Accurate Threshold Research Articles

Rancang Bangun Sistem Keamanan Laboratorium Menggunakan Face Recognition Berbasis Internet of Thing (IoT)

An Internet of Things (IoT)-based laboratory security system is designed to improve security and monitoring efficiency. This study aims to develop such a system using ESP-32 CAM (Embedded Serial Peripheral Interface 32-bit Camera), a camera module with an ESP32 microcontroller with Wi-Fi connectivity and image processing capabilities for real-time access control and room monitoring. Firebase is a cloud-based facial data storage platform, and the Telegram application is a medium that allows automatic notifications. Access control testing was conducted involving five user samples. The system recorded a facial recognition success rate of 90% (9 out of 10 tests were successful), which is considered "good" based on the accuracy threshold of ≥ 85% for security applications. However, accuracy decreases in nonideal conditions, such as when the user's distance from the camera exceeds 30 cm or the angle of view of the face is not aligned with the camera. Non-ideal refers to operational situations where optimal parameters, such as distance and facial orientation, are unsatisfied. The notification delivery delay to the Telegram application was tested using Wireshark, with an average time of 18,528 ms, indicating that the system has a fast response in real-time. In addition, the monitoring camera (ESP-32 CAM) successfully sent stable real-time video to the laboratory manager's web without significant interference. The test results show that this system meets the design objectives as an efficient laboratory security solution. Some improvements are needed to improve accuracy under non-ideal conditions and expand monitoring testing to various network scenarios.

POST PROCESSING OF PREDICTIONS TO IMPROVE THE QUALITY OF RECOGNITION OF WATER SURFACE OBJECTS

Context. The significance of this work stems from the growing need for UAV technologies integrated with artificial intelligence, aimed at detecting and identifying objects on the surface of water bodies. Modern needs in water body monitoring, especially in the context of environmental monitoring, protection and resource management, require accurate and reliable solutions. This work demonstrates methods for improving the performance of neural networks and offers approaches for processing NN predictions, even if they are trained on irrelevant data, which increases the versatility and efficiency of the technology. Objective. The goal of the work is to solve the problem of false recognition of objects on the surface of water bodies, which is due to a decrease in the accuracy threshold for the neural network. This provides more accurate and reliable detection, reducing the number of false positive predictions and increasing the efficiency of the system in general. Method. It is proposed to add a stage of post-processing of NN predictions, which inherits concepts of min-max suppression used by YOLO models. This algorithm suppresses the re-detection of the object by the network and relies on the cross-sectional area of the detected rectangles. It uses a threshold value of 0.8 for the two points of the rectangle, which can effectively reduce the number of re-predictions and improve the accuracy. Results. As a result of the implementation of the proposed algorithm and the script created on its basis, a result was achieved in which groups from several predictions are combined and filtered. The received data is stored in the database as found and detected objects. The proposed post-processing algorithm effectively removes redundant predictions while maintaining forecast accuracy. This ensures the reliability of the system and increases its performance in real conditions. Conclusions. Detected images of objects on the surface of water bodies are stored in the database in the form of records with unique file name identifiers. After tests with pre-taken images algorithm proved it`s persistence against data duplication scenarios. This increases the efficiency and reliability of the monitoring system, ensuring accurate and timely detection of objects on the surface of water bodies.

