ISSN
2424-8460(Online)
2251-2608(Print)
published
Table of Contents
Open AccessArticles
by Kunji Wang
2025,12(2);
145 Views
Abstract
The utilization of the millimeter wave (mmWave) frequency band has accelerated the development of
5G wireless communication systems. Although mmWave offers abundant bandwidth resources and faster data
rates, channel estimation and compensation techniques face significant challenges in high-mobility scenarios
and complex urban environments. This paper focuses on the proposal and enhancement of channel estimation
and compensation technologies within mmWave-based 5G systems. We have developed a novel channel
estimation algorithm that integrates compressed sensing and machine learning techniques. This approach not only
significantly improves the accuracy of channel estimation but also effectively reduces implementation complexity.
Experimental results demonstrate that the proposed method delivers superior performance across various channel
conditions, achieving approximately a 30% reduction in error rate compared to traditional methods.
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Open AccessArticles
by Yong Liu
2025,12(2);
176 Views
Abstract
Driven by technologies such as 5G, there is an urgent demand for high-capacity, long-distance, and
low-power transmission in fiber optic communication. Traditional modulation and coding techniques face
bottlenecks in terms of spectrum efficiency and error correction. This article explores the engineering applications
of new modulation and coding technologies, analyzes the integration of high-order coherent modulation and
multidimensional multiplexing, and combines LDPC codes and machine learning to study long-distance
transmission and DCI implementation paths. The provincial project verifies the advantages of new technologies,
including a 60% increase in spectrum efficiency, a 25% delay in transmission distance, and a two order of
magnitude reduction in bit error rate. Their joint optimization provides key support for breaking through the
capacity bottleneck of fiber optic communication and developing high-speed optical networks.
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Open AccessArticles
by Zhizhong Zhu
2025,12(2);
117 Views
Abstract
In this study, a high-voltage pulse generator and a multifunctional waveform generator based on lateral
heterojunction structure are designed to meet the requirements of fast switching and frequency domain correction
in high-frequency signal processing of wide-band gap semiconductors. The system adopts N-type β-gallium
oxide /p-type silicon carbide lateral heterojunction device, which realizes ultra-high-speed switching under the
peak voltage of 800V and has the characteristics of low on-resistance. The digital programmable gate drive
module based on PLL synchronization dynamically adjusts polynomial coefficients through FPGA to compensate
the nonlinear transmission deviation of transistors; The measuring unit combines high-speed ADC and Wiener
filtering algorithm to correct amplitude-frequency distortion in 10GHz bandwidth and ensure waveform synthesis
accuracy. The system integrates Jacobian matrix control strategy and active voltage clamping technology, and
connects the traditional module structure in series to maintain the dynamic stability of high-speed conversion.
The two-dimensional electron gas channel formed by heterojunction polarization effect significantly optimizes
the switching performance, which is verified by synchronous pulse excitation mechanism and real-time DSP
analysis. The scalable waveform generation scheme proposed in this paper provides accurate waveform support
for semiconductor detection, especially suitable for the application scenarios of high voltage and high frequency
wide band gap devices.
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Open AccessArticles
by Qingyang Yuan, zuoxin Huang
2025,12(2);
134 Views
Abstract
Aiming at the problem of viscosity increase in polyisobutylene succinimide (PIBSI)-type ashless
dispersants during thermal storage, this study integrates electronic science and technology to explore the
mechanism. Methods such as Fourier transform infrared spectroscopy (FTIR), gel permeation chromatography
(GPC), and reaction kinetics calculations were used to analyze the viscosity increase mechanism under thermal
storage conditions. The effects of chemical structure changes on properties were evaluated through spot tests and
simulated crankcase tests. The results show that the nucleophilic substitution reaction between the primary amine
terminal group and the carbonyl carbon of the five-membered ring succinimide generates secondary amide,
leading to an increase in relative molecular weight and kinematic viscosity. However, the changes in chemical
structure and viscosity have no significant impact on dispersibility and high-temperature detergency. This study
provides a theoretical basis for improving the viscosity increase problem and highlights the application value of
electronic science and technology in fine chemistry.
