The idea of electric-drive-reconstructed onboard charger (EDROC) systems, along with the concept of dual-channel charging, offers a novel design, thought to enhance the integration and fault tolerance of the charging system of electric vehicles (EVs). This article investigates a dual-channel EDROC incorporating an asymmetrical six-phase permanent magnet synchronous machine (ASPMSM). A unique operation mode, called the unbalanced charging voltage operation mode, exists in this topology, in case the voltages of the two batteries are unequal. This unbalance results in different winding currents following through two channels, leading to an undesired charging torque in the machine. To ensure the safety of the system, an effective charging torque elimination method, based on dual-channel winding current balance, is proposed, which achieves a dot-shaped current path of torque generation-associated subspace (i.e., α–β subspace) by balancing the dual-channel charging power. Eventually, a controller is designed for the system and a prototype is created, to validate the effectiveness of the proposed method. Full article

Understanding the characteristics of coal spontaneous combustion (CSC) in goaf under different porosities is crucial for comprehending the mechanism of CSC and its prevention and control. In this paper, a multi-field coupled model of CSC in the goaf, considering porosity variation, is developed to investigate the effect of porosity on the CSC characteristics in the goaf. The results indicate that, as the goaf depth increases, both porosity and permeability decrease. When the highest goaf porosity is 25%, the average airflow velocity is between 0.00134 and 0.00139 m/s. In contrast, the average airflow velocity in the goaf with a porosity of 40% is approximately six times greater than that of the goaf with a porosity of 25%. As the goaf porosity increases, the overall oxygen concentration, temperature, and oxidized zone area also rise. Moreover, the oxidation zone area can be quantified and visualized, thereby enabling more effective prediction of the CSC risk in the goaf. The findings of the study have a positive significance in guiding the prevention and control of coal fires. Full article

This paper aims to provide an effective method for pricing forward starting options under the double fractional stochastic volatilities mixed-exponential jump-diffusion model. The value of a forward starting option is expressed in terms of the expectation of the forward characteristic function of log return. To obtain the forward characteristic function, we approximate the pricing model with a semimartingale by introducing two small perturbed parameters. Then, we rewrite the forward characteristic function as a conditional expectation of the proportion characteristic function which is expressed in terms of the solution to a classic PDE. With the affine structure of the approximate model, we obtain the solution to the PDE. Based on the derived forward characteristic function and the Fourier transform technique, we develop a pricing algorithm for forward starting options. For comparison, we also develop a simulation scheme for evaluating forward starting options. The numerical results demonstrate that the proposed pricing algorithm is effective. Exhaustive comparative experiments on eight models show that the effects of fractional Brownian motion, mixed-exponential jump, and the second volatility component on forward starting option prices are significant, and especially, the second fractional volatility is necessary to price accurately forward starting options under the framework of fractional Brownian motion. Full article

In recent years, various complex systems and real-world phenomena have been shown to include memory and hereditary properties that change with respect to time, space, or other variables. Consequently, fractional partial differential equations containing variable-order fractional operators have been extensively resorted for modeling such phenomena accurately. In this paper, we consider the two-dimensional fractional cable equation with the Caputo variable-order fractional derivative in the time direction, which is preferable for describing neuronal dynamics in biological systems. A point-wise scheme, namely, the Crank–Nicolson finite difference method, along with a group-wise scheme referred to as the explicit decoupled group method are proposed to solve the problem under consideration. The stability and convergence analyses of the numerical schemes are provided with complete details. To demonstrate the validity of the proposed methods, numerical simulations with results represented in tabular and graphical forms are given. A quantitative analysis based on the CPU timing, iteration counting, and maximum absolute error indicates that the explicit decoupled group method is more efficient than the Crank–Nicolson finite difference scheme for solving the variable-order fractional equation. Full article

Considering the Friedmann–Lemaître–Robertson–Walker (FLRW) metric and the Einstein scalar field system as an underlying gravitational model to construct fractional cosmological models has interesting implications in both classical and quantum regimes. Regarding the former, we just review the most fundamental approach to establishing an extended cosmological model. We demonstrate that employing new methodologies allows us to obtain exact solutions. Despite the corresponding standard models, we cannot use any arbitrary scalar potentials; instead, it is determined from solving three independent fractional field equations. This article concludes with an overview of a fractional quantum/semi-classical model that provides an inflationary scenario. Full article

