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The key to intelligent seed potato cutting technology lies in the accurate and rapid identification of potato bud eyes. Existing detection algorithms suffer from low recognition accuracy and high model complexity, resulting in an increased miss rate. To address these issues, this study proposes a potato bud-eye-detection algorithm based on an improved YOLOv8s. First, by integrating the Faster Neural Network (FasterNet) with the Efficient Multi-scale Attention (EMA) module, a novel Faster Block-EMA network structure is designed to replace the bottleneck components within the C2f module of YOLOv8s. This enhancement improves the model's feature-extraction capability and computational efficiency for bud detection. Second, this study introduces a weighted bidirectional feature pyramid network (BiFPN) to optimize the neck network, achieving multi-scale fusion of potato bud eye features while significantly reducing the model's parameters, computation, and size due to its flexible network topology. Finally, the Efficient Intersection over Union (EIoU) loss function is employed to optimize the bounding box regression process, further enhancing the model's localization capability. The experimental results show that the improved model achieves a mean average precision (mAP@0.5) of 98.1% with a model size of only 11.1 MB. Compared to the baseline model, the mAP@0.5 and mAP@0.5:0.95 were improved by 3.1% and 4.5%, respectively, while the model's parameters, size, and computation were reduced by 49.1%, 48.1%, and 31.1%, respectively. Additionally, compared to the YOLOv3, YOLOv5s, YOLOv6s, YOLOv7-tiny, and YOLOv8m algorithms, the mAP@0.5 was improved by 4.6%, 3.7%, 5.6%, 5.2%, and 3.3%, respectively. Therefore, the proposed algorithm not only significantly enhances the detection accuracy, but also greatly reduces the model complexity, providing essential technical support for the application and deployment of intelligent potato cutting technology. [ABSTRACT FROM AUTHOR]

Although reclaimed water (RW) has become a promising alternative source for alleviating water shortage in arid and semiarid regions, the ecological risks it poses to the receiving water bodies remain largely unknown. Dissolved organic matter (DOM) is crucial for affecting the quality of RW and strongly influences bacterial communities (BCs) in aquatic ecosystems. In this study, we aimed to unravel the role of DOM signatures on the spatiotemporal composition of microbial communities (MCs) in a seasonally ice-sealed urban river that had been chronically replenished by RW. We found that discharging RW resulted in elevated DOM levels in the receiving river. Notably, an increase of 10% in protein-like substances was revealed. The differences between compositional characteristics of DOM and the abundance of riverine BCs between freezing and non-freezing periods were revealed. In the freezing season, humic-like components, aromaticity, and hydrophobicity of DOM were more significant, and bacterial taxa such as Bacteriodetes and Flavobacterium were increased, while Proteobacteria was decreased. Similarly, co-occurrence network analysis revealed an enhanced interplay between DOM and BCs at the same time. However, Klebsiella pneumoniae markedly decreased during the ice-sealed period. These results suggest that variations in DOM characteristics have remarkable impacts on the dynamics of aquatic BCs, which points to the need for a DOM−oriented RW quality monitoring strategy. [ABSTRACT FROM AUTHOR]

Phlorizin, as a flavonoid from a wide range of sources, is gradually becoming known for its biological activity. Phlorizin can exert antioxidant effects by regulating the IL-1β/IKB-α/NF-KB signaling pathway. At the same time, it exerts its antibacterial activity by reducing intracellular DNA agglutination, reducing intracellular protein and energy synthesis, and destroying intracellular metabolism. In addition, phlorizin also has various pharmacological effects such as antiviral, antidiabetic, antitumor, and hepatoprotective effects. Based on domestic and foreign research reports, this article reviews the plant sources, extraction, and biological activities of phlorizin, providing a reference for improving the clinical application of phlorizin. [ABSTRACT FROM AUTHOR]

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%. [ABSTRACT FROM AUTHOR]

Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performed between the prediction model functioning with a buffer and without one, and the accuracy of the location model was verified by comparing the actual change trend and the predicted trend in two years. The following conclusions were obtained: (1) coffee shops in the main urban area of Beijing are clustered in an area within 12 km of the main urban center, and also around the core commercial agglomeration area; (2) the random forest (RF) model is the best model in this study, and the accuracy values before and after buffer analysis were 0.915 and 0.929, respectively; and (3) after verifying the accuracy of the model through two years of data, we recommend the establishment of a main road buffer zone for site selection, and the success rate of site selection was found to reach 72.97%. This study provides crucial insight for coffee shop prediction model selection and potential store location selection, which is significant to improving the layout of leisure spaces and promoting economic development. [ABSTRACT FROM AUTHOR]

Cord steel is used for making tire frames and wire saws for cutting silicon wafers. The diameter of mainstream cutting wire has been developed to be lower than 100 μm. The size and deformation ability of inclusions are very important to the wire breaking rate of cord steel during the drawing process. In order to improve the deformation ability of the inclusions in cord steel, alkali metal oxide was added into the molten steel to improve the inclusions in the steel so as to obtain good, plastic, low-melting-point inclusions. Mass fractions of 0.3%, 0.5% and 1.0% K2CO3, Na2CO3 and B2O3 were added into cord steel, which were melted in 10 furnaces (including 0% alkali metal oxides, mass fractions of 0.3%/0.5%/1.0% K2CO3, Na2CO3 and B2O3). The morphology and composition of inclusions were observed by SEM-EDS. Factsage phase diagram calculations and experimental results show that, with the increase in Na2CO3 content in cord steel, the aluminum content in the inclusions gradually decreased. When the mass fraction of Na2CO3 was 0.5% per ton, most of the inclusions in the steel fell in the low melting point region (less than 1300 °C). With the increase in K2CO3 content in cord steel, the silicon content in the inclusions decreased gradually. When the mass fraction of K2CO3 was 0.5% per ton, most of the inclusions in the steel fell in the low melting point region. The deformation ability of the inclusions added with 0.5% Na2CO3 in the steel during forging was better than that of the inclusions added with 0.5% K2CO3. After adding B2O3, the inclusions in the steel were SiO2-MnO-Al2O3 inclusions or inclusions with SiO2-MnO-Al2O3 as the core and BN wrapped around. Boron could not be dissolved into the inclusions for plastic modification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system's positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments. [ABSTRACT FROM AUTHOR]

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm's versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving. [ABSTRACT FROM AUTHOR]

Classifying mobile applications from encrypted network traffic is a common and basic requirement in network security and network management. Existing works classify mobile applications from flows, based on which application fingerprints and classifiers are created. However, mobile applications often generate concurrent flows with varying degrees of ties, such as low discriminative flows across applications and application-specific flows. So flow-based methods suffer from low accuracy. In this paper, a novel mobile application-classifying method is proposed, capturing relationships between flows and paying attention to their importance. To capture the inter-flow relationships, the proposed method slices raw mobile traffic into traffic chunks to represent flows as nodes, embeds statistical features into nodes, and adds edges according to cross-correlations between the nodes. To pay different attention to the various flows, the proposed method builds a deep learning model based on graph attention networks, implicitly assigning importance values to flows via graph attention layers. Compared to recently developed techniques on a large dataset with 101 popular apps using the Android platform, the proposed method improved by 4–20% for accuracy, precision, recall, and F1 score, and spent much less time training. [ABSTRACT FROM AUTHOR]

The corrosion behavior of reinforcing steel in the ITT under a submarine environment was investigated. Electrochemical tests were carried out to separately determine the linear polarization curves and the AC impedance spectra of rebars in the ITT scaled-down models subjected to pressurized seawater erosion, from which key parameters were obtained, including the self-corrosion potential (Ecorr), corrosion current density (icorr), polarization resistance (Rp), concrete resistance (Rc), and charge migration resistance (Rct). The results show that in the process of pressurized seawater erosion, the rebars on the seawater side of the ITT models corroded earlier than the rebars on the cavity side, and it is recommended that anti-chloride ion penetration measures be taken on the surface of the seawater side as a priority in the project. The corrosion rate of rebars on the seawater side was significantly higher than that on the cavity side, and the corrosion rate of rebars on the cavity side increased as the erosion time increased. The corrosion rate of rebars in the ITT models was affected by chloride ions to a greater extent than by oxygen. Furthermore, by regression equation, a linear function between Rp obtained from the polarization curves and Rct obtained from the AC impedance spectra was established. [ABSTRACT FROM AUTHOR]

Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer's experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model's generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability. [ABSTRACT FROM AUTHOR]

High-frequency ultrasound (HFUS), the imaging modality of choice for thyroid screening, is most commonly used in the study of diffuse thyroid disease (DTD) with Hashimoto's thyroiditis (HT) and Graves' disease (GD). DTD can involve thyroid function and severely affect life quality, so early diagnosis is important for the development of timely clinical intervention strategies. Previously, the diagnosis of DTD relied on qualitative ultrasound imaging and related laboratory tests. In recent years, with the development of multimodal imaging and intelligent medicine, ultrasound and other diagnostic imaging techniques have gradually become more widely used for quantitative assessment of the structure and function of DTD. In this paper, we review the current status and progress of quantitative diagnostic ultrasound imaging techniques for DTD. [ABSTRACT FROM AUTHOR]

Opioids are widely used in clinical practice, but drug overdoses can lead to many adverse reactions, and even endanger life. Therefore, it is essential to implement real-time measurement of drug concentrations to adjust the dosage given during treatment, keeping drug levels within therapeutic levels. Metal-Organic frameworks (MOFs) and their composite materials modified bare electrode electrochemical sensors have the advantages of fast production, low cost, high sensitivity, and low detection limit in the detection of opioids. In this review, MOFs and MOFs composites, electrochemical sensors modified with MOFs for the detection of opioids, as well as the application of microfluidic chips in combination with electrochemical methods are all reviewed, and the potential for the development of microfluidic chips electrochemical methods with MOFs surface modifications for the detection of opioids is also prospected. We hope that this review will provide contributions to the study of electrochemical sensors modified with MOFs for the detection of opioids. [ABSTRACT FROM AUTHOR]

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults. [ABSTRACT FROM AUTHOR]

In this work, poly (vinylidene fluoride) (PVDF) hierarchically porous membranes (HPMs) with isolated large pores and continuous narrow nano-pores have been fabricated from its blend with poly (methyl methacrylate) (PMMA) based on the combination of crystallization template with chemical or supercritical CO2 foaming. On the one hand, the decomposition of azodicarbonamide (ADC, chemical foaming agent) or the release of CO2 can produce isolated large pores. On the other hand, PMMA is expelled during the isothermal crystallization of PVDF in their miscible blend, yielding narrow nano-pores upon etching with a selective solvent. In the case of supercritical CO2, the attained PVDF HPMs fail to improve separation performance because of the compact wall of isolated-large-pore and consequent poor connectivity of hierarchical pores. In the case of ADC, the optimal HPM exhibits much higher flux (up to 20 times) without any loss of selectivity compared with the reference only with nano-pores. The enhanced permeability can be attributed to the shorter diffusion length and lower diffusion barrier from isolated large pores, while the comparable selectivity is determined by narrow nano-pores in THE matrix. [ABSTRACT FROM AUTHOR]

