Traditional Tests Research Articles

The Method and Experiment of Detecting the Strength of Structural Components Utilizing the Distributed Strain of Sensing Optical Fibers Demodulated by OFDR.

Defects occurring during the welding process of metal structural components directly affect their overall strength, which is crucial to the load-bearing capacity and durability of the components. This signifies the importance of accurate measurement and assessment of weld strength. However, traditional non-destructive testing methods such as ultrasonic and non-contact camera inspection have certain technical limitations. In response to these issues, this paper analyzes the detection principle of weld strength, revealing that weld defects reduce the effective area of the structural bearing section and cause stress concentration around them. Through repeated experimental data analysis of samples, strain distribution data along the one-dimensional direction caused by defects such as slag inclusion and porosity were obtained. Experimental results show that this method can identify defect types in welds, including slag inclusion, porosity, and unevenness, and accurately measure the location and size of defects with a precision of 0.64 mm, achieving qualitative analysis of weld defects. Additionally, by deploying distributed optical fiber sensors (DOFS) at different vertical distances along the weld direction, the propagation law of stress induced by different types of weld defects on samples was thoroughly analyzed. This further validates the advantages of this method in weld strength detection, including high spatial resolution, high sensitivity, and non-destructive measurement.

A dual-reaction sites fluorescent probe for accurate detection of benzoyl peroxide in food

Benzoyl peroxide (BPO) is widely used as a whitening agent in flour, but excessive intake of BPO will severely endanger human health. To quickly and accurately detect BPO on-site, we have rationally designed a novel fluorescent probe PTPY-BE with dual-reaction sites. PTPY-BE underwent a specific cascade reaction with BPO to achieve high-contrast fluorescence turn-on response along with significant achromic reaction. The probe has high sensitivity, excellent selectivity, strong anti-interference ability and low detection limit (LOD = 0.83 mg·kg−1) for BPO. Furthermore, a portable detection platform was fabricated, which offers the portability and color visualization of traditional test strips and the color recognition of a smart device, enabling on-site visualization and quantitative detection of BPO. This platform has been successfully used to determine BPO in real food samples with good recoveries (93.59% - 107.13%). Therefore, this platform possessed great prospect and potential application for the determination of BPO in food.

Prioritizing privacy and presentation of supportable hypothesis testing in forensic genetic genealogy investigations.

Investigative leads are not generated by traditional forensic DNA testing, if the source of the forensic evidence or a 1stdegree relative of unidentified human remains is not in the DNA database. In such cases, forensic genetic genealogy (FGG) can provide valuable leads. However, FGG generated genetic data contain private and sensitive information. Therefore, it is essential to deploy approaches that minimize unnecessary disclosure of these data to mitigate potential risks to individual privacy. We recommend protective practices that need not impact effective reporting of relationship identifications. Examples include performing one-to-one comparisons of DNA profiles of third-party samples and evidence samples offline with an "air gap" to the internet and shielding the specific shared single nucleotide polymorphisms (SNP) states and locations by binning adjacent SNPs in forensic reports. Such approaches reduce risk of unwanted access to or reverse engineering of third-party individuals'genetic data and can give these donors greater confidence to support use of their DNA profiles in FGG investigation.

Innovative 2D Nanomaterials-Based Electrochemical Microfluidic Sensors for Food Safety and Environmental Ecology

