bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Numerical Time Integration Procedure for Secondary Dendrite Arm Spacing in Hyper-Peritectic Steel Alloys
Authors: Mahomed Nawaz , Nanor Ebenezer T. , Ait El Haj Badiâ , Hickson Dylan , Bouayad Aboubakr 
Published: May 2024

AbstractA numerical time integration procedure for the calculation of the secondary dendrite arm spacing (SDAS) in FeC hyper-peritectic alloys is presented, a preferred group of low-carbon casting steel. The procedure incorporates a three-stage thin arm dissolution model, which is solved at each time step using Newton–Raphson iteration. This is coupled to a coarsening model based on the integral forms of the dissolution model, which are solved using Gaussian quadrature, as well as a growth model for solid fraction evolution. The procedure can easily be embedded into numerical models for solidification, in which the space–time evolution of the SDAS is required for determining the dynamic permeability in the mushy zone. Higher order approximations for both growth and solute concentration evolution can easily be incorporated. Temperature dependence of thermophysical parameters is taken into account using inter-dendritic solidification empirical models, and an alloy-specific peritectic reaction constant is used to determine the isothermal peritectic holding time. The procedure is validated against experimental data presented in literature. Various cases of SDAS as a function of local solidification time, cooling rate and carbon composition are investigated. The method is compared to experimental results of SDAS obtained from test castings of a hyper-peritectic steel alloy and can be used to iteratively determine the alloy-specific peritectic reaction constant by comparing the solid fraction evolution during the peritectic reaction with that found from the experimental cooling curve.
Clustering time series by extremal dependence
Authors: Alonso A. M. , Gabirondo P. , Scotto M. G. 
Published: May 2024

AbstractThe goal of this paper is to characterize the temporal dependence structure on the extremes of time series and use such dependency to group them. In particular, three similarity measures to capture extremal dependence are proposed, being their performance assessed in different scenarios. This will involve the use of classical time series clustering algorithms, as well as rigorous evaluation of their performance in both simulated scenarios and real-world time series data sets. The focus will be on comparing the performance of these similarity measures with different clustering methods, and illustrate the efficacy of extremal dependence-based clustering in meteorological data. To achieve this, we will consider a dataset consisting of daily maximum temperatures recorded at 500 stations across Europe.
Simultaneous Optimization of Launch Vehicle Stage and Trajectory Considering Flight-Requirement Constraints
Authors: Ko Jaeyoul , Kim Jaewoo , Choi Jimin , Ahn Jaemyung 
Published: May 2024

AbstractA conceptual design of a launch vehicle encompasses various disciplines, such as structural design, aerodynamics, propulsion systems, flight control, and stage sizing. This paper focuses on optimizing trajectory and stage size. Traditional approaches used for the conceptual design of a launch vehicle conduct the stage and trajectory designs sequentially, often leading to high computational complexity and suboptimal results. This paper presents an optimization framework that addresses both trajectory optimization and staging in an integrated way. The proposed framework aims to minimize the lift-off mass while satisfying the constraints on flight requirements (e.g., maximum dynamic pressure and the impact points of separated parts). A case study demonstrates the advantage of the proposed framework compared to the traditional sequential optimization approach.
Detection of Signal Integrity Issues in Vibration Monitoring Using One-Class Support Vector Machine
Authors: Kannan Vigneshwar , Dao Dzung Viet , Li Huaizhong 
Published: May 2024

AbstractThis paper presents an analysis of the common signal integrity issues in vibration monitoring caused by sensor saturation and signal distortion, or sensor loosening and detachment, and the development of a method of detecting the occurrence of vibration signal integrity issues using a one-class support vector machine. For this, vibration signals with distortions due to sensor saturation and/or sensor detachment are analysed to determine parameters sensitive to common integrity issues. These features are then extracted from training data of good quality vibration signals. Principal Component Analysis is used to dimensionally reduce the parameters which are then used in the training of the one-class support vector machine. The proposed method was validated on trained and untrained health conditions. Additionally, model sensitivity was also tested on trained health conditions with varying defect sizes. The method successfully detected all signal integrity issues tested proving to be an effective pre-processing step in condition monitoring systems for increased reliability and accuracy.
Efficient genome editing in rice with miniature Cas12f variants
Authors: Ye Zhengyan , Zhang Yuanyan , He Shiqi , Li Shaokang , Luo Longjiong , Zhou Yanbiao , Tan Junjie , Wan Jianmin 
Published: May 2024

