Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of microbial communities and their removal efficiency of multiple pharmaceutical micropollutants combined in Membrane-Bioreactors

Pharmaceuticals are of concern to our planet and health as they can accumulate in the environment. The impact of these biologically active compounds on ecosystems is hard to predict and information on their biodegradation is necessary to establish sound risk assessment. Microbial communities are promising candidates for the biodegradation of pharmaceuticals such as ibuprofen, but little is known yet about their degradation-capacity of multiple micropollutants at higher concentrations (100 mg/L). In this work, microbial communities were cultivated in lab-scale Membrane Bioreactors (MBRs) exposed to increasing concentrations of a mixture of six micropollutants (ibuprofen, diclofenac, enalapril, caffeine, atenolol, paracetamol). Key players of biodegradation were identified using a combinatorial approach of 16S rRNA sequencing and analytics. Microbial community structure changed with increasing pharmaceutical intake (from 1 mg/L to 100 mg/L) and reached a steady-state during incubation for 7 weeks on 100 mg/L. HPLC analysis revealed a fluctuating but significant degradation (30-100%) of five pollutants (caffeine, paracetamol, ibuprofen, atenolol, enalapril) by an established and stable microbial community mainly composed of Achromobacter, Cupriavidus, Pseudomonas and Leucobacter. By using the microbial community from MBR1 as inoculum for further batch culture experiments on single micropollutants (400 mg/L substrate, respectively), different active microbial consortia were obtained for each single micropollutant. Microbial genera potentially responsible for degradation of the respective micropollutant were identified, i.e. Pseudomonas sp. and Sphingobacterium sp. for ibuprofen, caffeine and paracetamol, Sphingomonas sp. for atenolol, and Klebsiella sp. for enalapril. Our study demonstrates the feasibility of cultivating stable microbial communities capable of degrading simultaneously a mixture of highly concentrated pharmaceuticals in lab-scale MBRs and the identification of microbial genera potentially responsible for the degradation of specific pollutants. Graphical abstract
Construction of the ETECFinder database for the characterisation of enterotoxigenic Escherichia coli (ETEC) and revision of the VirulenceFinder webtool at the CGE website

The identification of pathogens is essential for effective surveillance and outbreak detection, which has been facilitated by the decreasing cost of whole-genome sequencing (WGS). However, extracting relevant virulence genes from WGS data remains a challenge. In this study, we developed a web-based tool to predict virulence-associated genes in enterotoxigenic Escherichia coli (ETEC), including heat-labile toxin (LT) genes (eltA and eltB), heat-stable enterotoxin (ST) genes (est) for both human and animal, enterotoxin (espC), colonization factors CS1 through 30, F4, F5, F6, F17, F18, and F41, and toxigenic invasion and adherence loci (tia, tibAC, etpBAC, eatA, yghJ, and tleA). To construct the database, we revised the existing ETEC nomenclature and used the VirulenceFinder webtool at the CGE website (https://cge.cbs.dtu.dk/services/VirulenceFinder/). The database was tested on 1,083 preassembled ETEC genomes and the ETEC reference sequence for strain H10407. We added 455 new alleles, replaced or renamed 50 alleles, and removed two. Overall, our tool has the potential to greatly facilitate ETEC identification and improve the accuracy of WGS analysis. The revised nomenclature and expanded gene repertoire provide a better understanding of the genetic diversity of ETEC. Additionally, the user-friendly interface makes it accessible to users with limited bioinformatics experience.
Multi-time wavelet bicubic interpolation search algorithm and its application to high-resolution reconstruction of remote sensing image

Due to the far imaging distance of optical remote sensing and the limitation of the physical size of CCD, the resolution of the remote sensing image is usually low. In order to improve the resolution and PSNR of the obtained image without damaging its original information, the Wavelet Bicubic Interpolation Search Algorithm was proposed in the previous work. On the basis of deep analysis and investigation of the Wavelet Bicubic Interpolation Search Algorithm, the Multi-time Wavelet Bicubic Interpolation Search Algorithm is proposed. The new established algorithm can realize the multi-time and high-resolution image reconstruction of optical remote sensing images. The experiment results prove that the detail information and visual effect of the 2×2 times high-resolution image reconstructed by the proposed algorithm is better, the 3×3 times and 4×4 times is good, 5×5 times is bad and more reconstructed times is not suitable to be applied in practice.
Pesticides Spraying Using Non-GPS-Based Autonomous Drone

