bluuebunny/crossref_metadata_2025 huggingface dataset:
Spin-charge-lattice couplings and Co doping effect in Ba(Fe

1
−
x

Co

x

)2As2: an ab initio molecular dynamics study
Authors: He Hui-Hui , Yang Huan-Cheng , Lu Zhong-Yi , Liu Kai
Published: May 2024

As one of the typical iron-based superconductors, Ba(Fe

1
−
x

Co
x
)2As2 exhibits a dome-like evolution of superconducting transition temperature T
c with the Co doping (x), which provides an ideal platform to explore the mechanism of unconventional superconductivity. Through ab initio molecular dynamics (AIMD) studies, we find that the spin, charge, and lattice degrees of freedom in Ba(Fe

1
−
x

Co
x
)2As2 are intimately coupled with a characteristic frequency around 5.5 THz, which originates from the

A

1
g

phonon mode of the As atoms. Compared with the undoped BaFe2As2, the spin dynamics in Ba(Fe

1
−
x

Co
x
)2As2 (x = 0.0625, 0.1250, 0.1875) shows enhanced spatial inhomogeneities, though the features of the charge dynamics are rarely modified. Along with the increasing Co doping, the spatial inhomogeneities of Fe spin dynamics display nonmonotonic changes, meanwhile the lattice vibrational spectra first show peak broadening and then restore the main peak around 5.5 THz, being both consistent with the nonmonotonic behavior of superconducting T
c. Our study provides a molecular dynamics approach to investigate the couplings of spin, charge, and lattice in doped BaFe2As2, which could also be applied to explore the dome-like evolution of superconductivity in other Fe-based superconductors.
High critical current properties of multi-filamentary MgB2 superconducting wires fabricated using an internal Mg diffusion method
Authors: Chen Wenwu , Nong Xiaoyao , Wang Zhihan , Li Jianwei , Yang Luan , Lin He , Pan Xifeng
Published: May 2024

Although both the mass density and grain connectivity of MgB2 superconducting layers can be greatly improved via an internal Mg diffusion (IMD) process, the poor structural uniformity and low MgB2 filling factor of IMD wires limit further enhancement of their superconducting performance. Herein, we prepared 19-filament and 37-filament IMD-MgB2 superconducting wires using a combination of optimization of the component structure and the introduction of an intermediate annealing process. Microstructure analysis suggests that good structural uniformity and high layer density have been achieved in the multi-filamentary MgB2 wires, and the MgB2 filling factor reaches 9.3%–11.0%. The magnetic superconducting transition of MgB2 wires is relatively sharp, and the onset T
c is around 37 K. Remarkably, there is no magnetic flux jump for 37-filament wires in the low-field region at 5 K. At 4.2 K and 4 T, the transport layer J
c values of 19-filament and 37-filament MgB2 wires are as high as 1.5 × 105 A cm−2 and 2.2 × 105 A cm−2, respectively, with, accordingly, engineering J
e values of 1.7 × 104 A cm−2 and 2.0 × 104 A cm−2. These results indicate that the performance of multi-filamentary IMD-MgB2 wires can compete with traditional powder-in-tube-MgB2 wires applied in industry.
The average transmitted wave in random particulate materials
Authors: Karnezis Aristeidis , Piva Paulo S , Gower Art L
Published: May 2024

Microwave remote sensing is significantly altered when passing through clouds or dense ice. This phenomenon is not unique to microwaves; for instance, ultrasound is also disrupted when traversing through heterogeneous tissues. Understanding the average transmission in particle-filled environments is central to improve data extraction or even to create materials that can selectively block or absorb certain wave frequencies. Most methods that calculate the average transmitted field assume that it satisfies a wave equation with a complex effective wavenumber. However, recent theoretical work has predicted more than one effective wave propagating even in a material which is statistically isotropic and for scalar waves. In this work we provide the first clear evidence of these predicted multiple effective waves by using high-fidelity Monte-Carlo simulations that do not make any statistical assumptions. To achieve this, it was necessary to fill in a missing link in the theory for particulate materials: we prove that the incident wave does not propagate within the material, which is usually taken as an assumption called the Ewald–Oseen extinction theorem. By proving this we conclude that the extinction length—the distance it takes for the incident wave to be extinct—is equal to the correlation length between the particles.
Strong-coupling mean-field and classical percolation approach to produce the phase diagram of disordered Bose–Hubbard model at fixed n = 1 filling
Authors: Gupta Manjari
Published: May 2024

