Traditional Parameters Research Articles

Verbal memory is linked to average oxygen saturation during sleep, not the apnea-hypopnea index nor novel hypoxic load variables

IntroductionThe apnea-hypopnea index (AHI) is the current diagnostic parameter for diagnosing and estimating the severity of obstructive sleep apnea (OSA). It is, however, poorly associated with the main clinical symptom of OSA, excessive daytime sleepiness, and with the often-seen cognitive decline among OSA patients. To better evaluate OSA severity, novel hypoxic load parameters have been introduced that consider the duration and depth of oxygen saturation drops associated with apneas or hypopneas. The aim of this paper was to compare novel hypoxic load parameters and traditional OSA parameters to verbal memory and executive function in OSA patients. MethodA total of 207 adults completed a one-night polysomnography at sleep laboratory and two neuropsychological assessments, the Rey Auditory Verbal Learning Test and Stroop test. Results: Simple linear regression analyses were used to evaluate independent associations between each OSA parameter and cognitive performance. Associations were found between immediate recall and arousal index, hypoxia <90 %, average SpO2 during sleep, and DesSev100+RevSev100. Total recall was associated with all OSA parameters, and no associations were found with the Stroop test. Subsequently, sex, age, and education were included as covariates in multiple linear regression analyses for each OSA parameter and cognitive performance. The main findings of the study were that average SpO2 during sleep was a significant predictor of total recall (p < .007, β = −.188) with the regression model explaining 21.2 % of performance variation. Average SpO2 during sleep was also a significant predictor of immediate recall (p < .022, β = −.171) with the regression model explaining 11.4 % of performance variation. Neither traditional OSA parameters nor novel hypoxic load parameters predicted cognitive performance after adjustment for sex, age, and education. ConclusionThe findings validate that the AHI is not an effective indicator of cognitive performance in OSA and suggest that average oxygen saturation during sleep may be the strongest PSG predictor of cognitive decline seen in OSA. The results also underline the importance of considering age when choosing neurocognitive tests, the importance of including more than one test for each cognitive domain as most tests are not pure measures of a single cognitive factor, and the importance of including tests that cover all cognitive domains as OSA is likely to have diffuse cognitive effects.

Elevated ALT/AST ratio as a marker for NAFLD risk and severity: insights from a cross-sectional analysis in the United States.

The prevalence and incidence of Nonalcoholic fatty liver disease (NAFLD) are increasing worldwide, and NAFLD has emerged as a prominent global health concern. The link between serum alanine aminotransferase (ALT) to aspartate aminotransferase (AST) ratio and NAFLD remains unclear. This study investigated the association between the ALT/AST ratio and NAFLD prevalence, including liver steatosis and fibrosis levels in the population. We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018, including 4753 participants. Subgroup analyses, stratified by age, gender, and body mass index (BMI), were performed, along with adjusted multivariable logistic regression analyses to evaluate the relationship between ALT/AST levels and the likelihood of NAFLD, liver steatosis, and hepatic fibrosis stage. A generalized additive model examined the non-linear relationship between ALT/AST and the probability of developing NAFLD. Among 4753 participants, 1508 (31.73%) were diagnosed with NAFLD. Significant positive correlations between ALT/AST and NAFLD risk were found across all models. In addition, the subgroup analysis by gender, age, and BMI suggested that ALT/AST showed a positive correlation with NAFLD. The ALT/AST ratio was positively correlated with the degree of liver steatosis and liver fibrosis. The correlation between ALT/AST and the incidence of NAFLD showed a non-linear pattern. In women, the non-linear trend is particularly evident, showing an inverted U-shaped curve with an inflection point of 1.302.A receiver operating characteristic (ROC) analysis showed that the predictive value of ALT/AST for NAFLD was better than that of traditional liver enzyme parameters. A higher ALT/AST ratio was independently associated with a significantly higher risk of NAFLD and liver fibrosis within American cohorts. This link is robust among females, children, and adolescents. ALT/AST ratio can be used as a simple and effective noninvasive biomarker to identify individuals with high risk of NAFLD.

