A Survey on Federated Learning Poisoning Attacks and Defenses

As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized data collaboratively without explicitly aggregating the data. Due to its ability to break data silos, federated learning has received increasing attention in many fields, including finance, healthcare, and education. However, the invisibility of clients' training data and the local training process result in some security issues. Recently, many works have been proposed to research the security attacks and defenses in federated learning, but there has been no special survey on poisoning attacks on federated learning and the corresponding defenses. In this paper, we investigate the most advanced schemes of federated learning poisoning attacks and defenses and point out the future directions in these areas

Effects of aggregate size on water retention capacity and microstructure of lime-treated silty soil

International audienceLime treatment is a common technique of improving the workability and geotechnical properties of soils. In this study, the aggregate size effects on the water retention capacity and microstructure of lime-treated soil were investigated. Two soil powders with different maximum aggregate sizes (D max = 0·4 and 5 mm) were prepared and stabilised by 2% lime (by weight of dry soil). Soil samples were prepared by compaction at dry side of optimum water content (w = 17%) with a dry density of 1·65 Mg/m 3. Suction and pore size distribution were determined after different curing periods. The results obtained show that: (a) the treated soil with smaller D max presents relatively smaller modal sizes and lower frequency of macropores (10–330 μm); (b) lime addition effectively improves the soil water retention capacity and decreases both the modal sizes of macro-and micropores gradually over time. Moreover, a higher air entry value and larger water retention capacity were also observed for a smaller D max value, in agreement with the pore size distributions

Scavenging low-speed breeze wind energy using a triboelectric nanogenerator installed inside a square variable diameter channel

Over the recent years, triboelectric nanogenerator (TENG) have received widespread attention as a simple and efficient energy harvesting device. However, how to collect the breeze in daily life is an important issue that need to be solved for wind-powered triboelectric nanogenerator (W-TENG). Here, we propose a method of connecting a square variable diameter channel to the previously studied double-ended fixed W-TENG, which realizes the collection of energy in the breeze. As a result, after adding channels, the starting wind speed of W-TENG is optimized to as low as 0.4 m s−1, with an average output voltage of 6.1 V. This method not only enables W-TENG to start at the ultra-low wind speed, but also improves the output performance. When the external wind velocity is 2.0 m s−1, the output voltage is increased by 10.6 times after adding the channel structure. This work provides a good strategy for collecting the breeze without changing the original structure of the W-TENG, fully demonstrating the advantages of energy harvesting under the low wind velocity.</p

Scavenging low-speed breeze wind energy using a triboelectric nanogenerator installed inside a square variable diameter channel

Over the recent years, triboelectric nanogenerator (TENG) have received widespread attention as a simple and efficient energy harvesting device. However, how to collect the breeze in daily life is an important issue that need to be solved for wind-powered triboelectric nanogenerator (W-TENG). Here, we propose a method of connecting a square variable diameter channel to the previously studied double-ended fixed W-TENG, which realizes the collection of energy in the breeze. As a result, after adding channels, the starting wind speed of W-TENG is optimized to as low as 0.4 m s−1, with an average output voltage of 6.1 V. This method not only enables W-TENG to start at the ultra-low wind speed, but also improves the output performance. When the external wind velocity is 2.0 m s−1, the output voltage is increased by 10.6 times after adding the channel structure. This work provides a good strategy for collecting the breeze without changing the original structure of the W-TENG, fully demonstrating the advantages of energy harvesting under the low wind velocity.</p

Modeling and Optimizing of Producing Recycled PET from Fabrics Waste via Falling Film-Rotating Disk Combined Reactor

Recycling and reusing of poly (ethylene terephthalate) (PET) fabrics waste are essential for reducing serious waste of resources and environmental pollution caused by low utilization rate. The liquid-phase polymerization method has advantages of short process flow, low energy consumption, and low production cost. However, unlike prepolymer, the material characteristics of PET fabrics waste (complex composition, high intrinsic viscosity, and large quality fluctuations) make its recycling a technique challenge. In this study, the falling film-rotating disk combined reactor is proposed, and the continuous liquid-phase polymerization is modeled by optimizing and correcting existing models for the final stage of PET polymerization to improve the product quality in plant production. Through modeling and simulation, the weight analysis of indexes closely related to the product quality (intrinsic viscosity, carboxyl end group concentration, and diethylene glycol content) was investigated to optimize the production process in order to obtain the desired polymer properties and meet specific product material characteristics. The model could be applied to other PET wastes (e.g., bottles and films) and extended to investigate different aspects of the recycling process

Immune-related potential biomarkers and therapeutic targets in coronary artery disease

BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease

The Pinx1 Gene Downregulates Telomerase and Inhibits Proliferation of CD133+ Cancer Stem Cells Isolated from a Nasopharyngeal Carcinoma Cell Line by Regulating Trfs and Mad1/C-Myc/p53 Pathways

Background/Aims: Cancer stem cells (CSCs) are important factors for the continuous growth, recurrence, and metastasis of malignant tumors. They are responsible for the ineffectiveness of traditional radiotherapy and chemotherapy toward malignant tumors. Currently, stem cells or side-population cells have been isolated from many cancer cell lines and malignant tumor tissues, including nasopharyngeal carcinoma. Exploring the biological characteristics of CSCs for CSC-targeted therapy has gained importance. CSCs possess higher telomerase activity; thus, the use of the gene encoding telomerase inhibitor PinX1 gene to target telomerase in CSCs and inhibit proliferation, invasion, and metastasis of CSCs has become an important means for the treatment of malignant tumors. PinX1 may regulate complex pathways, including TRF1, Mad1/c-Myc, and p53. Methods: In this study, nasopharyngeal CD133+ CSCs were sorted using CD133 immunomagnetic beads by flow cytometry The successful isolation of CD133+ CSCs was confirmed by examining their surface markers, namely CD44, NaNOG, and SOX2 as well as their ability to undergo in vivo tumorigenesis and in vitro sphere formation, proliferation, migration, and invasion. In addition, CD133+ CSCs were transfected with the constructed PinX1 overexpression plasmid or siRNA and the resulting effects on their proliferation, migration, invasion, and apoptosis were detected using cell counting kit-8 (CCK-8), transwell assay, and scratch test, respectively. Furthermore, their effects on mRNA and protein levels of TRF1, TRF2, Mad1, c-Myc, and p53 were examined using quantitative real-time PCR and western blot, respectively. Results: The overexpression of PinX1 in CD133+ CSCs significantly decreased hTERT (P &#x3c; 0.001), inhibited proliferation, migration, and invasion, induced apoptosis, and significantly decreased c-Myc mRNA levels (P &#x3c; 0.001), while it increased TRF1, Mad1, and p53 mRNA levels (all P &#x3c; 0.001). On the other hand, PinX1 silencing in CD133+ CSCs significantly decreased TRF1, Mad1, and p53 mRNA levels (all P &#x3c; 0.01), while it increased hTERT and c-Myc mRNA levels (all P &#x3c; 0.05). Conclusion: These results indicate that PinX1 downregulates telomerase activity in CD133+ CSCs, inhibits its proliferation, migration, and invasion, and induces apoptosis possibly through TRF1, Mad1/c-Myc, and p53–mediated pathways

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches

Alcoholism Identification Based on an AlexNet Transfer Learning Model

Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis.Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10−4, and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning.Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set.Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images