ISSN
2424-8460(Online)
2251-2608(Print)
Published
Table of Contents
Open AccessArticles
by Haiyan Wu, Azli BinAriffin
2026,13(1);
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Abstract
With the acceleration of educational digital transformation and the deepening of college English teaching reform, artificial intelligence (AI) technology has provided a new practical path for classroom activity design. Taking college English teaching in China as the research scenario, this paper focuses on AI-driven classroom activity design. By analyzing its core characteristics and teaching adaptability, it explores its effectiveness in enhancing learning participation, realizing personalized teaching, and optimizing the evaluation system, while examining practical challenges such as insufficient technology adaptation, homogeneous activities, and inadequate teacher literacy. On this basis, it proposes optimization paths including a "technology-goal-discipline" three-dimensional adaptation framework, a teacher literacy improvement system, and data ethics norms. The study holds that AI-based classroom activity design must take teaching goals as the core and balance technology application with the essence of teaching to provide sustainable support for improving the quality of college English teaching.
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Open AccessArticles
by Hao Sun
2026,13(1);
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Abstract
This paper explores the pedagogical transformation of big data courses through generative AI, first examining traditional teaching challenges: content-industry mismatch, inadequate alignment between practice and real-world scenarios, and limitations in instructional implementation and evaluation systems. It then proposes a restructuring framework: clarifying the division of responsibilities between human and machine collaboration, developing a dynamically evolving curriculum system based on knowledge progression, and implementing precision teaching with multidimensional assessment through student analytics. Finally, case studies from Beijing No.11 School and Khan Academy demonstrate generative AI applications in data tracking, knowledge delivery, and personalized instruction, offering actionable insights for advancing big data course reform.
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Open AccessArticles
by Hui Wang, Haiying Liang
2026,13(1);
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Abstract
The algorithmic distribution of social media has changed the traditional pathways of cultural dissemination, with both trending and fragmented content coexisting, which can easily lead to symbolic consumption and shifts in value. This article takes a humanities-oriented approach, exploring methods such as semantic modeling, dynamic profiling, weight adjustment, collaborative production, and multimodal experiences to enhance the interpretability and transmission effectiveness of recommendations. It aims to provide strategic reference for platform governance and high-quality content supply, while promoting sustained cultural understanding among audiences during their routine browsing.
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Open AccessArticles
by Liu Yao
2026,13(1);
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Abstract
With the ever-increasing demands for machining accuracy and efficiency of complex parts in the high-end manufacturing industry, tool path planning has become a key link in numerical control (NC) machining, which directly affects the machining quality and production cost of parts. To improve the machining efficiency and surface quality of parts such as complex curved surfaces and thin-walled structures, this paper discusses the application of UG software in tool path planning. First, by utilizing the parametric modeling and machining modules of UG, a multi-strategy collaborative path generation scheme including contour layered machining, adaptive region segmentation and spiral cutting was designed for complex geometric features. Second, the genetic algorithm was integrated to optimize the tool path, so as to reduce non-cutting travel and abrupt steering. Finally, the feasibility and safety of the planned path were dynamically verified through the cutting simulation and collision detection functions of UG. By means of UG and knowledge fusion tools, intelligent algorithms are deeply integrated with the Computer-Aided Manufacturing (CAM) process, realizing the collaborative and autonomous optimization of tool paths and cutting parameters.
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Open AccessArticles
by Xudan Zhou, Qinglin Yang, Yuxin Zhang, Xiaoli Chen, Jin Luo, Guohui Ma, Wei Shu1
2026,13(1);
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Abstract
To identify core prognostic markers and key pathways of lung squamous cell carcinoma (LSCC),
this study adopted the interpretable Dual PASNet deep learning model with a dynamic pathway mask
mechanism to screen survival-related pathways/genes, and systematically validated their prognostic value
through multi-cohort verification, feature selection, and functional analysis. The model identified 10
survival-related core pathways in LSCC (P
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Open AccessArticles
by Yuan Kang, M. KazemChamran
2026,13(1);
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Abstract
Artificial intelligence (AI) is increasingly integrated into higher education through machinelearning (ML) and natural language processing (NLP) systems that support teaching, learning, student
services, and institutional decision-making. Learning analytics models can enable earlier and more targeted
student support, while adaptive and tutoring systems personalize practice and feedback. More recently,
generative AI and large language models (LLMs) have introduced dialog-based assistance for explanation,
feedback drafting, and study support at scale. However, these benefits come with nontrivial risks, including
unreliable output, bias, privacy leakage, and uncertainty about acceptable use in assessment. This article
presents a concise, human-centered implementation framework that links AI capability selection to learning
outcomes, data governance, assessment design, and post-deployment monitoring, enabling institutions to
deploy AI in ways that are pedagogically meaningful, accountable, and sustainable.