2D nanomaterials-infused electrochemical microfluidic sensors mark a paradigm shift in precision sensing technology. Leveraging the unique properties of materials like carbons, transition metal and molecularly imprinted polymers, these sensors redefine the benchmarks for electrochemical sensing with their extraordinary sensitivity, rapid response kinetics, and remarkably low detection limits. The integration of a microfluidic system further enhances their capabilities, providing meticulous control over sample volumes and reaction kinetics, ensuring unparalleled reliability and reproducibility in real-time monitoring applications. This amalgamation of advanced nanomaterials and microfluidic technology not only sets new standards for precision sensing but also opens avenues for transformative applications across diverse fields. In the realm of food safety, these sensors emerge as pioneering tools, offering exceptional specificity for the precise detection of contaminants, pathogens, and spoilage indicators. Their rapid, on-site analysis capabilities revolutionize traditional testing methods, enabling timely interventions and elevating the overall quality assurance in the food industry. Simultaneously, their impact extends to environmental ecology, where the sensors play a crucial role in monitoring pollutants, heavy metals, and environmental stressors in real-time. The compact design and portability of these sensors facilitate on-site environmental monitoring, transcending the limitations of conventional laboratory-based testing and contributing to more sustainable and resilient ecosystems. Beyond their technological prowess, these sensors carry significant societal and economic implications. Their compact and portable nature streamlines on-site analyses, minimizing logistical challenges associated with sample transportation and centralized laboratory facilities. This efficiency not only enhances monitoring processes but also holds the potential to revolutionize resource allocation in sectors reliant on rapid and reliable analytical insights. In essence, 2D nanomaterials-based electrochemical microfluidic sensors represent a transformative leap towards precision sensing, promising a future where safeguarding the integrity of our food supply and ecological systems is seamlessly intertwined with technological innovation. Figure 1

Molecular Factors Predicting Ovarian Chemotoxicity in Fertile Women: A Systematic Review.

Background: Recent advances in cancer diagnosis and treatment have significantly improved survival rates among women of reproductive age facing cancer. However, the potential iatrogenic loss of fertility caused by chemotherapeutic agents underscores the need to understand and predict chemotherapy-induced ovarian damage. This study addresses this gap by systematically reviewing the literature to investigate genetic markers associated with chemotherapy-induced ovarian failure (CIOF). Objective: The primary objective is to identify genetic markers linked to CIOF, contributing to a comprehensive understanding of the factors influencing fertility preservation in female cancer survivors. Methods: A systematic review was conducted using PubMed, EMBASE, Web of Science, Scopus, and OVID electronic databases from inception through December 2023. Studies were included if they featured genomic assessments of genes or polymorphisms related to CIOF in women with histologically confirmed tumors. Exclusion criteria comprised in vitro and animal studies, reviews, and pilot studies. The resulting four human-based studies were scrutinized for insights into genetic influences on CIOF. Results: Of the 5179 articles initially identified, four studies met the inclusion criteria, focusing on alkylating agents, particularly cyclophosphamide, and anthracyclines. Su et al. explored CYP3A41B variants, revealing modified associations with CIOF based on age. Charo et al. investigated GSTA1 and CYP2C19 polymorphisms, emphasizing the need to consider age and tamoxifen therapy in assessing associations. Oktay et al. delved into the impact of BRCA mutations on anti-Müllerian hormone (AMH) levels post-chemotherapy, supported by in vitro assays. Van der Perk et al. focused on childhood cancer survivors and revealed significant associations of CYP3A43 and CYP2B6*2 SNPs with AMH levels. Conclusions: This systematic review analyzes evidence regarding genetic markers influencing CIOF, emphasizing the complex interplay of age, specific genetic variants, and chemotherapy regimens. The findings underscore the need for a personalized approach in assessing CIOF risk, integrating genetic markers with traditional ovarian reserve testing. The implications of this study extend to potential advancements in fertility preservation strategies, offering clinicians a comprehensive baseline assessment for tailored interventions based on each patient's unique genetic profile. Further research is essential to validate these findings and establish a robust framework for integrating genetic markers into clinical practice.

AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

This study investigates the application of artificial intelligence (AI) to predict the compressive strength of self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing to sustainable construction practices. By leveraging advancements in computational techniques, specifically artificial neural networks (ANNs), we developed highly accurate predictive models to forecast the compressive strength of SCC based on ultrasonic pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved concrete quality, despite the general trend of decreased compressive strength with increased air-entraining admixture (AEA) concentrations. The ANN models show exceptional effectiveness in predicting compressive strength, with a correlation coefficient (R2) of 0.99 between predicted and actual values, providing a robust tool for optimizing SCC mix designs and ensuring quality control. This AI-driven approach enhances sustainability by improving material efficiency and significantly reducing the need for traditional destructive testing methods, thus offering a rapid, reliable, and non-destructive alternative for assessing concrete properties.

Evaluation of an innovative, open-source and quantitative approach for the kinematic analysis of rock slopes based on UAV based Digital Outcrop Model: A case study from a railway tunnel portal (Finale Ligure, Italy)

This paper presents a novel, cost-effective method for a rapid and quantitative characterization of discontinuities in fractured rock cliffs using Unmanned Aerial Vehicle-derived Digital Outcrop Models (DOMs). The proposed workflow combines manual extraction of discontinuity data from the DOM with a kinematic analysis using an automated algorithm (ROKA), which upgrades and renews the traditional Markland's test. The case study, a designed railway tunnel portal in NW Italy (Finale Ligure) demonstrates the advantages and limitations of this approach compared to current methods. Manual mapping is compared with (semi-)automatic extraction of discontinuity data from the DOM and validated with field-based scan lines. The results show that in complex geological settings, manual mapping provides more statistically robust and geologically reliable measurements of discontinuity orientation, validated by the user's expertise. Traditional stereographic approaches in kinematic analysis are limited in characterizing spatially variable slopes and discontinuity networks, providing only generic estimations of unstable slope surfaces and discontinuity intersections. This limitation is overcome by new algorithms that incorporate 3D spatial relationships of both the discontinuity network and the rock slope, resulting in a more accurate and site-specific kinematic characterization of the rock cliff, emphasized by 3D visualizations of the critical planes and potential rock failure volumes. The new approach significantly impacts the time, effort, and cost of the engineering projects, allowing to quantitatively define the potential unstable areas with a fast analysis. The Authors stress the importance of integrating digital workflows with comprehensive field-based characterizations of the local litho-stratigraphic and structural setting to effectively utilize large datasets and partially automated procedures.

Assessing parosmia patients: a study on the evaluation method using a self-administered odor questionnaire for parosmia.

Parosmia symptoms are difficult to quantify due to their heterogeneity among patients, and thus a clinical challenge. This study aimed to assess parosmia with Self-Administered Odor Questionnaire for Parosmia (SAOQ-P), a modification of the widely used SAOQ in Japan. The primary objective was to assess the effectiveness of SAOQ-P in identifying parosmia symptoms and its potential integration into the clinical assessment process. The study also explored traditional olfactory test differences between patients with and without parosmia. Patients at Jikei Smell Clinic that presented between May 2022 and November 2022 were recruited and administered the SAOQ-P, which had an added question about changes in the perception of 20 daily odors compared to the original SAOQ. Traditional olfactory tests utilized T&T olfactometry and Open Essence. Of 279 patients, 81 had parosmia, while 198 did not exhibit parosmic symptoms. Parosmia prevalence was influenced by the cause of olfactory dysfunction, with post-infectious and post-COVID-19 patients showing higher parosmia rates. Among parosmia patients, 87% reported changes in their perception of at least one odor assessed by SAOQ-P, with coffee, stool, and perfume most commonly affected. Traditional olfactory tests showed no significant differences between parosmia and non-parosmia groups. The number of odors causing parosmia was negatively correlated with age. SAOQ-P offers a promising approach to assess and quantify parosmia symptoms, seamlessly integrating into clinical assessments. SAOQ-P identified parosmia in 87% of patients and revealed insights into triggering factors. Traditional olfactory tests' limitations underscore the need for more accurate, patient-centric diagnostic approaches for parosmia.

Validity of a motor-cognitive dual-task agility test in elite youth football players.

