Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Botnet DGA Detection: Blending XAI and OSINT for Cyber Threat Intelligence Sharing

We investigated 12 years DNS query logs of our campus network and identified phenomena of malicious botnet domain generation algorithm (DGA) traffic. DGA-based botnets are difficult to detect using cyber threat intelligence (CTI) systems based on blocklists. Artificial intelligence (AI)/machine learning (ML)-based CTI systems are required. This study (1) proposed a model to detect DGA-based traffic based on statistical features with datasets comprising 55 DGA families, (2) discussed how CTI can be expanded with computable CTI paradigm, and (3) described how to improve the explainability of the model outputs by blending explainable AI (XAI) and open-source intelligence (OSINT) for trust problems, an antidote for skepticism to the shared models and preventing automation bias. We define the XAI-OSINT blending as aggregations of OSINT for AI/ML model outcome validation. Experimental results show the effectiveness of our models (96.3% accuracy). Our random forest model provides better robustness against three state-of-the-art DGA adversarial attacks (CharBot, DeepDGA, MaskDGA) compared with character-based deep learning models (Endgame, CMU, NYU, MIT). We demonstrate the sharing mechanism and confirm that the XAI-OSINT blending improves trust for CTI sharing as evidence to validate our proposed computable CTI paradigm to assist security analysts in security operations centers using an automated, explainable OSINT approach (for second opinion). Therefore, the computable CTI reduces manual intervention in critical cybersecurity decision-making.
Intelligent Classification for Emotional Issues by Deep Learning Network on EEG Signal Processing

Identifying the risk of emotional issues is of great significance to the development of adolescents. Electroencephalography (EEG) signals can reflect brain activities, and imply the emotional status, therefore can be used to identify emotional issues. In this study, we designed an experimental paradigm for high school adolescents by displaying emotion-inducing pictures as stimuli and recorded their EEG signals simultaneously. The EEG signals was preprocessed and analyzed. In this paper we applied a convolutional network EEGNet to classify four emotional issues in adolescents: depression symptom, manic symptom, anxiety symptom and the control group. The results showed that the classification accuracy of the four groups can reach 94.24%. In addition, this paper explored using different types of picture stimuli on the classification and reached a result above chance level. This work extended the previous work on the classification of emotional issues to four categories, and achieved a good classification accuracy.
Alzheimer's level classification by 3D PMNet using PET/MRI multi-modal images

The accurate diagnosis of Alzheimer's disease (AD) has an important impact on early treatment. Positron emission tomography (PET) and magnetic resonance imaging (MRI) are popular imaging methods and are used to facilitate the identification and evaluation of AD. In this paper, we proposed a VGG-style 3D convolutional neural network (3D CNN) model, which is named 3D PET-MRI Net (3D PMNet), and it uses DiffGrad optimizer to speed up the convergence of the model and Focalloss function to improve the classification performance of unbalanced data processing. The multi-modal feature information of 3D MRI and PET images can be extracted using the 3D PMNet model, which provides convenience for AD diagnosis. Tenfold cross-validation was performed on the data of each patient in the data set to determine the group classification. The results showed that the proposed method achieves 97.49%, 81.25%, and 76.67% accuracy in the classification tasks of AD: NC, AD: MCI, and NC: MCI, respectively. Our PMNet reached 72.55% accuracy in AD: NC: MCI three group classification, which is significantly better than the other reported network models.
Intelligent Music Style Classification System Based on K-Nearest Neighbor Algorithm and Artificial Neural Network

