Construction Of System Research Articles

Vulnerability Evolution Mechanism of High-Speed Railway Construction System in the Goaf Site of Qinshui Coalfield

The main reason for serious consequences caused by disasters is “insufficient emergency preparedness and obvious system vulnerability”. To ensure the safe operation and maintenance of the high-speed railway in goaf sites and achieve effective hierarchical management, the vulnerability theory was introduced, based on the system approach, to explore the characteristics of hierarchical evolution. Firstly, based on the analysis of the internal and external disturbances of the high-speed railway construction system with underlying goaf areas, the vulnerability characteristic elements were defined, and a conceptual vulnerability model was proposed. Secondly, based on the literature research, accident cases, and standards and specifications, 25 factors affecting vulnerability were identified and a two-dimensional cross matrix was used to establish a vulnerability evaluation index system. Then, ISM was used to build hierarchical relationships among indicators, analyze the vulnerability characteristics of direct factors, indirect factors and essential factors, and a vulnerability evolution analysis framework was proposed. Finally, the Taiyuan–Jiaozuo high-speed railway project was selected for hierarchical and progressive vulnerability management. The study analyzed the coupling effect and evolution mechanism of various elements of the safety management system under external disturbances, identified the weak links of the high-speed railway system in the goaf site, and provided a decision-making basis for continuous dynamic optimization and emergency response technology, which complied with the strategic policy of sustainable development.

Synthesis of DNA-Encoded Macrocyclic Peptides via Visible-Light-Mediated Desulfurative C-C Bond Formation.

DNA-encoded library (DEL) technology has been developed to serve as a practical platform for the discovery of biologically active macrocyclic peptide compounds. However, the cyclization of linear peptides has been widely regarded as the challenging step in the production of macrocyclic peptide DELs. Herein, we describe a novel DNA-compatible macrocyclization strategy, which enables the construction of ring systems via visible-light-mediated desulfurative C-C bond formation. The macrocyclization proceeds smoothly under mild conditions and in a good yield. Moreover, the reaction is compatible with a variety of linear substrates and can thus be employed to generate structurally diverse DNA-encoded macrocycles with various ring sizes.

Research on the Mechanisms of the Digital Art Talent Cultivation Mode in Colleges and Universities Based on the AIGC

In the field of digital art, artificial intelligence-generated content (AIGC) technology is gradually becoming the core driving force of innovation. AIGC technology can automatically generate images, music, text and other types of artworks by simulating the creative process of human beings, providing new tools and platforms for digital art creation. Through the three methods of the literature review method, case study method, and interviews and questionnaire surveys, this paper thoroughly explores the current status of the application of AIGC in digital art education in colleges and universities, its teaching effect and its impact on the construction of teaching resources, teaching modes and evaluation systems. The research results show that AIGC technology can be used not only as a teaching tool but also as a platform to stimulate creativity and provide students with a new learning experience. Through data analysis, this paper reveals the potential of AIGC technology in enhancing students' interest in learning, creative expression, and technical mastery and proposes corresponding optimization of the teaching mode and evaluation system. In addition, this paper explores the challenges faced by AIGC technology in educational applications, such as ethical and legal issues, technological and cost challenges, insufficient faculty, and students' adaptability, and proposes corresponding countermeasures. Finally, this paper looks forward to the future development trend of AIGC in digital art education, highlights the contributions and innovations of the study, and provides references and suggestions for universities to cultivate new digital art talent in the era of AIGC.

Quantitative Evaluation of Macao’s Education Policy: A PMC Index Modeling Approach Incorporating Text Mining

The education policy of the Macao Special Administrative Region is a driving factor in the investment of educational resources and the construction of a comprehensive education system. At the same time, the Macao Special Administrative Region Government closely integrates its education policy with the overall national development strategy, focuses on the training of teachers with a strong sense of national responsibility, and attaches importance to the needs of young people in national education, information technology education, and vocational education. Taking the policy addresses of the Macau Special Administrative Region over the past 25 years as the research sample, this study pioneered the use of policy modeling consistency index modeling combined with text mining to construct an evaluation system featuring the development of education policies. This approach provides a more in-depth quantitative analysis of the evolution and effectiveness of specific education policies. The results show that the MSAR has made significant progress in strengthening educational resources and aligning educational goals with national priorities. However, there is still room for improvement in areas such as information technology and vocational training. Based on this, the study recommends that these areas be targeted for strengthening, providing a reference basis for the future formulation or adjustment of existing education policies.