Agility, as the ability to react rapidly to unforeseen events, is an essential component of football performance. However, existing agility diagnostics often do not reflect the complex motor-cognitive interaction required on the field. Therefore, this study evaluates the criterion and ecological validity of a newly developed motor-cognitive dual-task agility approach in elite youth football players and compare it to a traditional reactive agility test. Twenty-one male youth elite football players (age:17.4±0.6; BMI:23.2±1.8) performed two agility tests (reactive agility, reactive agility with integrated multiple-object-tracking (Dual-Task Agility)) on the SKILLCOURT system. Performance was correlated to motor (sprint, jump), cognitive (executive functions, attention, reaction speed) and football specific tests (Loughborough soccer passing test (LSPT)) as well as indirect game metrics (coaches' rating, playing time). Reactive agility performance showed moderate correlations to attention and choice reaction times (r=0.48-0.63), as well as to the LSPT (r=0.51). The dual-task agility test revealed moderate relationships with attention and reaction speed (r=0.47-0.58), executive functions (r=0.45-0.63), as well as the game metrics (r=0.51-0.61). Finally, the dual-task agility test significantly differentiated players based on their coaches' rating and playing time using a median split (p<0.05; d=0.8-1.28). Motor-cognitive agility performance in elite youth football players seems to be primarily determined by cognitive functions. The integration of multiple object tracking into reactive agility testing seems to be an ecologically valid approach for performance diagnostics in youth football.

Unveiling potential: urinary exosomal mRNAs as non-invasive biomarkers for early prostate cancer diagnosis

BackgroundThis study investigated the use of urinary exosomal mRNA as a potential biomarker for the early detection of prostate cancer (PCa).MethodsNext-generation sequencing was utilized to analyze exosomal RNA from 10 individuals with confirmed PCa and 10 individuals without cancer. Subsequent validation through qRT-PCR in a larger sample of 43 PCa patients and 92 healthy controls revealed distinct mRNA signatures associated with PCa.ResultsNotably, mRNAs for RAB5B, WWP1, HIST2H2BF, ZFY, MARK2, PASK, RBM10, and NRSN2 showed promise as diagnostic markers, with AUC values between 0.799 and 0.906 and significance p values. Combining RAB5B and WWP1 in an exoRNA diagnostic model outperformed traditional PSA tests, achieving an AUC of 0.923, 81.4% sensitivity, and 89.1% specificity.ConclusionsThese findings highlight the potential of urinary exosomal mRNA profiling, particularly focusing on RAB5B and WWP1, as a valuable strategy for improving the early detection of PCa.

Improving Access to Hereditary Testing in Pancreatic Ductal Carcinoma.

Approximately 5%-10% of patients with pancreatic ductal adenocarcinoma (PDAC) have an inherited basis, yet uptake of genetic testing remains low and subject to disparities. This study compared two genetic testing pathways available to patients referred to a provincial cancer center, BC Cancer: a traditional hereditary cancer clinic-initiated testing (HCT) pathway and a new oncology clinic-initiated testing (OCT) pathway. Study subjects were patients with confirmed PDAC referred for genetic testing through the HCT or OCT pathway between June 1, 2020, and February 1, 2022. Charts were retrospectively reviewed for patient characteristics and testing outcomes. The study population was 397 patients (HCT, n = 279 and OCT, n = 118). OCT patients were more likely to have non-European ethnicity compared with HCT patients (41.9% v 25.6%, P = .004), to have earlier-stage disease (P = .012), and to have better Eastern Cooperative Oncology Group performance status than the HCT group (P = .001). A total of 306 patients completed testing (77%). OCT patients had higher test completion rates than HCT patients (odds ratio, 3.74 [95% CI, 1.66 to 9.62]). Median time for results was shorter in OCT than in HCT (53 days [IQR, 44-76] v 107 days [IQR, 63.8-158.3]). Pancreatic cancer susceptibility pathogenic gene variants were identified in 8.5% (26/306). The real-world observations in our study show that oncology clinic-initiated hereditary testing is more effective and faster than testing through hereditary cancer clinic referrals and reaches a more ethnically diverse population. This has important implications for publicly funded environments with limited resources for genetic counseling.

Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci.

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

Traditional Chinese-version reliability test of the Pieper-Zulkowski pressure ulcer knowledge: Psychometric and assessment.

This study was to translate the Pieper-Zulkowski pressure ulcer knowledge test (PZ-PUKT) into Traditional Chinese and evaluate its psychometric properties as well as identify the predictors of knowledge on pressure injury. The PZ-PUKT was translated into Traditional Chinese (TC-PZ-PUKT), and its content validity was evaluated. A total of 296 nurses participated in this study and completed the 72-item TC-PZ-PUKT online. The reliability of the TC-PZ-PUKT was analysed by evaluating its internal consistency and test-retest reliability. Hierarchical regression was used to determine factors associated with TC-PZ-PUKT scores. Content validity was achieved with a score of 0.986. Internal consistency was observed to be reliable, with a Cronbach's alpha of 0.858. The mean knowledge score on the TC-PZ-PUKT was 72.5%, with a 1-week test-retest reliability of r = 0.849. Education level, certification as a wound specialist and self-learning through reading articles, books or guidelines on pressure injury were significantly associated with TC-PZ-PUKT scores. The TC-PZ-PUKT is a valid and reliable tool. Education level, certification as a wound specialist and self-learning regarding pressure injury are related to knowledge of pressure injury.

Coronary Atherosclerotic Plaque Burden Assessment by Computed Tomography and Its Clinical Implications.

Recent studies have demonstrated that coronary plaque burden carries greater prognostic value in predicting adverse atherosclerotic cardiovascular disease outcomes than myocardial ischemia, thereby challenging the existing paradigm. Advances in plaque quantification through both noncontrast and contrast-enhanced computed tomography (CT) methods have led to earlier and more cost-effective detection of coronary disease compared with traditional stress testing. The 2 principal techniques of noninvasive coronary plaque quantification assessment are coronary artery calcium scoring by noncontrast CT and coronary CT angiography, both of which correlate with disease burden on invasive angiography. Plaque quantification from these imaging modalities has shown utility in risk stratification and prognostication of adverse cardiovascular events, leading to increased incorporation into clinical practice guidelines and preventive care pathways. Furthermore, due to their expanding clinical value, emerging technologies such as artificial intelligence are being integrated into plaque quantification platforms, placing more advanced measures of plaque burden at the forefront of coronary plaque evaluation. In this review, we summarize recent clinical data on coronary artery calcium scoring and coronary CT angiography plaque quantification in the evaluation of adverse atherosclerotic cardiovascular disease in patients with and without chest pain, highlight how these methods compare to invasive quantification approaches, and directly compare the performance characteristics of coronary artery calcium scoring and coronary CT angiography.

A mechanical characteristic capture method considering printing configurations for buildability modeling in concrete 3D printing

The structure failure modeling of 3D printing concrete (3DPC) during production is crucial for structure design, manufacturing process control and optimization. Serving as model inputs, the accuracy of 3DPC fresh material properties measurement highly affects the model prediction performance. The measured mechanical properties of freshly printed concrete strongly depend on the geometry, deformation and hardening process of used samples in testing. Herein, we propose an all-in-one method (AIOM) that synchronously considers these key factors (layer geometry, deformation, and hardening process) to more accurately capture the early-age mechanical performance distribution in printed structures. Different parametric-mechanical buckling models and two printable cementitious materials with distinct hardening characteristics (printable cement and printable geopolymer) were used to validate the performance of AIOM, with the traditional testing method, uniaxial unconfined compression test (UUCT), as the reference. Compared with UUCT, AIOM can improve the buildability prediction accuracy by 11.9 % to 50.8 % for different validation scenarios. This novel testing method for 3DPC fresh material properties contributes to improving the accuracy of 3DPC structure failure models, thereby facilitating a better production phase control for 3DPC.

