bluuebunny/crossref_metadata_2025 huggingface dataset:
Acoustic rotation of multiple subwavelength cylinders for three-dimensional topography reconstruction
Authors: Huang Laixin , Bao Shi-Chun , Cai Feiyan , Meng Long , Zhou Wei , Zhou Juan , Kong Deqing , Li Fei , Zheng Hairong
Published: November 2023

Accurate rotation of microparticles is of great significance in micro-rotors, multi-angle microscopic observation, microbial three-dimensional phenotyping, and microsystem assembly. However, most methods can only rotate a single object, thus limiting the throughput. In this study, we realized the simultaneous rotation of many trapped and aligned subwavelength glass cylinders inside an evanescent wave field excited by a resonant phononic crystal plate. The unique feature of the rotation lies in its periodic distribution as well as the rotation axis being perpendicular to the acoustic axis. The rotary power originates from viscous torque generated by the evanescent wave-induced near-boundary acoustic streaming's asymmetry distribution on the trapped cylinder. Furthermore, the three-dimensional topographies of rotated cylinders can be reconstructed from the microscopic images under different rotating angles. Our findings can pave the way toward developing simple, disposable, and scalable microfluidic devices for massive subwavelength acoustic rotation by carefully designing acoustic metamaterials.
Effect of amphiphilic polymers on phase separating binary mixtures: A DPD simulation study
Authors: Chauhan Avinash , Gogoi Dorothy , Puri Sanjay , Singh Awaneesh
Published: November 2023

We present the phase separation dynamics of a binary (AB), simple fluid (SF), and amphiphilic polymer (AP) mixture using dissipative particle dynamics simulation at d = 3. We study the effect of different AP topologies, including block copolymers, ring block copolymers (RCP), and miktoarm star polymers, on the evolution morphologies, dynamic scaling functions, and length scale of the AB mixture. Our results demonstrate that the presence of APs leads to significantly different evolution morphologies in SF. However, the deviation from dynamical scaling is prominent, mainly for RCP. Typically, the characteristic length scale for SF follows the power law R(t) ∼ tϕ, where ϕ is the growth exponent. In the presence of high AP, we observe diffusive growth (ϕ → 1/3) at early times, followed by saturation in length scale (ϕ → 0) at late times. The extent of saturation varies with constraints imposed on the APs, such as topology, composition ratio, chain length, and stiffness. At lower composition ratios, the system exhibits inertial hydrodynamic growth (ϕ → 2/3) at asymptotic times without clearly exhibiting the viscous hydrodynamic regime (ϕ → 1) at earlier times in our simulations. Our results firmly establish the existence of hydrodynamic growth regimes in low surfactant-influenced phase separation kinetics of binary fluids and settle the related ambiguity in d = 3 systems.
Non-stoichiometry induced 2H–1T phase interfaces and room-temperature ferromagnetism in defective molybdenum selenide
Authors: Liu Guang , Xing Xuejun , Zhang Xuanlin , Wang Xinwei , Wu Chen , Lu Yunhao , Yan Mi
Published: November 2023

Magnetic semiconducting materials offer tremendous prospects for spin electronics but is challenging to achieve room-temperature ferromagnetism with unambiguous origin. Herein, a non-stoichiometry strategy is proposed to induce tunable magnetization in MoSe2−x nanoflowers via vacancy-controlled 2H–1T phase transition. The resultant MoSe2−x exhibits robust room-temperature ferromagnetism with significant positive correlation to the content of 1T phase and 2H–1T interfaces. Significant magnetic hysteresis and Curie transition above room temperature have been achieved, confirming the ferromagnetic feature of MoSe2−x. To examine the origin of ferromagnetism, formation energy and spin-polarized calculations have been conducted, indicating that the Se vacancy is beneficial for the formation of the 1T phase and interfacial spin polarization. Localized magnetic moments induced at the 2H–1T interfaces exhibit enhanced magnetism as compared to the net moments from the 1T orbital splitting, giving rise to strong coupling bound magnetic polarons. This work not only advances the understanding on the origin of magnetism in magnetic semiconductors, but also provides an effective route to generate ferromagnetism by defect and/or interface engineering that could be applied to multiferroics, spintronics, and valleytronics.
A method to determine arsenic concentration in drinking water based on proton gradient and conductance measurements in filter paper media
Authors: Rozario Joan , Hussam Abul
Published: November 2023

This work describes a novel technique for the measurement of inorganic arsenic in water by generating arsine gas and detecting the conductivity of moving protons, H+ (aqueous or aq), produced by the reaction: Ag+ (aq) + AsH3 (gas, g) → AgAsH2 (solid, s) + H+ (aq). The detection is based on an electrochemical gradient of protons in a confined porous substrate (filter paper) and measures the change in the conductance due to the higher mobility of H+ compared to other ions. The conductance was measured with a pair of silver electrodes attached to opposite sides of the substrate with a bipolar pulse conductance technique. The method is established in theory and in practice. The theoretical equation for conductance change shows that a constant increase in conductance is directly proportional to the As(total) concentration. The method is validated with a standard reference material and applied to the measurement of the groundwater sample.
Ultra-efficient machine learning design of nonreciprocal thermal absorber for arbitrary directional and spectral radiation
Authors: Chen Zihe , Yu Shilv , Yuan Cheng , Hu Kun , Hu Run
Published: November 2023

