nils-herrmann/clean_scirepeval huggingface dataset:
A randomized study of coconut oil versus sunflower oil on cardiovascular risk factors in patients with stable coronary heart disease
Venue: Indian heart journal

Coronary artery disease (CAD) and its pathological atherosclerotic process are closely related to lipids. Lipids levels are in turn influenced by dietary oils and fats. Saturated fatty acids increase the risk for atherosclerosis by increasing the cholesterol level. This study was conducted to investigate the impact of cooking oil media (coconut oil and sunflower oil) on lipid profile, antioxidant mechanism, and endothelial function in patients with established CAD.  In a single center randomized study in India, patients with stable CAD on standard medical care were assigned to receive coconut oil (Group I) or sunflower oil (Group II) as cooking media for 2 years. Anthropometric measurements, serum, lipids, Lipoprotein a, apo B/A 1 ratio, antioxidants, flow mediated vasodilation, and cardiovascular events were assessed at 3 months, 6 months, 1 year, and 2 years.  Hundred patients in each arm completed 2 years with 98% follow up. There was no statistically significant difference in the anthropometric, biochemical, vascular function, and in cardiovascular events after 2 years.  Coconut oil even though rich in saturated fatty acids in comparison to sunflower oil when used as cooking oil media over a period of 2 years did not change the lipid related cardiovascular risk factors and events in those receiving standard medical care.
On Deep Multi View Representation Learning: Objectives and Optimization
Venue: ArXiv

We consider learning representations (features) in the setting in which we have access to multiple unlabeled views of the data for learning while only one view is available for downstream tasks. Previous work on this problem has proposed several techniques based on deep neural networks, typically involving either autoencoder like networks with a reconstruction objective or paired feedforward networks with a batch style correlation based objective. We analyze several techniques based on prior work, as well as new variants, and compare them empirically on image, speech, and text tasks. We find an advantage for correlation based representation learning, while the best results on most tasks are obtained with our new variant, deep canonically correlated autoencoders (DCCAE) We also explore a stochastic optimization procedure for minibatch correlation based objectives and discuss the time/performance trade offs for kernel based and neural network based implementations.
Robust Face Recognition with Deep Multi View Representation Learning
Venue: ACM Multimedia

This paper describes our proposed method targeting at the MSR Image Recognition Challenge MS Celeb 1M. The challenge is to recognize one million celebrities from their face images captured in the real world. The challenge provides a large scale dataset crawled from the Web, which contains a large number of celebrities with many images for each subject. Given a new testing image, the challenge requires an identify for the image and the corresponding confidence score. To complete the challenge, we propose a two stage approach consisting of data cleaning and multi view deep representation learning. The data cleaning can effectively reduce the noise level of training data and thus improves the performance of deep learning based face recognition models. The multi view representation learning enables the learned face representations to be more specific and discriminative. Thus the difficulties of recognizing faces out of a huge number of subjects are substantially relieved. Our proposed method achieves a coverage of 46.1% at 95% precision on the random set and a coverage of 33.0% at 95% precision on the hard set of this challenge.
Deep multi view representation learning for multi modal features of the schizophrenia and schizo affective disorder
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

This work is originated from the MLSP 2014 Classification Challenge which tries to automatically detect subjects with schizophrenia and schizo affective disorder by analyzing multi modal features derived from magnetic resonance imaging (MRI) data. We employ Deep Neural Network (DNN) based multi view representation learning for combining multimodal features. The DNN based multi view models include deep canonical correlation analysis (DCCA) and deep canonically correlated auto encoders (DCCAE) In addition, support vector machine with Gaussian kernel is used to conduct classification with the compact bottleneck features learned by the deep multi view models. Our experiments on the dataset provided by the MLSP Classification Challenge show that bottleneck features learned via deep multi view models obtain better results than the trimming features used in the baseline system in terms of the receiver operating characteristic (ROC) area under the curve (AUC)
Heat Exchanger Network Synthesis using a Genetic Algorithms Particle Swarm Optimization Hybrid Method and Parallel Processing

In Heat Exchanger Network (HEN) synthesis capital savings and pollutant emission reduction can be achieved. The mathematical modeling of the HEN synthesis problem requires elaborated solution strategies given the particularities of their non linear formulations and non convex problems. The use of heuristic approach accounts for a large computational load, and hence a high processing time until convergence. In the present paper a hybrid model for HEN synthesis using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is presented. The potentialities of applying parallel processing techniques to solve the problem were studied. Two examples from the literature were used as benchmarks for the solutions obtained. Comparative experiments were carried out to investigate the time efficiency of the method while implemented using series or parallel processing. The solutions obtained in both cases with the proposed methodology led to Total Annual Costs (TAC) equal or lower to those presented by the literature. As one could expect, parallel processing usage multiplied the algorithm speed by the number of cores used (processing time was close to 75% lower by using 4 processing cores) Hence, it can be concluded that the hybrid algorithm proposed has potential to find near optimal solutions, and the application of multiprocessing techniques to such non deterministic approaches represents a substantial reduction in the execution time.
Business innovation and government regulation for the promotion of electric vehicle use: lessons from Shenzhen, China

