Threshold Setting Research Articles

The Effect of Dehydration on Tooth Color: A Prospective In Vivo Study.

Introduction The influence of dehydration on tooth shade constitutes a significant aspect, making it a pivotal area of study, particularly within the field of restorative dentistry. This prospective study was done to analyze, using a spectrophotometer, how dehydration affects the shade of the teeth. Materials and methods Twenty-five participants were recruited in this prospective study that took place in the Dental Care Center of Saint Joseph University of Beirut (Lebanon) between October 2022 and January 2023. The inclusion criteria for this study primarily comprised four intact maxillary incisors. On the other hand, damage, cavities, restorations, and staining served as exclusion criteria. The VITA Easyshade Advance (Vident,Brea,CA) spectrophotometer was used to record the measurements. The measures of L (which represents darkness to lightness), a (which represents greenness to redness), and b (which represents blueness to yellowness)were registered at the center of each tooth's labial surface at baseline and then at 10-minute intervals for 30 minutes while the teeth were dehydrating due to the placement of a rubber dam. The color difference (ΔE) was later calculated using L*a*b* measures, with the perceptibility threshold set at ΔE00=0.8. Statistical analyses were performed using the Friedman test, the Bonferroni post hoc test, and the Wilcoxon signed-rank test. Results The different color changes represented byΔE(ΔE1, ΔE2, and ΔE3) were found to be significantly higher than the perceptibility threshold of 0.8 (p<0.001). A statistically significant difference was found between ΔE1 and ΔE3 (p<0.05). Moreover, a statistically significant difference was found between L0, L10, L20, and L30 (p<0.001). Statistically significant differences (p<0.05) exist between the means of L0 and L20, the means of L0 and L30, the means of L10 and L20, and the means of L10 and L30. Conclusion This study showed that dehydration affected the shade of the teeth: Lightness increased, and therefore, the teeth appeared whiter. Dentists should consider the hydration status of the teeth when evaluating color for treatments, as dehydration can significantly affect shade matching: the more the dehydration time elapses, the more the color difference compared to the baseline increases.

An Experimental Evaluation of Anomaly Detection in Time Series

Anomaly detection in time series data has been studied for decades in both statistics and computer science. Various algorithms have been proposed for different scenarios, such as fraud detection, environmental monitoring, manufacturing, and healthcare. However, there is a lack of comparative evaluation of these state-of-the-art approaches, especially in the same test environment and with the same benchmark, making it difficult for users to select an appropriate method for real-world applications. In this paper, we present a taxonomy of anomaly detection methods based on the main features, i.e., data dimension, processing technique, and anomaly type and six inner classes. We perform systematic intra- and inter-class comparisons of seventeen state-of-the-art algorithms on real and synthetic datasets with a point metric commonly used in classification problems and a range metric specifically designed for subsequence anomalies in time series data. We analyze the properties of these algorithms and test them in terms of effectiveness, efficiency, and robustness to anomaly rates, data sizes, number of dimensions, anomaly patterns, and threshold settings. We also test their performance in different use cases. Finally, we provide a practical guide for detecting anomalies in time series and discussions.

Evaluation of blood cell morphology with the RBC Advanced Application: Which cut-offs are most needed for which specific abnormalities?

The detection of erythrocyte morphological abnormalities is a valuable and sometimes overlooked element in the diagnostic management of anemias. The aim of this article is to evaluate the clinical performance of the different detection thresholds tested by our laboratory using the Cellavision RBC Advanced module, after manual reclassification by an experienced operator, and comparing them to the guidelines by the ICSH (International Council for Standardization in Haematology). We arbitrarily set thresholds at 1% for "critical" abnormalities (tear drop cells, target cells, schizocytes and spherocytes) except for sickle cells (threshold set at 0.01%). Our data show excellent sensitivity of 100% for the cut-offs defined by the investigation for tear drop cells and sickle cells, but low specificity for detection of associated clinical pathology compared with ICSH cut-offs, varying from 4% for teardrop cells (detection of myelofibrosis), 26% for target cells (detection of martial deficiency) to 55% for schizyocytes (presence of hemolytic anemia). Our results show a better specificity of the thresholds established by ICSH than our studied thresholds for the detection of the pathologies of concern, suggesting a better clinical relevance.