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Open AccessArticles
by Chenqi Ding, Lu Bai
2025,12(2);
128 Views
Abstract
With the increasingly urgent demand for high-precision flip chip bonding machines in integrated circuit
packaging technology, and in view of China’s reliance on imported high-end equipment, this study proposes an
accuracy optimization design method for flip chip bonding machines.Based on the multi-body system theory,
the spatial error model of the flip chip bonding machine is established, which reveals the coupling relationship
between the geometrical error of the moving parts and the overall bonding accuracy.Through the error sensitivity
analysis by matrix differentiation method, the critical geometric error parameters(such as line displacement error,
angular displacement error and guide verticality error)are identified, and the accuracy optimization design model
aiming at the minimization of manufacturing cost is constructed by this method.Combined with the genetic
algorithm, 15 line displacement error parameters are globally optimized, and the results show that the optimized
tolerance assignments meet the bonding accuracy requirements(X, Y, Z direction accuracy is significantly
improved)while expanding the acceptable error range and reducing the manufacturing cost, realizing the
synergistic optimization of performance and cost.In addition, the angular displacement error and perpendicularity
error are indirectly optimized through error parameter traceability and identification, which solves the problem
of synergistic optimization of multi-measurement error.This study provides theoretical support and practical
guidance for the design and manufacture of domestic high-precision flip chip loader.
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Open AccessArticles
by Yangchen Ji
2025,12(2);
127 Views
Abstract
This study investigates the differences in Chinese and English comprehension capabilities between the
open-source large language models Llama3-8B (Meta) and GLM-4-9B (Zhipu AI) using a controlled variable
approach. Employing a unified BPE tokenizer and cleaned monolingual corpora (850M tokens for Chinese,
1.1B tokens for English), both models were pretrained under identical hyperparameters (learning rate: 3e-5,
batch size: 32, epochs: 10). Performance was evaluated primarily using the F1 score on text classification tasks.
Results indicate that GLM-4-9B significantly outperformed Llama3-8B on Chinese tasks (F1: 92.3% vs. 89.7%),
while both models exhibited comparable performance on English tasks (F1: 94.1% vs. 93.8%). This suggests
that the Autoregressive Blank Infilling objective in GLM’s architecture may be better suited to the syntactic
characteristics of Chinese. The study notes that limitations in dataset scale and hyperparameter optimization
depth warrant further validation of these conclusions
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Open AccessArticles
by Shousen Chen, Jiliang Guo, Xuemei Li, Quanzhu Jiang
2025,12(2);
121 Views
Abstract
This paper presents a technical architecture model for an agricultural digital twin system driven by
data elements, aiming to advance agriculture’s digital transformation and enhance productivity and quality. The
model integrates IoT, 5G, SaaS, and AI technologies, forming a four-layer structure: perception, transmission,
application, and decision-making layers. The perception layer uses sensor networks to collect high-resolution data
on crop growth and field environments. The transmission layer supports reliable communication through WLAN,
Bluetooth, and ZigBee. The application layer, based on SaaS, provides integrated services for agricultural
operations. The decision-making layer utilizes cloud computing and big data analytics to process large datasets.
By enabling real-time data acquisition, efficient transmission, and intelligent analysis, the model facilitates
accurate mapping and smart management of agricultural activities. It supports data-driven decisions, helping
producers optimize resources, reduce risks, and improve efficiency. This architecture not only boosts agricultural
productivity and product quality but also fosters sustainable and environmentally friendly practices, offering a
robust framework for the intelligent evolution of modern agriculture.
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Open AccessArticles
by Sun Shaowei, Shen Guodong, Liu Mingzhou, Zou Jiao, Wu Yue
2025,12(2);
133 Views
Abstract
Node positioning technology is a hot research area in wireless sensor networks (WSN). In view of
the problems of large node localization errors in traditional DV-Hop algorithm, premature convergence and
local optimization in PSO algorithm, a node localization algorithm based on the combination of random weight
particle swarm optimization (RWPOS) and artificial fish swarm algorithm (AFSA) is proposed.By analyzing the
inertia weight, maximum velocity, and learning factor of particles, the particle swarm optimization algorithm
was improved and combined with the artificial fish swarm algorithm. Finally, four algorithms were compared
and analyzed in three-dimensional space to investigate the impact of different beacon node ratios, different node
communication radii, and different node numbers on node positioning error.The MATLAB simulation results
show that compared with the improvement of the single algorithm, the hybrid algorithm has strong convergence
and effectively improves the node positioning accuracy, thus providing an important basis for the application of
this algorithm in practical situations.
