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With the development of the times, figurative expressions no longer meet the creative needs of artists and the aesthetic demands of the people. In order to express art in a more profound way, the perfect use of abstract graphics plays a crucial role in the success of the work. In recent years, there has been a surge in the creation of digital art, but there is relatively little theoretical literature on the combination of abstract graphics as a visual language and digital art. In addition, research on the theoretical aspects of digital art design is also relatively weak, so it is essential to analyse the formal aesthetics and innovative applications. In fact, digital art design has a very important role to play in promoting the development of creative cultural industries. In other words, the healthy development of digital art design can influence the future prospects of a country's creative and cultural industries. Digital art design is an integrated and complex production process and labour outcome. In addition to its human, aesthetic, and social value, digital art also has an economic value. Digital art is a new art form that combines digital technology and artistic aesthetics. As such, digital art is characterised by high technology, diverse forms, popularised art, and the advantages of high communication, interactivity, and influence, which can provide more assistance for the innovation and application of abstract graphics. Digital art is multifaceted and has an artistic expression that cannot be matched by other forms of technology. Abstract graphics, driven by digital art, are full of novelty and interest and can greatly enrich people's emotions and senses. Abstract graphics bring the experience of digital art to its fullest potential. The combination of digital art and abstract graphics offers more innovation and possibilities for the development of art and will bring great prosperity to art communication. With the widespread use of computer and network technology, the Internet has developed rapidly. In this context, digital art, as art created in a digital way and concept, has gained widespread attention. As a result, how to integrate existing computer resources in the new environment to build a model of digital art design effectiveness will cause a direct influence on the quality of digital art design with digital content innovation as the core. At the same time, as digital art becomes more and more popular, the demand for digital talents becomes very urgent. As a result, the cultivation of high-quality digital talents has become a major concern for society. Therefore, in order to explore the success of digital art design and the cultivation of digital art talents, and to better serve the innovation of digital art, this paper proposes a digital art effectiveness model based on the K-medoids algorithm. This model can provide a deeper and more comprehensive understanding of digital art and abstract graphics and provide theoretical support for professional design creation. [ABSTRACT FROM AUTHOR]

An automotive battery, also known as a car battery, is a device that provides electrical energy to start a vehicle's engine; it uses chemical reactions between lead (Pb) and other chemicals to generate electricity. The automotive industry has been receiving positive feedback from consumers, and the industry has been striving to improve the quality of its product packaging, which protects the battery and facilitates its distribution. The research aims to increase customer satisfaction in the automotive industry by creating better battery packaging design. The research employs a range of methods to achieve this goal, including conducting an initial survey with customers who have purchased and used car batteries to identify problems, 3D computer-aided design (CAD) to create design packaging development, life-cycle assessment (LCA) to measure the environmental impact of various materials and contains sustainability analysis, and finite element analysis (FEA) simulation of the packaging corrugated paper using SolidWorks 2021 software, and lastly, a customer feedback survey is conducted to assess the attributes of packaging development. The design criteria prioritize green materials, ergonomics, durability, reliability, and ease of use. In conclusion, the study recommends the use of corrugated paper for battery packaging in the automotive industry. The corrugated paper has proven to be an ecofriendly and sustainable material that provides excellent protection for the battery while also being easy to handle and use. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device's accuracy. [ABSTRACT FROM AUTHOR]

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners' urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners' learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective. [ABSTRACT FROM AUTHOR]

In order to improve the effect of ballet training, this paper combines digital technology to improve the motion recognition process, analyzes the imaging process, analyzes the system process combined with the human node model, and combines digital feature recognition technology to construct a ballet training motion correction system. Moreover, this paper inputs the ballet training action recognition results into the system and compares the standard actions to judge the rationality of the actions and builds a ballet training action correction system based on digital feature recognition. In addition, this paper designs experiments to conduct ballet training movements in the system effect evaluation. The experimental research verifies that the digital feature recognition technology proposed in this paper can play an important role in ballet movement recognition and has a good action correction effect. [ABSTRACT FROM AUTHOR]

This paper combines the digital twin system modeling method to conduct an in-depth study and analysis of graph-theoretic combinatorial optimization. This paper provides new ideas and approaches for optimal numerical analysis work by studying the digital twin modeling method that integrates digital modeling and graph theory combination, provides theoretical support for safe, stable, and economic operation of the system, proposes a solution for digital twin model based on big data platform, focuses on the nearest neighbor propagation (AP) and graph theory combination, solves the digital twin real-time monitoring data asynchronous, incomplete problem, and applies the algorithm to the digital twin model based on the big data platform for data preprocessing to achieve better results. This paper also presents a web-based digital twin system based on intelligent practical needs, analysis, and comparison of existing models, combined with digital twin technology, detailing the differences and connections between the various levels of numerical analysis and the implementation of this data in various fields, such as user management, equipment health management, product quality management, and workshop 3D navigation and detailed modeling of the digital twin system based on this numerical analysis to realize remote online monitoring, analysis, and management. In this paper, for the numerical analysis process, firstly, the key technologies of modeling and simulation operation control of production line based on digital twin are studied, and the rapid response manufacturing system based on a digital twin is designed and validated. Secondly, a scheduling technology framework for capacity simulation evaluation and optimization is established, and batching optimization, outsourcing decision, and rolling scheduling techniques are thus proposed to form a batching optimization algorithm based on priority rules, which realizes batching processing, outsourcing decision, and rolling scheduling of production orders to optimize equipment utilization and capacity. Finally, digital twin-based modeling is designed, and the validation results demonstrate the system's superior performance in achieving information interaction between physical and virtual production lines, optimization of numerical analysis, and display of results. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, social sport is more and more valued and favored by the people as a universal and nationwide sport, but it should be noted that social sport involves a wide range of aspects, but due to its particularity, it is also constrained by economic development. In view of these needs and limitations, three technical methods of entropy method, RSR (rank-sum ratio), and TOPSIS are introduced, to sort out the development of social sports, national physical development, social sports guidance, and the number of people to be measured in various places based on the relevant technologies of smart cities in this paper, realize the application analysis of social sports in the public service level. This paper aims to provide a scientific evaluation and evaluation method for social sports information cloud services. The results of simulation experiments show that the evaluation of social sports information cloud service quality based on smart cities is effective, and the comprehensive application of methods can be implemented perfectly, and the further promotion and popularization of social sports services can be realized. [ABSTRACT FROM AUTHOR]

