Test Time Research Articles

Effects of Inertial Flywheel Training vs. Accentuated Eccentric Loading Training on Strength, Power, and Speed in Well-Trained Male College Sprinters.

This study aimed to evaluate and compare the effects of inertial flywheel training and accentuated eccentric loading training on the neuromuscular performance of well-trained male college sprinters. Fourteen sprinters were recruited and randomly assigned to either the flywheel training (FWT, n = 7) group or the accentuated eccentric loading training (AELT, n = 7) group. The FWT group completed four sets of 2 + 7 repetitions of flywheel squats, whereas the AELT group performed four sets of seven repetitions of barbell squats (concentric/eccentric: 80%/120% 1RM). Both groups underwent an eight-week squat training program, with two sessions per week. A two-way repeated ANOVA analysis was used to find differences between the two groups and between the two testing times (pre-test vs. post-test). The results indicated significant improvements in all measured variables for the FWT group: 1RM (5.0%, ES = 1.28), CMJ (13.3%, ES = 5.42), SJ (6.0%, ES = 2.94), EUR (6.5%, ES = 4.42), SLJ (2.9%, ES = 1.77), and 30 m sprint (-3.4%, ES = -2.80); and for the AELT group: 1RM (6.3%, ES = 2.53), CMJ (7.4%, ES = 3.44), SJ (6.4%, ES = 2.21), SLJ (2.2%, ES = 1.20), and 30 m sprint (-3.0%, ES = -1.84), with the exception of EUR (0.9%, ES = 0.63, p = 0.134), showing no significant difference. In addition, no significant interaction effects between group and time were observed for 1RM back squat, SJ, SLJ, and 30 m sprint (p > 0.05). Conversely, a significant interaction effect between group and time was observed for both CMJ and EUR (p < 0.001); post hoc analysis revealed that the improvements in CMJ and EUR were significantly greater in the FWT group compared to the AELT group (p < 0.001). These findings indicate that both FWT and AELT are effective at enhancing lower-body strength, power, and speed in well-trained male college sprinters, with FWT being particularly more effective in promoting elastic energy storage and the full utilization of the stretch-shortening cycle.

Aging Effects Observed in Automotive Fuel Cell Stacks by Applying a New Realistic Test Protocol and Humidity Control

ABSTRACTTraditional automotive proton exchange membrane fuel cell (PEMFC) endurance testing relies on the fuel cell (FC) dynamic load cycle (FC‐DLC) protocol, which inadequately reflects real‐world driving conditions. To address this limitation the “Investigations on degradation mechanisms and Definition of protocols for PEM Fuel cells Accelerated Stress Testing” (ID‐FAST) consortium defined the new representative “ID‐FAST driving load cycle,” a novel approach capturing the load distribution, transitions, temperature variations, and humidity fluctuations experienced by FCs in real‐world operation. We demonstrate the ID‐FAST driving cycle itself and the integration into a realistic durability test program for FC test benches and present the resulting test data. Furthermore, we showcase its implementation within an accelerated stress testing (AST) protocol, highlighting its potential to significantly reduce testing time by accelerating degradation. Additionally, a novel method for highly dynamic humidity adjustment within test benches is introduced. By overcoming limitations of existing methods and incorporating the ID‐FAST driving cycle, this work paves the way for a new era of efficient and realistic FC endurance testing, ultimately contributing to the development of more robust and durable automotive FC stacks.

Correlation of Binocular Perimetry Screening using imo, a Portable Head Mounted Perimeter with 10-2 Standard Automated Perimetry for Early Glaucoma with Central Visual Field Defects.

