Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovative Microstructural Transformation upon CO2 Supercritical Conditions on Metal-Nucleobase Aerogel and Its Use as Effective Filler for HPLC Biomolecules Separation

This work contributes to enlightening the opportunities of the anisotropic scheme of non-covalent interactions present in supramolecular materials. It provides a top-down approach based on their selective disruption that herein has been employed to process a conventional microcrystalline material to a nanofibrillar porous material. The developed bulk microcrystalline material contains uracil-1-propionic acid (UPrOH) nucleobase as a molecular recognition capable building block. Its crystal structure consists of discrete [Cu(UPrO)2 (4,4′-bipy)2 (H2 O)] (4,4′-bipy=4,4′-bipyridine) entities held together through a highly anisotropic scheme of non-covalent interactions in which strong hydrogen bonds involving coordinated water molecules provide 1D supramolecular chains interacting between them by weaker interactions. The sonication of this microcrystalline material and heating at 45 °C in acetic acid–methanol allows partial reversible solubilization/recrystallization processes that promote the cross-linking of particles into an interlocked platelet-like micro-particles metal–organic gel, but during CO2 supercritical drying, the microcrystalline particles undergo a complete morphological change towards highly anisotropic nanofibers. This unprecedented top-down microstructural conversion provides a nanofibrillar material bearing the same crystal structure but with a highly increased surface area. Its usefulness has been tested for HPLC separation purposes observing the expected nucleobase complementarity-based separation.
Amplitude-phase modulation metasurface hologram with inverse angular spectrum diffraction theory

Designed metasurfaces, composed of a two-dimensional array of meta-atoms, provide an alternative approach to achieving efficient electromagnetic wave manipulation. Metasurface holography is an emerging and promising imaging technology, with improved image quality and spatial resolution compared to traditional holography. Many devices are fabricated only by coding specific phase responses of the designed metasurfaces. However, the modulation of both the amplitude and phase responses of electromagnetic waves can significantly improve the quality of the holographic image. In this paper, we employ bi-layered split rings as meta-atoms, which can fully control the transmission amplitude and phase independently. Furthermore, we present an algorithm based on the inverse angular spectrum diffraction theory to obtain the amplitude and phase information for the shape and arrangement of the meta-atoms. The proof-of-concept experiments in the microwave regime demonstrate that the inverse angular spectrum diffraction theory shows better image quality than the conventional Gerchberg-Saxton algorithm, especially when the number of meta-atoms is the same or even slightly fewer. The proposed approach provides an innovative and effective method for hologram design and expands the route to versatile applications related to holographic technologies.
High Frequency Resonance Suppression Strategy of Three-Phase Four-Wire Split Capacitor Inverter Connected to Parallel Compensation Grid

With the continuous penetration and development of renewable energy power generation, the distributed grid and the microgrid are becoming increasingly important in modern power systems. In distribution networks and the microgrid, the grid impedance is comparatively large and cannot be ignored. Usually, the parallel compensation is used to improve the grid quality. In the grid with parallel compensation, the large phase angle difference between the impedance of the grid-connected inverter and the impedance of the grid at amplitude intersection will result in high frequency resonance (HFR). Because the inverter shows filter characteristics due to limited bandwidth of the controller, the parallel compensation grid, respectively, performs as the capacitance characteristic and inductance characteristic in different high frequency range. Compared with the three-phase, three-wire system, an additional zero-sequence path exists in the three-phase four-wire split capacitor inverter (TFSCI) system, so that the existing high frequency resonance suppression methods will be not effective. Since the zero-sequence component is neglected, HFR will also occur, in addition to the positive-sequence component and the negative-sequence component. Therefore, in order to suppress the high frequency resonance caused by positive-sequence, negative-sequence and zero-sequence components, an impedance reshaping strategy based on current feedback is proposed in this paper. This proposed method can reshape the amplitude and phase of the inverter impedance in a high frequency range without affecting the performance of the fundamental frequency control and ensure that the inverter contains a sufficient phase margin. Additionally, the proposed method can reshape the impedance of TFSCI within a wide frequency range, which makes it able to cope with the challenge of the parallel compensation degree change. Theoretical analysis and experiments verify the availability of the proposed control strategy.
Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber–physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment.
Exploring Multiliteracies and Multimodal Pedagogies in Chinese Language Teaching

