bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Climate Risk in Panama’s Urban Areas
Authors: Ruíz Michelle A. , Mack-Vergara Yazmin L.
Published: July 2024

To mitigate the effects of climate change and increase the resilience of cities, climate risks in urban areas are crucial issues to be addressed. This study analyzes the risks, vulnerability, capacity, degree of exposure, and characteristics of the threats to Panama’s urban areas that result from climate change. Data from DesInventar—a conceptual and methodological tool developed for the construction of databases regarding losses, damages, or effects caused by emergencies or disasters—were analyzed. The main current impacts are floods, landslides, and extreme winds in that order. From 1933 to 2019, Panama recorded 1903 flood reports, 625 landslide reports, and numerous extreme wind events. The affected population totaled 527,394 people, with 101,738 homes impacted. The most affected provinces are Panama, Panama Oeste, and Chiriquí, based on the number of reports. It is expected that in the future, the current effects will increase, and the country’s energy and water security will be put at risk. Strategies to address climate change include enhancing early warning systems and investing in climate-resilient infrastructure. Key measures involve developing public policies for renewable energy and sustainable transportation, preserving ecosystems, and financial mechanisms to support a transition to a sustainable economy.
Heat Transfer Fluids Based on Amino-Functionalized Silica Dispersed in 1,2-Propylene Glycol and in 50-50 Aqueous 1,2-Propylene Glycol
Authors: Kalbarczyk Marta , Skupiński Sebastian , Kosmulski Marek
Published: July 2024

1,2-propylene glycol and its 50-50 w/w mixture with water were used to prepare heat transfer fluids based on amino-functionalized silica. On top of pH-neutral dispersions (no reagents added except for the solvent and the particles), dispersions acidified with acetic acid and with HCl were used to enhance the positive electric charge of silica particles. The colloidal particles had a positive zeta potential >40 mV and showed apparent particle radii of 70 nm, and these properties remained unchanged on heating up to 80 °C for up to 28 days.
Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects
Authors: Balabanov Pavel , Egorov Andrey , Divin Alexander , Ponomarev Sergey , Yudaev Victor , Baranov Sergey , Abu Zetoonh Huthefa
Published: July 2024

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min.
Genetic Diversity of the Common Black Carp Strain (Cyprinus carpio var. baisenensis)
Authors: Sumana Sahr Lamin , Liao Yu , Zhang Chengfeng , Jing Xiaojun , Zhu Jian , Tang Yongkai , Liu Wenting , Su Shengyan
Published: July 2024

The Common Black Carp Strain (Cyprinus carpio var. baisenensis), known for its black skin, is commonly cultured in the integrated rice-agriculture (IRA) system in Guangxi province, China. This study aimed to compare the genetic diversity of three common carp strains/populations (Common Black Carp Strain, Huanghe, and Songpu) using resequencing data. The genome-based method reveals a significant difference (p < 0.05) in identified loci and SNP frequency (p < 1 × 10−6) between the Songpu (Sp) or mirror carp and Huanghe (Hh) new strain. Additionally, the Common Black Carp Strain (Bk) exhibits a higher number of Tajima’s D values, possibly due to its population size and mutations within its entire genome. The average value of population nucleotide diversity (π) for the Bk is 1.706 × 10−4 while the mean number for the Hh and Sp strains is 1.691 × 10−4 Heterozygosity analysis results indicate that the Bk has the highest F coefficient compared to the Sp and Hh hybrids. This suggests that the isolated population of the Bk may have experienced a decrease in population size as a result of environmental disturbances in the IRA system. PCA results further reveal that all individuals of the Bk, except for one, are clustered together, while individuals of the Hh form a separate group. On the other hand, Sp displays a distinct distribution pattern. The comparative study of the genetic diversity of the Bk provides baseline data on its genome makeup. Assessing genetic diversity and genetic structure is critical for fisheries management and the conservation of critically endangered fish species.
Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans
Authors: Gupta Siddharth , Dubey Arun K. , Singh Rajesh , Kalra Mannudeep K. , Abraham Ajith , Kumari Vandana , Laird John R. , Al-Maini Mustafa , Gupta Neha , Singh Inder , Viskovic Klaudija , Saba Luca , Suri Jasjit S.
Published: July 2024

