bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Ultrasound and Power-Assisted Devices through Lateral Incision: The OCCULT Technique for Gynecomastia—A Multicentric Large Series Study
Authors: Gupta Rajat , Bansal Priya , Chugh Anmol , Shah Saumil Girish , Chaudhary Gautam 
Published: February 2025

Abstract
Introduction Gynecomastia is a common condition characterized by male breast enlargement, which can have a profound psychological impact on affected individuals. Surgical intervention is often sought to alleviate these concerns. This study evaluates the lateral port technique for gynecomastia surgery, which combines power-assisted liposuction (PAL) and ultrasound-assisted liposuction (UAL) to achieve optimal surgical outcomes with no scar on front of chest.
Materials and Methods A retrospective analysis was conducted on 967 patients who underwent gynecomastia surgery using the Out of Sight, No Cut on Front of Chest Using Ultrasound and Power-Assisted Devices through Lateral Incision (OCCULT) technique between January 2022 and December 2023. The procedures were performed at multiple centers located in New Delhi, Gurugram, and Mumbai, India. This technique involves a single lateral chest wall incision and incorporates both PAL and UAL to ensure effective tissue removal and contouring. Outcomes assessed included surgical efficacy, complication rates, and patient satisfaction. The surgeries were performed by a team of experienced surgeons, ensuring the reproducibility and consistency of the method.
Results The OCCULT technique demonstrated high efficacy and high patient satisfaction. Among the 967 patients, 93.3% reported being satisfied with the surgical results. Excellent scar quality was reported by 97.1% of the patients. Moreover, the scar was out of sight, that is, no scar on the front of chest was made in the OCCULT technique for gynecomastia surgery. Complications were minimal, with only 0.8% experiencing seroma formation and no cases of necrosis observed.
Conclusion The OCCULT technique for gynecomastia surgery is an effective and reproducible method that provides aesthetically favorable results with minimal complications. Its ability to address the psychological and physical burdens of gynecomastia highlights its value in modern surgical practice.
Surface transport emission reduction strategy in Bhutan
Authors: Suberi Hari Kumar , Helmers Eckard , Asif Muhammad , Nadeem Talha Bin 
Published: February 2025

This study analyzes the surface transport emission reduction strategy in Bhutan. Considering 100% Electric Vehicle (EV) development by 2050, the estimated emission reduction is calculated based on kilometers traveled for passenger vehicles and ton kilometers traveled for freight mobility in the year 2022. The calculated values show combustion engine vehicle emissions (CO2: 1145 Gt, CO: 29.8 Gt, SO2: 2.9 Gt, NOx: 12.1 Gt, N2O: 0.009 Gt, CH4: 0.104 Gt, NMVOC: 5.8 Gt, and PM2.5: 0.072 Gt) compared to 100% EV emissions (CO2: 276 Gt) in gigatons (Gt) in 2050, where other emission categories are completely removed during the operation life of EVs. However, to meet the emission reduction potential, various technical challenges are to be fulfilled, such as electrification of freight mobility, rural road infrastructure quality development, the high modal share of public transport service, and at least the provision of 2500 MW as a base load in the electric grid network. Furthermore, qualifying for zero emissions by 2050 requires regular data updates, which is a concern. Bhutan is uniquely positioned to achieve EV zero emissions as its power generation mix is almost 100% renewables based predominantly driven by hydropower.
Increasing plant density increased rice (Oryza sativa) yield but decreased lodging resistance when grown under wheat (Triticum aestivum) straw using unmanned aerial seeding technology
Authors: Hu Qun , Zhu Haibin , Gu Yuankun , Wei Haiyan , Zhang Hongcheng 
Published: February 2025

