bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Advances in the Development of Liquid Crystalline Nanoparticles as Drug Delivery Systems
Authors: Leu Jassica S. L. , Teoh Jasy J. X. , Ling Angel L. Q. , Chong Joey , Loo Yan Shan , Mat Azmi Intan Diana , Zahid Noor Idayu , Bose Rajendran J. C. , Madheswaran Thiagarajan
Published: May 2023

Due to their distinctive structural features, lyotropic nonlamellar liquid crystalline nanoparticles (LCNPs), such as cubosomes and hexosomes, are considered effective drug delivery systems. Cubosomes have a lipid bilayer that makes a membrane lattice with two water channels that are intertwined. Hexosomes are inverse hexagonal phases made of an infinite number of hexagonal lattices that are tightly connected with water channels. These nanostructures are often stabilized by surfactants. The structure’s membrane has a much larger surface area than that of other lipid nanoparticles, which makes it possible to load therapeutic molecules. In addition, the composition of mesophases can be modified by pore diameters, thus influencing drug release. Much research has been conducted in recent years to improve their preparation and characterization, as well as to control drug release and improve the efficacy of loaded bioactive chemicals. This article reviews current advances in LCNP technology that permit their application, as well as design ideas for revolutionary biomedical applications. Furthermore, we have provided a summary of the application of LCNPs based on the administration routes, including the pharmacokinetic modulation property.
Relating Molecular Dynamics Simulations to Functional Activity for Gly-Rich Membranolytic Helical Kiadin Peptides
Authors: Rončević Tomislav , Maleš Matko , Sonavane Yogesh , Guida Filomena , Pacor Sabrina , Tossi Alessandro , Zoranić Larisa
Published: May 2023

Kiadins are in silico designed peptides with a strong similarity to diPGLa-H, a tandem sequence of PGLa-H (KIAKVALKAL) and with single, double or quadruple glycine substitutions. They were found to show high variability in their activity and selectivity against Gram-negative and Gram-positive bacteria, as well as cytotoxicity against host cells, which are influenced by the number and placing of glycine residues along the sequence. The conformational flexibility introduced by these substitutions contributes differently peptide structuring and to their interactions with the model membranes, as observed by molecular dynamics simulations. We relate these results to experimentally determined data on the structure of kiadins and their interactions with liposomes having a phospholipid membrane composition similar to simulation membrane models, as well as to their antibacterial and cytotoxic activities, and also discuss the challenges in interpreting these multiscale experiments and understanding why the presence of glycine residues in the sequence affected the antibacterial potency and toxicity towards host cells in a different manner.
First-Principle Prediction of Stress-Tunable Single-Photon Emitters at Telecommunication Band from Point Defects in GaN
Authors: Yuan Junxiao , Wang Ke , Hou Yidong , Chen Feiliang , Li Qian
Published: May 2023

Point defect-based single-photon emitters (SPEs) in GaN have aroused a great deal of interest due to their room-temperature operation, narrow line width and high emission rate. The room-temperature SPEs at the telecommunication bands have also been realized recently by localized defects in GaN in experiments, which are highly desired for the practical applications of SPEs in quantum communication with fiber compatibility. However, the origin and underlying mechanism of the SPEs remain unclear to date. Herein, our first-principle calculations predict and identify an intrinsic point defect NGa in GaN that owns a zero-phonon line (ZPL) at telecommunication windows. By tuning the triaxial compressive strain of the crystal structure, the ZPL of NGa can be modulated from 0.849 eV to 0.984 eV, covering the fiber telecommunication windows from the O band to the E band. Besides the ZPL, the formation energy, band structure, transition process and lifetime of the SPEs under different strains are investigated systematically. Our work gives insight into the emission mechanism of the defect SPEs in GaN and also provides effective guidance for achieving wavelength-tunable SPEs working in fiber telecommunication windows.
Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites
Authors: Karamov Radmir , Breite Christian , Lomov Stepan V. , Sergeichev Ivan , Swolfs Yentl
Published: May 2023

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high—(0.3 μm) and low-resolution (1.6 μm) statically acquired images. These networks were then applied to a low-resolution (1.1 μm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters.
Effect of 3D Printing Process Parameters and Heat Treatment Conditions on the Mechanical Properties and Microstructure of PEEK Parts
Authors: Zhen Honglei , Zhao Bin , Quan Long , Fu Junyu
Published: May 2023

