Real Gold Research Articles

In the realm of microbiology, a quality stool sample is as valuable as gold

Abstract In the microbiology curriculum a section is dedicated to parasitology in both Undergraduate and Post-graduate. Due to Deworming programs, decrease in the detection of parasite ova/cyst detection in stool samples. In current times, many institutes don’t have various specimens (like blood, and stool) with the presence of parasites. They are demonstrating the parasites using either photographs or videos. India is endemic for malarial parasites and they be found in blood specimens. However, to find out various helminth ova, cysts or trophozoites in human stool specimens is much difficult. Without practice and training, students can’t be an expert in parasitology. Its paucity should not be a hindrance to adequate and sound training and short-term solutions should not be encouraged. It may be awkward to hear this, but lately a desired stool sample is the real gold in microbiology.

Climate Risks and Real Gold Returns over 750 Years

Using data that cover the annual period from 1258 to 2023, we studied the link between real gold returns and climate risks. We documented a positive contemporaneous link and a negative predictive link. Our findings further show that the predictive link historically gave rise to significant out-of-sample forecasting gains. The positive contemporaneous link is consistent with the view that investors viewed gold as a safe haven in times of elevated climate risks. The negative predictive link, in turn, is consistent with an overshooting scenario in which the real gold price overshot in response to climate risks, only to return subsequently to a lower value. Our findings should provide important implications for investors and policymakers, given that our analysis covered the longest possible data sample involving the gold market, and hence, was independent of any sample selection bias.

Fire testing real gold: political following and shareholder-oriented ESG

Fire testing real gold: political following and shareholder-oriented ESG

Developing deep learning surrogate models for digital twins in mineral processing – A case study on data-driven multivariate multistep forecasting

The escalating demand for environmental and social sustainability underscores the critical need for large industries such as mining and metallurgy to function optimally. Achieving optimal operation of a mineral processing plant requires timely access to information and rapid implementation of corrective actions. Essential information, such as current and future production, plays a pivotal role in enhancing process efficiency by facilitating adjustments in key aspects, including feed, control and operating variables. This study delves into the application of deep learning algorithms to forecast the gold concentrate grade of a flotation process half an hour in advance, a valuable tool for short-term decision-making, e.g., with regard to optimising the operation and process control. Multivariate time series data from a real gold processing plant was acquired through the process instrumentation and control system and augmented with simulated data at 5-minute intervals over 236 days. Surrogate models based on LSTM, GRU and CNN architectures were developed and analysed for predicting the gold grade one to six steps ahead. Evaluation of the testing dataset indicates that the CNN exhibits superior performance, achieving an average error reduction of 36 %, 45 % and 35 % for the performance metrics RMSE, MAE and MAPE respectively compared to the baseline model. These findings reveal the high potential of deep learning surrogate methods for accurately forecasting process outputs, enabling effective data-driven process control and optimisation strategies.

Efficiency and controllability of stochastic boolean function generation by a random network of non-linear nanoparticle junctions

Amid efforts to address energy consumption in modern computing systems, one promising approach takes advantage of random networks of non-linear nanoscale junctions formed by nanoparticles as substrates for neuromorphic computing. These networks exhibit emergent complexity and collective behaviors akin to biological neural networks, characterized by self-organization, redundancy, and non-linearity. Based on this foundation, a generalization of n-inputs devices has been proposed, where the associated weights depend on all the input values. This model, called receptron, has demonstrated its capability to generate Boolean functions as output, representing a significant breakthrough in unconventional computing methods. In this work, we characterize and present two actual implementations of this paradigm. One approach leverages the nanoscale properties of cluster-assembled Au films, while the other utilizes the recently introduced Stochastic Resistor Network (SRN) model. We first provide a concise overview of the electrical properties of these systems, emphasizing the insights gained from the SRN regarding the physical processes within real nanostructured gold films at a coarse-grained scale. Furthermore, we present evidence indicating the minimum complexity level required by the SRN model to achieve a stochastic dynamics adequate to effectively model a novel component for logic systems. To support our argument that these systems are preferable to conventional random search algorithms, we discuss quantitative criteria based on Information-theoretic tools. This suggests a practical means to steer the stochastic dynamics of the system in a controlled way, thus focusing its random exploration where it is most useful.