Styrene–butadiene rubber (SBR) has been extensively applied to enhance the toughness of hardened cement. The instability of existing liquid latex leads to difficulties in storage and transportation, and even performance regression. Thus, the well-dispersed carboxylated butylbenzene (SISBR) latex powders were fabricated through the seed emulsion polymerization of liquid polybutadiene (LPB), styrene (St), itaconic acid (IA), and sodium p-styrenesulfonate (SSS) to overcome the difficulties. The dispersion performance of latex powders with various IA amounts was quantitatively evaluated using particle size distribution, zeta potential, and ultraviolet–visible spectrophotometry. Results showed that the carboxylic ionic (COO-) from IA enhanced the dispersing abilities of SISBR latex powders, which ensured the uniform distribution in water. Based on this, the influence of latex powder on cement was assessed mainly by fluidity, isothermal heat flow calorimetry, X-ray diffraction (XRD), and triaxial mechanical testing. Results showed the fluidity and dispersion performance of cement were improved with more IA in latex, while the hydration of cement was retarded due to excessive adsorption of carboxyl (-COOH) groups in IA. Triaxial mechanical testing showed that cement with SISBR-3 (latex containing 3% IA) exhibited the minimal elastic modulus of 3.16 GPa, which was lower than that of plain cement (8.34 GPa). [ABSTRACT FROM AUTHOR]

Musculoskeletal ultrasound (MSKUS) has been recognized as an important method for the evaluation of diseases of the musculoskeletal system, and contrast-enhanced ultrasound (CEUS) technology is becoming an important branch of it. The development of novel materials and tiny nano-formulations has further expanded ultrasound contrast agents (UCAs) into the field of nanotechnology. Over the years, nanoscale contrast agents have been found to play an unexpected role in the integration of precise imaging for diagnosis and treatment of numerous diseases. It has been demonstrated that nanoscale UCAs (nUCAs) have advantages in imaging over conventional contrast agents, including superior biocompatibility, serum stability, and longer lifetime. The potential value of nUCAs in the musculoskeletal system is that they provide more reliable and clinically valuable guidance for the diagnosis, treatment, and follow-up of related diseases. The frontier of advances in nUCAs, their applications, and insights in MSKUS are reviewed in this paper. [ABSTRACT FROM AUTHOR]

Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe. [ABSTRACT FROM AUTHOR]

A finite-time extended, state-observer-based, fixed-time backstepping control algorithm was designed for hypersonic flight vehicles. To enhance the robustness of the controller, two novel finite-time extended state observers were introduced to compensate for the negative effects of lumped disturbances such as uncertainties and external disturbances. Two hyperbolic sine tracking differentiators were used to approximate the derivatives of the virtual control signals and guidance commands, thereby alleviating the computational burden associated with traditional backstepping control. Furthermore, a fixed-time backstepping attitude controller was used to guarantee that the tracking errors converged to a small neighbor of the origin in fixed time. According to the simulation results, the proposed controller outperformed a fixed-time sliding mode disturbance, observer-based, finite-time backstepping controller in terms of the tracking precision and convergence rate. Moreover, the proposed controller was noted to be robust in simulations involving lumped disturbances. [ABSTRACT FROM AUTHOR]

The invariant imbedding T-matrix (IIM T-matrix) model is regarded as one of the most promising models for calculating the scattering parameters of non-spherical particles. However, the IIM T-matrix model needs to be iterated along the radial direction when calculating the T-matrix, which involves complex calculations such as matrix inversion and multiplication. Therefore, how to improve its computational efficiency is an important problem to be solved. Focused on particles with N-fold symmetric geometry, this paper deduced the symmetry in the calculation process of the IIM T-matrix model, derived the block iteration scheme of the T-matrix, and contracted the IIM T-matrix program for particles with N-fold symmetric geometry. Discrete Dipole Approximation (DDA) and Geometrical Optics Approximation (IGOA) were employed to verify the accuracy of the improved IIM T-matrix model. The results show that the six phase matrix elements (P11, P12/P11, P22/P11, P33/P11, P34/P11 and P44/P11) calculated by our model are in good agreement with other models. The computational efficiency of the improved IIM T-matrix model was further investigated. As demonstrated by the results, the computational efficiency for the particles with N-fold symmetry improved by nearly 70% with the improvement of the symmetry of U matrix and T matrix. In conclusion, the improved model can remarkably reduce the calculation time while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D) electron density nowcasting model for China and its adjacent regions was developed. Unlike the traditional Gaussian background covariance model, the error covariance of the IRI model, based on the IGS grid TEC data, is estimated in this work. Due to the large scale of the high-resolution covariance matrix, it cannot be stored and calculated directly on a personal computer. The covariance localization (CL) technique is introduced to sparse the covariance matrix while removing the pseudo-correlation of the covariance matrix. After localization, the covariance matrix can be converted into a sparse matrix for storage and calculation, which greatly reduces the computer memory requirement of the assimilation model and improves the calculation speed of the model. Based on this algorithm, a series of experiments were carried out in this work. The experimental results show that this algorithm can effectively assimilate the observed GNSS to the background field, make up for the temporal and spatial limitations of the observed data, and improve the accuracy of the ionospheric electron density nowcast. Compared with the digisonde observed foF2 (the critical frequency of the ionospheric F2 layer), the RMSE of the assimilation model is 0.44 MHz lower than that of the IRI model. [ABSTRACT FROM AUTHOR]

