bluuebunny/crossref_metadata_2025_split huggingface dataset:
Instagram is a ridiculous lie factory
Authors: Foley Jennifer , Hidalgo-Downing Laura 
Published: November 2023

Abstract
This article explores the way in which social media and its relation to mental health is metaphorically conceptualized in newspaper opinion discourse. We discuss the extent to which metaphoric expressions are used creatively and whether they convey positive or negative evaluations. For this purpose, a 10,000-word sample of opinion articles from two British newspapers was collected and analysed. The main research questions are: (1) How is social media conceptualized? (2) To what extent is social media conceptualized by means of creative expressions? (3) Are social media metaphors more likely to be evaluative or non-evaluative? If so, what is the predominant value? (4) How are mental health and well-being conceptualized? (5) Do authors identify positive or negative effects of social media on mental health and well-being? Results show that the main source domains used to conceptualize social media are person, drugs, place, object, war, journey and competition. Creative social media metaphors typically make use of the person, drugs and place source domains, and evaluative metaphors more frequently project a negative evaluation.
Variation of sign parameters in narrative and expository discourse
Authors: Cohen-Koka Shirit , Nir Bracha , Meir Irit 
Published: November 2023

Abstract
The paper presents an in-depth study of variation in sign parameters of Israeli Sign Language (ISL). We describe
and analyze specific sign parameters and their sub-parameters: handshape, orientation, place of articulation, and movement, as
they are distributed in two text-types: narrative and expository discourse. A group of 16 deaf native ISL signers produced 32
texts containing a total of 6,875 signs which were then analyzed from a bottom-up approach. A sub-set of 3,919 signs out of this
large database (excluding signs that are inherently variable) were found eligible for the detailed distributional analysis of
text-dependent variation in terms of their articulation structure, and were used for comparison between the narrative and
expository productions. Our findings show that narratives attract more variation in the sign parameters compared to expository
texts. The study thus highlights the impact of communicative context on the use of signs.
The directional antenna tracker for enhancing range communication between UAVs and Ground Control Station
Authors: Aizat I Muhammad , Hazry D , Azizi A Muhammad , Abadal-Salam T H , Hassan T M 
Published: November 2023

Abstract
This paper discusses the development of an antenna tracker project focused on evaluating a 2.4 GHz Yagi Uda directional antenna connected to an NRF24L01 transceiver. It employs a gear reduction system for precise yaw and pitch control using two servo motors. The tracker calculates rotation angles using the Haversine formula for coordinates and inverse tangent trigonometry for elevation. Experiments took place in an open area with a stationary Ground Control Station (GCS) and a moving Unmanned Aerial Vehicle (UAV) to assess performance. The experiments comprised three tests: first, GCS without the Yagi Uda antenna or tracker; second, GCS with the Yagi Uda antenna but without the tracker, positioned along a 30-degree angle away from the transmitter; and third, GCS with both the Yagi Uda antenna and the tracker. The UAV gradually moved away from the tracker in 5-meter intervals until data loss occurred. The test results are based on received coordinates, distance, temperature, humidity, and data loss. Among the three tests, Test 3 demonstrates the best outcome, where the Yagi Uda antenna successfully receives data up to 180 meters before experiencing data loss.
Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application
Authors: Jack Lee L , Hazry D , Muhammad Azizi A , Abadal-Salam T H , Hassan T M 
Published: November 2023

Abstract
This article introduces a lightweight and efficient model for measuring applications, aimed at enhancing the current UAV monitoring system. The primary objective of this project is to develop a measuring application capable of determining and displaying the distance between the camera on the UAV and the facial model. The YOLOV8 framework is employed as a detection model to identify and interpret objects within the region of interest. Additionally, the algorithm incorporates the concept of focal length in lenses to calculate the distance between the facial expressions of a human face and the camera. To assess the algorithm’s accuracy, facial models were placed at various distances from the camera during testing. The predicted distance values obtained through the algorithm were then compared to the actual measured distances using a measuring tape. The results demonstrated a maximum tolerance of ±0.9 cm, indicating the algorithm’s reliable performance in predicting distance measurements.
Assessment of QRS and Q-T segments efficacy as non-invasive biomarkers for sudden cardiac death forecasting
Authors: Alfarhan K A , Zakaria A , Kamarudin L M , Mamduh S M , Yousif H A , Sabry M M 
Published: November 2023

