Error Rate Research Articles

An RF-powered multi-device diamond relay IoT network using A-NOMA for WBAN

ABSTRACT This article presents an energy-efficient multi-device (MD) diamond relay network (DRN) using radio frequency (RF) energy harvesting (EH) using an improved adaptive non-orthogonal multiple access (A-NOMA) protocol for wireless body area network (WBAN) scenario. To understand the practical application of A-NOMA in WBAN, a realistic virtual environment has been modelled in Netsim v13.1, and subsequently the simulated values are processed in Matlab to compare the achievable sum rate (ASR) with OMA and NOMA. Next, theoretical derivations of ASR and energy efficiency (EE) are carried out and validated through extensive simulations. Moreover, the bit error rate (BER) analysis has been performed and compared with the baseline models. Additionally, an in-depth evaluation of EE with respect to ASR has been performed. Results show that our proposed approach is more effective than the existing benchmarks and reveal an intriguing trade-off between ASR and EE.

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning models have not sufficiently focused on the importance of active atoms and adsorbates, instead placing more emphasis on the overall structure of the catalytic materials. In this paper, the overall molecular graph and a masked graph, which ignores fixed atoms, are separately input into the Masked Graph Transformer (MGT) network to enhance the model's ability to recognize key sites in catalytic reactions. Second, we introduce a nonlinear message-passing mechanism to improve the dot-product attention in the Transformer and capture the directional information on the relative positions of nodes by integrating molecular geometric information through deep tensor products. Subsequently, we constructed the NLMP-TransNet framework, which combines MPNN and Transformer and optimizes the model's learning and prediction capabilities through weight sharing and residual connections. The MGT achieves an error rate of 0.5447 eV on the small data set OC20-Ni, surpassing existing technologies. Ablation studies confirm the necessity of focusing on site features for accurate adsorption energy prediction. Code is available at https://github.com/KristinSun/OCP-MGT.git.

Horst Oertel and the Russell Sage Institute of Pathology: Impacts on vital statistics and the origins of the short-lived heyday of autopsies in America.

Horst Oertel was an early 20th century pathologist who began his career as an instructor of clinical pathology at the New York University and Bellevue Hospital Medical College in 1899. In 1903, he was appointed pathologist for City Hospital, an institution for indigent patients on New York City's Blackwell's Island. In 1907, Oertel became the first director of the Russell Sage Institute of Pathology, a unique new blended public-private entity based at City Hospital, that was named after a wealthy benefactor. The Institute was established to utilize the Island's indigent population to perform anatomical pathology research on diseases of aging. Oertel and the Institute thrived until 1911, when a newly-appointed New York City bureaucrat seeking patronage and graft began meddling in the Institute's governance. After press coverage of a vitriolic one-week-long exchange of charges and counter-charges between the Institute's Board of Directors and the bureaucrat, the Institute imploded. While these events meant that the Institute's tenure on Blackwell's Island would soon be coming to an end, Oertel continued to work and submit articles from the Institute throughout 1912. In May 1913, he published "The Inaccuracy of American Mortality Statistics" in American Underwriter, a bombshell article documenting low autopsy rates and high clinical diagnostic error rates at Bellevue Hospital during 1912. This, along with similar studies by Richard Cabot at Massachusetts General Hospital in Boston, highlighted fundamental problems in hospital care and medical education across America. Simultaneously, Oertel led the Public Health, Hospital and Budget Committee of the New York Academy of Medicine which reported that, in a selected series of large American hospitals, the autopsy percentage when compared to the number of deaths was one-eighth the rates in Canada, Great Britain, Austria, and Germany. This paper addresses how American autopsy rates skyrocketed and then plummeted again during the mid-20th century.

