nils-herrmann/clean_scirepeval huggingface dataset:
Dyeing of UV irradiated cotton and polyester fabrics with multifunctional reactive and disperse dyes

The dyeing behaviour of UV irradiated cotton and polyester fabrics using multifunctional reactive and disperse dyes has been investigated. The plain, woven, mercerized, bleached, cotton and polyester fabrics were exposed to UV radiation (180 w, 254 nm) for 30, 60, 90 and 120 min. Dyeing was performed using irradiated fabric with a dye solution of un irradiated reactive and disperse/azo dyes. The dyeing parameters such as, temperature, time, pH and salt concentration have been optimized. The colour strength values of dyed fabrics were evaluated by comparing irradiated and un irradiated cotton and polyester fabrics in CIE Lab systems using spectra flash SF600. Finally ISO standard methods were employed to observe the effect of UV radiation on fastness properties. It was found that UV radiation has a potential to improve the colour strength values of cotton and polyester fabrics by using reactive and disperse dyes.
Controlled release of microencapsulated citronella essential oil on cotton and polyester matrices
Venue: Cellulose

Microencapsulated finishes are an important element in the development of new textiles. In this context, a large area to be explored is microencapsulation of essential oils in textiles. This technique offers the possibility of developing new products with many advantages over traditional fabrics, as traditional finishing may be ineffective for reasons related to uncontrolled release of the active principle while microencapsulation aims to achieve increased duration of the finishing effect. However, many studies present only the application of microcapsules in a textile but do not report how the release of the encapsulated material occurs or the influence of the textile matrix. This paper reports the mechanism and kinetics of controlled release of microencapsulated citronella oil in cotton and polyester. The microencapsulation was done by complex coacervation with gelatin and gum Arabic as shell materials. The resulting microcapsules were analyzed by optical microscopy, scanning electron microscopy, thermogravimetric analysis, and dynamic light scattering. They were then applied in cotton and polyester and evaluated by attenuated total reflection Fourier transform infrared spectroscopy. Finally, the controlled release of citronella from the microcapsules deposited on the fabrics was studied in vitro. It was found that the release was directly influenced by the type of fiber: the microcapsules in polyester showed diffusion by a Fickian mechanism, while a non Fickian kinetic model fit for the modified cotton. Comprehension of such controlled release processes is fundamental for achieving and developing more durable finishing effects.
Characterizing the sorption of polybrominated diphenyl ethers (PBDEs) to cotton and polyester fabrics under controlled conditions.
Venue: The Science of the total environment

Cotton and polyester, physically and chemically different fabrics, were characterized for sorption of gas phase polybrominated diphenyl ethers (PBDEs) Scanning electron microscopic (SEM) images and BET specific surface area (BET SSA) analysis showed cotton's high microsurface area; NMR analysis showed richness of hexose and aromatic carbon in cotton and polyester, respectively. Cotton and polyester sorbed similar concentrations of gas phase PBDEs in chamber studies, when normalized to planar surface area. However, polyester concentrations were 20 50 times greater than cotton when normalized to BET SSA, greater than the 10 times difference in BET SSA. The difference in sorption between cotton and polyester is hypothesized to be due to 'dilution' due to cotton's large BET SSA and/or greater affinity of PBDEs for aromatic rich polyester. Similar fabric air area normalized distribution coefficients (K'D, 10(3) to 10(4)m) for cotton and polyester support air side controlled uptake under non equilibrium conditions. K'D values imply that 1m(2) of cotton or polyester fabrics would sorb gas phase PBDEs present in 10(3) to 10(4)m(3) of equivalent air volume at room temperature over one week, assuming similar air flow conditions. Sorption of PBDEs to fabrics has implications for their fate indoors and human exposure.
Preparation and performance evaluation of phosphorus nitrogen synergism flame retardant water borne coatings for cotton and polyester fabrics
Venue: Journal of Polymer Research

Three phosphorus nitrogen content effective synergist flame retardant water borne coatings have been synthesized, and their structures were characterized by infrared spectroscopy. Cotton and polyester fabrics have been treated by coatings to improve their flame retardancy. The thermal performances and flame retardant properties of treated samples were investigated by thermogravimetric analysis (TGA) horizontal flame test, vertical burning test, limiting oxygen index (LOI) scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) respectively. The combustion performances of cotton and polyester fabrics have proven to be strongly affected by flame retardant coatings and HFD coating performed the best. As a result, the LOI value of treated cotton increased to 22.5, and the UL 94 value of treated polyester achieved V 0. In addition, the antistatic behavior, hydrostatic pressure, tearing strength and wrinkle recovery angle of samples were studied carefully, and the results showed that all of these performances were improved.
Zika virus phosphoproteome through the computational looking glass and what we found there?

