Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Adversarial Attack Against Explainable Deep Classification Models Based on Adversarial Images With Different Patch Sizes and Perturbation Ratios

In recent years, adversarial attack methods have been deceived rather easily on deep neural networks (DNNs). In practice, adversarial patches cause misclassification that can be extremely effective. However, many existing adversarial patches are used for attacking DNNs, and only a few of them apply to both the DNN and its explanation model. In this paper, we present different adversarial patches that misguide the prediction of DNN models and change the cause of prediction results of interpretation models, such as gradient-weighted class activation mapping. The proposed adversarial patches have appropriate location and perturbation ratios, which comprise visible or less visible adversarial patches. In addition, image patches within small arrays are localized without covering or overlapping with any of the main objects in a natural image. In particular, we generate two adversarial patches that cover only 3% and 1.5% of the pixels in the original image, while they do not cover the main objects in the natural image. Our experiments are performed using four pre-trained DNN models and the ImageNet dataset. We also examine the inaccurate results of the interpretation models through mask and heatmap visualization. The proposed adversarial attack method could be a reference for developing robust network interpretation models that are more reliable for the decision-making process of pre-trained DNN models.
Constrained Adaptive Control for Heat Equations with Disturbances

This paper considers the adaptive stabilization for uncertain heat equations via constrained boundary control and constrained distributed control, respectively. Quite essentially, distributed disturbance and boundary disturbance are simultaneously allowed in the considered heat equations, and moreover, only a boundary value is available for feedback in the case of boundary control. This renders the equations under investigation more practical and the control problem more challenging. In this paper, a new adaptive control scheme is proposed to effectively deal with disturbances and to properly satisfy the pre-specified control constraints. Specifically, adaptive dynamic compensation technique and appropriate projection operator are first introduced respectively to handle the disturbances and the control constraints. Then, the constrained boundary and distributed controllers are successfully constructed, which guarantee that all states of the resulting closed-loop systems are bounded in certain sense of norms.
POTENTIAL ANTI-INFLAMMATORY ACTIVITY OF PLUMBAGO ZEYLANICA

Objective: To determine the anti-inflammatory activity of dichloromethane extract of Plumbago zeylanica (DMEPZ), and its possible mechanism of action. Methods: Male Wistar rats (180-200 g) under controlled standard conditions (24±1°C, 55-58 humidity and 12 hrs light/dark cycle). The groups were divided into 5 groups (n=6/group) and assigned as positive control, negative control, and standard and two different test dose groups of P. zeylanica. Paw edema induced by subplantar injection of 0.1 mL of carrageenan (suspended in 1% carboxymethyl cellulose) into the right hind paw in all groups except negative control group. Granuloma induced by cotton pellets (10±1 mg) were implanted into groin region of each rat. The groups were divided into 4 groups (n=6/group) and assigned as possitive control, two different test dose groups of P. zeylanica and standard. Results: Oral administration of DMEPZ shown a significant (p<0.05) dose-dependent protection against carrageenan-induced paw edema. At 1st hr, P. zeylanica shown an inhibition effect of edema in the different doses of 250 mg/kg and 500 mg/kg were found to be 28.57 and 31.79%, respectively. At 3rd hrs, the paw edema inhibition was found to be 30.70 and 40.15%, respectively. Diclofenac (25 mg/kg) had effect of 34.10 and 41.73% (p<0.001) inhibition of paw edema at in 1 and 3 hrs. P. zeylanica 500 mg/kg showed percentage inhibition of wet and dry cotton pellet granuloma in rats 55.84% and 47.92%, respectively. Conclusion: Thus, the present study revealed that the DMEPZ offered significant protection against inflammation.
Nonlinear Modeling the Quadcopter Considering the Aerodynamic Interaction

