Development Of Technology Research Articles

A Review of Research Progress in Carbon Monitoring and Carbon Metering Methods: Comparison at Home and Abroad

Carbon monitoring and carbon measurement are not only important foundations for realizing the marketization of carbon trading, but also a key link in realizing China’s strategic “dual carbon” goal. The aim of this research is to comprehensively summarize and compare carbon monitoring and carbon metering technologies, as well as to analyze their current status and challenges. This study adopts literature research, comparative analysis, case analysis, policy interpretation, and other methods to comprehensively and deeply explore the relevant content of carbon monitoring and carbon metering technology. An in-depth exploration of relevant methods, standards, and applications provides a reference for promoting the sustainable development of global carbon monitoring and carbon metering technologies. By summarizing the difficulties of carbon monitoring and the characteristics of existing technologies, as well as comparing carbon measurement methods and the relevant measurement standards, this paper focuses on the difficulty of carbon monitoring, which lies in the credibility and accuracy of the data, where remote sensing technology possesses higher applicability. The principles of carbon measurement methods mainly include direct underlying data measurements, indirect measurements through statistical modelling, and market mechanism measurements. The relevance and precision of carbon measurement methods have been gradually strengthened as the measurement standards have been developed and implemented. Finally, future development directions and relevant suggestions will be described in detail and put forward in combination with the application of carbon monitoring and carbon measurement. Among them, blockchain technology is considered to be one potential area for future development, and data standardization will play an important role in the development of carbon monitoring and measurement technology. We recommended establishing and perfecting data-sharing mechanisms in future policies to improve the accuracy and credibility of data.

Operando visualisation of lithium plating by ultrasound imaging of battery cells.

While developing battery cells, the achievement of fast-charging capability is heavily dependent on avoiding metallic plating on the anode surface (i.e., lithium plating in lithium-ion cells). However, this objective hinges on the effectiveness of plating detection. Currently, measurement techniques are either inadequate in providing spatial, temporal, or causal information, incur high costs when employing, e.g., neutron imaging, or are lengthy due to destructive post-mortem examinations that additionally lack operando data. In this work, we demonstrate an ultrasound imaging method for operando visualization of the interior of a multi-layer pouch battery cell. Here we show that this method can non-invasively visualize the formation and stripping of lithium plating during cycling. Extensive reference electrode studies and ex-situ analysis verify the effectiveness of our method for plating detection. Ultimately, this work enables researchers and industry to significantly accelerate the development of new cell technologies and their optimized utilization.

Applications of Human-Computer Interaction in the Field of Cultural Heritage Preservation

The development of digital technologies has opened up some possibilities for practices across various fields, while the discipline of human-computer interaction has introduced new paradigms that enable humans to engage with digital technologies more conveniently. An increasing number of disciplines are seeking to intersect with the field of human-computer interaction, continuously advancing technology and exploring new research areas. This paper aims to assess the current applications of human-computer interaction technologies in the field of cultural heritage preservation and to provide guidance for potential future developments. To this end, this paper conducts a literature review to analyze and discuss the research progress in relevant academic journals. This paper specifically discusses three mainstream applications of human-computer interaction in the field of cultural heritage preservation: heritage information collection and preservation, restoration and display, and gamification. It analyzes the current research progress and key case studies for each application. Studies have shown that the introduction of human-computer interaction technologies in cultural heritage preservation primarily differs from traditional research in terms of public participation. The project cases documented in the literature mainly focus on conceptual designs. The conclusion of the article provides a summary of the entire text and offers an outlook for future developments.

Progress and Application of Unsupervised Feature Extraction Methods

Unsupervised feature extraction is crucial in machine learning and data mining for handling high-dimensional and unlabeled data. However, existing methods often ignore feature relationaships, resulting in suboptimal feature subsets. This paper reviews the current state of unsupervised feature extraction methods, discussing the limitations of traditional methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), particularly in terms of interpretability, sensitivity to outliers, and computational resource challenges. In recent years, improvement strategies such as information theory, sparse learning, and deep learning (e.g., deep autoencoders and generative adversarial networks) have significantly progressed in feature extraction. This paper analyzes the practical applications of these methods in image processing, gene analysis, text mining, and network security. For example, in image processing, deep autoencoder-based methods such as Matrix Capsules with EM Routing can effectively extract key features from complex images. In text mining, unsupervised feature selection methods combined with generative adversarial networks significantly improve the efficiency of processing high-dimensional text data. Additionally, this paper explores future research directions such as multimodal data processing, improving real-time processing capabilities, and integration with other machine learning techniques (e.g., reinforcement learning, transfer learning), providing insights for further development of unsupervised feature extraction technologies.