Comparing Medical Record Abstraction (MRA) error rates in an observational study to pooled rates identified in the data quality literature

BackgroundMedical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework.MethodsA comparison of the error rates derived from MRA-centric studies identified as part of a systematic literature review was conducted against those derived from an MRA-centric study that employed an MRA-QC framework to evaluate the effectiveness of the MRA-QC framework. An inverse variance-weighted meta-analytical method with Freeman-Tukey transformation was used to compute pooled effect size for both the MRA studies identified in the literature and the study that implemented the MRA-QC framework. The level of heterogeneity was assessed using the Q-statistic and Higgins and Thompson’s I2 statistic.ResultsThe overall error rate from the MRA literature was 6.57%. Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate), 4.00–5.53% points less than the observed rate from the literature (p < 0.0001).ConclusionsReview of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study.

Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.

Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making

In an attempt to make algorithms fair, the machine learning literature has largely focused on equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate, consider a hypothetical government rideshare program that provides transportation assistance to low-income people with upcoming court dates. Following this literature, one might allocate rides to those with the highest estimated treatment effect per dollar while constraining spending to be equal across race groups. That approach, however, ignores the downstream consequences of such constraints and, as a result, can induce unexpected harm. For instance, if one demographic group lives farther from court, enforcing equal spending would necessarily mean fewer total rides provided and potentially more people penalized for missing court. Here we present an alternative framework for designing equitable algorithms that foregrounds the consequences of decisions. In our approach, one first elicits stakeholder preferences over the space of possible decisions and the resulting outcomes—such as preferences for balancing spending parity against court appearance rates. We then optimize over the space of decision policies, making trade-offs in a way that maximizes the elicited utility. To do so, we develop an algorithm for efficiently learning these optimal policies from data for a large family of expressive utility functions. In particular, we use a contextual bandit algorithm to explore the space of policies while solving a convex optimization problem at each step to estimate the best policy based on the available information. This consequentialist paradigm facilitates a more holistic approach to equitable decision making. This paper has been This paper was accepted by Catherine Tucker, Special Issue on The Human-Algorithm Connection. Funding: This work was supported by Stanford Impact Labs [Start-Up Impact Labs/GEJBU], the Stanford Institute for Human-Centered Artificial Intelligence, and Harvard Data Science Initiative. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00345 .

Kolam-Inspired Frequency Hopping: An Innovative Approach for Interference Reduction in Wireless Communication

The integration of cultural patterns into engineering applications opens new avenues for innovation and efficiency. This research explores the application of Kolam patterns, a traditional Indian art form characterized by its symmetry and periodicity, in frequency hopping for wireless communication systems. Kolam-inspired frequency hopping sequences are designed and compared with conventional pseudorandom sequences using key performance metrics, including Bit Error Rate (BER) and Signal-to-Interference Ratio (SIR). Simulations conducted under varying conditions of frequencies, number of users, hops, and Signal-to-Noise Ratio (SNR) reveal that Kolam-based sequences demonstrate superior interference management and lower error rates. The results underscore the potential of Kolam patterns to improve spectral efficiency and reliability in modern communication networks. This study paves the way for further exploration of cultural and mathematical patterns in optimizing wireless technologies.

EXPRESS: Effects of central vs peripheral attentional-oculomotor exercise on lexical processing.

Past research from our lab has suggested visual demands in video games serve to exercise attentional-oculomotor processing in a manner beneficial to reading. However, testing the effect of video games on reading typically requires long timeframes (e.g., multi-week training or years of accumulated video game experience). The current study manipulated within-experiment peripheral and central demands to evaluate the effects of attentional-oculomotor (A-O) exercise on task performance. Our study included two tasks: an orthographic lexical decision task (OLDT), designed to optimize orthographic lexical processing, and a novel graphic-based health bar decision task (HBDT). In Experiment 1, the stimuli were presented centrally in one block and peripherally in another block to manipulate A-O exercise. We observed greater improvements in the peripheral-first than the central-first group, particularly for the OLDT. In Experiments 2 and 3, we focused on the OLDT, with the HBDT serving as the A-O exercise task and observed improvements in both centrally and peripherally trained participants. We additionally observed, through analyses of word and bigram frequency, a double-dissociation whereby increased target word frequency was associated with faster target reaction times and improved error rates, while increased foil bigram frequency was associated with slower foil reaction times and worse error rates. Taken together, the experiments demonstrate a mechanism beyond simple task learning that drives reading improvements, and A-O exercise, even if movements are small, appears to play a role in the improvements observed. We suggest future research should further develop this paradigm, and examine its utility for reading remediation in dyslexia.