Development of nanophotonics has made it possible to control the wavelength and direction of thermal radiation emission, but it is still limited by Kirchhoff's law. Magneto-optical materials or Weyl semimetals have been used in recent studies to break the time-reversal symmetry, resulting in a violation of Kirchhoff's law. Currently, most of the work relies on the traditional optical design basis and can only realize the nonreciprocal thermal radiation at a specific angle or wavelength. In this work, on the basis of material informatics, a design framework of a multilayer nonreciprocal thermal absorber with high absorptivity and low emissivity at any arbitrary wavelength and angle is proposed. Through a comprehensive investigation of the underlying mechanism, it has been discovered that the nonreciprocal thermal radiation effect is primarily attributed to excitation of the cavity mode at the interface between the metal and the multilayer structure. Moreover, the impact of factors, such as layer count, incidence angle, extinction coefficient, and applied magnetic field on nonreciprocal thermal radiation, is thoroughly explored, offering valuable insights to instruct the design process. Additionally, by expanding the optimization objective, it becomes feasible to design fixed dual-band or even multi-band nonreciprocal thermal absorbers. Consequently, this study offers essential guidelines for advancing the control of nonreciprocal thermal radiation.
Extremely energy-efficient, magnetic field-free, skyrmion-based memristors for neuromorphic computing
Authors: Joy Ajin , Satheesh Sreyas , Anil Kumar P. S.
Published: November 2023

The human brain can process information more efficiently than computers due to the dynamics of neurons and synapses. Mimicking such a system can lead to the practical implementation of artificial spiking neural networks. Spintronic devices have been shown to be an ideal solution for realizing the hardware required for neuromorphic computing. Skyrmions prove to be an effective candidate as information carriers owing to their topological protection and particle-like nature. Ferrimagnet and antiferromagnet-based spintronics have been employed previously to obtain an ultrafast simulation of artificial synapses and neurons. Here, we have proposed a ferromagnetic device of stack Ta3nmPt3nmCu0.65nmCo0.5nmPt1nm that is capable of ultrafast simulation of artificial neurons and synapses, owing to the high velocity of the stabilized skyrmions in the system. Electrical pulses of nanosecond pulse width were used to control the accumulation and dissipation of skyrmions in the system, analogous to the variations in the synaptic weights. Lateral structure inversion asymmetry is used to bring about a field-free switching in the system, leading to an energy-efficient switching process. Magnetic field-free deterministic switching and low pulse width current pulses drastically reduce energy consumption by 106 times compared to the existing ferromagnet-based neuromorphic devices. Artificial neuron, synapse, and memristor functionalities have been reproduced on the same device with characteristic time scales and field-free switching, better than any existing ferromagnet-based neuromorphic devices. The results recognize ferromagnet-based skyrmions as viable candidates for ultrafast neuromorphic spintronics capable of executing cognitive tasks with extremely high efficiency.
Magic angle spinning effects on longitudinal NMR relaxation: 15N in L-histidine
Authors: Afrough Armin , Christensen Nichlas Vous , Jensen Rune Wittendorff Mønster , Juhl Dennis Wilkens , Vosegaard Thomas
Published: November 2023

Solid-state magnetic resonance is a unique technique that can reveal the dynamics of complex biological systems with atomic resolution. Longitudinal relaxation is a mechanism that returns longitudinal nuclear magnetization to its thermal equilibrium by incoherent processes. The measured longitudinal relaxation rate constant however represents the combination of both incoherent and coherent contributions to the change of nuclear magnetization. This work demonstrates the effect of magic angle spinning rate on the longitudinal relaxation rate constant in two model compounds: L-histidine hydrochloride monohydrate and glycine serving as proxies for isotopically-enriched biological materials. Most notably, it is demonstrated that the longitudinal N15 relaxation of the two nitrogen nuclei in the imidazole ring in histidine is reduced by almost three orders of magnitude at the condition of rotational resonance with the amine, while the amine relaxation rate constant is increased at these conditions. The observed phenomenon may have radical implications for the solid-state magnetic resonance in biophysics and materials, especially in the proper measurement of dynamics and as a selective serial transfer step in dynamic nuclear polarization.
Unitary coupled-cluster for quantum computation of molecular properties in a strong magnetic field
Authors: Culpitt Tanner , Tellgren Erik I. , Pavošević Fabijan
Published: November 2023

