bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bericht zum Workshop: Fach- und Erfahrungsaustausch für onkologisch
tätige Sport- und PhysiotherapeutInnen
Authors: Meyer-Schwickerath Corinna , Paratte Jean-Luc , Wiskemann Joachim 
Published: February 2025

ZusammenfassungAm 27. und 28.09.24 fand das erste Therapeut*innentreffen im Rahmen eines
Workshops für onkologisch tätige Sport- und Physiotherapeut*innen am Uniklinikum
in Heidelberg statt. Der Workshop wurde als gemeinsame Veranstaltung der
Stiftung Leben mit Krebs und des Netzwerks OnkoAKtiv von
Sportwissenschaftler*innen des NCT Heidelbergs vorbereitet und durchgeführt.
Ziel des Treffens war die Schaffung eines Raumes für Fach- und
Erfahrungsaustausch sowie die stärkere Vernetzung onkologisch tätiger Sport- und
Physiotherapeut*innen.
Determination of mid-infrared optical properties of complex media using partial Mueller matrix ellipsometry
Authors: Yang Chiyu , Wang Xueji , Jacob Zubin , Cai Wenshan , Zhang Zhuomin M. 
Published: February 2025

Tailoring optical and radiative properties has attracted significant attention recently due to its importance in advanced energy systems, nanophotonics, electro-optics, and nanomanufacturing. Metamaterials with micro- and nanostructures exhibit exotic radiative properties with tunability across the spectrum, direction, and polarization. Structures made from anisotropic or nanostructured materials have shown polarization-selective absorption bands in the mid-infrared. Characterizing the optical and radiative properties of such materials is crucial for both fundamental research and the development of practical applications. Mueller matrix ellipsometry offers a nondestructive and noninvasive technique for characterizing radiative properties. Although such ellipsometers have long been used to measure optical properties, their operational bandwidth is usually limited to the visible to near-infrared range, leaving the mid-infrared largely unexplored. In this work, a broadband mid-infrared ellipsometer, operating from 2 to 15 μm, is designed and constructed to measure 12 elements of the Mueller matrix. The results may be used to determine the full Mueller matrix under specific conditions. The performance of the ellipsometer is evaluated using nanostructured materials, including a 1D grating and a chiral F-shaped metasurface. The measurement results compared well to those obtained from rigorous-coupled-wave analysis and finite-difference time-domain simulations, suggesting that this setup offers a useful tool in optical property retrieval and the assessment of nanostructured materials.
Multi-cation synergy improves crystallization and antioxidation of CsSnBr3 for lead-free perovskite light-emitting diodes
Authors: Wang Jie , Han Dongyuan , Ji Huiyu , Zang Ziang , Zhou Jianheng , Wang Ning 
Published: February 2025

Abstract
Tin (Sn) perovskites have emerged as promising alternatives to address the toxicity concerns associated with lead-based (Pb) perovskite light-emitting diodes (PeLEDs). However, the inherent oxidation of Sn perovskite films leads to a serious efficiency roll-off in PeLEDs at increased current densities. Although three-dimensional (3D) CsSnBr3 perovskites exhibit decent carrier mobilities and thermal stability, their rapid crystallization during solution processing results in inadequate surface coverage. This inadequate coverage increases non-radiative recombination and leakage current, thereby hindering Sn PeLED performance. Herein, we present a multi-cation synergistic strategy by introducing the organic cations formamidinium (FA+) and thiophene ethylamine (TEA+) into CsSnBr3 perovskites. The addition of organic cations delays crystallization by forming hydrogen bonds interacting with the CsSnBr3. The smaller FA+ enters the perovskite lattice and improves crystallinity, while the larger TEA⁺ cation enhances surface coverage and passivates defect states. By further optimizing the interface between PEDOT:PSS and perovskite layers through the use of ethanolamine (ETA) and a thin layer of LiF, we achieved a red Sn-based PeLED with an emission wavelength of 670 nm, a maximum luminance of 151 cd m-2, and an external quantum efficiency (EQE) of 0.21%.&#xD;
A chaos-based lightweight encryption scheme using hash-code and cyclic rotation
Authors: KAYA Yasin , AYDIN Zeynep Gürkaş , Akgül Akif 
Published: February 2025

