Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis of Intermittent Scan Chain Faults Through a Multistage Neural Network Reasoning Process

Diagnosis of intermittent scan chain failures still remains a hard problem. In this article, we demonstrate that the use of artificial neural networks (ANNs) can lead to significantly higher accuracy. The key of this method is a multistage process incorporating ANNs with gradually refined focuses. During this process, the final fault suspect is elected through multiple rounds of ANN inference, instead of just one round. At each stage, identification of a proper Affine Group, used as the “candidate set of scan cells for the next round of ANN inference,” will influence the final diagnostic accuracy. Thus, we propose a validation-based learning procedure for Affine Group derivation to further boost the final diagnostic accuracy. The experimental results on benchmark circuits have shown that this method is, on the average, 17.46% more accurate than a state-of-the-art commercial tool for intermittent stuck-at-0 faults.
A Study on the Relationship between Paradox Cognition, Green Industrial Production, and Corporate Performance

Based on the theory of paradox cognition, a relationship model among paradox cognition, industrial green production, and enterprise performance has been constructed, which mainly focuses on a study on whether the paradox cognition can have positive influences on the green production behavior of industrial enterprises, and then further promote the improvement of enterprises’ economic benefits. The author wrote this thesis on the basis of results obtained from 305 sample surveys and verified the direct and indirect influence relationships among variables in the model with structural equation path coefficient and mediation effect. The empirical results show that: firstly, paradox cognition has a positive and significant impact on the industrial green production behavior. The higher the level of paradox cognition, the more likely the enterprises are to implement the industrial green production behavior. Secondly, paradox cognition can improve the potential performance of enterprises by affecting “green product provision”, “green production management”, and “green production technology”, and then indirectly improve the financial performance of enterprises.
Social and Cognitive Effects of Smartphone Use in Face-to-Face Verbal Interactions

From 2011 to 2018, the percentage of U.S. adults possessing smartphones has rapidly doubled from 35 to 77%. This number is expected to grow, considering that 94% of individuals aged 18–29 have a smartphone (Pew Research Center, 2018). A 2014 survey of eight European countries suggested that 69% of children use a mobile phone, indicating 10 years old as the modal age for first phone ownership (GSMA, 2014). The impact that the growing rate of mobile phone possession has on relationships and human interactions continues to be explored, with the current focus on smartphone use. Although some consider smartphones as a tool for maintaining relationships (Borae & Peña, 2010; Lundquist, Lefebvre, & Garramone, 2014) by allowing connections with far-away friends or sustained constant communication with others, some suggest that smartphones, or even just mobile phones, might be a detriment to more intimate human interactions (Lundquist et al., 2014) or that they may change the dynamics of face-to-face (f2f) interactions completely (Humphreys, 2005). Many studies on smartphones’ interactions with human relationships have focused on individuals’ beliefs about smartphone use in their relationships ABSTRACT. Smartphone possession is on the rise in the United States, and their presence may have detrimental effects in face-to-face (f2f) interactions. Two potential theories were explored: expectation violation theory, which suggests that smartphone presence in a conversation could lead to negative social evaluations, and cognitive load theory, which suggests that smartphone use during f2f conversations could be a distraction. Participants engaged in a scripted dyadic conversation scenario with a confederate, where the confederate did or did not appear to use a smartphone during the conversation, and/or the participant did or did not use a smartphone for a simultaneous texting task. In each condition, participants were asked to socially evaluate their confederate partner’s conversation behavior and perform a conversation recognition task. As predicted, participants had more negative social evaluations for behaviors of confederate partners using their smartphones (ηp2 = .07) and scored lower on the recognition task (ηp2 = .22) when engaged in their own smartphone use. Results of this study suggest social and cognitive rationales for not using smartphones in f2f interactions and encourage continued research on the effects smartphones may have in different f2f interactions.
Incorporating User's Preference into Attributed Graph Clustering

