Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Partially Shaded Conditions on Photovoltaic Array's Maximum Power Point Tracking

Maximum power point tracking algorithm is widely implemented in photovoltaic system to maximize the PV array output power. In general, Perturb and Observe (P&O) is simple thus being selected to continuously track the array maximum power point (MPP). Under uniform solar irradiance, PV array characteristic is non-linear and consisting only one MPP along the functional operating voltage. However, when the PV array is partially shaded, the P-V characteristic becomes more complex with multiple MPPs. The occurrence of multiple MPP might cause the PV array to be trapped at the local MPP. At this operating condition of local MPP, PV array will generate lesser output power. In this study, the performance of PV array is explored especially when each PV module is at 30% and 70% shaded conditions. Simulation results show that PV array at absolute MPP can generate greater output power with the largest increased by 74.6% hence achieving higher power efficiency.
Statistical Weighted Approach to Identify Sentiments over Movie Reviews

Online movie reviews are collected by different agencies to predict or analyze the user response towards the movie. It is considered as the business plan to estimate the movie response and earning. But the review is collected in the form of textual feedback that requires some intelligent automated check called sentiment analysis. In this present work, a weighted statistical approach is suggested to perform the movie sentiment analysis. The work includes the analysis on overall movie review as well as feature level review analysis. The obtained results shows the clear identification of positive and negative sentiments at feature level so that the effective decision based on sentiment analysis can be taken
Do Pharmacists Buy Bayer? Sophisticated Shoppers and the Brand Premium

We estimate the effect of information on consumers’ willingness to pay for branded goods in physically homogeneous consumer packaged goods categories. In a case study of headache remedies, we find that college education, working in a healthcare occupation, and other proxies for product knowledge predict more purchases of private labels relative to brands. Pharmacists devote almost 90 percent of headache remedy purchases to private labels, against 71 percent for the average consumer. The effect of knowledge is similar across a broad set of health products, and in a set of relatively homogeneous food products, but smaller for food and drink products overall. We conclude that a significant share of the willingness to pay for brands in these categories would disappear in a world where consumers were fully informed.
Volatility in International Tourism Demands for Small Island Tourism Economies

Volatility in monthly international tourist arrivals is defined as the squared deviation from mean monthly international tourist arrivals. Consequently, volatility is directly related to the standard deviation, which is a common measure of financial risk. Fluctuating variations, or conditional volatility, in international monthly tourist arrivals are typically associated with unanticipated events. There are timevarying effects related to SITEs, such as natural disasters, ethnic conflicts, crime, the threat of terrorism, and business cycles in tourist source countries, among others, which cause variations in monthly international tourist arrivals. In this paper, we show how the generalised autoregressive conditional heteroscedasticity (GARCH) model can be used to measure the conditional volatility in monthly international tourist arrivals to six SITEs, namely Barbados, Cyprus, Dominica, Fiji, Maldives and Seychelles, and to appraise the implications of conditional volatility of SITEs for modelling tourist arrivals.
Performance evaluation of different routing protocols to minimize congestion in heterogeneous network

In this paper, we have implemented Multipath routing algorithms for heterogeneous network. Multipath routing separates the traffic among different paths to minimize congestion in terms of multiple alternative paths through a network which can provide a variety of benefits such as minimize delay and congestion, maximize bandwidth, or improved security. We propose a newly improved QoS multipath routing algorithm for heterogeneous networks. Different types of adhoc routing protocols are discussed in this paper such as Ad-Hoc On-Demand Distance Vector (AODV), Ad-Hoc On Demand Multipath Distance Vector (AOMDV), QoS Ad-Hoc On Demand Multipath Distance Vector (QAOMDV), AOMDV is the extension of AODV routing protocol. QAOMDV is QoS version of AOMDV. These routing protocol are used in wireless network which is designed to form multiple routes from source to the destination and also avoid the loop formation so that it reduces congestion in the channel. The performance of AODV, AOMDV, QAOMDV protocols are compared and proved the new routing protocol is better than others. The NS2 simulation result shows that improved performance of the heterogeneous network for newly proposed multipath routing protocol. The QAOMDV works better than other protocols in terms of delay, bandwidth, load balance, routing overhead and packet delivery ratio have been considered by varying the traffic load in the network.
An Object-Oriented Represenation Language to Support Multiple Perspective Explanations

