Test Setting Research Articles

Spatial and temporal characterization of Aedes albopictus oviposition activity in candidate urban settings for sterile insect technique testing in La Reunion Island

BackgroundUnderstanding of mosquito spatiotemporal dynamics is central to characterize candidate field sites for the sterile insect technique (SIT) testing, and is critical to the effective implementation and evaluation of pilot sterile male release programs. Here, we present a detailed description of Aedes albopictus (Skuse) egg-laying activity over a 6-year period in urban areas identified as potential SIT testing sites on Reunion Island.MethodWeekly entomological collections using ovitraps were carried out in residential and adjacent uninhabited habitats in two urban areas, Duparc and Bois Rouge, in the municipality of Sainte Marie, Reunion Island. Time-series data incorporating the frequency of positive ovitraps and the total number of eggs/ovitrap recorded each time at each locality during the study period from May 2013 to December 2018 were analyzed with multifaceted statistical approaches including descriptive statistics and spatiotemporal analyses incorporating the role of climatic factors on overall ovitrap productivity.ResultsDuring the ovitrap survey, the proportion of egg-positive ovitraps differed among study sites (χ2 = 50.21, df = 2, P < 0.001), being relatively lower in Duparc (89.5%) than in Bois-Rouges (95.3%) and the adjacent buffer zone (91.2%). Within each neighborhood, Ae. albopictus egg abundance varied by month in a roughly seasonal pattern marked by a single peak occurring more regularly February each year, a decline at the onset of the austral winter in July, followed by a period of lower ovitrap productivity in August and September. Fluctuation in both positivity rate and eggs densities per ovitraps were related to annual and seasonal variations in local temperature and rainfall (P < 0.001 in all cases). The spatial analysis also captured substantial between- and within-habitats heterogeneity, whereby the overall ovitrap productivity was higher in residential areas than in the buffer zone.ConclusionsCollectively, these results reveal that the distribution of Ae. albopictus oviposition activity is shaped by local habitat heterogeneity and seasonal climatic factors. Overall, this study provides baseline insights into the reproductive dynamics of Ae. albopictus, which would assist in planning locally tailored SIT interventions, while addressing concerns related to focal areas of high egg-laying intensity and potential immigration of females from natural areas.

53 Clinical characteristics and outcomes of children hospitalized with RSV-associated acute respiratory infections

Abstract Background Respiratory syncytial virus (RSV) is one of the most common acute respiratory infections (ARI) in infants and young children. After the relaxation of COVID-19 pandemic restrictions, children’s hospitals worldwide experienced unprecedented volumes and severity in presentation of children admitted with ARIs, such as RSV. It is unknown whether the increase in volumes was also associated with changes in the typical demographic, severity and outcomes of patients with RSV infections. Objectives To describe sociodemographic characteristics, clinical presentation, management including diagnosis and disposition and clinical outcomes of children under the age of 18 years admitted to tertiary care paediatric hospital with RSV-associated ARI. Design/Methods Observational cohort of children and adolescents aged 0-18 years admitted to two large Canadian children’s hospitals between July 1, 2022 - December 31, 2022, who had microbiological evidence of RSV infection. Detailed clinical and demographic information were extracted, including admission and discharge diagnoses, diagnostic tests, viral and bacterial testing, interventions, and respiratory settings. Clinical outcomes and complications were collected, including length of hospital admission, paediatric intensive care unit (PICU) admission, need for invasive mechanical ventilation, disposition, and death. Results There were 455 patients admitted with an RSV infection, of which a third (35.0%, n=146) were transfers from other hospitals. Cases were 41.0% male and the median age was 1.22 years (interquartile range [IQR] 0.19-3.06). Nearly two-thirds of patients (62.0%, n=284) were younger than 2 years of age, 8.6% had a history of prematurity. One third (31.0%, n=142) of patients had at least one documented comorbid condition, the most common diagnosis was asthma (6.6%). In addition to RSV, 3.5% (n=16) of patients had a simultaneous bacterial pathogen identified. Most patients (73%, n=330) were only RSV positive, while 24% (n=109) had two pathogens identified. One quarter (25.9%) were admitted to the PICU. The median length of hospital stay was 3 days (IQR 2.00-5.00) and for those admitted to the PICU, nearly two thirds of patients (63.0%, n=75) were admitted for less than 4 days. 0 patients died. The most common discharge diagnosis in 61.0% of patients (n=279) was bronchiolitis. There were 56 patients (12.3%) who had an unscheduled return visit to the ED within 30 days of discharge with 23.0% (n=13) requiring readmission to the hospital. Conclusion There continues to be a substantial volume of hospitalizations secondary to RSV infection, predominantly in those &amp;lt;2 years of age, with a prominent diagnosis of bronchiolitis. A quarter of patients had notable severity requiring PICU admission.

