nils-herrmann/clean_scirepeval huggingface dataset:
Assessment of Sediment Concentration Through RBNN and SVM FFA in Arid Watershed, India
Venue: Smart Intelligent Computing and Applications
Year: 2019

For scheduling, designing and management of hydraulic structure and hydrologic model prediction of sediment concentration play a crucial role in water resources engineering. Here, prediction of the sediment concentration using SVM FFA and radial basis function network (RBFN) models are done. Parameters like rainfall, temperature, and sediment concentration on a monthly basis from Puintala, Odisha, India watershed are considered. SVM FFA model shows better performance than the RBFN model. Among five input scenarios, S_{t 1} S_{t 2} S_{t 3} S_{t 4} S_{t 5} give most excellent WI value 0.965 and 0.943 for testing and training phases. In case of RBFN, best WI value is 0.8636 and 0.8911 for training and testing phase when 3 0.5 1 architecture is used as an input.
The Numerical Solution of Singularly Perturbed Nonlinear Partial Differential Equations in Three Space Variables: The Adaptive Explicit Inverse Preconditioning Approach
Year: 2019

Critical comments on the complexity of computational systems and the basic singularly perturbed (SP) concepts are given. A class of several complex SP nonlinear elliptic equations arising in various branches of science, technology, and engineering is presented. A classification of complex SP nonlinear PDEs with characteristic boundary value problems is described. A modified explicit preconditioned conjugate gradient method based on explicit inverse preconditioners is presented. The numerical solution of a characteristic 3D SP nonlinear parabolic model is analytically given and numerical results for several model problems are presented demonstrating both applicability and efficiency of the new computational methods.
Efficient Encoding of Quasi Cyclic Low Density Parity Check Codes
Venue: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2018

A compact encoding process with three phases is proposed for quasi cyclic low density parity check (QC LDPC) codes. A back substitution circuit is shared at the first and third phases. A high speed vector dense matrix multiplier is well designed for the second phase, which offers tradeoffs between speed and memory. The proposed encoding method just removes some nonzero circulants of a sparse parity check matrix \pmb{H} while the classical Richardson Urbanke (RU) approach splits \pmb{H} into multiple submatrices and destroys its integrity. It is shown that the proposed encoder runs fast, has low complexity, and is well compatible with multiple code rates.
Cancer Cachexia: Cause, Diagnosis, and Treatment.
Venue: Nutrition in clinical practice official publication of the American Society for Parenteral and Enteral Nutrition
Year: 2017

Patients with cancer frequently experience unintended weight loss due to gastrointestinal (GI) dysfunction caused by the malignancy or treatment of the malignancy. However, others may present with weight loss related to other symptoms not clearly associated with identifiable GI dysfunction such as anorexia and early satiety. Cancer cachexia (CC) is a multifactorial syndrome that is generally characterized by ongoing loss of skeletal muscle mass with or without fat loss, often accompanied by anorexia, weakness, and fatigue. CC is associated with poor tolerance of antitumor treatments, reduced quality of life (QOL) and negative impact on survival. Symptoms associated with CC are thought to be caused in part by tumor induced changes in host metabolism that result in systemic inflammation and abnormal neurohormonal responses. Unfortunately, there is no single standard treatment for CC. Nutrition consequences of oncologic treatments should be identified early with nutrition screening and assessment. Pharmacologic agents directed at improving appetite and countering metabolic abnormalities that cause inefficient nutrient utilization are currently the foundation for treating CC. Multiple agents have been investigated for their effects on weight, muscle wasting, and QOL. However, few are commercially available for use. Considerations for choosing the most appropriate treatment include effect on appetite, weight, QOL, risk of adverse effects, and cost and availability of the agent.
A Quantitative 1H NMR Method for Screening Cannabinoids in CBD Oils
Year: 2021

