Manual Reading Research Articles

Evaluation of PhenoMATRIX and PhenoMATRIX PLUS for the screening of MRSA from nasal and inguinal/perineal swabs using chromogenic media.

The objective of this study was to assess the clinical performances of PhenoMATRIX and PhenoMATRIX PLUS for the screening of methicillin-resistant Staphylococcus aureus (MRSA) from nasal and inguinal/perineal ESwabs using chromogenic media. The automated performances were compared to the manual reading. Additionally, we evaluated PhenoMATRIX PLUS for the automatic release of the negative results to the Laboratory Information System (LIS) and the automatic discharge of the negative plates from the incubators. A total of 6,771 non-duplicate specimens were used by PhenoMATRIX as a machine learning model. The validation of these settings was performed on 17,223 non-duplicate specimens. The MRSA positivity rate was 5% (866/17,223). Validated settings were then used by PhenoMATRIX PLUS on another 1,409 non-duplicate specimens. The sensitivities of PhenoMATRIX and PhenoMATRIX PLUS were 99.8% [95% confidence interval (CI), 99.2%-99.9%] and 100% (95% CI, 92.1%-100%), respectively. The specificities of PhenoMATRIX and PhenoMATRIX PLUS were 99.1% (95% CI, 99.0%-99.2%) and 95.2% (95% CI, 93.8%-96.1%), respectively. All the 1,297 MRSA-negative specimens analyzed by PhenoMATRIX PLUS were automatically released and sent to the LIS immediately after availability of the culture image on the WASPLab (100% accuracy). All negative media plates were automatically discarded. PhenoMATRIX PLUS decreases the time spent by technologists on negative plates and ensures optimal usage of the incubators' capacity.

High-precision FBG-based sensor for soil settlement monitoring: A comparative study with magnetic settlement gauges and PIV technique

The primary objective of layered settlement monitoring is to detect subsidence issues in soil or foundational structures, in order to implement appropriate measures for ensuring the safety and stability of engineering projects. Conventional monitoring techniques have problems such as errors in manual readings, high costs, susceptibility to interference, and low levels of automation. A layered soil settlement sensor based on fiber Bragg grating (FBG) technology is proposed in this study. This sensor utilizes an equal-strength beam as an "intermediate bridge" to make the FBG sensor respond. The sensor is prepared by the laboratory's 3D fused deposition modelling (FDM) and calibrated by applying displacement loads through a fine-tuning platform. The sensitivity coefficient is 0.068 nm/mm, and the correlation coefficient exceeds 0.99. Finally, the sensor exhibited high precision when compared to measurements obtained from magnetic settlement gauges and particle image velocimetry (PIV). The discrepancies between the measurement outcomes of the sensor and the actual values of soil settlement does not exceed 3.33%.

Development of an automatic conversion method for the conversion of a CIE‐XYZ color system to a DIN color system

AbstractThe DIN color system is a visually uniform system adopted as the standard by several countries in central Europe. Currently, the conversion method from CIE‐XYZ color system to DIN color system consists of the DIN 6164 method combined with the manual reading method. However, the DIN 6164 method cannot be used for the edges of a chromaticity diagram. The manual reading method can read all areas in a chromaticity diagram; however, it is too inefficient to convert large quantities of data. This study builds on the manual reading method to create a new method entitled the program reading method. Upon comparing the relative error, absolute error and conversion times of all methods, it was determined that the program reading method achieved higher accuracy than that of the manual reading method. The maximum relative error of the program reading method was 3.24%; however, the conversion time for each sample was 0.07 s, which was 3.5 times slower than that of the DIN 6164 method. Based on these findings, this study proposes an automatic conversion method that combines the strengths of both approaches: the DIN 6164 method to be used for interpolable areas and the program reading method to be used for areas that cannot be interpolated. The automatic conversion method outlined in this study provides an efficient, precise and fast way to convert a CIE‐XYZ color system to a DIN color system.