Optimizing hardware configuration for solar powered energy management in battery ultracapacitor hybrid electric vehicles

The design and construction of an adaptive energy management system incorporating a 12 V–2 Ah battery and a 1F ultracapacitor for solar powered hybrid electric vehicles are presented in this paper. The primary storage battery’s longevity and overall system efficiency are intended to be increased by the EMS’s ability to forecast driving circumstances and lessen the load on it. The method optimizes power distribution among numerous energy storage sources by using sophisticated hardware configurations. Using Arduino Uno software, the EMS continuously modifies power distribution in response to driving circumstances and vehicle energy needs. To conduct a thorough assessment of the system, a mechanical configuration was created to replicate the vehicle’s dynamics. This included situations in which the vehicle moved only by using its moment of inertia and not the accelerator. Testing under diverse operating conditions, such as braking, acceleration, and varying slopes is made possible by this configuration. According to experimental data, peak battery stress is significantly reduced by about 33.33%, resulting in longer battery life. With minimal energy losses of 4.3–4.9%, the system regularly achieves power efficiency ranging from 95.1 to 95.7%. Furthermore, the suggested hardware-based EMS lowers overall energy loss by roughly 10.36%, highlighting its dependability and effectiveness in contrast to traditional techniques. The longevity and efficiency of the system are enhanced by the balanced power distribution between the battery, ultracapacitor, and solar sources, which prevents any one source from being overloaded. Overall, the suggested energy management system is successful in improving energy distribution and extending the lifespan and performance of HEV components.

Simulation of dynamic heat dissipation energy optimization based on IoT and image recognition in low carbon building VR design process

With the global emphasis on low-carbon buildings, how to effectively optimize the thermal management of buildings has become a research hotspot. This article constructs a three-dimensional model of low-carbon buildings and implements real-time heat dissipation detection of buildings through image recognition to evaluate their heat dissipation performance. Then a heat load prediction model was proposed, which obtained the heat load prediction results of buildings under different environmental conditions through data analysis. Thus, it lays the foundation for the subsequent optimization of dynamic heat dissipation energy. The core of the research is to establish a dynamic heat dissipation optimization strategy. This article provides a detailed introduction to the principle of dynamic heat dissipation synergy, combined with the thermal network temperature compensation model and dynamic adjustment model, to achieve the expected energy efficiency improvement. Finally, the construction of a dynamic heat dissipation energy optimization control system based on the Internet of Things has strengthened the collection and analysis of real-time temperature data, and its effectiveness and reliability in practical applications have been verified through system testing.

Recombinant Live-Attenuated Salmonella Vaccine for Veterinary Use

Vaccination is essential for maintaining animal health, with priority placed on safety and cost effectiveness in veterinary use. The development of recombinant live-attenuated Salmonella vaccines (RASVs) has enabled the construction of balanced lethal systems, ensuring the stability of plasmid vectors encoding protective antigens post-immunization. These vaccines are particularly suitable for production animals, providing long-term immunity against a range of bacterial, viral, and parasitic pathogens. This review summarizes the progress made in this field, with a focus on clinical trials demonstrating the efficacy and commercial potential of RASVs in veterinary medicine.

