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Repair and protection systems can have a positive impact on sustainability by prolonging the life of a structure and reducing the amount of new construction works. Over recent years, new products and technologies for use as concrete repair mortars and protective coatings, have been developed and launched on the market with lower environmental footprints than their traditional counterparts. To help visualise the environmental impact of these new technologies and products with improved environmental footprints, this paper will use a Life Cycle Analysis (LCA) of a repair project in Johannesburg, South Africa, which was completed in 2021. Two different scenarios will be compared and assessed – the first, is an existing product range for concrete repair and protection works that was used on the project, and the second is an alternative solution using the new and more sustainable technologies-based product range. This paper will provide an example of the positive environmental impact for such asset owners, by using more sustainable concrete repair and protection materials, by focusing on the Global Warming Potential (GWP) and Abiotic Depletion Potential fossil (ADP fossil) indicators.

High Performance Concrete is used to not only minimize the environmental footprint of the cement and concrete industry, but also to enable the design and rapid construction of taller and more slender structures. Despite the significant change in concrete property requirements, the properties of concrete as used during design of concrete structures are mostly still based on 28-day water cured compressive strength, stiffness, long term drying shrinkage and creep. The relevance of these measured properties on the actual behaviour of the structure should be questioned as high strength concrete is known to have low water/cement ratios resulting in significant early age shrinkage, which is currently not taken into account by designers. This paper is based on a study where the early age properties of concrete was investigated, with specific focus on the first 24 hours after casting. The effect of factors such as low water/cement ratio, admixtures, ambient and material temperatures, shutter type and cast volume on properties such as setting time, early age shrinkage, strength development and stiffness was studied and observed trends discussed in this paper.

Facing the aging building stock, challenging times can be expected with a sharp increase of reinforced concrete buildings requiring maintenance, repair and/or replacement. For the condition assessment of existing reinforced concrete structures an in-depth and reliable inspection strategy and evaluation is essential to come to a durable and service-life extending repair strategy. A stepwise protocol to evaluate the condition and remaining bearing capacity of cantilevered reinforced concrete (RC) balconies is presented in this paper. Corrosion causing the reduction of the steel section, incorrect positioning of the reinforcement and/or higher loads are the main reasons service life might not be reached or the failure of the balcony can occur. As mentioned in EN 1504-9 and NEN 8700, an adequate diagnosis prior to repair showing the cause and extent of the damage is important (i) to determine the actual bearing capacity, (ii) to make an estimation of the residual lifespan and (iii) to select a durable repair technique and/or define maintenance requirements. In this paper a newly developed protocol for the assessment of the condition and the bearing capacity of the cantilevered RC balconies of a high-rise residential building is demonstrated. A semi-probabilistic method is used by applying partial safety factors based on Eurocode guidelines and fib Bulletin 80. The condition of the structure is determined based on NEN2767 regulations. As corrosion is the main contributor to the degradation of existing concrete structures, a chloride contaminated building with damaged reinforced concrete balconies is selected as case study in order to demonstrate the applicability of the developed protocol and tools.

In order to stay competitive, printing companies need to continually improve their business, both in the technical and organizational fields. In this paper are presented some aspects related to the optimization of the technological processes of printing. Printed products such as magazines, brochures and notebooks are often made by offset technology. In order to achieve the actual printing, both web and sheet-fed printing machines are needed. The paper presents the initial version of the production workflow taking into account the available current endowment, both equipment and human resources of the printing house where the case study was conducted. After the study of the time required for each machine cycle, a new, automated production workflow was proposed. The advantages and disadvantages of each proposed option have been analyzed and criteria for optimizing the production flows have been established.

This paper presents the Fuzzy Logic method in order to evaluate the performance of a real flow-line. The flow-line comprises a number of machines that are arranged in series and the flow of products is continuous. The objective of this paper consists in the production rate prediction of a manufacturing system using the Fuzzy Logic method through ANFIS SUGENO and FIS MAMDANI. Input data are processed in ANFIS SUGENO to view the construction mode of the input membership functions in order to achieve our own model of FIS MAMDANI. Input variables' membership functions are modified by removing the values which exceed the representation range, after which the inference rules and the representation range of the output variables are to be established. The forms of membership functions are very important because they influence the prediction accuracy. A case study is carried out to demonstrate the performance and flexibility of the proposed approaches.

