Test Time Research Articles

Comparative analysis of Real-Time PCR and Chemiluminescence for the detection of SARS-CoV-2

SARS-CoV-2 infection is capable of causing a multi-organ syndrome, mainly involving the lungs with Acute Respiratory Insufficiency. The easy access to a rapid diagnosis of COVID-19 is a key point to improve the management of SARS-CoV-2 infection to contain its spread. The aim of this study is to evaluate the use of chemiluminescence technology in the diagnosis of SARS-CoV-2. In particular, a retrospective analysis was carried out on nasopharyngeal swabs from oncology patients referred to the National Cancer Institute of Naples, comparing the results of the Elecsys® SARS-CoV-2 Antigen chemiluminescence assay with those obtained by molecular testing in Real Time RT-PCR. The concordance rate between the antigen test and Real Time RT-PCR was 70.83%. The false negative and false positive rates were 63.79% and 0%, respectively. Although the Elecsys® SARS-CoV-2 Antigen test showed a high specificity, it is not as sensitive as the molecular test which remains the reference method. Therefore, to compensate for the potential decrease in test sensitivity, negative results should be analysed together with more patient-related factors, such as history of exposure to COVID-19 and clinical symptoms, in order to guide the diagnosis and subsequent treatment of the patient. In the end, from a clinical point of view, the antigen test is useful for the identification of acute or early infection in a rapid and cost-effective way.

Deep learning models for separate segmentations of intracerebral and intraventricular hemorrhage on head CT and segmentation quality assessment.

The volume measurement of intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) provides critical information for precise treatment of patients with spontaneous ICH but remains a big challenge, especially for IVH segmentation. However, the previously proposed ICH and IVH segmentation tools lack external validation and segmentation quality assessment. This study aimed to develop a robust deep learning model for the segmentation of ICH and IVH with external validation, and to provide quality assessment for IVH segmentation. In this study, a Residual Encoding Unet (REUnet) for the segmentation of ICH and IVH was developed using a dataset composed of 977 CT images (all contained ICH, and 338 contained IVH; a five-fold cross-validation procedure was adopted for training and internal validation), and externally tested using an independent dataset consisting of 375 CT images (all contained ICH, and 105 contained IVH). The performance of REUnet was compared with six other advanced deep learning models. Subsequently, three approaches, including Prototype Segmentation (ProtoSeg), Test Time Dropout (TTD), and Test Time Augmentation (TTA), were employed to derive segmentation quality scores in the absence of ground truth to provide a way to assess the segmentation quality in real practice. For ICH segmentation, the median (lower-quantile-upper quantile) of Dice scores obtained from REUnet were 0.932 (0.898-0.953) for internal validation and 0.888 (0.859-0.916) for external test, both of which were better than those of other models while comparable to that of nnUnet3D in external test. For IVH segmentation, the Dice scores obtained from REUnet were 0.826 (0.757-0.868) for internal validation and 0.777 (0.693-0.827) for external tests, which were better than those of all other models. The concordance correlation coefficients between the volumes estimated from the REUnet-generated segmentations and those from the manual segmentations for both ICH and IVH ranged from 0.944 to 0.987. For IVH segmentation quality assessment, the segmentation quality score derived from ProtoSeg was correlated with the Dice Score (Spearman r=0.752 for the external test) and performed better than those from TTD (Spearman r=0.718) and TTA (Spearman r=0.260) in the external test. By setting a threshold to the segmentation quality score, we were able to identify low-quality IVH segmentation results by ProtoSeg. The proposed REUnet offers a promising tool for accurate and automated segmentation of ICH and IVH, and for effective IVH segmentation quality assessment, and thus exhibits the potential to facilitate therapeutic decision-making for patients with spontaneous ICH in clinical practice.

Does the short-term learning effect impact vertical jump performance assessment on a portable force plate system?

