Miracle-dz/UniFAR-FTUs huggingface dataset:
Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection has attracted extensive interests for its wide use in military and civilian fields, and three main categories of detection methods have been developed successively over past few decades, including statistical model-based, representation-based, and deep-learning-based methods. Most of these algorithms are essentially trying to construct proper background profiles, which describe the characteristics of background and then identify the pixels that do not conform to the profiles as anomalies. Apparently, the crucial issue is how to build an accurate background profile; however, the background profiles constructed by existing methods are not accurate enough. In this article, a novel and universal background purification framework with extended morphological attribute profiles is proposed. It explores the spatial characteristic of image and removes suspect anomaly pixels from the image to obtain a purified background. Moreover, three detectors with this framework covering different categories are also developed. The experiments implemented on four real hyperspectral images demonstrate that the background purification framework is effective, universal, and suitable. Furthermore, compared with other popular algorithms, the detectors with the framework perform well in terms of accuracy and efficiency.
Improvement of front-junction GaInP by point-defect injection and annealing

Traditional front junction GaInP solar cells are radiation tolerant and can have good diffusion length, but have limited voltage and thus efficiency. Here, we investigate the impact of annealing on GaInP device performance. First, Zn-doped GaInP/AlGaInP double heterojunction structures are studied in order to investigate the impact of annealing in a simple structure. While standard anneals lower diffusion length and lifetime, annealing after injecting point-defects improves material quality, presumably by eliminating sources of non-radiative recombination. Atomic ordering is reduced by the anneal, which raises the device bandgap and requires consideration. These results can be used to improve front junction device performance while controlling the bandgap. Then, GaInP front-junction devices are created using anneals with and without point-defect injection. Voc and EQE trends follow the diffusion length and lifetime trends from the DH structures. Baseline devices have Woc of 0.484 V, raising to 0.504 V after a high temperature anneal. However, injecting point defects prior to the anneal results in a front junction device with Woc of 0.405 V and a diffusion length greater than 8 µm. Optimized front-junction GaInP devices with an ARC have ~20% efficiency without a rear reflector.
Augmented Noise Learning Framework for Enhancing Medical Image Denoising

Deep learning attempts medical image denoising either by directly learning the noise present or via first learning the image content. We observe that residual learning (RL) often suffers from signal leakage while dictionary learning (DL) is prone to Gibbs (ringing) artifacts. In this paper, we propose an unsupervised noise learning framework that enhances denoising by augmenting the limitation of RL with the strength of DL and vice versa. To this end, we propose a ten-layer deep residue network (DRN) augmented with patch-based dictionaries. The input images are presented to patch-based DL to indirectly learn the noise via sparse representation while given to the DRN to directly learn the noise. An optimum noise characterization is captured by iterating DL/DRN network against proposed loss. The denoised images are obtained by subtracting the learned noise from available data. We show that augmented DRN effectively handles high-frequency regions to avoid Gibbs artifacts due to DL while augmented DL helps to reduce the overfitting due to RL. Comparative experiments with many state-of-the-arts on MRI and CT datasets (2D/3D) including low-dose CT (LDCT) are conducted on a GPU-based supercomputer. The proposed network is trained by adding different levels of Rician noise for MRI and Poisson noise for CT images considering different nature and statistical distribution of datasets. The ablation studies are carried out that demonstrate enhanced denoising performance with minimal signal leakage and least artifacts by proposed augmented approach.
Study of Solvent Type Effect on Preparation of Ethyl Cellulose Microparticles by Solvent Evaporation Method Double Emulsion System (W/O/W) Using Focused Beam Reflectance Measurement (FBRM)

The purpose of this study was to investigate effect of solvent type on solidification rate of ethyl cellulose (EC) microparticles and particle size/distribution of the emulsion droplets/hardened microparticles during solvent evaporation process using focused beam reflectance measurement (FBRM). EC microparticles were prepared by a W/O/W-solvent evaporation method using various solvents, including dichloromethane, dichloromethane:methanol (1:1), ethyl acetate and chloroform. The particle size/distribution of the emulsion droplets/hardened microparticles was monitored by FBRM. The morphology of EC microparticles was characterized by scanning electron microscopy (SEM). The transformation of the emulsion droplets into solid microparticles for all solvents occured within the first 10-90 min. The square weighted mean chord length of EC microparticles which were prepared using chloroform was smallest, but the chord counts was no the highest. The chord length distribution (CLD) measured by FBRM showed that a larger particle size mean gave longer CLD and a lower peak of particle number. SEM data revealed that the morphology of microparticles was influenced by type of solvent. FBRM can be employed for online monitoring of the shift in the microparticle CLD and detect transformation of the emulsion droplets into solid microparticles during the solvent evaporation process. The microparticle CLD and transformation process was strongly influenced by solvent type.
Real-time Detection and Classification for Targeted Marine Mammals

