Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Enhancement of Guar Gum−Based Hydrogel by Polydopamine and Nanocellulose

The development of green, biomedical hydrogels using natural polymers is of great significance. From this viewpoint, guar gum (GG) has been widely used for hydrogel preparation; however, its mechanical strength and adhesion often cannot satisfy the biomedical application. Therefore, in the present study, gelatin and a cellulose nanocrystal (CNC) were first applied to overcome the defects of guar gum hydrogel. Dopamine was self−polymerized into polydopamine (PDA) on the gelatin chain at alkaline condition, and gelatin−polydopamine (Gel−PDA) further cross−linked with guar gum and CNC via the borate−didiol bond, intramolecular Schiff base reaction, and Michael addition. CNC not only interacted with guar gum using borate chemistry but also acted as a mechanical reinforcer. The obtained Gel−PDA+GG+CNC hydrogel had an excellent self−healing capacity, injectability, and adhesion due to the catechol groups of PDA. Moreover, dopamine introduction caused a significant increase in the anti−oxidant activity. This hydrogel was cyto− and hemo−compatible, which implies a potential usage in the medical field.
Biopolymers Produced by Lactic Acid Bacteria: Characterization and Food Application

Plants, animals, bacteria, and food waste are subjects of intensive research, as they are biological sources for the production of biopolymers. The topic links to global challenges related to the extended life cycle of products, and circular economy objectives. A severe and well-known threat to the environment, the non-biodegradability of plastics obliges different stakeholders to find legislative and technical solutions for producing valuable polymers which are biodegradable and also exhibit better characteristics for packaging products. Microorganisms are recognized nowadays as exciting sources for the production of biopolymers with applications in the food industry, package production, and several other fields. Ubiquitous organisms, lactic acid bacteria (LAB) are well studied for the production of exopolysaccharides (EPS), but much less as producers of polylactic acid (PLA) and polyhydroxyalkanoates (PHAs). Based on their good biodegradability feature, as well as the possibility to be obtained from cheap biomass, PLA and PHAs polymers currently receive increased attention from both research and industry. The present review aims to provide an overview of LAB strains’ characteristics that render them candidates for the biosynthesis of EPS, PLA, and PHAs, respectively. Further, the biopolymers’ features are described in correlation with their application in different food industry fields and for food packaging. Having in view that the production costs of the polymers constitute their major drawback, alternative solutions of biosynthesis in economic terms are discussed.
Microwave background anisotropies and large-scale structure in universes with cold dark matter, baryons, radiation, and massive and massless neutrinos

Calculations of microwave backgrond anisotropies at various angular scales and of expected large-scale bulk velocities and mass correlation functions are presented for a variety of models which include baryons, radiation, cold dark matter (CDM), and massive and massless neutrinos. Nearly 100 different models are considered. Open and flat models with both adiabatic and isocurvature initial conditions are calculated for models without massive neurinos. A set of flat models with both massive neutrinos and CDM with adiabatic initial conditions is also considered. Fitting functions for the mass transfer function and small-angle radiation correlation function are provided for all of the models. A discussion of the evolution of the perturbations is presented. Results are compared with some recent observations of large-scale velocities and limits on microwave background anisotropies. CDM and baryon models have difficulty satisfying observational limits, although they are not completely ruled out. Hybrid models with massive neutrinos and CDM satisfy current observational data. 42 refs.
Sensorless Temperature Estimation for Li-Ion Battery Cells: An Overview, Practical Considerations, Challenges and Future Trends

Sensorless temperature estimation methods for batteries can be classified into three categories: analytical methods, observer-based methods and data-driven methods. In general, analytical methods are easy to derive and implement but have a limited performance due to their open-loop nature. Observer-based methods have a high performance due to their closed-loop nature but demand accurate dynamic and measurement models with up-to-date parameters for accurate estimation. Data-driven methods are extremely stable and robust due to their massive parallel structure, but they demand huge amount of data for training. This paper presents a comprehensive review of state-of-the-art sensorless temperature estimation methods for lithium-ion (Li-ion) batteries and demonstrates the advantages and limitations of each. A comparison between the presented methods in terms of their performance and implementation requirements is carried out. In addition, practical considerations, challenges and future trends are discussed.
Ultraviolet line identifications for Tau Scorpii

Identifications are presented for absorption features found in the ultraviolet spectrum of the BO V star, tau Scorpii. The spectrum, produced by the Copernicus satellite, covers the wavelengths from 949 to 1560 A. A correction to the first-order wavelength scale of the tau Scorpii atlas is derived.
Refining the multi-field effects of Higgs inflation

