bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of shells on the charge tunneling behavior in quantum-dot light-emitting diodes
Authors: Zhu Bingyan , Wang Ting , Wang Song , Chi Xiaochun , Zhang Han , Zhang Hanzhuang , Ji Wenyu
Published: November 2024

Due to the advances in quantum dot (QD) synthesis and device design, quantum-dot light-emitting diodes (QLEDs) have gained much progress in luminance, efficiency, and operational stability. However, fabricating high-performance QLEDs remain to be empirical, lacking full understanding of the electroluminescent mechanism of QDs. The formation and recombination of excitons is one of the most crucial charge-carrier dynamics in determining device design and performance. As a fingerprint, the ideality factor of diodes is used here to evaluate the transportation and recombination of charge carriers in QLEDs. The ideality factor of the current for QLEDs deviates significantly from 2, indicating that tunneling behavior dominates the charge injection and transportation processes in the low driving region. The ideality factor of the luminance strongly depends on the core-shell structure of the QDs. For QLEDs with I-QDs that have an alloying shell, the luminance ideality factor falls between 1 and 2, suggesting the coexistence of both trap-assisted recombination and Langevin bimolecular radioactive recombination processes. In contrast, for II-QDs with a sharp core-shell structure, the luminance ideality factor is approximately 2, indicating that trap-assisted recombination prevails in the device.
Comparison measurements of natural constants based on laser-focused atomic deposition and extreme ultraviolet interference lithography
Authors: Deng Xiao , Tang Zhaohui , Song Song , Xiao Guangxu , Yin Zhijun , Shen Junyu , Xue Dongbai , Xie Yuying , Dun Xiong , Cheng Xinbin , Li Tongbao
Published: November 2024

Traceability is the foundation of accuracy and consistency in dimensional metrology. Meter in the International System of Units is defined and reproduced by natural constants. Comparing the differences in traceability approaches based on different natural constants can better understand the real size of manufacturing structures. This paper compares the consistency of three traceability approaches based on chromium atomic transition wavelength, iodine-stabilized laser wavelength (top-down approach), and silicon lattice spacing (bottom-up approach) in nanoscale dimensional metrology. For comparison, the measured objects are silicon gratings manufactured by laser-focused atomic deposition (LFAD) and extreme ultraviolet interference lithography (EUV IL). Notably, the pitch of the silicon gratings can be traced from the transition wavelength of chromium (7S3→ 7P4, λ = 425.5533 nm). For silicon gratings manufactured by LFAD and EUV IL at diffraction order m = 1, the expected pitch of the silicon grating is

λ
4

. The pitch of the silicon grating is measured using a top-down (metrological atomic force microscope) approach, and the deviation between the measured result and the pitch based on the transition wavelength of chromium is 0.1 nm. For silicon gratings manufactured by LFAD and EUV IL at diffraction order m = 2, the expected pitch of the silicon grating is

λ
8

. This silicon grating is measured using a bottom-up approach, and the deviation between the measured results and the pitch based on the transition wavelength of chromium is 0.2 nm. This study provides a feasible solution for integrating various measurement methods and different traceability approaches at the nanoscale. It demonstrates the potential and feasibility of a nanoscale dimensional metrology traceability chain based on self-traceable gratings.
Multi-objective tree-structured Parzen estimator optimized Res-Net for ITSC fault diagnosis of PMSM
Authors: Zhang Wei , Xu Qiwei , Zhang Yixuan , Wang Yiming , Yang Yun , Cai Huaxiang
Published: November 2024

With the advancement of artificial intelligence technology, fault diagnosis methods based on deep learning have been extensively studied due to their ability to automatically extract fault latent features and develop end-to-end diagnostic models. However, the existing methods focus on achieving high accuracy while neglecting model complexity. Therefore, this paper proposes an inter-turn short circuit fault diagnosis method of permanent magnet synchronous motor using data-level fusion and multi-objective tree-structured Parzen estimator (MOTPE) optimized Res-Net. In this method, the original three-phase current signals are fused into a new modal signal through Clarke transform at the data layer. Based on an improved Res-Net18, hyperparameters are optimized using MOTPE to achieve high-performance and lightweight model design. Experiments have validated the fault diagnosis model that integrates current signal fusion and MOTPE optimization, achieving an accuracy of 99.62%, with the best noise robustness and the lowest model complexity. Compared with single-objective tree-structured Parzen estimator, multi-objective Non-Dominated Sorting Genetic Algorithm-III, and Random algorithms, MOTPE not only maintains high accuracy but also achieves lower computational costs and a lightweight network structure.
Thermal expansion of boron nitride nanotubes and additively manufactured ceramic nanocomposites
Authors: Wang Dingli , Chen Rachel , Anjum Nasim , Ke Changhong
Published: November 2024

