nils-herrmann/clean_scirepeval huggingface dataset:
Partial rpoB Gene Sequencing Identification and Probiotic Potential of Floricoccus penangensis ML061 4 Isolated from Assam Tea (Camellia sinensis var. assamica)
Venue: Scientific Reports
Year: 2019

Assam tea or Miang is a local name of Camellia sinensis var. assamica in northern Thailand. By the local wisdom, Assam tea leaves are used as the raw material in tea fermentation to produce "Fermented Miang" consumed by people in northern Thailand and the countries nearby. In this study, twenty eight bacterial isolates were obtained from Assam tea leaf samples collected from Nan province, Thailand. Bacterial isolates were identified within 6 genera including Bacillus, Floricoccus, Kocuria, Lysinibacillus, Micrococcus and Staphylococcus. Among these, the strain ML061 4 shared 100.0 and 99.4% similarity of 16S rRNA and rpoB gene sequence with F. penangensis JCM 31735T, respectively. This is the first discovery of F. penangensis in Thailand. F. penangensis ML061 4 exhibited probiotic characteristics including lactic acid production (9.19 0.10 mg/ml) antibacterial activities (Escherichia coli ATCC 25922 and E. coli O157:H7 DMST 12743) acid and bile salt tolerance (71.1 and 54.9% respectively) autoaggregation (97.0% coaggregation (66.0% with E. coli O157:H7) cell surface hydrophobicity (90.0% bacterial adhesion (82.9% with Lactobacillus plantarum FM03 1) competitive inhibition (17.8% with E. coli O157:H7) and competitive exclusion (34.9% with E. coli O157:H7) Overall, the data suggested that F. penangensis ML061 4 had a great potential to be a probiotic.
Ionization of many electron atoms by the action of two plasma models.
Venue: Physical review. E
Year: 2021

The Hartree Fock equations for many electron atoms embedded in a plasma medium are solved using two different plasma models: (a) Debye Huckel screening (DHS) potential and (b) exponential cosine screened Coulomb (ECSC) potential. Roothaan's approach is implemented for these models after solving the inherent difficulties to evaluate integrals where screening appears explicitly. A corresponding computer code was developed using the method of global basis sets (GBS) The reliability of this approach was verified by solving the Hartree Fock equations through implementation of the finite differences and finite element grid methods and applied to two electron atoms, yielding excellent agreement with the Roothaan GBS (RGBS) method. The RGBS method was used to study the energy evolution and ionization threshold of several closed and open shell many electron atoms embedded either in weak or strong DHS or ECSC plasma conditions. In all cases, a critical value of the screening length is obtained for which ionization is achieved, being systematically larger for DHS conditions, indicating the effect of a more repulsive ECSC potential. For He like atoms in the ground state, we report a comprehensive set of accurate total energy data as a function of the screening constant using the Lagrange mesh method, which includes the electron correlation effects. The electron correlation energy is estimated using this data with reference to the RGBS estimates of energy as the Hartree Fock energy. The variation of correlation energy as a function of screening constant under the different plasma potentials is rationalized in terms of a conjectured comparison theorem. Finally, a discussion on the effect of plasma strength on localization or delocalization of the electronic density derived from the RGBS method is presented in terms of changes in the Shannon entropy, yielding consistent results for delocalization close to the ionization threshold.
Object oriented modeling of an Interior Permanent Magnet Synchronous Motor Drives for Dynamic Simulation of Vehicular Propulsion
Venue: 2006 IEEE Vehicle Power and Propulsion Conference
Year: 2006

This paper describes an object oriented modeling technique of an interior permanent magnet synchronous motor (IPMSM) drives for a dynamic simulation of a hybrid vehicle propulsion system. In order to enhance reusability of the model, each part of an IPMSM drive system is modeled in an object oriented manner according to the physical partitioning of the drive system. This model allows comprehensive analysis of the IPMSM drive, including torque fluctuation of IPMSM, switching strategies of an inverter, and complex algorithm of a controller and it is also possible to simulate in a simplified way for a reduced computation time without considering switching patterns of an inverter. This model is implemented in Simulink environment, which is a de facto standard tool in control and system engineering, and is easily integrated with other models of hybrid propulsion system. By use of the developed model, fundamental characteristics of IPMSM drive system were investigated and the simulation results show its practical effectiveness.
Machine learning based mechanical properties prediction in foundry production
Venue: 2009 ICCAS SICE
Year: 2009