In this thesis, I investigated the extent to which the inclusion of multiple scalar fields in models of Higgs inflation produces observable departures from the single field case. I designed simulations to numerically solve the differential equations of motion in the Einstein frame governing the evolution of two nonminimally coupled fields in a quartic potential. Determining the behavior of the Hubble parameter, I found that successful inflation results in this model from a range of initial conditions. Based on calculations of the evolution of field perturbations by Courtney Peterson and Max Tegmark [6], the results suggest that the turn rate of the fields' combined velocity through field space has been sufficiently small since Hubble crossing that it may be excluded as a source of the amplification of perturbations in the Cosmic Microwave Background to their present size. Thesis Supervisor: Professor David I. Kaiser Title: Germeshausen Professor and Department Head, Program in Science, Technology, and Society; and Senior Lecturer, Department of Physics
Non-Surgical Definitive Treatment for Operable Breast Cancer: Current Status and Future Prospects

Simple Summary For breast cancer patients who do not wish to undergo any form of surgery, various non-surgical treatments have been investigated. Radiotherapy is considered the most important modality, but conventional radiotherapy alone or concurrent chemoradiotherapy do not lead to high local control rates. So, to increase curability, radiosensitization strategies, including the use of hydrogen peroxide injection before radiation and hyperthermia plus oral tegafur-gimeracil-oteracil potassium (S-1), have been investigated. These strategies have yielded promising outcomes, with local control rates of ~97%. A trial of carbon ion radiotherapy is ongoing, and particle therapy should be further investigated in the future. Image-guided ablation therapy including radiofrequency ablation and focused ultrasound have been investigated; although complete ablation rates of ≥70% have been reported, combination with radiotherapy may be necessary to treat the extensive intraductal components. Non-surgical treatment of breast cancer is evolving steadily and will become a valuable treatment option for patients who refuse surgery. Abstract This article reviews the results of various non-surgical curative treatments for operable breast cancer. Radiotherapy is considered the most important among such treatments, but conventional radiotherapy alone and concurrent chemoradiotherapy do not achieve high cure rates. As a radiosensitization strategy, intratumoral injection of hydrogen peroxide before radiation has been investigated, and high local control rates (75–97%) were reported. The authors treated 45 patients with whole-breast radiotherapy, followed by stereotactic or intensity-modulated radiotherapy boost, with or without a radiosensitization strategy employing either hydrogen peroxide injection or hyperthermia plus oral tegafur-gimeracil-oteracil potassium. Stages were 0–I in 23 patients, II in 19, and III in 3. Clinical and cosmetic outcomes were good, with 5-year overall, progression-free, and local recurrence-free survival rates of 97, 86, and 88%, respectively. Trials of carbon ion radiotherapy are ongoing, with promising interim results. Radiofrequency ablation, focused ultrasound, and other image-guided ablation treatments yielded complete ablation rates of 20–100% (mostly ≥70%), but long-term cure rates remain unclear. In these treatments, combination with radiotherapy seems necessary to treat the extensive intraductal components. Non-surgical treatment of breast cancer is evolving steadily, with radiotherapy playing a major role. In the future, proton therapy with the ultra-high-dose-rate FLASH mode is expected to further improve outcomes.
Expression Detection Of Autistic Children Using CNN Algorithm

Facial emotion detection is a technique for identifying human emotions from facial expressions. Autism Spectrum Disorder is an advanced neurobehavioral disorder, autism is a vast field with many problems, including hyperactivity, sensory, speech, etc. Children with autism who have behavioral problems are the focus of this research. This research is on an automatic detection system that will detect the expressions of autistic children and generate a report explaining their behaviors during their therapy sessions. This automatic system is primarily focusing on three main behavioral issues: happy, neutral, and irritated. This research is done to provide facilities to the parents, therapists, and Autistic Rehabilitation Centers to keep records of their children’s and patients’ progress in the form of reports. This is carried out using machine learning and image processing methods. The autistic children's faces are used to extract the features using local binary patterns. The proposed system uses CNN, Haar Cascade object detection Algorithm, and TensorFlow. The classification of emotions and face detection is done using the CNN algorithm and Haar Cascade object detection algorithm. Helping Hands Rehabilitation Center, a Pakistan-based autism rehabilitation center has provided its utmost assistance to make this research possible.
Magnetorheological fluids: A concise review of composition, physicochemical properties, and models

Magnetorheological fluids (MRFs) are classified as intelligent materials whose rheological and mechanical properties can be modified by interaction with an external magnetic field. These unique features allow for a controlled change of their viscosity, which is applied in technology to build adaptive devices and effectively suppress vibrations in various mechanical systems. In this paper, we overview and discuss our previous results regarding advances and physicochemical MRF properties in the context of broader literature. We concentrated on such properties as flow, yield strength, and viscoelastic behavior under shearing flows. We briefly discussed continuum and discrete MRFs modeling. Since the magnetic core is mainly based on iron or its compounds, depending on its chemical composition, morphology, stabilizing agents, and the liquid medium’s viscosity, its rheological and micromechanical properties can be moderated. To predict the behavior of such a fluid, it is necessary to propose and implement an appropriate model. Simple models like Bingham can consider the quasi-static and dynamic behavior of the MRFs, while discrete models are applied to the development and implementation of the MRF control algorithms. Thus, analytical and numerical simulation compromise the accuracy, quantity of considered phenomena, and computational cost.
The Role of Midlatitude Cyclones in the Emission, Transport, Production, and Removal of Aerosols in the Northern Hemisphere

