bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single pixel readout of a MaPMT-based neutron anger camera system with waveform digitizers
Authors: Zhao Binqing , Huang Yu , Chen Longfei , Wang Cailin 
Published: January 2025

Abstract
In the neutron-sensitive Anger camera, the scintillator is
directly coupled to the photodetectors, such as multiplier tubes or
silicon-photomultipliers (SiPMs), and the incident position of the
neutron is determined by the center of gravity of the photon
distribution from the scintillating material. The photodetectors in
the neutron Anger camera are typically arranged in arrays with
multiple pixel outputs. To reduce the number of readout channels,
the front-end electronics often employ analog signal multiplexing
methods, such as row-column summation and resistor
networks. However, these methods increase channel crosstalk and
noise after compression. In this paper, we propose reading out each
pixel of the multi-anode photomultiplier tube (MaPMT) individually
and digitizing each analog channel via waveform capture. This
approach allows direct collection of the signal from each analog
channel, and we use the pulse amplitude method to achieve
neutron-gamma discrimination for GS20 scintillator. We tested the
neutron two-dimensional imaging capability of the anger camera
system using a Cf-252 slowed neutron beam in the laboratory. The
neutron position resolution was estimated using the knife-edge
function method with B4C mask. By combining this with the
traditional center-of-gravity method, we achieved a position
resolution of 1.2 mm (FWHM).
The potential for fuel reduction to reduce wildfire intensity in a warming California
Authors: Brown Patrick T , Strenfel Scott J , Bagley Richard B , Clements Craig B 
Published: January 2025

Abstract
Increasing fuel aridity due to climate warming has and will continue to increase wildfire danger in California. In addition to reducing global greenhouse gas emissions, one of the primary proposals for counteracting this increase in wildfire danger is a widespread expansion of hazardous fuel reductions. Here, we quantify the potential for fuel reduction to reduce wildfire intensity using empirical relationships derived from historical observations with a novel combination of spatiotemporal resolution (0.375 km, instantaneous) and extent (48 million acres, 9 years). We use machine learning to quantify relationships between sixteen environmental conditions (including ten fuel characteristics and four temperature-affected aridity characteristics) and satellite-observed fire radiative power. We use the derived relationships to create fire intensity potential (FIP) maps for sixty historical weather snapshots at a 2 km and hourly resolution. We then place these weather snapshots in differing background climatological temperature and fuel characteristic conditions to quantify their independent and combined influence on FIP. We find that in order to offset the effect of climate warming under the SSP2-4.5 emissions scenario, fuel reduction would need to be maintained perpetually on ∼3 million acres (or 600 000 acres per year, 1% of our domain, at a 5 year return frequency) by 2050 and ∼8 million acres (or 1.6 million acres per year, 3% of our domain, at a 5 year return frequency) by 2090. Overall, we find substantial potential for fuel reduction to negate the effects of climate warming on FIP.
Polyadic Cantor potential of minimum lacunarity: special case of super periodic generalized unified Cantor potential
Authors: Umar Mohammad , Hasan Mohammad , Narayan Singh Vibhav , Prasad Mandal Bhabani 
Published: January 2025

Abstract
To bridge the fractal and non-fractal potentials we introduce the concept of generalized unified Cantor potential (GUCP) with the key parameter N which represents the potential count at the stage S = 1. This system is characterized by total span L, stages S, scaling parameter ρ and two real numbers µ and ν. Notably, the polyadic Cantor potential system with minimal lacunarity is a specific instance within the GUCP paradigm. Employing the super periodic potential formalism, we formulated a closed-form expression for transmission probability

