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Projections of commodity prices have long been a significant source of dependence for investors and the government. This study investigates the challenging topic of forecasting the daily regional steel price index in the northeast Chinese market from January 1, 2010, to April 15, 2021. The projection of this significant commodity price indication has not received enough attention in the literature. The forecasting model that is used is Gaussian process regressions, which are trained using a mix of cross-validation and Bayesian optimizations. The models that were built precisely predicted the price indices between January 8, 2019, and April 15, 2021, with an out-of-sample relative root mean square error of 0.5432%. Investors and government officials can use the established models to study pricing and make judgments. Forecasting results can help create comparable commodity price indices when reference data on the price trends suggested by these models are used. [ABSTRACT FROM AUTHOR]

The forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short‐Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short‐term, medium‐term, and long‐term frequencies via fine‐to‐coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short‐term, medium‐term, and long‐term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry. [ABSTRACT FROM AUTHOR]

GARCH family models are the most widely-used methods for forecasting price volatility. Given that this approach usually has extremely high forecast errors, continuous studies have been conducted to improve forecast models using different techniques. In the present manuscript, we expanded the fields of expert systems, forecast, and modeling using an artificial neural network (ANN) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) method that created an ANN-GARCH model. The hybrid ANN-GARCH model was used to forecast steel price volatility, and its accuracy was evaluated based on mean absolute error (MAE) and mean square error (MSE) evaluation criteria. The results indicated a general improvement in forecasting using ANN-GARCH compared to the GARCH method alone. The results were realized using copper price returns, the dollar index, gold price returns, and oil price returns as inputs. We also discussed the research implications for this field in addition to practical applications. The research results indicated better performance of the hybrid ANN/GARCH/N model than other models. Furthermore, the neural-network-based hybrid models could better forecast prices than other time series models. [ABSTRACT FROM AUTHOR]

By masterfully balancing directed growth and passive mechanics, plant roots are remarkably capable of navigating complex heterogeneous environments to find resources. Here, we present a theoretical and numerical framework which allows us to interrogate and simulate the mechanical impact of solid interfaces on the growth pattern of plant organs. We focus on the well-known waving, coiling, and skewing patterns exhibited by roots of Arabidopsis thaliana when grown on inclined surfaces, serving as a minimal model of the intricate interplay with solid substrates. By modeling growing slender organs as Cosserat rods that mechanically interact with the environment, our simulations verify hypotheses of waving and coiling arising from the combination of active gravitropism and passive root-plane responses. Skewing is instead related to intrinsic twist due to cell file rotation. Numerical investigations are outfitted with an analytical framework that consistently relates transitions between straight, waving, coiling, and skewing patterns with substrate tilt angle. Simulations are found to corroborate theory and recapitulate a host of reported experimental observations, thus providing a systematic approach for studying in silico plant organs behavior in relation to their environment. [ABSTRACT FROM AUTHOR]

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1–2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions. [ABSTRACT FROM AUTHOR]

In ship construction, material costs constitute a substantial portion of the overall expenses. With the surging steel prices, the shipbuilding industry faces a pressing challenge. To counterbalance this issue, optimizing the structural components of ships has emerged as a viable solution. Genetic Algorithm (GA) methods, known for their application in structural optimization, have demonstrated their potential. However, the protracted computational time associated with GA remains a limiting factor. This research introduces a novel approach by merging GA with Finite Element Method (FEM) for optimizing plate sizes, resulting in a hybrid GA system. Moreover, the study incorporates sensitivity analysis (SA) due to its proven efficacy in enhancing optimization processes involving multiple variables. The SA component investigates plate interrelationships and effectively clusters them. After this grouping, the hybrid GA executes parallel optimization of plates that influence each other under tension. By integrating SA, the optimization process becomes faster and more time-efficient, while preserving optimal manufacturing costs. Remarkably, this methodology culminates in a substantial reduction in computational time when contrasted with the hybrid GA approach devoid of SA, all the while maintaining a parallel manufacturing cost trajectory. In conclusion, this study presents an innovative hybrid GA approach, supplemented with SA, as an effective strategy for mitigating the escalating steel costs in shipbuilding. The amalgamation of GA, FEM and SA synergistically simplifies the optimization process, ensuring optimal results in a faster way. [ABSTRACT FROM AUTHOR]

By using a dynamic panel data model, this study investigated how the production of thermal power affects China's regional pilot carbon markets with a focus on carbon price, trading volume, realized volatility, and market liquidity. Our results suggest that the production of thermal power has slightly increased the trading volume, but has not affected carbon prices based on the existence of excessive CO 2 allowances. Moreover, thermal power production has increased realized volatility in the pilot markets. However, this impact was reduced following the announcement of the National Emission Trading System (ETS). This demonstrates that thermal power companies in pilot carbon markets tend to trade carbon credit more actively in response to policies favouring further market liberalization, rather than passively following government instructions. Furthermore, we find that realized volatility and liquidity increased in the Hubei and Shanghai pilots following the announcement. This demonstrates that the announcement of the National ETS improved the market activity and efficiency, and pricing capacity of its host pilot markets. However, this improvement could only be observed in the short term. Therefore, the government should reallocate CO 2 allowances to match its emission reduction targets and incentivize industries with high carbon intensity to participate in carbon trading. [ABSTRACT FROM AUTHOR]

