Empirical Techniques Research Articles

Formation of multiple anodic fire spots inside an electrostatic cavity

AbstractCharged particles exhibit self‐organized behaviour during the discharge state transitions inside an electrostatic cavity, along with the energy redistribution in different coherent modes with progressive anode potential. Multiple sheath formations around the spherical metallic ball anode placed inside the cavity transit to intensely glowing multiple anode spot formations (fireballs) on the anode surface with progressive anode potential. The intensely glowing anode spots reorganize over the anode surface and multiply quickly on increasing the anode potential. The nonlinear analysis of the fluctuating discharge current reveals the presence of self‐organized criticality (SOC) during different states of discharge along with a strong memory effect within (10–104) time lags in every state of discharge. The multiple anode spots exhibit long‐range correlation in the absence of a magnetic field, indicating SOC behaviour. The present study investigates energy distribution among the coherent modes of discharge through the application of the Empirical Mode Decomposition technique. The analysis indicates that the introduction of a magnetic field results in the interchange of coherent modes carrying most of the energy during multiple spot formations. The present experimental configuration produces stable multiple coherent modes along with the evolution of anodic sheath layers in the absence and then in the presence of the magnetic field. Relating SOC with such discharge transitions may help to understand the relation between complex structure formations in plasma with nonlinear aspects of glow discharge, which is of vital importance in the investigation of complexities in space plasma.

Optimization of the structure of modern money circulation in the context of the role of cash payments

Introduction. The dynamics of the economic development of any country largely depends on the state and efficiency of money circulation, the functioning of which could not be untouched by modern trends of virtualization of the economic environment and digitalization of interaction between market entities. Under such circumstances, determining the role and place of cash payments in the general system of monetary relations, the development of which is significantly influenced by digital technologies, should be considered as an urgent task in the view of maintaining the stability of monetary circulation as one of the important tasks of the state regulatory policy.Purpose. Research of the conditions of organization and ensuring the stability of modern money circulation, taking into account the role and place of cash in it. Proving the impossibility of completely replacing cash with non-cash payments in modern conditions.Methods. General scientific and empirical techniques and tools of economics, methods of analysis and synthesis, comparison, compilation and grouping are used.Results. Despite the development of modern banking technologies and the spread of non-cash payments, it is premature to talk about the complete replacement of cash with electronic money. Cash and non-cash forms of money circulation will coexist for a long time. Cash remains an important instrument of payments, taking into account a number of shortcomings inherent in electronic payments. The possibilities of implementing the concept of “cashless society” in practice are currently unattainable, and cash retains a number of advantages over the other payment instruments. Therefore, proper providing of cash flow in the economy is a necessary condition for financial stability and efficiency of the organization of money circulation.Prospects. Studying, taking into account the experience of the banking systems of developed countries, possible directions for improving the organization of cash circulation by bettering the technical conditions of banks, minimizing cash service costs and creating optimal opportunities for all economic agents to freely choose the forms and methods of using money as means of payment.

The effects of economic growth on carbon dioxide emissions in selected Sub-Saharan African (SSA) countries

This research article concerns a study of economic growth influences on carbon dioxide emissions in 20 selected Sub Saharan African (SSA) countries. The study also intends to reexamine energy consumption, tourism sector and population effect on carbon dioxide emissions. The empirical research applies panel linear regression model for the data obtained in these 20 SSA countries throughout 2000 to 2020. The empirical estimation techniques employed in the analysis consist of pooled ordinary least square (OLS), fixed effects model (FEM), random effects model (REM) and robust fixed model, including diagnostic tests such as endogeneity, heteroscedasticity and other measurements. The empirical analysis using the robust fixed effects model has established significant associations between economic growth, energy consumption, tourism sector and population on carbon dioxide emissions in SSA countries between 2000 and 2020. This study has established that a 1% increase in economy growth increases the carbon dioxide emission level by approximately 0.02%. A study has identified that SSA countries' energy consumption, especially from oil, will only contaminate air quality. A study confirmed that international tourist arrivals are one of the factors that significantly caused air quality reduction among SSA countries. However, increasing population and future international agreements and protocols could also mean that carbon emissions can potentially cause less environmental degradation in the region.