Segmentation of Images Used in Unmanned Aerial Vehicles Navigation Systems

The paper presents the results of the study of a two-stage procedure for selecting a reference object in the current image formed by a correlation-extreme system used for autonomous navigation of unmanned aerial vehicles. The aim of this paper is to theoretically evaluate the probability of selecting low-dimensional low-contrast objects in the segmented current image according to the proposed two-stage procedure. To achieve this goal, the problem of segmentation of images of the sighting surface and subsequent selection of the reference object in the presence of heterogeneous objects differing in brightness and area characteristics is solved. The most significant result is the justification of application of two-stage procedure of selection of the reference object in the current image by brightness and area parameters using the set thresholds. The significance of the obtained results consists in establishing the dependence of the probability of correct selection of the reference object on the noise level of the current images. It is shown that the probability of correct selection of the object in the image is a function of the threshold value and can be maximised by choosing its value. This approach allows to consider the influence of various factors leading to image noise on the quality of images formed by the navigation system. It is shown that when noise distorts more than 31% of the image pixels, the proposed two-stage procedure allows to ensure the selection of the reference object in the image with a probability not lower than 0.9.

Optimization of power feedback control system for HCN interferometer on EAST Tokamak

The Hydrogen Cyanide (HCN) interferometer stands as an indispensable diagnostic tool, designated to measure the line-integrated electron density on the Experimental Advanced Superconducting Tokamak (EAST), thereby offering essential density feedback signals for EAST operation. The HCN laser used in the interferometer is a continuous glow discharge gas laser. However, owing to variations in external ambient temperature, the HCN laser exhibits output power instability. The current power automatic feedback control system for the HCN laser interferometer exhibits low actuator adjustment precision, thereby limiting its ability to achieve maximum output power. Moreover, the system may cause a temporary power drop to zero during adjustment, while EAST operation requires the HCN laser interferometer to maintain high power continuously. In order to solve the issues, this paper developed an innovative actuator that ingeniously amalgamates the virtues of piezoelectric ceramic and stepper motor. This novel actuator, demonstrating a broad adjustment range and high precision, is intended to replace the present one in the HCN interferometer power control system. While the stepper motor executes coarse adjustment, the piezoelectric ceramic enables precise adjustment, thus enhancing the adjustment accuracy of the automatic power control system to a sub-micrometer level and an adjustment range greater than 5 mm. Accurate determination of the regulator's adjustment direction can significantly improve the efficiency of adjustment. The slope of the laser output power peak decreases as it approaches the maximum value, and increases as it moves away from the maximum value. Therefore, this system replaces the original threshold algorithm with a slope algorithm. By correctly judging the adjustment direction, the efficiency of adjustment is improved. Furthermore, this algorithm can keep the output power stable on a power peak. There will be no instances of zero power output during the adjustment, allowing the power to remain stable above the set threshold for a long time. Currently, this system has been successfully applied to the HCN interferometer, and testing has found that the system can maintain stability for at least 16 hours. Furthermore, even when the laser output power was intentionally reduced to below the set threshold, the system was able to respond quickly and adjust the output, rapidly restoring it to above the predefined threshold. Multiple tests have shown the strong robustness of the system, indicating it meets the stringent demands of the EAST's complex environment.

Evaluating the effectiveness of concurrent sessions and counselors' attention allocation in online counseling.

