Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Transmission Efficiency of MIMO System in Presence of Noise Complex

Reception of signals in MIMO information transmission systems is carried out using spatial encoding and decoding algorithms. The algorithms are implemented taking into account the spatial properties of the signal transmission medium. The properties of the medium are set by a matrix of channel coefficients that determine the amplitude and phase changes of the signal in the information transmission channel. The sources of interference include the phase noise of the signal generator of the transmitter and the receiver's heterodyne. The active elements of the receiving path are affected by low-frequency flicker noise. The paper analyzes the influence of a complex of phase, thermal and flicker noise on the outage probability of the MIMO information transmission system. The model of fractal Brownian motion is chosen as the phase noise model. The required level of both low frequency noises is determined by the obtained results of investigation. Besides, we can develop recommendations for building a MIMO information transmission system.
Structure and Functions of HMGB2 Protein

High-Mobility Group (HMG) chromosomal proteins are the most numerous nuclear non-histone proteins. HMGB domain proteins are the most abundant and well-studied HMG proteins. They are involved in variety of biological processes. HMGB1 and HMGB2 were the first members of HMGB-family to be discovered and are found in all studied eukaryotes. Despite the high degree of homology, HMGB1 and HMGB2 proteins differ from each other both in structure and functions. In contrast to HMGB2, there is a large pool of works devoted to the HMGB1 protein whose structure–function properties have been described in detail in our previous review in 2020. In this review, we attempted to bring together diverse data about the structure and functions of the HMGB2 protein. The review also describes post-translational modifications of the HMGB2 protein and its role in the development of a number of diseases. Particular attention is paid to its interaction with various targets, including DNA and protein partners. The influence of the level of HMGB2 expression on various processes associated with cell differentiation and aging and its ability to mediate the differentiation of embryonic and adult stem cells are also discussed.
A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

Monitoring nutritional values in food can help an individual in planning a healthy diet. In addition, regular dietary assessment can improve and maintain the physical and mental health of individuals. Recent advancement in computer vision using Deep Learning has enabled researchers to develop various techniques for automatic food nutrition estimation frameworks. Researchers have also contributed to prepare large food image datasets consisting of various food classes for this purpose. However, automatic estimation of nutritional values from food images still remains a challenging task. This review paper critically analyzes and summarizes existing methodologies and datasets used for automated estimation of nutritional value from food images. We first define the taxonomies in order to categorize the existing research works. Then, we study different methods to detect the food value estimation from food images in those categories. We have critically analyzed existing methods and compared the performance of various approaches for estimating food value using conventional performance metrics such as Accuracy, Error Rate, Intersection over Union (IoU), Sensitivity, Specificity, Precision, etc. In particular, we emphasize the current trends and techniques of Deep Learning-based approaches for food value estimation from images. Moreover, we have identified the ongoing challenges associated with automated food estimation systems and outlined the potential future directions. This review can immensely benefit researchers and practitioners, including computer scientists, health practitioners, and nutritionists.
Design and characterization of Timolol maleate and Travoprost hydrogel drug delivery system

Hydrogels are comprised of a cross-linked network of polymers. Water penetrates these networks, resulting in swelling and giving the hydrogel a soft and rubbery consistency, thereby maintaining the integrity of the membrane. Because of the drawback of conventional therapy for ocular delivery, a hydrogel membrane containing a combination of timolol maleate and Travoprost were formulated for the treatment of glaucoma. In the present investigation, hydrogel membranes were prepared using polymers like gelatin, PVA and chitosan, which were cross‐ linked using physical and/or chemical methods. The cross‐linking of the membranes was confirmed by Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD) and Differential scanning calorimetry (DSC) studies. From the scanning electron microscopy (SEM) of the membranes, it appeared homogenous and showed no separation. The pH of the membranes ranged from 7.21‐7.4. The hydrogels showed a considerably good swelling ratio ranging from 91.66‐372.72%. The drug content ranged from 82.78‐95.62%. The in vitro drug release study indicated that there was a slow and sustained release of the drug from the membranes that were sufficiently cross‐linked and followed zero order release. The Intraocular pressure (IOP) lowering activity of the prepared formulation was compared with the marketed formulation, and it was found that the IOP lowering action was sustained for a long period of time. Stability studies proved that the formulations could be stable when stored at room temperature. Results of the study indicate that it is possible to develop a safe and physiologically effective hydrogel that is patient compliant.
Sliding Mode Control for Quadrotor-Slung Load Transportation System with State Constraints*