Controlling the thermal expansion of ceramic materials is important for many of their applications that involve high-temperature processing and/or working conditions. In this study, we investigate the thermal expansion properties of additively manufactured alumina that is reinforced with boron nitride nanotubes (BNNTs) over a broad temperature range, from room temperature to 900 °C. The coefficient of thermal expansion (CTE) of the BNNT-alumina nanocomposite increases with temperature but decreases with an increase in BNNT loading. The introduction of 0.6% BNNTs results in an approximate 16% reduction in the CTE of alumina. The observed significant CTE reduction of ceramics is attributed to the BNNT’s low CTE and ultrahigh Young’s modulus, and effective interfacial load transfer at the BNNT-ceramic interface. Micromechanical analysis, based on in situ Raman measurements, reveals the transition of thermal-expansion-induced interface straining of nanotubes, which shifts from compression to tension inside the ceramic matrix under thermal loadings. This study provides valuable insights into the thermomechanical behavior of BNNT-reinforced ceramic nanocomposites and contributes to the optimal design of ceramic materials with tunable and zero CTE.
A reconfigurable compliant robotic gripper based on flexible scissors mechanism
Authors: Xie Wenhao , Li Yunquan , Mo Liyan , Fu Shiling , Qu Jinting , Zhong Yong , Zhang Yuan-Fang , Hu Nan , Wu Changchun , Ren Tao
Published: November 2024

Traditional robotic grippers are constrained by their fixed structures and limited grasping range, which poses difficulties when handling objects of various shapes and sizes. In contrast, reconfigurable grippers can flexibly adjust their grasping mode and configuration based on object properties, enabling them to accommodate a wider range of geometries. However, existing reconfigurable grippers face challenges in achieving structural consistency, large-scale continuous deformation, and ease of manufacturing and operation. To overcome these limitations, this study proposes a reconfigurable compliant robotic gripper (RCRG) based on a flexible scissor mechanism finger (FSMF). The FSMF incorporates an anisotropic scissor unit design that decouples extension and bending motions, achieving up to 4.5 times continuous finger length adjustment. Multiple FSMF modules can be mounted on a rigid scissor mechanism base to form either a two-fingered or three-fingered RCRG, depending on task requirements. With this configuration, the RCRGs can transition from a compact, retracted form to a significantly expanded state by deforming both the base and the FSMFs, thereby reconfiguring the grasping space to accommodate objects of various sizes, shapes, and orientations for diverse tasks. Theoretical modeling and experimental validation were conducted to analyze the mechanical performance of the FSMF under different extension ratios. Various grasping tests results demonstrate that the proposed RCRGs design efficiently and stably handles various objects, with a grasping envelope variation of up to 64 times, showcasing high adaptability and flexibility.
Study on the dual-segment arctangent model of magnetorheological damper (MRD)
Authors: Zhang Yanjuan , Luo Tianzhou , Guo Jiaxuan , Li Xin
Published: November 2024

Magnetorheological fluid damper (MRD) is a damping device that utilizes the instantaneous variable characteristics of magnetorheological fluid in magnetic field to control damping force. At present, there are some problems with the existing mechanical parametric models of MRD, such as the complexity of parameters in the Bouc–Wen model, low model accuracy, and the inability of the Bingham model to accurately capture the hysteresis characteristics of damping force with velocity variation. In response to these issues, on the base of MRD performance testing, a dual-segment arctangent model based on arctangent function is proposed. This model uses the arctangent function to describe the nonlinearity of the damping force output curve. And using piecewise fitting method to accurately capture the hysteresis characteristics of the output curve. Compared with existing parametric models such as the Bingham model, Sigmoid model, Bouc–wen improved model, et al, the dual-segment arctangent model shows simpler parameters and higher model fitting accuracy. The comparative analysis of the fitting results based on experimental data confirms the effectiveness and accuracy of the proposed model, providing-valuable reference for the development of high-precision MRD mechanical models.
Local structure analysis of 300 keV He+ irradiated REBCO coated conductor using polarisation dependent Cu K edge EXAFS
Authors: Lewis Jarrod C , Adams Kirk , Iliffe William , Aramini Matteo , Grovenor Chris R M , Nicholls Rebecca J , Diaz-Moreno Sofia , Speller Susannah C
Published: November 2024

