Accuracy Rate Research Articles

A non-invasive heart rate prediction method using a convolutional approach.

The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the global challenge of heart failure, the study seeks to provide a rapid, accurate, and non-invasive alternative to traditional, uncomfortable blood pressure cuffs. To achieve more accurate and efficient heart rate estimates, a k-fold CNN model with an optimal number of convolutional layers is employed. While the findings show promising results, the study addresses potential sources of error in cuffless PPG-based heart rate measurement, including motion artifacts and skin color variations, emphasizing the need for validation against gold standard methods. The research optimizes a CNN model with optimal layers, operating on 1D arrays of 8-s data slices and employing k-fold cross-validation to mitigate probabilistic uncertainties. Finally, the model yields a remarkable minimum absolute error (MAE) rate of 6.98 beats per minute (bpm), marking a significant 10% improvement over recent studies. The study also advances medical diagnostics and data analysis, then lays a strong foundation for developing cost-effective, reliable devices that offer a more comfortable and efficient way of predicting heart rate.

Predictive Power of Key Financial Variables During the Unconventional Monetary Policy Era

ABSTRACTThis study investigates the forecasting power of three well‐established financial predictors during the prolonged era of unconventional monetary policy: the term spread, the short‐term interest rate, and stock returns. The focus is on predicting GDP growth in both the United States and the Euro area. Our out‐of‐sample forecasting analysis specifically targets the period characterized by the short‐term interest rate effectively bounded at or near the zero lower bound. We recognize that the information content of the term spread is likely to change under such circumstances. Similarly, the dynamics of the short‐term interest rate could be altered due to unconventional monetary policy measures. To address this, we modify the short rate calculation by incorporating the shadow interest. This shadow interest rate can go much lower on the negative side than normal interest rates, making it a potentially more accurate rate to describe the monetary policy stance of central banks. The forecasting analysis covers the period from 2009:1 to 2022:3. Our results unambiguously reveal that the predictive power of the term spread completely vanishes during the zero lower bound era. Although the shadow rate has minor predictive content, the strongest predictor consistently lies in real stock returns during unconventional monetary policy. Our findings challenge the conventional wisdom and the stylized fact of the term spread as the most reliable financial predictor for economic activity. According to our results, this does not hold true under unconventional monetary policy, and using the shadow interest rate does not make a major difference in that respect. By shedding light on the changing dynamics during unconventional monetary policy, our study contributes novel insights to the existing literature.

Functions of a Complex Variable with a Large Parameter and Construction of Regions

The paper describes an experimental setup for studying the solubility of substances in SCF media and the implementation of a supercritical fluid extraction process using a pure and modified polar solvent additive. The advantages and disadvantages of this installation are analyzed, ways of improving it are indicated, leading to achieving a more accurate GFR flow rate, maintaining the necessary constant pressure in the first extract collector for fractionation of a certain component of the mixture and eliminating multiple regulation of the GFR flow rate.

Erratum: Regional variation in low-value musculoskeletal surgery: a nationwide study from the Finnish Care Register.

We would like to inform of an error in the reported incidences in our published article,Regional variation in low-value musculoskeletal surgery: a nationwide study from the Finnish Care Register. Specifically, the incidences for rotator cuff repair, partial meniscectomy, ankle arthroscopy, distal radius fracture fixation, and wrist arthroscopy surgeries were calculated using a too small population size. Accordingly, corrected calculations resulted in higher incidence values for these surgeries, impacting Table 3andFigure 3, which now show accurate incidence rates. We have also updatedFigure 6and the corresponding sections of the results accordingly. These corrections did not affect any other figures or tables, not the overall conclusions, and the text in the discussion section remains unchanged. The correct data further emphasizes the findings in our original article. We apologize for any confusion caused by these errors and appreciate the opportunity to correct the publication.

Rate coefficients for C and O2 reactive collisions relevant to interstellar clouds from QCT and machine learning.

