Traditional Threshold Research Articles

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology, smart energy grids can realize the stable, efficient and clean operation of power systems. However, the emergence of cyber-physical attacks, such as dynamic load-altering attacks (DLAAs) has introduced great challenges to the security of smart energy grids. Thus, this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids. The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer. First, a data-driven method was proposed to predict the DLAA sequence in the cyber layer. By designing a double radial basis function network, the influence of disturbances on attack prediction can be eliminated. Based on the prediction results, an unknown input observer-based detection and localization method was further developed for the physical layer. In addition, an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs. Consequently, through the collaborative work of the cyber-physics layer, injected DLAAs were effectively detected and located. Compared with existing methodologies, the simulation results on IEEE 14-bus and 118- bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

A novel strategy for detecting mutations from circulating tumor DNA with blood tissue only.

e20031 Background: Molecular Residual Disease (MRD) test detects tumor-specific mutations in circulating tumor DNA (ctDNA), which can indicate the risk of tumor recurrence. However, without tumor-specific mutations, detecting mutations in blood tissue alone is extremely challenging due to the low concentration of ctDNA in blood. To address this challenge, we proposed an innovative mutation filtering and calling strategy by simulating noise’s distribution and quality assessment to greatly boost the performance. Methods: We performed 406 pairs of MRD tests on both tumor and blood tissue of postoperative patients with mixed cancer types, including lung, gastrointestinal (GI), hepatobiliary, and pancreatic. Patients that possess one or more tumor-specific mutations in their blood tissue were classified as recurrence high-risk (n = 66), others were classified as recurrence low-risk (n = 340). All sequence data were analyzed using both traditional and proposed method. In the traditional method, VAF threshold, end-reads, multiple-align reads and population frequencies were used as filtering criteria. While in the proposed method, VAF threshold was excluded and two new parameters were introduced: The distribution of variant allele frequency (VAF) of sequencing noise, which simulated 42 healthy individuals’ background noise with conf > 0.995; The normalized proportion of supported unique and total variants, to assess the likelihood of mutations originated from ctDNA. Results: Among 406 patients of mixed tumor types, the traditional approach barely reached a sensitivity of 39.4% and a specificity of 89.1%, while our strategy achieved a sensitivity of 71.2% and a specificity of 80.9%. Particularly among the 92 patients with gastrointestinal tumor, by filtering specific noise caused by the MRD panel (e.g. multi-alignment error of MUC gene family), we further boost the sensitivity from 47.1% to 82.4% with a loss of specificity from 93.3% to 90.7%. Moreover, among 248 patients with lung tumors, the sensitivity and specificity between traditional approach and proposed method were 15.8%, 93.9%, and 52.6%, 90.0% respectively, which might be caused by the low recurrence rate in the patient cohort (19 out of 248). Conclusions: The novel strategy based on simulated noise distribution and read quality assessment surpasses the traditional threshold-based approaches in mutation detection, especially in deep sequencing like MRD tests. The findings of the study indicate that the recurrence monitoring strategy is applicable across various tumor types and exhibits superior performance in GI tumor, albeit with certain limitations. In conclusion, this study boosts the feasibility of MRD as a non-invasive alternative for tissue biopsy by providing a novel mutation filtering strategy, which further strengthen the potential of MRD testing in enhancing cancer patients’ long-term prognosis and treatment.

#2415 Estimated glomerular filtration rate thresholds associated with poor long-term outcomes in older patients with diabetes