Determinants of vascular impairment in type 1 diabetes–impact of sex and connexin 37 gene polymorphism: A cross-sectional study

BackgroundThe associations of risk factors with vascular impairment in type 1 diabetes patients seem more complex than that in type 2 diabetes patients. Therefore, we analyzed the associations between traditional and novel cardiovascular risk factors and vascular parameters in individuals with T1D and modifications of these associations according to sex and genetic factors.MethodsIn a cross-sectional study, we analyzed the association of risk factors in T1D individuals younger than 65 years using vascular parameters, such as ankle brachial index (ABI) and toe brachial index (TBI), duplex ultrasound, measuring the presence of plaques in carotid and femoral arteries (Belcaro score) and intima media thickness of carotid arteries (CIMT). We also used photoplethysmography, which measured the interbranch index expressed as the Oliva-Roztocil index (ORI), and analyzed renal parameters, such as urine albumin/creatinine ratio (uACR) and glomerular filtration rate (GFR). We evaluated these associations using multivariate regression analysis, including interactions with sex and the gene for connexin 37 (Cx37) polymorphism (rs1764391).ResultsIn 235 men and 227 women (mean age 43.6 ± 13.6 years; mean duration of diabetes 22.1 ± 11.3 years), pulse pressure was strongly associated with unfavorable values of most of the vascular parameters under study (ABI, TBI, Belcaro scores, uACR and ORI), whereas plasma lipids, represented by remnant cholesterol (cholesterol – LDL-HDL cholesterol), the atherogenic index of plasma (log (triglycerides/HDL cholesterol) and Lp(a), were associated primarily with renal impairment (uACR, GFR and lipoprotein (a)). Plasma non-HDL cholesterol was not associated with any vascular parameter under study. In contrast to pulse pressure, the associations of lipid factors with kidney and vascular parameters were modified by sex and the Cx37 gene.ConclusionIn addition to known information, easily obtainable risk factor, such as pulse pressure, should be considered in individuals with T1D irrespective of sex and genetic background. The associations of plasma lipids with kidney function are complex and associated with sex and genetic factors. The decision of whether pulse pressure, remnant lipoproteins, Lp(a) and other determinants of vascular damage should become treatment targets in T1D should be based on the results of future clinical trials.Graphical

Analysis of the Effects of Epidural Anesthesia on the Nociception Level Index (NOL®) during Abdominal Surgery.

Background: The NOL® system (PMD-200™ Nociception Level Monitor; Medasense Ltd., Ramat Gan, Israel) is used for the real-time detection of physiological nociception in anesthetized patients by assessing the parameters indicative of sympathetic activity, such as photoplethysmography, skin conductance, peripheral temperature, and accelerometry, which are quantified into the NOL®-Index. This index is more sensitive than traditional clinical parameters in estimating pain and stress responses. While its effectiveness in general anesthesia is well documented, its efficacy in epidural anesthesia needs further investigation. Methods: This retrospective study analyzed NOL®-Index dynamics compared to conventional parameters after epidural administration of bupivacaine. Following ethics committee approval, 119 NOL® measurements were retrospectively analyzed after thoracic epidural catheter administration in 40 patients undergoing abdominal and urological surgery. The NOL-Index® was assessed at 0, 1, 3, and 5 min post application and compared to heart rate, blood pressure, and bispectral index dynamics. Results: This study showed a significant decrease in the NOL®-Index post-local-anesthetic administration with better sensitivity than classical clinical parameters (0 min = 38 ± 11; 1 min = 22 ± 13*; 3 min = 17 ± 11*; 5 min = 12 ± 10*). Higher doses of local anesthetics led to a significant, dose-dependent decrease in NOL®-Index (low dose, 5 min = 15 ± 10*; high dose, 5 min = 8 ± 8*). Conclusions: This study is the first to demonstrate the effectiveness of the NOL®-Index in measuring nociceptive effects following epidural administration, highlighting its potential superiority over conventional parameters and its sensitivity to dose variations.

