Features Of Interest Research Articles

Aerial and terrestrial root habits influence the composition of the cell walls of Vanilla phaeantha (Orchidaceae).

In response to the restrictions imposed by their epiphytic habit, orchids have developed structural traits that allow greater efficiency in water uptake and use, such as a complex adventitious root system with velamen. The composition of cell wall of this specialized epidermis can be altered according to the substrate to which it is fixed, influencing wall permeability, absorption, and storage of water in roots. The current study aimed to evaluate the cell wall composition of adventitious roots of Vanilla phaeantha (Orchidaceae) that grow attached to the phorophyte, fixed in the soil, or hung free. Immunocytochemical analyses were used to determine the protein, hemicellulose, and pectin composition of the cell walls of aerial and terrestrial roots. We observed that pectins are present in the different tissues of the aerial roots, while in the terrestrial roots, they are concentrated in the cortical parenchyma. The deposition of xyloglucans, extensins, and arabinogalactans was greater in the epidermis of the free side of the roots attached to the phorophyte. The strong labeling of pectins in aerial roots may be related to the influx of water and nutrients, which are generally scarce in this environment. The arrangement of hemicelluloses and proteins with the pectins may be associated with increased cell rigidity and sustainability, a feature of interest for the aerial roots. In summary, the habit of roots can interfere with the non-cellulosic composition of the cell walls of V. phaeantha, possibly related to changes in cell functionality.

Optimization test function synthesis with generative adversarial networks and adaptive neuro-fuzzy systems

This paper presents an approach to synthesizing optimization test functions that couples generative adversarial networks and adaptive neuro-fuzzy systems. A generative adversarial network produces optimization landscapes from a database of known optimization test functions, and an adaptive neuro-fuzzy system performs regression on the generated landscapes to provide closed-form expressions. These expressions can be implemented as fuzzy basis function expansions. Eight databases of two-dimensional optimization landscapes reported in the literature are used to train the generative network. Exploratory landscape analysis over the generated samples reveals that the network can lead to new optimization landscapes with features of interest. In addition, fuzzy basis function expansions provide the best approximation results when compared against two symbolic regression frameworks over several selected landscapes. Examples are used to illustrate the ability of these functions to model complex surface artifacts such as plateaus. The proposed approach can be used as a mathematical collaboration tool that couples generative artificial and computational intelligence techniques to formulate high-dimensional optimization test problems from two-dimensional synthesized functions.

Association Between Baseline Macular Morphologic Features on Optical Coherence Tomography and Visual Outcomes in Patients with Vogt-Koyanagi-Harada Disease.

The choroidal thickening and serous retinal detachments that characterize Vogt-Koyanagi-Harada (VKH) disease can be imaged in detail using spectral domain optical coherence tomography (SD-OCT). Whether specific qualitative and quantitative SD-OCT features at presentation were associated with visual outcomes in a randomized controlled trial comparing methotrexate to mycophenolate for steroid-sparing control of uveitis were evaluated. An exploratory subanalysis of data from the FAST trial in which SD-OCT images from VKH participants were analyzed for presence/absence of bacillary detachments, retinal pigment epithelium (RPE) folds, and internal limiting membrane (ILM) fluctuations was performed. A modified RPE undulation index was calculated to provide a quantifiable surrogate marker for choroidal folds. SD-OCT images were available from 158 eyes with VKH. At baseline, bacillary detachments were present in 23.5% of eyes, RPE folds in 22.8% of eyes, and ILM fluctuations in 35.2% of eyes. For each 0.1 unit increase in modified RPE undulation index, there was an associated 0.13 increase in mean logMAR BSCVA at baseline. None of the SD-OCT features were associated with BSCVA at the 6-month primary endpoint. Indeed, mean final BSCVA was similar in those with and without the SD-OCT features of interest at baseline, and was between 0.1 and 0.2 logMAR (Snellen visual acuity 20/25 to 20/30). While eyes with VKH may present with a variety of SD-OCT imaging pathology prior to starting immunosuppression with methotrexate or mycophenolate mofetil, final visual outcome in our study was excellent. With appropriate immunosuppression, good visual outcomes are possible in VKH.ClinicalTrials.gov Identifier NCT01829295Date of Registration: April 11, 2013.