Digital reading promotion service is a service way for libraries to provide readers with a series of digital resources, enjoy the service functions, and share the experience of using them in various digital reading platforms, which is to meet the reading interests and reading needs of more readers, and is also the focus of the current library work. In the era of new media, the characteristics of digital reading are subtly changing the readers' needs for reading environment, reading content, and reading style. Libraries should keep pace with the development of the times and provide readers with diversified, intelligent, and targeted digital reading platforms. The digital reading platform should continuously improve the digital reading service functions, broaden the service scope and dissemination channels, and finally realize the diversification, interest, and intelligence of digital reading service. This paper takes the digital reading platform of libraries in the region as the research theme and carries out research work on libraries. The province is divided into three regions according to the geographical map: southeastern region, central region, and northwestern region. The digital reading platforms of 14 prefecture-level public libraries and 58 libraries of higher education institutions in each region were accessed. Firstly, we check the construction of digital resources within the library websites, secondly, we count the opening of digital reading platform functions, and finally, we check the opening of digital reading platforms. Through the research, it is found that there are problems of unbalanced distribution of digital reading resources in regional libraries; unattractive design of readers' interface and inadequate reading service functions; lack of continuous training of readers' guidance; insufficient publicity and promotion; low efficiency of staff in responding to consultation; and low degree of platform openness and weak awareness of sharing. Finally, the problems found in the research are summarized, and the solution measures for the regional digital reading platform are proposed. Libraries in the digital era should give priority to systems that can manage all library resources comprehensively and effectively, adapt to more flexible library workflows, and enable libraries to provide better services to users. [ABSTRACT FROM AUTHOR]

The increasing environmental consideration and the growth of instability of the energy market call for methods that can process the organic fraction of municipal solid waste (OFMSW) into fuel instead of disposal. Due to the pyrolysis and gasification that are efficient procedures to achieve valuable products, gasification of OFMSW with different particle sizes and compositions was carried out in the presence of Ni and Ni-Ce-loaded Y-zeolite in a multizone tubular kiln reactor. During the experiments, 500°C was applied in the first reactor zone, while the 2nd zone was at an elevated temperature of 600°C or 900°C in the presence of steam. The combined pyrolysis and gasification experiments were also carried out without a catalyst with the same operating conditions. The feedstock was collected from the organic fraction of a Hungarian mechanical-biological treatment (MBT) plant and was separated into four different fractions based on particle size: <1 cm dry biomass and fine particles, 1-2 cm paper-rich, 2-6 cm paper and plastic-rich feedstocks, and <6 cm mixture of these fractions. During the experimental work, product yields; gaseous product composition; the ratio of H2/CO, CO2/CO, and CH4/CO; and lower heating value were determined in the function of feedstock composition, the applied temperature, and catalysts. It was found that the hydrogen content and H2/CO ratio of gaseous products were increased due to catalyst application and temperature elevation in the 2nd reactor zone. The addition of Ce to the Ni/Y-zeolite catalyst was advantageous in the case of hydrogen formation at a lower temperature (600°C). The hydrogen and methane content of products obtained from the catalytic pyrolysis-gasification of paper and plastic-rich OFMSWs on elevated temperatures were higher, which increased the lower heating value of those products. Based on the elemental analysis of the obtained solid residues, it was found that paper/plastic-rich feedstock released hydrogen and carbon with a higher extent. 1-2 cm feedstock-related solid residues had the highest H/C ratio which caused a 12.5-12.8 MJ/kg gross heating value. As a result, combined pyrolysis and gasification appear to be an efficient method to attain valuable outputs from OFMSW not only in gas but also in solid products. [ABSTRACT FROM AUTHOR]