Integrated model for simulating Coble creep deformation and void nucleation/growth in polycrystalline solids − Part II: Validation for material design

Part II of the paper presents comprehensive investigations aimed at demonstrating the validation and effectiveness of the proposed integrated model for simulating Coble creep deformation and void nucleation/growth in polycrystalline solids for material design. Quantitative comparisons between the model’s predictions of void area fraction in a 2D cross-section and experimental results using Alloy 201, a commercially pure nickel, demonstrated a high degree of agreement. Furthermore, the numerical simulation results align with theoretical equations, affirming the model’s validity and its capacity to represent complex phenomena accurately. Although traditional laboratory testing for pure Coble creep is challenging due to constraints such as time and equipment limitations, the proposed model offers a distinct advantage, allowing numerical simulation of Coble creep in materials with arbitrary polycrystalline morphologies under various environmental conditions. Leveraging this capability, the study conducted comprehensive numerical simulations to quantitatively explore the effects of environmental and polycrystalline morphology factors on Coble creep deformation and void nucleation/growth in polycrystalline solids. These aspects, which have not been comprehensively addressed in existing studies, were thoroughly investigated. The findings presented here establish a novel foundation for designing heat-resistant materials applicable across diverse equipment scenarios.

Machine Learning-Based Prediction Approach for COVID-19 Detection via Chest X-Ray Scans

Objectives: In medical research, the application of AI technologies has become a desirable approach for disease diagnosis due to their ability to increase diagnosis speed and accuracy. One major application of these technologies is in diagnosing diseases using medical imagery data such as X-ray and CT scan images. The objective of this study is to implement a COVID-19 diagnosis approach based on ML and compare it with laboratory testing approaches. Methods: The study utilizes the VGG16 architecture to extract features from X-ray and CT scan images, which are then used to build an ANN classifier to predict images as either COVID-positive or normal. Real-world datasets of varying difficulty were employed to evaluate the system's performance under different stressors, while comparative analysis with other classifiers was conducted to assess the model's improvement in accuracy and time complexity. Findings: The proposed architecture correctly classified 98.41% of the testing images. Among the images from infected patients in the testing set, 97.54% were accurately identified, while 98.72% of the normal cases were correctly classified. The model demonstrated good precision, with 96.49% of predicted COVID cases truly being from COVID-19 patients. Additionally, the F1 score for the model is 98.41%, indicating a good balance in predicting both categories. Showcasing its ability to handle multiple predictions quickly. The model's execution time for classifying a single case was computed as 0.0039 seconds, showcasing its ability to handle multiple predictions quickly. The proposed approach exhibits superior performance on datasets with balanced outcomes, surpassing the accuracy of SVM, DT, Logistic Regression, and Random Forest models. The approach outperforms laboratory-based testing approaches in terms of accuracy cost and assay times. Novelty: This study not only implements a robust ML-based approach for COVID-19 diagnosis but also provides a unique comparative analysis with traditional laboratory-based testing methods, such as Reverse Transcription Polymerase Chain Reaction (RT-PCR), reported in other literature. This dual focus on ML performance and its comparison with established testing methods highlights the practical advantages of ML in terms of diagnosis speed, accuracy, and cost-efficiency, offering a comprehensive perspective not commonly addressed in existing research. Keywords: COVID-19, Machine Learning techniques, F1 score, Classifier, Specificity, Accuracy, Sensitivity, Precision

Evaluation of Oxygen Saturation Levels Using a Custom-modified Finger Pulse Oximeter for Assessment of Pulp Vitality in Various Clinical Situations in Pediatric Dental Practice: An In Vivo Study.

