Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Production Research Enhanced Joint Pricing and Lotsizing Problem in a Two- Echelon Supply Chain with Logit Demand Function Enhanced Joint Pricing and Lotsizing Problem in a Two-echelon Supply Chain with Logit Demand Function

Taylor & Francis makes every effort to ensure the accuracy of all the information (the " Content ") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. In retail supply chains, ordering and pricing policies for the retailer and shipment plan for the supplier are the most important decisions. These policies are often conducted either individually or sequentially with poor overall performance for the whole supply chain resulting to extra inventory and other deficiencies. In this paper, an integrated marketing-inventory model in a two-echelon supply chain model is developed involving discount promotion, customer behaviour more realistically and operations aspects to determine optimal ordering, shipping and pricing quantities simultaneously. An efficient analytical solution procedure and a Particle Swarm Optimisation solution algorithm are also developed. Finally, a number of numerical tests are conducted to approve the interesting theoretical results of the analytical approach. 1. Introduction While supply chain management (SCM) models generally focus on supply side, trying to find cost-effective solutions, marketing function is more concerned with revenue by focusing on the demand side of the organisations. Evidently, together, they can determine the organisation's profitability. However, within the marketing as well as the supply chain literature, the need to link both sides has been emphasised. In today's increasingly competitive environment, production and marketing decisions have been among the most crucial management aspects and entrepreneurs are severely applying marketing tools to attract customers. Marketing decisions generally fall into the four controllable categories known as the marketing mix or 4 P's of marketing: price, promotion, product and place. The price promotion scheme may take the form of a simple percentage off, coupons, a 'multibuy' (discount for multiple items packaged together), an extra product, advanced sales discount (ASD), …
On-chip monitoring and compensation scheme with fine-grain body biasing for robust and energy-efficient operations

Aggressive technology scaling and strong demand for lowering supply voltage impose a serious challenge in achieving robust and energy-efficient circuit operation. This paper first overviews on device-circuit interactions to enable cross-layer resiliency, and energy optimization. We show that the ability to monitor and control device and circuit characteristics not only increase energy-efficiency by more than 20% but also relax the severe design constraints, which were required because of the uncertainties of variability. We then demonstrate two proof-of-concept circuits in a 65 nm process to show variability resiliency and energy optimization with local body biasing.
Fast and Precise 3D Computation of Capacitance of Parallel Narrow Beam MEMS Structures

Efficient design and performance of electrically actuated MEMS devices necessitate accurate estimation of electrostatic forces on the MEMS structures. This in turn requires thorough study of the capacitance of the structures and finally the charge density distribution on the various surfaces of a device. In this work, nearly exact BEM solutions have been provided in order to estimate these properties of a parallel narrow beam structure found in MEMS devices. The effect of three-dimensionality, which is an important aspect for these structures, and associated fringe fields have been studied in detail. A reasonably large parameter space has been covered in order to follow the variation of capacitance with various geometric factors. The present results have been compared with those obtained using empirical parametrized expressions keeping in view the requirement of the speed of computation. The limitations of the empirical expressions have been pointed out and possible approaches of their improvement have been discussed.
Stabilizability of Stochastic Linear Systems with Finite Feedback Data Rates

Feedback control with limited data rates is an emerging area which incorporates ideas from both control and information theory. A fundamental question it poses is how low the closed-loop data rate can be made before a given dynamical system is impossible to stabilize by any coding and control law. Analogously to source coding, this defines the smallest error-free data rate sufficient to achieve "reliable" control, and explicit expressions for it have been derived for linear time-invariant systems without disturbances. In this paper, the more general case of finite-dimensional linear systems with process and observation noise is considered, the object being mean square state stability. By inductive arguments employing the entropy power inequality of information theory, and a new quantizer error bound, an explicit expression for the infimum stabilizing data rate is derived, under very mild conditions on the initial state and noise probability distributions.
Hiding collaborative recommendation association rules on horizontally partitioned data