Ultimate tensile strength (UTS) is the force a material can resist until it breaks. The only way to examine this mechanical property is the employment of destructive inspections with the subsequent cost increment. Modelling the foundry process as an expert knowledge cloud allows properly trained machine learning algorithms to foresee the value of UTS. Extending previous research that presented outstanding results with a Bayesian network based approach, we have adapted an ANN and K nearest neighbour algorithm for the same objective. We compare the obtained results and show that artificial neural networks are more suitable than the rest of counterparts for the prediction of UTS.
Investigating the collision energy dependence of e/s in the beam energy scan at the BNL Relativistic Heavy Ion Collider using Bayesian statistics
Year: 2018

We determine the probability distributions of the shear viscosity over the entropy density ratio e/s in the quark gluon plasma formed in Au Au collisions at sNN=19.6,39, and 62.4GeV, using Bayesian inference and Gaussian process emulators for a model to data statistical analysis that probes the full input parameter space of a transport viscous hydrodynamics hybrid model. We find the most likely value of e/s to be larger at smaller sNN, although the uncertainties still allow for a constant value between 0.10 and 0.15 for the investigated collision energy range.
Extracellular Vesicles as an Emerging Frontier in Spinal Cord Injury Pathobiology and Therapy
Venue: Trends in Neurosciences
Year: 2021

Extracellular vesicles (EVs) are membrane delimited particles that are secreted by nearly all cell types. EVs mediate crucial physiological functions and pathophysiological processes in the CNS. As carriers of diverse bioactive cargoes (e.g. proteins, lipids, and nucleic acids) that can be modified in response to external stimuli, EVs have emerged as pathological mediators following neurotrauma such as spinal cord injury (SCI) We discuss the roles of endogenous EVs in the CNS as well as crosstalk with peripheral EVs in relation to neurotrauma, with a particular focus on SCI. We then summarize the status of EV based therapeutic advances in preclinical animal models for these conditions. Finally, we discuss new bioengineering strategies that are poised to enhance CNS specific therapeutic capabilities of EVs.
Low serum IgA and increased expression of CD23 on B lymphocytes in peripheral blood in children with regressive autism aged 3 6 years old
Venue: Archives of medical science AMS
Year: 2012

Immune system dysfunction is considered to be one of many medical disorders found in children with autism. The primary objective of the study was to assess if blood tests reflecting humoral immunity (IgA, IgG, IgM, IgE) are useful in identifying children with regressive autism. The secondary objective was to evaluate a part of the cellular arm of immunity (CD4/CD25 Tregs, CD4/CD23 cells) in those children.  Using a clinical case control design, the systemic levels of immunoglobulins and lymphocyte subpopulations analysed by flow cytometry were compared in children aged 3 6 years old with a new diagnosis of regressive autism (n 24; mean age: 4.25 1.70 years; male 23/24) and in sex and age matched healthy children (n 24; aged 4.25 2.20 years; male 23/24)  The humoral immunity profile, described by three binary variables, IgA 0.97 g/l, IgE 36 IU/ml, and IgG 6.3 g/l, with a sensitivity of 79% and a specificity of 83% (p 0.0001) was able to identify children with autism. The highest risk of autism diagnosis was associated with IgA 0.97g/l (OR 23.0; p 0.001) A higher number of CD19/CD23 was found in children diagnosed with autism than in the control group (36.82 6.72% vs. 18.20 3.95% p 0.02) No correlation between the number of CD23 positive cells and serum IgE levels was observed.  A subtle shift of serum immunoglobulins consisting of low normal IgA and B cell activation expressed by an increase of CD23 positive cells may characterize children with regressive autism aged 3 6 years old.
IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings
Venue: Sensors
Year: 2020

The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end users' interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.
On line component level soft fault diagnostics for power converters
Venue: 2016 Prognostics and System Health Management Conference (PHM Chengdu)
Year: 2016

The implementation of diagnostic methodologies to power devices and converters has become essential and critical in many fields. A lot of previous research demonstrated detection of hard faults, such as open or shorted switching elements in the power systems. However, little progress has been made on soft faults diagnosis. The soft faults related to components degradation degrade the system's performance but not completely affect the normal operation. A method of on line model based components soft faults diagnosis is proposed in this paper. The fault detection technique utilizes parameter identification method based on Particle Swarm Optimization algorithm to achieve the component level diagnostic purpose. Considering components degradation and variable operating conditions, several simulation experiments have been conducted to validate the presented approach. The results show that this technique characterizes high computation efficiency and good estimation accuracy. Also, it will be a basis of further study for the circuit level condition monitoring and remaining useful life prediction.
Techno economic analysis and life cycle assessment for electrochemical ammonia production using proton conducting membrane
Year: 2020

