rs110455063(C/G), located near IGF-I, was associated with milk yield, fat and total solid contents. rs109421300(T/C), associated with fat/protein content ratio, as well as fat and total solid content, is located in the DGAT1 gene intron. rs41587003(A/C), located in the DLG2 gene intron, was associated with lactose content. The economic analysis revealed differences between the variants of the three tested SNPs. The T/C variant of the rs136813430(T/C) SNP was characterized by the highest gross margin, the highest direct profitability index and the lowest costs incurred to produce 1 liter of milk. The T/A variant of rs110785912(T/A) was related to low lnSC and was characterized by the highest direct profitability index. In turn, the C/C variant of the rs41587003(A/C) was related to the lowest level of lactose and the highest costs of milk production. It appears that rs136813430(T/C) may be the most promising of the tested SNPs for increasing the profitability of milk production. To our knowledge, it is the first effort to assess directly a correlation between the DNA polymorphism and economic output of a dairy enterprise. [ABSTRACT FROM AUTHOR]

Background: The mismatch between the global burden of surgical disease and global health funding for surgical illness exacerbates disparities in surgical care access worldwide. Amidst competing priorities, governments need to rationally allocate scarce resources to address local needs. To build an investment case for surgery, economic data on surgical care delivery is needed. This study focuses on femur fractures. Methods: This prospective cohort study at Soroti Regional Referral Hospital (SRRH), captured demographic, clinical, and cost data from all surgical inpatients and their caregivers at SRRH from February 2018 through July 2019. We performed descriptive and inferential analyses. We estimated the cost effectiveness of intramedullary nailing relative to traction for femur fractures by using primary data and making extrapolations using regional data. Results: Among the 546 patients, 111 (20.3%) had femur fractures and their median [IQR] length of hospitalization was 27 days [14, 36 days]. The total societal cost and Quality Adjusted Life Year (QALY) gained was USD 61,748.10 and 78.81 for femur traction and USD 23,809 and 85.47 for intramedullary nailing. Intramedullary nailing was dominant over traction of femur fractures with an Incremental Cost Effectiveness Ratio of USD 5,681.75 per QALY gained. Conclusion: Femur fractures are the most prevalent and most expensive surgical condition at SRRH. Relative to intramedullary nailing, the use of femur traction at SRRH is not cost effective. There is a need to explore and adopt more cost-effective approaches like internal fixation. [ABSTRACT FROM AUTHOR]

Economic statistics are frequently produced at an administrative level such as the subnational division. However, these measures may lack sufficient local variation for effective analysis of local economic development patterns and exposure to natural hazards. Agricultural gross domestic product (GDP) is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend for their livelihoods, and it provides a key source of income for the entire household. Through a data-fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of agricultural GDP into a global gridded dataset at approximately 10×10 km for the year 2010 using satellite-derived indicators of the components that make up agricultural GDP, i.e., crop, livestock, fishery, hunting and forestry production. To illustrate the use of the new dataset, the paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, which amounts to around USD 432 billion of agricultural GDP circa 2010, with nearly 1.2 billion people living in those areas. The data are available on the World Bank Development Data Hub (10.57966/0j71-8d56;). [ABSTRACT FROM AUTHOR]

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020–October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the q-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 COVID-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments. [ABSTRACT FROM AUTHOR]

Simple Summary: This study attempts to analyse the technical and economic data of Iberian-breed pig farms that operate using two differentiated production models, i.e., the cebo or indoor fodder-fed pig production system where the animals are reared indoors, and the cebo campo or outdoor fodder-fed pig production system where the animals are reared outdoors with access to outdoor areas also during the fattening stage. The paper shows the study of three farms from each system with three batches per farm, where the data are obtained by way of a survey directly carried out with the farmers. Despite the conversion ratios being higher in the outdoor system, they do not translate into a significant increase in feed costs. However, labour costs are higher in the outdoor than in the indoor system. Gross margins are higher in the outdoor than in the indoor system, mainly due to the difference in incomes. Such incomes can vary temporarily subject to market prices. The outbreak of COVID-19 meant a sudden decrease in the gross profit, as the incomes went down and the costs remained at the same level. Crisis situations usually cause a reduction of the retail prices and a decrease in the profits. The current Quality Standard regulating the Iberian pig provides for various differentiated farming systems subject to the type of management implemented and the breed of the pigs. This study attempts to analyse the differences between two of these production systems, i.e., the outdoor and the indoor rearing systems by comparing the main technical and economic factors of six farms, three operating under each system, in order to ascertain the most profitable production system. This analysis is based on the information provided by the farm owners. It also evaluates the impact that the COVID-19 pandemic outbreak had on profitability. The results show that both systems spend the same resources on animal feed, which represents nearly 60% of the expense, with the price of purchase of piglets representing 30–32% of the total; however, there are differences in the cost of labour, which is higher in the outdoor variant. In economic terms, outdoor farms obtained a higher gross margin than indoor farms did. Although their production costs are higher, these are offset with larger incomes due to the higher market price of the pigs at the time of slaughter. Lastly, all the farms under study reveal large financial losses on account of COVID-19, given that there was a general decrease in the revenues due to the decrease in the selling price of the pigs, which seems to be the most determinant factor for the economic profits made by these kinds of farms. [ABSTRACT FROM AUTHOR]

A rectangular cartogram is a kind of graphical representation that uses a rectangular area to represent the attributes of geographical objects and is widely used in the representation of statistical data such as population, economic statistics, and epidemic distribution. However, there is currently little research on the effectiveness of rectangular cartograms for conveying quantitative data. In this paper, the rectangular cartogram and unclassed choropleth map are compared to evaluate the rectangular cartogram's effectiveness in quantity representation through task-driven eye-tracking cognitive experiments. In addition, the accuracy and efficiency of information communication and the differences in the reading processes of map readers when using the two types of maps for different tasks have been analyzed to provide references for more effective use of rectangular cartograms in the future. The main contributions of this paper are: (1) it has been proven by experiments that when completing the four tasks of locate, compare, find extreme, and estimate, the participants have higher accuracy rates with the rectangular cartogram; (2) the reading process shown by the eye tracking experiment shows that the more complex the task is, the more patient the participants would be with the rectangular cartogram, and the more pronounced the advantages of the rectangular cartogram are. [ABSTRACT FROM AUTHOR]