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.
Positron Emission Tomography/Computed Tomography in Polymyalgia Rheumatica: When and for What—A Critical Review
Authors: Heras-Recuero Elena , Blázquez-Sánchez Teresa , Landaeta-Kancev Laura Cristina , Martínez de Bourio-Allona Marta , Torres-Roselló Arantxa , Rengifo-García Fernando , Caraballo-Salazar Claritza , Largo Raquel , Castañeda Santos , González-Gay Miguel Ángel
Published: July 2024

Polymyalgia rheumatica (PMR) is an inflammatory disease common in people aged 50 years and older. This condition is characterized by the presence of pain and stiffness involving mainly the shoulder and pelvic girdle. Besides the frequent association with giant cell arteritis (GCA), several conditions may mimic PMR or present with PMR features. Since the diagnosis is basically clinical, an adequate diagnosis of this condition is usually required. Positron emission tomography/computed tomography (PET-CT) has proved to be a useful tool for the diagnosis of PMR. The use of 18F-FDG-PET imaging appears promising as it provides detailed information on inflammatory activity that may not be evident with traditional methods. However, since PET-CT is not strictly necessary for the diagnosis of PMR, clinicians should consider several situations in which this imaging technique can be used in patients with suspected PMR.
The Assessment of Retinal Image Quality Using a Non-Mydriatic Fundus Camera in a Teleophthalmologic Platform
Authors: Chan Tsung-Yueh , Wang Jen-Hung , Chen Nancy , Chiu Cheng-Jen
Published: July 2024

This study assesses the quality of retinal images captured using a non-mydriatic fundus camera within a teleophthalmologic platform in Taiwan. The objective was to evaluate the effectiveness of non-mydriatic fundus cameras for remote retinal screening and identify factors impacting image quality. From June 2020 to August 2022, 629 patients from five rural infirmaries underwent ophthalmic examinations, with fundus images captured without pupil dilation. These images were reviewed by senior ophthalmologists and graded based on quality. The results indicated that approximately 70% of images were of satisfactory diagnostic quality. Risk factors for poor image quality included older age, the presence of cataracts, pseudophakia, and diabetes mellitus. This study demonstrates the feasibility of using non-mydriatic fundus cameras for teleophthalmology, highlighting the importance of identifying and addressing factors that affect image quality to enhance diagnostic accuracy in remote settings.
Distribution of Microaneurysms and Hemorrhages in Accordance with the Grading of Diabetic Retinopathy in Type Diabetes Patients
Authors: Romero-Aroca Pedro , Garcia-Curto Eugeni , Pascual-Fontanilles Jordi , Valls Aida , Moreno Antonio , Baget-Bernaldiz Marc
Published: July 2024

(1) Underlying Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. There are automatic image reading systems that can aid screening for DR. (2) Methods: Using our automatic reading system we have counted the number of microaneurysms and hemorrhages in the four quadrants of the ETDRS grid and evaluated the differences between them according to the type of DR. The study was carried out using data from two different databases, MESSIDOR and MIRADATASET. (3) Results: The majority of microaneurysms and hemorrhages are found in the temporal and inferior quadrants of the ETDRS grid. Differences are significant with respect to the other two quadrants at p < 0.001. Differences between the type of DR show that severe-DR has a greater number of microaneurysms and hemorrhages in the temporal and inferior quadrant, being significant at p < 0.001. (4) Conclusions: The count of microaneurysms and hemorrhages is higher in the temporal and inferior quadrants in all types of DR, and those differences are more important in the case of severe-DR.
Problems and Prospects for the Development of Cluster Structuring in the Economy of Kazakhstan’s Agricultural Sector: Theory and Practice
Authors: Tkacheva Anastassiya , Saginova Saniya , Karimbergenova Madina , Taipov Timur , Saparova Gulnar
Published: July 2024

