Applied Science and Innovative Research (ASIR) is an international open-access journal published by Scholink. The journal aims to provide a high-level platform for scholars and researchers all over the world to share latest findings and views in the field of applied science. We would welcome scholars and researchers engaging in the related field to submit your manuscripts which are complete unpublished and original works and not under review in any other journals to Applied Science and Innovative Research. Both of online submission and E-mail submission (asir@scholink.org) are acceptable. -------------------------------------------------------------------------------- The journal covers all areas of applied sciences such as: Applied Behavioral Science Applied Biology Applied Chemistry and Materials Sciences Applied Computer and Information Research Applied Ecology Applied Economics Applied Education Research Applied Engineering Applied Genetics Applied Geography Applied Linguistics Applied Mathematics and Statistics Applied Mechanics Applied Philosophy Applied Physics Applied Physiology Applied Psychology Applied Sociology Artificial Intelligence Open access: Applied Science and Innovative Research is available online to the reader "without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself." Peer review: Applied Science and Innovative Research takes peer review policy. Peer review is the evaluation of work by one or more people of similar competence to the producers of the work (peers).
Authors who publish with this journal must agree to the following terms: Copyrights for articles published in our are retained by the authors. Authors should promise that their works are entirely original, and all the references in their works should be appropriately cited or quoted. The works have not been published previously. The behavior of submitting the same work to other journals is unacceptable. The journal editor has the right to edit the format of works.
An Exploration of AI-Driven Multimodal Teaching Model Construction for College English Reading Instruction
Current college English reading instruction is constrained by traditional single-modal approaches, making it hard to meet students’ learning needs in the digital age. The advancement of artificial intelligence (AI) technology has injected new vitality into education, and its integration with multimodal teaching—by incorporating resources like images, audios, and videos—shows great potential to break free from the text-centric paradigm and create a more multidimensional language input environment. Multimodal reading instruction enhances language comprehension, cognitive engagement, and student learning interest. AI integration facilitates individualized learning, shifting teaching from “one-size-fits-all” to “precision-oriented”, which is key to college English education reform.
Copyright (c) 2025 Yifan Peng http://creativecommons.org/licenses/by/4.0
Spatial-Temporal Analysis of Burglary Incidents in Leeds: A Study from 2021 to 2022
In the era of globalization and informatization, urban crime, especially residential burglary, poses a significant threat to societal well-being. This study critically examines the spatial distribution patterns of residential burglaries in Leeds for the years 2021 and 2022, and their temporal variations. Utilizing the global Moran's I and Local Moran's I for assessing spatial autocorrelation, the research identified a consistent and significant spatial association in burglary incidents, displaying persistent and stable patterns throughout the observed period. Additionally, with the aid of Kernel Density Estimation (KDE), the study pinpointed that burglary cases are predominantly concentrated in the central urban region, gradually dissipating towards the suburbs. Finally, employing the Ordinary Least Squares (OLS) model to analyze the determinants of residential burglaries, the results indicated a close association between burglary densities and environmental factors.
Copyright (c) 2025 Qiushi Jin http://creativecommons.org/licenses/by/4.0
About Integral Equation for the Symmetrical Loop Antenna
The derivation and solution’s problems of integra-differential equations for the currents in symmetrical loop antennas of rectangular, circular and other short-circuited forms with large length are considered. The results are compared with the properties of small antennas.
Copyright (c) 2025 Boris Levin http://creativecommons.org/licenses/by/4.0
With the in-depth implementation of the "Transportation Power" strategy and the explosive development of artificial intelligence (AI) technology, smart bridges, as the core carrier for the intelligent upgrading of transportation infrastructure, are undergoing a paradigm shift from traditional engineering models to "data-driven + intelligent decision-making". This paper systematically expounds on the application foundation of AI technology in the field of smart bridges, analyzes four full-life-cycle scenarios including design, construction, operation and maintenance, and disaster emergency response. Combined with typical cases such as the AI-assisted beam replacement project at Keqiao Station of Shaoxing Intercity Railway, it reveals the core value of AI technology in improving the accuracy, efficiency and safety of bridge engineering. Meanwhile, it analyzes current challenges in technology application such as data silos and algorithm reliability, and looks forward to the development direction of in-depth integration of multimodal large models and digital twins, providing references for technological innovation and engineering practice of smart bridges.
