法理学视阈下的生态文明法治

党的十八大以来,习近平总书记对生态文明建设作出一系列重要论述,提出“人与自然生命共同体”理念,强调人与自然关系在新高度上的辩证统一关系,让人类对人与自然之间关系的认识达到了一个全新的高度。“生态文明”成为我国的治国方略写进了宪法,标志着“生态法治”成为我国法治一项新的重要内容。“生态法治”已经成为我国法治的一个重要价值理念,成为我国社会主义法治所要追求的目标之一,是我国社会主义法治思想的升级进步。它把此前“保护环境”的制度规范上升到“法理价值”的哲学层面,从而将对我国整个法律体系带来根本性的影响。从法理学的角度阐释“生态法治”,可以丰富我国法学理论的内容,还能为“生态法治”建设提供理论指导。

A Pricing-Based Cooperative Relay Selection Scheme for Reliable Communications

With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

A graph neural network approach to the inverse design for thermal transparency with periodic interparticle system

Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and intriguing.Our earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for manipulating thermal transport behavior and achieving thermal transparency.In this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a thermal metamaterial-based periodic interparticle system with the desired thermal transport behavior.Our work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.

High Stable and Accurate Vehicle Selection Scheme Based on Federated Edge Learning in Vehicular Networks

Federated edge learning(FEEL)technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users.In the FEEL system,vehicles upload data to the edge servers,which train the vehicles’data to update local models and then return the result to vehicles to avoid sharing the original data.However,the cache queue in the edge is limited and the channel between edge server and each vehicle is time-varying.Thus,it is challenging to select a suitable number of vehicles to ensure that the uploaded data can keep a stable cache queue in edge server while maximizing the learning accuracy.Moreover,selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training,which further affects the model accuracy.In this paper,we propose a vehicle selection scheme,which maximizes the learning accuracy while ensuring the stability of the cache queue,where the statuses of all the vehicles in the coverage of edge server are taken into account.The performance of this scheme is evaluated through simulation experiments,which indicates that our proposed scheme can perform better than the known benchmark scheme.

Analysis and Optimization on Partition-Based Caching and Delivery in Satellite-Terrestrial Edge Computing Networks

As a viable component of 6G wireless communication architecture,satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial networks.In the paper,we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul load.Focusing on the interference effect from varied nodes in different geographical distances,we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic geometry.Considering the constraint of nodes cache space and file sets parameters,we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical user.The complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization(PSO)method and greedy based multiple knapsack choice problem(MKCP)optimization method.Numerical results show that compared with the terrestrial only cache strategy,Ground Popular Strategy,Satellite Popular Strategy,and Independent and identically distributed popularity strategy,the performance of the proposed scheme improve by 30.5%,9.3%,12.5%and 13.7%.

Incremental Learning of Radio Modulation Classification Based on Sample Recall

Radio modulation classification has always been an important technology in the field of communications.The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks.In this paper,we propose a sample memory and recall framework for incremental learning of radio modulation classification.For data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for memorizing.We compare the performance of our proposed method with three baselines through a large number of simulation experiments.Results show that our method achieves far higher classification accuracy than finetuning method and feature extraction method.Furthermore,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

Cooperative Content Caching and Delivery in Vehicular Networks: A Deep Neural Network Approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network.As a promising alternative,cooperative content caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and limited caching capacities.In addition,the high mobility of vehicles renders the more complicated caching environment.Therefore,performing efficient cooperative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communication link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is formulated.Furthermore,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition

The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.

亚言语的理论与应用

成晓光,辽 宁 师 范 大

国外社会创业研究述评与展望

本文试图通过大量的文献调研,探讨国外社会创业研究现状和问题,并且提出中国学者进一步研究的思路;本研究发现,社会创业作为一个独立的学术探寻领域,正在逐步被学术界认同。但理论文章甚多,严格意义之下的实证研究偏少。如果再进一步进行分析可以发现,目前社会创业研究存在的根本性问题是缺乏一个统一的科学、严谨、结构合理的基于社会创业原有模型的法则网络理论框架。通过对"社会创业导向"的有关研究进行综述发现,基于"社会创业导向"构建该理论框架并进行实证研究是未来社会创业研究的发展方向。目前,学者们对"创业导向"能运用于社会创业组织之中达成一定的共识。但对"创业导向"的五个维度包括创新、超前行动、风险承担、进攻性竞争和自主性是否都能反映社会创业过程的本质以及各维度在社会创业情境下如何定义和测量,争论不一,需要进一步研究。

效度概念新认识

本文主要讨论了效度概念的演变和存在的问题、效度概念新认识及如何寻找效度三个问题。效度概念的发展从内容效度、预测效度、构想效度和同时效度并存发展到现在由构想效度涵盖了其它所有效度。但一直以来,把效度概念作为一个法理学网络来理解,造成了我们对效度概念理解的很多误区。事实上效度概念是一种本体论的观点,重视现实参照并依据因果关系。我们应该通过建立一个理论假设、形成一个测验和确定测量的属性来寻找效度。

