Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of energy storage devices on energy price in decentralized wind-diesel utilities

The most widely spread method of supplying the customers in isolated areas is to use single or multiple diesel generators with constant or variable speed. The price of energy in such areas is highly influenced by the cost of fuel transportation to the place of consumption. We propose an approach to reduce this price by complementing the diesel generators with wind turbines and high-capacity energy storage devices. We create a model to define the proportion between the installed capacities and to provide the proper sizing of elements. The paper studies the case of an isolated area in central Russia, where the fuel transportation share of energy price reaches 65%. We propose a new energy supply system, which includes diesel generators, wind turbines and energy storage devices. We study the system statistically and model it in Homer software. To decrease the capital and operating costs, we introduce the specific algorithm: the diesel generator operates only when wind turbines are not covering the load and energy storage device is exhausted. We test the proposed algorithm using MATLAB to obtain both economic and technical operation results. The economic modeling then continues the system evaluation. We evaluate the investment criteria and define the new minimum price for electricity. The paper is concluded by a systematic analysis of the results and remarks towards the efficiency of investment into the new equipment.
Spermatozoon ultrastructure of Thysanotaenia congolensis ( Cyclophyllidea , Anoplocephalidae , Inermicapsiferinae ) : phylogenetic implications

The mature spermatozoon of Thysanotaenia congolensis, an intestinal parasite of black rat Rattus rattus from Cape Verde, is described by means of transmission electron microscopy. The ultrastructural organization of the sperm cell of T. congolensis follows Levron et al.’s type VII of the Eucestoda. It corresponds to a uniflagellate spermatozoon that presents crested bodies, periaxonemal sheath and intracytoplasmic walls, spiralled cortical microtubules and nucleus spiralled around the axoneme. These characteristics are also present in the spermatozoa of other inermicapsiferines and differ from the characters found in species belonging to the remaining subfamilies of anoplocephalids, namely Anoplocephalinae, Linstowiinae and Thysanosomatinae. Several authors consider the family Anoplocephalidae as a polyphyletic group, and its relationships with the Davaineidae are a matter of controversy. The phylogenetic implications of spermatological ultrastructural features presen t in inermicaps i fe r ines and in the remain ing anoplocephalids are discussed, and the available data on anoplocephalids are compared to similar results in davaineids in order to contribute to a better knowledge of relationships between these cyclophyllidean families.
Stable modeling of arbitrarily oriented thin slots in the FDTD method

A subcell model for thin wires in the finite-difference time-domain (FDTD) method using modified telegraphers equations has been developed by Holland et al. Edelvik has previously presented an extension of their algorithm, which allows for arbitrarily located and oriented wires with respect to the Cartesian grid. This is important to be able to accurately model wires that cannot be aligned to the Cartesian grid, e.g., tilted wires and circular loop wires. Recently, a dual set of equations has been proposed for modeling of thin slots. In this paper, we show that using a similar algorithm as for thin wires we can also handle slots of arbitrary location in Cartesian planes. Previous thin slot models have been susceptible for instabilities. We show that a symmetric coupling between field and slot yields a stable time-continuous field-slot system and that the fully discrete field-slot system is stable under a generalized Courant-Friedrich-Lewy (CFL) condition. The proposed method is demonstrated for scattering from a finite-length slot in an infinite conducting wall and a shielding enclosure including a slot. The results are in good agreement with published experimental data.
Emerging Roles for Platelets in Inflammation and Disease

Platelets and their interaction with cells of the immune system contribute through a variety of molecular mechanisms to support hemostasis and inflammation. These simple yet essential cells exert their effects in lymphocytes, monocytes, and neutrophils, both recruiting and modulating their function after activation. Emerging evidence is starting to define the mechanisms that allow platelets to also play pivotal roles in host defense. For example, platelet cell-surface expression of toll-like receptors allows platelets to direct neutrophil activation toward extracellular trap formation and facilitate the elimination of blood pathogens. In addition to these well-known receptors, two of the most recently discovered platelet receptors, C-type lectin receptor 2 (CLEC-2), and TREM-like transcript-1 (TLT-1), have been shown to modulate hemostatic and inflammation-related roles in platelets. This review will discuss the evolution of our understanding of platelet functions from hemostasis to inflammation, and highlight novel mechanisms that platelets use to mediate hemostasis under inflammatory pressure.
Buffer planning as an Integral part of floorplanning with consideration of routing congestion