This study explores one teacher's forays into multiliteracies practices and multimodal pedagogies in teaching a language other than English in an international school in Hong Kong. Using the Action Learning Circle as a guiding framework, this study analysed a Chinese language teacher's one-year self-initiated exploration of multiliteracies and multimodality with students of different proficiency levels. Data analysis is unpacked through interview reflections and three digital multimodal composition (DMC) projects that the teacher designed to explore the unique processes of incorporating multiliteracies and multimodality into Chinese language teaching. This study has filled in multiple research gaps by being one of the first to look at DMC in Chinese language teaching through a teacher-focused investigation. It has also included a more balanced focus on both visual and video projects to respond to the linguistic features of the Chinese language. Continuous professional learning and strong institutional support are required to fully embed multiliteracies into language education.
Mixed Micellization, Thermodynamic and Adsorption Behavior of Tetracaine Hydrochloride in the Presence of Cationic Gemini/Conventional Surfactants

In this approach, tensiometry and UV-visible techniques are used to determine the effect of cationic gemini and conventional surfactants on tetracaine hydrochloride (TCH), an anesthetic drug. We have estimated micellar, interfacial, and energetic constraints. To gain a deep understanding of their mixed association behavior, the outputs were examined using different theoretical models. The critical micelle concentration for single and mixed amphiphiles was estimated. The cmc values of mixed amphiphiles were found between the individual amphiphiles due to strong attractive interaction (synergism) between the components after mixing. The non-ideal behavior of mixtures was confirmed by the larger values of ideal cmc than the experimental cmc values. The negative values of interaction parameter (β) and values of activity coefficients less than unity indicate strong synergistic interaction between drug and surfactant. The stability of the mixed systems is demonstrated by the negative Gibbs free energy of micellization and excess free energy of micellization. In contrast to a single chain surfactant, a double chain surfactant (gemini) exhibits better interactions with the drug. Spectral measurements (UV-visible spectra) were used to monitor the binding of the drug with surfactant (conventional as well as gemini). Studying these mixed aggregates could help to optimize their compositions and find synergistic properties between TCH monomers and surfactants.
Maximized Effective Transmission Rate Model for Advanced Neighbor Discovery Process in Bluetooth Low Energy 5.0

Bluetooth low-energy (BLE) technology is one of the most promising communication technologies applicable to a variety of Internet of Things (IoT) services. The neighbor discovery process (NDP) plays a key role in BLE-enabled IoT services. The basic NDP (B-NDP) specified in BLE specification 4.0 has a limitation in supporting large numbers of BLE devices, owing to its use of three channels. To overcome the limitation of B-NDP, advanced NDP (A-NDP) has been recently introduced in BLE specification 5.0. However, most existing studies have focused on B-NDP, with very few studies having been conducted on A-NDP. In this article, we propose a model for analyzing the effective transmission rate for a BLE advertiser with an A-NDP operation. Using the proposed model, we also propose a performance model to maximize the transmission rate by optimally setting the BLE parameters. The proposed models are validated by comparing with extensive simulation results. It also demonstrates that the maximum transmission rate by the proposed models can be achieved with low energy consumption.
Expression of the Antimicrobial Peptide Piscidin 1 and Neuropeptides in Fish Gill and Skin: A Potential Participation in Neuro-Immune Interaction

Antimicrobial peptides (AMPs) are found widespread in nature and possess antimicrobial and immunomodulatory activities. Due to their multifunctional properties, these peptides are a focus of growing body of interest and have been characterized in several fish species. Due to their similarities in amino-acid composition and amphipathic design, it has been suggested that neuropeptides may be directly involved in the innate immune response against pathogen intruders. In this review, we report the molecular characterization of the fish-specific AMP piscidin1, the production of an antibody raised against this peptide and the immunohistochemical identification of this peptide and enkephalins in the neuroepithelial cells (NECs) in the gill of several teleost fish species living in different habitats. In spite of the abundant literature on Piscidin1, the biological role of this peptide in fish visceral organs remains poorly explored, as well as the role of the neuropeptides in neuroimmune interaction in fish. The NECs, by their role as sensors of hypoxia changes in the external environments, in combination with their endocrine nature and secretion of immunomodulatory substances would influence various types of immune cells that contain piscidin, such as mast cells and eosinophils, both showing interaction with the nervous system. The discovery of piscidins in the gill and skin, their diversity and their role in the regulation of immune response will lead to better selection of these immunomodulatory molecules as drug targets to retain antimicrobial barrier function and for aquaculture therapy in the future.
The Discussion of Remainders-Detecting Technique Based on Random Vibration for Space Relay

