nils-herrmann/clean_scirepeval huggingface dataset:
Optical Performance of Vertical Single Axis Tracked Solar Panels with Seasonal Adjustment of Tilt Angles
Year: 2011

To investigate the optical performance of vertical single axis tracked solar panels with the tilt angle of solar panels being seasonally adjusted (4T V A tracked solar panels, in short) as compared with fixed and full 2 axis tracked solar panels, a mathematical procedure to estimate the daily collectible radiation on fixed and tracked panels is suggested based on the monthly horizontal radiation. Calculation results showed that the optimal date on which tilt angle adjustments were seasonally made was about 23 days from the equinoxes, the seasonal optimal tilt angles of a 4T V A tracked solar panel for maximizing seasonal energy collection strongly depended on site latitudes, and the corresponding maximum annual collectible radiation on such tracked panel was about 97% of solar radiation annually collected by a dual axis tracked panel, slightly higher than that on those tracking the sun about south north axis inclined at a yearly fixed tilt angle from the horizon. Empirical correlations for a quick estimation of seasonal optimal tilt angle of vertical single axis tracked solar panels were also proposed based on climatic data of 32 sites in China.
Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan
Venue: Jurnal Teknologi dan Sistem Komputer
Year: 2019

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85% and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.
Improved Face Recognition based on Hidden Markov Model
Year: 2020

In this paper, a new face recognition technique based on Hidden Markov Model (HMM) Pre processing, and feature extraction (K means and the Sobel operator) is proposed. Two main contributions are presented; the first contribution in the pre processing were image's edges are normalized to enhance the HMM models to be non sensitive to different edges. The second contribution is a new technique to extract the image's features by splitting the image into non uniform height depending on the distribution of the foreground pixels. The foreground pixels are extracting by using the vertical sliding windows. The proposed technique is faster with a higher accuracy with respect to other techniques which are investigated for comparison. Moreover, it shows the capability of recognizing the normal face (center part) as well as face boundary
Algorithms for sea surface wind parameter extraction from x band shipborne nautical radar images
Year: 2014

In this thesis, research for improving sea surface wind parameter extraction from shipborne X band marine radar images is presented. First, the curve fitting based wind algorithms are investigated. To exclude the rain cases and low backscatter images, a data quality control process is designed. Then, a dual curve fitting technique is proposed to enhance the wind retrieval performance under low sea states. This modified curve fitting based wind algorithm is tested using radar images and shipborne anemometer data collected on the east coast of Canada. It is shown that the dual curve fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 5.7* and 0.3 m/s, respectively, under sea states with significant wave height lower than 2.30 m. Secondly, the intensity level selection (ILS based wind algorithms are studied. An additional process is implemented for the ILS based method to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. Moreover, a harmonic function that is least squares fitted to the selected range distances vector as a function of antenna look direction is applied. This modified ILS based wind algorithm is applied to the same radar data. Compared with the original ILS based algorithm, the modified one reduces the standard deviation (STD) of wind direction and speed by about 4* and 0.2 m/s, respectively. Also, the above mentioned two modified methods (dual curve fittingbased and modified ILS based) are compared. Finally, X band radar signatures of rain cells are elaborately described both in time and frequency domains. It is seen that the rain contaminated image pixels are more uniformly bright than the wave echoes and radar retrieved wind results are thus overestimated. This property of "uniformly bright" is used to identify the portions that are more affected by rain. To mitigate the effects of rain on wind retrieval from X band radar images, a novel texture analysis based data filtering process is presented and tested. By removing the data in the directions more affected by rain, significant improvements can be seen from the radar derived wind results using both of the two wind algorithms.
UN ANALISIS DE LA SEGURIDAD Y SALUD EN EL TRABAJO EN EL SISTEMA EMPRESARIAL CUBANO
Year: 2016

A partir de la existencia en Cuba de insuficiencias en los sistemas de gestion de la seguridad y salud en el trabajo (SGSST) en las empresas que pueden ser por la ocurrencia de incidentes y accidentes del trabajo con repercusiones en la responsabilidad social de las mismas. En este sentido la presente investigacion tiene como objeto, el estudio de los SGSST, cuestion de gran importancia para el sector empresarial, pues en los momentos actuales dentro del ambito empresarial a nivel internacional constituye un parametro para determinar si una empresa es socialmente responsable, por lo que nuestro pais no esta ajeno de estas exigencias en el contexto empresarial.
The morphosyntax of clause typing: single, double, periphrastic, and multifunctional complementizers in Korean
Year: 2019