The dominating contribution of interconnect to system performance has made it critical to plan the resources of the buffers and routes in the early stage of the layout. In this paper, we integrate floorplanning with buffer insertion for performance-driven design processes. We devise a two-step method to evaluate the feasible buffer insertion sites, which can improve the efficiency of the buffer-planning algorithm. By partitioning all empty spaces into blocks in the packing process, the buffer allocation is handled as an integral part of the floorplanning. Our buffer-planning algorithm maps the buffers into tiles with consideration of routing congestion. In this approach, we construct a distribution graph to model the possible routes. The buffer allocation method is performed on the updated distribution graph to find the buffer locations with their respective congestion costs. The method is based on a simulated annealing approach, which is composed of multiple phases to speed up the optimization. Since there is more freedom with floorplan optimization, the empirical results demonstrate better performance.
Ontology modeling for REST Open APIs and web service mash-up method

According as the web technology rapidly advances, web services are taking a step further up to the era of Web 2.0 which goes beyond the existing closed web services and enables ones to open and share information freely. Web services are usually provided in the form of Open APIs (Application Programming Interface). As the number of open APIs is increasing and its variety of the web services have wide spectrum, mash-up web services that combine several open APIs are fairly proliferated on Web 2.0 environment and even the service users are also interested in producing their own mash-up web services. When generating mash-up web services, however, the users have difficulties in that they should find Open APIs they need by looking up the web service information by their own and also they are forced to determine whether it is necessary. Also, when looking up the web service information, numerous unnecessary Open APIs are retrieved along with the desired Open API, which makes it difficult for users to find desired Open APIs readily and swiftly, because the current Open API search is performed only by key words. To overcome these obstacles, it is necessary to provide a mash-up platform for the service users to be able to generate their own new mash-up web service on their own comfort and purpose they want. In this paper, we present the key technologies for automatic generation of new mash-up service using open APIs. As the key technologies, ontology modeling method specifying the semantic open API information which is necessary for mash-up, is presented. And, the automatic service mash-up method using the ontology will be presented.
Doppler-Related Distortions in TOPS SAR Images

A direct consequence of the TOPS acquisition geometry and the steering in azimuth of the antenna is the time-varying Doppler centroid within bursts. If this fact is not properly accommodated during SAR image formation, undesired distortions in both azimuth and range dimensions of the focused SAR images may appear. Azimuth distortions are caused by the local mismatch of both squint and topography. Range distortions arise from the inaccurate accommodation of the intrapulse motion of the platform, usually known as the stop-and-go approximation. Conventional spaceborne SAR image formation schemes will be, in general, unable to provide accurate TOPS SAR images. These distortions are discussed and evaluated for exemplary low-Earth-orbit SAR scenarios. Compensation strategies are presented and validated with TerraSAR-X TOPS data. A discussion of the potential impact on the Sentinel-1 interferometric-wide-swath and extra-wide-swath modes (i.e, TOPS) is also given.
High-power widely tunable singly resonant optical parametric oscillator based on PPLN or MgO-doped PPLN

Tunable near-infrared and mid-infrared radiations have drawn enormous interest and obtained a variety of applications, such as, molecular analysis, remote sensing, laser radar, material spectroscopy, air pollution detection and so on. The periodically poled lithium niobate (PPLN) crystal is a suitable material to realize the infrared solid-state lasers. A QPM singly resonant OPO based on PPLN crystal is experimentally demonstrated and the measured results are reported in this paper. The OPO system is pumped by a commercial acousto-optically Q-switched Nd:YAG laser. Its output wavelength is 1.064 μm, pulse duration of 150 ns at a repetition rate of 10 kHz. A maximum laser output power of 1.23W at the idler wavelength of 3.09 μm has been obtained for the OPO when the pump power is 8.15 W. And now the optic - optic conversion efficiency is 15.1%. The widely tunable mid-infrared ranges of 2.80-3.17 μm, 3.34-3.60 μm, 3.77-3.97 μm, 4.15-4.32 μm, and 4.48-4.62 μm are obtained by changing both the crystal's temperature from 40 to 200 °C and grating periods from 26.5 μm to 30.5 μm. A QPM singly resonant OPO based on MgO-doped PPLN crystal is experimentally demonstrated and the measured results are presented in this paper, too. The pump source is also the acousto-optically Q-switched Nd:YAG laser. A maximum laser output power of 2.17 W at the idler wavelength of 3.33 μm has been obtained for the OPO when the pump power is 8.15 W. And now the optic - optic conversion efficiency is 26.7%. The tunable mid-infrared ranges are 2.83-2.89 μm, 3.10-3.38 μm, 3.57-3.78 μm, 3.95-4.12 μm, 4.28-4.46 μm, and 4.65-4.79 μm. Compared to PPLN crystal, we can obtain higher idler output power and optic - optic conversion efficiency by using MgO-doped PPLN crystal.
Adaptive Word Sense Tagging on Chinese Corpus

