Comprehensive Test Research Articles

The Impact of Using Say Something Strategy on Iraqi EFL University Students' Achievement in Reading Comprehension

Reading is the most important skill among the four skills in learning English and being able to comprehend various English texts as expected. Yet, the experience has not seemed to be realized. The goal of the "Say Something Strategy" is to assist students in understanding what they are reading by predicting, questioning, clarifying, connecting, or commenting. Telling students to "say something" about the reading, or offering them specific things to say, keeps them engaged with the content. In this study, the Say Something strategy is an appropriate strategy to progress accomplishment for improving students' reading comprehension success. The study's sample comprises of second-year university students from the English Department, the College of Art, and Tikrit University during the academic year 2022–2023. It has been randomly chosen and has been divided into two groups, the experimental group with (33) students taught by Say Something Strategy and the control group with (33) students without Say Something Strategy, both groups completed the same reading comprehension test. An accomplishment exam was developed, validated, and administered to the two groups involved. The essential data has been collected and statistically analyzed. Because of the implementation of the Say Something Strategy, the obtained results show that there are statistically significant differences in the success of the two groups of students in favor of the experimental group.

Enhancing Indonesian EFL Students’ Reading Comprehension of Report Texts through KWL Strategy with Multimedia: A Classroom Action Research

The significance of reading is undeniable in the English learning process. Thus, students need to be equipped with reading strategies to help them develop their English language. This study sought to improve students’ reading comprehension and attitude through the use of know-want-learn (KWL) strategy in conjunction with multimedia. Classroom Action Research was employed involving 28 students in the ninth grade. Several instruments were utilized, i.e., reading comprehension tests, interview, observation checklist, and field notes. The mean scores of the reading tests increased from baseline (53) to cycle 2 (80.54) indicating that the result had achieved the criteria of success. It could be inferred that implementing KWL strategy with multimedia in the pre-reading stage enhanced students’ reading comprehension. Two factors affecting the students’ reading comprehension were identified, i.e., the teacher’s participation and topic familiarity. The strategy was also found to foster students’ participation and provide students with a concise framework to develop their English reading skills. This study therefore has contributed to the existing literature of English reading strategy and offered meaningful pedagogical implications for English teachers to harness the strategy incorporated with the use of current media such as pictures and videos to teach English reading.

The Impact of Next-Generation Sequencing Workflows on Outcomes in Advanced Lung Cancer: A Retrospective Analysis at One Academic Health System.

Broad-based molecular testing with next-generation sequencing (NGS) is now the standard of care in advanced non-small cell lung cancer (NSCLC). Two approaches to molecular testing are (1) reflexive testing at pathologic NSCLC confirmation, often using an in-house molecular panel, and (2) send-out testing to private vendors, ordered by a clinician. This study explored the outcomes with reflex versus send-out testing. A retrospective chart review was conducted of patients diagnosed with de novo stage IV NSCLC in 2019 and 2020 at three hospitals in the same system, one academic hospital (Northwestern Memorial Hospital, or NMH) utilizing reflex, in-house NGS, and two community-based hospitals (Central DuPage Hospital, or CDH, and Delnor, or D) sending out tissue samples for testing. The outcomes assessed were the time from biopsy to results, biopsy to treatment, the incidence of first-line targetable mutations and the use of first-line targeted therapies, and overall survival. In total, 191 patients met the inclusion criteria, 85 at NMH, 106 at CDH + D, and in total, 131 in 2019 and 60 in 2020. The time to results was significantly shorter with reflexive NGS when compared with send-out testing; the time to treatment was also shorter but not statistically significant. At CDH + D, the time to results was significantly shorter with a limited panel than with comprehensive testing, but the time to treatment was similar. NGS testing rates were 95% at NMH and 84.5% at CDH + D (p = 0.009), with 31.0% at NMH receiving 1L targeted therapies versus 20.8% at CDH + D (p = 0.08). In 2019, the median time from biopsy to treatment was 35 days at NMH and 38 days at CDH and Delnor; in 2020, time to treatment was 26 days and 37 days, respectively. Overall survival trended longer in 2020 relative to 2019 independent of site. Reflexive NGS testing is associated with a shorter time to actionable results and higher rates of first-line targetable mutations than send-out testing. In practices with send-out testing, limited panels had slightly faster turnaround times but no difference in time to treatment. If resources allow, reflexive NGS should be considered in healthcare systems for patients with NSCLC.