The study of privacy preserving data mining has become more important in recent years due to the increasing amount of personal data in public, the increasing sophistication of data mining algorithms to leverage this information, and the increasing concern of privacy breaches. Association rule hiding in which some of the association rules are suppressed in order to preserve privacy has been identified as a practical privacy preserving application [5,9,12,16,19-21,23,25,28-31]. Most current association rule hiding techniques assume that the data to be sanitized are in one single data set. However, in the real world, data may exist in distributed environment and owned by non-trusting parties that might be willing to collaborate. In this work, we propose a framework to hide collaborative recommendation association rules where the data sets are horizontally partitioned and owned by non-trusting parties. Algorithms to hide the collaborative recommendation association rules and to merge the sanitized data sets are introduced. Performance and various side effects of the proposed approach are analyzed numerically. Comparisons with trusting-third-party approach are reported. The proposed non-trusting-third-party approach shows better processing time, with similar side effects.
An overview of the Amber biomolecular simulation package

Molecular dynamics (MD) allows the study of biological and chemical systems at the atomistic level on timescales from femtoseconds to milliseconds. It complements experiment while also offering a way to follow processes difficult to discern with experimental techniques. Numerous software packages exist for conducting MD simulations of which one of the widest used is termed Amber. Here, we outline the most recent developments, since version 9 was released in April 2006, of the Amber and AmberTools MD software packages, referred to here as simply the Amber package. The latest release represents six years of continued development, since version 9, by multiple research groups and the culmination of over 33 years of work beginning with the first version in 1979. The latest release of the Amber package, version 12 released in April 2012, includes a substantial number of important developments in both the scientific and computer science arenas. We present here a condensed vision of what Amber currently supports and where things are likely to head over the coming years. Figure 1 shows the performance in ns/day of the Amber package version 12 on a single‐core AMD FX‐8120 8‐Core 3.6GHz CPU, the Cray XT5 system, and a single GPU GTX680. © 2012 John Wiley & Sons, Ltd.
Blackhole Tolerant Protocol for ZigBee Wireless Networks

ZigBee is a specification formalized by the IEEE 802.15. 4 standard designed for low rate wireless personal area networks (LR-WPAN). It consumes extremely low power for wireless connectivity which allows battery power to sustain for a long time. For routing in ZigBee mesh network, Ad-hoc on Demand Distance Vector (AODV) routing algorithm is extensively used. However the performance of the AODV degrade when the network is under black hole attack. In this paper, a technique is proposed to detect and defend black hole attack using AODV routing protocol with little modification. Analysis of AODV and suggested protocol under black hole prone network is carried out using NS-2. The result shows that efficacy of proposed protocol.
Options with Combined Reset Rights on Strike and Maturity

Reset clauses on the strike price and maturity date are commonly found in derivative contracts, like insurance segregated funds, bonds and executive warrants. We analyze the optimal reset policy adopted by the holder of an option that possesses the reset rights on the strike price and date of maturity. The optimal reset policy relates closely to the temporal rate of change of the value of the new option received by the holder at the reset moment. The characterization of the optimal reset policy requires the solution of a free boundary value problem. As part of the solution procedure, we determine the critical asset price for a given time to expiry at which the holder chooses to activate the reset clause optimally. Depending on the specific nature of the reset clauses, the reset policies exhibit a wide variety of behaviors. We also manage to obtain analytic price formulas for several specific types of reset options.
BOUNDING NORMS OF LOCALLY RANDOM MATRICES

Recently, several papers proving lower bounds for the performance of the Sum Of Squares Hierarchy on the planted clique problem have been published. A crucial part of all four papers is probabilistically bounding the norms of certain “locally random” matrices. In these matrices, the entries are not completely independent of each other, but rather depend upon a few edges of the input graph. In this paper, we study the norms of these locally random matrices. We start by bounding the norms of simple locally random matrices, whose entries depend on a bipartite graph H and a random graph G; we then generalize this result by bounding the norms of complex locally random matrices, matrices based off of a much more general graph H and a random graph G. For both cases, we prove almost-tight probabilistic bounds on the asymptotic behavior of the norms of these matrices.
H.265 Codec over 4G Networks for Telemedicine System Application

This paper presents the exploratory stages of a project to develop Telemedicine services in the Republic of Kazakhstan. The project intends to take advantage of recent technological developments. It proposes a new video-encoding scheme as a replacement for other video codecs currently used in telemedicine systems. H.265 is suggested for telemedicine systems because it can support high-quality video streaming for telemedicine systems and at the same time it requires less bandwidth than its predecessor H.264. This perfectly fits within 4G wireless network capabilities such as WiMAX and Long Term Evolution (LTE) networks. The project targets patients whom cannot easily access the hospital to be monitored or be treated by a specialist even though this is needed for patient cases such as rehabilitation, and paraplegics. Using LTE and WiMAX to form the telemedicine framework means that the system could be used either from the home to monitor the patient or it could be used from mobile devices such as smart-phones, PDAs, or laptops. In addition to the initial results, future investigations are proposed for optimal streaming of H.265 over 4G wireless networks.
An experimental comparison of source separation and beamforming techniques for microphone array signal enhancement