Graph clustering has been studied extensively on both plain graphs and attributed graphs. However, all these methods need to partition the whole graph to find cluster structures. Sometimes, based on domain knowledge, people may have information about a specific target region in the graph and only want to find a single cluster concentrated on this local region. Such a task is called local clustering. In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs). Currently, very few methods can deal with this kind of task. To this end, we propose two quality measures for a local cluster: Graph Unimodality (GU) and Attribute Unimodality (AU). The former measures the homogeneity of the graph structure while the latter measures the homogeneity of the subspace that is composed of the designated attributes. We call their linear combination as Compactness. Further, we propose LOCLU to optimize the Compactness score. The local cluster detected by LOCLU concentrates on the region of interest, provides efficient information flow in the graph and exhibits a unimodal data distribution in the subspace of the designated attributes.
Representation and learning schemes for argument stance mining

Argumentation is a key part of human interaction. Used introspectively, it searches for the truth, by laying down argument for and against positions. As a mediation tool, it can be used to search for compromise between multiple human agents. For this purpose, theories of argumentation have been in development since the Ancient Greeks in order to formalise the process and therefore remove the human imprecision from it. From this practice the process of argument mining has emerged. As human interaction has moved from the small scale of one-to-one (or few-to-few) debates to large scale discussions where tens of thousands of participants can express their opinion in real time, the importance of argument mining has grown while its feasibility in a manual annotation setting has diminished and relied mainly on a human-defined heuristics to process the data. This underlines the importance of a new generation of computational tools that can automate this process on a larger scale. In this thesis we study argument stance detection, one of the steps involved in the argument mining workflow. We demonstrate how we can use data of varying reliability in order to mine argument stance in social media data. We investigate a spectrum of techniques, from completely unsupervised classification of stance using a sentiment lexicon, automated computation of a regularised stance lexicon, automated computation of a lexicon with modifiers, and the use of a lexicon with modifiers as a temporal feature model for more complex classification algorithms. We find that the addition of contextual information enhances unsupervised stance classification, within reason, and that multi-strategy algorithms that combine multiple heuristics by ordering them from the precise to the general tend to outperform other approaches by a large margin. Focusing then on building a stance lexicon, we find that optimising such lexicons using an empirical risk minimisation framework allows us to regularise them to a higher degree than competing probabilistic techniques, which helps us learn better lexicons from noisy data. We also conclude that adding local context (neighbouring words) information during the learning phase of the lexicons tends to produce more accurate results at the cost of robustness, since part of the weights is distributed from the words with a class valence to the contextual words. Finally, when investigating the use of lexicons to build feature models for traditional machine learning techniques, simple lexicons (without context) seem to perform overall as well as more complex ones, and better than purely semantic representations. We also find that word-level feature models tend to outperform sentence and instance-level representations, but that they do not benefit as much from being augmented by lexicon knowledge. This research programme was carried out in collaboration with the University of Glasgow, Department of Computer Science.
Modeling dependency between industry production and energy market via stochastic copula approach

Abstract In this article, the dependence structure between industrial production and energy markets is modeled via stochastic copula autoregressive approach. This model based on latent process is nonlinear and has time-varying parameters. The parameters that follow AR latent process are estimated by means of maximum likelihood-efficient importance sampling due to integral problem with high dimensions. It is found that dependence structures between industrial production and energy markets evolve over time. In addition, although relationships between industrial production and petrol price as well as dependency between industrial production and producer price do not affected by extraordinary events such as financial crisis, dependence structure between natural gas and industrial production is influenced by extreme events.
Profiling Secondary Metabolites of Cocoa (Theobroma cacao L.) Endophytic Fungi Lasiodiplodia pseudotheobromae PAK-7 and Lasiodiplodia theobromae TN-R-3 and Their Antimicrobial Activities