With its anonymity and accessibility, text-based online counseling has shown great potential in reaching people with mental health needs. One strategy adopted to meet the service gap is concurrent counseling, that is, each counselor attending to more than one client at a time. To date, there is no reported evidence supporting its rationality and effectiveness. This study investigated the potential opportunities, effectiveness, and caveats in concurrent service delivery and identified the optimal cutoff number of concurrent sessions while maintaining the quality of service at or above a set threshold. We analyzed the transcript of 54,716 online counseling sessions from Open Up, a free, 24/7 text-based counseling service, to develop an attention score that measures the attention allocation of counselors and examined the impact of the counselor's attention allocation on client satisfaction and service outcomes. On average, compared to nonconcurrent sessions, concurrent sessions were longer, more likely to end prematurely, and had lower client satisfaction. We also identified an optimal attention score of approximately 0.4 (out of 1.0, which denotes full attention), which translates to two to three concurrent sessions. This study provides empirical evidence for the feasibility of conducting multiple text-based sessions concurrently without compromising service quality and client experience. Our method of measuring the counselor attention allocation offers a way to systematically assess and evaluate concurrent sessions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Abstract 150: Arterial Calcification and Neural Metabolic Activity: Assessing Neurovascular Status Through FDG and NaF PET/CT Imaging

Introduction Atherosclerosis, the occlusion of arteries due to the accumulation of fatty substances, cholesterol, calcium, and fibrin in the arterial walls, can result in reduced blood flow and a subsequent decrease in oxygen delivery to the organs supplied by the affected artery, including the brain. Positron emission tomography‐computed tomography (PET‐CT) utilizing 18F‐fluorodeoxyglucose (FDG) and 18F‐Sodium Fluoride (NaF) have demonstrated efficacy in the assessment of cerebral glucose metabolism and atherosclerotic calcification, respectively. However, the reported literature has not clearly correlated arterial disease throughout the body with cerebral hypometabolism. Utilizing a novel integration of FDG and NaF PET/CT imaging, our study explores the link between cardiovascular risk factors and regional cerebral metabolism. Methods We compared 79 healthy controls (mean age 44.5 ± 13.8 years, 53.2% males) and 40 individuals (mean age 55.9 ± 11.9 years, 50% males) at increased risk for cardiovascular disease (CVD) as assessed by the systematic coronary risk evaluation (SCORE) tool. All subjects underwent whole‐body FDG‐PET/CT imaging. [18F]fluoride PET/CT imaging was performed using hybrid PET/CT scanners with image correction and reconstruction processes following standard protocols. The quantitative regional analysis of PET images was performed using MIMneuro version 7.1.5 (MIM Software, Inc., Cleveland, Ohio). 70 whole brain structures were analyzed in each subject. MIMneuro provided region‐based analysis, with z‐scores generated by comparing the patient to the selected age‐matched set of normal controls. Using OsiriX MD software, [18F]fluoride PET/CT scans were analyzed with regions of interest (ROIs) manually drawn to assess carotid calcification and standardized uptake value mean (SUVmean), along with ROI volume values. Correlations between the degree of molecular calcification and cerebral FDG uptake in patients with cardiovascular risk factors were evaluated using Pearson’s R, with the significance threshold set at P &lt; 0.05. Results Unhealthy patients demonstrated significant negative correlations between increased calcification in the carotid arteries, as assessed by NaF‐PET, and metabolic activity in the anterior cingulate (r = ‐0.42, p = 0.008) and cingulate regions (r = ‐0.34, p = 0.036). Conversely, healthy patients exhibited significant positive correlations in the pons (r = 0.25, p = 0.042) and pontine tegmentum (r = 0.34, p = 0.005), suggestive of a compensatory mechanism to maintain autonomic and motor functions. Conclusion The present study provides insight regarding the interplay between arterial calcification and neural metabolic activity, particularly as it pertains to regions supplied by the anterior cerebral artery. Our investigation suggests that calcification in the carotid arteries, which give rise to the ACA, has a deleterious impact on the function of the cingulate gyrus. Additionally, the observed positive correlation in the pons and pontine tegmentum in healthy individuals raises intriguing questions regarding potential compensatory mechanisms that may help maintain autonomic and motor functions despite vascular impediments. Understanding the temporal progression of vascular calcification allows for the optimization of surgical strategy and timing, potentially reducing stroke risk and improving patient outcomes. These findings underscore the necessity of persistent surveillance and targeted management strategies concerning carotid calcification.