The chemical reactions between certain interstellar molecules are exothermic in nature and barrierless in the entrance channel, allowing these reactions to occur rapidly even at low astronomical temperatures, e.g., C and O2 interaction. Obtaining detailed rovibrational transition parameters for the reaction between C and O2, such as state-selected rate coefficients, is crucial for studying the associated atmospheric and astronomical environments. Hence, this work presents an approach that combines quasi-classical trajectory calculations with machine learning techniques based on Neural Network (NN) and Gaussian Process Regression (GPR) to determine state-selected rate coefficients. Employing this approach, we significantly reduced the computational requirements while simultaneously obtaining the accurate state-selected reaction cross sections and rate coefficients for the collision of C and O2. Both the NN-based and GPR-based models established in this work accurately predict the results calculated from explicit numerical calculations in the explored temperature range of 50-1500K, achieving a coefficient of determination R2 > 0.96. Most importantly, the current work provides the most comprehensive dataset of rovibrational rate coefficients of v = 0-4, j = 0-70 → v' = 0-15 for the astrophysical modeling of the C-O2 collision system.

Enhancing Eyringpy: Accurate Rate Constants with Canonical Variational Transition State Theory and the Hindered Rotor Model.

The recrossing effect and hindered rotations can lead to significant inaccuracies in rate constant calculations using transition state theory and the harmonic oscillator approximation. To address these issues, we enhanced Eyringpy, a Python-based computational tool, by integrating the canonical variational transition state theory (CVT) and the hindered rotor model, effectively mitigating the limitations of traditional methods. CVT rate constants are calculated using electronic structure data from nonstationary points along the minimum-energy path. Additionally, we developed an algorithm based on reaction force analysis to autonomously select relevant nonstationary points. Torsions are modeled using one-dimensional hindered rotor approaches proposed by Pitzer-Gwinn and Ayala-Schlegel.

Comparative analysis of GPT-4, Gemini, and Ernie as gloss sign language translators in special education

While several comparative studies have analyzed the effectiveness of various large language models (LLMs), most of them were technical (i.e., comparing execution time, among others). Additionally, these comparative studies did not discuss special education. Consequently, scant information exists about how effective LLMs are in special education. To address this research gap, this study conducted a comparative study of three LLMs, namely GPT-4o, Gemini, and Ernie, as gloss sign language translators for learners with hearing impairments. Specifically, a mixed method was adopted, where the translated outputs of the three LLMs were compared (quantitatively and qualitatively) to two sign language outputs from a sign language expert. The obtained results highlighted that Gemini outperformed both GPT-4o and Ernie as an accurate gloss sign language translator. Additionally, GPT-4o had a high accurate rate, while Ernie had a very low translation performance. The findings of this study can help to raise awareness about the use of LLMs in special education as well as the best ones to use especially with hearing impairment learners.

Crop Leaf Type Classification and Multi-Class Leaf Disease Identification with Tangent Hunter Prey Optimization-Based LeNet

The early detection and accurate rate of classification of plant leaf disease are more important for reliable and good agriculture, which prevents unwanted financial loss and resources. In this study, Tangent Hunter Prey Optimization-based LeNet (THPO-LeNet) is utilized for crop leaf classification and multi-class plant leaf disease identification. In this case, the input image that goes through the image pre-processing step is obtained from the plant village dataset. An adaptive Wiener filter is applied at the pre-processing stage to reduce needless mistakes and improve image quality. The pre-processed image is then sent to the leaf segmentation step, where a Mask Region-based Convolution Neural Network (Mask R-CNN) performs the segmentation. Consequently, image augmentation is performed using techniques such as scaling, rotation, translation, flipping, contrast, saturation, and hue. Afterwards, first-level classification plant leaf classification is processed by LeNet, which is optimized by Tangent Hunter Prey Optimization (THPO). The THPO is the incorporation of a Tangent Search Algorithm (TSA) and Hunter–Prey Optimizer (HPO). At last, the second-level classification of plant leaf disease is conducted by THPO-LeNet. Furthermore, the efficiency of THPO-LeNet is examined based on measures, like accuracy, Negative Predictive Value (NPV), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), loss value, and False Positive Rate (FPR), and the value attained is 95.68%, 92.50%, 91.48%, 92.25%, 92.54%, 8.321%, and 7.754%, respectively.