Machine learning allows expert level classification of intraoperative motor evoked potentials during neurosurgical procedures

ObjectiveTo develop and evaluate machine learning (ML) approaches for muscle identification using intraoperative motor evoked potentials (MEPs), and to compare their performance to human experts. BackgroundThere is an unseized opportunity to apply ML analytic techniques to the world of intraoperative neuromonitoring (IOM). MEPs are the ideal candidates given the importance of their correct interpretation during a surgical operation to the brain or the spine. In this work, we develop and test a set of different ML models for muscle identification using intraoperative MEPs and compare their performance to human experts. In addition, we provide a review of the available literature on current ML applications to IOM data in neurosurgery. MethodsWe trained and tested five different ML classifiers on a MEP database developed from six different muscles in patients who underwent brain or spinal cord surgery. MEPs were obtained by both transcranial (TES) and direct cortical stimulation (DCS) protocols. The models were evaluated within a single patient and on previously unseen patients, considering signals from TES and DCS both independently and mixed. Ten expert neurophysiologists classified a set of 50 randomly selected MEPs, and their performance was compared to the best performing model. ResultsA total of 25.423 MEPs were included in the study. Random Forest proved to be the best performing model with 99 % accuracy in the single patient dataset task and a 78 %–94 % accuracy range on previously unseen patients. The model performance was maximized by representing MEPs as a set of features typically employed in signal processing compared to traditional neurophysiological parameters. The classification ability of the Random Forest model between six different muscles and across different MEP acquisition modalities (79 %) significantly exceeded that of human experts (mean 48 %). ConclusionsCarefully selected ML models proved to have reliable capacity of extracting meaningful information to classify intraoperative MEPs using a limited number of features, proving robustness across patients and signal acquisition modalities, outperforming human experts, and with the potential to act as decision support systems to the IOM team. Such encouraging results lay the path to further explore the underlying nature of clinically important signals, with the aim to continue to produce useful applications to make surgeries safer and more efficient.

TAKAGI–SUGENO–KANG FUZZY SYSTEM MODELING BASED ON LOW-RANK SPARSE SUBSPACE LEARNING FOR MOTOR IMAGERY ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION

The classification of electroencephalogram (EEG) signals derived from motor imagery (MI) has always been a hot topic in the field of brain–computer interfaces. Due to its ability to handle the nonstationary and uncertain information contained in EEG signals, the Takagi–Sugeno–Kang fuzzy system (TSK-FS) has become an advantageous classification algorithm. To train a fuzzy system with strong discrimination capabilities from EEG data interspersed with redundant information, this paper proposes a TSK-FS modeling method based on low-rank sparse subspace learning (TSK-LSSL). This method focuses on consequent parameter learning, which transforms the traditional consequent parameter learning strategy into low-rank subspace and sparse subspace learning processes. Low-rank subspace learning is used to mine the global structural information of data and effectively reduce the number of fuzzy rules. During sparse subspace learning, [Formula: see text]-norm regularization is used to constrain the consequent parameters and causes the number of redundant consequent parameters to be zero, thereby simplifying the fuzzy rules. In addition, a local boundary term based on graph matrices is embedded into the objective function to mine the local structural information of the given data. TSK-LSSL simplifies the number of rules and the consequent part of the fuzzy rules. It exhibits good classification performance on two BCI Competition databases.

A Comparative Study of Machine Learning Models for Predicting Meteorological Data in Agricultural Applications

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector.

Constructing a temperature transferable coarse-grained model of cis-1,4-polyisoprene with the structural and thermodynamic consistency aided by machine learning

Polyisoprene (PI) is a widely used polymer and constructing a systematic coarse-grained (CG) PI model with the structural and thermodynamic consistency with the underlying atomic model over a wide range of thermodynamic conditions is very important for the predictive capability of CG model on overall properties of PI polymer materials and the establishment of their structure-property relationship. However, as the number of tunable CG potential parameters and target properties grows, traditional parameter tuning methods become impractical. In this work, we present a novel approach for determining the optimal CGPI non-bonded potential parameters by employing Particle Swarm Optimization as the calibrator with machine learning-based models trained using molecular dynamics data. The resulting CG model is further augmented with temperature factors through a multistate parameterization approach. This enhancement ensures the model's temperature transferability of structure and thermodynamics in a wide temperature of 150K∼750K.

