False Alarm Rate Research Articles

Maternal hair lead and cytokine pro-inflammatory effects in preterm birth

This case-control study analyzed the lead (Pb), interleukin 6 (IL-6), and tumor necrosis factor alpha (TNF-α) levels in pregnant women with preterm birth (PTB) in Central Java, Indonesia. Hair samples from 72 pregnant women were collected non-invasively. The prenatal exposure to Pb was determined with the total reflection X-ray fluorescence (TXRF) method. Serum IL-6 and TNF-α were examined using enzyme-linked immunosorbent assays (ELISA). The Pb concentration in hair was slightly higher in women with PTB than those without PTB; however, this difference was not statistically significant. An elevated hair Pb level was not associated with increased PTB risk (OR 24.69, 95% CI 0.93–653.82, p>0.05). A serum TNF-α level ≥27 pg/ml, a serum IL-6 level ≥9 pg/ml, and the spouse’s smoking frequency were significantly associated with increased PTB risk (TNF-α OR 42.25, 95% CI 5.26–339.61; IL-6 OR 22.33, 95% CI 3.12–158.54; spouse’s smoking frequency OR 1.28, 95% CI 1.09–1.5), while the maternal hemoglobin concentration significantly decreased PTB risk (OR 0.43, 95% CI 0.2–0.927). This study demonstrates that maternal hair Pb concentration has no significant relationship with PTB. Serum TNF-α, IL-6, and the spouse’s smoking frequency potentially increased PTB risk, while the maternal hemoglobin level is a protective factor.

Role of videourodynamics, imaging, and cystoscopy in patients with recurrent urinary tract infections: Should we throw in the kitchen sink?

Purpose:Recurrent urinary tract infection (rUTI) remains a common outpatient problem with discordance and paucity of evidence for management. This study aims to evaluate the role of videourodynamics (VUD), additional imaging, and cystoscopy in the complete workup of these patients. Materials and Methods:A retrospective review was performed on 1421 consecutive patients referred for physician performed VUD. After exclusion criteria, 170 patients were included. Ethics approval was obtained, followed by data collection, and analysis of demographics, symptoms, cystoscopy results, imaging, and VUD parameters. Statistical analyses were performed with IBM SPSS Statistics Version 28. Statistical significance was defined by an alpha level of P ≤0.05. Results:Overall, 117/170 (69%) had identifiable causes of rUTI identified on VUD. There was a statistically significant difference (p<0.001) in identifying a cause in those with voiding symptoms (93/114 = 82%) compared to those without (24/56 = 43%). Gender was not predictive of an identifiable cause on VUD (p=0.47). Neither was a neurogenic history (p=0.11), diabetes (p=0.97), or age (p=0.89). Additional imaging was not diagnostic for rUTI cause. No malignancy was identified on imaging or cystoscopy. Conclusion:In patients with rUTI, VUD may be an important investigative step to find a possible underlying cause, but it is a scarce resource. As VUD has a higher detection rate in patients with voiding symptoms, by first screening for these patients on history, VUD can be used judiciously. Cystoscopy and additional imaging were not as helpful in identifying a target treatment plan for rUTI, when a VUD had already been done.

Efficacy and Safety of Ruxolitinib Cream by Anatomic Region in Children Aged 2 to 11 Years With Atopic Dermatitis: Results From TRuE-AD3