In this dissertation I provide an account of the distribution of Korean clause typing markers from the perspective of a formal typological model, the Universal Spine Hypothesis (Wiltschko, 2014, 2017) Although Korean clause typing markers have both syntactic properties (expressing force/mood, Chomsky 2000, 2001) and pragmatic properties (expressing speech styles, Sohn 1999) my investigation focuses on the morphosyntactic properties of clause typing markers in single layered and doublelayered CPs. I detail their ability to transmit clause type, their compatibility with TAM elements, and their incompatibility with subordinators and speech act elements. My central claim is that, through an association with the linking spine (i.e. CP in generative grammar) clause typing markers, including ta and e, construct Korean language specific categories. Clause typing markers interact with a syntactic domain encoding the common ground of speech participants, the grounding spine. My dissertation has two major findings. First, the morphophonological realization of C is obligatory in both finite and non finite clauses. I therefore propose a Clause Complementation Parameter (CCP) all clauses must have a complementizer, and a C must have a correspondent PF realization. This accounts for the expletive like dummy complementizers e and ci which can fill the head of the three basic clause types. Second, functional elements selecting clause typing markers support the existence of syntactic projections above the traditional CP. For instance, iterative ko and hearsay y in reiterated and hearsay utterances, respectively, must be associated with the syntactic domain above CP. I argue that along with polite yo and intonation, they construct Korean languagespecific categories through their association with the three universal categorizers k: linking, k: grounding, and k: responding. This is formulated as [RespP [GroundP [LinkingP  the periphrastic irrealis markers show that C with T can restrict the person feature on the subject. The interpretations of multifunctional markers in different morphosyntactic contexts show that their properties emerge in two ways: through interaction with local elements in the domain or by virtue of their association with hierarchically distinct domains. Assuming the Universal Spine Hypothesis, I have accounted for the morphosyntactic properties of Korean clausetyping markers by proposing languagespecific categories considering the functional layers. This dissertation offers a more complete account of Korean grammar but also will provide an explanation for crosslinguistic differences in encoding of clause typing Units of Languages change how C appears.
Visual Inertial Sensor Fusion Models and Algorithms for Context Aware Indoor Navigation
Year: 2019

Positioning in navigation systems is predominantly performed by Global Navigation Satellite Systems (GNSSs) However, while GNSS enabled devices have become commonplace for outdoor navigation, their use for indoor navigation is hindered due to GNSS signal degradation or blockage. For this, development of alternative positioning approaches and techniques for navigation systems is an ongoing research topic. In this dissertation, I present a new approach and address three major navigational problems: indoor positioning, obstacle detection, and keyframe detection. The proposed approach utilizes inertial and visual sensors available on smartphones and are focused on developing: a framework for monocular visual internal odometry (VIO) to position human/object using sensor fusion and deep learning in tandem; an unsupervised algorithm to detect obstacles using sequence of visual data; and a supervised context aware keyframe detection. The underlying technique for monocular VIO is a recurrent convolutional neural network for computing six degree of freedom (6DoF) in an end to end fashion and an extended Kalman filter module for fine tuning the scale parameter based on inertial observations and managing errors. I compare the results of my featureless technique with the results of conventional feature based VIO techniques and manually scaled results. The comparison results show that while the framework is more effective compared to featureless method and that the accuracy is improved, the accuracy of feature based method still outperforms the proposed approach. The approach for obstacle detection is based on processing two consecutive images to detect obstacles. Conducting experiments and comparing the results of my approach with the results of two other widely used algorithms show that my algorithm performs better; 82% precision compared with 69% In order to determine the decent frame rate extraction from video stream, I analyzed movement patterns of camera and inferred the context of the user to generate a model associating movement anomaly with proper frames rate extraction. The output of this model was utilized for determining the rate of keyframe extraction in visual odometry (VO) I defined and computed the effective frames for VO and experimented with and used this approach for context aware keyframe detection. The results show that the number of frames, using inertial data to infer the decent frames, is decreased.
Testing Klein and Sorra's innovation implementation model: An empirical examination
Year: 2008