Application and evaluation of artificial intelligence TPS-assisted cytologic screening system in urine exfoliative cytology

Objective: To explore the application of manual screening collaborated with the Artificial Intelligence TPS-Assisted Cytologic Screening System in urinary exfoliative cytology and its clinical values. Methods: A total of 3 033 urine exfoliated cytology samples were collected at the Henan People's Hospital, Capital Medical University, Beijing, China. Liquid-based thin-layer cytology was prepared. The slides were manually read under the microscope and digitally presented using a scanner. The intelligent identification and analysis were carried out using an artificial intelligence TPS assisted screening system. The Paris Report Classification System of Urinary Exfoliated Cytology 2022 was used as the evaluation standard. Atypical urothelial cells and even higher grade lesions were considered as positive when evaluating the recognition sensitivity, specificity, and diagnostic accuracy of artificial intelligence-assisted screening systems and human-machine collaborative cytologic screening methods in urine exfoliative cytology. Among the collected cases, there were also 1 100 pathological tissue controls. Results: The accuracy, sensitivity and specificity of the AI-assisted cytologic screening system were 77.18%, 90.79% and 69.49%; those of human-machine coordination method were 92.89%, 99.63% and 89.09%, respectively. Compared with the histopathological results, the accuracy, sensitivity and specificity of manual reading were 79.82%, 74.20% and 95.80%, respectively, while those of AI-assisted cytologic screening system were 93.45%, 93.73% and 92.66%, respectively. The accuracy, sensitivity and specificity of human-machine coordination method were 95.36%, 95.21% and 95.80%, respectively. Both cytological and histological controls showed that human-machine coordination review method had higher diagnostic accuracy and sensitivity, and lower false negative rates. Conclusions: The artificial intelligence TPS assisted cytologic screening system has achieved acceptable accuracy in urine exfoliation cytologic screening. The combination of manual screening and artificial intelligence TPS assisted screening system can effectively improve the sensitivity and accuracy of cytologic screening and reduce the risk of misdiagnosis.

A Study on the Teaching Model of Cross Culture and Speculation on EFL Writing

Under the background of post critical pedagogy, teachers should pay attachment to the awakening of students’ self-identity consciousness and the cultivation of critical thinking skills. The teaching model of writing course adopted by the author is as follows: firstly, the writing course is equipped with a dedicated reading manual. It is compiled to establish cultural reading materials which include 12 theme reading passages to introduce Chinese and American culture and 12 writing sharing to promote identity thinking based on the relevant theories of Constructivism and Output Hypothesis by using the Flesch Kincaid Readability Test. The teaching process emphasizes the integration of students’ vertical and horizontal knowledge. Referring to the Knowledge Transformation Model of Writing proposed by Bereiter and Scardamalia, the teaching model vertically emphasizes the integration of pre-class cultural reading, in-class skill training, and post-class thinking reinforcement; it horizontally emphasizes on the integration with reading courses, cultural courses, and speculative courses. Overall, the model stresses the cultivation of cross-cultural communication skills. It emphasizes understanding oneself and the other and stimulates students’ writing interest in cultural reading; it emphasizes that the other is for my own use and enrich students’ cultural illustrations in skill training; it emphasizes that I see the world and reflect on my own learning, life, and social hotpots in theme writing. In short, the author actively explores “playing” with learning, “stealing” with learning, and “using” in learning.

Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

PurposeThe aim of this study was to investigate the performance of an artificial intelligence (AI)-based software for fully automated analysis of leg alignment pre- and postoperatively after high tibial osteotomy (HTO) on long-leg radiographs (LLRs).MethodsLong-leg radiographs of 95 patients with varus malalignment that underwent medial open-wedge HTO were analyzed pre- and postoperatively. Three investigators and an AI software using deep learning algorithms (LAMA™, ImageBiopsy Lab, Vienna, Austria) evaluated the hip–knee–ankle angle (HKA), mechanical axis deviation (MAD), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA). All measurements were performed twice and the performance of the AI software was compared with individual human readers using a Bayesian mixed model. In addition, the inter-observer intraclass correlation coefficient (ICC) for inter-observer reliability was evaluated by comparing measurements from manual readers. The intra-reader variability for manual measurements and the AI-based software was evaluated using the intra-observer ICC.ResultsInitial varus malalignment was corrected to slight valgus alignment after HTO. Measured by the AI algorithm and manually HKA (5.36° ± 3.03° and 5.47° ± 2.90° to − 0.70 ± 2.34 and − 0.54 ± 2.31), MAD (19.38 mm ± 11.39 mm and 20.17 mm ± 10.99 mm to − 2.68 ± 8.75 and − 2.10 ± 8.61) and MPTA (86.29° ± 2.42° and 86.08° ± 2.34° to 91.6 ± 3.0 and 91.81 ± 2.54) changed significantly from pre- to postoperative, while JLCA and mLDFA were not altered. The fully automated AI-based analyses showed no significant differences for all measurements compared with manual reads neither in native preoperative radiographs nor postoperatively after HTO. Mean absolute differences between the AI-based software and mean manual observer measurements were 0.5° or less for all measurements. Inter-observer ICCs for manual measurements were good to excellent for all measurements, except for JLCA, which showed moderate inter-observer ICCs. Intra-observer ICCs for manual measurements were excellent for all measurements, except for JLCA and for MPTA postoperatively. For the AI-aided analyses, repeated measurements showed entirely consistent results for all measurements with an intra-observer ICC of 1.0.ConclusionsThe AI-based software can provide fully automated analyses of native long-leg radiographs in patients with varus malalignment and after HTO with great accuracy and reproducibility and could support clinical workflows.Level of evidenceDiagnostic study, Level III.

Hacking Threat in Arab Cyberspace: How al-Hākirz al-Muslimūn Construct Narratives against Zionists and Anti-Islamists

The attack on the al-Aqsa mosque on April 6, 2023, and the burning of the Qur'an on January 21, 2023, have sparked a reaction from hacker group who call themselvesالهاكرز المسلمون (al-hākirz al-muslimūn) 'Muslim Hackers' (MH). Through the “Hacker News” YouTube channel, they threaten to hack several strategic and vital facilities of the Israeli and Swedish governments. About this phenomenon, this study aims to explain the construction of their threat narratives to those whom they call Zionists and anti-Islamists. Explanative qualitative research was conducted to answer research questions consisting of the formulation of themes, sequence of the scheme, and micro linguistic elements. Research data in the form of 2 videos of MH hacking threats were collected through manual transcription, reading and observation, printing, selection, and classification. Meanwhile, data analysis through the stages of reduction, presentation, analysis, interpretation, and conclusion was carried out by referring to Teun van Dijk's discourse structure model as an analytical framework. The results of the thematic structure analysis show that the theme highlights the actions as a response to attacks on Muslims and Islamic sacred symbols. MH asserts their identity as Muslims explicitly on the theme to attract the audience's attention. In line with these findings, the results of the schematic structure analysis show that the MH threat narrative scheme consists of the cause of threat, form of threat, detail of threat, and impact of threat. Microstructure analysis shows that MH uses clause structures, meaning intentions, and speech acts to launch psychological warfare against Zionists and anti-Islamists. Through this narrative, MH spreads fear with the target of putting political pressure on the Israeli and Swedish governments. Observing the increasingly massive and dynamic phenomenon of cyber warfare, it is predicted that MH's hacking threat narratives will often be found in conflicts and political dynamics in various countries.

