Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.

The journal "Applied Sciences (Switzerland)" (ISSN 2076-3417) provides an advanced forum for all aspects of applied natural sciences. It publishes reviews, research papers, and communications, aiming to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There are no restrictions on the length of the papers, and full experimental details must be provided to ensure reproducibility. If the complete details of calculations or experimental procedures cannot be published in the usual manner, electronic files and software can be deposited as supplementary electronic material.

《Applied Sciences (Switzerland)》期刊（ISSN 2076-3417）为应用自然科学的各个方面提供了一个先进的论坛。期刊发表综述、研究论文和通讯，旨在鼓励科学家尽可能详细地发布他们的实验和理论结果。对论文的长度没有限制，必须提供完整的实验细节，以便他人能够重复实验。如果无法以正常方式发布的计算或实验程序的完整细节，电子文件和软件可以作为补充电子材料存放。

Applied Sciences (Switzerland)

To improve the performance of the tension control system in gravure printed electronic equipment, it is necessary to study the tension system model of gravure-printing electronic equipment. This paper focuses on the coupling characteristics of multi-span tension systems. Firstly, based on the single-span tension model, the mathematical model of the printing tension subsystem is established and simplified into a general multi-span tension coupling model. Then, using the relationship between the inputs and outputs of the system, the coupling model is simulated using MATLAB/Simulink and verified through experiments on the gravure printed electronic platform. Finally, the coupling relationship and influence laws among multi-physical quantities of the system are analyzed. The research results show that changes in the input web tension of the multi-span system will affect the steady-state tension values of all subsequent spans. Moreover, the speed change in a roller in the multi-span system will not only affect the steady-state tension value of its own span, but also cause transient tension fluctuations in all subsequent spans. The findings of this paper provide an important theoretical basis for the research of the tension system in gravure printed electronic equipment, contributing to the enhancement of printed electronic product quality.

Kui He

Pengbo He

Shifa Li

Jian Li

Xingmei Wei

Applied Sciences

Study on Multi-Span Tension Coupling Relationship of Gravure Printed Electronic Equipment

In the world, brain tumor (BT) is considered the major cause of death related to cancer, which requires early and accurate detection for patient survival. In the early detection of BT, computer-aided diagnosis (CAD) plays a significant role, the medical experts receive a second opinion through CAD during image examination. Several researchers proposed different methods based on traditional machine learning (TML) and deep learning (DL). The TML requires hand-crafted features engineering, which is a time-consuming process to select an optimal features extractor and requires domain experts to have enough knowledge of optimal features selection. The DL methods outperform the TML due to the end-to-end automatic, high-level, and robust feature extraction mechanism. In BT classification, the deep learning methods have a great potential to capture local features by convolution operation, but the ability of global features extraction to keep Long-range dependencies is relatively weak. A self-attention mechanism in Vision Transformer (ViT) has the ability to model long-range dependencies which is very important for precise BT classification. Therefore, we employ a hybrid transformer-enhanced convolutional neural network (TECNN)-based model for BT classification, where the CNN is used for local feature extraction and the transformer employs an attention mechanism to extract global features. Experiments are performed on two public datasets that are BraTS 2018 and Figshare. The experimental results of our model using BraTS 2018 and Figshare datasets achieves an average accuracy of 96.75% and 99.10%, respectively. In the experiments, the proposed model outperforms several state-of-the-art methods using BraTS 2018 and Figshare datasets by achieving 3.06% and 1.06% accuracy, respectively.

Mohammed Aloraini

Asma Khan

Suliman Aladhadh

Shabana Habib

Mohammed F. Alsharekh

Muhammad Islam

Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images

The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.

Samreen Naeem

Aqib Ali

Salman Qadri

Wali Khan Mashwani

Nasser Tairan

Habib Shah

Muhammad Fayaz

Farrukh Jamal

Christophe Chesneau

Sania Anam

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

In order to investigate the damage to microstructure and some other micromechanical responses during a fatigue test on asphalt mixture, Particle Flow Code (PFC) was used to reconstruct a two-dimensional discrete element model of asphalt mixture, based on computed tomography (CT) images and image-processing techniques. The indirect tensile fatigue test of asphalt mixture was simulated with this image-based microstructural model, and verified in the laboratory. It was found that there were four stages during the fatigue failure: no crack, crack initiation, crack developing, and interconnected crack. Cracks mainly developed between the aggregate and asphalt mortar, near the loading axis. The corresponding stages of failure, the developing trend and the distribution characteristics of the cracks matched well with those in the laboratory test. Furthermore, the trends of both the time-load curve and time-displacement curve from the simulation test were also consistent with those from the experimental test. In short, the distribution characteristics of cracks and internal forces of asphalt mixture show that it is feasible to simulate the fatigue performance of the asphalt mixture by a discrete element method (DEM).