Optimization for the Process Parameters of Nickel–Titanium Nitride Composites Fabricated via Jet Pulse Electrodeposition

The corrosion resistance of nickel–titanium nitride (Ni/TiN) composites is significantly influenced by the operation parameters during the jet pulse electrodeposition (JPE) process. The effect of current density, jet rate, TiN concentration, and duty cycle impact on the anti-corrosion property of Ni/TiN composites were investigated and optimized using the response surface method (RSM). After the optimization of the operation parameters, the corrosion current of Ni/TiN composites decreased from 9.52 × 10−5 A/cm2 to 4.63 × 10−5 A/cm2. The corrosion current of Ni/TiN composites decreased initially and then increased with an increase in current density, jet rate, TiN concentration, and duty cycle. During the jet electrodeposition process, the influence of the duty cycle on the corrosion current of Ni/TiN composites was comparatively insignificant, whereas the concentration of TiN had a significant effect on the corrosion current. The error rate between the predicted value and the measured result from the corrosion current of Ni/TiN composites was only 0.64%, indicating the high accuracy of fitting the model. Furthermore, X-ray diffraction (XRD) patterns and scanning electron microscope (SEM) images revealed that the optimized Ni/TiN composites comprised significant Ti content, fine nickel gain, and a compact, smooth structure. In addition, the electrochemical measured results demonstrated that the optimized Ni/TiN composites possessed a low self-corrosion current and high self-corrosion potential. These findings show that the optimized composites have a substantially greater corrosion resistance compared to two other unoptimized Ni/TiN composites.

Effect of Four Process Parameters on Flexural Strength and Porosity of Metakaolin Ceramics Fabricated by Material Extrusion: Optimization and Predictive Models via Orthogonal Experiments

A high‐strength metakaolin‐based porous ceramics are fabricated using slurry‐based material extrusion via optimizing four key process parameters, including nozzle internal diameter, height to diameter ratio, filling rate, and printing speed. The orthogonal experiments are used to adjust flexural strength and porosity, and the optimal process parameters are obtained by comprehensive scoring and range analysis methods. The predictive models of strength‐parameters, porosity‐parameters, and strength‐porosity are established and validated. The results show that the optimal process parameters for high‐strength and high‐porosity ceramics are 0.51 mm nozzle internal diameter, 70% height to diameter ratio, 100% filling rate, and 15 mm s−1 printing speed. The correctness and predictability of three predictive models are proved by two methods, which are the mutual validation and comparison between theoretical and actual values. And the error rates between theoretical and actual results are less than 7%. This work provides guidance for the rapid fabrication of ceramics with adjustable strength and porosity by material extrusion, and the established predictive models can pave the way for its wider application in the practice.

A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5

In recent years, research on opinion mining from X (formerly Twitter) has rapidly advanced, focusing on processing tweets to determine user sentiments about events. Many researchers prefer using machine and deep learning techniques for this analysis. This work proposes a novel approach integrating the C4.5 procedure, fuzzy rule patterns, and convolutional neural networks. The approach involves six steps: pre-processing to remove noisy data, vectorizing tweets with word embedding, extracting sentiment and contextual features using convolutional neural networks, fuzzifying outputs with a Gaussian fuzzifier to handle ambiguity, constructing a fuzzy tree and rule base using a fuzzy version of C4.5, and classifying tweets with fuzzy General Reasoning. This method combines the benefits of convolutional neural networks and C4.5 while addressing imprecise data with fuzzy logic. Implemented on a Hadoop framework-based cluster with five computing units, the approach was extensively tested. The results showed that the model performs exceptionally well on the COVID-19_Sentiments dataset, surpassing other classification algorithms with a precision rate of 94.56%, false-negative rate of 5.28%, classification rate of 95.15%, F1-score of 94.63%, kappa statistic of 95.12%, execution time of 11.81 s, false-positive rate of 4.26%, error rate of 4.26%, specificity of 95.74%, recall of 94.72%, stability with a mean deviation standard of 0.09%, convergence starting around the 75th round, and significantly reduced complexity in terms of time and space.