IOT based Energy Monitoring for Practical Loads using NodeMCU

Electrical energy monitoring is increasingly important nowadays for residential and commercial usage. The energy meters are installed in consumer’s houses to track their energy usage. There are many chances of human mistakes for every electricity consumer while recording the manual energy meter reading in their houses. Also, the consumer does not have updated information about current electricity usage in each hour, day, and month. To overcome the above problems, the system is developed to remotely monitor from any world location using the NodeMCU and Arduino IDE. The energy monitoring is carried out by using the Node MCU, Arduino IDE, and PZEM-004T sensor. The PZEM-004T module is coupled to the Arduino controller. The sensor module receives a signal from the CT coil which is connected to load. The load parameters such as voltage current power etc., are measured and transferred to the Arduino. A NodeMCU ESP8266 is utilized as a Wi-Fi chip system. By using a Wi-Fi connection and the internet, the acquired data is sent to Thing Speak to save in the cloud to monitor the measured parameters remotely. The power analyzer instrument is also used to measure the real-time parameters consumed by the load which is used to cross verification and compare with the sensor measuring parameters. The passive load resistance, inductance, and 100-watt lamp loads are experimentally connected and tested. The load consumed electrical parameters such as current, voltage, power, power factor, real power, and frequency are monitored in online. Energy monitoring online may reduce the consumer's mistakes by recording the parameters and consumers can know each hour, day, and month's energy consumption up to date according to their usage.

Water level recognition based on deep learning and character interpolation strategy for stained water gauge

AbstractDue to the diversity of climate and environment in China, the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention. Water level measurement is one of the important research topics of flood prevention. Recently, the image‐based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation, low cost, and zero need of manual reading. However, there are two mainly shortcomings of the existing image‐based water level recognition methods: (1) severely affected by light intensity and (2) low accuracy of water level recognition for stained water gauges. To solve these two problems, this paper proposes a water level recognition method in consideration of complex scenarios. This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask, and then uses the YOLOv5 object detection network to extract the letter “E” on the water gauge. Based on the character sequence inspection strategy, the algorithm dynamically compensates for the missed detection of characters of stained water gauges. Through a large number of experiments, the proposed water level measurement method has good robustness in complex scenarios, meeting the needs of flash flood defense.

Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that integrates data augmentation techniques and SwinTransformer, to detect AMD and distinguish between different subtypes using color fundus photographs (CFPs). The DeepDrAMD was trained on the in-house WMUEH training set and achieved high performance in AMD detection with an AUC of 98.76% in the WMUEH testing set and 96.47% in the independent external Ichallenge-AMD cohort. Furthermore, the DeepDrAMD effectively classified dryAMD and wetAMD, achieving AUCs of 93.46% and 91.55%, respectively, in the WMUEH cohort and another independent external ODIR cohort. Notably, DeepDrAMD excelled at distinguishing between wetAMD subtypes, achieving an AUC of 99.36% in the WMUEH cohort. Comparative analysis revealed that the DeepDrAMD outperformed conventional deep-learning models and expert-level diagnosis. The cost-benefit analysis demonstrated that the DeepDrAMD offers substantial cost savings and efficiency improvements compared to manual reading approaches. Overall, the DeepDrAMD represents a significant advancement in AMD detection and differential diagnosis using CFPs, and has the potential to assist healthcare professionals in informed decision-making, early intervention, and treatment optimization.

Online verification and management scheme of gateway meter flow in the power system by machine learning

Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.

Automated analysis of the total choline resonance peak in breast proton magnetic resonance spectroscopy.

The aim of the current study was to compare the performance of fully automated software with human expert interpretation of single-voxel proton magnetic resonance spectroscopy (1H-MRS) spectra in the assessment of breast lesions. Breast magnetic resonance imaging (MRI) (including contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted imaging) and 1H-MRS images of 74 consecutive patients were acquired on a 3-T positron emission tomography-MRI scanner then automatically imported into and analyzed by SpecTec-ULR 1.1 software (LifeTec Solutions GmbH). All ensuing 117 spectra were additionally independently analyzed and interpreted by two blinded radiologists. Histopathology of at least 24 months of imaging follow-up served as the reference standard. Nonparametric Spearman's correlation coefficients for all measured parameters (signal-to-noise ratio [SNR] and integral of total choline [tCho]), Passing and Bablok regression, and receiver operating characteristic analysis, were calculated to assess test diagnostic performance, as well as to compare automated with manual reading. Based on 117 spectra of 74 patients, the area under the curve for tCho SNR and integrals ranged from 0.768 to 0.814 and from 0.721 to 0.784 to distinguish benign from malignant tissue, respectively. Neither method displayed significant differences between measurements (automated vs. human expert readers, p > 0.05), in line with the results from the univariate Spearman's rank correlation coefficients, as well as the Passing and Bablok regression analysis. It was concluded that this pilot study demonstrates that 1H-MRS data from breast MRI can be automatically exported and interpreted by SpecTec-ULR 1.1 software. The diagnostic performance of this software was not inferior to human expert readers.