One of the reoccurring questions that arises during the countermovement vertical jump (CVJ) assessment is whether the learning effect has an impact on the accuracy of the results obtained. Thus, the purpose of the present investigation was to examine the impact of the short-term learning effect on the assessment of lower-body neuromuscular performance characteristics when performed on a portable one-dimensional force plate system. Sixteen recreationally active college-age males volunteered to participate in the present study. Each participant completed four sets of three non-consecutive CVJs with no arm swing throughout a single day. Besides strong verbal encouragement, participants were constantly instructed to focus on pushing the ground as explosively as possible. Fourteen force-time metrics were selected for CVJ performance analysis purposes: eccentric and concentric peak and mean force and power, eccentric and concentric duration, contraction time, jump height, reactive strength index-modified, and countermovement depth. Repeated measures multivariate analysis of variance was used to examine statistically significant differences across four testing time points (p < 0.05). The results indicate an absence of any meaningful differences across four testing time points in force-time metrics of interest during both eccentric and concentric phases of the CVJ. Moreover, no differences were observed in CVJ outcome metrics such as countermovement depth, suggesting that the movement strategy tends to remain consistent. Overall, these findings reveal that CVJ test repeatability is not affected by the short-term learning effect and that data are stable at least within the scope of this study and within this population.

ВЛИЯНИЕ ВИБРАЦИИ НА ИЗНОС БУКСОВОГО ПОДШИПНИКА

The study objective is to determine the effect of vibration on the wear of the parts of a rolling axle box bearing. The tasks of the study are to obtain dependences of the speed and wear intensity of bearing elements depending on time, to determine the most unfavorable factors affecting the bearing, to evaluate methods for increasing wear resistance and vibration resistance of parts of a rolling axle box bearing. The bearing is tested on 1K62 screw-cutting lathe under the action and without the action of axial vibration. Wear is calculated for the treadmill using a mass method, by stopping tests and measuring mass loss (with an accuracy of 1g). The friction path of the roller to determine the wear intensity is calculated analytically. The scientific novelty of the work consists in confirming the hypothesis about the adverse effect of vibration on wear and suggesting methods and ways to increase the vibration resistance of rolling axle box bearing parts. The results of the tests and calculations are presented as graphs showing the effect of vibration on the axle box bearing parts in the form of a wear curve, which has a nonlinear dependence on the test time, as well as a wear curve for tests without vibration. The main factors influencing the fretting wear of rolling axle bearings are determined, methods for increasing their vibration resistance and wear resistance are proposed.

Prediction of time-dependent concrete mechanical properties based on advanced deep learning models considering complex variables

Evaluating the mechanical properties such as compressive strength, tensile strength, and modulus of elasticity (MOE) of concrete is crucial for the design, construction, and quality control of engineering projects. Developing a comprehensive theoretical-based numerical model to predict the concrete mechanical properties is considered challenging, owing to the diversity of admixtures and curing environment affecting the performance of modern concrete. The deep learning (DL) model gives a feasible approach as data-driven methods for accurate prediction of various material properties these years. However, predicting time-dependent mechanical properties of modern concrete with complex mix constituents by DL algorithms is still confined so far. In this article, artificial neuron network (ANN) and long short-term memory (LSTM) based DL models were established to predict concrete compressive strength, tensile strength, and MOE at different ages, considering the mix design, curing condition, curing time and tension testing method as input variables. LSTM outperformed ANN in terms of the statistical indicators for accuracy evaluation with high training and testing time costs. Bidirectional modification and multi-head attention mechanism were implemented to further improve the prediction accuracy of LSTM. It is shown that the multi-head attention bidirectional LSTM had more precise predicting results (R2=0.9355 for compressive strength, R2=0.8930 for tensile strength, R2=0.9584 for MOE) and fitting of the mechanical property-age curves. The LSTM-based models provided a promising prediction tool for time-dependent concrete mechanical properties with complex input parameters.

In situ deposition of bismuth on pre-anodized screen-printed electrode for sensitive determination of Cd2+ in water and rice with a portable device

Electrochemical detection is favorable for the rapid and sensitive determination of heavy metal cadmium. However, the detection sensitivity needs to be further improved, and a portable, low-cost device is needed for on-site detection. Herein, an in-situ bismuth modified pre-anodized screen-printed carbon electrode (SPCE) was developed for Cd2+ determination by square wave anodic stripping voltammetry (SWASV). The in-situ bismuth modification enhances the enrichment of Cd2+, and together with pre-anodization improve the electron transfer rate of electrode, thus enhancing the detection sensitivity. The electrode modification method combines pre-anodization and in-situ bismuth deposition, which is very easy and effective. Furthermore, a self-made PSoC Stat potentiostat coupled with a stirring device was fabricated for portable and low-cost electrochemical detection. After comprehensive optimization, the developed method can reach a testing time of 3 min, a detection limit of 3.55 μg/L, a linear range of 5–100 μg/L, and a recovery rate of 91.7–107.1% in water and rice samples for Cd2+ determination. Therefore, our method holds great promise for the rapid, sensitive and on-site determination of Cd2+ in food samples.