With the continuous development of offshore engineering, major offshore projects develop rapidly. During the development of offshore engineering, different degrees of noise will be generated, and the man-made noise can have harmful effects on marine mammals. Currently, The researchers usually use Passive Acoustic Monitoring(PAM) method to monitor the marine mammals. However, it is impossible to acquire, monitor and analyze the sound of marine mammals in real time and lack of comprehensive information of marine mammals monitoring, and the data analysis and study of vocalization rules can only be carried out after data collection is completed and equipment is recovered. Therefore, this paper proposes a real-time automatic detection and classification technology to monitor targeted marine mammals efficiently and continuously timely in offshore engineering areas.
Research on Flexible Control Strategy of Controllable Large Industrial Loads Based on Multi-Source Data Fusion of Internet of Things

With the rapid development of the power grid, the high penetration of new energy sources and the diversity of loads have further aggravated the uncertainty of “source-load”, which has brought huge challenges to the peak shaving of the power grid. In order to ensure the reliable operation of the power system during the electricity peak, this paper combines the IoT technology to propose a flexible control strategy for controllable large industrial loads that considers the interrupt priority. Firstly, the perception and fusion framework of large industrial load information is constructed based on the IoT technology. After that, the improved TOPSIS method is adopted to establish the evaluation model of the adjustable potentials of large industrial loads and the load interruption priority is further divided. Finally, a three-stage rolling regulation model for controllable large industrial loads to participate in the deep peak shaving of the power grid is constructed to achieve the goal of bidirectional peak shaving on the power generation side and the demand side. The case uses an improved IEEE 30-node system for simulation. As the simulation results show, the method proposed in this paper can not only take the cost of peak load regulation into account, but also effectively achieve the goal of ‘peak shaving and valley filling’.
Design, synthesis, and conformational studies of [DOTA]‐Octreotide analogs containing [1,2,3]triazolyl as a disulfide mimetic

Somatostatin (SS) is a cyclic tetradecapeptide able to inhibit the release of growth hormone (GH) mainly through the binding to two G‐protein coupled receptor (GPCR) subtypes, SSTR2 and SSTR5. These receptors are overexpressed in approximately 90% of carcinoid tumors. However, the clinical use of somatostatin is limited by its short half‐life in vivo. In order to overcome this severe drawback, a huge number of analogs have been prepared, leading to the development of Octreotide, which is currently used in the clinic, among other applications, to treat various neuroendocrine tumors and, radiolabeled by, for example, 111In, 11C, and 68Ga, for imaging SS‐secreting tumors. Despite the success of Octreotide, there is an unmet need for the development of novel, more stable and selective Octreotide‐derived radiotherapeutics. To this end, the Cu(I)‐catalyzed azide‐alkyne 1,3‐dipolar Huisgen's cycloaddition, the prototypic click reaction, presents a promising opportunity to replace the susceptible disulfide bridge with a durable [1,2,3]triazolyl containing bridge and to introduce conformational constraints increasing specific receptor binding. Herein we report the design and synthesis of a series of i‐to‐i + 5 1,4‐ and 4,1‐disubstituted [1,2,3]triazolyl‐bridged cyclopeptides derived from the Octreotide scaffold and their detailed conformational analysis via NMR spectroscopy.
Stacked-Interconnect for Monolithic Integration of Low-Temperature Polysilicon and Amorphous Metal-Oxide Thin-Film Transistors

While offering a range of practical benefits, the monolithic integration of low-temperature polysilicon (LTPS) and amorphous metal-oxide thin-film transistors presents several incompatibility issues regarding materials and processes. Presently addressed are two critical ones arising from the back-end processes of contact treatment and metallization. Both are resolved by employing a stacked-interconnect consisting of two conductor layers, with each layer forming the preferred contact electrodes for one of the two types of transistors. At the expense of a slight increase in process complexity, a narrow distribution of low specific contact resistance ( $\sim 10^{-5}\Omega \cdot $ cm2) for both types of transistors was obtained, thus giving rise to more consistent transistor characteristics. Inverters consisting of complementary top-gate LTPS pull-up and bottom-gate indium-gallium-zinc oxide pull-down transistors were demonstrated, exhibiting a gain of 40 V/V and a rail-to-rail full swing.
Preliminary Evaluation of a Performance-based Stiffness Control for Upper Limb Elbow Joints Rehabilitation