Understanding irradiation damage of REBCO is increasingly of interest for compact tokamak fusion reactor designs, as these materials are critical for the proposed magnetic plasma confinement systems. Here commercially sourced samples of REBCO coated conductor are irradiated with 300 keV He+ ions to a damage level of 169 × 10−3 displacements-per-atom, to the point where superconductivity is no longer detectable, meaning these samples correspond to a non-functional end-of-life component in a fusion reactor context. Subsequent analysis of the crystal structure through a combination of x-ray diffraction and x-ray absorption spectroscopy measurements reveals a complex variation away from the as-grown structure. The local structure probed by the spectroscopy measurements is further observed to change as a function of the relative polarisation of the incident x-ray beam, indicating that within this damage regime the structural anisotropy of the REBCO unit cell plays a determining role in where defects accumulate within the material. Here the local structure measurements probing the a-b plane of the system vary significantly less than those probing the c-axis direction following irradiation, mirroring the observed trend in the x-ray diffraction data that the a:b ratio is preserved upon irradiation whilst the absolute values increase, whereas the c-axis parameter expands. These observations highlight the role of oxygen defect formation in driving the degradation of superconductivity within irradiated REBCO. These changes are observed to preferentially accumulate along the c-axis of the material, indicating a possible mechanistic signature of the degradation of the superconducting properties within these systems that are evident using a local structure probe such as extended x-ray absorption fine structure.
Tensor quantum programming
Authors: Termanova A , Melnikov Ar , Mamenchikov E , Belokonev N , Dolgov S , Berezutskii A , Ellerbrock R , Mansell C , Perelshtein M R
Published: November 2024

Running quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications appears daunting. In this article, we propose a novel approach called Tensor Quantum Programming, which leverages tensor networks (TNs) for hybrid quantum computing. Our key insight is that the primary challenge of algorithms based on TNs lies in their high ranks (bond dimensions). Quantum computing offers a potential solution to this challenge, as an ideal quantum computer can represent tensors with arbitrarily high ranks in contrast to classical counterparts, which indicates the way towards quantum advantage. While tensor-based vector-encoding and state-readout are known procedures, the matrix-encoding required for performing matrix-vector multiplications directly on quantum devices is much less studied. We introduce an algorithm that encodes matrix product operators into quantum circuits with a depth that scales linearly with the number of qubits. We demonstrate the algorithm’s performance for matrices commonly encountered in differential equations, optimization problems, and quantum chemistry, for systems involving up to 50 qubits.
Characterization and effectiveness analysis of ternary organic eutectic mixture/hybrid-nanoparticles based phase change material for cold storage applications
Authors: Jain Nilesh Kumar , Paliwal Deepak , Jain Pankaj
Published: November 2024

A novel organic ternary mixture composed of Lauric acid, Myristic acid, and Dodecanol (referred to as LMD) has been synthesized as a Phase Change Material (PCM), specifically designed for cold storage applications. Although organic PCMs are highly effective, their low thermal conductivity frequently restricts their heat transfer performance. To address this issue, a hybrid Nano-enhanced PCM (HNe-PCMs) has been developed in this study. This involved dispersing two types of nanoparticles, Graphene nanoparticles (GNP)-Al2O3 and GNP-CuO, each at a 1% weight fraction, within the LMD matrix. The thermal and chemical characteristics of developed pure LMD and HNe-PCMs were studied using Electron microscope scanning (SEM), Fourier transform infrared spectrophotometer (FTIR), x-ray diffractometer (XRD), Thermal conductivity analyser (TC), Differential scanning calorimeter (DSC), and Thermo gravimetric analyser (TGA) and explored their potential in cold storage application. Chemical and thermal characterization revealed that the freezing and melting temperatures of LMD are 8.4 ± 0.1 °C and 15.6 ± 0.1 °C, respectively, with corresponding latent heats (LH) of freezing/melting of 125.4 ± 1.8 J g−1 and 131.5 ± 1.8 J g−1. Upon incorporation of the nanoparticles, the thermal conductivities of LMD/GNP-Al2O3 and LMD/GNP-CuO were notably enhanced by 57.4% and 49.8%, respectively, compared to pure LMD. However, a slight deviation in the melting/freezing LH of 2.9% and 1.9%, and in phase change temperature of 12.1% and 7.1%, respectively, was observed for the LMD/GNP-Al2O3 HNe-PCMs compared to the pure LMD. Through effectiveness analysis, it was depicted that the LMD/GNP combined with Al2O3 exhibits a reduction of 13.94% and 12.4% in charging-discharge time compared to pure LMD. In contrast, the LMD/GNP with CuO showed a 12.73% and 10.87% reduction in these times respectively. Overall, LMD/GNP-Al2O3 emerges as a promising material for passive cold storage applications, improving thermal conductivity while preserving similar phase change characteristics to pure LMD performance.
Accurate density measurement of a small solid sample using a combination of hydrostatic weighing and pressure-of-flotation methods
Authors: Nishihashi Kanako , Waseda Atsushi , Kondo Yohan , Bitou Youichi , Kuramoto Naoki
Published: November 2024

