High Level Of Precision Research Articles

Quantitation of micro and nanoplastics in human blood by pyrolysis-gas chromatography–mass spectrometry

The limited available data on human internal exposure poses a significant challenge in assessing the risks associated with micro and nanoplastics (MNPs) to human health. A contributing factor to this challenge is the scarcity of sensitive analytical methods to quantify the mass concentration of plastic polymers in human blood. In this study we present an improved and validated method for quantitatively analysing polyethylene (PE), polyethylene terephthalate (PET), poly(vinyl chloride) (PVC), poly(methyl methacrylate) (PMMA), polypropylene, and polystyrene in human whole blood samples. We introduce and apply stringent quality assurance and quality control procedures, including the validation of the method using quality control samples and continuous monitoring of batch analyses to ensure data reliability. Expanding upon prior pioneering work by Leslie et al. (2022), we optimised the pyrolysis-gas chromatography–mass spectrometry (Py-GC-MS) conditions to enhance method sensitivity and selectivity. Recovery experiments demonstrated a high level of accuracy and precision, with values ranging from 68 to 109% for quality control samples. Applying this method to whole blood samples (n = 68), we identified plastic polymers in 64 samples, with PE as the predominant polymer, followed by PVC, PET, and PMMA. In 17 blood samples, polymer concentrations were found to exceed the limit of quantitation, with a mean of 1070 ng/mL for the summed polymer concentrations, ranging between 170 and 2490 ng/mL. The mean of the sum of polymers across all blood samples (n = 68) was 268 ng/mL. These findings underscore the pressing need for further research aimed at comprehensive MNP quantification in human matrices, considering the potential health implications.

Empowering Communication: A Deep Learning Framework for Arabic Sign Language Recognition with an Attention Mechanism

This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to reduce the barriers to effective communication and social integration for deaf communities. The model design incorporates LSTM units and an attention mechanism to handle the input sequences of extracted keypoints from recorded gestures. The attention layer selectively directs its focus toward relevant segments of the input sequence, whereas the LSTM layer handles temporal relationships and encodes the sequential data. A comprehensive dataset comprised of fifty frequently used words and numbers in ArSL was collected for developing the recognition model. This dataset comprises many instances of gestures recorded by five volunteers. The results of the experiment support the effectiveness of the proposed approach, as the model achieved accuracies of more than 85% (individual volunteers) and 83% (combined data). The high level of precision emphasises the potential of artificial intelligence-powered translation software to improve effective communication for people with hearing impairments and to enable them to interact with the larger community more easily.

Versatile hybrid transparent triboelectric nanogenerator-solar cell device enables efficient energy harvesting and personal handwriting recognition

The continuous energy supply for wearable electronic devices, including health monitoring sensors and human-machine interface devices, poses a significant challenge to their development. Renewable energy sources, such as nanogenerators and solar cells, offer promising solutions to this challenge. In this study, we explore the potential of a versatile device based on a smart hybrid triboelectric nanogenerator (SH TENG) and photovoltaic (PV) cell for efficient energy harvesting and personal handwriting recognition. A transparent, self-cleaning, UV-protective, bidirectional, non-rigid, lightweight, ultra-durable, eco-friendly, and uniform composite of epoxy resin/ZnO/Ag nanowires-based sandwich-like TENG can produce a power density of 2.3 W m−2. Integrating SH TENG with PV led to improved energy harvesting and increased the charging voltage of the capacitor by up to 5 times. Moreover, this material is a multifunctional sensor capable of detecting temperature, humidity, pressure, handwriting content, and the writer. These features make it an intriguing candidate for e-skin applications. By employing a generalized deep neural network (DNN) model, our device can accurately recognize handwritten letters, numbers, and personal identification with a high level of precision, achieving an accuracy of 97 %. This device can function as an intelligent touch screen with dual-purpose energy production and detection, paving the way for developing highly efficient and sustainable wearable electronic devices.

OncoGPT: Enhancing oncological decision-making with ASCO guideline–trained AI.

