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This study aims to employ a causal network model based on transfer entropy for the early warning of systemic risk in commodity markets. We analyzed the dynamic causal relationships of prices for 25 commodities related to China (including futures and spot prices of energy, industrial metals, precious metals, and agricultural products), validating the effect of the causal network structure among commodity markets on systemic risk. Our research results identified commodities and categories playing significant roles, revealing that industry and precious metal markets possess stronger market information transmission capabilities, with price fluctuations impacting a broader range and with greater force on other commodity markets. Under the influence of different types of crisis events, such as economic crises and the Russia–Ukraine conflict, the causal network structure among commodity markets exhibited distinct characteristics. The results of the effect of external shocks to the causal network structure of commodity markets on the entropy of systemic risk suggest that network structure indicators can warn of systemic risk. This article can assist investors and policymakers in managing systemic risk to avoid unexpected losses. [ABSTRACT FROM AUTHOR]

Neurological diseases are a significant health threat, often presenting through abnormalities in electroencephalogram (EEG) signals during seizures. In recent years, machine learning (ML) technologies have been explored as a means of automated EEG pathology diagnosis. However, existing ML-based EEG binary classification methods largely focus on extracting EEG-related features, which may lead to poor performance in classifying EEG signals by overlooking potentially redundant information. In this paper, we propose a novel Kruskal–Wallis (KW) test-based framework for EEG pathology detection. Our framework first divides EEG data into frequency sub-bands using wavelet packet decomposition and then extracts statistical characteristics from each selected coefficient. Next, the piecewise aggregation approximation technique is used to obtain the aggregated feature vectors, followed by the KW statistical test methodology to select significant features. Finally, three ensemble learning classifiers, random forest, categorical boosting (CatBoost), and light gradient boosting machine, are used to classify the extracted significant features into normal or abnormal classes. Our proposed framework achieves an accuracy of 89.13%, F1-score of 87.60%, and G-mean of 88.60%, respectively, outperforming other competing techniques on the same dataset, which shows the great promise in EEG pathology detection. [ABSTRACT FROM AUTHOR]

The deformation force is an important factor affecting the forming accuracy of parts in the incremental forming process of sheet metal. This paper proposes an analytical calculation method of the deformation force based on pure shear deformation. After assuming and simplifying the factors affecting the deformation force, a graphical method is used to approximate the contact area between the forming tool and the sheet metal. A forming test is also designed. In addition, the deformation force is measured in the experiment, and its theoretical analysis value is compared with the actual measurement value of the forming test to validate the analytical method of deformation force calculation. The results show that the radial forming deviations are 28.5% and 22.5%, the axial deformation force deviations are 9.8% and 16.1%, and the forming force deviations are 6.3% and 10.3%, which demonstrates the effectiveness of using the analytical method to calculate the deformation force. [ABSTRACT FROM AUTHOR]

Fused Filament Fabrication (FFF) is a popular additive manufacturing process to produce printed polymer components, whereby their strength is highly dependent on the process parameters. The raster angle and infill pattern are two key process parameters and their effects on flexural properties need further research. Therefore, the present study aimed to print test specimens with varying raster angles and infill patterns to learn their influence on the in-plane and edgewise flexural properties of acrylonitrile–butadiene–styrene (ABS) material. The results revealed that the highest in-plane and edgewise flexural moduli were obtained when printing was performed at 0° raster angle. In comparison, the lowest values were obtained when the printing was executed with a 90° raster angle. Regarding the infill pattern, the tri-hexagon pattern showed the largest in-plane modulus, and the quarter-cubic pattern exhibited the greatest edgewise flexural modulus. However, considering both the modulus and load carrying capacity, the quarter-cubic pattern showed satisfactory performance in both planes. Furthermore, scanning electron microscopy was used to investigate the failure modes, i.e., raster rupture, delamination of successive layers and void formation. The failure occurred either due to one or a combination of these modes. [ABSTRACT FROM AUTHOR]

In this study, a Glutinous rice 89-1 (Gr 89-1) × Shuhui 527 recombinant inbred line population (RIL) comprising 309 F9-generations was used to screen gradient molecular markers. The phenotypic variation and distribution of eight agronomic traits obtained from multiyear and multilocation samples, as well as the network expression relationships between agronomic traits and molecular markers, were investigated. The results showed that there were 14 phenotypic lines with significant differences in the RILs, and the molecular testing results of most of the lines were consistent with the phenotype. The correlation degree between the first-level molecular markers and the eight agronomic traits was 100%. Excluding the correlations of third-level markers with grain width and grain length, the degree of correlation between molecular markers and agronomic traits decreased with an increase in marker levels. The RILs were divided into eight core populations and one approximate population, revealing genetic correspondence between agronomic traits and molecular markers. [ABSTRACT FROM AUTHOR]

