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Why does the mind wander? Recent theoretical models suggest mental content depends on a calculation that measures the expected rewards gained from the current task compared to other cognitive tasks and procedures. In Focused Attention Meditation (FA), participants practice attentional control by maintaining attention to an internal stimulus. Throughout the task, attentional lapses occur, in which there is an abrupt shift to mind wandering. We propose a model that formalizes attentional lapses as the interaction between a controller that boosts attentional resources to a target according to expected value calculations and a metacognitive monitoring procedure that stochastically observes internal contents. The model is applied to explain individual variation in button press data on an FA meditation task.

Large-scale behavioral datasets enable researchers to use com-plex machine learning algorithms to better predict human be-havior, yet this increased predictive power does not always leadto a better understanding of the behavior in question. In thispaper, we outline a data-driven, iterative procedure that allowscognitive scientists to use machine learning to generate mod-els that are both interpretable and accurate. We demonstratethis method in the domain of moral decision-making, wherestandard experimental approaches often identify relevant prin-ciples that influence human judgments, but fail to generalizethese findings to “real world” situations that place these prin-ciples in conflict. The recently released Moral Machine datasetallows us to build a powerful model that can predict the out-comes of these conflicts while remaining simple enough to ex-plain the basis behind human decisions.

Highlights In the present study, the basalt fibers (BFs) were treated with hybrid sizings consisting silane coupling agent (3‐Glycidoxypropyl)trimethoxysilane, and silicon dioxide nanoparticles (SNPs). Using acetone washing and heat treatment, the commercial sizing previously on the BFs was removed. Next, the fibers were dip‐coated, and silanes condensed on the surface of the fibers. Fiber samples were characterized using thermogravimetric analysis (TGA), atomic force microscopy (AFM), X‐ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The test findings demonstrate that acetone extraction was an effective way to remove sizings. The AFM analysis and SEM micrographs demonstrate that SNP and silane particles were dispersed uniformly across the fiber surface. The single fiber tensile testing results indicated that the deposition of hybrid sizings enhanced the fibers strength. Because of the better adhesion between the fiber and epoxy, the hybrid sizings also improved the flexural strength by 22.28% and the interlaminar shear strength by 20.75%. Hybrid sizings (silanes and nano‐particles) on the basalt fiber surface. Characterization of the sizings and understanding of surface morphology. Hybrid sizings resulted in better tensile strength. Failure mechanisms of the fibers were observed using fractography. The effect of hybrid sizing on ILSS and flexural properties was observed. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the usefulness of machine learning (ML) and neural network (NN) models versus traditional statistical methods for estimating biochemical recurrence (BCR) in men following robot-assisted radical prostatectomy (RARP). Methods: Patients who underwent RARP from November 2011 to July 2022 were taken in the study. Patients with BCR were assigned to Group 2, whereas those without BCR were placed in Group 1. Preoperative and postoperative parameters, together with demographic data, were recorded in the database. This study used one NN, the radial basis function NN (RBFNN), and two ML approaches, the K-nearest neighbor and XGboost ML models, to predict BCR. Results: Following the application of exclusion criteria, 516 patients were deemed eligible for the study. Of those, 234 (45.3%) developed BCR, and 282 (54.7%) did not. The results showed that the median follow-up period was 24 (15--42) months, and the median BCR diagnosis was 12.23 ± 15.58 months. The area under the curve (AUC) for the Cox proportional hazard analysis was 0.77. The receiver-operating characteristic curves (AUCs) for the XGBoost and K closest neighbor models were 0.82 and 0.69, respectively. The RBFNN's AUC was 0.82. Conclusions: The classical statistical model was outperformed by XGBoost and RBFNN models in predicting BCR. [ABSTRACT FROM AUTHOR]