On a Holm-related MTP for rejecting at least k hypotheses: general validity, optimality property, confidence regions, and applications.

This article concerns p-value-based multiple testing procedures (MTPs) that can be used in a confirmatory clinical study under minimal assumptions in case the requirement for study-success is that at least k out of m primary/important hypotheses become rejected. Recently, a simple, generally valid Holm-type MTP was discussed that can be used for such a requirement for any k from one to m. It can only reject at least k (or zero) hypotheses, but this increases the power to reject k or more hypotheses compared to Holm's step-down MTP. The present article provides a simple formulation and proof of strong family-wise error rate (FWER) control for a stepwise MTP that is sharper in that for any k strictly between one and m it: (a) always rejects at least as much, and (b) can potentially reject fewer than k hypotheses. This sharper MTP too is generally valid, without any assumption about logical or stochastic relationships. It has a gatekeeping step, followed by m steps where ordered primary p-values are compared to critical constants and rejections are made in a step-down manner. These constants have the optimality property that under a natural monotonicity restriction, they cannot be increased without losing the general strong FWER control. Confidence regions like those for Holm's MTP are provided. Applications are discussed in connection with three interesting approaches proposed earlier for confirmatory studies: (a) the Superiority-Noninferiority approach; (b) Fallback tests for co-primary endpoints; and (c) Multistage gatekeeping MTPs that utilize so-called k-truncated Holm MTPs in some stages.

Omnidirectional Bending Sensor with Bianisotropic Structure for Wearable Electronics.

Bending sensors are critical to the advancement of wearable electronics and can be applied in the dynamic monitoring of flexible object morphology. However, current bending sensors are constrained by sensing range and precision, especially in full-range detection. The maximum sensing range of existing flexible bending sensors is 0-240°. This study introduces a bianisotropic responsive structure into the design of an all-textile bending sensor, thereby realizing 0-360° full-range omnidirectional bending sensing. First, the project elucidated the sensing mechanism of the piezoresistive bianisotropic structured bending sensor and identified critical factors through a numerical simulation method. Then, the bianisotropic structured bending sensors were produced through the stitch method and analyzed on their electromechanical performance. Further, the recognition model for both bending angle and direction parameters was developed via numerical calculation, achieving a high accuracy with an error rate of 2.82%. Last, according to the ergonomics of body joints, the sensors were customized and validated in body joint monitoring scenarios. This work significantly enhances the performance of flexible bending sensors in sensing range, accuracy, and comfort for the wearer. The versatility of this bending sensor positions it as a promising candidate to supplant traditional heavy equipment or rigid devices, particularly in wearable joint motion monitoring and soft robotics.

Predicted breeding values for relative scrapie susceptibility for genotyped and ungenotyped sheep

BackgroundScrapie is an infectious prion disease in sheep. Selective breeding for resistant genotypes of the prion protein gene (PRNP) is an effective way to prevent scrapie outbreaks. Genotyping all selection candidates in a population is expensive but existing pedigree records can help infer the probabilities of genotypes in relatives of genotyped animals.ResultsWe used linear models to predict allele content for the various PRNP alleles found in Icelandic sheep and compiled the available estimates of relative scrapie susceptibility (RSS) associated with PRNP genotypes from the literature. Using the predicted allele content and the genotypic RSS we calculated estimated breeding values (EBV) for RSS. We tested the predictions on simulated data under different scenarios that varied in the proportion of genotyped sheep, genotyping strategy, pedigree recording accuracy, genotyping error rates and assumed heritability of allele content. Prediction of allele content for rare alleles was less successful than for alleles with moderate frequencies. The accuracy of allele content and RSS EBV predictions was not affected by the assumed heritability, but the dispersion of prediction was affected. In a scenario where 40% of rams were genotyped and no errors in genotyping or recorded pedigree, the accuracy of RSS EBV for ungenotyped selection candidates was 0.49. If only 20% of rams were genotyped, or rams and ewes were genotyped randomly, or there were 10% pedigree errors, or there were 2% genotyping errors, the accuracy decreased by 0.07, 0.08, 0.03 and 0.04, respectively. With empirical data, the accuracy of RSS EBV for ungenotyped sheep was 0.46–0.65.ConclusionsA linear model for predicting allele content for the PRNP gene, combined with estimates of relative susceptibility associated with PRNP genotypes, can provide RSS EBV for scrapie resistance for ungenotyped selection candidates with accuracy up to 0.65. These RSS EBV can complement selection strategies based on PRNP genotypes, especially in populations where resistant genotypes are rare.

