bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advances in Tumor Targeting Biomimetic Drug Delivery Systems: A Promising Approach for Antitumor Therapy
Authors: Mo Ziyi , He Jiao , Li Man , Guo Rong , He Qin 
Published: May 2024

AbstractCancer is one of the most fatal diseases that attract numerous efforts and attention from researchers. Among plentiful therapeutic agents, chemotherapy is frequently used in treating virulent tumors, and its insistent administration is useful in the ablation of cancers; however, it also produces side effects. Biomimetic drug delivery systems (BDDSs) provide an alternative route for antitumor therapy. Their endogenous substances may be extracellular vesicles, living cells, cell membranes, etc., which optimize single-agent chemotherapy. They “upgrade” traditional drug delivery platforms by combining the original drug with itself, disguised as a Trojan Horse, to trick the immune system or tumor tissues to achieve higher targeting and lower immunogenicity. Herein, we review three BDDS strategies being used recently in antitumor drug development and their advances, aiming at providing general guidelines and opportunities in this field in the future.
Effect of Acidic Media on Surface Topography and Color Stability of Two Different Glass Ceramics
Authors: Makkeyah Fatma , Elsahn Nesrine A. , Bakr Mahmoud M. , Al Ankily Mahmoud 
Published: May 2024

Abstract
Objective The aim of this study was to determine the effect of two acidic beverages (orange juice and H3PO4-containing fizzy drink) on the surface topography and color stability of two commonly used computer-aided design/computer-aided manufacturing (CAD/CAM) ceramic materials.
Materials and Methods Sixty samples of two CAD/CAM ceramic materials, lithium disilicate (IPS e-max CAD) and zirconia reinforced lithium silicate (Vita Suprinity), were prepared according to the manufacturer's instructions. The samples were immersed in one of three media (artificial saliva, orange juice and H3PO4-containing fizzy drink) and then stored in an incubator at 37 °C for 24 hours). Before and after immersion in different media, the surface roughness (Ra) of the samples was assessed using profilometer (JITAI8101 Surface Roughness Tester—Beijing Jitai Tech Detection Device Co. Ltd, China) and the color parameters were measured using VITA Easyshade Advance 4.01 (VITA shade, VITA made, VITA). Surface topography was observed using scanning electron microscope (SEM) and surface mineral content was compared before and after immersion. Paired sample t-test was used to determine the change in Ra before and after immersion. Two-way analysis of variance was used to determine the effect of different CAD/CAM materials and immersion media on the mean ∆Ra and mean ∆E of the studied groups. Tukey's honest significant difference posthoc test was used for multiple comparisons at a level of significance (α = 0.05).
Results A significant increase in Ra and a decrease in the color stability of the two investigated ceramic materials were detected after immersion in the acidic media than in artificial saliva. SEM and energy-dispersive X-ray results revealed the dissolution of the glassy matrix and the exposure of silicate crystals.
Conclusion The surface topography and color stability of glass ceramics are affected by the pH of different acidic media.
Collaborative reflection and discussion using the narrative medicine approach: speech acts and physician identity
Authors: Li Shanshan , Zhong Libo , Cai Yaping 
Published: May 2024

AbstractNarrative medicine has become a meaningful solution to promote medical students’ professional and personal growth. However, there is a lack of study on how students benefit from the narrative medicine approach when developing identities. This study aimed to establish and implement an integrated English reading and writing curriculum based on collaborative reflection and discussion using the narrative medicine approach, exploring how students presented and constructed physician identities by performing speech acts. First-year undergraduate students majoring in clinical medicine were recruited. Collaborative reflection and discussion were conducted in the integrated course involving 14 pieces of medical-related narrative works, along with essential elements that guided the students to engage in classroom activities. In the following analysis, the qualitative analysis software NVivo was used to identify the main speech acts and their frequency to analyze students’ perception and recognition of different physician identities. Our analysis presented 33 major speech acts and revealed their frequency and characteristics, which we summarize in five interrelated physician identities. The fine-grained analysis showed how these identities were influenced by the narrative medicine approach, which provides enlightenment for supporting students to develop their professional identities. Another unique contribution of this study was to present the teaching basis and essential elements in an integrated humanistic curriculum. The results helped clarify the relationship between speech acts and physician identity and suggested that collaborative reflection and discussion using the narrative medicine approach improved the humanistic component that was in short supply in medical education through its interdisciplinary advantages, thus promoting professional identity development that can lead to higher-quality medical and emotional care.
Learning a stable approximation of an existing but unknown inverse mapping: application to the half-time circular Radon transform
Authors: Cam Refik Mert , Villa Umberto , Anastasio Mark A 
Published: May 2024

