Smart Management Information Systems(Smis):Concept,Evolution,Research Hotspots and Applications

Management information system(MiS),a human-computer system that deeply integrates next-generation information technology and management services,has become the nerve center of society and organizations.With the development of next-generation information technology,Mis has gradually entered the smart period.However,research on smart managementinformation systems(SMIS)is still limited,lacking systematic summarization of its conceptual definition,evolution,research hotspots,and typical applications.Therefore,this paper defines the conceptual characteristics of SMIS,provides an overview of the evolution of SMIS,examines research focus areas using bibliometric methods,and elaborates on typical application practices of sMis in fields such as health care,elderly care,manufacturing,and transportation.Furthermore,we discuss the future development directions of SMIS in four key areas:smart interaction,smart decisionmaking,efficient resource allocation,and flexible system architecture.These discussions provide guidance and a foundation for the theoretical development and practical application of SMIS.

Current status and construction scheme of smart geothermal field technology

To address the key problems in the application of intelligent technology in geothermal development,smart application scenarios for geothermal development are constructed.The research status and existing challenges of intelligent technology in each scenario are analyzed,and the construction scheme of smart geothermal field system is proposed.The smart geothermal field is an organic integration of geothermal development engineering and advanced technologies such as the artificial intelligence.At present,the technology of smart geothermal field is still in the exploratory stage.It has been tested for application in scenarios such as intelligent characterization of geothermal reservoirs,dynamic intelligent simulation of geothermal reservoirs,intelligent optimization of development schemes and smart management of geothermal development.However,it still faces many problems,including the high computational cost,difficult real-time response,multiple solutions and strong model dependence,difficult real-time optimization of dynamic multi-constraints,and deep integration of multi-source data.The construction scheme of smart geothermal field system is proposed,which consists of modules including the full database,intelligent characterization,intelligent simulation and intelligent optimization control.The connection between modules is established through the data transmission and the model interaction.In the next stage,it is necessary to focus on the basic theories and key technologies in each module of the smart geothermal field system,to accelerate the lifecycle intelligent transformation of the geothermal development and utilization,and to promote the intelligent,stable,long-term,optimal and safe production of geothermal resources.

A Thermochromic, Viscoelastic Nacre-like Nanocomposite for the Smart Thermal Management of Planar Electronics

Cutting-edge heat spreaders for soft and planar electronics require not only high thermal conductivity and a certain degree of flexibility but also remarkable self-adhesion without thermal interface materials, elasticity, arbitrary elongation along with soft devices, and smart properties involving thermal self-healing, thermochromism and so on. Nacre-like composites with excellent in-plane heat dissipation are ideal as heat spreaders for thin and planar electronics. However, the intrinsically poor viscoelasticity, i.e., adhesion and elasticity, prevents them from simultaneous self-adhesion and arbitrary elongation along with current flexible devices as well as incurring high interfacial thermal impedance. In this paper, we propose a soft thermochromic composite(STC) membrane with a layered structure, considerable stretchability, high in-plane thermal conductivity(~30 Wm^(-1) K^(-1)), low thermal contact resistance(~12 mm^2 KW^(-1), 4–5 times lower than that of silver paste), strong yet sustainable adhesion forces(~4607 Jm^(-2), 2220 Jm^(-2) greater than that of epoxy paste) and self-healing efficiency. As a self-adhesive heat spreader, it implements efficient cooling of various soft electronics with a temperature drop of 20℃ than the polyimide case. In addition to its self-healing function, the chameleon-like behavior of STC facilitates temperature monitoring by the naked eye, hence enabling smart thermal management.

