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In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model's performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper. [ABSTRACT FROM AUTHOR]

Metal material processing using femtosecond lasers is a useful technique, and it has been widely employed in many applications including laser microfabrication, laser surgery, and micromachining. The basic mechanisms of metal processing using femtosecond lasers are reviewed in this paper and the characteristics and theory of laser processing are considered. In addition to well-known processes, the recent progress relating to metals processing with femtosecond lasers, including metal material drilling, metal ablation thresholds, micro/nano-surface modification, printed circuit board (PCB) micromachining, and liquid metal (LM) processing using femtosecond lasers, is described in detail. Meanwhile, the application of femtosecond laser technology in different fields is also briefly discussed. This review concludes by highlighting the current challenges and presenting a forward-looking perspective on the future of the metal laser processing field. [ABSTRACT FROM AUTHOR]

Grain is a commodity related to the livelihood of the nation's people, and the volatility of its futures price affects risk management, investment decisions, and policy making. Therefore, it is very necessary to establish an accurate and efficient futures price prediction model. Aiming at improving the accuracy and efficiency of the prediction model, so as to support reasonable decision making, this paper proposes a Bi-DSConvLSTM-Attention model for grain futures price prediction, which is based on the combination of a bidirectional long short-term memory neural network (BiLSTM), a depthwise separable convolutional long short-term memory neural network (DSConvLSTM), and an attention mechanism. Firstly, the mutual information is used to evaluate, sort, and select the features for dimension reduction. Secondly, the lightweight depthwise separable convolution (DSConv) is introduced to replace the standard convolution (SConv) in ConvLSTM without sacrificing its performance. Then, the self-attention mechanism is adopted to improve the accuracy. Finally, taking the wheat futures price prediction as an example, the model is trained and its performance is evaluated. Under the Bi-DSConvLSTM-Attention model, the experimental results of selecting the most relevant 1, 2, 3, 4, 5, 6, and 7 features as the inputs showed that the optimal number of features to be selected was 4. When the four best features were selected as the inputs, the RMSE, MAE, MAPE, and R 2 of the prediction result of the Bi-DSConvLSTM-Attention model were 5.61, 3.63, 0.55, and 0.9984, respectively, which is a great improvement compared with the existing price-prediction models. Other experimental results demonstrated that the model also possesses a certain degree of generalization and is capable of obtaining positive returns. [ABSTRACT FROM AUTHOR]

This article introduces an innovative extension of the Kelly criterion, which has traditionally been used in gambling, sports wagering, and investment contexts. The Kelly criterion extension (KCE) refines the traditional capital growth function to better suit dynamic market conditions. The KCE improves the traditional approach to accommodate the complexities of financial markets, particularly in stock and commodity trading. This innovative method focuses on crafting strategies based on market conditions and player actions rather than direct asset investments, which enhances its practical application by minimizing risks associated with volatile investments. This paper is structured to first outline the foundational concepts of the Kelly criterion, followed by a detailed presentation of the KCE and its advantages in practical scenarios, including a case study on its application to blackjack strategy optimization. The mathematical framework and real-world applicability of the KCE are thoroughly discussed, demonstrating its potential to bridge the gap between theoretical finance and actual trading outcomes. [ABSTRACT FROM AUTHOR]

The silver fir (Abies alba Mill.) is among the most valuable conifers in Europe for ecological and economic reasons. Throughout the course of history, primarily in the 20th century, its share in stands has been declining due to ill-suited management practices, especially clear-cut management, air pollution (SO2 and NOX emissions), and wildlife-induced damage. This literature review compiles findings from 338 scientific papers. It describes futures for silver fir and its distribution, ecological requirements, threats and diseases, seed production and nurseries, and forest management practices with emphasis on ongoing climate change. Based on recent knowledge of fir ecology and population dynamics, small-scale shelterwood and selection management have been introduced in fir stands, which have also stabilized them. Fir is an essential species for maintaining high stability and biodiversity, especially on planosols and in waterlogged habitats. Owing to its shade tolerance and environmental plasticity, it can coexist very well with many tree species in mixtures, which can increase the productive potential of stands within the natural range in Europe. The average stand volume of mature fir stands ranges from 237–657 m3 ha−1. For its successful natural regeneration, it is essential to reduce cloven-hoofed game and thus prevent bud browsing damage. The attractiveness of fir in terms of heavy browsing is the highest of all conifers (52% damage). On the other hand, fir is a species relatively resistant to bark stripping and the spread of secondary rot compared with Norway spruce (Picea abies [L.] Karst.). Under global climate change, fir is expected to shift to higher elevations with sufficient precipitation, while in the southern part of its natural range or at lower elevations, outside water-influenced habitats, it is likely to decline. Climate change is intricately linked to the heightened prevalence of forest pathogens with significant damage potential in Europe, necessitating careful consideration and strategic adaptation within management practices of fir forests. [ABSTRACT FROM AUTHOR]