We perform strong-coupling mean-field along with classical percolation analysis to produce the phase diagram of disordered Bose–Hubbard model with chemical potential disorder in two dimensional square lattice. The phase diagram in the disorder strength (Δ) and the on-site repulsion (U), for two and three dimensional system at fixed n = 1 filling show interesting re-entrant superfluid phase sandwiched between Bose-glass phases, as observed by the previous QMC results. We produce the phase diagram exhibiting the re-entrant phenomenon as well as phenomenologically demonstrate the robustness of the re-entrant phenomena within this method. We also discuss the importance of the method and applicability in further analysis of the same phase diagram.
Synergistic effect of stearic acid/bismuth oxychloride/cupric oxide for thermal storage applications: preparation, stability, rheological and thermophysical analysis
Authors: K Gopi Kannan , R Dhivagar , Singh Pawan Kumar , Nandhakumar E
Published: May 2024

The present study reports the preparation of nanophase change materials (NPCMs) using a two-step method with an optimized ratio of 0.5 wt% of nanoparticles for thermal storage applications. Bismuth oxychloride (BiOCl), cupric oxide (CuO), and a mixture of BiOCl/CuO (composite) were used as nanoparticles, and stearic acid was used as the PCM. The crystallography, chemical/functional groups and morphologies of the prepared NPCMs have been analysed by XRD, FT-IR and SEM, respectively. Observations revealed that the presence of nanoparticles in NPCMs did not affect crystal formation or chemical disruption of molecular interactions. TGA was used to analyse the thermal stability and rate of deterioration of the NPCMs. The deterioration of the PCM occurs at 243 °C with a weight loss of 1.3% while 0.5 wt% concentrated BiOCl, CuO, and composite NPCMs exhibit weight losses of 1.8%, 2.3%, and 3.4%, respectively at 257 °C, 262 °C and 258 °C. In the DSC study, the phase-changing attributes of the NPCMs manifested in the onset temperature range between 53.4 °C and 59.8 °C. The potential enthalpy of the PCM is 209.1 kJ kg−1, whereas those of the 0.5 wt% concentrated BiOCl, CuO, and composite NPCMs are 204.6, 198.3, and 201.7 kJ kg−1, respectively. However, the enhanced thermal conductivities of 0.5 wt% concentrated BiOCl, CuO, and the composite NPCMs are 0.18, 0.22, and 0.20 W/(m.°C), respectively, which are 5.9%, 29.4% and 17.6% greater than that of PCM. Additionally, the congruent melting rate increased by 31% for 0.5 wt% CuO concentrated NPCM; therefore, it is a potential candidate than other NPCMs/PCM. Cyclic tests were conducted to assess the reliability of the NPCMs, and compatible results were obtained even after 500 cycles. The findings of this work indicate that all the prepared NPCMs could be a viable option for practical applications, including thermal comfort buildings, solar heating, and electronic cooling.
Effect of graphene oxide loaded on TiO2-nanotube-modified Ti on inflammatory responses
Authors: Cao Xu , Luo Bin , Wang Caiyun , Lu Ran , Chen Su
Published: May 2024

Although a titanium matrix modified with titanium dioxide nanotube (TNT) arrays can have anti-inflammatory effects in vitro, these effects are limited. In this study, the TNT surface was modified by electrodepositing graphene oxide (GO) to enhance the anti-inflammatory effect of the material. Scanning electron microscopy (SEM), Raman spectroscopy, and x-ray diffraction were used to characterize each of these materials. Cell Counting Kit-8 (CCK-8) was used to determine the cell proliferation status. Enzyme-linked Immunosorbent Assay (ELISA), immunofluorescence staining, and RNA sequencing were used to assess the regulation of inflammation in each group. Raman spectroscopy confirmed that GO was successfully loaded onto the surface. The SEM, ELISA, fluorescence staining, and RNA sequencing results indicated that TNT-GO can effectively inhibit the inflammatory response and induce the M2 polarization of macrophages. TNT-GO can weaken the surface inflammatory responses of materials, suppress the secretion of pro-inflammatory factors, and promote the M2 polarization of macrophages. These advantageous properties render TNT-GO a promising material for dental implants.
The preparation and selection process for a new form stable phase change material (FSPCM) intended for use as thermal storage in applications at intermediate temperatures
Authors: Patel Kalpeshkumar , Patel Tushar M
Published: May 2024

