Determination Of Threshold Research Articles

Prognostic value of neutrophil to lymphocyte ratio and lymphocyte counts before durvalumab consolidation after radio-chemotherapy in locally advanced non-small cell lung cancer

BackgroundDurvalumab, an anti-PD-L1 immune checkpoint inhibitor, after radio-chemotherapy (RCT) has changed the management of locally advanced non-small cell lung cancer (LA NSCLC). A series of retrospective studies have investigated different cut-off of lymphocyte count (LyC) and neutrophil-to-lymphocyte ratio (NLR) to predict survival in LA NSCLC. None of these studies has validated their threshold in an independent group of patients. We wanted to assess the OS prognostic value of NLR and LyC in patients with LA NSCLC treated by RCT and durvalumab, with threshold determination and their validation in an external cohort.MethodsPatients were enrolled in four institutions between Oct. 2017 and Jan. 2022. Pre durvalumab LyC, neutrophils count (NC) and NLR were collected. To define NLR and LyC cut-off value predicting survival event, time dependent Receiver Operating Characteristics (ROC) curves was performed. Survival outcomes were estimated by the Kaplan-Meier method and differences were compared using univariate and multivariate Cox proportional hazard models.ResultsWe included 76 patients in the training set and 85 in the test set. The best cut off were 2,94 for NLR and 0,61 G/l for LyC to predict OS in the training set. For patients with NLR > 2,94, univariate analysis showed no significant deterioration in OS in either the training set (p = 0,066) or the test set (p = 0,12). Patients with LyC > 0,61 G/L, in univariate analysis, had longer OS in training set (p = 0,030) and in test set (p = 0,0062). This OS increase was not found in multivariate analysis (p = 0,057) in training set but was confirmed in test set (0,039).ConclusionLyC > 0,61 G/l is associated with longer OS for LA NSCLC patient’s treated with RCT and durvalumab in univariate analysis. In this context, a particular expectation for organs at risk sparing during RT to avoid lymphopenia seems important.Trial registrationRetrospectively registered.

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

A novel method for determining ventilatory and gas exchange dynamics during exercise: the "chirp" waveform.

Quantitating exercise ventilatory and gas exchange dynamics affords insights into physiological control processes and cardiopulmonary dysfunction. We designed a novel waveform, the chirp waveform, to efficiently extract moderate-intensity exercise response dynamics. In the chirp waveform, work rate fluctuates sinusoidally with constant amplitude as sinusoidal period decreases progressively from ∼8.5 to 1.4 min over 30 min of cycle ergometry. We hypothesized that response dynamics of pulmonary ventilation (V̇e) and gas exchange [oxygen uptake (V̇o2) and carbon dioxide output (V̇co2)] extracted from chirp waveform are similar to those obtained from stepwise transitions. Thirty-one participants [14 young healthy, 7 older healthy, and 10 patients with chronic obstructive pulmonary disease (COPD)] exercised on three occasions. Participants first performed ramp-incremental exercise for gas exchange threshold (GET) determination. In randomized order, the next two visits involved either chirp or stepwise waveforms. Work rate amplitude (20 W to ∼95% GET work rate) and exercise duration (30 min) were the same for both waveforms. A first-order linear transfer function with a single system gain (G) and time constant (τ) characterized response dynamics. Agreement between model parameters extracted from chirp and stepwise waveforms was established using Bland-Altman analysis and Rothery's concordance coefficient (RCC). V̇e, V̇o2, and V̇co2 Gs showed no systematic bias (P > 0.178) and moderate-to-good agreement (RCC > 0.772, P < 0.01) between waveforms. Similarly, no systematic bias (P = 0.815) and good agreement (RCC = 0.837, P < 0.001) was found for τV̇o2. Despite moderate agreement for τV̇co2 (RCC = 0.794, P < 0.001) and τV̇e (RCC = 0.722, P = 0.083), chirp τ was less [-6.9(11.7) s and -12.2(22.5) s, respectively]. We conclude that the chirp waveform is a promising method for measuring exercise response dynamics and investigating physiological control mechanisms.NEW & NOTEWORTHY We investigated the ability of a novel waveform to extract exercise ventilatory and gas exchange dynamics. In the chirp waveform, work rate fluctuates sinusoidally with constant amplitude as sinusoidal period decreases progressively over 30 min of exercise. In a study of 31 healthy individuals and patients with COPD, comparison of exercise dynamics derived from chirp to those from stepwise waveforms suggests that the chirp waveform is a promising method for derivation of exercise response dynamics.