Toxicologically relevant levels of the psychoactive 9 tetrahydocannabinol (9 THC) as well as high levels of non psychoactive cannabinoids potentially occur in CBD (cannabidiol) oils. For consumer protection in the fast growing CBD oil market, facile and rapid quantitative methods to determine the cannabinoid content are crucial. However, the current standard method, i.e. liquid chromatography combined with tandem mass spectrometry (HPLC MS/MS) requires a time consuming multistep sample preparation. In this study, a quantitative nuclear magnetic resonance spectroscopy (qNMR) method for screening cannabinoids in CBD oils was developed. Contrary to the HPLC MS/MS method, this qNMR features a simple sample preparation, i.e. only diluting the CBD oil in deuterochloroform. Pulse length based concentration determination (PULCON) enables a direct quantification using an external standard. The signal intensities of the cannabinoids were enhanced during the NMR spectra acquisition by means of multiple suppression of the triglycerides which are a major component of the CBD oil matrix. The validation confirmed linearity for CBD, cannabinol (CBN) 9 THC and 8 THC in hemp seed oil with sufficient recoveries and precision for screening. Comparing the qNMR results to HPLC MS/MS data for 46 commercial CBD oils verified the qNMR accuracy for 9 THC and CBD, but with higher limits of detection. The developed qNMR method paves the way for increasing the sample throughput as a complementary screening before HPLC MS/MS.
Perpetration of Dating Violence Among Belgian Youth: Impulsivity, Verbal Skills, and Empathy as Risk and Protective Factors?
Venue: Violence and Victims
Year: 2020

Dating violence (DV) is a serious social problem among adolescents and emerging adults, both boys and girls are victims and perpetrators. The current study aimed to investigate correlates of DV perpetration among Belgian youths. We examined facets of empathy and verbal skills as protective factors, and facets of impulsivity as risk factors for psychological and physical DV perpetration. Eight hundred and thirteen adolescents and emerging adults (71.7% females, mean age 18.9) completed self administered questionnaires investigating their physical and psychological DV perpetration, empathy, verbal skills, and impulsivity. Findings show that male and female respondents have different patterns of correlations between the studied variables. Facets of empathy and verbal skills hinder DV perpetration, whereas facets of impulsivity foster it. The impact of the different facets can vary across regression models, gender of the perpetrator, and forms of DV. The present study contributes to a better understanding of DV by examining facets of empathy, verbal skills, and impulsivity. Prevention programs are encouraged to target specific facets of these variables among adolescents and emerging adults.
Perry's Chemical Engineers' Handbook
Year: 2007

Conversion Factors.Physical and Chemical Data.Heat and Mass Transfer.Transport and Storage of Fluids.Heat Transfer Operations and Equipment.Drying, Humidification and Evaporative Cooling.Distillation.Gas Absorption and Other Gas Liquid Operations and Equipment.Solid State Operations and Equipment.Size Reduction and Size Enlargement.Handling of Bulk Solids and Packaging of Solids and Liquids.Alternative Separation Processes.Safety and Handling of Hazardous Materials.Energy Sources, Conversion and Utilization.Process Machinery Drives.Analysis of Plant Performance.
Introduction To Protein Structure
Year: 2016

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Cervical precancerous lesions classification using pre trained densely connected convolutional networks with colposcopy images
Venue: Biomed. Signal Process. Control.
Year: 2020

Colposcopy is currently a common medical technique for preventing cervical cancer. However, with the increase of the workload, screening by artificial vision has the problems of misdiagnosis and low diagnostic efficiency. Based on transfer learning, pre trained densely connected convolutional networks are used to propose a computer aided diagnosis (CAD) method for automatic classification of cervical precancerous. The proposed method is applied to determine CIN2 or higher level lesions in cervical images. In the present work, image data are initially prepossessing with ROI extraction and data augmentation. Then, parameters of all layers are fine tuning with pre trained DenseNet convolutional neural networks from two datasets (ImageNet and Kaggle) The impact of different training strategies on the model performance with limited training data is analyzed, including random initialization (RI) training from scratch, fine tuning (FT) pre trained model, different size of training data and K fold cross validation. Experimental results show that our method (FT) achieves an accuracy of 73.08% (AUC 0.75) in 600 test images. Compared with previous related work and clinicians, the performance of our approach can effective diagnosis CIN2+ and comparable with a senior physician, which proves the feasibility and promising of the proposed computer aided diagnostic method.
Passive radiative cooling impact on commercial crystalline silicon based photovoltaics
Year: 2019