For post-stroke patient, the interaction force between the impaired limb and the rehabilitation robotic system is an important issue for patient safety in clinical treatment. The compliance control scheme can be achieved by the rehabilitation robotics with variable stiffness actuator (VSA) for the safe physical human robot interaction. The output stiffness would be adjusted independently via variable stiffness actuator during the rehabilitation training processing. In addition, when the stiffness changing, the different impedance characteristic can be realized according to the environmental applied force. The compliance control of VSA can be utilized into the rehabilitation application scenario for improving the training safety. In this paper, a performance-based stiffness control strategy for maximum the rehabilitation effect and increasing the patient participant was proposed utilizing the compliance characteristic of VSA. The elbow joint output stiffness would be regulated according the patient's training performance which will be determined using the real-time position tracking error. It is noted that the training performance should be different to adapt the patient individual-specific. Therefore, an acceptable position tracking error range should be set in advance for the suitable training plan. The experimental results show that the proposed method can adjust the elbow joint stiffness for patients according to the real-time training performance.
Dynamic alterations of amplitude of low-frequency fluctuations in adolescents with autism spectrum disorder

Numerous studies showed autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder together with disturbed static brain activity. However, little is known about alterations of dynamic brain activity and its associations with social behavior in ASD. In this study, the dynamic amplitude of low-frequency fluctuation (dALFF) based on sliding-window technique was applied to the resting-state functional magnetic resonance imaging data of 91 adolescents with ASD and 122 age-matched typically developing controls (TDC group), which obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. We found that the increased dALFF variance in the right middle temporal gyrus (MTG) was detected in adolescent ASD relative to TDC, which is positively correlated with communication deficits. Our findings reveal that atypical dynamic brain activity in MTG is associated with social deficits in ASD. Results also highlight the crucial role of MTG in the social-cognitive deficits of ASD and provide a novel insight into understanding dynamic pathophysiologic mechanisms underlying ASD.
An Auto-Encoding Generative Adversarial Networks for Generating Brain Network

Generative adversarial networks (GANs) are powerful generative models that have led to breakthroughs in image generation. The aim of our work is to adapt the idea of GANs to the graph data based on fMRI, which can solve the issue of limited data and improve the classification performance in the domain of the Autism spectrum disorder (ASD) diagnosis. In this work, we present a data augmentation method that generates synthetic graph data with the proposed graph generation model named α-GCNGAN, which is able to handle the intrinsic challenge of generating brain network with considering flexible context-structure. Extensive experiments on Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate the effectiveness and generalizability of α-GCNGAN.
Fabrication and Properties of Polycaprolactone Composites Containing Calcium Phosphate-Based Ceramics and Bioactive Glasses in Bone Tissue Engineering: A Review

ABSTRACT Polycaprolactone (PCL) is a bioresorbable and biocompatible polymer that has been widely used in long-term implants and controlled drug release applications. However, when it comes to tissue engineering, PCL suffers from some shortcomings such as slow degradation rate, poor mechanical properties, and low cell adhesion. The incorporation of calcium phosphate-based ceramics and bioactive glasses into PCL has yielded a class of hybrid biomaterials with remarkably improved mechanical properties, controllable degradation rates, and enhanced bioactivity that are suitable for bone tissue engineering. This review presents a comprehensive study on recent advances in the fabrication and properties of PCL-based composite scaffolds containing calcium phosphate-based ceramics and bioglasses in terms of porosity, degradation rate, mechanical properties, in vitro and in vivo biocompatibility and bioactivity for bone regeneration applications. The fabrication routes range from traditional methods such as solvent casting and particulate leaching to novel approaches including solid free-form techniques.
Investigation on the conformational changes of bovine serum albumin in a wide pH range from 2 to 12