Effect of access to natural shade on scrotal thermoregulatory capacity, integrity of the testicular parenchyma and sperm morphology of Nelore (Bos indicus) and Canchim (Bos taurus x Bos indicus) bulls.

The objective of this work was to evaluate the effect of using naturally shaded pastures on scrotal thermoregulatory capacity, testicular echotexture, and sperm morphology of Nelore (Bos indicus) and Canchim (5/8 Bos taurus x 3/8 Bos indicus) bulls in a tropical climate region. Sixty-four adult Nelore and Canchim bulls were used, equally allocated in Full Sun (FS, n = 32) or Crop-Livestock-Forestry (CLF, n = 32) pasture systems. During five consecutive climate seasons, the bulls underwent monthly breeding soundness evaluations and the biometeorological variables in the systems were continuously monitored. Microclimate was significantly different between systems. CLF system had lower BGHI than FS throughout the experimental period. No triple interaction (Season x Breed x Treatment, P > 0.05) was observed for any of the variables. Animals in CLF showed lower body temperature in Summer (FS:39.41 ± 0.05 vs. CLF:39.30 ± 0.05°C; P = 0.005) and in Autumn (FS:39.54 ± 0.05 vs. CLF:39.35 ± 0.05°C; P = 0.005). Access to shading did not determine differences in the evolution of scrotal biometry, temperatures, and scrotal thermal gradients (P > 0.05). Regardless of breed, animals in CLF showed greater right testicular volume (FS:247.5 ± 5.7 vs. CLF:259.0 ± 5.7cm³; P < 0.05), more suitable parenchyma echotexture, and fewer microlithiasis spots in the Spring and Summer. Testosterone concentration was higher in FS (FS:2.6 ± 0.2 vs. CLF:2.1 ± 0.2 ng/mL; P = 0.035). Canchim bulls presented higher total sperm defects during the Autumn and Winter (P = 0.010), but the total defects levels for Canchim and Nelore bulls were in normal range for adult bulls. Thus, the natural shade in CLF system was effective in improving the microclimate of pastures and minimizing adverse environmental effects on some reproductive features of interest in beef cattle.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3500 Inherited Retinal Disease Patients from the United Kingdom.

To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire MEH imaging dataset. Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Video Game Addiction in Young People (8-18 Years Old) after the COVID-19 Pandemic: The Grey Area of Addiction and the Phenomenon of "Gaming Non-Pathological Abuse (GNPA)".

In the literature, video game addiction in youths is correlated with dysfunctional symptoms of anxiety, emotional disorders, and mood disorders, and the pandemic period of 2020-2022 has favored the aggravation of this behavioral addiction. Therefore, we identified the need to analyze this phenomenon with an emphasis on the risks and correlates related to deviance and maladjustment from a prospective perspective, seeking to understand the impact of the individual variables examined. To demonstrate whether the condition of "gaming non-pathological abuse" (GNPA) promotes psychopathological features of clinical interest, in the absence of a diagnosis of "gaming disorder" (GD). A search performed on PubMed and administration of an ad hoc sociological questionnaire were used to investigate individual variables of criminological interest in a representative population sample (531 males/females, 8-18 years old, M: 14.4, SD: 2.5). Statistical analysis showed that after the pandemic period, digital video game addiction was reinforced, feeding psychopathological traits consistent with anxiety, emotional disorders, and mood disorders. Variables correlated with impulsive, aggressive, and violent behavior related to age, gender, socio-environmental and economic background, and the severity of digital video game addiction. In the youth population (8-18 years), "gaming non-pathological abuse" (GNPA) is related to aggressive, impulsive and violent behaviors that foster phenomena of social maladjustment and deviance, especially in individuals living in disadvantaged or otherwise complex socio-economic and family contexts. Looking forward, the study of structural and functional personality profiles is essential in order to anticipate and reduce the future risk of psychopathological and criminal behavior.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BackgroundCells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.ResultsWe analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.ConclusionsOur integrative approach identifies several putative hits—genes demonstrating possible transcriptional adaptation—to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity

The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics.

