nils-herrmann/clean_scirepeval huggingface dataset:
THE VELOCITY DISPERSION AND ATTENUATION OF MARINE HYDRATE BEARING SEDIMENTS

P wave and S wave velocity will increase and the attenuation will vary when the concentration of gas hydrate increases. The analysis of velocity dispersion and attenuation for hydrate bearing sediments (GHBS) would contribute to the estimate of gas hydrate concentration. Based on effective medium theory (EMT) we study the nonlinear variation feature of P wave and S wave velocity for marine unconsolidated hydrate bearing sediments. Moreover, we use BISQ model to replace Gassmann equation in the EMT, and research the velocity dispersion and attenuation of hydrate bearing sediments in the full frequency band. Based on this model, the velocity and attenuation always increase with the increasing amount of gas hydrate, and the rock porosity and clay content doesn't make any differences to the attenuation. After the numeral modeling, we apply the sonic logging (20 kHz) and VSP (100 Hz) data from Ocean drilling Program (ODP) leg 164 in obtaining the concentration of gas hydrate stability zone (GHSZ) In the application, the average hydrate concentration of GHSZ from site 995 in ODP leg 164 is about 5%~7% and corresponds to the pressure core sample (PCS) data, Helgerud et al.'s research conclusions and the prediction data from neural network (NN) Due to the velocity dispersion, the estimated hydrate concentration from VSP data would be lower than the estimated results from sonic logging data. The prediction results of three hydrate Sites (SH2, SH3 and SH7) from Shenhu area also coincide with the PCS, South China Sea. Moreover, based on the peak frequency method, the effective seismic quality factors (Q) of the BSR are estimated from the inline prestack seismic gathers. The Q values suggest that the effective saturation of gas hydrate estimated by this model fluctuates between 15%~30% The results of numeral modeling and applications indicate the velocity dispersion and attenuation of GHBS always vary with the concentration of hydrate. The study of velocity dispersion and attenuation feature for GHBS in the full frequency band would contribute to the estimate of gas hydrate concentration.
Diversity in Fish Auditory Systems: One of the Riddles of Sensory Biology
Venue: Front. Ecol. Evol.

An astonishing diversity of inner ears and accessory hearing structures (AHS) that can enhance hearing has evolved in fishes. Inner ears mainly differ in the size of the otolith end organs, the shape and orientation of the sensory epithelia, and the orientation patterns of ciliary bundles of sensory hair cells. Despite our profound morphological knowledge of inner ear variation, two main questions remain widely unanswered. (i) What selective forces and/or constraints led to the evolution of this inner ear diversity? (ii) How is the morphological variability linked to hearing abilities? Improved hearing is mainly based on the ability of many fish species to transmit oscillations of swim bladder walls or other gas filled bladders to the inner ears. Swim bladders may be linked to the inner ears via a chain of ossicles (in otophysans) anterior extensions (e.g. some cichlids, squirrelfishes) or the gas bladders may touch the inner ears directly (labyrinth fishes) Studies on catfishes and cichlids demonstrate that larger swim bladders and more pronounced linkages to the inner ears positively affect both auditory sensitivities and the detectable frequency range, but lack of a connection does not exclude hearing enhancement. This diversity of auditory structures and hearing abilities is one of the main riddles in fish bioacoustics research. Hearing enhancement might have evolved to facilitate intraspecific acoustic communication. A comparison of sound producing species, however, indicates that acoustic communication is widespread in taxa lacking AHS. Eco acoustical constraints are a more likely explanation for the diversity in fish hearing sensitivities. Low ambient noise levels may have facilitated the evolution of AHS, enabling fish to detect low level abiotic noise and sounds from con and heterospecifics, including predators and prey. Aquatic habitats differ in ambient noise regimes, and preliminary data indicate that hearing sensitivities of fishes vary accordingly.
Exterior lighting computer automated design based on multi criteria parallel evolutionary algorithm: optimized designs for illumination quality and energy efficiency
Venue: Expert Syst. Appl.

