Assessment Methods Research Articles

Association between obstructive sleep apnea and temporomandibular disorders: A meta-analysis.

Obstructive sleep apnea (OSA) is a very common condition in patients with temporomandibular disorders (TMD). However, there is little evidence of a connection between them. The aim of this systematic review and meta-analysis is to assess the association between OSA and TMD in adult population. Case-control, cross-sectional and cohort studies on the association between TMD and OSA were searched in the EMBASE, LILACS, LIVIVO, PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, Open Grey and Pro Quest databases. TMD should be assessed using Research Diagnostic Criteria (RDC/TMD) or Diagnostic Criteria (DC/TMD) and OSA using polysomnography (PSG) and/or a validated questionnaire. The risk of bias was evaluated using the Joanna Briggs Institute Critical Assessment Checklists; and an association meta-analysis was performed. The effect measure included the odds ratio (OR) in dichotomous variables and a 95% confidence interval (CI). Certainty of evidence was determined by analysing groups using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Out of the 1024 articles screened, 7 met the inclusion criteria for the qualitative synthesis, and 6 for quantitative analysis. All articles were classified at low risk of bias. A positive association with OSA was found in patients with TMD (OR = 2.61; 95% CI = 2.31, 2.95). A significant association was also found irrespective to the OSA diagnostic methods applied (for studies using PSG + validated questionnaires: OR = 2.74; 95% CI = 2.11, 3.57; for studies using validated questionnaires only: OR = 2.55; 95% CI = 2.22, 2.92). GRADE was moderate. Patients with TMD presented a significant association with OSA regardless of the OSA diagnostic method (PSG and/or validated questionnaires). OSA screening should be part of the TMD examination routine. Furthermore, due to the different OSA assessment methods used and the small number of studies included, there is a need to include a larger number of studies using PSG to better elucidate this association.

Running safety assessment method of trains under seismic conditions based on the derailment risk domain

Running safety assessment method of trains under seismic conditions based on the derailment risk domain

Internal pipe corrosion assessment method in water distribution system using ultrasound and convolutional neural networks

Internal pipe corrosion within water distribution systems leads to iron oxide deposits on pipe walls, potentially contaminating the water supply. Consuming iron oxide-contaminated water can cause significant health issues such as gastrointestinal infections, dermatological problems, and lymph node complications. Therefore, non-destructive and continuous monitoring of pipe corrosion is imperative for water sustainability initiatives. This study introduces a dual-mode methodology utilizing advanced ultrasound technology and convolutional neural networks (CNN) to quantify pipe corrosion. Scanning acoustic microscopy (SAM) employs high-frequency ultrasound to generate high-resolution images of pipe thickness, indicating iron oxide accumulation. SAM also captures internal pipe data to measure iron oxide concentration in the water. This data, analyzed by CNN, achieves an impressive 95% accuracy. This dual-mode system effectively assesses both the extent of pipe corrosion and water contamination, exemplifying the successful integration of SAM and CNN for precise and reliable monitoring.

Smart Chemometric‑Assisted Spectrophotometric Approaches for Simultaneous Quantification of Tertiary Combination Recommended for COVID-19 Supportive Treatments with Their Greenness Assessment.

There is an increasing interest of scientific community in developing innovative methodologies for their analysis needs within green analytical chemistry framework. UV spectrophotometry is one of the most promising eco-friendly methods, which is integrated with advanced chemometric tools to enhance the selectivity of the analysis of complex mixtures with severe overlapped signals. Simultaneous determination of a triple-combination of pseudoephedrine hydrochloride (PSE), carbinoxamine maleate (CRX), and paracetamol (PAR) using an artificial intelligence system and multivariate calibration methods. This combination is recently recommended for COVID-19 home-treated patients as part of a symptomatic treatment. Namely, the suggested models are: Artificial Neural Networks, Partial Least Squares, and Principal Component Regression. The proposed algorithms were optimized and developed with the aid of a five-level, three-factor experimental design. The investigated methods were applied over the concentration range of 100-180 μg/mL, 18-16 μg/mL, and 4-12 μg/mL for PSE, CRX, and PAR, respectively. The models validation results demonstrated excellent recoveries (around 98 to 102%), signaling the approaches outstanding resolution capacity for the cited compounds in the presence of common excipients. The outcomes of the studied methods were statistically compared to the official approaches, and no significant difference was found. The suggested models were efficiently employed to determine the selected drugs in their combined tablets without any initial separation steps. The impact of these methods on the environment was evaluated via greenness tools, namely; National Environmental Method Index, Raynie and Driver's green assessment method, analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. Green chemometric quality assessment of PSE, CRX, and PAR in their pure and pharmaceutical dosage forms. The established approaches are innovative, sustainable, smart, fast, selective, and cost-effective. These models are potential green nominees for routine analysis of the investigated mixture in quality control laboratories.

Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery

Noises widely exist in the actual working environment of rotating machinery, which makes it difficult to extract high-quality fault features. Recently, the superiority of graph neural network (GNN) in the aspect of fault feature representation has been confirmed. By converting one-dimensional signals into graph structural data, hidden structural information inside the signals can be revealed. In this study, a novel graph-level fault diagnosis method based on spatial–temporal graph feature learning is developed. It mainly contains the following steps: First, Short-Time Fourier Transform (STFT) is used to construct time–frequency energy heatmaps, which can reflect the distribution of frequencies over time windows and the temporal variation characteristics. Second, a time–frequency similarity assessment method based on dynamic frequency warping is presented. On this basis, a time–frequency similarity graph (TFSG) is constructed to describe the frequency relationship over different time windows, which can bring extra structural information for the domain of time–frequency to support the subsequent classification task. Then, with the input of TFSG, a graph-level fault diagnosis model based on an improved GraphSAGE network (IGN) is developed. Finally, the fault labels for all TFSGs are recognized with a SoftMax classifier. Two experimental cases based on axial flow pumps and a public gearbox dataset verified the effectiveness of the proposed fault diagnosis method.

Digital occupancy assessment for lighting evaluation: a pilot study to prepare for real-time research results

The relationship between lighting and human well-being is pivotal in architecture and environmental design. Adequate lighting enhances visual appeal and significantly influences occupant comfort, productivity, and satisfaction. This pilot study aimed to integrate digital occupancy assessment methods to understand indoor lighting conditions' impact on occupant well-being. Hypotheses explored the potential link between lighting conditions and occupant well-being, as assessed through physiological indicators and the influence of interior attributes on occupants' perceptions and physiological responses. The identified main components were satisfaction with artificial and daylighting, ambiance, and stimulation. The study explored the relationship between lighting conditions, physiological responses, and subjective assessments, revealing potential influences on occupant perceptions. A real-time data visualization could be valuable for data-intensive projects, enhancing comprehension and decision-making for facility management. While limited by the small sample size, trends, and moderate correlations were observed, indicating the need for larger, more diverse samples in future research.

The efficacy and safety of modified transbronchial cryobiopsy in the diagnosis of interstitial lung disease.

The objective of this study is to investigate the efficacy and safety of flexible transbronchial cryobiopsy (TBCB) in the diagnosis of diffuse parenchymal lung disease (DPLD) in a routine bronchoscopy examination room under analgesia and sedation, using neither endotracheal intubation or rigid bronchoscope nor fluoroscopy or general anesthesia. The data from 50 DPLD patients with unknown etiology who were treated in the Affiliated Hospital of Guilin Medical College from May 2018 to September 2020 were collected, and 43 were eventually included. The specimens obtained from these 43 patients were subjected to pathological examination, pathogenic microorganism culture, etc, and were analyzed in the clinical-radiological-pathological diagnosis mode to confirm the efficacy of TBCB in diagnosing the cause of DPLD. Subsequently, the intraoperative and postoperative complications of TBCB and their severity were closely observed and recorded to comprehensively evaluate the safety of TBCB. For the 43 patients included, a total of 85 TBCB biopsies were performed (1.98 [1, 4] times/case), and 82 valid tissue specimens were obtained (1.91 [1, 4] pieces/case), accounting for 96.5% (82/85) of the total sample. The average specimen size was 12.41 (1, 30) mm2. Eventually, 38 cases were diagnosed, including 11 cases of idiopathic pulmonary fibrosis, 5 cases of connective tissue-related interstitial lung disease, 5 cases of nonspecific interstitial pneumonia, 4 cases of tuberculosis, 4 cases of occupational lung injury, 3 cases of interstitial pneumonia with autoimmune characteristics, 1 case of lung cancer, 2 cases of interstitial lung disease (unclassified interstitial lung disease), 1 case of hypersensitivity pneumonitis, 1 case of pulmonary alveolar proteinosis, and 1 case of fungal infection. The remaining 5 cases were unclarified. For infectious diseases, the overall etiological diagnosis rate was 88.4% (38/43). With respect to complications, pneumothorax occurred in 4 cases (9.3%, 4/43, including 1 mild case and 3 moderate cases), of which 3 cases (75%) were closed by thoracic drainage and 1 case (25%) was absorbed without treatment. In addition, 22 cases experienced no bleeding (51.2%) and 21 cases suffered bleeding to varying degrees based on different severity assessment methods. TBCB is a minimally invasive, rapid, economical, effective, and safe diagnostic technique.

Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.

Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patients with AML, MDS, and MDS transformed into AML. Seven machine learning algorithms were established based on all of 26 m6A or main differentially expressed m6A regulator genes, and attempted to establish a risk assessment method to distinguish AML from MDS and predict the transformation of MDS into AML. In collective of m6A regulators sets, support vector machine (SVM) and neural network (NNK) model best distinguished AML or MDS from control, with area under the ROC curve (AUROC) 0.966 and 0.785 respectively. The SVM model best distinguished MDS from AML, with AUROC 0.943, sensitivity 0.862, specificity 0.864, and accuracy 0.864. In differentially expressed gene sets, SVM and logistic regression (LR) model best distinguished AML or MDS from control, with AUROC 0.945 and 0.801 respectively. The random forest (RF) model best distinguished between MDS and AML, with AUROC 0.928, sensitivity 0.725, specificity 0.898, and accuracy 0.818. For predictive capacity of MDS transformed into AML, SVM model showed the best predicted in collective m6A regulators sets, with AUROC 0.781 and accuracy 0.740. The LR model showed the best predicted in differential expression m6A regulators sets, with AUROC 0.820 and accuracy 0.760. All results suggested that machine learning model established by m6A regulators can be used to distinguished AML or MDS from control, distinguished AML from MDS and predicted the transformation of MDS into AML.

Assessing the multidimensional comfort of earplugs in virtual industrial noise environments

Earplugs’ comfort is primarily evaluated through cost-effective laboratory evaluations, yet these evaluations often inadequately capture the multidimensional comfort aspects due to design limitations that do not replicate real-world conditions. This paper introduces a novel laboratory method for comprehensive assessment of the multidimensional comfort aspects of earplugs, combining questionnaire-based evaluations and objective perceptual tests within virtual industrial sound environments replicating in-situ noise exposure. Objective perceptual results confirm that the sound environment affect participants’ ability to detect alarms in a noisy environment and comprehend speech-in-noise while wearing earplugs. Subjective questionnaire results reveal that the earplugs family has an effect on the primary attributes of the acoustical, physical and functional comfort’s dimension. Participants reported the physical dimension as the most important factor they take into account when evaluating earplugs’ comfort. The functional dimension was considered the second most important factor by the participants, followed by the psychological dimension, and the acoustical dimension.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next?

Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.

Factors influencing the relationship between perceptions of ecosystem services and well-being of farmers in the ore-agriculture zone, China

Understanding the coupling relationship between ecosystem service perceptions (ESP) and farmers’ well-being (FWB) in the ore-agriculture zone plays an important strategic role in maintaining regional ecological security and improving the well-being of regional farmers. Based on field research, questionnaire surveys and other methods, this study took farmers around the coal mining development zone as research subjects and used a comprehensive multi-index assessment method to evaluate the level of ESP and the FWB in the research area. A coupling coordination degree model was used to analyze the coupling relationship between ESP and FWB, and the random forest model was used to explore the influencing factors of the coupling relationship between the two. The results showed: (1) The FWB index was moderate and the ESP index was low. Farmers’ perceptions of the different ecosystem services varied, with provisioning services being perceived most strongly and supporting services least strongly. Satisfaction with basic material needs was highest on the well-being assessment. (2) The overall state of ESP and FWB coupling was good, with most of them in the state of coordination improvement. From the perspective of dimensional differentiation, the coupling of the dimensions of ESP and FWB was poorly coordinated, and all of them were in the state of dysfunction regression. (3) The coupling relationship between ESP and FWB was influenced by both environmental and household characteristics, with the former having a stronger effect. The results of this study can serve as a reference for promoting the coordination of ESP and FWB in the ore-agriculture zone, and ultimately realize the sustainable development of ecology and human in the ore-agriculture zone.