Predicting the need for collateral ligament repair in transolecranon fractures of the elbow: a traffic light model.

Biomechanical studies have shown that translation of the proximal radius relative to the capitellum in the sagittal plane can predict integrity of the collateral ligaments in a transolecranon fracture model; no studies have examined this in clinical practice. Nineteen consecutive transolecranon fracture dislocations were retrospectively reviewed. Data collection included patient demographics, fracture classifications, surgical management, and failure with instability. Distance between the center of the radial head and the center of the capitellum was measured on initial radiographs by 2 independent raters on 3 separate occasions. Statistical analysis was used to compare the median displacement between patients who required collateral ligament repair for stability and those who did not. Sixteen cases with a mean age of 57 years (range 32-85) were analyzed with an inter-rater Pearson coefficient of 0.89 for displacement measurement. Median displacement where collateral ligament repair was needed and performed was 17.13 mm (interquartile range [IQR]=10.43-23.88) compared with 4.63 mm (IQR=2.68-6.58) where collateral ligament repair was not performed and not required (P=.002). In 4 cases, ligament repair was not performed initially but deemed necessary based on clinical outcome and postoperative and intraoperative images. Of these, the median displacement was 15.59 mm (IQR=10.09-21.20), and 2 of these required revision fixation. Where displacement on initial radiographs exceeded 10 mm, lateral ulnar collateral ligament (LUCL) repair was required in all cases (red group). If less than 5 mm, ligament repair was not required in any case (green group). Between 5 and 10 mm, following fracture fixation, the elbow must be screened carefully to assess for any instability and a low threshold set for LUCL repair to prevent posterolateral rotatory instability (amber group). Using these findings, we propose a traffic light model to predict the need for collateral ligament repair in transolecranon fractures and dislocation.

Design and Implementation of Environmental Monitoring System for Substation Based on Internet of Things

At present, the environmental monitoring of the substation suffers from complex wiring, low intelligence, high staff work intensity, and a lack of information-based overall management. Moreover, the distribution of substations is relatively scattered, making it complex and costly to set up a dedicated communication network. In response to these problems, this paper combines modern communication technology, sensor technology, and mobile intelligent terminal development technology to study an overall information-based substation environmental monitoring system. The monitoring system proposed in this paper includes a main controller, a data collection terminal, and a mobile communication terminal. The environmental data collected by the main controller is sent to the server through the communication method of subscription and substation using wireless communication technology, and the interface of the mobile communication terminal can display indoor environmental monitoring data. When the environmental data of the Substation exceeds the set threshold value to issue an early warning alert, the user can control it remotely through the mobile communication terminal to improve the indoor environment of the Substation. The system has a simple operation interface, low power consumption, and strong expansion compatibility, which effectively realizes remote monitoring and control of the substation environment and has good engineering application value.

Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.

Local Second-Order Møller-Plesset Theory with a Single Threshold Using Orthogonal Virtual Orbitals: Theory, Implementation, and Assessment.

It has long been clear that electron correlation methods exhibit unphysical compute scalings with molecular size, which has motivated the development of local correlation methods to discard effectively zero contributions in a controlled way to yield an approximate correlation energy. The ideal local correlation method should have a single numerical threshold that controls the dropping of terms with the ability to have that threshold set small enough so that the correlation energy is reproduced to enough significant figures such that the result is chemically identical. This work reports such a method for the second-order Møller-Plesset (MP2) theory. The theory, implementation, and testing of this local MP2 theory are reported. Thresholds ranging from 10-5 to 10-8 and basis sets ranging from split valence plus polarization through to quadruple-ζ are assessed for local MP2 calculations on a range of molecules, including linear chains and molecules with two- and three-dimensional character. The implementation is shared memory parallel via OpenMP and yields roughly 50% parallel efficiency with 16 cores for a large job. Considerable efforts were made to minimize memory demands, which increased as thresholds were tightened. A variety of relative energy calculations are presented as a function of threshold to provide some guidance to users on how to obtain adequate precision at a low compute cost. It is particularly clear that derivative properties require tighter thresholds in order to achieve an adequate precision.