Toward Intelligent Head Impulse Test: A Goggle-Free Approach Using a Monocular Infrared Camera.

To assess vestibular function, video head impulse test (vHIT) is taken as the gold standard by evaluating the vestibulo-ocular reflex (VOR). However, vHIT requires the patient to wear a specialized head-mounted goggle equipment that needs to be calibrated before each use. For this, we proposed an intelligent head impulse test (iHIT) setting with a monocular infrared camera instead of the head-mounted goggle and contributed correspondingly a video classification approach with deep learning to vestibular function determination. Within the iHIT framework, a monocular infrared camera was set in front of the patient to capture test videos, based on which a dataset DiHIT of HIT video clips was set up. We then proposed a two-stage multi-modal video classification network, trained on the dataset DiHIT, that took as input the eye motion and head motion data extracted from the facial keypoints via HIT clips and outputted the identification of the semicircular canal (SCC) being tested (SCC identification) and determination of VOR abnormality (SCC qualitation). Experiments on this dataset DiHIT showed that it achieved the accuracy of 100% in prediction of SCC identification. Furthermore, it attained predictive accuracies of 84.1% in horizontal and 79.0% in vertical SCC qualitation. Compared with existing video-based HIT, iHIT eliminates goggles, does not require equipment calibration, and achieves complete automation. Furthermore, iHIT will bring more benefits to users due to its low cost and ease of operation. Codes and use case pipeline are available at: https://github.com/dec1st2023/iHIT. 3 Laryngoscope, 2024.

Zero-Shot Relation Classification Through Inference on Category Attributes.

The goal of relationship classification (RC) is to predict the semantic relationship between two entities in a given sentence. With the advent of deep learning and pretrained language models, RC research has progressed by leaps and bounds. However, the current studies are focused mainly on predicting semantic relationships from a predefined set. How to recognize unseen relationships remains a challenge, which is also known as the zero-shot RC (ZSRC) task. Some ZSRC-related methods directly map relationship categories to numerical indices, constraining the model's ability to autonomously infer and understand these relationships, while others rely heavily on manual definitions. To address these issues and inspired by the way of reasoning in which humans perform RC tasks, we propose a new framework to handle the ZSRC task through inference on category attributes (ICAs). The main idea of ICA is to detect the semantic relationship between promises, which are RC sentences, and hypotheses, which are relational sentences of entities created by templates. Specifically, instead of manual design, we introduce two hypothesis templates derived from the label words (LWs) and descriptions (LDs) associated with each relationship. These templates are used to automatically convert the RC data into the textual entailment (TE) format. Furthermore, they are fine-tuned with a pretrained TE model, facilitating the acquisition of relational knowledge and enabling the generalization of semantic reasoning rules learned from seen classes to unseen classes. Moreover, to implement multirelationship semantic inference for all unseen classes, we propose an entailment difference mechanism to enhance the reasoning capability of the model. Besides the current ZSRC test setting, we also examine our method in an even more challenging setting to deal with data scarcity in real-world applications. The outstanding performance of ICA on the FewRel and Wiki-ZSL datasets demonstrates its effectiveness in the ZSRC task.

Diagnostic serology test comparison for Q fever and Rift Valley fever in humans and livestock from pastoral communities.