Bioinspired skin-like in vitro model for investigating catheter-related bloodstream infections

Intravenous (IV) catheter-related bloodstream infections (CRBSIs) cause significant risks in healthcare, necessitating advancements in catheter design and materials. This study investigates the effectiveness of Ecoflex, a silicone-based material, in studying CRBSIs through the development of skin-like replicas that mimic human skin properties for use in wearable sensing devices. We characterized the replica’s bioinspired surface roughness, wettability, bacterial adhesion, and mechanical properties and validated its performance using in vitro IV simulation. The results demonstrated that the bioinspired model replicates human skin textures with less than 7.5% error for surface roughness ranging from 0.05 μm to 6.3 μm. Wettability tests revealed that the artificial sebum application significantly reduced the static contact angles for deionized water and artificial sweat. Comprehensive mechanical testing revealed material high elasticity and resilience, suitable for dynamic biomedical applications. Bacterial adhesion studies using Staphylococcus epidermidis showed varying adhesion patterns influenced by surface roughness, highlighting the potential for material texture to impact infection risk. In IV therapy simulations, we observed bacterial growth dynamics over the incubation period. Our findings suggest that Ecoflex-based skin-like replicas can serve as a valuable tool for developing and testing new catheters, while the potential for use in other medical innovation devices, including wearable sensing devices, ultimately contributes to improved patient outcomes and infection control strategies.

Serum anti-NMDA receptor antibodies are linked to memory impairment 12 months after stroke.

Patients suffering from strokes are at increased risk of developing post-stroke dementia. Serum anti-NMDA receptor autoantibodies (NMDAR1-abs) have been associated with unfavorable post-stroke outcomes. However, their effect on specific cognitive domains remains unclear. We used data from the prospective multicenter DZNE-mechanisms after stroke (DEMDAS) cohort, and measured NMDAR1-abs in serum at baseline. Cognitive function was assessed with a comprehensive neuropsychological test battery at 6- and 12-months follow-up. We employed crude and stepwise confounder adjusted linear and logistic regression models as well as generalized estimating equation models (GEE) to determine the relevance of NMDAR1-abs seropositivity on cognitive function after stroke. 10.2% (58/569) DEMDAS patients were NMDAR1-abs seropositive (IgM:n = 44/IgA:n = 21/IgG:n = 2). Seropositivity was not associated with global cognitive impairment after stroke. However, NMDAR1-abs seropositive patients performed lower in the memory domain (βadjusted = -0.11; 95%CI = -0.57 to -0.03) and were at increased risk for memory impairment (ORadjusted = 3.8; 95%CI = 1.33-10.82) compared to seronegative patients, 12 months after stroke. Further, NMDAR1-abs were linked to memory impairment over time in GEE from 6- to 12-months follow-up (ORadjusted = 2.41; 95%CI = 1.05-5.49). Our data suggests that NMDAR1-abs contribute to memory dysfunction 1 year after stroke while not affecting other cognitive subdomains. Hence, antineuronal autoimmunity may be involved in distinct mechanisms of post-stroke memory impairment. Clinical trial name and registration number: The Determinants of Dementia After Stroke (DEMDAS; study identifier on clinical trials.gov: NCT01334749).

DPDCD: The Intelligent Prediction Strategy for the Course Development of Diabetic Kidney Disease

Diabetic nephropathy (DKD) is a severe diabetes complication and a leading cause of mortality. While urine microalbuminuria and eGFR are common markers, some advanced cases may exhibit normal values. The progression from microalbuminuria to overt proteinuria in DKD is gradual and requires comprehensive physiological tests for diagnosis. This study employs deep reinforcement learning to predict DKD progression based on patients’ physiological data, aiming to assist clinical diagnosis and treatment. A multivariate logistic regression algorithm is used to describe the DKD prediction probability. An optimized DKD progression prediction algorithm (DPDCD) based on deep reinforcement learning determines the best model coefficients, enabling accurate prediction of DKD progression using routine clinical data. Experimental results demonstrate that DPDCD outperforms other algorithms in predicting DKD progression, providing valuable support for clinicians.

