bluuebunny/crossref_metadata_2025 huggingface dataset:
Gray-Scale Extraction of Bone Features from Chest Radiographs Based on Deep Learning Technique for Personal Identification and Classification in Forensic Medicine
Authors: Kim Yeji , Yoon Yongsu , Matsunobu Yusuke , Usumoto Yosuke , Eto Nozomi , Morishita Junji
Published: August 2024

Post-mortem (PM) imaging has potential for identifying individuals by comparing ante-mortem (AM) and PM images. Radiographic images of bones contain significant information for personal identification. However, PM images are affected by soft tissue decomposition; therefore, it is desirable to extract only images of bones that change little over time. This study evaluated the effectiveness of U-Net for bone image extraction from two-dimensional (2D) X-ray images. Two types of pseudo 2D X-ray images were created from the PM computed tomography (CT) volumetric data using ray-summation processing for training U-Net. One was a projection of all body tissues, and the other was a projection of only bones. The performance of the U-Net for bone extraction was evaluated using Intersection over Union, Dice coefficient, and the area under the receiver operating characteristic curve. Additionally, AM chest radiographs were used to evaluate its performance with real 2D images. Our results indicated that bones could be extracted visually and accurately from both AM and PM images using U-Net. The extracted bone images could provide useful information for personal identification in forensic pathology.
From Phantoms to Patients: Improved Fusion and Voxel-Wise Analysis of Diffusion-Weighted Imaging and FDG-Positron Emission Tomography in Positron Emission Tomography/Magnetic Resonance Imaging for Combined Metabolic–Diffusivity Index (cDMI)
Authors: Deininger Katharina , Korf Patrick , Lauber Leonard , Grimm Robert , Strecker Ralph , Steinacker Jochen , Lisson Catharina S. , Mühling Bernd M. , Schmidtke-Schrezenmeier Gerlinde , Rasche Volker , Speidel Tobias , Glatting Gerhard , Beer Meinrad , Beer Ambros J. , Thaiss Wolfgang
Published: August 2024

Hybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses. Using research software, we evaluated the precision of image co-registration and voxel-wise analyses including the rigid and elastic 3D registration of DWI and [18F]-Fluorodeoxyglucose (FDG)-PET from an integrated PET/MR system. We analyzed DWI distortions with a volume-preserving constraint in three different 3D-printed phantom models. A total of 12 PET/MR-DWI clinical datasets (bronchial carcinoma patients) were referenced to the T1 weighted-DIXON sequence. Back mapping of scatterplots and voxel-wise registration was performed and compared to the non-optimized datasets. Fusion was rated using a 5-point Likert scale. Using the 3D-elastic co-registration algorithm, geometric shapes were restored in phantom measurements; the measured ADC values did not change significantly (F = 1.12, p = 0.34). Reader assessment showed a significant improvement in fusion precision for DWI and morphological landmarks in the 3D-registered datasets (4.3 ± 0.2 vs. 4.6 ± 0.2, p = 0.009). Most pronounced differences were noted for the chest wall (p = 0.006), tumor (p = 0.007), and skin contour (p = 0.014). Co-registration increased the number of plausible ADC and SUV combinations by 25%. The volume-preserving elastic 3D registration of DWI significantly improved the precision of fusion with anatomical sequences in phantom and clinical datasets. The research software allowed for a voxel-wise analysis and visualization of [18F]FDG-PET/MR data as a “combined diffusivity–metabolic index” (cDMI). The clinical value of the optimized PET/MR biomarker can thus be tested in future PET/MR studies.
Lid-Driven Cavity Flow Containing a Nanofluid
Authors: Alruwaele Wasaif H. R. , Gajjar Jitesh S. B.
Published: August 2024

In this paper, we consider the flow of a nanofluid in an enclosed lid-driven cavity using a single-phase model. Two cases are considered: one in which the top and bottom walls are kept at adiabatic conditions, and a second case in which the left- and right-side walls are kept in adiabatic conditions. The impact of different viscosity models on the mixed convection heat transfer is examined, and numerical methods are used to obtain solutions for the Navier–Stokes equations for various parameter ranges. Using our robust methods, we are able to obtain novel solutions for large Reynolds numbers and very small Richardson numbers. Using water as the base fluid and aluminium oxide nanoparticles, our results suggest that heat transfer enhancement occurs with increasing particle concentration and decreasing Richardson numbers. There are also significant differences depending on the viscosity model used in terms of the impact of reducing corner recirculation regions in the cavity.
A Low-Frequency Oscillation Suppression Method for Regional Interconnected Power Systems with High-Permeability Wind Power
Authors: Hu Yi , Luo Jinglin , Yan Kailin , Wang Tao , Zeng Qingzhu , Huang Tao
Published: August 2024

