Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermostable Glycoside Hydrolases in Biorefinery Technologies

Glycoside hydrolases, which are responsible for the degradation of the major fraction of biomass, the polymeric carbohydrates in starch and lignocellulose, are predicted to gain increasing roles as catalysts in biorefining applications in the future bioeconomy. In this context, thermostable variants will be important, as the recalcitrance of these biomass-components to degradation often motivates thermal treatments. The traditional focus on degradation is also predicted to be changed into more versatile roles of the enzymes, also involving specific conversions to defined products. In addition, integration of genes encoding interesting target activities opens the possibilities for whole cell applications, in organisms allowing processing at elevated temperatures for production of defined metabolic products. In this review, we overview the application of glycoside hydrolases related to the biorefining context (for production of food, chemicals, and fuels). Use of thermostable enzymes in processing of biomass is highlighted, moving from the activities required to act on different types of polymers, to specific examples in today’s processing. Examples given involve (i) monosaccharide production for food applications as well as use as carbon source for microbial conversions (to metabolites such as fuels and chemical intermediates), (ii) oligosaccharide production for prebiotics applications (iii) treatment for plant metabolite product release, and (iv) production of surfactants of the alkyl glycoside class. Finally future possibilities in whole cell biorefining are shown.
Media persuasion, ethnic hatred, and mass violence : a brief overview of recent rfesearch advances

This chapter outlines the fundamental empirical challenges when studying media effects on conflict and discusses some recent methodological advances designed to overcome them. The evidence in this emerging literature indicates that mass media can be an effective tool for political elites to orchestrate mass violence. Both direct and indirect persuasion matter. The emerging evidence indicates that direct persuasion is stronger when propaganda targeting ethnic minorities is aligned with political predispositions of the ethnic majority, and indirect persuasion may occur from coordination of violence and from spillovers arising from social interactions.
Discontinuous Galerkin method for two-component miscible liquid-gas porous media flows ∗

We consider two-component (typically, water and hydrogen) compressible liquid-gas porous media flows including mass exchange between phases possibly leading to gas-phase (dis)appearance, as motivated by hydrogen production in underground repositories of radioactive waste. Following recent work by Bourgeat, Jurak, and Smäı, we formulate the governing equations in terms of liquid pressure and dissolved hydrogen density as main unknowns, leading mathematically to a nonlinear elliptic-parabolic system of PDEs, in which the equations degenerate when the gas phase disappears. We develop a discontinuous Galerkin method for space discretization, combined with a backward Euler scheme for time discretization and an incomplete Newton method for linearization. Numerical examples deal with gas-phase (dis)appearance, ill-prepared initial conditions, and heterogeneous problem with different rock types.
Variable selection for BART: An application to gene regulation

We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266‐298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.
Discretization Errors of Random Fields in Finite Element Analysis

The mechanical properties of natural materials such as rocks and soils vary spatially. This randomness is usually modelled by random field theory so that the material properties can be specified at each point in space. When these point-wise material properties are mapped onto a finite element mesh, discretization errors are inevitable. In this study, the discretization errors are studied and suggestions for element sizes in relation with spatial correlation lengths are given.
A NOVEL SECURED BOOLEAN BASED SECRET IMAGE SHARING SCHEME

A novel (k, n) secret image sharing scheme with high security by using Boolean XOR operations and circular shift operations for gray-scale and color secret images is designed and its performance is evaluated in this paper. The original secret image is encoded into n noise-like share images, transmitted over channel, and any k or more number of share images is gathered to reconstruct the secret image at the receiver side. The share images with less than k in number never reconstruct the original secret image. In this technique, the security of the secret image is improved by combining the secret with same sized random image and using distinct 8-bit identifier to each share. The generated shares have high randomness which indicates high security to the secret image. The overheads like codebook design, pixel expansion and basis matrices are not needed in this proposed method compared to other methods. The performance evaluation parameters such as correlation, mean square error and peak signal-to-noise ratio gives the performance and consistency of the proposed design. The experimental results prove the feasibility and security of the proposed Boolean based image sharing scheme.
Do Speakers Produce Discourse Connectives Rationally?

