Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and Antibacterial Activity of Water‐soluble Chitin Derivatives

Diethylaminoethyl–chitin (DEAE–chitin) was synthesized by introducing DEAE groups onto the C(6)–OH in chitin. DEAE–chitin was N-deacetylated by heating in aqueous 10% sodium hydroxide containing sodium borohydride to prepare the diethylaminoethyl–chitosan (DEAE–chitosan). In addition, DEAE–chitin was quaternized with ethyl halide to produce triethylaminoethyl–chitin (TEAE–chitin). Their antibacterial activities were evaluated by using colony count against Staphylococcus aureus and Escherichia coli by means of shake flask method. The antibacterial activity was found to increase in order of DEAE–chitin, DEAE–chitosan, TEAE–chitin. DEAE–chitin was hydrolyzed by enzymes to investigate the effect of molecular weight on its antibacterial activities. The antibacterial activity was dependent on the structure, particularly on the molecular weight of chitin derivatives. © 1997 John Wiley & Sons, Ltd.
Highly ordered mesostructured silica spheres as template for polyaniline nanofibres: synthesis, characterisation and their electrochemical properties

Ordered micrometre-sized silica spheres with uniform morphology and mesoporous structures were prepared by a temperature-induced and polyethylene glycol-assisted assembly method. The porous surface of the silica spheres was used for the chemical polymerisation of aniline, as well as a sacrificial template for synthesis of polyaniline (PANI) nanofibres. The morphologies of PANI nanostructures were characterised by scanning electron microscopy and transmission electron microscopy. Microstructural analysis and properties evaluation of the as-prepared products were characterised by X-ray diffraction, thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy and N2 adsorption–desorption analysis. TGA results demonstrated that about 95% of the mesoporous material has been utilised for the synthesis of PANI nanofibres. Their electron transfer properties were investigated by cyclic voltammetry (CV) and electrochemical impedance spectroscopy. CV experiment shows that the obtained PANI nanofibres have good electrochemical activity, which can be attributed to a higher number of accessible redox sites available during the synthesis process. The value of time constant (Γ) of PANI nanofibres based electrode was 0.0217 ms. The lower Γ-value is usually preferred for electrochemical capacitor for fast charge–discharge processes; hence, these PANI nanofibres based materials and their composites have the potential to be used as supercapacitor electrodes for supercapacitor applications.
International Union of Crystallography. Commission on Crystallographic Apparatus and Commission on Charge, Spin and Momentum Densities. The theoretical models of extinction. Their domain of applicability

The various theoretical models of extinction are critically reviewed. It is shown that the Darwin energy transfer equations are valid as long as the mean coherence distance in the sample is smaller than the extinction length, as demonstrated recently by Kato. The available solution is correct when the entrance and exit surface do not overlap and the approximation is seen to be reasonable for more complicated cases. The correlation between primary and secondary extinction is discussed. It is emphasized, in practical applications, that many tests, both theoretical and experimental (when possible) of the correctness of the correction should be made. Only samples that fit a given diffraction theory should be used for accurate studies like charge density determination.
Adsorption of Gases on Quartz Single Crystals

Approximate adsorption isotherms of hexane and argon on the crystal face perpendicular to the Y axis of quartz have been obtained, making use of the alteration of the mechanically resonant frequency of a 10‐Mc, Y‐cut quartz crystal as a measure of the mass adsorbed. The ``area'' of the hexane molecule was found to be 50 A2 and the heat of adsorption at (B.E.T.) monolayer coverage was 1.5 kcal/mole in excess of the heat of liquefaction. A piezoelectric crystal vibrating in a thickness‐shear mode appears to constitute a microbalance of sensitivity sufficient to permit measurement, in favorable cases, of isotherms on adsorbents of very low specific area.
Fat suppression in the time domain in fast MR imaging

Two gradient‐recalled lipid suppression sequences are proposed. A two‐excitation sequence cycles the TE interval between excitations to alter the lipid phase which is fol‐lowed by complex subtraction. A four‐excitation variant which improves the extent of lipid suppression by partially compensating for errors resulting from spin‐spin relaxation and Bo inhomogeneities is outlined. © 1988 Academic Press, Inc.
Honeycomb Reactor Washcoated with Mordenite Type Zeolite Catalysts for the Reduction of NOx by NH3