The screening program using imo strongly correlates with 10-2 Standard automated perimetry in detecting central visual field defects in early glaucoma, highlighting its potential as a valuable tool for efficient screening and severity quantification. To investigate the correlation between abnormalities within the central 10° of the screening program using imo, a portable head-mounted perimeter, and 10-2 standard automated perimetry in early-stage glaucoma with central visual field defects. This retrospective study included early glaucoma with (26 eyes) and without (38 eyes) central visual field defects. The correlation between the numbers of abnormal points within the central 10° of the imo screening program and those in the probability plot of 10-2, and mean total deviation and pattern deviation, were analyzed. The imo screening program demonstrated sensitivity (80%) and specificity (98.7%) in detecting abnormalities, with a 91.6% agreement rate of 10-2. The mean number of anomalies in the 10-2 plot (pattern deviation) with P<1% was 0.85, 7.75, and 9.69 (95% confidence interval: 0.18-1.51, 5.38-10.1, and 6.89-12.5, respectively) for no, one, and two anomalies in the imo screening program, respectively. The number of anomalies with P<1% was significantly higher when the imo screening program detected one anomaly versus none (P<0.01), and two versus one (P<0.05). Mean total deviation values were -1.15, -7.5, and -15.2 (95% confidence interval: -2.32 to 0.03, -11.21 to -3.78, and -19.7 to -10.6, respectively), while mean pattern deviation values were -1.57, -9.0, and -16.0 (95% confidence interval: -2.57 to -0.57, -12.5 to -5.49, and -21.4 to -10.7, respectively), for no, one, and two anomalies in the imo screening program, respectively. The imo portable head-mounted perimeter was found to correlate with 10-2 standard automated perimetry in glaucoma patients with central visual field defects and has the potential to shorten visual field testing times.

Detection of Road Risk Sources Based on Multi-Scale Lightweight Networks.

Timely discovery and disposal of road risk sources constitute the cornerstone of road operation safety. Presently, the detection of road risk sources frequently relies on manual inspections via inspection vehicles, a process that is both inefficient and time-consuming. To tackle this challenge, this paper introduces a novel automated approach for detecting road risk sources, termed the multi-scale lightweight network (MSLN). This method primarily focuses on identifying road surfaces, potholes, and scattered objects. To mitigate the influence of real-world factors such as noise and uneven brightness on test results, pavement images were carefully collected. Initially, the collected images underwent grayscale processing. Subsequently, the median filtering algorithm was employed to filter out noise interference. Furthermore, adaptive histogram equalization techniques were utilized to enhance the visibility of cracks and the road background. Following these preprocessing steps, the MSLN model was deployed for the detection of road risk sources. Addressing the challenges associated with two-stage network models, such as prolonged training and testing times, as well as deployment difficulties, this study adopted the lightweight feature extraction network MobileNetV2. Additionally, transfer learning was incorporated to elevate the model's training efficiency. Moreover, this paper established a mapping relationship model that transitions from the world coordinate system to the pixel coordinate system. This model enables the calculation of risk source dimensions based on detection outcomes. Experimental results reveal that the MSLN model exhibits a notably faster convergence rate. This enhanced convergence not only boosts training speed but also elevates the precision of risk source detection. Furthermore, the proposed mapping relationship coordinate transformation model proves highly effective in determining the scale of risk sources.

TCAD approach for negative capacitance ferroelectric devices for radiation detection applications

Negative capacitance (NC) emerges as a promising technology for low-power, high-performance transistors. The HiEnD project intends to analyze and design high-energy efficient electronic devices based on innovative ferroelectric materials. It explores NC technology for high-resolution tracking detectors in future high-energy physics experiments. It aims to create ultra-thin, radiation-resistant devices for superior signal detection in harsh environments. This paper presents detailed modeling, simulation results, and a comparison with experimental data, highlighting the potential of the developed methodology but also of the technology for the realization of compact, high-performance detectors. Advanced Technology CAD modeling has been employed to investigate the potentiality of NC devices in unconventional application domains, such as high-energy physics experiments. Numerical simulations, capable of verifying experimental results, enhance predictive power, reducing time and cost in detector design and testing.

SiMix: A domain generalization method for cross-site brain MRI harmonization via site mixing

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

Preparation and Characterization of Chitosan-Gelatin Film for Wound Dressing

This study aims to explore the effect of various components of chitosan gelatin on its characteristics. The method used was drying at room temperature using petridish for 2-3 days, by mixing all raw materials with chitosan : gelatin weight ratio of 1:0, 0:1, 1:1, 1:2, and 2:1. The dried film was taken and tested for film characteristics. The analysis conducted was FTIR analysis, swelling analysis, and degradation analysis. The results of the swelling analysis showed that the more the addition of gelatin composition, the percentage of swelling increased as shown in the ratio of 1:2, namely 85.178%, 144.072%, 102.068%, 184.660%, 135.272%. Meanwhile, the degradation test results showed that the chitosan and gelatin films with various composition variations increased as the test time progressed. FTIR analysis revealed that gelatin (Ge), chitosan (Ch), and chitosan-gelatin mixture (Ch-Ge) with 1:1 ratio had similar absorption peaks.