Copyright (c) 2025 Shouling Luan, Huanrong Zhang, Aihong Qin, Sai Zhang, Linlin Ma, Liya Han http://creativecommons.org/licenses/by/4.0
Applying Micro lecture Technology in the Laboratory Course “Jewelry Processing and Production”
Against the backdrop of the deepening national strategy for digital education, enhancing the teaching effectiveness of practical experimental courses in application-oriented undergraduate programs has become a pressing issue. Taking the course "Jewelry Processing and Production" as an example, the high demands for skill training and the limited resources for teacher guidance create a core tension in the teaching process. Specifically, excessive pressure on instructors and prolonged waiting times for student operations constrain the overall improvement of teaching quality. To address this structural contradiction, this study introduces micro-lectures as a core vehicle, systematically constructing and implementing a digitally assisted model aimed at optimizing the teaching process. Practice has shown that by embedding standardized, reusable micro-lecture resources proactively into various teaching stages, this model effectively alleviates the burden of repetitive demonstrations on teachers and significantly reduces student idle time in the classroom. Consequently, it creates the necessary conditions for personalized teacher guidance and refined student training. Post-reform, students' skill acquisition and project completion rates have substantially improved. This study confirms that digital resources, represented by micro-lectures, play a fundamental role in restructuring teaching processes and unleashing instructional effectiveness. The practical pathway of "problem diagnosis—resource intervention—process optimization—efficacy release" established herein provides a valuable preliminary paradigm for the digital reform of similar courses.
Copyright (c) 2025 Zhen Gao, Qi Hao http://creativecommons.org/licenses/by/4.0
Intelligent Monitoring of Individual Nest-building Behavior in Group-raised Geese
Breeding geese often exhibit frequent nest-building behaviors under group rearing conditions, which significantly reduces egg-laying efficiency. However, it remains challenging to accurately identify the occurrence and duration of individual nest-building in such environments. To address this, this study integrates wearable activity monitoring sensors with RFID identification technology to develop a nest-building behavior characterization system based on activity rhythms and posture features. Building on this foundation, we propose an Integrated Feature Engineering-Driven Stacking Recognition Model (IFESM-GND) that enhances nest-building behavior recognition accuracy by leveraging interpretable feature engineering. Experimental results demonstrate a 93.5% detection accuracy, representing a 15%-21% improvement over traditional methods. The application value of nest-building behavior monitoring in early egg-laying anomaly detection and breeding management decision-making is validated. This precise monitoring method for individual nest-building behaviors in breeding geese provides crucial technical support for optimizing reproductive performance, improving egg-laying efficiency, and achieving precision management. The research holds significant theoretical and practical implications for advancing the intelligent and modernization of China's goose industry.
Copyright (c) 2025 Tanyu Lin, Siting Lü, Kaiwen Huang, Zhoucai Ou, Yuanyang Mao http://creativecommons.org/licenses/by/4.0
Optimization of Structural Parameters for UAV Spray Nozzles Using Response Surface and Genetic Algorithms
A fan-shaped nozzle for unmanned aerial vehicle spraying was designed to investigate the influence of three key parameters—nozzle outlet diameter, relative cutting depth, and groove angle—on outlet velocity and pressure drop. Using a central composite design (CCD) with 20 sample points, numerical simulation methods were employed to solve for outlet velocity and pressure drop. A response surface model linking outlet diameter, relative cutting depth, and groove angle to outlet velocity and pressure drop was constructed using Kriging. A multi-objective genetic algorithm was applied to optimize structural parameters, targeting maximum outlet velocity and minimum pressure drop. The optimal structural parameters were determined as: outlet diameter 3.0 mm, relative cutting depth 1.2 mm, and groove angle 120.0°. Compared to the original prototype, this configuration achieved a 15.7% increase in outlet velocity and a 7.8% reduction in pressure drop.
Copyright (c) 2025 Yujie Li, Qingzheng Liu, Chaojie Zhai http://creativecommons.org/licenses/by/4.0
An IGA-BP–Based Estimation Method for Water-Film Pressure of Marine Water-Lubricated Bearings
Water-film pressure is a key parameter characterizing the load-carrying capacity, frictional performance, and operational stability of water-lubricated bearings; therefore, real-time and accurate monitoring is crucial to the safety of ship propulsion systems. Existing analytical, CFD, and experimental approaches are limited by operating-condition adaptability, computational burden, and cost, while conventional data-driven methods are prone to initialization sensitivity and entrapment in local optima, resulting in inadequate timeliness and accuracy. To address these issues, this study applies a back-propagation (BP) neural network to water-film pressure estimation and introduces an improved genetic algorithm (IGA) with a sine-decay mechanism to optimize the BP network’s weights and biases, thereby constructing an IGA-BP model. Using rotational speed, load, and friction coefficient(COF) as inputs, the model estimates the maximum water-film pressure and its corresponding journal circular angle. Comparative evaluations on multi-condition experimental datasets show that IGA-BP attains an R² of 0.976; relative to BP, the root-mean-square error (RMSE) and mean absolute error (MAE) are reduced by about 53% and 79%, respectively. Compared with GA-BP, RMSE and MAE are further reduced by approximately 34% and 52%, and the model exhibits faster convergence and greater stability. Moreover, against several optimized BP variants, IGA-BP achieves the best overall metrics, offering a useful reference for research on water-lubricated bearings.
Copyright (c) 2025 Donghui Li, Nan Wang, Jia Wang, Huabing Jing, Deyang Zhao http://creativecommons.org/licenses/by/4.0