Economic Applications Research Articles

Facile and Simple Post Treatment Ball Milling Strategy for the Production of Low-Cost TiO2 Composites with Enhanced Photocatalytic Performance and Applicability to Construction Materials.

A facile and cost-effective approach assisted by ball milling (BM) of commercial titanium dioxide (TiO2), has been utilized to develop cheaper and efficient construction materials. At least three of the commercial and cheaper TiO2 samples (BA01-01, BA01-01+ and R996, designated as A1, A4 and R1, respectively) were selected and subjected to BM treatment to enhance their photocatalytic efficiencies, if possible. It was noted, that the samples A1, A4 and R1 were typical composites of TiO2 and calcium carbonate (CaCO3) and contained varying proportions of anatase, and rutile phases of TiO2 and CaCO3. Two of the highly efficient commercial TiO2 samples, Degussa P25 (simply designated as P25) and ST01 (Ishihara Ind.) were selected for making benchmark comparisons of photocatalytic efficiencies. The BM treated TiO2 samples (designated as TiO2-BM with respect to A1, A4 and R1) were evaluated for photocatalytic efficiencies both in both aqueous (methylene blue (MB)) and gaseous (NOx) photodegradation reactions. Based on detailed comparative investigations, it was observed that A1-BM photocatalyst exhibited superior photocatalytic performances over A4-BM and R1-BM, towards both MB and NOx photodegradation reactions. The difference of NOx photodegradation efficiency between the mortar mixed with A1-BM and that mixed with ST01, and P-25 at 15% were 16.6%, and 32.4%, respectively. Even though the mortar mixed with A1-BM at 15% composition exhibited a slightly lower NOx photodegradation efficiency as compared to mortar mixed with the expensive ST01 and P-25 photocatalysts, the present work promises an economic application in the eco-friendly construction materials for air purification considering the far lower cost of A1. The reasons for the superior performance of A1-BM were deduced through characterization of optical properties, surface characteristics, phase composition, morphology, microstructure and particle size distribution between pristine and BM treated A1 using characterization techniques such as diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction analysis, field emission scanning electron microscopy and particle size analysis.

In-situ production of lactate driving the biotransformation of waste activated sludge to medium-chain fatty acid

Medium-chain fatty acids (MCFAs) have drawn great attention due to their high energy density and superior hydrophobicity. Waste activated sludge (WAS) has been documented as a renewable feedstock for MCFAs production via anaerobic fermentation. However, MCFAs production from WAS depends on exogenous addition of electron donor (ED, e.g., lactate) for chain elongation (CE) bioprocess, which results in increased economic cost and limited practical application. In this study, a novel biotechnology was proposed to produce MCFAs from WAS with in-situ self-formed lactate by inoculating Yoghurt starter powder containing with Lactobacillales cultures. The batch experimental results revealed that the lactate was in-situ generated from WAS and the maximum production of MCFAs increased from 1.17 to 3.99 g COD/L with the increased addition of Lactobacillales cultures from 6✕107 to 2.3✕108 CFU/mL WAS. In continuous long-term test over 97 days, average MCFA production reached up to 3.94 g COD/L with a caproate yield of 82.74% at sludge retention time (SRT) 12 days, and the average MCFA production increased to 5.87 g COD/L with 69.28% caproate and 25.18% caprylate at SRT 15 days. A comprehensive analysis of the metagenome and metatranscriptome demonstrated that the genus of Lactobacillus and Streptococcus were capable of producing lactate from WAS and upgrading to MCFAs. Moreover, another genus, i.e., Candidatus Promineofilum, was firstly revealed that it might be responsible for lactate and MCFAs production. Further investigation of related microbial pathways and enzyme expression suggested that D-lactate dehydrogenase and pyruvate ferredoxin oxidoreductase contributed to lactate and acetyl-CoA production, which were the crucial steps for MCFAs generation and were most actively expressed. This study provides a conceptual framework of MCFAs from WAS with endogenous ED, potentially enhancing the energy recovery from WAS treatment.