A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice. One recent account is the Rational Speech Acts theory, which predicts that speakers try to maximize the informativeness of an utterance such that the listener can interpret the intended meaning correctly. Existing prior work uses referential language games to test the rational account of speakers’ production of concrete meanings, such as identification of objects within a picture. Building on the same paradigm, we design a novel Discourse Continuation Game to investigate speakers’ production of abstract discourse relations. Experimental results reveal that speakers significantly prefer a more informative connective, in line with predictions of the RSA model.
Methodus nova accurata & facilis inveniendi radices æqnationum quarumcumque generaliter, sine praviæ reductione

Artis analyticæ præcipuus quidem usus est problemata mathematica ad æquationes perducere, easque terminis quantum sieri poslit simplicissimis exhibere. Ars autem isla manca quodammodo, nee satis Analytica merito videretur, nisi methodi quædam subministrarentur, quarum ope radices, sive linæ sive numeri sint, ex jam inventis æquationibus elicere liceret, eoque nomine problemata soluta dare.
Identifying product opportunities using topic modeling and sentiment analysis of social media data

Detecting customer needs is the most prerequisite for new product development and current product improvement. In th is study, we propose a novel approach to identify product opportunities by applying topic modeling and sentiment analysis. First, this approach defines major contextual features of a product in need of product planning by applying topic modeling to social web data of the product. Second, it computes customer satisfaction for each feature by keyword(or key phrase)-level sentiment analysis. Finally, this approach quantifies the opportunity level of each feature through the opportunity algorithm based on the concepts of importance and satisfaction. This approach will contribute to monitoring customer needs in real time and identifying product development concepts.
SYNTHESIS OF GOLD NANOPARTICLES BY TETRACHLOROAURATE REDUCTION WITH CYCLODEXTRINS

One-step synthesis of gold nanoparticles functionalized with α-, βand γ-cyclodextrins was carried out using a synthetic method of direct tetrachloroaurate reduction in alkaline solutions. The two experimental conditions studied were direct heating and microwave irradiation. This is the first publication comparing the characteristics of gold nanoparticles produced using three cyclodextrins under different conditions. The resulting nanoparticles were characterized by absorbance spectra, FTIR spectra, dynamic light scattering data and transmission electron microscopy images. The reducing ability of cyclodextrins increased in the following order: β-cyclodextrin < α-cyclodextrin < γ-cyclodextrin. Therefore, γ-cyclodextrin was the most active reductant and the only cyclodextrin that was able to reduce tetrachloroaurate at room temperature. In this case, nanoparticles were observed only after 4 days of storage. The nanoparticle size was dependent on the cyclodextrin type and correlated with the difference in the reducing ability. Using microwave irradiation, most of nanoparticles were found to be free nanoparticles. Using direct heating, aggregates formed due to the high ionic strength of the solution after prolonged boiling. Therefore, nanoparticles synthesized by direct heating were less stable during storage than those synthesized by microwave irradiation.
Multi-Label Learning from Crowds

We consider multi-label crowdsourcing learning in two scenarios. In the first scenario, we aim at inferring instances’ groundtruth given the crowds’ annotations. We propose two approaches NAM/RAM (Neighborhood/Relevance Aware Multi-label crowdsourcing) modeling the crowds’ expertise and label correlations from different perspectives. Extended from single-label crowdsourcing methods, NAM models the crowds’ expertise on individual labels, but based on the idea that for rational workers, their annotations for instances similar in the feature space should also be similar, NAM utilizes information from the feature space and incorporates the local influence of neighborhoods’ annotations. Noting that the crowds tend to act in an effort-saving manner while labeling multiple labels, i.e., rather than carefully annotating every proper label, they would prefer scanning and tagging a few most relevant labels, RAM models the crowds’ expertise as their ability to distinguish the relevance between label pairs. In the second scenario, we care about cost-efficient crowdsourcing where the labeling and learning process are conducted in tandem. We extend NAM/RAM to the active paradigm and propose instance, label, and worker selection criteria such that the labeling cost is significantly saved compared to passive learning without labeling control. The proposals’ effectiveness are validated on simulated and real data.
A “SIGHT-SPEED” HUMAN-COMPUTER INTERACTION FOR AUGMENTED GEOSPATIAL DATA ACQUISITION AND PROCESSING SYSTEMS