Sustainability-aware measurement design: How to select the right measuring system

Environmental sustainability assessment has been establishing itself as a requirement when selecting any good or service. This approach also applies to the Information and Communication Technology (ICT) infrastructure, and to Measurements, which represent integral components of ICT. This means that, in the measurement design, the traditional parameters of target uncertainty and costs should be accompanied by environmental sustainability ones.To address this aspect, among the steps of the measurement design process, this paper focuses on the selection, for a given measurand, of the most suitable measuring system from a sustainability perspective. Specifically, to guarantee also consistency of the approach, the proposed methodology is developed within a framework that takes into account the pillars of measurement uncertainty (i.e. the Guide to the Expression of Uncertainty in Measurement, GUM) and those of sustainability assessment (i.e. the ISO14000 family of standards).As a case study, three resistance measuring systems are compared under different simulated and experimental scenarios. The obtained results demonstrate how, through the incorporation of sustainability considerations alongside metrological performance criteria, this methodology facilitates a holistic approach to the selection of measuring systems, promoting environmentally conscious practices within the field of Metrology.

Confinement strength prediction of corroded rectangular concrete columns using BP neural networks and support vector regression

The application of machine learning in predicting the mechanical performance of reinforced concrete columns subjected to reinforcement corrosion was investigated in this study. Traditional models, often relying on empirical formulas or simplified assumptions, are known to struggle in capturing all variables and relationships in complex problems, leading to inadequate prediction accuracy. Through theoretical analysis, the confinement effect of stirrups on core concrete and its performance deterioration caused by reinforcement corrosion were determined. To improve the prediction accuracy and generalization ability, the error Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models were employed. Parameter effects in the BPNN model were interpreted using the SHapley Additive exPlanations (SHAP) and compared with traditional parameter sensitivity analyses. The investigation indicates that the machine learning models have a high degree of fit and low error levels, particularly within the training set. After analyzing using the SHAP approach, it is found that the corrosion ratio of the stirrup and longitudinal reinforcement have a significant effect on the confinement strength of the concrete, whereas the effects of the stirrup configuration and size effect are negligible, which is different from conventional parametric sensitivity analysis. As to the stability analysis of model predictions, the SVR model shows lower volatility and higher prediction stability compared with the BPNN model, despite the latter occasionally providing superior generalization ability. This research underscores the significant advantages offered by machine learning models in addressing longstanding challenges associated with strength prediction, and thus presents new perspectives for future study.

Implementing an Adaptive Climate Control Strategy: Collection Monitoring and Sustainability Outcomes

ABSTRACT In 2022, the authors presented the first stage of implementing a revised climate control strategy at the National Gallery of Victoria, monitoring collection object response in collaboration with the Getty Conservation Institute using acoustic emissions (AE) monitoring. The research and education, motivating factors, and interdepartmental alignment required for adopting the Bizot Green Protocol (BGP) were discussed, as well as the experimental design and set-up of the AE system on a large Flemish altarpiece in an active gallery. This second and concluding paper presents the results of the AE monitoring study along with energy used by the HVAC system for the gallery housing the AE monitoring study, during the transition from traditional environmental parameters through to broadened parameters as defined in the BGP. The project highlights the importance of understanding individual gallery and building behaviour as well as the HVAC system in place. In the case of the gallery housing the AE study, the implementation of BGP parameters resulted in smaller environmental fluctuations than expected. That HVAC energy efficiency could be improved with a relatively low impact on the gallery environment was an unexpected but welcome outcome. This case study describes some common challenges faced when considering such a change operationally, including resourcing and communication needs. Identifying points of resistance, framing problems clearly, and identifying common goals were essential to making progress. In this case, sustainability has compelled many stakeholders into alignment and enabled a shift in institutional culture.

Exploring the Effect of Thyroid Hormone on Serum Lipoprotein (a) Levels in Patients With Thyroid Hormone Dysfunction: A Systematic Review.