Xuelian Li

Xinchao Lv

Xueying Liu

Junhong Ye

Discrete Element Analysis of Indirect Tensile Fatigue Test of Asphalt Mixture

Aspect-level sentiment analysis is a task of identifying and understanding the sentiment polarity of specific aspects of a sentence. In recent years, significant progress has been made in aspect-level sentiment analysis models based on graph convolutional neural networks. However, existing models still have some shortcomings, such as aspect-level sentiment analysis models based on graph convolutional networks not making full use of the information of specific aspects in a sentence and ignoring the enhancement of the model by external general knowledge of sentiment. In order to solve these problems, this paper proposes a sentiment analysis model based on the Syntax-Aware and Graph Convolutional Network (SAGCN). The model first integrates aspect-specific features into contextual information, and second incorporates external sentiment knowledge to enhance the model’s ability to perceive sentiment information. Finally, a multi-head self-attention mechanism and Point-wise Convolutional Transformer (PCT) are applied to capture the semantic information of the sentence. The semantic and syntactic information of the sentences are considered together. Experimental results on three benchmark datasets show that the SAGCN model is able to achieve superior performance compared to the benchmark methods.

Qun Gu

Zhidong Wang

Hai Zhang

Siyi Sui

Rui Wang

Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks

基于语法感知和图卷积网络的方面级情感分析

This study explores the transformative impact of substituting cement with raw marble powder (RMP) and calcined marble powder (CMP) at varying levels (0%, 5%, 10%, 15%, 20%, and 25%) on the physical and mechanical properties of cement-based composites. Additionally, the influence of two different mixing waters—tap water (TW) and magnetized water (MW)—was assessed to determine their combined effects on the composite performance. The evaluation encompassed fresh properties (initial and final setting times, and consistency) and hardened properties (flexural strength (ffs), compressive strength (fcs), ultrasonic pulse velocity (UPV), water absorption, porosity, and unit weight) of the composites. The results reveal that CMP-substituted composites significantly outperformed RMP-based counterparts across all indices. Notably, CMP-substituted mortars produced with TW showed a 10.8% to 15.8% increase in 28-day fcs values compared to RMP-substituted mortars, while those prepared with MW exhibited 7.8% to 10.9% higher fcs values than TW-prepared samples. A microstructural analysis via SEM indicated that CMP enhances hydration and microstructure densification, resulting in improved mechanical performance and durability. Overall, the combination of CMP and MW demonstrated a superior potential for producing eco-friendly, high-performance cementitious composites, supporting sustainable construction practices through significant material savings and environmental benefits.

Erdinc Halis Alakara

Ozer Sevim

Gazi Günel

İlhami Demir

Effect of Calcined Marble Powder and Magnetized Water on the Performance of Cement-Based Composites

煅烧大理石粉和磁化水对水泥基复合材料性能的影响

Achieving precise target localization for UAVs is a complex problem that is often discussed. In order to achieve precise spatial localization of targets by UAVs and to solve the problems of premature convergence and easy to fall into local optimum in the original dung beetle algorithm, an error handling method based on the coordinate transformation of an airborne measurement system and the dung beetle optimization with crisscross and 3 Sigma Rule optimization (CCDBO) is proposed. Firstly, the total standard deviation is calculated by integrating the carrier position, the attitude angle, the pod azimuth, the pitch angle, and the given alignment error of the pod’s orientation. Subsequently, the Taylor series expansion method is adopted to linearize the approximated coordinate transformation process and simplify the error propagation model. Finally, in order to further improve the positioning accuracy, a target position correction strategy with the improved dung beetle optimization algorithm is introduced. The simulation and flight experiment results show that this method can significantly reduce the target positioning error of UAVs and improve the positioning accuracy by 20.42% on average compared with that of the original dung beetle algorithm, which provides strong support for the high-precision target observation and identification of UAVs in complex environments.

