bluuebunny/crossref_metadata_2025_split huggingface dataset:
Threshold bounce — occupancy-dependent modulation of the discriminating threshold in silicon detectors
Authors: Basso M.J. , Buchanan E. , Gallop B.J. , John J.J. , Kaplon J. , Keener P.T. , Phillips P.W. , Poley L. , Sawyer C.A. , Sperlich D. , Warren M. 
Published: June 2024

Abstract
The front-end electronics of silicon detectors are typically
designed to ensure optimal noise performance for the expected input
charge. A combination of preamplifiers and shaper circuits result in
a nontrivial response of the front-end to injected charge, and the
magnitude of the response may be sizeable in readout windows
subsequent to that in which the charge was initially injected. The
modulation of the discriminator threshold due to the superposition
of the front-end response across multiple readout windows is coined
“threshold bounce”.

In this paper, we report a measurement of threshold bounce using
silicon modules built for the Phase-II Upgrade of the ATLAS detector
at the Large Hadron Collider. These modules utilize ATLAS Binary
Chips for their hit readout. The measurement was performed using a
micro-focused 15 keV photon beam
at the Diamond Light Source synchrotron. The effect of the choice of
photon flux and discriminator threshold on the magnitude of the
threshold bounce is studied. A Monte Carlo simulation which accounts
for the front-end behaviour of the silicon modules is developed, and
its predicted hit efficiency is found to be in good agreement with
the measured hit efficiency.
Optimization study of a new β
+-γ coincidence positron annihilation lifetime spectrometer using Geant4
Authors: Xu W. , Li Y.H. , Pan Z.W. , Liu J.D. , Zhang H.J. , Ye B.J. 
Published: June 2024

Abstract
Positron annihilation lifetime (PAL) spectroscopy is a
unique method for characterizing atomic-scale defects and
ultramicropores in materials. The conventional PAL spectrometer
adopts the γ-γ coincidence principle, and its
performance, especially the coincidence counting rate (CCR), can
hardly be further increased. Another coincidence principle,
β
+-γ coincidence, has the potential to
simultaneously improve the CCR and coincidence time resolution (CTR)
of PAL spectrometers. However, early β
+-γ
coincidence PAL spectrometers have not been widely applied due to
the considerable room for improvement in their performance. In this
work, we proposed a new β
+-γ coincidence PAL
spectrometer utilizing silicon photomultiplier (SiPM) array as the
positron detector and conducted a comprehensive optimization of its
structure with the aim of achieving a breakthrough in
performance. The effects of start signal threshold and structure
parameters on its CTR, CCR, and proportion of source contribution
(P
SC) were studied using Geant4. The simulation
results show that, with a 68Ge positron source of 30 μCi,
the optimized β
+-γ coincidence PAL spectrometer
can achieve an extremely high CCR exceeding 10000 counts per second
(cps) and an outstanding CTR below 160 picoseconds (ps) while
maintaining a low P
SC below 12%. This study
provides valuable guidance for constructing high-performance
β
+-γ coincidence PAL spectrometers.
Assessment of 180 nm double SOI technology for analog front-end design with back-gate voltage
Authors: Li Y. , Liu F. , Lu B. , Li Z. , Chen S. , Zhang C. , Zhu H. , Yao X. , Bu J. , Wan J. , Xu Y. , Cristoloveanu S. , Li B. , Luo J. , Ye T. 
Published: June 2024

Abstract
This paper provides an assessment of the electrical and noise performance in the 180 nm double silicon-on-insulator (DSOI) technology, which shows advantages for analog front-end radiation detectors. For the first time, the impact of the back-gate voltage on the electrical and noise performance of DSOI MOSFETs is investigated. The transconductance-to-current (gm
/ID
) ratio and low-frequency (1/f) noise were measured as a function of the MOS device types (NMOS/PMOS), gate length, and bias condition of front- and back-gates. Experimental results show that positive back-gate voltage deteriorates the gm
/ID
ratio of the MOSFETs in weak inversion region. The DSOI NMOS devices overwhelm the PMOS with better gm
/ID
and 1/f performance. The DSOI devices have a comparable 1/f noise with the 180 nm SOI counterparts. With negative back-gate voltage applied, the low frequency noise performance of NMOS is improved. This assessment of DSOI technology gives a guideline for the readout circuit design in detector front-end systems.
Climate and air quality impact of using ammonia as an alternative shipping fuel
Authors: Wong Anthony Y H , Selin Noelle E , Eastham Sebastian D , Mounaïm-Rousselle Christine , Zhang Yiqi , Allroggen Florian 
Published: June 2024