The origin, evolution, and formation mechanism of MgO-based inclusions in Si-Mn-killed steel were studied in industrial trials with systematical samplings of the refining ladle, casting tundish, and as-cast bloom. In the present study, there were large numbers of MgO-based non-metallic inclusions, which started to form in the LF final process, and the MgO content in the lime-silicate slag increases from LF to VD process. The reason for the formation of MgO-based inclusions in refining process was analyzed using FactSage8.1 software. It was found that MgO-based inclusions were caused by the violent reaction between the slag and steel and the serious erosion of MgO-C refractory. The MgO solubility decreased in the lime-silicate slag and precipitated the periclase phase with basicity increasing. The solubility of MgO increased with an increase in the temperature. Measures were taken to optimize the refining process based on the above result. By increasing the slag basicity and increasing the content of MgO in the slag, erosion of the MgO-C refractory was reduced and the number of MgO-based non-metallic inclusions decreased from 0.2 to 0.04 per square millimeter. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) aims to automatically evaluate image perceptual quality by simulating the human visual system, which is an important research topic in the field of image processing and computer vision. Although existing deep-learning-based IQA models have achieved significant success, these IQA models usually require input images with a fixed size, which varies the perceptual quality of images. To this end, this paper proposes an aspect-ratio-embedded Transformer-based image quality assessment method, which can implant the adaptive aspect ratios of input images into the multihead self-attention module of the Swin Transformer. In this way, the proposed IQA model can not only relieve the variety of perceptual quality caused by size changes in input images but also leverage more global content correlations to infer image perceptual quality. Furthermore, to comprehensively capture the impact of low-level and high-level features on image quality, the proposed IQA model combines the output features of multistage Transformer blocks for jointly inferring image quality. Experimental results on multiple IQA databases show that the proposed IQA method is superior to state-of-the-art methods for assessing image technical and aesthetic quality. [ABSTRACT FROM AUTHOR]

This study investigates the variations of middle and low latitude sporadic-E (Es) layers in response to a geomagnetic storm. Es layers are observed by five ionosondes located in the Eastern Asian sector. The critical frequencies of Es layers (foEs) at six stations increased in sequence from high latitude stations to low latitude stations after IMF/Bz turning southward. Lomb–Scargle analysis shows the amplification of semidiurnal oscillation amplitude in the vertical height of Es layers during geomagnetic disturbance. Modeling results of the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) show the enhancement of the wind field in the mesosphere and the lower thermosphere (MLT) region. Our study provides evidence that the enhanced wind field in the MLT region during the storm period could result in the enhancement of Es layers at middle and low latitude. [ABSTRACT FROM AUTHOR]

Obesity is an increasingly severe public health problem, which brings huge social and economic burdens. Increased body adiposity in obesity is not only tightly associated with type 2 diabetes, but also significantly increases the risks of other chronic diseases including cardiovascular diseases, fatty liver diseases and cancers. Adipogenesis describes the process of the differentiation and maturation of adipocytes, which accumulate in distributed adipose tissue at various sites in the body. The major functions of white adipocytes are to store energy as fat during periods when energy intake exceeds expenditure and to mobilize this stored fuel when energy expenditure exceeds intake. Brown/beige adipocytes contribute to non-shivering thermogenesis upon cold exposure and adrenergic stimulation, and thereby promote energy consumption. The imbalance of energy intake and expenditure causes obesity. Recent interest in epigenetics and signaling pathways has utilized small molecule tools aimed at modifying obesity-specific gene expression. In this review, we discuss compounds with adipogenesis-related signaling pathways and epigenetic modulating properties that have been identified as potential therapeutic agents which cast some light on the future treatment of obesity. [ABSTRACT FROM AUTHOR]