The Application of VR Technology in the Field of Gaming

With the rapid advancement of Virtual Reality (VR) technology, its application in the gaming sector has introduced an unprecedented level of immersion for players. This paper aims to explore the integration of VR technology in game design and assess its potential and challenges in enhancing player experiences. The motivation for this research stems from the fundamental differences between VR and traditional games in terms of immersion, interactivity, and multisensory feedback. The paper provides an overview of the basic concepts and principles of VR technology, compares it with traditional gaming technologies, and discusses its role in delivering immersive gaming experiences, enhancing player interaction, and providing multisensory feedback, particularly in multiplayer online gaming contexts. Despite the rapid growth of the VR gaming market, high hardware costs, lack of content, and technical challenges remain significant barriers to widespread adoption. The conclusion of this paper underscores the significance and potential for future development of VR technology in the gaming field, while also identifying key areas for improvement necessary for mainstream acceptance.

Application of big data and artificial intelligence in visual communication art design

In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications’ reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.

A Review on Human Pose Estimation Based on Deep Learning

With the rapid development of computer vision technology, deep learning-based human pose estimation has become a hot topic of research. This review outlines the progress made in this field in recent years, with a particular focus on the development of single-person and multi-person pose estimation. Single-person pose estimation primarily focuses on identifying and locating the joints of an individual, while multi-person pose estimation further extends to simultaneously recognizing the poses of multiple individuals. The article begins by introducing the basic concepts of pose estimation, then discusses in detail the application of deep learning models in single-person and multi-person pose estimation, as well as the advantages and disadvantages of the existing modules. In addition, the limitations of current models are analyzed at the end of the paper, and possible future optimization directions are explored. The aim of this article is to provide researchers with a comprehensive perspective to understand current technological trends and potential innovation points.

The Impact of Passive House Construction on Energy Consumption and Living Experience in Chongqing

This research investigates the impact of passive house construction on energy consumption and living experience in Chongqing, China. The study involves both primary and secondary methodology, which is represented in both qualitative and quantitative formats. The primary research evidence is collected from an online questionnaire and interviews with designers and engineers in the industry. The secondary evidence is collected from a range of online sources including relevant and credible academic articles, essays and news reports, and government reports. A case study based on a passive house project in Hebei is used to compare with passive house construction in Chongqing. The key areas considered in the study are the environment, policies, and social aspects and the impact of passive house construction on the living experience of the residents. The research concludes that passive house construction has positive results from both climate and policy perspectives. In terms of climate, it is environmentally friendly and energy-efficient. The development of more policies technologies will be the main driving force or motivation for more passive house construction.

Beyond Pixels: The Journey to Realistic NPCs in Gaming

Beyond Pixels: The Journey to Realistic NPCs in Gaming

A Review of YOLO-Based Target Detection Methods

Target detection is now a popular topic in computer vision. With the development and iteration of technology, deep learning is constantly emerging. The integration of deep learning in the target detection task has led to rapid improvement in accuracy and speed, among which the You Only Look Once (YOLO) series of methods have the most rapid and varied improvements and upgrades, which have been widely used in the fields of navigation, video surveillance, face detection, text detection, and aerospace, etc. This paper initially provides an overview of the research context, importance, and challenges associated with this domain, compares and analyzes the network structure and implementation of the single-phase target detection method represented by the YOLO series with the two-phase and other improved algorithms, and then introduces the research progress of the target detection algorithms of deep learning, the characteristics of the commonly used datasets, and the key parameters of the evaluation of performance indicators, and then presents a compilation of experimental outcomes associated with several widely recognized algorithms applied to prominent datasets. Subsequently, it enumerates the experimental findings of diverse algorithms on these established datasets. Ultimately, this paper anticipates future research trajectories and developmental trends pertaining to target detection algorithms.