Corrosion has always been an important factor affecting the life of steel, which causes huge economic losses every year. How to improve the corrosion resistance of steel has always been a research focus. Adding rare earth elements into steel is an important method to improve the corrosion resistance of steel. In this paper, the effects of rare earth elements on steel are summarized, including the purification of molten steel, modification and modification of inclusions, improvement of grain boundaries by solid solution strengthening, the influence of phase transformation and the refinement of microstructure, and reduction in C and N desolubilization. On this basis, the progress of research on the corrosion resistance mechanisms of rare earth steel is summarized, focusing on rare earth-modified inclusions. This includes the changes in composition and sizes of inclusions by rare earth addition, promoting the transformation of MnS and Al2O3 in rare earth inclusions with regular shapes, smaller sizes and better performance, or composite rare earth inclusions. The corrosion pits that form in the early stages of corrosion are shallow in depth, fewer in number and light in corrosion degree. The effects of rare earth materials on the rust layer include: rare earth promotes the formation of a more stable corrosion product α-FeOOH, and rare earth promotes the formation of a dense rust layer, which covers the surface of the matrix and hinders the transmission of corrosion ions. The protective effect of the rare earth atomic layer on the substrate and the corrosion inhibition effect of rare earth ions are formed by the segregation of rare earth at the interface. In the end, the existing problems in the research into rare earth steel and future research directions are briefly put forward, including improving the addition process of rare earth steel, theoretical guidance on enhancing the corrosion resistance mechanism of rare earth steel, and extending the research from La, Ce, and Y steel to more rare earth steels. [ABSTRACT FROM AUTHOR]

Short-season cotton is a type of cotton variety characterized by its abbreviated cycle, rapid development, and concentrated flowering and boll setting. Compared with full-season cotton, short-season cotton facilitates an easier attainment of desirable maturation even when sown relatively late. This advantage of late sowing and early maturation eliminates the necessity for plastic film mulching, thereby creating opportunities for diversified double cropping, such as cotton–wheat, cotton–garlic, cotton–rape, and cotton–triticale systems. This paper provides a comprehensive review of the morphological, physiological, and molecular biological mechanisms underlying early maturity in short-season cotton. Furthermore, the significance and application of short-season cotton is discussed in relation to optimizing planting patterns and methods, promoting its cultivation in saline fields, developing machine-harvested cotton, and encouraging plastic mulch-free cotton planting. Based on these analyses and discussions, the paper proposes future strategies aimed at enhancing the breeding and cultivation of short-season cotton. These findings serve as valuable references for global breeding and cultivation research, and application of short-season cotton in the future. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and future contracts, treasury bonds, stock indices, and growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale. [ABSTRACT FROM AUTHOR]

The bibliometric mapping approach is a quantitative methodology to analyze the structure and evolution of research activities in a scientific area or a discipline. The objective of the current study was to perform a bibliometric analysis of the worldwide durum wheat literature published from 1961 to 2022 to identify topics and trends and their evolution over time. A total of 7512 documents were analyzed to generate bibliometric maps illustrating the main research topics. Most of the articles (91.6%) were published in indexed journals, with a low percentage (3.4%) in conference proceedings. The most active journals were the Journal of Cereal Science, Euphytica, Theoretical and Applied Genetics, Cereal Research Communications, and Cereal Chemistry. Italy, the USA, Canada, Spain, and France were the countries publishing the most documents. Research interests were focused on mutagenesis, interspecific hybridization, and technological quality in 1961–1980 and moved to conservation farming, molecular genetics, and nutritional quality in the last two decades. Future durum wheat production is facing challenges from climate change, water scarcity, and rising demand for sustainable food production. Advancements in molecular breeding techniques, genome editing, precision agriculture, and conservation farming can expedite wheat improvement and pave the way toward a healthier environment. The analysis of a large amount of bibliographic data provides useful information for researchers and policymakers and represents a starting point for a comprehensive discussion for future research. [ABSTRACT FROM AUTHOR]

Thermomechanical processing (TMP) of ferritic–martensitic (FM) steels, such as HT9 (Fe–12Cr–1MoWV) steels, involves normalizing, quenching, and tempering to create a microstructure of fine ferritic/martensitic laths with carbide precipitates. HT9 steels are used in fast reactor core components due to their high-temperature strength and resistance to irradiation damage. However, traditional TMP methods for these steels often result in performance limitations under irradiation, including embrittlement at low temperatures (<~430 °C), insufficient strength and toughness at higher temperatures (>500 °C), and void swelling after high-dose irradiation (>200 dpa). This research aimed to enhance both fracture toughness and strength at high temperatures by creating a quenched and tempered martensitic structure with ultrafine laths and precipitates through rapid quenching and unconventional tempering. Mechanical testing revealed significant variations in strength and fracture toughness depending on the processing route, particularly the tempering conditions. Tailored TMP approaches, combining rapid quenching with limited tempering, elevated strength to levels comparable to nano-oxide strengthened ferritic alloys while preserving fracture toughness. For optimal properties in high-Cr steels for future reactor applications, this study recommends a modified tempering treatment, i.e., post-quench annealing at 500 °C or 600 °C for 1 h, possibly followed by a brief tempering at a slightly higher temperature. [ABSTRACT FROM AUTHOR]

Carbon steel materials are widely used in mechanical transmission. Under different temperature and pressure service conditions, the microscopic changes of stress and strain that are difficult to detect and analyze by experimental means will lead to failure deformation, thus affecting their operational stability and life. In this study, the molecular dynamics method is used to simulate the heating–cooling phase transition process of common carbon steel materials. Austenite transformation temperatures of 980 K (0.2 wt.%) and 1095 K (0.5 wt.%) are acquired which is determined by the volume hysteresis before and after transformation, which is consistent with the results of JMatPro phase diagram analysis. The internal stress state of the material varies between compressive stress and tensile stress due to the change of phase structure, and the dislocation characteristics during the phase transition period are observed to change significantly. Then, an α / γ two-phase interface model is constructed to study the migration of the phase interface and the change of the phase structure by applying a continuously changing external load. At the same time, the transition pressure of α → ϵ is obtained with a value of 37 GPa under three different initial loads showing the independence of the initial load and the historical path. Based on the molecular dynamics simulation and the phase diagram calculation of the carbon steel, the analysis method for the microstructure transformation and the stress–strain behavior of the phase interface under the external load can provide a reference for the design of microstructure and mechanical properties of alloy steel in the future. [ABSTRACT FROM AUTHOR]