Abstract
As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NO
x
, NH3 and N2O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NO
x
, NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 (high NO
x
, low NH3 emissions) vs pure NH3 (low NO
x
, very high NH3 emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem high performance global chemical transport model. We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 16 900 mortalities from PM2.5 and 16 200 mortalities from O3 annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr−1) from pure NH3 engines could lead to 668 100 additional mortalities from PM2.5 annually under current legislation. Requiring NH3 scrubbing within current emission control areas leads to smaller improvements in PM2.5-related mortalities (22 100 avoided mortalities for NH3–H2 and 623 900 additional mortalities for pure NH3 annually), while extending both Tier III NO
x
standard and NH3 scrubbing requirements globally leads to larger improvement in PM2.5-related mortalities associated with a switch to ammonia-powered ships (66 500 avoided mortalities for NH3–H2 and 1200 additional mortalities for pure NH3 annually). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.
A health economics perspective on behavioural responses to climate change across geographic, socio-economic and demographic strata
Authors: Borghi Josephine , Kuhn Michael 
Published: June 2024

Abstract
This perspective examines the relationship between climate change, health outcomes, and behavioural responses across the life course. It identifies three primary channels through which climate change impacts behaviours which in turn affect health: increased morbidity driving healthcare demand and accessibility, reduced productivity and income affecting health care investments, and combined health and economic risks shaping migration patterns, dietary choices and human capital investment across the life course and generations. Climate-induced changes in behaviours exacerbate existing health-related and socio-economic vulnerabilities. While climate-related shocks elevate demand for healthcare services, disruptions in infrastructure hinder access, especially for the poorest, widening health inequities. Loss of income and disrupted employment further compound health and economic risks, pushing vulnerable communities towards informal care options and impoverishment tied to health expenditures. Increased health and economic risks are associated with migration affecting healthcare access and health outcomes. They also influence dietary choices, with health consequences. Finally, deteriorating prospects of leading a long, prosperous and healthy life may induce individuals to reduce their time horizon and assign lower values to long-term survival, impacting human capital investments across the life course and generations. Again, these impacts are prone to exhibit a social gradient with vulnerable individuals being more likely to give up on striving for a healthier life. Effective policies must integrate climate, health, and socioeconomic factors, considering long-term behavioural responses and their health and socio-economic implications. Adapting health financing mechanisms to account for climate risks and incentivise resilience-building behaviours within health and social care systems is essential for protecting health across the life course, and avoiding widening inequities.
Topological analysis of the complex SSH model using the quantum geometric tensor
Authors: Cheng Eve , Batchelor Murray T , Cocks Danny 
Published: June 2024

Abstract
This paper presents two methods for topological analysis of the complex Hermitian Su–Schrieffer–Heeger (SSH) model using the quantum geometric tensor: Berry phase and topological data analysis. We demonstrate how both methods can effectively generate topological phase diagrams for the model, revealing two distinct regions based on the relative magnitudes of the parameters

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, it exhibits topologically non-trivial behavior. Our results contribute to building the groundwork for topological analysis of more complicated SSH-type models.
Study of bifacial photovoltaics with fluorinated ethylene propylene as an anti-reflective layer
Authors: Sivasubramanian Ramsundar , Aravind Vaithilingam Chockalingam , Tan Sin Jin 
Published: June 2024

Abstract
Bifacial photovoltaics is a type of solar photovoltaics technology that is fast growing in popularity owing to the several advantages it offers. In this study, the inclusion of fluorinated ethylene propylene polymer as one of the constituent materials in bifacial photovoltaic modules under various configurations and its effect on the module’s optical performance was investigated and compared with a commercial bifacial module. Monte Carlo ray tracing was used to conduct the study and the system was analyzed under both non tracking and uniaxial tracking conditions for varying surface albedo values corresponding to an ideal scattering surface, white concrete and sand. The analyses performed under normal incidence condition revelated that the net irradiance on the PV layers varied by as much as 96.0314 W m−2 between the best and worst performing bifacial configurations. Under uniaxial tracking, the top and rear surfaces of the PV modules could cumulatively be subjected to 21.799 kWh of solar irradiation flux per day over a generation window of eleven hours. The proposed changes could offer cost savings of USD 0.0118 per panel per day and up to an additional 5.802 kg of CO2 equivalent offsets per panel per year.
A simple physical model for simulation and design magneto-plethysmograph in application non-invasive hemoglobin measurement
Authors: Wardoyo Sony , Djamal Mitra , Budiman Maman 
Published: June 2024