Abstract Background and Aims Considering the natural decline of GFR with renal senescence, an age-adjusted estimated glomerular filtration rate (eGFR)-based chronic kidney disease (CKD) definition was recently proposed, which has been supported by that mortality risks start to increase at GFR <45 mL/min/1.73 m2 in older adults (≥65 years). However, the eGFR threshold for mortality and kidney outcomes in older patients with diabetes was less understood. We aimed to evaluate eGFR levels that raise the risk of mortality or end-stage kidney disease (ESKD) in older patients with diabetes. Method Older adult patients (≥65 years) with type 2 diabetes who visited our outpatient diabetes center during 2009 were retrospectively identified and followed up until 2017. Patients were categorized into four groups per their CKD-EPI equation-based eGFR: ≥60, 45–59, 30–44, and 15–29 mL/min/1.73 m2. Cox proportional hazard analysis for all-cause mortality and competing-risk analysis for ESKD (with a competing event of pre-ESKD death) were performed. Results Among 3,065 patients, 19%, 8%, and 2% of patients had eGFR 45–59, 30–44, and 15–29 mL/min/1.73 m2 at baseline, respectively. After adjusting various clinical covariates, including blood pressure, diabetes duration, urine albumin/creatinine ratio, HbA1c, serum cholesterol levels, and major comorbidities, patients with eGFR 30–44 and 15–29 mL/min/1.73 m2 had 1.51-fold (95% confidence interval [CI] 1.17–1.95, P < 0.001) and 2.66-fold (95% CI 1.87–3.79, P < 0.001) higher risks of mortality, respectively, whereas patients with eGFR 45–59 mL/min/1.73 m2 had a comparable risk (1.18, 95% CI 0.96–1.45, P = 0.127) to those with eGFR ≥60 mL/min/1.73 m2. Substitution hazard ratios for ESKD were 2.29 (95% CI 1.41–3.71, P = 0.001), 5.25 (95% CI 3.27–8.41, P < 0.001), and 16.74 (95% CI 9.73–28.80, P < 0.001) in patients with eGFR 45–59, 30–44, 15–29 mL/min/1.73 m2, respectively. In a subgroup of patients 75 or older (n = 800), patients with eGFR 45–59 mL/min/1.73 m2 exhibited comparable risks for both ESKD and mortality. Conclusion Reduced eGFR < 60 mL/min/1.73 m2 predicted an increased risk of ESKD in older patients with diabetes, suggesting that the current traditional eGFR threshold appears feasible. Thus, some high-risk individuals may be missed if age-adapted eGFR criteria with a lower eGFR threshold than 60 mL/min/1.73 m2 are universally applied for older adults ≥65 years.

Optimal Whole Slide Image Segmentation Using Generalized Normal Distribution Optimization

Whole slide image (WSI) segmentation is a crucial task aiding tumour and cancerous cell diagnosis. Generalized Normal Distribution Optimization (GNDO) algorithm is adopted for whole slide image segmentation based on thresholding in this paper. GNDO algorithm utilizes the generalized normal distribution's properties to determine the ideal thresholds for image segmentation. Through various metrics, the efficacy of GNDO in comparison to traditional Otsu thresholding methods is demonstrated. As demonstrated by the results, it can offer reliable and flexible solutions for different histopathology images.

Initial Study of Adaptive Threshold Cycle Slip Detection on BDS/GPS Kinematic Precise Point Positioning during Geomagnetic Storms

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth’s magnetosphere, ionosphere, and upper atmosphere affect GNSS’s positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms.

Improved DeepLabV3+ Network Beacon Spot Capture Methods

Improved DeepLabV3+ Network Beacon Spot Capture Methods

A preliminary study on the identification of microcracks on the aggregate surface of asphalt pavements under cumulative tire wear

This paper presents a microcrack segmentation method and two-dimensional planar parameters to quantitatively characterize microcracks in aggregates under tire loading. First, an aggregate sample with extensive repeated tire wear was collected from the pavement surface for rock CT scanning. The acquired grayscale images were pre-processed by image filtering and appropriate image enhancement. Then, a microcrack segmentation method for aggregate damage analysis was developed based on the edge region growth algorithm and compared with the traditional threshold segmentation method. Finally, based on the microcrack segmentation results, the distribution pattern of microscopic characteristic parameters of aggregate surface microcracks was revealed. The results show that the best image pre-processing scheme is to first reduce the noise in the gray-scale images by using the Gaussian and median filters together, and then enhance their local details by gamma transform. Compared with the traditional threshold segmentation method, the proposed method effectively improves the microcrack segmentation accuracy by setting a large number of seed positions and adjusting their growth patterns. Moreover, the microscopic characteristic parameter distribution pattern of aggregate microcracks in each layer suggests that the crack extension degree on the pavement aggregate surface directly subjected to tire loads is generally larger than that on other parts. In addition, the microcracks are not only densely distributed within the aggregate surface layer, but also tend to extend deeper, indicating that the effect of cumulative tire loads on frictional damage to the pavement surface needs to be investigated in the field of pavement skid resistance research.