Zika virus (ZIKV) a mosquito borne disease, has been linked to congenital anomalies in humans. Recent outbreaks of ZIKV have caused the scientific community to focus on this previously neglected pathogen. Information regarding phosphorylation potential of ZIKV proteins has yet to be elucidated. Deciphering phosphorylation potential of viral proteins is important because it assists in viral replication and its association with host receptors. Here, we present a vignette of the phosphorylation potential of ZIKV proteins using computational approach.
Humanoid interpreter for teaching basic sign language
Venue: 2016 IEEE International Conference on Automatica (ICA ACCA)

This article presents the design and construction of the torso of a humanoid robot with 22 degrees of freedom (22 GDL) built by additive manufacturing, with the combination of an interactive graphical interface, especially designed for children to self learn the language in basic sign Ecuador (ESL) through imitation. The interface is designed according to user requirements, should be easy and interactive, here the words is selected to interpret, the algorithm control activates the link mechanism of the humanoid, moving hands and body, transforming it in the form of signs, in accordance the Dictionary of Sign Language of Ecuador. For the construction of the torso, the model structure Inmoov took, made several modifications, according to the CAD/CAM/CAE philosophy and was constructed by additive manufacturing for a similar torso of a human model with multiple degrees of freedom. The humanoid was evaluated with children of Educational Specialized Unit Cotopaxi, using two teaching methods, one using traditional where the teacher teaches his students to interpret the sign and other interactive, where the interpreter is presented and the participants motivated to imitate the signs demonstrated by the humanoid repeated many times as they saw fit to its self learning. With the implementation of this interpreter, learning times were reduced by 25% compared to the traditional method, serves as an assistant teacher and is an interactive means of communication between the deaf community and listeners.
Basic Investigation for Improvement of Sign Language Recognition Using Classification Scheme
Venue: HCI

Sign language is a commonly used communication method for hearing impaired or speech impaired people. However, it is quite difficult to learn sign language. If automatic translation for sign language can be realized, it becomes very meaningful and convenient not only for impaired people but also physically unimpaired people. The cause of the difficulty in automatic translation is that there are so many variations in sign language motions, which degrades recognition performance. This paper presents a recognition method for maintaining the recognition performance for many sign language motions. A scheme is introduced to classification using a decision tree, which can decrease the number of words to be recognized at a time by dividing them into groups. The used hand, the characteristics of hand motion and the relative position between hands and face have been considered in creating the decision tree. It is confirmed by experiments that the recognition success rate increased from 41 and 59 to 59 and 82 respectively, for a basic 17 words of sign language with four sign language operators.
Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining
Venue: Nature communications

Fisheries data assembled by the Food and Agriculture Organization (FAO) suggest that global marine fisheries catches increased to 86 million tonnes in 1996, then slightly declined. Here, using a decade long multinational 'catch reconstruction' project covering the Exclusive Economic Zones of the world's maritime countries and the High Seas from 1950 to 2010, and accounting for all fisheries, we identify catch trajectories differing considerably from the national data submitted to the FAO. We suggest that catch actually peaked at 130 million tonnes, and has been declining much more strongly since. This decline in reconstructed catches reflects declines in industrial catches and to a smaller extent declining discards, despite industrial fishing having expanded from industrialized countries to the waters of developing countries. The differing trajectories documented here suggest a need for improved monitoring of all fisheries, including often neglected small scale fisheries, and illegal and other problematic fisheries, as well as discarded bycatch.
Measurements and modeling of current impulses in the lightning protection system and internal electrical installation equipped with household appliances

Open air experiments were conducted of impulse current distribution in the lightning protection system, supplying cable and electrical installation of a test house equipped with electrical and electronic household appliances. Impulse currents were injected from the generator into the installation. Current distributions in elements of tested system were measured with multi channel electro optical system. To verify the experimental results the simulations of surge current in the test house modeled in ATP EMTP were conducted. The obtained results show a good agreement between measured and numerically simulated currents and significant influence of frequency dependent parameters of the test house elements on both the amplitude and the shape of current waveforms.
Feasibility study of existing 50/60Hz electrical appliances for HFAC power distribution systems
Venue: 2016 IEEE International Telecommunications Energy Conference (INTELEC)