Subject and system of criminology in the views of Hans Gross

The article is devoted to the problem of systematization of forensic knowledge. It is noted that in modern conditions it is solved by creating two relatively separate theories - the doctrine of the subject and the doctrine of the forensic system. The thesis about the key role in the construction of the forensics system of a clear definition of its subject and its structure is substantiated. The subject of criminology is a system-forming factor for the content of this science. This necessitates the unification of this problem within the framework of a single forensic science. The significance of the use of the historical research method for the formation of such a teaching is emphasized. From this point of view, the historical monument of the late 19th and early 20th centuries is analyzed - the scientific work of Hans Gross «Manual for judicial investigative judges as a system of criminology». It is emphasized that this book had many editions in different years and gained extreme popularity among investigators, prosecutors, and judges of different countries of the world. It should be noted that this scientific work actually laid the foundation of criminology, on which this science developed in the following years. This development took place in both practical and theoretical aspects. Taking this into account, it is argued that Hans Gross’s views on the subject and system of forensics have not lost their significance in modern times. It is noted that their analysis allows modern researchers to orient themselves in a number of problematic issues of the development of criminology, to evaluate modern ideas about ways to solve them. In particular, two ideas of the outstanding scientist are noted: 1) criminology has as its subject the study of two types of activity - criminal activity and the activity of the investigator, which is reflected in the knowledge system of this science; 2) the system of criminology should consist of general and special parts based on the example of criminal law, the practical implementation of whose provisions in the judiciary is carried out precisely through criminology. It is claimed that these ideas are well-founded and productive and deserve their further development taking into account modern scientific processes.

Cooperative Construction of Renewable Energy and Energy Storage System: Research on Evolutionary Game Model Based on Continuous Strategy and Random Disturbance

Cooperative Construction of Renewable Energy and Energy Storage System: Research on Evolutionary Game Model Based on Continuous Strategy and Random Disturbance

The multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands

The rapid development of the urban logistics recycling industry, combined with the complexity of the pickup and delivery networks, has created a surge in dynamic customer demands and exacerbated the difficulty of logistics resource sharing. Accordingly, this work focuses on a multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands, which incorporates resource sharing. A bi-objective mathematical model is formulated to minimize the total operating cost and number of vehicles. A three-dimensional affinity propagation clustering and an adaptive nondominated sorting genetic algorithm-II are combined to find Pareto optimal solutions. A dynamic demand insertion strategy is proposed to determine the vehicle service sequences for dynamic situations. Combined with an elite iteration mechanism to prevent the proposed algorithm from falling into search stagnation and improve the convergence performance. The superiority of the proposed algorithm is verified by comparing with CPLEX solver (i.e., ILOG CPLEX Optimization Studio 12.10), multi-objective ant colony optimization, multi-objective particle swarm optimization, multi-objective evolutionary algorithm, multi-objective genetic algorithm, and decomposition-based multi-objective evolutionary algorithm with tabu search. Besides, the proposed model and algorithm are tested by a real-world case study in Chongqing city, China, and the further analysis indicates that significant improvement can be achieved. Furthermore, by incorporating the recognition and prediction techniques of artificial intelligence on dynamic demand data, the proposed approach can realize the self-optimization of multi-depot vehicle routes and the precise allocation of logistics resources in dynamic environments. This study is conducive to the construction of a digitally-intelligent urban logistics system.

Set-valued stochastic integrals for convoluted Lévy processes

In this study, we consider set-valued Lévy-driven Volterra-type stochastic integrals, defined as the closed decomposable hull of convoluted integral functionals. In addition to the well-established results for set-valued Itô integrals, we show that while set-valued stochastic integrals with respect to a finite-variation Poisson random measure are integrably bounded for bounded integrands, this is not true for infinite-variation random measures. For indefinite integrals, we prove that kernel singularity and jumps can lead to the possible explosiveness of set-valued integrals. These results have important implications for the construction of set-valued fractional dynamical systems. Two classes of set-monotone processes are studied for economic and financial modeling.

Construction and Application Analysis of an Intelligent Distribution Network Identification System Based on Deep Neural Networks

Construction and Application Analysis of an Intelligent Distribution Network Identification System Based on Deep Neural Networks

A Biolayer Interferometry-Based SARS-COV-2 Mpro-Targeted Active Ingredients Recognition System: Construction and Application in Ligand Screening From Herbal Medicines.