Abstract
Supervised deep learning-based methods have inspired a new wave of image reconstruction methods that implicitly learn effective regularization strategies from a set of training data. While they hold potential for improving image quality, they have also raised concerns regarding their robustness. Instabilities can manifest when learned methods are applied to find approximate solutions to ill-posed image reconstruction problems for which a unique and stable inverse mapping does not exist, which is a typical use case. In this study, we investigate the performance of supervised deep learning-based image reconstruction in an alternate use case in which a stable inverse mapping is known to exist but is not yet analytically available in closed form. For such problems, a deep learning-based method can learn a stable approximation of the unknown inverse mapping that generalizes well to data that differ significantly from the training set. The learned approximation of the inverse mapping eliminates the need to employ an implicit (optimization-based) reconstruction method and can potentially yield insights into the unknown analytic inverse formula. The specific problem addressed is image reconstruction from a particular case of radially truncated circular Radon transform (CRT) data, referred to as ‘half-time’ measurement data. For the half-time image reconstruction problem, we develop and investigate a learned filtered backprojection method that employs a convolutional neural network to approximate the unknown filtering operation. We demonstrate that this method behaves stably and readily generalizes to data that differ significantly from training data. The developed method may find application to wave-based imaging modalities that include photoacoustic computed tomography.
Inception of positive wire-cylinder corona discharges in air in crossed electric and magnetic fields
Authors: Naidis G V 
Published: May 2024

Abstract
A computational study of the inception of positive wire-cylinder corona discharges in low-pressure air in crossed electric and magnetic fields is performed. The inception voltages are calculated for a wide range of gas densities, wire radii, and applied magnetic fields. Conditions are considered when the reduced electric fields at wire electrodes reach extremely high values of about 10 kTd. An expression applicable at such strong fields for the ionization coefficient, which is a key parameter of the corona inception model, is presented against the values of electric and magnetic fields. Calculated inception voltages agree with a large quantity of available experimental data on low-pressure positive corona discharges, obtained both with and without the application of magnetic fields. The calculation results describe specific details of the non-monotonous dependence of the inception voltages on the magnetic field values, similar to those obtained in experiments.
Enhancing temperature stability of Ce: RIG films Faraday rotation through magnetic sublattice control
Authors: Zhang Hanxu , Zhu Senyin , Zhan Jin , Fan Xiangyu , Wang Xianjie , Yao Tai , Song Bo 
Published: May 2024

Abstract
Cerium doped rare-earth iron garnet (Ce: RIG) film is a promising candidate for magneto-optical devices in laser systems with giant Faraday effect; nevertheless, devices fail nonreciprocally with increasing temperature due to a negative Faraday rotation angle temperature coefficient. To mitigate this effect, the relationship between the magnetic moments of three distinct magnetic sublattices and the temperature coefficients of the Faraday rotation angle was investigated. Cerium doped holmium iron garnet (Ce: HoIG) film, where magnetic Ho3+ occupied the dodecahedrons, exhibited an enhanced Faraday rotation angle retention at a temperature of 400 K. However, the nonmagnetic ion doping in tetrahedral and octahedral sites yielded a negligible effect. The mechanism behind this occurrence is attributed to the magnetic compensation effect, which results in a small magnetic moment temperature coefficient within the range of 300–400 K. The study not only offers strategies for designing Ce: RIG components with reduced temperature coefficient, but also presents the development of a Ce: HoIG film exhibiting promising stability in Faraday rotation angle as a function of temperature.
Constraints on the dark sector from electroweak precision observables
Authors: Loizos B M , Wang X G , Thomas A W , White M J , Williams A G 
Published: May 2024

Abstract
We revisit the standard model fit to electroweak precision observables (EWPO) using the latest data and the particle data group value of the mass of the W boson. This analysis is repeated for the value reported by CDF. The constraints on the parameter space for dark photons arising from these EWPO are then evaluated for both values of the W boson mass. We also extend previous work by placing the first electroweak precision observable constraints on the coupling of dark photons to the fermionic dark matter sector.
An end-to-end learning framework for visual camera relocalization using RGB and RGB-D images
Authors: Zhang Kai , Meng Xiaolin , Wang Qing 
Published: May 2024