Shale porosity measurement by the saturated oil method: Removing the contribution from oils dissolved in kerogen

Shale porosity measurements have crucial scientific and economical applications in unconventional petroleum systems. As a standard technique, liquid saturation methods, including water saturation (WS) and oil saturation (OS), have been widely used to measure the porosity of many rock types. For clay-rich shale reservoirs with high organic matter content, it is well known that the WS method may cause clay swelling and induce structural changes in the pore system. The OS method affects the accuracy of porosity measurements because of some of the oil being dissolved by kerogen within the shale; however, this has not received sufficient research attention. In this study, we compare the previously reported and newly tested OS porosities with helium (He) expansion porosity. Results show that OS porosity generally exceeds the He porosity. Furthermore, the higher the total organic carbon (TOC) content and lower the maturity of shale, the greater the difference between the OS and helium porosities. When using the OS method, the effect of kerogen-dissolved oil causes an overestimation of the shale porosity by ∼30%. To the best of our knowledge, this is the first time to note the kerogen-dissolve oil effects on OS porosity. Herein, we propose a new, simple, and effective correction method for estimating OS porosity that involves subtracting the kerogen-dissolved oil content from raw OS porosity. In addition, the quantification model of kerogen-dissolved oil capacity is established, taking into account the abundance and maturity of organic matter. Taking the He porosity as the benchmark, the absolute error of the corrected OS porosity does not exceed 1% and the average relative error is only ∼10%. The obtained results can help improve the accuracy of shale porosity evaluation methods.

Practical machine learning: Forecasting daily financial markets directions

Financial time series prediction has many applications in economics, but producing profitable strategies certainly has a special place among them, a daunting challenge. Statistical and machine learning techniques are intensively researched in the search for a holy grail of stock markets forecasting. However, it is not clear to prospecting researchers how good those popular models are regarding useful predictions on a real scenario. This paper contributes to that discussion, providing decisive evidences contrary to the use of basic out-of-the-box models, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF) and Naive-Bayes (NB). Results consider optimistic and unreal variables often found in literature, as well as a more close-to-real simulation of the models usage. Specifically, current day closing prices direction forecasting results are contrasted with those on next day forecasts. As expected, when forecasting the current day, accuracy is almost perfect. However, when used to forecast next day closing direction, with a strict data separation policy and without direction or snooping bias, ANN, SVM, RF and NB produce results essentially equal to random guessing. The main achieved result is the demonstration of how a machine learning approach would fare in a support decision system for forecasting short-term future market direction, regardless of the level of market development, considering more than 100 securities in a 10 years period. Consequences for algorithmic trading relate to discouraging usage of the considered models as implemented here. On a more abstract sense, this paper presents more evidence to the Efficient Market Hypothesis (EMH).

Unsupervised Machine Learning for GNSS Reflectometry Inland Water Body Detection

Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and position of water bodies are crucial to the ecosystems. Mapping water bodies at a global scale is a challenging task due to the global variety of terrains and water surface. However, the sensitivity of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to different land surface properties offers the potential to detect and monitor inland water bodies. The extensive dataset available in the Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, is used in our investigation. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we show its capability of detecting and mapping inland water bodies. Both bistatic radar cross section (BRCS) and signal-to-noise ratio (SNR) can be used to detect, identify, and map the changes in the position and extent of inland waterbodies. We exploit the potential of unsupervised machine learning algorithms, more specifically the clustering methods, K-Means, Agglomerative, and Density-based Spatial Clustering of Applications with Noise (DBSCAN), for the detection of inland waterbodies. The results are evaluated based on the Copernicus land cover classification gridded maps, at 300 m spatial resolution. The outcomes demonstrate that CYGNSS data can identify and monitor inland waterbodies and their tributaries at high temporal resolution. K-Means has the highest Accuracy (93.5%) compared to the DBSCAN (90.3%) and Agglomerative (91.6%) methods. However, the DBSCAN method has the highest Recall (83.1%) as compared to Agglomerative (82.7%) and K-Means (79.2%). The current study offers valuable insights and analysis for further investigations in the field of GNSS-R and machine learning.

The contribution of economics to sustainable tourism research

This paper aims to identify and explore the interlinkages between sustainable tourism research and the study of economics with the goal of identifying main themes and gaps in the economics of sustainable tourism. It contributes to identifying the unique perspective that economics has contributed and can still contribute towards the sustainable tourism discourse. This review article retrieved and analysed 5593 papers using scientometric analysis. The findings indicate that economic theory and/or methods have had a clear influence on some, but not all clusters of sustainable tourism research. Clusters that are dominated by economic theory and methods include economic growth, climate change, the sharing economy and evolutionary framework. Current themes in economics, including migration, declining labour shares and rising inequality have not attracted attention in sustainable tourism research. Additionally, a much clearer focus on inter- and intragenerational effects, shadow prices and dealing with externalities deserves further scrutiny. Gaps also exist in terms of laboratory studies, field experimental studies and the application of behavioural economics. This article also launches the special issue – the Economics of Sustainable Tourism – which includes ten blind-peer-reviewed papers.