A new high-precision density measurement method for small non-silicon samples was developed. Hydrostatic weighing (HW) and the pressure-of-flotation method (PFM) are high-precision techniques for determination solid sample densities. However, in HW, the uncertainty of the density measurement is quite large when the volume of the sample is much smaller than that of a 1 kg silicon sample owing to the uncertainty of the auxiliary weights during the measurement. Additionally, the PFM is not applicable for measuring the density of non-silicon materials. We developed a new method that combines HW and the PFM. We prepared a small 45 g Si3N4 sphere sample with a diameter of 30 mm. By employing two silicon discs with these masses that were nearly equivalent to the apparent masses of the sample, the same auxiliary weights were used for both the standards and the sample. The new method was validated by measuring the density of the 45 g Si3N4 sphere with a relative standard uncertainty of 4.6 × 10−6, which is significantly lower than that obtained by traditional HW methods. This approach demonstrates substantial improvements in the precision of small-sample density measurements, thus offering a robust alternative to conventional techniques.
Errors in single pixel laser imaging emerging from spatial size limits in the bucket detector
Authors: Scheidt Dennis , Quinto-Su Pedro A
Published: November 2024

In single pixel imaging (SPI) an image is sampled with an orthogonal basis projected by a programmable optical element like a digital micromirror array or a spatial light modulator. The reflected or diffracted light is collected by a lens and measured with a photodiode or bucket detector. In this work we demonstrate that SPI of laser light fields can exhibit cross talk with its phase when the bucket detector is not sufficiently large (i.e. fast photodiodes) and does not integrate the full spectrum of each basis element. In that regime, structured phases can emerge from errors in the optical or light collection system (i.e. misaligned optical elements or aberrations) and can appear in the reconstructed amplitude image. We show this behavior in simulations and experiments where the reconstructed amplitude image will exhibit phase cross-talk if the detector size is smaller than between 50% and 75% of the maximum area spanned by the projected spectrum of the measurement basis. This is important because photodiodes with a fast response time have a small effective area.
Flat bands without twists through the use of a one harmonic Moiré systems: topological nature of modes and electron–electron pairing in periodic uniaxial strained or crenellated graphene nanoribbons
Authors: Andrade Elias , López-Urías Florentino , Naumis Gerardo G
Published: November 2024

Flat bands are known to produce highly correlated phases, leading to superconductivity and charge fractionalization. In two-dimensional systems, they have been extensively studied in magic-angle twisted bilayer graphene (MATBG). However, from both theoretical and experimental perspectives, MATBG remains a challenging system. Here, we present an alternative untwisted Moiré system to avoid such difficult twists. This system reproduces many of the interesting physical effects observed in Moiré systems, particularly flat bands and electron–electron pairing via a repulsive mechanism. The system consists of a graphene nanoribbon with periodic strain or corrugation, induced by a suitable substrate, such as a crenellated h-BN substrate. The strain is periodic with a single harmonic, but the key is to apply a spatial frequency slightly detuned from the condition that changes sign between neighboring sites. This produces a deformation that changes sign between neighbors in graphene while introducing a long-wavelength deformation visible only on each of the graphene’s bipartite lattices. This induces a spatially dependent effective mass, obtained using one harmonic. The system maps onto a Jackiw–Rebbi model, with flat-band modes identified as topological soliton modes. Electron–electron interactions are included in the system using the Hubbard Hamiltonian. The main result is the emergence of an effective attraction between electrons, accompanied by spin polarization coupled to the electron pseudospin. These observations align with the Kohn–Luttinger theory of superconductivity in other bipartite lattices, such as high-Tc cuprates. Since the Jackiw–Rebbi model exhibits charge fractionalization, similar phases are also expected to appear, as observed in MATBG.
Exploring the fresh and rheology properties of 3D printed concrete with fiber reinforced composites (3DP-FRC): a novel approach using machine learning techniques
Authors: Rasel Risul Islam , Hossain Md Minaz , Zubayer Md Hasib , Zhang Chaoqun
Published: November 2024

This study focuses on the prediction models for four parameters related to the fresh and rheological properties of 3DP-FRC: spreading diameters (SPD), dynamic yield stress (DYs), static yield stress (SYs) and plastic viscosity (PV), respectively. Five machine learning (ML) algorithms were employed, namely artificial neural network (ANN), random forest (RF), decision tree (DT), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost). An extensive dataset was compiled including 373 (SPD) and 219 (SYs, DYs, PV) from various literature comprising experimental results. Fifteen input parameters were identified as the most influential factors affecting the fresh and rheological properties. These parameters include OPC, W/B, W/S, FA, LP, SF, SP, VMA, W, h
f
, R
i
, AR, t
sf
, F
t
, and S
time
/R
time
. This study found strong correlations between the developed ML models and the experimental outcomes from both the training and testing datasets. The models demonstrated exceptional accuracy and provided precise predictions for SPD, SYs, DYs, and PV. The correlation coefficients (R2) ranged from 0.94 to 0.99 for SPD, 0.93 to 0.99 for SYs, 0.98 to 0.99 for DYs, and 0.98 to 1.00 for PV, with consistent results observed across both the training and testing datasets. Moreover, the model’s precision was assessed using different error metrics, including root mean square error (RMSE), mean square error (MSE), coefficient of variation in root-mean-square error (CVRMSE), and mean absolute error (MAE). Sensitivity analysis was performed to identify their impact. Additionally, fiber dependent analysis was conducted to assess the effectiveness of different fiber types on the fresh and rheological properties (SPD, SYs, DYs, and PV). In conclusion, the ML models were effectively trained and optimized, resulting in accurate and highly predictive capabilities for the parameters of interest.
Assessing the validity of a wearable shoulder motion tracking system through comparison with dartfish in patients undergoing shoulder joint replacement surgery
Authors: MilaniZadeh Sohrob , MacDermid Joy C , Langohr G Daniel , Johnson James
Published: November 2024