Q fever (QF) and Rift Valley fever (RVF) are endemic zoonotic diseases in African countries, causing significant health and economic burdens. Accurate prevalence estimates, crucial for disease control, rely on robust diagnostic tests. While enzyme-linked immunosorbent assays (ELISA) are not the gold standard, they offer rapid, cost-effective, and practical alternatives. However, varying results from different tests and laboratories can complicate comparing epidemiological studies. This study aimed to assess the agreement of test results for QF and RVF in humans and livestock across different laboratory conditions and, for humans, different types of diagnostic tests. We measured inter-laboratory agreement using concordance, Cohen's kappa, and prevalence and bias-adjusted kappa (PABAK) on 91 human and 102 livestock samples collected from rural regions in Chad. The serum aliquots were tested using ELISA in Chad, and indirect immunofluorescence assay (IFA) (for human QF and RVF) and ELISA (for livestock QF and RVF) in Switzerland and Germany. Additionally, we examined demographic factors influencing test agreement, including district, setting (village vs. camp), sex, age, and livestock species of the sampled individuals. The inter-laboratory agreement ranged from fair to moderate. For humans, QF concordance was 62.5%, Cohen's kappa was 0.31, RVF concordance was 81.1%, and Cohen's kappa was 0.52. For livestock, QF concordance was 92.3%, Cohen's kappa was 0.59, RVF concordance was 94.0%, and Cohen's kappa was 0.59. Multivariable analysis revealed that QF test agreement is significantly higher in younger humans and people living in villages compared to camps and tends to be higher in livestock from Danamadji compared to Yao, and in small ruminants compared to cattle. Additionally, RVF agreement was found to be higher in younger humans. Our findings emphasize the need to consider sample conditions, test performance, and influencing factors when conducting and interpreting epidemiological seroprevalence studies.

Association between working memory performance and parent and teacher ratings of working memory in 11-year-old children born preterm

ABSTRACT Working memory (WM) difficulties are often observed in children born preterm. We examined whether performance-based measures of WM components are associated with parent- and teacher-rated WM difficulties in the everyday life of children born very preterm and/or at very low birth weight (VPT/VLBW) at 11 years (n = 165). The WM components as defined in the original Baddeley’s model – phonological loop (PL), visuospatial sketchpad (VS), and central executive (CE) – were assessed with tasks from the Working Memory Test Battery for Children (WMTB-C) and the Wechsler Intelligence Scale for Children – Fourth edition (WISC-IV). Parents and teachers completed the WM subscale of the Behavioral Rating Inventory for Executive Functions (BRIEF). Measures of WM components were modestly associated with BRIEF scores, explaining 18.9% of the variance in parent-rated and 14.0% of teacher-rated WM difficulties. CE was the component most consistently associated with parent- and teacher-rated everyday WM. To conclude, our results suggest that tasks that utilize CE functions may best reflect WM outside of controlled test settings in the follow-up of VPT/VLBW children. However, performance and rating-scale measures provide unique information and are both needed to comprehensively assess WM skills.

B-232 One Year Post-Implementation Experience Using a Custom Designed DNA Contamination Detection Module for a Clinical Myeloid Neoplasm Next Generation Sequencing Assay

Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection module (MICon) using microhaplotype (MH) regions and an in-house designed analysis model was developed for use in a NGS assay for myeloid neoplasms (NGSHM) [1]. Variant detection for NGSHM has a validated analytical sensitivity of 2% variant allele frequency (VAF). Gross contamination events can potentially cause erroneous false positive variant detection whereas true low VAF mutations may be masked and evade detection. The following study was performed to evaluate MICon module performance on samples processed over the course of a year. Methods MH regions contain highly conserved tandem single nucleotide polymorphisms (SNPs) with high global heterogeneity occurring within a 300-nucleotide span. From the work of Kidd et al [2], 27 MH regions covering 92 SNPs and spanning 16 chromosomes were selected for inclusion in the 47 gene NGSHM target-capture panel. Sequencing was performed on a NovaSeq 6000 and analyzed through an internal bioinformatics pipeline, which incorporates the MICon module. MICon uses a binary classification model incorporating VAFs from MH regions, number of MH genotypes, and the contamination estimation score from verifyBamID to compute a single value on a 0-100 scale. Scores &amp;lt;50 are classified as non-contaminated and scores ≥50 require further investigation. Results The dataset was comprised of 10,990 samples. A total of 10,232 patient cases (93.1%) had non-contaminated MICon scores &amp;lt;50. Control replicates accounted for 261 samples (2.4%). There were 497 patient cases (4.5%) with MICon scores ≥50. Of the 497 patient cases, 137 (27.6%) had undergone hematopoietic stem cell transplantation (HSCT) and 114 (22.9%) contained gross chromosomal abnormalities, representing iatrogenic or tumor biological factors. An additional 135 (27.2%) of cases were contaminated from laboratory processing errors, 45 (9.1%) occurred due to chemistry failures during run processing, and 66 (13.2%) had an undetermined cause for a high score, representing laboratory-related and unknown factors. Overall, the 135 laboratory processing errors detected accounted for 1.3% of patient cases analyzed by the laboratory. These cases were repeated, and the non-contaminated results were accurately reported. Conclusions Herein it was shown the implementation of MICon has improved patient safety over the course of a year. The 1.3% of patient cases identified as arising from laboratory processing errors were successfully reprocessed. While the overall rate of laboratory processing errors appears low, the absolute number can be significant in a high-volume test setting, despite reliance on highly automated processes. Value is added by MICon through identifying errors that could result in the release of inaccurate patient results. By detecting previously unrealized errors, MICon has become an invaluable asset to the NGSHM assay by enhancing patient laboratory testing safety.

Experimental and numerical investigation of the sensor effect on the acoustic emission during Pencil-lead Break tests on PMMA plates

Pencil leads are broken on PMMA plates at various source-sensor distances to evaluate the influence of the propagation distance and the sensor type on AE signals. The resulting waves are detected using a broadband or a resonant sensor. The ability of five transducers, PKBBI sensor, MICRO80 sensor, MICRO200HF sensor, Nano30 sensor, and WD sensor, to recreate the characteristic forms of plate waves is evaluated. Because of the response spanning between 300 to 400 kHz frequency range, Nano30 and MICRO80 sensors have a bigger magnitude in the S0 mode than in the A0 mode, especially for small sensor-source distances. Their different responses demonstrate why similar test specimens and test settings can provide diverse findings due to the sensor effect, which is significant for the use of AE data in the classification of AE sources. Then, based on the normalization method, we suggest a procedure to acquire equivalent values for the selected descriptors using Laplacian Score and Principle Component Analysis. In order to gain a thorough understanding of the sensor effect through 3D finite element simulation, a comparative analysis is conducted between the perfect contact sensor and the resonant sensor with sensor effect. The sensor effect arises from the convolution of the Fourier Transform of the signal with the sensitivity curve in the frequency domain. By comparing the waveforms in the time and frequency domain, it is useful to determine how the different sensors differ from the AE signals.

ELECTRONIC ASSESSMENT ANXIETY SCALE: DEVELOPMENT, VALIDITY AND RELIABILITY

The widespread availability of internet access in daily life has resulted in a greater acceptance of online assessment methods. E-assessment platforms offer various features such as randomizing questions and answers, utilizing extensive question banks, setting time limits, and managing access during online exams. Electronic assessment enables real-time monitoring, customization, and scalability of feedback, benefiting students, academic staff, and administrative personnel. However, students encounter specific challenges in the electronic assessment environments. These challenges include limited control over test settings and the isolated nature of taking exams without peers. Furthermore, the technological proficiency of both instructors and students, along with resource constraints (computers, mobile devices, internet), can impede the effective utilization of these assessment tools. Technical issues like slow internet connection or disconnections can have significant consequences, especially in online exams, posing difficulties for corrections. The main goal of this study is to develop a Likert-type scale capable of measuring anxiety related to technical issues, social isolation, and the test interface experienced in e-assessment contexts. The study consists of two separate groups: the first group comprising 359 participants and the second group consisting of 356 participants. Both groups include undergraduate and pedagogical formation certificate program students from a university in the Eastern Anatolia Region of Turkiye. Construct validity was assessed through exploratory and confirmatory factor analyses. Item parameters were examined using item analysis based on classical test theory. As a result of the study, a two-factor scale structure comprising 21 items measuring social and technical anxiety was developed. These two dimensions account for 59.89% of the total variance. The Cronbach’s alpha coefficient for the entire scale was 0.93, the McDonald’s omega coefficient was 0.93, and the construct reliability was 0.99.

