False Alarm Rate Research Articles

Quantum Majorization in Market Crash Prediction

We introduce the Quantum Alarm System, a novel framework that combines the informational advantages of quantum majorization applied to tail pseudo-correlation matrices with the learning capabilities of a reinforced urn process, to predict financial turmoil and market crashes. This integration allows for a more nuanced analysis of the dependence structure in financial markets, particularly focusing on extreme events reflected in the tails of the distribution. Our model is tested using the daily log-returns of the 30 constituents of the Dow Jones Industrial Average, spanning from 2 January 1992 to 30 August 2024. The results are encouraging: in the validation set, the 12-month ahead probability of correct alarm is between 73% and 80%, while maintaining a low false alarm rate. Thanks to the application of quantum majorization, the alarm system effectively captures non-traditional and emerging risk sources, such as the financial impact of the COVID-19 pandemic—an area where traditional models often fall short.

Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers

Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors’ fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS.

Exploring the Efficacy of Joint Lavage in Knee Osteoarthritis: A Focus on Cytokines, Degrading Enzymes, and Oxidative Stress.

This study aimed to assess the effectiveness of joint lavage in managing knee osteoarthritis (OA) by evaluating its effect on pain relief, inflammatory markers, cartilage-degrading enzymes, and oxidative stress. Seventy patients with Kellgren-Lawrence grade 2 or 3 knee OA were selected for this single-center study. Joint lavage was performed, and pain and function were measured using the visual analog scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores at baseline and 24 weeks postintervention. Synovial fluid samples were collected at baseline, before lavage, and 24 weeks postintervention. Samples were stored at -80°C and analyzed in batches to minimize variability. At the time of analysis, the samples were thawed and evaluated for levels of proinflammatory cytokines, interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), matrix metalloproteinase-3 (MMP-3), and total oxidant status (TOS), and oxidative stress index (OSI). Postintervention, VAS, and WOMAC scores significantly decreased (P < 0.001), with 100% achieving the minimal clinically important difference (MCID). Patient acceptable symptom state (PASS) rates varied: VAS (80%), WOMAC pain (50%), function (81.4%), and total (84.3%). Cytokine levels (IL-1β, IL-6, TNF-α) and MMP-3 significantly decreased (P < 0.001), along with TOS and OSI. Baseline TNF-α, IL-6, and IL-1β levels were significantly correlated with improvements in VAS and WOMAC scores. Moderate correlations were observed between reductions in IL-6/TNF-α and improvements in VAS/WOMAC. No significant associations were found between confounders and outcomes. Joint lavage resulted in marked pain relief and functional improvement while significantly reducing inflammatory markers, cartilage-degrading enzymes, and oxidative stress.

Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection

The reliable operation of power transformers is essential for grid stability, yet existing fault detection methods often suffer from inaccuracies and high false alarm rates. This study introduces a machine learning framework leveraging voltage signals for early fault detection. Simulating diverse fault conditions—including single line-to-ground, line-to-line, turn-to-ground, and turn-to-turn faults—on a laboratory-scale three-phase transformer, we evaluated decision trees, support vector machines, and logistic regression models on a dataset of 6000 samples. Decision trees emerged as the most effective, achieving 99.90% accuracy during 5-fold cross-validation and 95% accuracy on a separate test set of 400 unseen samples. Notably, the framework achieved a low false alarm rate of 0.47% on a separate 6000-sample healthy condition dataset. These results highlight the proposed method’s potential to provide a cost-effective, robust, and scalable solution for enhancing transformer fault detection and advancing grid reliability. This demonstrates the efficacy of voltage-based machine learning for transformer diagnostics, offering a practical and resource-efficient alternative to traditional methods.