There is a greater focus on solar energy since the food processing business requires a lot of thermal energy to remove moisture from agricultural products. This study thoroughly explains how to choose Phase Change Materials (PCMs) within a specific temperature range. The best PCM is selected from the Multiple Criteria Decision Making (MCDM) procedures based on a few characteristics. Based on the results of the MCDM method, the eutectic of paraffin and palmitic acid is chosen. PCM is bound, and FSPCM is formed using clay. The method of preparation of Clay-based FSPCM is described in detail. The amount of clay materials and different clay percentage combinations are chosen from the leakage experiment. Kaolin has the least amount of leakage out of all the clay materials tested. Kaolin-based FSPCM Exhibits improvement in thermal conductivity compared to Pure PCM.
Deep learning-based auditory attention decoding in listeners with hearing impairment
*
Authors: Tanveer M Asjid , Skoglund Martin A , Bernhardsson Bo , Alickovic Emina
Published: May 2024

Objective. This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population. Approach. Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set had not seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with HI, listening to competing talkers amidst background noise. Main results. Using 1 s classification windows, DCNN models achieve accuracy (ACC) of 69.8%, 73.3% and 82.9% and area-under-curve (AUC) of 77.2%, 80.6% and 92.1% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9%, 80.1% and 97.5%, along with AUC of 94.6%, 89.1%, and 99.8%. Our DCNN models show good performance on short 1 s EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1 s EEG windows from participants with HI, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks. Significance. Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative DL architectures and their potential constraints.
Numerical simulation of neutral gas dynamics in the W7-X sub-divertor
Authors: Varoutis S. , Tantos C. , Strobel H. , Day C. , Dhard C.P. , Haak V. , Igitkhanov Y. , Naujoks D. ,   the W7-X Team
Published: May 2024

The present work presents a 2D and 3D modeling of the neutral gas flow in the sub-divertor region of the W7-X. The investigations have been done using the DIVGAS code. The complex 2D and 3D geometries of the divertor components in the sub-divertor region have been considered and the Standard and High-Iota magnetic configurations have been numerically simulated. The main objective of this study is to investigate the dynamics of neutral particles in the sub-divertor region including the effects due to geometry and toroidal and poloidal leakages located at the divertor targets and baffles on the achieved pumping efficiency. A sensitivity analysis has been performed for the effect of various geometrical and flow parameters on the pumping performance, under different plasma scenarios. The considered incoming fluxes in the sub-divertor range between 1020 to 1022 (H2 s−1). The main conclusions, which can be extracted from the present numerical analysis could be summarized as follows; a large fraction of incoming neutral particle flux i.e. >70% on the low iota side and >40% for the high iota side is leaked back to the main divertor region, while higher incoming neutral fluxes facilitate the increase of the pumped flux as well as the decrease of the outflux. It has been estimated that a small fraction ∼3%–4% of the incoming neutral flux is being pumped via the turbo-molecular pumps. The closure of the toroidal leakages as well as the inclination of the pumping gap panel by 9o facilitate the increase of the pumped flux, but considering the all the engineering constraints, the latter option seems to be more easy to be implemented. For low incoming neutral fluxes (∼1020 H2 s−1) and for the case of AEH section, free molecular flow conditions are estimated and therefore neutral-neutral collisions could be neglected. For higher incoming neutral fluxes and for both AEH and AEP sections neutral-neutral collisions play a significant role in the flow establishment. A comparison with available experimental measurements of the neutral pressure in the sub-divertor has been performed for Standard and High-Iota plasma discharges. The 3D DIVGAS simulations predict qualitatively the experimental data with relative deviation between 25 and 63%. All the above numerical findings will actively support the optimization of the W7-X particle exhaust, in view of the experimental campaign OP2.
Naturally biocompatible melanin based iron-complex nanoparticles for pH-responsive magnetic resonance imaging
Authors: Yan Jincong , Lu Zhongzhong , Xu Mingsheng , Liu Jihuan , Zhang Ye , Yin Jingbo , Pei Renjun , Cao Yi
Published: May 2024