指示优先级的程式化管理科学研究方法--治理学术界与企业界脱节顽症的一种手段

为治理管理学领域学术界与企业界脱节的顽症,基于法理学和指示优先级程式方法,提出指示优先级的程式化管理科学研究方法:将研究复杂管理问题的过程分解成相互关联的4个活动方面和8个主要活动,构成管理科学研究中的"指示优先级轮",以保持各个活动方面和主要活动的均衡为原则,纠正研究过程中的不平衡现象。分析了该方法的理论含义,剖析这一方法为什么有助于促进理论与实践相结合,从而解决理论与实践脱节问题,并阐述指示优先级程式方法的实践应用。实践应用表明:该方法的应用,可避免管理科学研究中重理论轻实践或重实践轻理论2种偏向,使管理研究过程的管理科学理论与管理实践行为相结合。

基于思维法则学的跨文化信任框架——综合中国本土化思想与西方理论的尝试

以"承诺式信任"和"说服式信任"作为2个极端点,以"调节式信任"作为连续体的中间状态,提出了"信任的连续统一体"模型。该模型解释了中国情境下基于"关系"和"面子"的信任,以及西方基于认知、基于情感的信任;从调节式信任的角度,提出了跨文化信任的框架,分析了跨文化信任构建过程的一些阶段及调节策略;给出了关于发展中国管理理论、促进管理科学中国学派走向世界的思考。

论人文、社会科学中的定律解释

定律解释是一切可以被称作是科学的知识体系的基础,是基本的科学解释。它不论在自然科学还是人文、社会科学中都是普遍适用的。针对人文、社会科学中缺乏普遍性定律的反对,可以普遍的“似定律陈述”取代定律解释要求的定律。现实的定律解释不是逻辑实证主义强调的经验定律对被解释项的覆盖或逻辑论证,而是理解与解释关系中的定律解释。后者在逻辑模式上强调解释项对于价值项的包含,它是“人的历史性的生存实践———前理解———理解、解释”的简洁表达。

进谏行为与组织公民行为的关系研究:诺莫网络视角

进谏行为和组织公民行为等角色外行为研究近年来备受关注。与组织公民行为相比,进谏行为是一个新的构念。本研究探索了进谏行为的构念内涵,并借鉴诺莫网络思路考察了进谏行为与组织公民行为的关系。问卷研究的结果表明:进谏行为能融入组织公民行为构念,领导公平和情感承诺对两者都具有正面影响,而大五中的宜人性维度对两者的影响表现出差异性;从事角色外行为会导致更高的工作满意感。研究证实了进谏行为与组织公民行为构念内涵的相似性及存在的细微差异。

员工建言和沉默之间的关系研究:诺莫网络视角

作为两种代表性的员工自愿性工作行为,建言与沉默的研究一直各行其道。它们之间的关系是否如其字面意思一样对立相反是本研究拟解答的关键问题。以长三角地区制造类民营和外资企业的350组配对数据为样本,基于诺莫网络视角,本研究从情绪、认知和领导因素的角度,探讨了建言与沉默之间的关系。结果发现,建言与沉默是两个独立的构念。在情绪特征上,正性情绪会促进员工建言,而负性情绪既会促进员工建言,更会导致员工沉默;在认知特征上,自我效能感会促使员工建言,心理安全会促进建言且抑制沉默;在领导特征上,领导成员关系与建言正相关。在作用效能上,建言比沉默更会得到领导的赏识。研究结果综合支持了建言与沉默并非对立相反的观点。

诺莫网络视角下产学研主体间知识共享与知识隐匿关系研究

以诺莫网络为主导逻辑,分析了产学研主体间知识共享与知识隐匿的关系,从知识、主体和环境3个层面选取6个前因变量,用知识粘性代表知识层,主体层包括自我效能、组织自尊和元胜任力,环境层包括组织心理所有权和差序氛围,用产学研知识溢出作为近果变量。采用Projection Pursuit和多元层次回归对构建的诺莫网络理论框架进行了实证分析,运用Mackinnon、Sobel检验和Multivariate Delta Method验证了多重中介效应。结果表明,知识共享与知识隐匿是两个相互独立的构念,知识共享与知识隐匿在前因变量与近果变量间有多重中介效应。

思维法则学框架下东西方管理理论的比较——探索管理科学中国学派走向世界的道路

思维法则学下的CmCvAwAs系统理论,汲取了哲学和心理学的成果,包含了东方哲学思想,可为东西方管理理论的比较提供一个结构化框架。在CmCvAwAs系统理论框架下,对需要层次理论、系统开发的生命周期理论、质量管理理论、群体发展阶段理论、物理-事理-人理系统理论、东方管理学、和谐管理理论、和合管理理论进行了比较,分析了CmCvAwAs系统理论对于管理研究和管理实践的理论含义,并基于此给出了管理科学中国学派走向世界的一些具体的机遇和挑战。

法理学教学方法改革初探

法理学是一门研究法的一般原理、原则、概念、制度的基础理论课程。为了取得更好的教学效果,需要将法理学的理论与法律实践更好地结合起来。为了弥合理论与实践的裂隙,法理学教学方法改革可以从两个方面展开:一方面,教师应当创造性地使用讲授的方法,引导学生深入理解法学基本原理;另一方面,教师还应当使用多种辅助教学方法,培养学生主动学习、解决实际问题的能力。