In recent years,high coercivity nanocrystalline magnets based on the critical single domain size theory have received increasing ***,nanocrystalline magnets are difficult to enhance the remanent magnetization by formi...
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In recent years,high coercivity nanocrystalline magnets based on the critical single domain size theory have received increasing ***,nanocrystalline magnets are difficult to enhance the remanent magnetization by forming textures with conventional magnetic field *** of nanocrystalline magnets is usually done using the hot pressing and hot deformation technique,but there are some difficulties in applying this technique to form texture in SmCo *** discover and solve the difficulties of obtaining texturized nanocrystalline magnets during the deformation process,samarium cobalt magnets with different degrees of deformation were first experimentally prepared by low strain rate hot deformation ***,moleculardynamics simulations were utilized to study the micro structural changes and the multi-phase formation process during hot *** process of deformation together with the reason why it is difficult to form textures in SmCo systems by lowtemperature hot deformation is discussed,and the strain energy densities of SmCo multiphase in multiple directions were calculated,which gives a feasible method to form low-temperature hot deformation textures in SmCo systems experimentally.
Neuronal plasticity in the central amygdala (CeA) is essential for modulating feeding behaviors and emotional responses, potentially influencing reactions to deoxynivalenol (dON). Acute oral administration of dON elic...
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Neuronal plasticity in the central amygdala (CeA) is essential for modulating feeding behaviors and emotional responses, potentially influencing reactions to deoxynivalenol (dON). Acute oral administration of dON elicits a dose-responsive reduction in food intake, accompanied by pronounced alterations in locomotor activity and feeding frequency. This study investigates circuitry adaptations that mediate dON's effects on feeding, by targeting of GABA neurons in the CeA. Following exposure to dON, an increase in connectivity between the paraventricular nucleus of the thalamus (PVT) and CeAGABA neurons is observed, suggesting the involvement of this pathway in dON's adverse effects on feeding and emotional states. Chemogenetic and optogenetic manipulations of CeAGABA neurons resulted in substantial alterations in mice's feeding and overall activity. These findings suggest that CeAGABA neurons are involved in dON-induced anorexia and aversive-like emotional responses. Additionally, the administration of the SCN10A antagonist (A-803467) effectively mitigateddON-induced anorexia and aversive-like emotions, highlighting the pivotal role of the PVT-CeA circuit and CeAGABA neurons in regulating the physiological and emotional impacts of dON. These findings have significant implications for public health and clinical interventions, offering potential therapeutic strategies to mitigate dON's adverse effects on human health.
Artificial intelligence (AI) has become a promising solution for meeting the stringent performance requirements on wireless physical layer in sixth-generation (6G) communication systems, due to its strong ability to l...
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Artificial intelligence (AI) has become a promising solution for meeting the stringent performance requirements on wireless physical layer in sixth-generation (6G) communication systems, due to its strong ability to learn complex model, achieve end-to-end optimization and adapt to dynamic environments. This article provides a comprehensive review with respect to artificial intelligence for wireless physical-layer technologies (AI4PHY). Specifically, we first analyze the characteristics of the classic AI techniques and their potential applications for physical-layer technologies. Then we study the AI-enhanceddesigns from the point of view of the basic physical-layer modules, including coding, modulation, multiple access, multiple-input-multiple-output (MIMO), channel estimation, as well as relay transmission. The standardization progress of AI4PHY in 3GPP is also discussed. Based on the current AI4PHY researches, we propose some potential future research directions to inspire and encourage the further exploration.
Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorec...
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Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorectal polyps with complex shapes, they have two main shortcomings: (1) multi-level semantic information at the output of the encoder may result in information loss during the fusion process and (2) failure to adequately suppress background noise during segmentation. To address these challenges, we propose a cross-scale interaction fusion transformer for polyp segmentation (CIFFormer). Firstly, a novel feature supplement module (FSM) supplements the missing details and explores potential features to enhance the feature representations. Additionally, to mitigate the interference of background noise, we designed a cross-scale interactive fusion module (CIFM) that combines feature information between different layers to obtain more multi-scale anddiscriminative representative features. Furthermore, a boundary-assisted guidance module (BGM) is proposed to help the segmentation network obtain boundary-enhanceddetails. Extensive experiments on five typical datasets have demonstrated that CIFFormer has an obvious advantage in segmenting polyps. Specifically, CIFFormer achieved an mdice of 0.925 and an mIoU of 0.875 on the Kvasir-SEG dataset, achieving superior segmentation accuracy to competing methods.
Wheat stripe rust or yellow rust (Yr), caused by Puccinia striiformis f. sp. tritici (Pst), causes substantial yieldreductions globally, but losses can be minimized by using resistance genes. Chinese wheat cultivar J...
