Hydrogels have emerged as promising candidates for flexible sensors due to their softness, biocompatibility, and tunable physicochemical properties. However, achieving synchronous satisfaction of conformality, conductivity, diverse biological functions in hydrogel remains a challenge. Here, we proposed multifunctional sensor by incorporating silver-loaded polydopamine nanoparticles (Ag@PDA) into thermally cross-linked methacrylamide chitosan (CSMA) acrylamide network, namely,...

Traditional methods for constructing synthetic nanobody libraries are labor-intensive and time-consuming. This study introduces a novel approach leveraging protein large language models (LLMs) to generate germline-specific sequences, enabling efficient library construction through statistical analysis. We developed NanoAbLLaMA, LLM based on LLaMA2, fine-tuned using low-rank adaptation (LoRA) 120,000 curated sequences. The model generates sequences conditioned germlines (IGHV3-301...

Abstract Motivation Virtual reality technology holds significant potential for applications in biomedicine, particularly the visualization and manipulation of protein molecular structures. To facilitate study molecules enable state-of-the-art VR hardware, we developed a novel software named VisionMol, which allows users to engage immersive exploration analysis three-dimensional structures using range virtual platforms (such as Rhino X Pro, Meta’s Oculus Quest Pro/3) well personal computers....

A brain tumor refers to an abnormal growth of cells in the that can be either benign or malignant. Oncologists typically use various methods such as blood visual tests detect tumors, but these approaches time-consuming, require additional human effort, and may not effective detecting small tumors. This work proposes approach detection combines segmentation feature fusion. Segmentation is performed using mayfly optimization algorithm with multilevel Kapur's threshold technique locate tumors...

Stock price prediction is an important and complex time-series problem in academia financial industries. market prices are voted by all kinds of investors influenced various factors. According to the literature studies, such as Elliott’s wave theory Howard’s cycle investment theory, cyclic patterns significant characteristics stock market. However, even several studies that do consider (or similar concepts) suffered from data leakage or boundary problems, which could be impractical for real...

Flexible temperature sensors have been widely used in electronic skins and health monitoring. Body as one of the key physiological signals is crucial for detecting human body's abnormalities, which necessitates high sensitivity, quick responsiveness, stable In this paper, we reported a resistive sensor designed an ultrathin laminated structure with serpentine pattern bioinspired adhesive layer, was fabricated composite poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/single-wall...

Emerging computing paradigm edge expects to store and process data at the network with reduced latency improved bandwidth. To best of our knowledge, key performance issues such as coding erasure-coded storage systems haven't been investigated for computing. In this paper, we present an system Unlike center cloud systems, it employs devices perform encoding decoding operations, which can be a bottleneck whole due limited power. Hence, comprehensive study erasure see if match 5G Wi-Fi 6 edge....

With the increasing popularity of social media, Sentiment Analysis (SA) Microblog has raised as a new research topic. In this paper, we present WDCRF: Word2vec and Dynamic Conditional Random Field (DCRF) based framework for Chinese Microblog. Our contributions include: firstly, to address drawbacks message such length Lexicon limitations, technology is leveraged enrich so that each word individual extended by its Top-k similar words. Secondly, DCRF model utilized combine conduct Subjectivity...

Due to the confusion of fault-prone software modules and non-fault-prone ones, limit traditional mothed such as LDA PCA, performance defect prediction model is difficult improve. In this paper, we present GMCRF, a method based on dimensionality reduction technique conditional random field (CRF) for prediction. our proposed method, firstly, leverage geometric mean subspace learning choose best combination features from data set. Secondly, propose apply which selected by mean-based approach in...

Abstract Extracting structured information from massive and heterogeneous text is a hot research topic in the field of natural language processing. It includes two key technologies: named entity recognition (NER) relation extraction (RE). However, previous NER models rarely consider about influence mutual attention among words on prediction labels, there are few researches how to more fully extract sentence for relational classification. In addition, treat RE as pipeline separated tasks,...

Abstract Summary Nanobodies, a unique subclass of antibodies initially discovered in camelids, characterized by the absence light chains and consisting solely heavy chain variable region. This distinctive structure endows nanobodies with inherent advantages realms disease treatment biopharmaceutical applications. Presently, research applications concerning are experiencing rapid growth. However, existing databases suffer from non-uniform data sources lack standardization. To address these...

Early detection of Atrial Fibrillation (AF) is essential for preventing heart failure, thrombosis, and cardio-embolic stroke. Traditional neural network (NN)-based methods primarily rely on clinical expert diagnoses, yet fully leveraging the intricate patterns inherent in AF episodes remains challenging. Herein, we propose an innovative framework that utilizes statistical inference probabilistic modeling to analyze cardiac inter-beat interval dynamics, incorporating 5 robust features: rate,...

Deep neural networks (DNNs) have achieved outstanding results in a wide range of applications. However, A DNN training is data- and compute-intensive task to obtain high accuracy. Multiple state-of-the-art AI accelerators such as GPUs can be deployed single machine for training, data loading dominates significant amount time the whole process. As result, becomes key bottleneck limit performance. In this paper, we present efficient trainings by using two methods: Based on access pattern...

To contribute software testing, and save testing costs, a wide range of machine learning approachs have been studied to predict defects in modules. Unfortunately, the imbalanced nature this type data increases difficulty such task. In paper, we present UCRF, method based on undersampling technique conditional random field (CRF) for defect prediction imbalance distribution. our proposed method, firstly, leverage meanshift clustering reduce samples majority class balancing train set. Secondly,...

This paper combines the deep learning technology and side-channel analysis method to achieve effective cross-technology for ECC Curve-25519 algorithm on an STM32F407 (ARM-CortexM4) chip. The implementation of target algorithm, which is protected with point randomization constant-time operations (Montgomery powering ladder), uses security countermeasures. Therefore, explores leakages from memory-related (conditional swap), happen before each addition or doubling operation. used determine...

In order to analyze the big data of people's livelihood appeal, this paper proposes a time series modeling and algorithm decompose {x(t)} into long-term change trend L(t), short-term S(t) occasional e(t). Then use method break down six types appeal such as unlicensed vendor, industrial noise, sewer cover, academic qualification, out-of-store operation public transportation, combine other for correlation analysis, find out cause event make predictions. The experimental results verify...

In this paper, the experimental research was done to 30 teenager table tennis players at Haidian District Amateur Sports School by method of imagery training. The training effects and traditional were compared. results show that athletes who adopt have significantly higher standard performance technical evaluation than those use routine methods. SPSS 22.0 test they reach significant level can effectively improve athletes' effect.

Extracting structured information from massive and heterogeneous text is a hot research topic in the field of natural language processing. It includes two key technologies: named entity recognition (NER) relation extraction (RE). However, previous NER models consider less about influence mutual attention between words on prediction labels, there how to more fully extract sentence for relational classification. In addition, treats RE as pipeline separated tasks, which neglects connection...