Empirical Techniques Research Articles

A Review of CFD Modeling of Erosion-induced Corrosion Formation in Water Jets Using FEA

A comprehensive review of all the related investigations carried on water jet cutting or AWJ (AWJ) cutting is performed. The experimental methods and results used on this subject were examined, defined and compared by the researchers. This research shows that further improvements are possible in the water jet cutting method. In order to reduce the high cost of the empirical technique and increase the time efficiency, it is important to perform computerized Finite Element Analysis (FEA) or Computational Fluid Dynamics (CFD) based AWJ processing. An attempt has been performed to systematically arranged the research investigations conducted on water jet cutting. The studies on the definition of the nozzle failure, calculate using CFD of water jet cutting or AWJ cutting processes, the studies on the parameters influencing water jet cutting are reviewed and the future work for the further improvement of water jet cutting process is highlighted.

Image-Guided Programing is Useful in DBS for Parkinson’s Disease (P4-11.007)

<h3>Objective:</h3> To evaluate the utility of image guided DBS programming. We aimed to determine the correlation between optimal DBS electrode placement predicted by imaging guided software and clinician-determined monopolar review in PD patients with deep brain stimulation. <h3>Background:</h3> The current paradigm for DBS programming, monopolar review - an empirical technique that is a time-consuming process that can last 60–90 minutes for each lead and is performed in a dopaminergic-off state. Recent developments in imaging software with detailed anatomical atlases allow visualization of each individual electrode in relation to the target structure. Pre-operative MRI and post-operative CT images are fused to determine the precise location of individual contacts and allows an unbiased determination of best contact and is associated with decreased programing time. <h3>Design/Methods:</h3> An automatic software algorithm (Brainlab, Munich) was used to fuse preoperative MRI with postoperative CT and visualize the location of each electrode inside the STN or GPI. Two independent reviewers assessed the fusion images generated for 50 patients who had undergone DBS lead placement for Parkinson’s disease. <h3>Results:</h3> Our analysis included 50 patients, 13 with GPi and 37 with STN implants. The majority of GPi contacts (71%) deemed best during initial monopolar review correlated with those predicted by the software and had full submersion in the target. In the case of STN, a strong correlation with the software was present in 42% of the cases for the left leads. However, the right STN contacts used for monopolar review had the largest percentage (48%) with no submersion in the target per the software. <h3>Conclusions:</h3> Optimal DBS contacts were successfully predicted by fusion images generated by the Brainlab software in the majority of patients with GPi targets and appreciable percentage of STN targets. This may help guide the monopolar review of patients who have undergone placement of deep brain stimulation electrodes. <b>Disclosure:</b> Mr. Moll has nothing to disclose. Ms. Bullock has nothing to disclose. Dr. Marmol has nothing to disclose. Dr. Hislop has nothing to disclose. Jonathan Jagid has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Medtronic Inc. The institution of Jonathan Jagid has received research support from Boston Scientific. The institution of Jonathan Jagid has received research support from Medtronic Inc. The institution of Dr. Haq has received research support from NINDS. The institution of Dr. Haq has received research support from the Parkinson’s Foundation. The institution of Dr. Haq has received research support from NIMH. Dr. Haq has a non-compensated relationship as a consultant with Medtronics that is relevant to AAN interests or activities. Dr. Haq has a non-compensated relationship as a consultant with Boston Scientific that is relevant to AAN interests or activities. Dr. Haq has a non-compensated relationship as a consultant with Abbott that is relevant to AAN interests or activities. Dr. Luca has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Boston Scientific. Dr. Luca has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Signant Health.

Combined empirical techniques reveal key role for variation in plasticity in a novel environment.

This article is a Commentary on Walter et al. (2023), 239: 374–387.