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Open AccessArticles
by Yangyang Yu
2026,13(1);
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Abstract
Organic solar cells (OSCs) have broad application prospects in fields such as photovoltaic
building integration and wearable electronics due to their advantages of flexibility, lightweight, solution
processability, and low cost. In recent years, their photoelectric conversion efficiency has exceeded 20%,
laying the foundation for commercial development. However, insufficient stability remains the core
bottleneck that restricts its practical application. Environmental factors such as light, heat, and humidity
can easily lead to rapid efficiency degradation of the device, with interface defects causing charge
recombination, interlayer delamination, and water oxygen permeation being the main causes. As a key
hub for charge separation, transmission, and collection in OSCs, the performance of the interface directly
determines the stability and long-term service capability of the device. Therefore, interface engineering has
become a key path to break through the bottleneck of OSCs stability. This article systematically reviews the
mainstream strategies for improving the stability of OSCs through interface engineering through literature
research, analyzes the mechanisms of each strategy, summarizes current research progress and existing
problems, and proposes future prospects that fit the perspective of undergraduate research. It provides a
basic reference for the preparation of high stability OSCs and promotes their commercialization process.
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Open AccessArticles
by Peng Yin
2026,13(1);
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Abstract
Remote sensing image classification is a core component of remote sensing technology
applications, widely serving fields such as resource exploration and environmental monitoring. Traditional
classification design schemes have limitations such as insufficient feature extraction and low classification
accuracy, making it difficult to adapt to complex scene requirements. Deep learning, with its powerful
ability to mine deep features, provides a new design concept for remote sensing image classification.
This article focuses on the design of a remote sensing image classification system based on deep learning,
reviews the relevant technical foundations, completes the design of each core module and overall
architecture, solves the pain points of traditional design, and provides efficient and feasible design solutions
for remote sensing image classification.
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Open AccessArticles
by Yan Li, Genjuan Ma, Yuwei Gu, Yuqing Zhang, Ziqing Yang, Jiahao Cao, Jiawen Yin
2026,13(1);
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Abstract
As a major rice-producing country, China's rice yield and quality are critical to food security and
livelihood protection. Rice diseases and pests have become a key constraint on rice production, reducing
both yield and quality and seriously affecting farmers' income and food supply stability. To realize accurate
prevention, real-time monitoring and scientific management of rice diseases and pests, and overcome
the low efficiency, poor accuracy and complex operation of traditional monitoring methods, this paper
designs an intelligent diagnosis system for rice diseases and pests based on the Keras model and Internet
of Things (IoT). The system integrates lightweight edge computing devices, UAV inspection terminals
and environmental sensors, taking the lightweight Keras deep learning model as the core to achieve rapid
identification and accurate diagnosis. With IoT and edge intelligence, it realizes real-time collection,
monitoring and analysis of field environmental parameters such as temperature, humidity and light
intensity, supporting intelligent monitoring, data recording and dynamic management throughout the rice
growth cycle. The system forms a complete digital and intelligent monitoring and diagnosis scheme with
standardized processes, providing efficient and scientific management support for farmers and effectively
improving the prevention and control efficiency of rice diseases and pests.
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Open AccessArticles
by Yining Gao
2026,13(1);
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Abstract
In the context of the deep integration of intelligent terminals and Internet of Things technology,
the multi-agent semantic perception network, formed by the combination of multi-agent systems and
semantic perception technology, is widely applied in various scenarios such as distributed information
processing and intelligent collaborative perception. The efficiency of its collaborative scheduling plays a
decisive role in the network's perception performance and service quality. This paper summarizes the basic
characteristics of the multi-agent semantic perception network, analyzes the core problems existing in the
current collaborative scheduling process, proposes an appropriate collaborative scheduling optimization
mechanism, and utilizes semantic unified adaptation and dynamic scheduling strategies to resolve issues
such as semantic heterogeneity and unreasonable resource allocation, thereby improving the network's
collaborative efficiency and stability. This enables the network to operate more efficiently and stably in
practical applications, providing strong support for the development of related fields.