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Open AccessArticles
by Jin quanZhang
2025,12(2);
185 Views
Abstract
With the rapid development of information technology, computer electronic information technology
has permeated all aspects of engineering management, significantly improving its efficiency and quality. This
paper begins with the research background, analyzing the current application status and development trends of
computer electronic information technology in engineering management. Through a combination of literature
review and case studies, it explores in detail the practical applications of key technologies such as big data, cloud
computing, the Internet of Things (IoT), and artificial intelligence (AI) in engineering management, focusing on
their effectiveness and advantages in project planning, resource allocation, schedule management, quality control,
and risk early warning. Research indicates that computer electronic information technology can effectively
reduce engineering management costs, enhance project execution efficiency, optimize resource allocation,
and improve risk monitoring capabilities. The paper further discusses challenges in its application, including
difficulties in technology integration, data security risks, and talent shortages, proposing countermeasures such
as increasing investment in R&D, strengthening information security systems, and implementing professional
training programs. The findings suggest that fully leveraging computer electronic information technology not
only enhances the scientific precision of engineering management but also injects new momentum into crossdisciplinary collaboration and smart engineering construction, providing significant reference value and practical
guidance for the innovative development of modern engineering management systems.
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Open AccessArticles
by Huijia Shun
2025,12(2);
134 Views
Abstract
With the continuous expansion of grid-connected wind power capacity, its output volatility, low inertia
characteristics, and anti-peak regulation behavior pose significant challenges to power system stability. This paper
investigates the impact mechanisms of wind power integration on both static and dynamic stability of power
systems, analyzing voltage collapse risks and power flow redistribution caused by wind power randomness, as
well as frequency instability and subsynchronous oscillations resulting from insufficient inertia support. Building
upon this foundation, we construct a multi-timescale scheduling optimization model that balances economic
efficiency with stability requirements, employing distributionally robust optimization to handle wind power
output uncertainty and designing decomposition-coordination algorithms to improve solution efficiency. For
stability enhancement, we propose coordinated “source-grid-load-storage” optimization strategies, including
combined wind-PV-storage dispatch, flexible transmission technologies, and demand-side response mechanisms.
This research provides theoretical support and practical solutions for ensuring secure and stable operation of
power systems with high renewable energy penetration, offering significant implications for low-carbon energy
transitions.
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Open AccessArticles
by Yunze Yang, Hucheng Chen, Yubing Gong
2025,12(2);
134 Views
Abstract
To solve the bottleneck problem of the disconnect between the supply of high-level applied talents in
the field of mechanical engineering in Guangxi and the demand for regional industrial clusters, this study focuses
on Guangxi’s characteristic industrial clusters such as automobiles, engineering machinery, and agricultural
equipment, and proposes strategies to optimize the master’s training mode of mechanical engineering in Guangxi.
On the demand side, through research on 52 enterprises such as Liugong and Yuchai, the core competency
gaps for master’s talents in the field of mechanical engineering in Guangxi were identified, such as intelligent
equipment research and development and cross-border technology adaptation; On the supply side, a three chain
collaborative training model of “industry chain innovation chain education chain” is constructed, and a three
in one training framework of “characteristic courses+industry education practice+international expansion” is
designed to provide a systematic solution for the western region to solve the dilemma of “talent supply lagging
behind industrial upgrading”.