This article discusses the issues of cluster policy formation in the Republic of Kazakhstan on the basis of studying the experience of leading countries. The research aim is to find new effective tools and institutions for the development of the cluster structuring of the agro-industrial complex economy of Kazakhstan. Cluster policy in the field of supporting regional clusters starts with the identification of already existing clusters in the region, because by viewing the regional economy through the prism of various local industries and innovative enterprises, regional authorities can identify measures of effective impact and support for their clusters. This research examines the possibilities of using clusters and cluster policy as one of the most important components of the policy for the development and support of small and medium-sized enterprises in Kazakhstan’s agro-industrial complex. The research methodology includes qualitative and quantitative data analysis methods, comparative analysis, and mathematical processing (the Pearson correlation coefficient), as well as the modeling of possible development scenarios. The obtained results show that there are significant opportunities for a wider involvement of small and medium-sized enterprises in the formation of cluster structures of the agro-industrial sector through joint efforts by the government and regional centers in the conditions of innovative development of the country’s economy.
Resident Evaluation of Reconstruction Challenges and Lessons Learned from the Great East Japan Earthquake: Recommendations for Reconstruction and Industrial Policies 12 Years after the Disaster
Authors: Nakamura Tetsuya , Lloyd Steven , Masuda Satoru
Published: July 2024

The year 2023 marks the 12th anniversary of the Great East Japan Earthquake (GEJE). Immediately after the disaster, the number of evacuees reached approximately 470,000, but by November 2022, the number had decreased to approximately 31,000. The reconstruction of housing, disposal of debris, public infrastructure development, and overall restoration and reconstruction has progressed steadily. However, a re-examination of the status of industrial restoration and reconstruction reveals that restoration and reconstruction have not progressed in some areas. This research statistically analyzes how the Japanese public perceives the issues around the recovery process and what memories and records they would like to learn from regarding the GEJE. The purpose of this study is to ask about reconstruction issues and lessons learned from the GEJE by conducting a web-based survey with 2000 respondents in Japan. The method of estimation is the use of ordinal logistic regression analysis to statistically estimate whether there are differences in recovery issues and lessons learned depending on individual attributes. The results suggest that those who are interested in, remember, and express anxiety about the recovery issues and lessons learned tend to be men, do not have children, are highly educated, and have a higher income. In sum, many of Japan’s citizens are highly interested in the reconstruction of agriculture, forestry, fisheries, housing, urban development, living environment, industry, and livelihood in the affected areas. In the future, they will play a central role in modernizing, scaling up, and integrating the agriculture, forestry, and fisheries industries, as well as in rebuilding towns and livelihoods. In the affected areas, it will be necessary to draw on the lessons learned from the GEJE and create reconstruction plans for the future, and then, policymakers will need to formulate reconstruction policies that reflect the concerns of the Japanese people.
Learn with M.E.—Let Us Boost Personalized Learning in K-12 Math Education!
Authors: Annuš Norbert , Kmeť Tibor
Published: July 2024

The traditional educational system, in certain aspects, limits personalized learning. This is mainly evident in the fact that average students, who do not have any learning difficulties, are required to solve the same tasks from the same textbook in the same order. Artificial intelligence and other smart learning tools present great opportunities for implementing a personalized learning system. Our previous surveys and literature reviews also show that educators see the greatest potential in personalized education for the assimilation of artificial intelligence into education. In this context, we have developed educational software called “Learn with M.E. as Math Educator”, which facilitates more personalized teaching of basic mathematical operations. This study presents the structure and operation of this application. We tested the usability of the software in several institutions. Our research target group consists of elementary school students, specifically those aged 11–15. This article provides a detailed overview of the accuracy and educational outcomes of the completed application. We evaluated the application and its effectiveness using both qualitative and quantitative methods. Our research design combined elements of educational technology development and effectiveness assessment. To evaluate student performance, we employed a control group methodology. Data were analyzed by comparing test results between students using the software and those receiving traditional instruction. We examined user satisfaction through survey questionnaires. Teachers’ opinions were gathered through structured interviews, and their responses were categorized using a SWOT analysis. The findings indicated that the use of the software significantly improved students’ mathematics performance compared to the control group. Students provided positive feedback on the software’s user interface, describing it as user-friendly and motivating. Teachers regarded the software as an effective educational tool, facilitating differentiated instruction and enhancing student engagement. The results suggest that digital educational tools, such as the developed software, can provide substantial added value in education.
Intelligent Agents at School—Child–Robot Interactions as an Educational Path
Authors: Di Stasio Margherita , Miotti Beatrice
Published: July 2024

