A 48-bp deletion upstream of LIGULELESS 1 alters rice panicle architecture

Panicle architecture is an agronomic determinant of crop yield and a target for cereal crop improvement.To investigate its molecular mechanisms in rice,we performed map-based cloning and characterization of OPEN PANICLE 1(OP1),a gain-of-function allele of LIGULELESS 1(LG1),controlling the spread-panicle phenotype.This allele results from a 48-bp deletion in the LG1 upstream region and promotes pulvinus development at the base of the primary branch.Increased OP1 expression and altered panicle phenotype in chimeric transgenic plants and upstream-region knockout mutants indicated that the deletion regulates spread-panicle architecture in the mutant spread panicle 1(sp1).Knocking out BRASSINOSTEROID UPREGULATED1(BU1)gene in the background of OP1 complementary plants resulted in compact panicles,suggesting OP1 may regulate inflorescence architecture via the brassinosteroid signaling pathway.We regard that manipulating the upstream regulatory region of OP1 or genes involved in BR signal pathway could be an efficient way to improve rice inflorescence architecture.

Sedimentary architecture of submarine channel-lobe systems under different seafloor topography:Insights from the Rovuma Basin offshore East Africa

Seafloor topography plays an important role in the evolution of submarine lobes.However,it is still not so clear how the shape of slope affects the three-dimensional(3-D)architecture of submarine lobes.In this study,we analyze the effect of topography factors on different hierarchical lobe architectures that formed during Pliocene to Quaternary in the Rovuma Basin offshore East Africa.We characterize the shape,size and growth pattern of different hierarchical lobe architectures using 3-D seismic data.We find that the relief of the topographic slope determines the location of preferential deposition of lobe complexes and single lobes.When the topography is irregular and presents topographic lows,lobe complexes first infill these depressions.Single lobes are deposited preferentially at positions with higher longitudinal(i.e.across-slope)slope gradients.As the longitudinal slope becomes higher,the aspect ratio of the single lobes increases.Lateral(i.e.along-slope)topography does not seem to have a strong influence on the shape of single lobe,but it seems to affect the overlap of single lobes.When the lateral slope gradient is relatively high,the single lobes tend to have a larger overlap surface.Furthermore,as the average of lateral slope and longitudinal slope gets greater,the width/thickness ratio of the single lobe is smaller,i.e.sediments tend to accumulate vertically.The results demonstrate that the shape of slopes more comprehensively influences the 3-D architecture of lobes in natural deep-sea systems than previously other lobe deposits and analogue experiments,which helps us better understand the development and evolution of the distal parts of turbidite systems.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Enhanced Differentiable Architecture Search Based on Asymptotic Regularization

In differentiable search architecture search methods,a more efficient search space design can significantly improve the performance of the searched architecture,thus requiring people to carefully define the search space with different complexity according to various operations.Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search.With this in mind,we propose a faster and more efficient differentiable architecture search method,AllegroNAS.Firstly,we introduce a more efficient search space enriched by the introduction of two redefined convolution modules.Secondly,we utilize a more efficient architectural parameter regularization method,mitigating the overfitting problem during the search process and reducing the error brought about by gradient approximation.Meanwhile,we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search procedure.Moreover,group convolution and data augmentation are employed to reduce the computational cost.Finally,through extensive experiments on several public datasets,we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space,thus validating the effectiveness of our approach.

Research on Information Architecture Design of Short-Form Video Social Platforms Based on Cognitive Psychology

This study investigates how cognitive psychology principles can be integrated into the information architecture design of short-form video platforms,like TikTok,to enhance user experience,engagement,and sharing.Using a questionnaire,it explores TikTok users’habits and preferences,highlighting how social media fatigue(SMF)impacts their interaction with the platform.The paper offers strategies to optimize TikTok’s design.It suggests refining the organizational system using principles like chunking,schema theory,and working memory capacity.Additionally,it proposes incorporating shopping features within TikTok’s interface to personalize product suggestions and enable monetization for influencers and content creators.Furthermore,the study underlines the need to consider gender differences and user preferences in improving TikTok’s sharing features,recommending streamlined and customizable sharing options,collaborative sharing,and a system to acknowledge sharing milestones.Aiming to strengthen social connections and increase sharing likelihood,this research provides insights into enhancing information architecture for short-form video platforms,contributing to their growth and success.