Currently, soft robots alone cannot obtain the same operating speed as rigid robots, while rigid robots are not safe enough for human-robot interaction. To address this problem, this paper describes a hybrid robot system that combines both rigid and flexible systems for unknown domain exploration. The system consists of a four-wheeled robot chassis and a cylindrical pneumatic soft actuator, and finally, a computer is used to coordinate and control both. The hardware of the robot system is designed, a bending motion model is proposed, and SOFA framework is used to carry out finite element simulation (FEM) to verify the reasonableness of the design; linear motion speeds of up to 0.5 m/s, higher than the existing soft robots investigated, were verified experimentally separately after carrying the new module, and steering ability was retained; and the robot carrying the navigation module is verified to have a certain map building and localization function through the construction of the simultaneous localization and mapping (SLAM) experimental platform. The hybrid robot introduced in this paper can move quickly on flat terrain and can use its soft part to avoid wear and tear. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to examine the characteristics of scientific production related to the use of filtered water in the field of dentistry. Material and Methods. A quantitative and descriptive observational study was carried out with a scientometric approach. Data were collected from the Web of Science (WOS) database during the period January 1991 to December 2023. A search strategy incorporating a combination of MeSH terms, including terms and thesauri related to "filtered water" and "dentistry", was used. R Studio version 4.3.2 and CiteSpace 6.2.R7 were used for data analysis. Results. Over the 32-year study period, 227 scholarly papers from 134 different sources were reviewed. The literature in this field has shown an annual growth rate of 10.44%. During the year 2010, a steady movement in the number of publications and authors was observed, with considerable collaborative interaction. In the year 2020, a large interaction between publications and their citations was found. The "Citation Burst" graph identified three references that have experienced the largest "burst" of citations in an evaluated period. Lotka's law described the productivity of authors, finding that most authors have published only one paper, while a smaller number of authors have published two papers. Most authors contributed a small number of articles, while a few authors contributed a large amount of the existing literature. Conclusion. A comprehensive overview of the scientific production related to the use of filtered water in dentistry over a span of 32 years is provided. The results highlight the growing interdisciplinarity and international collaboration in this field. Finally, the importance of filtered water in dentistry and its growing relevance in the scientific literature are emphasized. [ABSTRACT FROM AUTHOR]

Aim. To systematically map and identify key knowledge on organizational support for nurses' career planning and development. Design. Scoping review. Methods. Systematic electronic searches were carried out with the CINAHL, PubMed, Scopus, and Web of Science databases in May 2022. The searches were limited to scientific, peer-review papers that were published in English from January 2012 to May 2022. Data were extracted and synthetized and are presented in tables and text. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results. We identified 1,400 papers and 28 met the inclusion criteria. Organizations recognized nurses' career planning and development in relation to the individual's professional development and the organization's need to promote high-quality services and workforce engagement. The organizational support included strategic work to ensure there were adequate resources and purposeful vacancies and a structured framework based on objective qualification criteria and equal assessment. Organizations focused on sharing knowledge, structured career planning, and interpersonal support. Support within the nursing profession and multilayered interprofessional collaboration were also important. Conclusion. Nurses' career planning and development was linked to their personal development and the organization's aims and required support from both fellow nurses and other professionals. Implications for the Nursing Management. Identifying the organizational structures and methods that are needed to support nurses' career planning and development can help nursing management to evaluate and develop strategies that improve the attractiveness of a nursing career and nurses' engagement. [ABSTRACT FROM AUTHOR]

The aim of this work is a synthesis of suitable hydrogel to produce slow‐release fertilizer using recycled cellulose which is obtained from waste paper. For this purpose, initially, we extracted alpha cellulose from waste paper and modified it to obtain carboxymethyl cellulose (CMC). Then, the CMC was converted to a suitable hydrogel through in situ graft copolymerization of acrylic acid and vinyl acetate in the presence of methylene bisacrylamide as a crosslinker. The various factors that affect hydrogel synthesis, such as the amounts of CMC, monomers, initiator, and crosslinker, were evaluated. In the optimized formulation, the weight ratio of monomers to CMC is 7, the molar ratio of monomers to each other is 1, and the crosslinker is used as 3 molar percent of monomers. The products were characterized using Fourier transform infrared, thermal gravimetric analysis, and scanning electron microscope analyses. The swelling behavior of the synthesized hydrogels was evaluated in different environments, such as distilled water, tap water, salt water, and different pH levels. The swelling ratio increases with an increase in pH level. Between the synthesized hydrogels, the best one was selected for slow‐release fertilizer production and loaded with 20‐20‐20 fertilizer (NPK), and the release behavior was evaluated. In an alkaline pH, there was a long time for NPK release within a slow‐release medium and even after 361 h, the release process was continued. Also, the performance of the fertilizer‐loaded hydrogel in soil using water holding capacity and water retention ratio tests were evaluated. [ABSTRACT FROM AUTHOR]

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real‐time data anomaly detection system for small hydropower clusters (RDADS‐SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real‐time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real‐time data anomaly detection algorithm for small hydropower stations integrated with the Z‐score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived‐based hot‐standby RDADS‐SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision‐making. [ABSTRACT FROM AUTHOR]

In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double‐layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

With the development of air transport and improved ecological environment, bird strikes on aircraft have become a significant threat to flight safety. Accurate and efficient identification of airport birds can enhance the efficiency of airport staff in bird strike prevention. Public datasets for bird classification are typically limited in size and not specifically tailored for airport birds. Therefore, this paper introduces a self‐built airport bird dataset, ADB‐20, and proposes an image recognition method based on an improved ResNet model. First, this paper replaces traditional convolution in the residual structure with the Pyramidal Convolution method, ensuring the extraction of multiscale features. Second, it introduces the Parallel Convolutional Block Attention Moduleto the backbone network, considering features in both channel and spatial dimensions. Last, the Atrous Spatial Pyramid Pooling module is incorporated to capture contextual information at various scales. Experimental results show that the PPA‐ResNet model achieves an accuracy of 95.2%, with a recall of 93.7%, a precision of 96.8%, and an F1 score of 95.2% on the ADB‐20 dataset. The proposed algorithm significantly enhances classification performance compared to other mainstream image classification algorithms. These indicators confirm that the results of this study can precisely identify airport birds, aid airport personnel in making informed decisions, and guarantee the safety of aviation transport. [ABSTRACT FROM AUTHOR]