Forecasting commodity prices is a vital issue to a wide spectrum of market participants and policy makers in the metal sector. In this work, the forecast problem is investigated by focusing on the Chinese market, with the daily steel price indices of the composite index, long products, flat products, and rolled products spanning a 10-year period from June 15, 2011, to April 15, 2021. The non-linear auto-regressive neural network is employed as the forecasting model and model performance corresponding to a variety of settings is explored over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data for each of the four price indices. Models that are relatively simple and generate forecasts of high accuracy and stabilities are arrived at. Particularly, relative root mean square errors (RRMSEs) of 0.49%/0.49%/0.52%/0.55%, 0.51%/0.44%/0.51%/0.49%, and 0.52%/0.53%/0.56%/0.53%, respectively, for training, validation, and testing, and the overall RRMSE of 0.50%/0.49%/0.53%/0.54% are reached for the composite index/long products/rolled products/flat products. The results could, on the one hand, serve as standalone technical price forecasts. They could, on the other hand, be combined with other (fundamental) forecast results for forming perspectives of price trends and carrying out policy analysis. [ABSTRACT FROM AUTHOR]

For policy makers and a diverse spectrum of market participants, understandings of commodity price forecasts are considered as an important matter. In this work, we focus on the metal business sector and examine forecast problems for seven daily regional steel prices in the Chinese market over an eleven-year period spanning 2010–2021. Specifically, our analysis covers steel prices of east, south, north, central south, northeast, southwest, and northwest China through the use of non-linear auto-regressive neural networks. For each price series, we test forecast accuracy based upon one hundred and twenty settings over training algorithms for model estimations, numbers of hidden neurons, numbers of delays, and ratios used for segmenting the data into training, validation, and testing phases. Our analysis leads to the construction of a rather simple model that generates high forecast accuracy and stabilities for each of the seven price series. Correspondingly, forecast accuracy measured by the relative root mean square error is lower than 0.60% for all considered series based upon the overall sample. Forecast results here could be utilized on a standalone basis or combined with forecasts from other models by different forecast users as part of evaluating price trends and performing policy analysis. [ABSTRACT FROM AUTHOR]

The moisture induced stress test is used to simulate the high-temperature dynamic water scouring environment to evaluate the hydrothermal sensitivity of steel slag asphalt mixture. Through dynamic modulus test, triaxial repeated load creep test and indirect tensile fatigue test, the effects of high temperature dynamic water scouring on the mechanical properties, creep properties and fatigue properties of steel slag asphalt mixture are studied. The dynamic modulus master curve, permanent deformation viscoelastic mechanical model and stress fatigue equation of three kinds of steel slag steel slag asphalt mixture before and after high temperature dynamic water scouring are constructed. The results show that after high temperature dynamic water scouring, the dynamic modulus and fatigue life of steel slag asphalt mixture decrease, and the permanent deformation increases. The dynamic modulus master curve, permanent deformation viscoelastic model and stress fatigue equation of steel slag asphalt mixture are well fitted. [ABSTRACT FROM AUTHOR]

To improve the resource utilization of steel slag in road engineering, the chemical composition and immersion expansion rate of steel slag were tested first, and then 40%, 50%, and 60% of 4.75 ~ 16 mm basalt coarse aggregate were replaced by steel slag in drainage asphalt mixes by volume substitution method. To ensure the stability of the drainage asphalt mix structure, the oil-stone ratio was obtained by using raveling test and draindown test. Finally, the rutting and low-temperature trabecular bending tests were conducted on the drainage asphalt mixes with different steel slag dosing. The results show that: the amount of asphalt increases with the increase of steel slag admixture, and the addition of steel slag enhances the high and low-temperature performance of the mix. In terms of the water stability of the mix, the residual stability and the resistance to immersion water raveling loss were enhanced by incorporating the appropriate amount of steel slag. The steel slag adversely affected the freeze-thaw splitting strength ratio and bulk stability of the mixes, but within the specified permissible range. Considering the results of each test, a dosage of 60% steel slag in the drainage asphalt mix is recommended to provide good road performance. [ABSTRACT FROM AUTHOR]