Context Unmanned aerial seeding (UAS) of rice (Oryza sativa) has inherent advantages, but may not achieve a high and stable yield when extensive stubble is returned. Aims This study aimed to determine the optimal planting density for achieving high yield and lodging resistance in UAS rice. Methods The experiment evaluated yield and lodging of two rice varieties (conventional japonica and hybrid indica–japonica) at four planting densities 105 (D1), 150 (D2), 195 (D3), and 240 (D4) seedlings m−2 for conventional japonica; 45 (D1), 82.5 (D2), 120 (D3), and 157.5 (D4) seedlings m−2 for hybrid indica–japonica). Key results Our results showed that yield initially increased and then decreased with increasing density, and the highest yield was obtained under treatment D3. Increasing planting density increased the proportion of main stem panicles and the effective panicles, but decreased the number of grains per panicle, seed setting rate, and 1000-grain weight. The population growth rate was lower in seeding-jointing and heading-maturity stages for treatments D1 and D2, higher in seeding-jointing stage for treatment D4 but insufficient in subsequent stages, while treatment D3 had higher population growth rate throughout the whole growth stage, especially in heading-maturity stages, ultimately achieving high yield. Densification led to a reduction in plant height and ear length, as well as slender internodes and thinner base wall. Conclusions A planting density of 195 seedlings m−2 was optimal for conventional japonica rice, and 120 seedlings m−2 for indica–japonica. Implications Achieving high yield of UAS rice can be facilitated by increasing planting density, but may compromise lodging resistance.
Simultaneous estimation of electrical conductivity and permittivity in quantitative thermoacoustic tomography
Authors: Sahlström Teemu , Lahivaara Timo , Tarvainen Tanja 
Published: February 2025

Abstract
In this work, the inverse problem of quantitative thermoacoustic tomography is studied. In quantitative thermoacoustic tomography, dielectric parameters of an imaged target are estimated from an absorbed energy density induced by an externally introduced electromagnetic excitation. In this work, simultaneous estimation of electrical conductivity and permittivity is considered. We approach this problem in the framework of Bayesian inverse problems. The dielectric parameters are estimated by computing maximum a posteriori estimates, and the reliability of the estimates is studied using the Laplace's approximation. The forward model to describe electromagnetic wave propagation is based on a vectorial Maxwell's equation, that is numerically approximated using a finite element method with edge elements. The proposed methodology is evaluated using numerical simulations utilising one or two electromagnetic excitations at multiple excitation frequencies. The results show that the electrical conductivity and permittivity can be simultaneously estimated in quantitative thermoacoustic tomography. However, the problem can suffer from non-uniqueness, which could be overcome using multiple electromagnetic excitations.
Fundamental physical constants, operation of physical phenomena and entropy increase
Authors: Trachenko Kostya 
Published: February 2025

Abstract
Approaching the problem of understanding fundamental physical constants (FPCs) started with discussing the role these constants play in high-energy nuclear physics and astrophysics. Condensed matter physics was relatively unexplored in this regard. More recently, it was realised that FPCs set lower or upper bounds on key condensed matter properties. Here, we discuss a wider role played by FPCs in condensed matter physics: at given environmental conditions, FPCs set the observability and operation of entire physical effects and phenomena. We discuss structural and superconducting phase transitions and transitions between different states of matter. We also discuss metastable states, transitions between them, chemical reactions and their products. An interesting byproduct of this discussion is that the order of magnitude of the transition temperature can be calculated from FPCs. We show that the new states emerging as a result of various transitions increase the phase space and entropy. Were FPCs to take different values, these transitions would become inoperative at our environmental conditions and the new states due to these transitions would not emerge. This suggests that the current values of FPCs, by enabling various transitions and reactions which give rise to new states, promote entropy increase. We conjecture that entropy increase is a selection principle for FPCs considered to be variable in earlier discussions.
A Novel Two-Stage Fault Detection and Tracking Method of Hot-Melt Extrusion Process Based on Data-Driven Techniques
Authors: Zhang Mingjie , Chen Zhuyun , Deng Xu , Jin Gang 
Published: February 2025