Technological use in agriculture has a greater impact on efficient farming that elevates crop yield. The use of drones in the agriculture field helps farmers to a greater extent. This paper demonstrates the development of a non- GPS-based autonomous pesticide spraying drone, which is particularly useful for farms with limited land sizes and in areas where GPS is inaccurate. Because of poor GPS positioning and mapping, GPS became unreliable. This autonomous drone creates an optimized navigation path around the mapped field spraying pesticides uniformly over the crops. This agriculture drone carries a fluid pesticide tank to spray evenly all over the crop, reducing the time and effort of the farmer and preventing harmful effects caused by pesticides. This drone can adjust the altitude, liquid flow rate and speed depending on the type of crop. This drone included with the monitoring application platform provides real-time information. The web application is user-friendly, ensuring that drone performance is manageable and understandable by the user. With the help of these features, the farmer is able to comprehend the field’s spray in real-time.
Parameterized Adaptive Controller Design using Reinforcement Learning and Deep Neural Networks

This manuscript aims to build a parameterized adaptive controller using Reinforcement Learning (RL) and Deep Neural Networks (DNN). The main objective is to adapt parameters of any given controller structure using RL formulation and achieve better performance. In recent years, reinforcement learning has gained much attention, and its advantages make it ideal for adaptive tuning of parameterized controllers. With the advancement of computational power, it has become easier to approximate a complex policy function using a deep neural network to achieve better accuracy and performance. Conventional Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm may not be able to provide best possible training for a given number of episodes. To improve performance of the TD3 algorithm, dynamic action space is proposed along with modified reward function, designed to aid faster convergence. The proposed algorithm provides improved performance by dynamically modifying the action space in the existing TD3 algorithm. The effectiveness of the proposed RL-based parameterized controller is demonstrated through a standard first order system by designing an adaptive PI controller. A case study involving a 3 DOF (Degree of Freedom) gyroscope system, which is an unstable plant, is also presented. For the 3 DOF gyroscope system an adaptive Lead controller is designed, where the proposed algorithm provides faster convergence and better performance compared to the original TD3 algorithm.
3D Point Cloud Coarse Registration Algorithm Based on Center of Gravity and Centroid Transformation

It is easy to fall into a local optimum for registration algorithms, such as iterative closest point (ICP) algorithm in 3-D point cloud registration. In this study, a coarse registration algorithm based on the transformation of the geometric center of gravity and the centroid is proposed. Firstly, the noise and outliers are removed by using the filters for the point clouds. Then, a preliminary model of rotational transformation of point the clouds is established through the analysis of the relation of the gravity center and centroid of two point clouds. Moreover, according to the relation between rotation angle and registration error, an iterative rotation model is established to obtain the optimal rotation angle. Finally, ICP algorithm is applied to further improve registration performance. The experimental results show that the proposed coarse registration algorithm demonstrates superior performance, compared with the registration methods relying on the overlapped center of gravity.
Towards Privacy for Android Mobile Applications

The widespread adoption of Android phones has accelerated the deployment of android mobile apps. Mobile applications are software programs designed to operate on mobile devices such as smartphones and tablets. As a result, they will be able to access and use data saved on a device, such as a contact list and data from the device’s different sensors. In Android apps, third-party libraries are commonly used for leveraging functionalities like GPS, analytics, and advertisements. The third-party libraries have access to permissions the same as that of the host apps, these apps can easily become overly powerful and leak users’ personal information without the user’s knowledge. Users consent only to the applications they install, and they have no clue about the number of thirdparty apps integrated, as well as the permissions they access. Apart from this majority of the users lack the competence to discern if an application suits their privacy preferences. Hence, there’s a big difference between what’s perceived, and what’s preferred when it comes to privacy. Our motive is to create an Android application that will improve the user’s privacy. In this project, we perform a survey to collect the privacy preference of the user on one hand and compare it with what they share with the application as well as the hosted third-party apps. Though the permissions shared with the hosting app can be extracted from the user settings, it is not so with third-party apps. By using the Xposed framework to extract the list of third-party applications and the permissions accessed by them. Based on the sensitivity of the accessed API, a score is assigned and the listing is provided to the user. This would help the user in deciding to uninstall any app that accesses the sensitive APIs.
A Study of Memoryless Property Parameter Layer in Time Series Prediction

Time series prediction is an important research direction in neural networks. It can be applied into many different tasks, including traffic prediction, waveform prediction and so on. This paper presents a general method to improve the performance of time series neural networks. Specifically, the method uses the memoryless property of probability distributions such as exponential distribution and geometric distribution to improve the network structure: a memoryless property parameters layer (MPPL) is added in front of the basic time series prediction network to strengthen the distribution characteristics of time series data. According to the loss value, mean absolute error (MAE) and root mean square error (RMSE), the memoryless property parameter layer can effectively improve the performance of time series prediction model in both training and testing stage, hence demonstrate its superiority in accelerating the training convergence and improving the prediction accuracy.
Measures of Modal Controllability for Network Dynamical Systems