This paper presents a nonlinear model of the quadcopter to simulate the actual motion. An aerodynamic model of the rotor is obtained through wind-tunnel tests, which consider the variations in the rotor’s angular velocity, incoming flow speed, and angle of attack of the rotor ( $\alpha _{p}$ ). The quadcopter’s fuselage aerodynamics and mutual interference are simulated using the computational fluid dynamic (CFD) method. The mesh motion method is used to simulate the rotation of the rotor. It can be found that the interference has a significant impact on the aerodynamic load of the fuselage and the thrust and pitch moment of the rear rotor. The mathematical expression of the interference model is given by adopting the idea of the small disturbance hypothesis. The yaw dynamics model is also improved by considering the motor’s dynamics. The quadcopter’s nonlinear model can be obtained by combining the rotor model, fuselage model, motor model, and interference model. Then, flight experiments are done at hovering and forward flight states to verify the established model, and the experiment results are in good agreement with the nonlinear model. Finally, the quadcopter’s characteristic descriptive abilities of different accurate models are discussed to help designers to build a suitable quadcopter model according to the requirements.
Multi-zone indoor temperature prediction based on Graph Attention Network and Gated Recurrent Unit

Indoor temperature have significant influence on load forecasting, comfort control and security monitoring. Achieving accurate temperature prediction can provide key basic data for energy efficiency and building safety and comfort. In the case of multiple zones, the heat transfer process in adjacent zones can have an important impact on the dynamics of indoor temperature. This paper focuses on the influence of heat transfer process in multiple adjacent zones. To describe the interactions of temperature among the multiple zones, we consider the zones as nodes and the connected walls as edges based on actual layouts to construct the graph network. For the non-linearity of the heat transfer process, we propose a novel multi-zone indoor temperature prediction model based on graph attention mechanism and recurrent network to achieve one-step ahead and multi-step ahead temperature predictions. The accuracy of this model was further verified by using data generated from EnergyPlus simulations. The best predicted result had an RMSE value of 0.47, an MAE value of 0.37, and an R2 value of 0.94.
Multi-Scale Feature Fusion: Learning Better Semantic Segmentation For Road Pothole Detection

This paper presents a novel pothole detection approach based on single-modal semantic segmentation. It first extracts visual features from input images using a convolutional neural network. A channel attention module then reweighs the channel features to enhance the consistency of different feature maps. Subsequently, we employ an atrous spatial pyramid pooling module (comprising of atrous convolutions in series, with progressive rates of dilation) to integrate the spatial context information. This helps better distinguish between potholes and undamaged road areas. Finally, the feature maps in the adjacent layers are fused using our proposed multi-scale feature fusion module. This further reduces the semantic gap between different feature channel layers. Extensive experiments were carried out on the Pothole-600 dataset to demonstrate the effectiveness of our proposed method. The quantitative comparisons suggest that our method achieves the state-of-the-art (SoTA) performance on both RGB images and transformed disparity images, outperforming three SoTA single-modal semantic segmentation networks.
Experimental Validation of Domain Knowledge Assisted Robotic Exploration and Source Localization

In situations where toxic or dangerous airborne material is leaking, mobile robots equipped with gas sensors are a safe alternative to human reconnaissance. This work presents the Domain Knowledge Assisted Robotic Exploration and Source Localization (DARES) approach. It allows a multi-robot system to localize multiple sources or leaks autonomously and independently of a human operator. The probabilistic approach builds upon domain knowledge in the form of a physical model of gas dispersion and the a priori assumption that the dispersion process is driven by multiple but sparsely distributed sources. A formal criterion is used to guide the robots to informative measurement locations and enables inference of the source distribution based on gas concentration measurements. Small-scale indoor experiments under controlled conditions are presented to validate the approach. In all three experiments, three rovers successfully localized two ethanol sources.
Lidar Odometry Key Frame Selection Based on Displacement Vector Similarity

In order to improve the efficiency and reduce the cumulative error of lidar odometry caused by key frame update. This paper proposes a key frame selection method based on displacement vector similarity. First, calculate the pose between the current frame and the reference key frame according to the Normal Distribution Transform (NDT) algorithm; then, set the Manhattan distance between the two frames above, and calculate the cosine similarity of the displacement vector; finally, update the key frame according to the similarity threshold. The experimental results show that the key frame selection method proposed in this paper can effectively improve the key frame update efficiency and reduce the cumulative error of the odometry: compared with the NDT method updating key frame based on the distance, the maximum absolute error is reduced by 11.88 meters, and the relative error is reduced by 8%,time consumption has been reduced by 55 seconds.
Enhanced Performance of Strained Graphene Wrapped Channel Cylindrical FET

