Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Based Event-Triggered Adaptive Tracking Control for Discrete-Time Nonlinear Systems Under DoS Attacks

The manuscript highlights on a model-based event-triggered (ET) adaptive tracking control problem for uncertain discrete-time (DT) non-linear systems when denial of service (DoS) attacks exist in the communication network. To deal with this matter, this work firstly suggests a new and original dynamic ET mechanism to control the frequency of data transmission. Subsequently, using the approximation ability of highorder nonlinear neural network, a switching estimation model is constructed to resist the adverse effects of signal loss caused by DoS attacks. Then, a model-based neural network controller is derived. Through iterative techniques and Lyapunov’s theorem, the ultimately uniformly boundedness of the estimation error of the estimation model and the stability of the closed-loop system will be strongly demonstrated. The usefulness and effectiveness of the suggested approach are confirmed by a simulation study.
Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets

Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited from the proprietary interpretation of the dataset. Addressing comparability and reproducibility issues, we introduce Payload-Byte, an open-source tool for extracting and labeling network packets in this work. Payload-Byte utilizes metadata information and labels raw traffic captures of modern intrusion detection datasets in a generalized manner. Moreover, we transformed the labeled data into a byte-wise feature vector that can be utilized for training machine learning models. The whole cycle of processing and labeling is explicitly stated in this work. Furthermore, source code and processed data are made publicly available so that it may act as a standardized baseline for future research work. Lastly, we present a brief comparative analysis of machine learning models trained on packet-based and flow-based data.
MOSs-String-Triggered Silicon-Controlled Rectifier (MTSCR) ESD Protection Device for 1.8 V Application

In this work, a new low voltage-triggered silicon-controlled rectifier named MTSCR is realized in a 65 nm CMOS process for low voltage-integrated circuits electrostatic discharge (ESD) protections. The MTSCR incorporates an external NMOSs-string, which drives the internal NMOS (INMOS) of MTSCR to turn on, and then the INMOS drive SCR structure to turn on. Compared with the existing low trigger voltage (Vt1) ESD component named diodes-string-triggered SCR (DTSCR), the MTSCR can realize the same low Vt1 characteristic but less area penalty of ~44.3% reduction. The results of the transmission line pulsing (TLP) measurement shows that the MTSCR possesses above 2.42 V holding voltage (Vh) and a low Vt1 of ~5.03 V, making it very suitable for the ESD protections for 1.8 V input/output (I/O) ports in CMOS technologies.
A Lightweight Gesture Recognition Method on Ultralow-Power Nano-UAVs

Nano-scale unmanned aerial vehicles (UAVs) are a kind of aircraft with extremely small size (sub ten centimeters) and weight (a few tens of grams). It is challenging for resource constrained nano-UAVs to be equipped with intelligent human-drone interaction(HDI) functions such as gesture recognition based on artificial intelligence methods. Also, nano-UAVs can usually only carry low resolution gray-scale cameras due to the constrained onboard resource, which raise the difficulty of gesture recognition. This work attacks a complex task to enable the nano-drones with function of HDI ability. A lightweight deep learning method is proposed to realize static gesture recognition on resources severely limited nano-drones. This paper presents a vertically complete approach starting from the convolutional neural network (CNN) model design, model training, dataset collection and dataset augmentation down to 16-bit quantization, and onboard deployment. The proposed method achieves a recognition accuracy of more than 85% while meeting the real-time requirements. Even if the drones work in the energy-saving mode, the method can also achieve a speed of more than 6FPS. The data collection, augmentation and model training strategies for nano-drones improved the generalization and robustness of the model. This work also provides a solution for other similar human-drone interaction tasks on nano-drones.
Rosiglitazone Mitigates Dexamethasone-Induced Depression in Mice via Modulating Brain Glucose Metabolism and AMPK/mTOR Signaling Pathway