The pervasiveness of technologies leads us to talk about a code society. From an educational point of view, coding, computational thinking, and educational robotics are an open possibility. Nevertheless, new elements such as artificial intelligence are rapidly changing educational technology perspectives. In this work, we will analyze school policies and theoretical bases in order to understand if, and under what kind of, condition coding, computational thinking, and educational robotics still represent the qualifying elements of a framework for digital literacy and digital citizenship.
Non-Formal Education for the Inclusion of Unaccompanied Migrant Children in Italy
Authors: Fierli Catia , Roverselli Carla , Olmedo-Moreno Eva
Published: July 2024

The number of unaccompanied migrant children (UMC) that landed in Italy on a daily basis was 14,044 in 2022 and 18,820 in 2023 (as stated by the Italian Ministry of Interior). This research study examines Law 47/2017, which implements the United Nations Convention on the Rights of the Child (UNCRC). It ponders inclusive educational models other than formal learning by investigating non-formal education (NFE) that takes place outside formal learning settings, although in synergy with them, for personal and vocational fulfilment. By way of example, a particularly original case study on school children newly arrived in Italy from Ukraine in 2022 is reported herein. Thirty interviews were held in Italy for three years, from 2021 to 2023, with the people in charge of unaccompanied migrant children to investigate the integration actions implemented as well as the use of NFE. Novel data from the education and employment sectors were collected to fill some pre-existing gaps in the literature. The focus group conducted in 2023 with twelve volunteer tutors highlighted data and conclusions that can be cross-checked and generalised on the use of effective operating tools to identify European pathways to peace and democracy, which may be useful in forging new solidarity patterns.
An Evaluation of the Experimental Science Teaching Program for Primary Education from the Teachers’ Perspective: An Educational Design Research Journey
Authors: Silva Patrícia Christine , Rodrigues Ana Valente , Vicente Paulo Nuno
Published: July 2024

The concerns about ensuring scientific literacy for all are underpinned by international recommendations from organizations such as the UN and the OECD for science education. They advocate for science education from a contextualized, sequential, systematic, and active perspective on the students’ learning process, starting from the early years of schooling. It is based on these pedagogical principles that the research project the “Experimental Science Teaching Program” (PEEC) was founded. This article aims to disseminate an evaluation of the PEEC for primary education, which includes a science teaching curriculum proposal, teaching activities and resources (e.g., digital games), and assessment record instruments for learning. This program was developed using an Educational Design Research methodological approach based on iterative cycles, with the collaboration of a multidisciplinary team (e.g., teachers, researchers, and children). For a final evaluation of the PEEC components, we consulted the ten participating teachers through a questionnaire survey one year after the end of their official collaboration. The main results highlight the relevance and appropriateness of the proposals presented for the targeted age group and the assumptions and objectives of science education in terms of the curriculum, teaching resources, and digital assessment games. Teachers also mentioned continuing to use the PEEC resources after their participation in this study. These results underscore the need to expand and promote projects of this nature to contribute to more practical science education in Portugal and, consequently, to increase children’s levels of scientific literacy.
User Scheduling and Path Planning for Reconfigurable Intelligent Surface Assisted MISO UAV Communication
Authors: Gu Yang , Huang Zhiyu , Gao Yuan , Fang Yong
Published: July 2024

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system.
Determination of Parameters of Radio Frequency Identification Transponder Antennas Dedicated to IoTT Systems Located on Non-Planar Objects
Authors: Nizioł Magdalena , Jankowski-Mihułowicz Piotr , Węglarski Mariusz
Published: July 2024