In truncated coupled-cluster (CC) theories, non-variational and/or generally complex ground-state energies can occur. This is due to the non-Hermitian nature of the similarity transformed Hamiltonian matrix in combination with CC truncation. For chemical problems that deal with real-valued Hamiltonian matrices, complex CC energies rarely occur. However, for complex-valued Hamiltonian matrices, such as those that arise in the presence of strong magnetic fields, complex CC energies can be regularly observed unless certain symmetry conditions are fulfilled. Therefore, in the presence of magnetic fields, it is desirable to pursue CC methods that are guaranteed to give upper-bound, real-valued energies. In this work, we present the first application of unitary CC to chemical systems in a strong magnetic field. This is achieved utilizing the variational quantum eigensolver algorithm applied to the unitary coupled-cluster singles and doubles (UCCSD) method. We benchmark the method on the H2 molecule in a strong magnetic field and then calculate UCCSD energies for the H4 molecule as a function of both geometry and field angle. We show that while standard CCSD can yield generally complex energies that are not an upper-bound to the true energy, UCCSD always results in variational and real-valued energies. We also show that the imaginary components of the CCSD energy are largest in the strongly correlated region. Last, the UCCSD calculations capture a large percentage of the correlation energy.
Adsorption behavior of H2 in quartz silt-pores at high temperature and pressure
Authors: Yang Longxing , Mao Shide , Yi Li , Liu Hong , Wang Hanyu , Liu Haibo , Liu Lei
Published: November 2023

To understand the storage and transport of H2 in the Earth’s interior, the adsorption behaviors of H2 in the slit-like pore of quartz under different conditions were calculated by the grand canonical Monte Carlo method. The Poisson distributions of interaction energy show unimodal, indicating that the adsorption behavior is mainly affected by van der Waals interaction between molecular H2 and quartz, the adsorption potential energy increases, and when the pressure increases, the temperature and pore size decrease. Isosteric heat of adsorption is in the range of −5.0 to −1.7 kJ/mol, which indicates that the adsorption behavior belongs to physical adsorption. The results of isosteric heat of adsorption show that strong energy exchange occurs in the H2-quartz system at the initial stage of adsorption, which may affect the stability of quartz. The average isosteric heat of adsorption linearly increases with temperature. However, the increasing rate of average isosteric heat of adsorption decreases with the increase in the pore size. Adsorption snapshots show most of the H2 distributed randomly and there is no obvious adsorption layer of H2 in the pores. Excess adsorption amount increases with the decrease in temperature and the increase in pressure and pore size. The change rate of excess adsorption amount with temperature increases with the increase in pressure. Similarly, with the increase in pressure, the change rate of excess adsorption amount with pore size decreases slowly at first, then increases rapidly, and finally decreases. The results are helpful to reveal the migration and formation of H2 reservoirs in the Earth’s interior.
In situ insight into temperature-dependent microstructure evolution of carbon doped phase change materials
Authors: Qi Ruijuan , Zhang Jinzhong , Sui Fengrui , Song Sannian , Li Xi , Song Zhitang
Published: November 2023

Carbon-doped Ge2Sb2Te5 (CGST) is a potential candidate in phase change random access memory (PCRAM) with superb thermal stability and ultrahigh cycle endurance. Direct observation of the microstructure evolution of CGST is desirable to uncover the phase transformation mechanism on the relationship of nucleation/crystalline behaviors of the crystalline phase at elevated temperatures and the pristine amorphous phase at room temperature. Here, we investigate the structural evolution of CGST using combined in situ techniques. Our in situ x-ray diffraction and ellipsometry results demonstrate that CGST exhibits a much higher phase transition temperature than undoped one. Temperature-dependent in situ transmission electron microscopy observations further reveal that carbon doping plays a critical role in tailoring the properties of GST by tuning the stochasticity of nucleation/crystallization, stabilizing amorphous and crystalline GST via isolating and refining the grain size at room temperature and elevated temperature. Our work provides detailed information for understanding the microscopic origin of crystallization kinetics of carbon-doped phase change materials toward high-performance PCRAM.
Multiflagellarity leads to the size-independent swimming speed of peritrichous bacteria
Authors: Kamdar Shashank , Ghosh Dipanjan , Lee Wanho , Tătulea-Codrean Maria , Kim Yongsam , Ghosh Supriya , Kim Youngjun , Cheepuru Tejesh , Lauga Eric , Lim Sookkyung , Cheng Xiang
Published: November 2023