A Lab-Scale Experiment on the Spontaneous Combustion Behavior of Chronic Naturally–Oxidized Coal in Re-Mining Coal Mine

ABSTRACT Spontaneous combustion of coal in re-mining coal mine is a serious threat to the safety recovery of coal resources. In this paper, six coal samples, including two raw bituminous coal samples from the new mining working face and four oxidized bituminous coal samples from the re-mining working face, are selected. The pore structures, thermogravimetric properties, and spontaneous combustion characteristics of raw coal and oxidized coal are studied. The pore structures show that the surface of oxidized coals has more cracks and higher macropore volume and porosity as compared with raw coal. The thermogravimetric experiments demonstrate that the oxidized coal has a higher combustion rate and turns to the combustion reaction zone in advance. Moreover, the oxidized coals perform a better combustion property by showing a comprehensive combustion index of 6.7 × 10−9 min−2·°C−3 and 8.9 × 10−9 min−2·°C−3, higher than the raw coal of 4.0 × 10−9 min−2·°C−3. The spontaneous combustion characteristics show that the oxidized coal performs a greater rate of oxygen consumption and more oxidation products released than raw coal. The CPT of raw coal is higher than 157°C, while the CPT of oxidized coal is between 132°C and 140°C. The research results can provide a risk assessment method for coal spontaneous combustion in the re-mining coal mine, and provide guidance for avoiding the occurrence of coal fires.

Hydrogen leakage risk analysis of hydrogen emergency power supply vehicles using the AET-RM method

In recent years, research and application of hydrogen fuel emergency power supply vehicle technologies have been steadily advancing. However, due to the highly flammable and explosive nature of hydrogen gas, the safety risk of hydrogen leakage has been taken seriously. Addressing the current inadequacies in the understanding of hydrogen leakage risks, unclear sources of risk, incomplete coverage of risk scenarios, and insufficiencies in risk assessment methods associated with hydrogen fuel emergency power supply vehicles, this study firstly establishes a multi-scenario and multi-factor coupled hydrogen leakage risk event database and an accident risk system. This effort focuses on the diversity of application scenarios, types of hydrogen leakage hazards and complexity of interfering factors. Subsequently, by refining existing risk assessment methods, a more balanced subjective-objective evaluation and more granular risk ratings approach, termed AET-RM, is proposed, which ensures finer risk grading. Finally, through this method, the types of hydrogen leakage hazards and their harm boundaries are clarified, achieving a systematic and precise graded quantitative assessment of risk sources, probabilities, and consequences. The study results indicate that the established event database contains no risk events rated as I (acceptable) or V (unacceptable). There are 37 events classified as low risk (rating II), accounting for 17.54% of the total. Medium-risk events (rating III) comprise 142 incidents, making up 67.30%, while high-risk events (rating IV) include 32 cases, representing 15.17%. The highest risk score, determined by the AET-RM method, pertains to a risk event in the power generation scenario, Contingencies: metal corrosion within the battery.

Reflective Diaries as a Tool for Promoting Learning and Teaching in Higher Education

The concept of reflection has become increasingly prevalent in higher education over recent decades, particularly in the domains of teaching and learning. Being a reflective learner means that students can critically evaluate their learning, determine areas of their learning that need further development and make themselves more independent learners. This paper discusses a case study where reflective diaries were used to facilitate and assess both learning and teaching methods. As an assessment method, reflective diaries can assess students’ comprehension of the course content and can also promote critical self-reflection and enhance self-awareness (Biggs, 1999; O’Rourke, 1998). The data were collected through individual interviews and reflective diaries written by undergraduate students at the University of Dhofar. Data analysis indicates that the use of reflective diaries proved beneficial for enhancing both teaching and learning experiences. This is because the process of writing the diaries requires students to reflect on the learning activities that have taken place in class, analyze their own learning and express it in a personal way. Reflective diaries can also enable teachers to evaluate their teaching methods and generate feedback for improving their classroom practices.

Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images.

The purpose of this study was to establish and validate a deep learning model to screen vascular aging using retinal fundus images. Although vascular aging is considered a novel cardiovascular risk factor, the assessment methods are currently limited and often only available in developed regions. We used 8865 retinal fundus images and clinical parameters of 4376 patients from two independent datasets for training a deep learning algorithm. The gold standard for vascular aging was defined as a pulse wave velocity ≥1400 cm/s. The probability of the presence of vascular aging was defined as deep learning retinal vascular aging score, the Reti-aging score. We compared the performance of the deep learning model and clinical parameters by calculating the area under the receiver operating characteristics curve (AUC). We recruited clinical specialists, including ophthalmologists and geriatricians, to assess vascular aging in patients using retinal fundus images, aiming to compare the diagnostic performance between deep learning models and clinical specialists. Finally, the potential of Reti-aging score for identifying new-onset hypertension (NH) and new-onset carotid artery plaque (NCP) in the subsequent three years was examined. The Reti-aging score model achieved an AUC of 0.826 (95% confidence interval [CI] = 0.793-0.855) and 0.779 (95% CI = 0.765-0.794) in the internal and external dataset. It showed better performance in predicting vascular aging compared with the prediction with clinical parameters. The average accuracy of ophthalmologists (66.3%) was lower than that of the Reti-aging score model, whereas geriatricians were unable to make predictions based on retinal fundus images. The Reti-aging score was associated with the risk of NH and NCP (P < 0.05). The Reti-aging score model might serve as a novel method to predict vascular aging through analysis of retinal fundus images. Reti-aging score provides a novel indicator to predict new-onset cardiovascular diseases. Given the robust performance of our model, it provides a new and reliable method for screening vascular aging, especially in undeveloped areas.