Fused deposition modeling (FDM) processed Poly-ether-ether-ketone (PEEK) materials are widely used in aerospace, automobile, biomedical, and electronics industries and other industries due to their excellent mechanical properties, thermal properties, chemical resistance, wear resistance, and biocompatibility, etc. However, the manufacture of PEEK materials and parts utilizing the FDM process faces the challenge of fine-tuning a list of process parameters and heat treatment conditions to reach the best-suiting mechanical properties and microstructures. It is non-trivial to make the selection only according to theoretical analysis while counting on a vast number of experiments is the general situation. Therefore, in this paper, the extrusion rate, filling angle, and printing orientation are investigated to adjust the mechanical properties of 3D-printed PEEK parts; then, a variety of heat treatment conditions were applied to tune the crystallinity and strength. The results show that the best mechanical performance is achieved at 1.0 times the extrusion rate, varied angle cross-fillings with ±10° intervals, and vertical printing. Horizontal printing performs better with reduced warpage. Additionally, both crystallinity and mechanical properties are significantly improved after heat treatment, and the best state is achieved after holding at 300 °C for 2 h. The resulting tensile strength is close to 80% of the strength of injection-molded PEEK parts.
Optimization Strategy of the Electric Vehicle Power Battery Based on the Convex Optimization Algorithm
Authors: Wang Xuanxuan , Ji Wujun , Gao Yun
Published: May 2023

With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, finding a method that can effectively improve the performance of electric vehicles is of great significance. To improve the current performance of electric vehicles, a convex optimization algorithm is proposed to optimize the motor model and power battery parameters of electric vehicles, improving the overall performance of electric vehicles. The performance of the proposed convex optimization algorithm, dual loop DP optimization algorithm, and nonlinear optimization algorithm is compared. The results show that the hydrogen consumption of electric vehicles optimized by the convex optimization algorithm is 95.364 g. This consumption is lower than 98.165 g of the DCDP optimization algorithm and 105.236 g of the nonlinear optimization algorithm before optimization. It is also significantly better than the 125.59 g of electric vehicles before optimization. The calculation time of the convex optimization algorithm optimization is 4.9 s, which is lower than the DCDP optimization algorithm and nonlinear optimization algorithm. The above results indicate that convex optimization algorithms have better optimization performance. After optimizing the power battery using a convex optimization algorithm, the overall performance of electric vehicles is higher. Therefore, this method can effectively improve the performance of current electric vehicle power batteries, make new energy vehicles develop rapidly, and improve the increasingly serious environmental pollution and energy crisis in China.
Martyrs and Madonnas: Inácio de Azevedo, the Brazil Martyrs, and the Global Circulation of the Madonna of Santa Maria Maggiore
Authors: Nelles Paul
Published: May 2023

The article offers a revisionist account of the early circulation of copies of the Madonna of Santa Maria Maggiore, known since the nineteenth century as the Salus Populi Romani. Traditionally, the propulsion of the image into global circulation has been attributed variously to Pius V or Francisco Borja, the third Superior General of the Society of Jesus. The article argues that the circulation of the Saint Luke Madonna, as it was known at the time, was closely tied to the martyr’s cult that grew up around the Jesuit missionary Inácio de Azevedo and the so-called Brazil Martyrs, a group of Jesuits murdered by Calvinist corsairs off the Canary Islands in 1570. Azevedo had intended to carry a copy of the Roman icon to Brazil, a copy that perished at sea with Azevedo and the party of Jesuit missionaries. The article suggests that the popularity of the image among Jesuits in Europe and the overseas missions was fueled by the nascent martyr’s cult that followed Azevedo’s death. Painted copies of the Saint Luke Madonna came to function, together with relics of the Eleven Thousand Virgins of Cologne, as proxies for the missing material remains of the martyred Jesuits. The article argues that while the distribution of the image was globally extensive, circulation was restricted to an internal Jesuit martyr’s cult.
The “Adbhuta-Dharma” Narratives in Translated Chinese Sarvāstivāda Texts
Authors: Wang Lina , Chen Yingjin
Published: May 2023