Abstract
Sudden cardiac death (SCD) is a critical event occurring within an hour of sudden cardiac arrest (SCA). SCA often arises from disruptions in cardiac electrical signals, leading to fatality by hindering blood circulation. SCD, a significant contributor to cardiovascular-related deaths, impacts millions people globally. Most studies in the literature focus on heart rate variability (HRV) as a biomarker for predicting SCD while marginalizing other ECG morphological features. This study strives to assess and compare the QRS and Q-T efficacy as non-invasive biomarkers to predict SCD. The study aims to examine the QRS and Q-T segments of the ECG signal as potential biomarkers for predicting SCD effectively. The process involves selecting ECG segments from international databases, followed by preprocessing, delineation, empirical mode decomposition (EMD), and median frequency (MDF) feature extraction. Machine learning classifiers, namely support vector machine (SVM) and random forest (RF), are employed to classify SCD and normal sinus rhythm (NSR) classes based on the extracted features. The results underscore the superiority of the Q-T segment, with SVM achieving the best classification performance (accuracy = 83.88%, sensitivity = 90%, specificity = 77.77%). This suggests that the Q-T segment holds the potential to predict SCD better than the QRS segment.
Initial Study of Radio Tomographic Imaging for Human localization by using Simulation Model
Authors: Abdullah M. S. M. , Rahiman M. H. F. , Khalid N. S. , Nasir A. S. A. 
Published: November 2023

Abstract
This paper explains the details of modelling the simulation works designin setup for the RTI system. Th simulation modelling using software is focused on the interaction of electromagnetic behaviour in a dielectric medium of human inside a monitoring area. The modelling works have involved the criteria of the human, frequency and number of sensor nodes, dielectric properties of the human and last but not least, the configurations of the Radio tomography imaging (RTI) system. The model is then developed in the software to observe and investigate the result.
Indoor Automated Fire Extinguisher System using Computer Vision
Authors: Rosle Muhammad Hisyam , Chia Khoo Yan , Nasir Abdul 
Published: November 2023

Abstract
This paper presents the development of an automated fire extinguisher robot by employing fire recognition using computer vision. This project aims to develop a robot that can search, detect and extinguish small flames for indoor purposes. The robot is developed by applying open-source image processing to recognize the presence of fire. A control program was built to control the movement of the robot’s servo motor. The performance of the fire recognition was analysed in different light intensities and angles. The development of this project makes use of a camera and computer vision in place of various sensors, such as gas sensors, temperature sensors, and infrared sensors, to detect fires. Furthermore, a microprocessor is employed to operate the water pump and servo motor, which drive the nozzle to the position specified by the microcontroller. The results reveal that the proposed vision-based fire detection system has a high classification accuracy correspond to fire recognition.
Multi-detector simultaneous sampling design in the spot-scanning defects detection system
Authors: Zhou Z. , Qu D. , Ding R. , Luo H. , Xiong W. , Ru Y. , Jin W. , Li Z. , Hong J. 
Published: November 2023