BackgroundFew studies have examined the efficacy of drug-eluting stents (DES) for reducing aortocoronary saphenous vein bypass graft (SVG) failure compared with bare-metal stents (BMS) in patients undergoing stenting of de-novo SVG lesions. We assessed the risks and benefits of the use of DES versus BMS in de-novo SVG lesions.MethodsPatients were recruited to our double-blind, randomised controlled trial from 25 US Department of Veterans Affairs centres. Eligible participants were aged at least 18 years and had at least one significant de-novo SVG lesion (50-99% stenosis of a 2·25-4·5 mm diameter SVG) requiring percutaneous coronary intervention with intent to use embolic protection devices. Enrolled patients were randomly assigned, in a 1:1 ratio, by phone randomisation system to receive a DES or BMS. Randomisation was stratified by presence or absence of diabetes and number of target SVG lesions requiring percutaneous coronary intervention (one or two or more) within each participating site by use of an adaptive scheme intended to balance the two stent type groups on marginal totals for the stratification factors. Patients, referring physicians, study coordinators, and outcome assessors were masked to group allocation. The primary endpoint was the 12-month incidence of target vessel failure, defined as the composite of cardiac death, target vessel myocardial infarction, or target vessel revascularisation. The DIVA trial is registered with ClinicalTrials.gov, number NCT01121224.FindingsBetween Jan 1, 2012, and Dec 31, 2015, 599 patients were randomly assigned to the stent groups, and the data for 597 patients were used. The patients' mean age was 68·6 (SD 7·6) years, and 595 (>99%) patients were men. The two stent groups were similar for most baseline characteristics. At 12 months, the incidence of target vessel failure was 17% (51 of 292) in the DES group versus 19% (58 of 305) in the BMS group (adjusted hazard ratio 0·92, 95% CI 0·63-1·34, p=0·70). Between-group differences in the components of the primary endpoint, serious adverse events, or stent thrombosis were not significant. Enrolment was stopped before the revised target sample size of 762 patients was reached.InterpretationIn patients undergoing stenting of de-novo SVG lesions, no significant differences in outcomes between those receiving DES and BMS during 12 months of follow-up were found. The study results have important economic implications in countries with high DES prices such as the USA, because they suggest that the lower-cost BMS can be used in SVG lesions without compromising either safety or efficacy.FundingUS Department of Veterans Affairs Cooperative Studies Program.

To expand the applications of hybrid fiber composites in structural engineering applications, it is essential to study their fatigue behavior. The tensile and fatigue performance of unidirectional hybrid basalt/glass epoxy composites made with vacuum‐assisted resin infusion molding are investigated in this research. The sequencing influence on tensile properties was assessed using the two configurations of basalt/glass fiber epoxy hybrid fiber composites, namely GBBBG and BGBGB, having nearly equal volume fractions. According to the test results, hybrid fiber composites showed a positive hybrid effect on their tensile strength. Both hybrid layups showed higher tensile strengths (3.5% to 10.5%) than GFRPs and BFRPs. The GBBBG laminate has slightly higher fatigue resistance than BGBGB laminate under tension–tension (T–T) fatigue. Scanning electron microscopy (SEM) images verify the fatigue failure modes, which include fiber, matrix cracking, delamination, and debonding. The DMA analysis showed the lower adhesion efficiency of hybrid fiber composites, signifying lower damping properties. Highlights: The basalt/glass fiber hybrid fiber composites were fabricated using VARIM.The static and fatigue properties of basalt/glass fiber hybrid fiber composites were studied.The stiffness degradation and hysteresis were calculated for the composites tested.The DMA was performed, and Tg, storage modulus, and adhesion efficiency factor were calculated. [ABSTRACT FROM AUTHOR]