With the integration of large-scale wind power into the power grid, the impact on system stability, especially the issue of low-frequency oscillations caused by small disturbances, is becoming increasingly prominent. Therefore, this paper proposes a damping quantitative analysis method for regional interconnected power systems incorporating large-scale wind power. Using the cross-entropy particle swarm optimization (CE-PSO) algorithm, the control parameters of wind turbines are optimized to suppress low-frequency oscillations in interconnected systems. The method begins with the state equation of the interconnected power system in two regions; it deduces the characteristic polynomial of the interconnected system, including wind farms, and takes into account the influence of wind power integration on the electrical connectivity of the system. Subsequently, the influence of wind turbine control parameters on the system is quantified, and a quantitative analysis model of the impact of wind power integration on system damping characteristics is constructed. Based on this, an optimization model for wind turbine control parameters is established, and the CE-PSO algorithm is utilized to achieve suppression of low-frequency oscillations in interconnected power grids with wind power integration. Finally, the accuracy and effectiveness of the proposed method are verified through a typical electromagnetic transient simulation model of the two-region interconnected power system.
The Transformative Potential of Gender Equality Plans to Expand Women’s, Gender, and Feminist Studies in Higher Education: Grounds for Vigilant Optimism
Authors: Lopes Mónica , Santos Caynnã , Ferreira Virgínia , Monteiro Rosa , Vieira Cristina C.
Published: August 2024

The introduction of women’s, gender, and feminist studies (WGFS) into higher education (HE) curricula has primarily been driven by individual efforts rather than institutional objectives. This article employs a multimethod approach to comprehensively understand the potential contribution of gender equality plans (GEPs) to strengthening the integration of WGFS in HE curricula and pedagogical practices. The study’s documentary corpus includes data from GEPs in implementation, semi-structured interviews with 27 WGFS lecturers, and a web survey of 118 lecturers who teach courses related to or integrating gender aspects. This approach reveals divergent perspectives on the role of GEPs in HEIs, summarized into three categories: cautious optimism, skepticism, and simplistic views. The tensions and constraints associated with the contributions of the GEPs, as perceived by many involved in incorporating gender issues into teaching, outline a vigilant optimism for the future implementation of these mechanisms. Nonetheless, given the historically limited role of institutional initiatives in consolidating WGFS in Portuguese higher education, we argue that the potential contribution of GEPs to this field should not be underestimated, as integrating WGFS into institutional planning can legitimize, support, and leverage efforts that have predominantly been individual.
Unveiling University Students’ Perceptions on Their Teachers’ Digital Competence
Authors: Sjöberg Jeanette , Hoveskog Maya , Tell Joakim , Cherni Wiem
Published: August 2024

In contemporary society, digital competence has become increasingly important for people in everyday life as well as in working life. Hence, it is vital that today’s higher education contain an appropriate degree of digitization both in terms of content and approaches, something which is particularly important in higher education, where there is a pronounced expectation of what one should be able to handle in terms of digitization when one enters the workplace. Drawing on insights from previous research, this study seeks to explore the integration of digital elements in higher education pedagogy via students’ perceptions of digital integration in their education and evaluate their assessments of teachers’ digital competence. Special attention is given to collaborative learning practices facilitated by digital technologies. The research questions posed to guide the aim are: How do university students perceive the integration of digital elements within their education, and what are their assessments of their teachers’ digital competence in utilizing these technologies? The results show that teachers’ digital competence varies across disciplines which might influence their utilization of digital pedagogical methods and tools. Moreover, both engineering and non-engineering students reported varying levels of usage of digital collaborative learning methods which might reflect discipline-specific preferences and practices in collaborative learning. Also, despite high confidence levels in using digital tools, students exhibited limited awareness of existing digital functionalities. These results seek to inform pedagogical practices, institutional policies, and professional development initiatives to cultivate a digitally proficient educator workforce and have relevance globally for all involved in teaching and learning in higher education.
Insights from a Pre-Pandemic K-12 Virtual American Sign Language Program for a Post-Pandemic Online Era
Authors: Guynes Casey W. , Griffin-Shirley Nora , Guynes Kristen , Kackley Leigh
Published: August 2024