Multi objective evolutionary algorithm to computer automated exterior lighting design.Web client integrated to a cluster of computers to provide lighting design service.Solution to optimize both illumination quality and energy efficiency.Case study solution presents 37.5% power consumption and +227.3% global uniformity. A proper professional lighting design implies in a continuous search for the best compromise between both low power consumption and better lighting quality. This search converts this design into a hard to solve multi objective optimization problem. Evolutionary algorithms are widely used to attack that type of hard optimization problems. However, professionals could not benefit from that kind of assistance since evolutionary algorithms have been unexplored by several commercial lighting design computer aided softwares. This work proposes a system based on evolutionary algorithms which implement a computer automated exterior lighting design both adequate to irregular shaped areas and able to respect lighting pole positioning constraints. The desired lighting design is constructed using a cluster of computers supported by a web client, turning this application into an efficient and easy tool to reduce project cycles, increase quality of results and decrease calculation times. This ELCAutoD EA system consists in a proposal for a parallel multi objective evolutionary algorithm to be executed in a cluster of computers with a Java remote client. User must choose lighting pole heights, allowed lamps and fixtures, as well as the simplified blue print of the area to be illuminated, marking the sub areas with restrictions to pole positioning. The desired average illuminance must also be informed as well as the accepted tolerance. Based on user informed data, the developed application uses a dynamic representation of variable size as a chromosome and the cluster executes the evolutionary algorithm using the Island model paradigm. Achieved solutions comply with the illumination standards requirements and have a strong commitment to lighting quality and power consumption. In the present case study, the evolved design used 37.5% less power than the reference lighting design provided by a professional and at the same time ensured a 227.3% better global lighting uniformity. A better lighting quality is achieved because the proposed system solves multi objective optimization problems by avoiding power wastes which are often unclear to a professional lighting engineer in charge of a given project.
A STUDY ON THE FACTORS AFFECTING THE ACADEMIC PERFORMANCE OF DISTANCE EDUCATION STUDENTS AND FORMAL STUDENTS

Recently the numbers of distance education programs and students enrolling in them have increased significantly. This increase also carries the question of what factors may have an effect on academic success. The demographic features, motivation levels, personal development status and basic computer literacy of students have been taken into account and their effects on the academic success of students have been analyzed. With this purpose, surveys prepared with the help of professional opinion have been applied to certain groups. The sample group of this study consists students of the Kirikkale University Distance Education Center and the formal education students of the Kirikkale Vocational School. The results have been analyzed with the help of the SPSS data analysis program (version 22.0) and have been turned into figures. The academic success of distance learning students and formal education students have been compared and this data has been used to identify whether there are any significant connections between academic success and the factors determined. In the conclusion of this study, the figures have been explained separately, and suggestions have been made regarding both distance education and formal education.
Definition of a Learning Object from Perspectives of In Service Teachers (Case of Duzce Province)

Learning objects, as a relatively new technological concept, have drawn much attention from educators because these dijital resources are easily accessible, relatively easy to use due to their limited size and focus, interactive, and adaptable to many different educational contexts. Despite the fact that learning objects have the great potential to improve teaching and learning experiences by providing teachers reusable learning materials and reducing costs, the lack of a "working and clear" definition of these materials has restricted their effective and efficient use. This study aimed to explore elementary school teacher perceptions of their use of learning objects from a qualitative research paradigm in order to reveal the extent to which teachers understand concept of learning object and its instruction approach. The method of the study was based on descriptive phenomenology. Data were collected using multiple methods, including the semi structured interview, field observation reports, and photos from nine in service elementary school teachers from different departments in Duzce, Turkey. Methods of data analysis were based on Giorgi's method of descriptive phenomenology including four stages of content analysis: data coding, developing themes, organizing code and themes, describing findings. Overall findings of the study indicate that teachers use learning objects in their lesson activities without explicit recognition; however they generally fail to understand the exact meaning of a learning object approach and its applications in the classroom. Participants understood different properties of learning objects. Almost all participants perceive objectivity as the most important characteristic of the learning object. In addition, a majority of the teachers recognized the value of a learning object's reusability. In service teachers' vague perceptions of the definition and usage of learning objects indicated that they used these materials without clear directions and explanations on what a learning object is, and how these materials can be used in an efficient way. Implications for teacher education and development of curriculum materials related to reusable learning objects were discussed.  Learning objects, teacher education, elementary school teachers
A new design for coupling light between silicon strip waveguide and plasmonic slot waveguide
Venue: SPIE OPTO