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Wheat stripe rust or yellow rust (Yr), caused by Puccinia striiformis f. sp. tritici (Pst), causes substantial yieldreductions globally, but losses can be minimized by using resistance genes. Chinese wheat cultivar Jing 411 (J411) has continued to display an acceptable level of adult-plant resistance (APr) to Yr in varied field conditions since its release in the 1990s. A recombinant inbred line (rIL) population comprising 187 lines developed from a cross of J411 and Kenong 9204 (KN9204) was evaluated in multiple environments to identify genomic regions carrying genes for Yrresistance. A total of five quantitative trait loci (QTL) on chromosome arm 1BL, 3BS, 4BL, 6BS, and 7BL from J411 and two QTL on 3dS and 7dL from KN9204 were detected using inclusive composite interval mapping with the wheat 660 K SNP array. ***-1BL.5 and ***-4BL.3 from J411 were robust and showed similar effects in all environments. ***-1BL.5 was likely the pleiotropic gene of Yr29/Lr46. ***-4BL.3 was located within a 1.0 cM interval delimited by KASP markers AX-111609222 and AX-89755491. Based on haplotype analysis, Yr29 and ***-4BL.3 were identified as genetic components of quantitative resistance in a number of wheat cultivars. Moreover, rILs with Yr29 and ***-4BL.3 individually or when combined showed higherresistance to Yr in rust nurseries compared with rILs without them, and there was no negative effect of their presence on agronomic traits underrust-free conditions. These results suggest that effective polymerization strategy is important for breeding high yielding anddurable resistance cultivars.
Mobile communications networks are faced with gigantic challenges in meeting diversified service requirements. A tight coordination, or even convergence, of applications and mobile networks is required for the optimiz...
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Mobile communications networks are faced with gigantic challenges in meeting diversified service requirements. A tight coordination, or even convergence, of applications and mobile networks is required for the optimization of end-to-end quality of service and quality of experience (QoE). In this article, a multi-domain (e.g., application layer, transport layer, core network, radio access network, and user equipment) coordination scheme is presented, which facilitates a tight coordination between applications and networks for the current 5G networks. Toward the convergence of applications and networks, a network architecture with cross-domain coordination center (CdCC) is further proposed for 6G and beyond, which utilizes information from all the domains to optimize the end-to-end service provision. The benefits of the CdCC-enabled base station scheduling are further investigated via simulations. A new QoE-oriented fairness metric is proposed, which is capable of ensuring better fairness when different services are scheduled. Future research directions and their standardization impacts are also identified. Toward optimized end-to-end service provision, the paradigm shift from loosely coupled to convergeddesign of applications and wireless communication networks is indispensable.
This paperdesigns optimal control polices for networked multiagent pursuit-evasion game (MPEG) problems based on reinforcement learning (rL) technique. depending on the number of evaders, MPEG is formulated into seve...
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This paperdesigns optimal control polices for networked multiagent pursuit-evasion game (MPEG) problems based on reinforcement learning (rL) technique. depending on the number of evaders, MPEG is formulated into several simpler multiple-pursuer single-evader games (MPSEGs) by a divide and conquer approach. Then we propose optimal control policies for all the agents in each MPSEG, which constitute a distributed Nash equilibrium, and provide the capturability and Nash equilibrium analysis. Finally, a data-driven rL algorithm is developed to online learn optimal control polices using measurable behaviordata. A simulation example is given to verify the effectiveness of the proposed approach.
The popular AB /push-pull method fordistributed optimization problem may unify much of the existing decentralized first-order methods based on gradient tracking technique. More recently, the stochastic gradient varia...
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The popular AB /push-pull method fordistributed optimization problem may unify much of the existing decentralized first-order methods based on gradient tracking technique. More recently, the stochastic gradient variant of AB /push-pull method ( S - AB ) has been proposed, which achieves the linearrate of converging to a neighborhood of the global minimizer when the step-size is constant. This article is devoted to the asymptotic properties of S - AB with diminishing step-size. Specifically, under the condition that each local objective is smooth and the global objective is strongly convex, we first present the boundedness of the iterates of S - AB and then show that the iterates converge to the global minimizer with the rate O(1/k) in the mean square sense. Furthermore, the asymptotic normality of Polyak-ruppert averaged S - AB is obtained and applications on statistical inference are discussed. Finally, numerical tests are conducted to demonstrate the theoretic results.
In engineering measurements, metal foil strain gauges suffer from a limitedrange and low sensitivity, necessitating the development of flexible sensors to fill the gap. This paper presents a flexible, high-performanc...
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In engineering measurements, metal foil strain gauges suffer from a limitedrange and low sensitivity, necessitating the development of flexible sensors to fill the gap. This paper presents a flexible, high-performance piezoresistive sensor using a composite consisting of graphene nanoplatelets (GNPs) and polydimethylsiloxane (PdMS). The proposed sensordemonstrated a significantly widerrange (97%) and higher gauge factor (GF) (6.3), effectively addressing the shortcomings of traditional strain gauges. The microstructure of the GNPs/PdMS composite was observed using a scanning electron microscope, and the distribution of the conductive network was analyzed. The mechanical behavior of the sensor encapsulation was analyzed, leading to the determination of the mechanisms influencing encapsulation. Experiments based on a standard equal-strength beam were conducted to investigate the influence of the base and coating dimensions of the sensor. The results indicated that reducing the base thickness and increasing the coating length both contributed to the enhancement of the sensor's performance. These findings provide valuable guidance for future development anddesign of flexible sensors.
remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human fa...
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remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, andpartial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of keyregions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popularrPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collecteddataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.
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