Evaluation of Macroeconomic Variables in the Indian Stock Market Using TOPSIS Method

Asia's largest stock market, Indian stock market holds a pivotal position in the country's economy. Operating primarily through it is deeply influenced by government policies, global economic trends, corporate performance, and investor sentiment. These factors collectively shape the dynamics of the Indian economy as reflected in the market. Despite periodic fluctuations, the market has witnessed significant growth over the years, attracting both domestic and international investors. It offers diverse investment opportunities across various sectors such as technology, finance, pharmaceuticals, and manufacturing. Regulatory bodies like maintaining transparency and fairness in market procedures. the Indian stock market, conducting research holds significant importance. It serves as the cornerstone for making well-informed decisions, providing insights into market trends, company performance, and economic indicators. Thorough research empowers investors to identify profitable opportunities, mitigate risks, and enhance the long-term growth potential of their portfolios. Moreover, it facilitates understanding and compliance with regulatory changes and market dynamics, enabling investors to stay ahead of the curve. Essentially, research empowers investors in the Indian stock market, instilling confidence, minimizing risks, and maximizing opportunities. The TOPSIS ranking system, augmented with weighted averages and ambiguity comparisons, is commonly utilized. One approach involves addressing uncertainties to diminish ambiguity and adjusting both the weight and character of responses simultaneously. In TOPSIS, multiple responses are employed to adopt a holistic global perspective Mutual Funds, Exchange-Traded Funds (ETFs), Real Estate Investment Trusts (REITs), Gold and Precious Metals, Direct Equity Investments, Derivatives, Fixed Income Instruments, Portfolio Management Services (PMS) and Cryptocurrencies. Market Indices, Risk Management and Investor Sentiment. the Ranking of the Indian Stock Market. Direct Equity Investments got the first rank whereas the Real Estate Investment Trusts (REITs) have the Lowest rank.

The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models

The spread of the coronavirus has reduced the value of stock indexes, depressed energy and metals commodities prices including oil, and caused instability in financial markets around the world. Due to this situation, investors should consider investing in more secure assets, such as real estate property, cash, gold, and crypto assets. In recent years, among secure assets, cryptoassets are gaining more attention than traditional investments. This study compares the Bitcoin market, the gold market, and American stock indexes (S&P500, Nasdaq, and Dow Jones) before and during the COVID-19 pandemic. For this purpose, the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets. Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin, gold, and stock markets. In particular, we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period. This paper provides practical impacts on risk management and portfolio diversification.

Features of Gold Placer Formation in the Ukrainian Crystalline Shield And Southern Ukraine

The presence of numerous native gold deposits of the Ukrainian Crystalline Shield (UCS), as well as the spread of placer gold manifestations, determines the high probability of the formation here of gold placers with industrial parameters. The main factors and geological conditions that contribute to the formation of gold placers are manifested at the UCS. An important reason for the failures of previous geological works is the outdated scientific and methodological platform, which involved the search for typical gold-bearing deposits with metal of gravity size classes. They did not take into account the possibility of significant movement of placer gold in the geologically clay water flow and the formation of long-distance placers. The traditional Schlich method used in the study did not make it possible to determine the actual gold content when particles with a size smaller than 0.25 mm predominate in the sample, which is precisely the particle size of the primary sources of the UCS. The applied means of research could not be effective in the absence of the necessary analytical base for determining the gold content, modern methods of studying the geological structure and tectonics of the territories. The negative results did not provide any real grounds for further specialized large-scale research and prospecting for placer gold. Today, it is getting obvious that placer gold of small and fine classes can easily move over considerable distances in the water flow and form accumulations not only in boulder-pebble material, but also in pelitic sediments. Due to repeated block movements, erosion and redepositing of gold-bearing accumulations in secondary reservoirs could occur. In this aspect, neotectonic studies (using materials from space surveys) become the main predictive and prospecting method for identifying promising areas of placer gold concentration in the region. When carrying out fieldwork, drilling and modern methods of laboratory analysis of gold samples should be used. The results of many years of research give hope for the discovery of real placer gold deposits in the Northern Black Sea region in the near future.

