Miracle-dz/UniFAR-FTUs huggingface dataset:
Robustness of Smart Manufacturing Information Systems under Conditions of Resource Failure: A Complex Network Perspective

Resource failures frequently occur in a smart manufacturing information system (SMIS), which exerts significant impacts on the robustness of the system. From a complex network perspective, this paper develops a fresh methodology for analyzing the robustness of an SMIS suffering from resource failures. First, this methodology divides an SMIS into cyber and physical layers, dissects the resources within these layers and the relationships among these resources. Based on complex network thinking, the methodology then builds a network model incorporating different failure modes and link patterns. Finally, extensive simulations are performed using the case of an appliance manufacturer and one of its suppliers. The results show that an SMIS, along with its cyber layer, exhibits the property of being robust-yet-fragile, and that an assortative link pattern is the optimal link pattern to guarantee robustness for the SMIS under targeted failures.
Monitoring Beam-Column Joint in Concrete Structures Using Piezo-Impedance Sensors

Despite that piezoelectric ceramic lead zirconate titanate (PZT) has been used for structural health monitoring (SHM) in various engineering systems, limited work has been conducted on real size concrete structures. Beam-column connections are critical regions in reinforced concrete (RC) moment-resisting frame structures. The vulnerability of RC beam-column joints has been identified from structural damage investigations over the past decades, especially in the area of earthquake engineering. In the context of a terrorist bomb attack, the beam-column joints are very vulnerable, especially when the perimeter columns lose their load carrying capacity due to damage and the beam-column joints become one of the crucial load transfer mechanism of the structural frame. To avoid catastrophic failures, it is important to monitor beam-column joints under existing gravitational loads. In this paper, an experiment is carried out on four real size concrete frame structures with different detailing subjected to gradually increased loads. A number of PZT sensors are bonded to the frame structure to acquire PZT electro-mechanical (EM) admittance signature. The structural mechanical impedance (SMI) is extracted from the PZT EM admittance signature and its sensitivity is compared with that of the EM admittance. The relations between the damage index and the loading step and tip deflection of the concrete structure are obtained. Finally the sensitivity of the PZT sensors in detection of the critical loading level is discussed. The results show that the PZT sensors are capable of monitoring the integrity and behavior of the real size concrete structures.
Decoding the Three-Pronged Mechanism of NO3• Radical Formation in HNO3 Solutions at 22 and 80 °C Using Picosecond Pulse Radiolysis.

With nitric acid (HNO3) being at the core of nuclear technology through actinides separation and extraction processes, achieving a complete characterization of the complex processes involving concentrated HNO3 solutions under ionizing radiation equates bringing efficiency and safety into their operation. In this work, the three mechanisms contributing to the formation of nitrate radicals (NO3•) in concentrated nitric acid were investigated by measuring the radiolytic yield of NO3• in HNO3 solutions (0.5-23.5 M) at room (22.5 °C) and elevated (80 °C) temperatures on time scales spanning from picosecond to microsecond by pulse radiolysis measurements. We conclude that the formation yield of NO3•, just after the 7 ps electron pulse, is due to the direct effect and to the ultrafast electron transfer reaction between NO3- and the water cation radical, H2O•+. The absolute formation yield of NO3• radicals due to the direct effect, GNO3•dir, is found to be (3.4 ± 0.1) × 10-7 mol·J-1, irrespective of the concentration and temperature. On longer time scales, >1 ns, an additional contribution to NO3• formation from the reaction between •OH radicals and undissociated HNO3 is observed. The rate constant of this reaction, which is activation-controlled, was determined to be (5.3 ± 0.2) × 107 M-1·s-1 for 22.5 °C, reaching a value of (1.1 ± 0.2) × 108 M-1·s-1 at 80 °C.
Dual-reporter SERS-based biomolecular assay with reduced false-positive signals