Abstract
The magneto-plethysmograph method is a combination of magnetic field and sensors used to detect changes in blood flow pulsation. However, to detect the magnetic properties of blood related to hemoglobin concentration, physical modeling and simulation are required. This approach involves designing simulations using magnetic field equations and magnetic susceptibility, where a permanent magnet is placed on the surface of blood vessels, and sensors based on giant magnetoresistance are placed at a distance r. The design originates from a simple approach involving the magnetization and detection of Fe atoms in hemoglobin. Parameters involved include the magnetic susceptibility of oxyhemoglobin and deoxyhemoglobin, with an external magnetic field exceeding 1 Tesla. From the physical modeling and simulation, graphs are obtained depicting the influence of hemoglobin concentration on the number of Fe atoms and its magnetization. This enables the design of non-invasive hemoglobin measurement sensor devices. The uniqueness of this simple physical model and simulation lies in its ability to produce specially designed device models for measuring hemoglobin concentration. This differs from other research focusing on blood flow pulse measurements; the results of this study provide new insights into the benefits of simple physics equations that can be developed for medical diagnostic research and device development.
Reading of gafchromic EBT-3 film using an overhead scanner
Authors: Bantan H , Yasuda H 
Published: June 2024

Abstract
Gafchromic film, a commercially available radiochromic film, has been developed and widely used as an effective tool for radiation dose verification and quality assurance in radiotherapy. However, the orientation effect in scanning a film remains a concern for practical application in beam profile monitoring. To resolve this issue, the authors introduced a novel method using an overhead scanner (OHS) coupled with a tracing light board instead of a conventional flatbed scanner (FBS) to read Gafchromic EBT3 films. We investigated the orientation effect of the EBT3 film with a regular hexagonal shape after irradiation with 5 Gy x-rays (160 kV, 6.3 mA) and compared the digitized images acquired using a commercially available OHS (CZUR Aura) and a conventional FBS (EPSON GT-X980). As a result, RGB color intensities acquired from the OHS showed significantly lower orientation effect of the color intensities of RGB components than those from FBS. This finding indicates the high potential of the proposed method for achieving more precise two-dimensional dosimetry. Further studies are required to confirm the effectiveness of this method under different irradiation conditions over a wider dose range.
A chip-integrated homodyne detection system with enhanced bandwidth performance for quantum applications
Authors: Qi Ng Si , Zhang Gong , Lim Charles , Wang Chao 
Published: June 2024

Abstract
The rapid development of quantum technology has driven the need for high-performance quantum signal processing modules. Balanced homodyne detector (BHD) is one of the most promising options for practical quantum state measurement, providing substantial advantages of cost-effectiveness, no cooling requirement, and system compactness. However, due to the stringent requirements in BHD design, it typically suffers from a relatively small operating bandwidth which limits the overall speed of a quantum system. In this study, we propose comprehensive modelling for the BHD in quantum applications and enhance the performance of BHDs based on our modelling. Specifically, we utilise a photonic chip approach and optimise the electronic design to create the integrated BHD, which significantly boosts the 3 dB bandwidth to 4.75 GHz and achieves a shot-noise-limited bandwidth of 23 GHz. We demonstrate the capability of this setup to generate quantum random numbers at a rate of 240 Gbit s−1, highlighting its potential for ultra-high-speed quantum communication and quantum cryptography applications.
A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets
Authors: Sharma Abhinav , Dhanka Sanjay , Kumar Ankur , Maini Surita 
Published: June 2024

Abstract
Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model’s performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.
Multi-resolution short-time Fourier transform providing deep features for 3D CNN to classify rolling bearing fault vibration signals
Authors: Zhang Meng 
Published: June 2024