The present paper tackles certain issues regarding education, the educational system, the institution of education and it aims to provide a critical perspective on the Romanian higher education wellbeing in the contemporary international context. The outcome of the paper is to set forth specific recommendations regarding the improvement of education quality in order to ensure proper conditions for its sustainable development.

This paper deals with the use of three alternative methods in order to modelling, simulation and estimation of the production rate. The methods for evaluating the analytical performances are based on stochastic modelling of the production systems flows. Processes that imply random phenomena have traditionally been modelled through the use of statistical distributions. The paper shows that the Fuzzy Logic methods are able to achieve similar solutions to conventional methods. The objective of this work is to implement the main contributions brought in the industrial engineering field by modelling, simulating and estimation of the production rate, in a real case-study.

The aim of this paper is to analyze the parameters of significant wave height, wave period and wave length in the Black Sea basin in the period 2017 – 2021. To achieve this objective, satellite altimetry measurements from the IMOS database were used and compared with the values of SWAN model simulations. The results obtained from the processing of these two data sets contribute to a more in-depth understanding of the characteristics of wind waves in the Black Sea due to the special importance from a scientific, economic, political-military and strategic point of view of this geographical space.

This paper describes a novel PLC system that integrates cutting-edge hardware and software components, offering an immersive and interactive educational experience. The design, features, and benefits of the newly developed PLC are detailed, highlighting its potential to empower learners in various educational settings. Moreover, the cost-effectiveness of architectural decisions is also highlighted, with the end goal being to create e PLC that is both relevant to industry standards and affordable for universities and higher education schools.

This paper analyzes the contribution of the project RESTART_4Danube to urban regeneration in the Danube Region by enhancing transnational cooperation and coordination in Creative and Cultural Industries (CCIs) among the participating countries. From this perspective, the RESTART_4Danube project has a two-fold objective: (i) to strengthen the links between enterprises, research and development centers, universities and civil society, and (ii) to develop a new CCI-driven Urban Regeneration model that is rooted in regional Smart Specialization Strategies (S3) and can provide solid evidence for improving the framework conditions and public policies for research, innovation and regional development, in the sense of a transformative, systemic innovation policy, with an impact that goes beyond the CCIs and research & innovation eco-system, in order to produce broader effects in the economy, becoming more socially sustainable, according to the new orientation of smart specialization strategies for sustainable and inclusive growth (S4+).

Three cutting parameters are investigated in this study for their effect on the surface quality of aluminum alloy 7136 end milled. The major goal of the study is to use cluster analysis to discover the ideal cutting parameter combination to get the lowest surface roughness achievable. This paper examines cutting parameters and surface roughness for aluminum alloy end milling operations, finds literature gaps, and provides the experimental setup and research methodology. According to the findings, the data points are split into three categories depending on the characteristics of each individual data point. The findings are evaluated considering contemporary literature, and recommendations for further research are provided. As a consequence of this research, it is now clear how to optimize the cutting settings for end milling aluminum alloy 7136, potentially improving the usability and aesthetics of machined components.

The paper deals with some aspects regarding "Experimental Research and Laboratory Testing for Seismic Qualification DBE B, Seismic Edge HCLPF = 0.4g, Automatica 1 (2) - 52900 - PL2023 Junction Box". Seismic qualification was performed by physical experiment on the 150 kN Shaking Table at INCD URBAN INCERC IASI. The provisions of the - Technical Specification. Safety Systems Equipment Cabinets. Cernavodă 2. 82–68000–TS - 003, Revision 0, November 2001, - Addendum to Technical Specification 82–68000–TS–003, Rev. 0, Safety Systems Equipment Cabinets and - SR EN 60068-3-3:1994. Environmental testing. Part 3: Guidance. Seismic test methods for equipments, have been observed. The seismic qualification results and the related conclusions are contained in the paper.