Major depressive disorder (MDD) is a common, complex disease with poorly understood pathogenesis. Disruption of glucose metabolism is implicated in the pathogenesis of depression. AMP-activated protein kinase (AMPK) has been shown to regulate the activity of several kinases, including pAKT, p38MAPK, and mTOR, which are important signaling pathways in the treatment of depression. This study tested the hypothesis that rosiglitazone (RGZ) has an antidepressant impact on dexamethasone (DEXA)-induced depression by analyzing the function of the pAKT/p38MAPK/mTOR pathway and NGF through regulation of AMPK. MDD-like pathology was induced by subcutaneous administration of DEXA (20 mg/kg) for 21 days in all groups except in the normal control group, which received saline. To investigate the possible mechanism of RGZ, the protein expression of pAMPK, pAKT, p38MAPK, and 4EBP1 as well as the levels of hexokinase, pyruvate kinase, and NGF were assessed in prefrontal cortex and hippocampal samples. The activities of pAMPK and NGF increased after treatment with RGZ. The administration of RGZ also decreased the activity of mTOR as well as downregulating the downstream signaling pathways pAKT, p38MAPK, and 4EBP1. Here, we show that RGZ exerts a potent inhibitory effect on the pAKT/p38MAPK/mTOR/4EBP1 pathway and causes activation of NGF in brain cells. This study has provided sufficient evidence of the potential for RGZ to ameliorate DEXA-induced depression. A new insight has been introduced into the critical role of NGF activation in brain cells in depression. These results suggest that RGZ is a promising antidepressant for the treatment of MDD.
Editorial: Specialty grand challenge: Structure, spectroscopy, and imaging

In the interdisciplinary field of chemical biology, different scientists, generally chemists and biologists, undertake their best efforts to develop advanced methodologies and innovative approaches to address important scientific goals and societal demands. Furthermore, confronting the complexity of problems to be solved and sophisticated systems to be discerned, the partnership of diverse other specialists is demanded, including physicists, spectroscopists, pharmacologists, and physicians. Collectively, their complemented work brings together several areas of research, experimental as well as theoretical, to accomplish higher levels of knowledge about the structural and functional characterization of biological systems. A profound understanding of life and its wide-ranging processes are their final aims. Recently, AlphaFold, a revolutionary artificial intelligence (AI) network, predicted the structures of more than 200 million proteins in a universe of 1 million species, reaching to determine the protein structures of nearly every organism with known protein sequence data. It made it possible to understand the relationships between protein functions and its 3D structures, and the results are now available to scientists in a database (Callaway, 2022). These data can certainly encourage numerous types of studies in the future. A significant example of the structural feature’s importance in basic and applied investigations is the development of messenger RNA (mRNA)-based therapeutics (Dammes and Peer, 2020). Since its discovery in the 1960s, improvements in its engineering to express therapeutic proteins or manipulate specific genes’ expression have enabled a broad range of applications in intractable diseases, such as severe infections, varied forms of cancer, and genetic diseases, besides new vaccines, and accelerate its clinical translation (Kim, 2022; Qin, Tang, Chen et al., 2022). Because of the unfavorable characteristics of mRNA’s, such as large size, instability, immunogenicity, sensitivity to enzymatic degradation by RNases, and limited cellular uptake, many pivotal issues had to be solved during the development of mRNA-based therapeutics. Additionally, a variety of delivery strategies, including nanolipids, exosomes, or polymeric micelles as carriers, was crucial for their wide applicability (Uchida et al., 2020). Moreover, investigations in this chemico–biological field frequently lead to the conception, design, and provision of new molecules capable of interacting efficiently with selected biomolecules, and consequently causing remarkable modifications in their functional behavior. Results can establish new parameters and lead to new drugs that are more efficient, safe, and selective in facing illnesses and syndromes. Significant examples are found in the literature, illustrating important and innovative chemical–biological studies. They include small peptides and large protein effects in Alzheimer’s disease (Picone et al., 2022), emergence of bimolecular condensates as attractive targets for drug discovery (Mitrea OPEN ACCESS
Multi-channel terahertz wavelength division demultiplexer with defects-coupled photonic crystal waveguide

Abstract Terahertz (THz) wavelength division demultiplexer based on a compact defects-coupled photonic crystal waveguide is proposed and demonstrated numerically. This device consists of an input waveguide that perpendicularly coupled with a series of defects cavities, each of which captures the resonance frequency from the input waveguide. Coupled-mode theory and finite element method are used to analyze the transmission properties of the structure. It is found that the transmission wavelength centered around 1 THz can be adjusted by changing the geometrical parameters of defects cavities, which equals to THz waves generated by optical methods such as difference frequency generation and optical rectification. Applications in this frequency range are urgently needed. Furthermore, the highest transmission efficiency of 0.94 can be achieved when a perfect wavelength-selective mirror is set in the output waveguide.
Decoupling the effects of nutrition, age, and behavioral caste on honey bee physiology, immunity, and colony health