Abstract
With the decreases in semiconductor critical dimension and
increases in circuit intricacy, the impact of wafer defects on the
yield and manufacturing cost of integrated circuits is becoming
increasingly significant. In this paper, a multimodal spot-scanning
defect detection system designed for unpatterned wafer surfaces is
introduced. The defect detection system's mechanics, electronics,
and software are introduced, focusing on presenting a multi-detector
simultaneous sampling design. The signal sampling is based on the
position triggering of the rotary stage encoder. The focused
illumination spot is scanned in a helical trajectory relative to the
wafer in coordination with the motion of the linear stage and rotary
stage. The signal detection is carried out synchronously, and the
signal sampling is triggered by rotary stage encoder position pulses
to achieve equally spaced sampling. The detector signals are
trans-impedance amplified and transmitted differentially to transfer
the analogue signals from the optical head to the main control
board. Under FPGA control, the main control board completes the
simultaneous sampling and signal processing through nine
high-precision 16-bit SAR(successive-approximation-register)
ADCs(analogue-to-digital converter) under the trigger signal. The
defect detection system is designed with a pixel resolution of
4 × 4 μm, requiring a sampling position error of less
than 1 pixel and an inter-channel position deviation of less than
0.1 pixel. The rotary stage prototype has a design speed of
120 rpm, and the positioning accuracy of the encoder is
±8 arcsec. Therefore, the maximum sampling position error is
1.94 μm for a 4-inch wafer sample. The maximum phase
deviation between the nine channels is 32.5 ns, and the maximum
position deviation is 20.4 nm.
The annealing setup and associated monitoring system of the ELIADE HPGe clover detectors at ELI-NP
Authors: Dhal A. , Testov D. , Ramirez F. , Aogaki S. , Turturică G. , Suliman G. , Petcu C. , Tatulea B. , Ur C.A. , Balabanski D.L. 
Published: November 2023

Abstract
We report a dedicated setup built in-house for the annealing
of the HPGe clover detectors of the ELI-NP Array of DEtectors
(ELIADE) γ-ray spectrometer, as well as the post-annealing
testing of these detectors with the standard 60Co & 152Eu
radioactive calibration sources employing conventional analog
electronics. Both the design and assembly of the annealing setup
were performed at the Extreme Light Infrastructure — Nuclear
Physics (ELI-NP) facility, Măgurele, Romania. A `radiation
damage annealing assembly kit' (NRK-200 unit) from the detector
manufacturer Canberra is utilized in heating and controlling the
temperature of the Ge crystals of the annealed detector. The vacuum
inside the detector was maintained throughout the annealing process
by constantly pumping the system using a turbo-molecular pumping
station. The temperature of the germanium crystals, located inside a
vacuum sealed chamber, of the detector and the vacuum level of this
chamber were monitored throughout the annealing process, via both
in-person observation and remote (online) monitoring. The Graphic
User Interface (GUI) of an underlying LabVIEW script was utilized
for running the monitoring process of the temperature and vacuum
pressure values via a local network. To have the option of real-time
online monitoring of the temperature, vacuum pressure, we coupled
the web application Grafana with the Influx DB of the annealing
data, as well as the backup time information of an Uninterruptible
Power Supply (UPS) unit used in system.
Torsional Effects on Wind Turbine Blades and Impact on Field Damages
Authors: Jensen F. M. , Arconada J. O. , Werk M. , Berggreen C. , Sørensen J. D. , Zhong G. , Haans W. 
Published: November 2023

Abstract
The purpose of this paper is to present the importance of torsional loads when understanding the significant increase in structural damages on large wind turbine blades. By comparing the stress state of two blades of different length, it is shown that torsional loads grow with an exponent of four (4). Thereby, a function of the length of the blades leading to a new range of challenges that are not met by the current process for design, testing and certification.
The paper presents the study of these new challenges in detail, based upon:
- An experimental perspective, with results from large-scale cyclic loading tests on two blade cut-outs conducted under controlled laboratory conditions in. Both blades were tested to failure.
- A numerical perspective, non-linear geometrical 3D FEM simulations has been performed to study the impact of torsional loads on relevant current structural failure modes on large wind turbine blades.
The conclusion is that the larger torsional loads may explain shear-web disbond, as well as sandwich skin debonding in both the transition zone and in the max chord area of wind turbine blades. In view of the findings, the paper includes proposals for remedies that are expected to mitigate some of the significant failures currently seen in the field.
A strategic approach to wind turbine blade recycling: Using life cycle assessment to enable data driven decision making
Authors: Pender Kyle , Romoli Filippo , Fuller Jonathan 
Published: November 2023

