bluuebunny/crossref_metadata_2025_split huggingface dataset:
Langerhans Cell Histiocytosis Mimicking a Meningeal Lesion with Temporal Bone and Muscle Compromise in an Adult Patient: A Case Report
Authors: Durán-Ojeda Alejandro , Arce Jefferson , Campos-Fajardo Santiago , Jacomussi-Alzate Lorena , Rincón-Carreño Cristhian 
Published: April 2024

Abstract
Introduction Langerhans cell histiocytosis (LCH) is a rare proliferative systemic disease characterized by the growth of abnormal dendritic cells and wide-ranging organ involvement. This condition can affect individuals of all ages, but most commonly children, with a peak incidence in toddlers. Symptoms may vary depending on the affected organ or system.
Case Report A 43-year-old man presented with a left temporal stabbing headache unresponsive to management with therapy and nonsteroidal anti-inflammatory drugs. Initial evaluation revealed a contrast-enhanced left temporal extra-axial lesion with bone and muscle compromise. Differential diagnoses, including multiple myeloma, were explored. Initial laboratory tests and imaging studies showed no other abnormalities, except for splenomegaly and a residual granuloma in the left lung. En bloc resection of the lesion was recommended. The patient underwent surgical intervention, which included resection of the dural lesion and all borders of an infiltrating tumor within the temporalis muscle and the affected portion of the left temporal bone. Posterior pathological examination revealed LCH. Postoperative course was uneventful. Follow-up appointments were scheduled after pathology results confirmed the diagnosis. Patient has continued follow-up for the following 3 months after the surgical procedure. Further evaluations are pending.
Discussion This case report corresponds to a patient with LCH. These patients are individualized and stratified based on local or systemic involvement to determine the most appropriate type of management. This is a rare case as LCH is rare in older patients and the initial presented lesion initially mimicked a meningioma; however, its atypical behavior and associated lytic compromise led to consideration of possible differential diagnoses.
Conclusion LCH can present with lytic bone lesions, mimicking other conditions, including infiltrative neoplastic lesions. Early diagnosis and appropriate surgical management are essential for optimal patient outcomes. Long-term follow-up is crucial to monitor disease progression and response to treatment.
A compact six-axis force/torque sensor using photocouplers for impact robustness
Authors: Kim Hyun-Bin , Choi Keun-Ha , Kim Kyung-Soo 
Published: April 2024

This paper presents a novel six-axis force/torque (F/T) sensor design for robotics, combining ease of manufacturing with compactness. It features a high measuring range of ∼3700 N, an exceptional resolution of 0.1 N, and a rapid 5 kHz sampling rate. The sensor’s design, focusing on durability and a wider sensing range, utilizes noncontact sensors and a streamlined structure. A novel force sensing scheme aligns sensing elements in-plane on a single printed circuit board, reducing the part count to four and the weight to under 80 g, while integrating an analog-to-digital converter to eliminate the need for external communication devices. The lightweight, efficient prototype demonstrates a superior performance and a high response frequency, validated against a reference F/T sensor.
Ultrafast photochemistry and electron-diffraction spectra in n → (3s) Rydberg excited cyclobutanone resolved at the multireference perturbative level
Authors: Jaiswal V. K. , Montorsi F. , Aleotti F. , Segatta F. , Keefer Daniel , Mukamel Shaul , Nenov A. , Conti I. , Garavelli M. 
Published: April 2024

We study the ultrafast time evolution of cyclobutanone excited to the singlet n → Rydberg state through non-adiabatic surface-hopping simulationsperformed at extended multi-state complete active space second-order perturbation (XMS-CASPT2) level of theory. These dynamics predict relaxation to the ground-state with a timescale of 822 ± 45 fs with minimal involvement of the triplets. The major relaxation path to the ground-state involves a three-state degeneracy region and leads to a variety of fragmented photoproducts. We simulate the resulting time-resolved electron-diffraction spectra, which track the relaxation of the excited state and the formation of various photoproducts in the ground state.
Genomic prediction for targeted populations of environments in oat (Avena sativa)
Authors: Sandro Pablo , Bhatta Madhav , Bower Alisha , Carlson Sarah , Jannink Jean-Luc , Waring David J. , Birkett Clay , Smith Kevin , Wiersma Jochum , Caffe Melanie , Kleinjan Jonathan , McMullen Michael S. , English Lydia , Gutierrez Lucia 
Published: April 2024