In the past five years, the number of virtual American Sign Language (ASL) classes has dramatically increased from being a novel option to being a common course delivery mode across the country. Yet, little is known regarding virtual ASL course design and the implementation of evidence-based practices. Overarchingly, this programmatic case study sought insight from a small population of experienced virtual ASL teachers who had been teaching ASL online prior to the crisis teaching phenomenon that has laid the foundation for virtual ASL as it stands today. More specifically, the qualitative design utilized questionnaires, semi-structured interviews, member checks, and document reviews of five teachers who had been teaching ASL virtually to K-12 students prior to the onset of the COVID-19 pandemic. Rich qualitative data, analyzed through directed and summative content analysis, revealed many themes specific to virtual ASL education, including differences from traditional ASL instruction, specific job responsibilities, limitations, advantages, disadvantages, and suggestions for improvement. Additionally, aligning with previous literature, we explored teacher, student, and programmatic characteristics that were perceived to be conducive to virtual students’ success. Finally, all participants expressed broader concerns that continue to exist in the field of ASL education. Implications for stakeholders, including K-12 ASL students, their families, teachers, administrators, and teacher training programs are addressed, followed by suggestions for future research.
Learning from the Unexpected Journeys of Novice Teachers’ Professional Identity Development
Authors: Sydnor Jackie , Davis Tammi R. , Daley Sharon
Published: August 2024

This longitudinal qualitative inquiry delves into the experiences of three novice teachers in the United States. Over the first four years of their teaching career, participants were interviewed multiple times, during which they created artifacts to capture the complex and emotional aspects of their experiences. The researchers utilized thematic analysis and restorying to illuminate the participants’ professional identity development and career trajectories. The findings underscore the realities of entering the teaching profession during a global teacher shortage and the tensions and vulnerability inherent in teacher identity development. The insights gleaned from these novice teachers provide fresh perspectives for educators, policymakers, and teacher educators to reimagine support systems to better sustain teachers in the profession.
Reliability Evaluation of Multi-State Solar Energy Generating System with Inverters Considering Common Cause Failures
Authors: Zhao Shenmiao , Chen Jianhui , Li Baoqin , Zhang Hui , Liu Baoliang , Qiu Qingan
Published: August 2024

To ensure the efficient functioning of solar energy generation systems, it is crucial to have dependable designs and regular maintenance. However, when these systems or their components operate at multiple working levels, optimizing reliability becomes a complex task for models and analyses. In the context of reliability modeling in solar energy generation systems, researchers often assume that random variables follow an exponential distribution (binary-state representation) as a simplification, although this may not always hold true for real-world engineering systems. In the present paper, a multi-state solar energy generating system with inverters in series configuration is investigated, in which unreliable by-pass changeover switches, common cause failures (CCFs), and multiple repairman vacations are also considered. Furthermore, the arrivals of CCFs and the repair processes of the failed system due to CCFs are governed by different Markovian arrival processes (MAPs), and the lifetimes and repair times of inverters and by-pass changeover switches and the repairman vacation time in the system have different phase-type (PH) distributions. Therefore, the behavior of the system is represented using a Markov process methodology, and reliability measures for the proposed system are derived utilizing aggregated stochastic process theory. Finally, a numerical example and a comparison analysis are presented to demonstrate the findings.
Integration of Legacy Industrial Equipment in a Building-Management System Industry 5.0 Scenario
Authors: Korodi Adrian , Nițulescu Ioana-Victoria , Fülöp Adriana-Anamaria , Vesa Vlad-Cristian , Demian Petru , Braneci Robert-Adelin , Popescu Daniel
Published: August 2024

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost.
A Secure Data-Sharing Model Resisting Keyword Guessing Attacks in Edge–Cloud Collaboration Scenarios
Authors: Li Ye , Xiong Mengen , Yuan Junling , Zhang Qikun , Zhu Hongfei
Published: August 2024

In edge–cloud collaboration scenarios, data sharing is a critical technological tool, yet smart devices encounter significant challenges in ensuring data-sharing security. Attribute-based keyword search (ABKS) is employed in these contexts to facilitate fine-grained access control over shared data, allowing only users with the necessary privileges to retrieve keywords. The implementation of secure data sharing is threatened since most of the current ABKS protocols cannot resist keyword guessing attacks (KGAs), which can be launched by an untrusted cloud server and result in the exposure of sensitive personal information. Using attribute-based encryption (ABE) as the foundation, we build a secure data exchange paradigm that resists KGAs in this work. In our paper, we provide a secure data-sharing framework that resists KGAs and uses ABE as the foundation to achieve fine-grained access control to resources in the ciphertext. To avoid malicious guessing of keywords by the cloud server, the edge layer computes two encryption session keys based on group key agreement (GKA) technology, which are used to re-encrypt the data user’s secret key of the keyword index and keyword trapdoor. The model is implemented using the JPBC library. According to the security analysis, the model can resist KGAs in the random oracle model. The model’s performance examination demonstrates its feasibility and lightweight nature, its large computing advantages, and lower storage consumption.
KRA: K-Nearest Neighbor Retrieval Augmented Model for Text Classification
Authors: Li Jie , Tang Chang , Lei Zhechao , Zhang Yirui , Li Xuan , Yu Yanhua , Pi Renjie , Hu Linmei
Published: August 2024