The deployment of electric vehicles has attracted growing attention and is now seen as a possible pathway for a transition towards sustainable transportation. This paper provides insight into the commercialization of electric vehicles in Shenzhen focusing on business innovation and the regulatory context in which it occurs. Using the business model canvas framework, this paper analyzes interactions between enterprises and governments along the value chain of electric vehicles in the bus and taxi fleets. It also discusses the strengths and weaknesses of the Shenzhen model both in business innovation and government regulation for promoting electric vehicle use. This paper finds that Shenzhen has succeeded in fostering a distinct government enterprise cooperation model that not only reduces the financial pressure on the local government to promote electric vehicle use, but also gives enterprises significant leeway to experiment with various innovative business models. The joint result of these efforts is that the commercialization of electric vehicles has become feasible for delivering the public transport service (buses and taxis) in Shenzhen. Still, this paper argues that the current model of Shenzhen can be further enhanced by: 1) encouraging private investment in charging infrastructures by means of public private partnerships; and 2) standardizing electric vehicle technologies and production to break down the local protectionism in the electric vehicle market. The Shenzhen model acts as a source of inspiration by pointing out the significance of integrating business innovations and government regulations to facilitate the deployment of electric vehicles, which provides practical lessons for industrial players and policy makers in other cities. Furthermore, this work offers theoretical references regarding the application of the multi actor perspective and the business model canvas framework to analyze the actors and interactions along the value chain of innovative technologies.
The impact of business innovation modes on SME innovation performance in post Soviet transition economies: The case of Belarus

This study analyzes the most effective innovation modes (aEUR~science and technology based innovationaEUR(tm) STI, and aEUR~doing, using and interacting based innovationaEUR(tm) DUI) for business innovation performance in the context of post Soviet Transition Economies (PSTE) Their specificities are expected to influence both their business innovation modes and their impact on innovation output. In particular, we aim at identifying the specificities of PSTE in that the DUI mode alone (and its specific drivers) is more relevant than the STI mode alone (and its drivers) In our hypothesis, this outcome should be even stronger in the context of non technological types of innovation (e.g. organizational innovation)
Effect of soil properties, heavy metals and emerging contaminants in the soil nematodes diversity.
Venue: Environmental pollution

Among soil organisms, nematodes are seen as the most promising candidates for bioindications of soil health. We hypothesized that the soil nematode community structure would differ in three land use areas (agricultural, forest and industrial soils) be modulated by soil parameters (N, P, K, pH, SOM, CaCO3, granulometric fraction, etc. and strongly affected by high levels of heavy metals (Cd, Pb, Zn, Cr, Ni, Cu, and Hg) and emerging contaminants (pharmaceuticals and personal care products, PPCPs) Although these pollutants did not significantly affect the total number of free living nematodes, diversity and structure community indices vastly altered. Our data showed that whereas nematodes with r strategy were tolerant, genera with k strategy were negatively affected by the selected pollutants. These effects diminished in soils with high levels of heavy metals given their adaptation to the historical pollution in this area, but not to emerging pollutants like PPCPs.
The Abundance, Diversity, and Metabolic Footprint of Soil Nematodes Is Highest in High Elevation Alpine Grasslands
Venue: Front. Ecol. Evol.

Nematodes are key components of soil biodiversity and represent valuable bio indicators of soil food webs. Numerous community indices have been developed in order to track variations in soil ecosystem processes, but their use is mainly restricted to anthropogenic stresses. In this study, we propose to expand the use of nematodes' derived ecological indices in order to shed light on variations of soil food webs in natural systems distributed along elevation gradients. For this purpose, we aimed at determining how elevation affects the community structure and the trophic diversity by studying the abundance, the composition and the functional diversity of nematode communities. Nematode communities were sampled every 200 m across five transects that span about 2000 m in elevation in the Alps. To understand the underlying ecological parameters driving these patterns we studied both abiotic factors (soil properties) and biotic factors (trophic links, relationships with plant diversity) We found that (1) nematode abundance increases with elevation of lowland forests and alpine meadows; (2) differences in nematodes communities rely on habitat specific functional diversity (e.g. tolerance to harsh environments, "colonizer/persister" status) while most trophic groups are ubiquitous; and (3) the metabolic footprint of the complete nematode community increases with elevation. We thus conclude that the contribution of soil dwelling nematodes to belowground ecosystem processes, including carbon and energy flow, is stronger at high elevation. The resulting cascading effects on the soil food web structure are discussed from an ecosystem functioning perspective. Overall, this study highlights the importance of nematodes in soil ecosystems and brings insights in their enhanced role along ecological gradients.
Responses of soil nematodes to water and nitrogen additions in an old field grassland