Significance Whereas new biomolecular assays are continually being developed to achieve higher detection sensitivities, specificity is often the primary factor limiting an assay’s detection threshold—especially in single-label/signal amplification-based assays. This paper describes a sensitive and quantitative protein detection method that makes use of a dual-reporter approach with enhanced ability to distinguish true positives from false positives. This strategy gives rise to excellent sensitivity and accuracy of measurement, even for low target protein concentrations in complex biological samples. We present a sensitive and quantitative protein detection assay that can efficiently distinguish between specific and nonspecific target binding. Our technique combines dual affinity reagents with surface-enhanced Raman spectroscopy (SERS) and chemometric analysis. We link one Raman reporter-tagged affinity reagent to gold nanoparticles and another to a gold film, such that protein-binding events create a “hot spot” with strong SERS spectra from both Raman reporter molecules. Any signal generated in this context is indicative of recognition by both affinity labels, whereas signals generated by nonspecific binding lack one or the other label, enabling us to efficiently distinguish true from false positives. We show that the number of hot spots per unit area of our substrate offers a quantitative measure of analyte concentration and demonstrate that this dual-label, SERS-linked aptasensor assay can sensitively and selectively detect human α-thrombin in 1% human serum with a limit of detection of 86 pM.
Object-Oriented Zhangjiangkou Mangrove Communities Classification Using QuickBird Imagery

Mangroves are among the richest ecosystems in terms of natural resources. Because of the similar spectral characteristics and complex spatial structure among different mangrove communities, it is still difficult to extract accurate mangrove communities covers from the very high resolution (VHR) satellite images, based on the conventional pixel-based classification methods. Object-oriented classification methods are proposed to process VHR images because they can incorporate as much information on spatial neighbourhood properties as possible into the classification process. On the basis of object-oriented classification method, the paper extracted the Zhangjiangkou mangrove communities using Quickbird image, through image segmentation, merge segmentation, computing and selecting attributes, K Nearest Neighbor classification, et al. Mangrove communities were eventually divided into six types, i.e. Aegiceras corniculatum, Phragmites karka, Kandelia candel, Cyperus malaccensis, Avicennia marin, Aegiceras corniculatum & Kandelia candel. The extraction accuracy is 85.7%. In this paper, through many experiments, the scale level was determined to 50, and the merge threshold was chosen as 90. In this case, the resulting segments of mangrove communities had a obvious spatial characteristics, and the boundary can be delineated well, which would be help for later information extraction. Considering mangrove communities characters, the following object attributes are generated and added : NDVI, texture, and AVGBAND. This research shows very encouraging results for the use of image segmentation and Quickbird data for mapping mangrove communities.
Transgenerational Effects of Bisphenol A on Gene Expression and DNA Methylation of Imprinted Genes in Brain

Bisphenol A (BPA) is a ubiquitous man-made endocrine disrupting compound (EDC). Developmental exposure to BPA changes behavioral and reproductive phenotypes, and these effects can last for generations. We exposed embryos to BPA, producing two lineages: controls and BPA exposed. In the third filial generation (F3), brain tissues containing the preoptic area, the bed nucleus of the stria terminalis, and the anterior hypothalamus were collected. RNA sequencing (RNA-seq) and subsequent data analyses revealed 50 differentially regulated genes in the brains of F3 juveniles from BPA vs control lineages. BPA exposure can lead to loss of imprinting, and one of the two imprinted genes in our data set, maternally expressed gene 3 (Meg3), has been associated with EDCs and neurobehavioral phenotypes. We used quantitative polymerase chain reaction to examine the two imprinted genes in our data set, Meg3 and microRNA-containing gene Mirg (residing in the same loci). Confirming the RNA-seq, Meg3 messenger RNA was higher in F3 brains from the BPA lineage than in control brains. This was true in brains from mice produced with two different BPA paradigms. Next, we used pyrosequencing to probe differentially methylated regions of Meg3. We found transgenerational effects of BPA on imprinted genes in brain. Given these results, and data on Meg3 methylation in humans, we suggest this gene may be a biomarker indicative of early life environmental perturbation.
Haplotype analysis of HLA‐A, ‐B antigens and ‐DRB1 alleles in south Indian HIV‐1‐infected patients with and without pulmonary tuberculosis