Drug discovery research targeting SARS-CoV-2 and other emerging pathogens remains critically important. Active compounds derived from plants frequently serve as lead compounds for further drug discovery; however, numerous unrelated chemical constituents in crude extracts may obscure the effective ingredients in LC-MS analysis. The aim of this study is to construct a biolayer interferometry (BLI)-based system for recognizing active ingredients that inhibit the main protease (Mpro) of SARS-CoV-2 and to identify the active chemical components binding to Mpro from herbal medicines. We developed a novel FRET fluorogenic probe by linking the amino acid sequences of the fluorescent proteins Lssmorange and mKate2 (Ls-mK). The interaction between traditional Chinese medicine and Mpro was analyzed using BLI. Ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was employed to analyze the composition of herbal medicines. Fluorescence detection and spectroscopy confirmed the successful construction of an Mpro inhibitor screening system. Lanqin Oral Liquid (LQL) and Gardeniae fructus exhibited strong inhibitory effects on Mpro. Ten compounds were identified from G. fructus extracts; among them, deacetyl asperulosidic acid methyl ester (DAAME) and Gardoside were found to strongly bind to Mpro, with dissociation constants (KD) of 3.41 μM and 801 nM, respectively. The half-maximal inhibitory concentrations (IC50) of DAAME and Gardoside for Mpro were 27.46 and 13.7 μM, respectively. This study established a functional Mpro inhibitor screening system. Among the 10 components identified from G. fructus that bind to Mpro, DAAME and Gardoside displayed strong binding and inhibitory activity, indicating their potential as lead compounds for inhibiting SARS-CoV-2 viral replication.

Development of a Conceptual Scheme for Controlling Tool Wear During Cutting, Based on the Interaction of Virtual Models of a Digital Twin and a Vibration Monitoring System.

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process.

Research on the Construction of Grassroots Emergency Command System Under the Background of Informatization

Along with our economic development and public safety demand intensify., the task of fire rescue team is further expanded, and the construction of fire rescue command system becomes more and more important. This paper takes the information construction of the fire rescue team as the center, and studies the construction of the grass-roots emergency command system, aiming to promote the construction of the fire rescue system and improve the emergency rescue level of the fire rescue team. This paper takes the emergency rescue command system of the special service station of X Fire rescue detachment as the research object, and studies the construction of the basic emergency command system through literature research method and interview investigation methods.

Construction and Optimization of Comprehensive Risk Management System of Enterprises

In the complex and changing market environment, the construction and optimization of enterprise comprehensive risk management system is crucial. This study clarifies the connotation and objectives of enterprise comprehensive risk management, analyzes the main risks faced by the enterprise and the reasons for them, and constructs a comprehensive risk management system including risk management organization structure, process and information system. In terms of risk prevention and control in key areas, specific measures are proposed for strategic, financial and market risks. Meanwhile, through risk management technology innovation, culture construction and capacity enhancement, the efficiency and accuracy of risk management have been improved, and the risk awareness and professional quality of all staff have been enhanced. In conclusion, the comprehensive risk management system of an enterprise is a systematic project, which requires the joint efforts of all departments to adapt to the market environment and provide guarantee for the sound development of the enterprise.

Study on the Mechanism of Agricultural Greenhouse Gas Emission Reduction Under Macro Emission Reduction Measures

Clarifying the impact of macro emission reduction measures on the mechanism of agricultural greenhouse gas emission reduction is of great significance in promoting climate change governance and the construction of a carbon emission reduction policy system. This paper explores the mechanism of important macro emission reduction measures based on a multi-level progressive factor decomposition perspective and designs a coupled model of computable general equilibrium and structural path decomposition to identify the key emission reduction paths of major macro emission reduction measures and to decompose the drivers that promote emission reduction in each path. This study found that: (1) The emission reduction effect of the combination of carbon tax, carbon sink and carbon capture, utilization, and storage macro emission reduction measures is dominated by the indirect emission reduction triggered by the industrial chain, accounting for 95.67% of the total agricultural GHG emission reduction, and the emission reduction effect is gradually weakened with the increase in the production level. (2) The emission intensity effect and the industrial structure effect are the main drivers of the macro emission reduction portfolio measures to promote emission reduction, but there are differences in the roles of the different drivers on the various production levels and different emission reduction pathways. (3) Vegetables, fertilizers, the light industry, and other key industries are the main agricultural greenhouse gas emission reduction contributing industries, of which the emission reduction contribution from citizen consumption is the largest, and the emission reduction is mainly achieved by influencing the demand path of the vegetable industry and the light industry to the upstream high-energy-consuming or high-emission industries. Therefore, there is a need to fully utilize the mechanisms that drive emission reduction at different production levels and pathways by each key factor and to take targeted measures to promote synergistic emission reduction among industries. In the short term, focus on enhancing the role of the emission intensity effect, while in the medium and long term, pay much attention to the positive role of the industrial structure effect on agricultural greenhouse gas emissions.

