Ordinary Method Research Articles

Effect of Smuggling on Economic Status of Households of District Loralai: An Analysis

This study analyzes the effect of smuggling on the economic status of households in District Loralai. The data of 400 households were collected by using a simple random sampling technique from the Loralai district. The ordinary least square method was applied to estimate the results. Both rural and urban households were sampled. The economic status of the households was measured with household monthly income, household monthly expenditures, and household value of physical assets. The core independent variable used in the study was the participation of respondents in smuggling and price differences. Results show that smuggling boosts the economic status of households but harms the country's economic growth.

Role of green innovation technologies and urbanization growth for energy demand: Contextual evidence from G7 countries

In the context of rising energy demand, environmental degradation, and resource scarcity in G7 countries, the prime objective of this research is to explore the role of green innovation technologies, economic progress, and urbanization in energy demand. The long-standing relationship between green innovation technologies, urbanization, trade, and G7 countries from 1990 to 2020 was analyzed using the Kao and Pedroni cointegration test, panel causality, and dynamic and fully modified ordinary least squares methods. Empirical findings suggest that trade, gross domestic product per capita, and urbanization enhance energy demand, whereas environmental technology reduces energy consumption. The econometric results support promoting environmental technologies (“smart technology,” renewable energy, carbon dioxide removal, and electric vehicles) as policy tools to reduce energy demand and achieve sustainable development goals related to clean energy, a pollution-free climate, and green growth. Moreover, an energy conservation policy is recommended to ensure efficient energy use in G7 countries. In addition, novel implications in the context of the Sustainable Development Goals (SDG-7, 13) are discussed.

The role of resistance to change in determining organizational citizenship behavior: Evidence from MSMEs in Indonesia

Organizational citizenship behavior is vital to the company’s internal control system. This is because individual contributions that exceed role demands in the workplace increase company productivity and efficiency. This study aims to determine the role of resistance to change in moderating the influence of psychological capital and leader-member exchange on organizational citizenship behavior in micro, small, and medium enterprises in Indonesia. This paper uses a quantitative approach, and data collection is carried out through online surveys. The sample comprises 263 managers or owners of micro, small, and medium enterprises in Indonesia. This paper uses panel data; to test the interaction regression of resistance to change as a moderator, it uses the ordinary least squares method. The research results found empirical evidence that psychological capital and leader-member exchange impact increasing organizational citizenship behavior. Other findings show that resistance to change negatively moderates the influence of psychological capital and leader-member exchange on organizational citizenship behavior of Indonesia’s micro, small, and medium enterprises. AcknowledgmentThis study is supported by DPPMP Stikubank University; thanks to DPPMP and all team NGEJUS members.

Study on the regional ASF prediction method based on the ordinary kriging interpolation

The eLoran system functions as a robust backup to the Global Navigation Satellite System (GNSS), providing substantial signal power, robust anti-jamming capabilities, and an easily maintainable ground system. Nonetheless, the propagation time delay of the system, primarily driven by the Additional Secondary Phase Factor (ASF), significantly influences the positioning accuracy. Compensating for ASF can effectively enhance the positioning performance. Traditional propagation delay theories frequently yield substantial discrepancies between the predicted and measured values in regions characterized by extended propagation distances and varying topographic features. To address this issue, we conducted ASF measurements within a selected test area using a limited number of measurement points. We employed the ordinary Kriging interpolation method to predict ASF values across the entire test area, and used cross-validation to validate our predictions. The results confirmed the accuracy and effectiveness of the Kriging interpolation algorithm in predicting ASF values within specific regions. The cross-validation demonstrated that the errors remained within acceptable ranges. Furthermore, we applied Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function Interpolation methods to evaluate the positioning accuracy of the test area before and after ASF correction. Compared to other methods, using the Ordinary Kriging interpolation algorithm for predicting ASF values resulted in a corrected positioning accuracy of up to 68.8 m at various test locations. This approach effectively resolves the challenge of low accuracy in theoretical calculations in complex environments. By utilizing ordinary Kriging interpolation, we required measurements of only a few ASF values within a specific region to create an ASF correction map, addressing the challenges related to inaccurate theoretical calculations in complex pathways and avoiding time-consuming and labor-intensive large-scale measurements. The results of this study offer valuable theoretical support for improving the accuracy of land-based navigation systems.