This paper presents a case study on the use of Delmia Quest software in the simulation of a manual welding line. The goal of this paper is to better understand the line and its behaviour and to use the simulation software in order to optimize the line. To be able to optimize the welding line by adequate charge of the resources, the initial welding line layout has been used, where each operation had allocated a welder. Starting from the obtained results with the Delmia Quest software, solutions to reduce the number of welders from the welding line have been searched, without affecting the line productivity.

The paper proposes a prediction model of behavior spindle from the point of view of the thermal deformations and the level of the vibrations by highlighting and processing the characteristic equations. This is a model analysis for the shaft with similar electro-mechanical characteristics can be achieved using a hybrid analysis based on artificial intelligence (genetic algorithms - artificial neural networks - fuzzy logic). The paper presents a prediction mode obtaining valid range of values for spindles with similar characteristics based on measured data sets from a few spindles test without additional measures being required. Extracting polynomial functions of graphs resulting from simultaneous measurements and predict the dynamics of the two features with multi-objective criterion is the main advantage of this method.

One of the most important subsystems of the vehicles and machines operating currently in industry and transportation are the rotating subsystems. Monitoring of its technical state is an important issue of the engineering and academic research. As a result of the research a lot of new diagnostic methods have been formulated but one of the most important characteristic of them is the ability to industrial implementation. It depends on the insensitivity to disturbance which can arise in real operational environment. In the paper the method formulated by the authors is analysed. This is the method of the identification of inability states of rotating subsystems based on the vibrations analyses in the time domain. It constitutes the system approach to the considered issue. There are three applications of the method described - research on simulation laboratory stand, research on a real technical object conducted in the laboratory and research carried out on a real technical object under the real operation conditions. In each case different accuracy of the ability and inability states identification is achieved. Analysing the differences in accuracy of the method applications the conclusion of its sensitivity to the conditions of the experiment were formulated which are presented in the end of the paper.

The paper describes the methodology of annotating of road accidents centres of interactive accidents map. Based on the statistical analysis of the traffic accidents database the range of accident risk zones of a roadmap of the city are allocated as well as cause of accidents. These information in the form of text messages is applied to a vector map of a geographical area, in this paper – the map of Nizhny Novgorod. The annotation algorithm also takes into account the data of the road infrastructure, the weather, the driver (the user of the interactive crash map). The method was tested during the creation of e-cards of Nizhny Novgorod.

This article reports the results of an in-depth analysis of the transport situation in the Russian Federation and presents the main programmes, aimed at its improvement. The paper deals with the major ways of reducing risk of traffic accidents and increasing traffic safety. The paper proposes the scientific and methodological approach, providing the specialists with the opportunities of conducting a comprehensive assessment of the causes of emergencies and aiming at eliminating road accidents rates. The origin of the process of an emergency situation occurrence has been investigated and designed, the key components (constituents) of the process have been identified and the basic mechanisms, influencing the basic components, have been analyzed. The algorithm of the application of the proposed and developed scientific and methodological approach at the level of departmental organizations has been suggested. The article describes the mathematical model of the process of emergency situations occurrence, which, together with the applied telematics systems and intelligent transport systems, will help to achieve the basic objectives of the study.

Solar energy is a renewable energy abundant throughout the year in a tropical weather country like Malaysia. This paper investigates the viability of using solar PV systems as a cost-saving measure to supply electricity for Taylor’s University (TU) daily energy usage. Experimental values were compared with theoretical values and analyzed in this paper. In this experiment, four photovoltaic (PV) panels connected in parallel were linked to a maximum power point tracking (MPPT) charge controller acting as a voltage regulator. A lead-acid battery was also coupled to the controller that acts as an energy storage to store the harvested solar energy from PV panels and discharge it in electricity. Temperature sensors connected to an Arduino UNO were placed at different locations on the solar panels to monitor for irregularities in the temperature of the panels. The amount of electricity produced was calculated using the data obtained. The results showed that using a larger PV system will generate much more electricity and create a high return on investment (ROI) if the solar panels absorbed sunlight under good weather conditions, thus bringing forward a potential solution to reduce TU’s electricity consumption.