Laser-welding technology has recently enabled the production of corrugated-core steel sandwich panels (CCSSPs) as an innovative large-scale, lightweight structural solution in maritime and infrastructure applications. Detailed numerical analyses, specifically weld-region stress prediction in the presence of transverse patch loading and supports, are computationally challenging and time-consuming for their optimal design. This paper introduces an efficient, simplified combined sub-modelling approach for accurately predicting the detailed structural response of welded corrugated-core steel panels. The approach rests on the homogenisation of the three-dimensional (3D) panel into a two-dimensional equivalent orthotropic single layer (EOSL), where the effect of transverse compressive loads and local support conditions are captured separately via different 2D and 3D sub-modelling techniques, together with a model introduced for calculation of the weld region's equivalent spring stiffnesses. A laser-welded corrugated-core steel sandwich panel (CCSSP), as a future generation of the steel bridge deck, was examined using different modelling approaches. It was shown that the proposed combined sub-modelling approach can accurately predict stresses and displacements in all the constituent members of the cross-section, including the welds, in a reasonable calculation time when compared with a 3D reference model, unlike the conventional homogenisation approaches. [ABSTRACT FROM AUTHOR]

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China's crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security. [ABSTRACT FROM AUTHOR]

Vat photopolymerization (VPP) presents new opportunities for metals to achieve the design freedom of components. However, the material properties of copper powder and the inherent defects of the technology seriously hinder its application in high-precision metal additive manufacturing. Precision control is the key to obtaining minimal precision metal parts when copper is prepared by reduction photopolymerization. This paper employed variance analysis (ANOVA) and root mean square deviation (RMSD) to determine the significant parameters affecting dimensional accuracy and their optimal regions. The results show that printing accuracy is improved by optimizing exposure time, intensity, layer thickness, and sweeper moving speed. When the exposure time is 21 s, and the exposure intensity is 220 mW/cm2, a hole with a height of 1 mm and a diameter of 200 μm can be printed with a minimum size deviation of 51 μm. In addition, RMSD and ANOVA provide an effective method for realizing high-precision stereolithography 3D printing metal copper, expanding the material adaptation in the 3D printing metals field. The study highlights the potential of VPP as a method for preparing metals in future studies. [ABSTRACT FROM AUTHOR]

As ASEAN countries, including Vietnam, approach the living standards of developed countries, their copper demand is set to rise. This study investigates Vietnam's copper stock and flow from 1995 to 2050, employing dynamic material flow analysis and five socioeconomic pathway scenarios (SSPs). Based on this, the secondary copper reserves of Vietnam were assessed. The results showed that the domestic copper demand is expected to grow to 526–1062 kt, resulting in a rapid increase in scrap generation. In 2022, Vietnam's secondary copper reserves stood at 2.2 Mt and are projected to reach 6.8–8.6 Mt by 2050 under the SSP2 scenario. This corresponds to 3.6–4.6 times the 1.8 Mt primary copper reserve of Vietnam. However, these primary and secondary reserves cannot meet the cumulated demand by 2050. On the other hand, a large amount of copper, 8.9 Mt to 10 Mt, will become difficult-to-recover resources, such as waste in landfill sites, dissipated materials, or mixed metal loss. To promote the sustainable use of copper in Vietnam, we recommend increased geological expedition and mining investment, and improved waste management systems related to secondary resources. [ABSTRACT FROM AUTHOR]

Although China's grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting grain production and farmers' revenue by addressing the issue of land cultivation and farming methods. In this regard, the question of whether and how agricultural socialized services may resolve the present grain production conundrum is extremely practical. Therefore, the study employs the China Rural Revitalization Survey data of 3709 households. Based on the 2SLS model, stepwise regression method, and moderated effects model, it creatively takes into account a variety of agricultural production segments, investigates the mechanism of services on grain production from the standpoint of improved production efficiency and plot concentration, and further examines the effects of aging populations and regional variations in grain production areas. The study found the following: (1) The average proportion of grain production area of farmers in the sample is 49%, and 42% of farmers have purchased agricultural socialization services. (2) Agricultural socialization services can significantly promote farmers' grain cultivation behavior by facilitating connected transfers in and inhibiting connected transfers out to take advantage of plot concentration, and boosting the use of agricultural machines to enhance output efficiency. (3) The aging of the agricultural population will, to a certain extent, strengthen the promoting effect of agricultural socialization services on grain cultivation. Agricultural socialization services affect grain cultivation more in main grain-producing areas. Therefore, emphasizing the role of agricultural socialization services in accelerating the shift to moderate-scale operations, decreasing the non-grain component of the planting structure, and promoting the implementation of policies tailored to actual production needs are important steps to safeguard the production capacity of grain in different regions. [ABSTRACT FROM AUTHOR]

Waterlogging is one of the key abiotic factors that severely impedes the growth and productivity of soybeans on a global scale. To develop soybean cultivars that are tolerant to waterlogging, it is a prerequisite to unravel the mechanisms governing soybean responses to waterlogging. Hence, we explored the morphological, physiological, biochemical, and transcriptional changes in two contrasting soybean introgression lines, A192 (waterlogging tolerant, WT) and A186 (waterlogging sensitive, WS), under waterlogging. In comparison to the WT line, waterlogging drastically decreased the root length (RL), shoot length (ShL), root fresh weight (RFW), shoot fresh weight (ShFW), root dry weight (RDW), and shoot dry weight (ShDW) of the WS line. Similarly, waterlogging inhibited soybean plant growth by suppressing the plant's photosynthetic capacity, enhancing oxidative damage from reactive oxygen species, and decreasing the chlorophyll content in the WS line but not in the WT line. To counteract the oxidative damage and lipid peroxidation, the WT line exhibited increased activity of antioxidant enzymes such as peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT), as well as higher levels of proline content than the WS line. In addition, the expression of antioxidant enzyme genes (POD1, POD2, FeSOD, Cu/ZnSOD, CAT1, and CAT2) and ethylene-related genes (such as ACO1, ACO2, ACS1, and ACS2) were found to be up-regulated in WT line under waterlogging stress conditions. In contrast, these genes showed a down-regulation in their expression levels in the stressed WS line. The integration of morpho-physiological, biochemical, and gene expression analyses provide a comprehensive understanding of the responses of WT and WS lines to waterlogging conditions. These findings would be beneficial for the future development of soybean cultivars that can withstand waterlogging. [ABSTRACT FROM AUTHOR]