The Impact of Human Development Variables on Agricultural Growth in Iraq for The Period 1990 - 2022

Despite the importance of agricultural sector in Iraq, the reality indicates the inability of this sector to meet the needs required of it, as the reason for this lies in the fact that it is managed by individuals who do not possess the most basic elements of human development, the study aims to measure and explain the impact of human development variables in raising the agricultural growth rate in Iraq during the period from 1990-2022. In order to apply the quantitative aspect of the study, the agricultural output growth rate was adopted as a dependent variable Y. As for independent variables, they included agricultural capital X1, mechanical technology X2, chemical technology X3, agricultural labor X4 and agricultural education X5, depending on the statically software E-views10 and its tests according to the ordinary least squares method (OLS), the results showed the significance of the variables of capital and chemical technology with a positive effect in agricultural growth rate in short and long terms. The negative significant effect of mechanical technology and labor force appeared only in long term, while agricultural education appeared to have a significant and positive effect in the long term. The most important conclusions were reached was that human resources contribute to the development of agricultural businesses until they are considered the main means of achieving large-scale production. We recommend increasing government spending in the areas of education, training and rehabilitation in rural Iraq, as it enables the population to employ their skills and experience in raising the rate of agricultural growth.

Implementing Reliability Analysis of a Soil Slope using First-Order Second Moment Method, Monte-Carlo Simulation Method and Subset Simulation Method in a MS-Excel Spreadsheet

The spatial variation in the soil properties affects the analysis and design of geotechnical structures such as slope stability analysis. This paper presents the reliability analysis of a soil slope using three different methods namely, first-order second moment (FOSM) method, Monte-Carlo simulation (MCS) method and Subset simulation (SS) method. The analysis has been carried out by considering the spatial variation of the soil properties of the slope. The correlation function and the correlation distance (λ) for the uncertain parameters are modelled using random field theory developed by Vanmarcke. The factor of safety (FOS) of the slope is determined using Ordinary method of slices. The reliability based probabilistic slope stability analysis has been carried out in a MS-Excel spreadsheet to determine the probability of failure (P_f ) and reliability index (β) of the slope. The spreadsheet mainly consists of three parts i.e., deterministic analysis, uncertainty analysis and uncertainty propagation. The study shows that the SS method has shown better performance as compared to FOSM method and MCS method especially at low failure probability levels. Also, SS method ensures the generation of samples in the failure region which is not always possible in MCS method.

Long-term exposure to ambient air pollutants and increased risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease: a retrospective cohort study in Beijing, China

Limited data have examined the association between air pollution and the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). We aimed to investigate whether long-term exposure to air pollutants is related to the development of ESRD among patients with T2DM and CKD. A total of 1,738 patients with T2DM and CKD hospitalized in Peking University Third Hospital from January 1, 2013, to December 31, 2021 were enrolled in this study. The outcome was defined as the occurrence of ESRD. Data on six air pollutants (PM2.5, PM10, CO, NO2, SO2, and O3) from 35 monitoring stations were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center. Long-term exposure to air pollutants during the follow-up period was measured using the ordinary Kriging method. During a mean follow-up of 41 months, 98 patients developed ESRD. Multivariate logistic regression analysis showed that an increase of 10 μg/m3 in PM2.5 (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.03–1.36) and PM10 (OR 1.15, 95% CI 1.02–1.30) concentration were positively associated with ESRD. An increase of 1 mg/m3 in CO (2.80, 1.05–7.48) and an increase of 1 μg/m3 in SO2 (1.06, 1.00–1.13) concentration were also positively associated with ESRD. Apart from O3 and NO2, all the above air pollutants have additional predictive value for ESRD in patients with T2DM and CKD. The results of Bayesian kernel machine regression and the weighted quantile sum regression all showed that PM2.5 was the most important air pollutant. Backward stepwise logistic regression showed that PM2.5 was the only pollutant remaining in the prediction model. In patients with T2DM and CKD, long-term exposure to ambient PM2.5, PM10, CO, and SO2 was positively associated with the development of ESRD.

