nils-herrmann/clean_scirepeval huggingface dataset:
Mitigation of environmental pollution by genetically engineered bacteria Current challenges and future perspectives.
Venue: The Science of the total environment
Year: 2019

Industries are the paramount driving force for the economic and technological development of society. However, the flourishing industrialization and unimpeded growth of current production unit's result in widespread environmental pollution due to increased discharge of wastes loaded with baleful, hazardous, and carcinogenic contaminants. Physicochemical based remediation means are costly, create a secondary disposal problem and remain inadequate for pollution mitigating because of the continuous emergence of new recalcitrant pollutants. Due to eco friendly, social acceptance, and lesser health hazards, microbial bioremediation has received considerable global attention for pollution abatement. Moreover, with the recent advancement in biotechnology and microbiology, genetically engineered bacteria with high ability to remove environmental pollutants are widely used in the fields of environmental restoration, resulting in the bioremediation in a more viable and eco friendly way. This review summarized the advantages of genetically engineered bacteria and their application in the treatment of a wide variety of environmental contaminants such as synthetic dyestuff, heavy metal, petroleum hydrocarbons, polychlorinated biphenyls, phenazines and agricultural chemicals which will include herbicides, pesticides, and fertilizers. Considering the risk of genetic material exchange by using genetically engineered bacteria, the challenges and limitations associated with the application of recombinant bacteria on contaminated sites are also discussed. An integrated microbiological, biological and ecological acquaintance accompanied by field engineering designs are the desired features for effective in situ bioremediation of hazardous waste polluted sites by recombinant bacteria.
Event driven Information Integration for the Digital Oilfield
Year: 2012

Several operations in the Exploration and Production (E&P) sector are event driven in nature and are supported by specialized systems and applications. Narrow focus of applications results in application silos that restrict the information sharing across verticals, which is a critical requirement for coordinated cross functional efforts. Effective response to events warrants due emphasis on an integration strategy that facilitates desired information flow across verticals. Event driven methods can be used to make strategic asset management decisions across silos in real time, thus reducing response time and costs while improving asset performance. Complex event processing is an emerging research area that involves detecting complex events, processing the events, deciding actions for each event and notifying the relevant personnel about the event. In the E&P sector, the adoption of CEP and messaging based systems in conjunction with semantic methods can facilitate components of the oilfield to communicate in real time across different software platforms. Such an approach helps not only in detecting complex events across various sources, but also in processing them and deciding the actions to be taken, with the help of a knowledge base thereby reducing information overload. Consider a typical application scenario a pump failure event in an oilfield, which should elicit response not only by the pump operator but also by the maintenance engineers, production managers, reservoir engineers and other involved personnel. A proactive event driven system enables quick detection of the failure across heterogeneous data sources and takes corrective actions while notifying the appropriate personnel. This facilitates effective communication across the teams and software systems involved. We propose a semantic complex event processing architecture for the digital oilfield that facilitates enterprise information integration. We delineate an illustrative use case of such integration for production optimization. Value propositions of the proposed framework include efficient interaction patterns, reduction in data seeking efforts, faster response times, building of consistent best practices and management by exception.
Parameterized and GPU Parallelized Real Time Model Predictive Control for High Degree of Freedom Robots
Venue: ArXiv
Year: 2020

This work presents and evaluates a novel input parameterization method which improves the tractability of model predictive control (MPC) for high degree of freedom (DoF) robots. Experimental results demonstrate that by parameterizing the input trajectory more than three quarters of the optimization variables used in traditional MPC can be eliminated with practically no effect on system performance. This parameterization also leads to trajectories which are more conservative, producing less overshoot in underdamped systems with modeling error. In this paper we present two MPC solution methods that make use of this parameterization. The first uses a convex solver, and the second makes use of parallel computing on a graphics processing unit (GPU) We show that both approaches drastically reduce solve times for large DoF, long horizon MPC problems allowing solutions at real time rates. Through simulation and hardware experiments, we show that the parameterized convex solver MPC has faster solve times than traditional MPC for high DoF cases while still achieving similar performance. For the GPU based MPC solution method, we use an evolutionary algorithm and that we call Evolutionary MPC (EMPC) EMPC is shown to have even faster solve times for high DoF systems. Solve times for EMPC are shown to decrease even further through the use of a more powerful GPU. This suggests that parallelized MPC methods will become even more advantageous with the improvement and prevalence of GPU technology.
Introduction of a Thio Functional Group to Diazabicyclooctane: An Effective Modification to Potentiate the Activity of b Lactams against Gram Negative Bacteria Producing Class A, C, and D Serine b Lactamases.
Venue: ACS infectious diseases
Year: 2020