Text classification is a fundamental task in natural language processing (NLP). Deep-learning-based text classification methods usually have two stages: training and inference. However, the training dataset is only used in the training stage. To make full use of the training dataset in the inference stage in order to improve model performance, we propose a k-nearest neighbors retrieval augmented method (KRA) for deep-learning-based text classification models. KRA works by first constructing a storage system that stores the embeddings of the training samples during the training stage. During the inference stage, the model retrieves the top k-nearest neighbors of the testing text from the storage. Then, we use text augmentation methods to expand the retrieved neighbors, including traditional augmentation methods and a large language model (LLM)-based method. Next, the method weights the augmented neighbors based on their distances from the target text and incorporates their labels into the inference of the final results accordingly. We evaluate our KRA method on six benchmark datasets using four commonly used deep learning models: CNN, LSTM, BERT, and RoBERTa. The results demonstrate that KRA significantly improves the classification performance of these models, with an average accuracy improvement of 0.3% for BERT and up to 0.4% for RoBERTa. These improvements highlight the effectiveness and generalizability of KRA across different models and datasets, making it a valuable enhancement for a wide range of text classification tasks.
Detection of Scratch Defects on Metal Surfaces Based on MSDD-UNet
Authors: Liu Yan , Qin Yunbai , Lin Zhonglan , Xia Haiying , Wang Cong
Published: August 2024

In this work, we enhanced the U-shaped network and proposed a method for detecting scratches on metal surfaces based on the Metal Surface Defect Detection U-Net (MSDD-UNet). Initially, we integrated a downsampling approach using a Space-To-Depth module and a lightweight channel attention module to address the loss of contextual information in feature maps that results from multiple convolution and pooling operations. Building on this, we developed an improved attention module that utilizes image frequency decomposition and cross-channel self-attention mechanisms, as well as the strengths of convolutional encoders and self-attention blocks. Additionally, this attention module was integrated into the skip connections between the encoder and decoder. The purpose was to capture dense contextual information, highlight small and fine target areas, and assist in localizing micro and fine scratch defects. In response to the severe foreground–background class imbalance in scratch images, a hybrid loss function combining focal loss and Dice loss was put forward to train the model for precise scratch segmentation. Finally, experiments were conducted on two surface defect datasets. The results reveal that our proposed method is more advantageous than other state-of-the-art scratch segmentation methods.
ALPRI-FI: A Framework for Early Assessment of Hardware Fault Resiliency of DNN Accelerators
Authors: Mahmoud Karim , Nicolici Nicola
Published: August 2024

Understanding how faulty hardware affects machine learning models is important to both safety-critical systems and the cloud infrastructure. Since most machine learning models, like Deep Neural Networks (DNNs), are highly computationally intensive, specialized hardware accelerators are developed to improve performance and energy efficiency. Evaluating the fault resilience of these DNN accelerators during early design and implementation stages provides timely feedback, making it less costly to revise designs and address potential reliability concerns. To this end, we introduce Architecture-Level Pre-Register-Transfer-Level Implementation Fault Injection (ALPRI-FI), which is a comprehensive framework for assessing the fault resilience of DNN models deployed on hardware accelerators.
Integrated Mixed Attention U-Net Mechanisms with Multi-Stage Division Strategy Customized for Accurate Estimation of Lithium-Ion Battery State of Health
Authors: Fan Xinyu , Yang Xuxu , Hou Feifei
Published: August 2024

As a core component of electric vehicles, the state of health (SOH) of lithium-ion battery has a direct impact on vehicle performance and safety. Existing data-driven models primarily focus on feature extraction, often overlooking the processing of multi-level redundant information and the utilization of multi-stage battery features. To address the issues, this paper proposes a novel data-driven method, named multi-stage mixed attention U-Net (MMAU-Net), for SOH estimation, which is based on both the phased learning and an encoder–decoder structure. First, the geometric knee-point division method is proposed to divide the battery life cycle into multiple stages, which allows the model to learn the distinctive features of battery degradation at each stage. Second, to adeptly capture degraded features and reduce redundant information, we propose a mixed attention U-Net model for the SOH prediction task, which is constructed upon the fundamental U-Net backbone and is enhanced with time step attention and feature attention modules. The experimental results validate the proposed method’s feasibility and efficacy, demonstrating an acceptable performance across a spectrum of evaluative metrics. Consequently, this study offers a research within the domain of battery health management.
Decoupling Uplink and Downlink Access for NGEO Satellite Communications with In-Line Interference Avoidance
Authors: Liu Yilun , Liu Yujie , Kuai Xiaoyan
Published: August 2024