Interactive machine learning for segmenting pores of sandstone in computed tomography images

In the realm of gas field development and CO2/H2 geological storage, the precise characterization of reservoir parameters stands as a critical role. In recent years, digital rock technology has emerged as a cutting-edge tool for examining micro-pore structures, permeability parameter characteristics, and reservoir flow mechanisms. However, there are still certain defects in image segmentation, where low-density pore bodies in low-resolution images make it difficult to clearly distinguish between pores and solid minerals, affecting the accuracy of pore segmentation. This article aims to improve the accuracy of sandstone pore segmentation. We utilized the interactive machine learning segmentation method to segment pore structures and compare the segmented images obtained with the traditional ImageJ grayscale threshold segmentation methods. Furthermore, we evaluated the effectiveness of different segmentation methods quantitatively using the confusion matrix. The research results indicate that the segmentation quality (F1 score) of the machine learning method is higher than the grayscale methods, and the segmentation effectiveness of machine learning method is more stable. To further validate the accuracy of the machine learning method, we simulated the permeability and the results were compared with the experimental results, and the results show that the deviation in permeability segmented through machine learning methods is the smallest. Therefore, our results demonstrate the effective application of the machine learning method in segmenting sandstone pores in the digital rock images.

Control Moment Gyroscope Anomaly Detection Based on Sparse Autoencoder

The Control Moment Gyroscope (CMG) is an important executing part of spacecraft, and its anomaly detection has a key influence on the reliability of spacecraft. With the rapid growth of the number of spacecraft and telemetry data, data-driven spacecraft anomaly detection methods have received extensive attention, and such methods may become an important means of spacecraft anomaly detection. However, at present, the spacecraft is faced with the problem of unknown anomalies and lack of abnormal data during orbit. To solve this problem, a sparse autoencoder (SAE) based CMG anomaly detection method was proposed, which takes the telemetry signal of CMG as the object and lacks the sample of anomalous telemetry signal in training data. Firstly, data sample selection and processing were carried out to normalize the data of each telemetry value. Secondly, the SAE model was constructed, and the model was trained with the training set data. Then the sample reconstruction error of the test set was calculated to complete the anomaly detection of the CMG. Finally, it was compared with the traditional artificial threshold monitoring method. The results indicated that the CMG anomaly detection method based on SAE can effectively detect the anomaly samples of the CMG telemetry data, and has better anomaly detection performance than the traditional artificial threshold monitoring method.

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

Characterizing urban actively populated area growth in the Yangtze River Delta using nighttime light data

Understanding the spatiotemporal evolution of urban actively populated area (UAPA) in urban agglomerations is crucial for sustainable development. This study fills a gap in previous research that overlooked urban population dynamics, leading to inaccurate UAPA characterizations. We utilized nighttime light (NTL) data, which reflects both urban construction status and population dynamics, to characterize UAPA growth pattern in the Yangtze River Delta. We developed a novel search method for extracting UAPA that demonstrated greater accuracy and adaptability than traditional threshold method. We employed expansion rate (ER) and annual growth rate (AGR), and a method for recognizing urban expansion patterns, to illustrate the characteristics and spatial changes in the Yangtze River Delta. We divided the research stage (1992–2019) into six stages based on China’s Five-Year Plan. The results demonstrated that the UAPA size at the end of the sixth stage (2016–2019) was about 6 times greater than that at the end of the first stage (1992–1995). The ER and AGR were approximately 15% during the first stage (1992–1995), and dropped dramatically following the fourth stage (2006–2010). Edge expansion is a typical predominant spatial expansion pattern for UAPA. While the proportion of patches with newly generated expansion showed an initial upward and then a downward trend, the percentage of patches with infilling expansion exhibited a pattern of first decreasing and then increasing. This study aims to provide support for future development planning and policy formulation.

EMD-based noise reduction study of steel cored conveyor belt containing slag signal

This paper proposes a noise reduction technique that combines empirical mode decomposition (EMD) and wavelet thresholding to eliminate the noise interference caused by metal slag in the steel cord conveyor belt damage signal. Initially, the original signal undergoes decomposition by EMD into high- and low-frequency intrinsic mode function (IMF) components based on metal slag signal feature analysis. The wavelet threshold de-noising method is then applied to filter out the high-frequency noise while preserving the low-frequency IMF components. The IMF components containing slag noise are discarded, and the filtered signal and the low-frequency IMF components are used for signal reconstruction to obtain an optimal noise reduction effect. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

A New Denoising Method for Belt Conveyor Roller Fault Signals.

