Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Design of Multisource High-Flux Solar Simulators

We present a systematic approach to the design of a set of high-flux solar simulators (HFSSs) for solar thermal, thermochemical, and materials research. The generic simulator concept consists of an array of identical radiation modules arranged in concentric rows. Each module consists of a short-arc lamp coupled to a truncated ellipsoidal specular reflector. The positions of the radiation modules are obtained based on the rim angle, the number of concentric rows, the number of radiation modules in each row, the reflector radius, and a reflector spacing parameter. For a fixed array of radiation modules, the reflector shape is optimized with respect to the source-to-target radiation transfer efficiency. The resulting radiative flux distribution is analyzed on flat and hemispherical target surfaces using the Monte Carlo ray-tracing technique. An example design consists of 18 radiation modules arranged in two concentric rows. On a 60-mm dia. flat target area at the focal plane, the predicted radiative power and flux are 10.6 kW and 3.8 MW m(-2), respectively, and the predicted peak flux is 9.5MW m(-2).
Sensor-based navigation of air duct inspection mobile robots

12 This paper deals with an image sensor system and its position estimation algorithm for autonomous duct cleaning and inspection mobile robots. For the real application, a hierarchical control structure that consists of robot motion controller and image sensor system is designed considering the efficient and autonomous motion behaviors in narrow space such as air ducts. The sensor's system consists of a CCD camera and two laser sources to generate slit beams. The image of the structured lights is used for calculating the geometric parameters of the air ducts which are usually designed with a rectangular section. With the acquired 3D information about the environment, the mobile robot with two differential driving wheels is able to autonomously navigates along the duct path without any human intervention. For real time navigation, the relative position estimation of the robot are performed from 3D image reconstructed by the sensor system. The calibration and image processing methods used for the sensor system are presented with the experimental data. The experimental results show the possibility of the sensor based navigation which is important for effective duct cleaning by small mobile robots.
Challenges and Conflicts in Sustainable Supply Chain Management: Evidence from the Heavy Vehicle Industry

The purpose of this paper is to increase the understanding and explore the challenges and conflicts in sustainable supply chain management (SSCM) through empirical examples from the heavy vehicle industry in Sweden and China. An exploratory study of the case company’s supply chains in two countries is conducted for this paper. The major components of the empirical data are interviews with the company’s representatives and its downstream supply chain members in Sweden and China, as well as workshops with the logistics industry’s representatives in China. The findings show that the firms perceive the challenges on the regulatory and organisational levels. The conflicts can be found between several stakeholder groups, the main focus seems to be on environmental and economic aspects. Life-cycle solution for the vehicles’ utilisation is valuable but there are challenges to employing it, especially in the Chinese context. The results show that intensified international collaboration on environment and traffic safety can help tackle challenges and ease the conflicts in sustainable supply chain management.
Organization and Retrieval in a Conceptual Memory for Events or CON 54, Where Are You?

In the course of a normal day, people effortlessly recall past events and episodes from their lives. A reasonable goal in the design of computer programs is to construct a memory with that same capability. To facilitate such retrieval of events from a computer memory, we must first specify a reasonable memory organization. We must then design updating and retrieval processes to build up and access that information. This paper will present such a theory, and will describe a computer program named CYRUS which implements the theory.
Greedy is not Enough: An Efficient Batch Mode Active Learning Algorithm

Active learning algorithms actively select training examples to acquire labels from domain experts, which are very effective to reduce human labeling effort in the context of supervised learning. To reduce computational time in training, as well as provide more convenient user interaction environment, it is necessary to select batches of new training examples instead of a single example. Batch mode active learning algorithms incorporate a diversity measure to construct a batch of diversified candidate examples. Existing approaches use greedy algorithms to make it feasible to the scale of thousands of data. Greedy algorithms, however, are not efficient enough to scale to even larger real world classification applications, which contain millions of data. In this paper, we present an extremely efficient active learning algorithm. This new active learning algorithm achieves the same results as the traditional greedy algorithm, while the run time is reduced by a factor of several hundred times. We prove that the objective function of the algorithm is submodular, which guarantees to find the same solution as the greedy algorithm. We evaluate our approach on several largescale real-world text classification problems, and show that our new approach achieves substantial speedups, while obtaining the same classification accuracy.
Stochastic convergence among European economies

The aim of this paper is to test the stochastic convergence in real per capita GDP for 15 European countries using non-stationary panel data approaches over the period 1950-2003. Cross-sectional dependence is assumed due to the existence of strong linkages among European economies. However, tests derived under the assumption of cross-sectional independence are also carried out for completeness and comparison. We also split the whole sample into two sub-periods (1950-1976, 1977-2003) in order to take into account the effects of the first oil crisis (1973-1974) and to evaluate the robustness of the statistical analysis. Our results offer little support to the stochastic convergence hypothesis for the whole period, while suggest the presence of convergence in the first sub-period.
Comment based Seller Trust model for E-commerce