The radiative cooling of objects during daytime under direct sunlight has recently been shown to be significantly enhanced by utilizing nanophotonic coatings. Multilayer thin film stacks, 2D photonic crystals, etc. as coating structures improved the thermal emission rate of a device in the infrared atmospheric transparency window reducing considerably devices' temperature. Due to the increased heating in photovoltaic (PV) devices, that has significant adverse consequences on both their efficiency and life time, and inspired by the recent advances in daytime radiative cooling, we developed a coupled thermal electrical modeling to examine the physical mechanisms on how a radiative cooler affects the overall efficiency of commercial photovoltaic modules. Employing this modeling, which takes into account all the major processes affected by the temperature variation in a PV device, we evaluated the relative impact of the main radiative cooling approaches proposed so far on the PV efficiency, and we established required conditions for optimized radiative cooling. Moreover, we identified the validity regimes of the currently existing PV cooling models which treat the PV coolers as simple thermal emitters. Finally, we assessed some realistic photonic coolers from the literature, compatible with photovoltaics, to implement the radiative cooling requirements, and demonstrated their associated impact on the temperature reduction and PV efficiency. Providing the physical mechanisms and requirements for cooling radiatively solar cells, our study provides guidelines for utilizing suitable photonic structures as radiative coolers, enhancing the efficiency and the lifetime of PV devices.
ADRL: A Hybrid Anomaly Aware Deep Reinforcement Learning Based Resource Scaling in Clouds
Venue: IEEE Transactions on Parallel and Distributed Systems
Year: 2021

The virtualization concept and elasticity feature of cloud computing enable users to request resources on demand and in the pay as you go model. However, the high flexibility of the model makes the on time resource scaling problem more complex. A variety of techniques such as threshold based rules, time series analysis, or control theory are utilized to increase the efficiency of dynamic scaling of resources. However, the inherent dynamicity of cloud hosted applications requires autonomic and adaptable systems that learn from the environment in real time. Reinforcement Learning (RL) is a paradigm that requires some agents to monitor the surroundings and regularly perform an action based on the observed states. RL has a weakness to handle high dimensional state space problems. Deep RL models are a recent breakthrough for modeling and learning in complex state space problems. In this article, we propose a Hybrid Anomaly aware Deep Reinforcement Learning based Resource Scaling (ADRL) for dynamic scaling of resources in the cloud. ADRL takes advantage of anomaly detection techniques to increase the stability of decision makers by triggering actions in response to the identified anomalous states in the system. Two levels of global and local decision makers are introduced to handle the required scaling actions. An extensive set of experiments for different types of anomaly problems shows that ADRL can significantly improve the quality of service with less number of actions and increased stability of the system.
Music education in junior high school: Perception of emotions conveyed by music and mental imagery in students who attend the standard or musical curriculum
Venue: Psychology of Music
Year: 2019

The ability to perceive emotions conveyed by music and recognize multiple and mixed emotions improves with age. Several studies have found that mental imagery is one of the mechanisms that underlie emotional reactivity to music, and music has a facilitating effect on mental imagery. In particular, researchers have hypothesized a relationship between the perception of emotions expressed by music, visual imagery, and musical training. However, the results of previous studies are not homogenous. This study investigated the ability to perceive emotions in music in 130 students in their final year of junior high school and analyzed the contribution of musical training in visual imagery performances. Each student listened to one of two musical tracks, which were arranged to convey positive and negative emotions, respectively. After listening, the students reported the emotions they perceived and completed a visual imagery test. The results showed that the students could recognize simple, multiple and mixed emotions conveyed by the music. Moreover, the musically trained students showed higher visual imagery ability.
Fast depth map mode decision based on depth texture correlation and edge classification for 3D HEVC
Venue: J. Vis. Commun. Image Represent.
Year: 2017

