Test Time Research Articles

Design and manufacturing of Welded Vacuum Testing (WVT) tool

To ensure the quality of welded joints in the hull area, welding testing is very important and must be carried out. But unfortunately, currently the quality testing process of welded joints was still limited to penetrant tests and lime tests. The purpose of this study was to obtain a portable welding testing machine that was able to obtain fairly accurate test results on hull welding defects using a vacuum system. The research method is experimental by involving data collection through field experiments, testing is carried out with the resulting weld defect research subjects and the length of testing time on 1G and 3G position welding. The results of the study by compared tests among Welded Vacuum Testing (WVT) machines, Magnetic Particle tests (MP), and Penetrant Tests (PT). The three experiments detected leaking weld defects, spark sparks, pinholes, overlaps, and undercuts. For test results with machines made, welding defects that were successfully detected were leaks in the 1G position welding workpiece and undercut in the 3G position welding workpiece. Air bubbles at a vacuum pressure of 0.2 bar are detected, meaning that there is a defect in the welded joint. This tool can be used in bilge testing.

Non-Contact Acoustic Emission Monitoring of Corrosion in Mooring Chain Links

Mooring chains are key parts of floating energy production units, such as floating wind turbines, photovoltaic islands, and floating production-storage-offloading units (FPSOs). These structures are predominantly subject to corrosion and fatigue. Detailed integrity assessment of mooring chains can be challenging due to difficult access, complex geometry, surface conditions (often with the presence of corrosion pits), and weather conditions. Additionally, the presence of marine growth on the surface of the chain links often requires the need of surface cleaning to obtain a detailed assessment of the structural integrity. This paper highlights an experimental investigation of monitoring of corrosion-induced damage in mooring chain links by means of non-contact Acoustic Emission (AE) technique. Accelerated corrosion experiments were performed on a large-scale mooring chain sample recovered from the field. A dedicated experimental set-up was designed to apply accelerated corrosion on two chain links submerged in natural sea-water. An immersion tank, i.e. an instrumented 1000x1000x1200mm3 water tank, was used to submerge the chain links in natural sea-water. The corrosion process was accelerated by means of anodic polarization. Ultrasound signals were continuously measured using two arrays of underwater AE transducers (in the frequency range between 50-450 kHz) placed on two perpendicular planes at a fixed distance from the chain links. Corrosion-induced AE sources were localized on the 3D geometry of the tested links. The variation and evolution of AE parameters extracted from the measured signals were analyzed as a function of testing time. The results of the accelerated corrosion experiment suggest that corrosion-induced ultrasound signals can be detected and monitored by means of non-contact AE. The results of this study may be used for characterization of corrosion-induced AE signals in mooring chain links.

Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI

The rising prevalence of gastrointestinal (GI) tract disorders worldwide highlights the urgent need for precise diagnosis, as these diseases greatly affect human life and contribute to high mortality rates. Fast identification, accurate classification, and efficient treatment approaches are essential for addressing this critical health issue. Common side effects include abdominal pain, bloating, and discomfort, which can be chronic and debilitating. Nausea and vomiting are also frequent, leading to difficulties in maintaining adequate nutrition and hydration. The current study intends to develop a deep learning (DL)-based approach that automatically classifies GI tract diseases. For the first time, a GastroVision dataset with 8000 images of 27 different GI diseases was utilized in this work to design a computer-aided diagnosis (CAD) system. This study presents a novel lightweight feature extractor with a compact size and minimum number of layers named Parallel Depthwise Separable Convolutional Neural Network (PD-CNN) and a Pearson Correlation Coefficient (PCC) as the feature selector. Furthermore, a robust classifier named the Ensemble Extreme Learning Machine (EELM), combined with pseudo inverse ELM (ELM) and L1 Regularized ELM (RELM), has been proposed to identify diseases more precisely. A hybrid preprocessing technique, including scaling, normalization, and image enhancement techniques such as erosion, CLAHE, sharpening, and Gaussian filtering, are employed to enhance image representation and improve classification performance. The proposed approach consists of twenty-four layers and only 0.815 million parameters with a 9.79 MB model size. The proposed PD-CNN-PCC-EELM extracts essential features, reduces computational overhead, and achieves excellent classification performance on multiclass GI images. The PD-CNN-PCC-EELM achieved the highest precision, recall, f1, accuracy, ROC-AUC, and AUC-PR values of 88.12 ± 0.332 %, 87.75 ± 0.348 %, 87.12 ± 0.324 %, 87.75 %, 98.89 %, and 92 %, respectively, while maintaining a minimum testing time of 0.000001 s. A comparative study utilizes 10-fold cross-validation, ablation study and various state-of-the-art (SOTA) transfer learning (TL) models as feature extractors. Then, the PCC and EELM are integrated with TL to generate predictions, notably in terms of performance and real-time processing capability; the proposed model significantly outperforms the other models. Moreover, various explainable AI (XAI) methods, such as SHAP (Shapley Additive Explanations), heatmap, guided heatmap, Grad-Cam (Gradient-weighted Class Activation Mapping), guided Grad-CAM, and guided Saliency mapping, have been employed to explore the interpretability and decision-making capability of the proposed model. Therefore, the model provides practical intelligence for increasing confidence in diagnosing GI diseases in real-world scenarios.