Engineering Training in the Structure of Higher Education and Professional Choice of Youth

The structural elements of the university network of engineers’ training and its demand in different social groups of young people are analyzed using statistical data, monitoring and sociological research results. Recent shift in the choice by school graduates towards engineering specialties is associated, first of all, with the expansion of employment and career opportunities, as well as the growing attractiveness of advanced technical qualifications. The concentration of personnel training for the development of breakthrough technologies in top, selective universities is effected by combining investments from the state budget, large corporations, the development of innovative forms of education, in particular within the framework of the Advanced Engineering Schools project, as well as investments of families with high cultural and economic capital to ensure competitiveness of future students. Successful training in mass engineering education programs in regional universities, where numbers of budgetary admission places have been increased since 2022, is hampered by a keen shortage of well-prepared school students-applicants. The best results are shown under the practice of attracting target social groups of young people while maintaining value of technical labor, for example, in various forms of mobilization via the track “School-SPO-engineering training in higher education institutions”.

Trends of Green Cooperation in Agriculture

The goal of the research is to investigate key trends in green cooperation and the principle objective is to prove economic motivation of entrepreneurs towards green cooperation. In the research the following methods were used: analysis and synthesis and mathematic methods in economics. In order to substantiate economic motivation of ESGstandards introduction in agricultural industry economic and technological analysis was carried out and correlation between enterprise profitability, its financial indicators and ESG-rating was studied. The absence of rationalistic development potential at the state-owned corporation Rusagro was demonstrated. Therefore, the authors proposed to pass to heuristic development and innovation technologies. Investment in the development of innovation technologies can provide higher labour productivity and at the same time retain current values of technological equipment. These measures can affect GDP of Russia as a whole, as development and introduction of new technologies and passing over to heuristic development of the organization can raise quality of economic systems, which will provide the growth in entrepreneurs’ profit and gross product of the region and the country.

Flexible Physical Sensors Based on Membranes: From Design to Application

Abstract The flexible physical sensors have the advantage of good flexibility and ductility and can be freely bent or even folded. The development of flexibility from rigidity has significantly increased the application situations for sensors, especially in intelligent robots, tactile platforms, wearable medical sensors, bionic devices, and other fields. The research of membrane-based flexible physical sensors relies on the development of advanced materials and technologies, which have been derived from a wide range of applications. Various technical methods and principles have gradually matured according to the different applications and materials used. This review begins with a discussion of membrane substrates and functional materials, summarizing the development of flexible physical sensors. According to the technical sensing principles, this review focuses on research progress on physical sensing platforms. Finally, the challenges and opportunities for the design of emerging membrane-based flexible physical sensors in the coming years are presented.

Deep Learning-Based Information Security

Abstract. With the rapid development of network technology, information security is facing increasingly complex challenges. Deep learning technology, due to its strong capabilities in data processing and pattern recognition, has become a key technology to improve the detection efficiency and accuracy in the field of information security. This paper delves into the application of deep learning in various aspects such as malware detection, network intrusion identification, User and Entity Behavior Analytics (UEBA), privacy protection technology, model explainability, and network security vulnerability detection, and proposes deep learning-based information security methods. Through experimental validation, our methods have outperformed traditional machine learning models in multiple evaluation metrics, providing new solutions for the field of information security. In the future, we will continue to explore new applications of deep learning in the field of information security to cope with the ever-changing network security threats.

An Investigation of Parameter Sensitivity and a Dynamic Analysis of Subsurface Storage Chambers Utilizing the Finite Difference Method

Underground compressed air energy storage chambers are a promising emerging energy storage technology with strict limitations relating to the stability of the surrounding rock. This study conducted displacement and plastic zone analyses during the excavation and stabilization phases of the chamber utilizing the finite difference method based on engineering data, demonstrating that the stability of salt rock can effectively withstand internal pressures ranging from 0 to 9 MPa, with an average of 15 mm in the Z-axis and 19.23 mm in the X-axis. To further investigate the feasibility of subterranean energy storage reservoirs, the FOS for various surrounding rocks was calculated at different burial depths. These results facilitated a parameter sensitivity analysis on the stability of the surrounding rock of the underground energy storage reservoir. The dynamic reaction of the underground chamber was studied using synthetic seismic wave technology, demonstrating that the seismic capacity of the structure adhered to the code, and the post-seismic displacement remained within the safe range (Z-axis 34 mm, horizontal 19 mm). The results demonstrate the stability analysis method of the chamber and establish a foundation for the extensive implementation of CAES which will contribute to the development of energy storage technology.

Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.