Abstract
This study introduces a novel lightweight encryption scheme for platforms with limited resources utilizing chaotic systems. We employ a dual logistic map, utilizing the first map to determine cyclic rotation step counts for rows and columns in confusion (permutation) phase, and the second map for the XoR operation in diffusion phase. Input parameters of the first logistic map is derived from only secret key while input parameters of the second logistic map derived from secret key and hash value of plaintext together. Since we use hash value of the plaintext to determine input parameter of the second logistic map, our schema is highly resistant to differential attacks. On average, it produces over %99.57 NPCR value and about %33.45 UACI value in case of one pixel change in the plaintext. It also produces an information entropy value of over 7.9971 on average. Since we use double logistic map, our scheme is resistant to brute force attacks via about 196 bits key length. Firsty, we calculate the hash value of the plaintext which will be used to determine input parameter of the second logistic map. And then we employ cyclic rotation operation for all rows and all columns utilizing key stream generated by the first logistic map at the permutation stage. Subsequently, we performed the XoR operation to the output, utilizing the key stream of the second logistic map which is initialized with the hash value and secret key. Since we need the hash value of the plaintext in the decryption process, we need to save it in the ciphertext. So, we add a new row at the bottom of the ciphertext to make a room for the hash value. We save and distribute it into the matrix by swapping operation and obtain final ciphertext. To decrypt the ciphertext, we follow these steps in reverse order. First, we extract the hash value of the original plaintext from the ciphertext. Then, we apply XOR and cyclic rotation operations. Our test and analysis results demonstrate that our schema is pretty fast, reliable, robust, feasible and successful. It is a good lightweight encryption scheme alternative for resource-limited platforms.&#xD;
From dark matter minihalos to large-scale radiative feedback: a self-consistent 3D simulation of the first stars and galaxies using neural networks
Authors: Feathers Colton R. , Kulkarni Mihir , Visbal Eli 
Published: February 2025

Abstract
A key obstacle to accurate models of the first stars and galaxies is the vast range of distance scales that must be considered. While star formation occurs on sub-parsec scales within dark matter (DM) minihalos, it is influenced by large-scale baryon-dark matter streaming velocities (v
bc) and Lyman-Werner (LW) radiative feedback which vary significantly on scales of ∼100 Mpc. We present a novel approach to this issue in which we utilize artificial neural networks (NNs) to emulate the Population III (PopIII) and Population II (PopII) star formation histories of many small-scale cells given by a more complex semi-analytic framework based on DM halo merger trees. Within each simulation cell, the NN takes a set of input parameters that depend on the surrounding large-scale environment, such as the cosmic overdensity, δ(x⃗), and v
bc of the cell, then outputs the resulting star formation far more efficiently than is possible with the semi-analytic model. This rapid emulation allows us to self-consistently determine the LW background intensity on ∼100 Mpc scales, while simultaneously including the detailed merger histories (and corresponding star formation histories) of the low-mass minihalos that host the first stars. Comparing with the full semi-analytic framework utilizing DM halo merger trees, our NN emulators yield star formation histories with redshift-averaged errors of ∼7.3% and ∼5.2% for PopII and PopIII, respectively. When compared to a simpler sub-grid star formation prescription reliant on halo mass function integration, we find that the diversity of halo merger histories in our simulation leads to enhanced spatial fluctuations, an earlier transition from PopIII to PopII dominated star formation, and more scatter in star formation histories overall.
Towards a classification of UV completable Higgs inflation in metric-affine gravity
Authors: Mikura Yusuke , Tada Yuichiro 
Published: February 2025