Aims: To determine the chemical composition of secondary metabolite of cocoa endophytic fungi L. pseudotheobromae PAK-7, L. theobromae TN-R-3 and their anti-oomycete activities. Statistical Design: Multivariate analysis. Place and Duration of Study: Department of Plant Pathology, TNAU, Coimbatore, Tamil Nadu from April 2018 to December 2019. Methodology: Lipophilic extracellular secondary metabolites were extracted using ethyl acetate as a solvent and their chemical composition was detected by Gas Chromatography-Mass Spectrometry (GC-MS) and identified by NIST library and Pub Chem databases. Results: Metabolic profiling of cocoa endophytic fungi L. pseudotheobromae PAK-7 and L. theobromae TN-R-3 showed the presence of eleven peaks representing nine compounds. The most abundant compound observed were Acetic acid, 3-methyl-6-oxo-9oxabicyclo[3.3. 1]non-2-yl ester, 2H-Pyran-2-one, tetrahydro-4-hydroxy-6-pentyl-, Melezitose, Ethyl à-d-glucopyranoside collectively representing 58.01% area. In comparison to L. pseudotheobromae PAK-7 GC-MS analysis of L. theobromae TN-R-3 exhibited the presence of 29 peaks. The most abundant compounds were dl-Mevalonic acid lactone, Methyl 6-O-[1-methylpropyl]-á-d-galactopyranoside, 2H-Pyran-2-one, tetrahydro-4-hydroxy-6-pentyl-, Melezitose, Ethyl à-d-glucopyranoside, 1,6-Anhydro-á-d-talopyranose collectively representing 60.47% of the total area. Conclusion: Chemical compositions and anti-oomycete activities of crude secondary metabolites of L. pseudotheobromae PAK-7, L. theobromae TN-R-3 differed entirely depending on the property and abundance of bioactive metabolites.
How should quality of life assessment be integrated in the evaluation of patients with acute myeloid leukemia?

ABSTRACT Introduction: Recent studies in acute myeloid leukemia (AML) suggest that self-reported health status (including quality of life) should be a part of the pretherapy evaluation especially of elderly and unfit patients. However, there is also a need for additional studies to clarify the long-term effects of various antileukemic therapies. Areas covered: We searched for original articles in the PubMed database by the following combinations of terms: (i) acute myeloid leukemia combined with quality of life, geriatric assessment, or quality of life + allogeneic stem cell transplantation; or (ii) acute myeloid leukemia combined with either elderly, unfit, low-dose cytarabine or azacitidine. Expert commentary: We review and discuss the results from studies of quality of life for AML patients treated with conventional chemotherapy, autologous and allogeneic stem cell transplantation, and patients with acute promyelocytic leukemia. Self-reported health status (including quality of life) should be a part of the pretherapy evaluation especially of elderly and unfit AML patients together with performance status, comorbidity scoring and geriatric assessment. The risk of chemoresistance to intensive treatment should also be included. All these aspects should be considered when evaluation the risk for treatment-related mortality and deciding the intensity of the antileukemic therapy.
IoT privacy preservation using blockchain

ABSTRACT Internet of Things (IoT) security is a fundamental challenge for the enterprises working behind the buildup of the IoT, and it is related applications. In today’s ara, with IoT, another buzzword is a blockchain-based cryptocurrency and other blockchain applications. Blockchain technology has proven itself one of the most secure existing technology in trust and privacy. This paper discusses IoT and blockchain technology fundamentals, followed by a discussion regarding numerous consensus models. We also discuss mathematical foundations used by blockchain technology, and it is followed by the proposed privacy-preserving technique for IoT devices with better device identity management using blockchain technology. This paper presents a novel solution for privacy preservation and identity allocation using secure identity management through public key-based cryptography-based blockchain. Though we consider smart home management and smart health management’s ecosystem for applicability discussion of the proposed scheme, the proposed system can be applied to any IoT application for better privacy preservation. Finally, we conclude with the discussion regarding the futuristic scope of the proposed system. //
High-Gain Conical-Beam Traveling-Wave Array Antenna Based on a Slotted Circular Waveguide at $Ku$ -Band