Abstract The conformation of bovine serum albumin largely depends on its microenvironment pH and affects its physical functions and applications. In this study, we investigated the effects of pH (wide range 2–12) on the conformation of bovine serum albumin based on spectroscopic signals by various spectroscopic means including fluorescence, synchronous fluorescence, resonance light scattering, UV-visible absorption, and circular dichroism. The changes in spectroscopic signals, such as peak position and intensity, showed that the structure of bovine serum albumin varied significantly with pH. The conformation of bovine serum albumin was compact at pH 6–7, as indicated by the largest peak position values and peak intensities in the fluorescence, synchronous fluorescence, and RLS spectra. The structure of bovine serum albumin became loose in the acidic or alkaline environment, as indicated by the decreased peak position values and peak intensities. The microenvironment of the amino acid residues of bovine serum albumin also varied with pH, as indicated by the changing peak position values. At pH 7, the hydrophobicity of the tyrosine and tryptophan residues was the weakest, as indicated by the minimum synchronous fluorescence signals. In addition, the secondary structure of bovine serum albumin, especially α-helix, varied with pH. The content of α-helix reached the maximum value of 68% at pH 6–8, whereas it decreased in the acidic or alkaline environment. The study provides valuable details for studying the physiological function and applications of serum albumins.
Joint ptycho-tomography with deep generative priors

Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more efficient subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a denoiser parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.
Organic and printed process design kits: review, analysis and comparison

Organic and printed electronics (OE) is a promising technology for reducing the waste and the price of electronic systems for new devices. OE offers technological advantages not available in conventional silicon processes such as physical flexibility, large area fabrication, mask-less deposition, low process temperatures and fully additive manufacturing—processes that all enable low-cost and low-waste fabrication techniques. Despite these advantages, important difficulties remain for developing compact models for transistors due to intrinsic variability in the manufacturing process and the behaviour of organic materials, which make it difficult to match the model and the experimental data. Due to disparity in the fabrication methodologies and the different topologies of transistors used, the diversity in compact models is noticeable. These different models provide specific solutions for particular devices and require various process design kits (PDKs) for each manufacturing process. This review covers the different PDKs developed in the OE field and compares them with the electric models used for simulation purposes. Both simulated and fabricated systems are included with the aim of providing the reader with a clear state-of-the-art of the OE situation.
CuCr2O4 derived by the sol–gel method as a highly active and selective catalyst for the conversion of glycerol to 2,6-dimethylpyrazine: a benign and eco-friendly process

Vapour phase dehydrocyclization of crude glycerol in conjunction with 1,2-propanediamine (1,2-PDA) was examined over CuCr2O4 obtained by different preparation methods. A high proportion of copper species interacted with Cr2O3 in CuCr2O4 derived from the sol–gel route with a low ratio of Cu2+/Cu0 demonstrating higher dehydrocyclization activity and 2,6-dimethylpyrazine (2,6-DMP) selectivity. X-ray photoelectron spectroscopy analysis of the reduced CuCr2O4 revealed a lower fraction of ionic Cu and a high percentage of metallic Cu in the near surface region. The HCOOH and pyridine adsorbed DRIFT spectra of CuCr2O4 revealed that strong basic and moderate Lewis acid sites are responsible for the selective formation of 2,6-dimethylpyrazine which is consistent with the catalyst poisoning studies on CuCr2O4 co-feeding with pyridine as both Bronsted and Lewis acid site blocker and 2,6-lutidine as a selective Bronsted acid site blocker during the dehydrocyclization reaction. The presence of isolated CuO and Cr2O3 species led to a high selectivity for 2,6-dimethylpiperazine. The high intrinsic activity of CuCrsol–gel was also concomitant with the Cu metal surface areas of the catalysts. The fresh, reduced and some of the used catalysts are characterized by BET – surface area, powder XRD, FTIR, XPS, TEM, H2-TPR, TPD of NH3, pyridine, 2,6-dimethylpyridine and HCOOH adsorbed DRIFT spectroscopy.
Pharmaceutical Approaches on Antimicrobial Resistance: Prospects and Challenges

The rapid increase in pathogenic microorganisms with antimicrobial resistant profiles has become a significant public health problem globally. The management of this issue using conventional antimicrobial preparations frequently results in an increase in pathogen resistance and a shortage of effective antimicrobials for future use against the same pathogens. In this review, we discuss the emergence of AMR and argue for the importance of addressing this issue by discovering novel synthetic or naturally occurring antibacterial compounds and providing insights into the application of various drug delivery approaches, delivered through numerous routes, in comparison with conventional delivery systems. In addition, we discuss the effectiveness of these delivery systems in different types of infectious diseases associated with antimicrobial resistance. Finally, future considerations in the development of highly effective antimicrobial delivery systems to combat antimicrobial resistance are presented.