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Open AccessArticles
by Hilzati Kuzati, Baihetiya Mutalipu
2025,12(2);
147 Views
Abstract
With the rapid development of medical imaging technology, medical images collected by different
sensors containing different pathological information are also known as multi-modal medical images, which
are indispensable tools for modern medical analysis and disease diagnosis. Due to the inability of single-modal
images to provide complete pathological information for disease diagnosis, medical staff need to observe multiple
images of different modalities simultaneously, and this kind of image examination has errors, which increases
the uncertainty of clinical diagnosis. Therefore, some scholars have proposed multi-modal medical image
fusion technology, which can not only demonstrate the application advantages of different modal images but
also effectively make up for the application problems of single images, providing a reasonable basis for disease
diagnosis in the new era. Therefore, this paper mainly discusses the development and application effect of a
medical image diagnosis teaching assistant system supported by multi-modal deep learning.
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Open AccessArticles
by Yuliang Zhong
2025,12(2);
147 Views
Abstract
This paper comprehensively examines the integration of blockchain technology within China’s
“One - Net - All - Service” initiative, dissecting its transformative impact on government transparency. By
elucidating blockchain’s core features—decentralization, immutability, and traceability—and their alignment
with digital governance objectives, the study explores how these attributes address long - standing challenges in
administrative service delivery. Through in - depth analysis of technological applications, regulatory dynamics,
and public engagement, the paper identifies both opportunities and obstacles in leveraging blockchain for
transparency. It concludes by outlining future directions for blockchain - enabled governance, offering insights
applicable to global efforts in digital governance optimization.
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Open AccessArticles
by Hongbin Mou
2025,12(2);
141 Views
Abstract
Production scheduling in refining enterprises serves as a crucial link connecting planning and
operation. Current research mainly focuses on small-scale crude oil scheduling problems with a scheduling
cycle of 7 - 10 days and 3 or 4 scheduling links. However, there is a relative scarcity of research on largescale scheduling problems involving more than 5 storage and transportation links and a long cycle (≥30 days).
Aiming at the difficulties in global modeling under the trend of refining-chemical integration, this paper points
out the rationality of modeling complex processes from a spatial segmentation perspective. It also analyzes
the characteristics of modeling methods such as mathematical programming, constraint programming, genetic
algorithm, and reinforcement learning, clarifying the application advantages of mathematical programming in
maturity, constraint programming in logical processing, genetic algorithm in multi-objective optimization, and
reinforcement learning in potential. The importance of spatial segmentation modeling and the integration of
multiple methods in constructing a suitable-scale scheduling model is revealed. The research results provide
assistance for schedulers in selecting appropriate methods and constructing scheduling models that conform to
actual working conditions.
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Open AccessArticles
by Caiyun Shan, Yanbin Bu
2025,12(2);
141 Views
Abstract
Traditional signal processing methods struggle to meet the requirements of real-time performance
and accuracy, while deep learning-based approaches, despite their superior performance, suffer from high
computational complexity, which hinders real-time processing on embedded devices. To address this challenge,
this paper explores the design of lightweight neural networks for real-time signal processing in brain-computer
interfaces (BCIs). By analyzing the characteristics of electroencephalogram (EEG) signals, we summarize key
considerations for lightweight network design and present an efficient EEG signal classification model using
EEGNet as an example. Finally, we discuss the prospects and challenges of lightweight neural networks in BCIs,
providing insights for future research in this field.
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Open AccessArticles
by Kuan Ding
2025,12(2);
133 Views
Abstract
As a new form of power generation, photothermal power generation technology can convert solar
energy into heat energy and generate power according to the thermal conversion process. It can effectively use
China’s internationally leading thermal power turbine technology to reduce the impact of coal power capacity
reduction policy on conventional industries. The actual power generation is relatively stable and controllable,
the overall operation mode is flexible and changeable, and has good environmental benefits. Therefore, it has
been widely used in urban construction and development in recent years. This paper mainly studies the current
technical and economic status of thermal power generation in China and the corresponding construction and
development measures.