The concept of “adbhuta-dharma” (Ch. weicengyou fa 未曾有法) has developed and changed throughout Buddhism’s history. Indeed, the subject and the content of adbhuta texts as well as the meaning of the term “adbhuta” (marvelous phenomenon) varies across the scriptures. Looking through the scriptures, it would seem that they originated as narrative elaborations on various aspects of the Three Jewels—the Buddha, the Dharma, and the Saṃgha. Then gradually, “adbhuta-dharma” came to encapsulate those narrative elements in the scriptures related to the miraculous in the life of the Buddha as well as the careers of his disciples. As one of the twelve divisions of the Buddhist canon, the literary form of the adbhutas overlaps with other canonical genres, namely the nidānas, the avadānas, and the jātakas tales. This article will provide a careful analysis of adbhuta-dharma narratives in important Sarvāstivād in Sūtras, Vinayas, Abhidharmas, and other texts such as the Chuyao jing 出曜經 and the Buddhacarita (Ch. Fo suoxing zan 佛所行贊). These narratives are important because, while many monks and laypeople might have been left uninspired by the complexities of Buddhist thought and doctrine, miracle narratives were more accessible, playing an important role in popularizing Sarvāstivāda ideas and doctrines. These miracle stories reached audiences that other Sūtra narratives and exegetical works could not, helping in the dissemination of Buddhist thought and practice, as well as the later development and spread of Mahāyāna works in India and beyond.
An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment
Authors: Wang Feng , Zhou Guoqing , Xie Jiali , Fu Bolin , You Haotian , Chen Jianjun , Shi Xue , Zhou Bowen
Published: May 2023

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.
An ROI Optimization Method Based on Dynamic Estimation Adjustment Model
Authors: Li Ziyue , Zeng Qinghua , Liu Yuchao , Liu Jianye
Published: May 2023

An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) fusion positioning system to obtain the optimum size of the ROI is essential for further improvement of recognition accuracy. In this paper, we propose a dynamic estimation adjustment (DEA) model construction method to optimize the ROI. First, according to the residual variance of the integrated navigation system and the vehicle velocity, we divide the innovation into an approximate Gaussian fitting region (AGFR) and a Gaussian convergence region (GCR) and estimate them using variational Bayesian gated recurrent unit (VBGRU) networks and a Gaussian mixture model (GMM), respectively, to obtain the GNSS measurement uncertainty. Then, the relationship between the GNSS measurement uncertainty and the multi-sensor aided ROI acquisition error is deduced and analyzed in detail. Further, we build a dynamic estimation adjustment model to convert the innovation of the multi-sensor integrated navigation system into the optimal ROI size of the traffic lights online. Finally, we use the YOLOv4 model to detect and recognize the traffic lights in the ROI. Based on laboratory simulation and real road tests, we verify the performance of the DEA model. The experimental results show that the proposed algorithm is more suitable for the application of autonomous vehicles in complex urban road scenarios than the existing achievements.
High-Precision Segmentation of Buildings with Small Sample Sizes Based on Transfer Learning and Multi-Scale Fusion
Authors: Xu Xiaobin , Zhang Haojie , Ran Yingying , Tan Zhiying
Published: May 2023

In order to improve the accuracy of the segmentation of buildings with small sample sizes, this paper proposes a building-segmentation network, ResFAUnet, with transfer learning and multi-scale feature fusion. The network is based on AttentionUnet. The backbone of the encoder is replaced by the ResNeXt101 network for feature extraction, and the attention mechanism of the skip connection is preserved to fuse the shallow features of the encoding part and the deep features of the decoding part. In the decoder, the feature-pyramid structure is used to fuse the feature maps of different scales. More features can be extracted from limited image samples. The proposed network is compared with current classical semantic segmentation networks, Unet, SuUnet, FCN, and SegNet. The experimental results show that in the dataset selected in this paper, the precision indicators of ResFAUnet are improved by 4.77%, 2.3%, 2.11%, and 1.57%, respectively, compared with the four comparison networks.
LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion
Authors: Yang Dongxu , Zheng Yongbin , Xu Wanying , Sun Peng , Zhu Di
Published: May 2023