Yinglei Li

Qingping Hu

Shiyan Sun

Yuxiang Zhou

Wenjian Ying

Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform

基于无人机平台的目标定位误差优化研究

The critical charge of sensitive nodes decreases as transistors scale down with the advancement of CMOS technology, making SRAM cells more susceptible to soft errors in the space industry. When a radiation particle strikes a sensitive node of a conventional 6T SRAM cell, a single event upset (SEU) can occur, flipping in the stored data in the cell. Additionally, charge sharing between transistors can cause single-event multi-node upsets (SEMNUs), where data in multiple nodes are flipped simultaneously due to a single particle strike. Therefore, this paper proposes a radiation-hardened high stability 16T (RHHS16T) cell for space applications. The characteristics of RHHS16T are evaluated and compared with previously proposed radiation-hardened SRAM cells such as QUCCE12T, WEQUATRO, RHBD10T, RHD12T, RSP14T, RHPD14T, and RHBD14T. Simulation results for RHHS16T indicated that the proposed cell demonstrates improved performance in read stability, write access time, and write stability compared to all comparison cells. These improvements in the proposed cell are achieved with higher power consumption and a minor area penalty. Notably, isolating the storage node from the bit line during read operations and the feedback loop between nodes during write operations enables the proposed RHHS16T to achieve enhanced read stability and write stability, respectively. The proposed integrated circuit was implemented using a 90 nm CMOS process and operates at a supply voltage of 1V. Furthermore, RHHS16T provides high immunity against SEUs and SEMNUs. Through its enhanced read and write stability, it ensures reliable data retention for space applications.

Jong-Yeob Oh

Sung-Hun Jo

Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications

用于空间应用的改进读写稳定性的抗辐射16T SRAM单元

This study examines the hydrogeological response to the 12 September 2016 Gyeong-Ju earthquake (ML 5.8) in the southeastern region of the Korean Peninsula. Using 2D hydro-mechanical coupled bonded particle modeling, we simulated the dynamic fault rupture process to analyze stress redistribution and its impact on pore pressure and groundwater levels (GWLs). The results indicated that compressional areas correlated strongly with pore pressure increases and GWL rises, while extensional areas showed decreases in both. Observations from the groundwater monitoring Well 5 at Gyeong-Ju San-Nae and Well 8 at Gyeong-Ju Cheon-Buk, located approximately 15 km from the earthquake’s epicenter, aligned well with the model’s predictions and interpretation, providing validation for the simulation. These findings highlight the capability of hydro-mechanical models to capture fault-induced hydrological responses and offer valuable insights into the interplay between seismic activity and groundwater systems.

Hyunjin Cho

Se-Yeong Hamm

Jeoung Seok Yoon

Soo-Gin Kim

Jae-Yeol Cheong

Simulation of Groundwater-Level Behavior in Southeast Region of Korea Induced by 2016 Gyeong-Ju Earthquake Using 2D Hydro-Mechanical Coupled Bonded Particle Modeling

使用二维水力-力学耦合键合颗粒模型模拟2016年庆州地震引发的韩国东南地区地下水位变化行为

This study presents a novel methodology for the seismic vulnerability assessment of ground-supported liquid storage tanks, with a particular emphasis on tanks exhibiting deformed shells. The framework is built around the development of a simplified two-degree-of-freedom (2DoF) system, specifically designed to enable efficient probabilistic analysis through incremental dynamic analysis. The 2DoF model is calibrated using detailed nonlinear finite element simulations, which account for fluid–structure interaction and complex geometric effects. This approach allows for a significant reduction in computational effort while maintaining a high level of accuracy in predicting seismic fragility. A key feature of the proposed methodology is the explicit inclusion of deformed tank geometries, which are often encountered in real cases and can critically impact seismic performance. The study identifies and quantifies the effects of these deformations on various damage states, such as local buckling, overturning, and liquid overspill. Fragility curves are developed to characterize the probability of exceeding these damage states under different levels of seismic intensity. The proposed methodology is validated through a representative case study, demonstrating its ability to capture the key features of tank behavior and provide robust fragility estimates. The results highlight the potential of the 2DoF system to serve as a practical tool for seismic risk assessment, enabling engineers and researchers to evaluate tank vulnerabilities more efficiently.

Raihan Rahmat Rabi

Giorgio Monti

Seismic Vulnerability Assessment of Existing Ground-Supported Liquid Storage Tanks with Deformed Shells

现有地面支撑液态储罐带变形壳体地震易损性评估

Abbreviation	Appl. Sci.
Journal Impact	2.60
Quartiles(Global)	PHYSICS, APPLIED(Q2)

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: Semi-monthly，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2011，Proportion of original research papers: 94.96%，Self Citation Rate:12.00%， Gold OA Rate: 99.50%
Journal Fees	$Details
Average review cycle	期刊官网数据：平均first decision to authors: 17.4 days; acceptance to publication: 2.6 days网友分享经验：11 Weeks

Applied Sciences (Switzerland)

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