People underestimate the influence of repetition on truth judgments (and more so for themselves than for others)

People judge repeated statements as more truthful than new statements: a truth effect. In three pre-registered experiments (N = 463), we examined whether people expect repetition to influence truth judgments more for others than for themselves: a bias blind spot in the truth effect. In Experiments 1 and 2, using moderately plausible and implausible statements, respectively, the test for the bias blind spot did not pass the significance threshold set for a two-step sequential analysis. Experiment 3 considered moderately plausible statements but with a larger sample of participants. Additionally, it compared actual performance after a two-day delay with participants' predictions for themselves and others. This time, we found clear evidence for a bias blind spot in the truth effect. Experiment 3 also showed that participants underestimated the magnitude of the truth effect, especially so for themselves, and that predictions and actual truth effect scores were not significantly related. Finally, an integrative analysis focusing on a more conservative between-participant approach found clear frequentist and Bayesian evidence for a bias blind spot. Overall, the results indicate that people (1) hold beliefs about the effect of repetition on truth judgments, (2) believe that this effect is larger for others than for themselves, (3) and underestimate the effect's magnitude, and (4) particularly so for themselves.

The effect of feeding fermented distillers' grains diet on the intestinal metabolic profile of Guanling crossbred cattle

Fermented distiller's grains (FDG)-based diets are nutritious and can improve the growth and intestinal immunity in livestock. However, there is limited research examining the effect of feeding FDG-based diets on changes in intestinal metabolites and related pathways in livestock. In this study, nine Guanling crossbred cattle (Guizhou Guanling Yellow cattle × Simmental cattle) were selected and randomly divided into a basal diet (BD) group and two experimental groups fed with FDG replacing 15% and 30% of the daily ration concentrates (FDG-Case A and FDG-Case B), respectively, with three cattle in each group. Fresh jejunum (J) and cecum (C) tissues were collected for metabolomic analysis. Differential metabolites and metabolic pathways were explored by means of univariate and multivariate statistical analysis. Compared with the J-BD group, 30 and 100 differential metabolites (VIP &amp;gt; 1, p &amp;lt; 0.05) were obtained in the J-FDG-Case A group and J-FDG-Case B group, respectively, and the J-FDG-Case B vs. J-FDG-Case A comparison revealed 63 significantly differential metabolites, which were mainly divided into superclasses including lipids and lipid-like molecules, organoheterocyclic compounds, and organic acids and derivatives. Compared with the C-BD, 3 and 26 differential metabolites (VIP &amp;gt; 1, p &amp;lt; 0.05) were found in the C-FDG-Case A group and C-FDG-Case B group, respectively, and the C-FDG-Case B vs. C-FDG-Case A comparison revealed 21 significantly different metabolites, which were also mainly divided into superclasses including lipids and lipid-like molecules, organoheterocyclic compounds, and organic acids and derivatives. A total of 40 metabolic pathways were identified, with a significance threshold set at p &amp;lt; 0.05. Among them, 2, 14, and 18 metabolic pathways were significantly enriched in the J-FDG-Case A vs. J-BD, J-FDG-Case B vs. J-BD, and J-FDG-Case B vs. J-FDG-Case A comparisons, respectively. Meanwhile, 1, 2, and 3 metabolic pathways were obtained in the C-FDG-Case A vs. C-BD, C-FDG-Case B vs. C-BD, and C-FDG-Case B vs. C-FDG-Case A comparisons, respectively. Furthermore, four significant metabolic pathways, namely insulin resistance, biosynthesis of unsaturated fatty acids, linoleic acid metabolism, and primary bile acid biosynthesis, were significantly enriched in Guanling crossbred cattle fed FDG diets. These results suggest that feeding FDG diets may promote the growth and intestinal immunity of Guanling crossbred cattle by regulating metabolic patterns of lipid compounds and related metabolic pathways. This study sheds light on the potential metabolic regulatory mechanisms of FDG diets and offers some references for their use in livestock feed.