血府逐瘀汤对狼疮性肾炎患者胸腺肽β4及Ea-RFC的影响

目的探讨血府逐瘀汤辅助治疗狼疮性肾炎(LN)的效果及对患者胸腺肽β4及Ea花环细胞(Ea-RFC)形成率的影响。方法选取河北北方学院附属第二医院2020年1月至2021年6月收治的60例狼疮性肾炎患者,按随机数字表法分为观察组(行血府逐瘀汤联合泼尼松与环磷酰胺治疗)30例与对照组(行泼尼松与环磷酰胺治疗)30例,治疗18个月。观察组治疗后临床效果、中医症状、胸腺肽β4表达、Ea-RFC形成率、肾功能及不良反应。结果观察组治疗总有效率为96.67%,高于对照组的80.00%,差异有统计学意义(P<0.05)。治疗后,观察组纳少腹胀、腰膝酸软、面色不华、口黏口苦等中医症状积分低于对照组,差异有统计学意义(P<0.05)。治疗后,观察组红细胞沉降率、血清肌酐及胸腺肽β4表达低于对照组,Ea-RFC形成率高于对照组,差异有统计学意义(P<0.05)。治疗后,观察组不良反应发生率与对照组比较差异无统计学意义(P>0.05)。结论血府逐瘀汤辅助治疗LN患者疗效确切,能减少患者体内胸腺肽β4表达,提高Ea-RFC,改善肾功能,安全性较高。

Gravity modelling of crustal architecture and heterogeneity in the Nansha Block,South China Sea

The stretched structure and heterogeneity of the crust of the Nansha Block,the southern continental margin of the South China Sea(SCS),are not well understood.We used published ocean bottom seismic(OBS)/multichannel reflection seismic(MCS)profiles across the Nansha Block to establish five two-dimensional crustal structure models.Using gravity modelling with magnetic anomaly inversion,we obtained the distribution of density and local magnetic susceptibility of the crust.The models show that the distribution of density and thickness of the upper crust in the Nansha Block is uneven,and the thick upper crust is prevalent in the regions close to the continent-ocean transition(COT)showing different characteristics.The interpreted Mesozoic granite blocks and Precambrian rigid basement reflects the heterogeneity in the material composition of the SCS continental margin.Based on the thinning styles of different crustal layers,we suggest that the Nansha Block has a three-layer thinning pattern.The uppermost pre-rift layer was deformed via brittle fractures,the upper crust was sheared by discrete shear zones,and the lower crust experienced ductile deformation.The inherited pre-rift thermal regime,mechanical state,and material composition of the SCS continental margin affected the extensional structure of the crust.

Elaidic acid leads to mitochondrial dysfunction via mitochondria-associated membranes triggers disruption of mitochondrial calcium fluxes

Elaidic acid(EA)stimulation can lead to endoplasmic reticulum stress(ERS),accompanied by a large release of Ca^(2+),and ultimately the activation of NLRP3 inflammasome in Kupffer cells(KCs).Mitochondrial instability or dysfunction may be the key stimulating factors to activate NLRP3 inflammasome,and sustained Ca^(2+)transfer can result in mitochondrial dysfunction.We focused on KCs to explore the damage to mitochondria by EA.After EA stimulation,cells produced an oxidative stress(OS)response with a significant increase in ROS release.Immunoprecipitation experiments and the addition of inhibitors revealed that the increase in the level of intracellular Ca^(2+)led to Ca^(2+)accumulation in the mitochondrial matrix via mitochondria-associated membranes(MAMs).This was accompanied by a significant release of m ROS,loss of MMP and ATP,and a significant increase in mitochondrial permeability transition pore opening,ultimately leading to mitochondrial instability.These findings confirmed the mechanism that EA induced mitochondrial Ca^(2+)imbalance in KCs via MAM,ultimately leading to mitochondrial dysfunction.Meanwhile,EA induced OS and the decrease of MMP and ATP in rat liver,and significant lesions were found in liver mitochondria.Swelling of the inner mitochondrial cristae and mitochondrial vacuolization occurred,with a marked increase in lipid droplets.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

The Ecological Aesthetics of Funeral Architecture towards Death:A Case Study of Tomba Brion Family Cemetery

This paper discusses the design of funeral architecture with ecological aesthetics as the entry point,and makes a poetic interpretation of the gardening techniques and spiritual connotation of Tomba Brion Family Cemetery,a funeral architecture,in combination with architecture,ecological design aesthetics,landscape ecology,oriental aesthetics and other disciplines to explore the unique charm of the architectural field related to death for life,trying to provide the reference value of ecological aesthetics for the development of funeral architecture.