T

S

(
k
,
N
)

using the q-Pochhammer symbol and investigated the features of non-relativistic quantum tunneling through this potential configuration. We show that GUCP system exhibits sharp transmission resonances, differing from traditional quantum systems. Our analysis reveals saturation in the transmission profile with evolving stages S and establishes a significant scaling relationship between reflection probability and wave vector k through analytical derivations.
Unravelling the mechanism of transient coastal upwelling in the southeastern Arabian Sea triggered by Cyclone Michaung
Authors: Dev Rahul , Muraleedharan K R , Gireeshkumar T R , Shivaprasad S , Baliarsingh Sanjiba Kumar , Jayaprakash Anjana , Nair T M Balakrishnan 
Published: January 2025

Abstract
Cyclone Michaung formed and passed over the Bay of Bengal in early December 2023. In the current study, we investigated the dramatic changes in physical-biogeochemical conditions in the Southeastern Arabian Sea - and the underlying dynamics caused by Cyclone Michaung. Satellite observations indicated that temperature greatly decreased, but chlorophyll-a concentration increased at the sea surface. In the subsurface layer (5.5 m below the sea surface), an autonomous coastal observatory recorded a dramatic decrease in temperature and dissolved oxygen concentration but increased salinity. Further analyses proved that the physical-biogeochemical changes in the surface and subsurface layers in the Southeastern Arabian Sea were generated by transient coastal upwelling, which was forced by strong winds of Cyclone Michaung via driving offshore Ekman transport. The transient coastal upwelling brought cool and nutrient-rich subsurface waters to the surface and enhanced biological production. Coastal upwelling was unusual in the Southeastern Arabian Sea this season due to the prevalence of downwelling favourable to northeast monsoon winds. This study points out that large-scale weather events could have a prominent role in changing the hydrodynamic and biological features of remote regions due to their strong sea surface winds.
Advancing haptic realism: modelling grasp contact vibrations for enhanced virtual environment interaction
Authors: Najdovski Zoran , Pedrammehr Siamak , Qazani Mohammad Reza Chalak , Abdi Hamid , Asadi Houshyar 
Published: January 2025

Abstract
In haptic technology, achieving realistic tactile feedback is crucial for enhancing a user's experience in virtual environments. Previous studies lack effective methods for transmitting high-frequency vibrations crucial for realistic tactile feedback in haptic interfaces, highlighting the need for our research to address this gap. This paper explores the application of a tactile gripping interface to transmit the high-frequency vibrations produced when contacting a hard object’s surface. These short vibrations improve the tactile sensation of hard virtual surfaces when overlaid on traditional position-based force feedback within a haptic environment. The enhanced realism of virtual objects is achieved by effectively estimating the vibration composition from user-induced parameters. This work presents a prototype grasping interface and empirically demonstrates this device’s utility. We examine empirical grasp contact data, recorded and interpreted, to recognise the relationship between dynamic user-controlled parameters and the resulting vibration transients. This relationship effectively incorporates these changing dynamics to model the grasp impact and estimate the essential system parameters to understand the influence of the user’s grasp force. Through our multi-point grasping interface design, this work demonstrates a mathematical relationship between the user’s grasp force and the high-frequency vibrations from contact with hard surfaces. The study found that the proposed haptic interface achieved an RMSE of 0.05, demonstrating a high level of accuracy. This low RMSE value signifies that the predicted vibrations closely matched the actual measured vibrations, validating the system’s capability to generate precise high-frequency transients. Such accuracy is critical for practical applications, including realistic tactile feedback in virtual environments, where precise modelling enhances user experience and interaction reliability. This work provides a foundational model for developing advanced haptic technologies, enabling more immersive virtual environments and precise control in teleoperation and training simulations.
A design method to optimize GaN-based class E resonant inductive wireless power transfer systems – application to non-mating connectors
Authors: Côte Nathis , Garraud Nicolas , Sterna Léo , Périchon Pierre , Frassati François , Boisseau Sébastien 
Published: January 2025