E-commerce is on a boom and consumer reviews have become an essential aspect of this growing business. Various models have been proposed that use user feedback reviews to compute seller trust profile. The feedback reviews consist of ratings as well as comments given by the user. Models that compute seller trust based on user feedback ratings overlook the important subjective details in the text and give inaccurate trust scores based on ratings which are objective in nature. We have proposed a novel model for computing seller trust profile based on fine grained analysis of feedback comments. In our approach, we have proposed a unique methodology to compare and verify our result with two e-commerce websites. A few common seller on Amazon and Flipkart have been identified and trust scores have been computed for them. The computed trust scores for the sellers on Amazon are considerably lesser than those displayed on the website while those for Flipkart are nearly equal to those mentioned on its website. The calculated relative error is considerably higher on Amazon as compared to Flipkart. The experiment is unique of its kind in the field of natural language processing and sentiment analysis, and achieves the stated objective.
Clustering of Medical X-ray Images by Merging Outputs of Different Classification Techniques

Clustering x-ray images is a complex task, due to the great variations within each class including orientation, alignment and deformation. In this paper, an automatic medical x-ray image clustering is developed by merging the outputs from five different neural networks classifiers. Each classifier employs a set of features derived through different feature-extraction techniques. Such techniques are based on (i) pixel-value, (ii) local binary patterns, (iii) global means of rows and columns, (iv) local means of rows and columns, and (v) local histogram. A test accuracy of 86.2 % was achieved from merged output of the five NN classifiers using the ImageCLEF 2015 database. A somewhat higher accuracy of 87.2% was obtained when merging outputs of only three classifiers.
Crisis Economics: A Crash Course in the Future of Finance

Find loads of the crisis economics a crash course in the future of finance book catalogues in this site as the choice of you visiting this page. You can also join to the website book library that will show you numerous books from any types. Literature, science, politics, and many more catalogues are presented to offer you the best book to find. The book that really makes you feels satisfied. Or that's the book that will save you from your job deadline.
Study on extra-high voltage power line scatterers in time series SAR

Though the power line's diameter was only 1~3 centimetres, it could generate strong reflection in high resolution strip mode SAR images while the line direction was along the radar azimuth. This paper chose one set of line scatterers and analysed the changes among time series images in HK area. There were 8 line scatterers in these set which reflected by 6 power wires and 2 ground wires. Each of the line scatterers' magnitude, area and position had been analysed during data acquiring period. In time series SAR images, the maximum magnitude of the power line scatterers, the scattering area above 50db and the central position of the scatterers changed in various trend. Adaptive window maximum coherence algorithm has been applied to extract the reflection position. This paper showed how the power line sag changed and reflection position changed in time series images. It seemed that the temperature should be the main factor that changed the sag position and changed the reflection geometry.
The geometry of beam tracing

A solution to the hidden surface elimination problem called Beam Tracing is described. Beam tracing is related to ray tracing but uses spatial coherence within the scene, and area coherence within the image to batch computations. Beam tracing is an object space solution to the hidden surface problem. Beam tracing is formulated in terms of its principal subprocesses: intersection, sorting, and clipping. A Hierarchical Scene Representation is proposed. This incorporates the space decomposition idea of the BSP tree [Fuchs, Kedem and Naylor, 80] along with the convex polytope intersection detection technique of [Dobkin and Kirkpatrick, 83] to interleave and efficiently solve the intersection and sorting subproblems of beam tracing.
TR 12-016 Forming Long Term Teams to Exploit Synergies among Heterogeneous Agents

In this work, we describe how agents can take advantage of synergies among each other and increase their efficiency at accomplishing tasks by working in teams. We present different team structures for heterogeneous agents, which range from static teams that are formed upfront and never change, to dynamic teams where agents can change teams as need arises, to teams that allow only some types of agents to be members. We also develop teaming strategies that are strongly domain specific for the RoboCup Rescue simulation environment. RoboCup Rescue is a simulated environment which is characterized by uncertainty in available information and by severely limited communications among agents. The tasks to be accomplished are saving civilians who are trapped in buildings and preventing fires from spreading in the city. The locations of civilians and fires are not known upfront and have to be discovered. The domain constraints limit the applicability of some popular team formation algorithms and require adaptive strategies. We measure the effectiveness of the various teaming strategies we propose. Our experimental results support the hypothesis that teaming improves performance, and that more specialized and knowledge rich teaming arrangements perform better.
Fuzzy sliding mode control for anti-lock braking systems