Agreement between Body Composition Estimates using a 2D Imaging System Across different Body Positions and Days

ABSTRACT Relative adiposity (%Fat) was measured using a smartphone-based application in a convenience sample of adults aged 20–52 years (n = 32, 68.7% female, 84.3% White/Caucasian, 26.7 ± 3.5 kg/m2) across different body positions (Anterior versus Posterior) on consecutive days (Day 1 versus Day 2). A reference photo was obtained from the posterior view on Day 1 (POSTDay1), with an additional photo obtained from the anterior view (ANTDay1). A third photo from the posterior view was obtained on Day 2 (POSTDay2). Potential differences were assessed using an adjusted alpha level (p < .025). ANTDay1 (26.44 ± 6.19%Fat) was not different than POSTDay1 (26.84 ± 6.07%Fat, MAPE = 3.63%, p = .04) (ICC = 0.99, SEE = 1.1%Fat, p < .001). In addition, POSTDay2 (26.65 ± 6.17%Fat) was not different than POSTDay1 (MAPE = 3.30%, p = .31) (ICC = 0.99, SEE = 1.0%Fat, p < .001). Both ANTDay1 and POSTDay2 were considered equal to POSTDay1, suggesting %Fat can be consistently measured from a single digital image using a smartphone-based application in healthy young and middle-aged adults.

Detection of Flight Target via Multistatic Radar Based on Geosynchronous Orbit Satellite Irradiation

As a special microwave detection system, multistatic radar has obvious advantages in covert operation, anti-jamming, and anti-stealth due to its configuration of spatial diversity. As a high-orbit irradiation source, a geosynchronous orbit satellite (GEO) has the advantages of a low revisit period, large beam coverage area, and stable power of ground beam compared with traditional passive radar irradiation sources. This paper focuses on the key technologies of flight target detection in multistatic radar based on geosynchronous orbit satellite irradiation with one transmitter and multiple receivers. We carry out the following work: Firstly, we aim to address the problems of low signal-to-noise ratio (SNR) and range cell migration of high-speed cruise targets. The Radon–Fourier transform constant false alarm rate detector-range cell migration correction (RFT-CFAR-RCMC) is adopted to realize the coherent integration of echoes with range cell migration correction (RCM) and Doppler phase compensation. It significantly improves the SNR. Furthermore, we utilize the staggered PRF to solve the ambiguity and obtain multi-view data. Secondly, based on the aforementioned target multi-view detection data, the linear least square (LLS) multistatic positioning method combining bistatic range positioning (BR) and time difference of arrival positioning (TDOA) is used, which constructs the BR and TDOA measurement equations and linearizes by mathematical transformation. The measurement equations are solved by the LLS method, and the target positioning and velocity inversion are realized by the fusion of multistatic data. Finally, using target positioning data as observation values of radar, the Kalman filter (KF) is used to achieve flight trajectory tracking. Numerical simulation verifies the effectiveness of the proposed process.

Riemannian manifold-aided data-driven diagnosis of photovoltaic hot spots

Abstract This paper proposes a novel data-driven approach for hot-spot fault detection in photovoltaic (PV) modules, utilizing a curved Riemannian manifold (RM) to characterize the sample space. The proposed method detects hot spots in PV systems by mining geometric features in high-dimensional data. The RM-based method combines the geometric properties of RMs for fault detection. In addition, it also provides information about the severity of the hot spots. The proposed method has three main advantages: (1) it is capable of actively learning fault characteristics and can be applied to diagnose various degrees of hot spots; (2) unlike other data-driven methods, the proposed method considers non-Euclidean spatial data features, which further improves the accuracy of the method and reduces the false alarm rate; (3) it does not rely on mathematical models or expert knowledge, making it able to meet the real-time monitoring needs of actual PV systems. The effectiveness and superiority of the proposed approach are verified through 12 sets of hot spots tests conducted on a PV experimental platform.