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Open AccessArticles
by Cenglin Yao
2025,12(2);
147 Views
Abstract
With the improvement of China’s industrial level, the application of big data (hereinafter referred to as
BD) technology in industry has been deepened, which provides important help for the improvement of China’s
industrial level. With the proposal of German industry 4.0, IOT (hereinafter referred to as IOT) technology will
play a very important role in industry. Industrial BD is a new industrial ecosystem deeply integrated with the
new generation of information technology and industrial field, which promotes the collection, analysis and indepth application of industrial BD. Based on the characteristics of industrial BD, IOT technology has changed the
source and collection method of industrial BD. Combined with data models and algorithms, we can analyze the
applications of industrial BD in manufacturing enterprise R & D and design, production and manufacturing,
remote operation and maintenance and digital operation management, which can give suggestions to promote the
deepening utilization of industrial BD. Firstly, this paper analyzes the functional requirements of BD processing
platform. Then, this paper proposes related algorithms. Finally, this paper constructs an industrial BD platform
based on IOT technology.
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Open AccessArticles
by Lesheng Liu
2025,12(2);
136 Views
Abstract
BP neural network algorithm can help the battery charged state data estimation, along with the
development of the car networking and cloud platform technology, more and more state of the electric car
manufacturers of electric vehicles in the vehicle and the battery status information through the car terminal
uploaded to the cloud platform, through the analysis of the data of upload, the staff can better grasp the status of
the vehicle, early warning to the potential risk of electric cars.If the current SOC is predicted using the batteryrelated data on the cloud platform and verified with the SOC estimated by BMS, it is of great significance for the
online diagnosis of the data monitoring cloud platform.
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Open AccessArticles
by Jinze Li, Yi Zhao, Lei Zhang
2025,12(2);
149 Views
Abstract
Complex problems in chemical processes often need to be transformed into optimization problems
for solution, and intelligent optimization algorithms provide efficient strategies for this. Based on the WilliamsOtto (WO) process and the crude oil atmospheric and vacuum distillation process, this study compares the
performance of five optimization algorithms, including the Sequential Quadratic Programming (SQP) method,
Particle Swarm Optimization (PSO) algorithm, etc., and evaluates them from dimensions such as running time,
number of iterations, and the quality of results. The results show that the traditional gradient algorithm SQP
is suitable for low-dimensional nonlinear problems, while heuristic algorithms exhibit better global search
capabilities and robustness in the strongly coupled and multi-stable industrial process of crude oil atmospheric
and vacuum distillation. It can be seen that different optimization algorithms have their own advantages and
disadvantages, and a reasonable selection should be made according to specific problems.
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Open AccessArticles
by Junwei Liang
2025,12(2);
151 Views
Abstract
This paper presents an advanced implementation framework for Industrial Ethernet addressing critical
real-time communication requirements in smart manufacturing. We propose a novel architecture optimizing
network topology, implementing enhanced clock synchronization, and integrating time-sensitive networking
capabilities to achieve deterministic performance. Experimental evaluation on a manufacturing testbed
demonstrates significant improvements in communication latency (42% reduction), jitter (67% decrease), and
reliability (99.997% packet delivery) compared to conventional implementations. A real-world case study in a
precision machining facility validates our approach’s practical applicability, showing 28% increased production
throughput and 35% reduced system downtime. This work contributes insights into effective Industrial Ethernet
infrastructure deployment supporting stringent timing requirements of next-generation smart manufacturing
systems, particularly for applications requiring sub-millisecond control loops and synchronized operations.
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Open AccessArticles
by Zhe Yu, Feiyan Jin
2025,12(2);
170 Views
Abstract
Against the backdrop of global economic integration and rapid technological development, engineering
projects are facing increasingly complex demands and challenges. This article explores the application and
optimization strategies of intelligent engineering management mode from the perspective of the entire project
lifecycle. By analyzing the characteristics and challenges of each stage of project planning, design, construction,
and operation, a solution based on intelligent technology is proposed, and its impact on improving project
management level is discussed, aiming to provide scientific and reasonable guidance for enterprises.
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Open AccessArticles
by Yanrui Chen
2025,12(2);
144 Views
Abstract
Owing to the prevalent growth of internet of things (IoT) electronics devices, there has been an increasing requirement for processing real - time data and optimizing it in a faster manner. In this paper, a low
- overhead and a pragmatic python - based tool set is proposed that can perform processing and optimizing of
streaming data generated by IoT sensors. By integrating mature open-source libraries (e.g., pandas, NumPy,
and scikit-learn), we constructed a real-time feature engineering framework that enables asynchronous data
cleaning, feature extraction, and dynamic tuning of model parameters. System validation was conducted using
the public UCI IoT telemetry dataset, with quantitative analyses of core performance metrics including system
latency, memory requirements, and signal throughput. Furthermore, the accompanying Python code examples
demonstrate specific implementations of data parsing, anomaly detection, and adaptive signal filtering.