Environmental valuation of hydro-environmental services of the Tropical Montane Cloud Forest

Environmental externalities are just one class of externalities (or external effects). They are not more important than any other type of economic externalities, but it would not be possible to find rational arguments to justify that they are less so. If more complex and therefore better decisions are to be taken, these externalities should be duly quantified and incorporated in the framework of an economic analysis of public or private decisions of society. In that case, they can be accounted for as costs of our decisions. Environmental economics has different environmental valuation methods to include ecosystems and its dynamics into an economic system. The most applied methods are contingent valuation, hedonic prices, and travel cost. These methods may be used as a guide to formulate public policies to protect the environment and have an efficient management. The present research is an analysis for hydro-environmental services (recreational services) of the tropical montane cloud forest (La Granada waterfall) of Coatepec due to the increasing risks caused by human activity. Therefore, the travel cost method will be applied to estimate the economic value associated with the ecosystem. This method applies conventional empirical techniques used by economists to estimate economic values based on market prices. The findings showed different travel costs for every chosen zone and a consumer surplus of $47.10 MXN and an estimated yearly value of $70,645 MXN.

The importance of theory at the Information Systems Journal

The importance of theory at the <i>Information Systems Journal</i>

Interdisciplinary approaches to predicting evolutionary biology.

Interdisciplinary approaches to predicting evolutionary biology.

Migrant remittances, agriculture investment and cropping patterns

AbstractWe investigate how the receipt and amount of domestic or international transfers influences household decisions regarding farm investment and the selection of capital and labour‐intensive crops. We argue that, even though recipient households may use additional income to increase agricultural investment, investment can fall in the short run if labour constraints arising from the migrant member's absence are binding and capital accumulation is suboptimal. Employing a set of endogenous treatment estimates, we test this hypothesis on data from 5636 rural households in Pakistan. Our findings show a substantial difference between recipient and non‐recipient households in terms of their economic behaviour. Recipient households make 100% less agricultural investment and generate 82% less production compared to non‐recipient households. The estimates are found to be robust when tested with alternate empirical techniques (Heckman Selection and matching). The impact is stronger in the case of households that receive domestic transfers, with 100% less farm investment and 77% less production than non‐recipient households. Remittances result in a decrease in production of both capital‐ and labour‐intensive crops, reflecting a decline in overall farm activity. Similar farm investment and cropping patterns are observed relative to the amount of remittances received. The results are robust to different model specifications and estimation procedures.

Artificial Intelligence-Empowered Logistic Traffic Management System Using Empirical Intelligent XGBoost Technique in Vehicular Edge Networks

Recent advancements in computation and communication technologies and the increasing adoption of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies have paved the way to tremendous developments in modern transportation systems. Driven by the massive number of connected vehicles and the stringent requirements of the public traffic management system, the transportation of data to and from the centralized cloud servers poses a great challenge. As a result, to meet the computational requirements and handle the massive amount of sensory data efficiently, the potential solution is to process/analyze the data at the edge of the network. Motivated by the challenges mentioned above, in this paper, we design a new empirically intelligent XGboost (EIXGB)-enabled logistic transportation system at the edge network for analyzing the data efficiently. Besides that, the proposed EIXGB technique intends to obtain real-time results based on the monitoring parameters of the public traffic management system with higher accuracy and minimum error. Extensive simulation results demonstrate the efficiency of the proposed EIXGB technique over the standard machine learning techniques using a set of parameters. The proposed technique achieves 87-97% accuracy over the different sets of features of a real-time dataset as per the simulation results.

Implementation of Halal Tourism in Lombok Island (Study on the Compliance of Tourism Actors with the Halal Tourism Regulation)

The tourist business of Lombok, particularly the Muslim-friendly sector, was severely impacted by the declaration of COVID-19 as a worldwide pandemic in March 2020. The difficulty that still exists at the present day is the application of the policies mandated by law. One of the criteria for the establishment of halal tourism in line with Regional Regulation No. 2 of 2006 is whether or not the process has been carried out correctly and is supported by government monitoring. According to Soerjono Soekanto's theory of legal efficacy, this study employs a sociological or empirical legal research technique (sociology of law). A community's response to a law provides insight into the effectiveness of the law. In order for a law or legislation to be successful, it must result in changes in behavior on the part of the community members that bring about the intended outcome.&#x0D;

Обзор методов прогноза гидравлического разрыва противофильтрационного ядра каменно-земляной плотины

