bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integration of BOTDA and φ-OTDR Distributed Fiber Sensing for Multiple Parameters Monitoring
Authors: Wang Zi , Zou Yang-En , Guo Chen-Yin , Liaw Shien-Kuei , Yang Ya-Mei , Lee Bo-Heng , Yeh Chien-Hung 
Published: February 2025

Abstract
In this article, the integration of the Brillouin optical time-domain analysis (BOTDA) and the phase-sensitive optical time domain reflectometry (φ-OTDR) distributed fiber sensing systems is discussed. By combining the two systems and sharing common instruments, the cost of the experiments is minimized, enabling the sensing of three parameters: temperature, strain, and vibration on the same 18.7 km fiber. In the BOTDA part, the focus is on temperature and strain analysis. Two 4m dispersion shifted fibers (DSF) are used to simulate strain at different positions, causing the Brillouin frequency shift (BFS) to shift from 10.88 GHz to 10.52 GHz, allowing for strain sensing. The two 4m single mode fibers and DSF are separately heated from room temperature to 200°C, resulting in an R2 value of 0.97654 and 0.99958, respectively, indicating a good linear relationship. In the φ-OTDR section, vibration point frequencies were demodulated using an external interferometer system based on Mach-Zehnder interferometer (MZI). This article presents comprehensive experiments that demonstrate the ability to achieve multi-parameter sensing on a single fiber using a hybrid system, all within a low-cost framework.
Electrochemical investigations of B-Site modified La2BMnO6 (B = Ni, Co, Fe) electrospun fibers for supercapacitor performance
Authors: Yasmin Yasmin , J Gracie P , D Geetha , Jeyanthi Narthana 
Published: February 2025

Abstract
Perovskite oxides have attracted attention in recent years owing to their superior electrochemical performance. Here, we report the series of single-phase double perovskite oxides fibers of La2NiMnO6, La2CoMnO6 and La2FeMnO6 using a simple sol-gel assisted electrospinning method and its application in supercapacitors, investigated first time. The crystal structure, functional groups and fibrous morphology were investigated using X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR) and High-Resolution Scanning Electron Microscopy (HRSEM-EDAX). The Goldschmidt tolerance factor and cell parameters were refined using Rietveld refinement program. The synthesized fibers presented an unfragmented network of thickness in the range of 200-380nm. The electrochemical performance of electrodes were elucidated using three-electrode system in 3M KOH. The specific capacitances of La2NiMnO6, La2CoMnO6 and La2FeMnO6 electrodes were 1272 F/g, 1048 F/g and 238 F/g at 4mA/g. The Nyquist plots of La2NiMnO6 and La2CoMnO6 electrode displayed the sloping line in low frequency and a semicircle in the high frequency region while La2FeMnO6 electrode experienced a flat line which was observed due to large internal resistance of Fe.Among the synthesized fibers, La2NiMnO6 electrode showcased the highest specific capacitance with 87.5% cycling stability after 3000 cycles which shows the better stability of La2NiMnO6 electrode and its use for practical applications.&#xD;
Deep learning models as learners for EEG-based functional brain networks
Authors: Yang Yuxuan , Li Yanli 
Published: February 2025

Abstract
Objective. Functional brain network (FBN) methods are commonly integrated with deep learning (DL) models for EEG analysis. Typically, an FBN is constructed to extract features from EEG data, which are then fed into a DL model for further analysis. Beyond this two-step approach, there is potential to embed FBN construction directly within DL models as a feature extraction module, enabling the models to learn EEG representations end-to-end while incorporating insights from FBNs. However, a critical prerequisite is whether DL models can effectively learn the FBN construction process. Approach. To address this, we propose using DL models to learn FBN matrices derived from EEG data. The ability of DL models to accurately reproduce these matrices would validate their capacity to learn the FBN construction process. This approach is tested on two publicly available EEG datasets, utilizing seven DL models to learn four representative FBN matrices. Model performance is assessed through mean squared error (MSE), Pearson correlation coefficient (Corr), and concordance correlation coefficient (CCC) between predicted and actual matrices. Main Results. The results show that DL models achieve low MSE and relatively high Corr and CCC values when learning the Coherence network. Visualizations of predicted and error matrices reveal that while DL models capture the general structure of all four FBNs, certain regions remain difficult to model accurately. Additionally, a paired t-test comparing global efficiency and nodal degree between predicted and actual networks indicates that most predicted networks significantly differ from the actual networks (p < 0.05). Significance. These findings suggest that while DL&#xD;models can learn the connectivity relationships of certain FBNs, they struggle to capture the intrinsic topological structures. This highlights the irreplaceability of traditional FBN methods in EEG analysis and underscores the need for hybrid strategies that combine FBN methods with DL models for a more comprehensive analysis.
Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome
Authors: Ding Ling , Zou Qingyu , Zhu Junming , Wang Yueming , Yang Yuxiao 
Published: February 2025