Abstract
Wireless Power Transfer (WPT) systems have demonstrated their efficiency over a broad frequency spectrum. However, the design and the optimization of multi-MHz frequency WPT systems remain complex due to parasitic components specifically. This paper outlines a methodology for designing and optimizing a compact 13.56 MHz resonant inductive coupling WPT systems based on a GaN Class E inverter. The proposed design approach combines analytical modeling and advanced numerical simulations taking into account parasitic elements. The effectiveness of this approach is experimentally confirmed through a prototype, which shows an efficiency of 78% for a transmitted power of 43 W to a 5.54 cm3 coupler. The advanced modeling results perfectly match with the experimental ones (mismatch of only 2% for the evaluation of efficiency). The WPT system developed thanks to this method shows a power density among the highest ones in the non-mating connectors’ state of the art.
Underwater target detection algorithm based on feature enhancement and feature fusion
Authors: Liu Qinxiao , Ji Longlong , Zhao Fen 
Published: January 2025

Abstract
In the field of target detection, identifying underwater targets remains a formidable challenge due to the complexity of the underwater environment and the substantial variations in target scale. To tackle the difficulties of extracting complex features in such conditions, this study introduces a novel feature extraction module built upon the YOLOv7 model. The module incorporates a custom-designed information attention mechanism and deformable convolution, enhancing the network’s ability to capture and process intricate underwater features. Additionally, a feature enhancement and interaction module was developed further to strengthen the extraction and utilization of key features. An adaptive feature fusion module was also introduced, allowing for dynamic adjustment of fusion weights based on the characteristics of the input features, thereby enabling more efficient and accurate multi-scale feature fusion. Experimental results reveal that, compared to the original YOLOv7 model on the URPC2020 dataset, the proposed method achieves improvements of 3.3% and 2.7% in mAP
50 and mAP
50:95 metrics, respectively. These significant performance gains highlight the effectiveness of the proposed approach in underwater target detection and provide valuable new directions for research and applications in this domain.
A vision-based approach to estimate enzymatic glucose concentration and to investigate the effects of temperature and time on colorimetric responses
Authors: Goswami Rakesh , Hazarika Nityananda , Roy Ram Kishore , Bezboruah Tulshi 
Published: January 2025

Abstract
The estimation of glucose is important for managing health conditions, like diabetes and supporting advancements in non-invasive diagnostic tools. The propose work introduces a simple, fast, reliable, and cost-effective vision-based colorimetric method for detection of enzymatic glucose by using a Raspberry Pi. Unlike traditional methods, which often require invasive procedures, complex equipment, or expert intervention, the proposed approach leverages image processing and open-source computing to provide a non-invasive, portable, and efficient solution. The concentration of sample solutions varying from 0.02 M to 1.20 M is prepared for detection. The principle of the proposed technique is based on the change of color by using ammonium metavanadate and sulphuric acid upon interaction with glucose solution. A camera module interfaced with a Raspberry Pi single-board computer captures the colorimetric changes. The captured images are studied by using HSV (Hue, Saturation, and Value) color model. This model is chosen for its ability to separate color information (hue) from intensity (value), enabling more accurate quantification of colorimetric responses. We present here the results of the effect of temperature and time on the colorimetric responses of the prepared samples. The novelty of the technique is its simple design, portability, rapid analysis, cost-effectiveness, suitable for environmental conditions, non-invasive in nature, and simple measurement procedure.
FedFP: personalized federated learning based on feature extraction guidance and parameter decoupling
Authors: Zhang Qian , Wei Mingjun , Liu Jinyun , Li Hui , Xu Rongyang 
Published: January 2025