Context Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs. Aims The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders. Methods The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers. Key results Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs. Conclusions Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs. Implications We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets.
Measuring affix rivalry as a gradient relationship
Authors: Salvadori Justine , Varvara Rossella , Huyghe Richard 
Published: April 2024

Abstract
In many languages, affixes can be used to derive
words with the same semantic types and can therefore compete in word
formation. This paper discusses how to quantitatively assess the
competition between derivational affixes based on their semantic
similarity. Two possible measures of affix rivalry drawn from
studies in ecology are examined: the Sørensen index, which considers
the proportion of shared functions between rival affixes; and the
Percentage similarity coefficient, which is based on the realization
frequency of functions. Two complementary measures (Balanced
richness and Balanced abundance) are also proposed to further
analyze the semantic dissimilarity between rival affixes. Using the
semantic competition between six French deverbal suffixes as a case
study, we show how these four measures suit the quantification of
affix rivalry and help capture different aspects of the
phenomenon.
A diachronic perspective on competition in denominal verb
formation in Italian
Authors: Iacobini Claudio , De Rosa Maria Pina 
Published: April 2024

Abstract
Three derivational processes compete in the
formation of Italian denominal verbs: suffixation, conversion and
parasynthesis. The rivalry between these processes goes back to the
early history of the Italian language. Based on the analysis of an
original dataset, this chapter aims to offer a comprehensive
overview of denominal verb formation in Italian from a diachronic
perspective. The results of a macro-level analysis concern the
availability of nouns to form verbs, the distribution of the
competing verbalising processes over different diachronic stages, a
comparative overview of the meanings encoded by each process, and
the competition between synonymous verbs formed by means of
different processes from a same noun. A micro-level analysis
outlines the productivity of the specific derivational means
expressing each process. The data suggest a higher differentiation
of the processes over time, along with their enduring vitality, a
decreased abundance of verbs derived from the same noun, and a
recent growth in the employment of suffixation, which, however, does
not coincide with a significant decline in the resort to either of
the other two processes.
The shock wave evolution of the laser-induced breakdown under non-spherical symmetry using a 1064 nm nanosecond laser
Authors: Liu Qingxi , Tian Ye , Song Wenhua , Jia Ziwen , Zhong Qiang , Yao Zhifeng , Zheng Ronger , Yuan Guang 
Published: April 2024

Abstract
In acoustics-related interdisciplinary areas, the shock wave of laser-induced breakdown has garnered significant attention. However, research on the propagation of shock waves in non-spherical symmetry is insufficient in both theoretical and experimental aspects. This paper aims to thoroughly study the evolution of underwater shock wave directivity by employing the method of shadowgraph. The shock wave front is determined by the dark fringes in the shadowgraph image and the normal propagation speed of the shock wave is calculated using Huygens principle. Subsequently, normal propagation speed is converted to pressure in different directions by employing the equation of state of the medium. It has been found that the spherical plasma produces an isotropic shock wave, whereas filamentary plasma generates a highly anisotropic one. To evaluate the anisotropic property of the shock wave, we introduce pressure directivity, which is defined as the pressure at any direction normalized by the maximum value. The temporal evolution of shock wave pressure directivity is obtained based on the shadowgraph images. In the case of filamentary plasma, there is a sudden transition of the pressure directivity in the axial from 40 ns to 165 ns, after which the pressure directivity is consistent with the hydrophone measurement. Based on the moving breakdown model of the plasma and the superposition principle, we propose a theoretical model to explain the experimental result of the pressure directivity. The outcome of our model exhibits considerable consistency with the experiment.
Flexible piezoelectric pressure sensor based on BaTiO3-CNT/RTV composite film with filter paper microstructures for monitoring hand motions
Authors: Dong Yukun , Xu Ce , Chen Shenghua , Wang Zhihua 
Published: April 2024