Research on failure rate evaluation test method of energy meter in charging pile

Abstract To verify the failure rate of intelligent energy meter in charging pile and whether it can meet the reliability requirements in “general technical specification for single-phase intelligent electric energy meter (2020)”. Take 20 smart meter samples from four manufacturers, use the accelerated life model and carry out the accelerated test in the way of stress acceleration for a total of 4000 hours, and evaluate the failure rate indicators under normal working stress. The test results show that the single-phase intelligent watt hour meters produced by the four manufacturers can meet the reliability requirements such as annual failure rate index. The accelerated test theory is applied to the reliability life and failure rate evaluation of intelligent electric energy meter to shorten the test time, improve the test efficiency and reduce the test cost.

Exploring Head-Mounted Virtual Visual Fields for Glaucoma: How do They Stack Up Against Humphrey Tests?

Abstract The integration of virtual reality (VR) technology into visual field (VF) testing has introduced innovative head-mounted perimetry devices, which offer numerous advantages over traditional methods. This review examines the features, accuracy, and reliability of commercially available head-mounted VR perimeters and compares them with the gold standard, the Humphrey Visual Field Analyzer (HFA). While traditional devices like the HFA and Goldmann Perimeter have been reliable for glaucoma management, they are limited by fixed testing environments, long testing durations, and high costs. VR-based perimetry addresses these limitations by providing portable, cost-effective, and engaging testing environments that can be conducted in various settings, including patients’ homes. Head-mounted displays (HMDs) equipped with high-resolution screens, motion sensors, and eye-tracking technology create an immersive testing experience that minimizes distractions and external light interference, potentially enhancing test accuracy. These devices also offer customizable testing protocols, improving patient comfort and compliance, especially among children and elderly patients. However, concerns remain about the reproducibility and accuracy of VR perimetry compared to established standards. Variability among VR platforms and the lack of large normative databases for visual fields pose challenges to their widespread adoption. This review highlights the need for further validation studies and the development of standardized testing protocols to ensure the reliability and accuracy of VR perimetry. Despite these challenges, the flexibility, reduced testing times, and potential for telemedicine applications make VR head-mounted perimetry a promising tool for improving glaucoma management and expanding access to VF testing in diverse clinical environments.

