Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficiency analysis of grid-connected photovoltaic inverter with storage battery

PV is an intermittent renewable power source. Grid-connected PV inverters conventionally use grid as the energy reservoir. As the level of PV penetration increases substantially in recent years, battery is proposed as the energy storage in grid-connected PV system. This is to increase on-site consumption, which in turn avoids grid voltage rise and reduces the loss in transmission line. Additionally, it allows PV inverter to dispatch power similar to the conventional power generations. This paper analyzes the overall efficiency of grid-connected PV inverter with storage battery using system level regression models. The analysis postulates nonlinear relation between the total power loss and the battery charging. Voltage droop battery charge controller that simplifies the efficiency optimization is developed based on the regression models. The analysis and the controller are verified by the component level power loss calculations. The controller is compared in simulation to the conventional constant-current/constant-voltage battery charging using PV outdoor measurement data.
Hyperbaric oxygen for acute carbon monoxide poisoning.

BACKGROUND Patients with acute carbon monoxide poisoning commonly have cognitive sequelae. We conducted a double-blind, randomized trial to evaluate the effect of hyperbaric-oxygen treatment on such cognitive sequelae. METHODS We randomly assigned patients with symptomatic acute carbon monoxide poisoning in equal proportions to three chamber sessions within a 24-hour period, consisting of either three hyperbaric-oxygen treatments or one normobaric-oxygen treatment plus two sessions of exposure to normobaric room air. Oxygen treatments were administered from a high-flow reservoir through a face mask that prevented rebreathing or by endotracheal tube. Neuropsychological tests were administered immediately after chamber sessions 1 and 3, and 2 weeks, 6 weeks, 6 months, and 12 months after enrollment. The primary outcome was cognitive sequelae six weeks after carbon monoxide poisoning. RESULTS The trial was stopped after the third of four scheduled interim analyses, at which point there were 76 patients in each group. Cognitive sequelae at six weeks were less frequent in the hyperbaric-oxygen group (19 of 76 [25.0 percent]) than in the normobaric-oxygen group (35 of 76 [46.1 percent], P=0.007), even after adjustment for cerebellar dysfunction and for stratification variables (adjusted odds ratio, 0.45 [95 percent confidence interval, 0.22 to 0.92]; P=0.03). The presence of cerebellar dysfunction before treatment was associated with the occurrence of cognitive sequelae (odds ratio, 5.71 [95 percent confidence interval, 1.69 to 19.31]; P=0.005) and was more frequent in the normobaric-oxygen group (15 percent vs. 4 percent, P=0.03). Cognitive sequelae were less frequent in the hyperbaric-oxygen group at 12 months, according to the intention-to-treat analysis (P=0.04). CONCLUSIONS Three hyperbaric-oxygen treatments within a 24-hour period appeared to reduce the risk of cognitive sequelae 6 weeks and 12 months after acute carbon monoxide poisoning.
Towards a Unified OWL Ontology of Cloud Vendors' Appliances and Services at PaaS and SaaS Level

Virtualization technologies represent the basis of many software architectures and frameworks existing today, Cloud Computing being probably the most influenced paradigm in the current scenery. With the steady growth of new Cloud and Virtualization platforms, which have caused the proliferation of many Cloud Services and Virtual appliances, it seems necessary to put order in the chaos of overlapping functionalities such services and appliances provide. A common representation of both Cloud Services and Virtual Appliances can be the perfect starting point to define a functional classification that could help customers choose the resources which best adhere to their objectives. A semantic based representation, implemented through an OWL [1] ontology, is probably the best candidate for this purpose, thanks to the flexibility of the OWL language and the possibility to navigate the ontology using both visual tools and query languages.
Image Segmentation with Tensor – Based Classification of N – Point Correlation Functions

In this paper, we investigate the use ofN–Point correlation functions from material science literature, for medical image segmentation, and introduce a classifier suitable for use with these functions. TheN–point correlation functions serve as good estimators of component material distributions and their packing in a multi–phase heterogeneous media. We represent these multi–phase properties with tensor structures and employ these functions as features in our tensor decomposition based classification algorithm. We use a variant of Higher Order Singular Value Decomposition (HOSVD) to extract the multi– linear properties of the tensor feature space and reduce the dimensionality with respect to several modes. The preliminary results of segmenting a placenta image with these functions and classifier, are very promising.
Space vector PWM control of dual inverter fed open-end winding induction motor drive

A space vector PWM technique is developed based on the combination of space vectors from dual inverters feeding the induction motor from both ends (open-end winding without neutral point). A total of 64 voltage space vector combinations are available for PWM voltage control of the inverter fed machine with open-end winding. A space phasor based PWM scheme is proposed with minimum number of switching in a cycle per inverter coupled with equal number of switching for each inverter.
Temperature affects the T-DNA transfer machinery of Agrobacterium tumefaciens