To swim through a viscous fluid, a flagellated bacterium must overcome the fluid drag on its body by rotating a flagellum or a bundle of multiple flagella. Because the drag increases with the size of bacteria, it is expected theoretically that the swimming speed of a bacterium inversely correlates with its body length. Nevertheless, despite extensive research, the fundamental size–speed relation of flagellated bacteria remains unclear with different experiments reporting conflicting results. Here, by critically reviewing the existing evidence and synergizing our own experiments of large sample sizes, hydrodynamic modeling, and simulations, we demonstrate that the average swimming speed of
Escherichia coli
, a premier model of peritrichous bacteria, is independent of their body length. Our quantitative analysis shows that such a counterintuitive relation is the consequence of the collective flagellar dynamics dictated by the linear correlation between the body length and the number of flagella of bacteria. Notably, our study reveals how bacteria utilize the increasing number of flagella to regulate the flagellar motor load. The collective load sharing among multiple flagella results in a lower load on each flagellar motor and therefore faster flagellar rotation, which compensates for the higher fluid drag on the longer bodies of bacteria. Without this balancing mechanism, the swimming speed of monotrichous bacteria generically decreases with increasing body length, a feature limiting the size variation of the bacteria. Altogether, our study resolves a long-standing controversy over the size–speed relation of flagellated bacteria and provides insights into the functional benefit of multiflagellarity in bacteria.
Sustainable agricultural practices in South Asia: A comprehensive review
Authors: Panchami L.R. , Gudi Nachiket , Patil Divya Sussana
Published: November 2023

Overpopulation is a root cause that impedes food production and supply to the continuously growing population. Since agriculture is the primary source of food production, producers are inclined towards various unsustainable agriculture practices contributing to climate change. Therefore, sustainable agricultural practices have gained the spotlight to reduce the risk of climate change and increase crop yields. Specifically, South Asian countries take a significant hit with respect to agriculture and climate change because much of the population is earning their livelihood through agriculture. We conducted a comprehensive review to map different sustainable agriculture practices and understand how these practices impact climate change, the livelihood of farmers, and food security in South Asia. A systematic search carried out on PubMed (NCBI) and Scopus (Elsevier), resulted in the inclusion of 42 articles for analysis. The review identified agroforestry and organic farming as the most commonly used practices in the South Asian region, followed by zero tillage practices, use of hybrid seeds, residue and integrated pest management, and cropping systems. These sustainable agriculture practices ensure that sufficient food can be produced to feed the rising population with efficient use of natural resources. In addition, sustainable agriculture gains economic profitability for farmers and improved employment opportunities for women. Since sustainable agricultural practices are the best way forward, we recommend encouraging it by incentivizing the farmers. Due to contextual variations, future research can focus on identifying best practices for specific contexts.
UAV Swarm Formation Control Based on Disturbance Observer and Backstepping Controller
Authors: Zhao Hongchao , Zhao Jianzhong , Dong Xuexia
Published: November 2023

The formation control problem was researched for a fixed-wing unmanned aerial vehicle (UAV) swarm. The kinematic model was built for a UAV considering the lumped disturbances. The disturbance observers were designed to estimate the lumped disturbances in finite time. The desired UAV swarm formation was represented by a virtual structure. The backstepping controller was designed for every UAV to complete the formation maintaining a task. The sigmoid tracking differentiator (STD) was added to the backstepping controller, in order to settle the problem of “explosion of complexity”. The numerical simulation was executed to show the formation maintaining procedure of the UAV swarm. The simulation results demonstrate that the disturbance observer has good disturbance rejection capability and the backstepping controller based on the STD has good formation maintaining performance.
Machine Learning-based Investigation of the Influencing Factors on the Hardness of Steel Materials
Authors: Wu Shaoxiong , Wang Shiyu , Xie Daoxin , Zhu Shun-an , Li Junjie
Published: November 2023

The hardness of steel is often regarded as a crucial indicator of its production and application. The influences of chemical composition and process parameters on the hardness of steel exhibit strong non-linearity, making it challenging to accurately predict hardness through traditional multivariate regression or orthogonal experiments. Although machine learning has achieved distinguished success in diverse applications, its use in studying Metal materials has emerged only recently. Inspired by Sunčana et al.’s work, we select the Jominy distance as an input variable for the chemical composition, and instead of his subtly finding the best artificial neural networks, we simply use Generalized Regression Neural Network (GRNN) and due to the heterogeneity of the data, a second-order clustering method is employed as a data pre-processing step. The predicted hardness values were obtained using leave-one-out cross-validation, and the optimal smoothing factor (spread) was selected based on the Root Mean Square Error (RMSE) criterion. The optimal prediction results showed that in Configuration I, the RMSEs of the two types of data were 62.41 HV and 20.51 HV, respectively, while in Configuration II, they were 66.38 HV and 29.51 HV. Results showed Configuration I was more successful than Configuration II. This research presents a novel approach for predicting the hardness of metal materials using GRNN which is designed to address the need for quick adaptation to changes in design methods and the increasing demand for high-quality manufactured products.
Ballet Form Training Based on MediaPipe Body Posture Monitoring
Authors: Lei Miaowen , Wang Zuxuan , Chen Fang
Published: November 2023