Current technology trends have been gradually integrated into the production of all walks of life, which play an indispensable part in promoting the intelligent development of enterprises, and have brought a greater impact on production and reformation. With the rapid development of the economy and technology, the manufacturing industry has played a very important role. For this reason, the introduction of artificial intelligence into machinery manufacturing can not only improve production efficiency but also save labor and reduce labor costs. The application of artificial intelligence in machinery manufacturing has a critical good role in promoting industrial upgrading and transformation. This time, through the application of smart algorithms in machinery manufacturing and its automation, we expect that such a technological revolution can provide a new development prospect for the development of manufacturing intelligence and automation. Taking the malfunction identification of string striking machinery as an example, this paper studies the smart identification method of mechanical malfunction based on multisensor. In the process of malfunction identification of keyboard stroke machinery, the accuracy of malfunction identification results is low due to the influence of the identification model. Moreover, a malfunction identification and analysis method for keyboard stroke machinery based on BP optimized by GA is proposed. The mechanical data of keyboard chords are acquired by sound-sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. The results show that the intelligent identification method proposed has exhibited strong adaptability and superiority compared with the traditional method. [ABSTRACT FROM AUTHOR]

The common perception of people about recycling involves reusage of aluminum cans, glass bottles, and newspapers, whereas recycling of water resources is a most important aspect nowadays. Water recycling is known to reuse cleaned wastewater for beneficial applications including agricultural and landscape irrigation, industrial activities, and replenishing a groundwater using the latest technologies. The current methods have some flaws in the evaluations of water resources' recycling systems such as large mean square error, time complexity, and low-evaluation efficiency; therefore, this paper proposes an online evaluation method for the recycling process of water resources in urban landscaping. The health model of water resources recycling in urban landscaping has been analyzed using fuzzy-based approach. Second, the evaluation index system of water resources' recycling is also analyzed using the ecological water-level analysis results, water resource quality, water resource abundance, and water resource utilization rate. Then, the extension of analytic hierarchy process (AHP) is utilized to calculate the weight of water recycling evaluation index. Then a fuzzy-based comprehensive evaluation method is used to find the online evaluation model of the water recycling. Eventually, the evaluation of the effect of water recycling in urban landscaping is performed and analysis is made for decision-making. The results prove that the proposed AHP and fuzzy method has a low mean square error and high accuracy. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

During the fruit ripening period, the orchard temperature difference between day and night has a substantial impact on the fruit quality, and it is not easy to be controlled like that of in greenhouse, this paper designed the smart orchard Internet of Things (IoT), through the fuzzy PID (Proportion-Integration-Differentiation) algorithm to control the water spraying to adjust the temperature difference Δ T between day and night in the orchard, intelligently influenced the orchard energy conversion process, and promoted the fruit sugar accumulation. Experiments show that the maximum Δ T can reach 20°C, the intelligent energy regulation based on the IoT has a good effect, and a scheme to intervene the microclimate in farmland (quite different from the greenhouse environment) is provided. [ABSTRACT FROM AUTHOR]

Smart fiber reinforced concretes (FRC) are good in intelligent environmental response. With the smart FRC being used in buildings such as bridges and tunnels, the real-time online distributed monitoring of concrete state can be realized. It provides an excellent solution to reduce the emergencies caused by the accumulation of damages, resistance attenuation, etc. This paper analyzes smart FRC's self-sensing and self-healing response characteristics under different environmental response fields. Furthermore, the intelligent response mechanism of smart carbon fiber reinforced concrete (CFRC), optical fiber reinforced concrete (OFRC), glass fiber reinforced concrete (GFRC), and nanofiber reinforced concrete (NFRC) would be summarized. The response fields include the stress field, temperature field, electromagnetic field, chemical field, and humidity field, among which the field response mechanism of smart NFRC is classified and summarized. Finally, the advantages and disadvantages, the intelligent response parameters, and the applications of smart FRC under different environmental fields are compared. [ABSTRACT FROM AUTHOR]

The fabrication of heterostructures via solution process is one of the essential technologies for realizing efficient advanced-generation optoelectronics. Hybrid structures comprising colloidal quantum dots (CQD) and organic semiconducting molecules are garnering considerable research interest because of their complementing optical and electrical properties. However, blending both the materials and forming a stable electronic ink are a challenge owing to the solubility mismatch. Herein, a CQD:organic ternary-blended hybrid solar ink is devised, and efficient hybrid solar cells are demonstrated via single-step spin coating under ambient conditions. Specifically, the passivation of the benzoic acid ligand on the CQD surface enables the dissolution in low-polar solvent such as chlorobenzene, which yields a stable CQD:organic hybrid ink. The hybrid ink facilitates the formation of favorable thin-film morphologies and, consequently, improves the charge extraction efficiency of the solar cells. The resulting hybrid solar cells exhibit a power conversion efficiency of 15.24% that is the highest performance among all existing air-processed CQD:organic hybrid solar cells. [ABSTRACT FROM AUTHOR]