The EXPLAINER tool [Redmiles 93] was developed to help programmers solve tasks in computer graphics by allowing them to explore previously worked-out examples from several different perspectives. Perspectives are used by EXPLAINER to provide programmers access to information about an example through code listings, execution output, text statement of a problem domain, text statement of program features, and diagrams of program components and features.
Automatic Synthesis of Rules for Planning in Belief Space

Robots operating in complex, unknown environments have to deal explicitly with uncertainty. Sensing is increasingly reliable, but remains inescapably local: robots cannot see, immediately, inside cupboards, under collapsed walls, or into nuclear containment vessels. Task planning in household and disaster-relief domains requires explicit consideration of uncertainty and the selection of actions at both the task and motion levels to support gathering information. We have previously sketched an overall methodology for hierarchical task and motion planning in uncertain domains, and demonstrated it on a real robot [5]. A critical foundation of that methodology is the idea of planning in belief space: that is, the space of the robot’s beliefs (represented as probability distributions and other data structures) about the state of its environment. For planning purposes, the initial state is a belief state and the goal is a set of belief states: for example, a goal might be for the robot to believe with probability greater than 0.99 that all of the groceries are put away in an acceptable location, or that there are no survivors remaining in the rubble. Our approach is founded on the notion of integrating estimation, planning, and execution in a feedback loop. A plan is made, based on the current belief state; the first step is executed; an observation is obtained; the belief state is updated; the plan is recomputed, if necessary, etc. This approach allows planning to be approximate: it is important that the first step of the plan be useful, but the rest will be reexamined in light of the results of the first step. For this reason, we construct plans using a deterministic approximation of the probabilistic dynamics, which is discussed in detail in [5]. In order to support task planning in belief space, it had previously been necessary to write down a description of the domain by articulating the effects of different actions (moving an object, making an observation) in terms of their effects on the robot’s belief state. Even experts in both the domain and in probabilistic reasoning found it difficult to write a correct and effective specification. In this paper, we address that problem by providing a formalism for describing the stochastic transition and observation models that characterize the domain and an algorithm for converting those models into rules for planning in belief space. In addition to simplifying the problem of domain specification, this conversion process yields additional insight into conditions under which such a formalism could be correct and complete. This is a preliminary presentation of the work: the implementation and detailed solution is currently confined to discrete underlying state spaces.
The minimal important difference of exercise tests in severe COPD

Our aim was to determine the minimal important difference (MID) for 6-min walk distance (6MWD) and maximal cycle exercise capacity (MCEC) in patients with severe chronic obstructive pulmonary disease (COPD). 1,218 patients enrolled in the National Emphysema Treatment Trial completed exercise tests before and after 4–6 weeks of pre-trial rehabilitation, and 6 months after randomisation to surgery or medical care. The St George's Respiratory Questionnaire (domain and total scores) and University of California San Diego Shortness of Breath Questionnaire (total score) served as anchors for anchor-based MID estimates. In order to calculate distribution-based estimates, we used the standard error of measurement, Cohen's effect size and the empirical rule effect size. Anchor-based estimates for the 6MWD were 18.9 m (95% CI 18.1–20.1 m), 24.2 m (95% CI 23.4–25.4 m), 24.6 m (95% CI 23.4–25.7 m) and 26.4 m (95% CI 25.4–27.4 m), which were similar to distribution-based MID estimates of 25.7, 26.8 and 30.6 m. For MCEC, anchor-based estimates for the MID were 2.2 W (95% CI 2.0–2.4 W), 3.2 W (95% CI 3.0–3.4 W), 3.2 W (95% CI 3.0–3.4 W) and 3.3 W (95% CI 3.0–3.5 W), while distribution-based estimates were 5.3 and 5.5 W. We suggest a MID of 26±2 m for 6MWD and 4±1 W for MCEC for patients with severe COPD.
Political polarization and economic performance