Quadrotor-slung load transportation is an important way to transport goods, but the swing of the load bring a great deal of challenges to the stability of the system. In this paper, a barrier Lyapunov function based sliding mode controller is proposed to limit the load swing, which also satisfies the speed limitation of the quadrotor. An inner-outer loop control strategy is proposed, where the outer one is the virtual position controller which guarantees the stability of the quadrotor while eliminating the load swing, and the inner one is the attitude controller which driver the actual force to the position controller to ensure the effectiveness of the system. Specifically, the dynamics of quadrotor-slung load transportation system is reconstructed as a cascade form. Then, a barrier Lyapunov function based sliding mode controller is designed to ensure the trajectory tracking errors uniformly bounded with state constraints valid. Finally, comparative simulation results illustrate the effectiveness of the proposed method.
Towards Forecasting Engagement in Children with Autism Spectrum Disorder using Social Robots and Deep Learning

The personalization of therapy for children with Autism Spectrum Disorder (ASD) has been found to be crucial in comparison to a universal approach. This personalization in therapy demands the ability to adapt to the individual’s needs and engagement levels to avoid disinterest or meltdowns. This paper proposes the first step towards forecasting engagement of children with ASD during therapy sessions using Blood Volume Pulse (BVP). The BVP data is collected from an interactive session between two children with ASD in the presence of a NAO robot, and the forecast is made using a Deep Learning architecture combining Convolutional Neural Networks (CNNs) and Long-short term Memory (LSTM). Out of the three networks tested: LSTM, CNN and CNN+LSTM, the latter was found to outperform the others and gave a coefficient of determination of 0.955. The forecast was done using less than 3 minutes of prior BVP data to forecast 3 minutes into the future time steps.
Trusted Deep Neural Execution—A Survey

The growing use of deep neural networks (DNNs) in various applications has raised concerns about the security and privacy of model parameters and runtime execution. To address these concerns, researchers have proposed using trusted execution environments (TEEs) to build trustworthy neural network execution. This paper comprehensively surveys the literature on trusted neural networks, viz., answering how to efficiently execute neural models inside trusted enclaves. We review the various TEE architectures and techniques employed to achieve secure neural network execution and provide a classification of existing work. Additionally, we discuss the challenges and present a few open issues. We intend that this review will assist researchers and practitioners in understanding the state-of-the-art and identifying research problems.
NIR camera and spectrograph SWIMS for TAO 6.5m telescope: array control system and its performance

SWIMS (Simultaneous-color Wide-field Infrared Multi-object Spectrograph) is a near-infrared imager and multi-object spectrograph as one of the first generation instruments for the University of Tokyo Atacama Observatory (TAO) 6.5m telescope. In this paper, we describe an array control system of SWIMS and results of detector noise performance evaluation. SWIMS incorporates four (and eight in future) HAWAII-2RG focal plane arrays for detectors, each driven by readout electronics components: a SIDECAR ASIC and a JADE2 Card. The readout components are controlled by a HAWAII-2RG Testing Software running on a virtual Windows machine on a Linux PC called array control PC. All of those array control PCs are then supervised by a SWIMS control PC. We have developed an "array control software system", which runs on the array control PC to control the HAWAII-2RG Testing Software, and consists of a socket client and a dedicated server called device manager. The client runs on the SWIMS control PC, and the device manager runs on the array control PC. An exposure command, issued by the client on the SWIMS control PC, is sent to the multiple device managers on the array control PCs, and then multiple HAWAII-2RGs are driven simultaneously. Using this system, we evaluate readout noise performances of the detectors, both in a test dewar and in a SWIMS main dewar. In the test dewar, we confirm the readout noise to be 4.3 e- r.m.s. by 32 times multiple sampling when we operate only a single HAWAII-2RG, whereas in the case of simultaneous driving of two HAWAII-2RGs, we still obtain sufficiently low readout noise of 10 e- r.m.s. In the SWIMS main dewar, although there are some differences between the detectors, the readout noise is measured to be 4:1−4:6 e- r.m.s. with simultaneous driving by 64 times multiple sampling, which meets the requirement for background-limited observations in J band of 14 e- r.m.s.
Efficient Artificial Neural Network for Smart Grid Stability Prediction