The quantification of controllability has gained renewed interest in the context of large, complex network dynamical systems. In some application areas such as com-putational neuroscience, there is a large interest in modal controllability, which describes the ability of an input to control the modes of a system. In case of a linear system, the modes of the system are given by the left eigenvectors associated with the system matrix. In this work, we identify mode specific and gross metrics for modal controllability for both discrete and continuous systems. Our metrics are based on energy requirements to move along a given mode and find applications in problems involving selection of driver nodes for minimizing control effort along particular modes of the network. We illustrate the use of these metrics on real world networks.
Numerical Modelling with the Usage of High Order Accuracy Methods

In this contribution we consider the application of the WENO scheme and the ILES method to simulations of 3D laminar and turbulent incompressible flow. As a test problem a 3D lid-driven cavity flow was modelled. The results of the three- dimensional Taylor-Green vortex example were carried out. For the low and high Reynolds numbers the case of flow past an obstacle were simulated. The used model consists of the Navier -Stokes equations for the laminar flow and the continuity equation in the form for incompressible fluid flow. To solve the system of the equations the conservative high order methods were used. The advection terms are reconstructed using the 5th order weighted essentially non oscillatory (WENO) scheme and the time evolution is solved by the application of the explicit TVD Runge- Kutta scheme. The ILES, as a turbulent model, was employed.
A Rapid Smoke Detection in Real-Time CCTV Videos Using Non-Rigid Bodies Algorithm

World-changing technologies have rolled out in recent years to address an ever-increasing demand for safety and security. While fire accidents are increasing due to a variety of factors, the need for enhanced technology to detect smoke and fire at an early stage has been declared. An algorithm for detecting incipient stage smoke under any backdrop conditions has been proposed in this study using non-rigid regional bodies. The feature vector for multi-background frames was optimized in order to identify the swift and cost-effective way. Consequently, the non-rigid bodies algorithm (NRBA) was created to detect the onset of smoke or fire with a detection accuracy of 1 second for the high frame rate camera and 4 seconds for the low frame rate camera.
Volt/VAR Optimization and Control of Smart-Grid Enabled Distribution System using CVR

The conservation voltage reduction (CVR) technique is being deployed globally for peak load reduction and energy conservation. In this paper CVR technique optimized with Firefly optimization algorithm is applied to a distribution system enabled with smart grid technology. In the proposed scheme Volt/VAR control operation is used for the Advanced Distribution Management System (ADMS). The operation of CVR is based on a reduction in different levels of voltages which impact impedance, current, and power of a ZIP load. So, the level of CVR decides the conservation of energy. When the voltage reduction level is high, capacitor banks are injected to maintain the voltage profile within acceptable limits by injecting additional reactive power. The effect of proposed CVR is considered in IEEE 13-node test system which is an unbalanced radial distribution system. The system performance is simulated on OpenDSS software interfaced with MATLAB. The result of the simulation shows the energy saving during the peak load demand with different voltage reduction levels. It also shows voltage levels maintained with additional reactive power support by CVR in different modes operation.
Strategic Multi-class Classification for Non-uniform Preferences and its Relation to Incentive Compatibility

Motivated by the problem of university admissions, we study the problem of correctly classifying the label of a feature vector when a sender can manipulate this vector to get a desired label. There are two players: sender and receiver and both know the target function over the feature vectors. The sender privately observes a feature vector and manipulates it according to its payoff criteria and sends it to the receiver. The receiver would like to perform multi-class classification to maximize the number of points for which it can recover the original label of the feature vector known to the sender. We pose this problem as a Stackelberg game with the receiver as the leader. Through this we characterise the optimal classifier in terms of the independence number of a certain graph which depends on the utility and cost of the sender and the target function. As a corollary, we characterise the optimal strategy of the receiver for the problem of strategic communication with cost. Finally we show that if the target function is incentive compatible condition, then the receiver can correctly classify all feature vectors.
Lempel-Ziv-Welch (LZW) based Horizontally Scalable Route Prediction

String sequence indexing is the basis of many applications including compression, route prediction, bioinformatics, text mining, string matching, etc. where the goal is to index huge sequences. Moreover, prediction application requires a probabilistic tree. Probabilistic Lempel-Ziv- is one of the widely used techniques for text compression as well as string sequence indexing. In many applications, it serves as a model for prediction. In this case, demonstrate the distributed computation in the case of route prediction. LZW model construction from the large corpus of historical data by processing sequentially is a challenge in the efficient implementation. Most of the current implementations are based on time-space complexity on a vertical salability on a single machine. Extending them to distributed parallel execution is still challenging and is very less researched. This work implements the computing on distributed computing clusters which achieves parallelism without sacrificing the accuracy using Hadoop distributed file system (HDFS). The objective is two-fold – first, applies LZW for route prediction, and second, addresses challenges in distributed computation.
Impact of Renewable Energy Resources and Energy Storage Device on Transmission System