Introduction. The phenomenon of hydraulic fracture is associated with the failure of some earth-rock dams, in which the integrity of the core made of clay soil has been compromised. It is believed that hydraulic fracture occurs when the pressure of water contained in pores and microcracks overcomes the compressive stresses in the soil. At this point, several methods have been proposed for the computational prediction of hydraulic fracture. They are aimed at determining the conditions under which hydraulic fracture occurs and at determining the critical water pressure that causes fracture growth. Materials and methods. A review of foreign scientific publications was carried out to systematise information on methods and techniques for critical pressure determination. Results. The review gives a classification of methods for predicting hydraulic fracture. Theoretical methods are based on the usage of elasticity theory, plasticity theory or fracture mechanics. The simplest method is based on the analysis of the stress state of the soil mass. This review describes methods based on the analysis of the stress-strain state of soil around a cavity with internal pressure. They consider several mechanisms of crack propagation, including the loss of tensile and shear strength. Empirical formulas based on the results of laboratory experiments are also given. In the framework of the analytical review, a comparison of some methodologies has been carried out. Most of the methodologies agree that the critical pressure primarily depends on the minimum principal stress σ3 in the ground. The comparison showed that the critical pressure calculated by the theoretical triaxial stress analysis technique is noticeably lower than that calculated by the empirical techniques. In the presence of a cavity, the more likely cause of fracturing is the loss of shear strength of the soil rather than tensile strength. Conclusions. Despite the abundance of techniques for predicting hydraulic fracturing, no theoretically verified and accurate methodology has been established at the present time.

Two birds with one stone: Reducing corruption raises national income

AbstractObjectiveThis article estimates the causal effect of corruption reform on economic growth across a sample of 122 countries from 1980 to 2015.MethodsWe first identify countries that experienced large and sustained increases in corruption control, we refer this group as the treatment group. We then utilize two empirical estimation techniques to estimate the causal effect of corruption on economic growth—matching methods and a doubly robust, difference‐in‐difference, event study model.ResultsWe find that sustained corruption reform leads to dramatic increases in real Gross Domestic Product (GDP). Across a variety of methods, the effect at 10 years after reform is on the order of a 20 percent to 25 percent increase in average living standards.ConclusionsEven though corruption is often cited as a fundamental obstacle to economic development, lasting anti‐corruption reform is relatively rare. We find, however, that the benefits of this type of reform might be significantly higher than policymakers believe. Thus, successful reform brings a win–win outcome of less corruption and higher average incomes.

Применение сверточных нейронных сетей для прогнозирования критической частоты fₒF2

Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies on a regression approach to the prediction based on the known daily quasi-periodicity of ionospheric parameters related to solar illumination. Algorithmically, this approach is implemented in the form of convolutional neural networks with two-dimensional convolution. The input data for the analysis is presented as two-dimensional solar time — date matrices. The model was trained on data from the mid-latitude ionosonde in Irkutsk (RF) and tested using data from several mid-latitude ionosondes: Arti (RF), Warsaw (Poland), Mohe (China). It is shown that the main contribution to the prediction value of fₒF2 is made by the data on the nearest few days before the prediction; the contribution of the remaining days strongly decreases. This model has the following forecast quality metrics (Pearson correlation coefficient 0.928, root mean square error 0.598 MHz, mean absolute error in percent 10.45 %, coefficient of determination 0.861) and can be applied to fₒF2 forecast in middle latitudes.

Применение сверточных нейронных сетей для прогнозирования критической частоты fₒF2

Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies on a regression approach to the prediction based on the known daily quasi-periodicity of ionospheric parameters related to solar illumination. Algorithmically, this approach is implemented in the form of convolutional neural networks with two-dimensional convolution. The input data for the analysis is presented as two-dimensional solar time — date matrices. The model was trained on data from the mid-latitude ionosonde in Irkutsk (RF) and tested using data from several mid-latitude ionosondes: Arti (RF), Warsaw (Poland), Mohe (China). It is shown that the main contribution to the prediction value of fₒF2 is made by the data on the nearest few days before the prediction; the contribution of the remaining days strongly decreases. This model has the following forecast quality metrics (Pearson correlation coefficient 0.928, root mean square error 0.598 MHz, mean absolute error in percent 10.45 %, coefficient of determination 0.861) and can be applied to fₒF2 forecast in middle latitudes.