Strategic Rhythms: Insights and Research Directions

Strategic rhythms concern how managers intentionally organize strategic activities by mapping them to the time continuum in hopes of improving a firm's long-term prospects. We develop a clear conceptual scope for strategic rhythms research. We take stock of the research on: rhythms of repeated strategic activity (e.g., serial acquisitions); rhythms of multiple strategic activities (e.g., sequencing activities for innovation); and comparison of strategic rhythms (e.g., for the firm, its rivals, the environment). We synthesize this literature in an integrative framework explaining the antecedents, content, and outcomes of strategic rhythms. We identify firm-level and environmental constraints that limit managers’ discretion in enacting strategic rhythms, and we demonstrate two pathways through which managers may loosen those constraints. Finally, we identify promising opportunities for future research and suggest empirical techniques that will likely be useful for studying strategic rhythms.

Analysing customers' reviews and ratings for online food deliveries: A text mining approach

AbstractThe purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. An Indian food delivery portal (Zomato.com) was used, where 50 restaurants on Presence Across Nation (PAN) basis were selected through stratified random sampling. A total of 2530 reviews were collected, scrutinized, and analysed. Using the NVivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub‐themes. Linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub‐themes. Regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. These variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. Similarly, negative sentiments were observed in pricing and hygiene sub‐themes. Restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. Similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost‐prohibitive. This study devised a standardized methodology for analysing vast amounts of online user‐generated content (UGC). Findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.

Reversal of Neuromuscular Blockade Based on Train of Four Response: a Prospective Randomized Controlled Trial

Background and aims: Use of neostigmine to reverse the non-depoloarizing neuromuscular block at the end of the surgery is a standard practice. Reversal with neostigmine based on body weight is still commonly followed. However, neostigmine may affect adversely if used empirically in the absence of residual blockade. This study compares the empirical technique of reversal based on body weight with reversal using neostigmine dose adjusted to train-of-four (TOF) response. Methods: This prospective, double-blinded, randomized controlled trial included 126 patients undergoing surgery under general anaesthesia, lasting for >1 hour. They were randomized into group control receiving weight based reversal (0.05 mg/kg neostigmine) and group study receiving dose determined by TOF response. Signs of residual paralysis after extubation were observed. TOF ratios/count, reversal to extubation time, dose and side-effects of reversal agent were also noted. Results: Patient characteristics were comparable in both groups. Number of patients with signs of residual weakness was less in group study (26/63), but comparable to group control (40/63, p=0.094). Number of patients with TOF≥0.9 at reversal and extubation was significantly high in group study (40/63) than group control (22/63), and number of patients with TOF count <4 were significantly high in group control than group study (17/63 vs 8/63). Overall TOF ratio at reversal and extubation, reversal-extubation time, time taken to reach TOF 0.9 and side-effects were comparable. Conclusion: Reversal of neuromuscular block with neostigmine dose based on TOF ratio is comparable to weight based reversal with respect to postoperative residual weakness, reversal-extubation time and side effects of reversal agent.

The Spring Minimum in Subseasonal 2-m Temperature Forecast Skill over North America

Abstract On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.

Effective adsorption of crystal violet onto aromatic polyimides: Kinetics and isotherm studies.

The motive of this work is to synthesis aromatic polyimides by a two-step poly condensation process and the prepared aromatic polyamides (APIs) is been used as an effective functionalized adsorbent for the removal of carcinogenic crystal violet (CV) from aqueous medium. The adsorption efficiency of the APIs was enhanced by incorporation different functional moieties (varying aromatic dianhydrides with -O-, -(CF3)2-, -(CH3)2-) in the polymer structure. The initial and final concentration of CV was measured using UV-Vis spectrometer. The adsorption process was optimized by varying the parameters such as the effect of solution pH, contact time, initial dye concentration, and adsorbent dosage. Kinetics and isotherms of the adsorption system were appraised using data obtained from effect of contact time and initial dye concentration with corresponding empirical modelling techniques respectively. The evaluated results of the adsorption kinetic studies confirmed that the adsorption of API onto CV is followed a pseudo-second-order kinetic model. The adsorption behaviour and their interactions between APIs and CV are well established. The experimental results of this research output could be confirmed that APIs is a very effective adsorbent for the removal of cationic dye from aqueous.