Green ammonia production is facing increasing interest on a global scale as a hydrogen carrier for power generation as well as fertilizer for food production. The conventional Haber Bosch method for ammonia synthesis is energy demanding, requires high purity hydrogen, and is based on fossil fuels. A preliminary techno economic model for electrochemical ammonia synthesis at near ambient pressure using feed rates of 32 metric ton/day for hydrogen and 135 metric ton/day for nitrogen is presented in this study. Various pathways using different methods for nitrogen generation and hydrogen production were investigated to gain insight into added energy savings per metric ton of ammonia. Electrochemical synthesis using electrolysis for hydrogen and cryogenic nitrogen was found to be a potentially viable pathway for green ammonia. The profitability metrics including discounted cash flow rate of return, net present value and discounted payback period were estimated to be 8% $40 MM, and 4 6 years respectively for this pathway. The cost of electricity, conversion rate, and conversion efficiency dominate the tonnage cost of ammonia and were used to assess the feasibility of the model. A life cycle assessment was also conducted to assess the environmental impact of a well to product ammonia process.
Medical Concept Representation Learning from Multi source Data
Venue: IJCAI
Year: 2019

Representing words as low dimensional vectors is very useful in many natural language processing tasks. This idea has been extended to medical domain where medical codes listed in medical claims are represented as vectors to facilitate exploratory analysis and predictive modeling. However, depending on a type of a medical provider, medical claims can use medical codes from different ontologies or from a combination of ontologies, which complicates learning of the representations. To be able to properly utilize such multi source medical claim data, we propose an approach that represents medical codes from different ontologies in the same vector space. We first modify the Pointwise Mutual Information (PMI) measure of similarity between the codes. We then develop a new negative sampling method for word2vec model that implicitly factorizes the modified PMI matrix. The new approach was evaluated on the code cross reference problem, which aims at identifying similar codes across different ontologies. In our experiments, we evaluated cross referencing between ICD 9 and CPT medical code ontologies. Our results indicate that vector representations of codes learned by the proposed approach provide superior cross referencing when compared to several existing approaches.
North Indian Ocean variability during the Indian Ocean dipole
Year: 2008

The circulation in the North Indian Ocean (NIO henceforth) is highly seasonally variable. Periodically reversing monsoon winds (southwesterly during summer and northeasterly during winter) give rise to seasonally reversing current systems off the coast of Somalia and India. In addition to this annual monsoon cycle, the NIO circulation varies semiannually because of equatorial currents reversing four times each year. These descriptions are typical, but how does the NIO circulation behave during anomalous years, during an Indian Ocean dipole (IOD) for instance? Unfortunately, in situ observational data are rather sparse and reliance has to be placed on numerical models to understand this variability. In this paper, we estimate the surface current variability from a 12 year hindcast of the NIO for 1993?2004 using a 1/2 deg resolution circulation model that assimilates both altimetric sea surface height anomalies and sea surface temperature. Presented in this paper is an examination of surface currents in the NIO basin during the IOD. During the non IOD period of 2000?2004, the typical equatorial circulation of the NIO reverses four times each year and transports water across the basin preventing a large sea surface temperature difference between the western and eastern NIO. Conversely, IOD years are noted for strong easterly and westerly wind outbursts along the equator. The impact of these outbursts on the NIO circulation is to reverse the direction of the currents when compared to non IOD years during the summer for negative IOD events (1996 and 1998) and during the fall for positive IOD events (1994 and 1997) This reversal of current direction leads to large temperature differences between the western and eastern NIO.
Role of corporations in addressing climate change
Year: 2018

Climate change is a proven scientific fact, documented through thousands of peer reviewed, meticulously compiled studies and reports, indicating threats to the world (Fletcher 2013;  2014; Urban 2015) Climate change is a threat to the global economy and the current way of life, with a potential to affect all facets of life, from social to the economic fabrics of the global society. Businesses as a core component of the social fabric, and businesswomen and businessmen play an integral role in being engaged on issues potentially impacting their corporate bottom lines. While a smaller number of corporations are significant contributors to global warming and GHG emissions, companies in effect contribute to pollution, which leads to global warming and the ensuing climate change impact (Griffin 2017; Riley 2017; Starr 2016) However, companies are actively engaged in pursuing actions to stem the negative impact of climate change and threats from extreme weatherrelated events, in knowing the risk of failed cities, communities, and ultimately infrastructure on their operations. The consequences for corporations may include supply change and transportation disruptions, higher insurance premiums for doing business in certain parts of the world, the negative impact on human resources, and changes in energy supply. Each industry sector from tourism to logistics will be negatively and adversely affected. Linnenluecke and Griffiths (2010) documented the risks of climate change and extreme weather events to business organizations and suggested the strategies for dealing with those beyond traditional adaptation approaches. Corporations may be able to quickly adjust and seek opportunities for long term solutions
Robust kernel fisher discriminant analysis with weighted kernels
Year: 2012