Atopic dermatitis (AD) is a chronic, pruritic inflammatory skin disease. Ruxolitinib (Janus kinase [JAK]1/JAK2 inhibitor) cream has demonstrated efficacy and safety in patients aged ≥2 years with AD. In adolescents (aged ≥12 y) and adults with AD, application of ruxolitinib cream resulted in significant improvements across all anatomic regions vs vehicle. The objective of this analysis was to evaluate the efficacy and safety of ruxolitinib cream by anatomic region in the randomized, double-blind, phase 3 TRuE-AD3 study (NCT04921969). Children aged 2–11 years with AD for ≥3 months and an Investigator’s Global Assessment score of 2/3 were randomized 2:2:1 to apply 0.75% or 1.5% ruxolitinib cream twice daily (BID) or vehicle BID for 8 weeks. Efficacy was evaluated using the Eczema Area and Severity Index (EASI) subscores for head/neck, trunk, upper limbs, and lower limbs. Of 330 patients in TRuE-AD3, the mean (SD) age was 6.5 (2.9) years, and 179 patients (54.2%) were female; the mean (SD) EASI score was 8.6 (5.4). Improvements in EASI score were demonstrated with 0.75% and 1.5% ruxolitinib cream vs vehicle at first observation (Week 2), with statistically significant differences (at 0.05 alpha level and all P values below are nominal) at Week 8 in the head and neck (−0.63 [P=0.0108] and −0.70 [P=0.0011] vs −0.41), trunk (−0.98 [P=0.0310] and −1.18 [P=0.0003] vs −0.70), upper limbs (−1.81 [P&lt;0.0001] and −2.06 [P&lt;0.0001] vs −1.18), and lower limbs (−2.91 [P&lt;0.0001] and −3.23 [P&lt;0.0001] vs −1.43). Improvements in induration/papulation/edema, erythema, excoriation, and lichenification were observed for both strengths of ruxolitinib cream vs vehicle in all regions at Week 2, with statistical significance (at 0.05 alpha level) in nearly all AD signs and regions at Week 8. Ruxolitinib cream was well tolerated in both treatment groups (n=264), with 4.5% of patients experiencing application site reactions, which was similar to the frequency of application site reactions (4.3%) among patients with head/neck involvement (n=161) . In conclusion, ruxolitinib cream demonstrated significant improvements in AD vs vehicle in children with AD across anatomic regions, including on the head/neck, and was well tolerated. (Funding, Incyte Corporation)

Optimal metacognitive decision strategies in signal detection theory

AbstractSignal detection theory (SDT) has long provided the field of psychology with a simple but powerful model of how observers make decisions under uncertainty. SDT can distinguish sensitivity from response bias and characterize optimal decision strategies. Whereas classical SDT pertains to “type 1” judgments about the world, recent work has extended SDT to quantify sensitivity for metacognitive or “type 2” judgments about one’s own type 1 processing, e.g. confidence ratings. Here we further advance the application of SDT to the study of metacognition by providing a formal account of normative metacognitive decision strategies – i.e., type 2 (confidence) criterion setting – for ideal observers. Optimality is always defined relative to a given objective. We use SDT to derive formulae for optimal type 2 criteria under four distinct objectives: maximizing type 2 accuracy, maximizing type 2 reward, calibrating confidence to accuracy, and maximizing the difference between type 2 hit rate and false alarm rate. Where applicable, we consider these optimization contexts alongside their type 1 counterparts (e.g. maximizing type 1 accuracy) to deepen understanding. We examine the different strategies implied by these formulae and further consider how optimal type 2 criterion setting differs when metacognitive sensitivity deviates from SDT expectation. The theoretical framework provided here can be used to better understand the metacognitive decision strategies of real observers. Possible applications include characterizing observers’ spontaneously chosen metacognitive decision strategies, assessing their ability to fine-tune metacognitive decision strategies to optimize a given outcome when instructed, determining over- or under-confidence relative to an optimal standard, and more. This framework opens new avenues for enriching our understanding of metacognition.

Skinny fat model of metabolic syndrome induced by a high-salt/sucrose diet in young male rats.