Deep Learning Research on Smart Home Compatibility

Abstract. In recent years, with the rapid development of technology, the concept of Smart Home has gradually emerged into people's view., Because of its remarkable convenience, it has won the favor of many consumers in the present era. However, as the utilization of smart home devices becomes more widespread, several challenges have emerged that need to be addressed for future development. One such challenge is the compatibility problem, which has now transformed into a significant barrier hindering the development of Smart Home. Therefore, in order to bring back a truly intelligent smart home environment, it is of utmost urgency to address the compatibility problem. In this paper, the author mainly focuses on this issue. By leveraging the knowledge of IoT and transport protocols, and using Python as the research and test platform, the researcher strives to either work out or alleviate the compatibility problem within the Smart Home domain.

Optical Clearing of Ex Vivo Adipose Tissue.

The paper focuses on the development of technology of adipose tissue optical clearing using different complex hyperosmotic optical clearing agents and tissue permeability enhancers. To quantify optical clearing efficiency, reduced scattering coefficient was estimated from the ex vivo spatially resolved backreflectance measurements using a multi-distant fiber optical device. Tissue morphology modification was monitored with the help of histological studies. Kinetics and efficiency of the optical clearing were evaluated for ex vivo abdominal fat tissue at action of dimethyl sulfoxide, diatrizoic acid, metrizoic acid, sucrose, and fructose solutions accompanied by tissue permeability enhancers, such as various modes of fractional laser microablation and sonophoresis and their combinations. Histological studies allowed us to find agents that cause minimal morphological changes of the adipose tissue. Maximal optical clearing efficiency of 83.5% was observed for the samples subjected to action of the fructose-ethanol solution during 90 min in combination with gentle modes of fractional laser microablation and sonophoresis.

Pool boiling on the aluminum alloy, copper and WC-coated copper with micro-finned textures and developed multimodal roughness formed by nanosecond laser radiation

Boiling remains one of the most effective processes allowing heat to be removed from heat-loaded surfaces of technological equipment. Due to the development of technologies for metal surface treatment with laser radiation to form specified near-surface properties, including the type, texture configuration and wetting properties, the task of substantiating the use of heat transfer surfaces modified by laser radiation to intensify the boiling process has become relevant. The pool boiling characteristics in distilled degassed water (before the boiling crisis onset) were compared experimentally on the surfaces of samples made of aluminum alloy, copper, and WC-coated copper. Experimental samples were treated by two different methods, including widely used polishing with abrasive materials and laser radiation. Nanosecond laser treatment was used to make micro-finned and anisotropic (developed hierarchical roughness) textures. Textured samples were then hydrophobized. As part of the experiments on pool boiling, the characteristics of the forming bubbles were recorded on the prepared surfaces, heat transfer coefficients, and critical heat flux values were determined. Experimental results were compared with predicted characteristics using well-known models. The evolution of the functional properties of laser-textured metal surfaces after prolonged exposure to heat flux, which is typical of the operating modes of modern heat-loaded equipment, was assessed.

A Talent Cultivation and Performance Evaluation Model Based on a Fuzzy Control Algorithm

The Internet era has raised the demand for skilled workers in businesses and for economic expansion. Businesses find it difficult to expand when trained personnel are shortages. Evaluating and managing exceptionally gifted persons have become more difficult due to technological developments, which have slowed down technological progress and business expansion. The study suggests a technique to quantify how firms develop and employ talent to address this issue. An algorithm called the fuzzy optimized talent cultivation engine (FOTCE) is introduced to evaluate the effectiveness of talent development. The algorithm combines an adaptive neuro-Fuzzy Inference System with Adaptive Hybrid Particle Swarm Optimization. By considering factors including work-life balance, training, involvement, job performance, and satisfaction, the suggested FOTCE algorithm hopes to improve talent strategies and boost organizational competitiveness. The FOTCE algorithm assesses talent development on three levels: overall efficacy, major categories (such as job satisfaction and involvement), and individual indicators within each category. Fuzzy logic handles subjective viewpoints and unclear data in the study, allowing for a more comprehensive assessment. The technique uses fuzzy math to show different levels of employee retention by giving different factors different weights. The results show that the FOTCE technique helps evaluate and upgrade talent development programs and provides valuable insights for better people management when tested with accurate HR data from a company. The findings demonstrate that the suggested FOTCE algorithm outperformed state-of-the-art models like IAA-NRM (83% retention rate) and FAHP (87% retention rate) by a wide margin. Comparatively, FAHP had an EEI of 83% and DEA had 86%. The EEI for this organization achieved 95%. In addition, the FOTCE model showed a higher Talent Development Efficiency Score (TCDES) of 93%, proving that it boosts organizational competitiveness and employee happiness.