In order to perform logic and memory operation with high speed and accuracy, transistors should have high ON/OFF current ratio and high charge carrier mobility. But it is difficult to achieve both of these criteria in a single transistor. Graphene is considered as one of the most promising candidates due to its high mobility. But graphene field effect transistor (GFET) gives very low ON/OFF current ratio as graphene is a semi-metallic material. In this work, we have modeled strained graphene on silicon FET with high k dielectric material like HfO2, TiO2 and obtained notable improvement in the ON/OFF current ratio along with the drain current. But the structure suffers from significant channel length modulation effect. Finally, we have proposed a cylindrical FET structure with strained graphene layer to minimize the channel length modulation effect while maintaining the high ON/OFF current ratio and high charge carrier mobility.
Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator

This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunov-like composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78° to 1.09°, and 21.09% to 3.99%, respectively, within three iterations.
Collagen Fragments Produced in Cancer Mediate T Cell Suppression Through Leukocyte-Associated Immunoglobulin-Like Receptor 1

The tumor microenvironment (TME) is a complex structure comprised of tumor, immune and stromal cells, vasculature, and extracellular matrix (ECM). During tumor development, ECM homeostasis is dysregulated. Collagen remodeling by matrix metalloproteinases (MMPs) generates specific collagen fragments, that can be detected in the circulation of cancer patients and correlate with poor disease outcome. Leukocyte-Associated Immunoglobulin-like Receptor-1 (LAIR-1) is an inhibitory collagen receptor expressed on immune cells in the TME and in the circulation. We hypothesized that in addition to ECM collagen, collagen fragments produced in cancer can mediate T cell immunosuppression through LAIR-1. Our analyses of TCGA datasets show that cancer patients with high tumor mRNA expression of MMPs, collagen I and LAIR-1 have worse overall survival. We show that in vitro generated MMP1 or MMP9 collagen I fragments bind to and trigger LAIR-1. Importantly, LAIR-1 triggering by collagen I fragments inhibits CD3 signaling and IFN-γ secretion in a T cell line. LAIR-2 is a soluble homologue of LAIR-1 with higher affinity for collagen and thereby acts as a decoy receptor. Fc fusion proteins of LAIR-2 have potential as cancer immunotherapeutic agents and are currently being tested in clinical trials. We demonstrate that collagen fragment-induced inhibition of T cell function could be reversed by LAIR-2 fusion proteins. Overall, we show that collagen fragments produced in cancer can mediate T cell suppression through LAIR-1, potentially contributing to systemic immune suppression. Blocking the interaction of LAIR-1 with collagen fragments could be an added benefit of LAIR-1-directed immunotherapy.
Correlation of Early Nutritional Supply and Development of Bronchopulmonary Dysplasia in Preterm Infants <1,000 g

Background: Bronchopulmonary dysplasia (BPD) has multifactorial origins and is characterized by distorted physiological lung development. The impact of nutrition on the incidence of BPD is less studied so far. Methods: A retrospective single center analysis was performed on n = 207 preterm infants <1,000 g and <32 weeks of gestation without severe gastrointestinal complications to assess the impact of variations in nutritional supply during the first 2 weeks of life on the pulmonary outcome. Infants were grouped into no/mild and moderate/severe BPD to separate minor and major limitations in lung function. Results: After risk adjustment for gestational age, birth weight, sex, multiples, and antenatal steroids, a reduced total caloric intake and carbohydrate supply as the dominant energy source during the first 2 weeks of life prevailed statistically significant in infants developing moderate/severe BPD (p < 0.05). Enteral nutritional supply was increased at a slower rate with prolonged need for parenteral nutrition in the moderate/severe BPD group while breast milk provision and objective criteria of feeding intolerance were equally distributed in both groups. Conclusion: Early high caloric intake is correlated with a better pulmonary outcome in preterm infants <1,000 g. Our results are in line with the known strong impact of nutrient supply on somatic growth and psychomotor development. Our data encourage paying special attention to further decipher the ideal nutritional requirements for unrestricted lung development and promoting progressive enteral nutrition in the absence of objective criteria of feeding intolerance.
Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

