Applied Science and Innovative Research
type: channel
description: 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 We would welcome scholars and researchers engaging in the related field to submit your manuscripts which are complete unpublished and original works and not under review in any other journals to Applied Science and Innovative Research.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). <img src="[binary]" alt="" />
publisher: SCHOLINK INC.
language: en-US
publication name: Applied Science and Innovative Research
issn: 2474-4972
copyright: 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.
items: Intelligent Optimization Method of Shield Tunneling Parameters Based on Machine Learning
items: Transition from Reactive to Proactive Maintenance: A Data-Driven Approach for Expressway Asset Management
items: Research on Book Borrowing Prediction Model Based on Transformer
items: Adsorption of Methylene Blue and Congo Red by Silica Nanowires
items: AI Analysis of Sun Tracking Efficiency
items: Optimal Design of Fire Water Supply System for Automated Storage/Retrieval System of Automotive Rubber Product
items: Research on the Practice of Digital Low-carbon Management of Construction Projects from the Perspective of Globalization

Intelligent Optimization Method of Shield Tunneling Parameters Based on Machine Learning
type: item
description: Aiming at the problems that the parameter setting in the process of shield tunneling depends on artificial experience and is difficult to adapt to complex and changeable geological conditions, this paper studies the intelligent optimization of shield tunneling parameters based on machine learning. Combined with the nonlinear and multi-factor coupling characteristics of shield construction, three machine learning algorithms, random forest (RF), support vector machine (SVM) and BP neural network, are selected to construct a parameter optimization model with geological parameters and tunneling parameters as input and tunneling speed as output. The mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²) were selected as evaluation indexes to evaluate the performance of the model. The results show that all three models can predict the tunneling speed, but the performance difference is significant. Among them, the random forest model performs best in nonlinear mapping and anti-interference ability through ensemble learning and feature random selection. Its R² reaches 0.967, and the prediction accuracy is significantly better than that of BP neural network and support vector machine model. This study provides an effective method support for intelligent prediction and dynamic optimization of shield tunneling parameters, and has practical reference value for improving the intelligent level of shield construction.
creator: Li Liu
creator: Xiaotao Du
creator: Qianqian Cheng
creator: Xiaoyan Ma
rights: Copyright (c) 2026 Li Liu, Xiaotao Du, Qianqian Cheng, Xiaoyan Ma http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-04-07
publication date: 2026-04-07
volume: 10
number: 2
starting page: 1
doi: 10.22158/asir.v10n2p1

Transition from Reactive to Proactive Maintenance: A Data-Driven Approach for Expressway Asset Management
type: item
description: China's highways have fully entered a new stage of maintenance management. The traditional passive maintenance mode, which focuses on post repair and experience decision-making, has problems such as high cost, slow response, and rapid road condition degradation, making it difficult to meet the long-term operational needs of the road network. Promoting the transformation of highway asset management from passive maintenance to active maintenance has become an inevitable trend for the high-quality development of the industry. This article refers to the standardized academic paper architecture, systematically elaborates on the core differences between passive maintenance and active maintenance, constructs a data-driven framework with full lifecycle data as the core, multi-source perception as the support, and intelligent decision-making as the engine, studies the key implementation paths of data collection, fusion analysis, dynamic prediction, active decision-making, and closed-loop control, proposes a technology system and guarantee measures for transformation and implementation, aiming to provide theoretical and practical support for improving the quality and efficiency of highway asset management, extending facility life, and reducing full cycle costs.
creator: Jian Li
creator: Jifen Wei
creator: Min Ji
rights: Copyright (c) 2026 Jian Li, Jifen Wei, Min Ji http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13
volume: 10
number: 2
starting page: 8
doi: 10.22158/asir.v10n2p8

Research on Book Borrowing Prediction Model Based on Transformer
type: item
description: With the increasing demand for intelligent library services, accurately predicting user borrowing behavior has become a key issue for optimizing resource allocation and improving service quality. Traditional prediction methods have significant shortcomings in long sequence modeling, feature interaction, and cold start scenarios. This paper proposes a book borrowing prediction model based on the Transformer architecture. It dynamically captures the long-term dependency of user behavior through the self-attention mechanism and combines book metadata and temporal context features to achieve multi-dimensional information fusion. Experiments show that it significantly outperforms the baseline model. The study further explores the application potential of the model in dynamic inventory management, personalized recommendation, and other scenarios, providing theoretical support and technical solutions for the intelligent transformation of libraries.
creator: Baiyu Chen
rights: Copyright (c) 2026 Baiyu Chen http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-05-25
publication date: 2026-05-25
volume: 10
number: 2
starting page: 15
doi: 10.22158/asir.v10n2p15