Analyzing single-cell bisulfite sequencing data with MethSCAn

Single-cell bisulfite sequencing (scBS) is a technique that enables the assessment of DNA methylation at single-base pair and single-cell resolution. The analysis of large datasets obtained from scBS requires preprocessing to reduce the data size, improve the signal-to-noise ratio and provide interpretability. Typically, this is achieved by dividing the genome into large tiles and averaging the methylation signals within each tile. Here we demonstrate that this coarse-graining approach can lead to signal dilution. We propose improved strategies to identify more informative regions for methylation quantification and a more accurate quantitation method than simple averaging. Our approach enables better discrimination of cell types and other features of interest and reduces the need for large numbers of cells. We also present an approach to detect differentially methylated regions between groups of cells and demonstrate its ability to identify biologically meaningful regions that are associated with genes involved in the core functions of specific cell types. Finally, we present the software tool MethSCAn for scBS data analysis (https://anders-biostat.github.io/MethSCAn).

Phytomelatonin: From Intracellular Signaling to Global Horticulture Market.

Melatonin (N-acetyl-5-methoxytryptamine), a well-known mammalian hormone, has been having a great relevance in the Plant World in recent years. Many of its physiological actions in plants are leading to possible features of agronomic interest, especially those related to improvements in tolerance to stressors and in the postharvest life of fruits and vegetables. Thus, through the exogenous application of melatonin or by modifying the endogenous biosynthesis of phytomelatonin, some change can be made in the functional levels of melatonin in tissues and their responses. Also, acting in the respective phytomelatonin biosynthesis enzymes, regulating the expression of tryptophan decarboxylase (TDC), tryptamine 5-hydroxylase (T5H), serotonin N-acetyltransferase (SNAT), N-acetylserotonin O-methyltransferase (ASMT), and caffeic acid O-methyltransferase (COMT), and recently the possible action of deacetylases on some intermediates offers promising opportunities for improving fruits and vegetables in postharvest and its marketability. Other regulators/effectors such as different transcription factors, protein kinases, phosphatases, miRNAs, protein-protein interactions, and some gasotransmitters such as nitric oxide or hydrogen sulfide were also considered in an exhaustive vision. Other interesting aspects such as the role of phytomelatonin in autophagic responses, the posttranslational reprogramming by protein-phosphorylation, ubiquitylation, SUMOylation, PARylation, persulfidation, and nitrosylation described in the phytomelatonin-mediated responses were also discussed, including the relationship of phytomelatonin and several plant hormones, for chilling injury and fungal decay alleviating. The current data about the phytomelatonin receptor in plants (CAND2/PMTR1), the effect of UV-B light and cold storage on the postharvest damage are presented and discussed. All this on the focus of a possible new action in the preservation of the quality of fruits and vegetables.

Evaluating a sigmoid dark energy model to explain the Hubble tension

AbstractIn this study, we analyze Type Ia supernovae (SNe Ia) data sourced from the Pantheon+ compilation to investigate late‐time physics effects influencing the expansion history, , at redshifts . Our focus centers on a time‐varying dark energy (DE) model that introduces a rapid transition in the equation of state, at a specific redshift, , from the baseline, , value to the present value, . The change in the equation of state is implemented as a transition in the DE density scale factor driven by a sigmoid function. The constraints obtained for the DE sigmoid phenomenological parametrization have broad applicability for dynamic DE models that invoke late‐time physics. Our analysis indicates that the sigmoid model provides a slightly better, though not statistically significant, fit to the SNe Pantheon+ data compared to the standard cold dark matter () model. The fit results, assuming a flat geometry and maintaining constant at the 2018‐Planck value of , are as follows: km s Mpc, , . The errors represent statistical uncertainties only. The available SN dataset lacks sufficient statistical power to distinguish between the baseline model and the alternative sigmoid models. A feature of interest offered by the sigmoid model is that it identifies a specific redshift, , where a potential transition in the equation of state could have occurred. The sigmoid model does not favor a DE in the phantom region (). Further constraints to the dynamic DE model have been obtained using CMB data to compute the distance to the last scattering surface. While the sigmoid DE model does not completely resolve the tension, it offers a transition mechanism that can still play a role alongside other potential solutions.