We have shown earlier the association of human leucocyte antigen (HLA)‐A11 with resistance and HLA‐B40 and ‐DR2 with susceptibility to HIV and HIV‐TB. In the present study, we have attempted to find out the HLA‐DR2 subtypes and the possible HLA‐A/‐B/‐DRB1 haplotype combinations that are associated with susceptibility or resistance to HIV and HIV with pulmonary tuberculosis (HIV+PTB+). HLA‐DR2 subtyping was carried out by polymerase chain reaction‐based sequence‐specific oligonucleotide probe method. Overrepresentation of HLA‐DRB1*1501 in HIV‐positive PTB‐negative (HIV+PTB–) patients (P = 0.004, Pc = 0.06) and ‐DRB1*1502 in HIV‐positive PTB‐positive (HIV+PTB+) patients (P = 0.019) was observed as compared to healthy controls. Haplotype analysis revealed an increased frequency of HLA‐A2‐DRB1*1501 haplotype in HIV+PTB– patients (P = 0.008) and HLA‐A2‐DRB1*1502 among HIV+PTB+ patients (P = 0.01) compared to healthy controls. The haplotypes B40‐DRB1*1501 and B40‐DRB1*04 were found to be moderately increased in HIV+PTB– and HIV+PTB+ patients (P < 0.05). The study suggests that HLA‐A2‐DRB1*1501 haplotype may be associated with HIV infection while HLA‐A2‐DRB1*1502 haplotype might be associated with susceptibility to PTB in HIV patients. Moreover, HLA‐B40‐DRB1*1501 and HLA‐B40‐DRB1*04 haplotypes may be associated with susceptibility to HIV infection and to PTB in HIV patients.
Multilayer feature learning for polarimetric synthetic radar data classification

Features are important for polarimetric synthetic aperture radar (PolSAR) image classification. Various methods focus on extracting feature artificially. Compared with them, we have developed a method to learn feature automatically. The method is based on deep learning which can learn multilayer features. In this paper, stacked sparse autoencoder (SAE) as one of the deep learning models is applied as a useful strategy to achieve the goal. For improving the classification result, we use a small amount of labels to fine-tuning the parameters of the proposed method. Finally, a real PolSAR dataset is used to verify the effectiveness. Experiment result confirms that the proposed method provides noteworthy improvements in classification accuracy and visual effect.
Surface identification, meshing and analysis during large molecular dynamics simulations

Techniques are presented for the identification and analysis of surfaces and interfaces in atomistic simulations of solids. Atomistic and other particle-based simulations have no inherent notion of a surface, only atomic positions and interactions. The algorithms we develop here provide an unambiguous means to determine which atoms constitute the surface, and the list of surface atoms and a tessellation (meshing) of the surface are determined simultaneously. The tessellation is then used to calculate various surface integrals such as volume, area and shape (multiple moment). The principle of surface identification and tessellation is closely related to that used in the generation of the r-reduced surface, a step in the visualization of molecular surfaces used in biology. The algorithms have been implemented and demonstrated to run automatically (on the fly) in a large-scale parallel molecular dynamics (MD) code on a supercomputer. We demonstrate the validity of the method in three applications in which the surfaces and interfaces evolve: void surfaces in ductile fracture, the surface morphology due to significant plastic deformation of a nanoscale metal plate, and the interfaces (grain boundaries) and void surfaces in a nanoscale polycrystalline system undergoing ductile failure. The technique is found to be quite robust, even when the topology of the surfaces changes as in the case of void coalescence where two surfaces merge into one. It is found to add negligible computational overhead to an MD code.
Screening for Mild Cognitive Impairment and Dementia with Automated, Anonymous Online and Telephone Cognitive Self-Tests