Childhood and puberty can affect metabolism, leading to tissue injury and malfunction later in life. The consumption of high-processed foods rich in salt and sugar is increasing in middle- and high-income countries, especially among young people. It is necessary to evaluate the effects of high salt and sugar levels in the youth on most injured organs during metabolic challenges. We aimed to investigate whether high salt/sucrose intake affects whole-body development and leads to end-organ injury. Weaned Male Wistar rats were divided into two groups: a control group fed a standard diet (CO) and tap water, and an experimental group (SS) fed a standard diet and a beverage containing 1.8% NaCl and 20% sucrose instead of tap water. The animals were treated for 60 days, starting after weaning at 21 days of age, after which the animals were subjected to glucose and insulin tolerance tests, urine collection, and heart rate monitoring, and euthanized for sample collection at 81 days of age. SS showed reduced body weight gain and increased food intake of sodium/sucrose solution. Interestingly, high salt/sucrose intake led to increased body adiposity, liver lipid inclusion, heart rate, and renal dysfunction. SS exhibits increased levels of peroxisome proliferator-activated receptor alpha to counterbalance the hypertrophy of brown adipose tissue. Our findings reveal that the SS rat model exhibits non-obvious obesity with end-organ damage and preserved brown adipose tissue function. This model closely parallels human conditions with normal BMI but elevated visceral adiposity, providing a relevant tool for studying atypical metabolic disorders.

Transfection of 12/15-lipoxygenase effectively alleviates inflammatory responses during experimental acute pancreatitis

BACKGROUND Acute pancreatitis (AP), the initially triggered inflammatory process in the pancreas, can be life-threatening. It has been reported that 15-lipoxygenase may promote the removal of damaged intracellular components, maintain intracellular homeostasis, and promote apoptosis by upregulating the activity of caspases. Despite an increased understanding of the lipoxygenase pathway in inflammation and immune diseases, the role of the Alox15 gene product in modulating the inflammatory changes during AP is not well defined. AIM To investigate the effect of Alox15 expression in cerulein-induced AP in rats. METHODS Model rats were transfected with Alox15 by injecting a recombinant lentivirus vector encoding Alox15 into the left gastric artery before inducing AP. The expression of Alox15 was then assessed at the mRNA and protein levels. RESULTS Our in vivo results showed that serum amylase activity and pancreatic tissue water content were significantly reduced in Alox15 -transfected rats. Further, the mRNA expression levels of tumor necrosis factor alpha, interleukin (IL)-1β, IL-6, and monocyte chemoattractant protein-1, as well as the protein expression of nuclear factor kappa B in pancreatic tissue were reduced. Additionally, we observed an upregulation of cleaved caspase-3 that implies an induction of apoptosis in pancreatic cells. The transfection of Alox15 resulted in a lower number of autophagic vacuoles in AP. CONCLUSION Our findings demonstrate a regulatory role of Alox15 in apoptosis and autophagy, making it a potential therapeutic target for AP.

Physiotherapists’ knowledge, attitude and practice of clinical prediction rules in low-back pain

Abstract Background and aim Clinical prediction rules (CPRs) are mathematical tools that are intended to guide clinicians in clinical decision making or predict a future outcome, but they seem rather unknown, under-utilized, or avoided by clinicians. This study aimed to assess knowledge, attitude, and practice of CPRs in low-back pain (LBP) among physiotherapists. Methods A cross-sectional study involving 45 consenting specialist musculoskeletal physiotherapists from three public-funded teaching hospitals in Nigeria was carried out. An adapted validated questionnaire on facilitators and barriers to CPRs utilization, and a socio-demographic proforma were used to collect data. Descriptive and inferential statistics were employed to analyze data. Alpha level was set at p &lt; 0.05. Results Respondents were mostly males (71.1%), married (64.4%) and first-degree holders (55.6%). Twenty-eight (62.2%) of the respondents had above-average knowledge of CPRs in LBP. Rates for positive attitude towards, and utilization of CPRs were 37.8% and 15.6%. Knowledge and attitude about CPRs in LBP were not significantly influenced by socio-demographic factors (p &gt; 0.05). However, there was a significant association between the utilization of CPRs and years of experience (χ2 = 10.339 p = 0.016). Conclusion Most Nigerian physiotherapists had above-average knowledge, but a negative attitude and low utilization of CPRs in LBP. Clinicians’ years of clinical experience influence the usage of CPR. There is a need to incorporate training in CPRs into undergraduate and continuous professional development programmes.