Routine Coronary Angiography and Functional Cardiac Tests before Resection of Abdominal Aortic Aneurysm in Patients without Symptoms of Myocardial Infarction

INTRODUCTION: Cardiac complications are leaders in the structure of mortality of patients after resection of abdominal aortic aneurysms (AAA). In recent years more data have been obtained on the possibility of performing invasive coronary angiography (CAG) as a method of preoperative assessment of the condition of the coronary arteries. AIM: To assess the tactics of routine CAG and preventive revascularization of the myocardium in comparison with non-invasive functional tests in patients without clinical manifestations of coronary heart disease (CHD) from positions of the hospital outcomes of AAA resections. MATERIALS AND METHODS: A retrospective study included 205 patients without clinical and instrumental signs of CHD who underwent AAA resection at National Medical Research Center of Surgery named after A. Vishnevsky in 2001–2021. Patients of the first group (n = 55) underwent CAG before AAA resection and, if significant stenosis of coronary arteries was detected, revascularization of the myocardium was performed. In patients of the second group (n = 71), only functional cardiac tests were conducted before the operation. Patients of the third group were operated on without additional evaluation of cardiac status. RESULTS: In 30.9% of patients of the first group, significant coronary lesions were detected, in 29.5% of them with the involvement of the left main coronary artery. This required 10 percutaneous coronary interventions and 5 coronary artery bypass surgeries, which made 27.0% of patients in this group. No cases of myocardial infarction were recorded in this group. In the second and third groups, one case of myocardial infarction in each group was recorded (p = 0.688). There was no significant difference in the postoperative mortality in all the groups (1.8%; 4.2%; 2.5%, respectively; p = 0.704). To this end, maximally invasive diagnostic tactics increased the average waiting time for AAA resection: 52.2 ± 6.0 days in groups without coronary angiography and myocardial revascularization versus 99.2 ± 13.0 days in the first group. CONCLUSION: In a cohort of patients without anamnestic and clinical manifestations of coronary artery disease, the tactics of routine coronary angiography followed by prophylactic myocardial revascularization did not improve the short-term results of AAA resection, while the waiting period for intervention on the abdominal aorta increased. Preoperative tactics with functional cardiac tests also did not affect the treatment results.

Vibration serviceability assessment of ribbon-shaped large-span footbridge at high altitudes under wind-pedestrians coupling effects

Large-span footbridges have low structural stiffness, light deadweight and small damping and thus are prone to significant vibration under the wind-pedestrian coupling effect. Fast and accurate calculation of structural dynamic response is the key to vibration serviceability assessment of footbridges. This study proposed a method of vibration serviceability assessment considering wind-pedestrian coupling for the ribbon-shaped large-span footbridge at high altitudes. The simulated crowd load and wind load were obtained through theoretical derivation and wind tunnel tests. A refined numerical model was developed to analyze the dynamic response of a large-span footbridge under wind-pedestrian coupling. The peak acceleration of the footbridge under different cases was statistically analyzed for an empirical formula, and the serviceability assessment of the footbridge was realized based on the fitting formula. The results revealed that the dynamic response of the footbridge under wind-pedestrian coupling was larger than that under one type of load alone. The dynamic response under the coupling effect was not equal to the linear superposition of the dynamic response in the case of one load. The closer the excitation frequency is to the natural frequency of the structure, the larger the dynamic response of the structure is, and the superposition of multiple modes occurs.