Using ASR Tools to Produce Automatic Subtitles for TV Broadcasting

This paper discusses the potential use of Automatic Speech Recognition (ASR) tools to produce intralingual subtitles for broadcasting purposes. Two different ASR tools were trialled by an international broadcaster to produce automatic subtitles for pre-recorded content in English and in Italian, a British talk show and a US feature film dubbed into Italian. A study was commissioned to compare the performance of the two tools on the materials. Our evaluation focused on two key dimensions: the accuracy of the transcript and the readability of the subtitles. Accuracy was assessed quantitatively by using an adaptation of the NER and NTR models (Romero-Fresco & Martínez 2015, Romero-Fresco & Pöchhacker 2017), which focuses on ASR-generated errors and categorises them by error type (content- or form- related) and by level of severity (minor, standard and critical). Readability was assessed qualitatively by analysing text segmentation, namely line breaks and subtitle breaks. Our findings indicate that all the ASR outputs fell short of the 98% accuracy threshold expected in the broadcasting industry, although performance was notably better in English. Moreover, subtitle segmentation and timing were found to be relatively poor in the subtitles produced by both tools in both languages. Therefore, the ASR-generated subtitles from the samples provided by the broadcaster can only be considered an intermediate step. Substantial human input is required before the tools can be put to work (customisation) and after the ASR has generated the subtitles (human post-editing) to produce broadcast-ready subtitles. Lay summary This paper explores using Automatic Speech Recognition (ASR) tools to create intralingual subtitles (i.e. in the same language) for pre-recorded content. The study was commissioned by an international broadcaster to test two ASR tools for generating subtitles in English and Italian, covering a British talk show and a US film dubbed into Italian. The study compared the tools' performance, focusing on two dimensions, i.e. subtitles’ accuracy and readability. The former, i.e. accuracy, was measured using a model that enabled us to categorise and weigh errors generated by the ASR tools. The latter, i.e. readability, was measured by considering lines and subtitles breaks. The evaluation revealed that both tools fell short of the industry's expected 98% accuracy, especially in Italian. Additionally, subtitle segmentation and timing were found to be subpar in both languages. Consequently, substantial human involvement, including customisation and post-editing, is necessary to produce high-quality broadcast-ready subtitles.

Canonical coupled cluster binding benchmark for nanoscale noncovalent complexes at the hundred-atom scale.

In this study, we introduce two datasets for nanoscale noncovalent binding, featuring complexes at the hundred-atom scale, benchmarked using coupled cluster with single, double, and perturbative triple [CCSD(T)] excitations extrapolated to the complete basis set (CBS) limit. The first dataset, L14, comprises 14 complexes with canonical CCSD(T)/CBS benchmarks, extending the applicability of CCSD(T)/CBS binding benchmarks to systems as large as 113 atoms. The second dataset, vL11, consists of 11 even larger complexes, evaluated using the local CCSD(T)/CBS method with stringent thresholds, covering systems up to 174 atoms. We compare binding energies obtained from local CCSD(T) and fixed-node diffusion Monte Carlo (FN-DMC), which have previously shown discrepancies exceeding the chemical accuracy threshold of 1 kcal/mol in large complexes, with the new canonical CCSD(T)/CBS results. While local CCSD(T)/CBS agrees with canonical CCSD(T)/CBS within binding uncertainties, FN-DMC consistently underestimates binding energies in π-π complexes by over 1 kcal/mol. Potential sources of error in canonical CCSD(T)/CBS are discussed, and we argue that the observed discrepancies are unlikely to originate from CCSD(T) itself. Instead, the fixed-node approximation in FN-DMC warrants further investigation to elucidate these binding discrepancies. Using these datasets as reference, we evaluate the performance of various electronic structure methods, semi-empirical approaches, and machine learning potentials for nanoscale complexes. Based on computational accuracy and stability across system sizes, we recommend MP2+aiD(CCD), PBE0+D4, and ωB97X-3c as reliable methods for investigating noncovalent interactions in nanoscale complexes, maintaining their promising performance observed in smaller systems.

Convergent Concordant Mode Approach for Molecular Vibrations: CMA-2.