Constructing a novel double Z-type junction BaTiO3/ZnIn2S4/ZnTiO3 by a new chemical reaction for antibiotics degradation, H2 production, and CO2 reduction

At present, only single charge transfer channel is available in the study of BaTiO3/ZnIn2S4 heterojunction. In order to overcome this bottleneck, this paper adopts a novel chemical reaction to promote the transformation of ZnIn2S4 into ZnTiO3, thereby creating a novel triphase junction BaTiO3/ZnIn2S4/ZnTiO3 and a new transfer channel. This result leads to the photodegradation efficiencies of the levofloxacin and the lomefloxacin attaining 89 % and 92 % for the light irradiation of 30 minutes. Moreover, the removal efficiencies of the total organic carbon and the chemical oxygen demand reach 65 % and 84 % in the levofloxacin degradation, while these are 63 % and 81 % in the lomefloxacin degradation. Further, the H2 production rate attains 2.47 mmol/g/h, while the CO2 reduction rate to CO also reach 27.43 μmol/g/h. The accurate rate achieves 97.05 % for the prediction of the photocatalytic efficiency based on the Back Propagation neural network. The development toxicity indexes of the intermediate products reduce gradually to indicate the final solution is environmentally friendly. Therefore, the BaTiO3/ZnIn2S4/ZnTiO3 has a potential application in the sewage purification and the clean energy generation.

Toward Efficient Modeling of Nonradiative Decay in Extended INVEST: Overcoming Computational Challenges in Quantum Dynamics Simulations.

In recent years, an increasing number of fully organic molecules capable of thermally activated delayed fluorescence (TADF) have been reported, often with very small or even inverted singlet-triplet (INVEST) energy gaps. These molecules typically exhibit complex photophysics due to the close energy levels of multiple singlet and triplet states, which create various transition pathways toward emission. A predictive model for the rates of these transitions is thus essential for assessing the suitability of new materials for light-emitting devices. Quantum Dynamics (QD) calculations are ideal for this purpose, as they include quantum effects, without the limitations of first-order perturbative approaches, also allowing taking into account more than two electronic states at once. However, the huge computational demands of QD methodologies, especially for large molecules, currently limit their use as a standard tool. To address this problem, we here employ a strategy that allows us to include almost the whole set of the vibrational coordinates by selecting the key elements of the Hilbert space that significantly impact dynamics, thereby hugely reducing the computational burden. Application of this protocol to two relatively large INVEST molecules reveals that internal conversion in these systems is very fast, making indirect emissive pathways a possible channel for the population of the S1 state. More importantly, this study demonstrates that the dynamics can be accurately described even with a significantly reduced vibrational space, thus allowing quantum dynamics calculations that yield accurate transition rates in a few minutes of computational time.

A Neural Network Approach to Understanding Employee Retention Dynamics: Insights from Feature Importance Analysis

In today’s business environment, employee retention has become a significant challenge, as employee turnover can lead to decreased productivity, increased costs, and reduced morale. This study aims to leverage neural network technology to predict employees' retention intentions and conduct in-depth analyses based on employee data. A classification model was established considering various factors, including gender, marital status, number of children, education level, years of service, weekly working hours, career development opportunities, salary, and bonuses, to identify the potential risk of employee turnover. The results indicated that the model achieved an accurate classification rate of 95.12% on the test set, demonstrating high effectiveness in identifying employee retention intentions. Feature importance analysis revealed that education level (29.63%), bonuses (27.50%), and the number of children (21.81%) were the primary factors influencing retention decisions. Additionally, working hours, marital status, and career development opportunities also impacted employees’ retention intentions. This research not only provides insights into employee mobility but also offers data support for enterprises to develop effective retention strategies, suggesting that businesses prioritize enhancing education and training opportunities, designing competitive bonus systems, and providing flexible work arrangements and benefits tailored to employees with children.

MASER and dasar lines of disilicon carbide (SiCSi) using accurate collisional rate coefficients

MASER and dasar lines of disilicon carbide (SiCSi) using accurate collisional rate coefficients

Rain Fade Analysis on High Frequency Signal: A Review

A reliable transmission of high-frequency signal of more than 10 GHz is crucial for various communication systems, especially in the era of advanced technologies and wireless communications. Rain-induced signal fades pose a significant challenge to signal integrity, making accurate rain rate measurement methods essential for system optimization. This review critically examines the existing techniques for measuring rain rate and the applicability in analysing high-frequency signal fades. Several case studies and practical examples are presented to illustrate the significance of accurate rain rate measurements in predicting and mitigating signal degradation. Researchers are working to enhance existing models to better fit local environmental variables based on all the case studies. Having an accurate estimation of the level of rain attenuation in the signal is one of the components to ensuring the quality of service of a communication signal. Signal availability can be ensured with suitable rain attenuation modelling, assuming that all other communication elements are functioning as planned.

Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar.

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar's coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

Small fatigue crack behavior of CP-Ti in thin-walled cruciform specimens under biaxial loading

This study investigates the small fatigue crack propagation behavior of commercially pure titanium (CP-Ti) using thin-walled cruciform specimens under in-plane biaxial loading, considering the effects of biaxial ratio and phase angle. Increasing phase angle results in more secondary cracks merging with main cracks perpendicular to the rolling direction (RD) and transverse direction (TD), a phenomenon attributed to the rise in shear stress that accelerates main crack growth. Higher loading biaxiality or a lower phase angle leads to decreased crack propagation rates and increased biaxial fatigue life. Electron backscatter diffraction (EBSD) analysis reveals that when the maximum normal stress aligns with the RD, prismatic slip primarily governs crack propagation, thereby accelerating crack propagation rates. Conversely, alignment with the TD reduces prismatic slip activity and crack propagation rates. Under equi-biaxial loading, prismatic slip activity decreases further, and crack propagation is dominated by multiple slip and twinning, consequently resulting in the slowest propagation rates. Additionally, a higher proportion of prismatic slip under high phase angle also accelerates crack propagation. Finally, incorporating Findley equivalent stress into the Chapetti model, which considers the crack length-dependent threshold effect, a highly accurate biaxial small fatigue crack propagation rate model is proposed.

Converting Multiple- to Single-DNA-Tethered Beads and Removing Only-One-End-Tethered DNA in High-Throughput Stretching.

S-DNA is a double-stranded DNA that forms under tensions of >65 pN. Here, we report that S-DNA resists the cleavage of Cas12a and the restriction endonuclease SmaI. Taking advantage of this resistance, in magnetic tweezer experiments, we developed an assay to convert multiple-DNA-tethered beads into single-DNA-tethered beads and remove the only-one-end-tethered DNA molecule by cleaving the DNA that does not transition to S-DNA at about 80 pN. When multiple DNA molecules are tethered to a single bead, they share the tension, exist in the B-form, and allow the cleavage. Only-one-end-tethered DNA molecules, free of tension, are also cleaved. In versatile types of experiments, we proved the broad applications of this assay: measuring the correct DNA elasticity and DNA condensation dynamics by avoiding the false results due to interference of only-one-end-tethered DNA molecules and quantifying the accurate cleavage rates of Cas12a and the restriction endonucleases by eliminating the error caused by multiple-DNA-tethered beads. This convenient assay ensures correct and accurate results in high-throughput DNA stretching experiments.

AI-Based Smart Proxy Models for Accurate Oil Rate Prediction and Efficient Pipeline Monitoring

AI-Based Smart Proxy Models for Accurate Oil Rate Prediction and Efficient Pipeline Monitoring

Committor Guided Estimates of Molecular Transition Rates.

The probability that a configuration of a physical system reacts, or transitions from one metastable state to another, is quantified by the committor function. This function contains richly detailed mechanistic information about transition pathways, but a full parametrization of the committor requires the construction of a high-dimensional function, a generically challenging task. Recent efforts to leverage neural networks as a means to solve high-dimensional partial differential equations, often called "physics-informed" machine learning, have brought the committor into computational reach. Here, we build on the semigroup approach to learning the committor and assess its utility for predicting dynamical quantities such as transition rates. We show that a careful reframing of the objective function and improved adaptive sampling strategies provide highly accurate representations of the committor. Furthermore, by directly applying the Hill relation, we show that these committors provide accurate transition rates for molecular systems.

Methanimine in Cool Cosmic Objects Using Accurate Collisional Rate Coefficients

Methanimine in Cool Cosmic Objects Using Accurate Collisional Rate Coefficients

Correlation Fuzzy measure of multivariate time series for signature recognition.

Distinguishing different time series, which is determinant or stochastic, is an important task in signal processing. In this work, a correlation measure constructs Correlation Fuzzy Entropy (CFE) to discriminate Chaos and stochastic series. It can be employed to distinguish chaotic signals from ARIMA series with different noises. With specific embedding dimensions, we implemented the CFE features by analyzing two available online signature databases MCYT-100 and SVC2004. The accurate rates of the CFE-based models exceed 99.3%.