Original Test Research Articles

3D point cloud regularization method for uniform mesh generation of mining excavations

Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations. Together with the quality of the measurement data (diverse point-cloud density, missing samples, holes induced by obstructions in the field of view, measurement noise), this creates problems that require multi-stage processing of the obtained data. The authors propose a robust methodology to process a single segmented section of the mining system. The presented approach focuses on obtaining a point cloud ready for application in the computational fluid dynamics (CFD) analysis of airflow with minimal need for additional manual corrections on the generated mesh model. This requires the point cloud to have evenly distributed points and reduced noise (together with removal of objects inside) while keeping the unique geometrical properties and shape of the scanned tunnels. Proposed methodology uses trajectory of the excavation either obtained during the measurements or by skeletonization process explained in the article. Cross-sections obtained on planes perpendicular to the trajectory are processed towards the equalization of point distribution, removing measurement noise, holes in the point cloud and objects inside the excavation. The effects of the proposed algorithm are validated by comparing the processed cloud with the original cloud and testing within the CFD environment. The algorithm proved high effectiveness in improving skewness rate of the obtained mesh and geometry mapping accuracy (standard deviation below 5 centimeters in cloud-to-mesh comparison).

Improving the primary care clinical testing process in southwest Scotland: a systems-based approach

IntroductionAcross all healthcare environments, inadequately specified patient test requests are commonly encountered and can lead to wasted clinician time and healthcare resources, in addition to either missed or unnecessary testing...

A novel healthcare 4.0 system for testing respiratory diseases based on nanostructured biosensors and fog networking

New digital healthcare models based on advanced bio-sensing technologies are regarded as a possible solution to improve the screening and prevention processes and the overall performance of healthcare supply chains. This is particularly relevant for respiratory diseases, which are currently among the first causes of death and medical expenditures in industrialized countries. This research proposes a new e-health model based on a fog architecture and a smart device, enabling remote diagnostics of respiratory diseases and allowing for decentralized patient testing and self-testing. According to such a model, the patients’ testing is executed through the analysis of the exhaled breath collected using a smart device based on a novel nanostructured sensor and transferring relevant information to the medical staff involved in the diagnosis process. This research proposes an original testing method and system, validated in the lab through a comparative analysis of culture media samples collected from healthy patients, and subsequently exposed to cigarette smoke extract (CSE, an inducer of oxidative stress). The preliminary results obtained demonstrate the validity of the approach proposed, encouraging further experimental analyses on human patients for the implementation into clinical practice.

Translate Arabic Text to Arabic Gloss for Sign Language

Arabic Sign Language (ArSL) is a language used by the deaf community across Arab countries, but the lack of familiarity with ArSL among the hearing population often leads to social isolation for deaf individuals. The structural differences between ArSL and spoken Arabic pose significant challenges for machine translation. In this study, we enhance Arabic to ArSL gloss translation by employing data augmentation techniques, expanding the dataset from 600 to over 23,328 samples using sequence-to-sequence transformer models. Our approach achieved a substantial performance improvement, increasing the BLEU score from 11.1% in the baseline model to 52.72% on original test set. The best model achieved a BLEU score of 85.17% on augmented data test, underscoring the effectiveness of data augmentation in enhancing ArSL translation quality.

Automatic setting optimization for robotic upper-extremity rehabilitation in patients with stroke using ReoGo-J: a cross-sectional clinical trial