Routine Coronary Angiography and Functional Cardiac Tests before Resection of Abdominal Aortic Aneurysm in Patients without Symptoms of Myocardial Infarction

INTRODUCTION: Cardiac complications are leaders in the structure of mortality of patients after resection of abdominal aortic aneurysms (AAA). In recent years more data have been obtained on the possibility of performing invasive coronary angiography (CAG) as a method of preoperative assessment of the condition of the coronary arteries. AIM: To assess the tactics of routine CAG and preventive revascularization of the myocardium in comparison with non-invasive functional tests in patients without clinical manifestations of coronary heart disease (CHD) from positions of the hospital outcomes of AAA resections. MATERIALS AND METHODS: A retrospective study included 205 patients without clinical and instrumental signs of CHD who underwent AAA resection at National Medical Research Center of Surgery named after A. Vishnevsky in 2001–2021. Patients of the first group (n = 55) underwent CAG before AAA resection and, if significant stenosis of coronary arteries was detected, revascularization of the myocardium was performed. In patients of the second group (n = 71), only functional cardiac tests were conducted before the operation. Patients of the third group were operated on without additional evaluation of cardiac status. RESULTS: In 30.9% of patients of the first group, significant coronary lesions were detected, in 29.5% of them with the involvement of the left main coronary artery. This required 10 percutaneous coronary interventions and 5 coronary artery bypass surgeries, which made 27.0% of patients in this group. No cases of myocardial infarction were recorded in this group. In the second and third groups, one case of myocardial infarction in each group was recorded (p = 0.688). There was no significant difference in the postoperative mortality in all the groups (1.8%; 4.2%; 2.5%, respectively; p = 0.704). To this end, maximally invasive diagnostic tactics increased the average waiting time for AAA resection: 52.2 ± 6.0 days in groups without coronary angiography and myocardial revascularization versus 99.2 ± 13.0 days in the first group. CONCLUSION: In a cohort of patients without anamnestic and clinical manifestations of coronary artery disease, the tactics of routine coronary angiography followed by prophylactic myocardial revascularization did not improve the short-term results of AAA resection, while the waiting period for intervention on the abdominal aorta increased. Preoperative tactics with functional cardiac tests also did not affect the treatment results.

Digital Divide as a Dynamic Challenge and a Leveraging Opportunity: A Phenomenological Study on Seasoned TLE/TVL Teachers' Adaptive Pedagogy

This study explores the perspectives and strategies of seasoned Teachers of Technology and Livelihood Education / Technical-Vocational Education and Training (TLE/TVL) in addressing the challenges posed by the Digital Divide in educational settings. The participants are four Master Teachers who have 10 or more teaching experience and currently teaching in the secondary school in the Province of Ilocos Sur. Two of them are from rural secondary schools while the other two are from secondary schools situated in urban areas. Through a phenomenology study and cool and warm analysis, the research investigates how these seasoned teachers view their teaching experience in using adaptive pedagogical approaches and perceive the impact of unequal access to technology and varying levels of digital literacy among learners on engagement and learning outcomes. Findings reveal that seasoned TLE/TVL teachers view the Digital Divide as both a dynamic challenge and a leveraging opportunity. They employ adaptive pedagogical approaches, including differentiated instruction, flexible grouping, and varied assessment methods, to cater to diverse learner needs. Continuous assessment of individual learner digital competencies and promotion of collaborative learning emerge as effective strategies to bridge digital gaps and enhance student engagement. The study underscores the importance of ongoing professional development in digital competencies and the need for institutions to prioritize access to technology and support flexibility in teaching practices. However, the study also identifies limitations, including potential sampling bias and the subjectivity of self-reported teacher experiences. Despite these constraints, the findings contribute valuable insights into effective strategies for fostering inclusive learning environments in vocational education amidst technological disparities.

Prediction of alkali–silica reaction expansions of mortars containing glass waste

The majority of existing findings regarding expansion (EXP) risks in concretes containing waste glass stem from experimental studies. There is a need for rapid assessment methods to ensure safer recycling of glass waste in cementitious composites. In this study, an artificial neural network (ANN) model was developed to accurately predict alkali–silica reaction EXP/mitigation resulting from the integration of glass waste in mortars. The analysis considered glass incorporation either separately as waste glass powder (WGP) and waste glass aggregates (WGAs), or in combination, at contents of up to 100% for WGA and 30% for WGP. A set of 175 mixtures was analysed, considering five distinct variables, which encompassed different mix proportions, involving varying components of cement, natural aggregates, WGP and WGA, in addition to the duration of environmental exposure. The results show that the EXP of WGA mortars decreased with the increased incorporation of WGP. The EXP values obtained from validation and experience confirm the high accuracy of the developed ANN model, with validation coefficients reaching up to 98.061% and a small value of the mean square error.