Early studies on Agrobacterium tumefaciens showed that development of tumors on plants following infection by A. tumefaciens was optimal at temperatures around 22 degrees C and did not occur at temperatures above 29 degrees C. To assess whether this inability to induce tumors is due to a defect in the T-DNA transfer machinery, mobilization of an incompatibility group Q (IncQ) plasmid by the T-DNA transfer machinery of A. tumefaciens was tested at various temperatures. Optimal transfer occurred when matings were performed at 19 degrees C, and transfer was not seen when matings were incubated above 28 degrees C. Transfer of the IncQ plasmid was dependent upon induction of the virB and virD operons by acetosyringone but was not dependent upon induction of the tra genes by octopine. However, alterations in the level of vir gene induction could not account for the decrease in transfer with increasing temperature. A. tumefaciens did successfully mobilize IncQ plasmids at higher temperatures when alternative transfer machineries were provided. Thus, the defect in transfer at high temperature is apparently in the T-DNA transfer machinery itself. As these data correlate with earlier tumorigenesis studies, we propose that tumor suppression at higher temperatures results from a T-DNA transfer machinery which does not function properly.
Markov random field optimization for intensity-based 2D-3D registration

We propose a Markov Random Field (MRF) formulation for the intensity-based N-view 2D-3D registration problem. The transformation aligning the 3D volume to the 2D views is estimated by iterative updates obtained by discrete optimization of the proposed MRF model. We employ a pairwise MRF model with a fully connected graph in which the nodes represent the parameter updates and the edges encode the image similarity costs resulting from variations of the values of adjacent nodes. A label space refinement strategy is employed to achieve sub-millimeter accuracy. The evaluation on real and synthetic data and comparison to state-of-the-art method demonstrates the potential of our approach.
Optimization-Aware Scheduling in Cloud Computing

Cloud computing allows delivery of computational resources via internet. Some of the computational resources include signals, codes and physical resources. Cloud users require computational resources for execution of their tasks. The computational elements, when thoroughly assigned to the cloud users according to requirement facilitate an efficient scheduling mechanism. The efficiency is dependent on the parameters chosen to promote the assignment of task to resources. Here, computational cost is chosen as the optimization factor, and the solution with the least cost is selected as the best assignment. A Cuckoo Search inspired technique is used, and task assignment to resources is done by validating the solution with the least value of cost.
An Exploration Strategy for Simultaneous Localization and Mapping in Large Environments

It’s preferred that a hierarchical map is adopted to describe large environments, as seen in the literatures about the hierarchical simultaneous localization and mapping (HSLAM). With an automaton map at the upper layer and a set of feature-based maps at the lower layer, both macro and micro localization are realized. A novel exploration strategy which guides and supervises the mapping progress based on supervisory control of discrete event systems is proposed. It’s embodied in the system and implemented as a supervisor which permits some desired actions to occur and prohibits undesired actions.
Effect of directional antennas on spatiotemporal sampling in clustered sensor networks

In a sensor cluster, a large number of geographically distributed sensor nodes each make local measurements and transmit them toward the clusterhead. The clusterhead may schedule simultaneous transmissions from nodes within the cluster by identifying non-interfering areas in the cluster. Directional antennas that are oriented toward the clusterhead may increase spatial reuse and thus increase the number of simultaneous transmissions. On the other hand, the antennas' narrower coverage areas may reduce connectivity, thus reducing the number of sensors from which data can be collected. In this paper, we investigate this tradeoff between spatial reuse and connectivity in the context of the spatiotemporal sampling rate that can be achieved by the cluster. We also propose a simple analytical approach to optimizing the antennas beam width in order to improve the spatiotemporal sampling rate by maximizing the transport capacity of the network
Competition for Exclusive Customers: Comparing Equilibrium and Welfare Under One-Part and Two-Part Pricing

This paper compares one-part and two-part pricing in a discrete-continuous choice model, providing more extensive welfare results than prior literature. Under two-part pricing, firms may set fixed fees with or without `unit-price commitment,' where the lack of unit-price commitment is consistent with `after-market monopolization. ' We find that two-part pricing with unit-price commitment is firms' dominant unilateral and joint pricing policy. Two-part pricing without unit-price commitment is the least desirable policy from a welfare standpoint. Under appropriate conditions, one-part pricing produces the highest consumer and social welfare, but the lowest profits.
Playing Both Sides of the Market: Success and Reciprocity on Crowdfunding Platforms