Abstract
Flexible piezoelectric sensors are widely used in various applications such as physiological signal monitoring and human-computer interaction. The present study introduces a BaTiO3-CNT/RTV piezoelectric sensor fabricated using a filter paper template. It incorporates micro-scale fiber stacking and a 1% CNT doping in the microstructure, resulting in a notable enhancement of sensor sensitivity, increasing it from 0.07 V N−1 to 0.69 V N−1, representing an almost tenfold improvement. Furthermore, the study investigates the influence of affecting factors like the flexible substrate of the sensing film, thickness, and mass fractions of various materials on the output voltage. The sensor exhibits superior characteristics such as good repeatability under 5000 cyclic loads, high elongation at break, fast response (80 ms) and recovery times (90 ms), and good linearity. It also demonstrates outstanding sensitivity (12 mV/10°) when monitoring different finger bending states, enabling real-time, sensitive, and reliable hand motion tracking. This sensor holds promising prospects for future developments in the fields of intelligent grasping and sign language translation.
In-situ measurement of subway train-induced vibration and noise of steel spring floating slab with MEMS-based sensing units
Authors: Liu Xiao-Zhou , Wang Yuan , Yin Ziren , Gao Tian-Ci , Luo Qin 
Published: April 2024

Abstract
The concrete floating slab, as a widely-used measure to reduce the subway-induced vibration and noise, can deteriorate with the accumulation of trainload. To monitor the dynamic performance of the floating slab during operation, this study develops a lightweight sensing unit based on microelectromechanical systems (MEMS). This sensing unit, which can be easily mounted on the concrete slab, enables synchronous monitoring of vibration and noise at different sampling frequencies with a very low power consumption. With the advantage of easy installation, the sensing units are mounted on various concrete slabs and tunnel walls to collect massive vibration and noise data. With the monitoring data, the dynamic performance and vibration reduction capability of the floating slab can be assessed. A case study is conducted on two floating slab sections: one section has a floating slab with a potential defect on its shear hinge and the other has a normal floating slab. The monitoring results show that: (1) the train condition has a significant effect on the amplitude of the track-side vibration; (2) the train-induced noise data last longer than vibration data during the passage of trains; (3) for the floating slab with shear hinge defect, the vibration levels at edge points are much higher than that in the middle. Through the monitoring test, the performance of the MEMS-based sensing unit and its adaptability in subway circumstances are verified.
A lightweight dynamic dual-damped wavelet-based convolutional neural network for interpretable bearing fault diagnosis
Authors: Zhao Lijuan , Mao Yongfang , Qin Yi 
Published: April 2024

Abstract
Wavelet-based convolutional neural networks (CNNS) have attracted widespread attention because they can improve the interpretability of intelligent fault diagnosis methods. However, the fault feature representation capability of typical wavelet-based convolution kernel frameworks must be strengthened to improve the diagnostic accuracy of complex faults. In the meantime, the large number of network parameters leads to high computational costs. To address these issues, a lightweight wavelet-based dynamic CNN, which comprises a dual-damping wavelet-based dynamic CNN (DWDC) block and a discrete wavelet transformation (DWT) enhancement (DWTE) block, is put forward. In the DWDC block, a wavelet convolution layer is initially designed, where a dual-damping wavelet is used as the kernel function to improve the match of the convolution kernel with fault impulses. Subsequently, a dynamic convolution layer with multiple parallel small-size convolutional kernels is designed to screen the fault features instead of a multilayer network structure, thereby greatly reducing the number of network parameters. Finally, the DWTE block is constructed by combining the DWT and residual dense block, and it can mine more fault information from the previously extracted features. The experiments on the variable speed bearing dataset, locomotive bearing dataset with constant speed and the Case Western Reserve University dataset prove that the proposed approach outperforms five classical CNN models and six advanced wavelet-based CNN models. In addition, it can effectively solve the issue of data imbalance because of its powerful feature extraction capability.
Emergence of rapid solidification microstructure in additive manufacturing of a Magnesium alloy
Authors: Tourret Damien , Tavakoli Rouhollah , Boccardo Adrian D , Boukellal Ahmed K , Li Muzi , Molina-Aldareguia Jon 
Published: April 2024