Genetic variations among people mainly determine the blood levels of lipoprotein (a) (Lp(a)), and it is relatively stable throughout one's lifetime. Nevertheless, there could still be other factors that control the Lp(a) level. Thyroid hormones are known to influence the serum lipid level by regulating the expression ofkeyenzymes that are involved in lipid metabolism. Both hypo and hyperthyroidism are associatedwith changes in lipid levels. Even though thyroid hormone abnormalities have been shownto alter traditional lipid parameters like low-density lipoprotein (LDL-C), its influence onLp(a) has not been established. This review aims to identify the relationship between Lp(a) and thyroid hormones by reviewing data from correlative studies and observing treatment-related Lp(a) level changes in thyroid disorders from interventional studies. We searched MEDLINE, Cochrane, and Google Scholar databases with predefined search criteria and search strategies for paper identification. Individual reviewers reviewed identified papers for selection.Finalized papers were reviewed forLp(a) levels and their responses to treatment in patients with thyroid disorders to establish the relationship betweenLp(a) and thyroid hormone.We concluded that the data were limited and sometimes contradicted one another to establish a clear relationship betweenLp(a) and thyroid hormones.Even though correlative studies data showed strong indications that overt-hypothyroidism was associated with high Lp(a) levels, thyroid hormone replacement studies did not show any significant changes inLp(a) levels compared to pre-treatment in patients with both overt-hypothyroidism and subclinical hypothyroidism. More clinical trials focusing on Lp(a) with longer periods of treatment and follow-up in thyroid patients are needed to establish the relationship between the two. The possibility of dose-related Lp(a) responses to thyroid hormone treatment should also be explored.

Ice Phase Classification Made Easy with Score-Based Denoising.

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. The denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics

Abstract In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are emerging and can also be addressed in a parameter estimation framework. These developments call for a new unified perspective, which is able to cover both traditional parameter estimation methods and novel approaches in which the state variables or the model structure itself are inferred as well. Adopting concepts discussed in the inverse problems community, we distinguish between all-at-once and reduced approaches. With this general framework, we are able to structure a large portion of the literature on parameter estimation in computational mechanics -- and we can identify combinations that have not yet been addressed, two of which are proposed in this paper. We also discuss statistical approaches to quantify the uncertainty related to the estimated parameters, and we propose a novel two-step procedure for identification of complex material models based on both frequentist and Bayesian principles. Finally, we illustrate and compare several of the aforementioned methods with mechanical benchmarks based on synthetic and experimental data.

Research on sound quality prediction of vehicle interior noise using the human-ear physiological model.

In order to improve the prediction accuracy of the sound quality of vehicle interior noise, a novel sound quality prediction model was proposed based on the physiological response predicted metrics, i.e., loudness, sharpness, and roughness. First, a human-ear sound transmission model was constructed by combining the outer and middle ear finite element model with the cochlear transmission line model. This model converted external input noise into cochlear basilar membrane response. Second, the physiological perception models of loudness, sharpness, and roughness were constructed by transforming the basilar membrane response into sound perception related to neuronal firing. Finally, taking the calculated loudness, sharpness, and roughness of the physiological model and the subjective evaluation values of vehicle interior noise as the parameters, a sound quality prediction model was constructed by TabNet model. The results demonstrate that the loudness, sharpness, and roughness computed by the human-ear physiological model exhibit a stronger correlation with the subjective evaluation of sound quality annoyance compared to traditional psychoacoustic parameters. Furthermore, the average error percentage of sound quality prediction based on the physiological model is only 3.81%, which is lower than that based on traditional psychoacoustic parameters.

Using Machine Learning to Predict Cloud Turbulent Entrainment‐Mixing Processes

AbstractDifferent turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models.

Testing triple oxygen isotope preservation in the new OPEnS totalizer against conventional monthly rainfall collectors