This paper investigates into the key parameters (e.g. voltage, frequency, waveform, etc. of HFAC Power Distribution Systems (PDS) ranging from smaller 0.5 kW laptop/desktop computer upto 1.0 MW domestic/commercial/industrial power distribution applications. Due to the high reactance and associated issues observed with increased frequency, different HFAC PDSs have been designed to operate with different frequencies. Advantages of using HFAC over utility AC (50/60Hz) or DC in PDSs predominantly connected with electronic converters has been presented in many publications. Although most electrical appliances are intended to operate mainly with utility AC, it would facilitate widespread uptake of HFAC in PDSs if they could operate also at HFAC. The possibility of cross matching and using a wide variety of electrical appliances on HFAC PDSs has been investigated and the findings are presented in this paper. Use of AC AC power converters as interfacing devices for the appliances that do not perform well with HFAC has also been proposed.
Advances in real time magnetic resonance imaging of the vocal tract for speech science and technology research
Venue: APSIPA Transactions on Signal and Information Processing

Real time magnetic resonance imaging (rtMRI) of the moving vocal tract during running speech production is an important emerging tool for speech production research providing dynamic information of a speaker's upper airway from the entire midsagittal plane or any other scan plane of interest. There have been several advances in the development of speech rtMRI and corresponding analysis tools, and their application to domains such as phonetics and phonological theory, articulatory modeling, and speaker characterization. An important recent development has been the open release of a database that includes speech rtMRI data from five male and five female speakers of American English each producing 460 phonetically balanced sentences. The purpose of the present paper is to give an overview and outlook of the advances in rtMRI as a tool for speech research and technology development.
Exploitation of Exosomes as Nanocarriers for Gene Chemo and Immune Therapy of Cancer.
Venue: Journal of biomedical nanotechnology

The bottleneck in current vector based cancer therapy is the targeted and controlled release of therapeutics in tumors. Exosomes are submicron sized vesicles that are secreted by all cell types and are involved in communication and transportation of materials between cells. Analogous in size and function to synthetic nanoparticles, exosomes offer many advantages, rendering them the most promising candidates for targeted drug or gene delivery vehicles. Patient specific customized therapeutic strategies can be engineered using exosomes derived from the patient's own healthy cells. Therefore, exosome based cancer therapy has the potential to become an important part of personalized medicine. Interest in exosomes as carrier organelles is relatively recent. Knowledge about exosomal biology and its applications remains limited. The present review is an attempt to describe the current status of the application of exosomes to cancer therapy and the potential challenges associated with their use.
The biology and function of exosomes in cancer.
Venue: The Journal of clinical investigation

Humans circulate quadrillions of exosomes at all times. Exosomes are a class of extracellular vesicles released by all cells, with a size range of 40 150 nm and a lipid bilayer membrane. Exosomes contain DNA, RNA, and proteins. Exosomes likely remove excess and/or unnecessary constituents from the cells, functioning like garbage bags, although their precise physiological role remains unknown. Additionally, exosomes may mediate specific cell to cell communication and activate signaling pathways in cells they fuse or interact with. Exosomes are detected in the tumor microenvironment, and emerging evidence suggests that they play a role in facilitating tumorigenesis by regulating angiogenesis, immunity, and metastasis. Circulating exosomes can be used as liquid biopsies and noninvasive biomarkers for early detection, diagnosis, and treatment of cancer patients.
Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks
Venue: ESANN

Robust vision based pedestrian detection is a crucial feature of future autonomous systems. Thermal cameras provide an additional input channel that helps solving this task and deep convolutional networks are the currently leading approach for many pattern recognition problems, including object detection. In this paper, we explore the potential of deep models for multispectral pedestrian detection. We investigate two deep fusion architectures and analyze their performance on multispectral data. Our results show that a pre trained late fusion architecture significantly outperforms the current state of the art ACF+T+THOG solution.
Advances in Brain Inspired Cognitive Systems
Venue: Cognitive Computation

