nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Framework of Internet of Things for Smart Cities: Case Studies
Venue: Sensors

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet of Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.
Evaluating Hair Growth Activity of Herbal Hair Oil

Herbs herbal drugs are clinically proved good for hair growth. Hair loss problem is of great concern to both males females the main problems associated with hair loss are hair fading, dandruff falling of hair. Various synthetic medicines are available for hair loss which does not treat permanently also shows severe side effects. The main objective of this work is to develop such an herbal hair oil formulation which can resolve the problem related to hair fall other hair diseases. Herbal drugs like Emblica officinalis, Bacopa monniera and Cyperus rotundus were selected for the formulations of poly herbal hair oil. The hair oil was prepared individually and in a varying concentration of all three herbs and a mixture of all the three herbs in fixed proportion using coconut oil as base. The formulated oil in varying concentration was evaluated physical, chemical and hair growth properties of formulated oil by applying it topically on shaved skin of albino rats. Primary skin irritation test, hair length test were performed and the hair growth was compared with standard Minoxidil 2% ethanolic solution using healthy albino rats. It was observed that hair oil formulation (HF3) showed the best result among the other formulation evaluated by showing an enlargement of follicular size and prolongation of the anagen phase.
Formulation and evaluation of hair oil for hair loss disorders

Hair plays a vital role in the personality of human and for their cure we use lots of cosmetic products. The fading (pigmentation problem) dandruff, alopecia (loss of hair) is the major problem associated with hairs. Ayurvedic system is the traditional system of medicine having major treatment across globe. The aim of study is to develop a hair oil formulation using Azadirachta indica (leaves) Semecarpus anacardium (fruits) Trigonella foenum graecum (seeds) Cocos nucifera (oil) for better growth of hair and diminution in loss of hair (alopecia) The oil was prepared according to Ayurvedic Formulary of India and was standardized according to Protocol for Testing Ayurvedic, Siddha Unani Medicines, Government of India. The Semecarpus anacardium (fruits) are semi poisonous in nature hence purification of fruits was performed according to Ayurvedic Formulary of India. The pharmacological evaluation was performed for qualitative, quantitative, mean hair length studies and its effect on alopecia.
Graphene oxide/poly(3,4 ethylenedioxythiophene):polystyrenesulfonate layers on silver nanowire working electrodes enhance the power conversion efficiencies of dye sensitized solar cells in a low temperature process

In this study, silver nanowire (AgNW) networks were applied, for the first time, as transparent conductive layers for the working electrodes of dye sensitized solar cells (DSSCs) To avoid corrosion of the AgNWs by iodine in the electrolyte, graphene oxide (GO) was used as a protective layer that isolated the AgNWs from the electrolyte; the effective prevention of corrosion probably arose from electrostatic repulsion between the electrolyte and the charged surface of the protective layer. After incorporation of 25% poly(3,4 ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) into the GO, the power conversion efficiency (PCE) of the resulting GO/PEDOT:PSS AgNW DSSC improved by 324% The PEDOT:PSS doping led to the work function of the GO from 5.63 to 5.24 eV. The result may be because of decreasing the strength of the C O dipole moment of the GO. The enhanced PCE can be attributed to the lower Schottky barrier between the AgNWs and the GO, due to the decreased work function of the GO.
Solution Processed Graphene Composite Films as Freestanding Platinum Free Counter Electrodes for Bendable Dye Sensitized Solar Cells

Sulfuric acid treated poly(3,4 ethylenedioxythiophene):poly(styrenesulfonate)/less defective reduced graphene oxide (ST PEDOT:PSS/L rGO) composite films were prepared via a solution processing route and used as the counter electrodes of dye sensitized solar cells (DSSCs) These platinum (Pt) and transparent conductive oxide (TCO) free counter electrodes exhibited strong catalytic activity and excellent flexibility, showing no obvious change in their sheet resistances after 10000 cycles of bending. The integrated quasi solid state DSSC device with a TiO2 nanotube/Ti mesh photoanode and a ST PEDOT:PSS/L rGO counter electrode exhibited an energy conversion efficiency (e) of about 1.8% It also displayed an excellent mechanical stability and durability after bending for 1000 cycles at a small curvature radius of 10 mm. The high flexibility, low cost and efficient catalytic activity make ST PEDOT:PSS/L rGO composite films promising counter electrodes for flexible DSSCs.
An ANN Based Decision Support System Fostering Production Plan Optimization Through Preventive Maintenance Management
Venue: Advances in Neural Networks