Ancient stamps are suffering from the destructive effects of different kinds of inks that were prepared from different ingredients. Two Egyptian historical postage stamps printed in blue and red printing inks were evaluated and examined for their composition using a light microscope, SEM-EDS, FTIR, and Raman spectroscopic analyses. Mechanical, chemical, and deacidification treatments were done for the two stamps. Model stamps were made from the cotton pulp in the book house to simulate historical stamp paper with an average thickness of 11 microns. The unprinted and printed paper samples with printing inks that aged and unaged were treated with 0.7% Klucel G, 0.2% TiO2 NP, or a mixture of 0.7% Klucel G + 0.2% TiO2 NP, and the color change was measured and compared with the blank samples. The two stamps are suffering from high pH, where the margin color of the stamps changed to yellow-brown with weakness of the stamp paper. By SEM examination, stamps have suffered from fibers' weakness and dryness resulting from the self-oxidation reactions. EDS elemental composition of the red stamp showed the presence of C, O, Na, Al, Si, Mg, S, Ca, Ba, and Fe, while in the blue stamp, it was C, O, Na, Al, Si, P, S, Cl, and Ca. Raman spectrometer wavelengths turn out that the blueprinting ink of the stamp was characterized with spectra of ultramarine blue (lazurite), while hematite was characterized by the red stamp. FTIR analysis for the printing inks identified that gum Arabic sample and linseed oil were the binding and color medium, respectively. From the model trials, it was observed that the treatment of a mixture of Klucel G and TiO2 NP had the best properties for the consolidation of stamps. The two historical stamps were documented through different spectroscopic analyses, and from the restoration trials, it was observed that the mixture of 7% Klucel G + 0.2% TiO2 NP appeared to be a new and effective method for recovering the historical postage stamps. [ABSTRACT FROM AUTHOR]

Crack inclination and material heterogeneity have important effects on the meso-mechanical mechanism and macroscopic mechanical behavior of rock-like materials. In order to study the failure characteristics of shotcrete body during the process of using shotcrete bolt mesh support in the deep fractured rock mass of Lannigou Gold Mine, this paper combined the Digital Image Processing Technique (DIP) and RFPA2D (Rock Failure Process Analysis System) to establish a real meso-structure numerical model of concrete with different inclination angle cracks, simulating its crack propagation law and failure process, and studied the influence of crack geometry distribution and meso-heterogeneity on the effect of concrete structure. The findings reveal that the crack inclination angle has a substantial impact on concrete materials' compressive strength and elastic modulus, and both of them all show a nonlinear increase with the increase of crack angle; Because of the inhomogeneity of the materials, the inclination and propagation pathways of wing cracks are random, and the aggregate inhibits crack initiation and propagation. The wing crack's initiation position moves closer to the tip as the crack inclination angle increases, and the length gets shorter; Acoustic emission(AE) evolution characteristics are similar in samples with varying dip angles. In the early stages of loading, the AE energy is minimal, and increases rapidly when approaching the peak stress. The fractal dimension was used to describe the damage evolution process inside the material, and a damage variable index (ω) based on the fractal theory was proposed. The more the ω, the greater the material's degree of degradation. The proposed index provided a new method for quantitative study of the damage evolution characteristics of rock-like materials. It has guiding significance for the research on the stability of wet shotcrete in the deep fractured rock mass of Lannigou Gold Mine. [ABSTRACT FROM AUTHOR]

Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted. [ABSTRACT FROM AUTHOR]

All living things, including plants, animals, and humans, need water in order to live. Even though the world has a lot of water, only about 1% of it is fresh and usable. As the population has grown and water has been used more, fresh water has become a more valuable and important resource. Agriculture uses more than 70% of the world's fresh water. People who work in agriculture are not only the world's biggest water users by volume, but also the least valuable, least efficient, and most subsidized water users. Technology like smart irrigation systems must be used to make agricultural irrigation more efficient so that more water is used. A system like this can be very precise, but it needs information about the soil and the weather in the area where it is going to be used. This paper analyzes a smart irrigation system that is based on the Internet of Things and a cloud-based architecture. This system is designed to measure soil moisture and humidity and then process this data in the cloud using a variety of machine learning techniques. Farmers are given the correct information about water content rules. Farming can use less water if they use smart irrigation. [ABSTRACT FROM AUTHOR]

Three-dimensional plant model visualization (3D-PMV) is based on plant biology and the plant structure construction model for virtual simulation three-dimensional display, reflecting plant structure characteristics and demonstrating the growth process. In this work, we used the method of systematic mapping study (SMS) to screen the relevant literature in order to explore and analyze the methods and goals of virtual plant visualization research and to provide assistance in future literature reviews. To this end, we conducted extensive searches to identify articles related to plant model visualization technology. We searched for papers from July 2010 to November 2020 from four mainstream databases, namely, ACM, IEEE Xplore, EI, and Web of Science, and found more than 2,900 papers on 3D-PMV. Finally, we selected 60 qualified papers. We mainly followed the SMS method to classify papers by answering seven questions, mainly extracting data for analysis on research types, research goals, model construction methods, visual model categories, number of publications, tools, and methods. The results show that solution proposals account for the largest proportion of research types, accounting for 75% of the total number of papers. This shows that the focus is on the improvement of original methods or the proposal of new methods in this field; research goals of research plant phenotypic characteristics account for the total 35%; 28% of the research objectives focus on showing the reflection of plant growth process, indicating that the research objective trends in this research field regard plant phenotypic characteristics and visualization of the growth process; static type model visualization also accounts for 83% of the total, indicating that the related research on visualization in this field is mostly static typing. Therefore, our work not only analyzes the challenges faced by 3D-PVM and the research directions that need more attention in the future but also provides some suggestions for researchers to find suitable research directions in this field. As the future direction of development, researchers need to pay more attention to the study of dynamic visualization methods of plant growth. We believe that for future research, it is indispensable to provide 3D-PMV with research methods, research goals, visualization model types, development, and other tools. This article analyzes the results of the extracted data based on the SMS method and makes an important contribution to the researcher's extensive understanding of the current status of 3D-PMV and the potential future research opportunities in the field. [ABSTRACT FROM AUTHOR]

Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals. [ABSTRACT FROM AUTHOR]

Software startups work in an uncertain environment with limited resources. Thus, user experience (UX) practices can help startups in their product development process and provide greater chances of survival for these companies. It is necessary to know what UX practices software startups have used, how they are using them, and their effects on the organization. Therefore, this paper aims to present a systematic mapping study (SMS) conducted through search engines and snowballing to identify UX practices used in software startups. The papers' analysis identified thirty-six UX practices used by startups for product validation, design, and evaluation. However, the most emergent result extracted from the analysis was that only two of these practices were specified for software startups; in contrast, the others could be applied to various company types. In addition, several functions and ways of generating customer value through UX were identified. [ABSTRACT FROM AUTHOR]

In order to improve the feature extraction effect of digital music and improve the efficiency of music retrieval, this paper combines digital technology to analyze music waveforms, extract music features, and realize digital processing of music features. Taking the extraction of waveform music file features as the starting point, this paper combines the digital music feature extraction algorithm to build a music feature extraction model and conducts an in-depth analysis of the digital music waveform extraction process. In addition, by setting the threshold, the linear difference between the sampling points on both sides of the threshold on the leading edge of the waveform is used to obtain the overthreshold time. From the experimental research results, it can be seen that the music feature extraction model based on digital music waveform analysis proposed in this paper has good results. [ABSTRACT FROM AUTHOR]

In order to improve the humanization and convenience of online art design, this paper applies digital simulation technology to the art online design system and establishes a set of sequential multi-free-form surface design methods. Based on the obtained front free-form surfaces, this paper establishes the relationship between the discrete points on the subsequent free-form surfaces and their spatial solid angles and, through extremely complex theoretical deduction, finally obtains the subsequent free-form surfaces. In addition, by combining the two free-form surfaces to enter the 3D modeling software, we can obtain an optical lens with multiple free-form surfaces to improve the digital simulation effect. Finally, this paper uses the intelligent system constructed in this paper to conduct multiple sets of simulation experiments to evaluate the digital effect and artistic design effect of the system constructed in this paper. From the experimental research, it can be known that the art online design system based on digital simulation technology constructed in this paper basically meets the expected goals of the system constructed in this paper. [ABSTRACT FROM AUTHOR]

Integration of theory and practice is the key element in the research on teachers' professional knowledge. Traditionally, this topic has created a polarization between practical and theoretical knowledge. The reform in the new Norwegian teacher education has introduced a new and watered-down concept of knowledge and research-based knowledge in education. With the reform, conflicting perceptions have arisen of what we can understand with professional knowledge in teacher education. Based on Maton's Legitimation Code Theory, and with examples from a study of students' R&D papers, this paper sheds light on the conflicting concept of professional knowledge in new Norwegian teacher education. The paper refutes a generic concept of knowledge and argues that teachers' professional knowledge is based on practices that are informed by specialized forms of knowledge in the subject area. [ABSTRACT FROM AUTHOR]

This paper proposes a smart grid distributed security architecture based on blockchain technology and SDN cluster structure, referred to as ClusterBlock model, which combines the advantages of two emerging technologies, blockchain and SDN. The blockchain technology allows for distributed peer-to-peer networks, where the network can ensure the trusted interaction of untrusted nodes in the network. At the same time, this article adopts the design of an SDN controller distributed cluster to avoid single point of failure and balance the load between equipment and the controller. A cluster head was selected in each SDN cluster, and it was used as a blockchain node to construct an SDN cluster head blockchain. By combining blockchain technology, the security and privacy of the SDN communication network can be enhanced. At the same time, this paper designs a distributed control strategy and network attack detection algorithm based on blockchain consensus and introduces the Jaccard similarity coefficient to detect the network attacks. Finally, this paper evaluates the ClusterBlock model and the existing model based on the OpenFlow protocol through simulation experiments and compares the security performance. The evaluation results show that the ClusterBlock model has more stable bandwidth and stronger security performance in the face of DDoS attacks of the same scale. [ABSTRACT FROM AUTHOR]

This paper presents a case report on detecting hijacked journals. Towards identification of a fake journal website and preventing a hijacked paper, we can use different tools including Google Scholar and Web of Science (WoS) and Scopus (both as scientometric databases) to distinguish a fake website from a legal journal website. Our evaluation shows that analysis of a doubtful website for a targeted journal based on Google Scholar is not reliable. In fact, the use of scientometric tools for tracking prior publications of the targeted journal is compulsory. Another result of this study is that in some uncommon cases, fake websites (clone versions) may sometimes convince a scientometric database in order to be fully/partially indexed along with an abstracting of their hijacked papers while these websites steal identity of the legal journals. Therefore, as a result, we should check both of WoS and Scopus at the same time for verifying a fake website to obtain more reliability. [ABSTRACT FROM AUTHOR]

Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication. [ABSTRACT FROM AUTHOR]

Review articles present comprehensive overview of relevant literature on specific themes and synthesise the studies related to these themes, with the aim of strengthening the foundation of knowledge and facilitating theory development. The significance of review articles in science is immeasurable as both students and researchers rely on these articles as the starting point for their research. Interestingly, many postgraduate students are expected to write review articles for journal publications as a way of demonstrating their ability to contribute to new knowledge in their respective fields. However, there is no comprehensive instructional framework to guide them on how to analyse and synthesise the literature in their niches into publishable review articles. The dearth of ample guidance or explicit training results in students having to learn all by themselves, usually by trial and error, which often leads to high rejection rates from publishing houses. Therefore, this article seeks to identify these challenges from a beginner's perspective and strives to plug the identified gaps and discrepancies. Thus, the purpose of this paper is to serve as a systematic guide for emerging scientists and to summarise the most important information on how to write and structure a publishable review article. [ABSTRACT FROM AUTHOR]