Explanatory efficiency: A framework for analyzing the dynamic properties of explanatory games

In this paper I will discuss the problem of evaluating dynamic properties of the procedural rules that govern science. I will propose a novel framework for evaluating dynamic properties of such rules. This framework is based upon an analogy from New Institutional Economics. I will argue that the concept of ‘adaptive efficiency’, as it has been developed by Douglass North, solves a problem in economics that is analogous to the problem of evaluating dynamic properties of the procedural rules that govern science. I will propose to apply the main ideas underlying this concept to Chrysostomos Mantzavinos’s theory of Explanatory Games. Based on these ideas, I will develop the concept of ‘explanatory efficiency’. This concept is meant to provide the means to evaluate the dynamic properties of Explanatory Games. I will argue that the proposed analogy also motivates more general applications of New Institutional Economics to traditional problems of philosophy of science.

Research on and Design of an Electric Drive Automatic Control System for Mine Belt Conveyors

Conveyor belts are widely used in ore transportation in large-scale mines for their long transportation range, high safety, and strong economic applicability. Coal mine belt conveyors are not only traditional, simple mechanical conveying devices but also automatic control system operating devices that integrate safety, stability, and low power consumption. In the process of coal mining, a conveyor belt control system also needs to be closely integrated with modern industrial systems and information systems, which greatly improves its work efficiency. The purpose of this article is to improve the methods for designing automatic control systems for electric motors in order to obtain mechanical characteristics close to a constant power line, which would ensure the reliable operation of belt conveyors. An automatic control system was designed based on the controller Siemens S7-1200; then, a mathematical model of an automated electric drive was developed. Based on the mathematical model, a simulation model of an automatic electric drive was built, and the modes were modeled. After designing, the obtained plots of transients completely corresponded to the required transients, which means that the Siemens frequency converter automatic control system (ACS) parameters were calculated quite accurately and the conveyor electric drive met all requirements.

A generalization of the increasing generalized failure rate unimodality condition

In this paper, we study unimodality conditions for distributions that describe markets with stochastic demand. Such conditions naturally emerge in the analysis of game-theoretic models of market competition (Cournot games) and supply chain coordination (Stackelberg games). We express the price elasticity of expected demand in terms of the mean residual life (MRL) function of the demand distribution and characterize optimal prices or equivalently, points of unitary elasticity, as fixed points of the MRL function. This leads to economic interpretable conditions on the demand distribution under which such fixed points exist and are unique. We find that markets with increasing price elasticity of expected demand that eventually become elastic correspond to distributions with decreasing generalized mean residual life (DGMRL) and finite second moment. DGMRL distributions strictly generalize the widely used increasing generalized failure rate (IGFR) distributions. We further elaborate on the relationship of the two classes, link their limiting behavior at infinity and examine moment and closure properties of DGMRL distributions that are important in economic applications. The DGMRL unimodality condition is useful in the analysis of optimal decisions under uncertainty in settings that are not covered by the widely-used IGFR condition; thus, it can be of broader interest to the game-theory and operations research literature.

Characteristics of the Low Molecular Weight Metabolome of Potamogeton Natans L. (Potamogetonaceae) from Lakes of Different Trophic State (Karelian Isthmus, Northwest Russia)

The qualitative and quantitative component composition of low molecular weight volatile organic compounds (VOCs) of the essential oil of the floating-leaf pondweed (Potamogeton natans L., Potamogetonaceae family) growing in various lakes of the Karelian Isthmus (North-West of the Russian Federation) in the fruiting stage was investigated in detail for the first time by gas chromatography-mass spectrometry. The low molecular weight metabolome (LMWM) of P. natans contained 138 components, 128 of which were identified. VOCs belonging to esters, alcohols, and various functional groups dominated the LMWM of floating leaf pondweed from mesotrophic and eutrophic lakes. A significant similarity was found between the component composition of VOCs of floating leaf pondweed from mesotrophic and eutrophic lakes. Many of the substances found in the LMWM of P. natans can be attributed to biologically active compounds. This opens up prospects for the use of this plant (particularly manool and ecdysteroids from its LMWM) for various economic applications as a valuable natural raw material. Due to the characteristic of the floating leaf pondweed's substantial resistance of its LMWM to the factor of the trophic status of the lakes, it is feasible to use it as an ecological indicator of significant disruptions in aquatic environments.