Abstract
Camera relocalization plays a vital role in the realms of machine perception, robotics, and augmented reality. Direct learning methods based on structures can have a learning-based approach that can learn scene coordinates and use them for camera position estimation. However, the two-stage learning of scene coordinate regression and camera position estimation can result in some of the scene coordinate regression knowledge being lost throughout the learning process of the final pose estimation system, thereby reducing the accuracy of the pose estimation. This paper introduces an innovative end-to-end learning framework tailored for visual camera relocalization by employing both RGB and RGB-D images. Distinguished by its integration of scene coordinate regression with pose estimation into a concurrent inner and outer loop during a singular training phase, this framework notably enhances pose estimation accuracy. Engineered for flexibility, it accommodates training with or without depth cues and necessitates merely a single RGB image during testing. Empirical evaluation substantiates the proposed method’s state-of-the-art precision, attaining an average pose accuracy of 0.019 m and 0.74° on the indoor 7Scenes dataset, together with 0.162 m and 0.30° on the outdoor Cambridge Landmarks dataset.
Influence of the configuration of metal sensing layers on the performance of a bimetallic (Ag–Cu) surface plasmon resonance biosensor
Authors: Pillai Anjitha M , Nair Niveditha , Das Mukul K , Ram Sanjay K 
Published: May 2024

Abstract
Bimetallic surface plasmon resonance (SPR) sensors have the potential to overcome the drawbacks of individual metals, but the effect of the configuration of the two metallic layers on the performance of the sensors has not been explored. This study examines the influence of different positions of a thin layer of silver in relation to a copper layer on the sensitivity of such a bimetallic SPR sensor. The design of this configuration aims to improve the SPR reflectance curve and strengthen the evanescent electric field to improve the sensor efficiency. Our findings indicate that, by optimizing the architectures of SPR sensors and using a silver–copper bimetallic structure, we can achieve superior performance compared to devices that utilize only silver or copper. The optimized Ag (5 nm)/Cu (55 nm) sensor design, with the best sensitivity of 299.09° RIU−1, can detect a change of 0.43°/(g dl−1) for hemoglobin in blood, 0.35°/(g dl−1) for glucose in urine, and 0.1°/(%) for methanol in ethanol. We also demonstrate the importance of signal quality by introducing two new parameters that offer a better quantitative indication of the efficiency of a sensor than is obtained by using only sensitivity.
Reconsider photoplethysmogram signal quality assessment in the free living environment
Authors: Su Yan-Wei , Hao Chia-Cheng , Liu Gi-Ren , Sheu Yuan-Chung , Wu Hau-Tieng 
Published: May 2024

Abstract

Objective. Assessing signal quality is crucial for biomedical signal processing, yet a precise mathematical model for defining signal quality is often lacking, posing challenges for experts in labeling signal qualities. The situation is even worse in the free living environment. Approach. We propose to model a PPG signal by the adaptive non-harmonic model (ANHM) and apply a decomposition algorithm to explore its structure, based on which we advocate a reconsideration of the concept of signal quality. Main results. We demonstrate the necessity of this reconsideration and highlight the relationship between signal quality and signal decomposition with examples recorded from the free living environment. We also demonstrate that relying on mean and instantaneous heart rates derived from PPG signals labeled as high quality by experts without proper reconsideration might be problematic. Significance. A new method, distinct from visually inspecting the raw PPG signal to assess its quality, is needed. Our proposed ANHM model, combined with advanced signal processing tools, shows potential for establishing a systematic signal decomposition based signal quality assessment model.
Nonvolatile bipolar resistive switching characteristics of aluminum oxide grown by thermal oxidation processes
Authors: Jhang Wun-Ciang , Chien Yu-Sheng , Hsu Chih-Chieh 
Published: May 2024

Abstract
This study proposes a bipolar resistive random-access memory (RRAM), which is fabricated using an aluminum oxide (AlO
x
) resistive switching (RS) layer. The RRAM shows a large memory window of 106 at a low read voltage of 0.5 V. In addition, high switching speed, long retention time, and superior read-disturb immunity are observed. AlO
x
layers are prepared by a thermal oxidation growth process. Aluminum metal films deposited on n+-Si wafers are oxidized at O2/(O2 + N2) flow rate ratios of 50%–100%. Al/AlO
x
/n+-Si device shows no RS behavior when the AlO
x
is grown in a pure O2 environment. As the O2/(O2 + N2) flow rate ratio decreases to 50%, Al/AlO
x
:N/n+-Si device reveals stable bipolar RS characteristics. A filamentary mode based on oxygen interstitial and Al vacancy is proposed to explain the difference in electrical characteristics of AlO
x
devices prepared at different O2 flow rates.
Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT
Authors: A Jasmine Xavier , N Suthanthira Vanitha , G Sudha , M Birunda 
Published: May 2024

Abstract
The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is its heavy energy consumption while transmitting data, which makes scaling difficult. To overcome such issues, a hybrid approach of Crayfish Optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD, node localization is performed that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering, along with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection, combining data analysis for optimizing energy utilization in heterogeneous IoT environments. The ‘combined CFO-FCM and SSA-RBM’ has been implemented in MATLAB and achieved an accuracy of 94.50%. As a result, the overall performance of the system is improved.
Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation
Authors: Mujahid Muhammad , Rehman Amjad , Alamri Faten S , Alotaibi Sarah , Saba Tanzila 
Published: May 2024