The concordant mode approach (CMA) is a promising new scheme for dramatically increasing the system size and level of theory achievable in quantum chemical computations of molecular vibrational frequencies. Here, we achieve advances in the CMA hierarchy by computations targeting CCSD(T)/cc-pVTZ (coupled cluster singles and doubles with perturbative triples using a correlation-consistent polarized-valence triple-ζ basis set) benchmarks within the G2 molecular test set, executing a statistical analysis for 1501 frequencies from 111 compounds and then separately solving the refractory case of pyridine. First, MP2/cc-pVTZ (second-order Møller-Plesset perturbation theory with the same basis set) proves to be an excellent and preferred choice for generating the underlying (Level B) normal modes of the CMA scheme. Utilizing this Level B within the CMA-0A method reproduces the 1501 benchmark frequencies with a mean absolute error (MAE) of only 0.11 cm-1 and an attendant standard deviation of 0.49 cm-1. Second, a convergent CMA-2 method is constituted that allows efficient computation of higher level (Level A) frequencies to any reasonable accuracy threshold by using only Hartree-Fock (HF) and MP2 or density functional theory (DFT) data to generate ξ parameters, which select the sparse off-diagonal force field elements for explicit evaluation at Level A. When Level B = MP2/cc-pVTZ, a cutoff of ξ = 0.02 provides an average maximum absolute error per molecule of only 0.17 cm-1 by incurring merely a 33% increase in average cost over CMA-0A. This CMA-2 method also eradicates the 4 problematic CMA-0A outliers of pyridine with even less effort (ξ = 0.04, 22% increase). Finally, the newly developed CMA procedures are shown to be highly successful when applied to 1-(1H-pyrrol-3-yl)ethanol, a new test molecule with diverse types of vibration.

Plasma proteomics for novel biomarker discovery in childhood tuberculosis.

Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, and identifying novel non-sputum-based TB biomarkers to improve pediatric TB diagnosis is a global priority. We sought to develop a plasma biosignature for TB by probing the plasma proteome of 511 children stratified by TB diagnostic classification and HIV status from sites in four low- and middle-income countries, using high-throughput data-independent acquisition mass-spectrometry (DIA-PASEF-MS). We identified 47 proteins differentially regulated (BH adjusted p-values < 1%) between children with microbiologically confirmed TB and children with non-TB respiratory diseases (Unlikely TB). We further employed machine learning to derive three parsimonious biosignatures encompassing 4, 5, or 6 proteins that achieved AUCs of 0.86-0.88 all of which exceeded the minimum WHO target product profile accuracy thresholds for a TB screening test (70% specificity at 90% sensitivity, PPV 0.65-0.74, NPV 0.92-0.95). This work provides insights into the unique host response in pediatric TB disease, as well as a non-sputum biosignature that could reduce delays in TB diagnosis and improve detection and management of TB in children worldwide.

Efficiency of Artificial Intelligence for Interpretation of Chest Radiograms in the Republic of Tajikistan

The article presents data from recent publications and own data on screening studies with interpretation of chest radiographs using artificial intelligence CAD (Computer-Assisted Diagnosis), which, according to WHO recommendations, provides more accurate clinical thresholds for deciding who needs to take a sputum test. Another aspect of the WHO recommendations is the cost-effectiveness of CAD as a tool for triaging patients with tuberculosis symptoms in low-income countries with a high incidence of tuberculosis. Compared with smear microscopy and GeneXpert, without preliminary sorting, the use of mobile digital X-ray machines equipped with a CAD tool reduces costs, allowing sorting of individuals suspected of having tuberculosis for testing on GeneXpert, while reducing the time to start tuberculosis treatment. Thus, conducting a study using portable X-ray machines using a CAD program is a low-cost and easy-to-implement method, does not require large funds, does not require separate rooms, is highly effective, has good image quality, allows you to quickly clarify individuals suspected of having tuberculosis, differentiating it from other pathological changes in the lungs. Our experience shows that machine analysis of chest computed tomography data, due to the higher resolution capabilities of the method and the absence of fundamental disadvantages of radiography, including the effect of shadow summation, the presence of “blind” zones, etc., is finding increasing application in both diagnostics and screening of respiratory diseases. Our use of this tool allowed us to identify additional new cases of phthisio-onco-pulmonary diseases in field conditions.

Post-CCSD(T) Thermochemistry of Chlorine Fluorides as a Challenging Test Case for Evaluating Density Functional Theory and Composite Ab Initio Methods.