Integration of Radio Frequency Identification (RFID) technology with conductive textiles has greatly expanded the possibilities for creating smart devices that fit perfectly into the concept of the Internet of Things. The use of e-textiles for antenna manufacturing has enabled the development of a textronic RFID tag. Integration of such tags into products with often non-flat surfaces may result in exposure to changes in antenna geometry caused by bending. As a result, the antenna parameters may change, resulting in disruption of the entire tag operation. The authors, through simulation and experimental studies, analyzed the effects of bending the antennas of RFID tags operating in the HF (High Frequency) band.
NXRouting: A GPU-Enhanced CAD Tool for European Radiation-Hardened FPGAs
Authors: Portaluri Andrea , Azimi Sarah , Saracino Andrea , Sterpone Luca , Kilic Alp , Dupuis Damien
Published: July 2024

Field Programmable Gate Arrays (FPGAs) have witnessed an increase in space applications in the last years, mainly due to their cost-effective high-performances and flexibility. However, the susceptibility of these devices to radiation-induced effects when working in such an environment is well known. When common mitigation techniques are not sufficient to ensure the correct completion of a task, radiation-hardened FPGAs represent one of the most effective solutions. NanoXplore, in this context, is the first European developer of rad-hard FPGAs, which embed intrinsic high complexity in their architectures preventing the user from using or developing custom placement and routing algorithms. In this paper, we overcame these issues by proposing the first tool tailored to NanoXplore devices which allows the exploration of NanoXplore device architectures and routing of points through a Python interface. We developed a model that reflects the one used by the vendor, allowing the user to extract info about routes, nets and additional logic, otherwise unavailable. The tool also performs routing of points in the programmable logic, computing the optimal path. An implementation of the router on Graphic Processing Unit (GPU) is proposed to exploit the highly parallelizable nature of the problem. Finally, routing timing analyses on different benchmarks have been performed, improving the routing routine time.
A Robust AR-DSNet Tracking Registration Method in Complex Scenarios
Authors: Lei Xiaomei , Lu Wenhuan , Yong Jiu , Wei Jianguo
Published: July 2024

A robust AR-DSNet (Augmented Reality method based on DSST and SiamFC networks) tracking registration method in complex scenarios is proposed to improve the ability of AR (Augmented Reality) tracking registration to distinguish target foreground and semantic interference background, and to address the issue of registration failure caused by similar target drift when obtaining scale information based on predicted target positions. Firstly, the pre-trained network in SiamFC (Siamese Fully-Convolutional) is utilized to obtain the response map of a larger search area and set a threshold to filter out the initial possible positions of the target; Then, combining the advantage of the DSST (Discriminative Scale Space Tracking) filter tracker to update the template online, a new scale filter is trained after collecting multi-scale images at the initial possible position of target to reason the target scale change. And linear interpolation is used to update the correlation coefficient to determine the final position of target tracking based on the difference between two frames. Finally, ORB (Oriented FAST and Rotated BRIEF) feature detection and matching are performed on the accurate target position image, and the registration matrix is calculated through matching relationships to overlay the virtual model onto the real scene, achieving enhancement of the real world. Simulation experiments show that in complex scenarios such as similar interference, target occlusion, and local deformation, the proposed AR-DSNet method can complete the registration of the target in AR 3D tracking, ensuring real-time performance while improving the robustness of the AR tracking registration algorithm.
LA-Net: An End-to-End Category-Level Object Attitude Estimation Network Based on Multi-Scale Feature Fusion and an Attention Mechanism
Authors: Wang Jing , Liu Guohan , Guo Cheng , Ma Qianglong , Song Wanying
Published: July 2024