Nutritional stress, especially a dearth of pollen, has been linked to honey bee colony losses. Colony-level experiments are critical for understanding the mechanisms by which nutritional stress affects individual honey bee physiology and pushes honey bee colonies to collapse. In this study, we investigated the impact of pollen restriction on key markers of honey bee physiology, main elements of the immune system, and predominant honey bee viruses. To achieve this objective, we uncoupled the effects of behavior, age, and nutritional conditions using a new colony establishment technique designed to control size, demography, and genetic background. Our results showed that the expression of storage proteins, including vitellogenin (vg) and royal jelly major protein 1 (mrjp1), were significantly associated with nursing, pollen ingestion, and older age. On the other hand, genes involved in hormonal regulation including insulin-like peptides (ilp1 and ilp2) and methyl farnesoate epoxidase (mfe), exhibited higher expression levels in young foragers from colonies not experiencing pollen restriction. In contrast, pollen restriction induced higher levels of insulin-like peptides in old nurses. On the other hand, we found a strong effect of behavior on the expression of all immune genes, with higher expression levels in foragers. In contrast, the effects of nutrition and age were significant only the expression of the regulatory gene dorsal. We also found multiple interactions of the experimental variables on viral titers, including higher Deformed wing virus (DWV) titers associated with foraging and age-related decline. In addition, nutrition significantly affected DWV titers in young nurses, with higher titers induced by pollen ingestion. In contrast, higher levels of Black queen cell virus (BQCV) were associated with pollen restriction. Finally, correlation, PCA, and NMDS analyses proved that behavior had had the strongest effect on gene expression and viral titers, followed by age and nutrition. These analyses also support multiple interactions among genes and virus analyzed, including negative correlations between the expression of genes encoding storage proteins associated with pollen ingestion and nursing (vg and mrjp1) with the expression of immune genes and DWV titers. Our results provide new insights into the proximal mechanisms by which nutritional stress is associated with changes in honey bee physiology, immunity, and viral titers.
Classification of Cardiac Abnormality using PPG Fiducial Parameters and Probabilistic Neural Network

In the current era among all the untimed or premature root of mortality throughout the world is cardiovascular disease or abnormalities (CVD). For an effective prevention to save the human life is its early-stage detection is much more important. The medical professionals consider the predictor of these cardiac abnormalities by considering the physiological biomedical signals, not only Electrocardiogram (ECG) but also Phonocardiogram (PCG) and photoplethysmogram (PPG). To monitor these anomalies typically ECG is used to visualize the electrical activity of the heart, which is quite important but tedious for continuous and too lengthy time monitoring. In contrast, optical PPG has gained the popularity because of its less-cost, wireless and very tiny handy size. The cardiac abnormalities such as Hypertension, Heart failure, Ischemia, Myocardial infarction, Chronic obstructive pulmonary disease (COPD) and many more is reflected in a PPG waveform as they disrupt volumetric blood flow rate in the body. Here our work proposes an automatic method for recognizing these abnormalities in PPG signal only without the need for ECG. The proposed method suggests that a probabilistic neural network is trained with fiducial parameters of large number of PPG samples with its respective classes as cardiac abnormalities. The trained PNN is then utilized to predict the class of unknown PPG samples for quick diagnosis. The accuracy of prediction is tested as more than 95%.
Algorithm for Retinal Vessel Segmentation based on Axial Transformer and Convolutional Neural Network

The analysis of retinal vessel images plays a crucial role in the early diagnosis of various diseases. Existing retinal vascular segmentation algorithms rely only on convolutional neural networks (CNN) to carry out feature extraction. However, due to the local nature of the convolutional operation, the CNN-based algorithms are difficult to extract the global features of the picture and are insensitive to the position information of each feature, which leads to poor connectivity among the segmented retinal vessels and insufficient segmentation of fine vessels. Therefore, we propose a retinal vessel segmentation algorithm based on an axial transformer and CNN. In this algorithm, the axial transformer is used to encode the position of each feature of the image and extract their global features, and the CNN is used to extract the local features of the image. The experimental results on the CHASE DB1 dataset indicate that the proposed algorithm can effectively segment the fine vessels in retinal images and significantly improve the segmentation accuracy of retinal vessels.
Dihydrotanshinone I inhibits hepatocellular carcinoma cells proliferation through DNA damage and EGFR pathway

Background The incidence and mortality of hepatocellular carcinoma (HCC) are globally on the rise. Dihydrotanshinone I, a natural product isolated from Salvia miltiorrhiza Bunge, has attracted extensive attention in recent years for its anti-tumour proliferation efficiency. Methods Cell proliferations in hepatoma cells (Huh-7 and HepG2) were evaluated by MTT and colony formation assays. Immunofluorescence (IF) of 53BP1 and flow cytometry analysis were performed to detect DNA damage and cell apoptosis. Furthermore, network pharmacological analysis was applied to explore the potential therapeutic targets and pathway of dihydrotanshinone I. Results The results showed that dihydrotanshinone I effectively inhibited the proliferation of Huh-7 and HepG2 cells. Moreover, dihydrotanshinone I dose-dependently induced DNA-damage and apoptosis in vitro. Network pharmacological analysis and molecular simulation results indicated that EGFR might be a potential therapeutic target of dihydrotanshinone I in HCC. Collectively, our findings suggested that dihydrotanshinone I is a novel candidate therapeutic agent for HCC treatment.
Discrete wave number representation of elastic wave fields in three-space dimensions