Abstract
Towards a classification of UV completable Higgs inflation in the framework of parity-even
metric-affine gravity, we investigate the particle spectrum of a deformed theory in the large-N
limit. In a simple Higgs inflation model in metric-affine gravity, it is known that its UV cutoff
is much smaller than the Planck scale. While it calls for UV completion, a concrete example has
not yet been found, even with the large-N limit known as a successful technique to complete an
original Higgs inflation defined on the Riemannian geometry. This motivates us to study how small
deformation of the simple Higgs inflation affects the emergence and properties of dynamical fields
particularly in the large-N limit. As a UV theory has to be free of ghosts or tachyons at least
around Minkowski space, we perform the parameter search and find the healthy parameter region
where a new heavy particle can propagate without these pathologies.
Effective Strategy of Enhancing Piezoelectricity in Stable CrSiN4Sn Semiconductor Monolayers by Atom-Layer-Pair-Effect
Authors: He Qi-Wen , Zhu Dan-Yang , Wang Jun-Hui , Zhang He-Na , Shang Xiao , Cui Shou-Xin , Wang Xiao-Chun 
Published: February 2025

Abstract
It is a challenge to reveal the design strategy of strong piezoelectricity nano-materials applied for self-powered and smart nano-devices. The atom-layer-pair effect is found in MoSi
x
N
y

R

z
monolayers with remarkable piezoelectricity by first-principles calculations. The absolute values of the vertical piezoelectric coefficients have a linear relation with the total electronegativity difference dipole moments. Based on this effect, the promising CrSiN4Sn monolayer is found with the highest piezoelectricity among the above monolayers. The work expands our understanding of the piezoelectric physical mechanism and provides the design strategy for piezoelectric nano-devices.
Evaluation of Sinusoidal Wave Energy Dissipation over a Gyroid Triply Periodic Minimal Surface-based Porous Breakwater
Authors: Duy Khanh Le Thanh , Nguyen Khuong D. 
Published: February 2025

Abstract
Breakwaters with porosity have been considered one of the most prospective keys to the wave’s energy reduction issues in the shoreline engineering area. In this project, the specimen test would be a permeable porous breakwater design relied on the structural Triply Periodic Minimal Surface (TPMS) unit cells-Gyroid. The paper’s primary purpose is to utilize the numerical method Finite Volume Method (FVM) to evaluate approximately the wave’s dissipation before and after crashing against the structures. To be detailed, the experiment has two stages: validation and verification. For the first stage, to validate the accuracy of the mathematical sinusoidal wave model, a computational fluid dynamics software (CFD) Ansys Fluent was utilized to approximate the wave’s characteristics and compared with the empirical experiment, which is generated by a plunger-type wavemaker controlled with three different rounds per minute(RPM), separately 22(r/min), 44(r/min) and 66(r/min) in a wave flume without the breakwater. The wave’s characteristics, such as period T, wave height H, and wavelength L, would be considered in this process. Next, those set-ups are reapplied for a numerical wave tank containing the Gyroid breakwater to evaluate the effective performance regarding the wave prevention proficiency, based on the transmission coefficient (Ct
). In conclusion, the proposed wave model is validated, and there is a strong agreement between the numerical and experimental results. Finally, the Gyroid breakwater has exhibited outstanding efficacy in wave transmission reduction.
Positive Emotional State Recognition Using Nonlinear Dynamics Analysis and Machine Learning Algorithms with Electroencephalogram Signals
Authors: Gia Quynh Nguyen , Khai Le Quoc 
Published: February 2025

