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Abstract This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

Purpose: Purpose: Subtotal skin electron beam therapy may be an option for patients with cutaneous lymphoma receiving radiation therapy to treat large areas of their skin but may benefit from sparing specific areas that may have had previous radiation therapy, are of specific cosmetic concern, and/or show no evidence of disease. We report here on the design, implementation, and dosimetric characteristics of a reusable and transparent customizable shield for use with the large fields used to deliver total skin electron beam therapy at extended distance with a conventional linear accelerator. Methods and Materials: A shield was designed and manufactured consisting of acrylic blocks that can be mounted on a steel frame to allow patient-specific shielding. The dosimetry of the device was measured using radiochromic film. Results: The shield is easy to use and well-tolerated for patient treatment, providing minimal electron transmission through the shield with a sharp penumbra at the field edge, with no increase in x-ray dose. We report on the dosimetry of a commercial device that has been used to treat more than 30 patients to date. Conclusions: The customizable shield is well suited to providing patient-specific shielding for subtotal skin electron beam therapy.

In this paper, we study the sensitivity-tunable terahertz (THz) liquid/gas biosensor in a coupling prism–three-dimensional Dirac semimetal (3D DSM) multilayer structure. The high sensitivity of the biosensor originates from the sharp reflected peak caused by surface plasmon resonance (SPR) mode. This structure achieves the tunability of sensitivity due to the fact that the reflectance could be modulated by the Fermi energy of 3D DSM. Besides, it is found that the sensitivity curve depends heavily on the structural parameters of 3D DSM. After parameter optimization, we obtained sensitivity over 100°/RIU for liquid biosensor. We believe this simple structure provides a reference idea for realizing high sensitivity and a tunable biosensor device.

The fast advancement and deployment of sequencing technologies after the Human Genome Project have greatly increased our knowledge of the eukaryotic genome sequences. However, due to technological concerns, high-quality genomic data has been confined to a few key organisms. Moreover, our understanding of which portions of genomes make up genes and which transcript isoforms synthesize these genes is scarce. Therefore, the current study has been designed to explore the reliability of the tiger lily (Lilium lancifolium Thunb) transcriptome. The PacBio-SMRT was used for attaining the complete transcriptomic profile. We obtained a total of 815,624 CCS (Circular Consensus Sequence) reads with an average length of 1295 bp. The tiger lily transcriptome has been sequenced for the first time using third-generation long-read technology. Furthermore, unigenes (38,707), lncRNAs (6852), and TF members (768) were determined based on the transcriptome data, followed by evaluating SSRs (3319). It has also been revealed that 105 out of 128 primer pairs effectively amplified PCR products. Around 15,608 transcripts were allocated to 25 distinct KOG Clusters, and 10,706 unigenes were grouped into 52 functional categories in the annotated transcripts. Until now, no tiger lily lncRNAs have been discovered. Results of this study may serve as an extensive set of reference transcripts and help us learn more about the transcriptomes of tiger lilies and pave the path for further research.

PurposeThe current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome.Methods and MaterialUsing MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images.ResultsBy the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87.ConclusionsThe proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.

The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.