Background: Many older people worry about cognitive decline. Early cognitive screening in an anonymous and easily accessible manner may reassure older people who are unnecessarily worried about normal cognitive aging while it may also expedite help seeking in case of suspicious cognitive decline. Objective: To develop and validate online and telephone-based automated self-tests of cognitive function. Methods: We examined the feasibility and validity of the self-tests in a prospective study of 117 participants of whom 34 had subjective cognitive decline (SCD), 30 had mild cognitive impairment (MCI), and 53 had dementia. The ability of these self-tests to accurately distinguish MCI and dementia from SCD was examined with ROC curves. Convergent validity was examined by calculating rank correlations between the self-tests and neuropsychological tests. Results: Both the online and telephone cognitive self-tests were feasible, because the majority of participants (86% and 80%, respectively) were able to complete them. The online self-test had adequate diagnostic accuracy in the screening for MCI and dementia versus SCD with an Area under the Curve (AUC) of 0.86 (95% CI: 0.78–0.93). The AUC of the MMSE was 0.82 (95% CI: 0.74–0.89). By contrast, the telephone self-test had lower diagnostic accuracy (AUC = 0.75, 95% CI: 0.64–0.86). Both self-tests had good convergent validity as demonstrated by moderate to strong rank correlations with neuropsychological tests. Conclusion: We demonstrated good diagnostic accuracy and convergent validity for the online self-test of cognitive function. It is therefore a promising tool in the screening for MCI and dementia.
Evaluation of Strain Distribution for the P91 Steel under Static Load Using Espi System

Abstract The goal of the research was to evaluate the change of displacement/strain phases in the P91 steel under static load conditions. Undertaken tests were aimed at estimation and analysis of the impact of the material state, which was subjected to loading conditions, on the distribution of stress pattern using ESPI system. Specimen made of high temperature creep resistant steel X10CrMoVNb9-1 (P91) used as a construction material for boiler steam feed heaters, vapor tanks, pressure vessels and vapor pipelines, is used in the service conditions of temperature range up of 650°C. Test samples were taken from two P91 steel pipes. One sample came from a segment of a pipeline transporting fresh vapor in time 80 000 h, under the pressure of 8.4 MPa and temperature 540 °C. The second sample was the same material but in the delivery state.
The Berkeley Dry Eye Flow Chart: A fast, functional screening instrument for contact lens-induced dryness

Purpose In this article, we introduce a novel flow chart-based screening tool for the categorization of contact lens-induced dryness (CLIDE) and its impact on daily visual activities: the Berkeley Dry Eye Flow Chart (DEFC). Methods One hundred thirty (130) experienced soft contact lens wearers discontinued lens wear for 24 hrs, passed a baseline screening and eye health examination, completed the Ocular Surface Disease Index (OSDI) then were dispensed fresh pairs of their habitual lenses. After 6 hrs of wear, subjects were administered a battery of symptom questionnaires, and underwent non–invasive tear breakup time (NITBUT) measurement, grading of distortion in reflected topographer mires, grading of lens surface wettability, and a fluorescein examination of the ocular surface. Subjects returned after at least 48 hrs and repeated all assessments after 6 hrs of wear of a second fresh pair of habitual lenses. Results The repeatability of the DEFC between visits was within 1%, and Limits of Agreement and Coefficient of Repeatability were comparable to those of the other CLIDE assessments. Higher DEFC score was significantly related to shorter pre-lens NITBUT, higher OSDI score, and higher Visual Analog Scale (VAS) ratings of average and end-of-day severity and frequency of dryness (all p < 0.001). For CLIDE as diagnosed based on DEFC score, the highest sensitivities and specificities were achieved by the OSDI and VAS ratings; pre-lens NITBUT exhibited good sensitivity but poor specificity. The optimum pre-lens NITBUT diagnostic threshold was found to be ≤ 2.0 sec for debilitating CLIDE, and the OSDI threshold was ≥ 11.4. Conclusions The DEFC provides a means of quickly categorizing CLIDE patients based on severity and frequency of symptoms, and on the degree to which symptoms impact daily life. The DEFC has several potential advantages as a CLIDE screening and monitoring tool, has good repeatability, and is significantly related to commonly employed clinical assessments for CLIDE.
Climate change alters the structure of arctic marine food webs due to poleward shifts of boreal generalists