Precision Detection of Infrared Small Target in Ground-to-Air Scene

Reliable infrared small target detection plays an important role in infrared search and track systems. In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. When the size of the target is small or the target is close to the background, the statistical features of the rectangular window would reduce the significance of the target. Moreover, such methods have limited effect on interfering targets, high brightness background, background edges, and clutter suppression in complex backgrounds, and are likely to misdetect the target or even miss it. This paper proposes a non-window, structured algorithm for precision detection of infrared small targets under ground-to-air complex scenes. The non-window, structured local grayscale descent intensity and local gradient watershed (LGDI-LGW) filter can detect a 1 × 1 pixel infrared small target, and effectively suppress interfering targets and background edges. By using the adaptive threshold and centroid algorithm on the target area, the precision of target coordinates reaches sub-pixel accuracy. The results of 9 simulation experiments show that the algorithm has the lowest false alarm rate and the highest detection rate compared with the eight baseline algorithms. It can effectively detect targets with Gaussian distribution of grayscale values and targets with grayscale values approximating tree stump structure. The results of 2 engineering experiments show that under simulated near-sun conditions, a uniform target is precisely detected, and the UAV point target is precisely detected in complex ground-to-air scenes.

Efficient indoor 3D object detection in point clouds using the Kinect sensor

Abstract Only using the point clouds collected by a Kinect sensor to perform 3D object detection is a very challenging task for indoor sense understanding. In this paper, we propose a novel point clouds based 3D object detection framework named Weighted VoteNet (W-VoteNet), which systematically enhances seed points, voting points, and proposal objects to varying degree, thereby enhancing the detection accuracy of indoor scene point clouds. Firstly, a new neighborhood feature enhancement module was designed to enhance the features of the seed point, which can better utilize the neighborhood feature of the seed point. Secondly, a weighted voting module is implemented to increase the accuracy of voting, allowing more prospect seed points to participate in voting. Finally, a new semantic relation reasoning module is proposed to get the semantic relationship features of the proposal object, which can further decrease false alarm rate. The three modules we proposed all contribute to more accurate voting and more effective proposals. The experimental results on two benchmark indoor 3D object detection datasets, SUN RGB-D and ScanNet V2, demonstrate the effectiveness of our approach. The source code is available at: https://github.com/Guo-JQ/W-VoteNet.

Gyroscope in-assembly drift anomaly detection based on decision re-optimized deep auto-encoder

Abstract In dynamic servo gyroscope assembly, frequent drift performance overproof causes massive part re-assembly and write-off, for which in-assembly gyro drift anomaly detection is required. However, when utilizing common anomaly detection methods, imbalanced assembly data distribution causes severe accuracy reduction and false alarms. To tackle these problems, we first propose a decision re-optimized deep autoencoder model to conduct in-assembly drift anomaly detection under imbalanced assembly data distribution. Second, a decision-based training strategy is introduced to lower the false alarm rate in anomaly detection, for which models based on different training strategies are compared for better performance. Third, a modified SMOTE data augmentation method is utilized to settle the impact of data imbalance under small-sample conditions. The experimental results show that the proposed method can achieve in-assembly drift anomaly detection under imbalanced data distribution with high precision and outperforms all other existing methods, lowering the assembly repetition rate and improving assembly efficiency.

