基于AIGC技术的民族服饰设计研究——以畲族为例

民族服饰的当代创新需要在创作过程中寻求民族传统与现代审美的最佳平衡点,生成式人工智能(AIGC)技术的出现为民族服饰的当代设计应用提供了新的路径和方法。文章通过分析目前人工智能技术在民族服饰生成过程中遇到的问题,提出基于专属资源库模型训练的方法并以畲族服饰为例进行实验。实验表明,通过对畲族资源库中的服饰样本进行品类归纳和图像标注进行专属模型的训练,可以使被训练的模型理解、学习到资源库样本中畲族服饰的特征,进而使生成的内容具有畲族服饰风格的图像。通过这一实验,展示了人工智能技术给民族服饰创新设计带来的全新思路和方法,旨在建立一条民族服饰设计与AIGC技术相结合的创新实践路径,通过AIGC技术能促进民族服饰设计的创新性发展和创造性转化。

Neurogenesis dynamics in the olfactory bulb:deciphering circuitry organization, function, and adaptive plasticity

Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.

Physically Constrained Adaptive Deep Learning for Ocean Vertical-Mixing Parameterization

Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.

Review on physiological and ecological characteristics and agronomic regulatory pathways of intercropping to delay root and canopy senescence of crops

Intercropping has been widely used in arid and semi-arid regions because of its high yield,stable productivity,and efficient utilization of resources.However,in recent years,the high yield of traditional intercropping is mainly attributed to the large amount of purchased resources such as water and fertilizer,plastic film,and mechanical power.These lead to a decline in cultivated land quality and exacerbate intercrops'premature root and canopy senescence.So,the application of traditional intercropping faces major challenges in crop production.This paper analyzes the manifestations,occurrence mechanisms,and agronomic regulatory pathways of crop senescence.The physiological and ecological characteristics of intercropping to delay root and canopy senescence of crops are reviewed in this paper.The main agronomic regulatory pathways of intercropping to delay root and canopy senescence of crops are based on above-and blow-ground interactions,including collocation of crop varieties,spatial arrangement,water and fertilizer management,and tillage and mulch practices.Future research fields of intercropping to delay root and canopy senescence should focus on the aspects of selecting and breeding special varieties,application of molecular biology techniques,and developing or applying models to predict and evaluate the root and canopy senescence process of intercrops.Comprehensive analysis and evaluation of different research results could provide a basis for enhancing intercropping delay root and canopy senescence through adopting innovative technologies for regulating the physio-ecological characteristics of intercrops.This would support developing and adopting high-yield,efficient,and sustainable intercropping systems in arid and semi-arid areas with high population density,limited land,and abundant light and heat resources.

Study on influencing factors of adapters separating with the underwater missile

To analyze main factors affecting the separation reliability between a missile and adapters for the launching process, a six DOF underwater dynamic model for the missile and adapters is utilized to simulate the separation process, considering elastic forces of separating springs, hydrodynamic forces, gravity and buoyancy. Moreover, a criterion based on the maximum separating distance is put forward to determine whether adapters separate with the missile reliably. The results show that the magnitude and position of elastic force, the wedge angle and mass of the adapter significantly affect the separating process. The local sensitivity analysis for the reference status of design parameters demonstrates that the wedge angle of adapters has the maximum influence about 70. 4% on the separating distance.

Adapters．com推出新款芯片改装适配器

Adapters．com的芯片改装(Chip—Changer)适配器系列又添新品。这类适配器可使设计人员充分利用因器件更新、器件布局及高昂的电路板修改费用而报废的现有电路板。

中国改编指南的现状分析

目的分析中国改编指南的制订现状及其方法学质量和报告质量,为我国改编指南的制订和发展提供参考。方法系统检索中国知网、万方数据知识服务平台、中国生物医学文献数据库、PubMed数据库,纳入2015—2023年由中国研究者或机构牵头制订的改编指南,提取改编指南的基本信息,采用AGREEⅡ和RIGHT-Ad@pt工具评价改编指南的方法学质量和报告质量。结果共纳入14部改编指南,主要集中于护理领域(42.9%,6/14),最常采用的改编方法是ADAPTE方法(71.4%,10/14),参照的原指南主要来自美国、英国和国际组织。AGREEⅡ工具的评估结果显示,改编指南的方法学质量平均得分为36.6%,表达明晰性和制订严谨性领域得分相对较高(44.4%和54.9%),应用性领域得分最低(15.3%);RIGHT-Ad@pt工具的评估结果显示,改编指南的报告质量相对较好,报告率最高为91.2%,最低为35.3%,其中7部报告率均高于70%。结论中国改编指南仍处于起步阶段,数量少且主要集中于护理领域,最常采用的改编方法是ADAPTE方法;改编指南的方法学质量和报告质量有待进一步提升,未来应针对改编指南制订的具体条件和时机开展更多相关研究。