Abstract
Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction. Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance. Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance. Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.
Digitization of analog systems of older radiation monitoring instruments based on emerging technologies for radiation protection and nuclear security
Authors: Kountchou Noube Michaux , Ioannidou Alexandra , Sa\"{\i}dou  , Ioannidou Eleftheria , Folifack Signing Vitrice Ruben , Mbarndouka Taamté Jacob , Konstantakos Vasileios , Nikolaidis Emmanouil 
Published: February 2025

Abstract
This work addresses the problem of converting the analog
measurement systems of older radiation monitoring instruments into
fully digital automated measurement systems based on emerging
technologies. For the purposes of this study, the outdated analog
“BICRON Analyst” instrument will be completely transformed with
new, more efficient digital instruments that are also easier to
maintain, repair, and calibrate. Unlike the analog instrument, the
digital instrument is more accurate providing the exact number of
radioactive particles detected and the battery level. New
technologies and efficient, cost-effective electronic components
were used, namely a microcontroller board, which represents the
central element of the digitization process, a digital display to
replace the analog display and a micro-SD memory card module to
record and store the measurement data in real time as well as a GPS
module to determine the date, time and GPS coordinates of the
location where the measurement was taken. First, laboratory tests
were carried out to verify the proper functioning of the “BICRON
Analyst” according to its operating instructions. The complete
schematic circuit diagram and main board assembly (component side)
of the instrument were used to identify the points (exact location)
where the signals to be digitized must be taken from the electronic
boards. The instrument was carefully disassembled to add and attach
the new electronic components, and then the Arduino board was
appropriately programmed to complete the digitization
process. Calibration and operational control of the proper
functioning of the fully digitized equipment was carried out using a
Cs-137 gamma ray calibration source to determine the response of the
instrument as a function of its distance from the radiation
source. Given the generally high costs of laboratory equipment,
cost-effective recycling of old, outdated and bulky analogue
equipment is of great interest, particularly from an economic
perspective.
Structural, microstructural, dielectric, mechanical properties of PVDF/HAP nanocomposite films for bone regeneration applications
Authors: Kajal Parida A P , Mishra Balaram , Gupta Mukesh Kumar , Kumar Pawan 
Published: February 2025

Abstract
PVDF/HAP nanocomposite films, incorporating hydroxyapatite (HAP) nanoparticles as filler within a PVDF matrix, were successfully synthesized by solution casting method. Increasing the HAP concentration in the nanocomposite significantly enhances its electroactive properties, with synergistic effects on surface, electrical and biological characteristics are investigated comprehensively. Improvements in topographical and mechanical parameters reveal the nanocomposite films for biomimetic suitability. Notably, the impact of dielectric and ferroelectric properties on biological studies is well established. With increasing the HAP concentration, we observed significant improvements in remnant polarization from 0.28 to 1.87 µC/cm², saturation polarization from 1.1 to 2.10 µC/cm², and coercive field from 88.55 to 243.65 kV/cm. In in-vitro experiments with osteosarcoma cells, the nanocomposite films with 40% HAP showed higher cell proliferation and viability. Present finding indicated 60PVDF/40HAP nanocomposite films as a biomimicry candidate for bone regeneration applications.
Single-cone Dirac edge states on a lattice
Authors: Donis Vela Alvaro , Beenakker Carlo W J 
Published: February 2025

Abstract
The stationary Dirac equation, confined to a two-dimensional (2D) region, supports states propagating along the boundary and decaying exponentially away from the boundary. These edge states appear on the 2D surface of a 3D topological insulator, where massless fermionic quasiparticles are governed by the Dirac equation and confined by a magnetic insulator. We show how the continuous system can be simulated on a 2D square lattice, without running into the fermion-doubling obstruction. For that purpose we adapt the existing tangent fermion discretization on an unbounded lattice to account for a lattice termination that simulates the magnetic insulator interface.
A review of graded scaffolds made by additive manufacturing for tissue engineering: design, fabrication and properties
Authors: Wang Yue , Chen Shangsi , Liang Haowen , Bai Jiaming 
Published: February 2025