As the number of aging population increases, their Quality of Life (QoL) becomes a concern in the society. The elderly is not only vulnerable due to their chronic degeneration issues but may also be insensitive to technological innovation which could possibly improve their QoL in the Industrial Revolution 4.0. The outbreak of COVID-19 has significantly threatened their well-being in their living context and will consequentially change people’s perspective towards normal lifestyle after this public health crisis. Recent studies have highlighted the usability of the drone technology in the automation of navigation, monitoring, and load carrying which can potentially facilitate various purposes of use in our daily life. This paper reviews recent academic works related to Drone-Based Internet of Things (DIoT) technology and extracts the advantages of DIoT applications, which have the potential to assist elderly’s Activity of Daily Living (ADL) in post-epidemic time. Our results suggest that with the low energy consumption, the DIoT techniques are capable of reducing the body exposure under pandemic situation and satisfying the appreciation to normal and digital-connected life in the future. Nevertheless, the limited flying range and low technology penetration among elderly users significantly impede the implementation of DIoT application. Importantly, the DIoT technology upgrades manpower-based manual work. This paper updates to the knowledge of drone technology application in the context of elderly centre during post-pandemic.

The utilization of regenerative braking energy is of great significance to the energy saving of subway. Therefore, this paper proposes an optimization method for scheduling and control integration, which not only adjusts the timetable but also optimizes the speed curves of trains. When there is a train braking, this method will try to find a train that accelerates to absorb the regenerative energy generated by the braking train. Firstly, this paper establishes the timetable energy saving optimization model, based on which the speed curves will be optimized. Furthermore, we design a scheduling optimization algorithm based on genetic algorithm, and optimizes speed curves of trains by binary search method to obtain a good solution. Finally, simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the integrated scheduling and control optimization method can reduce energy consumption by 15.18%. In the random disturbance simulations, the proposed method shows good robustness, which makes it possible to apply this method to the real subway operations.

With the development of sensor, computer and communication, the researcher attaches more and more attentions to the research of the wireless sensor networks because of its bright future in applications. This paper introduces the structure, characteristics of wireless sensor networks. Then, some key questions of the research field are discussed based on the basic principles. The end of paper discusses the application fields of wireless sensor networks and comes to a conclusion.

With the development of new generation Internet technology, Digital Object Architecture (DOA)/Handle system plays an important role in industrial system. The development and technical characteristics of DOA/Handle technology is studied in this paper. Then function architecture of distributed data acquisition system based on DOA/Handle technology is proposed. Through the detailed design of the functions and characteristics of the architecture, the core functions such as distributed data acquisition, distributed data management, data security sharing, and data security governance are realized in the industrial Internet environment. At the end of the paper, the typical scenarios of this architecture are summarized, and a prospect of the application prospect of DOA/Handle technology is introduced.

This paper introduces the structure of the existing on-line monitoring system, and analyzes the problems of poor compatibility and reliability of IED in different manufacturers. Then it gives two ways to transform the on-line monitoring system of substation equipment to the Internet of things, which is to retain IED and to remove IED. On this basis, this paper introduces the application scenarios of the on-line monitoring system of substation equipment connected to the Internet of things, including accurate perception of equipment status, accurate evaluation of equipment status and intelligent research and judgment of equipment status.

To solve the problem that current communication network training tools have limited functions and can’t effectively support the electric power communication network training, a new communication network comprehensive training simulation system is designed and implemented. This paper introduces the architecture of the system and function of each part. Then the simulation model principle is explained. The paper introduces the key technologies of 3D modeling, mechanism modeling, communication network scenario automatic generation and network fault simulation. Finally, the system application is introduced and the results show that the system builds a multi-functional training tool to meet the requirements of training and assessment for electric power communication network personnels.