Solution studies, synthesis and antibacterial activity of Ga(III) complexes with bis-kojate derivatives

Given the recognized major problem of microbial drug resistance for human health, new metal-based drugs have been currently explored for their antimicrobial properties, including gallium-based compounds as potential metallophores that could perturb Fe’s interactions with proteins. Herein we have designed and synthesized two bis-kojate ligands (named L4 and L6) and studied their Ga(III) complexes for their physico-chemical and biological properties. In particular a detailed study of their complexation properties in aqueous solution, showed equilibrium models with formation of quite stable dinuclear 2:3 metal:ligand complexes, though with different stability. Solid state complexes were also prepared and characterized and complementary DFT studies indicated that [Ga2(L4)3] complex, with higher stability, seems to adopt a three-ligand bridging conformation, while that for L6 adopt a one ligand bridging conformation. Preliminary investigation of the antibacterial activity of these gallium complexes showed antipseudomonal activity, which appeared higher for the complex with L4, a feature of potential interest for the scientific community.

Features of Importance in Nasal Endoscopy: Deriving a Meaningful Framework.

Critical components of the nasal endoscopic examination have not been definitively established for either the normal examination or for clinical disorders. This study aimed to identify concordance among rhinologists regarding the importance of examination findings for various nasal pathologies. A consortium of 19 expert rhinologists across the United States was asked to rank the importance of findings on nasal endoscopy for 5 different sinonasal symptom presentations. An online questionnaire was distributed in July 2023. The questionnaire utilized JotForm® software and featured 5 cases with a set of 4 identical questions per case, each covering a common indication for nasal endoscopy. Rankings were synthesized into Normalized Attention Scores (NASs) and Weighted Normalized Attention Scores (W-NASs) to represent the perceived importance of each feature, scaled from 0 to 1. General concordance was found for examination findings on nasal endoscopy within each case. The perceived features of importance differed between cases based on clinical presentation. For instance, in evaluating postnasal drip, the middle meatus was selected as the most important structure to examine (NAS, 0.73), with mucus selected as the most important abnormal finding (W-NAS, 0.66). The primary feature of interest for mucus was whether it was purulent or not (W-NAS, 0.67). Similar analyses were performed for features in each case. The implicit framework existing among rhinologists may help standardize examinations and improve diagnostic accuracy, augment the instruction of trainees, and inform the development of artificially intelligent algorithms to enhance clinical decision-making during nasal endoscopy.

The Wedding Feast of Caranus the Macedonian by Hippolochus

Andrew Dalby, who furnished a new translation of The Banquet by Philoxenus in PPC 26, here performs the same service for another important text. Like the piece by Philoxenus, that by Hippolochus survives only in the pages of The Deipnosophists by Athenaeus. Dating back to the 3rd century BC, it seems to be the earliest surviving text which is devoted exclusively to the description of a banquet; and it has many other features of interest. This is the first article to be written about it, and the first time when it has been presented on its own.

A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.

Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the public dataset Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, the combination of modalities and different tasks are external factors that affect the performance of multimodal learning. Above all, we summrise three main problems in the early diagnosis of Alzheimer's disease: (i) unimodal vs multimodal; (ii) different combinations of modalities; (iii) classification of different tasks. In this paper, to experimentally verify these three problems, a novel and reproducible multi-classification framework for Alzheimer's disease early automatic diagnosis is proposed to evaluate and verify the above issues. The multi-classification framework contains four layers, two types of feature representation methods, and two types of models to verify these three issues. At the same time, our framework is extensible, that is, it is compatible with new modalities generated by new technologies. Following that, a series of experiments based on the ADNI-1 dataset are conducted and some possible explanations for the early diagnosis of Alzheimer's disease are obtained through multimodal learning. Experimental results show that SNP has the highest accuracy rate of 57.09% in the early diagnosis of Alzheimer's disease. In the modality combination, the addition of Single Nucleotide Polymorphism modality improves the multi-modal machine learning performance by 3% to 7%. Furthermore, we analyse and discuss the most related Region of Interest and Single Nucleotide Polymorphism features of different modalities.

Reduction of the model prediction error in vibration-based SHM applications

The accuracy of an SHM scheme is partly controlled by the accuracy of the predictive models involved within the design process of the detector that associates sensor data with a corresponding health state. On the other hand, the accuracy of the predictive model, which is quantified by the error between predictions and observations, is partly controlled by its parameter magnitudes. This work provides a unified framework that allows for reducing the model prediction error by updating the model parameters in the light of experimental observations. The problem is cast in a probabilistic setting and tackled through inferential statistics. Forward and inverse uncertainty quantification approaches are employed in a sequential manner. The framework is able to yield point estimates in accordance to a Maximum Likelihood Estimator (MLE) and is also able to construct credible intervals through computational Bayesian updating. The former is treated by genetic algorithms whereas the later through Markov Chain Monte Carlo computational statistics. The framework is showcased on a vibration problem of the simplified benchmark case named as “Jim Beam”. The first ten modal parameters are considered as the discriminant features of interest . A surrogate of the finite element model of the considered specimen serves as the predictive model and the eigenfrequencies are extracted from a linear modal analysis. The parameters of inferential interest are the Young’s modulus and an artificial stiffness related parameter that is introduced so as to model the flexural rigidity of the specimen’s bolted connections. Experimental Modal Analysis and Operational Modal Analysis are the two approaches that were employed for experimentally identifying the modes and their corresponding frequencies. Results are provided from both the MLE and the Bayesian updating process and the effectiveness of each approach is discussed.

Generalized Quasi-Static Mooring System Modeling with Analytic Jacobians

This paper presents a generalized and efficient method for quasi-static analysis of mooring systems, including complex scenarios such as when shared mooring lines interconnect multiple floating wind or wave energy devices. While quasi-static mooring models are well established, most published formulations are focused on specific applications, and no publicly available implementations provide efficient handling of large mooring system networks. The present formulation addresses these gaps by: (1) formulating solutions for edge cases not typically supported by quasi-static models; (2) creating a fully generalized model structure such that any combination of mooring lines, point masses, and floating bodies can be assembled; and (3) deriving analytic expressions for the system Jacobians (stiffness matrices) so that systems with many degrees of freedom can be solved efficiently. These techniques form the theory basis of MoorPy, an open-source mooring analysis library. The model is demonstrated on nine scenarios of increasing complexity with features of interest for offshore renewable energy applications. When compared with steady-state results from a lumped-mass dynamic model, the results show that the quasi-static formulation accurately calculates profiles and tensions and that its analytic approach provides more efficient and reliable computation of system stiffness matrices than finite-differencing methods. These results verify the accuracy of the MoorPy model.