An analysis of the determinants of money laundering in the United Arab Emirates (UAE)

PurposeIt is widely argued that money laundering (ML) is not a new phenomenon and the pervasiveness of ML is associated with some severe economic, social and political costs. Due to the lack of studies on the ML’s issue in the UAE, this study aims to examine the determinants of ML in the country between 1975 and 2020.Design/methodology/approachThe autoregressive distributed lag bounds testing results demonstrate the presence of long-run relationship between ML and the selected macroeconomics variables. The analysis is validated by the dynamic ordinary least squares, the fully modified ordinary least squares and the canonical co-integration regression estimators.FindingsThe estimation result reveals that while the real estate market, outflow of money, arms procurement and size of the underground economy influences the size of ML positively, gold trade, the level of financial development and the size of economic activities are negatively associated with ML, both in the short- and long-run.Originality/valueUp to date from a country-level analysis, no study has been devoted to the ML in UAE, except for Aljassmi et al. (2023). To the best of the authors’ knowledge, this study is the first to investigate the determinants of laundered money in the UAE economy. Based on these outcomes, strategies and measures which will deter the laundering of illicit funds through the real estate and gold market, remittance system, financial system and arms procurement contracts in the UAE are recommended.

A comparison of ligament geometries in real and computer-generated nanoporous gold based on cross-section descriptors

Nanoporous gold (np-Au) exhibits a bi-continuous network structure of nanoscale pores and solid ligaments, which form a random 3D network. Geometrical descriptors are developed that allow for characterization, statistical analysis and comparison of a large number of ligaments in real and computer generated np-Au structures. In addition to the voxel resolution and ligament length, the cross-sections of the ligaments are described by roundness, circularity, and compactness. The structures are analyzed in initial and coarsened conditions for various solid fractions. Although the structures do not change topologically, the results confirm significant changes in the geometry during coarsening. It is found that after coarsening the computer-generated structures compare much better to tomographic reconstructions of annealed np-Au structures, although there are still differences in the free ligament length. Therefore, in addition to the topological similarity, similarity of geometrical descriptors is found to be equally important for deriving predictive models based on computer-generated structures.

Evaluation of Macroeconomic Variables Indian Stock Market using TOPSIS Method

Asia's largest stock market, Indian stock market holds a pivotal position in the country's economy. Operating primarily through it is deeply influenced by government policies, global economic trends, corporate performance, and investor sentiment. These factors collectively shape the dynamics of the Indian economy as reflected in the market. Despite periodic fluctuations, the market has witnessed significant growth over the years, attracting both domestic and international investors. It offers diverse investment opportunities across various sectors such as technology, finance, pharmaceuticals, and manufacturing. Regulatory bodies like maintaining transparency and fairness in market procedures. the Indian stock market, conducting research holds significant importance. It serves as the cornerstone for making well-informed decisions, providing insights into market trends, company performance, and economic indicators. Thorough research empowers investors to identify profitable opportunities, mitigate risks, and enhance the long-term growth potential of their portfolios. Moreover, it facilitates understanding and compliance with regulatory changes and market dynamics, enabling investors to stay ahead of the curve. Essentially, research empowers investors in the Indian stock market, instilling confidence, minimizing risks, and maximizing opportunities. The TOPSIS ranking system, augmented with weighted averages and ambiguity comparisons, is commonly utilized. One approach involves addressing uncertainties to diminish ambiguity and adjusting both the weight and character of responses simultaneously. In TOPSIS, multiple responses are employed to adopt a holistic global perspective Mutual Funds, Exchange-Traded Funds (ETFs), Real Estate Investment Trusts (REITs), Gold and Precious Metals, Direct Equity Investments, Derivatives, Fixed Income Instruments, Portfolio Management Services (PMS) and Cryptocurrencies. Market Indices, Risk Management and Investor Sentiment. the Ranking of the Indian Stock Market. Direct Equity Investments got the first rank whereas the Real Estate Investment Trusts (REITs) is having the Lowest rank.