Federated learning-based medical data privacy sharing can promote the development of medical industry intelligence, but limited by its own security and privacy deficiencies, federated learning still suffers from a single point of failure and privacy leakage of intermediate parameters. To address these problems, this paper proposes a privacy protection framework for medical data based on blockchain and cross-silo federated learning, using cross-silo federated learning to establish a collaborative training platform for multiple medical institutions to enhance the privacy of medical data, introducing blockchain and smart contracts to realize decentralized federated learning to enhance trust between distrustful medical institutions and solve the problem of a single point of failure. In addition, a secure aggregation scheme is designed using threshold homomorphic encryption to prevent the privacy leakage problem during parameter transmission. The experimental and analytical results show that the accuracy of this paper's scheme is consistent with the original federated learning scheme, effectively deals with the problems of single-point failure and inference attacks of federated learning, improves system robustness, and is suitable for medical scenarios with more stringent requirements on security and accuracy. [ABSTRACT FROM AUTHOR]

With the increasing number of college students, the difficulty and workload of ideological and political education are also increasing. For the calculation and analysis of big data, it is most suitable to use data mining algorithm for calculation and research. In order to help college students' ideological and political education to develop and innovate, this paper establishes a new model of college students' ideological and political education combined with particle swarm optimization algorithm to help carry out college students' ideological and political education. The particle swarm optimization algorithm is tested. Therefore, this paper uses data mining algorithm to calculate and analyze the research in this paper. The algorithm is tested and analyzed. Through the test of data mining algorithm, the calculation accuracy of the algorithm is proved, and the feasibility of this paper is proved. This paper studies the ideological and political education by using data mining algorithm. The computational efficiency of the optimized data mining algorithm is twice that of the traditional algorithm, and the optimized algorithm has also been significantly improved. In general, the data mining algorithm optimized in this paper is reasonable, which can improve the calculation accuracy and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications. [ABSTRACT FROM AUTHOR]

Advanced sensor technologies in geospatial sciences and engineering research have contributed to (1) geographic mapping and (2) understanding the geological, ecological, hydrological, and environmental characteristics of Earth surfaces. W. Zhu et al. in their paper entitled "Pre- and Postcollapse Ground Deformation Revealed by SAR Interferometry: A Case Study of Foshan (China) Ground Collapse" investigated the ground deformation in the vicinity of the collapsed sinkhole in the Foshan collapse event using multitemporal SAR interferometry. W. Zhu et al. in their paper entitled "Spatiotemporal Characterization of Land Subsidence in Guandu (China) Revealed by Multisensor InSAR Observations" investigated the time-series two-dimensional ground deformation in Guandu using the multisensor InSAR observation. [Extracted from the article]

Modelica is a simulation language that is able to model systems from many different fields of scientific and engineering contexts, which can coexist in individual models. Modelica Standard Library is an open‐source library that contains many ready‐to‐use open‐source models built in Modelica language but does not have any code to determine transmission line parameters from its geometry, thus limiting its usability for power system studies. This paper first describes important issues arising when modelling power transmission lines, then presents how they can be solved using Modelica code. Readers of this paper will, therefore, be able to model power lines from the geometry of conductors using any of the existing (some freely available) Modelica‐capable simulation tools. [ABSTRACT FROM AUTHOR]

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study is to explore the scientific literature between ultraprocessed foods (UPFs) and cancer using a scientometric mapping. Materials and Methods: A Scopus search was conducted on February 4, 2024, limited to papers published between 2018 and 2023. We found 662 articles, 189 reviews, 68 book chapters, 13 conference papers, and 9 notes. The whole analysis included the evaluation of scholarly output by country/region, the number of scholarly papers produced (scholarly output), the number of views (view count), the field‐weighted citation impact (FWCI). Results: In the analysis conducted for the period 2018–2023, a dataset was examined where the annual growth rate was 5.96%, indicating a sustained expansion of the literature. The average number of citations per paper was 18.56, underlining the impact and relevance of the publications. Sixty‐six single‐authored papers were identified, and international collaborations accounted for 27.23% of the collaborative efforts. The most prominent authors were Inge Huybrechts, Marc J.R. Gunter, and Edward Luciano Giovannucci. In terms of impact and visibility, Harvard University leads with 52 contributions and a field‐weighted impact of 3.39. Conclusions: The literature in the field of UPFs and cancer has experienced a sustained expansion. The scientometric indicators reveal a high activity of recent academic contributions with significant impact. [ABSTRACT FROM AUTHOR]