This paper is concerned with stable trading between the coal mining and power generation companies in China. Under the current marketized coal and planned electricity price systems, barriers to price shifting between coal and electricity are created and conflicts between the two sectors are aggravated. The stable trading matching between coal mining and power generation companies is not only an effective means to resolve the conflict in the coal trading market, but also a ballast stone for price stabilization and supply guarantees in coal trading. Based on the two-sided matching theory, this paper starts from the micro market preference and matching willingness of coal mining and power generation companies, puts forward the conceptual framework of the pairwise stable matching of both sides, innovates a mechanism for trading between coal mining and power generation companies, and designs a stable trading matching algorithm. The algorithm has certain theoretical innovation significance from the matching problem of non-separable commodities to that of separable commodities considering the trading volume between coal mining and power generation companies. Furthermore, it is a complement and perfection of the existing coal–power trading platform in its transaction mechanism and trading function. The results reveal that the trading relations between coal mining and power generation companies under the stable matching mechanism are resistant to disintegration and that the pairwise stable matching result is sensitive. [ABSTRACT FROM AUTHOR]

The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21- and 125-day periods. The main findings of the study are that in a calm economic environment, the estimation accuracy is higher (1.5% vs. 4%), and that the AI-based estimation methods provide the most accurate estimates for both time horizons. These models provide the most accurate forecasts over short and medium time periods. Incorporating these forecasts into the ERM can significantly help to hedge purchase prices. Artificial intelligence-based models are becoming increasingly widely available, and can achieve significantly better accuracy than other approximations. [ABSTRACT FROM AUTHOR]

In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products' prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA–LSTM model were compared with those of other models. The results showed that the IGA–LSTM model could effectively capture the characteristics and trends of time-series changes. The results showed that the proposed model obtained the minimum values (MSE = 0.00107, RMSE = 0.03268, and MAPE = 0.0691) in the forecasting of futures prices for two types of chemical products, showing excellent forecasting performance. [ABSTRACT FROM AUTHOR]

The mitigation of CO2 emissions is one of the major areas of research in iron ore-based steelmaking. In this study, four simulated current and potential future reheating scenarios with different fuel and oxidizer gases were studied regarding the amount of oxide formation and the adhesion of the steel–oxide interface: (1) methane–air; (2) coke oven gas–air; (3) hydrogen–air; (4) and an oxyfuel scenario with 50:50 methane/hydrogen as fuel gases. Isothermal oxidation tests were conducted at temperatures of 1150, 1230 and 1300 °C. Four low-carbon steel grades were tested in the previously mentioned gas atmospheres. The structure and composition of the formed oxide scales was analyzed with FESEM-EDS microscopy. The amount of oxide formation correlated with the water vapor content of the gas atmosphere for all four steel grades; however, notable differences were found between individual steel grades regarding the degree of oxidation increase. No clear evidence was found of the gas atmospheres affecting the adhesion of oxide scales to the steel substrate. The adhesion of the interface was mainly determined by the content of silicon in the steel grade and the test temperature. [ABSTRACT FROM AUTHOR]

Copper in the international market has been priced by copper futures contracts from the London Metal Exchange (LME) and the New York Commodity Exchange (COMEX). Copper prices initially showed a downward trend until March 2020, but after the outbreak of COVID-19, they continued to rise and reached a record high in May 2021. The rise in copper demand also stimulated the continuous growth of copper production. However, a significant amount of smelting flue gas is produced in the copper smelting process. The main component of the flue gas is SO2 and other acid gases, which pollute the environmental atmosphere. At the same time, due to the Chinese government's effective control of the pandemic, China's economy continued to grow. Therefore, as one of the world's largest copper consumers and producers, China's futures market has attracted attention for its influence on copper pricing and the pollution caused by copper smelting. In this paper, we used the grey entropy method to compare the influence of copper prices on the three futures markets and changes in China's air pollution in recent years. Our results show that before the pandemic, the influence of the LME futures copper price was the same as the COMEX but greater than the Shanghai Futures Exchange (SHFE). After the outbreak of the pandemic, the influence of the SHFE copper futures price significantly improved and slightly exceeded the LME and COMEX. This result echoes our finding that SO2 has caused serious air pollution in recent years. [ABSTRACT FROM AUTHOR]