Material Inspection of Historical Built Heritage with Multi-Band Images: A Case Study of the Serranos Towers in Valencia

Built heritage materials assessment is an important task for planning and managing future conservation works. The uniqueness of each historical building makes reconnaissance operations more complex and specific for every single building. In the past, visual inspection and invasive techniques were widely used to investigate surface materials. Non-destructive techniques (NDTs) such as multi-band photogrammetry and remote sensing can help to assess the buildings without any contact with the investigated objects, restricting the disruptive tests on limited areas and reducing the testing time and costs of the surveys. This paper presents the results obtained using multi-band images acquired with a low-cost imaging solution after interchanging several filters, and the application of the principal components analysis (PCA) to recognize different materials of a significant historical monument. The Serranos Towers, built between 1392 and 1398, suffered several interventions in the past that affected their state of conservation with the replacement of different materials. The results of the study show the usefulness of applying PCA to distinguish different surface materials, often similar to the original ones, in a fast and efficient way to investigate and analyze our heritage legacy.

Impact of Hepatitis B Virus Point-of-care DNA Viral Load Testing Compared With Laboratory-based Standard-of-care Approaches on Uptake of HBV Viral Load Testing, Treatment, and Turnaround Times: A Systematic Review and Meta-analysis.

Point-of-care (PoC) hepatitis B virus (HBV) DNA viral load (VL) assays represent an alternative to laboratory-based standard-of-care (SoC) VL assays to accelerate diagnosis and treatment. We evaluated the impact of using PoC versus SoC approaches on the uptake of VL testing, treatment, and turnaround times from testing to treatment across the HBV care cascade. We searched 5 databases, 6 conference websites, and contacted manufacturers for unpublished reports, for articles with or without a comparator (SoC VL testing), and had data on the uptake of VL testing, treatment, or turnaround times between hepatitis B surface antigen (HBsAg) testing, VL testing, and treatment in the cascade. We performed a random-effects meta-analysis on rates of VL testing and treatment initiation. Six studies, composing 9 arms, were included. Three PoC arms reported less than 1 day between screening for HBsAg positivity and VL testing, and the other one (2 arms) reported it between 7 and 11 days. Five arms reported the time to available VL test results (<1 day). Three studies reported 1-8 days between VL testing results and treatment initiation. Two studies reported the turnaround times between a positive HBsAg screening and treatment initiation (the same day and 27 days). Overall, 84.1% of those with HBsAg positivity were tested for DNA VL and 88.3% of eligible people initiated treatment. HBV PoC DNA testing appears to be associated with a turnaround time of <1 day for receipt of VL results and appears associated with high rates of DNA testing and initiation of treatment among those eligible. PROSPERO CRD42023398440.

Reliability of SiC MOSFETs in the High Cycle Fatigue Regime under Fast Power Pulses

Device reliability is an important factor in application, especially in the field of electric mobility. In this paper high cycle fatigue power cycling results of SiC devices in baseplate free modules are presented. To minimize testing time, the devices were stressed with load pulses corresponding to a 50 Hz load. The reliability results are the first fatigue results for temperature swings below 30 K for SiC devices. The lifetime in the high cycle fatigue area is limited by solder fatigue for high virtual junction temperatures of 150 °C. In theory, the reliability should increase exponential since the elastic-plastic transition area is reached. The experiment revealed that the lifetime can still be described by the Coffin-Manson approach also in the high cycle fatigue area. It can be observed that the high junction temperatures weaken the stability of the solder layer, so no major lifetime increase can develop. The measured temperature data are additionally corrected by a three-dimensional (3D) simulation to ensure a validity of the results.