Implementing a genetic testing station in a safety net setting for equitable genetic testing and counseling.

167 Background: Genetic testing (GT) in patients with hereditary cancers has significant implications for treatment options and risk reduction among family members. Recent GT guidelines have expanded the eligibility criteria with limited genetic counseling staff. Patients who are from marginalized groups, are under or uninsured, or have limited English proficiency continue to experience disparities in GT, underscoring the need for innovations in care delivery. Methods: From March 2021 to June 2023, we conducted a single-arm intervention study of a point-of-care genetic testing station (GTS) in a safety net hospital. Patients who met NCCN criteria for GT were eligible. We compared this group to a historical cohort who received GT either in person or via telehealth in the preceding two years and non- GTS cohort. The GTS intervention consisted of (1) screening of clinic schedules for eligible patients, (2) GT video-based education in English, Spanish, and Cantonese, (3) consent and collection of blood or saliva samples by a genetic counseling assistant, and (4) telehealth-based post-test counseling with a genetic counselor (GC). The historical cohort underwent GT in a traditional model: (1) oncologist-initiated electronic referral, (2) pretest counseling with a GC, (3) GT if appropriate, and (4) post-test counseling to review and discuss results. Our primary outcomes were completion of GT and time to result delivery. We evaluated all associations with the Chi-square test for categorical variables. Results: We enrolled a total of 256 participants, 26% were Asian, 18% were Black, 30% were Hispanic/Latinx, 13% were Non-Hispanic White, and 2% were Native Hawaiian/Pacific Islander. The most common languages were English 48%, Spanish 28%, Cantonese 20%, and Other 4%. 251 (98%) of GTS participants completed GT, and 241 (95%) completed the post-test GC visit, compared to the Non-GTS cohort (79%, p&lt;0.001), pre-GTS cohort (85%, p&lt;0.001) with traditional GT model with in-person at 122 (95%) and telehealth (72%). The median time to result delivery was 35 days (IQR 27 - 52) for the GTS cohort, 56 days for non-GTS cohort, 97 days for pre-GTS telehealth (IQR 24.7 - 111), and 21 days ( IQR 14-31) days for in-person. Conclusions: GTS resulted in more patients completing GT and promptly receiving their results with telehealth genetic counseling. As GT eligibility criteria broaden, innovative interventions, workflows, and tailored educational resources for diverse populations are needed to ensure equitable access to GT.

Identification of activating somatic mutations in the &lt;i&gt;PIK3CA&lt;/i&gt; gene in breast tumors and determination of their minimal set for clinical diagnostic testing

Background: For effective screening of breast cancer patients for candidates for target therapy with alpelisib, it is necessary to identify activating somatic mutations in the PIK3CA gene by allele specific polymerase chain reaction (PCR); this requires that an optimal list of mutations should be compiled. Aim: To determine the spectrum of somatic mutations in the PIK3CA gene in breast cancer tumors by means of high performance sequencing (next generation sequencing, NGS) and to identify their minimal set for clinical diagnostic testing by allele specific PCR. Methods: Targeted NGS was used to identify mutations in the PIK3CA gene in DNA obtained from paraffin blocks with tumor material from 431 patients with HR+HER2- breast cancer. A set of the most common somatic mutations was also detected by allele specific PCR. Results: We have developed a set of reagents and a protocol for targeted NGS of frequently mutating regions of the PIK3CA and ESR1 genes, which was used to analyze samples from 451 HR+/HER2- breast cancer patients. Clinically significant activating mutations in the PIK3CA gene were found in 32.7% of the samples (141/431). The frequency of the mutant allele ranged from 0.15 to 0.65. Six mutations were most common: c.3140AG p.His1047Arg (69), c.1633GA p.Glu545Lys (32), c.1035TA p.Asn345Lys (12), c.1624GA p.Glu542Lys (9), c.3140AT p.His1047Leu (8), Cys420Arg c.1258TC (3). In total, these mutations amounted to 94.3% (133/141). In 3.5% of the samples (15/431), there were clinically significant somatic mutations in the ESR1 gene: c.1613AG p, Asp538Gly (7), c.1610AC p.Tyr537Ser (6), c.1609TA p.Tyr537Asn (1), c.1610AG p.Tyr537Cys (1), causing resistance to hormone therapy in patients with breast cancer. While rare mutations comprised only 5.7% of our sample, we validated a set of reagents to identify the six mutations described above by allele specific PCR. NGS and PCR were completely concordant. Conclusion: PCR testing of activating somatic mutations of the PIK3CA gene meets the requirements for sensitivity ( 90%) and specificity (100%) for a clinical test and can be used in the selection of patients for targeted therapy with PIK3CA inhibitors.