针对低资源场景下连续情感分析任务的持续注意力建模

目前情感分析的研究普遍基于大数据驱动型模型,严重依赖高昂的标注成本和算力成本,因此针对低资源场景下的情感分析研究显得尤为迫切.然而,存在的低资源场景下的情感分析研究主要集中在单个任务上,这导致模型难以获取外部任务知识.因此构建低资源场景下的连续情感分析任务,旨在利用持续学习方法,让模型随时间步学习多个情感分析任务.这样可以充分利用不同任务的数据,并学习不同任务的情感信息,从而缓解单个任务训练数据匮乏问题.认为低资源场景下的连续情感分析任务面临两大核心问题,一方面是单个任务的情感信息保留问题,另一方面是不同任务间的情感信息融合问题.为了解决上述两大问题,提出针对低资源场景下连续情感分析任务的持续注意力建模方法.所提方法首先构建情感掩码Adapter,用于为不同任务生成硬注意力情感掩码,这可以保留不同任务的情感信息,从而缓解灾难性遗忘问题.其次,所提方法构建动态情感注意力,根据当前时间步和任务相似度动态融合不同Adapter抽取的特征,这可以融合不同任务间的情感信息.在多个数据集上的实验结果表明:所提方法的性能显著超过了目前最先进的基准方法.此外,实验分析表明,所提方法较其他基准方法具有最优的情感信息能力和情感信息融合能力,并且能同时保持较高的运行效率.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

直接抽吸取栓术与支架取栓术治疗急性大脑中动脉粥样硬化性闭塞的疗效对比研究

目的 比较首选直接抽吸技术(a direct aspiration first-pass technique, ADAPT)与支架取栓(stent-retriever thrombectomy, SRT)治疗急性大脑中动脉粥样硬化性闭塞(intracranial atherosclerotic stenosis related large vessel occlusion, ICAS-LVO)所致急性缺血性卒中(acute ischemic stroke, AIS)患者的临床疗效。方法 回顾性连续纳入2020年1月至2023年1月西安交通大学第一附属医院神经内科及陕西省中医药大学第二附属医院脑病二科接受血管内治疗的急性大脑中动脉ICAS-LVO所致AIS患者,根据首选取栓装置将患者分为ADAPT组和SRT组,比较两组患者的基线资料、检验及检查结果、手术过程及临床预后等。结果 共纳入117例患者,其中ADAPT组48例,SRT组69例,两组患者的基线资料无统计学差异。SRT组首次取栓成功再通率(P=0.014)及首选取栓装置成功再通率(P<0.001)均高于ADAPT组,且医源性夹层(P<0.001)及血管痉挛(P=0.003)的发生率显著降低;而ADAPT组更换取栓装置补救治疗比例显著高于SRT组(P<0.001);但在最终血管成功再通率、症状性及无症状性颅内出血发生率和90 d良好预后率等方面,两组无统计学差异。结论 对于急性大脑中动脉ICAS-LVO患者,首选SRT治疗相较于ADAPT治疗具有更高的即刻成功再通率,且血管损伤风险更小,但二者改善患者预后的作用相似。

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Revolutionizing gastric cancer treatment:The potential of immunotherapy

Gastric cancer,a prevalent malignancy worldwide,ranks sixth in terms of frequency and third in fatality,causing over a million new cases and 769000 annual deaths.Predominant in Eastern Europe and Eastern Asia,risk factors include family medical history,dietary habits,tobacco use,Helicobacter pylori,and Epstein-Barr virus infections.Unfortunately,gastric cancer is often diagnosed at an advanced stage,leading to a grim prognosis,with a 5-year overall survival rate below 5%.Surgical intervention,particularly with D2 Lymphadenectomy,is the mainstay for early-stage cases but offers limited success.For advanced cases,the National Comprehensive Cancer Network recommends chemotherapy,radiation,and targeted therapy.Emerging immunotherapy presents promise,especially for unresectable or metastatic cases,with strategies like immune checkpoint inhibitors,tumor vaccines,adoptive immunotherapy,and nonspecific immunomodulators.In this Editorial,with regards to the article“Advances and key focus areas in gastric cancer immunotherapy:A comprehensive scientometric and clinical trial review”,we address the advances in the field of immunotherapy in gastric cancer and its future prospects.

Interplay between mesenchymal stromal cells and the immune system after transplantation: implications for advanced cell therapy in the retina

Advanced mesenchymal stromal cell-based therapies for neurodegenerative diseases are widely investigated in preclinical models.Mesenchymal stromal cells are well positioned as therapeutics because they address the underlying mechanisms of neurodegeneration,namely trophic factor deprivation and neuroinflammation.Most studies have focused on the beneficial effects of mesenchymal stromal cell transplantation on neuronal survival or functional improvement.However,little attention has been paid to the interaction between mesenchymal stromal cells and the host immune system due to the immunomodulatory properties of mesenchymal stromal cells and the long-held belief of the immunoprivileged status of the central nervous system.Here,we review the crosstalk between mesenchymal stromal cells and the immune system in general and in the context of the central nervous system,focusing on recent work in the retina and the importance of the type of transplantation.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Vibration Control of A Flexible Marine Riser System Subject to Input Dead Zone and Extraneous Disturbances

An observer-based adaptive backstepping boundary control is proposed for vibration control of flexible offshore riser systems with unknown nonlinear input dead zone and uncertain environmental disturbances.The control algorithm can update the control law online through real-time data to make the controller adapt to the environment and improve the control precision.Specifically,based on the adaptive backstepping framework,virtual control laws and Lyapunov functions are designed for each subsystem.Three direction interference observers are designed to track the timevarying boundary disturbance.On this basis,the inverse of the dead zone and linear state transformation are used to compensate for the original system and eliminate the adverse effects of the dead zone.In addition,the stability of the closed-loop system is proven by Lyapunov stability theory.All the system states are bounded,and the vibration offset of the riser converges to a small area of the initial position.Finally,four examples of flexible marine risers are simulated in MATLAB to verify the effectiveness of the proposed controller.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.

Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.