People are increasingly turning to the cloud in the context of “healthy China” to engage in online exercise. The use of artificial intelligence technology to address broad population health-related challenges has become increasingly important as information technology has matured. The MediaPipe artificial intelligence framework, which Google recently released, is used in this article to optimize video feedback and support the “cloud movement” of widespread home ballet instruction in order to examine the effects of digital technology-enabled ballet training on the general improvement of physical health. Based on the experiment’s findings, trainers can use MediaPipe video feedback as an additional method of “cloud movement” training in public homes. This allows trainers to visually reflect on any issues that arise during the exercise process and to promptly modify training according to motion monitoring in order to reduce the risk of physical injury brought on by improper motion training. It is possible to envision a day in the future when video feedback built on the MediaPipe architecture would combine artificial intelligence with exercise training to achieve training objectives that enhance the accuracy of trainers’ motions and enhance physical balance.
A novel quantum genetic algorithm with the application in LS-SVR
Authors: Xiang Hao
Published: November 2023

The characteristics of LS-SVR are analyzed. LS-SVR is fitted for modeling small samples and high dimensional data, but the performance of LS-SVR is related to the specific data distribution, the kind of kernel function, its related kernel parameter, and the penalty coefficient. In this paper, the radial basis function is applied as the kernel function of LS-SVR, and the real double-chain coding target gradient quantum genetic algorithm (DCQGA) is applied to optimize the kernel parameter and penalty item coefficient of LS-SVR, then the regression prediction model DCQGALSSVR is proposed. It is of great significance to build an accurate and reliable fault prediction model for the health monitoring and fault diagnosis of liquid rocket engines. The thrust of a liquid rocket engine is an important factor in its health monitoring. By predicting the thrust change value and comparing the predicted value with the engine thrust threshold, it can be predicted whether the engine will fail at a certain time. In this paper, the proposed DCQGALSSVR model is used to model the thrust of a liquid rocket engine. The simulation results show that the average relative error is 0.37% using LSSVR for modeling on 12 test samples, and is 0.3186% using DCQGALSSVR on the same samples. It can be seen that DCQGALSSVR is effective for the health monitoring of liquid rocket engines, so it has a certain promotion significance.
DualGGAN: A New Facial Style Transfer Network
Authors: Zhao Yu , Peng Chunyan , Zhang Xiaojuan
Published: November 2023

In response to the background penetration problem of unsupervised style transfer algorithms in most cases, a Transformer style transfer network DualGGAN based on dual generators and fusion of relative position encoding is proposed. The network is trained using the least squares generative adversarial network, and the neural network is used as the image feature extractor to generate feature maps to obtain facial image features with attention weights from the feature maps, utilizing relative position encoding and mask loss to jointly constrain feature region style transfer. The experimental results show that the DualGGAN network effectively reduces artifact generation when implementing facial style transfer, maintains good background consistency, and has good generalization ability. Experiments have shown that the FID and KID indicators on the cat2dog and older2adult datasets are significantly improved compared to other algorithms.
Fast-DecoupledNet: An Improved Multi-branch Edge Enhanced Semantic Segmentation Network
Authors: Xue Junyu , Zhang Zhiyuan
Published: November 2023

There are existing semantic segmentation methods that incorporate the idea of edge detection by using multi-branch networks to focus on edges and subjects separately, but there are a large number of unfocused aspects and limited improvement. In this paper, we suggest the Fast-DecoupledNet, a semantic segmentation network. We design Edge Feature Extractor to extract the target’s edge features more accurately, and the global features obtained by joint downsampling are computed to obtain the subject features and the final features. In addition, we employ a shallower ResNet as the backbone network to reduce computational complexity while ensuring computational accuracy. Our proposed methods achieve the state-of-the-art 72.59 F-score and 77.64% mIoU on the Deepglobe Land Cover Classification dataset.
Separability Measure Supervised Network for Radar Target Recognition
Authors: Xue Lingyan , Zhang Xinyu , Qiu Xiangfeng , Jiang Weidong
Published: November 2023

In the radar automatic target recognition (RATR) field, radar high-resolution range profiles (HRRPs) have garnered significant attention. While traditional methods focus on extracting features having physical explanations, including power spectra, FFT magnitudes, etc, the effectiveness of these features relies heavily on personal experience and skills. In contrast, deep learning networks have shown strong competence in extracting discriminative features of HRRPs. However, the deep learning networks’ feature extraction procedure is solely based on the targets’ label information, which has almost no correlation with the feature separability. As a result, this approach can lead to poor convergence and limited recognition performance. To address this issue, we propose a Separability Measure Supervised Network (SMSN), which integrates a separability measure based on the rate-distortion function into the loss function to direct the training of the network. Comparative experiments on the airplane electromagnetic simulation HRRP dataset demonstrate that SMSN achieves higher recognition accuracy compared to the backbone networks, with significantly improved feature separability.
Resource allocation algorithm in Heterogeneous Network Based on Non-orthogonal Multiple Access
Authors: Wu Guowei , Chen Guifen , Zhu Tong
Published: November 2023