Enhancing the light yield of He:CF4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document} based gaseous detector

The CYGNO experiment aims to build a large (O(10)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(10)$$\\end{document} m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}) directional detector for rare event searches, such as nuclear recoils (NRs) induced by dark matter (DM), such as weakly interactive massive particles (WIMPs). The detector concept comprises a time projection chamber (TPC), filled with a He:CF4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document} 60/40 scintillating gas mixture at room temperature and atmospheric pressure, equipped with an amplification stage made of a stack of three gas electron multipliers (GEMs) which are coupled to an optical readout. The latter consists in scientific CMOS (sCMOS) cameras and photomultipliers tubes (PMTs). The maximisation of the light yield of the amplification stage plays a major role in the determination of the energy threshold of the experiment. In this paper, we simulate the effect of the addition of a strong electric field below the last GEM plane on the GEM field structure and we experimentally test it by means of a 10 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 10 cm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} readout area prototype. The experimental measurements analyse stacks of different GEMs and helium concentrations in the gas mixture combined with this extra electric field, studying their performances in terms of light yield, energy resolution and intrinsic diffusion. It is found that the use of this additional electric field permits large light yield increases without degrading intrinsic characteristics of the amplification stage with respect to the regular use of GEMs.

Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

ABSTRACTThe research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

Machine Learning Model To Automate Ventilatory Threshold Determination During Vo2max Assessments

Machine Learning Model To Automate Ventilatory Threshold Determination During Vo2max Assessments

Changes in the ratio of neutrophils to albumin depending on the severity of abdominal sepsis in patients with benign and malignant non-traumatic perforation of the left colon

Background. According to the Third International Consensus (Sepsis-3), sepsis is defined as a life-threatening organ dysfunction caused by an uncontrolled response of the affected organism to a new infection, and septic shock as a sepsis-related condition characterized by the development of circulatory failure accompanied by arterial hypotension, with lactatemia and requiring maintenance of blood pressure with vasopressors. Despite progress in diagnosing and treating sepsis in patients with intra-abdominal infections, it continues to be the leading cause of mortality among patients in emergency departments worldwide. Purpose – the objective of this study was to ascertain the capacity of the neutrophilto-albumin ratio (NAR) before surgery to predict the severity of abdominal sepsis and 30-day mortality in patients with benign and malignant non-traumatic perforation left side of the colon. Methods. This was a retrospective, cross-sectional study that analyzed 117 patients of different age groups with sepsis caused by colon perforation who were hospitalized and operated on from January 2014 to March 2024. To evaluate the diagnostic value of INA in predicting sepsis severity and 30-day mortality, we analyzed the biomarker’s ROC curves, including the determination of optimal cut-off thresholds and associated specificity and sensitivity. The association of INA, sepsis severity, and perforation type with 30-day mortality was investigated using survival analysis, including the comparison of Kaplan–Meier curves and the development and analysis of Cox proportional hazards regression models. Comparisons between groups of study patients concerning quantitative characteristics were conducted, with the presence of septic shock and treatment outcome serving as the independent variables. This was done using nonparametric criteria for independent measurements, specifically the Kruskal–Wallis and Mann–Whitney tests. The statistical significance of the relationships between the qualitative indicators of the study was assessed based on the χ2 criterion and the odds ratio. Results. The results demonstrated that the INR has a high prognostic value in determining the severity of sepsis. This was evidenced by a high value of the area under the ROC curve (AUC = 0.948, p = 2, 309×10–41 &lt; 0.05, 95% CI = 0.882–1.013). It has been demonstrated that the development of septic shock can be predicted by a preoperative INA value not exceeding 22.1, with the highest possible specificity (93.4%) and sensitivity (92.3%). The INA was also found to be an effective predictor of 30-day mortality in our data. The optimal INA threshold for mortality prediction was 23.8, with a specificity of 77.1 and sensitivity of 78.7% (AUC = 0.862, p = 6,072 ×10–28 &lt; 0.05, 95% CI = 0.786-0.919). Furthermore, no statistically significant association was observed between the type of perforation and patient outcomes (χ2 = 0.00493, p = 0.944, p &gt; 0.05). However, our study did confirm higher 30-day mortality in patients with colonic perforation in the presence of septic shock compared to patients with sepsis. Conclusions. The neutrophil-to-albumin ratio has been identified as an accurate and reliable biomarker for predicting the severity of abdominal sepsis. The biomarker demonstrated a specificity of 93.4%, a sensitivity of 92.3%, and a cut-off value of 22.1 for stratifying patients with septic shock. Furthermore, this biomarker was identified as an important predictor of 30-day mortality in both types of perforation, with an area under the ROC curve (AUC) of 0.862. Nevertheless, the value of this indicator for predicting mortality was somewhat lower than for determining septic shock. Furthermore, the results of the Cox regression analysis indicated that the impact of IA on survival was more pronounced in patients with malignant colon perforation. In patients with benign colon perforation, the model demonstrated a satisfactory fit to the input data. The presence or absence of septic shock did not influence the survival of patients in this category. The findings of the study indicated a significant correlation between INA and sepsis severity, suggesting that this indicator may serve as a straightforward prognostic tool for determining the severity of sepsis before surgery. Ultimately, the stratification of patients according to the severity of sepsis before surgery may lead to improved clinical outcomes for these patients.