Objective assessments of shoulder motion are paramount for effective rehabilitation and evaluation of surgical outcomes. Inertial Measurement Units (IMU) have demonstrated promise in providing unbiased movement data. This study is dedicated to evaluating the concurrent construct validity and accuracy of a wearable IMU-based sensor system, called ‘Motion Shirt’, for the assessment of humero-thoracic motion arcs in patients awaiting shoulder replacement surgery. This evaluation was conducted by comparing Motion Shirt data with the Dartfish Motion Analyzer software during the Functional Impairment Test-Hand and Neck/Shoulder/Arm (FIT-HaNSA) test. Thirteen patients (age > 50), who were awaiting shoulder replacement surgery, were recruited. The Motion Shirt was employed to measure angular humero-thoracic movements in two planes during the FIT-HaNSA test. Simultaneously, two cameras recorded the participants’ movements to provide reference data. Bland-Altman plots were generated to visualize agreement between the Motion Shirt and the reference data obtained from the Dartfish Motion Analyzer software. The data analysis on Bland-Altman plots revealed a substantial level of agreement between the Motion Shirt and Dartfish analysis in measuring humero-thoracic motion. In Task-1, no significant systematic errors were exhibited, with only 3.27% and 2.18% of points exceeding the limits of agreement (LOA) in both elevation and the Plane of Elevation (POE), signifying a high level of concordance. In Task-2, a high level of agreement was also observed in Elevation, with only 3.8% of points exceeding the LOA. However, 5.98% of points exceeded LOA in POE for Task-2. In Task-3, focused on sustained overhead activity, the Motion Shirt showed strong agreement with Dartfish in Elevation (2.44% points exceeded LOA), but in POE, 7.32% points exceeded LOA. The Motion Shirt demonstrated a robust concordance with Dartfish Motion Analyzer system in assessing humerothoracic motion during the FIT-HaNSA test. These results affirm the Motion Shirt’s suitability for objective motion analysis in patients awaiting shoulder replacement surgery.
Quantum phase transition detection via quantum support vector machine
Authors: Wang Youle , Cao Linyun
Published: November 2024

Unveiling quantum phase transitions (QPTs) is important for characterising physical systems at low temperatures. However, the detection of these transitions is encumbered by significant challenges, especially in the face of the exponential growth in ground state complexity with system scale. The emergence of quantum machine learning has lately gained traction as a promising method for elucidating the properties of many-body systems, providing a different avenue to study QPT. In this paper, we propose a novel and efficient quantum algorithm for identifying QPT synthesising quantum feature with quantum machine learning. Our approach is anchored in the utilisation of quantum computers to directly encode the kernel matrix into Hilbert spaces, realised by the parallel implementation of the quantum feature map. Specifically, we generate a quantum state encoding the information of ground states of the given quantum systems by employing the parallel quantum feature map. The resultant state preparation circuit is then used to implement a block-encoding of the kernel matrix. Equipped with the associated labels and this encoding, we devise a new quantum support vector machine (QSVM) algorithm, forming the main ingredient of the classifier. The presented method refines the efficiency of the prevailing QSVM algorithm for processing quantum and classical data. We demonstrate the effectiveness of our quantum classifier in predicting QPT within the transverse-field Ising model. The findings affirm the efficacy of quantum machine learning in recognising QPT in many-body systems and offer insights into the design of quantum machine learning algorithms.
A review of phononic-crystal-based energy harvesters
Authors: Bahrami Ali , Motaei Farzaneh
Published: November 2024

Energy harvesting is a technique in which ambient energy is converted to useful energy to prevent wastefulness. Piezoelectric energy harvesting is described as conversion of mechanical energy into electrical energy. Vibrations, sounds, ocean motions, human activities, etc. are various nature sources of mechanical waves. Energy harvesting is used to directly power electronics or recharge a battery. Efficiency is the important parameter of an energy harvester. To achieve high electrical energy harvesting from mechanical waves, phononic crystals have been utilized. Phononic crystals are a type of metamaterial that can manipulate the propagation of mechanical waves. Researchers have proposed different techniques to concentrate acoustic waves in phononic crystals. In this paper, a comprehensive review of phononic-crystal-based energy-harvesting techniques has been conducted. An energy-harvesting capability comparison between proposed phononic-crystal-based energy harvesters is also included.
Joining of thin aluminum sheets by micro-friction stir welding (μFSW) using a pinless tool
Authors: Sen Meghnath , Puri Asit Baran
Published: November 2024