Solving Problems of Physical or Basic Sciences via Rohit Transform

The problems such as evolution and withering of population, immersion of glucose by the body, escalation of epidemics and reversal of gross domestic product (GDP) over time, Law of heating or cooling given by Newton, radioactive disintegration law, and velocity of an object which is falling vertically downwards under the operation of gravity pull, in physical or basic sciences, are delineated by differential equations. These problems delineated by differential equations are generally solved by the calculus method which requires complicated computations. In the present article, an advanced integral transform-Rohit transform (RT) is conferred for solving the problems delineated by differential equations in physical or basic sciences. The results come by confirming that the RT is a simple and more effective scientific tool for dealing with such problems delineated by differential equations in physical or basic sciences than the ordinary calculus method.

Uncertainty in Land Value Modeling of the San José Metropolitan Region, Costa Rica

Land value patterns show very distinct spatial associations with accessibility to urban centralities and physical factors in a territory. However, predictions based on models of this structure can be highly uncertain, as the underlying data also may show clustering (thus allowing for better predictions in more densely sampled areas). An assessment of this uncertainty for land value extrapolations in the the San José Metropolitan Region of Costa Rica is presented, via conditional Gaussian simulation, and the determinants of this uncertainty were explored, to find spatial strengths and weaknesses in the modeling efforts. The E-Type prediction from the conditional Gaussian simulation was found to marginally improve on ordinary kriging methods and it also provided explicit uncertainty patterns, which are the inverse of the land value prediction. The estimated uncertainty was found to decrease with characteristics that identify suitability for urban land use (and thus higher land values).

Energy-related financial literacy and energy consumption: a study of residential households in West Bengal, India

PurposeEnergy-related financial literacy (ERFL) which consists of energy literacy, financial literacy and lifecycle cost literacy, can play an instrumental role in addressing climate change by ensuring efficient energy consumption (macro level benefit) and promoting financial well-being (micro level benefit) of households. This study aims to highlight the ERFL level and its effect on the energy consumption of the sample households in the state of West Bengal, India.Design/methodology/approachThe study used primary data on 155 sample households from the two districts, i.e. Hooghly and North 24 Parganas in West Bengal, India, surveyed from September 2022 to November 2022 using a structured questionnaire. The study used the conceptual framework suggested by Blasch et al. (2018) to measure the ERFL. Pertinent statistical techniques and the ordinary least square regression method were used to attain the objectives of the study.FindingsThe outcome of the study showed that the average ERFL score was found to be moderate (63%). The findings of the study also indicated that the ERFL exerts a positive influence on reducing energy consumption among the sample households in India.Originality/valueThere is a dearth of research studies on the topic of ERFL around the globe. The very few studies so far conducted are mostly in the context of European economies and Nepal. Perhaps, to the best of the our knowledge, this is the first study on the issue of ERFL in the Indian context. Therefore, the present study will make an original contribution to the small but growing scholarship on ERFL.

Effects of landscape simplicity on crop yield: A reanalysis of a global database.