We consider the problem of separating one or more speech signals from a noisy background. Although blind source separation (BSS) and beamforming techniques have both been exploited in this context, the former have typically been applied to small microphone arrays and the latter to larger arrays. In this paper, we provide an experimental comparison of some established beamforming and post-filtering techniques on the one hand and modern BSS techniques involving advanced spectral models on the other hand. We analyze the results as a function of the number of microphones, the number of speakers and the input Signal-to-Noise Ratio (iSNR) w.r. t. multichannel real-world environmental noise recordings. The results of the comparison show that, provided that a suitable post-filter or spectral model is chosen, beamforming performs similar to BSS on average in the single-speaker case while in the two-speaker case BSS exceeds beamformer performance. Crucially, this claim holds independently of the number of microphones.
Synchronous generator model identification and parameter estimation from operating data

A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model armature circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the armature circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.
Stochastic Lane Shape Estimation Using Local Image Descriptors

In this paper, we present a novel measurement model for particle-filter-based lane shape estimation. Recently, the particle filter has been widely used to solve lane detection and tracking problems, due to its simplicity, robustness, and efficiency. The key part of the particle filter is the measurement model, which describes how well a generated hypothesis (a particle) fits current visual cues in the image. Previous methods often simply combine multiple visual cues in a likelihood function without considering the uncertainties of local visual cues and the accurate probability relationship between visual cues and the lane model. In contrast, this paper derives a new measurement model by utilizing multiple kernel density to precisely estimate this probability relationship. The uncertainties of local visual cues are considered and modeled by Gaussian kernels. Specifically, we use a linear-parabolic model to describe the shape of lane boundaries on a top-view image and a partitioned particle filter (PPF), integrating it with our novel measurement model to estimate lane shapes in consecutive frames. Finally, the robustness of the proposed algorithm with the new measurement model is demonstrated on the DRIVSCO data sets.
Enhance exploring temporal correlation for data collection in WSNs

Continuous data collection applications in wireless sensor networks require sensor nodes to continuously sample the surrounding physical phenomenon and then return the data to a processing center. Battery-operated sensors have to avoid heavy use of their wireless radio by compressing the time series sensed data instead of transmitting it in raw form. One of the most commonly used compacting methods is piecewise linear approximation. Previously, Liu et al. proposed a greedy PLAMLiS algorithm to approximate the time series into a number of line segments running in Theta(n2logn) time, however this is not appropriate for processing in the sensors. Therefore, based on our study we propose an alternative algorithm which obtains the same result but needs a shorter running time. Based on theoretical analysis and comprehensive simulations, it is shown that the new proposed algorithm has a competitive computational cost of Theta(nlogn) as well as reducing the number of line segments and so it can decrease the overall radio transmission load in order to save energy of the sensor nodes.
Atypical Presenting Symptoms of Acute Onset Systemic Lupus Erythematosus with Enteritis and Cystitis

Lupus enteritis and lupus cystitis are relatively rare manifestations of systemic lupus erythematosus. Some patients develop severe complications such as bowel perforation, infarction, obstruction, or irreversible bladder dysfunction. Early diagnosis is critical for management of lupus enteritis and cystitis. We report a 48-year-old Japanese man who presented with initial manifestations of abdominal pain, severe diarrhea, and bloody feces. The diagnosis was delayed due to atypical initial symptoms, resulting in clinical worsening. Physicians should be aware of typical computed tomography findings of lupus enteritis and lupus cystitis.
Vulnerability to Fine Grain Traffic Analysis