How to control the movement of the mechanical arm is a challenging topic recently. It requires much time and energy to program every joint. This paper proposes a new method based on 3D coordinates of the human arm joints, which helps the mechanical arm to imitate the movement of human arms and only needs to calculate angles of human arms’ joints according to the 3D coordinates. Here in this process the key is to get the information of 3D coordinates of human arms. This paper applies an AR (Augmented Reality) application of body detection with LiDAR on iPad Pro 2020 to get 3D coordinates. Then the calculated angle information is transferred from the host computer to the Arduino board controlling the movement of the mechanical arm. Eventually the same movements will be made by the mechanical arm and the human arm.

The construction of index system is the basis of equipment system contribution rate evaluation, and is also one of the main contents of equipment test and appraisal. This paper discusses the concept of systems contribution rate, introduces the evaluation process of system contribution rate of information system equipment, and gives the construction principles and ideas of information system equipment system contribution rate evaluation index system. Then, referring to the theory of operational loop, the evaluation index system of system contribution rate is given from the perspectives of system capability and system effectiveness. The evaluation index of this paper can provide decision support for the development of equipment planning system.

This paper presents a personalized clothing customization system using Augmented Reality (AR) technology. The goal of this paper is creating a more interactive and immersive experience for tailor designer by bridging physical personalized clothing customization and digital resources. This system can make the traditional clothing customization process more convenient and efficient and has strong interaction and experience. At the same time, it reduces the time cost, labor cost and material cost of sample making in the traditional clothing customization process. This paper discusses the feasibility of the system, introduces the system development, testing, and application environment, the overall system design scheme and system workflow, and details the specific functions of each module. It also introduces the system detection methods and observation indicators. The system uses technologies such as augmented reality (AR) technology, Azure Kinect somatosensory technology, and OpenGL 3D rendering, combined with the functional advantages of the somatosensory virtual fitting system, focusing on the improvement and role of AR technology in the clothing customization process, effectively improving user satisfaction with clothing customization and saves production time and costs.

Aiming at the propagation process of elastic wave in spacecraft stiffener structure. In this paper, the phase and energy changes of elastic waves propagating in the wall structure of stiffeners at different frequencies are analyzed by using finite element method and laser Doppler vibrometer (LDVS). Meanwhile, the relationship between the parameters of stiffeners and the diffusion of acoustic wave energy and the phase change of elastic waves respectively is obtained. The results show that in the range of parameters involved in this paper, the stiffener has a significant effect on elastic waves propagation. When wavelength λ > 10mm, most of the elastic waves are reflected by the stiffener, while when λ < 6mm, elastic waves propagate more easily across the stiffener, and the proportion of those propagated by the stiffener increases. Moreover, the functional expression of the relationship between the height of the stiffener and the frequency of the elastic wave which can easily propagate through the stiffener is obtained.

In this paper, evolutionary algorithm is used to optimize the flicker function of band-limited Gaussian noise interference, which disturbs the convergence of adaptive algorithm based on LMS in GPS receiver, so that the average null depth of GPS receiver under equal power is lower, and better jamming effect is achieved. The simulation results show that the jamming algorithm in this paper has better jamming effect than the band-limited Gaussian noise jamming in different jamming quantity.

Based on the precise sheepskin contour extracted by computer vision technology in the previous research of the team, this paper proposes the shape description technology based on the structure contour to extract the local features of the sheepskin, such as the head and hooves and the waste edge, which is the basis for the automatic edge removal of the sheepskin in the future. The algorithm uses Angle and position relation to segment the precise contour track of raw sheepskin into graph elements, and then uses geometric parameter shape description operator to describe and extract the edges that need to be removed, so as to obtain the starting point and end point of each local contour that needs to be removed. In this paper, the principle and implementation steps of this method are introduced in detail, and the experimental simulation verification shows that the extraction effect is good, which can meet the requirements of subsequent industrial production of automatic sheepskin cutting.