The sensitivity and diagnostic accuracy of magnetic resonance imaging mainly depend on the relaxation capacity of contrast agents (CAs) and their accumulated amount at the pathological region. Due to the better biocompatibility and high-spin capacity, Fe-complexes have been studied widely as an alternative to replace popular Gd-based CAs associated with potential biotoxicity. Compared with a variety of Fe complex-based CAs, such as small molecular, macrocyclic, multinuclear complexes, the form of nanoparticle exhibits outstanding longitudinal relaxation, but the clinical transformation was still limited by the inconspicuous difference of contrast between tumor and normal tissue. The enhanced effect of contrast is a positive relation as relaxation of CAs and their concentration in desired region. To specifically improve the amount of CAs accumulated in the tumor, pH-responsive polymer poly(2-ethyl-2-oxazoline) (PEOz) was modified on melanin, a ubiquitous natural pigment providing much active sites for chelating with Fe(III). The Fe(III)-Mel-PEOz we prepared could raise the tumor cell endocytosis efficiency via switching surface charge from anion to cation with the stimuli of the decreasing pH of tumor microenvironment. The change of pH has negligible effect on the r
1 of Fe(III)-Mel-PEOz, which is always maintained at around 1.0 mM−1s−1 at 0.5 T. Moreover, Fe(III)-Mel-PEOz exhibited low cytotoxicity, and satisfactory enhancement of positive contrast effect in vivo. The excellent biocompatibility and stable relaxation demonstrate the high potential of Fe(III)-Mel-PEOz in the diagnosis of tumor.
Electrospun fibers modified with polydopamine for enhancing human mesenchymal stem cell culture
Authors: Yang Da Hyun , Lee Nae Yoon
Published: May 2024

In this study, we coated electrospun polycaprolactone (PCL) fibers with polydopamine (PDA) to modify their hydrophobicity and fabricated a matrix for culturing mesenchymal stem cells (MSCs). Additionally, we incorporated Arg-Gly-Asp (RGD) peptides into PDA to enhance MSCs culture performance on PCL fibers. PDA and RGD were successfully coated in one step by immersing the electrospun fibers in a coating solution, without requiring an additional surface activation process. The characteristics of functionalized PCL fibers were analyzed by scanning electron microscopy with energy-dispersive x-ray analysis, Fourier transform infrared spectroscopy, water contact angle measurement, and fluorescence measurements using a carboxylic-modified fluorescent microsphere. MSCs cultured on the modified PCL fibers demonstrated enhanced cell adhesion, proliferation, and osteogenic- and chondrogenic differentiation. This study provides insight into potential applications for scaffold fabrication in MSCs-based tissue engineering, wound dressing, implantation, and a deeper understanding of MSCs behavior in vitro.
Design of a high-resolution magneto-plasmonic biosensor for analyte detection
Authors: Abbasi Sajad , Salehi Mohammad Reza , Emami Farzin
Published: May 2024

This paper introduces the design of a magneto-plasmonic refractometric sensor aimed at achieving high resolution. This sensor consists of arrays of gold nanowires and layers of SiO2 and Co6Ag94, where the analyte is placed on the gold nanowires. A p-polarized optical field with a wavelength of 631 nm is used to excite the structure, which is applied in the range of 1° to 45°. A magnetic field is applied to z-axis to create the magneto-optical effect. The reflected optical field of the samples is used to calculate the signal of the transverse magneto-optical Kerr effect, which shows significant changes in the refractive index of the samples and the direction of the magnetic field. The highest displacement is 4°. The highest value of the figure of merit is 3611 RIU−1, and the maximum sensitivity is obtained as 71 °/RIU.
Evaluating scanning electron microscopy for the measurement of small-scale topography
Authors: Chadha Vimanyu , Miller Nathaniel C , Ding Ruikang , Beschorner Kurt E , Jacobs Tevis D B
Published: May 2024

For predicting surface performance, multiscale topography analysis consistently outperforms standard roughness metrics; however, surface-characterization tools limit the range of sizes that can be measured. Therefore, we evaluate the use of scanning electron microscopy (SEM) to systematically measure small-scale topography. While others have employed SEM for similar purposes, the novelty of this investigation lies in the development and validation of a simple, flexible procedure that can be applied to a wide range of materials and geometries. First, we established four different options that can be used for sample preparation, and we measured quantitative topography of each using the SEM. Then the power spectral density (PSD) was used to compare topography among the four preparations, and against other techniques. A statistical comparison of PSDs demonstrated that SEM topography measurements outperformed AFM measurements at scales below 100 nm and were statistically indistinguishable from (highly labor-intensive) TEM measurements down to 16 nm. The limitations of SEM-based topography were quantified and discussed. Overall, the results show a simple generalizable method for revealing small-scale topography. When combined with traditional stylus profilometry, this technique characterizes surface topography across almost seven orders of magnitude, from 1 cm down to 16 nm, facilitating the use of physical models to predict performance.
Analog quantum simulation of partial differential equations
Authors: Jin Shi , Liu Nana
Published: May 2024