Despite a substantial volume of research activities on innovation implementation [Holahan,  Schoorman, F.D. 2004. Implementing computer technology: a multiorganizational test of Klein and Sorra's model. Journal of Engineering and Technology Management 21 (1) 31 50; Klein, K.J. Sorra, J.S. 1996. The challenge of innovation implementation. Academy of Management Review 21 (4) 1055 1080; Kwon, T.H. Zmud, R.W. 1987. Unifying the fragmented models of information systems implementation.  Hirschheim, B.R. (Ed. Critical Issues in Information Systems Research. John Wiley and Sons Ltd. New York, ] implementation outcomes continue to disappoint particularly those related to large scale information systems (ISs) implementation projects [Aiman Smith, L. Green, S.G. 2002. Implementing new manufacturing technology: the related effects of technology characteristics and user learning activities. Academy of Management Journal 45 (2) 421 430; The Standish Group International Inc. 1995. Chaos (Application Project Failure and Success) Access http:/www.standishgroup.com/chaos.html; Whittaker, B. 1999. What went wrong? Unsuccessful information technology projects. Information Management Computer Security 7 (1) 23 29] In 1996, Klein and Sorra introduced a promising model that posited key determinants of implementation effectiveness. In this paper we present new validated construct measures, and then test the Klein and Sorra model using a survey of 209 employees in seven organizations. Our results demonstrate that IS implementation effectiveness is influenced directly and indirectly by innovation values fit, and indirectly by implementation climate.
3D thermo mechanical solution of transversely isotropic and functionally graded graphene reinforced elliptical plates
Year: 2018

This paper presents an analytical investigation on the thermo elastic response of a clamped elliptical plate made of either transversely isotropic functionally graded materials (FGMs) or a novel functionally graded nanocomposite reinforced with graphene nanoplatelets (GPLs) whose weight fraction varies continuously and smoothly along the thickness direction according to three uniform and non uniform distribution patterns. Based on three dimensional elasticity theory and the generalized Mian and Spencer's method, 3D elasticity solutions are obtained for the elliptical plate under thermo mechanical loading whose mid plane displacements are constructed to satisfy the clamped boundary conditions in which the unknown constants are determined from the governing equations of the plate. The present analytical solutions are validated through comparisons with those available in open literature. A parametric study is then conducted, with a particular focus on the effects of GPL weight fraction, distribution pattern, geometry and size as well as the major to minor axis ratio of the plate on the thermo mechanically induced stress and deformation fields.
The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes
Venue: Science
Year: 2008

Genetics aims to understand the relation between genotype and phenotype. However, because complete deletion of most yeast genes ~80% has no obvious phenotypic consequence in rich medium, it is difficult to study their functions. To uncover phenotypes for this nonessential fraction of the genome, we performed 1144 chemical genomic assays on the yeast whole genome heterozygous and homozygous deletion collections and quantified the growth fitness of each deletion strain in the presence of chemical or environmental stress conditions. We found that 97% of gene deletions exhibited a measurable growth phenotype, suggesting that nearly all genes are essential for optimal growth in at least one condition.
Novel side chain type cardo poly(aryl ether sulfone) bearing pendant sulfoalkyl groups for proton ex
Year: 2011

A series of cardo poly(aryl ether sulfone) copolymers bearing pendant hydroxyl ethyl groups CH(2)CH(2)OH) were synthesized from a new biphenol monomer 2 (2 hydroxyethyl) 3,3 bis(4 hydroxyphenyl)isoindolin 1 one (PPH OH) 4,4' biphenol(BP) and 4,4' difluorodiphenylsulfone (DFDPS) Sulfonic acid groups were readily grafted to the copolymers by reaction of the hydroxyl group with 1,3 propanesultone. The resulting sulfonated copolymers SPES C X (where X refer to the content of PPH OH in the bisphenol monomers) contain side chain sulfoalkyl groups CH(2)CH(2) O CH(2)CH(2)CH(2)SO(3)H) TEM photographs revealed that SPES C 100 membrane exhibited characteristic hydrophilic/hydrophobic microphase separated morphology with small interconnected and hydrophilic clusters (1.5 2 nm) Such microphase separated morphology renders the membrane high proton conductivity of 0.056 S/cm (IEC 1.41 mequiv./g) and low dimensional changes of SPES C 100 at room temperature. SPES C X membranes exhibited the methanol permeability in the range of 13.8 5.3 x 10( 8) cm(2)/s, which were much lower than those of SBPES (8.7 x 10( 7) cm(2)/s) and Nafion 117 (2.4 x 10( 7) cm(2)/s)
Fungi regulate response of N 2 O production to warming and grazing in a Tibetan grassland
Year: 2018