Diversity of depositional architecture and sandbody distribution of sublacustrine fans during forced regression: A case study of Paleogene Middle Sha 3 Member in Dongying Sag, Bohai Bay Basin, East China

Currently, the differences in gravity flow deposits within different systems tracts in continental lacustrine basins are not clear. Taking the middle submember of the third member of Paleogene Shahejie Formation(Sha 3 Member) in the Shishen 100 area of the Dongying Sag in the Bohai Bay Basin as an example, the depositional architecture of sublacustrine fans during forced regression and the impact of the fourth-order base-level changes on their growth were investigated using cores, well logs and 3D seismic data. Sublacustrine fans were mainly caused by hyperpycnal flow during the fourth-order base-level rise, while the proportion of slump-induced sublacustrine fans gradually increased during the late fourth-order base-level fall. From rising to falling of the fourth-order base-level, the extension distance of channels inside hyperpycnal-fed sublacustrine fans reduced progressively, resulting in the transformation in their morphology from a significantly channelized fan to a skirt-like fan. Furthermore, the depositional architecture of distributary channel complexes in sublacustrine fans changed from vertical aggradation to lateral migration, and the lateral size of individual channel steadily decreased. The lobe complex's architectural patterns evolved from compensational stacking of lateral migration to aggradational stacking, and the lateral size of individual lobe steadily grew. This study deepens the understanding of depositional features of gravity flow in high-frequency sequence stratigraphy and provides a geological foundation for the fine development of sublacustrine fan reservoirs.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

Hybrid beamforming(HBF)has become an attractive and important technology in massive multiple-input multiple-output(MIMO)millimeter-wave(mmWave)systems.There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains.This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems.The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems.First,we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem,and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture.The overlapped subarray architecture which balances well between hardware costs and system performance is investigated.We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm.Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

GEP核算能代表生态产品价值核算吗?——基于SEEAEA对我国生态系统核算若干问题的探讨

文章介绍了中国GEP核算推行的背景及实施进程,阐述了国内外GEP概念的提出和研究进展,以及SEEA框架下的生态系统核算概念。文章指出:由于没有涉及编制价值量生态系统资产账户、没有扣除生态系统服务供应中产生的任何相关生态系统退化,GEP核算不能反映生态系统资产是否实现可持续利用或生态产品流量供给在数量或质量上是否可持续。文章认为:生态产品就是SEEA框架下的生态系统服务。由于对生态产品等关键概念界定不严谨,没有考虑“供给=使用”核算原则,对按交换价值进行估值等基本核算原则考虑不足,导致GEP核算实物量核算范围过大、价值量核算取价过高。文章特别指出《生态产品总值核算规范》在供给服务指标设计中缺失“供水”这一重要指标。文章结合GEP试算工作中反映出来的部分问题,就中国生态产品价值核算应依据的标准、执行主体、数据来源、核算方法、发展方向与核算重点,给出了针对性的政策建议。

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Timing effect of high temperature exposure on the plasticity of internode and plant architecture in maize

The occurrence of high temperature(HT)in crop production is becoming more frequent and unpredictable with global warming,severely threatening food security.The state of an organ’s growth and development is largely determined by the temperature conditions it is exposed to over time.Maize is the main cereal crop,and its stem growth and plant architecture are closely related to lodging resistance,and especially sensitive to temperature.However,systematic research on the timing effect of HT on the sequentially developing internode and stem is currently lacking.To identify the timing effect of HT on the morphology and plasticity of the stem in maize,two hybrids(Zhengdan 958(ZD958),Xianyu 335(XY335))characterized by distinct morphological traits in the stem were exposed to a 7-day HT treatment from the V6 to V17 stages(Vn presents the vegetative stage with n leaves fully expanded)in 2019-2020.The results demonstrated that exposure to HT during V6-V12 accelerated the rapid elongation of stems.For instance,HT occurring at V7 and V12 specifically promoted the lengths and weights of the 3rd-5th and 9th-11th internodes,respectively.Meanwhile,HT slowed the growth of internodes adjacent to the promoted internodes.Interestingly,compared with control,the plant height was significantly increased soon after HT treatment,but the promotion effect became narrower at the subsequent flowering stage,demonstrating a self-adjusting mechanism in the maize plant in response to HT.Importantly,HT altered the plant architectures,including a rising of the ear position and increase in the ear position coefficient.XY335 exhibited greater sensitivity in stem development than ZD958 under HT treatment.These findings improve our systematic understanding of the plasticity of internode and plant architecture in response to the timing of HT exposure.