This paper expatiates on the importance of detecting remainders in space relays in national defense electronic system.Then it analyses the actualities of detection for relay remainders and points out the main existed problems.At last the total design thought for detecting relay remainders based on random vibration is proposed.The paper also brings forward the direction and goal of the future research.
Real-time Measure and Control Platform of Missile Electro-hydraulic Servo Mechanism Based on NI-cRIO

The CampactRIO hardware is introduced briefly.Then,a real-time measurement and control platform of missile electro-hydraulic servo mechanism is designed using NI compact reconfigurable I/O programmable automation controller.The system composition,hardware configuration and the function modules are introduced.The main function of software,FPGA VI program of PID control and PC interface are also introduced.The platform can be used not only for the missile servo mechanism control,but for the non-linear characteristics analysis of system.
Dividend Policy and The Role of Capital Structure and Corporate Governance: A Case Study of Cement Factory in Pakistan

This work investigates the impact of capital structure (CS) and corporate governance (CG) on dividend policy. The sample selected for this study includes all firms in the cement sector of the Pakistan stock exchange. The CS and CG are measured using the debt to equity ratio (DER) and managerial and institutional ownership mechanisms. The dividend policy (DP) is measure using the dividend payout ratio. The fixed effect model of regression is used for estimation and correlation matrix. The data normality, autocorrelation and multicollinearity are also used as a tool of analysis. The results show no outliers problem in the data set and have the pair of variables firmly. We also demonstrate that CS and CG has a significant impact on dividend policy. It is concluded that CS and managerial ownership positively impacted dividend policy, while there is an insignificant impact on institutional ownership.
PID decoupling controller for aero-engine based on support vector machines

To solve the coupling problem in aero-engine multivariable control system,a new PID(proportion integration differentiation) decoupling controller based on support vector machines(SVM) was proposed.The SVM was applied to identify the aero-engine model,and then linearized to extract the instantaneous linear model.The PID parameter was obtained based on the linear model.The convergence of PID decoupling controller was proved with the help of Lyapunov stability theorem.Simulation results of certain aero-engine system demonstrate the effectiveness and feasibility of the PID decoupling controller based on SVM.
„Toll. Ich bin froh dabei zu sein“. Studie zur Spende digitaler Körperdaten in der Corona-Krise

Die Verbreitung des Covid-19-Virus birgt sowohl für Gesundheitspolitik und -forschung sowie die Bevölkerung neue Herausforderungen. Als gesundheitspolitische Maßnahmen werden in dieser Zeit u.a. neue Apps beworben, die helfen sollen, die Krise zu bewältigen. Eine dieser Apps ist die durch das Robert-Koch-Institut (RKI) entwickelte Corona-Datenspende-App. Auf freiwilliger Basis können durch diese App Vitaldaten an das RKI transferiert werden, die durch privat eingesetzte Fitness-Tracker generiert wurden. Im Rahmen einer qualitativen Kurzstudie werden die Motive für und die Effekte von Praktiken mit dieser App analysiert. Informiert durch die Science and Technologies Studies und Ansätze des New Materialism rücken die Verschränkungen des Materiellen (u.a. Technologien, Körper) und des Diskursiven (u.a. Seuchendiskurse, Werturteile) in den Fokus.
Application of Deep Learning Technology in Glioma

A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily magnetic resonance imaging (MRI) oriented, is considered as one of common tools developed for doctors. These doctors use this system to examine, analyse, and diagnose appearance of the glioma's outward for both patients, i.e., indoor and outdoor. In the literature, a widely utilized approach for the segmentation of glioma is the deep learning-oriented method. To cope with this issue, a segmentation of glioma approach, i.e., primarily on the convolution neural networks, is developed in this manuscript. A DM-DA-enabled cascading approach for the segmentation of glioma, which is 2DResUnet-enabled model, is reported to resolve the problem of spatial data acquisition of insufficient 3D specifically in the 2D full CNN along with the core issue of memory consumption of 3D full CNN. For gliomas segmentation at various stages, we have utilized multiscale fusion approach, attention, segmentation, and DenseBlock. Moreover, for reducing three dimensionalities of the Unet model, a sampling of fixed region is used along with multisequence data of the glioma image. Finally, the CNN model has the ability of producing a better segmentation of tumor preferably with minimum possible memory. The proposed model has used BraTS18 and BraTS17 benchmark data sets for fivefold cross-validation (local) and online evaluation preferably official, respectively. Evaluation results have verified that edema's Dice Score preferable average, enhancement, and core areas of the segmentation of the glioma with DM-DA-Unet perform exceptionally well on the validation set of BraTS17. Finally, average sensitivity was observed to be high as well, which is approximately closer to the best segmentation model and its effect on the validation set of BraTS1 and has segmented gliomas accurately.
Magnetic biochar affects the metabolic pathway in methanogenesis of anaerobic digestion of food waste