The impact of digitalization on credit risk: the mediating role of financial inclusion (National Bank of Egypt (NBE) case study)

This article investigates potential direct and indirect impact of National Bank of Egypt (NBE) digitalization and its financial inclusion on credit risk. The methodology used in the study is an empirical technique through the collection of secondary data from the period between 2011 and 2021 in the Egyptian banking sector. To accomplish this, the study developed two hypotheses employing the empirical SEM model for testing whether there is a positive or negative impact for three chosen variables: digitalization, credit risk and financial inclusion. The investigational outcome shows that NBE digitalization has both direct and indirect impact through the mediator variable, financial inclusion. It also provides an understanding of the relationship between digitalization, credit and financial inclusion. The article proposes for future studies the impact of other Fintech factors directly and indirectly using the mediators on credit risk. The authors came up with many findings: first, financial inclusion indices can be built using portable money and banking services activities. Second, digitalization has a negative direct impact on credit risk. Also, digitalization has a positive indirect impact on credit risk through the mediator variable which is referred to as financial inclusion. Third, the model fit is adequate for the data being tested.

Significant impact of time-of-day variation on metformin pharmacokinetics

Aims/hypothesisThe objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations.MethodsA large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis.ResultsA significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy.Conclusions/interpretationMetformin’s pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes.Graphical abstract

Ecological footprint in a global perspective: the role of domestic investment, FDI, democracy and institutional quality

PurposeThe continuous increase in the negative gap between biocapacity and ecological footprint has remained globally persistent since early 1970. The purpose of this study is to examine the effect of foreign capital, domestic capital formation, institutional quality and democracy on ecological footprint within a global panel of 101 countries from 1995 to 2017.Design/methodology/approachThe empirical procedure is based on data mix. To this end, this study uses a battery of testing and estimation approaches both conventional (no cross-sectional dependence [CD]) and novel approaches (accounting for CD). Among the battery of estimation techniques used, there are the dynamic ordinary least square, the mean group, the common correlation effect mean group technique, the augmented mean group technique, the Pooled mean group and the dynamic common correlation effect technique with the desire to obtain outcomes robust to heteroskedasticity, endogeneity, cross-correlation and CD among others.FindingsThe estimated outcomes indicate that using different estimators’ domestic capital formation consistently degrades the environment through an increase in ecological footprint, while institutional quality consistently enhances the quality of the environment. Further, the outcome reveals that, though foreign capital inflow degrades the environment, the time period is essential, as it shows a short-run environmental improvement and a long-run environmental degradation. Democratic activities show a mixed outcome with short-run degrading effect and a long-run enhancement effect on environmental quality.Practical implicationsGreen investment should be the policy target of all economies, and these policies should be adopted to target both domestic capital and foreign capital alike. Second, the adoption of democratic practices will produce good leaders that will not just design short-term policies to blindfold the populace temporary but those that will produce long-term-oriented practices that will better and enhance the quality of the environment through the reduction of the global footprint. Equally, enhancing the institutional framework like respect for the rule of law in matters of abatement should be encouraged.Originality/valueAlthough much research on the role of macroeconomic indicators on environmental quality has been done this far, democratic practices, intuitional quality and domestic capital have been given little attention. This research fills this gap by considering robust empirical techniques.

Measures of Extropy Based on Concomitants of Generalized Order Statistics under a General Framework from Iterated Morgenstern Family

In this work, we reveal some distributional characteristics of concomitants of generalized order statistics (GOS) with parameters that are pairwise different, arising from iterated Farlie–Gumbel–Morgenstern (IFGM) family of bivariate distributions. Additionally, the joint distribution and product moments of concomitants of GOS for this family are discussed. Moreover, some well-known information measures, i.e., extropy, cumulative residual extropy (CRJ), and negative cumulative extropy (NCJ), are derived. Applications of these results are given for order statistics, record values, and progressive type-II censored order statistics with uniform marginals distributions. Additionally, the issue of estimating the CRJ and NCJ is looked into, utilizing the empirical technique and the concomitant of GOS. Finally, bivariate real-world data sets have been analyzed for illustrative purposes, and the performance of the proposed method is quite satisfactory.

Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems

This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.

Nonsystematic Rupture Directivity of Geothermal Energy Induced Microseismicity in Helsinki, Finland

AbstractThe rupture behavior of microseismicity in fluid‐injection settings with low fault stresses is generally believed to be controlled by the pore pressure, including a tendency of the larger induced earthquakes to rupture into the perturbed volume toward the injection well, implying a degree of predictability. Here, we examine directivity patterns to identify fault planes and rupture directions of the 21 largest earthquakes (local magnitudes, 1.3–1.9) recorded during the 2018 St1 Deep Heat geothermal project near Helsinki, Finland. We use the Empirical Green's Function technique to retrieve per‐seismic‐station corner frequencies, earthquake durations, and directivity trends. After combining the directivity trends with focal mechanisms calculated using principle component analysis, we resolve rupture planes and rupture directions of 10 events. In contrast to studies of induced events at other sites, we find that one event rupture toward, two rupture away, and the remaining rupture parallel to the well. Furthermore, we find that the events prefer mode II failures rather than mode III. These observations provide new constraints for mechanical models of rupture growth in pore‐pressure dominated settings.

The effects of station density in geostatistical prediction of air temperatures in Sweden: A comparison of two interpolation techniques

High fidelity gridded temperature datasets are difficult to obtain for areas with sparse coverage of meteorological stations given that sparsity of stations is known to introduce uncertainty in the interpolation of climatic variables generally. Inspired by their potential for optimal results especially for small sample datasets, the accuracy of interpolation results of Empirical Bayesian Kriging (EBK) and EBK-Regression Prediction (EBKRP) spatial prediction techniques were assessed and compared under varying sampling density scenarios, using monthly maximum temperature normals (1991–2020) for the entire area of Sweden. The objective of the was to understand how EBK and EBKRP interpolation techniques perform in different sampling density scenarios and in a sparse data setting, and the possible difference in the prediction accuracy between the two techniques. The 708 sampled stations obtained from the historical climatology database of the Swedish Meteorological and Hydrological Institute (SMHI) were split into seven sampling density subsets, ranging from 1 sample per 63,614 km 2 to 1 sample per 634,350 km 2 and representing both low and high sampling density scenarios. EBK interpolation technique was implemented using temperature data while land use land cover (LULC) and digital elevation model (DEM) were used as temperature covariates for the EBKRP interpolation models. The prediction accuracy assessment was based on five robust prediction performance indicators - mean error, mean absolute error, mean square error, root mean square error and Pearson correlation (R) - obtained from independent validation/cross-validation operations. Prediction accuracy was found to be generally positively related to sampling density and sampling density accounted for 85%–87% of interpolation accuracy for both EBK and EBKRP techniques. Although sampling density increased linearly, the rate of change in accuracy from one sampling density step to the next was not particularly proportional. A rapid increase in accuracy was observed when transiting from 40% to 60% sampling density scenario, but the rate of change in accuracy remained fairly stable before and after this (40% to 60%) sampling density scenario. For equivalent sampling density set-ups, EBKRP consistently performed better than EBK in all the accuracy metrics and EBKRP proved to be about 40% better than EBK. However, the two interpolation techniques produced generally very low prediction biases at all the sampling density scenarios investigated. Our study suggests that potential effects of low sampling density and non-stationarity of temperature data can be significantly reduced by applying EBK but especially EBKRP when coupled with relevant covariates. We also concluded that EBK and EBKRP can be applied to generate accurate prediction surfaces even in a widely varying sampling density setting including environments with moderately low sample density. This is especially true for continuous and slowly varying phenomena such as temperature and similar variables. • Station density is positively related to interpolation accuracy of air temperature but in non-linear fashion. • Univariate and multivariate Empirical Bayesian Kriging interpolation techniques can produce high prediction accuracy result even when data density is small or locally non-stationary. • Multivariate Empirical Bayesian Kriging technique performs better than the univariate technique in different sampling density settings when predicting air temperature. • Normally distributed data can generally improve prediction results even when data density is relatively small or sparse.