Perbandingan Algoritma SIMON dan SPECK Dalam Pengamanan Citra Digital

Cryptography is a data security technique by encoding data that is to be kept secret so that the original meaning of the data can no longer be understood. SIMON and SPECK are modern cryptographic algorithms issued by the National Security Agency (NSA). SIMON and SPECK are said to be algorithms that are known for their efficiency and strong security. This research will compare the performance of the SIMON and SPECK algorithms in securing digital images. Comparisons were made by testing time performance, changes in file size, and the level of randomness of image files using the Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) metrics. The research results show that the average encryption and decryption time required by the SIMON algorithm is greater when compared to the SPECK algorithm. The image file size resulting from encryption using the SIMON and SPECK algorithms both increased by 24% from the original image. The level of randomness of the resulting image based on the UACI value obtained using the SIMON algorithm was found to be an average of 19.65%, while the UACI value obtained using the SPECK algorithm was an average of 20.94%. This shows that there is a significant change in intensity between the original image and the encrypted image. However, not all pixels in the encrypted image change when compared to the original image, this is shown by the NPCR value obtained from the SIMON and SPECK algorithm encrypted image, with average results of 49.98% and 50.17%.

Comparison of Glaucoma Detection Performance of Binocular Perimetry Screening Program Using imo Perimetry With Frequency Doubling Technology.

We aimed to preliminarily compare the glaucoma detection accuracy of a head-mounted binocular visual perimeter "imo" screening program (ISP) with that of frequency doubling technology (FDT). This multicenter, diagnostic accuracy study based on prospectively collected data included 76 non-glaucoma (including pre-perimetric glaucoma) eyes and 92 glaucomatous eyes from patients visiting two hospitals. Patients underwent ISP and FDT (C-20-1 screening program) on the same day. Diagnostic efficacy was evaluated using receiver operating characteristic curves and areas under the curve (AUCs). In addition, we compared the ISP and FDT testing times. AUC values for ISP versus FDT were as follows: (1) mild-stage glaucoma (mean deviation [MD] > -6 dB), 0.82 (95% confidence interval [CI], 0.75-0.88) versus 0.76 (95% CI, 0.68-0.83); moderate-stage glaucoma (-6 dB ≥ MD ≥ -12 dB), 0.98 (95% CI, 0.95-1.00) versus 0.96 (95% CI, 0.93-1.00); and advanced-stage glaucoma (-12 dB > MD), 1.00 (95% CI, 1.00-1.00) versus 0.99 (95% CI, 0.98-1.00). In addition, mild-stage glaucoma was classified into two stages (MD > -3D) and (-3D ≥ MD > -6D). AUC values were 0.81 (95% CI, 0.73-0.88) versus 0.76 (95% CI, 0.68-0.84) for MD > -3D and 0.86 (95% CI, 0.77-0.94) versus 0.73 (95% CI, 0.61-0.86) for -3D ≥ MD > -6D. The testing time for the ISP was significantly shorter than that of FDT for all glaucoma stages (P < 0.001). The ISP demonstrates non-inferiority in detecting glaucoma and has a shorter testing time compared with FDT. These findings provide evidence for applied further studies on large-scale population-based glaucoma screening. Our study provides a non-inferior and quicker glaucoma screening than existing tools.

Can isometric testing substitute for the one repetition maximum squat test?

When measuring maximum strength, a high accuracy and precision is required to monitor the training adaptations. Based on available reliability parameters, the literature suggests the replacement of the one repetition maximum (1RM) by isometric testing to save testing time. However, from a statistical point of view, correlation coefficients do not provide the required information when aiming to replace one test by another. Therefore, the literature suggests the inclusion of the mean absolute error (MAE), the mean absolute percentage error (MAPE) for agreement analysis. Consequently, to check the replaceability of 1RM testing methods, the current study examined the agreement of isometric and dynamic testing methods in the squat and the isometric mid-thigh pull. While in accordance with the literature, correlations were classified high r = 0.638-0.828 and ICC = 0.630-0.828, the agreement analysis provided MAEs of 175.75-444.17 N and MAPEs of 16.16-57.71% indicating an intolerable high measurement error between isometric and dynamic testing conditions in the squat and isometric mid-thigh pull. In contrast to previous studies, using MAE, MAPE supplemented by CCC and BA analysis highlights the poor agreement between the included strength tests. The recommendation to replace 1RM testing with isometric testing routines in the squat does not provide suitable concordance and is not recommended.

Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs.

Human-Machine Interfaces (HMIs) have gained popularity as they allow for an effortless and natural interaction between the user and the machine by processing information gathered from a single or multiple sensing modalities and transcribing user intentions to the desired actions. Their operability depends on frequent periodic re-calibration using newly acquired data due to their adaptation needs in dynamic environments, where test-time data continuously change in unforeseen ways, a cause that significantly contributes to their abandonment and remains unexplored by the Ultrasound-based (US-based) HMI community. In this work, we conduct a thorough investigation of Unsupervised Domain Adaptation (UDA) algorithms for the re-calibration of US-based HMIs during within-day sessions, which utilize unlabeled data for re-calibration. Our experimentation led us to the proposal of a CNN-based architecture for simultaneous wrist rotation angle and finger gesture prediction that achieves comparable performance with the state-of-the-art while featuring 87.92% less trainable parameters. According to our findings, DANN (a Domain-Adversarial training algorithm), with proper initialization, offers an average 24.99% classification accuracy performance enhancement when compared to no re-calibration setting. However, our results suggest that in cases where the experimental setup and the UDA configuration may differ, observed enhancements would be rather small or even unnoticeable.

Rheological Changes in Bio-Based Filaments Induced by Extrusion-Based 3D Printing Process.

In this work, the authors investigated the impact of extrusion-based printing process on the structural characteristics of bio-based resins through rheological measurements. Two commercially available filaments made from unfilled and wood-filled polylactide (PLA) polymers were considered. Three-dimensional specimens were prepared by printing these filaments under various operating conditions, i.e., changing the extruder temperature and printing rate, and examined using time sweep tests. Specific cycle rheological testing was conducted on pelletized filaments to simulate temperature changes in the printing process. The rheological characteristics of unprocessed materials, in terms of storage (G') and loss (G″) moduli, were found to be slightly affected by temperature changes. For a pure polymer, the G' slope at a low frequency decreased over time, showing that the polymer chains evolved from a higher to a lower molecular weight. For wood-filled materials, the G' slope rose over the testing time, emphasizing the formation of a percolated network of structured filler within the matrix. On the other side, the rheological parameters of both materials were strongly impacted by the printing extrusion and the related conditions. At lower nozzle temperatures (200 °C), by decreasing the printing speed, the G' and G″ curves became increasingly different with respect to unprocessed resin; whereas at higher nozzle temperatures (220 °C), the influence of the printing speed was insignificant, and all curves (albeit distant from those of unprocessed matrix) mainly overlapped. Considerations on degradation kinetics of both materials during the printing process were also provided by fitting experimental data of complex viscosity with linear correlation over time.

Effects of Proprioceptive Neuromuscular Facilitation Technique on Balance Function and Muscle Health in Older Adults With High Fall Risk.

Older adults frequently encounter health challenges, such as impaired balance and muscle health, which increase risk of falls. The current study investigated the effectiveness of the proprioceptive neuromuscular facilitation (PNF) technique in improving balance and muscle health among older adults with high fall risk. A total of 160 older adults with high fall risk were randomized into control and intervention groups. Over 6 months, the control group received standard interventions, while the intervention group received the same interventions and additional PNF training. Both groups demonstrated improvements in balance function over time, with the intervention group exhibiting significant improvements in Berg Balance Scale scores, Timed Up and Go test times, and 30-Second Chair Stand Test counts (p < 0.05). Bone density significantly increased in the intervention group compared to the control group (p < 0.05), although no substantial differences in lower limb muscle mass were observed. Satisfaction rates were higher and fall incidents fewer in the intervention group. The PNF technique is effective in enhancing balance function and muscle health in older adults with high fall risk, demonstrating potential in reducing fall risk and improving quality of life among older adults. [Journal of Gerontological Nursing, 50(8), 37-44.].

Test-Retest Reliability and Visual Perturbation Performance Costs During 2 Reactive Agility Tasks.