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Open AccessArticles
Analysis and simulation verification of key factors affecting safe rescue in building fire scenarios
by Xinzhe Zhao, Yunqi Jing, Huixin Dai, Yuming Kuang, Chongtian Wan
2026,13(1);
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Abstract
To address the issues of low efficiency, prominent safety risks, significant congestion
interference, and communication disruption in building fire rescue operations, this paper proposes a rescue
optimization model that integrates multiple constraints. The model takes "efficiency-safety-balance" as
its core objective, incorporates the Two-in-Two-out safety rule, congestion coefficient, and the Bernoulli
distribution of communication failure, quantifying the trade-offs among multiple objectives. Through
Monte Carlo simulation and multi-scenario parameter analysis, the robustness of the model in 3-15 story
building scenarios is verified, revealing the coupling influence mechanism of communication failure rate
and risk threshold on rescue performance. The results show that the model can significantly reduce the total
rescue time and safety violation rate, providing quantitative decision support and practical solutions for
emergency dispatch in high-rise buildings.
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Open AccessArticles
by Long Zhao
2026,13(1);
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Abstract
Aiming at the problems of declining physical fitness and insufficient participation in sports
activities among contemporary college students, this study takes four types of aerobic exercises (rope
skipping, jogging, yoga, and swimming) as the carriers, combines the innovation of intelligent equipment
with the optimization of teaching models, and explores the practical effect of aerobic exercise on improving
college students' physical fitness. Adopting literature research method, field investigation method, group
experiment method and other research approaches, the study conducted physical fitness assessment and
demand analysis on 300 college students, and designed a practical model of "Personalized Program +
Intelligent Monitoring + Interactive Incentive". Through 1 to 3 months of intervention experiments, the
significant effectiveness of innovative aerobic exercise in improving core physical fitness indicators such as
cardiopulmonary function, endurance and flexibility was verified. The results show that scientific aerobic
exercise intervention can increase college students' vital capacity by 5%~15% and endurance indicators
by 5%~12%, with the exercise adherence rate reaching more than 70%. The integration of intelligent
equipment and interesting exercise forms can effectively stimulate students' interest in participation
and solve core problems such as irregular exercise and unscientific training methods. The closed-loop
model of "Physical Fitness Assessment - Program Customization - Dynamic Adjustment - Achievement
Transformation" constructed in this study can provide a reference for the reform of college physical
education teaching and the implementation of the National Fitness Strategy in colleges and universities.
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Open AccessArticles
by Ziyou Ma
2026,13(1);
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Abstract
To address the issues of low accuracy in intent recognition and poor user experience in singlemodal interaction in complex scenarios, this paper conducts research on user intent perception and
experience optimization in multi-modal intelligent interaction scenarios. The research integrates voice,
gesture, facial recognition, and environmental perception technologies, builds a feature fusion model
based on Convolutional Neural Network (CNN) + Transformer, designs a hierarchical intent understanding
mechanism and interaction execution optimization strategy, and realizes precise perception and efficient
response of user intentions. Experiments are selected to verify in three typical scenarios: smart home
control, immersive entertainment, and personalized services. The results show that this research scheme
can achieve an intent recognition accuracy of over 85%, and the system response delay is controlled
within 800ms. This effectively improves the naturalness and intelligence level of multi-modal interaction,
providing technical references for the design and optimization of intelligent terminal interaction systems.
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Open AccessArticles
by Haidi Yu
2026,13(1);
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Abstract
Breast cancer, with the highest morbidity and mortality among women globally, presents
significant challenges in early diagnosis and precision treatment. This paper proposes a breast cancer
defense nanorobot integrating intelligent patrol and targeted therapy functions. The nanorobot incorporates
biosensors, targeting technology, drug delivery systems, and intelligent navigation control, enabling highly
sensitive identification, precise localization, and controlled drug release. Structurally, it utilizes DNA
origami and biodegradable materials for biocompatibility and stability. Functionally, it combines FRET
sensing with CRISPR gene editing to enhance diagnostic accuracy and therapeutic efficacy. Through
multidisciplinary collaboration, the system underwent full-chain development from design to experimental
validation. Results demonstrate excellent motility, targeting ability, and therapeutic effect, showing broad
application prospects in breast cancer treatment.
