Miracle-dz/UniFAR-FTUs huggingface dataset:
Importance of culture history on 17α-ethinylestradiol cometabolism by nitrifying sludge

17α-ethinylestradiol (EE2), a synthetic estrogen which interfere the endocrine and reproductive function in living organisms, has been found extensively to be deposited into municipal wastewater treatment plants and the environment via human excretion. EE2 has long been known to be efficiently cometabolized by ammonia-oxidizing bacteria (AOB) during ammonia (NH3) oxidation. Current study aims to investigate the effect of culture history on the biotransformation of EE2 by nitrifying sludge which was enriched under different ammonia loading rates in continuous flow reactors. Result showed that past growth condition largely affected not only the metabolic rate of NH3 oxidation but also EE2 cometabolism. Sludge previously acclimated with higher NH3 loads as well as sludge dominated with AOB belong to high growth cluster (Nitrosomonas europaea-Nitrosococcus mobilis) showed higher rate of EE2 biotransformation than those one being acclimated with lower NH3 loads because of its ability to provide more reducing power from NH3 oxidation. Moreover, the correlation between the degradation rates of NH3 and EE2 was higher in sludge being acclimated with higher load of NH3 in comparison with other sludge. Implication of the findings emphasized the role of volumetric NH3 loading rate in determining EE2 removal in wastewater treatment system.
Application of SRNN-GRU in Photovoltaic power Forecasting

Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. As one of the Deep Learning Methods, Recurrent Neural Network (RNN) is widely used in time series prediction but lacks the ability of parallel computing. With good prediction effect, RNN is faced with the problem of long training time. In this paper, Sliced Recurrent Neural Network (SRNN) is applied to PV power prediction to guarantee the ability of parallel computing. The research result shows that compared to other commonly used models, SRNN can greatly speed up the training of Deep Learning Network with over 4 times higher training speed of the application of PV power prediction than that of ordinary RNN structure like LSTM and GRU. The accuracy of SRNN model is also improved by 0.1102 mae, which is significantly ahead of the others, as its parallel structure causes the more efficient parameter update, thus achieving ideal effect in PV prediction.
A wavelet approach to multiple cointegration testing

This paper introduces a class of cointegration tests based on estimated low-pass and high-pass regression coefficients from the same wavelet transform of the original time series data. The procedure can be applied to test the null of cointegration in a n + k multivariate system with n cointegrating relationships without the need of either detrending nor differencing. The proposed non residual-based wavelet statistics are asymptotically distributed as standard chi-square with nk degrees of freedom regardless of deterministic terms or dynamic regressors, thus offering a simple way of testing for cointegration under the null without the need of special tables. Small sample quantiles for these wavelet statistics are obtained using Monte Carlo simulation in different situations including I(1) and higher order cointegration cases and it is shown that these wavelet tests exhibit appropriate size and good power when compared to other tests of the null of cointegration.
Probiotic Biofilm: A Novel Approach to Produce a New Generation of Probiotics and Probiotic Foods

Background Probiotic biofilm is considered a new version of an advanced natural protection method recently placed on researchers’ agenda as the fourth generation of probiotics. In several studies, probiotic biofilms have been produced exclusively in the bacterial culture medium, but in this study, the biofilm of Lactobacillus plantarum PTCC 1745 and Lactobacillus rhamnosus PTCC 1637 were formed in food (milk) and evaluated for the first time. Methods The biofilm was produced in milk then was transferred to yogurt in whole and crushed forms to be tested in real conditions as probiotic bacteria carriers. Survival was assessed for 21 days as well as gastrointestinal conditions. Results Results demonstrate that the number of bacteria in biofilm did not change significantly during 21 days of refrigerated storage. In contrast, planktonic bacteria was decreased by about 2.8 log simultaneously. Another significant result is that the biofilm could appropriately protect the bacteria in the stomach and intestine simulated conditions. So, no significant reduction was observed in the number of bacteria during 120 min, but planktonic cells were destroyed after 30 min. Conclusions In conclusion results indicated that L. plantarum and L. rhamnosus could form a very desirable and strong biofilm in vitro and milk that can have a good protective effect on yogurt’s probiotic survival process and storage. Therefore, the probiotic biofilm technique can introduce a new generation of probiotics to the food and pharmaceutical industries
Structured Feature Similarity with Explicit Feature Map Takumi Kobayashi National Institute of Advanced Industrial Science and Technology Umezono 1-1-1 , Tsukuba , Japan