Quantum chemistry plays a key role in exploring the chemical properties of highly reactive chlorine polyfluoride compounds (ClFn). Here, we investigate the thermochemical properties of ClFn species (n=2-6) by means of high-level thermochemical procedures approximating the CCSDT(Q) and CCSDTQ5 energies at the complete basis set limit. We consider total atomization energies (TAEs), Cl-F bond dissociation energies (BDEs), F2 elimination energies (F2 elim.), ionization potentials (IPs), and electron affinities (EAs). The TAEs have significant contributions from post-CCSD(T) correlation effects. The higher-order triple excitations, CCSDT-CCSD(T), are negative and amount to -0.338 (ClF2), -0.727 (ClF3), -0.903 (ClF4), -1.335 (ClF5), and -1.946 (ClF6) kcal/mol. However, the contributions from quadruple (and, where available, also quintuple) excitations are much larger and positive and amount to +1.335 (ClF2), +1.387 (ClF3), +2.367 (ClF4), +2.399 (ClF5), and +3.432 (ClF6) kcal/mol. Thus, the contributions from post-CCSD(T) excitations exceed the threshold of chemical accuracy in nearly all cases. Due to their increasing hyper-valency and multireference character, the ClFn series provides an interesting and challenging test case for both density functional theory and low-level composite ab initio procedures. Here, we highlight the limitations in achieving overall chemical accuracy across all DFT and most composite ab initio procedures.

Computational approaches to apply the String Edit Algorithm to create accurate visual scan paths

Eye movement detection algorithms (e.g., I-VT) require the selection of thresholds to identify eye fixations and saccadic movements from gaze data. The choice of threshold is important, as thresholds too low or large may fail to accurately identify eye fixations and saccades. An inaccurate threshold might also affect the resulting visual scan path, the time-ordered sequence of eye fixations and saccades, carried out by the participant. Commonly used approaches to evaluate threshold accuracy can be manually laborious, or require information about the expected visual scan paths of participants, which might not be available. To address this issue, we propose two different computational approaches, labeled as “between-participants comparisons” and “within-participants comparisons.” The approaches were evaluated using the open-source Gazebase dataset, which contained a bullseye-target tracking task, where participants were instructed to follow the movements of a bullseye-target. The predetermined path of the bullseye-target enabled us to evaluate our proposed approaches against the expected visual scan path. The approaches identified threshold values (220°/s and 210°/s) that were 83% similar to the expected visual scan path, outperforming a 30°/s benchmark threshold (41.5%). These methods might assist researchers in identifying accurate threshold values for the I-VT algorithm or potentially other eye movement detection algorithms.

Abstract 4136330: It’s ‘Tachy’ To Be Fooled - Adenosine Use During Leadless Pacemaker Placement to Ensure Reliable Threshold Assessment in the Setting of Tachycardia

Introduction: Accurate assessment of pacing thresholds during permanent pacemaker implantation is critical to ensure device function and longevity. A few reports have described rate-related threshold variability during Micra TM leadless pacemaker (Medtronic Inc, Minneapolis, MN) procedures. Postulated mechanisms for this phenomenon include variable myocardial contact or micro-dislodgment, and inflammation-induced phase IV block. In this case series, we demonstrate the use of adenosine to induce transient heart block for accurate threshold assessment in patients with tachycardia during leadless pacemaker placement. Cases: A 69-year-old male with a history of atrial flutter presented with complete atrioventricular (AV) block requiring emergent transvenous pacing. A Micra placement was performed. During the procedure, the patient was tachycardic in rapid atrial flutter despite multiple doses of metoprolol. It was noted that with rapid pacing from the pacemaker at 120 pulses per minute (ppm) the threshold was ≤1V at 0.24 ms. An adequate “tug-test” was performed and at least 2 of 4 tines were noted to be fixated based on fluoroscopic motion. To confirm the capture threshold before final deployment, 12 mg of adenosine was given to induce heart block. However, at a rate of 60 ppm, there was no ventricular capture at 3V at 0.24ms. The device was repositioned and repeat threshold testing with adenosine at 60 ppm was 0.38V at 0.24ms. Pacing parameters were assessed the next day and remained stable. Similarly, a 75-year-old female with ischemic cardiomyopathy was admitted with new atrial fibrillation (AF). She then developed pauses prompting Micra placement. During initial threshold testing, she was in rapid AF despite receiving multiple doses of metoprolol. Heart block was induced with 12mg adenosine to assess the threshold at a lower pacing rate of 60 ppm. The capture threshold achieved was 0.5V at 0.24ms. Discussion: Nusbickel et al., Yoshiyama et al., and Sano et al. have reported rate dependent discrepancies in capture thresholds after Micra implantation. Our cases highlight the importance of capture threshold assessment at lower pacing rates during implantation. In patients who are tachycardic at the time of the procedure, adenosine may be useful to more reliably assess device capture threshold and determine the need for repositioning.