Abstract
Bioresorbable Mg-based alloys with low density, low elastic modulus, and excellent biocompatibility are outstanding candidates for temporary orthopedic implants. Coincidentally, metal additive manufacturing (AM) is disrupting the biomedical sector by providing fast access to patient-customized implants. Due to the high cooling rates associated with fusion-based AM techniques, they are often described as rapid solidification processes. However, conclusive observations of rapid solidification in metal AM—attested by drastic microstructural changes induced by solute trapping, kinetic undercooling, or morphological transitions of the solid-liquid interface—are scarce. Here we study the formation of banded microstructures during laser powder-bed fusion (LPBF) of a biomedical-grade Magnesium-rare earth alloy, combining advanced characterization and state-of-the-art thermal and phase-field modeling. Our experiments unambiguously identify microstructures as the result of an oscillatory banding instability known from other rapid solidification processes. Our simulations confirm that LPBF-relevant solidification conditions strongly promote the development of banded microstructures in a Mg–Nd alloy. Simulations also allow us to peer into the sub-micrometer nanosecond-scale details of the solid–liquid interface evolution giving rise to the distinctive banded patterns. Since rapidly solidified Mg alloys may exhibit significantly different mechanical and corrosion response compared to their cast counterparts, the ability to predict the emergence of rapid solidification microstructures (and to correlate them with local solidification conditions) may open new pathways for the design of bioresorbable orthopedic implants, not only fitted geometrically to each patient, but also optimized with locally-tuned mechanical and corrosion properties.
Exploring the interplay between the TGF-β pathway and SLN-mediated transfection: implications for gene delivery efficiency in prostate cancer and non-cancer cells
Authors: Moraes-Lacerda Thaís , Garcia-Fossa Fernanda , de Jesus Marcelo Bispo 
Published: April 2024

Abstract
Solid lipid nanoparticles (SLN) are widely recognized for their biocompatibility, scalability, and long-term stability, making them versatile formulations for drug and gene delivery. Cellular interactions, governed by complex endocytic and signaling pathways, are pivotal for successfully applying SLN as a therapeutic agent. This study aims to enhance our understanding of the intricate interplay between SLN and cells by investigating the influence of specific endocytic and cell signaling pathways, with a focus on the impact of the TGF-β pathway on SLN-mediated cell transfection in both cancerous and non-cancerous prostate cells. Here, we systematically explored the intricate mechanisms governing the interactions between solid lipid nanoparticles and cells. By pharmacologically manipulating endocytic and signaling pathways, we analyzed alterations in SLNplex internalization, intracellular traffic, and cell transfection dynamics. Our findings highlight the significant role of macropinocytosis in the internalization and transfection processes of SLNplex in both cancer and non-cancer prostate cells. Moreover, we demonstrated that the TGF-β pathway is an important factor influencing endosomal release, potentially impacting gene expression and modulating cell transfection efficiency. This study provides novel insights into the dynamic mechanisms governing the interaction between cells and SLN, emphasizing the pivotal role of TGF-β signaling in SLN-mediated transfection, affecting internalization, intracellular transport, and release of the genetic cargo. These findings provide valuable insight for the optimization of SLN-based therapeutic strategies in prostate-related applications.
Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets
Authors: Rahman Rahnuma , Ganguly Samiran , Bandyopadhyay Supriyo 
Published: April 2024

Abstract
Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.