Abstract
The emergence of tissue engineering (TE) has provided new vital means for human body tissue/organ repair. TE scaffolds can provide temporary structural support for cell attachment, growth, and proliferation, until the body restores the mechanical and biological properties of the host tissues. Since native tissues are inhomogeneous and in many situations are graded structures for performing their unique functions, graded scaffolds have become increasingly attractive for regenerating particular types of tissues, which aim to offer a more accurate replication of native interactions and functions. Importantly, the advances introduced by additive manufacturing (AM) have now enabled more design freedom and are capable of tailoring both structural and compositional gradients within a single scaffold. In this context, graded TE scaffolds fabricated by AM technologies have been attracting increasing attention. In this review, we start with an introduction of common graded structures in the human body and analyse the advantages and strengths of AM-formed graded scaffolds. Various AM technologies that can be leveraged to produce graded scaffolds are then reviewed based on non-cellular 3D printing and cell-laden 3D bioprinting. The comparisons among various AM technologies for fabricating graded scaffolds are presented. Subsequently, we propose several types of gradients, structural, material, biomolecular and multi-gradients for scaffolds, and highlight the design methods, resulting mechanical properties and biological responses. Finally, current status, challenges and perspectives for AM in developing graded scaffolds are exhibited and discussed.
Coupled ionizing-radiation/optical-photon transport Monte Carlo simulations for characterisation of light signal in an optical fiber radioluminescence dosimetry system
Authors: Moradi Farhad , Oresegun Adebiyi , Khodaei Amin , Bradley David A , Taheri Ali , Khandaker Mayeen Uddin , Abdul-Rashid Hairul A 
Published: February 2025

Abstract
Optical fiber radioluminescence (RL) dosimetry has gained prominence in modern radiation therapy, offering real-time measurement and high spatial resolution. Our research group has developed a system utilizing a polymethyl methacrylate (PMMA) transmission fiber coupled with a photodetector and various scintillators, including doped silica fibers. A critical challenge in RL dosimetry lies in distinguishing the stem signal, generated by the transmission optical fiber, from the primary light signal produced by the RL sensor. To address this issue, we employed the Geant4 simulation tool, allowing for the simultaneous tracking of ionizing radiation and optical photons. In this study, the Geant4-based code, TOPAS, was utilized to conduct Monte Carlo simulations, aiming to gain insights into the radioluminescence signal in an optical fiber RL dosimeter and specifically characterize the stem signal for enhanced measurement accuracy. The simulations encompassed interactions of a medical photon beam from an Elekta linac within a solid water phantom, subsequent energy deposition within the RL sensor, and the generation and transmission of light signals within the optical fiber. Our emphasis was placed on detailed characterization of the light signals originating from both the Ge-doped silica fiber and PMMA transmission fiber. The primary focus was not only to discern the stem signal from the main signal but also to differentiate between the fluorescence and Cerenkov signals. Importantly, our study showcases how Monte Carlo simulations can be used to spectrally distinguish the stem signal from the scintillation signal of the sensor. This provides valuable insights, especially in scenarios where spectrometry is unavailable, contributing to the understanding and refinement of optical fiber RL dosimetry systems.
Internal biogas reforming in solid oxide and proton conducting ceramic cells: Progress, challenges and perspectives
Authors: Wolf Stephanie E , Uecker Jan , Eyckeler Niklas , Schley Leon , de Haart L G J (Bert) , Vibhu Vaibhav , Eichel Rüdiger-A 
Published: February 2025

Abstract
The internal reforming of biogas, a mixture containing carbon dioxide (CO2) and methane (CH4), in Solid Oxide and Solid Proton Conducting Fuel Cells (SOFCs, SPCFCs) is a sustainable and efficient method to produce syngas (H2 + CO) in combination with highly efficient electrical power generation. Reforming processes convert biogas into syngas by steam reforming, dry reforming, or partial oxidation, which then undergoes electrochemical reactions in the SOFCs/SPCFCs to produce electricity and heat. The Dry Methane Reforming Process (DMR) of the anthropogenic greenhouse gases CH4 and CO2 in the biogas can result in co-generation of electrical power and syngas mixtures of CO:H2 relevant for large-scale industrial processes like the Fischer-Tropsch process. Herein, a short review of promising developments in the literature concerning the internal dry reforming of biogas (CH4 and CO2) in oxygen-ion conducting and proton-conducting fuel cells is provided. The thermodynamics of different reforming processes, the advantages, disadvantages, and the fundamental electrochemical processes in SOFCs and SPCFCs are discussed comprehensively and comparatively. In addition, this article aims to provide a perspective on current gaps and possible future research efforts.
The Traditional Conception of Metaphysics
Authors: Thomasson Amie L 
Published: February 2025