Stage-Specific Milk Yield Losses and Associated Sweating, Respiration, and Rectal Temperature Responses Under Varying THI Thresholds in Lactating and Dry Cows

Heat stress (HS) may result in changes in the behavior, endocrine system, and physiological characteristics of dairy cows, and it may even lead to death in severe cases. As the effects of global warming have become more notable, the prevalence of HS has increased among dairy cows. Therefore, comprehensive strategies, including not only cooling measures but also dietary adjustments and genetic improvements for heat tolerance, are required to help these animals regulate their body temperature and avoid HS. In addition, detecting HS signs is essential for both lactating and dry cows to ensure appropriate interventions. The temperature-humidity index (THI) is a widely used tool for evaluating the effects of HS on livestock. Since the physiological state of cattle significantly influences their responses to HS, it is imperative to establish specific THI thresholds for both lactating and dry cows to implement appropriate cooling regimens and optimize animal welfare. In this study, we used the THI to investigate the relationship between rectal temperature (RT), respiration rate (RR), and sweating rate (SR) in lactating and dry cows. We also explored the relationships between milk yield at different lactation stages and THI thresholds. The results indicated that lactating and dry cows had different THI thresholds based on their immediate physiological responses. Compared with lactating cows, dry cows had higher THI thresholds for RT, RR, and SR. In addition, cows in early-, intermediate-, and late-lactation stages under thermoneutral conditions produced significantly more milk than did those under mild, moderate, and severe HS conditions, indicating that milk yield losses occur under HS conditions. Taken together, these findings provide valuable insights into how HS can be mitigated in subtropical dairy farms. For lactating cows, implementing cooling measures is recommended when the THI reaches 66 to 67, whereas for dry cows, waiting until the THI reaches 73 is recommended. Milk yield losses may occur when lactating cows are under HS conditions. Therefore, appropriate cooling measures should be implemented at accurate THI thresholds to ensure optimal animal welfare for both lactating and dry cows.

Diagnostic accuracy of TB screening tests in a prospective multinational cohort: Chest-X-ray with computer-aided detection, Xpert TB host response, and C-reactive protein.

Accessible, accurate screening tests are necessary to advance tuberculosis (TB) case finding and early detection in high-burden countries. We prospectively screened adults with ≥2 weeks of cough at primary health centers in the Philippines, Vietnam, South Africa, Uganda, and India. Participants received chest-X-ray, Cepheid Xpert TB Host Response (Xpert HR) testing, and point-of-care C-reactive protein (CRP) testing (Boditech). Chest-X-ray images were processed using CAD4TB v7, a computer-aided detection algorithm. We assessed diagnostic accuracy against a microbiologic reference standard (sputum Xpert Ultra, culture). Optimal cut-points were chosen to maximize specificity at 90% sensitivity. Two-test screening algorithms were considered, using 1) sequential negative serial screening (positive defined as positive on either test) and 2) sequential positive serial screening (positive defined as positive on both tests). Between July 2021 and August 2022, 1,392 participants with presumptive TB had valid index tests and reference standard results, and 303 (22%) had confirmed TB. In head-to-head comparisons, CAD4TB v7 showed the highest specificity at 90% sensitivity (70.3% vs. 65.1% for Xpert HR, difference 95% CI 1.6 to 8.9; 49.7% for CRP, difference 95% CI 17.0 to 24.3). Three two-test screening algorithms met WHO target product profile (TPP) minimum accuracy thresholds and had higher accuracy than any test alone. At 90% sensitivity, the specificity was 79.6% for Xpert HR-CAD4TB [sequential negative], 75.9% for CRP-CAD4TB [sequential negative], and 73.7% for Xpert HR-CAD4TB [sequential positive]. CAD4TB achieves TPP targets and outperforms Xpert HR and CRP. Combining screening tests further increased accuracy. NCT04923958.