High secondary injury rates after orthopedic surgeries have motivated concern toward the construct validity of return-to-sport test batteries, as it is evident that common strength and functional assessments fail to elicit pertinent behaviors like visual search and reactive decision making. This study aimed to establish the test-retest reliability of 2 reactive agility tasks and evaluate the impact of visual perturbation on physical performance. Fourteen physically active individuals completed 2 agility tasks with reaction time (ie,4 corner agility), working memory, and pathfinding (ie,color recall) components. Participants completed both tasks 4 times in 2 sessions scheduled 7days apart. Outcomes included performance metrics of reaction time, time to target, number of targets, and total time assessed with reactive training timing gates. To assess test-retest reliability, we used intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and minimal detectable change (MDC). Stroboscopic goggles induced visual perturbation during the fourth trial of each task. To assess the effect of visual perturbation, we used paired t tests and calculated performance costs. The 4-corner agility task demonstrated excellent reliability with respect to reaction time (ICC3,1 = .907, SEM = 0.13, MDC = 0.35s); time to light (ICC3,1 = .935, SEM = 0.07, MDC = 0.18s); and number of lights (ICC3,1 = .800, SEM = 0.24, MDC = 0.66 lights). The color recall task demonstrated good-to-excellent test-retest reliability for time to lights (ICC3,1 = .818-.953, SEM = 0.07-0.27, MDC = 0.19-0.74s); test time (ICC3,1 = .969, SEM = 5.43, MDC = 15.04s); and errors (ICC3,1 = .882, SEM = 0.19, MDC = 0.53 errors). Visual perturbation resulted in increased time to target (P = .022-.011), number of targets (P = .039), and total test time (P = .013) representing moderate magnitude degradation of performance (d = 0.55-0.87, performance costs = 5%-12%). Both tasks demonstrated acceptable test-retest reliability. Performance degraded on both tasks with the presence of visual perturbation. These results suggest standardized reactive agility tasks are reliable and could be developed as components of dynamic RTS testing.

Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.

Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically-annotated medical images. Many two-dimensional pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain Positron Emission Transmission (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using two-dimensional randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6,000-20,000 trainable parameters) achieved a test mean-absolute-error of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare "classical" and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The mean-absolute-error fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g. DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest mean-absolute-error at test time (~0.45 - 0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.

Comparison of RT-PCR cycle threshold values between individual and pooled SARS-CoV-2 infected nasopharyngeal swab specimens.

The molecular reverse transcription-polymerase chain reaction (RT-PCR) testing of respiratory tract swabs has become mandatory to confirm the diagnosis of coronavirus disease 2019 (COVID-19). However, RT-PCR tests are expensive, require standardized equipment, and relatively long testing times, and the sample pooling method has been introduced to solve this issue. The aim of this study was to compare the cycle threshold (Ct) values of the individual sample and pooled sample methods to assess how accurate the pooling method was. Repeat RT-PCR examinations were initially performed to confirm the Ct values for each sample before running the pooled test procedure. Sample extraction and amplification were performed in both assays to detect ORF1ab, N, and E genes with a cut-off point value of Ct <38. Overall, there was no difference in Ct values between individual sample and pooled sample groups at all concentrations (p=0.259) and for all pooled sizes. Only pooled size of five could detect the Ct value in the pooled samples for all concentration samples, including low-concentration sample (Ct values 36 to 38). This study highlighted that pooled RT-PCR testing strategy did not reduce the quality of individually measured RT-PCR Ct values. A pool size of five could provide a practical technique to expand the screening capacity of RT-PCR.

Optimized k-nearest neighbours classifier based prediction of epileptic seizures

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.

Supply Chain System Optimization Model for Logistics Enterprises in the Internet of Things Era Based on Improved ECC Algorithm

In this article, a new method to optimize the supply chain system (SCS) of logistics companies in the era of Internet of Things (IoT) using an improved elliptic curve cryptography (ECC) algorithm was put forward.The focus of this research was to address the challenges faced by logistics organizations in adapting to the complexity of modern supply chains and meeting the growing demands of customers. The research aimed to improve the efficiency, security and reliability of supply chain management systems by utilizing advances in ECC algorithms.By conducting comprehensive performance tests of the proposed system, including interface testing, joggle testing, and system response time analysis, the study demonstrated the superiority of the system over traditional systems. The research findings indicated that the system based on the improved ECC algorithm outperformed traditional systems in terms of interface effectiveness, usability, and system response time. The improved ECC algorithm enhanced the security of the encryption process, making it more resistant to unauthorized access, and improved the system’s overall data processing load capacity. Additionally, the system demonstrated superior performance in managing logistics data and information, facilitating better decision-making and efficiency in logistics operations. Overall, this research contributes to the advancement of supply chain management in the IoT era by providing a practical and efficient solution for logistics enterprises to optimize their supply chain systems. The innovative application of the improved ECC algorithm offers a secure, reliable, and efficient approach to managing logistics operations, addressing the challenges posed by the complexities of modern supply chains.