Nonparametric regression on Lie groups with measurement errors

This paper develops a foundation of methodology and theory for nonparametric regression with Lie group-valued predictors contaminated by measurement errors. Our methodology and theory are based on harmonic analysis on Lie groups, which is largely unknown in statistics. We establish a novel deconvolution regression estimator, and study its rate of convergence and asymptotic distribution. We also provide asymptotic confidence intervals based on the asymptotic distribution of the estimator and on the empirical likelihood technique. Several theoretical properties are also studied for a deconvolution density estimator, which is necessary to construct our regression estimator. The case of unknown measurement error distribution is also covered. We present practical details on implementation as well as the results of simulation studies for several Lie groups. A real data example is also provided.

Predicting Sanding Potential Using Empirical Method in “Ebendo” Field, Niger Delta, Nigeria

Sand formations represent a large fraction of oil and/or gas reservoirs in the world, hence it becomes imperative to determine the competency of the reservoirs to produce sand-free hydrocarbon. This informed the empirical technique adopted in this study to evaluate elastic parameters such as shear modulus (G), bulk compressibility (Cb), shear modulus to bulk compressibility (G/Cb) ratio and Unconfined Compressive Strength (UCS) to determine sand influx in the “Ebendo” Field, Niger Delta, Nigeria. To achieve this goal, seven (7) hydrocarbon-bearing sand units with thicknesses ranging from 12.51 to 48.63 m were identified at depth range of 1884.79 - 3350.15 m across the four (4) wells. These elastic parameters were estimated at the interval of interest. The range of values obtained for G/Cb ratio in EBD (01 and 02) is 1.49 × 1012 - 5.40 × 1012 psi2 while the range of values for G/Cb ratio in EBD (04 and 06) is 0.06 × 1012 - 0.41 × 1012 psi2. This result suggests that EBD (01 and 02) have no potential to sanding while EBD (04 and 06) have a high probability of sanding when compared to the threshold value of 0.8 × 1012 psi2. The production history of the “Ebendo” Field also correlates with the findings of this study. The low values of UCS in EBD (04 and 06) also agrees with the observation. Thus, this study has shown the efficacy of using empirical method as a quick approach to predicting sand production in the “Ebendo” Field and this technique could be used in other fields with similar geological setting.

ON SOME NAMES OF TREES IN WRITTEN MONUMENTS OF THE UDMURT LANGUAGE OF THE 18th CENTURY

In the proposed article, the author refers to some names of trees and shrubs, recorded in the written monuments of the Udmurt language of the 18th century. The forest with all the trees, shrubs and various herbs growing in it has always had and still has invaluable ecological, economic and social significance in human life. The realities of the plant world often belong to the oldest layers of the vocabulary of any language and always arouse the genuine interest of researchers, since their names, which fix the process of knowing and mastering the world of flora, reveal characteristic features of the national mentality. The purpose of this study is to identify the history of written fixation, etymology and ethnocultural features of the Udmurt names of some trees and shrubs: бадь [bad'] ‘willow’, бадяр [bad'ar] ‘maple’, беризь [beriz'] ‘linden’, гузэмпу [guzempu] ‘honeysuckle’ and лемышпу [l'em2špu] ‘cherry’, each of which in one form or another is reflected in the monuments of early Udmurt writing. To achieve the task, an empirical approach, methods and techniques of comparative historical and comparative linguistics were applied. The names of the phytonyms under consideration, found in written documents of the 18th century, are correlated with the names from later sources in the Udmurt language, if necessary, parallels are given from related and unrelated, and in some cases, contacting languages. The etymological analysis made it possible to identify the original names of the analyzed trees and shrubs that arose in the Ural ( бадь ‘willow’, бадяр ‘maple’) and Finno-Permian ( гузэмпу ‘honeysuckle’) epochs, as well as borrowed lexemes from the Tatar ( беризь ‘linden’) and Iranian ( лемышпу ‘cherry’) languages.