Abstract
In the hot-melt extrusion (HME) process, process parameters play a crucial role in determining the quality of the extruded products. Abnormal processing parameters can lead to degraded product performance and quality inconsistencies. Therefore, real-time monitoring of the HME process is vital for maintaining product quality and consistency. However, most existing detection methods are limited in their application due to their material-specific nature. Moreover, when processing parameters vary only slightly, the corresponding changes in detection signals may not be significant, making it challenging to differentiate between normal and abnormal data. To address these challenges, this paper proposes a novel two-stage fault detection and tracking method for the HME process based on data-driven techniques. Firstly, comprehensive material detection is achieved by collecting and analyzing five types of data: near-infrared (NIR), Raman, ultrasound, melt pressure, and current. A GMV-KPCA anomaly detection model is then constructed to effectively identify four different abnormal conditions. T² and SPE statistical contribution maps are calculated to perform data dimensionality reduction and feature extraction. Subsequently, a CTRB-SVM anomaly localization model is developed. This model uses the extracted features as input and learns the mapping relationship between the five types of data and various abnormal situations to accurately locate the cause of the anomalies. Finally, experiments are conducted using HME process equipment to validate the proposed method. The experimental results demonstrate that the proposed two-stage fault detection and tracking method effectively identifies various faults and exhibits superior recognition performance compared to state-of-the-art methods. This method provides a robust solution for real-time monitoring and quality control in the HME process.&#xD;
The impact of cross-layer movement on epidemic spreading in time-varying multiplex networks
Authors: Zeng Lang , Han Lilei 
Published: February 2025

Abstract
In the context of real-world multiplex networks, the movement of individuals across different layers introduced interlayer coupling, which signiﬁcantly affected diffusion processes within each layer. Additionally, the relationships among individuals undergo changes over time. Despite the abundant literature on the dynamics of propagation in complex networks, the impact of interlayer transitions (or interlayer jumps, interlayer shifts) on the spread of epidemics in activity-driven time-varying multiplex networks remains a relatively unexplored area. In this study, we introduced a time-varying two-layer network model that incorporated a subset of nodes known as inter-layer travelers, allowing them to move between different layers. We developed a theoretical framework based on the quenched mean-ﬁeld approximation, which yielded precise estimations of epidemic thresholds and ﬁnal states in both layers. Our ﬁndings indicated that the hopping behavior of interlayer travelers and the time-varying nature of the network impeded the disease transmission process. Furthermore, stronger heterogeneity in the individual activities could promote epidemic spread. However, the hopping behavior of interlayer travelers enhanced epidemic transmission in sub-networks with lower node activity heterogeneity while inhibiting it in sub-networks with higher node activity heterogeneity. Additionally, stronger contact capacity of nodes promoted disease spread, and interlayer traveler hopping promoted epidemic spreading in the sparse layer while suppressing it in the dense layer. Notably, an enhanced positive correlation between activities in the two layers can greatly suppress the spreading dynamics, suggesting a practical and effective approach to controlling epidemics in the real world.&#xD;
Machine Learning Prediction and Experimental Validation of Microhardness in High-Entropy Alloy Reinforced Aluminium Composite
Authors: A Saravana Sundar , Radhika N. 
Published: February 2025

Abstract
This study introduces a novel approach for the prediction and experimental validation of microhardness in High Entropy Alloy (HEA) reinforced Aluminium composites using advanced Machine Learning (ML) techniques. For the first time, five ML models, including XGBoost, Stacking Regressor, Quantile Random Forest, Gaussian Process, and Sparse Polynomial Regression were employed to predict the microhardness of Al/HEA composites. The models were trained using an 80:20 train-test split, with hyperparameters optimized via random search and cross-validation. Among the models, XGBoost delivered the highest accuracy, with an R² value of 0.91, and predicted a microhardness of 177 HV. To validate these predictions experimentally, LM13 aluminium alloy was selected as the matrix material, reinforced with HEA (AlTiVZrCrMo) synthesized through stir casting route. Scanning Electron Microscopy (SEM) analysis confirmed uniform reinforcement distribution, contributing to an experimental microhardness of 168 HV, a 45% improvement over the base alloy. The minimal 5.8% deviation between predicted and experimental values demonstrates the effectiveness of ML in predicting material properties, significantly reducing the need for extensive experimental work.
FePt Small-grain Film with Au Doping for High Density Perpendicular Magnetic Recording Media
Authors: Zhang Li , Huang Zhicheng , Liu Caiyan , Lu Xikun , Xu Che , Li Chen 
Published: February 2025