Kajian Setting Time dan Permeabilitas pada Beton Variasi Limbah Granit Sebagai Subtitusi Parsial Agregat Kasar

Currently, infrastructure development is a priority of the Indonesian government which aims to improve the quality of life of the community and equitable distribution of infrastructure development. The need for concrete material as a construction material also increases in line with the increase in infrastructure development. The use of recycled aggregate materials, especially granite waste, is a promising option in sustainable infrastructure development. This study was conducted with the aim of knowing the effect of partial substitution of coarse aggregates using granite waste variations on the binding time and permeability of concrete. Experimental method was chosen in this research. The levels of granite waste used were 0%; 15%; 30%; and 45% by weight of coarse aggregate or gravel. The test specimens used for the concrete bond time study were fresh concrete poured into cube molds measuring 20 cm x 20 cm x 20 cm with a minimum height of 15 cm. Testing concrete bond time using a penetrometer tool. The test specimen for concrete permeability is a cube measuring 15 cm x 15 cm x 15 cm, totaling 12 pieces and tested with a Permeability Test Apparatus (Water Permeabilty Apparatus).The results showed that partial substitution of coarse aggregate with granite waste variation did not affect the setting time of concrete up to a certain level. Based on the results of the research conducted, the initial setting time and final setting time values of 0%, 15%, 30%, and 45% granite content variations fluctuate and the coefficient of determination (R2) value in linear regression is only 0.5 and 0.3. Partial replacement of coarse aggregate with granite waste has an effect on the impermeability of concrete which meets the requirements for medium aggressive impermeable concrete. Water pressure of 5 kg/cm2 for 72 hours applied to the concrete partial replacement of coarse aggregate with granite waste variation resulted in permeability coefficient values of 4.11 x 10-12 cm/s; 3.48 x 10-12 cm/s; 2.17 x 10-12 cm/s; and 3.25 x 10-12 cm/s. The minimum coefficient value of concrete with partial substitution of coarse aggregate with granite waste is obtained at 30% by weight of coarse aggregate. Granite, whose specific gravity is greater than gravel, produces denser and more water-resistant concrete.

Forming quality and static properties of self-piercing riveted joints of hot-rolled steel and aluminum alloy sheets

The self-piercing riveting (SPR) Forming and tensile tests of hot-rolled steel sheet BR1200HS and aluminum alloy sheet AA 6082-T6 were simulated by Simufact Forming software. The test results show that the diameter of the rivet leg opening, which is the most important parameter affecting the mechanical properties of the joints, shows a first increase and a second decrease with the increase of rivet length, and the Max. tensile loads of the joints have the same variation law. The larger the diameter of the rivet leg opening, the greater the Max. tensile load of the joint, and the greater the effective plastic strain of the rivet of the joint. The rivet length of the joints in the five preferred SPR formation schemes obtained were all 6.5 mm, and only one scheme had a rivet hardness of H4, the rest were H5. The SPR experiment is used to verify the current finite element simulation data can get the final research conclusion. The finite element simulation (FEM) would greatly reduce the test times of the SPR test, save the test consumables and save the test cost.

Aspiring to Aspirate: L2 Acquisition of English Word-Initial /p/ Over 10 Years.

This short report describes a longitudinal examination of the acquisition of English-aspirated stops by an initial cohort of 24 adult Slavic-language (Russian, Ukrainian, and Croatian) speakers. All had arrived in Canada with low oral English proficiency, and all were enrolled in the same language instruction program at the outset. Initial bilabial stops in CVCs were recorded at eight testing times: six during the first year of the study, again at year 7, and finally at year 10. Intelligibility was evaluated through a blind listening assessment of the stop productions from the first seven testing times. Voice onset times (VOT) were measured for /p/ from all eight times. Mean /p/ intelligibility improved-mainly during a proposed Window of Maximal Opportunity for L2 speech acquisition-but remained below 100%, even after 7 years. For some speakers, early /p/ productions were minimally aspirated, with VOT increasing over time but remaining intermediate between L1 English and L1 Slavic-Language values at 10 years. However, inter-speaker variability was dramatic, with some speakers showing full intelligibility throughout the study and others showing many unintelligible productions at all times. Individual learning trajectories tended to be non-linear and often non-cumulative. Overall, these findings point to a developmental process that varies considerably from one learner to another. It also demonstrates the serious drawbacks of relying on group means to characterize the process of L2 segmental learning.