We simulate and test a new structure for light coupling from silicon strip waveguide to plasmonic slot waveguide. The conventional approach of simply using a taper funnel structure for the mode matching between two types of waveguides is typically insufficient for high coupling efficiency. Here we propose the use of an additional silicon strip to slot mode converter, which has a low insertion loss itself and achieves better mode matching. The experimental results show the new design, with slot width fixed at 200nm, achieves a higher coupling efficiency than conventional one. The newly implemented design has 1.5 dB less loss than the conventional taper funnel coupler, with a theoretical coupling loss of 2.18 dB/coupler and an experimentally measured loss of 3 dB/coupler at 1640nm wavelength.
Compact millimeter wave switched beam antenna arrays for short range communications

The need for more data throughput is a requirement that will keep growing in future wireless standards, and during the past few years, millimeter wave technology has generated much excitement in the mobile and wireless communications sectors due to the large bandwidth it can offer. In this letter two different travelling wave, slot antenna arrays are proposed which can offer tunable switched beam capability at millimeter wave frequencies. The antenna systems are built on a single layer PCB with top and bottom side etching for operation at 28 GHz with at least 0.7 GHz of measured impedance bandwidth. For the first design, a planar 2 x 4 slot antenna array is proposed while the second design is implemented using a 4 x 4 slot array to demonstrate improved beam directivity. A Butler matrix for simple beam switching in the far field is also integrated within the compact antenna structure to provide the needed phased array operation. The total size of the two proposed systems with their feed networks are 55.2 x 55 x 0.13 mm3 and 53.7 x 61.2 x 0.13 mm3, respectively. These new travelling wave switched beam arrays can be placed within current handheld mobile devices for high bandwidth short range communications enabling 5G technologies.
A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments
Venue: Neural Computing and Applications

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real time video based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content based image retrieval phase. For motion object detection, we used the temporal differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e. human and non human) For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content based image retrieval. Finally, a software based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.
Automatic non parametric image parsing via hierarchical semantic voting based on sparse dense reconstruction and spatial contextual cues
Venue: Neurocomputing

Image parsing is vital for many high level image understanding tasks. Although both parametric and non parametric approaches have achieved remarkable success, many technical challenges still prevail for images containing things/objects with broad coverage and high variability, because it still lacks versatile and effective strategies to seamlessly integrate local global features selection, contextual cues exploitation, spatial layout encoding, data driven coherency exploration, and flexible accommodation of newly annotated labels. To ameliorate, this paper develops a novel automatic non parametric image parsing method with advantages of both parametric and non parametric methodologies by resorting to new modeling and inferring strategies. The originality of our new approach is to employ sparse dense reconstruction as a latent learning model to conduct candidate label probability analysis over multi level local regions, and synchronously leverage context specific local global label confidence propagation and global semantic spatial contextual cues to guide holistic scene parsing. Towards this goal, we devise several novel technical components to comprise a lightweight parsing framework, including local region representation integrating complementary features, anisotropic consistency propagation based on bi harmonic distance metric, bottom up label voting, semantic string generation of image level spatial contextual cues based on Hilbert space filling curve, and co occurrence priors analysis based on relaxed string matching algorithm, which collectively enable us to effectively combat the aforementioned obstinate problems. Moreover, we conduct comprehensive experiments on public benchmarks, and make extensive and quantitative evaluations with state of the art methods, which demonstrate the advantages of our method in accuracy, versatility, flexibility, and efficiency.
Inferring borrower network in a microfinancing framework (KIVA)
Venue: 2016 International Conference on Data Science and Engineering (ICDSE)

Microfinance institutions aim at offering financial services to people in low income category, who typically lack access to traditional banking systems. Till date, greater than 15 billion U.S dollars has been infused into microfinancing, assisting more than 160 million people in developing countries. With the tremendous growth in the World Wide Web, a number of microfinance institutions have recently moved online. One such noble initiative is KIVA, a crowd sourced online microfinance platform which connects borrowers (small entrepreneurs and individuals) to lenders through the field partners. One particular interest to such microfinancing institutions, is the analysis of the network of borrowers which can help them improve the percentage of loan requests fulfilled. KIVA provides a rich dataset capturing the lending activities on the website. In this paper, we analyze the data to find and extract the structure in the KIVA framework. We formulate a novel tripartite extension of SimRank using the network of lenders, loans and borrowers to capture the inherent pattern in the system. We also propose a Multipartite extension of SimRank useful for real world settings. Extensive experiments validate the effectiveness of our modeling and the proposed disambiguation scheme for borrowers.
Effects of Temperature, Aeration Rate and Reaction Time on Composting of Empty Fruit Bunches of Oil Palm