Lack of understanding of the effects of warming and winter grazing on soil fungal contribution to nitrous oxide (N 2 O) production has limited our ability to predict N 2 O fluxes under changes in climate and land use management, because soil fungi play an important role in driving terrestrial N cycling. Here, we examined the effects of 10 years' warming and winter grazing on soil N 2 O emissions potential in an alpine meadow. Our results showed that soil bacteria and fungi contributed 46 and 54 to nitrification, and 37 and 63 to denitrification, respectively. Neither warming nor winter grazing affected the activity of enzymes responsible for overall nitrification and denitrification. However, warming significantly increased the enzyme activity of bacterial nitrification and denitrification to 53 and 55 respectively. Warming significantly decreased enzyme activity of fungal nitrification and denitrification to 47 and 45 respectively, while winter grazing had no such effect. We conclude that soil fungi could be the main source for N 2 O production potential in the Tibetan alpine grasslands. Warming and winter grazing may not affect the potential for soil N 2 O production potential, but climate warming can alter biotic pathways responsible for N 2 O production. These findings indicate that characterizing how fungal nitrification/denitrification contributes to N 2 O production, as well as how it responds to environmental and land use changes, can advance our understanding of N cycling. Therefore, our results provide some new insights about ecological controls on N 2 O production and lead to refine greenhouse gas flux models.
A NOVEL METHOD OF BRAIN TUMOR SEGMENTATION USING EXTENSIVE FEATURE SET
Year: 2014

Brain tissue Segmentation from the MRI images is having significance in the medical research field. The accurate Segmentation of the normal as well as the abnormal tissues is the complex assignment in this process. Because of the inconsistency and difficulty of abnormal tissues, MRI Brain Image Segmentation turned into more hard procedure. In this paper, a technique is proposed for segmenting the abnormalities such as Tumor and Atrophy in the MRI Brain images. (1) Feature extraction (2) Classification (3) Segmentation are the three stages offered in this work. At first, the features such as energy, entropy, homogeneity, contrast and correlation from MRI Brain Images are extracted. Next, by utilizing Neuro Fuzzy classifier, the Classification process is carried out and for this process, the feature set is specified as the input. From the outcome of Classification, the images are categorized into normal as well as abnormal. The further procedure Segmentation is performed according to this outcome only. The abnormal MRI images are segmented into abnormal tissues like Tumor and Atrophy using Region Growing method. Utilizing MATLAB platform the implementation of the proposed technique is made. The experimentation is carried out on the MRI Brain Images by BrainWeb data sets. The performance of our proposed technique is assessed with the help of the metrics namely FPR, FNR, Specificity, Sensitivity and Accuracy. Therefore, using our proposed technique with enhanced classification, the abnormal tissues of MRI Brain images are segmented accurately.
Prioritization of medical equipment for maintenance decisions
Venue: J. Oper. Res. Soc.
Year: 2011

Clinical engineering departments in hospitals are responsible for establishing and regulating a Medical Equipment Management Program to ensure that medical devices are safe and reliable. In order to mitigate functional failures, significant and critical devices should be identified and prioritized. In this paper, we present a multi criteria decision making model to prioritize medical devices according to their criticality. Devices with lower criticality scores can be assigned a lower priority in a maintenance management program. However, those with higher scores should be investigated in detail to find the reasons for their higher criticality, and appropriate actions, such as 'preventive maintenance' 'user training' 'redesigning the device' etc, should be taken. In this paper,we also describe how individual score values obtained for each criterion can be used to establish guidelines for appropriate maintenance strategies for different classes of devices. The information of 26 different medical devices is extracted from a hospital's maintenance management system to illustrate an application of the proposed model.
DM2 ECOP
Venue: ACM Trans. Internet Techn.
Year: 2019

Mobile Edge Computing is a promising paradigm that can provide cloud computing capabilities at the edge of the network to support low latency mobile services. The fundamental concept relies on bringing cloud computation closer to users by deploying cloudlets or edge servers, which are small clusters of servers that are mainly located on existing wireless Access Points (APs) set top boxes, or Base Stations (BSs) In this article, we focus on computation offloading over a heterogeneous cloudlet environment. We consider several users with different energy and latency constrained tasks that can be offloaded over cloudlets with differentiated system and network resources capacities. We investigate offloading policies that decide which tasks should be offloaded and select the assigned cloudlet, accordingly with network and system resources. The objective is to minimize an offloading cost function, which we defined as a combination of tasks' execution time and mobiles' energy consumption. We formulate this problem as a Mixed Binary Programming. Since the centralized optimal solution is NP hard, we propose a distributed linear relaxation based heuristic approach that relies on the Lagrangian decomposition method. To solve the subproblems, we also propose a greedy heuristic algorithm that computes the best cloudlet selection and bandwidth allocation following tasks' offloading costs. Numerical results show that our offloading policy achieves a good solution quickly. We also discuss the performances of our approach for large scale scenarios and compare it to state of the art approaches from the literature.