This study describes a general framework for adaptive word sense disambiguation. The proposed framework begins with knowledge acquisition from the relatively easy context of a corpus. The proposed framework heavily relies on the adaptive step that enriches the initial knowledge base with knowledge gleaned from the partially disambiguated text. Once adjusted to fit the text at hand, the knowledge base is applied to the text again to finalize the disambiguation decision. The effectiveness of this approach was examined through sentences from the Sinica corpus. Experimental results indicated that adaptation significantly improved the performance of WSD. Moreover, the adaptive approach, achieved an applicability improvement from 33.0% up to 74.9% with a comparable precision.
Optical imaging of the intrinsic signal as a measure of cortical plasticity in the mouse

The responses of cells in the visual cortex to stimulation of the two eyes changes dramatically following a period of monocular visual deprivation (MD) during a critical period in early life. This phenomenon, referred to as ocular dominance (OD) plasticity, is a widespread model for understanding cortical plasticity. In this study, we designed stimulus patterns and quantification methods to analyze OD in the mouse visual cortex using optical imaging of intrinsic signals. Using periodically drifting bars restricted to the binocular portion of the visual field, we obtained cortical maps for both contralateral (C) and ipsilateral (I) eyes and computed OD maps as (C − I)/(C + I). We defined the OD index (ODI) for individual animals as the mean of the OD map. The ODI obtained from an imaging session of less than 30 min gives reliable measures of OD for both normal and monocularly deprived mice under Nembutal anesthesia. Surprisingly, urethane anesthesia, which yields excellent topographic maps, did not produce consistent OD findings. Normal Nembutal-anesthetized mice have positive ODI (0.22 ± 0.01), confirming a contralateral bias in the binocular zone. For mice monocularly deprived during the critical period, the ODI of the cortex contralateral to the deprived eye shifted negatively towards the nondeprived, ipsilateral eye (ODI after 2-day MD: 0.12 ± 0.02, 4-day: 0.03 ± 0.03, and 6- to 7-day MD: −0.01 ± 0.04). The ODI shift induced by 4-day MD appeared to be near maximal, consistent with previous findings using single-unit recordings. We have thus established optical imaging of intrinsic signals as a fast and reliable screening method to study OD plasticity in the mouse.
An incremental convex hull algorithm based online Support Vector Regression

Consider the time complexity and newly added samples, an incremental convex hull algorithm based online Support Vector Regression (ICH-OSVR) is proposed in this paper, which can significantly reduce the time consuming and realize fast online learning when added a new sample. There are two steps, called offline step and online step. Firstly, the convex hull vertices of training samples are selected by using convex hull offline algorithm and then regard the vertices of convex hull as the training samples, which are prepared for training. Secondly, when a new sample comes and it is out of the previous convex hull, update the vertices of convex hull and then the previous SVR model will be updated by the new convex hull, but if the new sample is within the previous convex hull, discard it and do not need to update the model. The effectiveness of our proposed methods has been confirmed according to the artificial data sets and real data sets.
A Novel Approach for Software Property Validation

