Development of copper price from July 1959 and predicted development till the end of year 2022

The increasingly meagre copper ore resources constitute one of the decisive factors influencing the price of this commodity. The demand for copper has been showing an accelerating trend since the Covid pandemic broke out. It is thereby imperative to estimate the future price movement of this material. The article focuses on a daily prediction of the forthcoming change in prices of copper on the commodity market. The research data were gathered from day-to-day closing historical prices of copper from commodity stock COMEX converted to a time series. The price is expressed in US Dollars per pound. The data were processed using artificial intelligence, recurrent neural networks, including the Long Short Term Memory layer. Neural networks have a great potential to predict this type of time series. The results show that the volatility in copper price during the monitored period was low or close to zero. We may thereby argue that neural networks foresee the first three months more accurately than the rest of the examined period. Neural structures anticipate copper prices from 4.5 to 4.6 USD to the end of the period in question. Low volatility that would last longer than one year would cut down speculators’ profits to a minimum (lower risk). On the other hand, this situation would bring about balance which the purchasing companies avidly seek for. However, the presented article is solely confined to a limited number of variables to work with, disregarding other decisive criteria. Although the very high performance of the experimental prediction model, there is always space for improvement – e.g. effectively combining traditional methods with advanced techniques of artificial intelligence.

Experimental prediction of internal defects according to defect area on NDI via water absorption behavior

This study analyzed the relationship between the defect area identified through a C-scan and the void volume in CF-PEKK composite materials through the water absorption behavior to predict the void volume. The water absorption content varies with the defect area; however, the defect area identified through a C-scan and the water absorption content did not show a proportional relationship. This is because voids are distributed in the through-thickness. The results indicated that the absorption behavior could be used to predict the void volume. Irreversible absorption was found to be independent of the void volume. Further, no matrix degradation was seen with water immersion at [Formula: see text]C; however, some local swelling was seen.

Efficiency of the Experimental Prediction Market: Public Information, Belief Evolution, and Personality Traits

This paper examines the ability of markets to aggregate information so that the price generated from the market contains the best estimate of all the available information. The paper investigates how individuals “update” their initial beliefs from their public and private information in light of market prices. In particular, the paper looks at individuals' weighting of public information versus private information. Also, the volume of information in the market via an increased number of traders with private information has a positive impact on the quality of the market price. Lastly, the personality traits of the traders seem to provide some positive impact if the traders are diverse in terms of the proportion of “efficient and organized” traders in the market. JEL classification numbers: C91, C92, D82 Keywords: Experimental economics, Prediction markets, Belief, Market efficiency, Personality traits.

An experimental prediction: granule cells can perform linearly non-separable computations

Multiple studies show how dendrites might extend some neurons' computational capacity. These studies leave a large fraction of the nervous system unexplored. Here we demonstrate how a modest dendritic tree can allow cerebellar granule cells to implement linearly non-separable computations. Granule cells' dendrites do not spike and these cells' membrane voltage is isopotential. Conjunction of Boolean algebra and biophysical modelling enable us to make an experimental prediction. Granule cells can perform linearly non-separable computations. The standard neuron model used in the artificial network, aka the integrate and fire, cannot perform such type of computations. Confirming the prediction we provide in the present work would change how we understand the nervous system.

Experimental Prediction of Lean Blowout Limits for 3kW Micro Gas Turbine Combustor fuelled with LPG

The Lean Blowout Limit of the combustor is one of the important performance parameters for a gas turbine combustor design. This study aims to predict the total pressure loss and Lean Blowout (LBO) limits of an in-house designed swirl stabilized 3kW can-type micro gas turbine combustor. The experimental prediction of total pressure loss and LBO limits was performed on a designed combustor fuelled with Liquefied Petroleum Gas (LPG) for the combustor inlet velocity ranging from 1.70 m/s to 11 m/s. The results show that the predicted total pressure drop increases with increasing combustor inlet velocity, whereas the LBO equivalence ratio decreases gradually with an increase in combustor inlet velocity. The combustor total pressure drop was found to be negligible; being in the range of 0.002 % to 0.065 % for the measured inlet velocity conditions. These LBO limits predictions will be used to fix the operating boundary conditions of the gas turbine combustor.

Numerical prediction of sporadic E layer occurrence using GAIA

A sporadic E layer has significant influence on radio communications and broadcasting, and predicting the occurrence of sporadic E layers is one of the most important issues in space weather forecast. While sporadic E layer occurrence and the magnitude of the critical sporadic E frequency (foEs) have clear seasonal variations, significant day-to-day variations as well as regional and temporal variations also occur. Because of the highly complex behavior of sporadic E layers, the prediction of sporadic E layer occurrence has been one of the most difficult issues in space weather forecast. To explore the possibility of numerically predicting sporadic E layer occurrence, we employed the whole atmosphere–ionosphere coupled model GAIA, examining parameters related to the formation of sporadic E layer such as vertical ions velocities and vertical ion convergences at different altitudes between 90 and 150 km. Those parameters in GAIA were compared with the observed foEs data obtained by ionosonde observations in Japan. Although the agreement is not very good in the present version of GAIA, the results suggest a possibility that sporadic E layer occurrence can be numerically predicted using the parameters derived from GAIA. We have recently developed a real-time GAIA simulation system that can predict atmosphere–ionosphere conditions for a few days ahead. We present an experimental prediction scheme and a preliminary result for predicting sporadic E layer occurrence.

Numerical prediction of sporadic E layer occurrence using GAIA

A sporadic E layer has significant influence on radio communications and broadcasting, and predicting the occurrence of sporadic E layers is one of the most important issues in space weather forecast. While sporadic E layer occurrence and the magnitude of the critical sporadic E frequency ( foEs ) have clear seasonal variations, significant day-to-day variations as well as regional and temporal variations also occur. Because of the highly complex behavior of sporadic E layers, the prediction of sporadic E layer occurrence has been one of the most difficult issues in space weather forecast. To explore the possibility of numerically predicting sporadic E layer occurrence, we employed the whole atmosphere–ionosphere coupled model GAIA, examining parameters related to the formation of sporadic E layer such as vertical ions velocities and vertical ion convergences at different altitudes between 90 km and 150 km. By comparing those parameters in GAIA with observed foEs data obtained by ionosonde observations in Japan, we found that variations in the vertical ion convergence at 120 km altitude agree fairly well with variations in foEs . This result suggests that sporadic E layer occurrence can be numerically predicted using the parameters derived from GAIA. We have recently developed a real-time GAIA simulation system that can predict atmosphere–ionosphere conditions for a few days ahead. We present an experimental prediction scheme and a preliminary result for predicting sporadic E layer occurrence.