Epidemiological trends in respiratory pathogens infections among children post-COVID-19: A cross-sectional study

ObjectiveThe aim of this study was to investigate the epidemiological trend of respiratory pathogens infections among children after the Coronavirus Disease 2019 (COVID-19) pandemic. MethodsThis study enrolled 575,373 children who came to our hospital for relevant respiratory pathogen antigen/antibody testing due to respiratory symptoms such as fever and cough. The demographic and laboratory data, including age, gender, testing time, and influenza A virus (IAV), influenza B virus (IBV), respiratory syncytial virus (RSV), adenovirus (ADV), and Mycoplasma pneumonia (MP) results, were collected from electronic medical records. SPSS (version 21.0) and GraphPad Prism 9 software were used for statistical analysis and figure creation. Results79,746 children tested positive for IAV IgM, and 3196 children tested positive for IBV IgM, with an overall positive rate of 28.5 % for IAV and 1.1 % for IBV. IAV infections peaked at 21,502 cases in March 2023. 80,699 children underwent RSV IgM testing from April to October 2023, with 5726 (7.1 %) testing positive. The apex of RSV infections occurred in May 2023, with 2140 cases. Regarding ADV, 100,460 children underwent testing from April to October 2023, with 1981 (11.9 %) testing positive. The pinnacle of ADV infections reached 4546 cases in November 2023. Concerning MP, 474,913 children underwent MP testing, with 73,833 (15.5 %) testing positive. The zenith of MP infections occurred in November 2023, with 25,291 cases. Further analysis revealed that the outbreaks of these pathogens are occurring earlier than in previous years. Additionally, our data showed that children aged >3 years accounted for 79.6 %, 87.8 %, 88.6 %, and 77.8 % of the total IAV‐positive, IBV‐positive, ADV-positive, and MP-positive children, respectively. Conversely, RSV primarily infected children <6 years. ConclusionVarious respiratory pathogens showed an epidemic trend in children among children post-COVID-19. These results indicated that we should pay timely attention to the epidemiological trends and characteristics of respiratory pathogens in children after the COVID-19 pandemic and provide relevant information for society and clinical practice.

Analysis of child development facts and myths using text mining techniques and classification models

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90 % accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 μs). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Wireless network topology optimization algorithm based on discrete variables

In response to the missing discrete variables in current wireless network topology optimization, an improved particle swarm optimization algorithm based on fusion of discrete variables is proposed to obtain network discrete variables. A wireless network topology optimization model is constructed. The research results indicate that it has better anti-interference performance in complex situations, which contributes to improving network load balancing. The topology obtained by this method has independence and predictability. When optimizing network topology, it has high network node coverage. When the network nodes are 50, 100, 150, and 200, the connectivity is 99.85 %, 93.64 %, 91.25 %, and 90.18 %, respectively. The testing time is 19s, 34s, 54s, and 64s respectively, which has the best optimization performance. The method can effectively improve the missing discrete variables in wireless network topology optimization, which has good performance.

Comparison of Four Rapid N-Glycan Analytical Methods and Great Application Potential in Cell Line Development

N-glycan, a critical quality attribute (CQA) of monoclonal antibodies, profoundly impacts potency, immunogenicity, safety, and clinical efficiency. Therefore, N-glycan should be monitored and controlled during development. The conventional 2-AB method is considered the gold standard for N-glycan analysis, which is labor intensive and time consuming. Moreover, its sample requirement is relatively high and cannot be met in early-stage development, including cell line development. In this study, we compared the performance of four rapid analytical methods in N-glycan analysis of mAbs, including the rapid 2-AB method, reduction method, off-line IdeS digestion method, and two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method. Our results showed that these four rapid analytical methods could provide comparable N-glycan data. Moreover, these four rapid methods shortened the testing time for the conventional 2-AB method from days to just minutes. They also reduced the sample requirement for the conventional 2-AB method from milligrams to micrograms. Among these four rapid methods, the 2D-LC-MS method demonstrated great potential for applications in time-consuming cell line development because it required less testing time and a lower sample requirement.