A low pressure drop reactor was prepared by washcoating Cu ion-exchanged mordenite on a honeycomb. The reactor configuration including reaction conditions was experimentally optimized both for low-pressure drop and for high catalytic activity of the honeycomb reactor. Over 90% of NO conversion was achieved by both Cu ion-exchanged synthetic zeolite (CuHM) and natural zeolite (CuNZA). The pressure drops due to honeycomb reactors were low enough to meet the constraint of the pressure drop (5 in. of H2O) for its application to a utility boiler. A mathematical model based on fluid flow, reaction kinetics, and pressure drop was derived for the design of the reactor, and then the reactor was simulated to examine the effects of operating conditions on NO conversion. Finally, the sulfur tolerance of the honeycomb reactor developed in this work has also been investigated.
Modeling fuel spray auto-ignition using the FGM approach : effect of tabulation method

The Flamelet Generated Manifold (FGM) method is a promising technique in engine combustion modeling to include tabulated chemistry. Different methodologies can be used for the generation of the manifold. Two approaches, based on igniting counterflow diffusion flamelets (ICDF) and homogeneous reactors (HR) are implemented and compared with Engine Combustion Network (ECN) experimental database for the baseline n-heptane case. Before analyzing the combustion results, the spray model is optimized after performing a sensitivity study with respect to turbulence models, cell sizes and time steps. The standard High Reynolds ( Re ) k-e model leads to the best match of all turbulence models with the experimental data. For the convergence of the mixture fraction field an appropriate cell size is found to be smaller than that for an adequate spray penetration length which appears to be less influenced by the cell size. With the optimized settings, auto-ignition and flame lift-off length are analyzed. In general, both techniques capture the qualitative trend of experimental results. However, typically, the HR tabulation method predicts shorter ignition delay and LOL results than the ICDF method. In a quantitative sense, the ICDF and HR methods give better results in LOL and auto-ignition predictions, respectively.
Molecular characterisation of high-strength polycyclic aromatic hydrocarbon (PAH)-degrading and phenol-tolerant bacteria obtained from thermal power plant wastewater

This article reports the successful isolation and molecular characterisation of nine different phenol-tolerant and polycyclic aromatic hydrocarbon (PAH)-degrading Gram-positive bacterial species of diverse genera from the effluents of various industries. Based on similarity matrix studies, isolates Corynebacterium sp. DST1, Lysinibacillus sp. and Planococcus sp. showed<97% similarity, suggesting the possibility of new species in their respective genera.
Survey of Imbalanced Data Methodologies

Imbalanced data set is a problem often found and well-studied in financial industry. In this paper, we reviewed and compared some popular methodologies handling data imbalance. We then applied the under-sampling/over-sampling methodologies to several modeling algorithms on UCI and Keel data sets. The performance was analyzed for class-imbalance methods, modeling algorithms and grid search criteria comparison.
Time–Space Decomposition-Based Generalized Predictive Control of a Transport-Reaction Process

This paper presents a generalized predictive control (GPC) strategy for a spatially distributed transport-reaction process based on time–space decomposition. First, the Karhunen–Loeve (K–L) decomposition is used for time–space decomposition to find the principal spatial structures and to reduce the dimension of the data. Then, an autoregressive exogenous (ARX) model is identified using the excitation input signals and the temporal coefficients obtained by the K–L decomposition. A GPC strategy is investigated based on the ARX model, with or without considering the system constraints. Numerical simulations on a catalytic rod illustrate the effectiveness of the proposed methods.
Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision

Visual re-localization means using a single image as input to estimate the camera’s location and orientation relative to a pre-recorded environment. The highest-scoring methods are “structure-based,” and need the query camera’s intrinsics as an input to the model, with careful geometric optimization. When intrinsics are absent, methods vie for accuracy by making various other assumptions. This yields fairly good localization scores, but the models are “narrow” in some way, e.g., requiring costly test-time computations, or depth sensors, or multiple query frames. In contrast, our proposed method makes few special assumptions, and is fairly lightweight in training and testing.Our pose regression network learns from only relative poses of training scenes. For inference, it builds a graph connecting the query image to training counterparts and uses a graph neural network (GNN) with image representations on nodes and image-pair representations on edges. By efficiently passing messages between them, both representation types are refined to produce a consistent camera pose estimate. We validate the effectiveness of our approach on both standard indoor (7-Scenes) and outdoor (Cambridge Landmarks) camera re-localization benchmarks. Our relative pose regression method matches the accuracy of absolute pose regression networks, while retaining the relative-pose models’ test-time speed and ability to generalize to non-training scenes.
The Dawes Review 9: The role of cold gas stripping on the star formation quenching of satellite galaxies