The regulatory mechanism of mannan from millet Huangjiu on flavor release

Huangjiu, the national wine of China, has the characteristics of low grain consumption, low alcohol content and high nutrition, which aligns with the world's beverage wine consumption trend. The research on glutinous rice huangjiu from southern China is more extensive, while millet huangjiu from northern China has received less attention, hindering the diversified development of huangjiu. Polysaccharides play an important role in huangjiu's health activity and flavor characteristics. In this study, the polysaccharide components in millet huangjiu were separated and identified for the first time, and the influence of polysaccharides on the flavor release was explored through sensory evaluation, SPME-GC–MS, threshold determination and isothermal titration calorimetry. The millet huangjiu polysaccharide HJ-1 was composed of →3)-α-Manp(1→, α-Manp-(1→, →2,6)-α-Manp-(1→, →3)-α-Xylp-(1→ and →3)-β-Arap-(1→. HJ-1 (0.1–1 mg/mL) could regulate the release performance of key flavor compounds in millet huangjiu, inhibit the herbal, fruity and alcoholic aromas, and promote the sweet and rice aromas. Further studies showed that HJ-1 formed complexes with ethyl acetate, acetic acid, 2,3-butanediol and γ-butyrolactone through van der Waals forces and hydrogen bonds, and the process was a spontaneous exothermic entropy reduction reaction. This study provides a new idea for optimizing the overall aroma of huangjiu and improving its sensory quality.

Threshold Determination for Effective Water Injection in Coal Seams: Insights from Numerical Simulation

Coal seam water injection is the most basic and effective dust control technology used on the coal mining face. Numerical simulations are helpful for predicting the humidity range of coal seam water injection. The results can provide guidance for the field design of coal seam water injection process parameters. In order to understand the influence of coal seam water injection pressure and water injection time on the coal seam wetting effect, this paper uses the Fluent 17.0 software system to study the process parameters of the coal seam water injection seepage process under different conditions. It is found that in the process of coal seam water injection, with the increase in water injection pressure and the prolongation of water injection time, there is a specific threshold value for the change in coal seam permeability. Only when the water injection pressure and time increase to the threshold value will the permeability of the coal seam be significantly enhanced and the wetting effect improved. The pressure threshold of the mine is 15 MPa–20 MPa, and the time threshold is the first 42 h.

Comparison of local muscle oxygenation and whole-body VO2 during incremental exercise