Formulation and Physical Quality Testing of Solid Soap From a Combination of Citronella Oil with Patchouli Leaf Oil Againts Staphylococcus aureus Bacteria

Bacteria on the skin can cause infections ranging from mild to quite serious. Bacteria that are commonly found on human skin are the pathogenic bacteria Staphylococcus aureus. The choice for making antibacterial solid soap based on active ingredients from patchouli oil and citronella oil is that this oil has inhibitory power in the growth of bacteria on the skin, one of which is Staphylococcus aureus. The aim of this research is to determine how large the inhibition zone is produced by the antibacterial solid soap formulation and to determine the characteristics and physical quality tests of the solid soap preparation combining citronella oil with patchouli leaf oil so that it is safe to use. The method used is a pure experimental method. Data collection was carried out for 4 weeks according to the testing time and then analyzed using the Graphad application. The results of the research on the inhibition zone test used 2 concentrations, namely 50% and 100%, each formulation had different average results, namely at F2 50% the average was 21 mm with a very strong category, at F2 100% the average was 19.5 mm with the strong category, at F3 50% we get an average of 16 mm in the strong category and at F3 100% we get an average of 18.6 mm in the strong category. In the tests that have been carried out, it was concluded that citronella oil and patchouli leaf oil can be made into antiseptic solid soap and can inhibit the growth of Staphylococcus aureus bacteria. The results of testing physical quality characteristics for 4 weeks have met the standard requirements for solid soap.

Temperature-influenced Bulk Emulsion (BE) Demulsification Method as a PIBSA-MEA Emulsifier Durability Test in Blasting Environments under 100 °C

This research attempts to provide a better method, examine more effective temperatures for testing emulsifiers, and determine the demulsification limit that indicates emulsifier durability. This experiment was conducted by varying the temperature (40, 60, 80, and 100 °C) for heating the product with a test time of 1, 2, 4, and 6 h, then detected using formaldehyde titration to determine the highest level of demulsification of ammonium nitrate (AN) salt at each temperature in the product. The results showed that 100 °C was the most effective and representative temperature for testing the durability of the emulsifier with the highest level of demulsification from the other temperatures. This was indicated by the weight of AN salt that came out of the emulsion reaching 2.05 g from 20 g of emulsion or about 10.25% of the total weight of the product within 6 h. Emulsifiers with AN levels below 2.05 g (10.25%) were considered to pass the test and could be used for further production or analysis. This new test method was expected that bulk emulsion manufacturers would be faster in eliminating PIBSA-base (Polyisobutylene succinic anhydride-base) emulsifier products widely used by emulsifier manufacturers in manufacturing BE. This was due to it only focusing on the ability of emulsifiers to hold the product in high-temperature exposure so that it remained unified and not demulsified.

Does the Effect of Mental Fatigue Created by Motor Imagery on Upper Extremity Functions Change with Diaphragmatic Breathing Exercises? A Randomized, Controlled, Single-Blinded Trial.

Background and Objectives: This study focused on the impact of mental fatigue induced by motor imagery on upper limb function, an area with limited research compared to lower limb performance. It aimed to explore how diaphragmatic breathing exercises influence these effects. Materials and Methods: This study included 30 participants, and Group 1 participated in 12 sessions of diaphragmatic breathing exercises under the supervision of a physiotherapist; Group 2 did not receive any intervention. For all the participants, mental fatigue was induced with motor imagery before and after the intervention, and evaluations were performed before and after mental fatigue. Upper extremity functions were evaluated using isometric elbow flexion strength, hand grip strength, upper extremity reaction time and endurance, finger reaction time, the nine-hole peg test, shoulder position sense, light touch-pressure threshold, and two-point discrimination. Results: The study results showed that after mental fatigue, there was a decrease in isometric elbow flexion strength, nondominant hand grip strength, and nondominant upper extremity endurance, and an increase in nondominant tactile sensation (p < 0.05). No changes were found in two-point discrimination, nine-hole peg test time, and position sense on either side (p > 0.05). The effect of mental fatigue on isometric elbow flexion strength and nondominant grip strength showed significant improvement following diaphragmatic breathing exercises (p < 0.05). Conclusions: This study found that mental fatigue from motor imagery can impact elbow flexion, hand grip strength, upper extremity endurance, and tactile sensitivity. Breathing exercises may help improve strength parameters affected by mental fatigue. It is crucial to consider these effects on upper extremity functions in rehabilitation programs.