Given that soil water and nitrogen (N) are two major limiting factors for plant growth, changing precipitation regime and atmospheric N deposition may have profound impacts on ecosystem structure and function. However, the effects of changing precipitation regime and atmospheric N deposition on soil fauna abundance and trophic complexity are still poorly understood. A field manipulation experiment was established to investigate the responses of soil nematode communities to water and N additions in an old field grassland ecosystem. Results showed that water addition significantly increased soil nematode abundance and generic richness, but had no effects on the relative abundance of different trophic groups. Nitrogen addition did not affect nematode abundance, but reduced their generic richness, and significantly altered community structure by promoting the abundance of bacterivores and suppressing that of fungivores and omnivores predators. Compared to water addition, N addition resulted in a community with higher enrichment index (EI) and lower maturity index (MI) and channel index (CI) consistent with a soil food web with domination of bacterial decomposition channel. The resource availability might be responsible for the observed changes in nematode population size, whereas the alterations in nematode community trophic structure could be attributed to the changes in soil nitrate concentration. Our findings reveal different but independent effects of water and N additions on soil nematode communities in an old field grassland of North China Plain.
Electromagnetic Thermography Nondestructive Evaluation: Physics based Modeling and Pattern Mining
Venue: Scientific reports

Electromagnetic mechanism of Joule heating and thermal conduction on conductive material characterization broadens their scope for implementation in real thermography based Nondestructive testing and evaluation (NDT&E) systems by imparting sensitivity, conformability and allowing fast and imaging detection, which is necessary for efficiency. The issue of automatic material evaluation has not been fully addressed by researchers and it marks a crucial first step to analyzing the structural health of the material, which in turn sheds light on understanding the production of the defects mechanisms. In this study, we bridge the gap between the physics world and mathematical modeling world. We generate physics mathematical modeling and mining route in the spatial time frequency and sparse pattern domains. This is a significant step towards realizing the deeper insight in electromagnetic thermography (EMT) and automatic defect identification. This renders the EMT a promising candidate for the highly efficient and yet flexible NDT&E.
Thermal energy storage using chloride salts and their eutectics

Achieving the goals of the U.S. Department of Energy (DOE) Sunshot initiative requires (1) higher operating temperatures for concentrating solar power (CSP) plants to increase theoretical efficiency, and (2) effective thermal energy storage (TES) strategies to ensure dispatchability. Current inorganic salt based TES systems in large scale CSP plants generally employ molten nitrate salts for energy storage, but nitrate salts are limited in application to lower temperatures generally, below 600 degC. These materials are sufficient for parabolic trough power plants, but they are inadequate for use at higher temperatures. At the higher operating temperatures achievable in solar power tower type CSP plants, chloride salts are promising candidates for application as TES materials, owing to their thermal stability and generally lower cost compared to nitrate salts. In light of this, a recent study was conducted, which included a preliminary survey of chloride salts and binary eutectic systems that show promise as high temperature TES media. This study provided some basic information about the salts, including phase equilibria data and estimates of latent heat of fusion for some of the eutectics. Cost estimates were obtained through a review of bulk pricing for the pure salts among various vendors. This review paper updates that prior study, adding data for additional salt eutectic systems obtained from the literature. Where possible, data are obtained from the thermodynamic database software, FactSage. Radiative properties are presented, as well, since at higher temperatures, thermal radiation becomes a significant mode of heat transfer. Material compatibility for inorganic salts is another important consideration (e.g. with regard to piping and/or containment) so a summary of corrosion studies with various materials is also presented. Lastly, cost data for these systems are presented, allowing for meaningful comparison among these systems and other materials for TES applications. Because chloride salts may be employed as either sensible heat storage in the molten phase or as sensible and latent heat thermal energy storage (LHTES) as phase change materials (PCMs) cost of the candidate salt systems are presented on a cost per unit mass basis (sensible heat storage application) and a cost per unit latent heat of fusion basis (latent heat storage application) A total of 133 chloride salt systems were investigated.
Preparation of binary eutectic chloride/expanded graphite as high temperature thermal energy storage materials