Retrospective Analysis of Head and Maxillofacial Injuries: FIFA World Cup 2022 Report

Objectives: The aim of this study is to analyze the incidence, causes and consequences of head and maxillofacial injuries in the 2022 FIFA World Cup. Methods: This retrospective study, following the STROBE guideline, conducted an online investigation during the 2022 World Cup, with a specific focus on injuries resulting in player substitutions or absences of at least one match. To mitigate potential injury exaggeration, journalistic reports were prioritized over video analysis. The analysis of injuries involved the utilization of descriptive statistics, the Kolmogorov-Smirnov test, and the Pearson correlation test, with a significance threshold set at p&lt;0.05. The analytical tools Microsoft Excel and RStudio were employed. Comparative insights were derived from previous FIFA World Cup data; however, injuries lacking sufficient recovery time were classified as preseason injuries, potentially introducing an element of bias to the analysis. Results: During the FIFA World Cup 2022, a comprehensive analysis revealed a total of 123 injuries leading to player substitutions or subsequent game absences, of which seven were localized in the head or maxillofacial region. Statistical analysis indicated a departure from normal data distribution, and a robust correlation was observed between the number of players in national leagues and injury incidence. Noteworthy is the participation of 42 distinct national soccer leagues in contributing players to the tournament's national squads, with no specific league demonstrating a predisposition to higher injury rates. Within the dataset, three maxillofacial injuries were identified. It is of interest that five athletes opted for facemasks as protective measures for the maxillofacial region, and remarkably, only one of them experienced an injury during the World Cup but subsequently resumed play in subsequent matches, while the remaining four athletes had sustained injuries prior to the tournament. Conclusion: The 2022 FIFA World Cup was associated with a low number of head and maxillofacial injuries, with collisions with other players being the most common cause. Despite their limited occurrence, these injuries can have severe implications. In response to the increase in concussions, FIFA implemented an improved safety protocol, which involves immediate game halts for medical assessment and allows player substitutions without affecting the team's allotted substitutions. This change has been well-received by stakeholders. Furthermore, the use of protective equipment, such as custom-made shields, is growing in popularity and has the potential to reduce injury severity and shorten recovery time. Modern technology enables the creation of comfortable and effective protective gear, enhancing player safety. Overall, the study emphasizes the importance of injury prevention strategies in sports, calling for continued advancements in protective equipment design and increased transparency in injury reporting.

Effect of sintering time on microstructure and optical properties of yttria-partially stabilized monolithic zirconia