The use of near-infrared spectroscopy (NIRS) in sports science has become increasingly important in the past few years (Perrey et al., 2024). One application is to determine thresholds from different NIRS parameters like oxygenated (O2Hb) or deoxygenated (HHb) hemoglobin to distinguish between hard and severe-intensity exercise (Caen et al., 2018). Such applications are frequently validated by comparison to established threshold determination concepts. However, it is not yet known which of the two parameters is more closely related to classical concepts. Therefore, the aim of this project was do analyze which NIRS parameter (oxygenated [O2Hb] or deoxygenated [HHb] hemoglobin) better reflects systemic behavior determined from ventilation (VE). Thirteen physically active participants (4 females, 9 males; mean age: 26.3 ± 4.4 years, height: 176.0 ± 10.3 cm, weight: 71.9 ± 9.3 kg) were included in the study. VE and NIRS data from a major locomotor muscle (vastus lateralis) during a step-incremental single leg cycle ergometer test were recorded. We used a custom MATLAB script to fit piecewise linear continuous functions by regression to estimate NIRS and VE thresholds. We used two distinct segments for NIRS data and three distinct segments for VE data. NIRS thresholds were defined as the point of intersection of the two segments in O2Hb and HHb signals, respectively. The ventilatory threshold was defined as the second increase in VE. We used a resampling strategy to reduce the impact of outliers or peculiar data distributions on threshold estimates. Thresholds from O2Hb and HHb in the active leg and VE were observed at 78.24%, 76.37%, and 84.15% of the maximum power achieved. Both O2Hb (r = 0.85, p &lt; 0.001) and HHb (r = 0.75, p = 0.0051) thresholds were significantly correlated, but mean values were significantly different (p &lt; 0.05) from the respiratory compensation point (RCP) mean value. Correlations between O2Hb and HHb values from the passive leg and the RCP values were in the same range (r = 0.88, p &lt; 0.001 and r = 0.79, p = 0.0023, respectively). The results indicate that, based on the correlations, O2Hb is slightly closer related to the second ventilatory threshold compared to HHb. The present study shows that thresholds from both NIRS parameters are closely related to the second ventilatory threshold which indicates a physiological relationship. However, thresholds cannot be used interchangeably as NIRS parameters derived from the working muscle appear earlier than systemic thresholds that demonstrate a delay in kinetics between the working muscle and the system.

Explosive percolation in finite dimensions

Explosive percolation (EP) has received significant research attention due to its rich and anomalous phenomena near criticality. In our recent study [], we demonstrated that the correct critical behaviors of EP in infinite dimensions (complete graph) can be accurately extracted using the event-based method, with finite-size scaling behaviors still described by the standard finite-size scaling theory. We perform an extensive simulation of EPs on hypercubic lattices ranging from dimensions d=2 to 6, and find that the critical behaviors consistently obey the standard finite-size scaling theory. Consequently, we obtain a high-precision determination of the percolation thresholds and critical exponents, revealing that EPs governed by the product and sum rules belong to different universality classes. Remarkably, despite the mean of the dynamic pseudocritical point TL deviating from the infinite-lattice criticality by a distance determined by the d-dependent correlation-length exponent, TL follows a normal (Gaussian) distribution across all dimensions, with a standard deviation proportional to 1/V, where V denotes the system volume. A theoretical argument associated with the central-limit theorem is further proposed to understand the probability distribution of TL. These findings offer a comprehensive understanding of critical behaviors in EPs across various dimensions, revealing a different dimension-dependence compared to standard bond percolation. Published by the American Physical Society 2024

Predictive framework for remaining useful life of roller bearings: Utilizing fractional generalized Pareto degradation model in performance evaluation

Research on predictive frameworks for the Remaining Useful Life (RUL) of mechanical systems is limited. This study proposes a comprehensive RUL prediction framework for roller bearings, integrating early failure assessment, adaptive failure threshold determination, and an adaptive drift fractional-order Pareto degradation model. To tackle data insufficiency, multi-sensor fusion combines time-domain, frequency-domain, and time–frequency features. The framework normalizes the health indicator (HI) using Mahalanobis distance and a sigmoid function, and employs the MD-CUSUM technique for early fault detection. Dynamic threshold updating is achieved through BOX-COX transformation and Chebyshev inequality, establishing confidence intervals. The model demonstrates significant results: lowest RMSE of 5.4267, MAE of 3.7857, highest SOR of 0.90936, HD of 0.93543, PICP of 57.143%, and the narrowest MPIW of 9.45, showing enhanced accuracy and reduced uncertainty. Validation with the XJTU-SY bearing dataset confirms improved performance.