This dissertation consists of four separate papers.Paper 1: Will Privatization Reduce Costs? I develop a model of public sector contracting. In this model, an agent can put effort into increasing the quality of a service or reducing costs. Being residual claimants, private owners have stronger incentives to cut costs than public employees. However, if quality cannot be perfectly measured, providing a private firm with incentives to improve quality forces the owner of the firm to bear risk. As a result, private firms will always be cheaper for low levels of quality but might be more expensive for high levels of quality.Paper 2: When Does Privatization Reduce Costs and Improve Quality? Theory and Evidence from Service ContractingI develop and test a model of service contracting in residential youth care. I find that private facilities have lower per-day cost of treatment for low levels of quality, but higher per-day cost for high levels of quality. Though public facilities generally have a higher level of quality, private facilities are relatively better at treating troublesome teenagers. Treatment periods are much longer under private provision, implying that the average total cost of treatment is twice as high in the private sector.Paper 3: Identity and Redistribution (co-author Robert Ostling)This paper models the interaction between individuals' identity choices and redistribution. Both redistributive polices and identity choices are endogenous, and there might be multiple equilibria. The model is applied to ethnicity and social class.Paper 4: Political Polarization and Economic Performance (co-author Robert Ostling)We study the effect of political polarization on economic performance using the dispersion of self-reported political preferences as our measure of polarization. We find that politically polarized countries are poorer and have smaller and inferior governments.
Development of a novel leg mechanism in the shape of backward convex with a mechanical joint stop for bipedal robot

The realization of natural, fast and versatile motions for bipedal robots is still a challenge topic. As one approach to enhance the performance of bipedal robots, actuation with high backdrivability, high power-to-weight ratio as well as high torque is necessary. In this paper, a novel mechanism of leg in the shape of backward convex with a mechanical joint stop is proposed to reduce the necessary torque by the actuator in knee joint and then to improve the actuation capability of knee joint and reduce the consuming energy by the actuator while by a light structure. The design method including simulation for the mechanism is described. A prototype of the leg robot has been developed and the basic feasibility of the mechanism is shown by experimental results.
Optimal lane-changing control at motorway bottlenecks

We propose an optimal feedback control strategy for lane-changing control at bottleneck locations, assuming that a percentage of vehicles, equipped with vehicle automation and communication systems, are capable of receiving and executing specific lane-changing orders or recommendations. Based on a simplified multi-lane motorway traffic flow model, we formulate an optimal feedback control problem, cast as a linear quadratic regulator problem, aiming at maximising the throughput at bottleneck locations, via optimal lane assignment of vehicles upstream of the bottleneck. The feedback control decisions are based on appropriate choice of set-points for traffic densities and real-time measurements of the state of the system. We also present an extremum seeking algorithm to seek the optimal set-points using only the measurement of a cost that is representative of the achieved traffic conditions. The proposed strategy is tested on a nonlinear first-order macroscopic multi-lane traffic flow model, which accounts also for the capacity drop phenomenon.
A deep wide-field optical survey in the young open cluster Collinder 359

We present the first deep, optical, wide-field imaging survey of the young open cluster Collinder 359, complemented by near-infrared follow-up observations. This study is part of a large programme aimed at examining the dependence of the mass function on environment and time. We have surveyed 1.6 square degrees in the cluster, in the I and z filters, with the CFH12K camera on the Canada-France-Hawaii 3.6-m telescope down to completeness and detection limits in both filters of 22.0 and 24.0 mag, respectively. Based on their location in the optical ($I-z$, I ) colour–magnitude diagram, we have extracted new cluster member candidates in Collinder 359 spanning 1.3-0.03 $M_{\odot}$, assuming an age of 60 Myr and a distance of 450 pc for the cluster. We have used the 2MASS database as well as our own near-infrared photometry to examine the membership status of the optically-selected cluster candidates. Comparison of the location of the most massive members in Collinder 359 in a ($B-V$, V ) diagram with theoretical isochrones suggests that Collinder 359 is older than α Per but younger than the Pleiades. We discuss the possible relationship between Collinder 359 and IC 4665 as both clusters harbour similar parameters, including proper motion, distance, and age.
Interconnection of DAB converters for application in solid-state transformers with redundancy

This paper presents the theoretical foundation, mathematical analysis and numerical simulation results of an interconnection between Dual-Active Bridge (DAB) converters applied as Solid-State Transformers (SST) with redundancy. This system enables current and voltage sharing among the converters and the achievement of high reliability as to the power supply. The main contribution of this study is to show an interconnection scheme that allows the construction of a fault-tolerant SST.
Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

H.264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.
Halo mass functions in early dark energy cosmologies