Nowadays, open network resources allow people to freely search and enjoy all kinds of music products. Music classification plays an important role in music information retrieval. It can quickly find users' favorite music varieties in massive network resources and recommend them to enjoy. The music style classification system selects the most popular pop music, uses Python library for audio processing, and uses GTZAN data set to find out the similarity of different music and classify it based on the K-nearest neighbor algorithm (KNN) and artificial neural network (ANN), so as to form ten different styles. Machine learning and problem identification become the key, which is also the innovation of this project: in addition to the original network database of the system, users can upload their favorite or adapted and original music sound to the system. The system automatically identifies the music style label and can be incorporated into the database to establish their favorite “song list. “Innovation and development is the theme of the times. Technology is blurring the boundary between music consumers and creators. I hope that the development of this system can make more people love music, not only as music consumers, but also music creators who can realize their own innovative ideas. If so, people's creativity will have an exhibition stage combined with entertainment, the innovative spirit will be held high, and the music cultures will prosper and bloom in its various varieties. The display of creative talent and the cultivation of innovative spirit are the ultimate goal of the system.
NARMA-L2 Control of a Nonlinear Half-Car Servo-Hydraulic Vehicle Suspension System

In this paper, the performance of a nonlinear, 4 degrees-of-freedom, servo- hydraulic half-car active vehicle suspension system is compared with that of a passive vehicle suspension system with similar model parameters. The active vehicle suspension system is controlled by an indirect adaptive Neural Network-based Feedback Linearization controller (NARMA-L2). Hydraulic actuator force tracking is guaranteed by an inner Proportional+Integral+Derivative-based force feedback control loop. The output responses of the vehicles are presented and analyzed in the frequency and time domains, in the presence of model uncertainties in the form of variation in vehicle sprung mass loading. The results show that the NARMA-L2-based active vehicle suspension system performed better than the passive vehicle suspension system within the constraints.
Design of IGBT gate drive circuit with SOA consideration

Over-current protection for IGBT (insulated gate bipolar transistor) has created a great amount of attention in recent years. Over-current protection covers both for overload protection and short circuit protection. The protection circuit should be able to distinguish these two fault conditions and its response should be different. However in order to implement proper sensing, comparison and time delay functions, the protection circuit usually becomes complicated where active components and integrated circuits are used. In this paper, a simple IGBT gate drive circuit with only 9 passive and 1 active components was proposed to implement the complete function of over-current protection. The circuit is able to provide a suitable delay time prior to the gate shutdown in order to operate the device closer to but still within the limit of the safe operation area (SOA). The circuit was analysed theoretically and the performance was verified with experimental results.
Extracellular Vesicle-Mediated Delivery of Ultrasmall Superparamagnetic Iron Oxide Nanoparticles to Mice Brain

Extracellular vesicles (EVs) are small lipid membrane-bound vesicles that can pass the blood–brain barrier. Therefore, EVs could be used for the delivery of therapeutics to the brain. Herein, we investigated the biodistribution of intranasal perfusion of ultrasmall superparamagnetic iron oxide (USPIO)-labeled astrocyte-derived EVs (ADEVs) in mice. We used Western blotting, transmission electron microscopy (TEM), and nanoparticle uptake assay to characterize ADEVs. In addition, intranasal perfusion coupled with magnetic resonance imaging (MRI) was employed to determine the distribution of USPIO-labeled ADEVs in mice. Our results showed the uptake of USPIO by mouse astrocytes and ADEVs. In addition, we confirmed the biodistribution of ADEVs in the brain and other internal organs, including the kidneys, liver, and spleen. Our results suggest that USPIO did not affect mouse astrocyte cell survivability and EV release. Therefore, intranasal delivery of therapeutic loaded EVs could be used for the treatment of various brain disorders.
Structural Basis of Human Dimeric α-Amino-β-Carboxymuconate-ε-Semialdehyde Decarboxylase Inhibition With TES-1025

Human α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (ACMSD) stands at a branch point of the de novo NAD+ synthesis pathway and plays an important role in maintaining NAD+ homeostasis. It has been recently identified as a novel therapeutic target for a wide range of diseases, including inflammatory, metabolic disorders, and aging. So far, in absence of potent and selective enzyme inhibitors, only a crystal structure of the complex of human dimeric ACMSD with pseudo-substrate dipicolinic acid has been resolved. In this study, we report the crystal structure of the complex of human dimeric ACMSD with TES-1025, the first nanomolar inhibitor of this target, which shows a binding conformation different from the previously published predicted binding mode obtained by docking experiments. The inhibitor has a K i value of 0.85 ± 0.22 nM and binds in the catalytic site, interacting with the Zn2+ metal ion and with residues belonging to both chains of the dimer. The results provide new structural information about the mechanism of inhibition exerted by a novel class of compounds on the ACMSD enzyme, a novel therapeutic target for liver and kidney diseases.
Seasonal Distributions of Methane in a Populous Urban Coastal Sea Area