This special issue focuses on recent advancements in the field of brain inspired cognitive systems. It comprises 18 articles which are carefully selected, significantly revised versions of papers presented at the seventh International Conference on Brain Inspired Cognitive Systems (BICS 2015) held at Hefei, China, on December 11 13, 2015. The aim of BICS 2015 was to bring together leading scientists, engineers, and educators who use analytic, syntactic, and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowledge to advance computational methods toward higher levels of cognitive competence. The conference featured plenary speeches given by renowned scholars and a range of technical sessions focusing on timely topics of interest to the scientific community. Based on the recommendation of symposium organizers and reviewers, a number of authors were invited to submit revised versions of their contributions to this special issue. All articles went through a rigorous review procedure involving at least three independent experts before being accepted for publication. Chen et al. propose an efficient algorithm for actionbased pedestrian identification using hierarchical matching pursuit and order preserving sparse coding to identify and classify features. Gepperth et al. present a novel system for performing multi sensor fusion with experience based learning. The authors demonstrate that near optimal fusion can be learned and is a resource efficient alternative to empirical estimation of joint probability distributions and Bayesian inference. They also show that their generative learning approach outperforms Bayesian optimum as it is capable of rejecting outliers through detection of systematic changes in input statistics. Mi et al. describe a novel system for occluded face recognition. They propose a new similarity matrix called 'averaged degree of aggregation of matched pixels' and show that their system is very robust against occlusion and competitive in terms of recognition accuracy and computation time. Jiang et al. present a cognitively distributed simultaneous localization and tracking algorithm, with applications in Gaussian distributed wireless sensor networks, based on adaptive distributed filtering for target tracking and sensor localization. Their proposed system shows higher accuracy in parameter estimation, coupled with low computational complexity. Zhao et al. propose a new method called 'elastic matching' for common visual pattern discovery, through inducing sparse solutions and conducting detection more robustly. Zhang et al. describe a discriminative Lasso in which sparsity and correlation are jointly considered. The method Amir Hussain
Use of a systematic risk analysis method (FMECA) to improve quality in a clinical laboratory procedure.
Venue: Annali di igiene medicina preventiva e di comunita

Risk management is a set of actions to recognize or identify risks, errors and their consequences and to take the steps to counter it. The aim of our study was to apply FMECA (Failure Mode, Effects and Criticality Analysis) to the Activated Protein C resistance (APCR) test in order to detect and avoid mistakes in this process.  We created a team and the process was divided in phases and sub phases. For each phase we calculated the probability of occurrence (O) of an error, the detectability score (D) and the severity (S) The product of these three indexes yields the RPN (Risk Priority Number) Phases with a higher RPN need corrective actions with a higher priority.  The calculation of RPN showed that more than 20 activities have a score higher than 150 and need important preventive actions; 8 have a score between 100 and 150. Only 23 actions obtained an acceptable score lower than 100.  This was one of the first experience of application of FMECA analysis to a laboratory process, and the first one which applies this technique to the identification of the factor V Leiden, and our results confirm that FMECA could be a simple, powerful and useful tool in risk management and helps to identify quickly the criticality in a laboratory process.
Mining Individual Mobility Patterns Based on Location History
Venue: 2016 IEEE First International Conference on Data Science in Cyberspace (DSC)

At present, it is a hot spot in the research of data mining to mine mobility patterns on the basis of GPS trajectories. In order to solve the problems that data sampling frequency uncertainty, the value of experience as period parameter by human input, and spatiotemporal noises and outliers, we propose a framework called PMPM (Periodic Mobility Pattern Mining) to adaptively detect periodic parameters of GPS trajectories, and mine individual mobility patterns. First of all, some approach and algorithms are proposed to extract reference spot set from raw trajectories. Secondly, period of every reference spot is detected by a probabilistic model. Then individual mobility patterns are mined using frequent pattern mining algorithms. Finally, we use real collected data to perform functions for mining individual mobility patterns and demonstrate effectiveness of our framework.
Mining Semantic Location History for Collaborative POI Recommendation in Online Social Networks
Venue: 2016 2nd International Conference on Open and Big Data (OBD)

Location based social networks (LBSNs) have recently attracted millions of mobile users to explore attractive locations and share their visited experiences. As the increasing use of the online social networks, people demand personalized service to recommend places of interests (POIs) based on their personal preferences. Among POIs recommendation approaches, collaborative filtering that predicts POIs of the user based on the geospatial location and users' opinions is suite for LBSNs. Despite this, it is still a challenge to infer the similarity between users because of the unique characteristics of spatial items in LBSN. In this paper, we propose an effective POI recommendation method for LBSNs based on collaborative filtering. Our method focuses on mining interest similarity of users based on their check in activities in LBSN. Since the geospatial locations cannot capture user's interests, we perform to mine semantic features of user's check in history based on semantic location descriptions to discover the user's interests. We finally perform recommending nearby places to a particular user by fusing opinions from similar users according to the user's current location. Experimental results with two real world datasets collected from Foursquare show that our proposed method can achieve satisfying precision and recall of recommended places.