Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of ultrathin Pt-Mo-Ni nanowire catalysts for ethanol electrooxidation

Researchers design ultrathin Pt-Mo-Ni NWs as cost-effective, active, and durable electrocatalysts. Developing cost-effective, active, and durable electrocatalysts is one of the most important issues for the commercialization of fuel cells. Ultrathin Pt-Mo-Ni nanowires (NWs) with a diameter of ~2.5 nm and lengths of up to several micrometers were synthesized via a H2-assisted solution route (HASR). This catalyst was designed on the basis of the following three points: (i) ultrathin NWs with high numbers of surface atoms can increase the atomic efficiency of Pt and thus decrease the catalyst cost; (ii) the incorporation of Ni can isolate Pt atoms on the surface and produce surface defects, leading to high catalytic activity (the unique structure and superior activity were confirmed by spherical aberration–corrected electron microscopy measurements and ethanol oxidation tests, respectively); and (iii) the incorporation of Mo can stabilize both Ni and Pt atoms, leading to high catalytic stability, which was confirmed by experiments and density functional theory calculations. Furthermore, the developed HASR strategy can be extended to synthesize a series of Pt-Mo-M (M = Fe, Co, Mn, Ru, etc.) NWs. These multimetallic NWs would open up new opportunities for practical fuel cell applications.
A Decision Tree Method for Finding and Classifying Names in Japanese Texts

This paper describes a system which uses a decision tree to find and classify names in Japanese texts. The decision tree uses part-of-speech, character type, and special dictionary information to determine the probability that a particular type of name opens or closes at a given position in the text. The output is generated from the consistent sequence of name opens and name closes with the highest probability. This system does not require any human adjustment. Experiments indicate good accuracy with a small amount of training data, and demonstrate the system's portability. The issues of training data size and domain dependency are discussed. 1 I n t r o d u c t i o n For some NLP applications, it is important to identify, "named entities" (NE), such as person names, organization names, time, date, or money expressions in the text. For example, in information extraction systems, it is crucial to identify them in order to provide the knowledge to be extracted, and in machine translation systems, they are useful for creating translations of unknown words or for disambiguation. However, it is not easy to identify these names, because they involve unknown words, and hence the strategy of listing candidates won' t work. Also, it is sometimes hard to determine the category of proper nouns, like distinguishing a person name from a company name. These phenomena are often different from domain to domain. One domain may use a special pattern which is not found in other domains. In this paper, we will present a supervised learning system whicil finds and classifies named entities in Japanese newspaper texts. Recently, several systems have been proposed for this task, but many of them use hand-coded patterns. Cre171 ating these patterns is laborious work, and when we adapt these systems to a new domain or a new definition of named entities, it is likely to need a large amount of additional work. On the other hand, in a supervised learning system, what is needed to adapt the system is to make new training data. While this is also not a very easy task, it would be easier than creating complicated rules. For example, based on our experience, 100 training articles can be created in a day. There also have been several machine learning systems applied to this task. However, these either 1) partially need hand-made rules, 2) have parameters which must be adjusted by hand, or 3) do not perform well by fully automatic means. Our system does not work fully automatically and also needs special dictionaries, but performs welt and does not have parameters to be adjusted by hand. We will discuss one of the related systems in a later section. The issue of training data size will be discussed based on experiments using different sizes of training data. In order to demonstrate the portability of our system, we ran the system on a new domain with a new type of named entity. The experiment shows that the portability of the system is quite good and the performance is satisfactory.
Use of Wood Macerated Elements for Classification of Salix L. Species in Kurdistan Region- Iraq

Characters of wood macerated elements were investigated for taxonomic application. Wood samples from 5 stems of each Salix acmophylla, S. alba, S. babylonica, S. aegyptiaca, and S. Purpurea were collected from different geographical regions of kurdistan to clasp most possible variation. Wood of most species contains vessel elements described of primitiveness together with those descriptive of more or less advanced. Vessels of different species are similar in possessing the simple perforation plate. The differences are huge in vessel length and vessel width and hence in length/width ratio. Differences are also huge in vessel type or the end wall characters of vessel elements described of primitiveness and advancement, occurrence or absence of spiral thickening, cell walls pitting intensity and distribution within and between species. All these features are found to be important for providing taxonomic application. Libriform fibers are the only fibers present in the wood of this genus, no fiber trachieds occur. All species have fibers possessing dentition, almost exclusively at the distal parts, very rarely situated throughout the fiber length. Dentition morphological structure, distribution, size and orientation form important diagnostic characters
Omega-3 Polyunsaturated Fatty Acids Down-Modulate CXCR 4 Expression and Function in MDA-MB-231 Breast Cancer Cells

Metastasis is the leading cause of death from breast cancer. A major factor of metastasis is the migration of cancerous cells to other tissues by way of up-regulated chemokine receptors, such as CXCR4, on the cell surface. Much is known of the beneficial effects of omega-3 polyunsaturated fatty acids (n-3 PUFA) on cancer; however, the mechanisms behind these effects are unclear. For this study, we investigated the effects of two n-3 PUFAs, docosahexaenoic acid and eicosapentaenoic acid, on CXCR4 expression and activity in the MDA-MB-231 breast cancer cell line. We compared the n-3 PUFAs with the saturated fatty acid stearic acid as a control. Treatment of the cells with n-3 PUFAs resulted in reduced surface expression of CXCR4, but had no effect on overall CXCR4 expression. Consequently, we found that the fatty acid treatment significantly reduced CXCR4-mediated cell migration. Successful CXCR4-mediated signaling and migration requires the cholesterol-rich membrane microdomains known as lipid rafts. Treatment with n-3 PUFAs disrupted the lipid raft domains in a manner similar to methyl-β-cyclodextrin and resulted in a partial displacement of CXCR4, suggesting a possible mechanism behind the reduced CXCR4 activity. These results were not observed in cells treated with stearic acid. Together, our data suggest that n-3 PUFAs may have a preventative effect on breast cancer metastasis in vitro. This suggests a previously unreported potential benefit of n-3 PUFAs to patients with metastatic breast cancer. The data presented in this study may also translate to other disorders that involve up-regulated chemokine receptors. (Mol Cancer Res 2009;7(7):OF1–8)
Trait-mediated trophic cascade creates enemy-free space for nesting hummingbirds