Many real-time tasks in geospatial data analysis are based on matching of visual data, i.e. finding similarity and/or disparity in geoimages either in the remotely sensed source data or in geospatial vector and raster products. When human eyes scrutinize a scene, the brain performs matching of visual streams, acquired by eyes and transmitted via all chains of human visual system. As the result the brain creates a comfortable visual model of the scene and alarms, in case some distinct disparity in visual perception is found. While observing a scene, the optical axes of both eyes are naturally directed to the same area of the object, which is particularly true for visual perception of stereoscopic images on a computer screen. If eye movements are recorded while observing the virtual stereoscopic model generated in the brain, it is possible to detect such regions of interest using fixations, identified in eye-tracking protocols. These fixations can be considered as coordinates of feature points of an object being observed (regions of interest) and can be used to reconstruct corresponding 3D geometric models by applying classical stereo photogrammetric procedures. This novel way of utilizing eye-tracking leads to establishment of “eye-grammetry” - a new approach which melds human visual abilities and the computational power of modern computers to provide “sight-speed” interaction between the human operator and the computer software in augmented geospatial data acquisition and processing systems. This paper reviews theoretical and practical aspects of eye-tracking for real-time visual data processing and outlines two particular fields of applications where the proposed technology could be useful: gaze-tracking based 3D modeling and geospatial knowledge elicitation.
Performance Analysis of Secure data distribution in Wireless Sensor Networks

A data discovery and dissemination protocol for wireless sensor networks (WSNs) is responsible for updating configuration parameters and distributing management commands to the sensor nodes. All existing data dis covery and dissemination protocols suffer from two drawbacks. First, they are based on the centralized approach; only the bas e station can distribute data items. Such an approach is not suit able for emergent multi-owner-multi-user WSNs. Second, those protocols were not designed with security in mind and hence a dversaries can easily launch attacks to harm the network. This paper proposes a first secure and distributed data discov ery and dissemination protocol named DiDrip. It allows the network owners to authorize multiple network users with diferent privileges to simultaneously and directly dissemina te data items to the sensor nodes. Moreover, as demonstrated by t he theoretical analysis, The proposed system addresses a number of possible security vulnerabilities were identified. Extensive security analysis show DiDrip is provably secure, Hence DiDrip is implemented ,in an experimental network of resou rce-limited sensor nodes to show its high efficiency in practic e Index Terms – DiDrip, DHV, DIP, Hash Key, ECDSA, WSN.
Personality prediction of Twitter users with Logistic Regression Classifier learned using Stochastic Gradient Descent

Twitter is a popular social media platform with millions of users. The tweets shared by these users have recently attracted the attention of researchers from diverse fields. In this research, we focus primarily on predicting user's personality from the analysis of tweets shared by the user. An associative study of different research works done in personality prediction shows that different techniques have been used to predict a user's personality from tweets but there are certain shortcomings which still need to be addressed. In the introduction section we have given the gist of personality prediction with Twitter research. In the next section we provide the importance and framework of predicting a user's personality with Twitter. In the consecutive section, literature survey, we have discussed briefly about the different research ideas in context to personality prediction with a side by side overview of various resources, tools and machine learning algorithms that were used alongside their potential and limitations, followed by research gap, which provides different fields of possibilities to which can be rectified to improve the efficiency of predicting personality with machine learning algorithms. Finally, a new approach is proposed to predict personality with new insights to predict personality on crucial factors such as scalability and counter measures to improve the research based on previous work by using a Logistic Regression Classifier with parameter regularization using stochastic gradient descent.
Forced-Exploration Based Algorithms for Playing in Stochastic Linear Bandits

We study stochastic linear payoff bandit problems and give a simple, computationally efficient algorithm whose regret, under certain regularity assumptions on the action set, is O(d √ T ), where d is the dimensionality of the action space and T is the time-horizon. However, this result is problem dependent and not a minimax bound. We show that our algorithm is able to achieve lower regret bounds when we have sparsity in the problem. Our experimental results support our upper bound and show that the algorithm proposed might be competitive with alternative algorithms when the payoffs of the actions are correlated and when the parameters vector is sparse.
Snapshots of Children's Changing Biases During Language Development: Differential Weighting of Perceptual and Linguistic Factors Predicts Noun Age of Acquisition