Using heart rate data from wrist worn activity trackers to define thresholds for moderate to vigorous physical activity in children and young people with cystic fibrosis

BackgroundChildren and young people with cystic fibrosis (CYPwCF) are encouraged to do an average of 60 min of moderate-to-vigorous physical activity (MVPA) daily. However, there are no agreed heart rate (HR) thresholds for defining MVPA, so it is difficult to ascertain whether these targets are actually achieved. Wearable activity trackers enable continuous monitoring of fitness-related measures such as HR and could be used to measure duration and intensity of habitual MVPA. We aimed to define personalized and responsive MVPA thresholds from HR in CYPwCF, to determine habitual time spent in MVPA during childhood and adolescence. MethodsContinuous daily HR data were collected from 142 CYPwCF wearing activity trackers over 16 months. Linear mixed-effects models were used to develop personalised estimates of resting heart rate (RHR), peak heart rate (PHR) and MVPA thresholds, which were defined using the American College of Sports Medicine heart rate reserve (HRR) method. Results309,926 days of physical activity data showed that both RHR and PHR declined with age in CYPwCF, with considerable variability within and between individuals. The HRR method produced personalised MVPA thresholds for each CYPwCF based on age, which inherently accounted for individual demographic variability and personal factors such as cardiovascular fitness or disease severity. ConclusionsBy accounting for within and between person variability in RHR and PHR, our novel method provides more accurate age-related personalised MVPA thresholds for CYPwCF than existing estimates. Our findings provide population-based estimates for RHR, PHR and MVPA thresholds at different ages in CYPwCF. This approach may help guide development of international standards for objective MVPA measurement in the era of remote HR and activity monitoring and facilitate accurate measurement of habitual physical activity in children and young people.

Validation of 3D thermal simulations of the Double C Block, a novel composite masonry unit, using in-situ U-value measurements

Energy efficient building envelopes, particularly innovative prefabricated building components, can significantly contribute to lower the building industry energy-related emissions. In this research the composite masonry unit Double C Block (DCB) is quantitatively tested by means of three-dimensional thermal simulation validated by in-situ U-value measurements carried out in both summer and winter conditions.The simulations were enhanced by the experimental measurement of the dry thermo-physical material properties (specific heat capacity, thermal conductivity, and density) in laboratory conditions for both concrete and polyurethane. The samples were extracted from real-scale DCB prototypes. Furthermore, site-specific boundary conditions of Malta — representative a case study of a Mediterranean climate − were utilised in the simulations. Both laboratory and in-situ assessments of thermal transmittance, or U-value, had their uncertainty specified as part of the calculations.The validation exercise was performed by means of hourly recorded surface temperature and the selected indices were the root mean square error and mean absolute error. Results showed that these indices were within the recommended accuracy thresholds during both winter and summer assessments. For the hourly-based heat flux measurements, the mean bias error and coefficient of variation of the root mean square error were found lower than values reported in literature, thus supporting the robustness of the overall simulation outputs. Hence the 3D CVM simulation was experimentally validated by in-situ assessments.The convergence value of the experimental U-value provided respectively 1.51 ± 0.20 W/(m2K) (winter) and 1.44 ± 0.19 W/(m2K) (summer). Then, the validated 3D CVM model was adapted to calculate the theoretical U-value of DCB, using monthly values of recorded surface temperatures obtained by the test cells (in both seasons). The predicted thermal transmittance was of 1.47 W/(m2K), in both conditions, and this almost matches the above-mentioned experimental assessments and thresholds established in previous works.