We examine the distribution of aerosol optical depth (AOD) across 27,707 northern hemisphere (NH) midlatitude cyclones for 2005–2018 using retrievals from the Moderate Resolution Spectroradiometer (MODIS) sensor on the Aqua satellite. Cyclone‐centered composites show AOD enhancements of 20%–45% relative to background conditions in the warm conveyor belt (WCB) airstream. Fine mode AOD accounts for 68% of this enhancement annually. Relative to background conditions, coarse mode AOD is enhanced by more than a factor of two near the center of the composite cyclone, co‐located with high surface wind speeds. Within the WCB, MODIS AOD maximizes in spring, with a secondary maximum in summer. Cyclone‐centered composites of AOD from the Modern Era Retrospective analysis for Research and Applications, version 2 Global Modeling Initiative (M2GMI) simulation reproduce the magnitude and seasonality of the MODIS AOD composites and enhancements. M2GMI simulations show that the AOD enhancement in the WCB is dominated by sulfate (37%) and organic aerosol (25%), with dust and sea salt each accounting for 15%. MODIS and M2GMI AOD are 60% larger in North Pacific WCBs compared to North Atlantic WCBs and show a strong relationship with anthropogenic pollution. We infer that NH midlatitude cyclones account for 355 Tg yr−1 of sea salt aerosol emissions annually, or 60% of the 30–80°N total. We find that deposition within WCBs is responsible for up to 35% of the total aerosol deposition over the NH ocean basins. Furthermore, the cloudy environment of WCBs leads to efficient secondary sulfate production.
DCGCN: Double-Channel Graph Convolutional Network for Passenger Flow Prediction in Urban Rail Transit

Passenger flow prediction is one of the auxiliary methods of urban rail transit (URT) control, which can predict traffic conditions in advance and guide decision-makers to take measures. With the development of big data and artificial intelligence, the accuracy of passenger flow prediction has been greatly improved. However, there are still some problems not to be solved, such as insufficient relationship extraction of node features and topology structure. So, a model based on the graph convolutional network (GCN) is proposed to mine the connection between node features and the topology structure. Firstly, for station adjacencies in different dimensions, a GCN-based double-channel feature extraction module (DCGCN) is constructed based on parameter sharing and independent parameters to capture the heterogeneity and consistency of two spaces. And the model introduces the attention mechanism for the adaptive fusion of heterogeneous features, long short-term memory (LSTM) is used to capture temporal correlation. To verify the accuracy and rationality of this model, two datasets are introduced to test it, and the results show that DCGCN performs better than other baselines.
Tornado Risk Analysis: Is Dixie Alley an Extension of Tornado Alley?

The term “Tornado Alley” is a gross approximation of the most tornado-prone region in the United States. Depending on calculation methods, Tornado Alley can vary dramatically across the area between the Rocky and Appalachian Mountains. There is some evidence that multiple alleys of peak tornado activity exist around the country, including “Dixie Alley” in the Southeast. Therefore, we assess the spatial tornado risk and seek any regions of elevated tornado risk that are distinctly separate from the traditional Tornado Alley of the Great Plains. Results show there are no tornado risk areas statistically separate from Tornado Alley, but there are large portions of the Southeast that experience more tornadoes than the rest of the country. It appears that Tornado Alley and Dixie Alley are part of a single large region of high tornado risk with a relative minimum near the middle due to the Ozark and Ouachita Mountains. Placement of the maximum tornado density in Mississippi, along with other regions of relative.
Infrared nerve stimulation: modelling of photon transport and heat conduction

Infrared neural stimulation (INS) is a novel technique for stimulating neurons with infrared light, rather than the traditional electrical means. There has been significant discussion in the literature on the mechanisms behind INS, while recent work has shown that infrared light stimulates neurons by causing a reversible change in their membrane capacitance. Nevertheless, the effect of different laser parameters on neuronal responses is still not well understood. To better understand this and to assist in designing light delivery systems, modelling of spatial and temporal characteristics of light delivery during INS has been performed. Monte Carlo modelling of photon transport in tissue allows the spatial characteristics of light to be determined during INS and allows comparisons of varying geometries and fibre designs. Finite element analysis of heat conduction can then be used to reveal the behavior of different pulse durations and the resulting temperature decay. The combination of the two methods allows for further insights into the mechanisms of INS and assists in understanding different mechanisms which promote INS. The model suggests there may be two regimes of INS, namely temperature limited for pulses under 100 μs and temperature gradient limited for longer pulses. this is compatible with previously published data, but requires further experimentation for confirmation. The model also provides a tool for optimising the design of emitters and implants.
The Convergence of the Tangential Scattered Magnetic Field Based on the Vertical Improper Integral