A combination of two control methods, sliding mode and fuzzy, for the wheel slip control in anti-lock braking system, is presented in this paper. The fuzzy block is used to determine the values of key parameters important for establishing switching function dynamics. It is demonstrated, via performed experiments, that proposed control algorithm gives good system performances.
Collapse of superhydrophobicity on nanopillared surfaces

The mechanism of the collapse of the superhydrophobic state is elucidated for submerged nanoscale textures forming a three-dimensional interconnected vapor domain. This key issue for the design of nanotextures poses significant simulation challenges as it is characterized by diverse time and length scales. State-of-the-art atomistic rare events simulations are applied for overcoming the long time scales connected with the large free energy barriers. In such interconnected surface cavities wetting starts with the formation of a liquid finger between two pillars. This break of symmetry induces a more gentle bend in the rest of the liquid-vapor interface, which triggers the wetting of the neighboring pillars. This collective mechanism, involving the wetting of several pillars at the same time, could not be captured by previous atomistic simulations using surface models comprising a small number of pillars (often just one). Atomistic results are interpreted in terms of a sharp-interface continuum model which suggests that line tension, condensation, and other nanoscale phenomena play a minor role in the simulated conditions.
ACTIVITY PATTERNS OF FEMALE RUFFED GROUSE DURING THE BREEDING SEASON

Ruffed Grouse (Bonusa umbellus) are difficult to observe for extended periods in the wild. Consequently, despite the considerable research attention this bird has received (e.g. Bump et al. 1947, Gullion and Marshall 1968, and others), few precise data are available concerning its activity patterns. Recent radiotelemetry studies in Minnesota (Archibald 1973 and several studies cited therein) have increased our knowledge of this aspect of the Ruffed Grouse’s behavior but activity patterns of female Ruffed Grouse during the breeding season remain poorly documented. This paper reports a radiotelemetry study of female Ruffed Grouse activity patterns from pre-incubation through post-incubation periods at the University of Minnesota’s Cedar Creek Natural History Area located about 45 km north of Minneapolis, Minnesota.
ALTERNATE COMPLEXITY MEASURES AND STABILITY ANALYSIS OF PROCESS AND BIOLOGICAL NETWORKS

Recent growth in measurement technology has provided insight into many unresolved complex systems. Associations amongst the components of such a system are well represented by formulating a complex network of nodes and edges. Large scale process and instrument diagrams of a process plant, species interactions in ecological food webs, world wide web of internet servers and sites, complex biological systems like gene regulations, metabolic pathways and protein interactions etc., are real world examples of such complex systems. Many methods are suggested in the literature to address the issue of complexity measure and its implication to structural and functional stability of such networks. Graph theoretic measurements of topological complexity fail to indicate the vital inter component associations that essentially cause the stability in complex systems. These associations are partially explained by analyzing the distribution of connectivity of nodes. Less emphasis has been given to analysis of cycles in the network which constitute an important part of network dynamics. The present study aims at addressing these issues related to complex network analysis. A new concept of “cycle based complexity” is developed and explained with examples. Different simulated process networks and real world networks are analyzed for their structural complexity using the new parameters. Effect of various random and targeted disturbances on the network stability is studied. Based on the comparative results, it is proposed that the new measures of complexity be employed for better understanding of process and biological networks.
Decision-theoretic entropy

We introduce an axiomatic approach to the problem of inferring a complete and transitive weak ordering representing the agent's preferences given a set of observed constraints. The axioms characterize a unique inference rule, which amounts to the constrained maximization of a certain formula we derive. The formula can be interpreted as the entropy of the agent's preference ordering, and its unique maximand identifies the simplest rationalization of the observed behavior.
The Onset Time of the Ownership Sensation in the Moving Rubber Hand Illusion

The rubber hand illusion (RHI) is a perceptual illusion whereby a model hand is perceived as part of one’s own body. This illusion has been extensively studied, but little is known about the temporal evolution of this perceptual phenomenon, i.e., how long it takes until participants start to experience ownership over the model hand. In the present study, we investigated a version of the rubber hand experiment based on finger movements and measured the average onset time in active and passive movement conditions. This comparison enabled us to further explore the possible role of intentions and motor control processes that are only present in the active movement condition. The results from a large group of healthy participants (n = 117) showed that the illusion of ownership took approximately 23 s to emerge (active: 22.8; passive: 23.2). The 90th percentile occurs in both conditions within approximately 50 s (active: 50; passive: 50.6); therefore, most participants experience the illusion within the first minute. We found indirect evidence of a facilitatory effect of active movements compared to passive movements, and we discuss these results in the context of our current understanding of the processes underlying the moving RHI.
Fitting a distribution to data using an alternative to moments