According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems.
Digital Neural Network Model of the Detector of Anomalous Changes in the Signals Reflecting the Operation of Automated Process Control Systems of Enterprises under the Influence of Cyber Attacks

The article presents a model of the system for detecting anomalous changes in the signals that reflect the operation of automated process control systems (APCS) of enterprises under the influence of cyber attacks. To solve the anomaly detection problem, it is proposed to apply the approach based on the usage of digital signal processing, artificial neural networks of autoencoders, self-organizing artificial neural networks, DBSCAN and K-MEANS clustering methods. The application of these methods makes it possible to develop the hybrid digital model of the system for early detection of anomalies in the monitored APCS signals in order to minimize the damage that enterprises may suffer as a result of cyber incidents.
Tuning of the Electrostatic Potentials on the Surface of the Sulfur Atom in Organic Molecules: Theoretical Design and Experimental Assessment

Noncovalent sulfur interactions are ubiquitous and play important roles in medicinal chemistry and organic optoelectronic materials. Quantum chemical calculations predicted that the electrostatic potentials on the surface of the sulfur atom in organic molecules could be tuned through the through-space effects of suitable substituents. This makes it possible to design different types of noncovalent sulfur interactions. The theoretical design was further confirmed by X-ray crystallographic experiments. The sulfur atom acts as the halogen atom acceptor to form the halogen bond in the cocrystal between 2,5-bis(2-pyridyl)-1,3,4-thiadiazole and 1,4-diiodotetrafluorobenzene, whereas it acts as the chalcogen atom donor to form the chalcogen bond in the cocrystal between 2,5-bis(3-pyridyl)-1,3,4-thiadiazole and 1,3,5-trifluoro-2,4,6-triiodobenzene.
Application of guar (Cyamopsis tetragonoloba L.) gum in food technologies: A review of properties and mechanisms of action

Abstract With the world continuing to push toward modernization and the consumption of processed foods growing at an exponential rate, the demand for texturizing agents and natural additives has also risen as a result. It has become increasingly common to use thickening agents in food products to modify their rheological and textural properties and enhance their quality characteristics. They can be divided into (1) animal derived (chitosan and isinglass), (2) fermentation produced (xanthan and curdlan), (3) plant fragments (pectin and cellulose), (4) seaweed extracts (agar and alginate), and (5) seed flours (guar gum and locust bean gum). The primary functions of these materials are to improve moisture binding capacity, modify structural properties, and alter flow behavior. In addition, some have another responsibility in the food sector, such as the main ingredient in the delivery systems (encapsulation) and nanocomposites. A galactomannan polysaccharide extracted from guar beans (Cyamopsis tetragonolobus), known as guar gum (GG), is one of them, which has a wide range of utilities and possesses popularity among scientists and consumers. In the world of modernization, GG has found its way into numerous industries for use in food, cosmetics, pharmaceuticals, textiles, and explosives. Due to its ability to form hydrogen bonds with water molecules, it imparts significant thickening, gelling, and binding properties to the solution as well as increases its viscosity. Therefore, this study is aimed to investigate the characteristics, mechanisms, and applications of GG in different food technologies.
A Low-Power V-Band Radar Transceiver Front-End Chip Using 1.5 V Supply in 130-nm SiGe BiCMOS

Energy-efficient, low-voltage, and low-power millimeter-wave (mm-wave) radars are gaining increasing attention for battery-powered commercial applications. In this article, the design of a low-power <inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>-band radar sensor based on a transceiver (TRX) front-end chip using 1.5 V supply in an advanced SiGe BiCMOS technology with 300 GHz <inline-formula> <tex-math notation="LaTeX">${f}_{T}$ </tex-math></inline-formula> and 500 GHz <inline-formula> <tex-math notation="LaTeX">${f}_{max}$ </tex-math></inline-formula> is presented. The monostatic front-end chip utilizes low-voltage low-power circuit-level design techniques to achieve measured 9-dBm transmitter (TX) output power and 27-dB receiver (RX) gain with a simulated 3.8-dB noise figure (NF) consuming a total of only 72 mW in continuous mode. The TRX chip is used to build a radar sensor, which is experimentally verified in an anechoic chamber. The low-power sensor achieves a 46-dB dynamic range and a ranging precision better than <inline-formula> <tex-math notation="LaTeX">$3.4 \mu \text{m}$ </tex-math></inline-formula> measured with a static target at 1 m. Phase measurements using the low-power radar in the continuous-wave (CW) mode demonstrate that submillimeter movements can be tracked, and notably main vital parameters of a human can be determined accurately. Experimental results show that the performance of the proposed low-power TRX front-end chip is very competitive with designs in modern CMOS technologies.
Machine Learning Approach to Predict Liver Disease Effectively