In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method's denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

Early Vital Sign Thresholds Associated with 24-Hour Mortality among Trauma Patients: A Trauma Quality Improvement Program (TQIP) Study.

Identifying patients at imminent risk of death is critical in the management of trauma patients. This study measures the vital sign thresholds associated with death among trauma patients. This study included data from patients ≥15 years of age in the American College of Surgeons Trauma Quality Improvement Program (TQIP) database. Patients with vital signs of zero were excluded. Documented prehospital and emergency department (ED) vital signs included systolic pressure, heart rate, respiratory rate, and calculated shock index (SI). The area under the receiver operator curves (AUROC) was used to assess the accuracy of these variables for predicting 24-hour survival. Optimal thresholds to predict mortality were identified using Youden's Index, 90% specificity, and 90% sensitivity. Additional analyses examined patients 70+ years of age. There were 1,439,221 subjects in the 2019-2020 datasets that met inclusion for this analysis with <0.1% (10,270) who died within 24 hours. The optimal threshold for prehospital systolic pressure was 110, pulse rate was 110, SI was 0.9, and respiratory rate was 15. The optimal threshold for the ED systolic was 112, pulse rate was 107, SI was 0.9, and respiratory rate was 21. Among the elderly sub-analysis, the optimal threshold for prehospital systolic was 116, pulse rate was 100, SI was 0.8, and respiratory rate was 21. The optimal threshold for ED systolic was 121, pulse rate was 95, SI was 0.8, and respiratory rate was 0.8. Systolic blood pressure (SBP) and SI offered the best predictor of mortality among trauma patients. The SBP values predictive of mortality were significantly higher than the traditional 90mmHg threshold. This dataset highlights the need for better methods to guide resuscitation as initial vital signs have limited accuracy in predicting subsequent mortality.

Blood pressure cutoffs at 11–13 weeks of gestation and risk of preeclampsia

A parallel has been drawn between first-trimester placental vascular maturation and maternal cardiovascular adaptations, including blood pressure. Although 140/90 mm Hg is well-accepted as the threshold for chronic hypertension in the general obstetric population in early pregnancy, a different threshold could apply to stratify the risk of adverse outcomes, such as preeclampsia. This could have implications for interventions, such as the threshold for initiation of antihypertensive therapy and the target blood pressure level. We evaluated the relationship between various blood pressure cutoffs at 11-13 weeks of gestation and the development of preeclampsia, overall and according to key maternal characteristics. This secondary analysis was of data from a prospective nonintervention cohort study of singleton pregnancies delivering at ≥24 weeks, without major anomalies, at 2 United Kingdom maternity hospitals, 2006-2020. Blood pressure at 11-13 weeks of gestation was classified according to American College of Cardiology/American Heart Association categories (mm Hg) as (1) normal blood pressure (systolic <120 and diastolic <80), (2) elevated blood pressure (systolic ≥120 and diastolic <80), stage 1 hypertension (systolic ≥130 or diastolic 80-89), and stage 2 hypertension (systolic ≥140 or diastolic ≥90). For blood pressure category thresholds and the outcome of preeclampsia, the following were calculated overall and across maternal age, body mass index, ethnicity, method of conception, and previous pregnancy history: detection rate, screen-positive rate, and positive and negative likelihood ratios, with 95% confidence intervals. A P value of <.05 was considered significant. There were 137,458 pregnancies screened at 11-13 weeks of gestation. The population was ethnically diverse, with 15.9% of Black ethnicity, 6.7% of South or East Asian ethnicity, and 2.7% of mixed ethnicity, with the remainder of White ethnicity. Compared with normal blood pressure, stage 2 hypertension was associated with both preterm preeclampsia (0.3% to 4.9%) and term preeclampsia (1.0% to 8.3%). A blood pressure threshold of 140/90 mm Hg was good at identifying women at increased risk of preeclampsia overall (positive likelihood ratio, 5.61 [95% confidence interval, 5.14-6.11]) and across maternal characteristics, compared with elevated blood pressure (positive likelihood ratio, 1.70 [95% confidence interval, 1.63-1.77]) and stage 1 hypertension (positive likelihood ratio, 2.68 [95% confidence interval, 2.58-2.77]). There were 2 exceptions: a blood pressure threshold of 130/80 mm Hg was better for the 2.1% of women with body mass index <18.5 kg/m2 (positive likelihood ratio, 5.13 [95% confidence interval, 3.22-8.16]), and a threshold of 135/85 mm Hg better for the 50.4% of parous women without a history of preeclampsia (positive likelihood ratio, 5.24, [95% confidence interval, 4.77-5.77]). There was no blood pressure threshold below which reassurance could be provided against the development of preeclampsia (all-negative likelihood ratios ≥0.20). The traditional blood pressure threshold of 140/90 mm Hg performs well to identify women at increased risk of preeclampsia. Women who are underweight or parous with no prior history of preeclampsia may be better identified by lower thresholds; however, a randomized trial would be necessary to determine any benefits of such an approach if antihypertensive therapy were also administered at this threshold. No blood pressure threshold is reassured against the development of preeclampsia, regardless of maternal characteristics.