A hybrid dynamic graph neural network framework for real-time anomaly detection

ABSTRACT The timely and robust detection of anomalies is essential for resilient and secure operations of critical water infrastructures against operational faults or malicious actions. However, real-world systems exhibit diverse and evolving spatiotemporal relationships among their components, posing an intricate challenge in anomaly detection task. This study proposes a hybrid dynamic graph neural network (GNN) that jointly maps long- and short-term spatiotemporal relationships in multivariate data streams. Those relationships are encoded via a hybrid graph, comprising an optimally learned static subgraph for persistent structural relationships and a complementary dynamic subgraph for dynamically shifting relationships. Additionally, an attention mechanism captures time-varying relational importances and shifts the model's focus towards significant relationships, while minimising contributions of less importance to the final outputs. The proposed architecture is showcased through a synthetic case study of a water distribution system with multivariate data streams from both on-site and soft sensors, and shows strong detection and localisation accuracy against all scenarios of operational faults and malicious actions explored. A comparative analysis with an equivalent static graph model indicates that the addition of the hybrid dynamic component enhances detection accuracy and reduces false alarm rates through more robust characterisation of behaviours, thus allowing actionable insights for more resilient and secure operations.

The influence of photobiomodulation on upper body muscular performance in collegiate athletes.

Photobiomodulation (PBM), has been shown to improve muscular performance during single-joint resistance exercises. It is unclear whether this benefit extends to multiple-joint exercises, such as the barbell bench press. In a within-group design, 15 collegiate athletes (mean±SD; age 20.2±1.6 yrs; BMI 26.0±4.5 kg.m-2) received PBM or sham treatment six hours prior and then immediately preceding a bench press protocol. Two treatment sites each on the pectoralis major, triceps, and deltoids were irradiated. Bench press one repetition maximum (1RM), volume load (VL) lifted during five sets to failure at 60% 1RM, and muscular soreness were compared between PBM and sham conditions. Testing was conducted over three sessions separated by a week each, with a baseline testing day occurring first, and the PBM and sham condition testing days following in a counterbalanced fashion. A repeated measures ANOVA was applied for analysis (alpha level=0.05). No significant differences were observed between PBM and sham conditions for bench press 1RM or soreness (P≥0.05). VL was greater in both PBM and sham conditions compared to baseline (P≤0.05). VL was not significantly different between PBM and sham. These findings suggest that muscular performance during multi-joint resistance exercises such as the bench press may not benefit from PBM in the same fashion as single-joint resistance exercises.

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&amp;gt;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.

STC-GraphFormer: Graph Spatial-Temporal Correlation Transformer for In-vehicle Network Intrusion Detection System

The integration of several developing technologies and their applications with Internet of Vehicles (IoVs) techniques has been improved. Utilizing these emerging technologies renders the in-vehicle network more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. The CAN protocol provides an efficient and broadcast-based protocol for facilitating serial data exchange between ECUs. However, it lacks provisions for security measures such as authentication and encryption. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. This paper proposes STC-GraphFormer, an innovative spatial-temporal model that utilizes a Graph Convolutional Network (GCN) and a transformer. The spatial GCN layers are utilized to construct and acquire local spatial features, while the temporal transformer layers are employed to capture the long-term global temporal dependencies. By employing this integrated approach, STC-GraphFormer can learn complex spatial-temporal correlations within the IVN data, enabling it to detect and classify malicious intrusions. The proposed STC-GraphFormer has been validated using five real in-vehicle CAN datasets that cover a wide range of attacks that have not been previously investigated together. The finding results indicate that the STC-GraphFormer is more efficient than the SOTA approaches. It demonstrates excellent performance, with Car-hacking (0.99983), IVN intrusion detection (0.9991), CAN Dataset for intrusion detection “OTIDS” (0.9992), CAR hacking: attack & defense challenge (0.9901), and Survival analysis (0.9982), with a minimal false alarm rate and the highest achievable F1 scores for various types of attacks.

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.

Video Analysis of Elite American Football Athletes During Vertical Jump.

The National Football League (NFL) combine tests the athleticism of prospects competing for the draft. The vertical jump is included to test lower extremity power, yet the components which lead to the greatest performance remain elusive. Therefore, this study aimed to utilize a sample of elite athletes to analyze vertical jump components associated with increased performance and the relationship between vertical jump performance and rookie-year success. Videos of 50 NFL prospects performing the vertical jump task were analyzed for various countermovement jump components. Regression analyses examined the components in relation to normalized jump height and rookie Approximate Value (AV) using an alpha level of 0.05. After analysis, only the overall model for normalized jump height was statistically significant (R^2^ = 0.69, p = 0.002). While no single variable predicted jump height, distinct strategies were evident between the top and bottom 25% performers based on component correlations. The regression model approached significance in predicting rookie AV (R^2^ = 0.94, p = 0.052), with notable components like heel pauses for skilled positions and greater knee flexion for linemen. By creating models that can predict jump height or AV, variables can be identified that can be used to improve one's jump height or, in the case of AV, that can be used to predict which draft prospects will perform better in the NFL.