Abstract 4137942: Comparing Outcomes in Low versus High Activated Partial Thromboplastin Time-guided Anticoagulation Monitoring during Extracorporeal Membrane Oxygenation

Introduction: Systemic anticoagulation in Extracorporeal Membrane Oxygenation (ECMO) aims to reduce the risk of patient and circuit thrombosis, but requires monitoring due to risk of bleeding. Activated partial thromboplastin time (aPTT) is one of many tests utilized for this purpose. However, it lacks validation in ECMO patients and its clinical utility necessitates further analysis. Aim: We aim to elucidate associations between low versus high aPTT-guided anticoagulation monitoring and total time, survival, bleeding, and thrombotic events during ECMO. Methods: A retrospective review was conducted on 93 patients requiring ECMO at our institution between January 2020 and March 2021. Study inclusion required more than 2 days on ECMO and maintenance on anticoagulation with aPTT targets predominantly greater (&gt;) or less than (&lt;) 60 seconds (s). Final cohort was 74 subjects; 30 subjects with majority aPTT targets &lt;60s composed the control “low” aPTT group and 44 subjects with aPTT targets &gt;60s composed the “high” aPTT group. Primary outcomes were survival to decannulation, survival to discharge, average time on ECMO, and incidence of bleeding and thrombotic events during ECMO. Bleeding and thrombotic events were classified into subtypes and incidence of each was recorded. Statistical analyses were performed with Fisher’s exact, Pearson’s chi-square, and Student’s t-tests with an alpha level of .05 on IBM SPSS software. Results: The high group required a longer average time on ECMO (29.5 days versus 14.3 days; p = .002; 95% CI [5.5; 24.7]). Chi-square tests did not show an association between the low versus high groups and survival to decannulation (22/30 and 35/44; p = .533), survival to hospital discharge (17/30 and 30/44; p = .312), or overall incidence of bleeding (18/30 and 20/44; p = .219) and thrombotic events (7/30 and 14/44; p = .427). Regarding subtypes of events, Fisher’s exact test showed an association between aPTT target &gt;60s and severe epistaxis (1/30 in the low and 9/44 in the high group; two-tailed p = .042). Though not significant, a greater incidence of oxygenator failure and circuit thrombosis was recorded in the high group. Conclusions: Our findings did not establish a relationship between aPTT target &lt;60s or &gt;60s and survival, bleeding, or thrombotic events. However, aPTT target &gt;60s was associated with longer average ECMO time and severe epistaxis. Further investigation of aPTT-guided anticoagulation monitoring in ECMO patients is needed.

Investigating the Effects of Gossypetin on Cardiovascular Function in Diet-Induced Pre-Diabetic Male Sprague Dawley Rats

Gossypetin (GTIN) is a naturally occurring flavonoid recognised for its pharmacological properties. This study examined the effects of GTIN on cardiovascular function in a diet-induced pre-diabetic rat model, which has not been previously studied. Pre-diabetes was induced using a high-fat high-carbohydrate (HFHC) diet supplemented with 15% fructose water for 20 weeks. Thereafter, the pre-diabetic animals were sub-divided into five groups (n = 6), where they were either orally treated with GTIN (15 mg/kg) or metformin (MET) (500 mg/kg), both in the presence and absence of dietary intervention for 12 weeks. The results demonstrated that the pre-diabetic (PD) control group exhibited significantly higher plasma triglyceride, total cholesterol, low-density lipoprotein and very low-density lipoprotein levels, along with decreased high-density lipoprotein (HDL) levels in comparison to the non-pre-diabetic (NPD) group. This was accompanied by significantly higher mean arterial pressure (MAP), body mass index (BMI), waist circumference (WC) and plasma endothelial nitric oxide (eNOS) levels in PD control. Additionally, there were increased heart malondialdehyde levels, reduced heart superoxide dismutase and glutathione peroxidase activity as well as increased plasma interleukin-6, tumour necrosis factor alpha and c-reactive protein levels present in the PD control group. Notably, both GTIN-treated groups showed significantly reduced plasma lipid levels and increased HDL, as well as decreases in MAP, BMI, WC and eNOS levels in comparison to PD control. Additionally, GTIN significantly decreased heart lipid peroxidation, enhanced antioxidant activity and decreased plasma inflammation markers. These findings may suggest that GTIN administration in both the presence and absence of dietary intervention may offer therapeutic potential in ameliorating cardiovascular disturbances associated with the PD state. However, future studies are needed to determine the physiological mechanisms by which GTIN improves cardiovascular function in the PD state.