Stroke-induced upper-extremity paralysis affects a substantial portion of the population, necessitating effective rehabilitation strategies. This study aimed to develop an automated program, incorporating the item response theory, for rehabilitation in patients with post-stroke upper-extremity paralysis, focusing on the ReoGo-J device, and to identify suitable robot parameters for robotic rehabilitation. ReoGo-J, a training device for upper-extremity disorders including 71 items, was administered to over 300 patients with varying degrees of post-stroke upper-extremity paralysis. Each item was rated on a three-point scale (0, very difficult; 1, adequate; 2, very easy). The results were analyzed using the graded response model, an extension of the two-parameter logistic model within the framework of the item response theory, to grade the training items based on ability of the patients. The relationship between the predicted ability, an indicator of the predicted ability the paralyzed upper-extremity to perform an item in the item response theory analysis (higher numbers indicate higher ability, lower numbers indicate lower ability), and the items in the Fugl–Meyer assessment (FMA), which indicate the degree of paralysis, was analyzed using Pearson’s correlation coefficient. This study included 312 patients with post-stroke upper-extremity paralysis. The predicted ability (θ) of the tasks included in the original ReoGo-J test for forward reaching, reaching for mouth, rotational reaching, radial reaching (2D), abduction reaching, reaching in eight directions, radial reach (3D), and reaching for head ranged from − 2.0 to − 0.8, − 1.3 to − 0.8, − 1.0 to − 0.1, − 0.7 to 0.3, − 0.2 to 0.4, − 0.4 to 0.6, − 0.1 to 0.6, and 0.5 to 0.6, respectively. Significantly high correlations (r = 0.80) were observed between the predicted ability of all patients and the upper-extremity items of shoulder-elbow-forearm in the FMA. We have introduced an automated program based on item response theory and determined the order of difficulty of the 71 training items in ReoGo-J. The strong correlation between the predicted ability and the shoulder-elbow-forearm items in FMA may be used to ameliorate post-stroke upper-extremity paralysis. Notably, the program allows for estimation of appropriate ReoGo-J tasks, enhancing clinical efficiency.Trial registration: https://www.umin.ac.jp/ctr/index-j.htm (UMIN000040127).

Testing for Equal Average Forecast Accuracy in Possibly Unstable Environments *

We consider the issue of testing the null of equal average forecast accuracy in a model where the forecast error loss differential series has a potentially non-constant mean function over time. We show that when time variation is present in the loss differential mean, the standard Diebold and Mariano (1995) test, which was proposed for evaluating forecasts in a stable environment, has an asymptotic size of zero, and, whilst consistent, can have reduced local power. This arises due to inconsistent estimation of the implicit long run variance estimator, which diverges under a time varying mean. We suggest a modified statistic that replaces the standard long run variance estimator based on full-sample demeaning of the loss differential series with one based on nonparametric local demeaning. The new long run variance estimator is consistent under both the null and alternative when the mean function is time varying or constant, and in both cases, the modified test recovers the asymptotic size and power properties associated with the original test in the constant mean case. The modified test therefore provides a robust method for testing the equal average forecast accuracy null, allowing for instability in the loss differential mean. The benefits of our test are demonstrated via Monte Carlo simulation and two empirical applications.

Application of the Contemporary Psychometrics for Assessing Economic Literacy

Currently, new skills and various types of "new literacies" relevant to the modern world are becoming issues of growing importance. One of them is economic literacy; however, there are only few assessment instruments that fulfil the academic requirements for its assessment among university students. One of such internationally established instruments is the Test of Understanding in College Economics (TUCE), which is a popular tool in empirical studies of economic literacy in many countries around the world. Despite its advantages, the currently available version of the TUCE designed for American colleges back in 2006, is prone to cheating and provides limited opportunities for formative feedback. The purpose of this paper is to present the Updated Test of Understanding in College Economics (U-TUCE). In developing the U-TUCE, we utilized the capabilities of contemporary psychometrics, which offer sufficient advances in overcoming all limitations of the original TUCE mentioned before. First, we present a revised theoretical framework of the U-TUCE, highlighting that the test measures different types of mastery of economic literacy. Second, we describe the approaches used for modifying the TUCE items and developing new items. A half of the original test items have been replaced or redesigned to reflect the economic context that has changed since 2006. Third, we utilize the logic of automatic item generation algorithms to increaseg the level of test protection against cheating. We made all changes in such a way as to maintain comparability with the previous versions of the TUCE test if necessary. Finally, the use of the Item Response Theory (IRT) is paired up with that of Cognitive Diagnostic Modeling (CDM) to ensure the quality of the U-TUCE and enhance its formative value. We show that IRT can be used to estimate the construct as a whole (which is of interest to researchers, administrators, and policy makers), while CDM provides information relating to each of the construct components, which are of interest to educational practitioners and students themselves. The results of the data analyses show that the test can be used for both purposes simultaneously.

New four-point bending test protocol for the strength grading of timber: relationship between local and global modulus of elasticity (MOE) of maritime pine

ABSTRACT This study focuses on the characterisation of maritime pine according to standards EN408 and EN384 required for timber grading. A comparison of the EN408 bending test setup is performed with a new four-point bending protocol, reducing as much as possible, the effects of the shear force in the span where the deflection is measured. The study highlights difficulties to estimate accurately, the elasticity modulus of wood by using EN408 bending test setup. This is probably due to inaccuracies in the measurements of small curvatures and due to the local heterogeneities of wood. An original four-point bending test is proposed with 75% of the span of the wood beams in pure bending. This configuration clearly exhibits the convergence of measurements, in comparison to EN408 setup. The deformations generated by this new loading protocol allow a better evaluation of the bending modulus E 0. Moreover, the study reveals that the proposed setup supplies an increase of elasticity modulus close to 20%. In addition, dynamic tests are also performed to characterise the Young’s modulus. The experimental results reveal a better agreement between static test, using the new four-point bending configuration and dynamic method, than considering EN408 as a reference.