e13607 Background: General-purpose language models like ChatGPT, while proficient in a wide array of topics, often lack the specialized knowledge and precision required for specific fields such as oncology. The American Society of Clinical Oncology (ASCO) guidelines offer a comprehensive resource for oncology professionals, yet sifting through extensive documents for specific information can be daunting. To bridge this gap, we introduce OncoGPT, a specialized GPT model, tailored to provide precise and guideline-adherent oncological advice. Methods: We leveraged GPT-4's advanced language understanding capabilities as the foundation for OncoGPT. This customization involved an extensive training regimen using the complete text of the ASCO clinical guidelines. The training process was meticulously designed to ensure that OncoGPT not only assimilates the detailed information within these guidelines but also understands the context and intricacies of oncological care. Additionally, we created disease-specific versions of OncoGPT, each fine-tuned with guideline subsets pertinent to particular oncological conditions. Further, the training was augmented with specific prompts to foster kind, sincere, and respectful interactions, especially for patient-oriented responses. The model was designed to discern whether the user is a healthcare professional or a patient and adjust the complexity of its responses accordingly. For patients, additional emphasis was placed on delivering information in a kind, responsible, and empathetic manner. Results: OncoGPT's performance was rigorously evaluated for accuracy and reasoning against real-world clinical scenarios. The responses generated by the model were meticulously reviewed by an experienced oncologist to confirm adherence to ASCO guidelines and clinical relevance. This assessment revealed a high level of precision and contextual understanding in OncoGPT's outputs, demonstrating its potential as a valuable tool for oncology practitioners. Importantly, it showed a remarkable ability to tailor its communication style based on the user's background, providing simplified explanations for patients and more detailed responses for professionals. An example of a specific OncoGPT: Breast Cancer OncoGPT: https://chat.openai.com/g/g-VcuECT3jg-oncogpt-breast-cancer Conclusions: OncoGPT represents a significant advancement in the application of AI for medical decision-making. By integrating the depth of ASCO guidelines into a responsive and accurate AI model, OncoGPT offers oncology professionals a powerful tool for accessible, guideline-compliant advice. This development marks a crucial step towards integrating AI more profoundly into specialized medical fields, enhancing both the efficiency and accuracy of clinical decision-making.

Cone-wedge anchored surgical templates for stackable metal guide: a novel technique

ObjectiveTo address the instability in implant surgical guides, this technique proposes an alternative anchoring mechanism in the stackable metal surgical guides utilizing cone-wedge anchors for improved stability.MethodsPostoperative implant position superimposed onto the preoperatively planned design using Mimics Medical 21.0 and Materialise Magics 24.0 to assess 3D coronal implant deviation, 3D apical implant deviation, and implant angular deviation.ResultsPostoperative cone-beam computed tomography (CBCT) revealed a high level of precision in the implant placement, with an average 0.97 mm deviation at implant coronal region, 1.56 mm at implant apexes, and 2.95° angular deviation.ConclusionThis technique introduces a novel cone-wedge anchoring mechanism to enhance the stability of stackable metal surgical guide templates, addressing inherent instability issues. The utilization of this approach significantly improves the accuracy of implant placement procedures.

ON THE INFLUENCE OF EXOGENEOUS PROCESSES ON THE RESULTS OF MEASURING MOVEMENTS OF GEODESIC MARKINGS

The influence of exogenous processes on geodetic marks is multifactorial, and in modern observation practice there is no clear scientifically based system of engineering and geodetic forecasting of neotectonics influence on the results of repeated high-precision leveling, on the basis of which it would be possible to solve the issues of stability of depth, soil and rock leveling marks, as well as modeling the mechanism of influence of exogenous processes on the results of repeated leveling. Therefore, the study of their influence is an urgent task. The study is aimed at determining the influence of exogenous processes on the results of measurements of geodetic marks displacements during observations at local geodynamic polygons in the Kryvyi Rih region. The work performed has shown that the temperature factor is periodic in nature and therefore its influence on the stability of the reps also naturally varies over time: it changes linearly in the time interval March-May and September-November, sinusoidally in June-August, if divided into separate parts. Although, in general, the temperature change can be described as a sinusoid. A linear correlation has been established, but it should be noted that it can be broken by the imposition of a stronger factor that determines the dynamics of the movement of the rappers. There is no clear connection between temperature changes Δt and exceedances Δh in the time interval May-July. The intensive increase in temperature corresponds to the lowering of soil references, which is inconsistent with the general theory of their stability. The phase shift is more than two months, which indicates the influence of another, stronger factor than temperature on the stability of soil references. Periodic changes in atmospheric temperature cause thermal deformations of rocks, which, due to differences in thermal properties and temperature conditions of rocks, have a complex differentiated structure in space and time, which is manifested in heterogeneous vertical displacements of the references underlying these rocks. The revealed regularities of the temperature factor and the instability of the references caused by this factor made it possible to predict its value and recommend a scheme of time periods for field measurements in repeated cycles.