Why Lewis’ Poetry Matters

In spite of Lewis’ sustained, earnest, and determined efforts at writing poetry through the middle 1920s, his reputation will always be based on his prose, both fiction and non-fiction. That this is the case begs the questions: Does Lewis’ poetry matter? Why bother sifting through his verse if the real gold is to found elsewhere? Should not Lewis’ poetry simply be written off as so much adolescent self-indulgence? Did not his commitment to older poetic forms and traditions hopelessly date his poetry? While much could be said about the literary merits of Lewis’ poetry, here the discussion is limited to how his poetry reflects upon his spiritual maturation.

Predict Gold Price Trend Based on ARIMA Model

As a financial product, gold is one of the more important spot and futures trading products in the commodity market. Based on the time series model, the gold price can be fitted and predicted, in order to explore the law of gold price changes. It has positive implications for investors and government managers. This article selects the Prime Day price in 2018 as the research object. Combined with domestic and foreign research content on financial time series. First, through the time series diagram test and the unit root test, it is obtained that the gold daily price series is a cycle-free and non-stationary series. Therefore, the time series needs to be differentiated. Second, a new stationary sequence is obtained by making second-order differences. Third, after the time sequence diagram test, ADF test, and white noise test, the sequence is a non-white noise sequence. Comparing the AIC values of multiple time series models, the most ideal model for the series should be the ARIMA (2,2,2) model. The significance test of the model shows that the fitted model is significantly effective. And the significance test of the model parameters is also passed. Then make predictions with this model. Comparing the predicted value with the future real gold price, it is found that the predicted value is close to the real value. This is a good reference for the country to formulate relevant economic policies.

A Genetic algorithm for Objective formulation effect on the shortfall of retirees in developing countries: a case study in Iran

The certainty about retirement income is dependent on the longevity and the selected investment policy by individuals during their working years. Attention to longevity and investment risks is of high significance in making enough income for retirees. In this research, the impact of the objective formulation selection on investment decisions has been investigated. Two functions including terminal wealth objective formulation and retirement income objective formulation are applied to investigate these decisions. Based on the investment alternatives, 5 asset classes including Equity, Certificate of deposit (cash), real estate investment trust (REIT), gold coin, and foreign exchange have been selected for investment. According to the complexity of modeling in the aforementioned functions, the Metaheuristic Genetic algorithm has been used. The results are indicative of the importance of objective formulation selection. The retirement income objective function has the nature of more wealth accumulation and more control over economic and market turbulences through higher cooperation in investment and as a result, it has been recommended as the optimal function.

Prerequisites for the formation of gold spreads on the Ukrainian Shield and on its southern framing

The presence of numerous native gold deposits of the Ukrainian Crystalline Shield (UCS), as well as the spread of placer gold manifestations, determines the high probability of the formation of precious metal placers with industrial parameters here. The main factors and geological conditions that contribute to the formation of gold placers are manifested in the UCS. An important reason for the failures of previous geological works is the outdated scientific and methodological platform, which involved the search for typical gold-bearing deposits with metal of gravity classes of size. The possibility of significant movement of placer gold in the clayey water flow and the formation of long-distance placers was not taken into account. The traditional slack method used in the study did not make it possible to determine the actual gold content when particles with a size smaller than 0.25 mm predominate in the sample, which is precisely the particle size of the primary sources of the UCS. The applied means of research could not be effective in the absence of the necessary analytical base for determining the gold content, modern methods of studying the geological structure and tectonics of the territories. The negative results did not provide any real grounds for further specialized large-scale research and prospecting for placer gold. Today, it is becoming obvious that placer gold of small and fine grades can easily move over considerable distances in the water flow and form accumulations not only in boulder-pebble material, but also in pelite sediments. As a result of repeated changes in the tectonic plan of block movements, gold-bearing accumulations could be eroded and redeposited in secondary reservoirs. In this aspect, neotectonic studies (using materials from space surveys) become the main predictive and prospecting method for identifying promising areas of placer gold concentration in the region. Field work should be carried out using drilling and modern methods of laboratory analysis of samples for gold. The results of many years of research give hope for the discovery of real placer gold deposits in the Northern Black Sea region in the near future.