In the present research, butt welding of 0.8 mm thick aluminum 6061–T6 sheets has been executed using a pinless tool with variable tool rotational speed (1000-2500 rpm), welding speed (100–250 mm min−1), and a constant plunging depth (0.1 mm). Tensile and microhardness tests have been conducted to evaluate the joint’s mechanical strength. The joint developed at 2500 rpm, and 100 mm min−1 shows a maximum strength of 277.65 MPa (88.4% of original strength), indicating that higher tool rotational speed and slower welding speed enhance joint formation through adequate heat generation and material intermixing. Small amount of defects like flash, surface galling, and irregular shoulder marks have been found in the weld region.
Forecasting high-dimensional spatio-temporal systems from sparse measurements
Authors: Song Jialin , Song Zezheng , Ren Pu , Benjamin Erichson N , Mahoney Michael W , Li Xiaoye S
Published: November 2024

This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.
SnO2 quantum dots dispersed TGCN nanosheets for improved ethanol sensing under ambient conditions
Authors: Kamathe Vishal , Kumar Arun , Khanuja Manika , Nagar Rupali
Published: November 2024

This study investigates ethanol sensing properties of a hybrid sensing material prepared by mechano-mixing of tin oxide (SnO2) quantum dots (SQDs) and template-free graphitic carbon nitride (g-C3N4) i.e. TGCN nanosheets (TNSs). Hybrid sensors comprising of various weight ratios of TNSs and SQDs ranging from 1:5 to 1:50 (T-SX where X denotes the proportion of SQDs) were prepared and tested for ethanol vapor sensing under ambient conditions. Structural, thermal, optical, and morphological properties of the hybrid samples were analyzed comprehensively using techniques such as x-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), UV–visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), and Raman microscopy. The results indicate that the SQDs crystallize in rutile crystal structure with an average diameter of ∼ 2.7 nm and are dispersed on the TNSs. The best response and recovery time were observed for T-S20 with a fast response time of 12 ± 3 s and recovery time of 20 ± 3 s under ambient conditions. The hybrid sensor showed a 48% faster response time, and 57% faster recovery time as compared to pure quantum dots. This improvement is attributed to dispersed SQDs on TNSs, optimum bond strength between SQDs and TNSs, surface plasmon states of SQDs, higher surface area of TNSs, catalytic activity of N-atoms and finally the heterojunction formed between SnO2 quantum dots and TNSs which attracts bipolar hyroxyl group of ethanol vapors under ambient conditions and aids in their faster detection.
Is it just about the parties? A social welfare approach to familiar problems of international arbitration
Authors: Kostcov Vladimir
Published: November 2024

Many doctrinal discussions of arbitration, such as the law applicable to arbitration agreement, joinder of third parties, and arbitrability, are based on formalist methodology that derives answers from categorical notions such as party autonomy. This paper attempts to take a different route and addresses arbitration from the viewpoint of social welfare, using economic methodology. The paper highlights that arbitration, due to its procedural features and private-regarding incentives of arbitrators, comes with a cost to society. Even though that cost is outweighed by its benefits, this fact can inform our thinking about arbitration and inspire novel legal solutions to familiar problems.
Efficient genome monomer higher-order structure annotation and identification using the GRMhor algorithm
Authors: Glunčić Matko , Barić Domjan , Paar Vladimir
Published: November 2024

Motivation

Tandem monomeric units, integral components of eukaryotic genomes, form higher-order repeat (HOR) structures that play crucial roles in maintaining chromosome integrity and regulating gene expression and protein abundance. Given their significant influence on processes such as evolution, chromosome segregation, and disease, developing a sensitive and automated tool for identifying HORs across diverse genomic sequences is essential.

Results

In this study, we applied the GRMhor (Global Repeat Map hor) algorithm to analyse the centromeric region of chromosome 20 in three individual human genomes, as well as in the centromeric regions of three higher primates. In all three human genomes, we identified six distinct HOR arrays, which revealed significantly greater differences in the number of canonical and variant copies, as well as in their overall structure, than would be expected given the 99.9% genetic similarity among humans. Furthermore, our analysis of higher primate genomes, which revealed entirely different HOR sequences, indicates a much larger genomic divergence between humans and higher primates than previously recognized. These results underscore the suitability of the GRMhor algorithm for studying specificities in individual genomes, particularly those involving repetitive monomers in centromere structure, which is essential for proper chromosome segregation during cell division, while also highlighting its utility in exploring centromere evolution and other repetitive genomic regions.