Wireless water consumption sensing system for building energy efficiency: A visual-based approach with self-powered operation

The use of smart wireless remote water meters has become popular due to their efficiency in avoiding manual labor and automate meter reading. The current implementation of these measurement instruments often requires expensive external power sources and separate data aggregators, posing challenges for circuit safety and the goal of reducing carbon emissions in aging communities. In response, a novel wireless system has been developed to accurately measure water consumption in buildings. This system incorporates advanced machine learning techniques and utilizes the potential of water-based energy in a seamless manner. This novel methodology utilizes optical sensors to acquire visual data of water gauges and exploits machine learning techniques, specifically the AVSM model also called Advanced Visual Sensing Model (AVSM). This methodology employs a rigorous approach to achieve accurate metric determinations, thereby eliminating the necessity for additional hardware or excessive power configurations. Moreover, an integrated micro water generator has been seamlessly implemented, demonstrating the capability to effectively transfer energy to a lithium battery for the purpose of storage. This implementation guarantees consistent functionality and a significant decrease in reliance on external power supplies. The data augmentation procedure involves the stochastic manipulation of image luminance, resulting in encouraging results when used in conjunction with the AVSM model. Significantly, the dimensions of the model have been significantly decreased, while maintaining a commendable mean average precision (mAP). By leveraging this method and visual-based features, the system can contribute to overall building energy efficiency and reduce the burdens of manual meter reading while supporting CO2 emissions reduction as a feasible and cost-effective solution for building management, promoting sustainability, and advancing energy-efficient practices in old communities. This new wireless system for measuring water usage provides a practical and affordable way to enhance energy efficiency in buildings, lessen the workload of manual meter reading, and help meet carbon emissions reduction goals. As well as optimizing resource and energy use, this method also aligns with eco-friendly solutions to combat global warming and climate change.

Deep learning-based analysis of infrared fundus photography for automated diagnosis of diabetic retinopathy with cataracts.

To develop deep learning-based networks for the diagnosis of diabetic retinopathy (DR) with cataracts based on infrared fundus images. Shanghai General Hospital, Shanghai Eye Disease Prevention & Treatment Center, Shanghai, China. Development and evaluation of an artificial intelligence (AI) diagnostic method. A total of 10 665 infrared fundus images from 4553 patients with diabetes were used to train and test the model. For image quality assessment, left and right eye classification, DR diagnosis and grading, and segmentation of 3 DR lesions, an end-to-end software using EfficientNet and UNet was developed. The accuracy and performance of the software in comparison to human experts was evaluated. The model achieved an accuracy of 75.31% for left and right eye classification, 100% for DR grading and diagnosis tasks, and 73.67% for internal test set, with corresponding areas under the curve (AUCs) of 0.88, 1.00, and 0.89, respectively. For DR lesion segmentation, the AUCs of hemorrhagic, microangioma, and exudative lesions were 0.86, 0.66, and 0.84, respectively. In addition, a contrast test of human-machine film reading confirmed the software's high sensitivity (96.3%) and specificity (90.0%) and consistency with the manual film reading group (κ = 0.869, P < .001). This easily deployable software generated reports quickly and promoted efficient DR screening with cataracts in clinical and community settings. AI-assisted software can perform automatic analysis of infrared fundus images and has substantial application value for the diagnosis of DR patients with cataracts.