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors, magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation. Empirical Mode Decomposition (EMD), an adaptive multiscale analysis method for nonlinear and non-stationary signals, is widely used in geophysical and geodetic data processing. Compared with traditional EMD, Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN) is more effective in addressing the problem of mode mixing. Based on the principles of 1D ICEEMDAN, this paper presents an alternative algorithm for 2D ICEEMDAN, extending its application to two-dimensional scenarios. The effectiveness of the proposed approach is demonstrated through synthetic signal experiments, which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD (BEMD) method. Furthermore, to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components, an improved soft thresholding technique is introduced. Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods. To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN, it is applied to ground magnetic survey data in the Yandun area of Xinjiang. The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data. In particular, practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD. In conclusion, as a potential tool for multi-scale decomposition, the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.

Evolved Massive Stars at Low Metallicity. VII. The Lower Mass Limit of the Red Supergiant Population in the Large Magellanic Cloud

The precise definition of the lower mass limit of red supergiant stars (RSGs) is an open question in astrophysics and does not attract much attention. Here, we assemble a spectroscopic evolved cool star sample with 6602 targets, including RSGs, asymptotic giant branch stars, and red giant branch stars, in the Large Magellanic Cloud based on Gaia DR3 and Sloan Digital Sky Survey IV/APOGEE-2. The reference spectrum of each stellar population is built according to the quantile range of relative intensity (1% ∼ 99%). Five different methods, e.g., χ 2, cosine similarity, machine learning (ML), equivalent width, and line ratio, are used in order to separate different stellar populations. ML and χ 2 provide the best and relatively consistent prediction of a certain population. The derived lower limit of the RSG population is able to reach the K s -band tip of the red giant branch (K s ≈12.0 mag), indicating a luminosity as low as about 103.5 L ⊙, which corresponds to a stellar radius of only about 100 R ⊙. Given the mass–luminosity relation of L/L⊙=f(M/M⊙)3 with f ≈ 15.5 ± 3 and taking into account the mass loss of faint RSGs up to now, the minimal initial mass of the RSG population would be about 6.1 ± 0.4 M ⊙, which is much lower than the traditional threshold of 8 M ⊙ for the massive stars. This is the first spectroscopic evidence, indicating that the lower mass limit of the RSG population is around 6 M ⊙. However, the destinies of such faint RSGs are still elusive and may have a large impact on stellar evolutionary and supernova models.

Prioritizing a high posterior probability threshold leading to low error rate over high classification accuracy: the validity of MorphoPASSE software for cranial morphological sex estimation in a contemporary population.

An increasing number of software tools can be used in forensic anthropology to estimate a biological profile, but further studies in other populations are required for more robust validation. The present study aimed to evaluate the validity of MorphoPASSE software for sex estimation from sexually dimorphic cranial traits recorded on 3D CT models (n = 180) from three populations samples (Czech, French, and Egyptian). Two independent observers performed scoring of 4 cranial traits (2 of them bilateral) in each population sample of 30 males and 30 females. The accuracy of sex estimation using traditional posterior probability threshold (pp = 0.5) ranged from 85.6% to 88.3% and overall classification error from 14.4% to 11.7% for both observers, and corresponds to the previously published values of the method. The MorphoPASSE method is also affected by the subjectivity of the observers, as both observers show agreement in sex assignment in 83.9% of cases, regardless of the accuracy of the estimates. Applying a higher posterior probability threshold (pp 0.95) provided classification accuracy of 97.9% and 93.3% of individuals (for observer A and B respectively), minimizing the risk of error to 2.1% and 6.7%, respectively. However, sex estimation can only be applied to 54% and 66% of individuals, respectively. Our results demonstrate the validity of the MorphoPASSE software for cranial sex estimation outside the reference population. However, the achieved classification success is accompanied by a high risk of errors, the reduction of which is only possible by increasing the posterior probability threshold.