ATP-based assessments of recent cleaning and disinfection for high-touch surfaces in low-resource shared toilets

Quality improvements and reduction of disease risk for low-resource shared sanitation facilities require cleanliness assessment approaches that are both rigorous and practical. Using Adenosine Triphosphate (ATP) bioluminescence testing, we assessed contamination on high-touch (HT) surfaces (inner door handles) at 32 shared toilet sites in Kisumu, Kenya. In public toilets, contamination was lowest after cleaning and disinfection (C&D) with 0.5% chlorine solution (adjusted difference in mean log10 Relative Light Units per 100 cm2 (aDiff): −1.61; CI: −2.43, −0.59), followed by C&D with 0.1% chlorine solution (aDiff: −1.16; CI: −1.77, −0.55). ATP levels were not associated with overall observable toilet cleanliness and had poor agreement with visually assessed HT surface cleanliness. Our findings demonstrate the utility of this field-feasible method for detecting the impact of recent C&D in low-resource shared toilets, a novel setting for ATP cleanliness testing, while also highlighting the importance of using effective C&D procedures and addressing HT surfaces within cleaning protocols.

Genomic Landscape in Prostate Cancer in a Latin American Population.

This study aims to describe genomic characteristics of patients with metastatic prostate cancer (mPC). This study is a retrospective, multicenter cohort study of patients with mPC and reports on genomic testing. Patients were included from 12 academic centers in five countries. A total of 349 patients with PC were included in this study. Most patients (209, 59.9%) were de novo metastatic. Genomic analysis was performed in 233 (66.6%) patients in the metastatic castration-resistant prostate cancer (mCRPC) setting, and only 115 (32.8%) patients had a tumor evaluation in the metastatic hormone sensitive prostate cancer scenario. The evaluation of somatic and/or germline mutations was performed through multigene panel analyses in 290 (83.09%) patients, and next-generation sequencing of BRCA1 and BRCA2 genes was performed in 59 (16.91%) patients. Analyzing the mCRPC subgroup, with a median follow-up of 15.6 months (IQR, 14-19.06), the median progression-free survival (PFS) was not reached (NR) and the PFS at 16 months was 58.7% (95% CI, 50.8 to 67.8). When comparing patients with BRCA mutations with those who are not BRCA-mutated in the mCRPC scenario, the median PFS was NR (95% CI, 14 to NR) and 26.3 months (95% CI, 16.7 to 36.5; P = .2), respectively. Two of six patients with BRCA mutations were treated with targeted therapies (poly-ADP-ribose polymerase inhibitors). Our study, to the best of our knowledge, represents one of the larger data sets for somatic testing in patients with PC in Latin America (LATAM). It adds valuable information to the growing body of knowledge about the genomic landscape of advanced PC in real-world daily practice scenarios in LATAM countries, which are not always well-represented in large-scale randomized clinical trials.