Feature matching is a fundamental process in a variety of computer vision tasks. Beyond the standard L2 metric, various methods to measure similarity between features have been proposed mainly on the assumption that the features are defined in a histogram form. On the other hand, in a field of image quality assessment, SSIM [27] produces effective similarity between images, taking the place of L2 metric. In this paper, we propose a feature similarity measurement method based on the SSIM. Unlike the previous methods, the proposed method is built on not a histogram form but a tensor structure of a feature array extracted such as on spatial grids, in order to construct effective SSIMbased similarity measure of high robustness which is a key requirement in feature matching. In addition, we provide the explicit feature map such that the proposed similarity metric is embedded as a dot product. It contributes to significant speedup in similarity measurement as well as to feature transformation toward an effective vector form to which linear classifiers are directly applicable. In the experiments on various tasks, the proposed method exhibits favorable performance in both feature matching and classification.
Preparation and Biological Activity of New Collagen Composites, Part III. Collagen/(Ag/RGO) and Collagen/(Ag/RGO/SiO 2 ) Composites

The aim of this study is preparation, characterization, and biological activity evaluation of 2 new collagen composites, Collagen/(Ag/RGO) and Collagen/(Ag/RGO/SiO2) with expected antimicrobial activity. The loading of collagen matrix by self-prepared Ag/RGO and Ag/RGO/SiO2 was varied. The morphology of these antimicrobial agents, as well as of the corresponding collagen composites, was observed by SEM. Their biological activity was evaluated against 3 Gram-negative and 3 Gram-positive microbial strains, as well as against 3 type eukaryotic cells: osteoblast, fibroblast, and epithelial. Dependent on the concentration of Ag/RGO, well pronounced activity against Gram-negative bacterium (E. coli), fungus (C. Lusistaniae) and 2 Gram-positive bacteria (S. epidermidis and B. cereus) was found for Collagen/(Ag/RGO) composites at weight ratios of 2:1, 2:0.8, and 2:0.6, at the last one accompanied by low cytotoxicity. Dependent on the concentration of Ag/RGO/SiO2 biological activity was found for the Collagen/(Ag/RGO/SiO2) composites also, better expressed than that of Collagen/(Ag/RGO) composites with the same level of antimicrobial agent loading. The experimental results demonstrated that the doping of RGO with Ag nanoparticles increases the antimicrobial activity of the Collagen/(Ag/RGO) composites. Improving its dispersion, the silane matrix in the Ag/RGO/SiO2 additionally increases the biological activity of the studied Collagen/(Ag/RGO/SiO2) composites and their well-pronounced, broad spectrum (against 3 Gram-negative, 2 Gram-positive bacteria and a fungus), antimicrobial activity is similar to that of the broad spectrum antibiotics. The novel Collagen/(Ag/RGO) and Collagen/(Ag/RGO/SiO2) composites are promising antimicrobial collagen biomaterials that widen the assortment of such for variety biomedical applications. Keywords: Collagen/(Ag/RGO) and Collagen/(Ag/RGO/SiO2) Composites; Cytotoxicity to Eukaryotic Cells: Fibroblast, Osteoblast and Epithelial; Antimicrobial Activity: 3 Gram-Negative (S. Enterica, P. Aeruginosa and E. Coli)
International Journal of Information Management Data Insights