With the background of emission peaks and carbon neutrality, light weight has become an irreversible trend in the development of the automobile industry. It is an inevitable choice to use a large amount of ultra-high-strength steels to realize light weight and safety of automobiles. Ultra-high-strength martensitic steels can be divided into hot-formed steels and cold-formed steels according to the forming process. In recent years, ultra-high-strength martensitic steels have been rapidly developed in automotive battery pack frameworks, door guard beams, bumpers, A-pillars, etc., depending on their good plasticity and advanced forming technology. In this paper, the recent progress of ultra-high-strength martensitic steels for automobiles is systematically reviewed, the mechanisms of alloying, strengthening, and toughening are emphatically expounded, and the hydrogen embrittlement problems in application are summarized. Finally, the prospects of manufacture and application of ultra-high-strength martensitic steels for automobiles in the future are forecasted. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of soybean and crude oil futures on palm oil indexes by utilising monthly data from three palm oil indexes listed in Bursa Malaysia, i.e., the Asian palm index, Malaysian palm index, and Plantation index, spanning from January 2010 to June 2020. The impacts were analysed using the Autoregressive Distributed Lag (ARDL) bounds test approach and causality test. The statistical findings revealed that the Asian palm index has a long-run relationship with crude oil futures and crude palm oil, and a short-run relationship with soybean futures, crude oil futures, and crude palm oil. On the other hand, the Malaysian palm index has a short-run relationship with soybean futures and crude palm oil, whereas the Plantation index has a short-run relationship with crude oil futures, crude palm oil, and exchange rate. For the long-run strategy, this study recommends close monitoring of crude oil futures. Meanwhile, the short-run strategy requires close monitoring of the crude oil and soybean futures. Eventually, the empirical findings proposed that interested parties such as fund managers, investors, and traders should pay attention to crude oil and soybean futures to mitigate risk and diversify their portfolios with greater emphasis on crude oil futures. [ABSTRACT FROM AUTHOR]

The threat of toxic metals to food security and human health has become a high-priority issue in recent decades. As the world's main food crop source, the safe cultivation of rice has been the focus of much research, particularly the restoration of toxic metals in paddy fields. Therefore, in this paper, we focus on the effects of toxic metals on rice, as well as the removal or repair methods of toxic metals in paddy fields. We also provide a detailed discussion of the sources and monitoring methods of toxic metals pollution, the current toxic metal removal, and remediation methods in paddy fields. Finally, several important research issues related to toxic metals in paddy field systems are proposed for future work. The review has an important guiding role for the future of heavy metal remediation in paddy fields, safe production of rice, green ecological fish culture, and human food security and health. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. This study aims to conduct an effect analysis of the events that occurred during the years 2020 and 2022, characterized by substantial investments in the field of clean energy. The analysed period encompasses the period from 13 July 2018 to 11 July 2023. The study is carried out in multiple stages with the aim of investigating a highly tumultuous period in the global economy. To assess long-term relationships, the econometric methodology proposed by Gregory and Hansen will be employed. The research shows a positive association between energy metals (excluding nickel futures) and clean energy indexes, suggesting their potential as secure investments for green investors diversifying their portfolios. Additionally, the study confirms the reliability of precious metals, such as gold, silver, and platinum as safe havens for clean energy stock indexes. These findings highlight the stability that both energy and precious metals can offer within clean energy portfolios during market volatility, emphasizing their value in such investment strategies. In brief, this study affirms that energy and precious metals are invaluable pillars in the structure of clean energy portfolios, offering unwavering support during market turbulence. [ABSTRACT FROM AUTHOR]

In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts. [ABSTRACT FROM AUTHOR]

Prior theory suggests a positive relation between volatility and market depth, while past empirical research finds contrasting results. This paper examines the relation between the volatility and the limit order book depth in commodity and foreign exchange futures markets during a turbulent time using the generalized method of moments (GMM). Results indicate a negative relation between volatility and depth and suggest that the depth in the limit order book decreases as volatility increases. Findings help to understand how market participants provide liquidity in response to shifts in prices. [ABSTRACT FROM AUTHOR]

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing. [ABSTRACT FROM AUTHOR]

Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering. [ABSTRACT FROM AUTHOR]

Fungi belonging to the Fusarium genus are commonly isolated from soybean plants and seeds but not all of them are pathogenic. The aim of this study was to compare the pathogenicity among different Fusarium isolates obtained from soybean plants with disease symptoms originating from an experimental field located in the southeast of Poland. Nineteen fungal isolates were selected for the pathogenicity assay, including eight isolates of F. oxysporum, six isolates of F. graminearum, four isolates of F. culmorum and one isolate of F. redolens. Species identification of these isolates was carried out using microscopic methods and sequencing of two genes: translation elongation factor 1-alpha (TEF1) and RNA polymerase second largest subunit (RPB2). To our knowledge, this is the first report of F. redolens being isolated from soybean in Europe. The pathogenicity test was set up by fungal inoculation of healthy soybean seeds of three cultivars: Abelina, Atlanta and Mavka. Symptoms were assessed seven days after inoculation. Disease area percentage of Fusarium inoculated seeds was significantly higher compared to uninoculated control. Nineteen isolates differed in their aggressiveness as the median disease area percentage ranged between 5.0 and 88.0% depending on isolate. The obtained isolates of four Fusarium species may be used in the future screening of soybean cultivars for resistance to these pathogens. [ABSTRACT FROM AUTHOR]

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are crucial electrochemical reactions that play vital roles in energy conversion and storage technologies, such as fuel cells and metal–air batteries. Typically, noble-metal-based catalysts are required to enhance the sluggish kinetics of the ORR and OER, but their high costs restrict their practical commercial applications. Thus, highly active and strong non-noble metal catalysts are essential to address the cost and durability challenge. Based on previous research, carbon-based catalysts may present the best alternatives to these precious metals in the future owing to their affordability, very large surface areas, and superior mechanical and electrical qualities. In particular, carbon aerogels prepared using biomass as the precursors are referred to as biomass-derived carbon aerogels. They have sparked broad attention and demonstrated remarkable performance in the energy conversion and storage sectors as they are ecologically beneficial, affordable, and have an abundance of precursors. Therefore, this review focuses on various nanostructured materials based on biomass-derived carbon aerogels as ORR/OER catalysts, including metal atoms, metal compounds, and alloys. [ABSTRACT FROM AUTHOR]

Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices. [ABSTRACT FROM AUTHOR]

The direct discharge of untreated organic wastewater poses significant threats to the environment and to human health. To address these threats, electrocatalytic oxidation technology has emerged as a key solution for organic wastewater treatment. Building on research conducted over the past three years, this review highlights the considerable advantages of electrocatalytic oxidation technology in the context of organic wastewater treatment, with a particular emphasis on the application of metal oxide electrodes. The review also provides a summary of the primary methods used in the preparation of such electrodes. Subsequently, the applications of both single-metal-oxide electrodes and metal oxide composite electrodes in organic wastewater treatment are summarized. Finally, we discuss the future development of metal oxide electrodes. [ABSTRACT FROM AUTHOR]

Nowadays, cost-effective, available, and flexible paper-based electronics play an essential role in the electronics industry. Herein, we present gold nanoparticles (AuNPs) as a potential raw material for gold inks in the future for such purposes. AuNPs in this research were synthesised using the ultrasonic spray pyrolysis (USP) technique from two precursors: gold (III) chloride tetrahydrate and gold (III) acetate. Synthesised AuNPs were collected in a suspension composed of deionised (D.I.) water and the stabiliser polyvinylpyrrolidone (PVP). AuNPs' suspensions were subjected to the rotavapor process to obtain gold inks with higher Au concentration (>300 ppm). ICP-MS measurements, the size and shape of AuNPs, ζ-potential, Ultraviolet-visible (UV-Vis) spectrophotometry measurements, and scanning electron microscop y (SEM) of gold inks were carried out in order to find the optimal printing parameters. In the final stage, the optical contact angle measurements were performed using a set of polar to non-polar liquids, allowing for the determination of the surface free energy of gold inks. Inkjet printing of gold inks as defined stripes on photo paper were tested, based on the characterisation results. [ABSTRACT FROM AUTHOR]

This article discusses the different forms of powder bed fusion (PBF) techniques, namely laser powder bed fusion (LPBF), electron beam powder bed fusion (EB-PBF) and large-area pulsed laser powder bed fusion (L-APBF). The challenges faced in multimetal additive manufacturing, including material compatibility, porosity, cracks, loss of alloying elements and oxide inclusions, have been extensively discussed. Solutions proposed to overcome these challenges include the optimization of printing parameters, the use of support structures, and post-processing techniques. Future research on metal composites, functionally graded materials, multi-alloy structures and materials with tailored properties are needed to address these challenges and improve the quality and reliability of the final product. The advancement of multimetal additive manufacturing can offer significant benefits for various industries. [ABSTRACT FROM AUTHOR]

Featured Application: The work can be applied to commodities, equities, e-commerce, and social networking platforms to detect anomalies in each user's account to provide timely notification and thus reduce losses. For bulk commodity, stock, and e-commerce platforms, it is necessary to detect anomalous behavior for the security of users and platforms. Anomaly-detection methods currently used on these platforms train a model for each user since different users have different habits. However, the model cannot be trained adequately due to insufficient individual user behavior data. In this study, to utilize information between users and avoid underfitting, we propose a contrastive learning framework to train a complete global model (GM) for anomaly detection in a trading platform. By confusing the data between different users to generate negative samples, the model can learn the differences between users by contrastive learning. To reduce the need for individual user behavior data, this framework uses a GM instead of a model for each user to learn similarities between users. Experiments on four datasets show that models trained using our framework achieve better area-under-the-curve (AUC) scores than do the original models, proving that contrastive learning and GM are useful for anomaly detection in trading platforms. [ABSTRACT FROM AUTHOR]

Fluorescent proteins (FPs) are optical probes that are used to track the functions of genetically encoded target molecules in molecular and cellular biology. FPs have intrinsic photophysical properties generated by the chromophore and its surrounding amino acid sequences. The intensity of the fluorescence emission of FPs can be changed using external factors such as pH or metal ions. Additionally, the fluorescence intensity of FPs can be reduced or quenched using specific transition metal ions, suggesting that they are attractive probes for measuring metal ion levels. A spectroscopical analysis of the metal-induced fluorescence quenching of several FPs revealed that they exhibited intrinsic fluorescence quenching behavior with specific metal ions. The quenchable metal-binding site of FP has been determined using chemical modification, crystal structure, and modeling, providing insights into the molecular mechanism and FP engineering. In this review, studies on the change in the fluorescence activity of FPs mediated by metal ions are comprehensively compared and reviewed, and the requirements for the development of fluorescent protein-based metal biosensors in the future are discussed. [ABSTRACT FROM AUTHOR]

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε , γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2 % for prediction periods of 5 and 10 days. [ABSTRACT FROM AUTHOR]

This review aims to provide a clear overview of the most important analytical development in aflatoxins analysis during the last decade (2013–2022) with a particular focus on nuts and nuts-related products. Aflatoxins (AFs), a group of mycotoxins produced mainly by certain strains of the genus Aspergillus fungi, are known to impose a serious threat to human health. Indeed, AFs are considered carcinogenic to humans, group 1, by the International Agency for Research on Cancer (IARC). Since these toxins can be found in different food commodities, food control organizations worldwide impose maximum levels of AFs for commodities affected by this threat. Thus, they represent a cumbersome issue in terms of quality control, analytical result reliability, and economical losses. It is, therefore, mandatory for food industries to perform analysis on potentially contaminated commodities before the trade. A full perspective of the whole analytical workflow, considering each crucial step during AFs investigation, namely sampling, sample preparation, separation, and detection, will be presented to the reader, focusing on the main challenges related to the topic. A discussion will be primarily held regarding sample preparation methodologies such as partitioning, solid phase extraction (SPE), and immunoaffinity (IA) related methods. This will be followed by an overview of the leading analytical techniques for the detection of aflatoxins, in particular liquid chromatography (LC) coupled to a fluorescence detector (FLD) and/or mass spectrometry (MS). Moreover, the focus on the analytical procedure will not be specific only to traditional methodologies, such as LC, but also to new direct approaches based on imaging and the ability to detect AFs, reducing the need for sample preparation and separative techniques. [ABSTRACT FROM AUTHOR]