Abstract
Federated learning (FL) is an emerging distributed machine learning paradigm that collaboratively trains a global model while preserving data privacy. However, in the presence of data heterogeneity, it encounters challenges such as slow convergence and reduced global model accuracy. To address this issue, we propose a novel personalized federated learning(PFL) algorithm, FedFP. First, the algorithm combines the global category embedding layer with a contrastive learning mechanism to address the inconsistency of feature distributions in heterogeneous data scenarios by sharing global category embedding information. Second, by incorporating personalized batch normalization and classifier layers, the algorithm mitigates the impact of data heterogeneity on model convergence and generalization, while better accommodating the personalized needs of clients. Finally, extensive experiments were conducted on widely used image classification datasets as well as real-world human activity recognition datasets. The experimental results show that FedFP outperforms existing federated learning algorithms.
Supporting older people with cognitive impairment during and after hospital stays with intersectoral care management (intersec-CM)—results of a randomised clinical trial
Authors: Boekholt Melanie , Nikelski Angela , Schumacher-Schoenert Fanny , Kracht Friederike , Vollmar Horst Christian , Hoffmann Wolfgang , Kreisel Stefan Henner , Thyrian Jochen René 
Published: January 2025

Abstract

Background
The transition from hospital to primary care is a risk factor for negative health outcomes in people with cognitive impairment.

Objective
To test the effectiveness of intersectoral care management during the transition from hospital to primary care on repeated admission to hospital, functionality and institutionalisation in people with cognitive impairment.

Design
Longitudinal multisite randomised controlled trial with two arms (care as usual and intersectoral care management) and two follow-ups 3 and 12 months after discharge.

Setting
Three hospitals in two different primary care regions in Germany.

Subjects
n = 401 people with cognitive impairment: community-dwelling, age 70+. Randomised into control (n = 192) or intervention (n = 209).

Methods
Primary outcomes for the study after 3 months: admission to hospital, physical and instrumental functionality. Primary outcome after 12 months: institutionalisation, physical and instrumental functionality. Secondary outcomes: health-related quality of life, depressive symptoms, cognitive status and frailty. Statistical analyses include descriptive analyses as well as univariate and multivariate regression models for all outcomes.

Results
There was no statistically significant effect of the intervention on hospital admission and activities of daily living after 3 months, as well as on institutionalisation and activities of daily living after 12 months. There were significantly fewer participants in the intervention group readmitted to the hospital 12 months after discharge.
Analyses show a significant effect on health-related quality of life 3 months and 12 months after discharge. Depressive symptoms were significantly less likely in the intervention group 3 months after discharge. No effects on cognition or frailty.

Conclusion
Intersectoral care management supports people with cognitive impairment during discharge and transition. Even though we were not able to show an impact of the intervention on the chosen primary outcomes everyday functionality and institutionalisation, the effects on health-related quality of life, hospital admission rate and mental health are solid indicators for an improved individual situation.

Trial registration
ClinicalTrials.gov, NCT03359408; https://clinicaltrials.gov/ct2/show/NCT03359408
A benchmarking study of individual somatic variant callers and voting-based ensembles for whole-exome sequencing
Authors: Guille Arnaud , Adélaïde José , Finetti Pascal , Andre Fabrice , Birnbaum Daniel , Mamessier Emilie , Bertucci François , Chaffanet Max 
Published: January 2025

Abstract
By identifying somatic mutations, whole-exome sequencing (WES) has become a technology of choice for the diagnosis and guiding treatment decisions in many cancers. Despite advances in the field of somatic variant detection and the emergence of sophisticated tools incorporating machine learning, accurately identifying somatic variants remains challenging.
Each new somatic variant caller is often accompanied by claims of superior performance compared to predecessors. Furthermore, most comparative studies focus on a limited set of tools and reference datasets, leading to inconsistent results and making it difficult for laboratories to select the optimal solution. Our study comprehensively evaluated 20 somatic variant callers across four reference WES datasets. We subsequently assessed the performance of ensemble approaches by exploring all possible combinations of these callers, generating 8178 and 1013 combinations for single-nucleotide variants (SNVs) and indels, respectively, with varying voting thresholds. Our analysis identified five high-performing individual somatic variant callers: Muse, Mutect2, Dragen, TNScope, and NeuSomatic. For somatic SNVs, an ensemble combining LoFreq, Muse, Mutect2, SomaticSniper, Strelka, and Lancet outperformed the top-performing caller (Dragen) by &gt;3.6% (mean F1 score = 0.927). Similarly, for somatic indels, an ensemble of Mutect2, Strelka, Varscan2, and Pindel outperformed the best individual caller (Neusomatic) by &gt;3.5% (mean F1 score = 0.867). By considering the computational costs of each combination, we were able to identify an optimal solution involving four somatic variant callers, Muse, Mutect2, and Strelka for the SNVs and Mutect2, Strelka, and Varscan2 for the indels, enabling accurate and cost-effective somatic variant detection in whole exome.
Children’s participation in social work research. A secondary data analysis of an umbrella review of children’s participation in child welfare
Authors: McCafferty Paul , Mercado García Esther 
Published: January 2025