Research on the Impact of Digital Transformation on Enterprise ESG Performance: Based On the Perspective of Enterprise Dynamic Capability

At a time when more attentions are paid to green environmental protection at home and abroad, enterprises need more driving forces to do ESG practices. Digital transformation with the help of digital technology, this new quality productivity provides enterprises with a new way of thinking and power to enhance ESG performance. Explore whether digital transformation can enable ESG performance of enterprises, so as to provide certain practical significance for enterprises to firmly carry out digital transformation, improve ESG performance of enterprises, and obtain new sustainable competitive advantages of enterprises. This paper focuses on the empirical study of the relationship between digital transformation and ESG performance of Shanghai-Shenzhen A-share listed multinational enterprises from 2011 to 2022, and explores the mechanism of digital transformation affecting ESG performance from the perspective of dynamic capabilities of enterprises. The results show that the degree of digital transformation has a positive relationship with ESG performance. Based on the research conclusions, the following suggestions are put forward: At the government level, the government should provide strong policy guidance and resource support for the promotion of digital transformation, solve difficulties and dredge blocked points. Continue to deepen the understanding of ESG concept, expand the influence of ESG, guide enterprises to take the initiative to practice ESG concept, cope with the voice of foreign countries in the ESG field, and support the acceleration of the construction of ESG rating system with unique Chinese characteristics. At the enterprise level, enterprises should integrate digital transformation into corporate strategy and management activities, actively seize the opportunities of digital development to achieve green transformation, improve the quality of internal control, and lay a good foundation for sustainable development of enterprises.

Synergistic catalysis of immobilized enzyme and peroxymonosulfate activation: CuFe2O4/kaolin-CTS-Laccase for enhanced performance of reactive blue 19 degradation

This work constructed an immobilized laccase - peroxymonosulfate (PMS) activation synergistic catalytic system for enhancing the efficient removal of reactive blue 19 (RB19). Superparamagnetic CuFe2O4/kaolin (CF/K) composites were prepared by one-step solvothermal and calcination methods and then modified with chitosan as immobilized carriers for laccase. The enzyme activity of CuFe2O4/kaolin-CTS-Laccase (CF/K-CTS-Lac) could reach 316.3 U/g under optimal immobilization conditions of 3 h and 2.6 mg/mL. CF/K-CTS-Lac exhibited improved pH stability and thermal stability in comparison to free laccase. The CF/K-CTS-Lac/PMS synergistic catalytic system achieved 98.4 % decolourization of RB19 within 90 min, which was 1.7 and 1.9 times higher than that of the CF/K-CTS-Lac system and CF/K-CTS/PMS system, respectively. Enhanced RB19 degradation efficiency attributed to synergistic catalysis by immobilized laccase and PMS activation. Free radical quenching experiments showed that hydroxyl radicals (•OH), sulfate radicals (SO4•-), superoxide radicals (•O2-) and singlet oxygen (1O2) were involved in the synergistic catalytic system, with •O2- and 1O2 playing a major role. Our investigations are expected to provide new insights into the construction of synergistic catalytic systems for pollutants.

Recent Developments in the Metal-Catalyzed Synthesis of Nitrogenous Heterocyclic Compounds.

Metal-catalyzed cyclization reactions have become a powerful and efficient approach for the stereoselective construction of both carbocyclic and heterocyclic ring systems. Transition metal complexes, with their ability to activate and selectively functionalize organic substrates, have revolutionized various areas of synthetic chemistry. This review highlights recent advancements in metal-catalyzed cyclization reactions, especially in the synthesis of nitrogen-containing heterocycles like imidazoles, pyridines, pyrimidines, and indoles. These advancements have significantly impacted fields such as natural product synthesis, pharmaceuticals, functional materials, and organic electronics. Novel catalytic systems, ligand designs, and reaction conditions continue to expand the capabilities of these reactions, driving further the progress made in synthetic organic chemistry. This review provides a comprehensive overview of recent research.