Performance evaluation of ChatGPT 4.0 on cardiovascular questions from the medical knowledge self-assessment program

Abstract Background Medical trainees are increasingly using online chat-based artificial intelligence (AI) platforms as supplementary resources for board exam preparation and clinical decision support. Prior studies have evaluated the performance of AI chatbots like ChatGPT on various general standardized tests such as the United States Medical Licensing Examination (USMLE), but little is known about their performance on subspecialty-focused exam questions, particularly related to clinical management and treatment. Objective This study aims to evaluate the performance of ChatGPT version 4.0 on the cardiovascular questions from the Medical Knowledge Self-Assessment Program (MKSAP) 19, a widely used resource for board exam preparation in the United States. Methods We submitted all cardiovascular questions from MKSAP 19 to ChatGPT 4.0, covering a broad range of cardiology topics in a multiple-choice format. Performance was gauged against both the official MKSAP answer key and average trainee scores obtained from the MKSAP website. Out of 129 questions, 4 were invalidated due to post-publication data, and 18 were excluded due to reliance on visual aids, leaving 107 questions for analysis. Results ChatGPT 4.0 correctly answered 93 out of 107 questions, reflecting an 87% accuracy rate, compared to a 60% accuracy rate averaged among all human users for the same questions (p&amp;lt;0.0001). ChatGPT accuracy rates for each question category (e.g., heart failure, electrophysiology, etc.) are provided in the figure. On the 14 questions that ChatGPT answered incorrectly, human users averaged a 47% accuracy rate. All 14 incorrectly answered questions were related to clinical management and treatment (as opposed to diagnosis or epidemiology), and the majority (10/14) involved choosing treatment medications or choosing between medications versus interventions. Conclusion ChatGPT 4.0 surpassed the typical 70% accuracy threshold required for passing the internal medicine board certification but performed less well in answering questions about clinical management and treatment. This performance suggests that ChatGPT 4.0 could be an effective adjunct tool for cardiology education, but raises some concerns about its use in clinical decision support for trainees.

Identification of the electric motor mathematical model based on a data sample with feature engineering

The object of this study is a mathematical model of a synchronous electric motor, obtained on the basis of experimental data, which takes into account the temperature mode and uses artificial features to increase the accuracy of its operation. A characteristic feature of this work is that the model takes into account the temperature mode as a component of the technical-operational state of the object. The resulting mathematical model could make it possible to synthesize an optimal automatic control system in terms of the operational state of the object. The problem addressed was to increase the accuracy of the identified mathematical models by applying the approach of feature engineering. The results showed that the identification of mathematical models by the initial data leads to a low level of accuracy of the obtained models, namely 65–70 % for the first output channel, 80–85 % for the second, and 75–80 % for the third, fourth, and fifth output channels. Accordingly, building models with a higher threshold of accuracy requires the use of other, more significant data for identification. This paper reports a method for reformatting the original data into artificial features and provides results of their effectiveness in relation to the original channels. The resulting artificial features and the original features were used for further identification; the resulting mathematical model has on average higher accuracy thresholds, namely 82 %, 93 %, 88 %, 85 % for the corresponding output channels. The results prove the effectiveness of applying the principle of feature engineering since the accuracy of the resulting model is 5–10 % higher compared to the baseline. The scope of practical application of the results includes the synthesis of automatic control systems based on mathematical models of control objects obtained as a result of identification.

Research on polarization effect suppression method of weak starlight simulation device.

Under the influence of the polarization effect, the background stray light of the weak starlight simulator and the polarization state of the simulated target star point are doped with each other, so it is difficult to achieve accurate suppression of polarized stray light. In this paper, the mechanism of background stray light induced by the optical engine in a weak starlight simulation device is analyzed, and the principle of mutual doping between stray light and simulated target starlight polarization state is studied. The correlation model of polarization parameters, point source transmittal, and simulated target magnitude was established, and the optimization process of polarization effect was constructed to achieve accurate suppression of polarized stray light in the star map background. The simulation results show that after the optimization of the optical engine, the maximum point source transmittivity is 6.084 × 10-6, which meets the 6-magnitude star accurate recognition threshold. The experimental results show that the stray light suppression ability of the weak starlight simulation device is improved by nearly 2 times, and the magnitude simulation accuracy is improved by 1.8 times.