Cumulative including the electrical power generation identifies as consumption of extra fossil-fuels, such as maximum power generations are through coal. These fuels produce precarious gases for example $\mathrm{C}\mathrm{O}_{2}, \mathrm{N}\mathrm{O}_{2}$ and SO2. The depreciation of such gases can be obtained by employing further sustainable power means such as wind, solar, hydro and pumped-hydroelectric stowage in generation system. Though, these sustainable means are indeterminate in environment, however owing to their low-cost and sustainable characteristics, their consumption have been augmented. The rising expansion of significant energy storage systems (ESSs) may diminish the vagueness for generation of renewable power and comforts to advance the structure progression. Therefore, it is obligatory to study the influences of these means on the transmission network. For this inspection, the problem of optimal sizing for ESS is inspected with the amalgamation of PV and wind power. The purpose is to diminish the total price of the system and study the impact on CO2 level. The optimization algorithm of GABC has been executed for resolving the anticipated problem. IEEE 24-bus investigation system is considered to investigate.
Emulation Alternating Direction Method of Multipliers

Generally, centralized version of an algorithm performs better as compared to its decentralised counter parts. So, decentralizing an algorithm, while imitating the centralized version, could preserve its certain convergence properties. In this paper, we propose a novel method to completely decentralize Consensus-ADMM (C-ADMM) algorithm and try to mimic its convergence properties, by emulating the functionality of the central coordinator. We show that our algorithm behaves similar to noise induced ADMM and converges sub-optimally, in practice. We also give the bound on sub-optimality and ways to achieve desired accuracy while using our algorithm.
Factors Influencing Driver’s Psychological Vulnerability Judgment When Sharing the Road With Driverless Vehicles

To determine whether drivers of manned vehicles sharing the road with driverless vehicles believe they are in a vulnerable state and to identify the factors that contribute to their feeling of “driver’s psychological vulnerability judgment”, this study conducted a questionnaire survey. The survey collected information on the attitudes, perceived usefulness, trust, perceived risk, and demographic characteristics of drivers in a hypothetical situation where they drive on the same road as driverless vehicles. The aim of the survey was to identify areas where drivers feel more vulnerable to poor psychological judgment. A total of 945 valid questionnaires were collected through the Credamo questionnaire survey platform. The results indicated that 43.7% of the respondents believed they would be in a vulnerable state in mixed traffic competition, while 30.2% of respondents did not know if they would feel vulnerable. Moreover, women, people with higher education, those with more aggressive driving personalities, older people, and those with more driving experience were less inclined to think they would be in a vulnerable state. Attitude, trust, and perceived usefulness had positive impacts on the driver’s psychological vulnerability judgment, while perceived risk had a negative impact. Therefore, early promotion of driverless technology should target people with higher education, more driving experience, older age, and more aggressive driving personality. The focus should be on attitudes toward this technology, as well as perceived usefulness, trust, and perceived risk.
A Coherent Way of detecting pupil’s emotions via live Camera using CNNs along with Haar -Cascasde Classifier

In terms of education, a person’s emotions influence their knowledge and ability to follow a show. Educators can use automated measurement of a person’s emotions to achieve educational goals and personalize delivery. In this study, we present video-based emotion identification technique in virtual learning programs. The main feelings we detect here are disappointment, disinterest, fear, satisfaction, commitment, sadness, and surprise. Continuity of affect, valence and stimulus states extracted from consecutive video frames, together with a new vector of latent affective and behavioral traits, were used to emotion detection. Deep learning-based temporal models and classical machine learning-based non-temporal models are trained and verified on image-level and video-level features, respectively. Images from the facial expression recognition dataset were used. The results show that the vast majority of methods can detect student learning outcomes. The model presented in this paper can provide technical support for the modeling of learners’ emotions in the teaching assistant system.
Roasted Coffee beans Classification based on Convolutional Neural Network

Coffee is a traditional drink in India after tea. The demand for coffee is increasing day by day among youngsters. There are different types of beverages made in coffee as per its aroma & color, and it depends on the roasting levels of coffee beans. Most specialty coffees have been sorted and processed by hand. The coffee roasting degree is mainly based on the coloring of beans and is directly related to the beverage quality. There are four levels of roasting: green, light roast, medium roast, and dark roast. This manuscript proposed a convolutional neural network to classify the four- roasting level of coffee beans and achieved 97.5% accuracy.