Decoupling uplink and downlink access (DUDA) has latterly proven to effectively enhance transmission efficiency in wireless communication systems, with particular effectiveness observed in both terrestrial and unmanned aerial vehicle (UAV) systems. In this paper, we propose an innovative DUDA approach specifically designed for non-geostationary orbit (NGEO) multi-layer satellite systems (MSS), integrating strategies to mitigate in-line interference to ensure spectral coexistence between geostationary Earth orbit (GEO) and NGEO satellites. Notably, the interference from the main lobe of directional antennas on NGEO satellites is meticulously characterized using a spherical surface model based on the geocentric angle. Within the framework of proposed DUDA method, a user terminal (UT) can establish communication with the satellite which provides the highest average power of received signal in compliance with the unique exclusion angle constraints of NGEO satellites. The association probability of DUDA is analyzed based on stochastic geometry. The performance evaluation, conducted in terms of transmission rate, reveals that the proposed DUDA methodology yields significant improvements when compared to conventional access schemes.
Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
Authors: Wang Yawei , Liu Rongpeng , Yuan Jia , Lu Jingwei , Wang Ziyang , Wu Ruihuan , Wei Zhongchao , Liu Hongzhan
Published: August 2024

As the era of sixth-generation (6G) communications approaches, there will be an unprecedented increase in the number of wireless internet-connected devices and a sharp rise in mobile data traffic. Faced with the scarcity of spectrum resources in traditional communication networks and challenges such as rapidly establishing communications after disasters, this study leverages unmanned aerial vehicles (UAVs) to promote an integrated multi-hop communication system combining free-space optical (FSO) communication, terahertz (THz) technology, and intelligent reflecting surface (IRS). This innovative amalgamation capitalizes on the flexibility of UAVs, the deployability of IRS, and the complementary strengths of FSO and THz communications. We have developed a comprehensive channel model that includes the effects of atmospheric turbulence, attenuation, pointing errors, and angle-of-arrival (AOA) fluctuations. Furthermore, we have derived probability density functions (PDFs) and cumulative distribution functions (CDFs) for various switching techniques. Employing advanced methods such as Gaussian–Laguerre quadrature and the central limit theorem (CLT), we have calculated key performance indicators including the average outage probability, bit error rate (BER), and channel capacity. The numerical results demonstrate that IRS significantly enhances the performance of the UAV-based hybrid FSO/THz system. The research indicates that optimizing the number of IRS elements can substantially increase throughput and reliability while minimizing switching costs. Additionally, the multi-hop approach specifically addresses the line-of-sight (LoS) dependency limitations inherent in FSO and THz systems by utilizing UAVs as dynamic relay points. This strategy effectively bridges longer distances, overcoming physical and atmospheric obstacles, and ensures stable communication links even under adverse conditions. This study underscores that the enhanced multi-hop FSO/THz link is highly effective for emergency communications after disasters, addressing the challenge of scarce spectrum resources. By strategically deploying UAVs as relay points in a multi-hop configuration, the system achieves greater flexibility and resilience, making it highly suitable for critical communication scenarios where traditional networks might fail.
Low Probability of Intercept Radar Signal Recognition Based on Semi-Supervised Support Vector Machine
Authors: Xu Fuhua , Hu Haoning , Mu Jiaqing , Wang Xiaofeng , Zhou Fang , Quan Daying
Published: August 2024

Low probability of intercept (LPI) radar signal recognition under low signal-to-noise ratio (SNR) is a challenging task within electronic reconnaissance systems, particularly when faced with scarce labeled data and limited resources. In this paper, we introduce an LPI radar signal recognition method based on a semi-supervised Support Vector Machine (SVM). First, we utilize the Multi-Synchrosqueezing Transform (MSST) to obtain the time–frequency images of radar signals and undergo the necessary preprocessing operations. Then, the image features are extracted via Discrete Wavelet Transform (DWT), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are input into the semi-supervised SVM to complete the classification and recognition of LPI radar signals. The experimental results demonstrate that the proposed method achieves high recognition accuracy at low SNR. When the SNR is −6 dB, its recognition accuracy reaches almost 100%.
Effects and Mechanisms of Dilute-Foam Dispersion System on Enhanced Oil Recovery from Pore-Scale to Core-Scale
Authors: Wang Xiuyu , Shen Rui , Gao Yuanyuan , Xiong Shengchun , Zhao Chuanfeng
Published: August 2024