The Rise of Bioinputs in the Brazilian Agri-Industry: Trends, Cases and Hurdles

This article explores the role of bioinputs in Brazilian agriindustry, a sector that is increasingly adopting biological inputs to improve crop protection and productivity. The major class of bioinputs, biopesticides, are derived from natural sources such as fungi, bacteria, nematodes, insects and viruses, and offer several advantages over conventional pesticides, such as lower environmental and health impacts, higher specificity and compatibility with integrated pest management. The article presents economic data, registration statistics and application examples of different types of biopesticides in Brazil. It also discusses the challenges and opportunities for biopesticide development and adoption in Brazil, taking into account the legislative framework, the research and development programs, the quality standards and integration with conventional inputs. The article concludes by emphasizing the importance of bioinputs for tropical agriculture and suggesting some areas for future research.

The role of the iodide/iodine couple as a redox mediator in the dissolution of chalcopyrite in sulfate media. An electrochemical study

A detailed study has been made of the effect of the addition of iodine/iodide on the electrochemistry of the dissolution of chalcopyrite in acidic sulfate media containing iron(III). Mixed potential measurements have revealed that the potential is higher with iodine as an oxidant compared to iron(III) although the solution potentials are similar. Potentiostatic measurements in the absence of the oxidants at the measured mixed potentials have shown that chalcopyrite should dissolve some five times faster with iodine as the oxidant. Linear sweep voltammetry has confirmed the much higher reversibility of the iodine/iodide couple on a chalcopyrite surface than that of the iron(III)/iron(II) couple. Limited dissolution tests have confirmed that chalcopyrite dissolves more rapidly with iodine as the oxidant at rates similar to those predicted from the electrochemical measurements. A preliminary spectrophotometric study of the kinetics of the oxidation of iodide by iron(III) in sulfate media has shown that the rate is significantly slower than previously assumed and that the reaction occurs in two stages with tri-iodide as the initial product that is slowly oxidised to iodine. At the low concentrations necessary for economic application, iodide ions do not effect the rate of oxidation of iron(II) by dissolved oxygen in chloride solutions. It is suggested that the use of iodide as a redox mediator during heap leaching of chalcopyrite ores by acidic ferric sulfate lixiviants is unlikely to change current problems with the slow rate of dissolution under ambient conditions.

Quantum monte carlo for economics: Stress testing and macroeconomic deep learning

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenges in doing so. We provide a detailed introduction to quantum computing and especially the QMC algorithm. Then, we illustrate how to formulate and encode into quantum circuits (a) a bank stress testing model with credit shocks and fire sales, (b) a neoclassical investment model solved with deep learning, and (c) a realistic macro model solved with deep neural networks. We discuss potential computational gains of QMC versus classical computing systems and present a few innovations in benchmarking QMC.

Теория экономического развития: эволюция подходов и современная парадигма

Introduction. The relevance of scholarly justification of approaches to economic development is caused by the insufficient study of the economic development phenomenon, its nature and qualitative characteristics of the factors influencing the implementation of qualitative structural changes in the economy of the country. Theoretical analysis. The article summarizes the evolution of economic thought and historical transformation of attitudes concerning the study of economic development. Scientific approaches and studies of leading foreign and domestic scholars, which constituted the theoretical and methodological basis of the economic development theory, are consistently presented. The tendencies that served the modern theories of economic development formation are considered. Empirical analysis. Based on statistical data analysis the difference between the categories of «economic growth» and «economic development» is justified. The concept of economic complexity as a phenomenon that contributes to the qualitative transformation of the economic development factors is considered. The dynamics of the economic complexity index in the Russian Federation is analyzed. Results. The analysis of qualitative changes in the economic development factors in the modern mixed economy is conducted. The relevance of sustainable development theory and the concept of economic complexity application in the study of the economic development of states are demonstrated. The criteria of economic development in modern socio-economic conditions are formulated.

A NOVEL APPROACH TO PREDICTIVE ACCURACY TESTING IN NESTED ENVIRONMENTS

We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g., stationary and local to unit root). A local power analysis also establishes their ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.