A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.

Research on recognition of slippery road surface and collision warning system based on deep learning.

Aiming at the problems of slow detection speed, large prediction error and weak environmental adaptability of current vehicle collision warning system, this paper proposes a recognition method of slippery road surface and collision warning system based on deep learning. Firstly, this paper uses the on-board camera to monitor the environment and road conditions in front of the vehicle in real time, and a residual network model FS-ResNet50 is proposed, which integrated SE attention mechanism and multi-level feature information based on the traditional ResNet50 model. The FS-ResNet50 model is used to identify the slippery states of the current road, such as wet and snowy. Secondly, the yolov5 algorithm is used to detect the position of the vehicle in front, and a driving safety distance model with adaptive traffic environment characteristics is established based on different road conditions and driving conditions, and an early warning area that dynamically changed with the speed and the road slippery states is generated. Finally, according to the relationship between the warning area and the position of the vehicle, the possible collision is predicted and timely warned. Experimental results show that the method proposed in this paper improves the overall warning accuracy by 6.72% and reduces the warning false alarm rate for oncoming traffic on both sides by 16.67% compared with the traditional collision warning system. It can ensure safe driving, especially in bad weather conditions and has a high application value.

THE IN-CONTROL PERFORMANCE OF THE ROBUST MULTIVARIATE SYNTHETIC CONTROL CHARTS FOR MONITORING MEAN SHIFT

Multivariate synthetic control chart enables monitoring of multiple process variable simultaneously and hence, negate the inflation of false alarm rates as can be seen in an individual statistical chart. A robust version of the synthetic chart is necessary to mitigate the issue faces by the traditional multivariate synthetic chart, which is unable to produce reliable parameter estimates when Phase I data are contaminated. Therefore, this study proposed three new robust multivariate synthetic control charts (CMRCD, CWS and CWP) which were constructed via minimum regularized covariance determinant (MRCD) and winsorized modified one-step M-estimator (WMOM) for efficient process monitoring. The effectiveness of the proposed robust charts was evaluated in terms of false alarm rates by comparing their in-control performances to the traditional multivariate synthetic chart, Cmean, which is based on the sample mean. Via extensive simulation studies, the findings indicate that the proposed robust control charts outperform the traditional chart regardless of the dimensions or the level of contaminations in the dataset. The real data study further validates that the CMRCD, CWS and CWP perform better than the Cmean. Specifically, the three robust control charts show significant observations, illustrating better capability in monitoring river water quality when compared to the Cmean, i.e., the traditional multivariate synthetic chart.

Marketing of Educational Services and School Image of Private Secondary Schools in Akwa Ibom State, Nigeria

The purpose of this study was to examine the impact of marketing of educational services on school image of private secondary schools in Akwa Ibom State, Nigeria. To achieve this purpose, five specific objectives, five research questions and five null hypotheses were formed for this study. The study adopted the correlational research design and the population was all 495 principals in the 495 private secondary schools in Akwa Ibom State. However, a sample size of 221 principals drawn through multi-stage sampling procedure was used as participants. A self- developed questionnaire named “Marketing of Educational Services and School Image Questionnaire” with reliability of 0.74 was used for data collection. Out of the 221 participants sampled, 213 principals participated in the study. The hypotheses were tested using linear regressions at 0.05 alpha level. The findings indicated that product (people, process and physical evidence, price (fees charged), place (location) and promotion have significant impact on school image of private secondary schools. Based on the findings, conclusion was made that marketing of educational services significantly impact school image of private secondary schools. It was recommended among others that owners of private secondary schools should adopt product (people, process and physical evidence), price, place and promotion in marketing their schools because these marketing elements would make their schools to have good image and make prospective customers to be aware of them.