Ensuring Quality and Consistency in OPC-53 Cement Testing: A Comprehensive Proficiency Testing Evaluation

The proficiency testing (PT) program is a critical part of quality assurance in laboratories, especially within industries like construction where cement testing is essential. This study provides a comprehensive evaluation of the fifth PT scheme for Ordinary Portland Cement (OPC) grade 53, conducted by the Nodal Laboratory. The key parameters assessed in this program were standard consistency, compressive strength, and soundness using the Le-Chatelier method. Twenty-four laboratories participated, and their performance was analyzed using advanced statistical methods, adhering to ISO 17043:2010 and ISO 13528:2015 standards. The results showed that most laboratories performed satisfactorily, with only one outlier identified in the soundness test. This paper covers all aspects of the PT program, from sample preparation to the distribution of materials, as well as the homogeneity and stability tests conducted on the samples. Statistical evaluation was carried out using Z-scores, which allowed the laboratories to be ranked according to their performance in each parameter. This method highlights discrepancies and provides a clear framework for participating laboratories to improve their testing procedures. Proficiency testing is essential for maintaining high standards in laboratory performance. It helps standardize testing methods across multiple laboratories, ensuring that OPC-53 cement meets strict quality and performance specifications. The program enables laboratories to identify performance gaps and take corrective actions, ensuring the reliability and accuracy of results. Participation in PT programs is a step toward continuous improvement, keeping laboratories aligned with international standards, ultimately benefiting the broader construction industry by ensuring the structural integrity of buildings and infrastructure. This study underscores the importance of regular PT participation. It not only provides a benchmark for laboratory performance but also ensures compliance with ISO standards. This fosters the consistent quality of cement used in large-scale construction, reducing the risk of structural failures and contributing to long-term safety and durability. The findings demonstrate the positive impact of PT programs on overall testing accuracy and laboratory reliability, emphasizing their role in advancing best practices in the construction industry.

Maternal and Paternal Education on Italian Monolingual Toddlers’ Language Skills

Background. Language development in toddlers can be influenced by social interactions in environments and proximal contexts with mothers and fathers. We present the literature on mothers’ and fathers’ education level and socioeconomic status on the child’s language development; further evidence is needed in the Italian-speaking context. Aims. The study aims to confirm the role of mother and father education level on toddlers’ language skills assessed with direct and indirect measures. Methods and Procedures. Participants were 51 Italian-speaking children aged 33 to 41 months. Children were tested with a lexical test (PinG test) for comprehension and production of nouns and predicates and a morpho-syntactic test for grammar comprehension (PCGO). Parents of the children completed a demographic form and the Italian adaptation of the MacArthur-Bates CDI long version. Two series of one-way ANCOVAs were performed to study the role of mothers’ and fathers’ level of education on separate measures of their child’s language. Outcomes and Results. Findings suggest that in most families, mothers’ level of education is higher than fathers’ level of education. There was no significant difference between children of parents with low–middle level of education and children of parents with high level of education for the grammar comprehension tasks (PCGO) and indirect measure of vocabulary production (MacArthur-Bates CDI). However, both mothers’ and fathers’ level of education appears to be significant for the direct measurement of word production. Conclusions and Implications. This study provides new evidence for the role of mothers’ and fathers’ education on the development of word production in children aged 33 to 41 months, contributing to enriching the literature on the Italian context; it lays the groundwork for future research on the social and environmental factors that can affect language development.

Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.

Experimental and Theoretical Studies on the Vibration Serviceability of Composite HCS Floors with RC Topping

Composite hollow–core slab floor with reinforced concrete topping (i.e. CHFT) is relatively new, which can be applied to various long-span structures. However, these systems are typically lightweight and exhibit low damping, posing potential serviceability concerns related to human vibrations. This paper presents a comprehensive vibration test on a 9[Formula: see text]m-long CHFT system. Natural frequencies, damping ratios, and mode shapes of the floor system were obtained through modal tests. The peak acceleration, root–mean–square acceleration, maximum transient vibration value, and perception factor of the floor under heel-drop excitation were obtained through the perceived vibration test and were checked against the available design codes and standards. Sensitivity studies using the finite element method were made to investigate the vibration performance of the CHFT system. Analytical formulas for the fundamental frequency and peak acceleration were derived, which are therefore suggested for practical use. Additionally, an approach for evaluating the vibration serviceability of CHFTs is described.