MDADroid: A novel malware detection method by constructing functionality-API mapping

As the Android ecosystem develops, malware also evolves to adapt to the changes. Consequently, malware remains a significant threat, posing a challenge in developing a low-resource consumption malware detection method that can adjust to updates in the Android API versions. We propose a novel method called MDADroid, which detects malware based on self-built Functionality-API mapping. We start by building a set of permission-related APIs using open-source knowledge. Then, we construct a Functionality-App-API heterogeneous graph based on collected data and establish a Functionality-API mapping from it. Finally, MDADroid transforms app features from the API level to the functionality level for malware detection, ensuring model resilience to API changes. We also design an API similarity calculation method that updates the Functionality-API mapping at a low cost. We evaluate MDADroid on multiple datasets, and the results show that MDADroid achieves an accuracy of 95.22%, 96.23%, 98.77%, and 99.56% on the AndroZoo, CICAndMal 2017, CICMalDroid 2020, and Drebin datasets, respectively, with training and testing times of 2 s, 0.6188 s, 1.34 s, and 1.02 s. Moreover, our method demonstrates excellent performance in the tests for resilience capabilities.

Immediate sequential changes in the tear film lipid layer following eyelid massage in dry eye syndrome: A comparative control study

Background/PurposeMeibomian glands are sebaceous glands that release meibum onto the ocular surface; enhancing the quality and quantity of meibum secretions has been proven to improve signs and symptoms of evaporative dry eye (EDE) and meibomian gland dysfunction (MGD). This study aimed to evaluate and compare the efficacy of a heated eye mask (HEM) and eyelid massage device EyePeace (EP) in alleviating signs and symptoms of evaporative dry eye. MethodsForty dry eye participants were recruited in a prospective, contralateral-eye trial study. After undergoing 10 min of HEM therapy, eyelid massage therapy was applied to one eye by the device. The efficiency was assessed at four time points: baseline (0 min), 5 min (5 min), 15 min (15 min), and 30 min (30 min). Non-invasive breakup time (NITBUT), redness score (RS Score), tear meniscus height (TMH), tear-film lipid layer (TFLL), endothelial cell count (ECC), meibomian gland expressibility (MGEx), meibomian gland quality (MGQ), conjunctivocorneal staining (CS), ocular surface temperature (OST), best corrected visual acuity (BCVA), intraocular pressure (IOP), central corneal thickness (CCT) flat-axis keratometry value (K1), and steep-axis keratometry value (K2), were examined. ResultsBaseline clinical measurements did not have statistically significant differences between the groups (all p > 0.05). After 30 min, a comparison was made between the HEM group and EP + HEM group, revealing significant changes only in the primary outcomes, TFLL (2.18 ± 0.45 versus 2.40 ± 0.50; p < 0.05), and MGEx grades (0.68 ± 0.53 versus 0.98 ± 0.70; p < 0.05). Improvements in NITBUT and TMH were sustained until 5 min and 15 min after using EP + HEM. No significant changes were observed in RS Score, MGQ, OST, CFS, BCVA, IOP, ECC, K1, K2, and CCT (all p > 0.05) at all test time points. ConclusionThe application of a heated eye mask followed by a gentle massage using EyePeace on the eyelids can have a sustained improvement in the tear film lipid layer and meibomian gland expressivity score but not clinically significant, and does not pose any significant immediate impact on the cornea.Trial registration number: NCT06158997.

Enhanced Place Specificity of the Parallel Auditory Brainstem Response: An Electrophysiological Study

PurposeThis study investigates the effect of parallel stimulus presentation on the place specificity of the auditory brainstem response (ABR) in human listeners. Frequency-specific stimuli do not guarantee a response from the place on the cochlea corresponding only to that characteristic frequency — especially for brief and high-level stimuli. Adding masking noise yields responses that are more place specific, and our prior modeling study has suggested similar effects when multiple frequency-specific stimuli are presented in parallel. We tested this hypothesis experimentally here, comparing the place specificity of responses to serial and parallel stimuli at two stimulus frequencies and three stimulus rates.MethodsParallel ABR (pABR) stimuli were presented alongside high-pass filtered noise with a varied cutoff frequency. Serial presentation was also tested by isolating and presenting single-frequency stimulus trains from the pABR ensemble. Latencies of the ABRs were examined to assess place specificity of responses. Response bands were derived by subtracting responses from different high-pass noise conditions. The response amplitude from each derived response band was then used to determine how much individual frequency regions of the auditory system were contributing to the overall response.ResultsWe found that parallel presentation improves place specificity of ABRs for the lower stimulus frequency and at higher stimulus rates. At a higher stimulus frequency, serial and parallel presentations were equally place specific.ConclusionParallel presentation can provide more place-specific responses than serial for lower stimulus frequencies. The improvement increases with higher stimulus rates and is in addition to the pABR’s primary benefit of faster test times.