The liver is a crucial organ that executes more than 500 operations in the body. Early diagnosis of any disturbance of the liver may prevent unintended consequences. Due to the surge of machine learning in recent years, researchers have explicitly employed this to diagnose diverse diseases. The Indian Liver Patient Dataset(ILPD) has been adopted for the prediction, with the motivation of improving the existing model's performance. To achieve that, we use different preprocessing approaches on the dataset, such as missing value imputation, encoding categorical data, balancing the imbalanced dataset, scaling features, and dimensionality reduction. The main goal of this research is to highlight the preprocessing techniques on the aforementioned dataset. When utilized in tandem with the Support Vector Machine (SVM) and Extra Trees Classifier (ETC) in the model, Our K Nearest Neighbor (KNN) classifier outperformed with 96.52% accuracy.
Prototype and Context-Enhanced Learning for Unsupervised Domain Adaptation Semantic Segmentation of Remote Sensing Images

In unsupervised domain adaptation (UDA) of remote sensing images (RSIs), the huge interdomain discrepancies and intradomain variances lead to complicated class-level relations. Specifically, the instances of the same class differ greatly, while instances of different classes are similar, whether across different RSIs domains or within the same RSIs domain. However, existing methods cannot fully consider these problems, limiting the performance of UDA semantic segmentation of RSIs. To this end, this article proposes a novel cross-domain multiprototypes learning method, the core idea of which is to abstract the cross-domain and intradomain class-level relations into multiple prototypes. Specifically, the multiple prototypes belonging to different classes can detailedly describe complex interclass relations, and the multiple prototypes within the same class can better model rich intraclass relations. Furthermore, the source and target samples are jointly used for prototypes calculation, to fully fuse the feature information of different RSIs. In a nutshell, utilizing the samples from different RSIs domains to learn multiple prototypes for each class can achieve better domain alignment at the class level. In addition, considering that RSIs simultaneously contain large targets with wide coverage and important small targets, two masked consistency learning strategies are designed to better explore the contextual structure of target RSIs and improve the quality of pseudo labels for prototype updating. The global consistency strategy can strengthen the utilization of global context relations, while the local consistency strategy can further improve the learning of local context details. Therefore, the proposed method is actually a prototype and context-enhanced learning method for UDA semantic segmentation of RSIs. Extensive experiments demonstrate that the proposed method can achieve better performance than existing state-of-the-art UDA methods.
North Atlantic subtropical mode water formation controlled by Gulf Stream fronts

ABSTRACT The North Atlantic Ocean hosts the largest volume of global subtropical mode waters (STMWs) in the world, which serve as heat, carbon and oxygen silos in the ocean interior. STMWs are formed in the Gulf Stream region where thermal fronts are pervasive and result in feedback with the atmosphere. However, their roles in STMW formation have been overlooked. Using eddy-resolving global climate simulations, we find that suppressing local frontal-scale ocean-to-atmosphere (FOA) feedback leads to STMW formation being reduced almost by half. This is because FOA feedback enlarges STMW outcropping, attributable to the mixed layer deepening associated with cumulative excessive latent heat loss due to higher wind speeds and greater air-sea humidity contrast driven by the Gulf Stream fronts. Such enhanced heat loss overshadows the stronger restratification induced by vertical eddies and turbulent heat transport, making STMW colder and heavier. With more realistic representation of FOA feedback, the eddy-present/rich coupled global climate models reproduce the observed STMWs much better than the eddy-free ones. Such improvement in STMW production cannot be achieved, even with the oceanic resolution solely refined but without coupling to the overlying atmosphere in oceanic general circulation models. Our findings highlight the need to resolve FOA feedback to ameliorate the common severe underestimation of STMW and associated heat and carbon uptakes in earth system models.
Stochastic analysis of covariance when the error distribution is long-tailed symmetric

ABSTRACT In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.