By the emergence and worldwide spread of multi drug resistant Gram negative bacteria, there have been growing demands for efficacious drugs to cure these resistant infections. The key mechanism for resistance to b lactam antibiotics is the production of b lactamases, which hydrolyze and deactivate b lactams. Diazabicyclooctane (DBO) analogs play an important role as one of the new classes of b lactamase inhibitors (BLIs) and several compounds such as avibactam (AVI) have been approved by the FDA, along with many derivatives under clinical or preclinical development. Although these compounds have a similar amide substituent at the C2 position, we have recently reported the synthesis of novel DBO analogs which possess a thio functional group. This structural modification enhances the ability to restore the antimicrobial activities of cefixime (CMF) against pathogens producing classes A, C, and D serine b lactamases compared with AVI and expands the structural tolerance at the six position. Furthermore, some of these analogs showed intrinsic microbial activities based on multipenicillin binding protein (PBP) inhibition. This is the unique feature which has never been observed in DBOs. One of our DBOs had a pharmacokinetic profile comparable to that of other DBOs. These results indicate that the introduction of a thio functional group into DBO is a novel and effective modification to discover a clinically useful new BLI.
CCNA Based Communication Technology Courses
Venue: IEEE Communications Magazine
Year: 2020

Many study programs include courses on communication technology. All these courses explain the basis of communication technology, but they have different levels of detail depending on the study program and the related organization. Several courses include experimental activities, and some of these are based on certification related to a network device manufacturer. This article is based on the experience at the University of Stavanger, Norway, where two Bachelor level courses based on the Cisco Certified Network Associate are offered. This article generalizes the outcome from this experience and aims to provide a guideline to create similar courses in different study programs. This article will address the course syllabus, the laboratory activity, and the examination by giving some best practice suggestions.
Handling Agents' Incomplete Information in a Coalition Formation Model
Venue: ANAC@AAMAS
Year: 2014

Coalition formation is a problem of great interest in AI, allowing groups of autonomous rational agents to form suitable teams. Our work specially focuses on agents which are self interested and want to negotiate for executing actions in their plans. Depending on its capabilities, an agent may not be able to perform actions alone. Then the agent needs to find partners, interested in the same actions, and agree to put their resources in common, in order to perform these actions all together. We propose in this paper a coalition formation mechanism based on: (1) an action selection algorithm, which allows an agent to select the actions to propose and deal with the incomplete information about other agents in the system and (2) a coalition evaluation algorithm, which allows an agent to select a group of agents to perform with these actions. Our coalition evaluation algorithm is designed for structured preference context, based on the use of the information gathered in the previous interactions with other agents. It allows the agents to select partners, which are more likely interested in the actions. These algorithms are detailed and exemplified. We have studied the quality of the solution, we have implemented and tested them, and we provide the results of their evaluation.
Investigation on SVM Backstepping sensorless control of five phase open end winding induction motor based on model reference adaptive system and parameter estimation
Venue: Engineering Science and Technology, an International Journal
Year: 2019

This paper deals with a new control technique applied to five phase induction motor under open end stator winding (FPIM OESW) topology using the backstepping nonlinear control. The main objective is to improve the dynamics of this kind of machine, which is intended to be used in high power industrial application, where the maintenance is difficult and the fault tolerant is needed to ensure the continuous motor operating mode with minimized number of interruption. This control technique is combined with the Space Vector Pulse Width Modulation (SVPWM) to maintain a fixed switching frequency. In addition, the Model Reference Adaptive System (MRAS) concept is used for the estimation of the load torque, the rotor flux and the rotor speed to overcome the main drawbacks presented with the previous sensorless systems concepts. However, the great sensitivity to the changes of the motor internal parameters and it operating instability problems, especially in low speed operating region, that causes an estimation error of the rotor speed, is the most disadvantage of the MRAS technique. Therefore, to solve this problem, an estimation method of the motor internal parameters such as the rotor resistance, the stator resistance and the magnetizing inductance, is proposed in this paper. Where, the main aim is to improve furthermore the control performance, to reduce the computational complexity and to minimize the rotor speed estimation error. The obtained simulation results confirm the enhanced performance and the clear efficacy of the proposed control technique under a variety of cases of different operating conditions.
Aspect Based Sentiment Classification Using Interactive Gated Convolutional Network
Venue: IEEE Access
Year: 2020

Aspect based sentiment classification aims to detect the sentiment polarity of a target in a given context. Most previous approaches use long short term memory (LSTM) and attention mechanisms to predict the sentiment polarity of targets, which are usually complex and need more training time. Some previous approaches are based on convolutional neural networks (CNN) and gating mechanisms, which are much simpler, efficient and takes lesser convergence time than LSTM due to parallelized computations during training. However, such CNN based networks ignore the separate modeling of targets via context specific representations. In this paper, we propose a novel interactive gated convolutional network (IGCN) that uses a bidirectional gating mechanism to learn mutual relation between the target and corresponding review context. IGCN also uses positional information of context words with respect to the given target, POS tags, and domain specific word embeddings for predicting the sentiment of a target. The experimental results on SemEval 2014 datasets show the effectiveness of our proposed IGCN model.
Towards better understanding of gradient based attribution methods for Deep Neural Networks
Venue: ICLR
Year: 2018

Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work, we analyze four gradient based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity n and test the gradient based attribution methods alongside with a simple perturbation based attribution method on several datasets in the domains of image and text classification, using various network architectures.
Work life Balance and Organisational Commitment Perceptions of Working Postgraduate Students
Year: 2017

The study intends to investigate the interplay between work life balance and organizational commitment amongst working post graduate students. The survey research design was used, multistage sampling technique was adopted using the correlation and regression approaches. The samples were drawn from working postgraduate students in Lagos State University, Lagos.Results indicate a positive but not statistically significant relationship between work life balance and organizational commitment. A positive relationship was found between Work Interference with Family (WIF) and organizational commitment; however it was not statistically significant. Finally, a positive relationship was found between family interference with work (FIW) and organizational commitment, but not statistically significant. The study recommended that government and management of organizations that have employees' that are students should ensure that they help them achieve work life balance, to aid organizational commitment.
Radio Resource Management for Relay Aided Device to Device Communication
Year: 2013

In this thesis, performance of relay assisted Device to device (D2D) communication is investigated where D2D traffic is carried through relay nodes. I develop resource management schemes to maximize end to end rate as well as conversing rate requirements for cellular and D2D UEs under total power constraint. I also develop a low complexity distributed solution using the concept of message passing. Considering the uncertainties in wireless links (e.g. when interference from other relay nodes and the link gains are not exactly known) I extend the formulation using robust resource allocation techniques. In addition, a distributed solution approach using stable matching is developed to allocate radio resources in an efficient and computationally inexpensive way under the bounded channel uncertainties. Numerical results show that, there is a distance threshold beyond which relay assisted D2D communication significantly improves network performance at the cost of small increase in end to end delay when compared to conventional approach.
A Fast and Simple Graph Kernel for RDF
Venue: DMoLD
Year: 2013

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state of the art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag of words feature vectors, which increases performance in two of the four experiments.
Dispersion and anisotropy of thermo optic coefficients in tetragonal GdVO(4) and YVO(4) laser host crystals: erratum.
Venue: Applied optics
Year: 2015

We addressed errors found in our measurements of thermo optic coefficients and thermal coefficients of the optical path (TCOP) in tetragonal vanadates, YVO(4) and GdVO(4) [ (2013) Modified thermo optic dispersion formulas are presented for these laser host crystals.
The Approximation Ratio of the 2 Opt Heuristic for the Metric Traveling Salesman Problem
Venue: Oper. Res. Lett.
Year: 2020

The 2 Opt heuristic is one of the simplest algorithms for finding good solutions to the metric Traveling Salesman Problem. It is the key ingredient to the well known Lin Kernighan algorithm and often used in practice. So far, only upper and lower bounds on the approximation ratio of the 2 Opt heuristic for the metric TSP were known. We prove that for the metric TSP with $n$ cities, the approximation ratio of the 2 Opt heuristic is \sqrt{n/2} and that this bound is tight.
Towards CNN Representations for Small Mass Spectrometry Data Classification: From Transfer Learning to Cumulative Learning
Year: 2020

Rapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non sample related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology. where training samples are scarce.
Impact analysis of traffic related air pollution based on real time traffic and basic meteorological information.
Venue: Journal of environmental management
Year: 2016

With the rapid development of urbanization and motorization in China, traffic related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic related air pollution with real time traffic and basic meteorological information and also for further evaluating the impact of traffic related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real time link based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e. traffic speed and traffic intensity) of the affecting area and traffic related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area.
Fluorescent Probes for Nanoscopic Imaging of Mitochondria
Venue: Chem
Year: 2019

Studying the ultra fine structure and functions of mitochondria at a nanoscale level has garnered tremendous attention from biologists. Mitochondria perform many more functions than merely generating adenosine triphosphate (ATP) and their functions can vary in different eukaryotic cells. In place of diffraction limited conventional imaging techniques, advanced nanoscopic technologies have been devised in the past decades to explore the unknown aspects of mitochondrial dynamics and complex structures with a sub diffraction resolution. The success of these super resolution microscopy and nanoscopy techniques is complemented by the advancements in designing smart fluorescent probes that target mitochondria. Therefore, this review includes the comprehensive aspects of the recent progress in developing fluorogenic systems for nanoscopic imaging of mitochondria. The review also critically assesses the associated benefits and limitations of such fluorophores when they are employed in practical experiments. Future scope and challenges in developing suitable fluorophores for several nanoscopic techniques are also judiciously evaluated.