A high-gain conical-beam traveling-wave array antenna based on a slotted circular waveguide is designed at the higher part of Ku-band. A single radiating element is formed by a ring of eight equally spaced slots fed by a circular waveguide. The excitation of the nonfundamental TM01 mode provides a uniform feeding distribution to each slot, generating an omnidirectional radiation performance. Thus, an array antenna formed by 48 rings of slots is proposed following the design rules of traveling-wave arrays. Every ring is properly designed to achieve a uniform power distribution and consequently maximum directivity for the array antenna performance. The distance between elements is also adjusted to obtain the better input matching response and mitigate the grating lobe appearance, generating a high-gain conical-beam radiation performance suitable for satellite and fifth-generation communications. The proposed antenna design has been manufactured in plastic material by using a 3-D printing process and plated by a spray metallization process. A −10 dB impedance bandwidth higher than 23%, high-gain values around 13.5 dB, or total efficiency higher than 98% has been achieved for the whole frequency band.
Drinking-Water Supply for CKDu Affected Areas of Sri Lanka, Using Nanofiltration Membrane Technology: From Laboratory to Practice

Installation of decentralized water-treatment plants is an ideal option to supply safe drinking water for rural communities. Presently in Sri Lanka, over 3.6 million villagers face acute water-quality problems, and chronic kidney disease of unknown etiology (CKDu) is also prevalent among this community. Most of the drinking water in these villages is unpalatable due to high hardness and salinity. As an interim measure, reverse-osmosis (RO) water-treatment plants are introduced to provide safe water. However, due to deficient electrolytes, RO-treated water tastes unpleasant to some consumers; hence, people refuse it after prolonged use. The operation, maintenance, and management of RO plants are other major problems. Aimed at providing safe drinking water to the rural sector in a cost-effective manner, in this study, we fabricated an automated drinking-water purification system based on nanofiltration (NF) membrane technology, which can remove divalent cations, dissolved organic carbon (DOC) and pathogens efficiently, and monovalent ions partially, and thus keep electrolytes to some degree. Ten commercial NF membranes were tested in a laboratory, for solute and DOC removal efficiency and robustness. The DF-90 membrane showed the highest removal of DOC and hardness, and it was therefore selected, to design a pilot NF drinking-water treatment plant. The adhered DOC by the membrane can be cleaned by NaOH solution (pH = 12). The pilot NF drinking-water treatment plant has been in use since September 2018, and it shows excellent performance of removing DOC, TDS, hardness, fluoride, and pathogens in groundwater, and the permeate water of the NF plant has been well-accepted by the stakeholders of the society. The dominant genus of source water, and throughout the two processes (NF and RO), is Pseudomonas, and their difference is significant in the concentrates of the NF and RO processes.
ATG8-interacting (ATI) 1 and 2 define a plant starvation-induced ER-phagy pathway and serve as MSBP1 (MAPR5) cargo-receptors

ER-phagy, the selective autophagy of endoplasmic reticulum (ER) components, is known to operate in eukaryotes from yeast and unicellular algae to animals and plants. Thus far, only ER-stress derived ER-phagy was reported and analyzed in plants. In this study we characterize an ER-phagy pathway in Arabidopsis thaliana that is triggered by dark-induced starvation and not by ER-stress. This pathway is defined by the previously reported ATG8-interacting proteins, ATI1 and ATI2 and is regulated by the TOR signaling pathway. We further identified ER-localized Membrane Steroid Binding Protein 1 (MSBP1) as an ATI1 and 2 interacting protein and an autophagy cargo, and show that ATI1 and 2 serve as its cargo receptors. Together, these findings expand our knowledge on plants responses during energy deprivation and highlight the role of this special type of ER-phagy in this process.
Preparation of Palm Oil Ash Nanoparticles: Taguchi Optimization Method by Particle Size Distribution and Morphological Studies