The research objective of the present study is the development of a model for increased accuracy of steel-price forecasts, which is of paramount importance for firms who use steel as an input and thus need to make informed decisions with regard to an optimal amount and type of hedge against unfavourable steel-price movement. To achieve its aim, the study forms weighted average combinations of steel price forecasts generated separately by a transfer function ARIMA model (ARIMA-TF) and an artificial neural network model (ANN), as both models are shown to contribute independent information with regard to target variable (steel price) movement. A generalized reduced gradient algorithm (GRG) method is employed to estimate the component model forecast weights, which is a novel approach introduced by this study. The data set employed includes a time series of monthly steel prices (cold rolled flat steel) from February, 2012 to November, 2020. Explanatory variables include iron ore price, coking coal price, capacity utilization, GDP and industrial production. With regard to the out of sample forecasts of all models (component and combining), mean absolute percentage forecast errors (MAPE) are calculated and model comparisons are made. The study finds that the combining model formed with the gradient algorithm approach in which the weights are constrained to be nonnegative and sum to one has the lowest MAPE of all models tested, and overall is found to be very competitive with other models tested in the study. The policy implication for firms that use steel as a major input is to base their hedging decisions on a combination of forecasts generated by ARIMA-TF and ANN models, with the forecast weights generated by a constrained generalized reduced gradient algorithm (GRG) method. [ABSTRACT FROM AUTHOR]

Increasing water stress and decreasing supplies caused by growth and climate variability have expanded demand for managed aquifer recharge (MAR) projects to provide water supply resilience. Some of the most important factors in determining the performance of a MAR project include site selection, subsurface hydrogeologic characteristics and associated properties of the storage zone. Costs for invasive subsurface investigations to address these factors have slowly increased over the past two decades, with drilling costs increasing dramatically by as much as 30% or more since COVID‐19 hit, a result of supply chain issues, steel prices, and manpower challenges. This paper provides a high‐level review of major geophysical methods that have become more mainstream over the past decade or two to supplement invasive subsurface investigations and are very cost effective when compared to drilling boreholes and installing wells, which provide only point data. The more commonly used surface geophysical methods include ground‐based and airborne time‐domain electromagnetic methods (TEM), electrical resistivity, and seismic reflection. Airborne TEM methods (AEM) collect data very quickly, avoiding ground‐based access constraints, and land‐based methods are especially efficient using towed arrays. Electrical resistivity measurements provide resolution comparable to TEM but require more time than towed methods. Seismic reflection surveys are more expensive than other methods but typically have a much greater depth of penetration and can provide high resolution information on aquifer geometry, geology, and faults. Borehole geophysics is one of the more common methods used in MAR, providing near hole formation data and ground truths surface geophysics. [ABSTRACT FROM AUTHOR]

As two main steelmaking materials, iron ore and scrap steel have different price lead-lag relationships (PLRs) on midstream and downstream steel products in China. The relationships also differ as the time scale varies. In this study, we compare the price influences of two important steel materials on midstream and downstream steel products at different time scales. First, we utilize the maximal overlap discrete wavelet transform (MODWT) method to decompose the original steel materials and products price series into short-term, midterm, and long-term time scale series. Then, we introduce the cross-correlation and Podobnik test method to calculate and test the price lead-lag relationships (PLRs) between two steel materials and 16 steel products. Finally, we construct 12 price lead-lag relationship networks and choose network indicators to present the price influence of the two materials at different time scales. We find that first, most scrap steel and steel products prices fluctuate at the same time lag order, while iron ore leads most steel products price for one day. Second, products that exist in the downstream industry chain usually lead to iron ore. Third, as the time scale becomes longer, the lead relationships from steel materials to steel products become closer. [ABSTRACT FROM AUTHOR]

This work aims to examine the relationship between tourism activities and house prices (HP) in first-tier cities (i.e., Beijing, Shanghai, and Guangzhou) of China using a quantile-on-quantile approach. This approach provides an ideal framework with which to capture the overall dependence structure between tourism activities and HP. The empirical results show that there is a positive and negative relationship between tourism and HP for the first-tier cities considered, with substantial variations across cities and across quantiles within each city. Important city-specific policy implications may be drawn from these findings. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between oil, gold and steel prices observed over a period of ten years with the aim of presenting their situation and trends using statistical methods and time series over a longer period of time and their impact on agricultural development. The high share of agriculture in the basic macroeconomic aggregates of the Republic of Serbia conditions that agriculture has a significant role in the foreign trade of the Republic of Serbia, especially in exports. The negative trend of rising fuel prices per farmer reduces the average fuel consumption per hectare. Also, the growth of the price of steel is conditioned by the growth of the prices of agricultural machinery. [ABSTRACT FROM AUTHOR]

In this paper, the network perspective is combined with the clustering theory to investigate the power structure, cooperation network, and spatial distribution of Chinese film clusters between 2010 and 2020. The top 20 grossing films are collected. Moreover, three types of network, including network between directors and companies, inter- and intra-firm network and inter-city network, are established on the basis of the social network analysis and the data processing in Python. In this paper, the imbalance between the power structure and the film resource distribution is demonstrated. In addition, it is revealed that small coteries exist in China's film clusters. The above small coteries are more significantly connected under the network, while there are relatively sparse connections outside the network. Several emerging eastern cities (e.g., Hangzhou and Nanjing) have stood out, which may be conducive to reducing the absolute monopoly of Beijing, Hong Kong and Shanghai in the China's film industry. [ABSTRACT FROM AUTHOR]