The shift towards green aviation necessitates the adoption of all‐electric aircraft, marking an essential trend in the aviation industry. Distributed propulsion systems enhance aircraft flexibility and reliability, synchronously reducing performance demands and costs of single propulsion units compared to centralized propulsion system. The high reliability and lightweight characteristics of brushless DC motors have made them increasingly popular in distributed propulsion systems. Despite its advantage which helps to build efficient aircraft aerodynamic layout, there are more cables in the distributed propulsion system, which accounts for most of the weight of the aircraft. To address this, all‐electric aircraft utilizes high‐voltage power supply to reduce the current, so it uses SiC MOSFETs as power devices to improve its efficiency and heat dissipation. The speed controller in all‐electric aircraft determines the efficiency and reliability of the propulsion unit. However, the traditional high‐voltage speed controller poses challenges for aircraft maintenance because of high cost. Therefore, this paper emphasizes the significance of designing a low‐cost, high‐performance speed controller for all‐electric aircraft. Challenges such as circuit interference, battery voltage fluctuation, and sampling noise affecting the high‐voltage controller during operation are solved, respectively. First, to address the likelihood of mutual interference in the bridge arm, a self‐bootstrapping voltage reduction drive mode is designed to turn off SiC MOSFET and enhance circuit anti‐interference. Second, to solve the challenge of wide battery voltage fluctuation range and inconsistent controller output, an adaptive pulse width modulation (PWM) regulation mode for battery voltage is proposed, ensuring stable controller output. Finally, to counteract the impact of the comparator threshold on zero‐crossing sampling, an electrical angle delay method based on delay compensation is designed to improve the efficiency of the brushless DC motor during operation. Experimental results show that the three solutions proposed in this paper are helpful to improve the anti‐interference, stability, and efficiency of the controller in the high‐voltage distributed electric propulsion system and contribute to reduce the cost and weight in the propulsion unit. [ABSTRACT FROM AUTHOR]

Malicious encrypted traffic detection is a critical component of network security management. Previous detection methods can be categorized into two classes as follows: one is to use the feature engineering method to construct traffic features for classification and the other is to use the end‐to‐end method that directly inputs the original traffic to obtain traffic features for classification. Both of the abovementioned two methods have the problem that the obtained features cannot fully characterize the traffic. To this end, this paper proposes a hierarchical multimodal deep learning model (HMMED) for malicious encrypted traffic detection. This model adopts the abovementioned two feature generation methods to learn the features of payload and header, respectively, then fuses the features to get the final traffic features, and finally inputs the final traffic features into the softmax classifier for classification. In addition, since traditional deep learning is highly dependent on the training set size and data distribution, resulting in a model that is not very generalizable and difficult to adapt to unseen encrypted traffic, the model proposed in this paper uses a large amount of unlabeled encrypted traffic in the pretraining layer to pretrain a submodel used to obtain a generic packet payload representation. The test results on the USTC‐TFC2016 dataset show that the proposed model can effectively solve the problem of insufficient feature extraction of traditional detection methods and improve the ACC of malicious encrypted traffic detection. [ABSTRACT FROM AUTHOR]

Over the last few years, business simulation games (BSGs) in higher education have attracted attention. BSGs tend to actively engage students with course material, promoting higher engagement and motivation and enabling learning outcomes. Increasingly, researchers are trying to explore the full potential of these games with an upsurge of research in the BSG field in recent years. There is a need to understand the current state of research and future research opportunities; however, there is a lack of recent systematic literature reviews in BSG literature. This study addresses this gap by systematically compiling online empirical research from January 2015 to April 2022. We followed PRISMA guidelines to identify fifty-seven (57) papers reporting empirical evidence of the effectiveness of BSGs in teaching and learning. Findings showed that BSGs improve learning outcomes such as knowledge acquisition, cognitive and interactive skills, and behaviour. The review also summarises different issues concerning the integration of BSGs into the curriculum, learning theories used in the selected studies, and assessment methods used to evaluate student achievement in learning outcomes. The findings of this review summarise the current research activities and indicate existing deficiencies and potential research directions that can be used as the basis for future research into the use of BSGs in higher education. [ABSTRACT FROM AUTHOR]

In this paper, we study a reaction-diffusion nutrient-phytoplankton model with nutrient recycling delay and toxin-producing phytoplankton. For the delay-free case, we prove that the release rate of toxic chemicals by the toxin-producing phytoplankton can destabilize the system and cause periodic oscillation. For the time-delayed case, we observe that nutrient recycling delay may bring about stability switches and Hopf bifurcation. Moreover, we derive the formula for determining the direction and stability of the bifurcating periodic solutions. Finally, we give some numerical simulations to support the theoretical analyses. [ABSTRACT FROM AUTHOR]

The text summarization task aims to generate succinct sentences that summarise what an article tries to express. Based on pretrained language models, combining extractive and abstractive summarization approaches has been widely adopted in text summarization tasks. It has been proven to be effective in many existing pieces of research using extract-then-abstract algorithms. However, this method suffers from semantic information loss throughout the extraction process, resulting in incomprehensive sentences being generated during the abstract phase. Besides, current research on text summarization emphasizes only word-level comprehension while paying little attention to understanding the level of the sentence. To tackle this problem, in this paper, we propose the SentMask component. Taking into account that the semantics of sentences that are filtered out during the extraction process is also worth considering, the paper designs a sentence-aware mask attention mechanism in the process of generating a text summary. By applying the extractive approach, the paper first selects the most essential sentences to construct the initial summary phrases. This information leads the model to modify the weights of the attention mechanism, which provides supervision for the generative model to ensure that it focuses on the sentences that convey important semantics while not ignoring others. The final summary is constructed based on the key information provided. The experimental results demonstrate that our model achieves higher ROUGE and BLEU scores compared to other baseline models on two benchmark datasets. [ABSTRACT FROM AUTHOR]

In this paper, we substituted the ordinary derivative with the covariant derivative in simple translation, that is built by applying the ordinary derivative and we investigate the new display of the simple translation. We have introduced a new translation operator in which its generator is exactly covariant derivative. To know the translation of a quantum particle around a space where the effect of gravity is significant, such as a black hole, it is enough to put the metric of that space in the translation derived in this paper. [ABSTRACT FROM AUTHOR]