The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention

The main frequency of microseismic signals has recently been identified as a dominant indicator for characterizing vibration events because it reflects the energy level of these events. Frequency information directly determines whether effective signals can be collected, which has a significant impact on the accuracy of predicting rock burst disasters. In this study, we adopted a characterizing method and developed a monitoring system for capturing rock failure events at various strata in an underground coal mine. Based on the rock break mechanism and energy release level, three types of rock failure events, namely, high roof breaking, low roof breaking, and coal fracture events, were evaluated separately using specific sensors and monitoring systems to optimize the monitoring accuracy and reduce the general cost. The captured vibration signals were processed and statistically analyzed to characterize the main frequency features for different rock failure events. It was found that the main frequency distribution ranges of low roof breaking, high roof breaking, and coal fracture events are 20–400 Hz, 1–180 Hz, and 1–800 Hz, respectively. Therefore, these frequency ranges are proposed to monitor different vibration events to improve detection accuracy and reduce the test and analysis times. The failure mechanism in a high roof is quite different from that of low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect both in the maximum amplitude and main frequency, which could be an indicator to distinguish failure locations in the vertical direction. This result can support the selection and optimization of the measurement range and main frequency parameters of microseismic monitoring systems. This study also discussed the distribution law of the maximum amplitude and main frequency of different events and the variation in test values with the measurement distance, which are of great significance in expanding the application of optimized microseismic monitoring systems for rock burst monitoring and prevention.

On Estimating the Feasible Solution Space of Multi-objective Testing Resource Allocation

The multi-objective testing resource allocation problem (MOTRAP) is concerned on how to reasonably plan the testing time of software testers to save the cost and improve the reliability as much as possible. The feasible solution space of a MOTRAP is determined by its variables (i.e., the time invested in each component) and constraints (e.g., the pre-specified reliability, cost, or time). Although a variety of state-of-the-art constrained multi-objective optimisers can be used to find individual solutions in this space, their search remains inefficient and expensive due to the fact that this space is very tiny compared to the large search space. The decision maker may often suffer a prolonged but unsuccessful search that fails to return a feasible solution. In this work, we first formulate a heavily constrained MOTRAP on the basis of an architecture-based model, in which reliability, cost, and time are optimised under the pre-specified multiple constraints on reliability, cost, and time. Then, to estimate the feasible solution space of this specific MOTRAP, we develop theoretical and algorithmic approaches to deduce new tighter lower and upper bounds on variables from constraints. Importantly, our approach can help the decision maker identify whether their constraint settings are practicable, and meanwhile, the derived bounds can just enclose the tiny feasible solution space and help off-the-shelf constrained multi-objective optimisers make the search within the feasible solution space as much as possible. Additionally, to further make good use of these bounds, we propose a generalised bound constraint handling method that can be readily employed by constrained multi-objective optimisers to pull infeasible solutions back into the estimated space with theoretical guarantee. Finally, we evaluate our approach on application and empirical cases. Experimental results reveal that our approach significantly enhances the efficiency, effectiveness, and robustness of off-the-shelf constrained multi-objective optimisers and state-of-the-art bound constraint handling methods at finding high-quality solutions for the decision maker. These improvements may help the decision maker take the stress out of setting constraints and selecting constrained multi-objective optimisers and facilitate the testing planning more efficiently and effectively.

A 12-week Taijiquan practice improves balance control and functional fitness in fall-prone postmenopausal women.