The paper takes the authentication gateway system for internal office network as the research object, for the performance bottleneck and single point of failure problem of the single authentication gateway deployed in the existing primary and standby modes, chooses an authentication gateway integration method based on CA certificate. The practical results show that: through implementing the authentication gateway cluster design and introducing load balancing mechanism, this method solves the performance, stability and single point of failure of the authentication gateway, and improves the resource utilization of the authentication gateway device. This method introduced in this paper can be used for reference for the network architecture optimization based on the server load balancing technology.

Aiming at the current low pre-diabetes detection rate, this paper proposes a PSO-SVM model to assist doctors in identifying the risk of patients with pre-diabetes. The paper uses the Support Vector Machine as the verification algorithm, takes the radial basis kernel as the kernel function, uses the adaptive Particle Swarm Optimization algorithm to optimize the penalty factor and kernel parameters of the Support Vector Machine, and establishes a PSO-SVM model, finally compares the model with Neural Network, Logistic Regression, and Naive Bayes model, and use Sensitivity, Specificity indicators and ROC curve to evaluate model performance. Empirical analysis proves that the combined model proposed in this paper can effectively identify the risk of patients with prediabetes.

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.

The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect in the past three years: embedding layer, propagation update layer, and prediction layer. Although there are subtle differences between different models, they are all this framework can be applied, and different models can be regarded as variants of this general model, that is, other models are fine-tuned on the basis of this framework. At the end of the paper, the latest research progress is introduced, and the main challenges and research priorities that will be faced in the future are pointed out.

The current rapid development of cloud computing and networks has put forward new requirements for the construction of new infrastructure such as data centers. This paper compares traditional data centers and high-density data centers, proposes a three-tier infrastructure for high-density data centers, and analyzes the data center environment. To solve the obvious problem of software and hardware heterogeneity in high-density data centers, this paper uses virtualization technology to pool resources in high-density data centers and introduces SOA architecture to manage software and hardware resources hierarchically. Finally, the data center infrastructure and environment matching technology are studied.

As a complex and safety-critical system, any failure in the Next Generation Train Control System (NGTC) departure scenario may cause serious personal injuries and property losses. It is very necessary to study NGTC scenario scheme and effective modeling and verification methods. This paper investigates the key technologies of the NGTC, optimizes the system structure and redistribution functions, and proposes the train control system scheme and typical operating scenarios. Firstly, the structure, equipment function and information interaction of NGTC are analyzed, and the operation scenarios of the system scheme are designed. This paper also uses UML language to describe the train departure scenario, and uses NuSMV modeling to verify accessibility and certainty of the scenario scheme. The results show that, the scheme proposed in this paper provides a reference for the design and implementation of the NGTC.

The development of new energy vehicles is inseparable from the drive of consumers. Therefore, to explore the influencing factors of purchase behavior from the consumer's personal level is helpful for businesses to adopt corresponding sales strategies and the government to adopt relevant policies. Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles. First of all, this paper proposes a quantitative method of consumer individual level factors, which combines word-of-mouth reviews with statistics. In this method, word2vec is used to train word vectors in word-of-mouth corpus to mine initial keywords, and core keywords are selected through statistical correlation analysis. Secondly, based on the core keywords of consumers' personal level, the gbdt model is constructed to predict the purchase behavior of new energy vehicles. The results show that the probability of correctly predicting consumers' purchase behavior is more than 72%.

Chinese enterprises are facing the opportunity of digital transformation, and all enterprises are advancing in exploration. This paper introduces the trend and challenge of digital transformation of Chinese enterprises. This paper expounds the digital transformation strategy of enterprises as the center. This paper discusses the four development processes of enterprise digital transformation, including digital empowerment, process optimization, digital transformation and digital reengineering. This paper puts forward the methods that Chinese enterprises should adopt in the process of transformation. First of all, the digital transformation of enterprises should be scientifically planned. Then select pilot projects in enterprises and try them out first. After that, the trial effect was evaluated. After summarizing and improving, copy and enlarge the scope. Finally, optimize the operation of the enterprise, and constantly adjust.