Numerical and experimental investigation on angle multiple slit bolted connections for precast wall panels

Flat Multiple Slit Devices (MSDs) have been introduced in steel constructions located in earthquake-prone areas as compact hysteretic dampers connected to braces, and were further proposed to be employed also in other structural typologies, including aligned precast concrete walls. Nevertheless, similar angle connections needed for horizontal joints of orthogonal walls are not tackled in literature. The shape of MSDs, obtained by selective weakening by removing part of the steel area of flat plates, may allow to attain a highly ductile and stable dissipative behaviour, given local plate buckling is avoided, protecting the bolted joints through capacity design. Laser cutting as an alternative to mechanical milling introduces the possibility to easily optimise the shape of the slits in order to attain better performance under laterally imposed cyclic load. This paper analyses the application of right-angle-bent MSDs for the horizontal connection between orthogonal precast walls typical of panel structures with rigid diaphragms. In particular, bolted angle plates with hourglass-shaped multiple slits obtained after laser cutting are investigated numerically via non-linear finite shell elements and experimentally with original cyclic tests. The effect of the aspect ratio of the plate and of the restraint condition, possibly affecting the performance under imposed shear deformation due to the presence of the bent corner, is investigated by parametric analysis. Finally, general design rules and recommendations are presented.

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

Methodological concerns underlying a lack of evidence for cultural heterogeneity in the replication of psychological effects

The multi-site replication study, Many Labs 2, concluded that sample location and setting did not substantially affect the replicability of findings. Here, we examine theoretical and methodological considerations for a subset of the analyses, namely exploratory tests of heterogeneity in the replicability of studies between “WEIRD and less-WEIRD cultures”. We conducted a review of literature citing the study, a re-examination of the existing cultural variability, a power stimulation for detecting cultural heterogeneity, and re-analyses of the original exploratory tests. Findings indicate little cultural variability and low power to detect cultural heterogeneity effects in the Many Labs 2 data, yet the literature review indicates the study is cited regarding the moderating role of culture. Our reanalysis of the data found that using different operationalizations of culture slightly increased effect sizes but did not substantially alter the conclusions of Many Labs 2. Future studies of cultural heterogeneity can be improved with theoretical consideration of which effects and which cultures are likely to show variation as well as a priori methodological planning for appropriate operationalizations of culture and sufficient power to detect effects.

Effect of symmetrical biaxial tension on the magnetic properties of the composite specimen of two steel plates with different mechanical and magnetic properties

The results of studying the behavior of the magnetic characteristics of two-layer material containing layers of annealed sheet low-carbon Steel 15 and sheet metastable austenitic 12Kh18N9Т steel, subjected to cold rolling with a compression of 50 %, under biaxial symmetric tension have been presented. Experiments on biaxial deformation were carried out on the original biaxial testing machine, allowed to determine the physical properties of materials during elastic-plastic deformation independently along two axes. It has been shown that the coercive force of the studied two-layer material vary monotonically over the entire range of elastic-plastic biaxial deformation and can be used as an informative parameter for assessing the it’s stresses and deformations.

Cultural adaptation and psychometric analysis of Communication Activities of Daily Living third edition in Spanish and Catalan for people with aphasia.