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the local binary pattern (LBP)-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce LBP into GANs, enabling the network to have stronger texture feature extraction and utilization capabilities, as well as anti-interference capabilities. In the feature extraction stage, we introduce a pseudo-Siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN by fusing RGB and infrared images on the RoadScene dataset and medical images. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain richly detailed information.
Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation
Authors: Pathiranage Dinithi Siriwardana , Leigh Larry , Pinto Cibele Teixeira
Published: May 2023

This paper evaluates the Arable Mark 2 sensor, an automated and low-cost radiometer, for its potential to retrieve surface reflectance data and validate orbital sensors such as the Landsat-8 (L8) Operational Land Imager (OLI) Level 2 product. While orbital sensors are widely used for monitoring solar radiation changes, managing natural resources, and understanding climatic trends, atmospheric effects can make it challenging to obtain accurate measurements. Equipped with multiple sensors, including long-wave and short-wave radiometers, the Arable Mark 2 sensor can measure upwelling and downwelling irradiance to calculate surface reflectance. To assess the accuracy and consistency of the Arable Mark 2 sensor, the study performed a cross-calibration using a ground truth measurement collected with the Analytical Spectral Device (ASD) as the reference point. Additionally, a spectral band adjustment factor (SBAF) was applied across the calibrated Arable surface reflectance to compare it against the orbital sensor. An automated library aided in calculating SBAF for the days with unavailable hyperspectral data. The study found that the Arable Mark 2 sensor can provide accurate surface reflectance data that can be used for orbital sensor validation. The Arable sensor was successfully calibrated against the ASD FieldSpec with an average difference of less than 1/10 reflectance unit (reflectance unit = 0.01) for the blue, green, yellow, and red bands. The red-edge and NIR-1 bands showed an average difference of less than 1/2 reflectance units, while the NIR-2 band had an average difference of less than 1/10 reflectance unit of calibration accuracy. The calibrated Arable surface reflectance data was then compared against orbital sensor surface reflectance data, and the results showed good agreement between the two datasets. The study concludes that the low-cost and automated nature of the Arable Mark 2 sensor makes it a promising tool for surface reflectance retrieval and orbital sensor validation.
Enhanced Doppler Resolution and Sidelobe Suppression Performance for Golay Complementary Waveforms
Authors: Zhu Jiahua , Song Yongping , Jiang Nan , Xie Zhuang , Fan Chongyi , Huang Xiaotao
Published: May 2023

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes.
Dim and Small Space-Target Detection and Centroid Positioning Based on Motion Feature Learning
Authors: Su Shengping , Niu Wenlong , Li Yanzhao , Ren Chunxu , Peng Xiaodong , Zheng Wei , Yang Zhen
Published: May 2023

The detection of dim and small space-targets is crucial in space situational awareness missions; however, low signal-to-noise ratio (SNR) targets and complex backgrounds pose significant challenges to such detection. This paper proposes a space-target detection framework comprising a space-target detection network and a k-means clustering target centroid positioning method. The space-target detection network performs a three-dimensional convolution of an input star image sequence to learn the motion features of the target, reduces the interference of noise using a soft thresholding module, and outputs the target detection result after positioning via the offsetting branch. The k-means centroid positioning method enables further high-precision subpixel-level centroid positioning of the detection network output. Experiments were conducted using simulated data containing various dim and small space-targets, multiple noises, and complex backgrounds; semi-real data with simulated space-targets added to the real star image; and fully real data. Experiments on the simulated data demonstrate the superior detection performance of the proposed method for multiple SNR conditions (particularly with very low false alarm rates), robustness regarding targets of varying numbers and speeds, and complex backgrounds (such as those containing stray light and slow motion). Experiments performed with semi-real and real data both demonstrate the excellent detection performance of the proposed method and its generalization capability.
SIGNet: A Siamese Graph Convolutional Network for Multi-Class Urban Change Detection
Authors: Zhou Yanpeng , Wang Jinjie , Ding Jianli , Liu Bohua , Weng Nan , Xiao Hongzhi
Published: May 2023