Abstract
Metaphysics has traditionally been characterized as aiming to discover highly general, basic, or fundamental facts about the world. But this way of thinking of metaphysics has become outdated and problematic. This chapter presents three central problems that arise for this conception of metaphysics, including a rivalry with science, proliferation of views in a way that leads to skepticism, and epistemological mystery. The chapter also prefigures the diagnosis of where various approaches to metaphysics go wrong: in assuming that all the types of discourse of interest in metaphysics function to track or describe features of the world. One goal of the book is to show how we can identify the different functions various areas of discourse serve, in ways that enable us to unravel old metaphysical problems. Another goal is to show how we can avoid problems with the traditional conception of metaphysics by reconceiving work in metaphysics as conceptual engineering.
Fundamentality and Grounding Projects
Authors: Thomasson Amie L 
Published: February 2025

Abstract
In the last two decades it has become popular to think of metaphysics as properly concerned with fundamentality or grounding. This chapter argues that severe difficulties arise for the project of determining what’s absolutely fundamental, or for developing a “layered” picture of reality. We might still aim to identify local grounding or dependence relations between specific sorts of entity—but two cautions are needed. First, there needs to be a clear epistemology for determining what the relevant grounding relations are. Second, we need to avoid illegitimately generalizing, and assuming that questions about what “grounds” the relevant entities are always appropriate. The closing lesson of Part I is that, before we take on any metaphysical project, criterion, or standard of assessment, we must take a step back to first determine what functions the discourse serves and what rules it follows.
Identifying Linguistic Functions
Authors: Thomasson Amie L 
Published: February 2025

Abstract
To do a conceptual-engineering project well, we need a good notion of linguistic function, so that we can identify the functions extant parts of language have served and are to serve. This chapter confronts various sources of skepticism about our ability to identify linguistic functions, and makes use of work in systemic functional linguistics to show how to identify the functions of various parts and subsystems of language. Systemic functional linguistics begins from identifying the three metafunctions nearly all clauses simultaneously serve: ideational, interpersonal, and textual. It also identifies the important functions of “grammatical metaphors” (which are introduced via shifts from one part of speech to another) in developing scientific theories and bureaucracies. This approach to function not only enables us to overcome various old sources of skepticism about identifying linguistic functions but also enables us to disentangle ourselves from many old metaphysical problems.
IBC Oxford University Oral Abstract 4 - The impact of abdominoplasties in patients with prior sleeve gastrectomy on de novo GERD and the need for conversion to Roux-en-Y gastric bypass
Authors: Berk R , Lima D L , Park M , Serra J , Echeverri C , Wynn M , Camacho D 
Published: February 2025

Abstract

Introduction
The sleeve gastrectomy (SG) often requires conversion to Roux-en-Y gastric bypass (RYGB) due to gastroesophageal reflux disorder (GERD). Many post-bariatric patients seek body-contouring surgery such as abdominoplasty to remove unwanted skin and fat. While the number of abdominoplasties performed in post-bariatric patients is increasing each year, the number of conversion surgeries is increasing in accordance. This study evaluates the impact of abdominoplasties in patients with prior SG on the development of GERD and the need for conversion to RYGB.

Methods
A retrospective study was conducted with 630 patients who underwent conversions from SG to RYGB at our institution between January 2014 and September 2023.  Outcomes were stratified for comparison between patients with GERD as an indication for conversion and patients with inadequate weight loss as an indication for conversion. Between the two groups we compared the number of patients with post-SG abdominoplasty and the number of hiatal hernias seen during conversion surgery.

Results
There was a statistically significant higher number of abdominoplasties in patients who underwent conversion to RYGB for GERD (28 patients, 8.4%) compared to inadequate weight loss (13 patients, 4.3%), P-value 0.037. Conversely, there were statistically significantly more hiatal hernias present in patients with inadequate weight loss as an indication for conversion (55 patients, 18.3%) compared to patients with GERD as an indication for conversion (17 patients, 5.1%), P-value &lt; 0.001.

Conclusion
Our data supports the hypothesis that abdominoplasty surgery alone can increase the development of GERD in post-SG patients, often requiring conversion to RYGB.