Kernel Fisher discriminant analysis (KFDA) is a very popular kernel classification technique that performs well in situations where linear classifiers fail. The performance of the KFD classifier is however adversely affected by outliers or noise in the data. In this paper we propose a more robust KFD classifier by making use of weighted kernels. The performance of the proposed classifiers is compared to that of the KFD classifier with a Gaussian kernel in Monte Carlo simulation studies as well as on several benchmark data sets. Based on the results we conclude that the proposed weighted kernels are successful in achieving a lower error rate than the Gaussian kernel when used in a KFD classifier.
In Vehicle Occupancy Detection With Convolutional Networks on Thermal Images
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019

Counting people is a growing field of interest for researchers in recent years. In vehicle passenger counting is an interesting problem in this domain that has several applications including High Occupancy Vehicle (HOV) lanes. In this paper, present a new in vehicle thermal image dataset. We propose a tiny convolutional model to count on board passengers and compare it to well known methods. We show that our model surpasses state of the art methods in classification and has comparable performance in detection. Moreover, our model outperforms the state of the art architectures in terms of speed, making it suitable for deployment on embedded platforms. We present the results of multiple deep learning models and thoroughly analyze them.
InertialNet: Toward Robust SLAM via Visual Inertial Measurement
Venue: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Year: 2019

SLAM (simultaneous localization and mapping) is commonly considered as a crucial component to achieving autonomous robot navigation. Currently, most of the existing visual 3D SLAM systems are still not robust enough. Image blur, variation of illumination, and low texture scenes may lead to registration failures. To let the visual odometry (VO) deal with these problems, the workflow of traditional approaches becomes bulky and complicated. On the other hand, the advancement of deep learning brings new opportunities. In this paper, we use a deep network model to predict complex camera motion. It is different from previous supervised learning VO researches and requires no camera trajectories which are difficult to obtain. Using the image input and IMU output as an end to end training pair makes data collection cost effective. The optical flow structure also makes the system independent of the appearance of training sets. The experimental results show that the proposed architecture has faster training convergence, and the model parameters are also significantly reduced. Our method is able to remain certain robustness under image blur, illumination changes, and low texture scenes. It can correctly predict the new EuRoC dataset, which is more challenging than the KITTI dataset.
El proceso de formacion de un derecho global de la contratacion publica
Year: 2012

A pesar de sus peculiaridades con respecto a los sistemas administrativos nacionales, resulta muy dificil cuestionar en nuestros dias tanto la existencia de un Derecho Administrativo Global, como su trascendente impacto en los ordenamientos administrativos nacionales. Uno de los ambitos que mejor refleja en la actualidad el proceso de formacion, desarrollo y consolidacion de este Derecho Administrativo Global es, sin duda alguna, el de la contratacion publica. Hasta tal punto ha llegado esta evolucion, que se puede concluir con firmeza que es un sector paradigmatico en la consolidacion del Derecho Administrativo Global. En efecto, en los ultimos anos los contratos publicos han sido objeto tanto de importante legislacion y acuerdos internacionales de jurisprudencia de organos con jurisdiccion internacional que han fijado principios generales como de actuaciones administrativas, entre otras, de solucion de controversias entre Estados u otras partes en disputa. En este sentido, se puede destacar el Acuerdo Mundial sobre Contratacion Publica de la Organizacion Mundial del Comercio, la Ley Modelo de Naciones Unidas sobre Compras Publicas de Bienes, Servicios y Obras, las normas en la materia del Banco Mundial y la ocde,4 o el muy desarrollado Derecho de la Union Europea de la contratacion publica, integrado por normas de Derecho originario (Tratado de la Comunidad Europea) y derivado (directivas sobre contratos publicos) y por una jurisprudencia del Tribunal de Justicia de la Union Europea que ha construido unos principios generales que dotan de unidad a todo el sistema administrativo de contratacion.
Hybrid Saliency Detection for Images
Venue: IEEE Signal Processing Letters
Year: 2013

Saliency information interpreted from the visual stimuli can predict the attentional behaviour of human perception, thus playing a key role in visual signal processing. In this letter, we present a hybrid saliency detection method for images by which we automatically predict the saliency regions based on low level and high level cues. Unlike existing bottom up and top down attentional methods, we consider the high level cue imposed by the photographer. Based on this assumption, we estimate the defocus map of the image and integrate it with other low level features based on the Bayesian framework. We compare our algorithm to several state of the art saliency detection methods based on the well known 1000 image EPFL database, and demonstrate the superior performance of our proposed algorithm.