The dilute-foam dispersion system improves oil recovery by reducing interfacial tension between oil and water, altering wettability, and diverting displaced fluids by plugging larger pores. An optimized foaming system is obtained by formability evaluation experiments, in which the half-life for drainage and foaming volume by different types and concentrations of surfactants are analyzed, followed by the addition of partially hydrolyzed polyacrylamide (HPAM) with varied concentrations to enhance the foam stability. Using COMSOL Multiphysics 5.6 software, the Jamin effect and plugging mechanism of the water–gas dispersion system in narrow pore throats were simulated. This dispersion system is applied to assist CO2 huff-n-puff in a low-permeability core, combined with the online NMR method, to investigate its effects on enhanced oil recovery from the pore scale. Core-flooding experiments with double-pipe parallel cores are then performed to check the effect and mechanism of this dilute-foam dispersion system (DFDS) on enhanced oil recovery from the core scale. Results show that foam generated by combining 0.6% alpha-olefin sulfonate (AOS) foaming agent with 0.3% HPAM foam stabilizer exhibits the strongest foamability and the best foam stability. The recovery factor of the DFDS-assisted CO2 huff-n-puff method is improved by 6.13% over CO2 huff-n-puff, with smaller pores increased by 30.48%. After applying DFDS, the minimum pore radius for oil utilization is changed from 0.04 µm to 0.029 µm. The calculation method for the effective working distance of CO2 huff-n-puff for core samples is proposed in this study, and it is increased from 1.7 cm to 2.05 cm for the 5 cm long core by applying DFDS. Double-pipe parallel core-flooding experiments show that this dispersion system can increase the total recovery factor by 17.4%. The DFDS effectively blocks high-permeability layers, adjusts the liquid intake profile, and improves recovery efficiency in heterogeneous reservoirs.
A Review of Hydrogen Leak Detection Regulations and Technologies
Authors: Qanbar Mohammed W. , Hong Zekai
Published: August 2024

Hydrogen (H2) is positioned as a key solution to the decarbonization challenge in both the energy and transportation sectors. While hydrogen is a clean and versatile energy carrier, it poses significant safety risks due to its wide flammability range and high detonation potential. Hydrogen leaks can occur throughout the hydrogen value chain, including production, storage, transportation, and utilization. Thus, effective leak detection systems are essential for the safe handling, storage, and transportation of hydrogen. This review aims to survey relevant codes and standards governing hydrogen-leak detection and evaluate various sensing technologies based on their working principles and effectiveness. Our analysis highlights the strengths and limitations of the current detection technologies, emphasizing the challenges in achieving sensitive and specific hydrogen detection. The results of this review provide critical insights into the existing technologies and regulatory frameworks, informing future advancements in hydrogen safety protocols.
Optimizing Mean Fragment Size Prediction in Rock Blasting: A Synergistic Approach Combining Clustering, Hyperparameter Tuning, and Data Augmentation
Authors: Krop Ian , Sasaoka Takashi , Shimada Hideki , Hamanaka Akihiro
Published: August 2024

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression model. A dataset of 110 blasts was divided into 97 blasts for training and testing, whereas a separate set of 13 new, unseen blasts was used to evaluate the robustness and generalization of the model. Hierarchical Agglomerative (HA) and K-means clustering algorithms were used, with HA clustering providing a higher cluster quality. To address class imbalance and improve model generalization, a synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) was employed. Hyperparameter tuning was conducted using HyperOpt by comparing Random Search (RS) with the Advanced Tree-structured Parzen Estimator (ATPE). The combination of ATPE with HA clustering and SMOGN in an expanded search space produced the best results, achieving superior prediction accuracy and reliability. The proposed HAC1-SMOGN model, which integrates HA clustering, ATPE tuning, and SMOGN augmentation, achieved a mean squared error (MSE) of 0.0002 and an R2 of 0.98 on the test set. This study highlights the synergistic benefits of clustering, hyperparameter optimization, and data augmentation in enhancing machine learning models for regression tasks, particularly in scenarios with class imbalance or limited data.
The Effect of Maltose on Structural, Physicochemical, and Digestive Properties of Lentil Starch under Electron Beam Irradiation
Authors: Liang Danyang , Liu Qing , Luo Haiyu , Luo Lin , Temirlan Khamiddolov , Li Wenhao
Published: August 2024