Falls are the leading cause of accidental death among older persons, with postmenopausal women facing a greater hazard of falling due to osteoporosis. This study aimed to examine the effects of Taijiquan practice on balance control and functional fitness in at-risk females. Chinese women who self-reported a tendency to fall and had a baseline one-leg stand test time (4.1 s in the Taijiquan group) below the national average for their age group (60-64 years: 10.9 s, 65-69 years: 9.9 s) were assigned to either a control group (n = 26, mean age = 63.9 years) or a Taijiquan group (n = 24, mean age = 63.9 years). The Taijiquan group participated in a 12-week supervised intervention, while the control group maintained their daily activities. The average duration of each exercise session was 52 min. Static balance and functional fitness were assessed at the beginning and end of the intervention. After 12 weeks, the Taijiquan group significantly outperformed the control group in terms of balance, flexibility, and muscular fitness (all p < 0.05). Participants in the Taijiquan group improved their one-leg stand by 61.0% (+2.5 s, Hedge's g = 0.85), arm curl by 8.3% (+1.7 repetitions, g = 0.53), handgrip strength by 8.3% (+1.9 kg, g = 0.65), and sit-and-reach by 163.2% (+6.2 cm, g = 1.17). The improvement in balance, coupled with other functional fitness benefits, suggests that Taijiquan could serve as a useful exercise for older women with an elevated risk of falling.

Relative foveal dark adaptation: a potential method for assessing macular health.

Dark adaptation measures photoreceptor recovery following intense light stimulation. Time to recovery reflects retinal function. We describe a novel method of relative foveal dark adaptation using an iPhone. Data from a small number of healthy subjects were studied to assess reproducibility, effects of age, and consider potential clinical utility. Relative foveal dark adaption was studied in 6 normal subjects across ages from 20 to 81 years and across differing testing conditions. Foveal bleaching is produced by fixating a bright white circle on an iPhone for variable times. After foveal bleaching an annular surround appears to complete a bullseye stimulus with surround initially brighter than centre. As the fovea recovers the centre regains brightness. Relative foveal dark adaptation, the time for the visual anchor to shift from surround to centre, was studied across a range of bleaching times, ages, and testing conditions. Dispersion of dark adaptation times grows with increasing age. Foveal bleaching for 30 s was as effective as longer times. Testing times with a 30 s bleach were less than 1 min. Foveal dark adaptation was reproducible within each subject and was unaffected by ambient room lighting, pupil size, and light attenuation. Repeat, immediately sequential testing was similarly reproducible except after long bleaching. This method of dark adaptation is intuitive, repeatable, and relatively unaffected by testing condition. Testing times are brief, requiring only an iPhone screen positioned at reading distance. Relative foveal dark adaptation may be a useful tool to assess macular health.

A Practical Framework for the Integration of Structural Data Into Perimetric Examinations.

We sought to develop and evaluate a practical framework that supports structurally enhanced perimetric examinations. Two perimetric strategies were compared: standard Zippy Estimation through Sequential Testing (ZEST) procedure, a traditional visual field test with population-based prior distributions, and structural-ZEST (S-ZEST), enhanced with individual optical coherence tomography data to determine the starting parameters. The integration and collection of data was facilitated by a bespoke application developed in Shiny R (R Studio). The test was implemented using the Open Perimetry Interface on the Compass perimeter (CentreVue-iCare, Italy). The strategies were evaluated via simulations and on 10 visually healthy participants. The usability of the application was assessed in a simulated environment with 10 test users. In simulations, the S-ZEST improved test speed in patients with glaucoma. In the practical implementation, there was a statistically significant decrease in the testing time (approximately 26%) and in the number of presentations per test with S-ZEST (P < 0.001). The structure-function relationship was similar between the two strategies. The time taken for users to complete the sequence of actions on the application was 52.9 ± 11.5 seconds (mean ± standard deviation). Structurally enhanced perimetric examination can significantly improve test time in healthy subjects and can be delivered through a user-friendly interface. Further testing will need to assess feasibility and performance of S-ZEST in patients with glaucoma. We have developed a user-friendly web application based within the Shiny environment for R, which implements an automated extraction of optical coherence tomography data from raw files and performs real-time calculations of structural features to inform the perimetric strategy.