Abstract One of the key open questions in extragalactic astronomy is what stops star formation in galaxies. While it is clear that the cold gas reservoir, which fuels the formation of new stars, must be affected first, how this happens and what are the dominant physical mechanisms involved is still a matter of debate. At least for satellite galaxies, it is generally accepted that internal processes alone cannot be responsible for fully quenching their star formation, but that environment should play an important, if not dominant, role. In nearby clusters, we see examples of cold gas being removed from the star-forming discs of galaxies moving through the intracluster medium, but whether active stripping is widespread and/or necessary to halt star formation in satellites, or quenching is just a consequence of the inability of these galaxies to replenish their cold gas reservoirs, remains unclear. In this work, we review the current status of environmental studies of cold gas in star-forming satellites in the local Universe from an observational perspective, focusing on the evidence for a physical link between cold gas stripping and quenching of the star formation. We find that stripping of cold gas is ubiquitous in satellite galaxies in both group and cluster environments. While hydrodynamical mechanisms such as ram pressure are important, the emerging picture across the full range of dark matter halos and stellar masses is a complex one, where different physical mechanisms may act simultaneously and cannot always be easily separated. Most importantly, we show that stripping does not always lead to full quenching, as only a fraction of the cold gas reservoir might be affected at the first pericentre passage. We argue that this is a key point to reconcile apparent tensions between statistical and detailed analyses of satellite galaxies, as well as disagreements between various estimates of quenching timescales. We conclude by highlighting several outstanding questions where we expect to see substantial progress in the coming decades, thanks to the advent of the Square Kilometre Array and its precursors, as well as the next-generation optical and millimeter facilities.
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking

In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specifications, similar to those required by search-and-rescue missions. We decompose the original mission into a sequence of tracking sub-problems under safety constraints. We impose the safety conditions by utilizing barrier functions to map the constrained optimal tracking problem in the physical space to an unconstrained one in the transformed space. Furthermore, we develop policies that intermittently update the control signal to solve the tracking sub-problems with reduced burden in the communication and computation resources. Subsequently, an actor-critic algorithm is utilized to solve the underlying Hamilton-Jacobi-Bellman equations. Finally, we support our proposed framework with stability proofs and showcase its efficacy via simulation results.
Backdoor Attack in the Physical World

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of infected models will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger. Currently, most existing backdoor attacks adopted the setting of static trigger, $i.e.,$ triggers across the training and testing images follow the same appearance and are located in the same area. In this paper, we revisit this attack paradigm by analyzing trigger characteristics. We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training. As such, those attacks are far less effective in the physical world, where the location and appearance of the trigger in the digitized image may be different from that of the one used for training. Moreover, we also discuss how to alleviate such vulnerability. We hope that this work could inspire more explorations on backdoor properties, to help the design of more advanced backdoor attack and defense methods.
Towards Consistent Hybrid HMM Acoustic Modeling

High-performance hybrid automatic speech recognition (ASR) systems are often trained with clustered triphone outputs, and thus require a complex training pipeline to generate the clustering. The same complex pipeline is often utilized in order to generate an alignment for use in frame-wise cross-entropy training. In this work, we propose a flat-start factored hybrid model trained by modeling the full set of triphone states explicitly without relying on clustering methods. This greatly simplifies the training of new models. Furthermore, we study the effect of different alignments used for Viterbi training. Our proposed models achieve competitive performance on the Switchboard task compared to systems using clustered triphones and other flat-start models in the literature.
EasyCall corpus: a dysarthric speech dataset