Abstract
The real participation of children and young people (C&YP) in decisions affecting their lives and rights gained traction with the 1989 adoption of the UN Convention on the Rights of the Child, particularly Article 12, which asserts children's rights to express their views and have them respected. This principle recognizes children as active societal agents, not just passive subjects. This study, based on these principles, analyses secondary data from a prior umbrella review by the authors, focusing on children’s involvement in research processes. Using an adapted version of Shier's (2019) model, the study highlights a worrying dependence on traditional, adult-centred research methods that largely overlook young participants' perspectives. Although some creative, child-friendly methods are used, they are sporadic, revealing a gap in engaging younger children effectively. The study also finds that children's participation is often tokenistic rather than genuinely collaborative. These findings emphasize the need for more inclusive and innovative research practices to empower C&YP as co-creators of knowledge. Bridging these gaps is essential for promoting a more child-centred, equitable approach in research, which values every child's contribution and fosters a more inclusive society.
Performance and validation of a digital memory test across the Alzheimer’s disease continuum
Authors: Toniolo Sofia , Attaallah Bahaaeddin , Maio Maria Raquel , Tabi Younes Adam , Slavkova Elitsa , Klar Verena Svenja , Saleh Youssuf , Idris Mohamad Imran , Turner Vicky , Preul Christoph , Srowig Annie , Butler Christopher , Thompson Sian , Manohar Sanjay G , Finke Kathrin , Husain Masud 
Published: January 2025

Abstract
Digital cognitive testing using online platforms has emerged as a potentially transformative tool in clinical neuroscience. In theory, it could provide a powerful means of screening for and tracking cognitive performance in people at risk of developing conditions such as Alzheimer’s disease. Here we investigate whether digital metrics derived from an in-person administered, tablet-based short-term memory task—the ‘What was where?’ Oxford Memory Task—were able to clinically stratify patients at different points within the Alzheimer’s disease continuum and to track disease progression over time. Performance of these metrics compared to traditional neuropsychological pen-and-paper screening tests of cognition was also analysed. A total of 325 people participated in this study: 49 patients with subjective cognitive decline, 57 with mild cognitive impairment, 63 with Alzheimer’s disease dementia and 156 elderly healthy controls. Most digital metrics were able to discriminate between healthy controls and patients with mild cognitive impairment and between mild cognitive impairment and Alzheimer’s disease patients. Some, including Absolute Localization Error, also differed significantly between patients with subjective cognitive decline and mild cognitive impairment. Identification accuracy was the best predictor of hippocampal atrophy, performing as well as standard screening neuropsychological tests. A linear support vector model combining digital metrics achieved high accuracy and performed at par with standard testing in discriminating between elderly healthy controls and subjective cognitive decline (area under the curve 0.82) and between subjective cognitive decline and mild cognitive impairment (area under the curve 0.92), while performing worse in classifying between mild cognitive impairment and Alzheimer’s disease patients (area under the curve 0.75). Memory imprecision was able to predict cognitive decline on standard cognitive tests over one year. Overall, these findings show how it might be possible to use a digital memory test in clinics and clinical trial contexts to stratify and track performance across the Alzheimer’s disease continuum.