Leveraging Geospatial Technologies for Resource Optimization in Livestock Management

Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Driving Smartization Through Intelligent Transport

Smart cities initiatives are developing all over the world, due to their support in favouring a better provision of services, in connection with the aim of achieving efficiency in cities management. More and more big corporations are interested in this topic to develop their businesses, as several issues are linked to these projects, like environment, water management, energy policies, education, culture, and so on. In literature these issues, known as drivers, were also related to sustainability aims which could be reached through the accomplishment of better performances within environment, economy, and society. This study synthesizes all the different drivers identified by scholars and industry players in a model, in order to use it as a framework to depict the linkages among the various smart cities projects. Since transport resulted as one of the most considered drivers in the analyzed contributions, the research questions were delineated to describe its role in the different phases of smart initiatives, and to understand if it could encourage and enable the other drivers＇ development. The multiple case study was chosen as the fittest methodological approach and it was conducted with the selection of three smart cities, one for each of the most cited platform models developed by industry players, which had started transport initiatives at least three years ago： Singapore （IBM）, Amsterdam （Accenture）, and San Francisco （Microsoft）. The analysis of the official documents set up by city departments and by the industry players showed transport as a driving force in smart cities projects which conveys a smart approach to different domains, especially energy savings, environment, and safety. The results also showed smart mobility as a pivot in stimulating investments and citizens＇ participation on different city drivers, especially as it concerns environment and services to both citizens and businesses. Smart transport initiatives, hardware and software infrastructures, technological devices, and people participation to smartization were closely interrelated in smart cities projects and they could be successfully used to achieve sustainability aims for different stakeholders.

是谁偷走流量 三星Smart Manager告诉你

还没到月底,流量就用没了,这难道不是个令人悲伤的话题吗?追本溯源,你的手机为什么流量总不够用?最大的问题一定是——流量偷跑,而这也是困扰所有智能手机用户的'顽疾'。导致用户流量增加的原因有几个方面:一是使用了4G网络后,网速更快、消耗更大,用户消耗流量有所增长;二是部分APP有'偷流量'现象,这当中有一部分是由于手机中病毒、木马等导致的流量消耗,但更多是由于应用自动连接网络。

Introducing CHIPDRIVE MyKey: Computer Lock and Password Manager on a Smart Card-Secured USB Stick

Santa Ana, Calif., March 30, 2010-Having trouble remembering all your passwords? Safely store your passwords, take them wherever you go, and lock up your computer whenever you leave with

A case study of digital-twin-modelling analysis on power-plant-performance optimizations

China Energy’s National Institute of Clean-and-Low-Carbon Energy(NICE)is developing a Power Plant Smart Management(PPSM)platform that employs digital-twin technology to undertake techno-economic modelling analysis on China Energy’s existing coal-fired power-plant units and explore cost-effective solutions to improve those plant units’thermal efficiencies and operating performance.This paper presents a case study of PPSM on a 320-MWe coal-fired thermal power-plant unit,demonstrating how the digital-twin technology was employed to explore and analyse optimization solutions.Various optimization solutions and their cost-effectiveness were assessed using the digital-twin-modelling analysis;the results indicated the optimization solutions are expected to improve the plant unit’s operating efficiency and reduce its current electricity-generation coal consumption by up to 3.5 g/kWh standard coal equivalent(sce),worth annual fuel-cost savings of approximately 4 million RMB for a single unit or 8 million RMB for the two identical 320-MWe units that the power plant currently operates.The digital twin was also employed to assess the power-plant unit’s operating economics during both summer and winter.In summer,when the unit operates in electricity-generation-only mode,the unit’s operating thermal efficiency could drop by up to 6%points following the grid demand of load changes from 100%maximum continuous rating(MCR)down to 30% MCR,resulting in an~45 RMB/MWh increase of electricity-generation cost.In winter,when the unit operates in combined heat and power(CHP)cogeneration mode,for the same boiler load,the CHP operation increases the plant unit’s operating profit with increasing district-heating duty,although the relative profit gain from the CHP cogeneration could start to decrease when the district-heating steam-extraction flow increases to a certain point that varies depending on the market prices of heat and electricity,while the fuel cost was found to be equivalent to~50% of the unit’s total CHP income cogenerated from its electricity and district heat outputs.