Abstract
The time-frequency domain features of vibration signals provide valuable information for deep learning-based rolling bearing fault diagnosis methods, where fault signal classification aiding in the identification of nominal fault types during diagnosis. The Short-Time Fourier Transform (STFT) is a widely used time-frequency transformation method, and its window length is the key parameter that determines the trade-off between time and frequency resolution. The primary motivation of this study is to address the limitation in traditional STFT-based 2D CNN methods: the inability to adapt the window length to different types of signals. To achieve accurate classification of bearing fault types, this study proposes a method based on three-dimensional convolutional neural networks (3D CNNs) to deeply explore the time-frequency domain information of one-dimensional vibration signals from faulty bearings. This method first applies STFT with multiple window sizes to perform multi-resolution time-frequency transformations on the time-domain vibration signals, yielding three-dimensional data. Subsequently, a classifier is trained based on the proposed 3D CNN. Experimental results on public datasets show that, without any sophisticated techniques, the proposed method achieves an average classification accuracy of 99.2% for six types of bearing faults using a relatively simple CNN structure. Compared to 1D CNN and 2D CNN methods that use fixed window sizes for STFT, the proposed method significantly enhances classification performance. Furthermore, it demonstrates robust classification results even on small-scaled bearing datasets.
Design of bionic active folding flapping wing vehicle
Authors: Zhang Pingxia , Yu Shiyuan , Dong Qirui , Wu Wenjin , Zhu Yongqiang 
Published: June 2024

Abstract
Flapping wing vehicles mimic the wing flapping of flying creatures such as birds, bats and insects, and are characterized by simplicity, lightness, good concealment, high maneuverability and diversified flight attitudes. Currently, the development of wing-fluttering vehicles mainly focuses on wing-fluttering configurations, while there is little research on mimicking the large-scale active folding of wings of birds and bats. Here, we developed two types of large-scale folding wing vehicles with light mass and actively folded wings respectively, based on the property that the wings of flying organisms can be actively folded and contracted in a large scale, and tested their flight capabilities. The test results show that the latter vehicle, which combines the flapping wing mechanism and the active folding mechanism, has good flight performance and is able to accomplish the airborne folding of the wings on the basis of the flapping wing flight.
A comprehensive review of hydrophobic concrete: surface and bulk modifications for enhancing corrosion resistance
Authors: Gnanaraj S Joseph , K Vasugi 
Published: June 2024

AbstractCorrosion in concrete structures constitutes significant challenges, threatening integrity and requiring high maintenance costs. Hydrophobic concrete presents an emerging solution for addressing corrosion in concrete structures, especially in marine environments. This review article investigates advanced techniques for surface and bulk modifications of hydrophobic concrete, focusing on their efficacy in enhancing corrosion resistance. Surface modifications using silane-siloxane coatings and polyurethane treatments significantly reduce water absorption by up to 30% and chloride ion permeability by up to 50%. Bulk modifications incorporating nano-silica and calcium stearate demonstrate marked improvements in mechanical properties and corrosion resistance, with calcium stearate reducing corrosion rates by 40%. These treatments also contribute to an average increase in compressive strength of 15% and a decrease in corrosion current densities, indicating practical utility in environments such as marine conditions. Additionally, innovative techniques like fluorosilane treatments and steel fibres further fortify impermeability and structural integrity, demonstrating the versatility of hydrophobic concrete in addressing corrosion challenges. Moreover, using eco-friendly ingredients underscores its potential for sustainable infrastructure development. In summary, hydrophobic concrete offers a comprehensive strategy for combating corrosion, providing surface and bulk modifications that enhance durability, reduce maintenance costs, and ensure the long-term sustainability of modern infrastructure.
Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads
Authors: He Dongze , Gao Yuan , Chan Spencer Skylar , Quintana-Parrilla Natalia , Patro Rob 
Published: June 2024

Abstract

Motivation
Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments. As a result, the splicing status of many reads in scRNA-seq is ambiguous because of a lack of definitive evidence. We are therefore in need of methods that can recover the splicing status of ambiguous reads which, in turn, can lead to more accuracy and confidence in downstream analyses.

Results
We develop Forseti, a predictive model to probabilistically assign a splicing status to scRNA-seq reads. Our model has two key components. First, we train a binding affinity model to assign a probability that a given transcriptomic site is used in fragment generation. Second, we fit a robust fragment length distribution model that generalizes well across datasets deriving from different species and tissue types. Forseti combines these two trained models to predict the splicing status of the molecule of origin of reads by scoring putative fragments that associate each alignment of sequenced reads with proximate potential priming sites. Using both simulated and experimental data, we show that our model can precisely predict the splicing status of many reads and identify the true gene origin of multi-gene mapped reads.

Availability and implementation
Forseti and the code used for producing the results are available at https://github.com/COMBINE-lab/forseti under a BSD 3-clause license.