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes. Full article

Water is essential for maintaining plant health and optimal growth in agriculture. While some crops depend on irrigation, others can rely on rainfed water, depending on regional climatic conditions. This is exemplified by grapevines, which have specific water level requirements, and irrigation systems are needed. However, these systems can be susceptible to damage or leaks, which are not always easy to detect, requiring meticulous and time-consuming inspection. This study presents a methodology for identifying potential damage or leaks in vineyard irrigation systems using RGB and thermal infrared (TIR) imagery acquired by unmanned aerial vehicles (UAVs). The RGB imagery was used to distinguish between grapevine and non-grapevine pixels, enabling the division of TIR data into three raster products: temperature from grapevines, from non-grapevine areas, and from the entire evaluated vineyard plot. By analyzing the mean temperature values from equally spaced row sections, different threshold values were calculated to estimate and map potential leaks. These thresholds included the lower quintile value, the mean temperature minus the standard deviation ($T_{mean}-\sigma$), and the mean temperature minus two times the standard deviation ($T_{mean}-2\sigma$). The lower quintile threshold showed the best performance in identifying known leak areas and highlighting the closest rows that need inspection in the field. This approach presents a promising solution for inspecting vineyard irrigation systems. By using UAVs, larger areas can be covered on-demand, improving the efficiency and scope of the inspection process. This not only reduces water wastage in viticulture and eases grapevine water stress but also optimizes viticulture practices. Full article

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively. Full article

This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example. Full article

Wall proximity affects the accuracy of pressure probe measurements with a particularly strong impact on multi-hole probes. The wall-related evolution of the calibration of two hemispheric L-shaped 3D-printed five-hole probes was investigated in a low-speed wind tunnel. Pressure measurements and 2D particle image velocimetry were performed. The wall proximity causes the probe to measure a flow diverging from the wall, whereas the boundary layer causes the probe to measure a velocity directed towards the wall. Both angular calibration coefficients are affected in different manners. The error in angle measurement can reach 7°. These errors can be treated as calibration information. Acceleration caused by blockage is not the main reason for the errors. Methods to perform measurements closer to the wall are suggested. Full article

The gut microbiome reflects health and predicts possible disease in hosts. A holistic view of this community is needed, focusing on identifying species and dissecting how species interact with their host and each other, regardless of whether their presence is beneficial, inconsequential, or detrimental. The distribution of gut-associated eukaryotes within and across non-human primates is likely driven by host behavior and ecology. To ascertain the existence of free-living amoebae (FLA) in the gut of wild and captive non-human primates, 101 stool samples were collected and submitted to culture-dependent microscopy examination and DNA sequencing. Free-living amoebae were detected in 45.4% (46/101) of fecal samples analyzed, and their morphological characteristics matched those of Acanthamoeba spp., Vermamoeba spp., heterolobosean amoeboflagellates and fan-shaped amoebae of the family Vannellidae. Sequence analysis of the PCR products revealed that the suspected amoebae are highly homologous (99% identity and 100% query coverage) with Acanthamoeba T4 genotype and Vermamoeba vermiformis amoebae. The results showed a great diversity of amoebae in the non-human primate’s microbiome, which may pose a potential risk to the health of NHPs. To our knowledge, this is the first report of free-living amoebae in non-human primates that are naturally infected. However, it is unknown whether gut-borne amoebae exploit a viable ecological niche or are simply transient residents in the gut. Full article