Availability and implementation

Source code and example binaries freely available for download at github.com/gluncic/GRM2023.
HAlign 4: a new strategy for rapidly aligning millions of sequences
Authors: Zhou Tong , Zhang Pinglu , Zou Quan , Han Wu
Published: November 2024

Motivation

HAlign is a high-performance multiple sequence alignment software based on the star alignment strategy, which is the preferred choice for rapidly aligning large numbers of sequences. HAlign3, implemented in Java, is the latest version capable of aligning an ultra-large number of similar DNA/RNA sequences. However, HAlign3 still struggles with long sequences and extremely large numbers of sequences.

Results

To address this issue, we have implemented HAlign4 in C++. In this version, we replaced the original suffix tree with Burrows–Wheeler Transform and introduced the wavefront alignment algorithm to further optimize both time and memory efficiency. Experiments show that HAlign4 significantly outperforms HAlign3 in runtime and memory usage in both single-threaded and multi-threaded configurations, while maintains high alignment accuracy comparable to MAFFT. HAlign4 can complete the alignment of 10 million coronavirus disease 2019 (COVID-19) sequences in about 12 min and 300 GB of memory using 96 threads, demonstrating its efficiency and practicality for large-scale alignment on standard workstations.

Availability and implementation

Source code is available at https://github.com/malabz/HAlign-4, dataset is available at https://zenodo.org/records/13934503.
Efficacy of needle tract embolization using gelfoam in reducing incidence of pneumothorax in CT-guided transthoracic lung biopsies: a systematic review and meta-analysis
Authors: Singh Parneet , Nayak Manoj Kumar , Singhal Tejasvini , Parida Girish Kumar , Agrawal Kanhaiyalal , Sahoo Biswajit , Deep (Bag) Nerbadyswari
Published: November 2024

Objective

Transthoracic CT-guided lung biopsy is associated with risk of pneumothorax, requiring chest tube placement at times. From available literature, we tried to compare the incidence rate of pneumothorax in cases undergoing CT-guided lung biopsy with versus without use of gelfoam slurry for tract occlusion.

Methods

Databases like SCOPUS, PubMed, Google Scholar, and EMBASE were searched for original studies analysing the efficacy of gelfoam for needle tract embolization following CT-guided lung biopsy till September 2023. Bias in the selected studies was assessed using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. Pooled odds ratio of the effect of gelfoam slurry on the rate of pneumothorax and chest tube placement following CT-guided lung biopsy was calculated and represented with 95% confidence intervals (95% CI) and prediction interval (PI).

Results

Pooled analysis of six studies revealed that patients undergoing needle tract embolization when compared to non-embolized patients had a significantly decreased risk of pneumothorax with odds reduced by 59% (OR = 0.41, 95% CI = 0.25-0.66, P = .01; PI = −1.694 to 0.094). Also, it led to a significant reduction in severe pneumothorax requiring chest tube placement with reduced odds by 63% (OR = 0.37, 95% CI = 0.20-0.69, P = .01; PI = −1.855 to 0.115).

Conclusion

Needle tract embolization using gelfoam has a significant protective effect against pneumothorax as well as chest tube insertion in patients undergoing CT-guided lung biopsy.

Advances in knowledge

Gelfoam needle tract embolization can effectively reduce the risk of pneumothorax and chest tube requirement after lung biopsy.

PROSPERO registration ID

CRD42024505788
Impact of fixation duration on messenger RNA detectability in human formalin-fixed paraffin-embedded brain tissue
Authors: Hurler Charlene-Annett , Liebscher Sabine , Arzberger Thomas , Jäkel Sarah
Published: November 2024

Technologies to study mRNA in post-mortem human brain samples have greatly advanced our understanding of brain pathologies. With ongoing improvements, particularly in formalin-fixed paraffin-embedded tissue, these technologies will continue to enhance our knowledge in the future. Despite various considerations for tissue and mRNA quality, such as pre-mortem health status and RNA integrity, the impact of the tissue fixation time has not been addressed in a systemic fashion yet. In this study, we employed RNAscope to assess mRNA detectability in human post-mortem brain tissue in relation to fixation time. Our results reveal a dynamic change in mRNA detection across varying fixation durations, accompanied by an increase in signal derived from the negative probe and autofluorescence background. These findings highlight the critical relevance of standardized fixation protocols for the collection of human brain tissue in order to probe mRNA abundancy to ensure reliable and comparable results.
Effects of template molecules on the molecular permeability of molecularly imprinted polysaccharide composite films
Authors: Sagawa Takuya , Yamamoto Tamao , Hashizume Mineo
Published: November 2024