To alleviate pollution and promote clean energy production, food waste can be used to produce methane through anaerobic digestion (AD). In this study, wheat straw was used to produce biochar (BC) and magnetic biochar (MB) as additives for AD of food waste. MB2.5% and BC2.5% led to the highest methane production. The main action mechanism of the additives in AD was the change in environmental factors upon stimulating microorganisms to rapidly metabolize volatile fatty acids. The stimulation of microbial function increased the abundance of the methanogenesis pathways. Methanosarcina was the most dominant archaea in the four methanogenesis modules, and the microbial community changed more clearly with time than with treatment. This study is innovative in finding the effectiveness of BC and MB treatments attributed to the abundance of methanogenesis modules, especially MB2.5% treatment retained its advantage until the later stage of digestion in AD process.
Simulation of the Automatic Train Protection Based on Chinese Train Control System-2

The paper introduced an architecture of the CTCS2 train control system and the speed curve control mode, and designed and realized a simulation system of CTCS2 train control system based on the track circuit and balise. This paper described the calculating principle of target-distance mode curve and the algorithm of train position calculation and the automatic train protection in the simulation system in details. The simulation system conducted a simulation with the data of Jiao-Ji railway line and provided an environment for the advanced research on the train control system.
A Method for Large Underground Structures Geometry Evaluation Based on Multivariate Parameterization and Multidimensional Analysis of Point Cloud Data

In underground mining, new workings (tunnels) are constructed by blasting or mechanical excavation. The blasting technique used in underground mines is supported by economic aspects, especially for deposits characterized by hard rocks. Unfortunately, the quality of the result may be different than expected in terms of the general geometry of work or the roughness of excavation surfaces. The blasting technique is also a source of vibrations that may affect other existing structures, affecting their stability. Therefore, it is of great importance to monitor both the quality of the new tunnels and changes in existing tunnels that may cause rockfall from the sidewalls and ceilings of both new and existing tunnels. The length of mining tunnels and support structures in underground mines is massive. Even if one would like to limit monitoring of tunnel geometry to those used every day for major technological processes such as transport, it is a vast amount of work. What is more, any stationary monitoring system is hard to utilize both due to everyday blasting procedures and mobile machine operation. The method proposed here is based on quick LiDAR/Terrestrial Laser Scanner measurements to obtain a cloud of points, which allows generating the spatial model of a mine’s geometry. Data processing procedures are proposed to extract several parameters describing the geometry of the tunnels. Firstly, the model is re-sampled to obtain its uniform structure. Next, a segmentation technique is applied to separate the cross sections with a specific resolution. Statistical parameters are selected to describe each cross section for final 1D feature analysis along the tunnel length. Such a set of parameters may serve as a basis for blasting evaluation, as well as long-term deformation monitoring. The methodology was tested and validated for the data obtained in a former gold and arsenic mine Zloty Stok, Poland.
Optimization on blade-grid system of hydraulic reducer and braking dynamics simulation

The hydraulic reducer is an auxiliary brake necessary for high speed and heavy loaded vehicles that possesses advantages of big moment in high speed braking and no mechanical wear,and is especially suitable for the application on vehicles of velocity reduction at high speed and running in long downhill path.The blade-grid system decides the property of hydraulic reducer.On the basis of empirical design this paper carried out optimization on blade-grid system by combining with the multi-islands genetic algorithm,developed the virtual prototype and finally carried through braking property simulation by letting the hydraulic reducer be added into the transmission system of the entire vehicle.The result of simulation showed that the hydraulic reducer possesses good braking property after optimization,and extended the braking range of vehicle,thus laid a foundation for the development and manufacture of hydraulic reducer.