Besides pursuing the economic goals of low costs and high profits, companies are becoming more and more aware of the environmental and social impact of their actions. Companies striving for the integrated optimization of environmental and economic perspectives within their production processes, need to be supported by tools helping to understand the effects of the decision making process. In this context, this paper describes the Artificial Intelligence developed for a Decision Support System (DSS) which enables the early identification of problems occurring on manufacturing. The decision making process beneath the DSS starts from the aggregation of production lines sensors data in  Performance Indicators (KPI) The data are then processed by means of an Artificial Neural Networks (ANN) based knowledge system which enables to suggest preventive maintenance interventions. The proposed maintenance activities, elaborated throughout a scheduling engine, are integrated within the weekly production schedule, according to the selected optimization policy.
A neuroimaging point of view on the diversity of social cognition: evidence for extended influence of experience and emotion related factors on face processing
Venue: Culture and brain

Faces are key social stimuli that convey a wealth of information essential for person perception and adaptive interpersonal behaviour. Studies in the domain of cognitive, affective, and social neuroscience have put in light that the processing of faces recruits specific visual regions and activates a distributed set of brain regions related to attentional, emotional, social, and memory processes associated with the perception of faces and the extraction of the numerous information attached to them. Studies using neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have allowed localizing these brain regions and characterizing their functional properties. Magnetoencephalography (MEG) and electroencephalography (EEG) techniques are complementary to fMRI in that they offer a unique insight into the temporal dynamics of mental processes. In this article, I review the contribution of neuroimaging techniques to the knowledge on face processing and person perception with the aim of putting in light the extended influence of experience related factors, particularly in relation with emotions, on the face processing system. Although the face processing network has evolved under evolutionary selection pressure related to sociality related needs and is therefore highly conserved throughout the human species, neuroimaging studies put in light both the extension and the flexibility of the brain network involved in face processing. MEG and EEG allow in particular to reveal that the human brain integrates emotion and experience related information from the earliest stage of face processing. Altogether, this emphasizes the diversity of social cognitive processes associated with face perception.
Robustness of airline route networks

Airlines shape their route network by defining their routes through supply and demand considerations, paying little attention to network performance indicators, such as network robustness. However, the collapse of an airline network can produce high financial costs for the airline and all its geographical area of influence. The aim of this study is to analyze the topology and robustness of the network route of airlines following Low Cost Carriers (LCCs) and Full Service Carriers (FSCs) business models. Results show that FSC hubs are more central than LCC bases in their route network. As a result, LCC route networks are more robust than FSC networks.
Open innovation in smart cities: Civic participation and co creation of public services
Venue: ASIST

The purpose of this panel is to discuss actual developments in the co creation of public services and the role of information science within it. With the advent of the knowledge society, participation and co creation of public services have become crucial in smart city decision making processes. Transfer of knowledge through face to face interaction and the transfer of information through digital networks are spurring the process of innovation. The combination of both dimensions needs particular attention in the field of information science to enable suitable methods of knowledge management at a city level. This panel will bring together best practice examples and research frameworks. In real world scenarios, citizens are involved in decision making in the case of public library development. First, frameworks of smart city assessment and of knowledge management at the city level are discussed. Finally, the role of information science in open innovation processes will be the focus of this panel. For this purpose, the panel brings together researchers and practitioners from library and information science, as well as from neighboring disciplines, to discuss how information and communication technology (ICT) and open innovation are changing our society, culture, and urban space.
Analysis of old master paintings by direct temperature resolved time of flight mass spectrometry: Some recent developments

Direct temperature resolved mass spectrometry (DTMS) is an analytical technique in which small (mg) amounts of sample are applied to a filament and introduced into the ion source of a mass spectrometer. It is a fast fingerprinting method particularly suitable for the characterization of oils, resins, waxes, and other compound classes in tiny complex samples from paintings. DTMS results reported thus far have been obtained using instruments with nominal mass resolution. Higher mass resolutions can be achieved by magnetic sector mass spectrometers only at the expense of a severe loss of sensitivity. Modern time of flight mass spectrometers, however, do provide both high resolution and high sensitivity simultaneously. The availability of accurate mass information adds another dimension to DTMS. The difference between the nominal and accurate mass, the mass defect, may be graphically presented in so called 'Kendrick plots' These can be used as fingerprints, enabling a quick overview of the main features in high resolution mass spectra of complex mixtures. This paper combines DTMS with Kendrick mass defect analysis applied to a series of reference compounds commonly found in paintings. Finally, we also present the results of analysis of samples taken from the 17th century painting of Saul and David by Rembrandt van Rijn (Mauritshuis, The Hague) that has been recently subjected to extensive conservation treatment.
Reduced hippocampus volume and memory performance in bipolar disorder patients carrying the BDNF val66met met allele.
Venue: Journal of affective disorders