In this thesis, we apply the pattern recognition and data processing strengths of machine learning to accomplish traffic analysis objectives. Traffic analysis relies on the use of observable features of encrypted traffic in order to infer plaintext contents. We apply a clustering technique to HTTPS encrypted traffic on websites covering medical, legal and financial topics and achieve accuracy rates ranging from 64% 99% when identifying traffic within each website. The total number of URLs considered on each page ranged from 176 to 366. We present our results along with a justification of the machine learning techniques employed and an evaluation which explores the impact on accuracy of variations in amount of training data, number of clustering algorithm invocations, and convergence threshold. Our technique represents a significant improvement over previous techniques which have achieved similar accuracy, albeit with the aid of supporting assumptions simplifying traffic analysis. We examine these assumptions more closely and present results suggesting that two assumptions, browser cache configuration and selection of webpages for evaluation, can have considerable impact on analysis. Additionally, we propose a set of minimum evaluation standards for improved quality in traffic analysis evaluations.
Collocation based word sense disambiguation using clustering for Tamil

In this paper we present an unsupervised approach for Word Sense Disambiguation using clustering technique. This approach relies on the automatically extracted collocations for tagging the ambiguous words with the appropriate senses. The advantage of this approach is that the dependence of word sense disambiguisation algorithms, on either a sense-tagged corpus or a knowledge-source, such as dictionary, thesaurus etc. is removed. The algorithm is first trained on a training corpus to extract relevant collocations for each ambiguous words. We use the clustering technique to group the occurrences of an ambiguous word in the training corpus, where each group corresponds to a particular sense of the ambiguous word. Prominent collocations are then identified for each group. These collocations are then tagged with the appropriate sense manually and this information is used for disambiguating unseen occurrences of the ambiguous word. This method reduces the human effort in terms of sense tagging compared to the ealier approaches and at the same time gives reasonable levels of accuracy.
Perceptual evaluation of color gamut mapping algorithms

The recommendation of the CIE has been followed as closely as possible to evaluate the accuracy of five color gamut mapping algorithms (GMAs)—two nonspatial and three spatial algorithms—by psychophysical experiments with 20 test images, 20 observers, one test done on paper and a second one on display. Even though the results do not show any overall “winner,” one GMA is definitely perceived as not accurate. The importance of a high number of test images to obtain robust evaluation is underlined by the high variability of the results depending on the test images. Significant correlations between the percentage of out-of-gamut pixels, the number of distinguishable pairs of GMAs, and the perceived difficulty to distinguish them have been found. The type of observers is also important. The experts, who prefer a spatial GMA, show a stronger consensus and look especially for a good rendering of details, whereas the nonexperts hardly make a difference between the GMAs. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 470–476, 2008
Ecological Interfaces for Improving Mobile Robot Teleoperation

Navigation is an essential element of many remote robot operations including search and rescue, reconnaissance, and space exploration. Previous reports on using remote mobile robots suggest that navigation is difficult due to poor situation awareness. It has been recommended by experts in human-robot interaction that interfaces between humans and robots provide more spatial information and better situational context in order to improve an operator's situation awareness. This paper presents an ecological interface paradigm that combines video, map, and robot-pose information into a 3-D mixed-reality display. The ecological paradigm is validated in planar worlds by comparing it against the standard interface paradigm in a series of simulated and real-world user studies. Based on the experiment results, observations in the literature, and working hypotheses, we present a series of principles for presenting information to an operator of a remote robot.
zt: A Sofware Tool for Simple and Partial Mantel Tests

Different methods of data analysis (e.g. clustering and ordination) are based on distance matrices. In some cases, researchers may wish to compare several distance matrices with one another in order to test a hypothesis concerning a possible relationship between these matrices. However, this is not always self-evident. Usually, values in distance matrices are, in some way, correlated and therefore the usual assumption of independence between objects is violated in the classical tests approach. Furthermore, often, spurious correlations can be observed when comparing two distances matrices. A classic example is the comparison between genetic and environmental distances. Colonies that are in close proximity of each other tend to have similar environments and therefore there will be a positive correlation between environmental and geographical distances. Such colonies will also be more likely to exchange migrants so that genetic distances will be positively correlated with spatial distances. The consequence is that an observed positive association between genetic and environmental distances may be simply due to spatial effects. The most widely used method to account for distance correlations is a procedure known as the Mantel test (Mantel,'67; Mantel and Valand,'70 following the pioneering work of Daniels,'44 ; Daniels and Kendall'47). The simple Mantel test considers two matrices while an extension known as the partial Mantel test considers three matrices. These tools are widely used in different fields of research such as population genetics, ecology, anthropology, psychometrics and sociology.