A treadmill exercising device comprises a rigid frame having spaced first and second side members and front and rear end members with a plurality of rollers positioned laterally across the frame. The rollers are rotatably supported in plate members affixed to the side members and have opposite end portions, one of which has an end cap so as to provide a longitudinally extending series of end caps. An endless belt extends longitudinally of the frame along the first side member in contact with the series of end caps in driving engagement whereby rotation of several of the rollers rotatably drives other of the rollers in unison therewith. A braking carriage is slidably mounted to the first side member and has a plurality of grooved wheels rotatably mounted therein. The braking carriage is positioned adjacent the aligned roller end caps and the grooved wheels engage the belt. An adjustable jack screw is affixed to the front end member and extends to a forward end of the braking carriage to slide the braking carriage forwardly and rearwardly between non-braking and braking positions.
Chicken primordial germ cells use the anterior vitelline veins to enter the embryonic circulation

Summary During gastrulation, chicken primordial germ cells (PGCs) are present in an extraembryonic region of the embryo from where they migrate towards the genital ridges. This is also observed in mammals, but in chicken the vehicle used by the migratory PGCs is the vascular system. We have analysed the migratory pathway of chicken PGCs, focusing on the period of transition from the extraembryonic region to the intraembryonic vascular system. Our findings show that at Hamburger and Hamilton developmental stage HH12–HH14 the majority of PGCs concentrate axially in the sinus terminalis and favour transport axially via the anterior vitelline veins into the embryonic circulation. Moreover, directly blocking the blood flow through the anterior vitelline veins resulted in an accumulation of PGCs in the anterior region and a decreased number of PGCs in the genital ridges. We further confirmed the key role for the anterior vitelline veins in the correct migration of PGCs using an ex ovo culture method that resulted in defective morphogenetic development of the anterior vitelline veins. We propose a novel model for the migratory pathway of chicken PGCs whereby the anterior vitelline veins play a central role at the extraembryonic and embryonic interface. The chicken model of PGC migration through the vasculature may be a powerful tool to study the process of homing (inflammation and metastasis) due to the striking similarities in regulatory signaling pathways (SDF1–CXCR4) and the transient role of the vasculature.
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning

Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses.
SCoT: a Python toolbox for EEG source connectivity

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.
HGrid: A Data Model for Large Geospatial Data Sets in HBase

Cloud-based infrastructures enable applications to collect and analyze massive amounts of data. Whether these applications are newly developed or they are being evolved from existing RDBMS-based implementations, NoSQL databases offer an attractive platform with which to address this challenge. However, developers find it difficult to effectively manage data in NoSQL databases, because these platforms do not offer much support for data organization. Since poor data organization may abuse the features of the NoSQL database and result in unsatisfactory performance, developing a systematic method for NoSQL database data-schema design is a timely and important problem. In this paper, we focus on geospatial applications, as a family of big-data systems with distinct data types and usage patterns, in need of scalability. We propose the HGrid data model for HBase, based on a hybrid index structure, combining a quad-tree and a regular grid as primary and secondary indices correspondingly. We have comparatively evaluated the performance of HGrid with uniform and skewed data, against two other data models based on quad-tree and regular-grid indices. Our results demonstrate that HGrid scales well and supports efficient performance for range and k-nearest neighbor queries. Although this model does not outperform all its competitors in terms of query response time, it is more flexible for discontinuous and skewed space, and its index requires less space than the corresponding quad-tree and regular-grid indices, which makes its deployment possible with less resources. Through this study, we also formulate a set of guidelines on how to organize data for geospatial applications in HBase.
Towards an Autonomic Auto-scaling Prediction System for Cloud Resource Provisioning

This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted to compare the accuracy of time-series prediction algorithms for different performance patterns. In the experiment, workload was considered as the performance metric, and Support Vector Machine (SVM) and Neural Networks (NN) were utilized as time-series prediction techniques. In addition, we used Amazon EC2 as the experimental infrastructure and TPC-W as the benchmark to generate different workload patterns. The results of the experiment show that prediction accuracy of SVM and NN depends on the incoming workload pattern of the system under study. Specifically, the results show that SVM has better prediction accuracy in the environments with periodic and growing workload patterns, while NN outperforms SVM in forecasting unpredicted workload pattern. Based on these experimental results, this paper proposes an architecture for a self-adaptive prediction suite using an autonomic system approach. This suite can choose the most suitable prediction technique based on the performance pattern, which leads to more accurate prediction results.