METTL14 Promotes Lipopolysaccharide-Induced Myocardial Damage via m6A-Dependent Stabilization of TRPM7 mRNA.

Sepsis-induced myocardial injury (SIMI) is a vital pathological component of severe sepsis and septic shock. As a prevalent internal mRNA modification in eukaryotic cells, N6-methyladenosine (m6A) modification is implicated in sepsis and immune disorders. Methyltransferase-like 14 (METTL14), a core subunit of the methyltransferase complex that catalyzes messenger RNA m6A modification, is involved in the regulation of human cardiomyocyte cell line (AC16) injury. This study aimed to explore the role and mechanism of METTL14 in lipopolysaccharide (LPS) -induced myocardial injury.Cell viability and apoptosis were analyzed via 3- (4,5-Dimethylthiazol-2-yl) -2,5-diphenyltetrazolium bromide (MTT), flow cytometry, and TdT-mediated dUTP nick-end labeling (TUNEL) assay. The Tumor necrosis factor alpha (TNF-α) and Interleukin-1beta (IL-1β) levels were analyzed via Enzyme linked immunosorbent assay (ELISA). Caspase-3 activity, reactive oxygen species activity, malondialdehyde level, and glutathione level were assessed using special assay kits. The levels of transient receptor potential melastatin 7 (TRPM7) and METTL14 mRNA were determined via Real-time quantitative polymerase chain reaction (RT-qPCR). Meanwhile, the protein levels of TRPM7, METTL14, phospho-p65 (p-p65), total p65 (p65), p-IκBα, and total IκBα (IκBα) were examined via western blot assay.LPS treatment repressed AC16 cell viability and induced cell apoptosis, inflammatory response, oxidative stress, and ferroptosis in vitro. METTL14 and TRPM7 were upregulated in LPS-treated AC16 cells. At the molecular level, METTL14 could increase the stability of TRPM7 mRNA via m6A methylation. Moreover, METTL14 deficiency could abolish LPS-triggered AC16 cell injury and ferroptosis via TRPM7 regulation.METTL14 knockdown reversed LPS-caused myocardial cell damage mainly by regulating the stability of TRPM7 mRNA, providing a novel therapeutic target for septic cardiomyopathy treatment.

Food Choices of Contemporary Cuisine and Traditional Foods: Effects on Family Ties.

Background: Technological advancement has evolved dynamics in the pace of day-to-day life. Economic and social development has introduced new meanings at individual and societal levels. Modernity and development have transformed the social fabric and relationships. Social media has instigated a tremendous multifaceted transformation in lifestyle. An increase in disposable income from prepared food, especially contemporary cuisines, has evolved diversified changes in consumers' behavior. These changes include trends, perceptions, consumption patterns, and modes of fast food (quality, quantity, tastes), including delivery systems, among all age groups and genders. This study investigated the factors that influence food choices towards contemporary cuisine, the influence of contemporary cuisine food choice on homemade/traditional foods, and how food choices of contemporary cuisines affect family ties. Methods: In this exploratory study, a cross-sectional quantitative survey research method was used to obtain the insights of youths. A systematic random sampling procedure was adopted. We recruited at a public sector university of Lahore, namely, the Institute of Social and Cultural Studies, University of the Punjab. The sample size was 260. We used modified versions of the Food Frequency Questionnaire and the Food Choice Questionnaire to assess the food choices of contemporary cuisine, homemade/traditional food, and family ties. The Cronbach's coefficient alpha level varied from 0.62 to 0.85 among scales. We performed descriptive and inferential statistics (factor analysis and multivariate regression analysis) by using SPSS 23. Results: Age and education showed a significant relationship with traditional food. Taste was the only factor identified for food choices of contemporary cuisine. Traditional food and family ties were the factors identified in the analysis. Conclusions: Food choices of contemporary cuisines were found to be just for good taste and fun. Homemade food regulates the traditions of traditional food. Cooking and dinning together not only maintained the food choices but also encourage and motivate the connectedness and closeness that strengthen family ties.