We present the generalization to three dimensions of the discrete wave number representation method of Bouchon and Aki (1977). The method is developed to study the near field of a three-dimensional seismic source embedded in a layered medium. The elastic wave fields are represented by a superposition of plane waves propagating in discrete directions. The discretization is exact and results from a periodic two-dimensional arrangement of sources. The accuracy of the method is checked, in the case of a rectangular dislocation source radiating in an infinite medium, by comparing the results obtained with Madariaga's (1978) exact solution. Examples of the calculation of strong ground motion produced by a thrust fault and a strike slip fault are presented.
Nested DWT–Based CNN Architecture for Monocular Depth Estimation

Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth. Depth prediction from 2D images poses a problem that is both ill–posed and non–linear. Such networks are computationally and time–wise expensive as they have dense configurations. Further, the network performance depends on the trained model configuration, the loss functions used, and the dataset applied for training. We propose a moderately dense encoder–decoder network based on discrete wavelet decomposition and trainable coefficients (LL, LH, HL, HH). Our Nested Wavelet–Net (NDWTN) preserves the high–frequency information that is otherwise lost during the downsampling process in the encoder. Furthermore, we study the effect of activation functions, batch normalization, convolution layers, skip, etc., in our models. The network is trained with NYU datasets. Our network trains faster with good results.
New Application of Polyoxometalate Salts as Cathode Materials in Single Chamber MFC Using Wastewater for Bioenergy Production

Microbial fuel cells (MFCs) are a promising technology that can be applied in a bifunctional process in which wastewater treatment is used for renewable electric power generation. In this study, novel transition metal-modified Keggin-type lacunar polyoxometalate salts (L-POMs) Cs5PMo11M(H2O)O39 (M = Fe, Co), were synthesized and characterized by X-ray diffraction, SEM, EDX, IR, TGA/DSC, and UV-Vis/DSR spectroscopies to be tested, for the first time, as a cathode component in wastewater-fed air chamber MFCs. Both materials were tested in the presence and absence of light to evaluate their photocatalytic behavior. The best performance in terms of electricity production was obtained for the MFC containing the Co-modified POM-based cathode, which showed a maximum power of 418.15 mW/m2 equivalent to 331 mW per cubic meter of treated wastewater, and a maximum COD removal percentage of 97% after 96 h of MFC operation. Co- and Fe-modified POMs had outstanding optical behavior with lower energy gap values, 1.71 and 2.68 eV, respectively. The newly developed materials can be considered as promising alternative cathode catalysts in a new generation of MFC devices integrating full carbon removal from wastewater and a fast reduction of oxygen.
Surface gradient integrated profiler for x-ray and EUV optics: self calibration method of measured position for an off-axis parabolic mirror (f=150mm) measurement

A new ultra-precision profiler has been developed in order to measure such as asymmetric and aspheric profiles. In the present study, the normal vectors at each points on the surface are determined by the reflected light beam goes back exactly on the same path as the incident beam. The surface gradients at each point are calculated from the normal vector and the surface profile is obtained by integrating the gradient. The measuring instrument was designed according to the above principle of the measuring method. In the design, four ultra-precision goniometers were applied to the adjustment of the light axis for the normal vector measurement. In the measuring instrument, the angle-positioning resolution and accuracy of each goniometer are respectively 1.8x10-8rad and 2x10-7rad. A coaxial with an off-axis parabolic mirror has been developing for applying as an optical cavity. The most important engineering technique is to measure the profile of the reflective surface with sub nanometer. The present measuring instrument is evaluating to have capability to the surface measuring accuracy with nanometer for such parabolic mirror profile measurement. A coaxial off-axis parabolic mirror with 150 mm focal length has been polished. The outside and inside diameter of the mirror is 360 mm and 258 mm respectively. The thickness of the coaxial direction is 50 mm. The focal point is located on the center of the coaxial and the center of the coaxial direction of the mirror. The profile measurement such a mirror has been demonstrated. Specially, self calibration method for increasing the measured position accuracy from measured data is discussed.