Zigbee based Wireless Sensor Network for Smart Energy Meter

Wireless sensor networks are expanding across a wide range of application scenarios. The most widely used transmitter is "ZigBee," which is used in wireless sensor networks. Based on the IEEE standard known as IEEE 802.15.4, ZigBee is an enabling low-cost technology that offers minimal energy consumption and a low data rate. It is used for remote control, medical aid, home automation, industry control, and other wireless sensor applications, in addition to wireless sensor networks and personal area network applications. This paper aims to develop a wireless sensor network and a protocol for smart energy meter applications. Our proposed system comprises a digital energy meter, a ZigBee coordinator, and a management application. A terminal alert and a cover alarm can be automatically sent to the management software by the wireless meter reading system once it has read the unit. Mistakes from Errors in leakage metering reading to manual meter reading can be avoided. This proposed system will improve efficiency by reducing labor intensity to liberate labor and force. The system setup can accommodate a large number of energy meters with sufficient hop network depth to detect a new energy meter automatically. The technology can be widely used in wireless monitoring and control applications because of its low cost, low power consumption, extended battery life, and mesh networking's ability to extend high reliability to a broader range. To connect a variety of low-power devices wirelessly, ZigBee will satisfy the rising demand. For the future generation of industrial technologies, ZigBee will be deployed.

Evaluation of height prediction models: from traditional methods to artificial intelligence.

Traditional methods for predicting adult height (AHP) rely on manual readings of bone age (BA). However, the incorporation of artificial intelligence has recently improved the accuracy of BA readings and their incorporation into AHP models. This study aimed to identify the AHP model that fits the current average height for adults in Mexico. Using a cross-sectional design, the study included 1173 participants (5-18 yr). BA readings were done by two experts (manually) and with an automated method (BoneXpert®). AHP was carried out using both traditional and automated methods. The best AHP model was the one that was closest to the population mean. All models overestimated the population mean (males: 0.7-6.7 cm, females: 0.9-3.7 cm). The AHP models with the smallest difference were BoneXpert for males and Bayley & Pinneau for females. However, the manual readings of BA showed significant interobserver variability (up to 43% of predictions between observers exceeded 5 cm using the Bayley & Pinneau method). Traditional AHP models relying on manual BA readings have high interobserver variability.Therefore, BoneXpert is the most reliable option, reducing such variability and providing AHP models that remain close to the mean population height. Traditional models for predicting adult height often result in overestimated height predictions. The manual reading of bone age is prone to interobserver variability, which can introduce significant biases in the prediction of adult height. The BoneXpert method minimizes the variability associated with traditional methods and demonstrates consistent results in relation to the average height of the population. This study is the first to assess adult height prediction models specifically in the current generations of Mexican children.

Use of Digital Stethoscope to Measure Heart Rate in Birds: Comparison of Different Counting Methods Using Phonocardiograms.

The high cardiac contractility of birds poses a challenge to traditional cardiac auscultation, particularly for the accurate determination of heart rate (HR). The objectives of this study were to 1) evaluate the feasibility of using phonocardiograms of adequate length and quality to assess HR in different avian species with a commercially available digital stethoscope, 2) compare 5 counting methods, including 2 direct reading methods (manual counting and using a semiautomatic computerized algorithm as a reference method) and 3 listening methods (progressive mental counting, counting by 10s, and counting with a smartphone application tap counter), and 3) obtain the HR in selected birds and identify a correlation between body weight and HR in different avian species. An inverse correlation on a logarithmic scale was identified between the mean body weight and HR in 60 different bird species (n = 211; R = -0.72, P < 0.0001). Manual reading of phonocardiograms was the most reliable method and had the highest agreement with the reference method; this was followed by the counting by 10s method, the tapping method, and the progressive mental counting method, which was the least reliable. The agreement levels for the different methods were comparable for HRs <200 beats per minute (bpm) in birds weighing >1 kg. For HRs >500 bpm in birds weighing <150 g, only the reading method maintained a good agreement level. A digital stethoscope can be a useful tool for accurately determining the HR in birds, including very small species with high HRs.

Ki67 proliferation index in medullary thyroid carcinoma: acomparative study of multiple counting methods and validation of image analysis and deep learning platforms.