The importance of data-driven decisions and support is increasing day by day in every management area. The constant access to volume, variety, and veracity of data has made big data an integral part of management studies. New sub-management areas are emerging day by day with the support of big data to drive businesses. This study takes a systematic literature review approach to uncover the emerging management areas supported by big data in contemporary times. For this, we have analyzed the research papers published in the reputed management journals in the last ten years, ﬁr using network analysis followed by natural language processing summarization techniques to ﬁnd the emerging new management areas which are yet to get much attention. Furthermore, we ran the same exercise in each of these management areas to uncover these areas better. This research will act as a reference for future information systems (IS) scholars who want to perform analysis that is deep-dive in nature on each of these management areas, which in the coming times will get all the due attention to become dedicated research domains in the management area. We ﬁnally conclude the study by identifying the scope of future research in each of these management areas, which will be a true value addition for IS researchers.
Response of Tropical Precipitation to Global Warming

Abstract Using high-resolution cloud-resolving simulations with different CO2 concentrations, local precipitation fluxes are found to obey Clausius–Clapeyron (CC) scaling. Previous studies of the effect of CO2 concentration on precipitation extremes have used general circulation models, which are poor platforms for studying tropical convection because convection is parameterized. In idealized cloud-resolving simulations, it is possible to identify not only the changes in local precipitation fluxes, but also the factors responsible for those changes. There are many properties of convection that can change as the atmosphere warms, each of which could produce deviations from CC scaling. These properties include the effective water-vapor gradient, cloud pressure depth, and cloud velocity. A simple theory is developed that predicts the changes in these properties consistent with CC scaling. Convection in the cloud-resolving simulations is found to change as predicted by this theory, leading to an ∼20% increase.
Industrial Cyber–Physical System Defense Resource Allocation Using Distributed Anomaly Detection

An industrial cyber–physical system (ICPS) tightly integrating both physical processes and information and communication technologies (ICTs) leads to increasing cyberspace threats and attacks for the critical electrical infrastructure. With the limited defense resources availability, the efficient threat perception and mitigation of potential impacts of cyber attacks are essential to enhance the ICPS operational security. This article proposes an optimal defense resource allocation solution to prioritize the ICPS asset protection based on the distributed network traffic anomaly detection. The traffic anomalies and attack paths can be timely detected simultaneously over multiple security zones of the electrical infrastructure through local computing devices. The defense resource allocation is formulated as a multiobjective optimization (MOO) problem considering the tradeoff among the asset vulnerability, cost, and criticality, and solved by the Pareto optimal solution generation approach. The proposed solution is extensively evaluated using a realistic electrical CPS (ECPS) testbed for a range of cyber-attack scenarios. The numerical results confirm the effectiveness of the proposed distributed anomaly detection model and defense resource allocation strategy for varying defense resource availabilities.
Value added milk products: Constraints to women in milk micro enterprises in Kenya

As economic crisis deepens in Kenya, more and more women are joining the micro enterprise sector to earn a living. The dairy industry, in particular, provides opportunities for the Kenyan women to operate micro enterprises. However, these women face constraints in their efforts to sell high quality value added milk products. This paper is based on a study carried out in three districts in Kenya: Nakuru, Nairobi and Kiambu. The study used interviews and observations as data collection methods. The findings of the study indicated that women in milk micro enterprises used basic value addition technologies that were operated at low costs but which at the same time hindered their performance in the milk business. The major obstacle to acquiring appropriate technology for value addition was lack of finance. The women had problems accessing credit facilities due to high interest rates and lack of collateral. They also had problems accessing appropriate value addition information due to lack of knowledge on sources of information and lack of time to look for this information. Further, income realized from the sale of milk products was hampered by limited marketing skills among these entrepreneurs. If more income is to be generated from women owned micro enterprises to improve the lot of these women, it is imperative that the above issues are addressed. Value addition technologies targeting women should be relevant, accessible and affordable. Similarly, women entrepreneurs should be facilitated to acquire credit, appropriate training in value addition and marketing skills.
Guided Path Planning for Proximity Location Sensors