We examine the linear density contrast at collapse time, δc, for large-scale structure in dynamical dark energy cosmologies, including models with early dark energy. Contrary to previous results, we find that as long as dark energy is homogeneous on small scales, δc is insensitive to dark energy properties for parameter values fitting current data, including the case of early dark energy. This is significant since using the correct δc is crucial for accurate Press–Schechter prediction of the halo mass function. Previous results have found an apparent failing of the extended Press–Schechter approach (Sheth–Tormen) for early dark energy. Our calculations demonstrate that with the correct δc, the accuracy of this approach is restored. We discuss the significance of this result for the halo mass function and examine what dark energy physics would be needed to cause significant change in δc, and the observational signatures this would leave.
Pressure-induced metallization phase transitions of PbSe and PbTe by electrical conductivity measurements and first principle methods

In situ electric conductivity measurements of PbSe and PbTe have been performed under high pressure in a diamond anvil cell. The results reveal the change of electron transport behaviors. As the pressure increased from ambient to 27 GPa, two and three abnormal conductivity changes were observed at room temperature in PbSe and PbTe, respectively. The abnormal conductivity changes in PbSe and PbTe can be ascribed to the structure phase transition. At high pressure, the conductivities in both PbSe and PbTe rise obviously and their values are near metal order. The first-principles calculations performed by CASTEP code based on the density functional theory illustrate that the direct band gap of PbTe vanishes at 15 GPa, which results in semiconductor to metal in PbTe. The results fully convinced that PbSe and PbTe can be metalized under pressure.
Short- and long-term plasticity of the perforant path synapse in hippocampal area CA3.

The direct perforant path (PP) projection to CA3 is a major source of cortical input to the hippocampal region, yet relatively little is known about the basic properties of physiology and plasticity in this pathway. We tested whether PP long-term potentiation (LTP) in CA3 possesses the Hebbian property of associativity; i.e., whether the firing of fibers of different orders can induce PP LTP. We stimulated PP with weak trains of high-frequency stimulation (HFS), which by itself was below the threshold for LTP induction. The identical HFS was effective in inducing LTP when the mossy fiber pathway (MF) was activated simultaneously, thus demonstrating associative plasticity between the two pathways. We also demonstrated associative LTP between PP and recurrent collateral fibers (RC). PP LTP was blocked by the N-methyl-D-aspartate receptor (NMDAR) antagonist 2-amino-5-phosphonovaleric acid in both the associative and homosynaptic induction conditions. Neither MF nor RC fiber HFS alone resulted in permanent changes in PP field excitatory postsynaptic potential (fEPSP) amplitude. However, HFS delivered to either MF or RC alone led to transient heterosynaptic depression of the PP fEPSP. Our results support the conceptual framework that regards CA3 as an autoassociative memory network in which efficient retrieval of previously stored activity patterns is mediated by associative plasticity of the PP synapse.
Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing

Online social networks nowadays enjoy their worldwide prosperity, as they have revolutionized the way for people to discover, to share, and to distribute information. With millions of registered users and the proliferation of user-generated contents, the social networks become "giants", likely eligible to carry on any research tasks. However, the giants do have their Achilles Heel: extreme data sparsity. Compared with the massive data over the whole collection, individual posting documents, (e.g., a microblog less than 140 characters), seem to be too sparse to make a difference under various research scenarios, while actually they are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. We formulate a socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users. An efficient algorithm is designed to learn the proposed factor graph model. Finally we propagate term counts to smooth documents based on the estimated influence. Experimental results on Twitter and Weibo datasets validate the effectiveness of the proposed model. By leveraging the smoothed language model with social factors, our approach obtains significant improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP results.
Phrase-Level Pattern-Based Machine Translation

To overcome the conventional method based on Composi- tional Semantics, the Analogical Mapping Method was developed. Im- plementing this method requires a translation method based on sentence patterns. Although a word-level pattern-based translation system already exists in Japanese-English machine translation, this paper describes the new phrase-level pattern-based translation system. The results of trans- lation experiments show that the quality of phrase translation is still low. However, these problems are to be resolved in our future work.
Betti Bounds of Polynomials

We initiate a classification of complex polynomials f of degree d having the top Betti number of the general fibre close to the maximum. We find a range in which the polynomial must have isolated singularities and another range where it may have at most a line singularity of Morse transversal type, besides controlled singularities at infinity. Our method uses deformations into particular pencils with non-isolated singularities.