In order to improve the performance of alliance collaborative innovation and stimulate members' willingness and behavior to participate in collaborative innovation, this paper puts forward the incentive mechanism of benefit distribution. This paper divides the needs of members to participate in collaborative innovation into two stages: "risk avoidance-return on investment". Firstly, an effective benefit distribution model is established by using Logistic function. Then, by building a game model, we can get the best effort. The results show that building the benefit distribution model of alliance collaborative innovation according to the different needs of alliance members can fully stimulate members to participate in collaborative innovation and improve the performance of alliance collaborative innovation.

In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.

The pumped storage power station is flexible to start, can realize effective storage of electric energy, and has superior peak and frequency modulation effects, which is beneficial to provide comprehensive benefits of the power system. But at present, the construction of pumped-storage power plants has obviously exceeded the speed of policy and market development, resulting in problems in pumped-storage power station operation and cost reduction. Aiming at this problem, this paper further expounds the influence of the construction and operation of pumped storage power station on the electricity price of power grid companies. The revenue of pumped storage power station mainly comes from the auxiliary service market. In China, the peak regulation effect of pumped storage power station is significant and the revenue is considerable. Therefore, this paper studies the formulation of time-of-use price and subsection price of pumped storage power station.

Nowadays, Tibetan speech synthesis based on neural network has become the mainstream synthesis method. Among them, the griffin-lim vocoder is widely used in Tibetan speech synthesis because of its relatively simple synthesis.Aiming at the problem of low fidelity of griffin-lim vocoder, this paper uses WaveNet vocoder instead of griffin-lim for Tibetan speech synthesis.This paper first uses convolution operation and attention mechanism to extract sequence features.And then uses linear projection and feature amplification module to predict mel spectrogram.Finally, use WaveNet vocoder to synthesize speech waveform. Experimental data shows that our model has a better performance in Tibetan speech synthesis.

Violent behavior recognition is an important direction of behavior recognition research. For traditional violent behavior recognition algorithms, there is too much background information when processing video information, which will cause greater interference in feature extraction, so the recognition accuracy is not high. Improved on the basis of effective recurrent convolutional network, a long-term recurrent convolutional network with attention mechanism is proposed. In the video preprocessing stage, a variety of attention mechanisms are introduced. In the feature extraction stage, the lightweight end-to-side neural network architecture GhostNet and convLSTM are selected to build a long-term recurrent convolutional network. The global average pooling and fully connected layer are used in the classification process. The combined approach realizes the classification of behaviours. The final results show that in the Hockey dataset, the algorithm in this paper has increased by 0.4% compared to LRCN, in the RWF-2000 dataset with more samples, it has increased by 10.5% compared to LRCN, and has increased by 1.75% compared to I3D, indicating that the algorithm in this paper can effectively suppress the background information. Interference, improve the performance of the algorithm.

The visual information that can't be detected by consciousness but can affect individual's behavior and attitude under specific conditions is called subliminal visual messages. In order to better apply subliminal visual messages to commercial advertising, education and other fields, this paper studied the process of subliminal visual messages in the brain. First, this paper designed a experiment to allow the subjects to see a series of pictures stimulation of different durations and collect the EEG signals, then analyzed the impact of stimulation time on classification accuracy. The experimental results showed that when the stimulus time is short, the classification accuracy increases with the increase of time, resulting in subliminal visual effects. However, with the increase of stimulus time, the classification accuracy began to decline. We speculated that the visual information changed from subthreshold to suprathreshold. The subliminal visual effects were disturbed until disappeared.

