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 of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

Background and Objective: Heart rate variability (HRV) is an effective predictor of congestive heart failure (CHF). However, important challenges exist regarding the effective temporal feature extraction and efficient classification using high-dimensional HRV representations. To solve these challenges, an ensemble method for CHF detection using short-term HRV data and deep neural networks was proposed. Methods: In this study, five open-source databases, the BIDMC Congestive Heart Failure Database (BIDMC-CHF), Congestive Heart Failure RR Interval Database (CHF-RR), MIT-BIH Normal Sinus Rhythm (NSR) database, Fantasia database (FD) and Normal Sinus Rhythm RR Interval database (NSR-RR), were used. Additionally, three RR segment length types (N = 500, 1000 and 2000) were used to evaluate the proposed method. Firstly, we extracted the expert features of RR intervals (RRIs) and then built a long short-term memory-convolutional neural network-based network to extract deep-learning (DL) features automatically. Finally, an ensemble classifier was used for CHF detection using the above features. Results: With blindfold validation (three CHF subjects and three normal subjects), the proposed method achieved 99.85%, 99.41% and 99.17% accuracy on N = 500, 1000 and 2000 length RRIs, respectively, using the BIDMC-CHF, NSR and FD databases. With blindfold validation (six CHF subjects and six normal subjects), the proposed method achieved 83.84%, 87.54% and 85.71% accuracy on N = 500, 1000 and 2000 length RRIs, respectively, using the NSR-RR and CHF-RR databases. Based on feature ranking, the significant effectiveness provided by DL features has been proven. Conclusions: The results have shown that the deep ensemble method can achieve reliable CHF detection using short-term heart rate signals and enable the CHF detection through intelligent hardware.

CO₂ electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO₂ electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO₂ reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.

The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) in catalyst design. Presently, the fine-tuning of 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 large language model tailored to the domain of electrocatalytic materials. Our findings demonstrate that CataLM exhibits remarkable potential for facilitating human-AI collaboration in catalyst knowledge exploration and design. To the best of our knowledge, CataLM stands as the pioneering LLM dedicated to the catalyst domain, offering novel avenues for catalyst discovery and development.

The electrocatalytic CO₂reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from the abundant scientific literature. An annotated and verified corpus made from massive literature can assist the development of natural language processing (NLP) models, which can offer insight to help guide the understanding of these underlying mechanisms. To facilitate data mining in this direction, we present a benchmark corpus of 6,086 records manually extracted from 835 electrocatalytic publications, along with an extended corpus with 145,179 records in this article. In this corpus, nine types of knowledge such as material, regulation method, product, faradaic efficiency, cell setup, electrolyte, synthesis method, current density, and voltage are provided by either annotating or extracting. Machine learning algorithms can be applied to the corpus to help scientists find new and effective electrocatalysts. Furthermore, researchers familiar with NLP can use this corpus to design domain-specific named entity recognition (NER) models.