Quantum simulators were originally proposed for simulating one partial differential equation (PDE) in particular—Schrödinger’s equation. Can quantum simulators also efficiently simulate other PDEs? While most computational methods for PDEs—both classical and quantum—are digital (they must be discretised first), PDEs have continuous degrees of freedom. This suggests that an analog representation can be more natural. While digital quantum degrees of freedom are usually described by qubits, the analog or continuous quantum degrees of freedom can be captured by qumodes. Based on a method called Schrödingerisation, we show how to directly map D-dimensional linear PDEs onto a

(
D
+
1
)

-qumode quantum system where analog or continuous-variable (CV) Hamiltonian simulation on D + 1 qumodes can be used. This very simple methodology does not require one to discretise PDEs first, and it is not only applicable to linear PDEs but also to some nonlinear PDEs and systems of nonlinear ordinary differential equations. We show some examples using this method, including the Liouville equation, heat equation, Fokker–Planck equation, Black–Scholes equations, wave equation and Maxwell’s equations. We also devise new protocols for linear PDEs with random coefficients, important in uncertainty quantification, where it is clear how the analog or CV framework is most natural. This also raises the possibility that some PDEs may be simulated directly on analog quantum systems by using Hamiltonians natural for those quantum systems.
Feed-in-tariff is key to Japan’s current biomass power’s viability, even with environmental externalities
Authors: Miyatake Kosuke , Haraguchi Masahiko , Toyota Tomoyo , Nagai Yu , Taniguchi Makoto
Published: May 2024

Bioenergy is increasingly recognized as an effective tool for removing carbon dioxide from the atmosphere. However, its economic feasibility remains underexplored, particularly when accounting for environmental impacts. This study proposes a quantitative assessment framework to calculate the cost-benefit ratio of biomass power generation and to assess the sustainability of its supporting policy tools, such as feed-in-tariffs (FIT). The framework accounts for benefits through electricity generation and environmental externalities, namely emissions from feedstock production and procurement, such as the transportation of biomass materials. This allows for quantification and a detailed discussion of multiple environmental burdens of biomass energy and economic costs. As a case study, this framework was applied to a hypothetical biomass plant in Japan, which has the fifth-largest biomass market globally. We prepare several scenarios to consider diverse conditions within the Japanese biomass industry, including the types of biomass materials used (pellets versus chips), their sources (domestic versus international), and the biomass technologies employed. The results show that using pellets, predominantly imported, significantly increases biomass energy costs. The increase in cost is directly proportional to the quantity of utilized pellets and their transportation distances. However, pellet production location —whether in Vietnam or Canada—doesn’t significantly change the overall cost calculations in our study. Our result is consistent across various biomass technologies, showing that the high selling price under the feed-in-tariff system, rather than material type, supply origin, or transportation mode, plays the most critical role in economic feasibility, even when accounting for environmental externalities. Thus, decision-makers must reevaluate the efficacy of FIT policies for wood biomass powers, where fuel costs share a substantial portion. We also discuss its synergies with local industries and trade-offs with other land-use objectives.
Computational investigation of plasma arc welding process for aluminium alloys
Authors: Hossain Ibrak , Bhowmik Abhijit , Pattanaik Ashutosh , Kumar Rahul , Singh Abhishek Kumar , Pandey Shatrudhan
Published: May 2024