Previous studies investigated the impact of brain derived neurotrophic factor (BDNF) val66met (rs6265) on hippocampus volumes and neurocognition in bipolar disorders (BD) but the results were not consistent. This study aimed to investigate the effect of BDNF polymorphism on hippocampus volumes and memory performance in well characterized adult populations diagnosed with type I BD (BD I) and major depressive disorder (MDD) compared with healthy controls (HC) METHODS 48 BD I patients, 33 MDD patients and 60 HC were genotyped for BDNF rs6265 using DNA isolated from white blood cells. Individuals with val/met and met/met genotypes were grouped as met carriers and compared to those with the val/val. Brain segmentations were obtained from structural magnetic resonance imaging (MRI) using the Freesurfer. Memory performance was assessed with the California Verbal Learning Task (CVLT)  We found a significant diagnosis effect and marginal interaction between diagnosis and BDNF genotype group for both hippocampus volumes and memory performance. BDNF met allele carrier BD patients had smaller hippocampus volumes and reduced performance on multiple CVLT scores compared to MDD patients and HC.  We provide strong evidence for the BDNF val66met polymorphism as a putative biological signature for the neuroanatomical and cognitive abnormalities commonly observed in BD patients.
Bringing Antonovsky's salutogenic theory to life: A qualitative inquiry into the experiences of young people with congenital heart disease.
Venue: International journal of qualitative studies on health and well being

Antonovsky coined sense of coherence (SOC) as the central concept of his salutogenic theory focusing on the origins of well being. SOC captures the degree to which one perceives the world as comprehensible, manageable, and meaningful. Life events and resources are considered to be the building blocks of a person's SOC. However, mainly quantitative studies have looked into the role of life events and resources. Therefore, the present study aims to gain a deeper insight into the experiences of patients with congenital heart disease (CHD) regarding resources and life events.  For this qualitative study, patients were selected from the sample of a preceding study on development of SOC (n 429) In total, 12 young individuals with CHD who had either a weak (n 6) or strong SOC (n 6) over time were interviewed (8 women, median age of 20 years) Data analysis was based on the constant comparative method as detailed in the Qualitative Analysis Guide of Leuven. Commonalities and differences between patients from both groups were explored.  The following themes emerged: (1) self concept; (2) social environment; (3) daytime activities; (4) life events and disease related turning points; (5) stress and coping; and (6) illness integration. Additionally, the degree of personal control was identified as an overarching topic that transcended the other themes when comparing both groups of patients.  These results may have implications for the structure and content of interventions improving well being in young people with CHD.
Cryo EM Visualization of Nanobubbles in Aqueous Solutions.
Venue: Langmuir the ACS journal of surfaces and colloids

The detection of nanobubbles on surfaces is well established (e.g. AFM and optical microscopy methods) but currently no methods exist for the direct detection of bulk nanobubbles. Here, cryo electron microscopy (cryo EM) has been employed to observe bubbles in aqueous solutions for the first time. Nitrogen bubbles generated by a chemical reaction were observed in amorphous ice trapped between two carbon films. The cryo EM images of bubbles showed the same features as predicted by theory. The fact that no bubbles were observed near an air water interface suggests that bubbles may diffuse to the nearby air water interface and escape. The estimate of the bubble diffusion coefficient is about 30 250 mm2/s.
Learning from Experts: Developing Transferable Deep Features for Patient Level Lung Cancer Prediction
Venue: MICCAI