Macrobrachium rosenbergii is a warm water species, and low temperature is a limiting factor for its growth and survival. In order to explore the role of the acetyl-CoA-carboxylase (ACC) gene in response to the cold stress of M. rosenbergii, we investigated the effects of RNA interference (RNAi) with the ACC gene on the expression of fatty acid metabolism-related genes and the mortality of M. rosenbergii under cold stress. The results showed that different siRNA sequences and different injection concentrations had different inhibiting effects on ACC gene expression, and siRNA-III with an injection concentration of 2.0 μg/g (siRNA/prawn body weight) had the best interference effect. With the optimal siRNA and the optimal concentration under cold stress, the expressions of three fatty acid metabolism-related genes, FabD, echA, and ACOT, were generally significantly down-regulated. Compared to negative (scrambled-siRNA) and blank (PBS) control groups, the expression of FabD in the interference group was extremely significantly down-regulated at 12 h in the hepatopancreas and at 18 h in the muscles and gills; EchA was highly significantly down-regulated at 6 and 12 h in the muscles and gills; and ACOT was extremely significantly down-regulated and kept declining in the gills. Within 6–18 h after injection under cold stress, the mortality rate of the siRNA interference group (75%) was much lower than that of the negative (95%) or blank control group (97.5%), and all prawns died after 24 h. In conclusion, RNA interference with the ACC gene inhibited the expression of some fatty acid metabolism-related genes, and could partly improve the tolerance of M. rosenbergii to cold stress, indicating that the ACC gene might play an important role in the response of M. rosenbergii to cold stress. Full article

The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm’s efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm’s escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm’s exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance. Full article

The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard. This paper delves into essential research queries to achieve visually lossless images. The paper describes the influence of compression on image quality, appropriate objective image quality metrics (IQMs), and the effectiveness of subjective assessment methods. It also provides an overview of the existing literature, surveys, and subjective and objective image quality assessment (IQA) methods. Our aim is to offer insights, identify challenges in existing methodologies, and assist researchers in selecting the most effective assessment approach for their needs. Full article

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models. Full article

Blended electrodes are becoming increasingly more popular in lithium-ion batteries, yet most modeling approaches are still lacking the ability to separate the blend components. This is problematic because the different components are unlikely to degrade at the same pace. This work investigated a new approach towards the simulation of blended electrodes by replicating the complex current distributions within the electrodes using a paralleling model rather than the traditional constant-current method. In addition, a blending model was used to generate three publicly available datasets with more than 260,000 unique degradations for three exemplary blended cells. These datasets allowed us to showcase the necessity of considering all active components of the blend separately for diagnosis and prognosis. Full article

In this study, we systematically explored how changing groove surfaces of iron oxide/water nanofluid could affect the pool boiling heat transfer. We aimed to investigate the effect of three types of grooves, namely rectangular, circular, and triangular, on the boiling heat transfer. The goal was to improve heat transfer performance by consciously changing surface structure. Comparative analyses were conducted with deionized water to provide valuable insights. Notably, the heat transfer coefficient (HTC) exhibited a significant increase in the presence of grooves. For deionized water, the HTC rose by 91.7% and 48.7% on circular and rectangular grooved surfaces, respectively. Surprisingly, the triangular-grooved surface showed a decrease of 32.9% in HTC compared to the flat surface. On the other hand, the performance of the nanofluid displayed intriguing trends. The HTC for the nanofluid diminished by 89.2% and 22.3% on rectangular and triangular grooved surfaces, while the circular-grooved surface exhibited a notable 41.2% increase in HTC. These results underscore the complex interplay between groove geometry, fluid properties, and heat transfer enhancement in nanofluid-based boiling. Hence, we thoroughly examine the underlying mechanisms and elements influencing these observed patterns in this research. The results provide important insights for further developments in this area by shedding light on how surface changes and groove geometry may greatly affect heat transfer in nanofluid-based pool boiling systems. Full article

This paper tests the hypothesis that action-based fiscal consolidations have a negative effect on GDP growth. Using the IMF’s dataset on action-based fiscal consolidations, instrumental variables’ regressions show that action-based fiscal consolidations have a significant positive effect on GDP growth. The instrumental variables’ regressions also show that action-based fiscal consolidations significantly increase investment and productivity. The findings presented in this paper thus strongly reject the hypothesis that action-based fiscal consolidations reduce growth. The paper argues that least squares estimates presented in previous literature suffer from negative reverse causality bias: GDP growth has a significant positive effect on both the likelihood and the magnitude of action-based fiscal consolidations. To uncover causal effects of action-based fiscal consolidations, researchers need to use an instrumental variables approach. Full article