Thermal stress is a very common phenomenon that occurs at the welded joint. Determination of the same at the joint is however difficult due to inhomogeneity of the weld joint metals and spreading of heat to the surroundings from the Heat Affected Zone (HAZ). Thermal stress induced at the welded joint changes the microstructure of grains which affects the mechanical properties of the welded material. Due to this, cracks may appear in the joint leading to failure of the weld. In the present study, three-dimensional model of two types of welded joint, i.e., Tee Joint and lap joint of two plates having dimensions 100 mm × 75 mm × 5 mm are prepared using ANSYS Workbench 2020 R2. Hex dominant meshing is chosen in order to have clear picture of the spread of temperature over the entire region. The change of Residual stress with variation of welding current and keeping welding voltage constant is also observed for weld joint made of Aluminium Alloy. In this study, conduct steady-state thermal analysis and structural analysis on an aluminium alloy 6063 to assess von Mises stress, von Mises strain, and deformation distribution induced by heating. Evaluate various welding joints to identify the most effective technique.
Unveiling the dynamics of seam performance: a regression analysis approach for predictive modeling
Authors: Amir Muhammad , Asim Fareha
Published: May 2024

The performance of seams is crucial for the garment manufacturing industry's sustainability. This study delves into the factors impacting seam slippage, strength, and efficiency. By analyzing fabric specifications (weight and weave), machine settings (presser foot pressure and speed), and seam direction, the study uncovers their influence on seam properties. Samples of 100% cotton plain and twill fabrics, both of low (120

g
/

m

2

to140

g
/

m

2

) and high (275

g
/

m

2

to320

g
/

m

2

)

weights, were chosen. Lock stitch type (301) and superimposed seam class were employed, with samples stitched in both weft and warp directions using the same stitch type, seam class, and substantial core spun sewing thread. An experimental design was created using variable parameters (fabric weight, weave, machine speed, and presser foot pressure), resulting in a 23 × 32 regression analysis. This design facilitated the development of 72 stitched samples. The regression analysis yielded an optimized solution, with square regressions indicating significant seam strength (85%) and efficiency (93%). In summary, this research provides insights into optimizing seam performance by understanding the interplay of fabric, machine, and stitching parameters, crucial for enhancing the quality and durability of garment seams.
Hot rolled steel surface defect detection and classification using an automatic ensemble approach
Authors: Vasan Vinod , Sridharan Naveen Venkatesh , Sugumaran Vaithiyanathan , Jeyavadhanam Balasundaram Rebecca
Published: May 2024

This study introduces an ensemble-based Deep Neural Network (DNN) model for detecting defects on steel surfaces. The method suggested in this study classifies steel surface conditions into six possible fault categories, namely, crazing, inclusion, rolled in, pitted surface, scratches, and patches. The images undergo preprocessing and extraction of features in spatial and frequency domains using image segmentation techniques such as grey level difference method (GLDM), fast Fourier Transform (FFT), grey level co-occurrence matrix (GLCM), texture analysis and discrete wavelet transform (DWT). The ensembling of image features into a fused feature pool is carried out after the preprocessing of input images that are provided as input to a light-weight neural network model for training and testing. The performance of the model is comprehensively evaluated via an ablation study both before and after ensembling. In addition, the model capability is effectively analyzed using receiver operating characteristics (ROC) curve, confusion matrix from which classification accuracy of the model could be obtained and other parameters including precision and f1-score. It was observed that the proposed deep learning network presents phenomenally high accuracy of 99.72% for detection and classification of steel surface faults. This result was found to be superior when compared with the performance of the same neural network over each feature type individually. This study also compares the classification results of the model built based on the ensembled feature set with the results of various other classification approaches available in literature. The ensemble-based model could potentially be integrated into existing inspection systems for real-time, efficient and robust condition monitoring of steel surfaces.
Unifying O(3) equivariant neural networks design with tensor-network formalism
Authors: Li Zimu , Pengmei Zihan , Zheng Han , Thiede Erik , Liu Junyu , Kondor Risi
Published: May 2024

Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increase, maintaining parsimony and equivariance becomes increasingly challenging. In this paper, we propose using fusion diagrams, a technique widely employed in simulating SU(2)-symmetric quantum many-body problems, to design new spatial equivariant components for neural networks. This results in a diagrammatic approach to constructing novel neural network architectures. When applied to particles within a given local neighborhood, the resulting components, which we term ‘fusion blocks,’ serve as universal approximators of any continuous equivariant function defined on the neighborhood. We incorporate a fusion block into pre-existing equivariant architectures (Cormorant and MACE), leading to improved performance with fewer parameters on a range of challenging chemical problems. Furthermore, we apply group-equivariant neural networks to study non-adiabatic molecular dynamics of stilbene cis-trans isomerization. Our approach, which combines tensor networks with equivariant neural networks, suggests a potentially fruitful direction for designing more expressive equivariant neural networks.
Beyond dynamics: learning to discover conservation principles
Authors: Belyshev Antonii , Kovrigin Alexander , Ustyuzhanin Andrey
Published: May 2024