Abstract
In this study, we focused on a FePtAu-C small-grain film specifically designed for very-high density perpendicular magnetic recording media, surpassing 1 Tbits/in2 density. The film layer had a composition of Fe47Pt43Au10-C40% with 7.2 nm thickness. It was deposited onto a silicon substrate with one 12nm thick magnesium oxide sandwich-layer at 560oC temperature. Notably, the film exhibited a perpendicular coercivity of 39 kOe. Based on transmission electron microscopy (TEM) analysis, the film small-grain size was determined to be approximately 8.2±1.6 nm. Furthermore, our investigation revealed that the grain size of magnesium oxide sandwich-layer was about 12 nm, which was larger than that of FePt. As a consequence, the FePt small-grains were not directly influenced by magnesium oxide small-grains beneath them. Moreover, a more in-depth examination using high-resolution TEM imaging demonstrated outstanding L10 ordering within the thin-film, corroborated by texture examination conducted via X-ray diffraction (XRD). It is noteworthy that magnesium oxide sandwich-layer exhibited a polycrystalline structure, while the FePt growing on top of magnesium oxide occurred in an epitaxial manner.
An ultra-high sensitivity methane gas sensor based on the vernier effect with cryptophane-A film deposited in the photonic crystal fiber
Authors: Zhou Di , Li Kaifeng , Zhang Sa , Li Shuguang 
Published: February 2025

Abstract
Methane gas, due to its high flammability, is convenient for people’s daily lives, but its flammability poses safety hazards. Methane tends to explode when mixed with air. Therefore, it is particularly important to be able to effectively detect methane gas concentrations. A new methane gas sensor is presented in this paper. It is extremely sensitive to the reaction of methane gas. It consists of two fiber Sagnac interferometer (SI) loops, which utilize the vernier effect. The two fiber SI loops remain parallel. The optical fiber structure of the effective sensing in the sensing SI loop and the effective sensing in the reference SI loop are the same. Both of them adopt the polarization-maintaining photonic crystal fiber (PM-PCF) designed in this paper. However, the sensing SI loop uses a two-step filling technique to block small air holes and an immersion technique to deposit methane gas sensitive film on large air holes in PM-PCF. The sensing capability of the methane gas sensor was evaluated utilizing the finite element method (FEM). The numerical simulation results show that under the condition of the concentration of methane gas in the environment is 0-3.5%, the average sensitivity of two parallel Sagnac loops is 260.86 nm/%. Compared with other fiber optic methane gas sensors, the sensitivity of the sensor designed by this scheme is significantly improved, which provides a valuable reference and direction for the development of new methods and the design of ultra-sensitive methane gas sensors.
A Study on the Radiation Effects on Flexible GaInP/GaAs/InGaAs Triple-Junction Solar Cells Using Photoluminescence Measurements
Authors: Aierken Abuduwayiti , Li Meng , Hamzawy Sameh , Bi Jinshun , Zhang Shuyi , Wang Tingbao , Liu Xuefei , Ma Teng 
Published: February 2025

Abstract
Herein, the photoluminescence (PL) measurements were used to study and investigate the degradation behavior of flexible GaInP/GaAs/InGaAs inverted metamorphic triple-junction (IMM3J) photovoltaics (PV) under 1 MeV electron irradiation. The changes in PL intensity with varying electron irradiation fluence enabled the acquisition of effective minority carrier lifetimes (τeff) for top GaInP, middle GaAs, and bottom InGaAs subcells under different irradiation conditions. Comparing the changes of τeff before and after the electron irradiation, it was found that the τeff of the InGaAs bottom subcell deteriorates most severely with increasing electron irradiation fluence. Based on the degradation behaviour of the solar cell output characteristics, it was confirmed that the overall performance degradation of the flexible GaInP/GaAs/InGaAs IMM3J solar cell is mainly due to the degradation of the current density of the InGaAs bottom subcell.
Fluctuation-driven synergy, redundancy, signal to noise ratio and error correction in protein allostery
Authors: Erman Burak 
Published: February 2025