Mapping the composite nature of clay matrix in mudstones: integrated micromechanics profiling by high-throughput nanoindentation and data analysis

AbstractMudstones and shales serve as natural barrier rocks in various geoenergy applications. Although many studies have investigated their mechanical properties, characterizing these parameters at the microscale remains challenging due to their fine-grained nature and susceptibility to microstructural damage introduced during sample preparation. This study aims to investigate the micromechanical properties of clay matrix composite in mudstones by combining high-speed nanoindentation mapping and machine learning data analysis. The nanoindentation approach effectively captured the heterogeneity in high-resolution mechanical property maps. Utilizing machine learning-based k-means clustering, the mechanical characteristics of matrix clay, brittle minerals, as well as measurements on grain boundaries and structural discontinuities (e.g., cracks) were successfully distinguished. The classification results were validated through correlation with broad ion beam-scanning electron microscopy images. The resulting average reduced elastic modulus (Er) and hardness (H) values for the clay matrix were determined to be 16.2 ± 6.2 and 0.5 ± 0.5 GPa, respectively, showing consistency across different test settings and indenter tips. Furthermore, the sensitivity of indentation measurements to various factors was investigated, revealing limited sensitivity to indentation depth and tip geometry (when comparing Cube corner and Berkovich tip in a small range of indentation depth variations), but decreased stability at lower loading rates. Box counting and bootstrapping methods were applied to assess the representativeness of parameters determined for the clay matrix. A relatively small dataset (indentation number = 60) is needed to achieve representativeness, while the main challenges is to cover a representative mapping area for clay matrix characterization. Overall, this study demonstrates the feasibility of high-speed nanoindentation mapping combined with data analysis for micromechanical characterization of the clay matrix in mudstones, paving the way for efficient analysis of similar fine-grained sedimentary rocks.

Variability of cardiopulmonary exercise testing in patients with atrial fibrillation and determination of exercise responders to high-intensity interval training and moderate-to-vigorous intensity continuous training.

Disabling atrial fibrillation (AF)-related symptoms and different testing settings may influence day-to-day cardiopulmonary exercise testing (CPET) measurements, which can affect exercise prescription for high-intensity interval training (HIIT) and moderate-to-vigorous intensity continuous training (M-VICT) and their outcomes. This study examined the reliability of CPET in patients with AF and assessed the proportion of participants achieving minimal detectable changes (MDC) in peak oxygen consumption (V̇O2peak) following HIIT and M-VICT. Participants were randomized into HIIT or M-VICT after completing two baseline CPETs: one with cardiac stress technologists (CPETdiag) and the other with a research team of exercise specialists (CPETresearch). Additional CPET was completed following 12 weeks of twice-weekly training. Reliability of CPETdiag and CPETresearch was assessed by intraclass correlation coefficient (ICC) and dependent t-tests. The MDC score was calculated for V̇O2peak using a reliable change index. The proportion of participants achieving MDC was compared between HIIT and M-VICT using chi-square analysis. Eighteen participants (69±7 years, 33% females) completed two baseline CPETs. ICC was significant for all measured variables. However, peak power output (POpeak: 124±40 vs. 148±40 watts, p<0.001) and HR (HRpeak: 136±22 vs. 148±30 bpm, p=0.023) were significantly greater in CPETresearch than CPETdiag. Few participants achieved MDC in V̇O2peak (5.6 mL/kg/min) with no difference between HIIT (0%) and M-VICT (10.0%, p=0.244). POpeak and HRpeak differed significantly in patients with AF when CPETs were repeated under different settings. Caution must be practiced when prescribing exercise intensity based on these measures as under-prescription may increase the number of exercise non-responders.

Prediction of Oil–Water Two-Phase Flow Patterns Based on Bayesian Optimisation of the XGBoost Algorithm

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil–water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclination angles, water cut rates, and daily flow rates were the most significant features contributing to the predictions. This study confirms the efficacy and superiority of the Bayesian-optimised XGBoost (BO-XGBoost) algorithm in predicting oil–water two-phase flow regimes, offering a robust and effective methodology for investigating complex subsurface fluid dynamics. The research outcomes are crucial in improving the accuracy of oil–water two-phase flow predictions and introducing innovative technical approaches within the domain of petroleum engineering. This work lays a foundational stone for the advancement and application of multiphase flow studies.

In silico development of novel angiotensin-converting-enzyme-I inhibitors by Monte Carlo optimization based QSAR modeling, molecular docking studies and ADMET predictions

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.

A Psychometric Perspective on the Associations between Response Accuracy and Response Time Residuals.