A Novel IoT Architecture, Assessment of Threats and Their Classification withMachine Learning Solutions

The Internet of Things(IoT)will significantly impact our social and economic lives in the near future.Many Internet of Things(IoT)applications aim to automate multiple tasks so inactive physical objects can behave independently of others.IoT devices,however,are also vulnerable,mostly because they lack the essential built-in security to thwart attackers.It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment.As a result,the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT,such as Cloud computing,machine learning techniques,and lightweight encryption techniques.This paper presents a detailed analysis of the security requirements,attack surfaces,and security solutions available for IoT networks and suggests an innovative IoT architecture.The Seven-Layer Architecture in IoT provides decent attack detection accuracy.According to the level of risk they pose,the security threats in each of these layers have been properly categorized,and the essential evaluation criteria have been developed to evaluate the various threats.Also,Machine Learning algorithms like Random Forest and Support Vector Machines,etc.,and Deep Learning algorithms like Artificial Neural Networks,Q Learning models,etc.,are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.

Enhancing MXene-based supercapacitors:Role of synthesis and 3D architectures

MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite its various desirable properties including intrinsic flexibility,high specific surface area,excellent metallic conductivity and unique abundance of surface functionalities,its full potential for electrochemical performance is hindered by the notorious restacking phenomenon of MXene nanosheets.Ascribed to its two-dimensional(2D)nature and surface functional groups,inevitable Van der Waals interactions drive the agglomeration of nanosheets,ultimately reducing the exposure of electrochemically active sites to the electrolyte,as well as severely lengthening electrolyte ion transport pathways.As a result,energy and power density deteriorate,limiting the application versatility of MXene-based supercapacitors.Constructing 3D architectures using 2D nanosheets presents as a straightforward yet ingenious approach to mitigate the fatal flaws of MXene.However,the sheer number of distinct methodologies reported,thus far,calls for a systematic review that unravels the rationale behind such 3D MXene structural designs.Herein,this review aims to serve this purpose while also scrutinizing the structure–property relationship to correlate such structural modifications to their ensuing electrochemical performance enhancements.Besides,the physicochemical properties of MXene play fundamental roles in determining the effective charge storage capabilities of 3D MXene-based electrodes.This largely depends on different MXene synthesis techniques and synthesis condition variations,hence,elucidated in this review as well.Lastly,the challenges and perspectives for achieving viable commercialization of MXene-based supercapacitor electrodes are highlighted.

A Comparative Analysis of Visualization Methods in Architecture:Employing Virtual Reality to Support the Decision-Making Process in the Architecture,Engineering,and Construction Industry

The design process of the built environment relies on the collaborative effort of all parties involved in the project.During the design phase,owners,end users,and their representatives are expected to make the most critical design and budgetary decisions-shaping the essential traits of the project,hence emerge the need and necessity to create and integrate mechanisms to support the decision-making process.Design decisions should not be based on assumptions,past experiences,or imagination.An example of the numerous problems that are a result of uninformed design decisions is“change orders”,known as the deviation from the original scope of work,which leads to an increase of the overall cost,and changes to the construction schedule of the project.The long-term aim of this inquiry is to understand the user’s behavior,and establish evidence-based control measures,which are actions and processes that can be implemented in practice to decrease the volume and frequency of the occurrence of change orders.The current study developed a foundation for further examination by proposing potential control measures,and testing their efficiency,such as integrating Virtual Reality(VR).The specific aim was to examine the effect of different visualization methods(i.e.,VR vs.construction drawings)on,(1)how well the subjects understand the information presented about the future/planned environment;(2)the subjects’perceived confidence in what the future environment will look like;(3)the likelihood of changing the built environment;(4)design review time;and(5)accuracy in reviewing and understanding the design.