Ecological theory on diversity suggests that agriculture requires sufficient biodiversity, ecological function, and critical ecosystem services to remain sustainable and resilient. As such, research related to the effect of ecosystem services and diversity on crop yields has increased significantly in the past decade. One such study by Dainese and colleagues that presented a global synthesis of a compiled database of 1,475 crop experiments related to pollination and pest control ecosystem services and crop yields quickly garnered attention in the literature with more than 540 citations since its publication in 2019. Given the strong influence of this study on the research on diversity and agricultural production, we conduct a reanalysis on the publicly available dataset from the global synthesis study to test the robustness of findings to modeling approach and assumptions. In our reanalysis we apply ordinary least squares regression methods rather than Bayesian path analysis to the same data to examine the robustness of observed field-scale landscape diversity-ecosystem services-crop yield relationships. The result of our reanalysis supports the findings of Dainese and colleagues, illustrating the robustness of findings that suggest that increasing landscape simplicity is associated with lower rates of pollination and pest control ecosystem service provisioning and lower crop yields. However, our analyses also suggest that provisioning of pollination and pest control services account for only a small fraction of the total effect of landscape simplicity on crop yields. Furthermore, we find that management and soil health may mediate the effects of landscape simplicity on ecosystem services and crop yields. While our results complement previous findings for landscape simplicity and ecosystem services, they also indicate that above and below ground ecosystem services are not mutually exclusive but concurrently contribute to support crop production in agriculture.

MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud Environments

Cloud computing and relevant emerging technologies have presented ordinary methods for processing edge-produced data in a centralized manner. Presently, there is a tendency to offload processing tasks as close to the edge as possible to reduce the costs and network bandwidth used. In this direction, we find efforts that materialize this paradigm by introducing distributed deep learning methods and the so-called Federated Learning (FL). Such distributed architectures are valuable assets in terms of efficiently managing resources and eliciting predictions that can be used for proactive adaptation of distributed applications. In this work, we focus on deep learning local loss functions in multi-cloud environments. We introduce the MulticloudFL system that enhances the forecasting accuracy, in dynamic settings, by applying two new methods that enhance the prediction accuracy in applications and resources monitoring metrics. The proposed algorithm’s performance is evaluated via various experiments that confirm the quality and benefits of the MulticloudFL system, as it improves the prediction accuracy on time-series data while reducing the bandwidth requirements and privacy risks during the training process.

A comparative study between the two methods GM6.IDRGP(RMVN) and the GM6 method for analyzing the multiple linear regression model in the presence of outliers using the simulation method.

Multiple linear regression is one of the most widely used statistical analysis methods in many scientific fields. Its parameters are estimated based on the ordinary least squares method. Which gives the best unbiased linear estimate if its assumptions are met. The most important of these assumptions is that it has a normal distribution of error with a mean of zero and a constant variance. If the data does not meet certain assumptions, the sample estimates and results may be misleading. The linear regression model is sensitive to the appearance of outliers and leverage points. Therefore, statistical techniques have been developed capable of dealing with or detecting outliers. This led to the emergence of many alternative methods to the OLS method, such as M-Huber, s, LTS, and MM, which have high efficiency and breakdown points, but are affected by HLPs, which result in the problem of Masking and Swamping. The GM6 method is one of the methods that was developed in order to treat such problems through the use of a weight function, but the weight function depends on the individual diagnosis, which gives inaccurate results. In order to overcome this problem, comprehensive diagnostic methods have been proposed, such as the DRGP and IDRGP.RMVN methods. Therefore, in this study, we proposed to employ the IDRGP.RMVN method in the GM6 method algorithm. And comparing it with some hippocampal regression methods through a simulation study in determining the best methods.

Time Series Prediction of Solar Power Generation Using Trend Decomposition

High‐accuracy predictions of future solar power generations are important for monitoring, maintenance, dispatching, and scheduling. The goal of this study is to create a forecasting workflow that increases prediction accuracy independent of the machine learning method and has minimal computational requirements. The proposed trend decomposition method incorporates irradiance and seasonal features as exogenous inputs. In order to extract the linear part of the data, a moving average filter is used. The nonlinear (stable) component of the time series is then calculated by subtracting this linear part from the original data. The stable portion is modeled using several machine learning methods, while the ordinary least squares method is applied to the linear series. By aggregating both results, the final forecast is obtained. The forecasting performances of the machine learning algorithms on unprocessed data are used as baselines for evaluations. Improvements up to 39% in the mean absolute error and up to 31% in the root mean square error metrics are observed compared to the baselines. Experimental results show that the proposed trend decomposition with extrapolation method increases the forecasting performance and generalization capacity of machine learning algorithms.

