图档管理系统软件在图书管理中的应用

该文介绍了图档管理系统软件在图书管理中的应用,通过软件技术、图档管理概述、图书分类树设计、建立数据库,解决了图档管理系统软件在图书管理中的应用的一系列难题,并对某勘察设计院全部的图书进行管理。实现了图书档案一体化管理,节约投入资金,毋需另外购买专门的图书管理软件。     

三峡大坝应用碾压混凝土的研究

三峡工程具有工程量大、工期长的特点,为加快施工进度、缩短工期、节约水泥用量,拟在大坝的部分部位采用碾压混凝土。根据研究,考虑到坝体结构布置、构造要求、ＲＣＣ施工方法等因素,各坝段采用ＲＣＣ的部位将有所不同,总的采用原则是在坝体多钢筋的大孔口以下和结构布置简单而较规则的部位应用ＲＣＣ,但在基岩面上仍铺设１．０－１．５米厚的常态混凝土垫层,按上述原则,各坝段应用ＲＣＣ混凝土的总量约占大坝总混凝土量的１     

质量能够兴厂

通过赤天化推行全面质量管理从而建成全国化工行业中唯一的“双满意”企业的事例,阐述了创造优质名牌产品能够促进企业兴旺的道理。     

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国有企业要建立符合审场经济要求的现代企业制度,中心任务是建立现代公司法人制度和组织制度,并逐步实现产权的多元化。但天然气行业具有由输送网络等形成的自然垄断特征和行业总公司具有的双重职能（行政职能、法人团体）,因而管理不明晰,产业政策不明确,给体制改革带来很大困难。这些难点主要有：①对现在天然气的成本不完全和油气资源的流动矿藏特征,如何进行资产评估和产权界定;②企业组织由行政机构演变而来,如何建立法人治理机构;③如何建立内、外部约束机制;④如何处理计划经济时代遗留下来的“大而全”、“小而全”、“企业办社会”、冗员过多等问题。最后提出了公司制改造方案和政府管制方式。     

An introduction to TMN

The Telecommunication Management Network standard (ITU-T M.3010), the Network Magagement Forum OMNIPoint program, and many related developments are summarized. TMN and OMNIPoint are presented as a means to achieve integrated network management in the telecommunications industry by calling for all management systems to include an interoperable interface which permits each system to be integrated into a larger management hierarchy. By means of such interfaces, hierarchical integration of management systems can be achieved within the administration of a single service provider. Interoperable interfaces also make possible the interconnection of multiple service providers and the connection of customer to service provider, resulting in the possibility of industry wide integration of management infrastructures at all levels.     

含多电解槽的新能源制氢能量管理优化

新能源制氢系统是提升风能、太阳能等新能源发电消纳的有效途径。目前国内外关于电解槽能量管理的相关研究以单电解槽为主。单电解槽能量管理未考虑电解槽非线性的工作特性,难以兼顾多个电解槽制氢效率,影响系统经济性。论文针对含有多电解槽新能源制氢系统的能量管理问题进行了研究。以新能源消纳率、经济收益、制氢率为目标,考虑单个电解槽运行特性及生产约束等条件,建立包含风电、光伏、蓄电池、电解槽多个模块的能量管理优化模型,并采用SPEA2算法求解多目标优化问题。仿真研究表明,所提能量管理策略能够实现新能源发电的100%消纳,单位制氢效益可提升5.15%。因此,多电解槽制氢系统进行有效的能量管理有助于提高制氢效率,有效克服单电解槽运行及能量管理的不足。     

An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.     

Relaxation-Induced Significant Room-Temperature Dielectric Pulsing Effects

Thermo-responsive dielectric materials are in urgent demand owing to the rapid development of smart electronic/electrical systems. Although different types and structures of thermally responsive dielectric materials have been continuously reported, their dielectric response behaviors all originate from thermodynamic phase transitions. Herein, it is demonstrated that structural relaxation in poly(vinylidene fluoride) (PVDF), a non-thermodynamic phase transition, can induce a significant thermal dielectric pulse at room temperature. The dielectric pulse strength of up to 6.3 × 10⁵ at 20 Hz, with a dielectric pulsing temperature of 24 °C, is achieved from polyethylene glycol (PEG)-PVDF coaxial nanofibrous films (PVDF@PEG), fabricated via a continuous blow spinning method. Moreover, the films exhibit excellent flexibility, adjustable strength and toughness, switchable hydrophilicity/hydrophobicity, and effective thermal management capability. The relaxation-induced dielectric pulsing effect, outstanding multifunctionality, and simple preparation combine to promote further scalability and prospects of PVDF@PEG. In particular, the work contributes to the discovery of the relaxation-induced dielectric response mechanism, which provides a new strategy for the generation of thermo-responsive dielectric materials.     

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.