Determination of outdoor thermal comfort thresholds for hot and semi-arid climates: A field study of residential neighbourhoods in Jaipur city

Outdoor thermal comfort (OTC) plays a vital role in enhancing the livability and well-being of urban residents, making it an essential area of research, particularly in the context of climate change. However, few studies have specifically focused on hot and semi-arid regions, especially within residential environments. This study seeks to address this gap by assessing OTC in various residential areas classified as Local Climate Zones (LCZs), ensuring a diverse range of survey locations to evaluate people's comfort and stress levels. Using the Thermal Sensation Vote (TSV) index, the study calibrated the Physiologically Equivalent Temperature (PET) scale for hot and semi-arid climates. In Jaipur, micrometeorological data was monitored alongside thermal perceptions, resulting in 2,428 valid responses gathered during both summer and winter seasons, between 09:00 and 21:00 h, when outdoor activity is common. The analysis revealed that microclimate parameters significantly influence thermal sensations in outdoor environments. Respondents showed high tolerance levels during summer, with variations in thermoneutrality, acceptability, preferences, and comfort across different built typologies. The threshold values identified in this study offer valuable insights for urban planners and designers, enabling them to enhance outdoor thermal comfort during the planning stages, ultimately creating more comfortable and livable urban spaces for residents.

Profiles of muscle-specific oxygenation responses and thresholds during graded cycling incremental test.

Compared to the determination of exercise thresholds based on systemic changes in blood lactate concentrations or gas exchange data, the determination of breakpoints based on muscle oxygen saturation offers a valid alternative to provide specific information on muscle-derived thresholds. Our study explored the profiles and timing of the second muscle oxygenation threshold (MOT2) in different muscles. Twenty-six cyclists and triathletes (15 male: age = 23 ± 7years, height = 178 ± 5cm, body mass = 70.2 ± 5.3kg; 11 female: age = 22 ± 4years, height = 164 ± 4cm, body mass = 58.3 ± 8.1kg) performed a graded exercise test (GXT), on a cycle ergometer. Power output, blood lactate concentration, heart rate, rating of perceived exertion, skinfolds and muscle oxygen saturation were registered in five muscles (vastus lateralis, biceps femoris, gastrocnemius medialis, tibialis anterior and triceps brachii) and percentage at which MOT2 occurred for each muscle was determinated using the Exponential Dmax. The results of Statistical Parametric Mapping and ANOVA showed that, although muscle oxygenation displayed different profiles in each muscle during a GXT, MOT2 occurred at a similar percentage of the GXT in each muscle (77% biceps femoris, 75% tibalis anterior, 76% gastrocnemius medialis and 72% vastus lateralis) and it was similar that systemic threshold (73% of the GXT). In conclusion, this study showed different profiles of muscle oxygen saturation in different muscles, but without notable differences in the timing for MOT2 and concordance with systemic threshold. Finally, we suggest the analysis of the whole signal and not to simplify it to a breakpoint.

An adaptive trajectory compression and feature preservation method for maritime traffic analysis

Ship trajectory data extracted from Automatic Identification System (AIS) has been extensively used for maritime traffic analysis. Yet the enormous volume of AIS data has come with substantial challenges related to storing, processing, analyzing, transmitting, and transferring. Trajectory compression techniques have been widely investigated to remedy the challenge. However, conventional compression techniques such as Douglas-Peucker (DP) algorithm mainly depend on line simplification algorithms, falling short in accurately identifying and preserving crucial information within trajectories. Moreover, using kinematic information from AIS data has posed difficulties associated with compression threshold determination. Hence, an adaptive method capable of considering multiple information from AIS is required. In this paper, a Top-Down Kinematic Compression (TDKC) algorithm aimed at adaptive trajectory compression and feature preservation is proposed. By incorporating time, position, speed, and course attributes from AIS data, TDKC exploits a Compression Binary Tree (CBT) method to address the recursion termination problem and determine the threshold automatically. A case study was conducted to evaluate the performance of TDKC using AIS data from Gulf of Finland, where a comparison with conventional algorithms and their improved versions based on specific performance evaluation metrics was involved. The results demonstrate TDKC's superiority in facilitating maritime traffic analysis.

Rockburst early-warning method based on time series prediction of multiple acoustic emission parameters

Rockburst early-warning is crucial for ensuring safety in deep underground engineering. Existing methods primarily focus on classifying rockburst grades, making it challenging to provide timely warnings. This paper proposes a novel rockburst early-warning framework based on time series prediction of acoustic emission (AE) parameters. Six AE parameters (rise time, count, duration, amplitude, absolute energy, and peak frequency) were identified as potential indicators for rockburst early-warning based on rockburst tests. A sliding window method was applied to process normalized AE data, calculating statistical parameters of the local duration. An LSTM-based time series prediction model was developed to forecast the future evolution of these AE parameters. This, in turn, enabled the establishment of a comprehensive multi-indicator early-warning system. The Isolation Forest (IF) algorithm, an outlier detection method, was used to determine the warning thresholds for each indicator. The CRITIC weighting method was employed to integrate the six rockburst indicators into a single early-warning coefficient (EC), with EC=100 signifying the warning trigger condition. The results demonstrate that the proposed framework effectively captures the evolution trends of AE parameters, enabling proactive early warnings. This approach addresses the limitations of existing methods, such as reliance on experience for threshold determination, lack of a clear basis for multi-indicator weights, and difficulty in quantifying early-warning trigger conditions. The framework provides a new perspective for rockburst early-warning systems.