Abstract
The persistent lack of circular, scalable, and low-impact solutions for decommissioned wind blades remains a challenge for the wind energy sector. With a saturation of proposed technologies, the sector needs to focus efforts on pulling through technologies both for the immediate and longer-term wind blade waste streams. In this work, models for high-profile EoL scenarios have been developed and, using lifecycle assessment, applied to representative onshore and offshore wind blade designs. In their current iterations, not all composite recycling approaches are environmentally beneficial when considering global warming potential (GWP). This is the case particularly for blades consisting of only glass fibre reinforcement, where only cement kiln and mechanical recycling scenarios resulted in GWP lower than landfilling. The introduction of carbon fibre reinforcement leads to reductions in GWP for almost all end-of-life scenarios, the most significant of which coming in the chemical and thermal scenarios. This indicates that to minimise carbon footprint a combination of approaches is required. These findings have been coupled with material circularity indicators and show that, while recycling can be beneficial, it is only one piece of the puzzle. Methods for integrating restorative materials into wind turbine blade designs, as well as finding circular solutions to reduce waste associated with current blade manufacturing practices, are also critical areas of research needed to mitigate the impact of future blade production.
Complex damage mechanisms and roughness evolution of wind turbine blade surface: Multiphysics and stochastic effect modelling
Authors: Mishnaevsky L. , Kuthe N. , Tempelis A. , Mahajan P. 
Published: November 2023

Abstract
Leading edge erosion of wind turbine blades is the most often observed damage mechanism of wind turbines. The surface erosion of blades is influenced by many multiphysics and stochastic factors including humidity and related degradation processes, rough uneven surface and roughness development, random defects in the materials. In this paper, the effects of these factors and possibilities of their computational modelling and prediction are discussed. Competing damage mechanisms in erosion, including debonding and impact damage, as well moisture ingress and weathering are investigated. A predictive model for roughness evolution of leading edge due to the surface damage is presented.
Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe
Authors: XU 徐 Xutao 栩涛 , XU 徐 Tianchao 田超 , XIAO 肖 Chijie 池阶 , ZHANG 张 Zuyu 祖煜 , HE 何 Renchuan 任川 , YUAN 袁 Ruixin 瑞鑫 , XU 许 Ping 平 
Published: November 2023

Abstract
The diagnostic of poloidal magnetic field (

) in field-reversed configuration (FRC), promising for achieving efficient plasma confinement due to its high β, is a huge challenge because

is small and reverses around the core region. The laser-driven ion-beam trace probe (LITP) has been proven to diagnose the

profile in FRCs recently, whereas the existing iterative reconstruction approach cannot handle the measurement errors well. In this work, the machine learning approach, a fast-growing and powerful technology in automation and control, is applied to

reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach. The machine learning approach achieves a more accurate reconstruction of

profile when 20% detector errors are considered, 15%

fluctuation is introduced and the size of the detector is remarkably reduced. Therefore, machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.
Experimental results of a magnetic field modification to the radio frequency driver of a negative ion source
Authors: XIE 谢 Junwei 俊炜 , XIE 谢 Yahong 亚红 , WEI 韦 Jianglong 江龙 , LIANG 梁 Lizhen 立振 , PENG 彭 Xufeng 旭峰 , YANG 杨 Yuwen 宇雯 , GU 顾 Yuming 玉明 , HU 胡 Chundong 纯栋 , XIE 谢 Yuanlai 远来 
Published: November 2023