This study investigated the effects of electron beam irradiation (EBI) on the structural, physicochemical, and functional properties of lentil starch with varying maltose content. EBI did not significantly disrupt the starch’s surface structure or cause amorphization of starch and maltose crystals, but it significantly reduced the intensity of starch’s XRD peaks. The presence of maltose intensified internal growth ring damage, leading to more cross-link and rearrangement between short chains, improving short-range ordering of lentil starch and enhancing starch’s solubility and thermal stability. Additionally, adding maltose that EBI then treats can lead to an increased content of slowly digestible starch in samples.
A Report of the Observed Intensity and Structural Damage during the Mw 5.3 Earthquake in Santo Domingo (Province of Chiriquí, Panamá) on 8 July 2024
Authors: Pinzón Luis A. , Vargas Yessica , Hidalgo-Leiva Diego A.
Published: August 2024

On 8 July 2024, a magnitude 5.3 earthquake struck the province of Chiriquí in Panama, primarily impacting areas characterized by informal settlements and low-income neighborhoods. The earthquake was recorded by both the Panama Accelerographic Network and the Costa Rican Strong Motion Network, with accelerations exceeding 150 cm/s2. The National Civil Protection System (SINAPROC) reported damage to 24 residences and public infrastructure, including hospitals and schools. Despite the material damage, no fatalities were reported. The Ministry of Housing and Land Management (MIVIOT), the Ministry of Education (MEDUCA), and the Ministry of Social Development (MIDES) also participated in the assessment and response efforts. This report presents the measurements and damage observed during the event.
The Specialist Nurse in European Healthcare 2030: ESNO Congress 2024 Highlights
Authors: Stievano Alessandro , Caruso Rosario , Friganović Adriano
Published: August 2024

The European Specialist Nurses Organization (ESNO), after a series of congresses in Brussels, organised its 6th International Congress in Milan, Italy. The ESNO Congress 2024 focused on “The Specialist Nurse in European Healthcare 2030”, addressing the evolving roles and increasing importance of specialist nurses. The event featured keynote presentations and discussions on enhancing clinical practice through advanced education, bridging policy–practice gaps, and improving working conditions. The ESNO Declaration emphasised lifelong learning, harmonised qualification recognition, and interdisciplinary collaboration. A dynamic hackathon preceded the congress, generating innovative solutions to pressing nursing challenges. New inductees of the ESNO Fellowship Program were celebrated. The congress highlighted critical advancements and set a strategic roadmap for the future of specialist nursing in Europe.
Sedentary Behavior, Obesity, and Disabilities in Community-Dwelling Older Adults: Analysis of the Brazilian National Health Survey 2019
Authors: Prates Freitas Bruno , Martins Cândido Letícia , Jakovljevic Pudla Wagner Katia , Rodrigues Lacerda Ana Cristina , Amaral Mendonça Vanessa , De Micheli Roberta , Sartorio Alessandro , Carelli Pereira de Avelar Núbia , Danielewicz Ana Lúcia
Published: August 2024

Research suggests that sedentary behavior (SB) and obesity are associated with disabilities in basic activities (BADL) and instrumental (IADL) activities of daily living. However, there is a lack of studies investigating this association in community-dwelling older adults. Thus, the aim of this study was to investigate the association between different SB typologies, isolated and in conjunction with obesity, and their associations with BADL and IADL disabilities in community-dwelling Brazilian older adults. This was a cross-sectional study using data from older adults (≥60 years) who participated in the Brazilian National Health Survey (2019). The exposures were obesity (BMI > 27 kg/m2) and the amount of time spent daily on SB watching television (SB TV < 3 and ≥3 h/day) and engaging in leisure activities (SB leisure < 3 and ≥3 h/day), analyzed both separately and jointly. The outcomes were BADL and IADL disabilities. The main results showed that isolated SB TV ≥ 3 h/day (OR: 1.26; 95% CI: 1.14; 1.39) and SB TV ≥ 3 h/day combined with obesity (OR: 1.55; 95% CI: 1.37; 1.75) increased the odds of BADL and IADL disabilities. Obesity alone (OR: 1.21; 95% CI: 1.07; 1.36) increased only the odds of BADL disabilities. Moreover, SB leisure ≥ 3 h/day without obesity reduced the odds of IADL disabilities (OR: 0.56; 95% CI: 0.41; 0.76). Ideally, older adults should be encouraged to prevent obesity, reduce excessive periods spent in SB watching TV, and increase the daily periods spent in leisure activities, thus minimizing the likelihood of disabilities in functional activities.
Potentially Bioactive Compounds and Sensory Compounds in By-Products of Several Cultivars of Blackberry (Rubus fruticosus L.)
Authors: Čechovičienė Indrė , Viškelis Jonas , Viškelis Pranas , Hallman Ewelina , Kruk Marcin , Tarasevičienė Živilė
Published: August 2024