In this paper, the economic benefits of prefabricated buildings which are not directly reflected in the economic returns of investors are called indirect economic benefits. Based on the literature mining of the indirect economic relationship of a large number of prefabricated buildings, this paper constructs an analysis framework of indirect environment and social and economic benefits. Through BIM modeling software, the three prefabricated building models are modified into traditional building models. The indirect economic benefits of the project are calculated by using the index system. The functional relationship between the indirect economic benefits of prefabricated buildings and the assembly rate is established by using the SSPS statistical data processing software, which more intuitively shows the law of the indirect economic benefits of prefabricated buildings with the assembly rate It shows the impact of prefabricated building on environment and society, which is of great significance for the harmony between prefabricated building and society and environment, and the healthy and sustainable development of construction industry.

This paper firstly models the relationship between power companies, photovoltaic power generation, wind power generation networks, and users as a three-stage Steinberg game. In this game model, the power company is the dominant player and the leader of the entire game. Secondly, with the game model, a detailed analysis is carried out to obtain the optimal strategy of each game party. Finally, the simplified model was simulated and verified. The simulation result is consistent with the theoretical solution result. The research results of this paper have improved the level of energy services and innovated energy service models.

Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients. Researchers are trying multiple analytical methods to dig out more valuable information about CHM and reveal the principle of TCM. Machine learning is playing an important role in the studies. Knowledge discovery of CHM using machine learning mainly includes quality control of CHM, network pharmacology in CHM, and medical prescriptions composed by CHM, aiming to understand TCM better, provide more efficiency methods in the production of CHM and find novel treatment of disease not curable nowadays. In this paper, we summarized the basic idea of frequently used classification and clustering machine learning algorithms, introduced pre-processing algorithms commonly used to simplify and accelerate machine learning procedure, presented current status of machine learning algorithms’ applications in knowledge discovery of CHM, discussed challenges and future trends of machine learning’s application in CHM. It is believed that the paper provides a valuable insight for the starters trying to apply machine learning in the study of CHM and catch up the recent status of related researches.

in this paper, we construct a data-driven trust evaluation model based on perceptual source. The model takes the monitoring module as the evaluation unit, and the relay node completes the trust evaluation of the sensing node in its monitoring module. The direct trust calculation is realized by the relationship between the sensing node's own data, and the recommended trust is calculated by using the relationship between the neighbor nodes in the monitoring module. Combined with the historical trust, the comprehensive trust of the sensing node is output. In this paper, the credibility of existing database driven cognitive software is summarized, and then the key technologies of reliability evaluation and classification of database driven cognitive software are analyzed.

Investment, consumption and export are the troika which promotes China's economic development. Investment is the most important factor and plays a vital role to drive economic growth. However, due to the differences in the geographical location, education level and infrastructure construction of various regions in China, the structure of economic development and the imbalance of growth still exist. This paper takes the fixed asset investment of 31 provinces, municipalities and autonomous regions in my country from 1999 to 2017 as the research objects in our country and uses functional data analysis methods to functionalize discrete data, uses Fourier basis functions to fit the smooth curve, and then uses FPCA analysis of the characteristics of the principal component function shows that the contribution rates of the first two principal components to the regional economy are 96.4% and 2.8% respectively, and the cumulative contribution rate reaches 99.2%, which can represent most of the sample information to a certain extent. Therefore, this paper uses the first two main components to analyse the overall change for law of China's regional economy, and roughly divides my country's 31 provinces and municipalities into 4 categories and adjust my country's economic development structure and promote coordinated regional development.

This paper proposes a hybrid financial time series forecast model based on LSTM and LightGBM, namely LSTM_LightGBM model. Use the LightGBM model to train the processed stock historical data set, and save the training results. Then the opening price, closing price, highest price, lowest price, trading volume and adjusted closing price are separately input into the LSTM model for prediction. The prediction result of each attribute is used as the test set of the prediction after LightGBM training. Constantly adjust the parameters of each model, and finally get the optimal stock price forecast model. The model is validated with the rise and fall of AAPL stock. Through the comparison of evaluation index root mean square error RMSE, mean absolute error MAE, prediction accuracy Accuracy and f1_score. It is found that the LSTM_LightGBM model exhibits stable and better prediction performance in the stock prediction. That is to say, the LSTM_LightGBM model proposed in this paper is stable and feasible in the stock price fluctuation forecast.