Predicting physical fitness levels from resting ECG data: a machine learning approach in cardiovascular assessment

Abstract Background Exercise Stress Testing (EST), an inexpensive noninvasive modality, is widely used for preliminary cardiovascular evaluation and ongoing monitoring of cardiac patients. The MET (Metabolic Equivalent of Task) score from EST, indicating physical fitness, is associated with overall mortality risk and is extremely helpful for preoperative evaluation. Artificial Intelligence (AI) advances, particularly Deep Learning (DL) for resting ECG analysis, have significantly improved cardiac diagnostic accuracy. Objectives This study explores AI's ability to predict MET scores from resting ECGs, aiming to streamline fitness assessments and detect early changes in fitness levels. Methods The current analysis encompasses 45,518 consecutive EST evaluations from 31,532 individual patients from 2017 to 2023. MET scores were automatically computed using an established formula, while interpretations by certified cardiologists provided a benchmark for the AI model. Poor fitness was defined as being within the lowest quartile of MET scores, adjusted for sex and age. A deep learning framework was developed to process a 10-second snippet of resting phase ECG in raw waveform, predicting the likelihood of diminished fitness and thus acting as a binary classifier. The efficacy of this Poor Fitness AI model (PF-AI model) was assessed through metrics such as the area under the receiver operating characteristic curve (AUROC), along with sensitivity, specificity, and accuracy rates. Results The average age of the study group was 55±10 years, predominantly male (68.8%, n = 31,337). Among them, 1,152 were identified as having poor fitness. Applying the model to the entire cohort through 5-fold cross-validation yielded a sensitivity of 78%, a specificity of 81%, and an AUROC of 0.87 in predicting poor fitness. Conclusions Leveraging merely 10 seconds of resting ECG data, the PF-AI model proves to be a robust tool in predicting poor fitness levels, comparable to the outcomes of comprehensive stress testing.

Special Series on the Wechsler Adult Intelligence Scale-IV Performances in Five Asian Countries: Summary of Findings, Clinical Implications, and Future Directions.

This special series of exploratory studies compared WAIS-IV performances in five Asian countries: Japan, South Korea, Taiwan, India, and Indonesia with the U.S. The studies examined cognitive profiles across age groups to develop hypotheses on how culture can impact cognition. This summary article integrates the findings from each study to determine similarities and differences across Asian countries and proposes possible underlying cultural factors impacting cognition. In each study, raw scores corresponding to a subscale score of 10 were scored with U.S. norms across all age groups. Index scores were calculated with the adjusted scale scores. The impact of education on test performances was determined by correlating WAIS-IV scores with a ratio of educational attainment for each country with the U.S. for each age group. Findings from each country were then integrated to develop hypotheses on cultural factors that impacted cognitive profiles. East Asian countries (Japan, South Korea, Taiwan) demonstrated weaker Verbal Comprehension Index scores and stronger Processing Speed Index scores than the U.S. sample, although the latter was moderated by age. East Asian countries demonstrated stronger performances on Perceptual Reasoning versus Verbal Comprehension tests. East Asians and the Java subset of the Indonesian sample demonstrated a pattern of stronger performances in younger versus older cohorts which was significantly correlated with educational attainment. Cultural factors impacting cognition were proposed. Clinical implications of the findings were discussed and directions for future studies to examine the relationship between culture and cognition were recommended.

Mechanical properties of TPDH-graphene: atomistic aspect

Abstract TPDH-graphene is a new type of two-dimensional carbon material predicted by first-principles calculations to have tetragonal (T), pentagonal (P), decagonal (D) and hexagonal (H) carbon ring structures. First-principles calculations show that this special structure gives it excellent mechanical properties and promising applications in nanoelectronics. In this paper, a comprehensive test of its mechanical properties was carried out using the classical molecular dynamics (MD), mainly exploring the effects of factors such as tensile direction and temperature on its mechanical properties, and exploring the effects of introducing rectangular and circular defects on its mechanical properties. The results show that: TPDH-graphene exhibits significant anisotropy in zigzag and armchair directions, and the material exhibits some tensile toughness in armchair direction; the mechanical properties of the material are weakened at higher temperatures; the adding of defects leads to the reduction of the mechanical properties of the material in different directions to different degrees, and the The tensile toughness in the armchair direction is weakened by the addition of defects.