This study aimed to determine the amounts of phenols, antioxidant activity, and sensory compounds in three commercial cultivars of blackberries popular in Lithuania: ‘Polar’, ‘Brzezina’, and ‘Orkan’. Blackberry pomace was analyzed by the spectrophotometric method for total phenolic content, total flavonoid content and radical-scavenging capacity using the DPPH• and ABTS•+ assays. The phenolic profiles, organic acids, and sugars were analyzed by HPLC. The Heracles II electronic nose, which is based on ultrafast gas chromatography, was used for the quantification of volatile organic compounds. The results show that the total phenolic content of blackberry pomace varied from 2380.60 to 2088.00 mg 100 g−1 and that the total flavonoid content varied from 161.29 to 148.10 mg 100 g−1, depending on the cultivar. A total of 14 polyphenols were also identified, with epigallocatechin and anthocyanin cyanidin-3-O-glucoside being quantified in the highest concentrations (7.28 to 9.72 and 6.19 to 9.79 mg g−1, respectively) and being the predominant phenolic compounds in the blackberry-pomace samples. The odor profiles of blackberry pomace from different cultivars varied. The main volatile organic compounds found in all blackberry pomace were 1-Nonanol and cis-3-Hexen-1-ol, are associated with herbaceous and citrusy aromas. All these results show the potential of using blackberry pomace to enrich food products with bioactive phytochemicals.
The Relationship between Financial Literacy Misestimation and Misplacement from the Perspective of Inverse Differential Information and Stock Market Participation
Authors: Lee Yun-Ho , Ma Weihua
Published: August 2024

This study proposes the inverse differential information theory, which predicts a positive relationship between misestimation and misplacement, two types of overconfidence. The inverse differential information theory contrasts with the existing theory of differential information, which argues for a negative relationship between these two types of overconfidence. This study shows that these differences arise from opposing perspectives on the accuracy with which individuals assess their own abilities or performance compared to others’. The inverse differential information theory posits that people tend to evaluate others more objectively than they do themselves. A positive relationship between misestimation and misplacement predicts that overestimation and overplacement, as well as underestimation and underplacement, tend to occur together. Analysis using financial literacy data from South Korean adults supports the prediction of the inverse differential information theory. When these two types of overconfidence form a positive relationship, they are expected to have systematically a significant impact on human decision-making and behavior. This study empirically demonstrates that the positive relationship between misestimation and misplacement in financial literacy significantly influences individuals’ financial behavior, specifically in the context of stock market participation experience. The inverse differential information theory requires further empirical validation across various domains, not just in the field of behavioral finance, to establish whether the positive interaction between misestimation and misplacement consistently influences human decision-making and behavior.
Identifying the Nonlinear Impacts of Road Network Topology and Built Environment on the Potential Greenhouse Gas Emission Reduction of Dockless Bike-Sharing Trips: A Case Study of Shenzhen, China
Authors: Zhao Jiannan , Yuan Changwei , Mao Xinhua , Ma Ningyuan , Duan Yaxin , Zhu Jinrui , Wang Hujun , Tian Beisi
Published: August 2024

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation.
Binding to the Other Side: The AT-Hook DNA-Binding Domain Allows Nuclear Factors to Exploit the DNA Minor Groove
Authors: Battista Sabrina , Fedele Monica , Secco Luca , Ingo Alberto Maria Davide , Sgarra Riccardo , Manfioletti Guidalberto
Published: August 2024

The “AT-hook” is a peculiar DNA-binding domain that interacts with DNA in the minor groove in correspondence to AT-rich sequences. This domain has been first described in the HMGA protein family of architectural factors and later in various transcription factors and chromatin proteins, often in association with major groove DNA-binding domains. In this review, using a literature search, we identified about one hundred AT-hook-containing proteins, mainly chromatin proteins and transcription factors. After considering the prototypes of AT-hook-containing proteins, the HMGA family, we review those that have been studied in more detail and that have been involved in various pathologies with a particular focus on cancer. This review shows that the AT-hook is a domain that gives proteins not only the ability to interact with DNA but also with RNA and proteins. This domain can have enzymatic activity and can influence the activity of the major groove DNA-binding domain and chromatin docking modules when present, and its activity can be modulated by post-translational modifications. Future research on the function of AT-hook-containing proteins will allow us to better decipher their function and contribution to the different pathologies and to eventually uncover their mutual influences.