A robot can estimate its position by detecting RFID or barcode tags. The tags and their reader have small detecting range less than 0.3 meters, and provide position of the robot through proximity. It is difficult to distribute the tags all over the space, so a path planner needs to guide paths toward tags to hit the tags more frequently. This paper enumerates methods to generate the guided path on the road map and grid map. Especially, two approaches on the grid map were described in detail and analyzed in experiments. Keywords— Path Planning, Proximity Sensor, Tag, RFID, Barcode, A*
TrajMesa: A Distributed NoSQL-Based Trajectory Data Management System

With the development of positioning technology, a large number of trajectories have been generated, which are very useful for many urban applications. However, it is challenging to manage trajectory data for its spatio-temporal dynamics and high-volume properties. Existing trajectory data management frameworks suffer from efficiency or scalability problem, and support only limited trajectory query types. This paper takes the first attempt to build a holistic distributed NoSQL trajectory query engine, named TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa can manage a prohibitively large number of trajectories, and support plenty of query types efficiently. Specifically, we first design a novel trajectory storage schema, which reduces the storage size tremendously. We then devise a novel indexing key schema for time ranges, based on which ID (i.e., moving object identifier) temporal query can be supported efficiently. To reduce the amount of retrieved trajectory data for a spatial range query, we propose a position code to indicate the spatial location of trajectories accurately. We also propose a bunch of pruning strategies for similarity query and <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3079880.gif"/></alternatives></inline-formula>-NN query in the NoSQL environment. Extensive experiments are conducted using two real datasets and one synthetic dataset, verifying the powerful query efficiency and scalability of TrajMesa. The results show that TrajMesa is about <inline-formula><tex-math notation="LaTeX">$100 \sim 1000$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>100</mml:mn><mml:mo>∼</mml:mo><mml:mn>1000</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq2-3079880.gif"/></alternatives></inline-formula> times faster than the state-of-the-art trajectory management frameworks in our experimental settings. TrajMesa is currently deployed in JD company, processing over 1T trajectories of JD Logistics every day.
Impedance Adaptive Dual-Mode Control of Grid-Connected Inverters With Large Fluctuation of SCR and Its Stability Analysis Based on D-Partition Method

The stable operation of grid-connected inverters (GCIs) with traditional current source mode (CSM) control is affected by the large fluctuations of short-circuit ratio (SCR) under weak grids. Improved CSM control enhances the stability of GCIs under weak grids but remains unstable for very weak grids, while the stability of GCIs with voltage source mode (VSM) is just the opposite. Therefore, previous research works have proposed the impedance adaptive dual-mode control strategy: when the SCR is large, the GCI is controlled by CSM, and when the SCR is small, it is controlled by VSM. However, the existing literature still lacks the analysis of the parameter stability region and mode switching boundary of GCI in different modes. Therefore, based on the D-partition method, this article derives the parameter stability region of GCI in different modes under the constraints of multiple performance indexes such as phase and gain margin, current-loop bandwidth and phase-locked loop bandwidth, and the switching boundaries of CSM and VSM are also proposed. Finally, based on the grid impedance identification algorithm, the impedance adaptive dual-mode control is realized, which effectively improves the stability of GCI when SCR fluctuates greatly, and the experimental results verify the correctness of the above analysis.
Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty

Entrained flow gasification is a commonly used method for conversion of coal into syngas. A stable and efficient operation of entrained flow coal gasification is always desired to reduce consumption of raw materials and utilities, and achieve higher productivity. However, uncertainty in the process hinders the stability and efficiency. In this work, a quantitative analysis of the effect of uncertainty on the conversion efficiency of the entrained flow gasification is performed. A data-driven, i.e., ensemble, model of the process was developed to predict conversion efficiency of the process. Then sensitivity analysis methods, i.e., Sobol and Fourier amplitude sensitivity test, were used to analyze the effect of each individual process variables on conversion efficiency. For analyzing the collective impact of uncertainty in process variables on conversion efficiency, a non-intrusive polynomial chaos expansion (PCE) method was used. The PCE predicts probability distribution of the conversion efficiency. Reliability of the process was determined on the basis of percentage of the probability distribution falling within control limits. Measured data is used to derive the control limits for off-line reliability analysis. For on-line reliability analysis of the process, measured data is not available so a just-in-time method, i.e., k–d tree, was used. The k–d tree searches the nearest neighbor sample from a database of historical data to determine the control limits.
Quantum-orbit theory of low-energy above-threshold ionization on and off axis

Intense-laser-induced above-threshold ionization of a bound electron into continuum states with low energy is investigated in the context of the strong-field approximation that allows for one act of rescattering of the revisiting electron. The quantum orbits for forward and backward scattering are evaluated and generalized to arbitrary scattering angles. The velocity map of the liberated electron exhibits the well-known low-energy structure as well as other features off the polarization axis.
Human SCO 1 and SCO 2 have independent , cooperative functions in copper delivery to cytochrome c oxidase

Human SCO1 and SCO2 are paralogous genes that code for metallochaperone proteins with essential, but poorly understood, roles in copper delivery to cytochrome c oxidase (COX). Mutations in these genes produce tissue-specific COX deficiencies associated with distinct clinical phenotypes, although both are ubiquitously expressed. To investigate the molecular function of the SCO proteins, we characterized the mitochondrial copper delivery pathway in SCO1 and SCO2 patient backgrounds. Immunoblot analysis of patient cell lines showed reduced levels of the mutant proteins, resulting in a defect in COX assembly, and the appearance of a common assembly intermediate. Overexpression of the metallochaperone COX17 rescued the COX deficiency in SCO2 patient cells but not in SCO1 patient cells. Overexpression of either wild-type SCO protein in the reciprocal patient background resulted in a dominant-negative phenotype, suggesting a physical interaction between SCO1 and SCO2. Chimeric proteins, constructed from the C-terminal copper-binding and N-terminal matrix domains of the two SCO proteins failed to complement the COX deficiency in either patient background, but mapped the dominant-negative phenotype in the SCO2 background to the N-terminal domain of SCO1, the most divergent part of the two SCO proteins. Our results demonstrate that the human SCO proteins have non-overlapping, cooperative functions in mitochondrial copper delivery. Size exclusion chromatography suggests that both the proteins function as homodimers. We propose a model in which COX17 delivers copper to SCO2, which in turn transfers it directly to the CuA site at an early stage of COX assembly in a reaction that is facilitated by SCO1.
Nonlinear Observer for Tightly Coupled Integrated Inertial Navigation Aided by RTK-GNSS Measurements

A modular nonlinear observer is considered for tightly coupled integration of inertial measurements with global satellite measurements. A real-time-kinematic (RTK) approach is utilized where the rover and base station measure pseudoranges, carrier-phase, and carrier-phase derived Doppler, to be used in a dual receiver configuration. The modular observer design consists of a nonlinear attitude observer and a translational motion observer (TMO). The attitude observer represents the vehicle attitude as unit quaternions and estimates the gyro bias. A TMO based on the double-differenced measurements between the rover and base station receiver is proposed. The ambiguities introduced by the carrier-phase measurements are included in the state vector and are initially considered real valued, later to be resolved to integer values. The advantages of the nonlinear observer compared with a Kalman filter are reduced computational load, no linearization requirement of the model, and proven stability. The proposed observer is verified using experimental data from flights with an unmanned aerial vehicle, where the position estimates are shown to be within 2–4 cm of a global positioning system $L_{1}$ -based RTK reference solution.