Detecting changes in urban areas presents many challenges, including complex features, fast-changing rates, and human-induced interference. At present, most of the research on change detection has focused on traditional binary change detection (BCD), which becomes increasingly unsuitable for the diverse urban change detection tasks as cities grow. Previous change detection networks often rely on convolutional operations, which struggle to capture global contextual information and underutilize category semantic information. In this paper, we propose SIGNet, a Siamese graph convolutional network, to solve the above problems and improve the accuracy of urban multi-class change detection (MCD) tasks. After maximizing the fusion of change differences at different scales using joint pyramidal upsampling (JPU), SIGNet uses a graph convolution-based graph reasoning (GR) method to construct static connections of urban features in space and a graph cross-attention method to couple the dynamic connections of different types of features during the change process. Experimental results show that SIGNet achieves state-of-the-art accuracy on different MCD datasets when capturing contextual relationships between different regions and semantic correlations between different categories. There are currently few pixel-level datasets in the MCD domain. We introduce a new well-labeled dataset, CNAM-CD, which is a large MCD dataset containing 2508 pairs of high-resolution images.
Global Visual–Inertial Localization for Autonomous Vehicles with Pre-Built Map
Authors: Hao Yun , Liu Jiacheng , Liu Yuzhen , Liu Xinyuan , Meng Ziyang , Xing Fei
Published: May 2023

Accurate, robust and drift-free global pose estimation is a fundamental problem for autonomous vehicles. In this work, we propose a global drift-free map-based localization method for estimating the global poses of autonomous vehicles that integrates visual–inertial odometry and global localization with respect to a pre-built map. In contrast to previous work on visual–inertial localization, the global pre-built map provides global information to eliminate drift and assists in obtaining the global pose. Additionally, in order to ensure the local odometry frame and the global map frame can be aligned accurately, we augment the transformation between these two frames into the state vector and use a global pose-graph optimization for online estimation. Extensive evaluations on public datasets and real-world experiments demonstrate the effectiveness of the proposed method. The proposed method can provide accurate global pose-estimation results in different scenarios. The experimental results are compared against the mainstream map-based localization method, revealing that the proposed approach is more accurate and consistent than other methods.
Improving Compressed Video Using Single Lightweight Model with Temporal Fusion Module
Authors: Kuo Tien-Ying , Wei Yu-Jen , Su Po-Chyi , Chao Chang-Hao
Published: May 2023

Video compression algorithms are commonly used to reduce the number of bits required to represent a video with a high compression ratio. However, this can result in the loss of content details and visual artifacts that affect the overall quality of the video. We propose a learning-based restoration method to address this issue, which can handle varying degrees of compression artifacts with a single model by predicting the difference between the original and compressed video frames to restore video quality. To achieve this, we adopted a recursive neural network model with dilated convolution, which increases the receptive field of the model while keeping the number of parameters low, making it suitable for deployment on a variety of hardware devices. We also designed a temporal fusion module and integrated the color channels into the objective function. This enables the model to analyze temporal correlation and repair chromaticity artifacts. Despite handling color channels, and unlike other methods that have to train a different model for each quantization parameter (QP), the number of parameters in our lightweight model is kept to only about 269 k, requiring only about one-twelfth of the parameters used by other methods. Our model applied to the HEVC test model (HM) improves the compressed video quality by an average of 0.18 dB of BD-PSNR and −5.06% of BD-BR.
Design and Study of a Reflector-Separated Light Dispersion-Compensated 3D Microscopy System
Authors: Li Hui , Tan Xin , Jiao Qingbin , Li Yuhang , Liu Siqi , Pei Jian , Zhang Jiahang , Zhang Wei , Xu Liang
Published: May 2023

The secondary-phase grating-based tomographic microscopy system, which is widely used in the biological and life sciences, can observe all the sample multilayer image information simultaneously because it has multifocal points. However, chromatic aberration exists in the grating diffraction, which seriously affects the observation of the image. To correct the chromatic aberration of the tomographic microscope system, this paper proposes a system that adopts blazed gratings and angle-variable reflectors as chromatic aberration correction devices according to the principle of dispersion compensation and Fourier phase-shift theory. A reflector-separated light dispersion-compensated 3D microscopy system is presented to achieve chromatic aberration correction while solving the problem of multilayer image overlap. The theoretical verification and optical design of the system were completed using ZEMAX software. The results show that the proposed system reduced the chromatic aberration of ordinary tomographic microscopy systems by more than 90%, retaining more wavelengths of light information. In addition, the system had a relatively wide range in the color difference compensation element installation position, reducing the difficulty of dispersion compensation element installation. Overall, the results indicate that the proposed system is effective in reducing chromatic aberration in grating diffraction.
Effects of Walking Speed and Added Mass on Hip Joint Quasi-Stiffness in Healthy Young and Middle-Aged Adults
Authors: Fang Shanpu , Vijayan Vinayak , Reissman Megan E. , Kinney Allison L. , Reissman Timothy
Published: May 2023