Green governance and corporate social responsibility: The role of big data analytics

AbstractThis study considers the role and importance of Big Data analytics (BDA) in green governance and corporate social responsibility, as well as the prospects for their development. The purpose of the research is to determine how BDA can contribute to green governance and corporate social responsibility. In addition, the concepts of green governance and corporate social responsibility are studied in terms of their relationship with the concept of sustainable development. The study suggests that currently BDA is not widely used in the formation of corporate social responsibility rules and mechanisms, but it has great potential for the expansion in this area. At the moment, this potential is not being fully realized due to the lack of both relevant empirical studies and data interpretation techniques. Important Big Data sources include social networks and company reports on corporate social responsibility, which are in the public domain.

The Benefit of Cross-Border Investments in the Chinese Emerging Venture Capital and Private Equity Market

We compare the success rates of cross-border and domestic venture capital (VC) and private equity (PE) investments to determine whether foreign investors improve the development of an emerging VC/PE market. We examine a sample of 22,602 VC and PE investments made in 14,618 privately held Chinese companies between 2007 and 2016, analyzing the impact of sourcing capital abroad on the transaction outcome. We apply various empirical techniques to provide evidence that cross-border investments significantly increase the likelihood of a successful exit. Our results are robust with respect to syndication, reverse causality, and competing exit routes. This suggests that foreign investors can use their deep, international transaction experience, gained in mature markets, to support their investees and the development of emerging VC/PE markets.JEL Codes: G24, G32, G34

Deep learning: A taxonomy of modern weapons to combat Covid‐19 similar pandemics in smart cities

SummaryThe Covid‐19 pandemic has affected many lives over the past year. In addition to the enormous health cost, the necessary lockdowns and government‐mandated suspension to prevent the spread of the virus had a huge economic impact. The new challenges in 2021 were combating new virus mutations and providing effective vaccines globally. Artificial intelligent (AI) and machine learning have made significant improvements in many different applications during the last decades. One of the advanced and robust technologies in machine learning is deep learning (DL), which can be employed to help prevent initial infections and detect and monitor their progress and side effects. Fast and accurate Covid‐19 infection detection and treatment of suspected patients is essential to make better decisions, ensure treatment, and even save patients' lives. Modern technologies are required to achieve these objectives and create a sustainable society. This article presents a taxonomy in DL algorithms to cover both the technical novelties and empirical results techniques for Covid‐19 in smart cities. In this regard, (i) we demonstrate possible DL algorithms capable of combating Covid‐19; (ii) we propose an up‐to‐date perspective of DL algorithms in social prevention and medical treatment; and (iii) we identify the challenges in combating Covid‐19 outbreaks.

Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity

Type 2 diabetes mellitus (T2DM) is reported to cause widespread changes in brain function, leading to cognitive impairments. Research using resting-state functional magnetic resonance imaging data already aims to understand functional changes in complex brain connectivity systems. However, no previous studies with dynamic causal modelling (DCM) tried to investigate large-scale effective connectivity in diabetes. We aimed to examine the differences in large-scale resting state networks in diabetic and obese patients using combined DCM and graph theory methodologies. With the participation of 70 subjects (43 diabetics, 27 obese), we used cross-spectra DCM to estimate connectivity between 36 regions, subdivided into seven resting networks (RSN) commonly recognized in the literature. We assessed group-wise connectivity of T2DM and obesity, as well as group differences, with parametric empirical Bayes and Bayesian model reduction techniques. We analyzed network connectivity globally, between RSNs, and regionally. We found that average connection strength was higher in T2DM globally and between RSNs, as well. On the network level, the salience network shows stronger total within-network connectivity in diabetes (8.07) than in the obese group (4.02). Regionally, we measured the most significant average decrease in the right middle temporal gyrus (-0.013 Hz) and the right inferior parietal lobule (-0.01 Hz) relative to the obese group. In comparison, connectivity increased most notably in the left anterior prefrontal cortex (0.01 Hz) and the medial dorsal thalamus (0.009 Hz). In conclusion, we find the usage of complex analysis of large-scale networks suitable for diabetes instead of focusing on specific changes in brain function.