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Open AccessArticles
by Guan wei
2025,12(2);
137 Views
Abstract
This study examines the design and evaluation of an intelligent system augmented by DeepSeek
for chronic disease patient management.In response to the global burden of chronic diseases and healthcare
resource shortages, this research targeted three common conditions:diabetes, hypertension, and COPD.The
study employed mixed methods including surveys, interviews, clinical data analysis, and resource utilization
assessment.Results demonstrated significant improvements in patient satisfaction(27% improvement), clinician
efficiency(32% decrease in documentation time), and clinical outcomes(HbA1c reduction from 7.8% to 7.1%
among diabetic patients;12.4 mmHg reduction in systolic BP among hypertensive patients).Rehabilitation targets
were achieved 28% earlier and readmissions reduced by 36% among diabetics.Implementation challenges
included data integration, user reluctance, workflow disruption, and regulatory uncertainties.Optimization
strategies encompassed technical improvements, user acceptance enhancement, simplified operations, and policy
solutions.This study demonstrates DeepSeek technology’s ability to revolutionize chronic disease management
through personalized monitoring, smart interventions, and resource optimization, providing valuable insights for
healthcare facilities seeking to implement AI-assisted patient management.
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Open AccessArticles
by Feiyan Jin, Yu Zhe
2025,12(2);
143 Views
Abstract
With the rapid development of information technology, cloud computing has gradually become an
important means for enterprises to optimize information system integration. This article explores the integration
strategy and optimization methods of information technology systems based on cloud computing. Through
research, it is found that the reasonable use of cloud computing technology can significantly improve the
flexibility, scalability, and security of the system. The aim is to provide valuable references for researchers and
practitioners in related fields, and promote the efficient integration and optimization of information technology
systems.
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Open AccessArticles
by Tonggang Shao
2025,12(2);
144 Views
Abstract
This article focuses on the intelligent irrigation robot arm and explores its design and control strategy
in depth.On the basis of elaborating on the development background and significance of intelligent irrigation
robot arms, a detailed analysis of mechanical structure design is conducted, including arm configuration, joint
design, and material selection;In depth research on control strategies, covering motion control algorithms, sensor
applications, and intelligent decision-making systems;And test and evaluate its performance.The research aims to
provide theoretical and practical support for the optimization design and wide application of intelligent irrigation
robot arms, and promote the development of intelligent agricultural irrigation.
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Open AccessArticles
by Zhiyuan Chang
2025,12(2);
129 Views
Abstract
With the rapid development of cloud computing and web development technologies, traditional
teaching methods can no longer meet the industry’s demand for talents. Project-driven teaching method, as a
teaching approach that emphasizes the combination of practice and theory, has shown significant advantages
in cloud computing and web development courses. This paper deeply analyzes the challenges faced by current
cloud computing and web development course teaching, expounds the theoretical basis of project-driven teaching
method, and proposes a reform path for cloud computing and web development courses based on project-driven
teaching from the aspects of curriculum design, teaching implementation, and evaluation system, aiming to
provide references for improving the quality of course teaching and cultivating high-quality talents that meet the
needs of industry development.
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Open AccessArticles
by Tian Qi
2025,12(2);
125 Views
Abstract
Statistical learning methods serve as critical tools in data science, playing a key role in the analysis and
modeling of complex data. This study focuses on three statistical learning methods—regression, clustering, and
classification—conducting empirical research using the UCI “Online Retail” dataset. Linear regression models
were employed to evaluate the impact of key features on the performance of continuous variable prediction.