This work compares the tensile and fatigue behavior of unidirectional glass fiber/epoxy (GFRPs) and basalt fiber/epoxy (BFRPs) composites manufactured using vacuum-assisted resin infusion molding (VARIM). The tensile and fatigue tests are performed, and results show that both GFRPs and BFRPs have very similar tensile behavior, whereas BFRPs perform better than the GFRPs in terms of fatigue life and degradation of properties, showing about 75 MPa more fatigue limit. The S-N curve shows that BFRPs resist fatigue failure and stiffness degradation due to their better interfacial adhesion. Fractographic observations reveal fatigue failure mechanisms of both BFRPs and GFRPs. From the regression analysis performed, an S-N curve was fitted with a model available in the literature, and regression parameters were calculated. Dynamic mechanical analysis of both BFRPs and GFRPs was performed to evaluate parameters like glass transition temperature, storage modulus, and loss modulus. From the findings of this research, BFRPs may be recommended as a suitable alternative to GFRPs. Highlights • This article compares the tension-tension fatigue properties of unidirectional GFRPs and BFRPs. • From the research work, the authors found that BFRPs perform better than GFRPs under fatigue loading. • The stiffness degradation behavior of GFRPs and BFRPs are studied and compared. • Scanning electron micrographs of fractured samples are used to observe fatigue failure mechanisms. • Dynamic mechanical analysis (DMA) was performed to determine the storage modulus, loss modulus, glass transition temperature, and damping coefficient (tand) for both composites. [ABSTRACT FROM AUTHOR]

In Visual question answering (VQA), a natural language answer is generated for a given image and a question related to that image. There is a significant growth in the VQA task by applying an efficient attention mechanism. However, current VQA models use region features or object features that are not adequate to improve the accuracy of generated answers. To deal with this issue, we have used a Two‐way Co‐Attention Mechanism (TCAM), which is capable enough to fuse different visual features (region, object, and concept) from diverse perspectives. These diverse features lead to different sets of answers, and also, there is an inherent relationship between these visual features. We have developed a powerful attention mechanism that uses these two critical aspects by using both bottom‐up and top‐down TCAM to extract discriminative feature information. We have proposed a Collective Feature Integration Module (CFIM) to combine multimodal attention features and thus capture the valuable information from these visual features by employing a TCAM. Further, we have formulated a Vertical CFIM for fusing the features belonging to the same class and a Horizontal CFIM for combining the features belonging to different types, thus balancing the influence of top‐down and bottom‐up co‐attention. The experiments are conducted on two significant datasets, VQA 1.0 and VQA 2.0. On VQA 1.0, the overall accuracy of our proposed method is 71.23 on the test‐dev set and 71.94 on the test‐std set. On VQA 2.0, the overall accuracy of our proposed method is 75.89 on the test‐dev set and 76.32 on the test‐std set. The above overall accuracy clearly reflecting the superiority of our proposed TCAM based approach over the existing methods. [ABSTRACT FROM AUTHOR]

The UiO-6x family of metal-organic frameworks has been extensively studied for applications in chemical warfare agent (CWA) capture and destruction. An understanding of intrinsic transport phenomena, such as diffusion, is key to understanding experimental results and designing effective materials for CWA capture. However, the relatively large size of CWAs and their simulants makes diffusion in the small-pored pristine UiO-66 very slow and hence impractical to study directly with direct molecular simulations because of the time scales required. We used isopropanol (IPA) as a surrogate for CWAs to investigate the fundamental diffusion mechanisms of a polar molecule within pristine UiO-66. IPA can form hydrogen bonds with the μ 3 -OH groups bound to the metal oxide clusters in UiO-66, similar to some CWAs, and can be studied by direct molecular dynamics simulations. We report self, corrected, and transport diffusivities of IPA in pristine UiO-66 as a function of loading. Our calculations highlight the importance of the accurate modeling of the hydrogen bonding interactions on diffusivities, with about an order of magnitude decrease in diffusion coefficients when the hydrogen bonding between IPA and the μ 3 -OH groups is included. We found that a fraction of the IPA molecules have very low mobility during the course of a simulation, while a small fraction are highly mobile, exhibiting mean square displacements far greater than the ensemble average. [ABSTRACT FROM AUTHOR]