Climate-driven poleward shifts, leading to changes in species composition and relative abundances, have been recently documented in the Arctic. Among the fastest moving species are boreal generalist fish which are expected to affect arctic marine food web structure and ecosystem functioning substantially. Here, we address structural changes at the food web level induced by poleward shifts via topological network analysis of highly resolved boreal and arctic food webs of the Barents Sea. We detected considerable differences in structural properties and link configuration between the boreal and the arctic food webs, the latter being more modular and less connected. We found that a main characteristic of the boreal fish moving poleward into the arctic region of the Barents Sea is high generalism, a property that increases connectance and reduces modularity in the arctic marine food web. Our results reveal that habitats form natural boundaries for food web modules, and that generalists play an important functional role in coupling pelagic and benthic modules. We posit that these habitat couplers have the potential to promote the transfer of energy and matter between habitats, but also the spread of pertubations, thereby changing arctic marine food web structure considerably with implications for ecosystem dynamics and functioning.
Empirical Research in Information Systems: The Practice of Relevance

This commentary discusses why most IS acade- mic research today lacks relevance to practice and suggests tactics, procedures, and guidelines that the IS academic community might follow in their research efforts and articles to introduce rel- evance to practitioners. The commentary begins by defining what is meant by relevancy in the context of academic research. It then explains why there is a lack of attention to relevance with- in the IS scholarly literature. Next, actions that can be taken to make relevance a more central aspect of IS research and to communicate impli- cations of IS research more effectively to IS pro- fessionals are suggested.
Emotion recognition using prosodie and spectral features of speech and Naïve Bayes Classifier

Emotion plays an important role in human communication as we can convey our feelings through it. In recent years this emotion detection from speech has become one of the major and challenging research areas in speech processing domain. Extracting suitable features from speech that influence emotions and the categorization of several emotional classes make this task more challenging. This paper proposes emotion detection using both prosodic and spectral features of speech using Naïve Bayes Classifier. Although many works consider only spectral features or prosodic features, this paper considers both. We have used pitch and Mel-Frequency Cepstral Coefficients (MFCC) as the prosodic and spectral features respectively. The classification is performed using Naïve Bayes Classifier. We have developed both gender dependent and gender independent system considering seven emotional classes. After performing the classification the accuracy of the system is tested using speech samples of popular speech database Berlin Emo-db and the results are considerably better than many existing systems.
Contrast-accumulated histogram equalization for image enhancement

Among image enhancement methods, histogram equalization (HE) has received the most attention because of its intuitive implementation quality, high efficiency, and the monotonicity of its intensity mapping function. However, HE is indiscriminate and overemphasizes the contrast around intensities with large pixel populations but little visual importance. To address this issue, we propose an HE-based method that adaptively controls the contrast gain according to the potential visual importance of intensities and pixels. Observing that in natural scenes image details are usually hidden in darker regions that have noticeable local differences, we formulate the potential visual importance on the basis of the multi-resolution, dark-pass filtered gradients in the image. Experiments show that our method is highly discriminating in terms of noises and trivial image gradients, and it guarantees great global contrast preservation.
Non-local approximation of power flow equations with guaranteed error bounds

Progressive increase of distributed generation penetration levels into the power grid has motivated studies on network stability and control methods in order to maintain the grid in operational conditions. A key challenge to better study features of the power grid is to have a reliable network model under different circumstances. In this paper, we derive a structured nonlinear model for the network and then propose an approximate model that captures the system nonlinearities. We then design voltage control functions and derive a dynamic network model with embedded controllers. We analyse voltage stability of the controlled network for both the exact and approximate models. In particular, we give conditions for the voltage to ultimately lie within quantifiable bounds for both models. This result also allows to compute a bound on the difference between the exact and approximate model voltages.