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Open AccessArticles
by Luo Hong
2026,13(1);
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Abstract
The structural characterization integrity of molecular fingerprints directly impacts the efficiency
and accuracy of drug molecule screening. Single molecular fingerprints suffer from limitations such as
incomplete structural representation and insufficient generalization capabilities. To address this challenge,
this study employs three complementary molecular fingerprints (MACCS, PubChem, Pharmacophore ErG)
to construct a multidimensional molecular characterization system. Molecular similarity is calculated to
screen the top 5 candidate molecules by similarity ranking. Subsequently, the screened candidate molecules
undergo molecular docking validation with the 4mbs receptor to assess binding affinity and drug-like
potential. Experimental results demonstrate that the five selected candidate molecules exhibit high binding
affinity and excellent docking scores with the 4mbs receptor, while maintaining good consistency with the
query molecules in both chemical structure and molecular fingerprint similarity. This study indicates that
the proposed method effectively overcomes the limitations of single fingerprint approaches, significantly
enhancing the accuracy and robustness of drug molecule screening. This methodology provides efficient
and reliable technical support for candidate molecule selection in the drug discovery process.
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Open AccessArticles
by Xiaolan Zhu, Jing Feng
2026,13(1);
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Abstract
This paper focuses on classroom interaction in vocational business English teaching under
educational digital transformation. Traditional classrooms are limited by monotonous interaction, low
participation, weak personalization, and delayed feedback, which can hardly meet the needs of cultivating
interdisciplinary talents. Supported by TAM and constructivist theory, this study integrates AI and the
BOPPPS model to construct an optimized teaching strategy, verifies its effect through empirical research,
and puts forward supporting measures. It provides theoretical and practical references for the digital,
intelligent, and personalized reform of vocational business English education.
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Open AccessArticles
by Yishi Xue
2026,13(1);
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Abstract
The expanding deployment of unmanned aerial vehicles (UAVs) across logistics, agriculture,
infrastructure inspection, and emergency response has generated corresponding growth in security
incidents. This paper presents a systematic survey of UAV security threats and their associated
countermeasure technologies. A three-layer risk taxonomy is proposed that classifies threats into physical
layer risks, communication layer risks, and data and privacy risks. The cross-layer cascade propagation
mechanisms through which localized anomalies escalate into systemic failures are also analyzed. For
each risk category, state-of-the-art countermeasures are reviewed, including multi-modal sensor fusion
detection, graduated-response counter-UAV systems, network security hardening, digital-twin-enabled
airspace governance, and AI-augmented intelligent management. The analysis reveals that effective UAV
security governance requires a cross-layer defense-in-depth architecture coordinating detection, prevention,
response, and regulatory enforcement.
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Open AccessArticles
by Wenhao Lu, Zizhen Shen
2026,13(1);
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Abstract
To address the whole-lifecycle safety monitoring needs of large steel structures, this study
developed an intelligent monitoring system integrating a four-layer architecture of perception, transmission,
platform, and application. By optimizing the deployment of sensor networks, it achieved synchronous
multi-source data acquisition of critical parameters including stress and deformation during construction,
dynamic response during operation, and fatigue damage during the aging phase. A data-driven model
was developed for structural state identification and safety assessment, establishing multi-level warning
mechanisms and methods for predicting remaining service life. Engineering applications demonstrate that
this system effectively enhances structural safety monitoring accuracy and warning timeliness, providing
technical support for the whole-lifecycle safety management of steel structures.
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Open AccessArticles
by Jing Feng, Xiaolan Zhu
2026,13(1);
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Abstract
In the era of digital intelligence, artificial intelligence (AI) provides strong technical support
for the innovation of business English teaching in higher vocational colleges. Aiming at the problems
of insufficient interaction, single evaluation mode, and weak practical application ability in traditional
business English teaching, this paper constructs an interactive strategy system of "AI + BOPPPS" blended
teaching. Based on the six links of the BOPPPS model, namely Bridge-in, Objective, Pre-assessment,
Participatory Learning, Post-assessment, and Summary, this study integrates AI technologies such as
intelligent teaching platforms, AI oral evaluation, and big data analysis to realize multi-dimensional
interactions including teacher-student interaction, student-student interaction, and human-computer
interaction. Empirical results show that the strategy significantly improves students' learning participation,
autonomous learning ability and business English application level. This research provides a referable
teaching mode and practical path for the blended teaching reform of higher vocational business English.
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Open AccessArticles
by Jiangeng Long, M. KazemChamran
2026,13(1);
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Abstract
Artificial intelligence (AI) can improve urban mobility by adapting traffic signals to changing
demand, incidents, and multimodal priorities. We propose a fieldable framework combining real-time state
estimation, short-horizon prediction, and constraint-aware reinforcement learning. The policy suggests
phase, split, and offset updates, while a safety and fairness layer enforces minimum green, clearance,
pedestrian service, and side-street protection. Neighborhood messaging enables corridor coordination, and
deployment uses monitoring and rollback. Illustrative studies show reduced delay and queues with higher
throughput.
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