Classification of autonomous vehicle crash severity: Solving the problems of imbalanced datasets and small sample size

Only a few researchers have shown how environmental factors and road features relate to Autonomous Vehicle (AV) crash severity levels, and none have focused on the data limitation problems, such as small sample sizes, imbalanced datasets, and high dimensional features. To address these problems, we analyzed an AV crash dataset (2019 to 2021) from the California Department of Motor Vehicles (CA DMV), which included 266 collision reports (51 of those causing injuries). We included external environmental variables by collecting various points of interest (POIs) and roadway features from Open Street Map (OSM) and Data San Francisco (SF). Random Over-Sampling Examples (ROSE) and the Synthetic Minority Over-Sampling Technique (SMOTE) methods were used to balance the dataset and increase the sample size. These two balancing methods were used to expand the dataset and solve the small sample size problem simultaneously. Mutual information, random forest, and XGboost were utilized to address the high dimensional feature and the selection problem caused by including a variety of types of POIs as predictive variables. Because existing studies do not use consistent procedures, we compared the effectiveness of using the feature-selection preprocessing method as the first process to employing the data-balance technique as the first process.Our results showed that AV crash severity levels are related to vehicle manufacturers, vehicle damage level, collision type, vehicle movement, the parties involved in the crash, speed limit, and some types of POIs (areas near transportation, entertainment venues, public places, schools, and medical facilities). Both resampling methods and three data preprocessing methods improved model performance, and the model that used SMOTE and data-balancing first was the best. The results suggest that over-sampling and the feature selection method can improve model prediction performance and define new factors related to AV crash severity levels.

Enhanced Video-Level Anomaly Feature Detection for Nuclear Power Plant Component Inspections Using the Latency Mechanism

The conditions of various nuclear power plant facilities are regularly examined through manual inspections. Remote visual inspection is commonly applied and requires engineers to watch lengthy inspection footage and seek anomaly features therein. This is a labor-intensive process as anomaly features of interest usually only appear in very short segments of the original whole video. Therefore, an automated anomaly detection system is preferred to lessen the intensive labor cost in the inspection process. The detection process could also benefit from useful information that could potentially contribute to addressing reasoning traceability. With a well-prepared training data set of the anomaly feature, a convolutional neural network (CNN) can be developed to automatically detect anomaly indications in the inspection video. However, false-positive detections may occur and can be difficult to remove without seeking manual verification. To overcome this problem, we present a new automated video-level anomaly detection framework that utilizes the latency mechanism to effectively lessen false-positive occurrences, and therefore, increase detection accuracy. In this framework, a CNN-based anomaly classifier first performs initial scanning of the anomaly type of interest in every region of the sampled frames. Then our latency mechanism is applied to refine the initial scanning results by flagging up a region as an “anomaly” indication only when “anomaly” is detected by CNN in the current frame and also in a sequence of previous consecutive frames of the same region. We present a case study of crack feature detection in superheater inspection videos to illustrate the performance of the proposed framework. The results show that the latency mechanism can effectively remove the original false-positive detections seen in the initial scanning. In order to provide a primary exploration of suggesting possible formats for addressing reasoning traceability, knowledge graphs of the reasoning process in the video-level detection framework are built to provide a better understanding of why a specific section of the video is flagged as anomaly contents by the video-level detection framework.

Causal analysis of positive Reaction Systems

Cause/effect analysis of complex systems is instrumental in better understanding many natural phenomena. Moreover, formal analysis requires the availability of suitable abstract computational models that somehow preserve the features of interest. Our contribution focuses on the analysis of Reaction Systems (RSs), a qualitative computational formalism inspired by biochemical reactions in living cells. The primary challenge lies in dealing with inhibition mechanisms. On the one hand, inhibitors enhance the expressiveness of the computational abstraction; on the other hand, they can introduce nonmonotonic behaviors that can be computationally hard to deal with in the analysis. We propose an encoding of RSs into an equivalent formulation without inhibitors (called Positive RSs, PRSs for short) that is easier to handle, because PRSs exhibit monotonic behaviors. The effectiveness of our transformation is witnessed by its impact on two different techniques for cause/effect analysis. The first, called slicing, allows detecting the causes of some unforeseen phenomenon by reasoning backward along a given computation. Here, PRSs can be exploited to improve the quality of the analysis. The second technique, predictor analysis, is addressed by introducing a novel tool called MuMa, which is based on must/maybe sets, whence the tool name, an original abstraction for approximating ancestor formulas. MuMa exploits PRSs to improve the performance of the analysis.