A four-parameter probability distribution that is useful in fitting data is considered. This distribution can approximate many well-known distributions and provides a simple and effective algorithm for generating random variates. Its utility, however, is limited by one's ability to determine its parameters. The method of moments has been suggested as a means of selecting the four parameter values when the first four moments are specified or can be estimated. Sample moments, however, are sensitive to extreme observations and are subject to large sampling variability. Hence, the method of moments is generalized to allow the use of surrogate measures of location, scale, symmetry, and tailweight. A new procedure using two statistics that are functions of order statistics is developed and is compared with the method of moments by means of an example and a Monte Carlo study.
Do Unions Make Enterprises Insolvent?

This study investigates the impact of unionization on closures of firms, business lines, and establishments. Analyzing data from two major data sets—one (from the COMPUSTAT files) on the union status of solvent and insolvent enterprises and business lines, and one (obtained by matching files from the Current Population Survey) on the union status of workers who have lost their jobs due to permanent plant closures or business failures—the authors find little support for the hypothesis that unionization increases the insolvency of firms. The results are consistent with the hypothesis that unions behave in an economically rational manner, pushing wages to the point where union firms may expand less rapidly than nonunion firms, but not to the point where the firm, plant, or business line closes down.
A dynamic framework for maintaining customer profiles in e-commerce recommender systems

Recommender systems have been successfully applied to enhance the quality of service for customers, and more importantly, to increase the sale of products and services in e-commerce business. In order to provide effective recommendation results within an acceptable response time, a recommender system is required to have the scalability to handle a large customer population in real time. In this paper, we propose a new recommender system framework based on the incremental clustering algorithm in order to dynamically maintain the customer profiles. Using the incremental clustering technique, the dynamic changes in the number of customers and products purchased could be handled effectively. Experiments on real data sets showed that the proposed framework helps to reduce the recommendation time, while retaining accuracy.
Proteinase-activated receptor 2 promotes TGF-β-dependent cell motility in pancreatic cancer cells by sustaining expression of the TGF-β type I receptor ALK5

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high expression of transforming growth factor (TGF)-β and the G protein-coupled receptor proteinase-activated receptor 2 (PAR2), the latter of which functions as a cell-surface sensor for serine proteinases asscociated with the tumour microenvironment. Since TGF-β and PAR2 affect tumourigenesis by regulating migration, invasion and metastasis, we hypothesized that there is signalling crosstalk between them. Depleting PDAC and non-PDAC cells of PAR2 by RNA interference strongly decreased TGF-β1-induced activation of Smad2/3 and p38 mitogen-activated protein kinase, Smad dependent transcriptional activity, expression of invasion associated genes, and cell migration/invasion in vitro. Likewise, the plasminogen activator-inhibitor 1 gene in primary cultures of aortic smooth muscle cells from PAR2−/− mice displayed a greatly attenuated sensitivity to TGF-β1 stimulation. PAR2 depletion in PDAC cells resulted in reduced protein and mRNA levels of the TGF-β type I receptor activin receptor-like kinase 5 (ALK5). Forced expression of wild-type ALK5 or a kinase-active ALK5 mutant, but not a kinase-active but Smad-binding defective ALK5 mutant, was able to rescue TGF-β1-induced Smad3 activation, Smad dependent transcription, and cell migration in PAR2-depleted cells. Together, our data show that PAR2 is crucial for TGF-β1-induced cell motility by its ability to sustain expression of ALK5. Therapeutically targeting PAR2 may thus be a promising approach in preventing TGF-β-dependent driven metastatic dissemination in PDAC and possibly other stroma-rich tumour types.
Supervised Coloring of Volumes

We present a novel approach for assigning realistic colors to a scalar volume. Color assignment is typically done using a transfer function. However, the linear mapping between the scalar value and color produces unrealistic-looking results. In our approach, we use a reference multichannel scalar volume and its corresponding color volume to build the mapping. Using the Visible Human Male (VHM) dataset as the reference volume, we employ various machine learning techniques to find the best mapping between the VHM multichannel scalar data and the color data. We generate a feature vector for each voxel in the VHM dataset based on its 3D neighborhood. The feature vectors and their associated colors are then used to train the learning machine. After training, feature vectors from the scalar volume to be colored are fed into the learning machine to find the color. We have performed experiments on the VHM and Visible Human Female (VHF), foot and head datasets. The resultant color volumes correspond well with the full-color cross-sections of the original VH dataset.