Crowdfunding platforms constitute two-sided markets, bringing together entrepreneurs and potential backers; this peer-based fundraising schema introduces new dynamics into the fundraising process. We focus on platform agents who play on both sides of the market, supporting the ventures of their fellow entrepreneurs and subsequently raising money for their own venture. Acting on both sides of the market is a peer-economy phenomenon that has not yet received much attention. We find that an entrepreneur’s backing-history has a significant effect on financing outcomes; campaigns initiated by entrepreneurs who have previously supported others have higher success rates, attract more backers and collect more funds. We find evidence that supports the existence of a causal channel from playing both sides of the market to increased crowdfunding success. We also provide evidence as to the existence of reciprocity. Project owners back their backers, when possible, at a rate that is significantly higher than other comparable projects. We estimate the effect of such actions on the performance of crowdfunding campaigns and show that playing both sides of the market is a rewarding strategy.
P2X7 receptors mediate ischemic damage to oligodendrocytes

Brain ischemia leading to stroke is a major cause of disability in developed countries. Therapeutic strategies have most commonly focused on protecting neurons from ischemic damage. However, ischemic damage to white matter causes oligodendrocyte death, myelin disruption, and axon dysfunction, and it is partially mediated by glutamate excitotoxicity. We have previously demonstrated that oligodendrocytes express ionotropic purinergic receptors. The objective of this study was to investigate the role of purinergic signaling in white matter ischemia. We show that, in addition to glutamate, enhanced ATP signaling during ischemia is also deleterious to oligodendrocytes and myelin, and impairs white matter function. Thus, ischemic oligodendrocytes in culture display an inward current and cytosolic Ca2+ overload, which is partially mediated by P2X7 receptors. Indeed, oligodendrocytes release ATP after oxygen and glucose deprivation through the opening of pannexin hemichannels. Consistently, ischemia‐induced mitochondrial depolarization as well as oxidative stress culminating in cell death are partially reversed by P2X7 receptor antagonists, by the ATP degrading enzyme apyrase and by blockers of pannexin hemichannels. In turn, ischemic damage in isolated optic nerves, which share the properties of brain white matter, is greatly attenuated by all these drugs. Ultrastructural analysis and electrophysiological recordings demonstrated that P2X7 antagonists prevent ischemic damage to oligodendrocytes and myelin, and improved action potential recovery after ischemia. These data indicate that ATP released during ischemia and the subsequent activation of P2X7 receptor is critical to white matter demise during stroke and point to this receptor type as a therapeutic target to limit tissue damage in cerebrovascular diseases. © 2009 Wiley‐Liss, Inc.
A CANONICAL AND/EXOR FORM THAT INCLUDES BOTH THE GENERALIZED REED-MULLER FORMS AND KRONECKER REED-MULLER FORMS

| The paper gives a positive answer to a question posed by Tsutomu Sasao-whether there exists some family of canonical forms that would include both the Generalized Reed-Muller Forms and the Kro-necker RM Forms as its special cases. We call the new forms Generalized Kronecker Forms. We present also the corresponding decision diagrams. They can be used for representation of large functions and also for optimal synthesis of highly testable two-level and multilevel circuits in several technologies, including Field Programmable Gate Arrays (FPGAs).
Implementing jalapeño in Java

Jalapeño is a virtual machine for Java#8482; servers written in Java. A running Java program involves four layers of functionality: the user code, the virtual-machine, the operating system, and the hardware. By drawing the Java / non-Java boundary below the virtual machine rather than above it, Jalapeño reduces the boundary-crossing overhead and opens up more opportunities for optimization. To get Jalapeño started, a boot image of a working Jalapeño virtual machine is concocted and written to a file. Later, this file can be loaded into memory and executed. Because the boot image consists entirely of Java objects, it can be concocted by a Java program that runs in any JVM. This program uses reflection to convert the boot image into Jalapeño's object format. A special MAGIC class allows unsafe casts and direct access to the hardware. Methods of this class are recognized by Jalapeño's three compilers, which ignore their bytecodes and emit special-purpose machine code. User code will not be allowed to call MAGIC methods so Java's integrity is preserved. A small non-Java program is used to start up a boot image and as an interface to the operating system. Java's programming features — object orientation, type safety, automatic memory management — greatly facilitated development of Jalapeño. However, we also discovered some of the language's limitations.
Using Association Rules for Course Recommendation

Students often need guidance in choosing adequate courses to complete their academic degrees. Course recommender systems have been suggested in the literature as a tool to help students make informed course selections. Although a variety of techniques have been proposed in these course recommender systems, combining data mining with user ratings in order to improve the recommendation has never been done before. This paper presents RARE, a course Recommender system based on Association RulEs, which incorporates a data mining process together with user ratings in recommendation. Starting from a history of real data, it discovers significant rules that associate academic courses followed by former students. These rules are later used to infer recommendations. In order to benefit from the current students’ opinions, RARE also offers to users the possibility to rate the recommendations, thus leading to an improvement of the rules. Therefore, RARE combines the benefits of both former students’ experience and current students’ ratings in order to recommend the most relevant courses to its users.
UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB

Motivation: Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequence similarity are attracting increased attention. One of these novel approaches is to use the organization of the structural domains in proteins. Results: We propose a method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of this method was measured through a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/Swiss-Prot. The results demonstrate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85. We applied the method on nearly 55.3 million uncharacterized proteins in UniProtKB/TrEMBL resulted in 44 818 178 GO term predictions for 12 172 114 proteins. 22% of these predictions were for 2 812 016 previously non-annotated protein entries indicating the significance of the value added by this approach. Availability and implementation: The results of the method are available at: ftp://ftp.ebi.ac.uk/pub/contrib/martin/DAAC/. Contact: tdogan@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
EARLY REFLECTION THRESHOLDS FOR VIRTUAL SOUND SOURCES

Data on auditory thresholds for acoustic reflections were obtained from 29 subjects as a function of both spatial position and time delay in a simulated 5.1 surround sound listening environment. Absolute thresholds (perception of any type of change) were measured at the 70.7% level using a one up-two down staircase algorithm, for both anechoic and reverberant speech stimuli conditions, from 18 subjects. Additional data were gathered from 11 subjects for tone burst stimuli. Reflection threshold data are useful in the context of building acoustics, since path length attenuation and absorption can make potential reflections inaudible. Audibility of reflections is desirable for 3-D sound headphone simulations that require sound source externalization. The information is also useful for determining engineering parameters for the real-time simulation of virtual acoustic environments, such as headmounted displays that include head tracking. For all types of stimuli, results indicate that a single early reflection should be inaudible when less than 21 dB below the direct sound at 3 ms, and less than 30 dB at 15- 30 ms.
Calendar Spreads, Risk Premium and the Convenience Yield

This paper studies optimal calendar spreads in commodity futures markets while taking into account a stochastic convenience yield. We show that a convenience yield imperfectly correlated with the spot commmodity price results in an optimal strategy composed of two commodity futures contracts. These strategies reveal a calendar spread effect through the positive correlation between the two futures contracts. These strategies can easily be computed and analyzed under the Samuelson hypothesis.
Robust control strategies of stick-slip type actuators for fast and accurate nanopositioning operations in scanning mode

This paper deals with robust closed-loop control of a nano-robotic system dedicated to fast scanning probe microscopy. The nano-robotic system is actuated by piezoelectric stick-slip actuators able to produce a millimeter range displacement with a nanometer resolution. In order to meet the requirements of fast scanning in terms of closed-loop bandwidth and vibration damping, robust control strategies are studied. We first show that a commonly used one degree of freedom (1-DOF) H∞ controller is limited to satisfy robust performances required for fast and accurate positioning of the actuators. As such, the control strategy is defined considering two closed-loops. Results show that the 2-DOF H∞ control scheme allows robust performances for the positioning of nanorobotic systems and lead to new perspectives for fast scanning probe microscopy using stick-slip actuators.
Tactile-Data Classification of Contact Materials Using Computational Intelligence

The two major components of a robotic tactile-sensing system are the tactile-sensing hardware at the lower level and the computational/software tools at the higher level. Focusing on the latter, this research assesses the suitability of computational-intelligence (CI) tools for tactile-data processing. In this context, this paper addresses the classification of sensed object material from the recorded raw tactile data. For this purpose, three CI paradigms, namely, the support-vector machine (SVM), regularized least square (RLS), and regularized extreme learning machine (RELM), have been employed, and their performance is compared for the said task. The comparative analysis shows that SVM provides the best tradeoff between classification accuracy and computational complexity of the classification algorithm. Experimental results indicate that the CI tools are effective in dealing with the challenging problem of material classification.
Competition, Time Horizon and Corporate Social Performance

Abstract: This paper develops and tests a conceptual framework on the relationships between competition, time horizon and corporate social performance (CSP). We hypothesize that more intense competition discourages CSP by lowering the time horizon of companies. We test the hypothesis on a sample of 4696 of mainly small and medium-sized companies from twelve European countries. We distinguish between price competition, market position and technological competition. We find that companies with a longer time horizon have a higher CSP and that price competition and a ‘level playing field’ market position shorten the time horizon. The intensity of technological competition has a positive effect on time horizon, but also exerts a direct positive influence on CSP. Test results show that time horizon significantly mediates the influence of price competition, the market position and technological competition on CSP. The analysis implies that, from the perspective of CSP, the economic policy of the government should not focus on fostering price competition, but rather on strengthening competition in innovation.