Computer vision tools for early post-disaster assessment: Enhancing generalizability

Remote sensing data, particularly satellite imagery, have made early, post-hazard aerial damage assessment possible due to its fast availability and extensive coverage. Despite breakthroughs in using deep computer vision with satellite image inputs, achieving high generalizability across diverse hazards and locations remains the main obstacle to effectively deploying early assessment tools in real-world scenarios, as the same hazard can manifest differently across various landscapes and urban textures. This challenge may be overlooked when working with curated datasets with minimal (test-to-train) shifts in urban textures and hazard damage features (e.g., due to undersized study regions), leaving models ill-prepared for real-world scenarios. The primary objective of this study was to understand non-trivial generalizability challenges by taking the 2023 Turkiye earthquake and the Maui wildfire incidents as “training” and “unseen test” events that simulated a demanding scenario. Subsequently, strategies such as augmenting image channels with damage proxy maps, data fusion, deep ensemble learning, and Test Time Augmentation were exclusively designed and implemented to address those challenges. These measures significantly improved the damage detection, with or without severity classification, with F1 scores increased from 0.71 to 0.82 and 0.40 to 0.87, respectively. Furthermore, and through data fusion, the proposed framework accommodates estimating socioeconomic loss metrics at the individual building level, supporting both response and recovery phases. This research has the potential to enhance the effectiveness of rapid aerial damage assessment models, ultimately aiding in more efficient and targeted disaster response and recovery efforts. Data, models, and codes are available at https://github.com/TRG-AI4Good/Lahaina_Generalizability.

Stochastic Class‐Attention Net to Detect the Breast Carcinoma Subtypes With Test Time Augmentation

ABSTRACTDespite advances in medical sciences, breast cancer remains a deadly disease globally, primarily affecting women. Fortunately, studies claim that breast cancer is treatable if diagnosed early. Late diagnoses have poor prognoses and can affect the patient's quality of life. Therefore, a significant research body is dedicated to establishing and identifying the disease at an initial stage. Deep learning (DL) techniques are garnering attention for aiding medical professionals in detecting this disease using histopathology (HP) image modality. The heterogeneous nature of this disease subtypes results in the imbalances of benign and malignant subtypes. From a DL point of view, this becomes an imbalanced problem deserving special care. Unfortunately, current DL‐based techniques do not fully address this issue and suffer from poor metrics and robustness. In this work, we present a DL‐based breast cancer automatic detection system (BCADS) using a novel architecture stochastic class‐attention net (SCAN). This technique performed better when combined with label smoothing and test time augmentation. This work outperforms the previously reported results for binary and multiclass on the BreaKHis dataset. Also, we validated our method on separate BACH and BCNB datasets to prove its effectiveness and clinical relevancy. We hope that the designed BCADS will help the treating doctor and pathologist in a meaningful way and thus help to reduce the impact of this deadly disease.

Spatiotemporal parameters and kinematics differ between race stages in trail running-a field study.

Trail running is an emerging discipline with relatively few studies performed in ecological conditions. The aim of this work was to investigate if and how spatiotemporal parameters (STP) and kinematics differ between initial and final stage of a field trial. Twenty trail runners (10 F, 10 M) were recruited and ran a solo 9.1 km trial. During the test, participants wore a GPS watch and an IMU-based motion capture system. Running speed, elapsed time, STP and kinematics were compared between initial and final stage, separately for uphill (UH) and downhill (DH) sections. Running speed decreased in the final stage ( ). Total test time was more correlated to the time elapsed in UH sections. In the final stage and in both UH and DH sections, contact time and duty factor increased, whilst stride length and flight time decreased ( ). In the final stage, ankle joint was more dorsiflexed in stance and swing phases in UH sections and stance phase only in DH sections ( ). In the final stage, knee joint was less extended in swing phase in UH and DH sections, as well as less extended in stance in UH sections ( ). In the final stage, hip joint was less flexed in the swing phase in UH and DH sections ( ). In the final stage, forward trunk lean was higher across the entire gait cycle in in UH sections ( ). Trunk contralateral axial rotation was lower, in DH sections ( ). During the final stage, results indicate a less efficient propulsion phase, in both UH and DH sections. In UH sections, results suggest lower energy generation at the ankle joint. In DH sections, results suggest that the kinematics of swing leg may play a role in sub-optimizing propulsion phase. This study demonstrates how, in UH and DH sections, similar changes in spatiotemporal parameters can be elicited by dissimilar changes in running kinematics. To optimize performance in trail running, coaches and practitioners are advised to work on different (incline-specific) aspects of running technique.

Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection

Misinterpreted medical prescriptions had led to casualties due to the illegible cursive handwriting of medical practitioners. Many studies focused on this problem. However, the accuracy was unsatisfactory and needed improvement. The study evaluated the performance of the Canny edge detection with other preprocessing methods, including RGB to Grayscale Conversion, Binarization, and Inversion, which was used to process the images of cursive handwritten medical prescriptions using Alexnet Convolutional Recurrent Neural Network (ACoRNN). The CRNN model developed by previous researchers was used as the basis for comparison, and the researchers created a faster and more accurate model. The best combination of preprocessing methods for ACoRNN was with RGB to Grayscale Conversion, Binarization, Canny edge detection, and Inversion. The researchers’ model had faster preprocessing and testing time and achieved 90.76% average accuracy through five trials.

Mother-child engagement in a categorisation activity: implications for parental pedagogy and early education research

ABSTRACT Maternal support for children's cognitive competence has received considerable attention in the developmental and early childhood education literature. However, little research has examined the link between maternal behaviour and children’s categorisation performance. The current study explored the strategies that mothers and children used as they engaged in a categorisation task. Twenty-seven mother–child dyads from middle-class mothers and their three-to-five-year-old preschool children were videotaped while engaging in a categorisation activity. Total testing time, including video recordings, did not exceed one hour. The mother-child interaction codes were analysed using frequency analyses to extract empirical estimates. Certain maternal strategies (e.g. interrogative strategies) were positively related to children’s verbal responses. However, maternal stylistic (taxonomic and thematic) differences were not associated with children’s strategies. The study found that mothers use a variety of strategies to engage with their children in the categorisation activity. Theoretical perspectives and implications of the current research for parental pedagogy and early childhood education research are discussed.

Developmental acquisition of stops by multilingual speakers

ABSTRACT The study aimed to analyse the production of plosive sounds by trilingual speakers of L1 Polish – L2 English – L3 Norwegian. The main objective was to trace any changes in the VOT production of fortis and lenis stops in all three languages (Polish, English and Norwegian) produced by novice L3 learners with a focus on possible cross-linguistic interactions. The investigation was conducted across three testing times throughout the first year of L3 learning. The results show that participants maintained separate VOT patterns in all three languages. What is more, VOT production patterns changed over time, but only in the voiced series of stops. The study also provides evidence for asymmetry between voiced and voiceless stops, as the two series turned out to be affected by CLI in a different manner.

Intraoperative lateral laxity greater than 4° is associated with inferior functional improvement in posterior-stabilised total knee arthroplasty.

The study aimed to determine the impact of intraoperative lateral laxity at extension on clinical and functional outcomes 1 year after posterior-stabilised total knee arthroplasty (PS-TKA). In total, 91 varus-type osteoarthritic knees that underwent PS-TKA using the medial preservation gap technique were included. After the femoral trial component placement and patellofemoral joint reduction, the soft-tissue balance was assessed using an offset-type tensor with a 40-lb joint-distraction force. Patients were divided into the following three groups according to the intraoperative lateral laxity at extension (i.e., varus ligament balance) using the mean ± 1 standard deviation: Groups A, ≤0°; B, 0-4°; and C, >4°. The 2011 Knee Society Score (KSS) and 3-m timed up-and-go test (TUG) time 1-year postoperatively, and their improvements were compared among the groups. While significant improvements were observed in all subscales of the 2011 KSS and TUG post-TKA (p < 0.05), the improvement of functional activities and TUG time were significantly lower in Group C than in Group B (p < 0.05). However, no significant differences were observed in symptom improvement, patient satisfaction or patient expectation scores among the groups. An excessive lateral laxity (varus angle) >4° at extension was associated with lower improvement in functional ability 1-year postoperatively. Therefore, excessive intraoperative lateral laxity should be avoided in PS-TKA. Level IV.