Abstract
Electroencephalogram (EEG) signals processing has gathered increased interest from the scientific community for a long time, especially in the field of emotion recognition. The objective of this research is to propose a method that analyzes EEG signals from a publicly available dataset called SJTU Emotion EEG Dataset (SEED) to classify different emotional states, especially positive emotions. The dataset consists of EEG signals recorded from fifteen subjects while watching different film clips, each of which corresponds to a type of emotions. Initial analysis involves the extraction of features from the pre-processed data including time-domain analysis, frequency-domain analysis using Fast Fourier Transform (FFT) and nonlinear dynamics method. Machine learning algorithms are then applied as classifiers in order to determine the emotional states of each subject – categorized as positive, negative, or neutral – with the input is the extracted features. In this work, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Subspace Discriminant (SD) are used for training and testing the labeled data. The classification step achieved the highest accuracy level of 82.2% while the highest micro-F1 score value is 0.92 for positive emotions. The established methodology not only proposes an automated model for determining positive emotional states but also plays an important role at the beginning of the investigation further into emotion recognition by providing better visualization of how different emotional states are determined.
Applying convolutional neural networks in identifying and classifying Acute Lymphocytic Leukemia and its subtypes using Matlab
Authors: Huy Phan Nguyen Bao , Hau Tran Thien , Anh Tu Tran 
Published: February 2025

Abstract
Leukemia is a blood cancer originating in the bone marrow that leads to the production of abnormal blood cells, impacting cell functionality and efficiency. According to Globocan, statistics for 2022 show that Viet Nam has 180,480 new cancer cases and 120,184 deaths, of which leukemia has 5,789 new cases, ranking 8th among cancer types (accounting for 3.2%) and 4,330 deaths, ranking 6th in mortality rate among cancer types (accounting for 3.6%). Depending on the progression rate, leukemia can be identified as either acute or chronic. Acute leukemia affects blood cells in the early stages of development, causing the disease to become more severe, while chronic leukemia affects more developed blood cells, and the disease tends to develop slowly. Another way to classify leukemia is by the specific blood cell type it targets, either myeloid or lymphoid cells. Understanding the affected cell type is essential for diagnosis and treatment planning, as it influences disease behavior and response to treatment. This paper focuses on identifying and classifying ALL (Acute Lymphoblastic Leukemia) blasts in the most prevalent childhood cancer type using transfer learning of the Alexnet network model in Matlab software. Diagnosing ALL disease using peripheral blood smear (PBS) images helps screen and treat the disease early, contributing to improving treatment effectiveness for patients. Laboratory users often face challenges when examining PBS images, as the non-specific signs and symptoms of ALL can result in frequent misdiagnoses and diagnostic errors. The research presents a model for analyzing peripheral blood smear images, aimed at identifying ALL cell types and distinguishing among its malignant subtypes. The ALexnet model will be trained in 3 different ways including: Train with no parameters (Training a Neural Network from Scratch), train the pretrained network but change the fullyConnected layer at the end and train the pretrained network but change the 14th layer onwards. The results show that the model using the second training method achieves the best performance when reaching 98.664% accuracy on the validation set, while methods 1 and 3 are 92.292% and 89.825% respectively. Finally, the second model is saved to design the user application using AppDesigner.
Classification of Lumbar Spine Degeneration Using Vision Transformer with the RSNA Dataset
Authors: Huynh Hoang Nhut , Ngoc An Dang Nguyen , Nguyen Trung An Dang , Phan Minh Hoang , Nguyen Van Chinh , Tran Trung Nghia 
Published: February 2025

Abstract
This study presents a deep-learning approach for classifying lumbar spine degeneration using the RSNA dataset by employing a Vision Transformer model. The classification task was divided into three categories normal/mild, moderate, and severe. The dataset comprises approximately 2000 MRI studies annotated by spine radiology specialists to indicate the presence, vertebral level, and location of any lumbar spinal stenosis. The methodology involves preprocessing the MRI and training a Vision Transformer to classify degeneration severity. The Vision Transformer model was chosen for its ability to capture global image dependencies and superior performance in medical image analysis tasks. The quantitative results demonstrate the effectiveness of our approach, with an accuracy of 93.6%, a precision of 92.3%, and a recall of 89.8%. The proposed method achieves high classification performance and aids in the early detection and treatment planning for lumbar spine degeneration. The significance of this research lies in its potential to automate and enhance the diagnostic process, providing reliable and rapid assessments that can support clinical decision-making and improve patient outcomes.
Assessment of the lateral displacement of a single pile in the sloping ground based on finite element analysis method
Authors: Chau Phuong Khanh , Tuan Le Anh , Duong Ngoc Tran , Danh Thanh lam , Nguyen Tan Hung 
Published: February 2025