The discovery of conservation principles is crucial for understanding the fundamental behavior of both classical and quantum physical systems across numerous domains. This paper introduces an innovative method that merges representation learning and topological analysis to explore the topology of conservation law spaces. Notably, the robustness of our approach to noise makes it suitable for complex experimental setups and its aptitude extends to the analysis of quantum systems, as successfully demonstrated in our paper. We exemplify our method’s potential to unearth previously unknown conservation principles and endorse interdisciplinary research through a variety of physical simulations. In conclusion, this work emphasizes the significance of data-driven techniques in deepening our comprehension of the principles governing classical and quantum physical systems.
Quantized non-volatile nanomagnetic domain wall synapse based autoencoder for efficient unsupervised network anomaly detection
Authors: Alam Muhammad Sabbir , Misba Walid Al , Atulasimha Jayasimha
Published: May 2024

Anomaly detection in real-time using autoencoders implemented on edge devices is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We further propose nanoscale ferromagnetic racetracks with engineered notches hosting magnetic domain walls (DW) as exemplary non-volatile memory-based autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses to write different magnetoresistance states. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are typically known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point synaptic weights that are extremely memory intensive. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying significant reduction in operation energy for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data.
Extensible grid sampling for quantile estimation
Authors: Tan Jingyu , He Zhijian , Wang Xiaoqun
Published: May 2024

Quantiles are used as a measure of risk in many stochastic systems. We study the estimation of quantiles with the Hilbert space-filling curve (HSFC) sampling scheme that transforms specifically chosen one-dimensional points into high dimensional stratified samples while still remaining the extensibility. We study the convergence and asymptotic normality for the estimate based on HSFC. By a generalized Dvoretzky–Kiefer–Wolfowitz inequality for independent but not identically distributed samples, we establish the strong consistency for such an estimator. We find that under certain conditions, the distribution of the quantile estimator based on HSFC is asymptotically normal. The asymptotic variance is of

O
(

n

−
1
−
1

/

d

)

O(n^{-1-1/d})

when using

n
n

HSFC-based quadrature points in dimension

d
d

, which is more efficient than the Monte Carlo sampling and the Latin hypercube sampling. Since the asymptotic variance does not admit an explicit form, we establish an asymptotically valid confidence interval by the batching method. We also prove a Bahadur representation for the quantile estimator based on HSFC. Numerical experiments show that the quantile estimator is asymptotically normal with a comparable mean squared error rate of randomized quasi-Monte Carlo (RQMC) sampling. Moreover, the coverage of the confidence intervals constructed with HSFC is better than that with RQMC.
Elliptic equations with matrix weights and measurable nonlinearities on nonsmooth domains
Authors: Byun Sun-Sig , Cho Yumi , Lee Ho-Sik
Published: May 2024

We study general elliptic equations with singular/degenerate matrix weights and measurable nonlinearities on nonsmooth bounded domains to obtain a global Calderón-Zygmund type estimate under possibly minimal assumptions that the logarithm of the matrix weight has a small bounded mean oscillation (BMO) norm, the nonlinearity is allowed to be merely measurable in one variable but has a small BMO norm in the other variables and that the boundary of the domain is sufficiently flat in Reifenberg sense.
A variance-sensitive Gaussian concentration inequality
Authors: Dung Nguyen
Published: May 2024

In this note, we obtain a Gaussian concentration inequality for a class of non-Lipschitz functions. In the one-dimensional case, our results supplement those established by Paouris and Valettas [Ann. Probab. 46 (2018), pp. 1441–1454].
Phylogenomic perspectives on speciation and reproductive isolation in a North American biodiversity hotspot: an example using California sages (Salvia subgenus Audibertia: Lamiaceae)
Authors: Rose Jeffrey P , Kriebel Ricardo , Sytsma Kenneth J , Drew Bryan T
Published: May 2024

Background and Aims

The California Floristic Province (CA-FP) is the most species-rich region of North America north of Mexico. One of several proposed hypotheses explaining the exceptional diversity of the region is that the CA-FP harbours myriad recently diverged lineages with nascent reproductive barriers. Salvia subgenus Audibertia is a conspicuous element of the CA-FP, with multiple sympatric and compatible species.