Influence of Motivation in Predicting Lecturers’ Retention in Colleges of Education in Bauchi State, Nigeria

This study investigated the influence of motivation in predicting lecturers’ retention in Colleges of Education in Bauchi State, Nigeria. The study adopted survey research design. The population of the study consisted of all the lecturers from Colleges of Education in Bauchi State, Nigeria. A sample of 50 lecturers was drawn from the population through the process of simple random sampling technique. Three objectives, three research questions and three null hypotheses were formulated to guide the study. Data collection was carried out with the use of research instruments titled “Motivation and “Lecturers’ Retention Questionnaire (MLRQ)”. The reliability index was 0.71. These figures confirmed that the instruments were reliable in achieving the objectives of the study. The mean, standard deviation and simple linear regression was used to test the research questions and hypotheses respectively generated for the study at 0.05 alpha levels. The results indicate that substantive enhancement in motivation level may yield a correspondingly improvement in lecturers’ retention rate. The findings revealed among others that promotion significantly predict lecturers’ retention. Also, the results of the study revealed that salary significantly predicts lecturers’ retention. Moreover, the results showed that self-efficacy significantly predict lecturers’ retention. Based on the findings, the researcher recommended that government, colleges’ authorities, education regulatory bodies and all stakeholders in the academia should pay increasing attention to the motivation of academic staff in order to boost their retention rate thereby enhancing high productivity. These results were discussed in relation to previous findings of similar studies and recommendations were also proffered.

A big data analysis algorithm for massive sensor medical images.

Big data analytics for clinical decision-making has been proposed for various clinical sectors because clinical decisions are more evidence-based and promising. Healthcare data is so vast and readily available that big data analytics has completely transformed this sector and opened up many new prospects. The smart sensor-based big data analysis recommendation system has significant privacy and security concerns when using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access, which might lead to identity theft and privacy violations, increases when sending and storing sensitive medical data on the cloud. Our effort will improve patient care and well-being by creating an anomaly detection system based on machine learning specifically for medical images and providing timely treatments and notifications. Current anomaly detection methods in healthcare systems, such as artificial intelligence and big data analytics-intracerebral hemorrhage (AIBDA-ICH) and parallel conformer neural network (PCNN), face several challenges, including high resource consumption, inefficient feature selection, and an inability to handle temporal data effectively for real-time monitoring. Techniques like support vector machines (SVM) and the hidden Markov model (HMM) struggle with computational overhead and scalability in large datasets, limiting their performance in critical healthcare applications. Additionally, existing methods often fail to provide accurate anomaly detection with low latency, making them unsuitable for time-sensitive environments. We infer the extraction, feature selection, attack detection, and data collection and processing procedures to anticipate anomaly inpatient data. We transfer the data, take care of missing values, and sanitize it using the pre-processing mechanism. We employed the recursive feature elimination (RFE) and dynamic principal component analysis (DPCA) algorithms for feature selection and extraction. In addition, we applied the Auto-encoded genetic recurrent neural network (AGRNN) approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and root mean square error (RMSE) are some metrics used to evaluate the proposed task.

An optimized ensemble model with advanced feature selection for network intrusion detection.