The trapping layer refers to the atmospheric layer with vertical gradient of atmospheric refractivity less than −157 N-Units/km or vertical gradient of atmospheric modified refractivity 0 M-unit/km, which has a significant impact on radar and radio communication systems. Based on COSMIC and other radio occultation data, we show the statistical characteristics of the global trapping layer during 2005–2020.The statistical results show that the occurrence rate of the trapping layers is mainly concentrated between 50°S and 50°N, and higher occurrences of the trapping layers with more than 50% mainly occur in the boundary area between ocean and land, such as the northwest coastal area of Mexico, the west coastal area of Africa, the Mediterranean Sea, the Red Sea and the Arabian Sea, and the northwest area of Australia, etc. The altitude of the trapping layer is lower near the land and increases with the distance away from the coastline. The intensity is mainly between 6 M-unit and 24 M-unit (an M-unit is the unit of atmospheric modified refractivity), and the average value in some regions is above 24 M-unit, such as in the Arabian Sea area. In addition, the thickness of the trapping layer is between 50 and 240 m, and is generally larger over the ocean than over the land. These results reveal that the generation of the trapping layer is the result of the interaction of various background environmental factors such as radiation band migration, trade winds, monsoons, solar radiation heating, sea–land breezes and so on. [ABSTRACT FROM AUTHOR]

Rapid urbanization has destroyed urban water systems and led to blackened and odorous rivers. The heavily polluted rivers are always facing eutrophication and heavy metal pollution, while the combined effects of these environmental factors on the microbial diversity and distribution of the river microbial communities have not been adequately reported, especially the archaeal communities. In this study, we investigated the community structure and microbial distribution of sediment archaeal communities from an urban blackened and odorous river basin of the Zhang river, in Nanling, China. Results showed that the archaeal community from the eight sediment sites have average values of Shannon and Chao1 at 3.4921 and 232.7202, respectively. The community diversity and richness were different among samples. Halobacterota and Euryarchaeota were the most abundant phylum and Crenarchaeota also took up a considerable amount of the archaeal community. To reveal the main environmental drivers of the distribution of archaeal communities in sediment, the environmental physicochemical factors (total nitrogen, total phosphorus, oxidation/reduction potential, nitrate nitrogen, ammonia nitrogen, pH and total organic carbon) and heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb and Hg) in sediment were determined. A redundancy analysis (RDA) revealed that Eh was the most prominent influencing factor, and As was the most influential heavy metal on the microbial distribution of archaeal communities. Furthermore, a variance partitioning analysis (VPA) was used to identify the impacts of physicochemical factors and heavy metals on the archaeal community distribution. Results showed that heavy metals have higher effects on archaeal community distribution than physicochemical factors. The present study suggested that the heavy metal pollution should be paid more attention in the microbial distribution in heavily polluted urban rivers, and also should be taken into consideration for improving the efficacies of ecological evaluation and remediation. [ABSTRACT FROM AUTHOR]