Methods

Using 305 nuclear loci and both organellar genomes, we reconstruct species trees, examine genomic discordance, conduct divergence-time estimation, and analyse contemporaneous patterns of gene flow and mechanical reproductive isolation.

Key Results

Despite strong genomic discordance, an underlying bifurcating tree is supported. Organellar genomes capture additional introgression events not detected in the nuclear genome. Most interfertility is found within clades, indicating that reproductive barriers arise with increasing genetic divergence. Species are generally not mechanically isolated, suggesting that it is unlikely to be the primary factor leading to reproductive isolation.

Conclusions

Rapid, recent speciation with some interspecific gene flow in conjunction with the onset of a Mediterranean-like climate is the underlying cause of extant diversity in Salvia subgenus Audibertia. Speciation has largely not been facilitated by gene flow. Its signal in the nuclear genome seems to mostly be erased by backcrossing, but organellar genomes each capture different instances of historical gene flow, probably characteristic of many CA-FP lineages. Mechanical reproductive isolation appears to be only part of a mosaic of factors limiting gene flow.
ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events
Authors: Hyung Daejin , Cho Soo Young , Lee Kyubin , Yu Namhee , Hong Sehwa , Park Charny
Published: May 2024

Motivation

Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor-binding sites and protein domains. These elements involve in exon usage and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes.

Results

With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein–protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing.

Availability and implementation

The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R.
Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data
Authors: Liu Xiaofan , Tao Yuhuan , Cai Zilin , Bao Pengfei , Ma Hongli , Li Kexing , Li Mengtao , Zhu Yunping , Lu Zhi John
Published: May 2024

Motivation

Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability.

Results

To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%–14.7% in F1 score for cancer survival prediction, 5.1%–12% for cancer stage prediction, and 8.1%–13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer’s potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients’ blood, providing potential candidate targets for cancer microenvironment study.

Availability and implementation

Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.
Targeted nanoparticles for imaging and therapy of endometriosis
Authors: Slayden Ov , Luo Fangzhou , Park Youngrong , Moses Abraham S , Demessie Ananiya A , Singh Prem , Korzun Tetiana , Taratula Olena , Taratula Oleh
Published: May 2024

In this brief review, we discuss our efforts to validate nanoplatforms for imaging and treatment of endometriosis. We specifically highlight our use of nonhuman primates and primate tissues in this effort. Endometriosis is a painful disorder of women and nonhuman primates where endometrium-like tissue exists outside of the uterus. There are no reliable, specific, and noninvasive diagnostic tests for endometriosis. Laparoscopic imaging remains the gold standard for identifying small endometriotic lesions in both women and monkeys. Visualizing and surgically removing microscopic lesions remains a clinical challenge. To address this challenge, we have created nanoparticle reagents that, when administered intravenously, enter endometriotic lesions both passively and by targeting endometriotic cells. The particles can carry payloads, including near-infrared fluorescent dyes and magnetic nanoparticles. These agents can be used for imaging and thermal ablation of diseased tissues. We evaluated this approach on macaque endometriotic cells, human and macaque endometrium engrafted into immunodeficient mice, in endometrium subcutaneously autografted in macaques, and in rhesus monkeys with spontaneous endometriosis. Employing these models, we report that nanoplatform-based reagents can improve imaging and provide thermal ablation of endometriotic tissues.
Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis
Authors: He Jia , Wang Shang-xian , Liu Peng
Published: May 2024

Objectives

To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.

Methods

We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before March 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity.

Results

A total of 1699 patients from 24 studies were included. For machine learning models in predicting pCR to nCRT, the meta-analysis calculated a pooled area under the curve (AUC) of 0.91 (95% CI, 0.88-0.93), pooled sensitivity of 0.83 (95% CI, 0.74-0.89), and pooled specificity of 0.86 (95% CI, 0.80-0.91). We investigated 6 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 6 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep-learning model was 0.93 and 0.89 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90 and 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.93, and was 0.83 in studies conducted in other countries.

Conclusions

This systematic study showed that machine learning has promising potential in predicting pCR to nCRT in patients with locally advanced rectal cancer. Compared to traditional machine learning models, although deep-learning-based studies are less predominant and more heterogeneous, they are able to obtain higher AUC.

Advances in knowledge

Compared to traditional machine learning models, deep-learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine learning-based models may bring us closer towards precision medicine.