Sub-ppm NDIR [formula omitted] gas sensor using ICEEMDAN-SG joint denoising algorithm

Due to the excellent insulation and arc extinguishing properties of SF6 gas, it is widely used in power equipment such as gas insulated switchgear or gas circuit breaker. Therefore, detection of SF6 gas leakage with high sensitivity is necessary. Non dispersive infrared (NDIR) technology have advantages such as miniaturization, low power consumption, simple structure, low cost, and non-destructive testing, making it suitable for building large-scale sensor networks. However, NDIR sensors are susceptible to noise coming from the emitter, the detector and the circuits, which receives little research attention. In this study, we propose a new denoising method by combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with Savitzky-Golay (S-G) to the noise suppression, which achieves a 7 dB signal-to-noise ratio improvement over the traditional wavelet threshold denoising, making the detection limit of the sensing system reach the level below 1 ppm.

Abstract 4932: UltiAnalyzer.AI: An automatic and robust AI-Driven tool for the quantification of multiplex immunofluorescence whole slide images

Abstract Introduction: Whole slide multiplexed immunofluorescence (mIF) facilitates in-depth study of the tumor microenvironment with multi-channel images resulting in an enormous amount of protein expression and spatial data across millions of single cells. Therefore, it has become crucial to process this huge amount of data automatically and reliably. Here we introduce UltiAnalyzer.AI, a software tool that integrates a set of artificial intelligence (AI) models specifically designed for the analysis of whole slide mIF slides, that is fully automatic, highly accurate, highly efficient, and fully scalable. Methods: UltiAnalyzer.AI processes are specialized for 1) tissue segmentation, in which tissue regions are identified using a semantic segmentation AI model on low magnification versions of the DAPI channel, 2) tiling, where slide images are divided into 512x512 overlapping tiles for efficient image processing and lower memory requirements, 3) tumor region segmentation based on a semantic segmentation AI model, 4) nucleus segmentation using the open-source StarDist model, and 5) marker-positive cell detection, in which AI object detection models are employed to classify nuclei as positive or negative in each marker channel using the Faster R-CNN architecture. Here, we take advantage of the full 16-bit dynamic range of images produced using Ultivue InSituPlex® amplification technology, although our approach could be applied to other platforms. Intensity thresholding is no longer necessary, and identification of a positive cell mainly considers the morphology of the cell in the context of its cellular neighborhood. Results: Trained on over 60 mIF slides and validated with manual annotations, UltiAnalyzer.AI has demonstrated strong performance, achieving f1-scores between 0.8 to 0.91 for cell detection, and average correlations of 0.9 for cell counts across 15 markers. Processing time is around 3s per tile, enabling the processing of large slides in under an hour. It has been tested on over 1000 slides, and in a comparative study, it matched a VisioPharm-based quantification with 0.95 correlation in positive counts for 8 markers. Cloud deployment allows for processing thousands of slides simultaneously and in considerably less time. Conclusions: UltiAnalyzer.AI represents a significant advancement in the field of whole-slide mIF analysis, offering high accuracy and scalability using fully automated processes. By utilizing the full dynamic range of Ultivue’s mIF technology and avoiding traditional thresholding methods, the tool also increases robustness and reproducibility. The incorporation of cutting-edge AI technology and scalable cloud computing capabilities positions UltiAnalyzer.AI as a leading-edge solution for mIF analysis in digital pathology. Citation Format: Ruben Cardenes, Douglas Wood, Martin Schulze, Je Lee, Lorenz Rognoni. UltiAnalyzer.AI: An automatic and robust AI-Driven tool for the quantification of multiplex immunofluorescence whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4932.