Abstract
A magnetic field produced by a current flowing through the plasma grid (PG) is one of the solutions to reduce the collisional loss of negative ions in a negative ion source, which reduces the electron temperature in front of the PG. However, the magnetic field diffused into the driver has some influence on the plasma outflowing. In order to investigate the effect of changing this magnetic field on the outflowing of plasma from the driver, a circular ring (absorber) of high permeability iron has been introduced at the driver exit, which can reduce the magnetic field around it and improve plasma outflowing. With the application of the absorber, the electron density is increased by about 35%, and the extraction current measured from the extraction grid is increased from 1.02 A to 1.29 A. The results of the extraction experiment with cesium injection show that both the extraction grid (EG) current and H− current are increased when the absorber is introduced.
Women’s knowledge and musical form: adapting historical identities in Martin Guerre
Authors: Barnden Sally 
Published: November 2023

Abstract
This essay assesses the afterlives in film, theatre, and scholarship of a sixteenth-century legal proceeding in which a Basque peasant named Martin Guerre was subject to identity theft. Focusing on the 1996 West End musical, the essay proposes that the conventions of musical theatre allowed this adaptation to revise earlier versions of the story in response to then-current concerns in historiography, in particular, those of new historicist criticism. It argues that the musical’s focus on the female lead’s knowledge and consent, informed by the cultural context of the 1990s, constituted a key intervention in the adaptive history of the Martin Guerre story. And it examines the musical’s engagement with new historicist ideas about the contingency of early modern identity, noting that the lyrics and structure present a contrast between a contingent identity reliant on property ownership, kinship bonds, and religious community with a more ‘modern’ identity based on psychological continuity and unique selfhood. In conclusion, the essay proposes Martin Guerre as a case study for broader questions about how historical individuals’ agency can be represented in adaptation.
mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data
Authors: Padron-Manrique Cristian , Vázquez-Jiménez Aarón , Esquivel-Hernandez Diego Armando , Martinez Lopez Yoscelina Estrella , Neri-Rosario Daniel , Sánchez-Castañeda Jean Paul , Giron-Villalobos David , Resendis-Antonio Osbaldo 
Published: November 2023

Abstract

Motivation
Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure.

Results
We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails.

Availability and implementation
The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).
Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations
Authors: Miranda-Escalada Antonio , Mehryary Farrokh , Luoma Jouni , Estrada-Zavala Darryl , Gasco Luis , Pyysalo Sampo , Valencia Alfonso , Krallinger Martin 
Published: November 2023

Abstract
It is getting increasingly challenging to efficiently exploit drug-related information described in the growing amount of scientific literature. Indeed, for drug–gene/protein interactions, the challenge is even bigger, considering the scattered information sources and types of interactions. However, their systematic, large-scale exploitation is key for developing tools, impacting knowledge fields as diverse as drug design or metabolic pathway research. Previous efforts in the extraction of drug–gene/protein interactions from the literature did not address these scalability and granularity issues. To tackle them, we have organized the DrugProt track at BioCreative VII. In the context of the track, we have released the DrugProt Gold Standard corpus, a collection of 5000 PubMed abstracts, manually annotated with granular drug–gene/protein interactions. We have proposed a novel large-scale track to evaluate the capacity of natural language processing systems to scale to the range of millions of documents, and generate with their predictions a silver standard knowledge graph of 53 993 602 nodes and 19 367 406 edges. Its use exceeds the shared task and points toward pharmacological and biological applications such as drug discovery or continuous database curation. Finally, we have created a persistent evaluation scenario on CodaLab to continuously evaluate new relation extraction systems that may arise. Thirty teams from four continents, which involved 110 people, sent 107 submission runs for the Main DrugProt track, and nine teams submitted 21 runs for the Large Scale DrugProt track. Most participants implemented deep learning approaches based on pretrained transformer-like language models (LMs) such as BERT or BioBERT, reaching precision and recall values as high as 0.9167 and 0.9542 for some relation types. Finally, some initial explorations of the applicability of the knowledge graph have shown its potential to explore the chemical–protein relations described in the literature, or chemical compound–enzyme interactions.
Database URL: https://doi.org/10.5281/zenodo.4955410