Abstract
This study explores the relationship between residue fluctuations and molecular communication in proteins, emphasizing the role of these dynamics in allosteric regulation. We employ computational tools including the Gaussian Network Model, mutual information, and interaction information, to analyze how stochastic interactions among residues contribute to functional interactions while also introducing noise. Our approach is based on the postulate that residues experience continuous stochastic bombardment from impulses generated by their neighbors, forming a complex network characterized by small-world scaling topology. By mapping these interactions through the Kirchhoff matrix framework, we demonstrate how conserved correlations enhance signaling pathways and provide stability against noise-like fluctuations. Notably, we highlight the importance of selecting relevant eigenvalues to optimize the signal-to-noise ratio in our analyses, a topic that has yet to be thoroughly investigated in the context of residue fluctuations. This work underscores the significance of viewing proteins as adaptive information processing systems, and emphasizes the fundamental mechanisms of biological information processing. The basic idea of this paper is the following: Given two interacting residues on an allosteric path, what are the contributions of the remaining residues on this interaction. This naturally leads to the concept of synergy, redundancy and noise in proteins, which we analyze in detail for three proteins CheY, Tyrosine Phosphatase and β-Lactoglobulin.&#xD;
Unveiling the role of high-order anharmonicity in thermal expansion: a first-principles perspective
Authors: Zhang Tianxu , Zhou Kun , Li Yingjian , Yi Chenhao , Faizan Muhammad , Fu Yuhao , Wang Xinjiang , Zhang Lijun 
Published: February 2025

Abstract
Thermal expansion is crucial for various industrial processes and is increasingly the focus of research endeavors aimed at improving material performance. However, it is the continuous advancements in first-principles calculations that have enabled researchers to understand the microscopic origins of thermal expansion. In this study, we propose a coefficient of thermal expansion (CTE) calculation scheme based on self-consistent phonon theory, incorporating fourth-order anharmonicity. We selected four structures (Si, CaZrF6, SrTiO3, NaBr) to investigate high-order anharmonicity's impact on their CTEs, based on bonding types. The results indicate that our method goes beyond the second-order quasi-harmonic approximation and thirdorder perturbation theory, aligning closely with experimental data. Furthermore, we observed that an increase in the ionicity of the structures leads to a more pronounced influence of high-order anharmonicity on CTE, with this effect primarily manifesting in variations of the Grüneisen parameter. Our research provides a theoretical foundation for accurately predicting and regulating the thermal expansion behavior of materials.
GraphSleepFormer: A multi-modal graph neural network for sleep staging in OSA patients
Authors: Wang Chen , Jiang Xiuquan , Lv Chengyan , Meng Qi , Zhao Pengcheng , Yan Di , Chao Feng , Xu Fangzhou , Lu Shanshan , Jung Tzyy-Ping , Leng Jiancai 
Published: February 2025

Abstract
Objective. Obstructive sleep apnea (OSA) is a prevalent sleep disorder. Accurate sleep staging is one of the prerequisites in the study of sleep-related disorders and the evaluation of sleep quality. We introduce a novel GraphSleepFormer (GSF) network designed to effectively capture global dependencies and node characteristics in graph-structured data. Approach. The network incorporates centrality coding and spatial coding into its architecture. It employs adaptive learning of adjacency matrices for spatial encoding between channels located on the head, thereby encoding graph structure information to enhance the model's representation and understanding of spatial relationships. Centrality encoding integrates the degree matrix into node features, assigning varying degrees of attention to different channels. Ablation experiments demonstrate the effectiveness of these encoding methods. The Shapley Additive Explanations (SHAP) method was employed to evaluate the contribution of each channel in sleep staging, highlighting the necessity of using multimodal data. Main results. We trained our model on overnight polysomnography data collected from 28 OSA patients in a clinical setting and achieved an overall Accuracy of 80.10%. GSF achieved performance comparable to state-of-the-art methods on two subsets of the ISRUC database. Significance. The GSF Accurately identifies sleep periods, providing a critical basis for diagnosing and treating OSA, thereby contributing to advancements in sleep medicine.