The International Medullary Thyroid Carcinoma Grading System, introduced in 2022, mandates evaluation of the Ki67 proliferation index to assign a histological grade for medullary thyroid carcinoma. However, manual counting remains a tedious and time-consuming task. We aimed to evaluate the performance of three other counting techniques for the Ki67 index, eyeballing by a trained experienced investigator, a machine learning-based deep learning algorithm (DeepLIIF) and an image analysis software with internal thresholding compared to the gold standard manual counting in a large cohort of 260 primarily resected medullary thyroid carcinoma. The Ki67 proliferation index generated by all three methods correlate near-perfectly with the manual Ki67 index, with kappa values ranging from 0.884 to 0.979 and interclass correlation coefficients ranging from 0.969 to 0.983. Discrepant Ki67 results were only observed in cases with borderline manual Ki67 readings, ranging from 3 to 7%. Medullary thyroid carcinomas with a high Ki67 index (≥ 5%) determined using any of the four methods were associated with significantly decreased disease-specific survival and distant metastasis-free survival. We herein validate a machine learning-based deep-learning platform and an image analysis software with internal thresholding to generate accurate automatic Ki67 proliferation indices in medullary thyroid carcinoma. Manual Ki67 count remains useful when facing a tumour with a borderline Ki67 proliferation index of 3-7%. In daily practice, validation of alternative evaluation methods for the Ki67 index in MTC is required prior to implementation.

Research trends and hot spots of Chuanxiong Rhizoma from 2010 to 2023

This study aims to objectively and quantitatively analyze the research status and hot spots of Chuanxiong Rhizoma and provide guidance for further research and clinical application of this herbal medicine. Firstly, the research articles involving Chuanxiong Rhizoma from 2010 to 2023 were retrieved from seven databases including Web of Science, PubMed, Medline, CNKI, VIP, Wanfang, and SinoMed. Then, NoteExpress and manual reading were employed to complete the de-duplication and screening of the articles, and the annual number of publications and journals was analyzed. Finally, CiteSpace was used for systematic analysis of the research institutions, authors, and keywords, and the corresponding knowledge maps were established. After screening, 1 137 articles in Chinese and 433 articles in English were included, and the annual number of publications showed an increasing trend. Chinese Journal of Experimental Traditional Medical Formulae and Journal of Ethnopharmacology were the top Chinese and English journal in the number of publications. Chengdu University of Traditional Chinese Medicine and Nanjing University of Chinese Medicine published the most articles in Chinese and English, respectively. PENG Cheng and FENG Yi were the authors published more articles in Chinese and English. Ferulic acid, signaling pathway, mechanism, headache, ligustrazine, and apoptosis were frequent keywords. A total of 20 clusters and 30 bursts were generated. The comprehensive analysis showed that the research trends and hot spots in this field mainly focused on pharmacological components and isolation, pharmacological effects and mechanism, clinical application and efficacy, compatibility and efficacy of drug pairs, quality evaluation and control, and cultivation and germplasm improvement.

Machine-learning and water energy harvesting based wireless water consumption sensing system in buildings

In recent years, smart wireless remote water meters have become popular, but this kind of smart water meter needs an external power supply, and every 100 to 200 installed this type of water meter, needs to set up an independent collector. This kind of water meter is expensive, and the external power supply will increase the energy consumption of the building, which is not conducive to the circuit safety of the old community. In order to reduce the burden of manual meter reading and reduce the building's "carbon emissions". A wireless water consumption sensing system for buildings based on machine learning and water energy collection is designed. Images are collected and recognized by visual sensors to realize the visual reading of water meter data. The system is powered by a 12V micro water generator and transferred to lithium battery energy storage to realize the self-energy supply of the whole system. The lightweight YOLOv5 was used to implement the model training process. Then, on the premise of the small sample data set, the data set was expanded by a data enhancement method which randomly changed the image brightness and hue. Finally, the training model is deployed on MCU through the TensorFlow Lite framework to realize edge AI detection. Experiments show that the size of the lightweight YOLOv5 is reduced by 87.1% and the mAP reaches 92.3%. The system can complete the water meter wireless meter reading and realize the system self-power stably. In addition, the system can be expanded for other meter reading tasks based on visual features.