In order to improve the initialization robustness of visual inertial SLAM, the complementarity of the optical flow method and the feature-based method can be used in vision data processing. The parallel initialization method is proposed, where the optical flow inertial initialization and the monocular feature-based initialization are carried out at the same time. After the initializations, the state estimation results are jointly optimized by bundle adjustment. The proposed method retains more mapping information, and correspondingly is more adaptable to the initialization scene. It is found that the initialization map constructed by the proposed method features a comparable accuracy to the one constructed by ORB-SLAM3 in monocular inertial mode. Since the online extrinsic parameter estimation can be realized by the proposed method, it is considered better than ORB-SLAM3 in the aspect of portability. By the experiments performed on the benchmark dataset EuRoC, the effectiveness and robustness of the proposed method are validated. [ABSTRACT FROM AUTHOR]

Ships sailing through cold regions frequently encounter floe ice fields. An air-bubble system that reduces friction between the hull and ice floes is thus considered useful for the reduction of ice-induced resistance. In this study, a numerical analysis procedure based on coupled finite volume method (FVM) and discrete element method (DEM) is proposed to simulate complicated hull-water-gas-ice interactions for ice-going ships installed with air-bubble systems. The simulations reveal that after turning on the air-bubble system ice floes in contact with the hull side wall are pushed away from the hull by the gas-water mixture, resulting in an ice-free zone close to the side hull. It is found that the drag reduction rate increases with the increase of ventilation, while the bow ventilation plays a deciding role in the overall ice-resistance reduction. The proposed procedure is expected to facilitate design of new generations of ice-going ships. [ABSTRACT FROM AUTHOR]

Neural networks are widely used in fruit sorting and have achieved some success. However, due to the limitations of storage space and power consumption, the storage and computing of a neural network model on embedded devices remain a massive challenge. Aiming at realizing a lightweight mango sorting model, the feature-extraction characteristics of the shallow and deep networks of the SqueezeNet model were analyzed by a visualization method, and then eight lightweight models were constructed by removing redundant layers or modifying the convolution kernel. It was found that the model designated Model 4 performed well after training and testing. The class activation mapping method was used to explain the basis of the classification decision, and the model was compared with ten classical classification models. The results showed that the calculation performance of the model was significantly improved without reducing accuracy. The parameter storage requirement is 0.87 MB, and the calculation amount is 181 MFLOPS, while the average classification accuracy can still be maintained at 95.64%. This model has a high-cost performance and can be widely used in embedded devices. [ABSTRACT FROM AUTHOR]

Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones' spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R2) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R2 = 0.64 and 0.59); and models for chlorophyll and ΔE* (R2 = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas. [ABSTRACT FROM AUTHOR]

This can help to prevent the build-up of corrosion products on the surface of the substrate and can significantly reduce the risk of corrosion. For example, this Special Issue will discuss the various types of coatings available for corrosion protection, including organic coatings (e.g., paints, lacquers), inorganic coatings (e.g., ceramics, metallic coatings) [[9]], and composite coatings (e.g., metal-matrix composites) [[11]]. Corrosion is a costly and dangerous phenomenon that affects many industries, leading to significant economic losses and, in some cases, catastrophic failures. Overall, advanced corrosion protection through coatings and surface rebuilding is an important and effective way to protect metals from corrosion. [Extracted from the article]

Porous microstructure is a common surface morphology that is widely used in antifouling, drag reduction, adsorption, and other applications. In this paper, the lattice gas automata (LGA) method was used to simulate the non-uniform electrochemical machining of porous structure at the mesoscopic level. In a cellular space, the metal and the electrolyte were separated into orderly grids, the migration of corrosive particles was determined by an electric field, and the influences of the concentration gradient and corrosion products were considered. It was found that different pore morphologies were formed due to the competition between dissolution and diffusion. When the voltage was low, diffusion was sufficient, and no deposit was formed at the bottom of the pore. The pore grew faster along the depth and attained a cylindrical shape with a large depth-to-diameter ratio. As the voltage increased, the dissolution rates in all directions were the same; therefore, the pore became approximately spherical. When the voltage continued to increase, corrosion products were not discharged in time due to the rapid dissolution rate. Consequently, a sedimentary layer was formed at the bottom of the pore and hindered further dissolution. In turn, a disc-shaped pore with secondary pores was formed. The obtained simulation results were verified by experimental findings. This study revealed the causes of different morphologies of pores, which has certain guiding significance for non-uniform electrochemical machining. [ABSTRACT FROM AUTHOR]

Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms. [ABSTRACT FROM AUTHOR]

The innovative Incremental Sheet Forming (ISF) process affects the post-forming properties of thermoplastic polymers. However, the effects of degree of plastic strain, and the orientation and size of specimen on the mechanical properties are still unknown. In the present study, therefore, the ISF process is performed on a polymer sheet by varying the plastic strain ranging from 6% to 108%. The corresponding effects on the properties and associated polymer structure are quantified by conducting a variety of mechanical and structural tests. The results reveal that the post-ISF tensile properties like yield stress, ultimate stress, drawing stress, elastic modulus and elongation decrease from 26.6 to 10 MPa, 30.5 to 15.4 MPa, 18.9 to 9.9 MPa, 916 to 300 MPa and 1107% to 457%, respectively, as the strain increases in the investigated range. The value of post-ISF relaxation properties, contrary to the tensile properties, increases with increasing strain up to 62%. Particularly, reductions in stress, strain and modulus increase from 41% to 202%, 37% to 51%, and 41% to 202%. As regard the orientation effect, the sheet in the feed direction shows greater strength than the transverse direction (up to 142% in yield stress and 72% in ultimate stress). Moreover, the smaller sample offers greater strength than the larger one (up to 158% in yield stress and 109% in ultimate stress). The analysis of the post-ISF tensile properties and structural results lead us to conclude that the drop in the tensile properties due to increasing strain occurs due to corresponding increase in the voids area fraction (1.25% to 31%) and a reduction in the crystallinity (38% to 31%). [ABSTRACT FROM AUTHOR]

The present study was focused on establishing guidelines for successful friction stir welding of Al alloys and Cu lap joints. Detailed investigations in respect to tool geometry, tool material, work-piece material, welding parameters, stacking sequence, and heat sink were carried out. The soundness of welded joints was tested through microscopic analysis and the lap shear test. The results revealed that the tungsten carbide (WC) tool with square-pin produced sound joints in terms of minimized defects and high strength. Further, the use of heat sink proved as an important pre-requisite when the stacking sequence was inversed (i.e., Cu-Al), and this stacking configuration in comparison with the Al-Cu stacking yielded weaker joints. The influence of the tool welding speed (F, mm/min) was found to depend upon the tool material. A range of tool welding speed (23.5–37.5 mm/min) worked well for the WC tool. However, only two values of welding speed (30 mm/min and 37 mm/min) were observed to be conducive when the tool material was HSCo (high-speed cobalt)-steel. Finally, it was concluded to employ the WC tool with square-pin, a welding speed of 30 mm/min, the rotational speed (S, rpm) of 1500 mm/min, and Al-Cu stacking sequence to successfully process the Al/Cu lap joints. [ABSTRACT FROM AUTHOR]

Laminated metals sheets have been used widely in various sectors including the optoelectronics, aerospace, ship, and automotive industries because of their desirable mechanical properties. Therefore, techniques for successful joining of such laminates are a focus of study. The objective of this study was to perform butt-welding of bi-layered aluminum laminated sheets using the friction stir welding (FSW) technique. The tensile properties, flexural properties, residual stress distribution, and fractured surfaces of the final weldments were analyzed. The effects of the process parameters on the mechanical properties of the weldments were also investigated. Mixing defects (voids and cracks) were observed in the stir zone (SZ) at the following combinations: low rotational speed (ω) and low tool traverse speeds (v) and also at high ω and high v, which substantially decreased the tensile and bending strength of the weldments as well as % elongation. Moreover, the sample welded at ω = 1500 rpm and v = 47.5 mm/min showed the best performance under mechanical loading: with a % elongation and tensile strength of 24.72% and 79.10 MPa, respectively. Similarly, the sample welded at ω = 750 rpm and v = 118 mm/min showed the highest flexural strength of 3.15 MPa. Finally, the suitable parameters are proposed for the joining of bi-layered aluminum laminated sheets. [ABSTRACT FROM AUTHOR]