Towards large-scale analyses of settlement patterns in urbanizing landscapes—findings of first studies for India, Egypt, and China

Improved ability to assess and categorize the spatial characteristics of settlement patterns is required for a deeper understanding of how urbanization is driving land use and land cover transformation and its effects. Two approaches to the globally available settlement maps of the World Settlement Footprint 3D support a detailed assessment of spatial characteristics of settlement patterns in rural to urban landscapes and across scales: graph-based spatial network analysis and elements of fractal theory. Based on first comprehensive tests for the Punjab (India), the Nile Delta (Egypt) and the North China Plain, the results of our study suggest that the presented methods allow a quantitative and qualitative characterization and comparison of settlement patterns between different regions of the world. The approache allows to generate standardized baseline data for arbitrary regions in the world to analyze structuring principles of settlement hierarchies (e.g., self-organized fractal geometries) and their dependence on - or interaction with - cultural, political, socioeconomic, or environmental conditions.

Response tests on the effects of particle size of waste glass sand and glass powder on the mechanical and durability performance of concrete

To investigate the effect of waste glass material particle sizes on the mechanical and durability properties of concrete, a two-phase experimental approach was conducted. First, comprehensive tests were performed to examine the effects of glass sand and glass powder of different particle sizes on concrete performance. Subsequently, based on the experimental results, an orthogonal test was designed to optimize the replacement amounts of composite particle sizes. The results indicated that an appropriate amount of glass sand can enhance the mechanical properties and durability of concrete, while excessive amounts or larger particle sizes may have adverse effects. The pozzolanic effect of glass powder also contributes to performance improvement, but the replacement rate should be limited to 20%. Under composite particle size conditions, the compressive, tensile, and shear strengths of concrete increased by 35.56%, 21.74%, and 13.79%, respectively, while durability significantly improved, with water absorption reduced by 20.73% and chloride ion permeability decreased by 63.10%. At a total replacement rate of 20%, the optimal proportions were determined to be 2.86% for 0.6 mm glass sand, 1.43% for 1.18 mm glass sand, 8.57% for 50–60 μm glass powder, and 7.14% for 60–70 μm glass powder. The incorporation of composite particle sizes improved the microstructure of concrete, thereby enhancing its mechanical and durability properties.

Nonlinear Reinforcement Learning-Based Dynamic Test Case Prioritization with Anomaly Detection for Continuous Integration

This research article introduces DynamicR-TCP, a nonlinear approach to dynamic test case prioritization (TCP) leveraging reinforcement learning and anomaly detection. Designed to enhance the efficiency of software testing in continuous integration (CI) environments, the proposed model dynamically adapts to changing conditions by learning complex patterns from historical test data. A reinforcement learning agent, employing policy and value networks, guides nonlinear prioritization by optimizing the sequence of test case executions. The model integrates a sliding window strategy for adaptive focus on smaller test subsets and uses an experience replay buffer for iterative learning. An anomaly detection layer monitors test outcomes for deviations, triggering retraining when needed to maintain reliability. Experimental evaluation using the Defects4J dataset demonstrates the model’s superior fault detection rates, faster time to first fault, and higher computational efficiency compared to contemporary methods. An ablation study highlights the nonlinear synergy among the key components—reinforcement learning, sliding windows, and anomaly detection—showing significant performance gains when combined. This comprehensive framework advances software testing by providing an adaptive, efficient, and scalable solution, ensuring robust performance in dynamic CI environments.