The proposed resource allocation algorithm with joint power control and user pairing addresses the problem of degraded uplink user fairness due to cross-cell interference in NOMA uplink and improves user fairness. In the power control section, by decoupling power control from user pairing, the WOA algorithm is proposed in the statistical channel state scenario to solve the nonconvex optimization problem of intra-user pair fairness; in the user pairing section, the PTS algorithm is proposed to introduce randomness into the algorithm to improve the algorithm’s global search performance, solve the local optimum problem of integer programming, and improve inter-user pair fairness. Simulation results show that relative to the α-fairness comparison algorithm, the algorithm in this paper can improve fairness by about 5% under different interfering user densities, cell coverage radius, and tiny area transmit power.
Myocardial Segmentation Algorithm of U-Net Network Based on Cardiac Ultrasound Images
Authors: Yang Wang , Li Xin , Li Yan , Hou Haoxiong , Chen Zuxing
Published: November 2023

The myocardial state is always regarded as an important basis for identifying cardiac diseases. In order to assist physicians in diagnosis in an accurate manner, this paper proposes myocardial segmentation using a U-Net network based on cardiac ultrasound images. Firstly, we collected a large amount of clinical data and employed professional cardiac ultrasound imaging physicians to mark the myocardial regions as the gold standard. Then, we built an optimized U-Net network to establish the relationship between images and semantics to extract original image features. Finally, a newly fused loss function for training the network is created. According to the experiments, it shows that the accuracy, precision, and recall rate of U-Net indexes proposed in this paper reaches more than 96%, and MIOU more than 94%, which can effectively assist doctors in diagnosis in an accurate manner.
A Novel Channel-attention-based Dense Network for Gas Recognition in Complex Airflow Environments
Authors: Li Fei , Li Yiran , Zhao Wenlong , Peng Xiaoyan
Published: November 2023

An electronic nose is a commonly used technology for gas detection. However, due to the complex diffusion mechanism of gaseous chemical analytes in the natural environment, the gas often exhibits irregular turbulent flow patterns. This variability results in different responses to the same gas in the e-nose, which presents significant challenges in the gas identification tasks. In this study, we propose an efficient method for gas recognition by combining a Dense Convolutional Network (DenseNet) with an Efficient Channel Attention Network (ECANet), which uses one-dimensional convolutional neural networks to improve the capability of extracting sequence signals. We evaluate the proposed method using an open-source dataset and observe that it outperforms the best current methods available, including the ResNet, Long Short-term Memory Network (LSTM) networks, and Gate Recurrent Unit (GRU) networks, with a classification accuracy of 99.8%.
Single Image 3D Reconstruction Algorithm Based on Parallel Double-Branch Pyramid Stacking Network
Authors: Ku Yintao , Zhang Yumei , Yang Honghong , Zhao Yanqing , Li Jinshan , Zhou Yulin , Wu Xiaojun
Published: November 2023

Limited visual information contained in single images and complex motion models of objects may lead to severe fragmentation and confusion of model backbone in the 3D reconstruction of objects. In order to fully extract feature information in a single image and reduce noise interference caused by environmental factors in 3D reconstruction, a Parallel Dual-Branch Pyramid Stacking Network (PDB-PSN) model is proposed. A parallel dual-branch network model is used to construct an encoder-decoder framework based on cascaded feature extraction, encode/decode high and low-resolution features in a single image, and then convert from 2D to 3D through implicit functions to achieve the 3D reconstruction of target objects in a single image. A cascaded feature extraction network is used as a low-resolution feature extraction network to extract global features of objects. In the high-resolution feature extraction branch, three concatenated hourglass networks and dilated convolutions are used in the hourglass network to increase the receptive field and obtain more global information in order to maintain the integrity of the reconstructed object limbs. A threshold processing module is set to remove irrelevant information and ensure the integrity of global information, and meanwhile, to reduce the interference of irrelevant noise information. Simulation experiments on a self-built terracotta dataset show that the PDB-PSN model can completely reconstruct the 3D model in a single image and effectively eliminate model fragmentation in the reconstruction results.
Small Object Detection Algorithm for Underwater Organisms based on Improved Transformer
Authors: Cao Ning
Published: November 2023

In view of the problem of difficult feature extraction and target missed detection, propose an improved transformer based underwater target detection algorithm. The proposed algorithm improves the linear embedding module of the Transformer to supplement the linear embedding information of small objects and builds the local relationship model of each layer feeling field, in order to achieve end-to-end accurate detection. An experiment on URPU underwater target detection data set is conducted. Compared with Swim Transformer, the proposed algorithm detection accuracy is 77.4%, 1. 5%, detection speed is 7 frame/s, 35.6%, the number is 30.4 MB, and compression is 84.1%. Experimental results have shown that the improved algorithm improves the performance of underwater small target detection
Variance-aware spatial decoupling attention for stereo matching
Authors: Zeng Pengcheng , Zhai Jucai , Ma Chihao , Liu Yang , Wang Xinan , Zhao Yong
Published: November 2023