In category-level object pose estimation tasks, determining how to mitigate intra-class shape variations and improve pose estimation accuracy for complex objects remains a challenging problem to solve. To address this issue, this paper proposes a new network architecture, LA-Net, to efficiently ascertain object poses from features. Firstly, we extend the 3D graph convolution network architecture by introducing the LS-Layer (Linear Connection Layer), which enables the network to acquire features from different layers and perform multi-scale feature fusion. Secondly, LA-Net employs a novel attention mechanism (PSA) and a Max-Pooling layer to extract local and global geometric information, which enhances the network’s ability to perceive object poses. Finally, the proposed LA-Net recovers the rotation information of an object by decoupling the rotation mechanism. The experimental results show that LA-Net can has much better accuracy in object pose estimation compared to the baseline method (HS-Pose). Especially for objects with complex shapes, its performance is 8.2% better for the 10°5 cm metric and 5% better for the 10°2 cm metric.
High Frequency and Addressable Impedance Measurement System for On-Site Droplet Analysis in Digital Microfluidics
Authors: Zeng Jin , Xu Hang , Song Ze-Rui , Zhou Jia-Le , Jiang Guo-Jun , Yan Bing-Yong , Gu Zhen , Wang Hui-Feng
Published: July 2024

Digital microfluidics is a novel technique for manipulating discrete droplets with the advantages of programmability, small device size, low cost, and easy integration. The development of droplet sensing methods advances the automation control of digital microfluidics. Impedance measurement emerges as a promising technique for droplet localization and characterization due to its non-invasive nature, high sensitivity, simplicity, and cost-effectiveness. However, traditional impedance measurement approaches in digital microfluidics based on the high-voltage actuating signal are limited in sensing accuracy in practical applications. In this paper, we propose a novel droplet impedance sensing system for digital microfluidics by introducing a low-voltage and addressable measurement circuit, which enables impedance measurement over a wide frequency range. The proposed measurement system has also been used for detecting the droplet composition, size, and position in a digital microfluidic chip. The improved impedance sensing method can also promote the applications of the digital microfluidic, which requires high accuracy, real-time, and contactless sensing with automatic sample pretreatment.
Cloud-Based Access Control Including Time and Location
Authors: Al Lail Mustafa , Moncivais Marshal , Benton Robert , Perez Alfredo J.
Published: July 2024

Location-based services (LBS) offer various functionalities, but ensuring secure access to sensitive user data remains a challenge. Traditional access control methods often need more detail to enforce location-specific restrictions. This paper proposes a new approach that utilizes the Generalized Spatio-Temporal Role-Based Access Control Model (GSTRBAC) within the context of LBS. GSTRBAC grants access based on user credentials, authorized locations, and access times, providing a detailed approach to securing LBS data. We introduce an optimized cloud-based GSTRBAC implementation suitable for deployment in modern LBS architectures. The system uses two secure communication protocols tailored to different security requirements. This allows for efficient communication for less-sensitive data while offering robust protection for highly sensitive information. Additionally, a proof-of-concept mobile application demonstrates the system’s functionality and efficiency within an LBS environment. Our evaluation confirms the effectiveness of the cloud-based GSTRBAC implementation in enforcing location-specific access control while maintaining resource and time efficiency.
Trademark Text Recognition Combining SwinTransformer and Feature-Query Mechanisms
Authors: Zhou Boxiu , Wang Xiuhui , Zhou Wenchao , Li Longwen
Published: July 2024

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data.
Automatic Parkinson’s Disease Diagnosis with Wearable Sensor Technology for Medical Robot
Authors: Ji Miaoxin , Ren Renhao , Zhang Wei , Xu Qiangwei
Published: July 2024

The clinical diagnosis of Parkinson’s disease (PD) has been the subject of medical robotics research. Currently, a hot research topic is how to accurately assess the severity of Parkinson’s disease patients and enable medical robots to better assist patients in the rehabilitation process. The walking task on the Unified Parkinson’s Disease Rating Scale (UPDRS) is a well-established diagnostic criterion for PD patients. However, the clinical diagnosis of PD is determined based on the clinical experience of neurologists, which is subjective and inaccurate. Therefore, in this study, an automated diagnostic method for PD based on an improved multiclass support vector machine (MCSVM) is proposed in which wearable sensors are used. Kinematic analysis was performed to extract gait features, both spatiotemporal and kinematic, from the installed IMU and pressure sensors. Comparison experiments of three different kernel functions and linear trajectory experiments were designed. The experimental results show that the accuracies of the three kernel functions of the proposed improved MCSVM are 92.43%, 93.45%, and 95.35%. The simulation trajectories of the MCSVM are the closest to the real trajectories, which shows that the technique performs better in the clinical diagnosis of PD.
Infill Well Location Optimization Method Based on Recoverable Potential Evaluation of Remaining Oil
Authors: Liu Chen , Feng Qihong , Zhou Wensheng , Li Shanshan , Zhang Xianmin
Published: July 2024