In the Spanish and Catalan context, there is currently a lack of standardized, linguistically adapted tools to assess people with communication disorders. This lack is especially evident when it comes to instruments designed to assess functional communication. The main objective of this study is to adapt the instrument entitled Communication Activities of Daily Living 3rd edition (CADL-3) into European Spanish (CADL-3VE) and Catalan (CADL-3VC), thus providing a new tool to assess the functional communication of patients with aphasia in the Spanish and Catalan populations. A total of 152 people, all residents of Catalonia, took part in the study. The CADL-3VE test was administered to 125 Spanish-speaking participants, who were divided into two groups, one consisting of patients with aphasia and the other a control group. The CADL-3VC test was administered to 27 Catalan-speaking patients with aphasia. Other tests and assessment scales were used for the external validation of the test. Reliability scores were recorded for both new versions of the test. There was a very strong correlation between the CADL-3VE test and external criteria. The scores for both of the new versions showed significant differences in terms of performance between the aphasia and control groups. Both versions displayed similarities with the original test with respect to most of the psychometric analyses carried out. The test makes it possible to assess everyday communicative functioning and participation in real-world contexts. As such, it helps inform the creation of personalized, interdisciplinary treatment plans aimed at functional objectives that consider the patient's context. What is already known on the subject In order to effectively assess aphasia based on a bio-psycho-social perspective, it is necessary to examine functional communication. In other words, there is a need to explore the kinds of communication difficulties that people face in their everyday lives. Communication Activities of Daily Living (CADL-3) is an English-language test that was developed to assess functional communication by simulating certain everyday activities. What this study adds This paper offers an analysis of the items, reliability and validity of the Spanish and Catalan versions of the CADL-3 test. What are the clinical implications of this work? This new instrument has the potential to play an essential role in assessing the everyday functional communication of people with aphasia in Spain.

Validation of Optimization Methods for Sensory Characteristics Using Rate-All-That-Apply and Intensity Scales: A Case Study of Apple Juice.

Preference mapping (PM), which integrates consumer and descriptive analysis (DA) data to identify attributes that drive consumer liking, is widely employed for product optimization. However, a limited group of trained panelists cannot fully represent the diverse consumer population or reliably predict market acceptance. Consequently, numerous studies have explored consumer-based methodologies as potential replacements for DA; however, their efficacy for product optimization remains limited. Therefore, this study was conducted to explore the potential of optimizing products using two consumer-based profiling techniques as alternatives to DA in external preference mapping (EPM). Overall, 8 trained panelists profiled 12 sensory attributes of 7 commercial apple juices, whereas 160 consumers assessed the same attributes using a 5-point rate-all-that-apply (RATA) scale and a 10 cm intensity scale (IS). Danzart's response surface ideal modeling was employed to identify optimal products using DA, RATA, and IS through barycenter calculations, focusing on three products from the original consumer test located around the group ideal point. Overall, the ideal products of the group and their sensory characteristics were successfully identified using DA, RATA, and IS. Regarding sensory intensities, high concordance was observed between DA and RATA (Rv = 0.92) and between DA and IS (Rv = 0.91). Overall liking and preference scores for products mixed at the optimal ratio for each method showed no significant differences in preference among the ideal products identified using DA, RATA, and IS. This study suggests that both RATA and IS are viable alternatives to DA in EPM for identifying ideal sensory profiles.

Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application

We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α = 0.001. Notably, when 5 % or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31 % of African Ancestry, mean age of 62, with 58 % women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on both pooled samples and within each ancestry group. Our results suggest that mtDNA-encoded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the RNR1 and RNR2 genes (p < 0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations (p < 0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.

Visual tests, touch responses: Computer-based neuropsychological tools

Objective: Investigate three new computer tests of visual abilities relative to traditional paper-and-pencil (P&P) tests on groups with and without cerebral neurological impairment (CNI, Non-CNI) based on MRI and EEG criteria. The new tests employ an intuitive interface with audio instructions, touch responses, non-response prompts, and videography of test behavior. The Presidents Test was designed as an achievement-related measure of visual knowledge; the Railroad Test – visual perception and delayed recognition; and the Swamp Test – visual attention. Attitudes toward testing were assessed with an original Testing Experience Questionnaire (TEQ). Method: Of the 129 participants, 84 were women, 73 identified as non-White, average age 45.5 and education 14.3 years. Procedures included the new computer tests and a selection of standard neuropsychological measures including performance validity tests (PVT). Participants who failed two or three PVTs or had missing PVT data were excluded from main analyses, resulting in N = 115. Results: The new computer tests demonstrated adequate reliability. Correlations and factor analyses confirmed the computer tests as functioning in accordance with design. The Presidents Test was associated with academic achievement. The Railroad and Swamp Tests were linked to visual perception and visual attention. Correlations between computer total test duration time and traditional speed of processing tasks were modest. Computer and traditional tests demonstrated similar discriminability between CNI and Non-CNI groups. TEQ indicated positive attitudes toward testing in general, and computer testing in particular. Conclusions: The new computer tests evaluated in this study were found to be reliable, functioned to assess the designed cognitive domains, and discriminated between CNI and Non-CNI participants similarly to the traditional neuropsychological measures. Attitudes toward computer testing were favorable.

Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset

Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. While X-ray imaging remains a prevalent diagnostic tool, the increasing misinterpretation rates highlight the need for more accurate analysis, especially considering the lack of specialized training among many surgeons and physicians. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. However, distinguishing subtle variations between pediatric wrist pathologies in X-rays remains challenging. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. In this paper, we address the challenge of pediatric wrist pathology recognition with a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention. We refine our fine-grained architecture through ablation analysis and the integration of LION. Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition.

Realizing computer vision rehabilitation assessment tests & evaluation applications for mobile devices

Upper extremity impairments are a common consequence of stroke, necessitating thorough rehabilitation monitoring and kinematic assessments to facilitate motor recovery. The Box and Block Test (BBT) and Sollerman Hand Function Test (SHFT) are two widely utilized and recommended tools for objectively measuring upper limb dexterity and evaluating fine motor skill rehabilitation in patients. However, these tests rely on specific equipment and therapist attendance, making the process time-consuming and clinic-dependent. This paper introduces a computer vision-based hand rehabilitation assessment suite specifically designed for mobile devices, such as smartphones and tablets, which serves as a virtual alternative to traditional methods while also incorporating an interactive exergame. Our application faithfully integrates the original tests’ guidelines and procedures into an engaging computer vision experience, utilizing advanced technologies like MediaPipe Hands for precise hand and finger tracking. This innovative solution obviates the need for additional computer peripherals such as smart gloves or VR headsets, as well as physical equipment like wooden boxes and blocks, relying solely on the built-in camera of everyday mobile devices. In addition, we address several technical challenges encountered in our approach and outline future directions for score normalization and feature expansion, ensuring the continued improvement and efficacy of our hand rehabilitation assessment suite.

Neuron Semantic-Guided Test Generation for Deep Neural Networks Fuzzing

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: ❶ Testing Adequacy : Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. ❷ Interpretability : The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which incorrect attributes or patterns of the model are triggered by the test inputs. This lack of interpretability hampers the subsequent debugging and fixing process. Therefore, there is an urgent need for a novel fuzzing criterion that offers improved testing adequacy, better interpretability, and more effective failure detection capabilities for DNNs. To alleviate these limitations, we propose NSGen, an approach for DNN fuzzing that utilizes neuron semantics as guidance during test generation. NSGen identifies critical neurons, translates their high-level semantic features into natural language descriptions, and then assembles them into human-readable DNN decision paths (representing the internal decision of the DNN). With these decision paths, we can generate more fault-revealing test inputs by quantifying the similarity between original test inputs and mutated test inputs for fuzzing. We evaluate NSGen on popular DNN models (VGG16_BN, ResNet50, and MobileNet_v2) using CIFAR10, CIFAR100, Oxford 102 Flower, and ImageNet datasets. Compared to 12 existing coverage-guided fuzzing criteria, NSGen outperforms all baselines, increasing the number of triggered faults by 21.4% to 61.2% compared to the state-of-the-art coverage-guided fuzzing criterion. This demonstrates NSGen’s effectiveness in generating fault-revealing test inputs through guided input mutation, highlighting its potential to enhance DNN testing and interpretability.

Ethnomathematics-based Problem Based Learning (PBL) Model to Increase Students’ Critical Thinking in Mathematics Learning

Process of learning mathematics in the school that the researcher is aiming for tends to be theoretical, less contextual and pseudo. Applies mathematical concepts in learning with local cultural wisdom able to touch aspects in the daily lives of students. This study aims to apply the ethnomathematics-ground Problem Based Learning (PBL) model to ameliorate critical thinking in mathematics literacy. This explored was type of quasi-experimental explored (pseudo-experiment), with The Non-Equivalent Control Group type design. The population of this study was all fourth grade students at SD Insan Mandiri Bandar Lampung, totaling 121 students. The sample of this studied was determined by intentional slice fashion, which is a sample determination fashion with consideration in opting for the class to be studied and attained by as numerous as 49 students. Data collection technique used valid and dependable test instruments. The data analysis fashion uses a mated t- test with the results attained for the experimental class with a significance value of 0.000 (p&lt;0.05) passing significant changes and in the control class which is not applied to the Ethnomathematics-based PBL model gets a value of 0.070 (p&gt;0.05) indicating that the results of the original test and final test do not witness significant changes. Grounded on the data attained is from the results of this study, the ethnomathematics-ground Problem Based Learning (PBL) model to ameliorate thinking is significant.