AbstractGenome editing, particularly using the CRISPR/Cas system, has revolutionized biological research and crop improvement. Despite the widespread use of CRISPR/Cas9, it faces limitations such as PAM sequence requirements and challenges in delivering its large protein into plant cells. The hypercompact Cas12f, derived from Acidibacillus sulfuroxidans (AsCas12f), stands out due to its small size of only 422 amino acids and its preference for a T-rich motif, presenting advantageous features over SpCas9. However, its editing efficiency is extremely low in plants. Recent studies have generated two AsCas12f variants, AsCas12f-YHAM and AsCas12f-HKRA, demonstrating higher editing efficiencies in mammalian cells, yet their performance in plants remains unexplored. In this study, through a systematic investigation of genome cleavage activity in rice, we unveiled a substantial enhancement in editing efficiency for both AsCas12f variants, particularly for AsCas12f-HKRA, which achieved an editing efficiency of up to 53%. Furthermore, our analysis revealed that AsCas12f predominantly induces deletion in the target DNA, displaying a unique deletion pattern primarily concentrated at positions 12, 13, 23, and 24, resulting in deletion size mainly of 10 and 11 bp, suggesting significant potential for targeted DNA deletion using AsCas12f. These findings expand the toolbox for efficient genome editing in plants, offering promising prospects for precise genetic modifications in agriculture.
Human control of AI systems: from supervision to teaming
Authors: Tsamados Andreas , Floridi Luciano , Taddeo Mariarosaria 
Published: May 2024

AbstractThis article reviews two main approaches to human control of AI systems: supervisory human control and human–machine teaming. It explores how each approach defines and guides the operational interplay between human behaviour and system behaviour to ensure that AI systems are effective throughout their deployment. Specifically, the article looks at how the two approaches differ in their conceptual and practical adequacy regarding the control of AI systems based on foundation models––i.e., models trained on vast datasets, exhibiting general capabilities, and producing non-deterministic behaviour. The article focuses on examples from the defence and security domain to highlight practical challenges in terms of human control of automation in general, and AI in particular, and concludes by arguing that approaches to human control are better served by an understanding of control as the product of collaborative agency in a multi-agent system rather than of exclusive human supervision.
Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study
Authors: Jenkins John , Roy Kaushik 
Published: May 2024

AbstractOver the past few years, there has been a proliferation of research in the area of generative adversarial networks (GANs). GANs present a novel approach to producing synthetic data in varying fields including medicine, traffic control, text transferring, image generation, and cybersecurity. To improve the quality of synthetic generation, specifically for images, the GAN technique was paired with convolutional neural networks (CNNs) to build deep convolutional generative adversarial networks (DCGAN). The DCGAN framework is a simple yet stable framework shown to generate quality photorealistic images. There are a number of studies reviewing GANs, providing a comparative analysis of performance, stabilization, and training methods. With respects to the DCGAN architecture, there are literature reviews reporting its usage in forensic sketch to face transformation and fuzzy face recognition. Here, we provide a review detailing the use of the DCGAN framework with biometrics samples for advancements in biometric authentication systems and cybersecurity. As GANs have shown to be a primary tool in generating deepfakes, we explore the use of DCGANs to generating synthetic biometrics that can deceive security systems and serve as quality training data for other machine learning models. The goal of this review is to contribute a concise consolidated review of techniques involving the DCGAN framework and biometric samples for the improvement of biometric recognition systems and to be used as a reference point for future work in cybersecurity.
Potential role of cyclin-dependent kinase 4/6 inhibitors in the treatment of mucosal melanoma
Authors: Shi Chaoji , Ju Houyu , Wu Yunteng , Ma Xuhui , Zhang Zhiyuan , Ren Guoxin 
Published: May 2024

AbstractMucosal melanoma (MM) is a rare and aggressive form of melanoma with a poorer prognosis compared to other subtypes. Recent large-scale next-generation sequencing studies, including our own research, have demonstrated that the molecular characteristics and potential oncogenic drivers of MM differ significantly from those of cutaneous melanoma. The emergence of selective CDK4/6 inhibitors, already approved for use in breast cancer and undergoing phase III clinical trials for other solid tumors, represents a promising development in the treatment of MM. Recent studies have shown that CDK4/6 inhibitors not only induce cell cycle arrest but also play a crucial role in facilitating the interaction between tumor cells and the host immune system. Moreover, our findings indicate that dysregulation of cell cycle progression due to cyclin‐dependent kinase 4 (CDK4) amplification is a significant genetic characteristic in a substantial portion of MM cases. Targeting CDK4 in specific MM patients shows promise for precision cancer therapy, utilizing molecularly characterized MM patient-derived xenograft (PDX) models and clinical trials. This paper provides an overview of existing literature on CDK4/6 dysregulation in MM, as well as preclinical and clinical investigations on CDK4/6 inhibitors and potential combination therapies for MM treatment.
Enhancing ergogenic performance and antioxidant benefits of red sugarcane juice through probiotic fermentation
Authors: Wan Mansor Wan Nusrah , Mohd Zaini Nurul Solehah , Gengan Gengghatarani , Abdullah Ariani Hoo , Mohd Sha’ari Ainnur Adnin , Roslan Anis Zulaikha , Abd Rahim Muhamad Hafiz 
Published: May 2024