Sales Strategy, Risk Investment and Property Rights Allocation: Analysis Based on Endowment Effect

The endowment effect suggests that people value what they have now and the status quo, and thereby want to avoid losing what they have or just step out of the comfort zone. Several scholars have identified and demonstrated that people’s utility is reduced by a given quantity of loss more than it is increased by an equal amount of gain. Due to the endowment effect, the decision-making process is out of balance, and are more likely to avoid harm rather than pursue profits. This paper reviews the concepts, experiments and applications of endowment effect and loss aversion theory in behavioral economics. Furthermore, it explains endowment effect using three economic applications of sales strategy, risk investment and property rights allocation. In all the three economic applications, people’s economic behavior totally conforms to the endowment effect: people buy things that they don’t need, make the investment irrationally, or bring trade frictions. Due to its commonness, endowment effect has been widely tested and applied, attracting heated discussion among academia research and policy regulators. In this sense, this paper also provides us a good understanding of endowment effect and behavior economics.

Weighted OWA (Ordered Weighted Averaging Operator) Preference Aggregation for Group Multicriteria Decisions

Group decision making is an integral part of operations and management functions in almost every business domain with substantial applications in finance and economics. In parallel to human decision makers, software agents operate in business systems and environments, collaborate, compete and perform algorithmic decision-making tasks as well. In both settings, information aggregation of decision problem parameters and agent preferences is a necessary step to generate group decision outcome. Although plenty aggregation information approaches exist, overcomplexity of the underlying aggregating operation, in most of them, is a drawback, especially for human based group decisions in practice. In this work we introduce an aggregation method for group decision setting, based on the Weighted Ordered Averaging Operator (WOWA). The aggregation is applied on decision maker preferences, following the majority concept to generate a unique set of preferences as input for the decision algorithm. We present the theoretical construction of the model and an application at a group multicriteria assignment decision problem, along with detailed numerical results. The proposed method contributes in the field, as it offers a novel approach that is simple and intuitive, and avoids overcomplexity during group decision process. The method can be also easily deployed into artificial environments and algorithmic decision-making mechanisms.

Capacity and Mechanisms of Pb(II) and Cd(II) Sorption on Five Plant-Based Biochars

China is a large agricultural country that produces a large amount of crop straw every year. Thus, the development of cost-effective and economic application of invasive plants is warranted. Biochars derived from crop straw have been proven to be promising for adsorbent materials. However, less studies have focused on biochar derived from different types of crop straw as adsorbent under the same conditions to compare their adsorption performance. Here, we characterized the five biochars in the same system (600 °C). In results, GBC has higher ash content, pH, CEC, specific surface area, mineral composition and oxygen-containing functional groups. The adsorption kinetics can be explained adequately by the pseudo-second-order model and the Langmuir model, indicating that the adsorption behavior of the biochar is both physical adsorption and chemical adsorption; the adsorption process includes complexation reaction, cationic π bond, ion precipitation and electrostatic adsorption. In conclusion, GBC exhibited higher metal equilibrium adsorption capacities (125 mg·g−1 for Pb2+, 29 mg·g−1 for Cd2+). The solution pH, biochar dosing, pyrolysis temperature and the properties of these heavy metals were responsible for adsorption capacity, thus showing stronger affinity and better adsorption effect. Our results are important for the selection and utilization of plant-based biochar for different heavy metals.

Economic Decision-Making Using Rough Topological Structures

This paper suggests new approximations that are inspired by topological structures. The primary goal of this work is to define four neighborhoods resulting from a binary relation. Thus, we have four distinct techniques for approximating rough sets. The suggested approaches represent topological generalizations of the previous works. The characteristics and connections of these approaches are investigated. For the sake of the application, we provide some useful examples to compare our techniques to those in the published literature. The merit of the current technique is to obtain a more accurate decision for the problems in which these cases are the appropriate frame to describe them; for instance, machine learning (ML, for short) applications of finance, etc. To demonstrate this fact, an economic application is proposed. We employ the proposed technique in defining accurate decisions to identify the growth of countries. An algorithm for decision-making problems is proposed and tested on fictitious data to compare our methods with the previous approaches.

Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning

The purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, “Sustainable Energy”, “DT in Health”, “E-Government”, “DT in Education”, and “Supply Chain” emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. “Sustainable Energy” and “Supply Chain” have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.