The interest in the utilization of palm oil ash is high, mainly due to their renewable material, opportunity to enhance the properties and possibility to use in a wide range of applications. Palm oil ash is the by-product of the palm oil mill boilers and locally available in the form of micro-size particles. In this research, optimization of the milling process was designed using the Taguchi method to find the most influencing parameters for the preparation of palm oil ash (POA) nanoparticles using a ball milling technique. The experiment was applied using a L9 orthogonal array and signal to noise ratio to investigate the performance of parameters, which are milling time, milling speed, and balls size. The results from signal to noise ratio reveal that to produce POA nanoparticles in optimum parameters, the size of balls shows the highest significant effect on the production of POA nanoparticles, followed by milling time and speed. The results of the parameters optimization experiment were validated by a confirmation test of milling machine operations.
The variation of the chemical composition of the main plant species in a subalpine grassland in northwestern Greece

This study presents the results of a survey conducted in the year 2013 in a subalpine grassland located in Epirus, in northwestern Greece, and concerns the determination of the chemical composition of nine wild plants species (Alopecurus gerardii V., Stipa pennata L., Phleum alpinum L., Trifolium repens L., Lathyrus aphaca L., Lotus corniculatus L., Ranunculus repens L., Achillea millefolium L., and Geranium lucidum L.) at various stages of growth. The highest mean neutral detergent fiber content was determined in S. pennata L. samples with 546 g kg−1 dry matter (DM). The highest mean acid detergent fiber content was determined in samples of A. gerardii L. with 358 g kg−1 DM. For calcium (Ca), the highest average content was determined in the samples of L. corniculatus L. with 9.6 g kg−1 DM. For phosphorus (P), iron (Fe), and copper (Cu), the highest average contents were determined in the samples of R. repens L. with 2.8 g kg−1 DM, 60.2 mg kg−1, and 5.6 mg kg−1 DM, respectively. For magnesium (Mg), the higher average content was found in L. aphaca L. samples with 1.6 g kg−1, for potassium (K) in G. lucidum L. samples with 15.6 g kg−1, for sodium (Na) in A. millefolium L. samples with 15.3 g kg−1, and for manganese (Mn) in P. alpinum L. samples with 37.9 mg kg−1. The chemical composition of plant species meets the nutritional requirements of grazing animals in the investigated area. Among the plant species, legumes and other forbs have the highest nutritional value.
Analysis of the inter-species power balance in JET plasmas

Most auxiliary heating methods provide heating to more than one particle species (electrons, ions, impurities) in a fusion plasma. This can lead to substantial temperature differences between species, depending on conditions such as heating power to the different species and collisionality, with temperature differences between species limited by inter-species thermal equipartition and transport. The analysis of the steady-state electron-ion and impurity-ion power balances presented in this paper are used for consistency-checking experimental ion and electron temperature measurements and for inferring the main ion temperature from measured impurity temperatures. As ion temperature measurements by charge exchange spectroscopy (CXS) based on impurity ions have become more difficult and time-consuming since the installation of the ITER-like wall (ILW) with Be and W PFC’s, knowing the maximum sustainable temperature difference between ions and electrons, |Ti − Te| allows rejecting erroneous measurements. It also obviates the need for an ion temperature measurement, if an electron temperature measurement is available and |Ti − Te| cannot be larger than the combined errors of the underlying measurements. A power balance analysis is also required for estimating the errors of the ion and electron heat fluxes prior to any species-resolved transport analysis. The ion-impurity temperature differences are usually found to be small due to strong thermal equipartition between ion species. However, they can approach 10% in JET-ILW low density, high power discharges, such the ones under development for a future JET deuterium–tritium campaign (Joffrin et al 2019 Nucl. Fusion). This has a generally small, but not always negligible effect on the calculation of fusion reaction rates, which depend on main ion temperatures. An important outcome of this analysis is that temperature differences between impurity species are always much smaller than between the impurities and hydrogenic species and can usually be neglected. The paper presents two methods for calculating the impurity-to-main ion temperature ratio. Finally, this analysis leads to a method for the reconstruction ion temperature profiles from ion temperature data available at only one or a small number of spatial locations.