pH-responsive materials comprising natural polysaccharides have attracted attention due to their high biocompatibility and biodegradability, and are potentially useful as biomaterials. In particular, polysaccharide film materials can be used as drug carriers, wound dressings, and separation materials. We have succeeded in the fabrication of polysaccharide composite films from polyion complexes of anionic polysaccharides and chitosan. These films showed molecular permeability and the permeation behavior can be controlled by applying a molecular imprinting approach while using a cationic molecule as the template. However, the details of the effects of template molecules have not been clarified. In this study, to investigate the effects of the characteristics of the template molecules on the molecular permeability of the film, molecularly imprinted polysaccharide composite films were prepared by using template molecules with different sizes and charge valences. The molecular permeation behaviors of the resulting films were then evaluated under different pH conditions. It was found that differences in template molecules affected the swelling ratio and surface charge of the films. Moreover, the permeation behavior was largely affected by the surface charge of the film, while the size of the template molecule had little effect. Based on the results, the mechanism for the molecular permeation is discussed. These results will contribute to the application of polysaccharide composite films as pH-responsive materials.
Brain–clinical pattern alterations after treatment in schizophrenia
Authors: Jing Rixing , Wang Qiandong , Liu Guozhong , Shi Jie , Fan Yong , Lu Lin , Lin Xiao , Li Peng
Published: November 2024

Discovering meaningful brain–clinical patterns would be a significant advancement for elucidating the pathophysiology underlying schizophrenia. In the present study, we analyzed associations between functional brain characters (average functional connectivity strength and its fluctuations) and clinical features (age onset, illness duration, and positive, negative, disorganized, excited, and depressed) using partial least squares. Also, we analyzed the brain–clinical relationship changes after 6-wk of treatment. At baseline, 2 identified latent brain–clinical dimensions collectively accounted for 33.2% of the covariance between clinical data and brain function. The illness onset age and duration significantly contributed to all latent dimensions. The disorganized symptoms contributed to the first latent variable, while the positive and depressed symptoms notably negatively contributed to the second variable. The average functional connectivity strength of first latent variable could positively predict the treatment effect, especially in the positive, negative, excited, and overall symptoms. No significant correlation between average functional connectivity strength and treatment effect was obtained in second latent variable. We also found that functional connectivity and its fluctuations altered after treatment, with similar patterns of brain characteristic alterations across the 2 latent variables. By simultaneously taking into account both clinical manifestations and brain abnormalities, the present results open new avenues for predicting treatment responses in schizophrenia.
An Empirical Study on Personal Data Protection in the Banking Sector across Hong Kong, Macau, and Mainland China
Authors: Wang Zhangyu , Du Li
Published: November 2024

This study examines the personal data protection practices within the banking sectors in the Greater Bay Area—Hong Kong, Macau, and Mainland China. Our analysis reveals significant inconsistencies in the application of related privacy policies of banks across the three regions, with specific focus on aspects such as personal and sensitive data, data processing, and the protection of minors’ personal data. The discrepancies stem from diverse regional laws, regulations, and judicial practices that generate legal uncertainties, subsequently impacting cross-border data transfer. The study emphasizes an urgent need for a harmonized approach to data protection within the Greater Bay Area and highlights practical challenges encountered in harmonizing the relevant laws across the three regions. It recommends developing data protection standards for the banking industry and using such standards as useful guidelines for addressing the existing discrepancies in data protection regulations and practices. This could ensure consistency, allow for daily banking operations that comply with the diverse data protection rules across the three regions, and foster data flow within the banking sector in the Greater Bay Area.
Converting dose-area product to effective dose in dental cone-beam computed tomography using organ-specific deep learning
Authors: Pauwels Ruben
Published: November 2024

Objective

To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.

Methods

A total of 24 384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width/height, and isocenter position. Equivalent organ doses as well as DAP values were recorded. Next, using the aforementioned scan parameters as inputs, neural networks (NN) were trained using Keras for estimating the equivalent dose per DAP for each organ. Two methods were explored for positional input features: (1) “Coordinate” mode, which uses the (continuous) XYZ coordinates of the isocentre, and (2) “AP/JAW” mode, which uses the (categorical) anteroposterior and craniocaudal position. Each network was trained, validated, and tested using a 3/1/1 data split. Effective dose (ED) was calculated from the combination of NN outputs using ICRP 103 tissue weighting factors. The performance of the resulting NN models for estimating ED/DAP was compared with that of a multiple linear regression (MLR) model as well as direct conversion coefficients (CC).

Results

The mean absolute error (MAE) for organ dose/DAP on the test data ranged from 0.18% (bone surface) to 2.90% (oesophagus) in “Coordinate” mode and from 2.74% (red bone marrow) to 14.13% (brain) in “AP/JAW” mode. The MAE for ED was 0.23% and 4.30%, respectively, for the two modes, vs. 5.70% for the MLR model and 20.19%-32.67% for the CCs.

Conclusions

NNs allow for an accurate estimation of patient dose based on DAP in dental CBCT.