Cognitive impairment in patients with bipolar disorder alone versus those with bipolar disorder comorbid with borderline personality disorder.

Bipolar disorder (BD) is a severe mental illness. BD often coexists with borderline personality disorders, making the condition more complex. To explore the differences in cognitive impairment between patients with BD and those with BD comorbid with borderline personality disorder. Eighty patients with BD and comorbid borderline personality disorder and 80 patients with BD alone were included in groups A and B, respectively, and 80 healthy volunteers were included as controls. Cognitive function in each group was evaluated using the Chinese version of the repeatable battery for the assessment of neuropsychological status (RBANS), the Stroop color-word test, and the Wechsler intelligence scale-revised (WAIS-RC). The indices of the RBANS, Stroop color-word test, and WAIS-RC in groups A and B were significantly lower than those of the control group (P < 0.05). Group A had significantly longer Stroop color-word test times for single-character, single-color, double-character, and double-color, lower scores of immediate memory, visual breadth, verbal function dimensions and total score of the RBANS, as well as lower scores of verbal IQ, performance IQ, and overall IQ of the WAIS-RC compared with group B (P < 0.05). Compared to group B, group A exhibited significantly longer single-character time, single-color time, double-character time, and double-color time in the Stroop color-word test (P < 0.05). The cognitive function of patients with BD complicated with borderline personality disorder is lower than that of patients with BD.

Behavior of Retained Austenite and Carbide Phases in AISI 440C Martensitic Stainless Steel under Cavitation

In this work emphasis was given to determine the evolution of the retained austenite phase fraction via X-ray diffractometry technique in the as-hardened AISI 440C martensitic stainless steel surface subjected to cavitation for increasing test times. Scanning electron microscopy results confirmed the preferential carbide phase removal along the prior/parent austenite grain boundaries for the first cavitation test times on the polished sample surface during the incubation period. Results suggest that the strain-induced martensitic transformation of the retained austenite would be assisted by the elastic deformation and intermittent relaxation action of the harder martensitic matrix on the austenite crystals through the interfaces between both phases. In addition, an estimation of the stacking fault energy value on the order of 15 mJ m−2 for the retained austenite phase made it possible to infer that mechanical twinning and strain-induced martensite formation mechanisms could be effectively presented in the studied case. Finally, incubation period, maximum erosion rate, and erosion resistance on the order of 7.0 h, 0.30 mg h−1, and 4.8 h μm−1, respectively, were determined for the as-hardened AISI 440C MSS samples investigated here.

An SDN-based Hybrid-DL-driven cognitive intrusion detection system for IoT ecosystem

Smart homes, healthcare, transportation, agriculture, manufacturing, and many more sectors can all benefit from IoT technology. This revolutionary way of interacting with the world could increase efficiency, convenience, and productivity. However, it also raises concerns regarding security, privacy, and data management. To mitigate security threats in IoT networks, this article presents a software-defined networking-enabled cognitive hybrid-deep learning model for intrusion detection in IoT ecosystem. The primary purpose of this framework is to continuously and efficiently identify cybersecurity threats in the IoT environment. With the influence of the cognitive computing paradigm, the designed system can analyse, understand, and even respond to various traffic approaching IoT devices. The proposed model is trained and tested using the cutting-edge N-BaIoT and CICDDoS2019 datasets. The experimental results demonstrate a high level of accuracy, yielding an acceptable false positive rate in conjunction with reasonable testing time. Furthermore, the architecture considers the limited resources of IoT devices, ensuring that they are not overburdened in the process. The proposed model obtained an acceptable accuracy rate of 99. 86 %, 99. 96 % precision, 99. 903 % recall, and 99.93 % F1-Score. The proposed model outperformed the other hybrid-DL models, such as Cu-GRU+LSTM and Cu-GRU+DNN.