Dual-Discriminator Conditional Generative Adversarial Network-based LDPC-Coded OFDM Free-Space OCS for Improving BER Analysis

This paper proposes a Dual-Discriminator Conditional Generative Adversarial Network-based LDPC-Coded OFDM Free-Space OCS for Improving BER Analysis (LDPC-OFDM-FSO-DDcGAN). The proposed LDPC-OFDM-FSO-DDcGAN method containsthe transmitter model, the channel model and the receiver model. The binary data are given to the encoder of LDPC in the transmitter model and the input data encode previously controlling the situation using QAM. To decrease the effects of fading caused by turbulence, the channel model intends a probability density function with FSO-DDcGAN. The maximal ratio combing (MRC), equal gain combining (EGC) and selection combining (SC) can be employed by the combiner to integrate the signal once it has been received from the FSO channel. The combiner is used to improve one signal by combining the several signals received. The bit of data is transformed towards a sequential stream after combiner. The proposed Probability Density Function (PDF) offers high SNR gain, BER reduction and power efficiency when combined with DDcGAN. To achieve large coding gain, the standard coding methods of LDPC are used through only a code percentage as realistic to imitation data. The proposed LDPC-OFDM-FSO-DDcGAN technique is executed in MATLAB. The LDPC-OFDM-FSO-DDcGAN method provides higher power efficiency and lower bit error rate analysed with the existing models.

How Chatbots Respond to NCLEX-RN Practice Questions: Assessment of Google Gemini, GPT-3.5, and GPT-4.

ChatGPT often "hallucinates" or misleads, underscoring the need for formal validation at the professional level for reliable use in nursing education. We evaluated two free chatbots (Google Gemini and GPT-3.5) and a commercial version (GPT-4) on 250 standardized questions from a simulated nursing licensure exam, which closely matches the content and complexity of the actual exam. Gemini achieved 73.2 percent (183/250), GPT-3.5 achieved 72 percent (180/250), and GPT-4 reached a notably higher performance with 92.4 percent (231/250). GPT-4 exhibited its highest error rate (13.3%) in the psychosocial integrity category.

Developmental features of multi-word expressions in spoken discourse by Chinese learners of English

Multi-word expressions (MWEs) serve as vital indicators of language development and have been a primary focus in second language acquisition research. The use of MWEs in spoken discourse, however, remains relatively under-explored. To address this gap, this study aims to investigate the current status and developmental features of MWE use in spoken English by Chinese learners of English across three proficiency levels based on Nattinger and DeCarricos’ classification framework of MWEs. A comparative analysis of MWE usage patterns across these groups yields two key findings: (1) The overall proficiency in using MWEs is relatively low, marked by an imbalance and inaccuracy in MWE types. Specifically, learners exhibit minimal use of “polywords” and “institutionalised expressions” within the structural dimension, and “discourse devices” and “social interactions” within the functional dimension. Moreover, learners demonstrate a high error rate across various MWE types. (2) Although the overall proficiency in using MWEs shows no significant improvement across the three levels, an upward trend is observed in the usage of overall tokens, types, and various categories of MWEs in both dimensions, culminating in a significant increase in the variation of MWEs at the highest proficiency level. Drawing upon these findings, this study proposes several pedagogical implications for enhancing the teaching and learning of MWEs in spoken discourse.

Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa

BackgroundThe use of the modern family planning method provides chances for women to reach optimal child spacing, increase quality of life, increase economic status, achieve the desired family size, and prevent unsafe abortions and maternal and perinatal deaths. However, use modern family planning is low among reproductive age women in East Africa. Therefore, this study aimed at predicting not using modern family planning and identifying its associated factors among reproductive age women in east Africa using machine learning methods.MethodsThe data set was obtained from a demographic health survey in East Africa. This study used data on weighted sample of 92,564 women who were in their reproductive age. Python software was utilized for data processing, and machine learning models such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), SVM (Support Victor Machine), and K-Nearest Neighbor Decision Tree (DT) were implemented. In this work, we evaluated the predictive models’ performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve.ResultIn this study, 92,564 reproductive women’s was used in the final analysis. Among the proposed machine learning models, XGB performed best overall in the proposed classifier, with an accuracy of 98.7%, precision of 99.8%, recall of 98%, 99.9% AUC score, had high specificity 98%, sensitivity 99.8%, and low error rate discovery was 0.013%. The most significant determinants of not using modern family planning in East Africa: lack of education, age 25–29, living in a rural area, being single, not knowing how to use a contraceptive, and smoking cigarette.ConclusionExtreme Gradient Boosting proved effective in predicting not using modern family planning, offering valuable insights for interventions and policy decisions in East Africa. Machine learning could help achieve early prediction and intervention on reproductive women’s not using modern family planning. This leads to a recommendation for policy direction to reduce child and maternal mortality and improve living standards in east Africa.

Precise and Accurate DNA-3′/5-Ends Polishing with Thermus thermophilus Phage vb_Tt72 DNA Polymerase

Tt72 DNA polymerase is a newly characterized PolA-type thermostable enzyme derived from the Thermus thermophilus phage vB_Tt72. The enzyme demonstrates strong 3′→5′ exonucleolytic proofreading activity, even in the presence of 1 mM dNTPs. In this study, we examined how the exonucleolytic activity of Tt72 DNA polymerase affects the fidelity of DNA synthesis. Using a plasmid-based lacZα gene complementation assay, we determined that the enzyme’s mutation frequency was 2.06 × 10−3, corresponding to an error rate of 1.41 × 10−5. For the exonuclease-deficient variant, the mutation frequency increased to 6.23 × 10−3, with an associated error rate of 4.29 × 10−5. The enzyme retained 3′→5′ exonucleolytic activity at temperatures up to 70 °C but lost it after 10 min of incubation at temperatures above 75 °C. Additionally, we demonstrated that Tt72 DNA polymerase efficiently processes 3′/5′-overhangs and removes a single-nucleotide 3′-dA overhang from PCR products at 55 °C. These characteristics make Tt72 DNA polymerase well suited for specialized molecular cloning applications.

Enhancing Personalized Mental Health Support Through Artificial Intelligence: Advances in Speech and Text Analysis Within Online Therapy Platforms

Automatic speech recognition (ASR) and natural language processing (NLP) play key roles in advancing human–technology interactions, particularly in healthcare communications. This study aims to enhance French-language online mental health platforms through the adaptation of the QuartzNet 15 × 5 ASR model, selected for its robust performance across a variety of French accents as demonstrated on the Mozilla Common Voice dataset. The adaptation process involved tailoring the ASR model to accommodate various French dialects and idiomatic expressions, and integrating it with an NLP system to refine user interactions. The adapted QuartzNet 15 × 5 model achieved a baseline word error rate (WER) of 14%, and the accompanying NLP system displayed weighted averages of 64.24% in precision, 63.64% in recall, and an F1-score of 62.75%. Notably, critical functionalities such as ‘Prendre Rdv’ (schedule appointment) achieved precision, recall, and F1-scores above 90%. These improvements substantially enhance the functionality and management of user interactions on French-language digital therapy platforms, indicating that continuous adaptation and enhancement of these technologies are beneficial for improving digital mental health interventions, with a focus on linguistic accuracy and user satisfaction.