Abstract
Brain tumors being ninth in terms of prevalence and one of the most frequently diagnosed malignant tumors, negatively impact millions of individuals. Identifying and classifying tumors from MRI used for health monitoring poses a challenge for radiologists, yet early detection could significantly enhance the chances of effective treatment. Researchers in the field of explainable AI are currently focused on developing sophisticated techniques to classify and diagnose brain diseases. This study presents a novel framework that enhances the interpretability of our proposed system for brain tumor detection by utilizing explainable AI techniques. To enhance interpretability, we integrate the optimized recursive feature elimination selection technique with support vector machines. This method effectively eliminates redundant features, identifies the most important ones, and enhances the efficiency of detecting tasks. Following that, the optimal recursive feature elimination (ORFE) features are combined using the supervised support vector machine (SVM) technique. While EfficientNet-CNN is a very useful feature extraction framework that extracts the most important features from a transparent model, we reduced the overall computational complexity through feature elimination and supervised models, and the Figshre dataset clearly demonstrated the efficacy of our model. This study achieved very exceptional results and reduced computational complexity as compared to a single CNN model. The experimental results indicate that the proposed SVM-RFE based technique accurately detects brain tumors with a 99.51% accuracy and a specificity score of 99.63%. The proposed approach obtained an accuracy of 98.93% with a standard deviation of 0.032 using 10-fold cross-validation. Additionally, it produced an optimal ROC_AUC of 100% for cases including meningiomas and pituitary tumors.
Qualitative analysis and new exact solutions for the extended space-fractional stochastic (3 + 1)-dimensional Zakharov-Kuznetsov equation
Authors: Elbrolosy Mamdouh 
Published: May 2024

Abstract
In this paper, the extended (3 + 1)-dimensional Zakharov-Kuznetsov equation, which describes the propagation of ion-acoustic waves in a magnetic environment, is investigated. Due to the exposure of the propagation to unpredictable factors, the stochastic model is assessed including the Brownian process, in addition to including the recent concept of truncated M-fractional derivative. A fractional stochastic transformation is applied to transform the model into an integer-order ordinary differential equation which in turn is equivalent to a conservative Hamiltonian model. Novel solutions, such as hyperbolic, trigonometric, and Jacobian elliptic functions, are established by employing both of the qualitative analysis of dynamical systems and the first integral of the Hamiltonian model. We explore and graphically display the effects of the fractional derivative order and noise intensity on the solutions structures. In the deterministic instance, i.e. in the absence of noise, solitary and cnoidal solutions among other traveling wave solutions of the Zakharov-Kuznetsov equation, are derived. Further, it is found that the curvature of the wave disturbs and the surface turns substantially flat by increasing the value of noise. While the curve in all cases loses its characteristic shape and degenerates into another deterministic shape by changing the fractional derivative order.
The role of divertor pumping combined with full drifts in particle exhaust and divertor plasma
Authors: Zhao Xuele , Sang Chaofeng , Wang Yilin , Zhang Chen , Wang Dezhen 
Published: May 2024

Abstract
The effect of drifts combined with pumping on particle exhaust is assessed using the SOLPS-ITER code package, considering full drifts. Both drift and pumping speed S can affect particle exhaust. Drifts change the neutral density by influencing plasma flow and the resulting particle recycling. This leads to the accumulation of neutral particles either far away or close to the pump opening location. The particle exhaust is enhanced as S rises. When the pump opening is positioned at the common flux region (CFR) of the outer divertor (referred to as Pump CFR/OD), particle exhaust is suppressed by drifts in forward Bt
, while it is enhanced by drifts in reversed Bt
, with fixed S. On the other hand, when the pump is situated in the private flux region (PFR) of the OD (referred to as Pump PFR/OD), particle exhaust is enhanced by drifts in both reversed and forward Bt
compared to the case without drifts. Moreover, the effective pumping in reversed Bt
is stronger than in forward Bt
. In the same Bt
direction, Pump PFR/OD has a higher effective pumping than Pump CFR/OD. Increased S results in higher particle exhaust in all Bt
direction and pump location cases. The plasma detachment is affected by drift, S and pump opening location, respectively. For the specified Bt
direction and pump opening location case, higher S suppresses plasma detachment. For identical particle exhaust rates, stronger pumping capacity can promote plasma detachment. Therefore, Pump PFR/OD can more easily achieve OD detachment than Pump CFR/OD in the same Bt
direction. Overall, placing the pump at the PFR side of the OD while running in reversed Bt
is the best option from the divertor particle exhaust and plasma detachment point of view.