Results show that the inclusion of “purchase frequency” improved the model’s goodness of fit (R2) to 0.87 and
reduced the mean squared error (MSE) to 12.3. For clustering tasks, the K-Means algorithm was applied, and
when K=3, the silhouette coefficient reached a maximum of 0.71, effectively distinguishing different consumption
behavior patterns. In the classification task, the random forest model achieved an accuracy of 93.5% and an
F1-score of 0.90, demonstrating its robustness in high-dimensional data scenarios. The findings highlight the
significant advantages of different statistical learning methods in their respective tasks while pointing out areas
for improvement, such as feature dependency, computational efficiency, and handling of outliers.
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Open AccessArticles
by Haonan Tian
2025,12(2);
204 Views
Abstract
Object detection technology plays a crucial role in the field of computer vision and has made
significant progress in recent years. Among them, the You Only Look Once(YOLO) object detection algorithms
have attracted attention due to their speed, accuracy, and end-to-end characteristics. In order to promote the
development and practical application of object detection, this paper provides a comprehensive review of the
development history, technical principles, strengths, weaknesses, and future development directions of the YOLO
object detection algorithms. Firstly, the technical principles of the YOLO algorithms are detailed, including the
end-to-end detection frameworks based on a single neural network and the concept of dense prediction. Then,
this paper reviews the evolution of YOLO algorithms, from YOLOv1 to the latest version, and combs their key
technical innovations and performance improvements.Finally, we discuss the potential research directions for the
future.
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Open AccessArticles
by Yufan Guo
2025,12(2);
159 Views
Abstract
With the intensification of global agi Brunel University of London ng, the adaptability of elderly
healthcare services has become an increasingly pressing issue. As a crucial interactive component in medical
systems, visual design significantly influences the operational experience, accessibility, and information
acquisition efficiency of elderly users. Starting from the evolution of artificial intelligence (AI) technologies, this
paper systematically reviews its core applications in visual design for elderly healthcare—focusing on intelligent
interface optimization, personalized visual presentation, and the construction of multimodal interaction systems
tailored to the aging population. By analyzing current technological achievements and representative system
cases, it explores the potential of AI-based visual systems to improve usability, aging-friendliness, and precision
in medical service delivery. At the same time, the study identifies existing technical bottlenecks, ethical risks, and
design limitations that hinder widespread adoption. Based on these findings, it proposes a set of recommendations
for optimizing aging-friendly visual design and outlines future directions for research and development. The
research concludes that AI will play a pivotal role in advancing healthcare systems toward more intelligent,
inclusive, and human-centered models—particularly in supporting elderly health, enhancing digital accessibility,
and promoting long-term societal well-being.
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Open AccessArticles
by Yukang Wang
2025,12(2);
170 Views
Abstract
The rapid expansion of the new energy vehicle market has brought great opportunities to the fuel cell
management system (BMS) industry. The high growth dividend of new energy vehicles has driven the explosion
of the demand for battery management system, and the industry has ushered in a golden era. Efficient, safe
fuel battery management system (BMS) is the key to guarantee normal operation of new energy vehicles, with
the increase of number of new energy vehicles, fuel battery management system (BMS) in the future market
will accelerate explosion, through the fuel battery management system (BMS) research and development,
manufacture, arrangement in use and the battery data would be more competitive. Establishing a unified industry
standard, improving the quality of fuel cell management system (BMS) products, and reducing production
costs and prices are the key issues in the domestic fuel cell management system (BMS) field, and the increase
of market concentration will help accelerate the solution of these problems. Promote the rapid and healthy
development of fuel cell management system (BMS) and new energy vehicle industry. This paper proposes and
“realizes” the design and use of new energy fuel cell management system (BMS) based on the Internet of Things
(IOT) technology through remote control, real-time data detection, emergency solution and other aspects.
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Open AccessArticles
by Cenglin Yao
2025,12(2);
142 Views
Abstract
In recent years, with the rapid development of artificial intelligence technology in our country, obtained
the swift and violent development of automobile industry, car since entering the social life, are developing fast,
due to the gradually reduce the cost of production, domestic auto prices dropped substantially, the car entered
innumberable families, at the same time, the motor drive systems market is very wide also, car service time is
long, hard to avoid can appear problem, artificial intelligence technology in the drive system has an important
role in application, this article carries on the thorough analysis.
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