Due to recent progress in Convolutional Neural Networks (CNNs) developing image based CNN models for predictive diagnosis is gaining enormous interest. However, to date, insufficient imaging samples with truly pathological proven labels impede the evaluation of CNN models at scale. In this paper, we formulate a domain adaptation framework that learns transferable deep features for patient level lung cancer malignancy prediction. The presented work learns CNN based features from a large discovery set (2272 lung nodules) with malignancy likelihood labels involving multiple radiologists' assessments, and then tests the transferable predictability of these CNN based features on a diagnosis definite set (115 cases) with true pathologically proven lung cancer labels. We evaluate our approach on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC IDRI) dataset, where both human expert labeling information on cancer malignancy likelihood and a set of pathologically proven malignancy labels were provided. Experimental results demonstrate the superior predictive performance of the transferable deep features on predicting true patient level lung cancer malignancy (Acc 70.69 AUC 0.66) which outperforms a nodule level CNN model (Acc 65.38 AUC 0.63) and is even comparable to that of using the radiologists' knowledge (Acc 72.41 AUC 0.76) The proposed model can largely reduce the demand for pathologically proven data, holding promise to empower cancer diagnosis by leveraging multi source CT imaging datasets.
Congenital Tongue Mass with Concomitant Cleft Palate and Bifid Tongue: A Case Report and Review of the Literature
Venue: The Cleft palate craniofacial journal official publication of the American Cleft Palate Craniofacial Association

A case report of a congenital, lingual, salivary gland choristoma with bifid tongue and cleft palate is presented. The patient was born with airway obstruction in supine positioning. Laryngoscopy revealed a midline tongue mass that extended into the hypopharyx and pathological examination showed a congenital ectopic salivary gland. The bifid tongue was repaired at the time of surgical excision. Literature review revealed nine additional cases of congenital lingual mass, bifid tongue, and cleft palate. The most common tongue mass reported was hamartoma (40% but the differential diagnoses include hamartoma, teratoma, and salivary choristoma.
Congenital bifid tongue with lingual hamartoma: A case report and review of the literature

A non syndromic, congenital bifid tongue is an extremely rare malformation, the details of which are largely unknown. A female neonate was referred to our division presenting with a bifid tongue. The cleft was located in the right anterior part of the tongue. In addition, 2 tumors (10 mm and 2 mm) were located at the base of the cleft. Seven days after birth, the tumors were excised and primary closure of the cleft was performed for correction of the bifid tongue. The postoperative course was uneventful, and follow up examination 20 months later showed no functional disturbance. Furthermore, all published cases were carefully reviewed to improve our understanding of the etiology of congenital bifid tongue. Formation of a congenital neoplasm during the early stages of tongue development could be the main etiology of a bifid tongue.
Covariance based outlier detection with feature selection
Venue: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The present covariance based outlier detection algorithm selects from a candidate set of feature vectors that are best at identifying outliers. Features extracted from biomedical and health informatics data can be more informative in disease assessment and there are no restrictions on the nature and number of features that can be tested. But an important challenge for an algorithm operating on a set of features is for it to winnow the effective features from the ineffective ones. The powerful algorithm described in this paper leverages covariance information from the time series data to identify features with the highest sensitivity for outlier identification. Empirical results demonstrate the efficacy of the method.
Covariance based outlier detection with feature selection.
Venue: Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

The present covariance based outlier detection algorithm selects from a candidate set of feature vectors that are best at identifying outliers. Features extracted from biomedical and health informatics data can be more informative in disease assessment and there are no restrictions on the nature and number of features that can be tested. But an important challenge for an algorithm operating on a set of features is for it to winnow the effective features from the ineffective ones. The powerful algorithm described in this paper leverages covariance information from the time series data to identify features with the highest sensitivity for outlier identification. Empirical results demonstrate the efficacy of the method.
Unique visible light assisted field emission of tetrapod shaped ZnO/reduced graphene oxide core/coating nanocomposites
Venue: Scientific reports

The electronic and the optoelectronic properties of graphene based nanocomposites are controllable, making them promising for applications in diverse electronic devices. In this work, tetrapod shaped zinc oxide (T ZnO)/reduced graphene oxide (rGO) core/coating nanocomposites were synthesized by using a hydrothermal assisted self assemble method, and their optical, photoelectric, and field emission properties were investigated. The ZnO, an ideal ultraviolet light sensitive semiconductor, was observed to have high sensitivity to visible light due to the rGO coating, and the mechanism of that sensitivity was investigated. We demonstrated for the first time that the field emission properties of the T ZnO/rGO core/coating nanocomposites could be dramatically enhanced under visible light by decreasing the turn on field from 1.54 to 1.41 V/mm and by increasing the current density from 5 to 12 mA/cm2 at an electric field of 3.5 V/mm. The visible light excitation induces an electron jump from oxygen vacancies on the surface of ZnO to the rGO layer, resulting in a decrease in the work function of the rGO and an increase in the emission current. Furthermore, a field emission light emitting diode with a self enhanced effect was fabricated making full use of the photo assisted field emission process.