Methane (CH4) is an important greenhouse gas, and its concentrations in aquatic areas are heavily influenced by anthropogenic activities, especially human-induced eutrophication, polluted river discharge and wastewater treatment plant (WWTP) effluents. Although coastal areas and estuaries contribute large amounts of global oceanic CH4 emissions, the relative contributions of different sources have not been well determined. The Tamsui River located in northern Taiwan is an urban river flowing through populated cities and thus likely carries large amounts of contaminants, such as nutrients and organic matter to the estuary. In this study, we characterized the spatial distribution and seasonal variations in CH4 in the Tamsui River estuary adjacent marine areas. The sea-to-air CH4 fluxes were also estimated to quantify the strength of the study area as an atmospheric CH4 source. Our results showed that CH4 concentrations in coastal sea areas were influenced by WWTP effluents, sediment and freshwater inputs. Thus, river discharge and nutrient levels as well as strong vertical mixing and disturbances might increase CH4 concentrations and emissions. The seasonal surface CH4 concentrations and sea-to-air CH4 fluxes were 13.7 ± 18.7 nM and 41.7 ± 68.0 μmol m-2 d-1 in autumn; 29.3 ± 19.8 nM and 61.3 ± 44.6 μmol m-2 d-1 in spring; 21.8 ± 13.9 nM and 37.0 ± 26.2 μmol m-2 d-1 in summer; and 27.0 ± 21.4 nM and 85.9 ± 75.4 μmol m-2 d-1 in winter.
Ectopic Expression of Poplar PsnCYCD1;1 Reduces Cell Size and Regulates Flower Organ Development in Nicotiana tabacum

The D-type cyclin (CYCD) gene, as the rate-limiting enzyme in the G1 phase of cell cycle, plays a vital role in the process of plant growth and development. Early studies on plant cyclin mostly focused on herbs, such as Arabidopsis thaliana. The sustainable growth ability of woody plants is a unique characteristic in the study of plant cyclin. Here, the promoter of PsnCYCD1;1 was cloned from poplar by PCR and genetically transformed into tobacco. A strong GUS activity was observed in the areas with vigorous cell division, such as stem tips, lateral buds, and young leaves. The PsnCYCD1;1-GFP fusion expression vector was transformed into tobacco, and the green fluorescence signal was observed in the nucleus. Compared with the control plant, the transgenic tobacco showed significant changes in the flower organs, such as enlargement of sepals, petals, and fruits. Furthermore, the stems of transgenic plants were slightly curved at each stem node, the leaves were curled on the adaxial side, and the fruits were seriously aborted after artificial pollination. Microscopic observation showed that the epidermal cells of petals, leaves, and seed coats of transgenic plants became smaller. The transcriptional levels of endogenous genes, such as NtCYCDs, NtSTM, NtKNAT1, and NtASs, were upregulated by PsnCYCD1;1. Therefore, PsnCYCD1;1 gene played an important role in the regulation of flower organ and stem development, providing new understanding for the functional characterization of CYCD gene and new resources for improving the ornamental value of horticultural plants.
Dual-Branch Convolutional Neural Network Based on Ultrasound Imaging in the Early Prediction of Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Breast Cancer

