The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation blood pressure (BP) using PPG method received considerable interest. In this paper, a for estimating systolic diastolic BP based only on signal is developed. multitaper (MTM) used feature extraction, an artificial neural network (ANN) estimation. Compared with previous...

In this paper, a dual-functional probe, 2-(benzothiazol)-4-(3-hydroxy-4-methylphenyl) imino phenol (BHMH), was synthesized and characterized for the simultaneous detection of Cu2+ Fe3+ in dimethyl sulfoxide/4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (DMSO/HEPES) (1:4, v/v, pH = 6.0). The limits detections (LODs) were 9.05 48 nM, respectively. Based on competitive coordination, complex BHMH-Cu2+/Fe3+ exhibited good sensitivity selectivity glyphosate. LODs BHMH-Cu2+ BHMH-Fe3+...

The field of catalysis holds paramount importance in shaping the trajectory sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) catalyst design. Presently, fine-tuning open-source large language models (LLMs) has yielded significant breakthroughs across various domains such as biology and healthcare. Drawing inspiration from these advancements, we introduce CataLM (Catalytic Language Model), a model tailored domain electrocatalytic...

Congestive heart failure (CHF) refers to the inadequate blood filling function of ventricular pump and it may cause an insufficient discharge volume that fails meet needs body metabolism. Heart rate variability (HRV) based on RR interval is a proven effective predictor CHF. Short-term HRV has been used widely in many healthcare applications monitor patients' health, especially combination with mobile phones smart watches. Inspired by inception module from GoogLeNet, we combined long...

Heart rate variability (HRV) is an effective predictor of congestive heart failure (CHF). However, important challenges exist regarding the temporal feature extraction and efficient classification using high-dimensional HRV representations. To solve these challenges, ensemble method for CHF detection short-term data deep neural networks was proposed. In this paper, five open-source databases, BIDMC database (BIDMC-CHF), RR interval (CHF-RR), MIT-BIH normal sinus rhythm (NSR) database,...

The electrocatalytic CO2 reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity selectivity can draw inspiration from abundant scientific literature. An annotated verified corpus made massive literature assist development natural language processing (NLP) models, which offer insight to help guide understanding these underlying mechanisms. To facilitate data mining in this...

Electrocatalysis takes a significant role in the production of sustainable fuels and chemicals. The combination artificial intelligence catalytic science is exhibiting great potential to extract, analyze, predict electrocatalysts. However, currently developed machine learning approach usually requires mass data from density functional theory calculations train optimize models. In contrast, knowledge graph has extract useful information large amount literature without referring theory....

In this paper, a support vector machine (SVM) approach using statistical features, P wave absence, spectrum and length-adaptive entropy are presented to classify ECG rhythms as four types: normal rhythm, atrial fibrillation (AF), other too noisy classify.The proposed algorithm consisted of three steps: (1) signal pre-processing based on the wavelet method; (2) feature extraction, extracted features including one power feature, two 17 RR interval-related 11 features; (3) classification SVM...

The electrocardiogram (ECG) has become an important tool for the diagnosis of cardiovascular diseases. As long-term ECG recordings more common, driven partly by development intelligent hardware, requirement automatic analysis continues to grow. Research attempted use expert knowledge optimise ECG-related algorithms, however, visual is tedious and operator dependent. In previous studies, beat clustering approach based on self-organising maps been applied reduce amount time must spend. This...

Sustainability draws increased supply chain management (SCM) attention. This article analyzes critical success to the assessment, evaluation, and attainment of sustainable (SSCM), assessed through critical‐success identification qualitative data analysis. Namely, a literature review selected 188 articles, published between January 1994 November 2016, helps identify most influential factors. The analysis pertains fifteen such successes, identified in our collaboration with other academic...

Abstract CO 2 electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts domain literature can help scientists find new effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques large models (LLMs) approaches extract relevant about electrocatalytic reduction process scientific literature. By applying extraction pipeline, present an...

Heart rate variability has been proven to be an effective prediction of risk heart failure. The tradition method required manual feature extraction, thus may lead potential error. In order improve the robustness, a deep learning based on long short-term memory presented in this paper. Three RR interval length (N) for detection are used. Without pre-processing, obtain 82.47%, 85.13% and 84.91% accuracy N=50 (average time is 37. 8s), N=100 73. 9s), N=500 369. 5s), respectively. This makes it...

Convolution Neural Networks (CNN) have recently achieved state-of-the art performance on handwritten Chinese character recognition (HCCR). However, most of CNN models employ the SoftMax activation function and minimize cross entropy loss, which may cause loss inter-class information. To cope with this problem, we propose to combine similarity ranking use it as function. The experiments results show that combination functions produce higher accuracy in HCCR. This report briefly reviews...

Large envelope deformable aircraft pose higher requirements for guidance and control technology, requiring law parameters to be able adaptively optimize online based on flight uncertainty dynamically enhance capabilities. At present, coefficient freezing is commonly used in engineering design typical feature work points, but large flight, there are more traditional methods complex highly dependent manual experience; On the other hand, gain scheduling can only switch preset laws status. Once...

Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. Since the introduction new relational tasks can overshadow previously learned information, catastrophic forgetting becomes significant challenge in this domain. Current replay-based training paradigms prioritize all data uniformly and train memory samples through multiple rounds, which would result overfitting old pronounced bias towards because imbalances replay set. To...

The field of catalysis holds paramount importance in shaping the trajectory sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) catalyst design. Presently, fine-tuning open-source large language models (LLMs) has yielded significant breakthroughs across various domains such as biology and healthcare. Drawing inspiration from these advancements, we introduce CataLM Cata}lytic Language Model), a model tailored domain electrocatalytic...

Grain moisture is a key indicator of security, however, it lack online technology for detecting distribution. In this paper, the microwave image method using rotatable reflector has been proposed to solve problem. Firstly, detection probe was designed. Then, we use FDTD (Finite-Difference Time-Domain) simulate propagation process in grain silo. After that, one — dimensional distribution inverse model built reflection signal. At last, an generated based on linear combination all data....

Abstract An ECG baseline shift correction method is presented on the base of adaptive bionic wavelet transform. After modifying transform according to characteristics signal, we propose a novel BWT algorithm. Using contaminated and actual data in MIT-BIH database, fast simple can effectively correct under premise maintaining geometric signal. Evaluation proposed shows that average improvement SNR FABWT 2.187 dB more than CWT-based best case result.

City water demand forecasting is of great significance in reducing the cost electricity consumption and municipal planning. Back-propagation (BP) neural network has been widely adopted recent years. But BP performs unsatisfactorily terms training time global searching ability, so this paper we improve by two heuristic algorithms, namely, genetic algorithm (GA) particle swarm optimization (PSO), respectively. The testing verification three algorithms (BP, GA+BP, PSO+BP) have conducted on...

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