AbstractThe potential of red sugarcane as a functional probiotic drink was investigated, with a focus on determining its physicochemical, ergogenic, and antioxidant activities. Three different variants of Malaysian red sugarcane, namely Ragnar, Kapur, and Serai, were selected for analysis. The concentrations of electrolytes (Na, K, Ca, Mg, Zn, and Fe) in sugarcane juice were determined using an inductively coupled plasma mass spectrophotometer (ICP-MS), while the antioxidant activity and Vitamin C were assessed through colorimetric assays. Additionally, the functionality of the optimal variant, Ragnar, was enhanced by inoculating it with lactic acid bacteria (LAB) Lactobacillus plantarum ATCC8014 and Lactobacillus casei Shirota. High antioxidative properties (TPC: 71.63 mg GAE/mL, FRAP: 2.76 mmol TE/mL, DPPH EC50: 55.66 µg/mL, and Vitamin C: 0.72 mg/100 g) were observed in the Ragnar, which tends to exhibit an attractive blue-yellow hue. K exhibited the highest concentration in all samples (126.31 – 229.95 mg/mL), followed by Na, Mg, Fe, and Zn, which exceeded the commercial isotonic drink. The viability of LAB (above 107 CFU/mL) and the production of gamma-aminobutyric acid (GABA) were satisfactory while reducing sugars were generally lower after the fermentation. The findings present red sugarcane as a potential natural source for the development of functional drinks.
Graphical Abstract
Post-traumatic stress disorder symptoms among internally displaced persons: unveiling the impact of the war of Tigray
Authors: Gebreyesus Aregawi , Gebremariam Asqual Gebreslassie , Kidanu Kokob Gebru , Gidey Solomon , Haftu Hansa , Nigusse Afewerki Tesfahunegn , Shishay Fiyori , Mamo Liya 
Published: May 2024

Abstract
Introduction
Due to the war in Tigray, 2.1 million people (31% of the total population) were internally displaced. Epidemiological evidence shows that the burden of mental health is higher in war/conflict and post-conflict areas of the world compared to non-conflict places, especially for those who have experienced targeted ethnic violence as a result of civil and political unrest. Post-traumatic stress disorder is one of the common psychiatric disorders experienced during war. Thus, this study aimed to assess the level and aggravating factors of PTSD during the war in Tigray.

Methods
A community-based cross-sectional study was conducted among 2132 IDP family heads in Tigray from August 6–30, 2021. Study participants were recruited using a multi-stage sampling technique. Data were collected using a pretested structured questionnaire through face-to-face interviews. The PCL-C checklist, derived from DSM-IV criteria, was used to assess the magnitude of post-traumatic stress disorder. The entered data were exported to the SPSS version 26 statistical package for analysis. Summary statistics were computed, and logistic regression analysis was used to investigate factors associated with developing PTSD.

Results
A total of 2071 IDPs were surveyed with a response rate of 99.7%. The survey revealed that the level of PTSD among community-hosted IDPs was 57.7%; 95% CI 55.5%-59.8%. Older age (> 50) (AOR 3.1, 95% CI 1.497–6.421), primary and secondary school attendance (AOR 2.1, 95% CI 1.344–3.279; and 1.697, 95% CI 1.067–2.7) respectively, internally displaced persons with a family size of > 6 members (AOR 1.821, 95% CI 1.124–2.95), disability due to the war (AOR 1.702, 95% CI 1.077–2.69), and loss of contact with family members (AOR 1.472, 95% CI 1.032–2.099) were significantly associated with PTSD.

Conclusion
The overall level of PTSD among cIDPs was found to be high (57.7%). Almost every other IDP developed this serious mental health syndrome. Immediate psycho-social health intervention is needed by local and international organizations in collaboration with governmental and non-governmental institutions based on the study's findings.
Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania’s Historical Regions
Authors: Bâra Adela , Văduva Alin Gabriel , Oprea Simona-Vasilica 
Published: May 2024