Oil prices and gold prices on housing market in China: novel findings from the bootstrap approach

PurposeThis study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.Design/methodology/approachThis study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.FindingsThe ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.Originality/valueTo the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Rare disaster risks and gold over 700 years: Evidence from nonparametric quantile regressions

Using annual data on real gold returns and measures of rare disaster risks over the period of 1280–2016, we show the existence of nonlinearity and regime changes in the relationship between the two variables of concern, over and above the existence of non-normality in the data. In light of these issues, we rely on a nonparametric quantile regression model to show that real gold returns can hedge against such risks, but only when the market is in its bullish-state, with it being negatively impacted in its bearish-phase. Understandably, our results have important implications for investors seeking refuge in the safe haven of gold during rare disaster events. In addition, our findings, would require theoreticians to develop new asset pricing models, which would incorporate the state-specific impact of rare disaster risks on gold.

Contagious diseases and gold: Over 700 years of evidence from quantile regressions

We investigate the effect of the probability of fatality due to contagious diseases on real gold returns over the period 1258–2020 using a predictive quantile regression model, which is justified by the features of non-normality, nonlinearity, and structural breaks in the dataset involving real gold returns and the probability of fatality. We show that real gold returns hedge the probability of fatality due to contagious diseases primarily when the gold market is bullish. However, the hedging ability is insignificant when the gold market is bearish. These results are important for investors seeking refuge in gold during rare disaster events.

Mercury sequestration from synthetic and real gold processing wastewaters using Fe–Al bimetallic particles

The Fe–Al bimetallic material was synthesized, and its capability to eliminate mercury (Hg) pollution from both simulated and real gold processing wastewaters containing other heavy metals was examined. A high Hg removal from a chloride solution was achieved at a wide range of initial pH (3–11) and Cl− concentrations (0.2–3 M). The best result (99.9% of Hg removal) was obtained at pH of 3, Cl− concentration of 1.7 M, and bimetal dosage of 10 g/L. The removal of Hg(II) from nitrate solution decreased, while the presence of other metal ions did not interfere. It can be attributed to higher redox potential and, therefore, an increased affinity of nitrate for electrons than Hg and the other metals studied. A redox reaction involving electrons transfer to Hg(II) from Al and Fe, and Hg adsorption by Fe oxy-hydroxides were identified as the main pathways for Hg removal. XPS examination detected oxidized Hg on the bimetal surface (HgCl2) in addition to elemental Hg, which is likely due to re-oxidation of Hg0 by active Cl atoms on the bimetal. In addition, leaching tests on Hg loaded bimetal in acidic and neutral pH conditions indicated a strong bond between Hg ions and the bimetal. The bimetal was also shown to be highly effective for repeated use (≥90% of Hg removal after 4 cycles). On gold processing wastewater containing stable Hg-cyanide complexes, the Hg removal of 82% was achieved after 120 min using 20 g/L of bimetal dosage at an initial pH of 8. This study has provided evidence for the potential application of the Fe–Al bimetal for removing heavy metals from complex wastewater under varying conditions.

Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

Drill-hole spacing analysis (DHSA) and optimization are becoming commonplace for uncertainty assessment and management in the mining industry. However, there is no standardized DHSA workflow, and the outputs of certain methodologies are not interchangeable. We group available simulation-based DHSA methods according to their accounted uncertainty into (i) raw uncertainty, assessed by drawing realizations from synthetic data-sets with the drilling spacings to be tested; and (ii) model uncertainty, in which these synthetic data-sets are used to assess the variation between the estimated model and the (unknown) actual value. DHSA workflows available in the literature ignore the differences between both types of uncertainty. Commonly, the DHSA algorithm is chosen without a detailed analysis of its uncertainty output, which may lead to misleading results and suboptimal decisions. While available solutions are based only on assessing raw or model uncertainty, the proposed approach simultaneously analyses both and their relationship for models at different stages of the mine. The integrated analysis results deliver more information to support decision-making than available methods. Principles, practical considerations, and discussions of the advantages of the proposed integrated analysis are presented. The approach is applied to a real gold deposit to illustrate its use.