Abstract
The current study observed the effect of embankment height, pile diameter, and pile length on the lateral displacement of the reinforced concrete pile installed in the sloping ground using the finite element analysis program. While the embankment height in the study was 1.5, 3.0, and 4.5 m, the pile diameter was 0.25, 0.5, and 1.0 meters. The pile length was chosen as 10, 15, and 20 m for the analysis. This study figured that the embankment height affected remarkably the lateral displacement of the pile (LDP). As the height of the embankment increased, the LDP increased. Results of the LDP in the case of difference in pile diameter were similar. It could be seen that the pile diameter did not affect the LDP in the sloping ground. Finally, the study for the effect of pile embedment length showed that for the pile head, a pile with a longer embedment length resulted in a smaller lateral displacement. However, for the pile toe, piles with different embedment lengths produced similar lateral displacements. It is noted that the current study only evaluated the effect of embankment height, pile diameter, and pile length on the LDP based on the finite element analysis. Further studies on the effects of these factors based on experimental evaluation should be carried out.
The Modeling of Non-invasive Measurement of Fruit Sweetness using Near-Infrared (NIR) Spectroscopy Method
Authors: Tran Hong Duyen Trinh , Anh Tu Tran 
Published: February 2025

Abstract
The sugary content in fruit requires knowledge for both the maker and the consumer. Methods of evaluating the level of sweetness in fruits as previously described, for example through chemical means, is a long and tedious process. This paper demonstrates the benefits of using NIR in determining fruit sweetness as a rapid, non-destructive, and accurate technique compared to the conventional methods. Near infrared spectroscopy (NIR spectroscopy) is a rapid non-destructive technique for measuring the physical and chemical properties of fruit by analyzing the light absorption in the proximal part of infrared spectrum (780-2500 nm). This paper aims to determine the potential of applying food quality NIR spectroscopy spot check, at a wavelength of 940 nm, in the reflectance mode for the evaluation of the sweetness of apples. When light from a 940 nm LED is directed to illuminate the surface of the fruit, some of it is absorbed while the rest is reflected. The reflected light is detected using a photodiode then converted to an electrical quantity using a conversion circuit after which it is passed to a microcontroller. The study was conducted on 30 different apple samples to develop a model, with sweetness quantified using a Brix refractometer. The obtained results were used to construct a linear regression model, showing the correlation between the measured voltage values and the Brix values. The model yielded a correlation coefficient (R²) of 0.6261, demonstrating the feasibility of using the 940 nm near-infrared wavelength to non-invasively measure fruit sweetness. These preliminary results help us understand the experimental design and develop techniques to measure fruit sweetness using light at other wavelengths in the future.
An Optimal Method for Calculating Suspension Spring Stiffness Based on Roll Angle and Roll Acceleration Values
Authors: Nguyen Ngoc Tram , Nguyen Duc Ngoc , Pham Huu Dong , Nguyen Manh Long , Nguyen Tuan Anh 
Published: February 2025

Abstract
The stiffness of the suspension spring plays a vital role in maintaining the vehicle’s ride comfort during vibration. An optimal calculation method is proposed in this paper to find the ideal stiffness value of the suspension spring. Firstly, a half-dynamic model is established to describe the vehicle’s vibration. Then, an optimal calculation method is formulated, which consists of two steps: running the loop to find the Root Mean Square (RMS) values of the vibration and determining the ideal stiffness value based on finding the minimum values of roll angle and roll acceleration. According to the study’s findings, the ideal stiffness of the suspension spring is 36900 N/m, obtained in the first case. Even when the excitation amplitude increases (in the second case), the ideal stiffness value is still 36900 N/m, achieved using the proposed method. This helps to demonstrate the correctness of the optimal method established in this paper. Finally, the vehicle vibration is investigated using the optimal stiffness value above.