Non-ferrous metals, as one of the representative commodities with large international circulation, are of great significance to social and economic development. The time series of its prices are highly volatile and nonlinear, which makes metal price forecasting still a tough and challenging task. However, the existing research focus on the application of the individual advanced model, neglecting the in-depth analysis and mining of a certain type of model. In addition, most studies overlook the importance of sub-model selection and ensemble mode in metal price forecasting, which can lead to poor forecasting results under some circumstances. To bridge these research gaps, a novel forecasting system including data pretreatment module, sub-model forecasting module, model selection module, and ensemble module, which successfully introduces a nonlinear ensemble mode and combines the optimal sub-model selection method, is developed for the non-ferrous metal prices futures market management. More specifically, data pretreatment is carried out to capture the main features of metal prices to effectively mitigate those challenges caused by noise. Then, the extreme learning machine series models are employed as the sub-model library and employed to predict the decomposed sub-sequences. Moreover, an optimal sub-model selection strategy is implemented according to the newly proposed comprehensive index to select the best model for each sub-sequence. Then, by proposing a nonlinear ensemble forecasting mode, the final point forecasting and uncertainty interval forecasting results are obtained based on the forecasting results of the optimal sub-model. Experimental simulations are carried out using the datasets copper and zinc, which show that the present system is superior to other benchmarks. Therefore, the system can be used not only as an effective technique for non-ferrous metal prices futures market management but also as an alternative for other forecasting applications. [ABSTRACT FROM AUTHOR]

The phenomenon of cross adaptation refers to the ability of plants to improve their resistance to other stress after experiencing one type of stress. However, there are limited reports on how ultraviolet radiation B (UVB) pretreatment affects the enrichment, transport, and tolerance of cadmium (Cd) in plants. Since an appropriate UVB pretreatment has been reported to change plant tolerance to stress, we hypothesized that this application could alter plant uptake and tolerance to heavy metals. In this study, a woody plant species, 84K poplar (Populus alba × Populus glandulosa), was pretreated with UVB and then subjected to Cd treatment. The RT-qPCR results indicated that the UVB-treated plants could affect the expression of Cd uptake, transport, and detoxification-related genes in plants, and that the UVB-Pretreatment induced the ability of Cd absorption in plants, which significantly enriched Cd accumulation in several plant organs, especially in the leaves and roots. The above results showed that the UVB-Pretreatment further increased the toxicity of Cd to plants in UVB-Cd group, which was shown as increased leaf malonaldehyde (MDA) and hydrogen peroxide (H2O2) content, as well as downregulated activities of antioxidant enzymes such as Superoxide Dismutase (SOD), Catalase (CAT), and Ascorbate peroxidase (APX). Therefore, poplar plants in the UVB-Cd group presented a decreased photosynthesis and leaf chlorosis. In summary, the UVB treatment improved the Cd accumulation ability of poplar plants, which could provide some guidance for the potential application of forest trees in the phytoremediation of heavy metals in the future. [ABSTRACT FROM AUTHOR]

This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors' positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility. [ABSTRACT FROM AUTHOR]

In the recent past, China has expanded its grain production to achieve high food security and increased its partial dependence on imported agricultural commodities, of which soybean supply is highly import-dependent. This study systematically reviews the past trends in China's soybean demand, Brazil's soybean production and export, factors contributing to the soybean trade between China and Brazil, and future uncertainty in China's demand and Brazil's supply under climate change. We find that recently China imported ~64% of soybean from Brazil, while ~73% of Brazil's soybean exports were destined for China, making them key stakeholders in their international soybean trade. China's accession to the World Trade Organization, China–Brazil trade cooperation, and diversion from trade with the USA have played a pivotal role in China's increasing soybean imports from Brazil. China's soybean import has brought increasing virtual land to China (from 3.57 million hectares (Mha) in 2005 to 19.63 mha in 2020). This growing virtual land import could be one of the reasons behind Brazil's soybean harvested area, which increased from 22.95 Mha in 2005 to 37.19 Mha in 2020. In the future, climate change impacts on soybean production in Brazil can seriously affect China's soybean imports from Brazil and its domestic food security. We analyze these effects using a climate-crop–economic modeling approach, where yield changes from the crop model are incorporated into the economic model as lower land productivity. Our results show that Brazil's future soybean production and gross exports can drop up to 13.1% and 15.2% under the highest emissions scenario (RCP8.5). Consequently, China would face a decrease in its soybean imports from Brazil (−9.94 Mt). Due to these import reductions, China's domestic soybean supply will be reduced (−9.94 Mt). There would also be some reduction in China's meat supply and a drop in China's consumer welfare. Our results can contribute to devising policies to ensure China's food security and promote global sustainable development goals. [ABSTRACT FROM AUTHOR]

Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements (Cu, Fe, K, Mg, Mn, and Zn) by using three models for single-locus and six models for multi-locus analysis of a genome-wide association study (GWAS) using 174 diverse rice accessions and 6565 SNP markers. To declare a SNP as significant, −log10(P) ≥ 3.0 and 15% FDR significance cut-off values were used for single-locus models, while LOD ≥ 3.0 was used for multi-locus models. Using these criteria, 147 SNPs were detected by one or two GWAS methods at −log10(P) ≥ 3.0, 48 of which met the 15% FDR significance cut-off value. Single-locus models outperformed multi-locus models before applying multi-test correction, but once applied, multi-locus models performed better. While 14 (~29%) of the identified quantitative trait loci (QTLs) after multiple test correction co-located with previously reported genes/QTLs and marker associations, another 34 trait-associated SNPs were novel. After mining genes within 250 kb of the 48 significant SNP loci, in silico and gene enrichment analyses were conducted to predict their potential functions. These shortlisted genes with their functions could guide future experimental validation, helping us to understand the complex molecular mechanisms controlling rice grain mineral elements. [ABSTRACT FROM AUTHOR]

Given the food supply chain disruption from COVID-19 lockdowns around the world, we examine the predictive power of daily infectious diseases-related uncertainty (EMVID) on commodity traded futures within the agricultural bracket, sometimes known as the softs, using the heterogeneous autoregressive realised variance (HAR-RV) model. Considering the short-, medium-, and long-run recursive out-of-sample estimation approach, we estimate daily realised volatility by using intraday data within the 5 min interval for 15 agricultural commodity futures. During the COVID-19 episode, our results indicated that EMVID plays an important role in predicting the future path of agricultural commodity traded futures in the short, medium, and long run, i.e., h = 1, 5, and 22, respectively. According to the MSE-F test, these results are statistically significant. These results contain important implications for investors, portfolio managers, and speculators when faced with investment risk management and strategic asset allocation during infectious disease-related uncertainty. [ABSTRACT FROM AUTHOR]

Due to climate change, global warming, and reduced radiation, there is an urgent need for research to explore the effects on wheat grain filling and protein-related quality. In this study, two spring and two semi-winter varieties were analyzed. Six sowing dates were set in the experiment, at 10-day intervals from the beginning of the local sowing window. The seedling population of the first sowing date (S1) was 300 × 104 plants ha−1, which was observed to have increased by about 10% by the subsequent sowing date. During the experiment, due to the different dates of sowing, the treatments were in different growth stages; so, all the treatments were grown under different day and night temperatures and radiation to study the effects on post-anthesis grain filling and protein quality. The results showed that the sowing date decreased the effective accumulated temperature (EAT) and the cumulative radiation after anthesis and increased the daily average, maximum, and minimum temperatures. The decrease in the EAT of 94.99 °C d and the increase in the daily average temperature of 1.59 °C after antrum resulted in a decrease in the wheat grain weight of 4.5 g and an increase in the grain filling rate of 0.029 mg d−1. This may be due to the shortening of the wheat filling time with the increase in the day/night temperatures. Compared with the normal sowing date, the later sowing date caused a decrease in the EAT and an increase in the Tmean, which led to an increase in the wheat protein content, wet gluten content, and sedimentation value. There was a positive correlation between the grain filling rate and the protein content in the wheat. Compared with radiation, temperature significantly regulates wheat grain filling and protein formation. These results can be used to guide the sowing date to obtain a higher quality of wheat protein in the future climate change. [ABSTRACT FROM AUTHOR]

Flag leaf senescence is an important determinant of wheat yield, as leaf senescence occurs in a coordinated manner during grain filling. However, the biological process of early senescence of flag leaves post-anthesis is not clear. In this study, early senescence in wheat was investigated using a high-throughput RNA sequencing technique. A total of 4887 differentially expressed genes (DEGs) were identified, and any showing drastic expression changes were then linked to particular biological processes. A hierarchical cluster analysis implied potential relationships between NAC genes and post-anthesis senescence in the flag leaf. In addition, a large set of genes associated with the synthesis; transport; and signaling of multiple phytohormones (JA, ABA, IAA, ET, SA, BR, and CTK) were expressed differentially, and many DEGs related to ABA and IAA were identified. Our results provide insight into the molecular processes taking place during the early senescence of flag leaves, which may provide useful information in improving wheat yield in the future. [ABSTRACT FROM AUTHOR]

Copper is one of the chemical elements that is widely used in various sectors nowadays together with the development of civilization especially in agricultural and industrial sectors. Copper is also considered as one of the heavy metals that is commonly present in wastewater. This preliminary study conducted is mainly focused on the techniques of removal of copper in wastewater. There are a variety of approaches for treating industrial effluent contaminated with heavy metals such as copper. Copper separation can be accomplished using a variety of technologies, each of which has advantages that vary depending on the application. Chemical removal techniques that are commonly used for copper removal are adsorption, cementation, membrane filtration, electrochemical method, and photocatalysis. This study compares the fundamentals and performances of the treatment techniques in addition to the future perspective of copper removal in detail. The study highlights the present research in terms of its strengths and shortcomings, pointing out deficiencies that need to be addressed in future studies, pointing to future research prospects. [ABSTRACT FROM AUTHOR]

Grain yield and quality of wheat are both important components for food security. Great effort has been made in the genetic improvement of wheat grain yield in China. However, wheat grain quality (i.e., protein concentration and protein quality) has received much less attention and is often overlooked in efforts to improve grain yield. A timely summary of the recent process of wheat breeding for increasing yield and quality (which can be used to guide future breeding strategies) is essential but still lacking. This study evaluated the breeding efforts on grain yield and grain quality of 1908 wheat varieties in China over the past two decades, from 2001 to 2020. We found wheat yields show a 0.64–1.03% annual growth in the three-dominant wheat-growing regions in China. At the same time, there was no significant decrease in wheat protein concentration. Genetic yield potential was increased, and the genetic yield gap was closed. High grain yields and better quality can likely be achieved simultaneously by genomic selection in future wheat breeding. [ABSTRACT FROM AUTHOR]