Thermal histories reveal spatiotemporal distribution and population overlapping of Sepioteuthis lessoniana

Abstract Thermal histories describe ambient temperature experienced by cephalopod species from birth to death, reflecting their habitats and distributions. Thermal histories were reconstructed by analyzing ontogenetic oxygen isotopes (δ18O values) in statoliths of adult Sepioteuthis lessoniana collected from northeastern and southwestern Taiwan between 2017 and 2019. The probabilities of occurrence associated with thermal histories in the two populations were modeled using a spatial interpolation approach, ordinary kriging method. The northeastern population exhibited larger ontogenetic variations in experienced temperature (ranging from 9.1 to 10.4°C) than did the southwestern population (ranging from 4.3°C to 6.8°C). The two geographical populations exhibited distinct ontogenetic movement patterns and distribution. The southwestern population demonstrated wide dispersal influenced by seasonal wind directions and tended to remain in 20°C isotherm areas around the Penghu Islands for maturing and spawning. At the seasonal cohort level, the northeastern population exhibited a southward shift during the 2-year study period as a result of the El Niño event. The distribution of the two geographical populations overlapped in the adult stage primarily in the northern Taiwan Strait, supporting an assumption of population connection in Taiwan. This study revealed the thermal histories of S. lessoniana, providing insights into the field observation of distribution patterns and the progress in relating population dynamics to environmental variability, which are essential for the sustainable management of squid fisheries.

Inversion of soil properties with hyperspectral reflectance in construction areas of high-standard farmland

ABSTRACT High-standard farmland construction is an important process that can enhance food security and accelerate new-style modernization agriculture. Hyperspectral remote sensing can provide data and technical support for this type of construction to provide a reference when optimizing high-standard farmland construction areas. This study was performed in Xinzheng City, the primary grain-producing areas in Henan Province. Field sampling and indoor hyperspectral spectroscopy (350~2500 nm) were combined; spectral transformations such as continuum removal (CR) were performed after Savitzky‒Golay (SG) convolution smoothing; and the best hyperspectral bands were selected as the common index of the soil properties by correlation analysis and fuzzy clustering maximum tree. A hyperspectral inversion model was built for the panel data model of the fixed effect variable coefficient based on the ordinary least squares estimation method (OLS), including panel data describing pH, organic matter, nitrogen, phosphorus, potassium, iron, chromium, cadmium, zinc, copper, and lead of 116 samples in Xinzheng City. Results show that the panel data model is of good quality overall, and the goodness of fit is higher ( R 2 = 0.9991, F = 2195.67). The precision test results indicate that the models performed well at both description and prediction, including accurate quantification, with an RPD above 2.5. Thus, the proposed model provides an important basis for soil information management, resource evaluation, and a reference when optimizing high-standard farmland construction processes.

Export of Organic Cape Gooseberry (Physalis peruviana) as An Alternative Illicit Crop Substitution: Survey of Consumers in Namur, Belgium

The social situation that indigenous people encounter in rural areas of Colombia is linked to social disparities and a dearth of opportunities for their products. Illicitly harvesting coca plants affords them resources they would not receive in the conventional market. This study investigates the feasibility of exporting organic gooseberries cultivated in conflict-affected regions of Colombia to European countries. Such exports could offer a means of crop substitution for indigenous communities that currently rely on coca farming for their sustenance. Additionally, this article features an exploratory analysis of consumer attitudes towards organic cape gooseberry in Namur, Belgium. This study employed surveys of 158 individuals in 2022 to gauge consumer perception and used the ordinary least squares method in the econometric modeling of foreign trade behavior. The modeling utilized 6233 observations between 2015 and 2022 about exports to European nations. Surprisingly, dehydrated gooseberry snacks were found to be the preferred method of consumption in Namur, Belgium, as opposed to fresh fruit. The export of Colombian cape gooseberries has secured a strong presence in Europe, with its price–quantity correlation demonstrating a high inelastic demand. This implies that price changes have minimal impact on the quantities purchased overseas. Organic cape gooseberries have significant potential for indigenous land. However, this study is exploratory and limited to the variables identified in the ICECOMEX databases in Colombia. Further research should consider the consumption of organic cape gooseberries in other European cities.