Joint quasi-stiffness has been often used to inform exoskeleton design. Further understanding of hip quasi-stiffness is needed to design hip exoskeletons. Of interest are wearer responses to walking speed changes with added mass of the exoskeleton. This study analyzed hip quasi-stiffness at 3 walking speed levels and 9 added mass distributions among 13 young and 16 middle-aged adults during mid-stance hip extension and late-stance hip flexion. Compared to young adults, middle-aged adults maintained a higher quasi-stiffness with a smaller range. For a faster walking speed, both age groups increased extension and flexion quasi-stiffness. With mass evenly distributed on the pelvis and thighs or biased to the pelvis, both groups maintained or increased extension quasi-stiffness. With mass biased to the thighs, middle-aged adults maintained or decreased extension quasi-stiffness while young adults increased it. Young adults decreased flexion quasi-stiffness with added mass but not in any generalizable pattern with mass amounts or distributions. Conversely, middle-aged adults maintained or decreased flexion quasi-stiffness with even distribution on the pelvis and thighs or biased to the pelvis, while no change occurred if biased to the thighs. In conclusion, these results can guide the design of a hip exoskeleton’s size and mass distribution according to the intended user’s age.
Performance Analysis of Wirelessly Powered Cognitive Radio Network with Statistical CSI and Random Mobility
Authors: Kozić Nadica , Blagojević Vesna , Cvetković Aleksandra , Ivaniš Predrag
Published: May 2023

The relentless expansion of communications services and applications in 5G networks and their further projected growth bring the challenge of necessary spectrum scarcity, a challenge which might be overcome using the concept of cognitive radio. Furthermore, an extremely high number of low-power devices are introduced by the concept of the Internet of Things (IoT), which also requires efficient energy usage and practically applicable device powering. Motivated by these facts, in this paper, we analyze a wirelessly powered underlay cognitive system based on a realistic case in which statistical channel state information (CSI) is available. In the system considered, the primary and the cognitive networks share the same spectrum band under the constraint of an interference threshold and a maximal tolerable outage permitted by the primary user. To adopt the system model in realistic IoT application scenarios in which network nodes are mobile, we consider the randomly moving cognitive user receiver. For the analyzed system, we derive the closed-form expressions for the outage probability, the outage capacity, and the ergodic capacity. The obtained analytical results are corroborated by an independent simulation method.
Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene
Authors: Guo Yiran , Shen Qiang , Deng Zilong , Zhang Shouyi
Published: May 2023

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25–30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm’s robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm’s estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.
Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks
Authors: Wang Xiang , Yang Jie , Kasabov Nikola K.
Published: May 2023

Increasing violence in workplaces such as hospitals seriously challenges public safety. However, it is time- and labor-consuming to visually monitor masses of video data in real time. Therefore, automatic and timely violent activity detection from videos is vital, especially for small monitoring systems. This paper proposes a two-stream deep learning architecture for video violent activity detection named SpikeConvFlowNet. First, RGB frames and their optical flow data are used as inputs for each stream to extract the spatiotemporal features of videos. After that, the spatiotemporal features from the two streams are concatenated and fed to the classifier for the final decision. Each stream utilizes a supervised neural network consisting of multiple convolutional spiking and pooling layers. Convolutional layers are used to extract high-quality spatial features within frames, and spiking neurons can efficiently extract temporal features across frames by remembering historical information. The spiking neuron-based optical flow can strengthen the capability of extracting critical motion information. This method combines their advantages to enhance the performance and efficiency for recognizing violent actions. The experimental results on public datasets demonstrate that, compared with the latest methods, this approach greatly reduces parameters and achieves higher inference efficiency with limited accuracy loss. It is a potential solution for applications in embedded devices that provide low computing power but require fast processing speeds.
An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles
Authors: Unar Salahuddin , Su Yining , Liu Pengbo , Teng Lin , Wang Yafei , Fu Xianping
Published: May 2023