Background This study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM). Materials and Methods A retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model. Results After multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival. Conclusion Our study established an integrated model to predict the postoperative recurrence of AM.
Fast and Accurate Electric Field Gradient Calculations in Molecular Solids With Density Functional Theory

Modern approaches for calculating electric field gradient (EFF) tensors in molecular solids rely upon plane-wave calculations employing periodic boundary conditions (PBC). In practice, models employing PBCs are limited to generalized gradient approximation (GGA) density functionals. Hybrid density functionals applied in the context of gauge-including atomic orbital (GIAO) calculations have been shown to substantially improve the accuracy of predicted NMR parameters. Here we propose an efficient method that effectively combines the benefits of both periodic calculations and single-molecule techniques for predicting electric field gradient tensors in molecular solids. Periodic calculations using plane-wave basis sets were used to model the crystalline environment. We then introduce a molecular correction to the periodic result obtained from a single-molecule calculation performed with a hybrid density functional. Single-molecule calculations performed using hybrid density functionals were found to significantly improve the agreement of predicted 17O quadrupolar coupling constants (C q ) with experiment. We demonstrate a 31% reduction in the RMS error for the predicted 17O C q values relative to standard plane-wave methods using a carefully constructed test set comprised of 22 oxygen-containing molecular crystals. We show comparable improvements in accuracy using five different hybrid density functionals and find predicted C q values to be relatively insensitive to the choice of basis set used in the single molecule calculation. Finally, the utility of high-accuracy 17O C q predictions is demonstrated by examining the disordered 4-Nitrobenzaldehyde crystal structure.
Genome-Wide Identification and Comparative Analysis of the Teosinte Branched 1/Cycloidea/Proliferating Cell Factors 1/2 Transcription Factors Related to Anti-cancer Drug Camptothecin Biosynthesis in Ophiorrhiza pumila

Ophiorrhiza pumila (O. pumila; Op) is a medicinal herbaceous plant, which can accumulate camptothecin (CPT). CPT and its derivatives are widely used as chemotherapeutic drugs for treating malignant tumors. Its biosynthesis pathway has been attracted significant attention. Teosinte branched 1/cycloidea/proliferating cell factors 1/2 (TCP) transcription factors (TFs) regulate a variety of physiological processes, while TCP TFs are involved in the regulation of CPT biosynthesis remain unclear. In this study, a systematic analysis of the TCP TFs family in O. pumila was performed. A total of 16 O. pumila TCP (OpTCP) genes were identified and categorized into two subgroups based on their phylogenetic relationships with those in Arabidopsis thaliana. Tissue-specific expression patterns revealed that nine OpTCP genes showed the highest expression levels in leaves, while the other seven OpTCPs showed a higher expression level in the stems. Co-expression, phylogeny analysis, and dual-luciferase (Dual-LUC) assay revealed that OpTCP15 potentially plays important role in CPT and its precursor biosynthesis. In addition, the subcellular localization experiment of candidate OpTCP genes showed that they are all localized in the nucleus. Our study lays a foundation for further functional characterization of the candidate OpTCP genes involved in CPT biosynthesis regulation and provides new strategies for increasing CPT production.
Effect of ripening temperature on the texture of cow milk Kashkaval cheese

Texture development during ripening of cow milk Kashkaval cheese at different temperatures (9±1 °С, 11±1 °С and 13±1 °С) was studied. Texture parameters representing cheese hardness, cohesiveness, springiness, adhesiveness, gumminess and chewiness were determined by texture profile analysis. It was found that hardness, gumminess and adhesiveness of all studied samples increased, while springiness and cohesiveness decreased during ripening. An increase of chewiness values during the first stages of ripening was observed, followed by a decrease to the 60th day. It was found that ripening time, as well as ripening temperature had a significant effect on the changes in Kashakaval texture parameters. Cheese samples ripened at higher temperatures had lower values for hardness, cohesiveness, gumminess and chewiness. Ripening temperature had no significant effect on the changes in springiness and adhesiveness of the studied samples. The results obtained showed that by an appropriate combination of the two factors, ripening time and temperature, the changes in the Kashkaval cheese texture can be controlled, which is important for the quality of the final product.