How do young identify plants? Using the drawing method to explore early ethnobotanical knowledge in Madagascar

Abstract In small‐scale societies, people learn to identify plant species during childhood. Plant recognition is an important baseline knowledge, immediately useful to avoid intoxication risk due to wrong identification. Plant recognition is the basis of other ethnobotanical knowledge essential for safeguarding biocultural diversity. However, despite many studies on folk classification, we still have a narrow understanding of the criteria locally used for species identification; the gap being even larger regarding children's plant identification criteria. Here, we study the criteria used by Betsileo children and adolescents to identify wild edible plant (WEP) species using a child‐adapted method including drawings and follow‐up interviews. We worked with 80 teenagers (from 12 to 17 years old; 51 girls, and 29 boys). Our results suggest that teenagers use a large spectrum of visual criteria to identify plants and that these criteria match with botanical and ecological knowledge documented in the literature and herbarium vouchers. We found that 35% of the identification criteria used were non‐morphological (e.g. phenology, biotic interactions), suggesting deep ecological knowledge. On average, teenagers use more than nine distinct criteria per plant, which allows them to identify most plant species with a very high level of precision. The precision level of plant representation increases with age for boys, but remains constant for girls, suggesting different dynamics in plant identification knowledge acquisition. We also found that boys and girls use different identification criteria: girls focus on morphological criteria while boys also incorporate ecological criteria, such as landscape features and biotic interactions, in their spectrum of identification keys. Our results highlight the complexity of teenagers' plant knowledge and the importance of the ecological context and gender in plant identification's knowledge acquisition. This knowledge acquired very early in childhood, constitutes the foundation of future interactions with nature and should be at the heart of environmental humanities studies and knowledge co‐production projects to tackle socio‐ecological concerns. Hence, we urge further research to explore innovative methods that complement traditional ethnoecological tools and capture complex sensory aspects of folk children's taxonomy to better understand human‐plant interactions and knowledge. Read the free Plain Language Summary for this article on the Journal blog.

Mapping Longitudinal and Transverse Displacements of a Dam Crest Based on the Synergy of High-Precision Remote Sensing

Reservoirs are highly relevant infrastructure assets, and now, more than ever, they play an essential role in society’s welfare and national security. Their importance is related to regional socioeconomic development due to their capacity to store water for different uses, such as human consumption, agricultural irrigation, flood control, and hydroelectric energy production, among other important services. However, many reservoirs are reaching the end of their period of life, and others are showing undesired displacements and cracking. Four 3D surveys were conducted on a reservoir that serves the Metropolitan Area of Monterrey City in Mexico. These surveys were carried out over a period of 5 years using GNSS observation to assist in understanding the actual dam kinematics, i.e., the behavior of its longitudinal and transversal displacements and the possible correlation with the reservoir level. The high-precision leveling and close-range remote sensing data were assessed and then mapped. The high-precision geodetic and leveling techniques allowed us to locate and measure 84 established permanent control points with errors of about ± 0.003 m. The mapping of displacements was made possible by modeling the positive and negative translations. The highest uplifts (11 mm) occurred at the left riverbank, and the highest subsidences (−5 mm) occurred along the downstream piers from the middle of the dam crest to the right riverbank. A ground laser scanner (GLS) produced 3D digital models with geometrical and radiometric characteristics, detecting displacements among the dam crest elements. The synergy of GNSS and high-leveling techniques allows the possibility to measure displacements, while the use of geographical information system (GIS) and geomatic techniques allows a better visualization through 2D and 3D maps validated using traditional topographical methods.

Quality assurance and quality control of atmospheric organosulfates measured using hydrophilic interaction liquid chromatography (HILIC)

Abstract. ​​​​​​​As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOAs) influence public health, regional air quality, and global climate patterns. This paper highlights the use of hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze six standards, environmental standards (1648a and 1649b), and samples collected in urban environments in Guangzhou in the Pearl River Delta region, which serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method. Sodium octyl-d17 sulfate serves as the internal standard, with a linear correlation coefficient of the six standards, boasting a linear correlation coefficient r ranging from 0.993–0.9991 and a slope, k, of the linear equation from 0.966–1.882. The instrument detection limits (IDLs) are established at 0.03–0.20 µg mL−1, while the method detection limits (MDLs) fall within the range of 0.30–1.75 ng m−3, demonstrating the method's exceptional sensitivity. Since isoprene-derived organosulfates (iOSs) are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulfate, and as such showed strong retention using this method, this technique employs sodium ethyl sulfate and sodium octyl sulfate standards for semi-quantitative compound analysis of iOSs. The error in sample analysis (EA) ranged from 12.25 %–95.26 %, and the two standards maintained a consistent recovery rate between 116 %–131 % and 86.4 %–127 %. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for iOSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several iOSs using this method. Methyltetrol sulfate (m/z 215, C5H11SO7-) is the highest concentration in the ambient samples, up to 67.3 ng m−3 in the daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment.

Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study.

Disabled older adults exhibited a higher risk for cognitive impairment. Early identification is crucial in alleviating the disease burden. This study aims to develop and validate a prediction model for identifying cognitive impairment among disabled older adults. A total of 2138, 501, and 746 participants were included in the development set and two external validation sets. Logistic regression, support vector machine, random forest, and XGBoost were introduced to develop the prediction model. A nomogram was further established to demonstrate the prediction model directly and vividly. Logistic regression exhibited better predictive performance on the test set with an area under the curve of 0.875. It maintained a high level of precision (0.808), specification (0.788), sensitivity (0.770), and F1-score (0.788) compared with the machine learning models. We further simplified and established a nomogram based on the logistic regression, comprising five variables: age, daily living activities, instrumental activity of daily living, hearing impairment, and visual impairment. The areas under the curve of the nomogram were 0.871, 0.825, and 0.863 in the internal and two external validation sets, respectively. This nomogram effectively identifies the risk of cognitive impairment in disabled older adults.

Recognizing Sign Language using Machine Learning and Deep Learning Models

Individuals with hearing impairments communicate mostly through sign language. Our goal was to create an American Sign Language recognition dataset and utilize it in a neural network-based machine learning model that can interpret hand gestures and positions into natural language. In our study, we incorporated the SVM, CNN and Resnet-18 models to enhance predictability when interpreting ASL signs through this new dataset, which includes provisions such as lighting and distance limitations. Our research also features comparison results between all the other models implemented under invariant conditions versus those using our proposed CNN model. As demonstrated by its high levels of precision at 95.10% despite changes encountered during testing procedures like varying data sets or scene configurations where losses are minimal (0.545), there exists great potential for future applications in image recognition systems requiring deep learning techniques. Furthermore, these advancements may lead to significant improvements within various fields related explicitly to speech-language therapy sessions designed specifically around helping people overcome challenges associated with deafness while building bridges towards improved social integration opportunities.

Deep learning-based classification of anti-personnel mines and sub-gram metal content in mineralized soil (DL-MMD)

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors.

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

A Novel RSS Fingerprint to Locate User Equipment UE in Remote Area

Location determination algorithms are widely used in cellular networks, especially in the long-term evolution (LTE) network, to enable the provision of location-based services (LBS). The increasing global demand for cellular networks has resulted in the creation of new user equipment (UE) positioning systems that align with the network's momentum. Regrettably, all of these technologies are hindered by their incapacity to ascertain the location of the UE in distant regions. This article introduces a novel method utilizing Radio Frequency (RF) fingerprinting to precisely locate UEs in remote areas. The approach entails employing a proposed partitioning model with a high level of precision, incorporating artificial intelligence and machine learning AI/ML in its fundamental state to reduce the search area. Furthermore, two algorithms are suggested: The first aims to enhance the efficiency of the battery with limited capacity by decreasing the frequency of measurements transmission. The second utilizes Jaccard similarity and incorporates the prefix filtering technique to determine matches. The algorithm is used to speed up the process of matching the fingerprint recorded in the fingerprint database with the fingerprint captured in real time. The results shows that it can reduces the transmission rate by 77.08% and achieves the lowest error rate of 35.34 m. Additionally, it exhibits a response time of 8 seconds.

Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.

Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders. This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions. This study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems. Our findings emphasize the importance of ML analyses for early diagnoses of FASD, allowing a better understanding of FASD subtypes to potentially improve clinical practice and avoid misdiagnosis.

Advancements in pelvic ring fracture surgery: Assessing INFIX screw placement accuracy with CT navigation

The management of unstable pelvic ring fractures, typically resulting from high-energy trauma, presents a significant clinical challenge due to the complexity of injuries. While effective in many cases, the traditional stabilization methods are fraught with various complications that can significantly impact patient recovery and quality of life (QOL). This study aims to evaluate the efficacy and precision of the anterior subcutaneous internal fixator (INFIX) technique when used with intraoperative computed tomography (CT) navigation, a novel approach intended to mitigate the limitations of conventional treatment modalities.Our retrospective case series encompasses 43 patients who sustained traumatic pelvic injuries and were subsequently treated with the INFIX technique from December 2020 to January 2024. The focus of this analysis was to assess the accuracy of INFIX screw placement facilitated by intraoperative CT navigation.A total of 81 INFIX screws were inserted, and our study findings reveal a high level of precision in screw placement, with only one screw deviating, resulting in an inaccuracy rate of merely 1.2 %. This highlights the significant advantage provided by intraoperative CT navigation. The high level of accuracy not only enhances the stability of the pelvic fixation but also substantially reduces the risk of complications commonly associated with screw misplacement, such as abdominal damage, vascular injury, and issues related to incorrect hardware positioning.In conclusion, the integration of the INFIX technique with intraoperative CT navigation in the treatment of unstable pelvic ring fractures represents a significant advancement in orthopedic trauma surgery. This study provides compelling evidence supporting the efficacy and precision of this approach, suggesting its potential as a superior alternative to traditional fixation methods. Further research, ideally through prospective studies involving larger patient cohorts, is needed to validate these findings and explore the long-term implications of this technique on patient recovery and QOL.