The early prediction of a patient’s response to neoadjuvant chemotherapy (NAC) in breast cancer treatment is crucial for guiding therapy decisions. We aimed to develop a novel approach, named the dual-branch convolutional neural network (DBNN), based on deep learning that uses ultrasound (US) images for the early prediction of NAC response in patients with locally advanced breast cancer (LABC). This retrospective study included 114 women who were monitored with US during pretreatment (NAC pre) and after one cycle of NAC (NAC1). Pathologic complete response (pCR) was defined as no residual invasive carcinoma in the breast. For predicting pCR, the data were randomly split into a training set and test set (4:1). DBNN with US images was proposed to predict pCR early in breast cancer patients who received NAC. The connection between pretreatment data and data obtained after the first cycle of NAC was considered through the feature sharing of different branches. Moreover, the importance of data in various stages was emphasized by changing the weight of the two paths to classify those with pCR. The optimal model architecture of DBNN was determined by two ablation experiments. The diagnostic performance of DBNN for predicting pCR was compared with that of four methods from the latest research. To further validate the potential of DBNN in the early prediction of NAC response, the data from NAC pre and NAC1 were separately assessed. In the prediction of pCR, the highest diagnostic performance was obtained when combining the US image information of NAC pre and NAC1 (area under the receiver operating characteristic curve (AUC): 0.939; 95% confidence interval (CI): 0.907, 0.972; F1-score: 0.850; overall accuracy: 87.5%; sensitivity: 90.67%; and specificity: 85.67%), and the diagnostic performance with the combined data was superior to the performance when only NAC pre (AUC: 0.730; 95% CI: 0.657, 0.802; F1-score: 0.675; sensitivity: 76.00%; and specificity: 68.38%) or NAC1 (AUC: 0.739; 95% CI: 0.664, 0.813; F1-score: 0.611; sensitivity: 53.33%; and specificity: 86.32%) (p<0.01) was used. As a noninvasive prediction tool, DBNN can achieve outstanding results in the early prediction of NAC response in patients with LABC when combining the US data of NAC pre and NAC1.
Beam Studies with a New Longitudinal Feedback System at the ANKA Storage Ring

With the now fully commissioned longitudinal feedback system at the ANKA Storage Ring in addition to the already operational transverse feedback system the stability throughout the injection process was increased considerably. This opened up the possibility of being ablte to investigate beam dynamics and limitations during injection more systematically. This paper presents the results of these studies, an overview of the limiting parameters, and discusses possible approaches to increase the efficiency of the injection.
Bio-Matrix Pot Addition Enhanced the Vegetation Process of Iron Tailings by Pennisetum giganteum

The barrenness of large mine tailing sand reservoirs increases the risks for landslides and erosion that may be accompanied with transfer of contaminants into the surrounding environment. The tailing sand is poor in nutrients, which effectively complicates the vegetation process. We investigated direct planting of Pennisetum giganteum into tailing sand using two pit planting methods: the plants were transplanted either directly into pits filled with soil or into soil-filled bio-matrix pots made of organic material. After growing P. giganteum in iron tailing sand for 360 days, the dry weight of the plants grown in the bio-matrix pot (T2) was approximately twofold higher than that of the plants grown in soil placed directly into the sand (T1). At 360 days, the organic matter (OM) content in the soil below the pit was the lowest in the not-planted treatment (T0) and the highest in T2, the available N (AN) contents were higher in T1 and T2 than in T0, and the available P and K contents were the highest in T2. At 360 days, the Shannon diversity of the soil microbial communities was higher in T1 and T2 than in T0, and the community compositions were clearly separated from each other. The profiles of predicted C cycle catabolism functions and N fixation-related functions in T1 and T2 at 360 days were different from those in the other communities. The results showed that P. giganteum grew well in the iron tailing sand, especially in the bio-matrix pot treatment, and the increased nutrient contents and changes in microbial communities indicated that using the bio-matrix pot in planting had potential to improve the vegetation process in iron tailing sands effectively.
Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing

A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured environments is a challenge. In this context, we propose a novel tea chrysanthemum – generative adversarial network (TC-GAN) that attempts to deal with this challenge. First, we designed a non-linear mapping network for untangling the features of the underlying code. Then, a customized regularization method was used to provide fine-grained control over the image details. Finally, a gradient diversion design with multi-scale feature extraction capability was adopted to optimize the training process. The proposed TC-GAN was compared with 12 state-of-the-art generative adversarial networks, showing that an optimal average precision (AP) of 90.09% was achieved with the generated images (512 × 512) on the developed TC-YOLO object detection model under the NVIDIA Tesla P100 GPU environment. Moreover, the detection model was deployed into the embedded NVIDIA Jetson TX2 platform with 0.1 s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future.
The Potential of Metabolism-Related Gene OGDHL as a Biomarker for Myocardial Remodeling in Dilated Cardiomyopathy