AbstractThe extreme phenomena have been increased recently in frequency and intensity causing numerous damage that cannot be neglected by residents, local authorities and social media. More European countries are experiencing a growing number of occurrences, such as floods, powerful winds, storms, icing, and unusual temperature fluctuations. Particularly, the year 2023 has recorded the highest temperatures in the history of humanity. In this research, we compile a dataset that combines news reports with numerical data pertaining to weather conditions and air quality at the historical region level in Romania. We examine the news and recorded data spanning the years from 2009 to 2023 using anomaly detection and clustering techniques to compare the results. Specifically, we employ Isolation Forest and Autoencoders to identify anomalies within the data that are further clustered to analyse the detection process. We explore the occurrence frequency and duration of daily simultaneous extreme weather events over the years, conducting statistical tests like the Mann–Kendall test to discern trends in the extreme phenomena. The findings reveal statistically significant increasing trends in the incidence of heatwaves, storms and floods. When we set the Mean Squared Error (MSE) threshold to 95%, both methods detect nearly 16% of the anomalies, and this figure rises to over 25% when the MSE threshold is set to 90%. An analysis of anomalies at the regional level indicates that most anomalies are detected in the Transylvania and Muntenia regions, while the Banat region experiences the lowest level of anomalies.
Enhancing IoT Security: A Blockchain-Based Mitigation Framework for Deauthentication Attacks
Authors: Gopalan S. Harihara , Manikandan A. , Dharani N. P. , Sujatha G. 
Published: May 2024

AbstractThe proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security.
Integrated structural and energy building retrofit by reinforced concrete wall addition
Authors: Chen Baixi , Dong You 
Published: May 2024

AbstractIn light of the paramount considerations of environmental sustainability and the protection of both life and property, a significant number of existing reinforced concrete (RC) buildings fall short of meeting contemporary standards in terms of their structural and energy performances. In response to these pressing concerns, there is an imperative need for comprehensive building retrofitting processes that integrate both structural and energy considerations. One such approach, the addition of RC walls, is commonly employed as a structural retrofitting technique. However, its potential to enhance overall building efficiency has been relatively underexplored in previous research. In this study, we focus on a representative RC building and utilize two key performance indices: structural residual life and energy consumption, to comprehensively evaluate the impact of RC wall retrofitting on both structural and energy performance. Our investigation considers retrofitting on both the exterior and interior sides of the building. It is observed that the structural performance exhibits notable improvement with the addition of RC walls on either side, and this improvement becomes even more pronounced when complemented by local retrofits to adjacent beams. While the RC walls added on the interior side have negligible impact on the building energy efficiency, those installed on the exterior side could obviously reduce the energy consumption of the HVAC system by 7.9%. Hence, the outcomes of this study indicate that employing the RC wall retrofitting on the exterior side of the existing building is an efficient way to reach structural and energy performance targets simultaneously.
Assessing the ecological impact of industrial noise pollution on wildlife behavior in Okomu Forest Reserve, Nigeria
Authors: Ajibola-James Olufunmilayo Omolara , Awotoye Olusegun O. , Sonibare Jacob A. , Oroboade Jeremiah G. 
Published: May 2024

AbstractThe study identified sources and established the levels of industrial noise in Okomu National Park with the aim of determining the impact of industrial noise pollution on wildlife in Okomu National Park. It also modelled propagation of ambient noise levels associated with the established levels as well as determined impacts of ambient noise obtained from industrial activities on wildlife of the National Park. Locations of identified sources of noise including industrial activities sources relative to the National Park were established and digital maps of the identified locations was generated. Ambient noise levels were obtained for wildlife populations around selected locations. Noise assignment was done at each of the hotspots for industrial activity that had been detected using EXTEC sensors. The average noise levels from the industrial sources were then processed into a model using NoiseMap. The calculated noise levels from identified sources were represented with contour map. The results identified sources of noise pollution in Okomu Forest Reserve as: Okomu Palm Oil processing mill and Michelin rubber Processing factory. The result showed that activities within these industries produced at least 40 dB during the period of study. The effects of noise from the industrial activities forced the migration of the animals out of the National Park to favourable habitats with less noise sources. The retreat of the animals caused energy depletion and reduced the chances of their survival. The study concluded that industrial activities such as Palm oil processing, rubber processing, logging and vehicles are a major source of noise pollution that threatens the sustainability of wildlife in the Okomu National Park.
Heterogeneous and hybrid integration system in display technology
Authors: Huang Sixin , Long Haohui , Li Jianhui , Zhou Ziqing 
Published: May 2024

AbstractThe development tendency of “More than Display” is proposed for the display and semiconductor technologies, and the new-brand architecture of heterogeneous integration system in display (HiSID) is established in accordance with the demands of third generation Micro/Mini-LED devices. Many functional units (e.g., display units, storage units, sensing units, communication units and computing units) are integrated into one display main-board based on the semiconductor technology and electronic packaging. The advantages and details of miniaturization, intelligent, advanced integration, signal integrity with low latency performance, process compatibility and reliability are introduced. The interconnection requirements and design of the HiSID model with artificial intelligence are also summarized in this paper. It will provide technical guidance and references for the commercial application, core technology, and breakthrough direction of the HiSID module in display technology.