Understanding the mechanisms that drive spatial and temporal triple oxygen isotope (Δ′17O) variations in modern precipitation is the first step to expanding the utility of these measurements as an environmental tracer jointly with traditional stable isotope parameters δD, δ18O, and d-excess. However, “totalizers” designed to collect a single precipitation sample pooled over a calendar month and minimize evaporation and associated isotopic fractionation of the sample during that time have not been tested for Δ′17O. We conducted a 30-day laboratory experiment comparing mass losses and isotopic shifts in four totalizers: 1) the OPEnS (Openly Published Environmental Sensing) totalizer, 2) the classic oil-based totalizer, 3) the commercial tube dip-in/pressure equilibration totalizer (Palmex Ltd. RS1), and 4) a reference totalizer (the control, lacking any evaporation reduction mechanism). The OPEnS totalizer was designed as being readily user built with parts costs of about $10, oil-free to facilitate quick and easy sample preparation and risk-free sample analysis, and its collection device expands as it fills to maintain a small gas/water ratio and minimize internal evaporative losses. All totalizers were filled to 12 % of their 3-L volume and placed in a modified laboratory oven with a diurnal temperature change of 23 to 40 °C and an average relative humidity of 9.1 % to simulate extreme evaporative conditions. The OPEnS totalizer experienced the smallest mass loss of water (0.21 %) and smallest isotopic shifts (p < 0.05 for δ18O and d-excess), which were all within measurement error. The oil, tube, and reference totalizers showed larger mass losses (0.41, 1.37, and 1.61 %, respectively) and evaporative enrichment with respect to δD (+0.3, +0.8, and + 2.1 ‰), δ18O (+0.16, +0.23, and + 0.83 ‰), and d-excess (−0.9, −1.0, and − 4.5 ‰). The Δ′17O variations for all totalizers were within measurement error, so we suggest that in less harsh climates their triple oxygen isotope changes during secondary evaporation would be more acceptable. To test the OPEnS totalizer in field settings, we installed it alongside oil totalizers to collect monthly precipitation over three years in the towns of Jolly and San Antonio, Texas, with mean annual precipitation, temperature, and windspeed values of 556 and 563 mm, 18.7 and 21.9 °C, and 5.0 and 3.5 m/s, respectively. Results indicate that the OPEnS and oil totalizers can produce similar isotopic data in the field, but modifications to OPEnS have been implemented to minimize under-catch and stabilize the collection component where high winds are present and additional testing under a variety of environmental conditions is ongoing. OPEnS is scalable according to expected monthly precipitation amounts, providing a cost-effective, high-performance device for quantification of total rainfall and its isotopic composition without oil contamination risks.

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Development of an Intelligent Multi-Parameter Sleep Diagnosis and Analysis System

Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China's sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO 2 and O 2 concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.

Prognostic Significance of "High" Tumor Budding and "High" Poorly Differentiated Clusters in Endometrial Carcinomas: Independent Predictors of Lymphovascular Space Invasion and Lymph Node Metastasis.

Tumor budding (TB) and poorly differentiated clusters (PDCs) are well-established prognostic factors in various cancers. This study aimed to assess the independent prognostic role of these markers in endometrial carcinomas. Retrospective analysis of endometrial carcinoma resection specimens by examining traditional histologic prognostic parameters. TB and PDC were observed at 20× magnification in ten fields at the invasive front and categorized as present or absent. In addition, a count of ≥5 was stratified as "high." Clinical and follow-up details were extracted from Gynecologic Oncology records. Sixty-five endometrial carcinomas were studied and were predominantly endometrioid (n=47, 72.3%). TB was identified in 52.3% of cases, with high TB observed in 38.5%. PDC was evident in 44.6%, with high PDC seen in 29.2%. Associations were significant between the presence of TB/high TB and higher tumor grade (P < 0.001), deep myometrial invasion (P = 0.006/P = 0.002), diffuse pattern of invasion (P = 0.007/P = 0.03), microcystic elongated and fragmented pattern (P < 0.001), lymphovascular space invasion, lymph node metastasis (P=<0.001) and International Federation of Gynecology and Obstetrics stage (P = 0.000/P = 0.002). PDC/high PDC showed similar associations, and, in addition, with nonendometrioid histologic type (P = 0.02) and tumor location in a lower uterine segment (high PDC, P = 0.009). After adjusting for other significant parameters, both high TB (P = 0.03) and high PDC (P = 0.031) emerged as independent prognostic parameters for lymphovascular space invasion or Lymph node metastasis. No recorded deaths or significant events occurred, precluding commentary on overall survival status. High TB and PDC are independent predictors of Lymph node metastasis in endometrial carcinomas. Their association with the microcystic elongated and fragmented pattern makes them histologic predictors of epithelial-mesenchymal transition. Their simple application underscores their potential as valuable additional prognostic indicators for endometrial carcinomas.