The effects of replacing over-mature forest with secondary forests and plantations are significant for terrestrial ecosystem carbon (C) dynamics. However, the carbon balance and recovery time of this replacement process remain unclear. This study measured the fluxes of CH4 and CO2 in soils and the annual net C sequestration (ANCS) from seven ecosystems with different vegetation types (over-mature forest (OMF), Korean pine plantation (KPP), hardwood forest (HWF), Betula platyphylla forest (BPF), Populous davidiana forest (PDF), mixed deciduous forest (MDF), and Mongolian oak forest (MOF)) using the static chamber-gas chromatography method and the relative growth equation method. We examined the effects of environmental factors (e.g., air and soil temperature, soil volumetric water content (SVWC), soil pH, nitrate nitrogen (NO3−-N), ammonium nitrogen (NH4+-N), and soil organic carbon (SOC)) on CH4 and CO2 fluxes at the Maoershan Ecosystem Research Station in Northeast China. The carbon source or sink of OMF, KPP, and five secondary forests (HWF, BPF, PDF, MDF, and MOF) were then evaluated based on net ecosystem C balance. The results revealed that the mean annual CH4 fluxes varied between −0.046 and −0.077 mg m−2 h−1. The mean annual absorption of CH4 in the secondary forests and OMF were respectively 1.09–1.67 times and 1.11 times higher than that of KPP (0.046 mg m−2 h−1, p < 0.05). The mean annual CO2 fluxes varied between 140.425 and 250.023 mg m−2 h−1. The CO2 fluxes in the secondary forests and KPP soils were respectively 1.33–1.78 times and 1.16 times higher than that of OMF (140.425 mg m−2 h−1, p < 0.05). The CH4 and CO2 fluxes were mainly influenced by air and soil temperature, SVWC, soil pH, NO3−-N, NH4+-N, and SOC in Northeast China. The ANCS of vegetation (3.41 ± 0.27 − 6.26 ± 0.75 t C ha−1 y−1) varied widely among different forest types: KPP had the largest ANCS (6.26 ± 0.75 t C ha−1 y−1, which was higher than secondary forests and OMF by 1.20–1.84 times and 1.46 times, respectively, p > 0.05). Carbon sources and sinks were significantly different among the seven types of vegetation: OMF and KPP were observed to be the greatest C sinks, and secondary forests were shown to be the weakest carbon sinks or net C sources in the study region. [ABSTRACT FROM AUTHOR]

With the intensification of the energy crisis, it is urgent to vigorously develop new environment-friendly energy storage materials. In this work, coexisting ferroelectric and relaxor-ferroelectric phases at a nanoscale were constructed in Sr(Zn 1/3 Nb 2/3 )O 3 (SZN)-modified (Bi 0.5 Na 0.5 ) 0.94 Ba 0.06 TiO 3 (BNBT) ceramics, simultaneously contributing to large polarization and breakdown electric field and giving rise to a superior energy storage performance. Herein, a high recoverable energy density (W rec ) of 5.0 J/cm 3 with a conversion efficiency of 82% at 370 kV/cm, a practical discharged energy density (W d ) of 1.74 J/cm 3 at 230 kV/cm, a large power density (P D ) of 157.84 MW/cm 3 , and an ultrafast discharge speed (t 0.9 ) of 40 ns were achieved in the 0.85BNBT-0.15SZN ceramics characterized by the coexistence of a rhombohedral-tetragonal phase (ferroelectric state) and a pseudo-cubic phase (relaxor-ferroelectric state). Furthermore, the 0.85BNBT-0.15SZN ceramics also exhibited excellent temperature stability (25-120 °C) and cycling stability (10 4 cycles) of their energy storage properties. These results demonstrate the great application potential of 0.85BNBT-0.15SZN ceramics in capacitive pulse energy storage devices.

Exploiting the increasingly wide use of light emitting diodes (LEDs) lighting, in this paper we propose a reversed indoor positioning system (IPS) based on LED visible light communication (VLC) in order to improve indoor positioning accuracy. Unlike other VLC positioning systems, we employ two annular receivers with multi-photodiodes installed on the ceiling to locate the persons who carry LEDs. The basic idea is using multi-photodiodes to calculate the angle while using the received signal strength (RSS) method to calculate the distance. The experiment results show that the effective positioning range of the proposed system is 1.8 m when the distance between two receivers is 1.2 m. Moreover, a positioning error less than 0.2 m can be achieved under the condition that the radius of the PIN circle is between 0.16 m and 0.2 m, and the distance of the transmitter-receiver plane is less than 1.8 m, which will be effective in practice.