The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders’ willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets. Full article

Background: Myocardial infarction with non-obstructive coronary arteries (MINOCA) is a clinical situation characterized by evidence of acute myocardial infarction (AMI)—according to the Fourth Universal Definition of Myocardial Infarction—with normal or near-normal coronary arteries on angiographic study (stenosis < 50%). This condition is extremely variable in etiology, pathogenic mechanisms, clinical manifestations, prognosis and consequently therapeutic approach. Objective: The objective of the study was the evaluation of remnant cholesterol (RC), monocyte/high-density lipoprotein cholesterol ratio (MHR), platelet/lymphocyte ratio (PLR) and various lipoprotein ratios in patients with MINOCA in order to establish their validity as predictors of this event. Materials and Methods: We included 114 patients hospitalized in the Intensive Coronary Care Unit (ICCU) and Hospital Wards of our Hospital Center from 2015 to 2019 who received a diagnosis of MINOCA compared to a control group of 110 patients without previous cardiovascular events. RC was calculated with the following formula: RC = total cholesterol (TC) − HDL-C − LDL-C. MHR was calculated by dividing the monocyte count in peripheral blood by high-density lipoprotein cholesterol (HDL-C) levels; PLR was obtained by dividing platelet count by lymphocyte count. We also calculated various lipoprotein ratios, like total cholesterol/high-density lipoprotein cholesterol (TC/HDL-C), low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C), triglycerides/high-density lipoprotein cholesterol (TG/HDL-C), and non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol (non-HDL-C/HDL-C) ratios. Results: The MINOCA group had higher mean levels of RC (21.3 ± 10.6 vs. 13.2 ± 7.7 mg/dL), MHR (23 ± 0.009 vs. 18.5± 8.3) and PLR (179.8 ± 246.1 vs. 135 ± 64.7) than the control group. Only the mean values of all calculated lipoprotein ratios were lower in MINOCA patients. Statistical significance was achieved only in the RC evaluation. Conclusions: Higher levels of RC and MHR were found in patients with MINOCA. We also observed higher levels of PLR than in the control group. Only various lipoprotein ratios were lower, but this could reflect the extreme heterogeneity underlying the pathogenic mechanisms of MINOCA. In patients who receive a diagnosis of MINOCA with a baseline alteration of the lipid profile and higher levels of cholesterol at admission as well, the evaluation of these parameters could play an important role, providing more detailed information about their cardiometabolic risk. Full article

Infectious hematopoietic necrosis virus (IHNV) is a serious pathogen that causes great economic loss to the salmon and trout industry. Previous studies showed that IHNV alters the expression patterns of splenic microRNAs (miRNAs) in rainbow trout. Among the differentially expressed miRNAs, miRNA146a-3p was upregulated by IHNV. However, it is unclear how IHNV utilizes miRNA146a-3p to escape the immune response or promote viral replication. The present study suggested that one multiplicity of infection (MOI) of IHNV induced the most significant miR-146a-3p expression at 1 day post infection (dpi). The upregulation of miR-146a-3p by IHNV was due to viral N, P, M, and G proteins and relied on the interferon (IFN) signaling pathway. Further investigation revealed that Wingless-type MMTV integration site family 3a (WNT3a) and G1/S-specific cyclin-D1-like (CCND1) are the target genes of miRNA-146a-3p. The regulation of IHNV infection by miRNA-146a-3p is dependent on WNT3a and CCND1. MiRNA-146a-3p was required for the downregulation of WNT3a and CCND1 by IHNV. Moreover, we also found that WNT3a and CCND1 are novel proteins that induce the type-I IFN response in RTG-2 cells, and both of them could inhibit the replication of IHNV. Therefore, IHNV-induced upregulation of miRNA-146a-3p promotes early viral replication by suppressing the type-I IFN response by targeting WNT3a and CCND1. This work not only reveals the molecular mechanism of miRNA-146a-3p during IHNV infection but also provides new antiviral targets for IHNV. Full article