Denoising of partial discharges in switchgear insulation material using hybrid wavelet denoising-optimization-machine learning

Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance.

Unsupervised online poor contact detection in the secondary circuit of voltage transformers

Voltage transformers (VTs) are fundamental and crucial instrument devices for accurate voltage measurement. Generally, measurement errors can be caused by both the VT and the secondary circuit. Most research focused on the VT but ignored the secondary circuit between it and the measurement device. Poor contact in the secondary circuit is more challenging to capture than those caused by faulty VTs. In this study, we present an unsupervised online poor contact detection method. It first exploits a time series similarity matrix derived from the measured voltage data. The physical constraints deriving from the electrical topology are utilized to eliminate the non-fault fluctuation. To ensure the versatility of the proposed method, an adaptive threshold determination strategy is designed based on knee point location. Numerical experiments compared with some state-of-the-art methods have validated the effectiveness of the proposed method. The versatility of the proposed method is further validated in three real-world cases.

Unraveling the impact of patent transfers on regional innovation: Empirical insights through the lens of entity relationships

Over the past two decades, the market for patent transfers (PT) has experienced substantial growth in China, driven by the expansion of main relationship types, including inter-firm, industry-university-research (IUR), and individual-to-firm PTs. This growth raises critical questions regarding the influence of these diverse PT types on regional innovation. Utilizing data mining on 1,914,750 PT events, this study constructs panel data models for China's 31 provinces from 2011 to 2020, investigating the presence, dimensions, effect decomposition, and threshold determinations of PTs on regional innovation. Findings reveal heterogeneous impacts of PT types on innovation, with individual-to-firm PT being most effective and IUR PTs the least. Among the dimensions of impact, PT strength demonstrates the most circumscribed promotional effect, predominantly within patent inflows, and particularly hinders IUR PTs due to market frictions. The importance of PT breadth mainly appears in patent outflows, suggesting geographic expansion of PT enhances innovation remuneration, critical for sustaining innovation momentum in hub provinces. PT depth uniformly affects innovation across all PT categories irrespective of patent inflows or outflows, underscoring the significance of diversified technology transfers. A universal observation across PT types is that surpassing regional thresholds of digitalization significantly amplifies the promotion of PT on regional innovation.

Antinociceptive and anesthetic-sparing effects of levomethadone/fenpipramide cannot be enhanced by coadministration of metamizole in awake and anesthetized Beagles.

To determine the effect of levomethadone/fenpipramide and metamizole alone and in combination on acute nociception. 8 healthy, adult Beagles were used in 2 separate randomized, complete crossover, experimental trials (threshold testing and determination of minimal alveolar concentration [MAC]) with masked observers. In both trials, treatments were 0.2 mg·kg-1 levomethadone/fenpipramide (L), 75 mg·kg-1 metamizole (M), or their combination (LM). In conscious dogs, mechanical thresholds were determined using constantly rising force. Thermal thresholds were measured via ramped contact heat. The MAC of sevoflurane was determined using the bracketing method with electrical stimulus (50 V, 50 Hz, 10 ms) before and 1 and 4 hours after treatment. Mechanical thresholds in L and LM were significantly increased above baseline (BL) for 165 minutes and above M for 135 minutes. Percent thermal threshold excursion significantly increased above BL in L for 75 minutes and in LM for 135 minutes. In L and LM, the percent thermal threshold excursion was significantly higher than in M from 15 to 75 or 135 minutes, respectively. In L and LM, the MAC of sevoflurane was significantly reduced at 1 hour compared to BL and M. Duration but not the magnitude of thermal antinociception of levomethadone/fenpipramide was increased by metamizole. Mechanical antinociception in awake dogs and anesthetic-sparing effects of levomethadone/fenpipramide were not altered. Coadministration of levomethadone/fenpipramide and metamizole to increase antinociception is not justified.