Drug Use during Incarceration: A Comprehensive Quality and Prevalence Study in Three Danish Prisons

Background Drug use in Danish prisons has previously not been investigated in detail using confirmatory, laboratory analysis. The objective of the present quality study, initiated by the Danish Prison and Probation Service, was to i) evaluate the performance of an initial, on-site drug screening strategy; ii) gain insights into emerging drug trends; and iii) suggest evidence-based strategies for future drug testing. Methods Over a two-year period, routine urine samples (n = 1952 from 710 inmates) from three Danish prisons were subjected to comprehensive drug testing. Immunoassay screening was conducted on-site. A parallel sample aliquot was forwarded to laboratory analysis by confirmatory methods: High-performance liquid chromatography and tandem mass spectrometry (LC-MS/MS) and gas chromatography and mass spectrometry (GC-MS) targeting 56 drugs-of-abuse/medical drugs, 26 key metabolites, 41 new psychoactive substances (NPS), including specific biomarkers for heroin, crack cocaine, or ethanol (a total of 123 target analytes/sample). Results The on-site immunoassay method showed a sensitivity from 66 to 100%; specificity was above 98%; accuracy was above 95%. Laboratory analysis detected compounds not screened for including tramadol, oxycodone, buprenorphine, ketamine, MDMA, 4-fluoroamphetamine, and GHB. The prevalence of drug use was in the order: cannabis > ethanol > cocaine > benzodiazepines > amphetamine. Conclusion The performance of the immunoassay was found acceptable; however, the screening program was inadequate for detecting other significant substances. Based on these findings, a broader screening method will be implemented in future at Danish Prisons to minimize false negative results. The data did not indicate a trend of using NPS in Danish prisons.

Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach

This paper presents a robust position estimation technique for autonomous vehicles navigating urban and highway environments. It employs a probabilistic framework, integrating Monte Carlo simulation and Bayesian filtering via particle filters to represent position estimates beyond Gaussian distributions. To improve accuracy, Unscented Kalman Filtering (UKF) is used to fuse radar and lidar data, facilitating the detection of static, pole-like objects. These objects are matched with landmarks from a detailed reference map generated through 3D lidar scans. The proposed method addresses both lateral and longitudinal localisation challenges, ensuring precise vehicle positioning. Comprehensive simulation tests validate the system's effectiveness, demonstrating reliable performance across various driving conditions.

Molecular, Histological, and Functional Changes in Acta1-MCM;FLExDUX4/+ Mice.

DUX4 is the major gene responsible for facioscapulohumeral dystrophy (FSHD). Several mouse models expressing DUX4 have been developed, the most commonly used by academic laboratories being ACTA1-MCM/FLExDUX4. In this study, molecular and histological modifications in the tibialis anterior and quadriceps muscles were investigated in this model at different time points. We investigated several changes that could be used as markers of therapeutic efficacy. Our results confirm the progressive muscular dystrophy previously described but also highlight biases associated with tamoxifen injections and the complexity of choosing the genes used to calculate a DUX4-pathway gene composite score. We also developed a comprehensive force test that better reflects the movements made in everyday life. This functional force-velocity-endurance model, which describes the force production capacities at all velocity and fatigue levels, was applied on 12-13-week-old animals without tamoxifen. Our data highlight that previously unsuspected muscle properties are also affected by the expression of DUX4, leading to a weaker muscle with a lower initial muscle force but with preserved power and endurance capacity. Importantly, this force-velocity-endurance approach can be used in humans for clinical evaluations.

Inferential and Macrostructural Ability in Multimodal Texts

Objective: To evaluate inferential and macrostructural ability through three types of multimodal artifacts: verbal, graphic-mathematical, and complete text (which integrates all semiotic modes). Theoretical Framework: This section explores the key concepts of multimodality theory, as well as the notions of inferences and multimodal ability, which are fundamental to addressing the research problem. The main theories and concepts supporting the research are presented, providing a solid foundation for understanding the study's context. Method: A mixed methodology was employed, combining quantitative and qualitative approaches. The quantitative phase adopted a correlational ex post facto design, while the qualitative phase was carried out through a focus group. The quantitative study followed a factorial experimental between-subjects design, considering two dimensions: the participants' professional program and the semiotic systems present in the comprehension test. Results and Discussion: It was concluded that the type of multimodal artifact does not significantly affect the efficient identification of inferences or macrostructural reduction. This suggests that students exhibit a low level of multimodal ability. Research Implications: The practical and theoretical implications of the research are analyzed, providing insights into how the results can influence or be applied to practices related to multimodal comprehension. Originality/Value: The research highlights that universities, in general, do not pay sufficient attention to multimodality, which negatively impacts students' multimodal competencies.