The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.
Miniaturization of an Osmotic Pressure-Based Glucose Sensor for Continuous Intraperitoneal and Subcutaneous Glucose Monitoring by Means of Nanotechnology
Authors: Pfützner Andreas , Tencer Barbora , Stamm Boris , Mehta Mandar , Sharma Preeti , Gilyazev Rustam , Jensch Hendrick , Thomé Nicole , Huth Michael
Published: May 2023

The Sencell sensor uses glucose-induced changes in an osmotic pressure chamber for continuous glucose measurement. A final device shall have the size of a grain of rice. The size limiting factor is the piezo-resistive pressure transducers inside the core sensor technology (resulting chamber volume: 70 µL. To achieve the necessary miniaturization, these pressure transducers were replaced by small (4000 × 400 × 150 nm³) nano-granular tunneling resistive (NTR) pressure sensors (chamber volume: 750 nL). For benchmark testing, we filled the miniaturized chamber with bovine serum albumin (BSA, 1 mM) and exposed it repeatedly to distilled water followed by 1 mM BSA solution. Thereafter, we manufactured sensors with glucose testing chemistry (ConcanavalinA/dextran) and investigated sensor performance with dynamic glucose changes between 0 and 300 mg/dL. Evaluation of the miniaturized sensors resulted in reliable pressure changes, both in the BSA benchmark experiment (30–35 mBar) and in the dynamic in vitro continuous glucose test (40–50 mBar). These pressure results were comparable to similar experiments with the previous larger in vitro sensors (30–50 mBar). In conclusion, the NTR pressure sensor technology was successfully employed to reduce the size of the core osmotic pressure chamber by more than 95% without loss in the osmotic pressure signal.
Propagation Constant Measurement Based on a Single Transmission Line Standard Using a Two-Port VNA
Authors: Hatab Ziad , Abdi Arezoo , Steinbauer Gregor , Gadringer Michael Ernst , Bösch Wolfgang
Published: May 2023

This study presents a new method for measuring the propagation constant of transmission lines using a single line standard and without prior calibration of a two-port vector network analyzer (VNA). The method provides accurate results by emulating multiple line standards of the multiline calibration method. Each line standard was realized by sweeping an unknown network along a transmission line. The network need not be symmetric or reciprocal, but must exhibit both transmission and reflection. We performed measurements using a slab coaxial airline and repeated the measurements on three different VNAs. The measured propagation constant of the slab coaxial airline from all VNAs was nearly identical. By avoiding disconnecting or moving the cables, the proposed method eliminates errors related to the repeatability of connectors, resulting in improved broadband traceability to SI units.
Incidence and Predictors of Soft Tissue Injuries during Basic Combat Training
Authors: Nikolaidis Pantelis , Havenetidis Konstantinos
Published: May 2023

Strenuous exercise, such as military training, is known to demand a high degree of physical performance and to cause injuries. The present study aimed to (a) monitor the incidence of soft tissue injuries (blisters, contusions, and lacerations) among cadets during Basic Combat Training (BCT), and (b) identify possible risk factors for these injuries. Participants were 315 first-grade cadets (women, n = 28; men, n = 287), recruited from the Hellenic Army Academy. Seven weeks of BCT resulted in an overall cadet injury rate of 24.1% (n = 76) with 13.7% being injured one time, whereas 10.4% of participants were injured 2–6 times. The incidence of injuries was 2.9 soft tissue injuries per 1000 training hours. The logistic regression model using sex, being an athlete, nationality, weight, height, body mass index, and percentage of body fat (BF) to predict soft tissue injury was not statistically significant (χ2(7) = 5.315, p = 0.622). The results of this study showed that BCT caused a large number of soft tissue injuries similar to the number reported for musculoskeletal injuries. In conclusion, following BCT, soft tissue injury characteristics (occurrence, severity, treatment) are similar to those applied in musculoskeletal injuries for Army cadets. However, risk factors such as sex, nationality, and BF have not been related to soft tissue injury prediction as previously shown for musculoskeletal injuries for the same sample group.
Biomass Zilla spinosa Fruit Functionnalized Polyethyleneimine Polymer: Analysis and Application for the Elimination of Calmagite in Water
Authors: Jabli Mahjoub , Elaissi Arwa , Altwala Afnan
Published: May 2023