Binary eutectic chloride (NaCl CaCl 2 /expanded graphite (EG) composite phase change materials (PCMs) used as high temperature thermal energy storage materials, were prepared by an impregnating method, and the effects of EG additives on thermal properties of compound salts were investigated by TEM, DSC and Hotdisk techniques. The results revealed that both the specific heat capacity and phase change latent heat of composite PCMs decreased with the increase of EG content, the variation of phase change temperature was negligible, and the thermal conductivities of composite PCMs increased linearly with the addition of EG content and density. In particular, the thermal conductivity of the composite PCM with 20 wt% of EG and 2 g/cm 3 of density was 4.937 W/(m K) which showed 701.1% higher than binary eutectic chloride. The heating and cooling rate of composite PCMs were higher than binary eutectic chloride, and the melting and freezing platform time also reduced with the addition of EG content.
Arduino based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module
Venue: 2016 IEEE Region 10 Humanitarian Technology Conference (R10 HTC)

Emergence of Controlled Environment Agriculture (CEA) ranging from computer controlled water irrigation system to lightning and ventilation has changed the conventional scenario of farming. This paper proposes and demonstrate an economical and easy to use arduino based controlled irrigation system. The designed system deals with various environmental factors such as moisture, temperature and amount of water required by the crops using sensors like water flow sensor, temperature sensor and soil moisture sensor. Datas are collected and received by arduino which can be linked to an interactive website which show the real time values along with the standard values of different factor required by a crop. This allows user to control irrigation pumps and sprinklers from far distance through a website and to meet the standard values which would help the farmer to yield maximum and quality crops. Studies conducted on laboratory prototype suggested the designed system to be applicable which can be implemented.
Self Powered Triboelectric Micro Liquid/Gas Flow Sensor for Microfluidics.
Venue: ACS nano

Liquid and gas flow sensors are important components of the micro total analysis systems (mTAS) for modern analytical sciences. In this paper, we proposed a self powered triboelectric microfluidic sensor (TMS) by utilizing the signals produced from the droplet/bubble via the capillary and the triboelectrification effects on the liquid/solid interface for real time liquid and gas flow detection. By alternating capillary with different diameters, the sensor's detecting range and sensitivity can be adjusted. Both the relationship between the droplet/bubble and capillary size, and the output signal of the sensor are systematically studied. By demonstrating the monitoring of the transfusion process for a patient and the gas flow produced from an injector, it shows that TMS has a great potential in building a self powered micro total analysis system.
An Electrochemical Impedance Based Thermal Flow Sensor for Physiological Fluids
Venue: Journal of Microelectromechanical Systems

A novel electrochemical thermal flow sensor was developed for use in physiological liquids. The sensor was constructed out of a platinum resistive heater and platinum sensing electrodes on a Parylene C substrate, rendering it flexible and fully biocompatible. During heating, changes in electrochemical impedance across the sensing electrodes were used to detect changes in temperature, and highly sensitive flow measurements were achieved with overheat temperatures of only 1 degC. The sensor's biocompatibility and low overheat temperature make it an ideal candidate for chronic in vivo applications.
Identification and quantification of microplastics using Nile Red staining.
Venue: Marine pollution bulletin

We investigated the applicability of Nile Red (NR) a fluorescent dye, for microplastic analysis, and determined the optimal staining conditions. Five mg/L NR solution in n hexane effectively stained plastics, and they were easily recognized in green fluorescence. The NR staining method was successfully applied to micro sized polyethylene, polypropylene, polystyrene, polycarbonate, polyurethane, and poly(ethylene vinyl acetate) except for polyvinylchloride, polyamide and polyester. The recovery rate of polyethylene (100 300mm) spiked to pretreated natural sand was 98% in the NR stating method, which was not significantly (p<0.05) different with FT IR identification. The NR staining method was suitable for discriminating fragmented polypropylene particles from large numbers of sand particles in laboratory weathering test samples. The method is straightforward and quick for identifying and quantifying polymer particles in the laboratory controlled samples. Further studies, however, are necessary to investigate the application of NR staining to field samples with organic remnants.
Digital forensics: the missing piece of the Internet of Things promise

As technology advances at a blinding pace, the promise of new gadgets to enhance every facet of our lives tempts every consumer and organisation. From the ease of automation, control and monitoring of the most mundane aspects of our lives to advanced lifesaving and monitoring capabilities, our world is changing daily. No reliable forensics application or digital forensics guidance exists to retrieve the data from IoT devices in the event of an attack, an active investigation or a litigation request. The digital forensics of Internet of things (IoT) technologies is the missing conversation in our headlong rush to the promise of connecting every device on the planet.  And they elaborate on how forensics practitioners, device manufacturers and legal authorities could share their efforts and minimise this gap.