Word learning is a lifelong activity constrained by cognitive biases that people possess at particular points in development. Age of acquisition (AoA) is a psycholinguistic variable that may prove useful toward gauging the relative weighting of different phonological, semantic, and morphological factors at different phases of language acquisition and development. Our aim here was to evaluate AoA as a statistical tool for taking “snapshots” of cognitive development. We examined a large corpus of English nouns (n = 1,381) with AoA as the outcome variable in three separate multivariate regressions, encompassing different age ranges (early–middle–late). Predictors included perceptual (e.g., imagery), phonological (e.g., phonological neighborhood density), and lexical (e.g., word length) factors. Different combinations of predictors accounted for significant proportions of the variance for different AoA ranges (i.e., early–middle–late). For example, imageability and frequency are stronger predictors of early relative to late word learning. These corpus analyses support a hybrid model of word learning in which multiple perceptual and linguistic factors are differentially weighted over time. This statistical approach may provide independent corroboration of and motivation for experimental studies in language learning and cognitive development.
Belief Formation Under Signal Correlation

Using a set of incentivized laboratory experiments, we characterize how people form beliefs about a random variable based on independent and correlated signals. First, we theoretically show that, while pure correlation neglect always leads to overvaluing of correlated signals, that may not happen if people also exhibit overprecision perceiving signals to be more precise than they actually are. Our experimental results reveal that, while subjects do overvalue moderately or strongly correlated signals, they undervalue weakly correlated signals, suggesting concurrent presence of correlation neglect and overprecision. Estimated parameters of our model suggest that subjects show a nearly complete level of correlation neglect and also suffer from a high level of overprecision. Additionally, we find that subjects do not fully benefit from wisdom of the crowd-they undervalue aggregated information about othersi¯ actions in favor of their private information. This is consistent with models of overprecision where people do not properly incorporate the variance reducing power of averages.
Molecular and Biological Studies of Papaya Ringspot Virus Isolates from Brazil and Cuba

The coat protein genes of 21 Papaya ringspot virus (PRSV) isolates from Brazil and 7 isolates from Cuba were sequenced and analyzed, and the symptoms induced by the virus isolates were evaluated in papaya (Carica papaya L. cv. Solo) and squash (Cucurbita pepo L. cv. Caserta). The virus isolates were grouped in phylogenetic trees by geographic region, presenting the highest nucleotide and amino acid identities with those isolates collected in the same regions. The American and Indian isolates, from GenBank were grouped together in the tree, and the Brazilian isolates were grouped by state; the Cuban isolates from the eastern region were closer to the American isolates than to those from the central-west region. Typical mosaic symptoms, grouped according to intensity, were observed in the inoculated papaya and squash seedlings. The variability of the coat protein genes confirmed that virus control using cross-protection and transgenic plants requires the selection of region-specific virus isolates in each country.
Exposure of honeybees ( Apis mellifera ) in Saskatchewan , YahyaALNAGGAR

Concentrations of 14 organophosphorus insecticides (OPs) were measured in Apis mellifera L. (European honeybee) and hive matrices (honey and bee bread). Samples were collected from seven randomly selected colonies in central Saskatchewan during the summer of 2013. LC-MS/MSwas used to identify and quantify individual OP by use of a modified quick easy cheap effective rugged safe (QuEChERS) method. Diazinon, dimethoate, and chlorpyrifos-oxon were the only OPs detected in honey with mean concentrations of 0.3, 1.5, and 0.2 ng/g, wet mass (wm), respectively. Fenamiphos, chlorpyrifos, and chlorpyrifos methyl were the only OPs detected in bee bread, with mean concentrations of 0.4, 2.7, and 15.8 ng/g, wm, respectively, while ethoprop, malathion, and dichlorvos were the only OPs detected in bees with mean concentrations of 1.4, 3.7, and 889.2 ng/g, wm, respectively. Total hazard quotients (HQs), based on lethality of bees exposed to OPs in honey and pollen consumed by bees ranged from 0.01 to 0.06 and based on lethality of bees from direct exposure to OPs ranged from 0.3 to 0.4 which suggests little hazard of OPs to Saskatchewan beehives. honey / OPs / risk assessment / agriculture / honeybees