ObjectivesTo evaluate the impact of speed sintering on the microstructure and optical properties of 3 and 5 mol% yttria-partially stabilized monolithic zirconias. Methods120 plate-shaped zirconia specimens (10x10x 0.4 mm) were prepared from three commercial 5 mol% yttria-partially stabilized zirconia blocks (5Y-PSZs); Katana UTML (Kuraray Noritake), Cercon xtML (Dentsply Sirona), and Zolid FX white (Amann Girrbach), and two commercial 3 mol% yttria-partially stabilized zirconia blocks (3Y-PSZs); Lava Plus (3 M ESPE) and InCoris (Sirona, GmbH). Specimens were either conventional sintered (CS) using a 7-hour program or speed sintered (SS) using a quick 90-minute program. The microstructure was inspected with a scanning electron microscope (SEM), and phase fractions were detected using x-ray diffraction analysis (XRD). Translucency (TP00), and contrast ratio (CR) were obtained using a spectrophotometer (VITA Easyshade V). Color difference (ΔE00) between both sintering processes was calculated with the CIEDE2000 formula. ΔE00 up to 1.8 was set as the acceptability threshold. Data were analyzed using two-way ANOVA, Krusakll-Wallis, and Mann-Whitney U tests. (n = 12, α = .05). ResultsGrain size was significantly decreased after SS for all tested materials (P < .0001). The average grain sizes of 5Y-PSZs were significantly larger than those of 3Y-PSZs. The atomic structure, microstructure, and transparency of CS and SS were all affected by the amount of yttria, the size of the crystals, and tetragonality. SS significantly reduced TP00 (F = 7135.95, P < 0.0001) and increased CR (F = 453.21, P < 0.0001). The CS Katana presented the highest TP00 and lowest CR value. ΔE00 between the CS and SS groups were clinically acceptable except for Lava, which had values above the set threshold (1.89). SignificanceSS altered the grain size and internal structure of the tested materials, which was reflected in translucency.

CatBoost-Based Automatic Classification Study of River Network

Existing research on automatic river network classification methods has difficulty scientifically quantifying and determining feature threshold settings and evaluating weights when calculating multi-indicator features of the local and overall structures of river reaches. In order to further improve the accuracy of river network classification and evaluate the feature weight, this paper proposes an automatic grading method for river networks based on ensemble learning in CatBoost. First, the graded river network based on expert knowledge is taken as the case; with the support of the existing case results, a total of eight features from the semantic, geometric, and topological aspects of the river network were selected for calculation. Second, the classification model, obtained through learning and training, was used to calculate the classification results of the main stream and tributaries of the river reach to be classified. Furthermore, the main stream river reaches were connected, and the main stream rivers at different levels were hierarchized to achieve river network classification. Finally, the Shapley Additive explanation (SHAP) framework for interpreting machine learning models was introduced to test the influence of feature terms on the classification results from the global and local aspects, so as to improve the interpretability and transparency of the model. Performance evaluation can determine the advantages and disadvantages of the classifier, improve the classification effect and practicability of the classifier, and improve the accuracy and reliability of river network classification. The experiment demonstrates that the proposed method achieves expert-level imitation and has higher accuracy for identifying the main stream and tributaries of river networks. Compared with other classification algorithms, the accuracy was improved by 0.85–5.94%, the precision was improved by 1.82–9.84%, and the F1_Score was improved by 0.8–5.74%. In this paper, CatBoost is used for river network classification for the first time, and SHAP is used to explain the influence of characteristics, which improves the accuracy of river network classification and enhances the interpretability of the classification method. By constructing a reasonable hierarchy, a better grading effect can be achieved, and the intelligence level of automatic grading of river networks can be further improved.

Aberrant spontaneous static and dynamic amplitude of low-frequency fluctuations in cerebral small vessel disease with or without mild cognitive impairment.