A fast depth map mode decision algorithm for 3D HEVC is proposed.The depth map texture video correlation is exploited throughout fast mode detection.The edge classification is employed in the procedure of intra/inter prediction.Experimental results demonstrate the effectiveness of the proposed algorithm. The 3D extension of High Efficiency Video Coding (3D HEVC) has been adopted as the emerging 3D video coding standard to support the multi view video plus depth map (MVD) compression. In the joint model of 3D HEVC design, the exhaustive mode decision is required to be checked all the possible prediction modes and coding levels to find the one with least rate distortion cost in depth map coding. Furthermore, new coding tools (such as depth modeling mode (DMM) and segment wise depth coding (SDC) are exploited for the characteristics of depth map to improve the coding efficiency. These achieve the highest possible coding efficiency to code depth map, but also bring a significant computational complexity which limits 3D HEVC from real time applications. In this paper, we propose a fast depth map mode decision algorithm for 3D HEVC by jointly using the correlation of depth map texture video and the edge information of depth map. Since the depth map and texture video represent the same scene at the same time instant (they have the same motion characteristics) it is not efficient to use all the prediction modes and coding levels in depth map coding. Therefore, we can skip some specific prediction modes and depth coding levels rarely used in corresponding texture video. Meanwhile, the depth map is mainly characterized by sharp object edges and large areas of nearly constant regions. By fully exploiting these characteristics, we can skip some prediction modes which are rarely used in homogeneity regions based on the edge classification. Experimental results show that the proposed algorithm achieves considerable encoding time saving while maintaining almost the same rate distortion (RD) performance as the original 3D HEVC encoder.
16.8% Monolithic all perovskite triple junction solar cells via a universal two step solution process
Venue: Nature communications
Year: 2020

Perovskite semiconductors hold a unique promise in developing multijunction solar cells with high efficiency and low cost. Besides design constraints to reduce optical and electrical losses, integrating several very different perovskite absorber layers in a multijunction cell imposes a great processing challenge. Here, we report a versatile two step solution process for high quality 1.73 eV wide 1.57 eV mid and 1.23 eV narrow bandgap perovskite films. Based on the development of robust and low resistivity interconnecting layers, we achieve power conversion efficiencies of above 19% for monolithic all perovskite tandem solar cells with limited loss of potential energy and fill factor. In a combination of 1.73 eV, 1.57 eV, and 1.23 eV perovskite sub cells, we further demonstrate a power conversion efficiency of 16.8% for monolithic all perovskite triple junction solar cells.
Deriving an Empirical Channel Model for Wireless Industrial Indoor Communications
Venue: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Year: 2019

Wireless system design on the physical layer is usually evaluated using comprehensive channel models. However, there is still a lack of publicly available stochastic channel models tailored to industrial use cases, which are recently considered more frequently. This paper presents the derivation of such a channel model for the 5 GHz ISM band and its parametrization. The frequency selective behaviour is modeled by the Saleh Valenzuela model. Based on a measurement campaign, the parameters of this model for a factory environment are determined and published the first time for the 5 GHz ISM band. Spatial correlation is modeled by the Kronecker model. The temporal variation of the channel is based on a theoretically derived Doppler spectrum assuming Laplacian distributed angle of arrivals. In addition to the description of the model components, key issues and common mistakes while constructing a channel model for industrial applications are discussed in order to advance the design and the deployment of future wireless industrial communications systems. The derived channel model is used in IEEE 802.11ax link layer simulations. It is shown that for industrial use cases specially tailored channel models are needed.
Caracteristicas biologicas del dzibaban (Melanoptila glabrirostris) en la Reserva de la Biosfera de Sian Ka'an, Mexico
Year: 2017