Parameter identification and NVH characteristic analysis of automotive suspension rubber bushing

Accurate prediction of the dynamic stiffness of rubber bushings is crucial for optimizing vehicle vibration and noise performance. However, this task is highly challenging due to various influencing factors such as frequency and amplitude. Consequently, there has been limited research conducted in this area thus far. This paper presents a novel approach for predicting the dynamic stiffness of rubber bushings. The frequency and amplitude dependencies of rubber bushings were thoroughly investigated through dynamic loading tests. A comprehensive model for rubber bushings, incorporating parallel connections of elastic elements, friction elements, and higher-order fractional derivative viscous elements, was established. The model parameters were accurately identified using the GA-BP method. The results demonstrate that the proposed model exhibits a high level of precision in estimating dynamic stiffness across a frequency range of 0–200 Hz. In comparison to the conventional model, the proposed approach enables more precise computation of interior sound pressure response and enhances vehicle simulation accuracy.

Parameter identification of disk-gap-band parachute during steady descent

In light of the challenges associated with acquiring the display model for identifying parachute aerodynamic parameters, this study aims to establish a correlation model for the aerodynamic parameters of a specific type of disk-gap-band parachute during the steady drop stage of transonic and subsonic speeds. This will be achieved by employing the genetic algorithm and utilizing observed data from airdrop experiments. The findings indicate that the genetic algorithm can accurately calculate the aerodynamic parameters in dynamic simulation with a high level of precision. Additionally, the numerical distribution state of the parameters to be identified exhibits similarity in the multiple disk-gap-band parachute airdrop experiments, thereby confirming the accuracy of the model and methodology. Finally, the numerical ranges for the drag coefficient and the stable equilibrium angle of attack of this particular type of disk-gap-band parachute are provided. The results are compared with both the airdrop experiments and the wind tunnel experiment. The errors in question satisfy the criteria of engineering precision, and the verification process confirms the accuracy of the identification outcomes.

Electronic structure, oscillator strength, and rovibrationally resolved photodissociation of 24MgH

Aims. A series of high-precision calculations for the electronic structure of MgH have been reported in the past two decades; however, most of them d not include the core-valence correlation and still exhibit distinct differences. Furthermore, the latest high-precision results have not been applied to the studies of photodissociation dynamics. The primary motivations of this paper are to calculate a more precise electronic structure of MgH consering a core-valence correlation and to prove the photodissociation cross-sections. Methods. The electronic structure of MgH is investigated by multi-reference configuration interaction calculations with Davson correction (MRCI+Q). We performed two different sets of calculations to investigate the core-valence correlation and, as a result, obtained accurate potential energy curves (PECs) and transition dipole moments (TDMs). An extrapolation procedure was also employed to eliminate the error of basis set. Then, the photodissociation cross-sections were calculated using high-precision PECs and TDMs. Results. The PECs and TDMs of the five lowest doublet electronic states, X2Σ+, B′2Σ+, E2Σ+, A2Π, and C2Π, are obtained from calculations including core-valence correlation, termed as CV-MRCI, while PECs of the ten lowest doublet states and three quartet states are also obtained from NCV-MRCI calculations without core-valence correlation. The spectroscopic constants and band oscillator strengths are also proved with high precision levels. The equilibrium Re and vertical excitation energy Te are only 0.1% different from the measurements. Based on the CV-MRCI results, the rovibrationally resolved photodissociation cross-sections for transitions from X2Σ+ to the other four states, as well as the total local thermodynamic equilibrium cross-sections for temperatures up to 10000 K, are calculated.

Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study.

Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study's validity. A comparative analysis was conducted between the AI model's diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings. The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3-93.2%] and specificity of 71.4% (95% CI: 53.7-85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5-99.8%), specificity of 100.0% (95% CI: 69.2-100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI's extensive training across diverse datasets. The experienced radiologist's sensitivity was 40.0% (95% CI: 21.1-61.3%), while the specificity was 80.0% (95% CIs: 63.6-91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system's consistent performance across different settings. The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.