The development of dilated cardiomyopathy (DCM) is accompanied by a series of metabolic disorders, resulting in myocardial remodeling or exacerbation, while the mechanism remains not completely clear. This study was to find out the key metabolism-related genes involved in the onset of DCM, providing new insight into the pathogenesis of this disease. The datasets of GSE57338, GSE116250, and GSE5406 associated with hearts of patients with DCM were downloaded from the Gene Expression Omnibus database. GSE57338 was analyzed to screen out metabolism-related differentially expressed genes (DEGs), while GSE116250 and GSE5406 were utilized to verify the optimal genes through R software. Support vector machine recursive feature elimination algorithm and least absolute shrinkage and selection operator algorithm were used to determine key genes. Finally, 6 of 39 metabolism-related DEGs were screened out and identified as the optimal genes. After quantitative reverse-transcription polymerase chain reaction (qRT-PCR) validation performed on the samples drawn from the left ventricles of human hearts, it showed that only the expression of oxoglutarate dehydrogenase-like (OGDHL) increased while PLA2G2 decreased significantly in patients with DCM compared with non-failing donors, respectively. Furthermore, the higher OGDHL protein expression, except the change of PLA2G2, was also found in DCM hearts, and its mRNA expression was negatively correlated with myocardial Masson’s scores (r = –0.84, P = 0.009) and left ventricular end-diastolic diameter (LVEDd; r = –0.82, P = 0.014), which might be regulated by miR-3925-5p through further bioinformatics prediction and qRT-PCR verification. The data then suggested that the metabolism-related gene OGDHL was associated with myocardial fibrosis of DCM and probably a biomarker for myocardial remodeling in patients with DCM.
Latent Profile Analysis to Survey Positive Mental Health and Well-Being: A Pilot Investigation Insight Tunisian Facebook Users

Background To examine mental health during COVID-19 peaks, lockdown, and times of curfew, many studies have used the LPA/LCA person-centered approach to uncover and explore unobserved groups. However, the majority of research has focused only on negative psychological concepts to explain mental health. In this paper, we take another perspective to explore mental health. In addition, the study focuses on a period of peak decline in the COVID-19 pandemic. Objective The present paper aim (a) empirically identifies different profiles among a cohort of Facebook users in Tunisia based on positive factors of mental health using a person-centered approach, (b) outline identified profiles across sociodemographic, internet use, and physical activity, and (c) establish predictors of these profiles. Methods Cross-sectional data were collected through an online survey among 950 Facebook users were female (n = 499; 52.53%) and male (n = 451; 47.47) with an average age =31.30 ± 9.42. Subjects filled Arabic version of Satisfaction with Life Scale, Scale of Happiness (SWLS), Gratitude Questionnaire (GQ-6), International Physical Activity Questionnaire (IPAQ), and the Spirituel Well-Being Scale (SWBS). Results The LPA results revealed three clusters. The first cluster (n = 489, 51,47%) contains individuals who have low scores on the positive psychology scales. The second cluster (n = 357, 37,58%) contained individuals with moderate positive psychology scores. However, a third cluster (n = 104, 10,95%) had high positive psychology scores. The selected variables in the model were put to a comparison test to ensure that the classification solution was adequate. Subsequently, the clusters were compared for the variables of socio-demographics, use of the internet for entertainment and physical activity, the results showed significant differences for gender (low mental well-being for the female gender), socio-economic level (low for the low-income class), and physical activity (low mental well-being for the non-exerciser). However, no significant differences were found for the variables age, location, and use of the Internet for entertainment. Conclusion Our results complement person-centered studies (LPA/LCA) related to the COVID-19 pandemic and can serve researchers and mental health practitioners in both diagnostic and intervention phases for the public. In addition, the GQ6 scale is a valid and reliable tool that can be administered to measure gratitude for culturally similar populations.