We provide an alternative psychometric perspective on the empirical statistical dependencies observed between response accuracy residuals (RARs) and response time residuals (RTRs) in the context of the van der Linden model. This perspective emphasizes the RAR (or parts of the RAR) as being exogenous and having a directional influence on response time. Our simple and theoretically justifiable perspective adds to previous joint response time/accuracy models and comports with recent generalizations of the D-diffusion IRT model incorporating person-by-item interactions, and thus similarly reproduces many of the recently highlighted empirical findings concerning the associations between RARs and RTRs. Using both empirical and simulation-based results, we show how our psychometric perspective has both applied and interpretational implications. Specifically, it would suggest that (1) studies of item parameter estimate heterogeneity in relation to response times may reflect more of a psychometric artifact (due to the exogenous effects of the RARs) as opposed to providing insights about the response process (e.g., the application of different response strategies) and that (2) efforts to use RTRs as indicators of latent proficiency should attend to the anticipated interactions between the latent proficiency and RAR on response times. The validity of our psychometric perspective against alternatives likely relies on appeals to theory; the best perspective to take may vary depending on the test setting.

Electronic Load Device for Testing DC Power Supply

The project deals with a programmable electronic load device capable of dynamic variations of its load parameters to simulate a myriad of electrical conditions. Hence, one of the objectives of this paper is to implement, in a designed setup, variously heated loads, such as constant current, voltage, resistance, and power modes. That said, the designed system has hardware and software aspects that integrate advanced microcontroller capabilities so that high accuracy is assured through a series of test procedures on the system. The hardware design of the project encompasses choosing various resistors and relays modules (ESP32), selection of the proper power supplies, and microcontrollers. A program is being developed in the Arduino IDE that will program the microcontroller to do dynamic load control.It all began with the circuit design and resistor value choice, taking into consideration their power rating about effective heat dissipation. Dynamic loading control by the ESP32 microcontroller allows variable changes which are automatic in nature. On top of this, relay modules were used for effective switching of load and its adjustments, hence making the system flexible and responsive. The firmware developed in Arduino IDE automates control processes, which enables the smooth running of operations. In this regard, our experiments output results that show the device—a programmable electronic load mimicking load situations and providing performance information. Notably, the automated control system coupled with adjustable load capabilities increased the testing process efficiency and accuracy to a great deal. The overall effect of the programmable electronic load device is that it makes testing more professional by way of automation and provision of precise management for the test settings of the load. Its versatility also provides simulation of a wide scope of scenarios accommodating test conditions. It is designed to cater to heat regulation and power dispersion for the reliability of performance whiles in operation

Flexural Improvement of RC Slabs by FRP or Steel Using Different Strengthening Systems and Novel Anchoring Techniques

An experimental study on reinforced concrete one-way slabs strengthened by various methods and materials is introduced in this paper. Innovative anchorage procedures are presented and evaluated to prevent the strengthening elements with FRP system from de-bonding at the initial stages. Externally bonded embedded in concrete cover (EBECC) strengthening technology was proposed to save the fiber strips from being subjected to heat, degradation, and sabotage. Nine RC one-way slabs, including a control slab and eight strengthened slabs, were cast. One RC slab was strengthened using externally bonded embedded in concrete cover (EBECC), whereas the other tested RC slabs were strengthened using either externally bonded (EB) or near-surface mounted (NSM) procedures. The following test variables are used in this study: the proposed anchors, the area of steel, the kind of material utilized in NSM rods (carbon fiber reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP), and steel), and the strengthening scheme. The ultimate and initial cracking loads, load–deformation response, cracking patterns, and failure behavior were recorded and discussed. Additionally, a comparison of the stiffness, ductility, and energy absorption of the examined slabs was reported. The strengthened slabs by various techniques showed a boost in flexural strength that varied from 67 to 107% compared to the control slab. In addition, RC slabs strengthened by NSM-CFRP bars showed a maximum flexural capacity when compared with slabs strengthened by GFRP and steel bars. Also, the results supported the superiority of a novel end anchorage. The ABAQUS program was employed to conduct a finite element analysis (FEA) employing 3-D geometries to compare and assess the numerical performance of the identical slabs under similar test settings. The results showed good agreement between the experimental and numerical findings.