The stereo matching algorithms which are based on feature matching build cost volume to regression disparity. Such cost volume shows a regular data distribution along the disparity dimension. The pixels with proper matching should have a single-peak distribution, while the pixels in the ill-conditioned regions have a multi-peak distribution. Using the variance of disparity dimension data, ill-conditioned regions can be identified. In this paper, an attention mechanism based on variance features is proposed, which promotes the neural network to deal with complicated regions. By decoupling the space of the image, a linear complexity attention mechanism is realized. It can be embedded into the algorithm based on feature matching with a slight increase in inference time, and a suitable trade-off is achieved in precision and speed. By embedding the module into BGNet, the accuracy of the designed model has been improved by 10 %, meanwhile, competitive performance has been achieved.
Conversion Control for Remote Sensing Stable Platforms for Transient and Continuous Disturbances
Authors: Zhou Jiaxing , Qixuan Huang , Chen Xiang , Yao Youxin
Published: November 2023

An airborne electro-optical stabilized platform is a device situated between the aircraft and the imaging components, which serves to isolate external disturbances and maintain imaging axis stability for the imaging components. In light of the impact of transient and continuous attitude changes of the aircraft on the axis-stabilized imaging in visible light unmanned aerial vehicle (UAV) remote sensing missions, a control method combining a proportional-integral (PI) controller and a backpropagation neural network (BPNN) PID conversion control was devised. The specific procedure involved the following steps: firstly, the transfer mechanism of external disturbances between platforms was derived using the state transition matrix through mechanistic modelling. Secondly, the multi-rigid body dynamics equations of the airborne electro-optical platform were established based on Newton’s laws of motion and Euler’s laws. Subsequently, the relationship between the stable platform rotation angle and external disturbances was obtained using Laplace transformation, and an analysis was conducted on the effects of different disturbances on the stable platform. Lastly, conversion control was incorporated into the system model for simulation analysis. The simulation results demonstrated that transient disturbances could be effectively and rapidly isolated by the PI controller, while continuous disturbances exhibited improved performance when handled by the neural network PID controller, thus validating the efficacy of the designed conversion control method.
A Mixed Safety and Reliability Evaluation Method for the Spacecraft Power Supply System
Authors: Wang Chao , Wang Heyu
Published: November 2023

A spacecraft power distribution system is a multi-parameter, strongly coupled complex system, and the safety of a spacecraft power distribution system directly affects the working status of the spacecraft. In order to achieve better research results in the safety analysis system of spacecraft energy systems, it is very necessary to establish an assessment method for the safety of spacecraft power distribution systems. Previous research mainly focuses on the safety assessment of power systems or aircraft power systems and has not formed a relatively complete assessment method for the safety of spacecraft power distribution systems. This paper proposes a comprehensive evaluation method for the safety of spacecraft power distribution systems. It combines objective and subjective evaluation methods to comprehensively evaluate the safety of spacecraft power distribution systems. The objective parameters of the spacecraft power distribution system are processed and objectively evaluated; Subjective evaluations are obtained using analytic hierarchy. The proposed method can reasonably evaluate the safety of spacecraft power distribution systems and assist in the selection of different spacecraft power distribution systems.
A PEDOT-Pt-TiO2 hybrid material synthesized by the casting method for photocatalytic hydrogen generation
Authors: Kobylański Marek P , Tercjak Agnieszka , Remita Hynd , Yuan Xiaojiao , Cavdar Onur , Gutierrez Junkal , Zaleska-Medynska Adriana
Published: November 2023

This study describes the synthesis and characterisation of a hybrid material consisting of titanium dioxide nanotube arrays (TiO2 NTs) modified by platinum nanoparticles (Pt-TiO2 NTs) via radiolysis and a conductive poly(3,4-ethylenedioxythiophene) (PEDOT) layer, for the first time. The NTs were fabricated by a two-step anodic oxidation process and exhibited different morphologies using electrolyte solutions with different water contents (2–10 vol%). The polymer layer of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) was coated on the Pt-TiO2 scaffold using the casting method. The PEDOT:PSS-PT-TiO2 NTs exhibited stability in the photocatalytic process after additional calcination which was carried out to remove the PSS part; the nanotubes with lengths of ∼3 μm exhibited the highest photocatalytic activity (∼4.5 × 10−3
μmol cm−2 of H2). Additionally, the samples obtained from electrolyte solutions containing 5 and 10 vol% water exhibited nanostructures with the highest catalytic activity.
Techno-economic optimization of renewable urea production for sustainable agriculture and CO2 utilization
Authors: Palys Matthew J , Daoutidis Prodromos
Published: November 2023