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers’ experience, often using single indices such as the remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers the factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize the differences in reservoir heterogeneity, scale of recoverable remaining oil reserves, water flooding conditions, and oil–water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing the infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of the remaining oil potential and infill well location optimization. Our results demonstrate that the assessment of the remaining oil potential comprehensively characterizes the influence of the reservoir’s physical properties and oil–water diversion capacity on the remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, infill well location optimization can effectively enhance reservoir development outcomes.
A Simulation Approach for Analysis of the Regenerative Potential of High-Speed Train Suspensions
Authors: Wang Haihua , Zhang Xinjue , Wang Ruichen , Feng Guosheng
Published: July 2024

This study primarily investigates the adaptability and performance of hydraulic–electric regenerative dampers for high-speed trains by substituting conventional primary dampers with hydraulic–electric regenerative dampers. The primary objectives are to develop a detailed model of primary suspension regenerative damper (PSRD) energy conversion that incorporates factors such as oil pressure loss, motor efficiency, and overall system efficiency, and to perform a comprehensive comparative analysis of vibration responses, wheel wear, comfort indices, and power generation using an integrated MATLAB and SIMPACK co-simulation platform. The results reveal that at an operational speed of 350 km/h, the dynamic responses of the carbody, bogie, wheelset, and dampers equipped with the proposed PSRD systems closely align with those of conventional primary vertical damper systems. The detailed PSRDs’ hydraulic–mechanical–electrical model effectively captures the subtleties of oil pressure fluctuations and their impacts. The wear distribution and magnitude across the vehicle remain consistent during acceleration, constant, and deceleration speeds, ensuring uniform wear characteristics. Under real-world railway operational conditions, the ride comfort metrics of vehicles fitted with regenerative dampers are comparable to those with conventional primary vertical dampers. Furthermore, each regenerative damper can generate up to 21.72 W of electrical power, achieving a generation efficiency of 45.28%. Finally, a test rig was designed and fabricated to validate the primary suspension regenerative damper (PSRD) model, showing good agreement between predicted and actual damping force and power regeneration, with results indicating a peak damping force of 12.5 kN and approximately 230 W of regenerated power. This research provides a theoretical foundation and experimental validation for implementing power regeneration mechanisms in railway transportation, demonstrating that the hydraulic–mechanical–electrical PSRD model can fulfil the performance criteria of conventional dampers while offering substantial energy harvesting capabilities. This advancement not only enhances energy efficiency but also contributes to the sustainable development of high-speed rail systems.
Predictive Modeling of Conveyor Belt Deterioration in Coal Mines Using AI Techniques
Authors: Parmar Parthkumar , Jurdziak Leszek , Rzeszowska Aleksandra , Burduk Anna
Published: July 2024

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.
Analysis of the APR1400 Benchmark Using High-Fidelity Pin-Wise Core Calculation Codes
Authors: Yu Hwanyeal , Hong Hyunsik , Yoon Jooil
Published: July 2024

The KEPCO Nuclear Fuel Company (KNF) has undertaken considerable efforts to improve the KARMA/ASTRA nuclear design code system to meet the increasing demand for high-fidelity core analyses. Through years of effort, the KARMA lattice transport code based on the method of characteristics (MOC) has evolved into KARMA2, a direct whole core calculation code using a 3D calculation method based on planar (2D) MOC principles. Simultaneously, ASTRA2, designed as the successor to the ASTRA nodal diffusion code, has been developed. ASTRA2 exhibits enhanced capabilities in multigroup pin-by-pin core calculations, achieved by decoupling the 3D whole core problem into a series of planar and axial problems. The domain size of each axial problem can be adjusted, ranging from pin-cell to assembly scale, thereby optimizing efficiency. The verification and validation process of the KARMA2/ASTRA2 code system involves various benchmark problems and measured data from operational PWRs. In this study, the APR1400 benchmark analysis was performed to verify the neutronics calculation capabilities of both codes. The results underscore the reliability and accuracy of the KARMA2 solutions across various core conditions, exhibiting close agreement with the McCARD reference solutions. Similarly, the ASTRA2 results agree with the corresponding KARMA2 results. These successful results demonstrate the high-fidelity core calculation capabilities of KNF’s next-generation code system.
Examining the Evolution of Energy Storing in the Ecuadorian Electricity System: A Case Study (2006–2023)
Authors: Oscullo Lala José , Carvajal Mora Henry , Orozco Garzón Nathaly , Vega José , Ohishi Takaaki
Published: July 2024