Time-domain extrapolation of vehicle durability load spectrum based on load decomposition and POT model threshold preference

In vehicle structural fatigue life assessment, the time-domain Peak over Threshold (POT) extrapolation method is widely used due to its ability to overcome testing constraints and accurately extrapolate whole life-cycle load while maintaining realistic probability distribution. However, random road load spectra often contain trend components influenced by terrain and driving maneuvers, which may reduce the POT method’s adaptability and lead to extrapolation distortions. Existing POT extrapolation methods also struggle with reliance on single threshold criteria and inadequate extreme value fitting. To address these issues, this paper proposes an improved time-domain POT extrapolation method that incorporates load decomposition and threshold preference. The wavelet decomposition is utilized to extract the principal and trend components from random signals, allowing for targeted extrapolation of principal component load that embodies the main characteristics. Meanwhile, during the extrapolation process, a comprehensive threshold preference model utilizing multiple evaluation criteria for Generalized Pareto Distribution (GPD) fitting is established, with the genetic algorithm determining the optimal threshold. Then, new extreme values are generated through GPD parameter estimation and Monte-Carlo simulation to replace the original ones. The extrapolated principal component load is subsequently superimposed with the original trend load to complete the extrapolation. Validation with a prototype vehicle’s durability spectrum under real driving conditions demonstrates the method’s efficacy in preventing distortions and achieving accurate threshold determination, with predicted load characteristics and fatigue damage highly aligning with measured verification data. It can provide a valuable technical reference for whole life-cycle fatigue load acquisition of mechanical structures.

Accurate optimal quantum error correction thresholds from coherent information

Quantum error correcting (QEC) codes protect quantum information from decoherence as long as error rates fall below critical error thresholds. In general, obtaining thresholds implies simulating the QEC procedure using, in general, suboptimal decoding strategies. In a few cases and for sufficiently simple noise models, optimal decoding of QEC codes can be framed as a phase transition in disordered classical spin models. In both situations, accurate estimation of thresholds demands intensive computational resources. Here we use the coherent information of the mixed state of noisy QEC codes to accurately estimate the associated optimal QEC thresholds already from small-distance codes at moderate computational cost. We show the effectiveness and versatility of our method by applying it first to the topological surface and color code under bit-flip and depolarizing noise. We then extend the coherent information based methodology to phenomenological and quantum circuit level noise settings. For all examples considered, we obtain highly accurate estimates of optimal error thresholds from small, low-distance instances of the codes, in close agreement with threshold values reported in the literature. Our findings establish the coherent information as a reliable competitive tool for the calculation of optimal thresholds of state-of-the-art QEC codes under realistic noise models. Published by the American Physical Society 2024

Optimal DaTQUANT Thresholds for Diagnostic Accuracy of Dementia with Lewy Bodies (DLB) and Parkinson's Disease (PD).

Background: Quantitative thresholds are helpful to define an abnormal DaT SPECT in patients with suspected nigrostriatal degenerative diseases (NSDD). The optimal DaTQUANT threshold for diagnostic accuracy of DaT SPECT across combined movement and cognitive disorder populations has been previously described. Methods: We established optimal DaTQUANT thresholds that enhance the discrimination between dementia with Lewy bodies (DLB) and non-DLB dementia types, as well as between Parkinsonian syndromes (PS) and conditions not characterized by nigrostriatal degeneration (non-PS). Results: Data from a total of 303 patients were used in this retrospective analysis. Posterior putamen of the more affected hemisphere (MAH) was shown to be an accurate single-variable predictor for both DLB and PS and was comparable to the most accurate multi-variable models. Conclusions: Automated quantification with DaTQUANT can accurately aid in the differentiation of DLB from non-DLB dementias and PS from non-PS. Optimal thresholds for assisting a diagnosis of DLB are striatal binding ratio (SBR) ≤ 0.65, z-score ≤ -2.36, and a percent deviation ≤ -0.54 for the posterior putamen of the MAH. Optimal posterior putamen thresholds for assisting a diagnosis of PS are SBR ≤ 0.92, z-score ≤ -1.53, and a percent deviation ≤ -0.33, which are similar to our previously reported posterior putamen threshold values using a blended patient pool from multiple study populations.