Abstract The tangential scattered magnetic field of a perfectly electric conducting surface is usually expressed as an improper integral. However, the improper integral is divergent. Hence, the field is expressed as a limit of an integral in some references. In this article, the limit of the integral is called a vertical improper integral. A stronger smooth condition of the surface is added to ensure the convergence of the vertical improper integral. The relationship between the vertical improper integral and the Cauchy principle value integral is established. An example shows that the tangential scattered magnetic field is infinite at a first-order smooth point if it is assumed that the surface current density is non-zero at the point. By the boundary condition, it is obtained that the surface current density should be zero at the point. A numerical example is given to demonstrate the effectiveness of the vertical improper integral method. The numerical result also shows that singular integrals need not be calculated when the magnetic field integral equation is computed based on the method of moments.
Analysis of k-Anonymity for Homogeneity Attack

The size of data is increasing exponentially day by day. It is as much important to provide privacy as to provide utility. Objective of privacy preserving data mining is to serve knowledge extraction with out leakage of private sensitive information, k-anonymity approach is a group based anonymization approach devised to achieve this objective. In this paper (α,k)-anonymization is discussed, that is a performance analysis metric, that finds the degree of association of sensitive value with an equivalence class. An algorithm is designed to return α value for each sensitive attribute. This paper represents a complete bipartite graph representation of (α,k)-anonymization, that can also generate accurate α values by implementing the algorithm on the graph. Performance analysis of k-anonymization is discussed with respect to the effects of homogeneity attack based on different parameters.
A Low Complexity Neural Network-Based BP Decoder for Polar Codes

In recent years, neural network-based BP decoders for polar codes have draw much attention, due to their faster convergence and better error-correction performance compared with conventional BP decoders. However, the prior neural BP decoders have high storage cost and training complexity due to massive number of training weights, and the performance improvement is slight. In this work, we first propose a 2D-MSMS algorithm with a shared weight scheme, in which the nodes in the same layer share a weight among different iterations. These learnable weights are optimized by deep learning techniques. Then, we propose a CRC-Aided relaxed 2D-MSMS polar de-coder by introducing relaxation method and CRC-Aided early termination scheme. Simulation results show that our proposed decoder can effectively improve BER performance at medium to high SNR and reduce the amount of training weights by more than 95%.
A Survey on Programmable Logic Controller Vulnerabilities, Attacks, Detections, and Forensics

Programmable Logic Controllers (PLCs), as specialized task-oriented embedded field devices, play a vital role in current industrial control systems (ICSs), which are composed of critical infrastructure. In order to meet increasing demands on cost-effectiveness while improving production efficiency, commercial-off-the-shelf software and hardware, and external networks such as the Internet, are integrated into the PLC-based control systems. However, it also provides opportunities for adversaries to launch malicious, targeted, and sophisticated cyberattacks. To that end, there is an urgent need to summarize ongoing work in PLC-based control systems on vulnerabilities, attacks, and security detection schemes for researchers and practitioners. Although surveys on similar topics exist, they are less involved in three key aspects, as follows: First and foremost, previous work focused more on system-level vulnerability analysis than PLC itself. Subsequently, it was not clear whether their work applied to the current systems or future ones, especially for security detection schemes. Finally, the prior surveys lacked a digital forensic research review of PLC-based control systems, which was significant for security analysis at different stages. As a result, we highlight vulnerability analysis at both a core component level and a system level, as well as attack models against availability, integrity, and confidentiality. Meanwhile, reviews of security detection schemes and digital forensic research for the current PLC-based systems are provided. Finally, we discuss future work for the next-generation systems.
A Miniaturized Dual-Polarized Patch Antenna With Low Cross Polarization

In this paper, a ±45° dual-polarized antenna is proposed, which mainly consists of a main radiator, a parasitic patch, a feed network and a box-type reflector plate. The miniaturization of the antenna is achieved by adding a dielectric between the main radiator and the reflector plate, and the size of the main radiator is $0.257\lambda_{0}\times 0.257\lambda_{0}$. The simulation results show that the antenna has the −14dB bandwidth of 2.42GHz~2.74GHz, the gain of 7.8dBi~8.3dBi, the half-power beamwidth of 67.2°~3.5°, the axial cross-polarization ratio greater than 33dB, and the ~60° cross-polarization ratio greater than 10dB.