Estimation in Semi-Varying Coefficient Heteroscedastic Instrumental Variable Models with Missing Responses

This paper studies the estimation problem for semi-varying coefficient heteroscedastic instrumental variable models with missing responses. First, we propose the adjusted estimators for unknown parameters and smooth functional coefficients utilizing the ordinary profile least square method and instrumental variable adjustment technique with complete data. Second, we present an adjusted estimator of the stochastic error variance by employing the Nadaraya–Watson kernel estimation technique. Third, we apply the inverse probability-weighted method and instrumental variable adjustment technique to construct the adaptive-weighted adjusted estimators for unknown parameters and smooth functional coefficients. The asymptotic properties of our proposed estimators are established under some regularity conditions. Finally, numerous simulation studies and a real data analysis are conducted to examine the finite sample performance of the proposed estimators.

Development of a fast and simple method for the isolation of superparamagnetic iron oxide nanoparticles protein corona from protein-rich matrices

The adsorption of proteins onto the surface of nanoparticle (NP) leads to the formation of the so-called “protein corona” as consisting both loosely and tightly bound proteins. It is well established that the biological identity of NPs that may be acquired after exposure to a biological matrix is mostly provided by the components of the hard corona as the pristine surface is generally less accessible for binding. For that reason, the isolation and the characterisation of the NP-corona complexes and identification of the associated biomolecules can help in understanding its biological behaviour. Established methods for the isolation of the NP-HC complexes are time-demanding and can lead to different results based on the isolation method applied. Herein, we have developed a fast and simple method using ferromagnetic beads isolated from commercial MACS column and used for the isolation of superparamagnetic NP following exposure to different types of biological milieu. We first demonstrated the ability to easily isolate superparamagnetic iron oxide NPs (IONPs) from different concentrations of human blood plasma, and also tested the method on the corona isolation using more complex biological matrices, such as culture medium containing pulmonary mucus where the ordinary corona methods cannot be applied. Our developed method showed less than 20% difference in plasma corona composition when compared with centrifugation. It also showed effective isolation of NP-HC complexes from mucus-containing culture media upon comparing with centrifugation and MACS columns, which failed to wash out the unbound proteins. Our study was supported with a full characterisation profile including dynamic light scattering, nanoparticle tracking analysis, analytical disk centrifuge, and zeta potentials. The biomolecules/ proteins composing the HC were separated by vertical gel electrophoresis and subsequently analysed by liquid chromatography-tandem mass spectrometry. In addition to our achievements in comparing different isolation methods to separate IONPs with corona from human plasma, this is the first study that provides a complete characterisation profile of particle protein corona after exposure in vitro to pulmonary mucus-containing culture media.

The correlation between state capture, grand corruption, petty corruption, and investment in Indonesia

This study aims to find the correlations between various types of corruption, namely state cap-ture, grand corruption, and petty corruption, with foreign direct investment and domestic investment in Indonesia. Using ordinary least squares method, this study analyzes data on corruption court decisions that occurred in 509 regencies from 2015 to 2019. The results reveal that, whilst domestic investment shows clear and significant correlation only with grand corruption, the foreign direct investment has definite and significant correlations with both grand and petty corruption. In addition, grand corruption has a stronger correlation with investment, especially with the foreign direct investment, in comparison to petty corrup-tion. The state capture corruption, on the other hand, shows no significant correlation to either foreign direct investment or domestic investment. These results highlight that the high level of investment in Indonesia is accompanied by an increase in the number of corruptions, especially large-scale corruption such as grand corruption.