En el presente estudio reportamos caracteristicas morfometricas y reproductivas del dzibaban, Melanoptila glabrirostris algunas de ellas descritas por primera vez. El dzibaban es un mimido tropical endemico de la peninsula de Yucatan muy poco estudiado El estudio lo realizamos en la costa norte de la Reserva de la Biosfera de Sian Ka'an, Mexico, entre febrero y agosto de 2008. Presentamos medidas de culmen, tarso y masa corporal. Proponemos un metodo de sexado valido durante la temporada no reproductiva, ya que los machos presentaron longitud de cuerda alar mayor que las hembras. La coloracion del ojo puede ser un criterio suplementario para determinar la edad, puesto que los individuos de segundo ano presentaron frecuentemente coloracion ocular marron, similar a los individuos de primer ano. La temporada reproductiva inicia en abril y termina a mediados de agosto. El ciclo del nido es de 27 y 32 dias y el tamano de nidada es de dos a tres huevos. El macho construye la mayor parte del nido y la hembra incuba los huevos. La mayoria de los nidos se construyo en la palma Thrinax radiata y el arbusto Pithecellobium keyense En conclusion, el dzibaban comparte caracteristicas similares con Dumetella carolinensis y las principales diferencias son debidas a su distribucion tropical.
Improving the fracture toughness and the strength of epoxy using nanomaterials a review of the current status.
Venue: Nanoscale
Year: 2015

The incorporation of nanomaterials in the polymer matrix is considered to be a highly effective technique to improve the mechanical properties of resins. In this paper the effects of the addition of different nanoparticles such as single walled CNT (SWCNT) double walled CNT (DWCNT) multi walled CNT (MWCNT) graphene, nanoclay and nanosilica on fracture toughness, strength and stiffness of the epoxy matrix have been reviewed. The Young's modulus (E) ultimate tensile strength (UTS) mode I (GIC) and mode II (GIIC) fracture toughness of the various nanocomposites at different nanoparticle loadings are compared. The review shows that, depending on the type of nanoparticles, the integration of the nanoparticles has a substantial effect on mode I and mode II fracture toughness, strength and stiffness. The critical factors such as maintaining a homogeneous dispersion and good adhesion between the matrix and the nanoparticles are highlighted. The effect of surface functionalization, its relevancy and toughening mechanism are also scrutinized and discussed. A large variety of data comprised of the mechanical properties of nanomaterial toughened composites reported to date has thus been compiled to facilitate the evolution of this emerging field, and the results are presented in maps showing the effect of nanoparticle loading on mode I fracture toughness, stiffness and strength.
Sustainability assessment of last mile logistics and distribution strategies: The case of local food networks
Year: 2020

Current trends related to increased sustainability requirements, the application of new digital technologies, and changes in consumer behavior have disrupted conventional food supply chains, entailing challenges for the last mile logistics and distribution of food products. The main aim of this study is to develop a toolset for exploring the sustainability potential of last mile logistics and distribution strategies, employing (1) a centralized distribution network with a click collect option, (2) a decentralized distribution network with a home delivery option, and (3) a distributed network based on a crowd logistics concept. For this, a system dynamics (SD) simulation and a multi criteria decision aid (MCDA) were applied to assess the sustainability performance of these distribution channel options for a case study of a local food cooperative and a logistics service provider in Austria. The sustainability potential of developing a new logistics system in collaboration with these players has been estimated for the first time, while considering the dynamic interplay of all relevant sustainability elements within operational, tactical, and strategic planning. The results show that an integration of the two players into a distributed network strategy based on a crowd logistics concept is the most viable and sustainable option. This highlights the significant role of the logistics sector in proactively innovating services to make sustainable choices easier for the customer.