Exploring Job Attitudes among Junior High School Teachers in Technology and Livelihood Education: A Foundation for Teacher Development and Enhancement

This research aimed to gather information about the “Job Attitudes of Junior High School Teachers Teaching Technology and Livelihood Education”. The objectives of the study were to determine the job attitudes of Junior High School TLE Teacher; and to find out the significant differences in the job attitudes of Junior High School TLE Teachers when grouped according to age and school assignment. This research utilizes a descriptive survey method. There were thirty purposively selected Junior High School TLE Teachers in the municipality of Barotac Nuevo. The result revealed that the job attitudes of TLE Teacher was high. As to age 25-30, 31-40, and 41-50 years old, job attitudes towards organizational commitment revealed in all the variables same with the school assignment, in Jose Monfort Science High School, Salihid National and Barotac Nuevo National Comprehensive High School TLE teachers’ job attitudes towards organizational commitment were very high. There are no significant differences in the job attitudes of Junior High School TLE Teachers when grouped according to age and school assignment with p-value higher than the set of .05 alpha level of significant, which means the null hypothesis were accepted. The following are the suggested researchers retooling activities to be used to improve TLE teachers’ attitude towards their job: 1. providing feedback and support on remote learning, 2. facilitating communication and collaboration, and 3. identifying upskills within existing teacher competencies.

Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers

The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.

Excellent Wavelength Selectivity of p-AlGaN/n-Ga2O3 Solar-Blind UVC PD.

Due to the advantages of ultrawide bandgap, chemical stability, self-powered, and low cost, gallium oxide (Ga2O3) based photodetectors (PDs) are considered as one of the most promising solar-blind ultraviolet PDs, having garnered significant attention in fields such as missile warning and flame arc detection. High selective ratios and excellent responsivity are important to reduce the false alarm rate in solar-blind detection. However, due to the lack of p-type Ga2O3, existing Ga2O3-based PN PDs utilized heterostructures with narrower bandgap p-type semiconductors (e.g., p-GaN, p-SiC, etc.), inducing poor selective ratios with visible light and ultraviolet-A/B (UVA/B). Therefore, a high Al content AlGaN was used in this study to form a p-AlGaN/n-Ga2O3 solar-blind ultraviolet-C (UVC) PD. Since the bandgap of p-AlGaN aligned with the UVC region, the device exhibited exceptionally high selective ratios with R247nm/R360nm = 3.71 × 105 and R247nm/R300nm = 1.47 × 105 at 0 V, which surpassed the reported Ga2O3-based PN PDs. The formation of the type-II heterojunction facilitated the effective separation and transport of photogenerated carriers, resulting in high responsivity (0.36 A/W) under zero bias. Overall, the high selective ratios and high responsivity of the p-AlGaN/n-Ga2O3 PD in this paper provided a reliable pathway for self-powered solar-blind PDs with a low false alarm rate.

Evaluation of the Accuracy of Interactive Multisensor Snow and Ice Mapping System (IMS) 1 km Product Using Ground Snow Depth Data Across China

This study evaluates the accuracy of the U.S. interactive multisensor snow and ice mapping system (IMS) 1 km product with recall, precision, false alarm rate, and overall accuracy by utilizing ground-observed snow depth data from meteorological stations in China over six snow seasons spanning from 2015 to 2020. Furthermore, it examines the correlation between recall and snow depth, as well as the correlation between overall accuracy and snow depth. The results reveal the following: The precision typically peaks during the middle of the snow season, predominantly exceeding 80% but rarely reaching 100%, with minimal spatial variation. The precision generally ranges from 75% to 80%, exhibiting slight spatial variations during this period. The overestimation rate remains below 5%, seldom exceeding 10%. The overall accuracy mostly exceeds 95%, showing no significant spatial variations. Across both China and its five regions, the recall demonstrates a significant logarithmic correlation with snow depth, while the overall accuracy exhibits an almost horizontal linear correlation with snow depth once it exceeds 3 cm.