Cerebral small vessel disease (CSVD) is considered an age-related degenerative neurological disorder and the most common risk factor for vascular cognitive impairment (VCI). The amplitude of fluctuation of low frequency (ALFF) can detect altered intrinsic brain activity in CSVD. This study explored the static and dynamic ALFFs in the early stage of CSVD with (CSVD-M) or without (CSVD-W) mild cognitive impairment (MCI) in these patients and how these changes contribute to cognitive deterioration. Thirty consecutive CSVD cases and 18 healthy controls (HC) were included in this study. All the participants underwent a 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence to obtain structural T1-weighted images. Simultaneous multislice imaging 5(SMS5) was used for resting-state functional MRI (rs-fMRI), and Data Processing and Analysis of Brain Imaging software helped determine static ALFF (sALFF). The dynamic ALFF (dALFF) was calculated using the sliding window method of DynamicBC software. Analysis of Covariance (ANCOVA) and two-sample t-test were used to evaluate the sALFF and temporal variability of dALFF among the three groups. The subjects were rated on a broad standard neuropsychological scale. Partial correlation analysis was used to evaluate the correlation between sALFF and dALFF variability and cognition (Bonferroni correction, statistical threshold set at p<.05). Compared with HCs, the CSVD-M group indicated decreased sALFF values in the bilateral cerebellum posterior lobe (CPL) and the left inferior Parietal Lobule (IPL), with increased sALFF values in the right SFG. For dALFF analysis, the CSVD-W group had significant dALFF variability in the right fusiform gyrus compared with HC. Moreover, the postcentral gyrus (PoCG) was significantly high in the CSVD-W group. While in the CSVD-M group, the bilateral paracentral lobules (PL) revealed significantly elevated dALFF variability and low dALFF variability in the left CPL and right IPL compared with HCs. The CSVD-M group had high dALFF variability in the bilateral PL but low dALFF variability in the left middle temporal gyrus (MTG) and right PoCG compared with the CSVD-W group. The partial correlation analysis indicated that dALFF variability in the left MTG was positively associated with EM (r=0.713, p=.002) in CSVD-W and CSVD-M groups. In the groups with CSVD-M and HC, altered dALFF variability in the bilateral PL was negatively correlated with EM (r=-0.560, p=.002). There were significant changes in sALFF and dALFF variability in CSVD patients. Abnormal spontaneous static and dynamic ALFFs may provide new insights into cognitive dysfunction in CSVD with MCI and may be valuable biomarkers for early diagnosis.

Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training

Intelligent anomaly detection for wind turbines using deep-learning methods has been extensively researched and yielded significant results. However, supervised learning necessitates sufficient labeled data to establish the discriminant boundary, while unsupervised learning lacks prior knowledge and heavily relies on assumptions about the distribution of anomalies. A long short-term memory-based variational autoencoder Wasserstein generation adversarial network (LSTM-based VAE-WGAN) was established in this paper to address the challenge of small and noisy wind turbine datasets. The VAE was utilized as the generator, with LSTM units replacing hidden layer neurons to effectively extract spatiotemporal factors. The similarity between the model-fit distribution and true distribution was quantified using Wasserstein distance, enabling complex high-dimensional data distributions to be learned. To enhance the performance and robustness of the proposed model, a two-stage adversarial semi-supervised training approach was implemented. Subsequently, a monitoring indicator based on reconstruction error was defined, with the threshold set at a 99.7% confidence interval for the distribution curve fitted by kernel density estimation (KDE). Real cases from a wind farm in northeast China have confirmed the feasibility and advancement of the proposed model, while also discussing the effects of various applied parameters.

Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model

Background Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk. Methods Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models. Results Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683–0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model. Conclusion With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.

Impact and Implication of the Proletariats Rendezvous in the 2023 General Election in Nigeria

The state of affairs over the years since Nigeria assumed the practice of democracy has been largely anti-democratic in practice, this scenario has led to an outcome of impoverishment, stagnation and the torn-stunk of the living conditions of the proletariats, which have undermined their faith and confidence in the electoral outcome and governance in Nigeria. The chunks of the Nigerian masses which represent the proletariats in Nigeria have been the most deleteriously affected group of the population in Nigeria. In view of the above, this paper examined the impact and implication of the proletariat’s rendezvous in the 2023 general elections in Nigeria. The paper adopted the qualitative research method, while utilizing textbooks, journals, newspapers, government publications, rules of proceedings and internet materials. Also applied was the Rational Choice Theory as propounded by Adam Smith in 1770 as its theoretical framework of analysis to provide understanding, explanation and as well make predictions germane to the problematic. The paper find out and concludes that the 2023 general election in Nigeria fell short of the credibility threshold set. Based on the findings, recommendations were made that additional reforms and improvements are required to ensure that the quality of future elections in Nigeria does not further decline, and that public confidence in the capacity of INEC to conduct elections is restored, while trust in the credibility of elections is re-established.