In today's digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. Traditional machine learning techniques have been extensively employed for this purpose due to their high accuracy and low false alarm rates. However, these methods often fall short in detecting sophisticated and evolving threats, particularly those involving subtle variations or mutations of known attack patterns. To address this challenge, our study presents the "Optimized Random Forest (Opt-Forest)," an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. The genetic algorithms based decision forest construction offers notable benefits by traversing a wider exploration space and mitigating the risk of becoming stuck in local optima, resulting in the discovery of more accurate and compact decision trees. Leveraging advanced feature selection techniques, including Best-First Search, Particle Swarm Optimization (PSO), Evolutionary Search, and Genetic Search (GS), along with contemporary dataset, this research aims to enhance the adaptability and resilience of NIDS against modern cyber threats. We conducted a comprehensive evaluation of the proposed approach against several well-known machine learning models, including AdaBoostM1 (AbM1), K-nearest neighbor (KNN), J48-Decision Tree (J48), multilayer perceptron (MLP), stochastic gradient descent (SGD), naïve Bayes (NB), and logistic model tree (LMT). The comparative analysis demonstrates the effectiveness and superiority of our method across various performance metrics, highlighting its potential to significantly enhance the capabilities of network intrusion detection systems.

Virtual Reality Exercise with Autonomy Support Increases Positive Affect During Time Trial Exercise.

Background: Being able to choose elements of an exercise session, known as autonomy support, improves motor performance and psychological responses. Virtual reality (VR) programs provide many options for embedding autonomy support in exercise sessions. The purpose of this study was to investigate the effects of autonomy support in a VR setting on physiological and psychological responses to self-regulated rowing exercise. Methods: Using a repeated-measures crossover design, healthy untrained men and women (N = 20, age = 23.0 ± 7.4) completed exercise sessions on a rowing ergometer coupled with a head-mounted immersive VR application. In the Choice condition, participants chose the virtual environment, and in the Control condition, the environment was assigned to the participant. Participants were instructed to complete 1500 m as quickly as possible in both conditions, while ratings of perceived exertion, affective valence, and heart rate were recorded throughout the trials. Finishing time and remembered pleasure were assessed at the end of each session. Repeated-measures analyses with an alpha level of 0.05 were used for all variables as appropriate, with Bonferroni adjustments applied for any post hoc tests. Results: There was a main effect of condition on affective valence which was higher in Choice (2.07 ± 1.67) than Control (1.64 ± 2.12, P = 0.03, η2 = 0.22). No other differences were detected between conditions for finishing time or the remaining variables. Conclusion: During self-regulated exercise accompanied by an immersive VR application, being able to choose the virtual environment oneself leads to a more positive affective state without compromising exercise effort, physiological strain, or performance.

Quantitative Assessment of Apically Extruded Debris During Retreatment Procedures Using Three Nickel-Titanium Rotary Systems: An In Vitro Comparative Study

Objectives: Apical extrusion of debris can affect the success of endodontic treatments, and the specific performance of certain retreatment systems has not been studied yet. Therefore, the aim of this in vitro study was to quantitatively assess the amount of apically extruded debris produced during retreatment procedures using three rotary NiTi retreatment systems in mature non-resorbed straight roots. Methods: Thirty extracted permanent human teeth with single straight roots were selected. The root canals were prepared with the ProTaper Next system up to size 30 and obturated with gutta-percha and AH Plus sealer using the continuous wave of condensation technique. The samples were stored for 30 days and randomized by computer sequence into three retreatment groups (n = 10): (1) ProTaper Universal Retreatment; (2) HyFlex Remover; and (3) VDW.Rotate Retreatment. Apically extruded debris was collected in Eppendorf tubes and weighed with a microbalance (10−5 g) before and after retreatment procedure. As the data were not normally distributed, the Kruskal–Wallis test was applied for comparing data among groups, with an alpha level set at α = 0.05. Dunn’s test was considered for post-hoc analyses, if appropriate. Results: Hyflex Remover was associated with the highest amount of extruded debris (0.85 ± 0.82 mg), followed by VDW.Rotate Retreatment (0.78 ± 0.41 mg) and ProTaper Universal Retreatment (0.62 ± 0.28 mg). However, the differences were not statistically significant (p &gt; 0.05). Conclusions: All the retreatment systems tested were associated with apical extrusion of debris in vitro, with no significant quantitative differences between them, suggesting that clinicians can choose a retreatment system with features appropriate to the specific clinical situation without risk of increasing the amount of apically extruded debris.