A major drawback of traditional pulp testing procedures is that they depend on neural responses rather than vascular circulation. Hence, this study aimed to develop a custom-modified dental sensor using a finger pulse oximeter (PO) that is applicable to any type of tooth so as to test its ability and accuracy in evaluating the oxygen saturation (SaO2) values of teeth at different developmental stages as a measure of pulp vitality. A customized finger PO was employed to determine the systemic and pulp SaO2 levels in 300 children. A total of 600 teeth (primary and permanent) were divided into group I (100 primary molars), group II (200 permanent molars), group III (200 permanent incisors), and 100 endodontically treated (primary and permanent) teeth were included in group IV. The mean SaO2 values thus obtained in various groups were compared. Intragroup comparison of mean SaO2% of the patient's finger (systemic), test teeth, and control teeth showed significant differences (p ≤ 0.001). Intergroup comparisons also revealed significant differences in most of the groups. The modified PO probe can be applied to any type of tooth. It was found to be accurate and sensitive enough to detect changes in SaO2 levels in various clinical situations irrespective of developmental stage. It proved to be a direct method of evaluation of pulp vitality by objective means. Patil A, Garg N, Pathivada L, et al. Evaluation of Oxygen Saturation Levels Using a Custom-modified Finger Pulse Oximeter for Assessment of Pulp Vitality in Various Clinical Situations in Pediatric Dental Practice: An In Vivo Study. Int J Clin Pediatr Dent 2024;17(S-1):S30-S36.

Controllability of heterogeneous multi-agent systems via cooperative output regulation

This paper investigates the controllability of heterogeneous linear multi-agent systems (MASs) based on the cooperative output regulation, in which the considered agents are of high-degree heterogeneity, including different dimensions, heterogeneous dynamics and nonidentical interactions varying with agent and its neighbors. A design framework is developed for nonidentical external input and interaction gains, with the help of the improved cooperative regulators and virtual triple. Some necessary and/or sufficient conditions are derived to ensure the controllability of MASs. The obtained controllability criteria require lower computational cost and are much easier to examine compared with directly performing the traditional controllability test on heterogeneous MASs. Furthermore, extended controllability analysis without regulation assumptions is also provided to illustrate the effect of interactions among diverse agents on the controllability. The effectiveness of theoretical results is substantiated finally by two examples: a 6-agents heterogeneous MAS and a practical MAS composed of heterogeneous aircrafts and vehicles.

Insignificant but Essential: The Critical Role of Non-Significant Variables through Necessity Logic in Sport Event Tourism Context

This study explores the application of Single Necessary Condition Analysis (NCA) alongside traditional regression-based techniques, such as Structural Equation Modeling (SEM), to provide a comprehensive understanding of consumer behavior in sport and tourism settings. Traditional methods, including SEM, often rely on additive logic, which predicts outcomes using multiple variables that can compensate for each other. While useful, this approach may offer an incomplete picture of complex consumer phenomena. In contrast, necessity logic, exemplified by NCA, identifies conditions that must be present for an outcome to occur. This study applies NCA to evaluate the essential factors influencing intentions to visit sport events in Bangkok, Thailand, specifically focusing on attitudes, subjective norms, and perceived behavioral control as necessary conditions. The findings indicate that if these conditions are not met, the intention to visit a sport event typically will not occur. By integrating both necessity and additive logics, this study reveals that all variables in the Theory of Planned Behavior (TPB) are indispensable for forming an intention to visit a sport event. The analysis shows that attitudes, subjective norms, and perceived behavioral control are necessary conditions, even though subjective norms and perceived behavioral control may not appear significant in traditional SEM tests. The study underscores the importance of considering multiple causal perspectives—deterministic, probabilistic, and typicality—to achieve a holistic understanding of consumer intentions. This dual approach ensures that significant variables are not overlooked, providing robust support for the TPB in the context of sport event marketing. Future research should continue to incorporate complementary perspectives to enhance the accuracy and depth of consumer behavior analysis in various settings.