Adsorption of Methylene Blue and Congo Red by Silica Nanowires
type: item
description: Silica nanowires (SiO2 NWs) were prepared via a PVP‑Na3Cit‑TMOS system and used as adsorbents for the removal of methylene blue (MB) and Congo red (CR) from aqueous solutions. The adsorption of MB reached equilibrium within 20 min at 25 °C and pH 9.0, with a capacity of 155.0 mg/g, while CR achieved equilibrium within 45 min at 25 °C and pH 2.0, with a capacity of 60.8 mg/g. The adsorption processes followed the pseudo‑second‑order kinetic model and the Langmuir isotherm model, indicating monolayer physical adsorption. Thermodynamic parameters (ΔG < 0, ΔH < 0) confirmed spontaneous exothermic adsorption. pH played a critical role: alkaline conditions favored MB adsorption due to enhanced electrostatic attraction, whereas strongly acidic conditions promoted CR adsorption. The SiO2 nanowires exhibited significantly higher affinity for the cationic dye MB, demonstrating promising potential for the treatment of cationic dye‑contaminated wastewater.
creator: Qing Li
rights: Copyright (c) 2026 Qing Li http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-05-26
publication date: 2026-05-26
volume: 10
number: 2
starting page: 21
doi: 10.22158/asir.v10n2p21

AI Analysis of Sun Tracking Efficiency
type: item
description: For two years in a row, 2025 and 2026, the electrical energy generated by PV constitutes about 10-12% of all electricity produced across the world [1]. Significant amount of solar energy was produced by the PV systems with fixed tilt. Only 40-45% of the PV energy was generated by sun tracking systems [2]. The amount of energy produced (in kWh/m2/day) is defined mostly by efficiency of solar panels. The second important factor of PV energy collection is the productivity of sun tracking, i.e. the control by algorithms of sun tracking. In the cost of energy units produced by solar panels there are some other economic factors to be considered, such as basic cost of tracking equipment, cost of maintenance and cleaning of panel surfaces. The initial low cost and maintenance of single-axis tracking systems (SATS), smaller basic size made this machinery dominant in sun tracking [3]. The methods of control of SATS and algorithms of usage at different weather conditions define real efficiency of tracking systems. Recently we developed instant measurements of sun irradiation using three optical sensors installed at sun tracker which cover entire sun spectrum [4]. As the scanner expected to make step from position “A” to position “B”, the control algorithm compares the sun intensity in both positions. The step is allowed if in the position “B” solar panels would gain energy. Various weather conditions are programmed in the algorithm what allows flexible tracking control [5]. In the current brief study, we subjected our novel control of sun tracking to be compared with other scanning systems by AI. Conclusions of AI analysis are presented.
creator: Sam Mil’shtein
rights: Copyright (c) 2026 Sam Mil’shtein http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-06-05
publication date: 2026-06-05
volume: 10
number: 2
starting page: 33
doi: 10.22158/asir.v10n2p33

Optimal Design of Fire Water Supply System for Automated Storage/Retrieval System of Automotive Rubber Product
type: item
description: Automated storage/retrieval systems for automotive rubber products usually have large racks and high storage density. Rubber products have dangerous characteristics such as high combustion calorific value, large amounts of smoke, and rapid fire spread. Once a fire occurs, it will cause serious casualties and property losses. At present, domestic fire protection design specifications for automated storage/retrieval systems of automotive rubber products are not yet complete, and systematic technical guidance is insufficient. In actual projects, there are common problems such as difficult system selection, complex pipeline laying, and difficult hydraulic calculations. Focusing on the above issues, this article focuses on discussing the main technical bottlenecks and their causes in the design of fire water supply systems, and proposes corresponding optimization strategies and solutions, in order to provide a reference for the formulation of fire protection plans, construction drawing design and engineering practice for similar projects in the future.
creator: Qing Yan
creator: Donghui Li
creator: Kefu Wang
creator: Daping Jiang
rights: Copyright (c) 2026 Qing Yan, Donghui Li, Kefu Wang, Daping Jiang http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-06-09
publication date: 2026-06-09
volume: 10
number: 2
starting page: 39
doi: 10.22158/asir.v10n2p39

Research on the Practice of Digital Low-carbon Management of Construction Projects from the Perspective of Globalization
type: item
description: Traditional construction projects have high energy consumption and low efficiency of low-carbon management mode. Under the background of global carbon neutrality development trend and digital transformation of construction industry, low-carbon management of construction projects in the whole life cycle has become the trend of industry development. From the whole life cycle of construction project decision-making stage, design stage, construction stage to operation and maintenance demolition stage, this paper explores the application path of BIM, big data, Internet of things, digital twin and other digital technologies in the low-carbon management of the whole life cycle of construction projects, so as to reduce costs and promote sustainable development. By analyzing the practice cases of digital low-carbon management of typical construction projects at home and abroad, this paper compares the differences of construction project management modes in different countries and regions, summarizes the implementation mode, core advantages and existing problems of digital low-carbon management, combs the specific low-carbon management mode from the whole life cycle of the project, and puts forward optimization countermeasures for the existing technology, system and talent challenges of the industry development.
creator: Juping Ge
creator: Qixiang Miao
creator: Xiaozhen Wang
creator: Yao Cai
creator: Keqiang Han
creator: Mengmeng Cao
rights: Copyright (c) 2026 Juping Ge, Qixiang Miao, Xiaozhen Wang, Yao Cai, Keqiang Han, Mengmeng Cao http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2026-06-16
publication date: 2026-06-16
volume: 10
number: 2
starting page: 50
doi: 10.22158/asir.v10n2p50