Composting is a biochemical process in a controlled aerobic environment where thermophilic microorganisms stabilize organic waste substrates into valuable humus like products. Three parameters which are known to affect the composting process including temperature, aeration rate and composting time. This research aims at developing a model to describe the relative influence of different temperatures, aeration rates and reaction time on the composting process and how it affects the final quality of EFB compost produced. EFB samples were mixed with urea as a source of nitrogen and fresh compost as inoculum. The composting process was carried out in a composting test bench for a total of 42 days. The moisture content was found to be significantly affected by temperature and reaction time. Carbon loss was significantly affected by all three factors. Nitrogen content was affected by aeration rate, reaction time as well as interaction between temperature and reaction time. Changes in total ions over time showed a positive correlation with the value of conductivity (Pearson correlation coefficient of 0.853) and the largest reduction in C/N ratio (from 30.2:1 to 17.6:1) was obtained at temperature of 40degC and aeration rate of 0.4 L/min kg. The results of this study could form a basis for palm oil mills to improve the quality of EFB composts produced within a short maturation period and with low C/N ratio.
Temperature Sensitivity as a Microbial Trait Using Parameters from Macromolecular Rate Theory
Venue: Front. Microbiol.

The activity of soil microbial extracellular enzymes is strongly controlled by temperature, yet the degree to which temperature sensitivity varies by microbe and enzyme type is unclear. Such information would allow soil microbial enzymes to be incorporated in a traits based framework to improve prediction of ecosystem response to global change. If temperature sensitivity varies for specific soil enzymes, then determining the underlying causes of variation in temperature sensitivity of these enzymes will provide fundamental insights for predicting nutrient dynamics belowground. In this study, we characterized how both microbial taxonomic variation as well as substrate type affects temperature sensitivity. We measured b glucosidase, leucine aminopeptidase, and phosphatase activities at six temperatures: 4, 11, 25, 35, 45, and 60degC, for seven different soil microbial isolates. To calculate temperature sensitivity, we employed two models, Arrhenius, which predicts an exponential increase in reaction rate with temperature, and Macromolecular Rate Theory (MMRT) which predicts rate to peak and then decline as temperature increases. We found MMRT provided a more accurate fit and allowed for more nuanced interpretation of temperature sensitivity in all of the enzyme x isolate combinations tested. Our results revealed that both the enzyme type and soil isolate type explain variation in parameters associated with temperature sensitivity. Because we found temperature sensitivity to be an inherent and variable property of an enzyme, we argue that it can be incorporated as a microbial functional trait, but only when using the MMRT definition of temperature sensitivity. We show that the Arrhenius metrics of temperature sensitivity are overly sensitive to test conditions, with activation energy changing depending on the temperature range it was calculated within. Thus, we propose the use of the MMRT definition of temperature sensitivity for accurate interpretation of temperature sensitivity of soil microbial enzymes.
Comparison of Thermal Performance Equations in Describing Temperature Dependent Developmental Rates of Insects: (II) Two Thermodynamic Models
Venue: Annals of the Entomological Society of America

There are many descriptive statistical models describing the temperature dependent developmental rates of insects without derivation of biophysical processes; thus, it is difficult to explain how temperature affects development from the thermodynamic mechanisms. Fortunately, two mathematical models (the Sharpe Schoolfield Ikemoto [SSI] model and Ratkowsky Olley Ross [ROR] model) based on thermodynamics have been built to explain temperature dependent reaction rates. Despite their differences in construction, both models produce similar functions when used to describe the effect of temperature on the probability of a theoretical rate controlling enzyme that is in its active state. However, the previous fitting method of the SSI model was unable to achieve global optimization of parameter estimates; that of the ROR model usually underestimates the maximal probability of the rate controlling enzyme that is in its active state, as found in some empirical data sets. In the present study we improved the fitting methods for these two models. We then used these two models to fit 10 data sets from published references. We found the models based on the improved fitting methods agree with the empirical data well and predict that the maximal probabilities of the rate controlling enzyme that is in its active state are close to 1. The SSI model produces a slightly better goodness of fit value for the model than the ROR model, whereas the latter predicts a more symmetrical curve for the probability of the rate controlling enzyme that is in its active state. If thermodynamic parameters of two or more different species are to be compared, we recommend that researchers use one or the other of these two models and follow the same fitting methods for all species.