Urea is the most used nitrogen fertilizer due to its ease of storage, transportation, and application. It is made by combining ammonia and carbon dioxide (CO2), both of which are produced predominantly from fossil fuels at present. The recent momentum behind ammonia production using renewable-powered electrolysis offers an opportunity to both make urea in a more sustainable way and utilize CO2 from external sources. In this work, we present a techno-economic optimization model to minimize the cost of making urea in this way. The model allows for time-varying chemical production in response to renewable variability by simultaneously optimizing production facility design and hourly operation. We performed a case study for Minnesota considering the use of byproduct CO2 from bioethanol production. We found that the present-day levelized cost of renewable urea is between $268 mt−1 and $413 mt−1 at likely implementable production scales up to 250 000 mt yr−1. This is within the range of historical conventional urea prices while offering at least 78% carbon intensity reduction. Projecting to 2030, there is a clear economic case for renewable urea production with levelized cost as low as $135 mt−1 due to technology improvement and electrolysis manufacturing expansion, facilitating a urea production scale increase to 525 000 mt yr−1. Optimal facilities use wind energy, with hydrogen and ammonia production operating in a flexible, time-varying way to minimize battery and hydrogen storage capacities. Urea production operates near steady state due to the relatively low cost of intermediate ammonia buffer storage. A mix of imported methane and locally produced hydrogen are used to provide heat for steam consumed in the urea synthesis.
Computational study of shock diffraction over convex edges
Authors: Banerjee Debiprasad , Halder Pabitra
Published: November 2023

Numerical analysis of normal shock diffraction over various step corners is presented using an implicit second-order upwind least squares cell-based method. Slipstream angle calculation for various corner angles and Mach numbers, variation of pressure load and specific heat flux with incident shock Mach numbers and different step corners along the step wall, separation point movement, and reattachment of the streamlines are some of the key features of the present study. Detailed studies have been conducted about the perturbed region enclosed within the diffracted shock wave and the last running expansion wave with the increase in the incident shock Mach numbers. The finite Volume Method is used to solve the governing equations numerically for the simulation of the moving shock. Various flow characteristics such as secondary shock, contact surface, expansion wave, slipstream, vortex, etc are well captured. Apart from isopycnics; Mach contours, isobars, and isotherms are plotted as well. The reattachment region formed after the flow separation near the step edge is mentioned and corresponding lengths over various steps are presented. Separation point movement along the step wall is highlighted and a relevant increase in reattachment length is reported. The slipstream angles get increased with incident shock Mach numbers and with corner angles. However, the rate of increment of slipstream angle decreases gradually. The core of the vortices generated and the reattachment of the separated streamlines are indicated by the two downfalls in the heat flux values.
Excess phosphoserine-129 α-synuclein induces synaptic vesicle trafficking and declustering defects at a vertebrate synapse
Authors: Wallace Jaqulin N. , Crockford Zachary C. , Román-Vendrell Cristina , Brady Emily B. , Hoffmann Christian , Vargas Karina J. , Potcoava Mariana , Wegman M. Elizabeth , Alford Simon T. , Milovanovic Dragomir , Morgan Jennifer R.
Published: November 2023

α-Synuclein is a presynaptic protein that regulates synaptic vesicle (SV) trafficking. In Parkinson’s disease (PD) and dementia with Lewy bodies (DLB), α-synuclein aberrantly accumulates throughout neurons, including at synapses. During neuronal activity, α-synuclein is reversibly phosphorylated at serine 129 (pS129). While pS129 comprises ∼4% of total α-synuclein under physiological conditions, it dramatically increases in PD and DLB brains. The impacts of excess pS129 on synaptic function are currently unknown. We show here that compared with wild-type (WT) α-synuclein, pS129 exhibits increased binding and oligomerization on synaptic membranes and enhanced vesicle “microclustering” in vitro. Moreover, when acutely injected into lamprey reticulospinal axons, excess pS129 α-synuclein robustly localized to synapses and disrupted SV trafficking in an activity-dependent manner, as assessed by ultrastructural analysis. Specifically, pS129 caused a declustering and dispersion of SVs away from the synaptic vicinity, leading to a significant loss of total synaptic membrane. Live imaging further revealed altered SV cycling, as well as microclusters of recently endocytosed SVs moving away from synapses. Thus, excess pS129 caused an activity-dependent inhibition of SV trafficking via altered vesicle clustering/reclustering. This work suggests that accumulation of pS129 at synapses in diseases like PD and DLB could have profound effects on SV dynamics.
XHap: haplotype assembly using long-distance read correlations learned by transformers
Authors: Consul Shorya , Ke Ziqi , Vikalo Haris
Published: November 2023

Reconstructing haplotypes of an organism from a set of sequencing reads is a computationally challenging (NP-hard) problem. In reference-guided settings, at the core of haplotype assembly is the task of clustering reads according to their origin, i.e. grouping together reads that sample the same haplotype. Read length limitations and sequencing errors render this problem difficult even for diploids; the complexity of the problem grows with the ploidy of the organism. We present XHap, a novel method for haplotype assembly that aims to learn correlations between pairs of sequencing reads, including those that do not overlap but may be separated by large genomic distances, and utilize the learned correlations to assemble the haplotypes. This is accomplished by leveraging transformers, a powerful deep-learning technique that relies on the attention mechanism to discover dependencies between non-overlapping reads. Experiments on semi-experimental and real data demonstrate that the proposed method significantly outperforms state-of-the-art techniques in diploid and polyploid haplotype assembly tasks on both short and long sequencing reads.

Availability and implementation

The code for XHap and the included experiments is available at https://github.com/shoryaconsul/XHap.