The valorization of natural polymeric substrates has increased due to their uses and applications in several fields. The existence of many functional groups in their chemical structures allows them to be easily subjected to chemical modifications. This work focuses on the exploration of a new low-cost and abundant cellulosic biomass, Zilla spinosa fruit. The biomaterial was functionnalized with polyethyleneimine (1%, 3%, 5%, and 8%) in order to impart new reactive sites on its surface. The virgin and functionnalized biomaterials were analysed using several analytical methods; X-ray Photoelectron Spectroscopy (XPS), Fourier Transform Infrared (FT-IR), Scanning Electron Microscopy (SEM), and Thermogravimetric analysis (TGA). XPS spectrum of Zilla spinosa-polyethyleneimine exhibited the appearance of a new peak at 399 eV, which corresponds to N1s (5.07%). The adsorption characteristics of the prepared adsorbents were evaluated toward calmagite, an azoic and anionic dye. The adsorption capacity of Zilla spinosa-polyethyleneimine (5%) reached 114 mg/g at pH = 5, T = 20 °C, and time = 60 min conditions; though, it does not exceed 8.4 mg/g for the virgin Zilla spinosa under the same experimental conditions. The kinetic data followed both pseudo-first-order and pseudo-second-order kinetic equations suggesting a physicochemical process. The adsorption mechanism was found to be exothermic and non-spontaneous. Overall, Zilla spinosa-polyethyleneimine has demonstrated a high adsorption level which could be considered a promising candidate to remove synthetic dye molecules from contaminated water.
The Cultural Dimension of Clinical Vulnerability: Repeated Access to Emergency Units and Discontinuity in Health and Social Care Pathway
Authors: Bertini-Soldà Laura
Published: May 2023

Swiss health and social care system is complex and is based on universal coverage. However, discontinuity in health and social path and repeated access to emergency units are symptoms of inequity. The aim of this paper is to highlight the interactions between vulnerable patients with socio-sanitary actors to propose some innovative solutions to promote social justice. A six-month ethnography of 15 vulnerable patients’ health and social transitions in the region of Ticino Canton in Switzerland gives new insight into conflict situations in assistance relationships, where reciprocal stereotyping between professionals and patients undermines continuity of care. The cultural dimension of health and social institutions is identified in the problem-solving approach which is legitimized as the only one for dealing with complex situations. The analysis shows how clinical vulnerability relates strongly to an unmanaged so-called liminality phase. Culture is an invisible dimension in care, but its effects on equity are major. Two possible interventions are discussed, which are culturally informed reorganization of the care network and collaboration with a sociosanitary cultural mediator.
Physiological Responses and Adaptations of the Halophyte Atriplex halimus to Soil Contaminated with Cd, Ni, and NaCl
Authors: Ishtiyaq Shumailah , Kumar Harsh , D’Souza Rohan J. , Varun Mayank , Favas Paulo J. C. , Paul Manoj S.
Published: May 2023

Soils contaminated with potentially toxic elements (PTEs) and salt manifest a large number of physical, chemical, and structural problems by various processes such as reduced water availability, water and air movement in soil space, water holding capacity of soil, as well as perilous effects on plant growth and physiology. Halophytes have the ability to grow in saline environments and are better adapted to accommodate environmental constraints including PTE ions. An experiment was designed to study the response of the halophyte Atriplex halimus to a range of salinities and different concentrations of Cd and Ni. Tolerance and soil remedial potential of the plant were quantified in terms of PTE uptake and portioning, plant biomass, root/shoot ratio, chlorophyll and anti-oxidative enzyme production, along with stress markers such as lipid peroxidation, proline, and glycine betaine. The plant was also evaluated for its potential to phytoremediate PTE contaminated soil. The results suggest that A. halimus can tolerate moderate concentrations of both the PTEs and salt. The species holds promise for bio-reclamation of saline and PTE-contaminated soil.