Ensuring a balance between supply and demand is critical within electricity grids, requiring a supply composition that guarantees consistent service provision in the short and medium term. Between 2008 and 2017, Ecuador’s electricity generation capacity expanded significantly, with an investment of approximately USD 8150 million into harnessing the potential energy of water. This led to the construction of five high-capacity hydroelectric projects by 2017, contributing 33.4% of the sector’s energy output by 2023. However, it is important to note that if installed hydroelectric projects operate as run-of-river plants, with limited reservoir capacity, they can only regulate water use for electricity generation on an hourly basis. As of 2023, these run-of-river plants represent 68.8% of Ecuador’s total hydroelectric capacity within the National Interconnected System (SNI). Consequently, during periods of low inflows, supplementary energy from other power generation plants is necessary to prevent energy crises. This paper addresses the impact on energy storing for electricity generation resulting from the evolution of hydroelectric power plant entry from 2006 to 2023. This aspect has not been thoroughly examined in hydrothermal systems, which primarily focus on potential energy obtained from dams. Our approach involves a statistical analysis of hydroelectric dam reservoir operational levels. We further explore the influence on demand service within Ecuador’s electricity system, particularly during observed energy crises towards the end of 2023.
Carbon Dioxide Oil Repulsion in the Sandstone Reservoirs of Lunnan Oilfield, Tarim Basin
Authors: Wu Zangyuan , Feng Qihong , Lian Liming , Meng Xiangjuan , Zhou Daiyu , Luo Min , Cheng Hanlie
Published: July 2024

The Lunnan oilfield, nestled within the Tarim Basin, represents a prototypical extra-low-permeability sandstone reservoir, distinguished by high-quality crude oil characterised by a low viscosity, density, and gel content. The effective exploitation of such reservoirs hinges on the implementation of carbon dioxide (CO2) flooding techniques. This study, focusing on the sandstone reservoirs of Lunnan, delves into the mechanisms of CO2-assisted oil displacement under diverse operational parameters: injection pressures, CO2 concentration levels, and variations in crude oil properties. It integrates analyses on the high-pressure, high-temperature behaviour of CO2, the dynamics of CO2 injection and expansion, prolonged core flood characteristics, and the governing principles of minimum miscible pressure transitions. The findings reveal a nuanced interplay between variables: CO2’s density and viscosity initially surge with escalating injection pressures before stabilising, whereas they experience a gradual decline with increasing temperature. Enhanced CO2 injection correlates with a heightened expansion coefficient, yet the density increment of degassed crude oil remains marginal. Notably, CO2 viscosity undergoes a substantial reduction under stratigraphic pressures. The sequential application of water alternating gas (WAG) followed by continuous CO2 flooding attains oil recovery efficiency surpassing 90%, emphasising the superiority of uninterrupted CO2 injection over processes lacking profiling. The presence of non-miscible hydrocarbon gases in segmented plug drives impedes the oil displacement efficiency, underscoring the importance of CO2 purity in the displacement medium. Furthermore, a marked trend emerges in crude oil recovery rates as the replacement pressure escalates, exhibiting an initial rapid enhancement succeeded by a gradual rise. Collectively, these insights offer a robust theoretical foundation endorsing the deployment of CO2 flooding strategies for enhancing oil recovery from sandstone reservoirs, thereby contributing valuable data to the advancement of enhanced oil recovery (EOR) technologies in challenging, low-permeability environments.