Financial markets' responses to COVID-19: A comparative analysis

We examine the effects of covid-19 infections and deaths on bond yields and stock returns and possible contagion effects across markets using data for the period 01.07.2019 through 30.06.2020. Deploying different empirical techniques, we find, unlike some papers in the literature that group countries and run panel regressions, that the effects of covid-19 on the financial markets differed by country depending on the way the outbreak was managed. First, covid-19 infections generally had a fleeting effect on stock returns and bond yields. Second, in countries that experienced a covid-19 health crisis during the study period (e.g., UK and Italy), covid-19 deaths were associated with bond yield increases. For countries employing less restrictive ways of managing the disease (e.g., Sweden), the covid-19 outbreak elicited investor fears of near-term burgeoning infections and possible concomitant economic effects, which caused bond yields to rise.

Assessing Supply Chain Management Ambidexterity, Integration of Knowledge Management Use and User Satisfaction

This study aims to address the knowledge gap in supply chain management between large corporations and small and medium enterprises by investigating the ambidexterity of supply chain management in small and medium enterprises. The study also focuses on the integration of knowledge management, user satisfaction, and supply chain management ambidexterity as its main novelty. A quantitative empirical technique was used, utilizing online data collection evaluated through partial least squares analysis with a sample of 372 reliable data points. This study presents five hypotheses, and the results of the Smart PLS 4 analysis indicate that all four theories have positive and significant influences. In addition, the results can be used for research in supply chain management and knowledge management, as well as for making plans to improve the quality of managed organizations. Doi: 10.28991/HEF-2022-03-03-08 Full Text: PDF

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha–1yr−1.

Study of temperature-dependent elastic, electronic, optical, and lattice dynamical properties of AlxIn1-xPySb1-y alloys

The electronic, optical, elastic, and lattice dynamical properties of AlxIn1-xPySb1-y alloys lattice-matched to InP, GaSb, and InAs substrates have been studied. We present a detailed investigation of the temperature dependence of these properties using the empirical pseudopotential technique (EPM) modified with virtual crystal approximation (VCA). There is a great match between our estimated results and accessible data in the literature for the binary compounds of the constituent which reinforces the case for the alloys under consideration. Due to the lack of the temperature dependence of the considered properties for the investigated alloys, the findings of this study appear to be potentially valuable as a reference.

Influence of segmentation approaches on the before-after evaluation of engineering treatments: A hypothetical treatment approach

The segmentation of highways is a fundamental step in estimating crash frequency models and conducting a before-after evaluation of engineering treatments, but the effects of segmentation approaches on the engineering treatment evaluations are not known very well. This study examined the effects of segmentation approaches on the before-after evaluation of engineering treatments. In particular, this study evaluated four segmentation approaches by applying the Empirical Bayes technique to a dataset for which the ground truth was known. Four segmentation approaches included Highway Safety Manual (HSM), Fixed (kilometre post), Fisher’s, and K-means segmentation. This study utilized a 440 km stretch of rural two-lane two-way highway in Queensland, Australia, to prepare a dataset with known ground truth. The treatment under evaluation was a hypothetical treatment, which should yield a crash modification factor (CMF) of 1. For assigning hypothetical treatment, a total of fifteen datasets were prepared, including ten datasets based on the random assignment and five datasets based on the hotspot identification method. Following the before-after evaluation using the Empirical Bayes technique, the results showed that HSM and Fixed segmentation approaches predict the ground truth in both dataset types. From random assignment datasets, the estimated CMFs using HSM, Fixed, Fisher’s, and K-means segmentation approaches deviated from the true CMF (i.e., 1) by 2.32 %, 5.30 %, 6.08 %, and 8.62 %, respectively. In the case of hotspots, the corresponding deviations of CMFs were 8.57 %, 9.37 %, 28.84 %, and 35.43 %, respectively. Overall, HSM segmentation best identified the actual treatment effect, followed by the Fixed segmentation. If the variables to define homogeneity for HSM segmentation are limited, then Fixed segmentation can yield reliable crash modification factors from the before-after treatment evaluations than the crash-based segmentation approaches.