This paper introduces a new dysarthric speech command dataset in Italian, called EasyCall corpus. The dataset consists of 21386 audio recordings from 24 healthy and 31 dysarthric speakers, whose individual degree of speech impairment was assessed by neurologists through the Therapy Outcome Measure. The corpus aims at providing a resource for the development of ASR-based assistive technologies for patients with dysarthria. In particular, it may be exploited to develop a voice-controlled contact application for commercial smartphones, aiming at improving dysarthric patients' ability to communicate with their family and caregivers. Before recording the dataset, participants were administered a survey to evaluate which commands are more likely to be employed by dysarthric individuals in a voice-controlled contact application. In addition, the dataset includes a list of non-commands (i.e., words near/inside commands or phonetically close to commands) that can be leveraged to build a more robust command recognition system. At present commercial ASR systems perform poorly on the EasyCall Corpus as we report in this paper. This result corroborates the need for dysarthric speech corpora for developing effective assistive technologies. To the best of our knowledge, this database represents the richest corpus of dysarthric speech to date.
Statistical limits of sparse mixture detection

We consider the problem of detecting a general sparse mixture and obtain an explicit characterization of the phase transition under some conditions, generalizing the univariate results of Cai and Wu. Additionally, we provide a sufficient condition for the adaptive optimality of a Higher Criticism type testing statistic formulated by Gao and Ma. In the course of establishing these results, we offer a unified perspective through the large deviations theory. The phase transition and adaptive optimality we establish are direct consequences of the large deviation principle of the normalized log-likelihood ratios between the null and the signal distributions.
Logging Practices with Mobile Analytics: An Empirical Study on Firebase

Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs remotely from their apps running on end user devices at the cost of recording and transmitting logs across the Internet to a centralised infrastructure.This paper makes a first step in characterising logging practices with a widely adopted mobile analytics logging library, namely Firebase Analytics. We provide an empirical evaluation of the use of Firebase Analytics in 57 open-source Android applications by studying the evolution of code-bases to understand: a) the needs-in-common that push practitioners to adopt logging practices on mobile devices, and b) the differences in the ways developers use local and remote logging.Our results indicate mobile analytics logs are less pervasive and less maintained than traditional logging code. Based on our analysis, we believe logging using mobile analytics is more user centered compared to traditional logging, where the latter is mainly used to record information for debugging purposes.
Neural networks in interpretation of electronic core-level spectra

We explore the applicability of artiﬁcial intelligence for molecular structure – core-level spectrum interpretation. We focus on the electronic Hamiltonian using the H 2 O molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We ﬁnd predicting spectra easier than predicting structures, where a tighter grid of the spectrum improves prediction. However, the accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space.
Enhancing the Diversity of Predictions Combination by Negative Correlation Learning

Predictions combination, as a combination model approach with adjustments in the output space, has ﬂourished in recent years in research and competitions. Simple average is intuitive and robust, and is often used as a benchmark in predictions combination. However, some poorly performing sub-models can reduce the overall accuracy because the sub-models are not selected in advance. Even though some studies have selected the top sub-models for the combination after ranking them by mean square error, the covariance of them causes this approach to not yield much beneﬁt. In this paper, we suggest to consider the diversity of sub-models in the predictions combination, which can be adopted to assist in selecting the most diverse model subset in the model pool using negative correlation learning. Three publicly available datasets are applied to evaluate the approach. The experimental results not only show the diversity of sub-models in the predictions combination incorporating negative correlation learning, but also produce predictions with accuracy far exceeding that of the simple average benchmark and some weighted average methods. Furthermore, by adjusting the penalty strength for negative correlation, the predictions combination also outperform the best sub-model. The value of this paper lies in its ease of use and e ﬀ ectiveness, allowing the predictions combination to embrace both diversity and accuracy.
Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about the engagement of the user on the discussed product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data, and that voice features alone can be sufficiently effective. The performance of the prediction algorithms are maximised when pre-processing the training data with the synthetic minority oversampling technique (SMOTE). The results of our work suggest that biofeedback and voice analysis can be used to facilitate prioritization of requirements oriented to product improvement, and to steer the interview based on users' engagement. Furthermore, the usage of voice features can be particularly helpful for emotion-aware requirements elicitation in remote communication, either performed by human analysts or voice-based chatbots.