The presence of hawks at their nests alters the foraging height of predatory jays, creating a safe nesting area for hummingbirds. The indirect effects of predators on nonadjacent trophic levels, mediated through traits of intervening species, are collectively known as trait-mediated trophic cascades. Although birds are important predators in terrestrial ecosystems, clear examples of trait-mediated indirect effects involving bird predators have almost never been documented. Such indirect effects are important for structuring ecological communities and are likely to be negatively impacted by habitat fragmentation, climate change, and other factors that reduce abundance of top predators. We demonstrate that hummingbirds in Arizona realize increased breeding success when nesting in association with hawks. An enemy-free nesting space is created when jays, an important source of mortality for hummingbird nests, alter their foraging behavior in the presence of their hawk predators.
Low-Rate Chaotic UWB Transceiver System Based on IEEE 802.15.4a

The chaotic UWB communication system based on IEEE 802.15. 4a is proposed for wireless headset applications. A compact architecture can be implemented by using a chaotic UWB signal and a non-coherent detection scheme. The chaotic UWB generator is designed with the BW of 3.1 to 5.1 GHz, and a baseband process is realized on an FPGA including an adaptive decision and a channel code for non-source coded data stream. The system performance is evaluated by transmitting MP3 audio/voice and measuring PERs at 32 byte length of a PSDU for the system sensitivity and the interferer compatibility. The proposed system can be an excellent candidate for short-range connectivity services, as well as an inexpensive system with good capability for narrow-band interferences
Hyperincursive Cogitata and Incursive Cogitantes: Scholarly Discourse as a Strongly Anticipatory System

Strongly anticipatory systems — that is, systems which use models of themselves for their further development — and which additionally may be able to run hyperincursive routines — that is, develop only with reference to their future states — cannot exist in res extensa, but can only be envisaged in res cogitans. One needs incursive routines in cogitantes to instantiate these systems. Unlike historical systems (with recursion), these hyper-incursive routines generate redundancies by opening horizons of other possible states. Thus, intentional systems can enrich our perceptions of the cases that have happened to occur. The perspective of hindsight codified at the above-individual level enables us furthermore to intervene technologically. The theory and computation of anticipatory systems have made these loops between supra-individual hyper-incursion, individual incursion (in instantiation), and historical recursion accessible for modeling and empirical investigation.
Approximating the Bandwidth via Volume Respecting Embeddings

A linear arrangement of an n-vertex graph is a one-to-one mapping of its vertices to the integers {1, ? , n}. The bandwidth of a linear arrangement is the maximum difference between mapped values of adjacent vertices. The problem of finding a linear arrangement with smallest possible bandwidth is NP-hard. We present a randomized algorithm that runs in nearly linear time and outputs a linear arrangement whose bandwidth is within a polylogarithmic multiplicative factor of optimal. Our algorithm is based on a new notion, called volume respecting embeddings, which is a natural extension of small distortion embeddings of Bourgain and of Linial, London and Rabinovich.
MACROD2 overexpression mediates estrogen independent growth and tamoxifen resistance in breast cancers

Significance Despite the widespread use and success of tamoxifen for treating ER-positive breast cancers, overcoming resistance to this drug remains an unmet need in clinical breast oncology. The results presented in this study demonstrate that overexpression of a novel gene, MACROD2, can mediate tamoxifen resistance and estrogen independent growth in human breast cancers, and that amplification of MACROD2 in primary breast tumors is associated with worse overall survival. Tamoxifen is effective for treating estrogen receptor-alpha (ER) positive breast cancers. However, few molecular mediators of tamoxifen resistance have been elucidated. Here we describe a previously unidentified gene, MACROD2 that confers tamoxifen resistance and estrogen independent growth. We found MACROD2 is amplified and overexpressed in metastatic tamoxifen-resistant tumors. Transgene overexpression of MACROD2 in breast cancer cell lines results in tamoxifen resistance, whereas RNAi-mediated gene knock down reverses this phenotype. MACROD2 overexpression also leads to estrogen independent growth in xenograft assays. Mechanistically, MACROD2 increases p300 binding to estrogen response elements in a subset of ER regulated genes. Primary breast cancers and matched metastases demonstrate MACROD2 expression can change with disease evolution, and increased expression and amplification of MACROD2 in primary tumors is associated with worse overall survival. These studies establish MACROD2 as a key mediator of estrogen independent growth and tamoxifen resistance, as well as a potential novel target for diagnostics and therapy.
Serum Transferrin Receptor : A Quantitative Measure of Tissue Iron Deficiency

This study was undertaken to evaluate the role of serum transferrin receptor measurements in the assessment of iron status. Repeated phlebotomies were performed in 14 normal volunteer subjects to obtain varying degrees of iron deficiency. Serial measurements of serum iron, total ironbinding capacity, mean cell volume (MCV), free erythrocyte protoporphyrin (FEP), red cell mean index, serum ferritin, and serum transferrin receptor were performed throughout the phlebotomy program. There was no change in receptor levels during the phase of storage iron depletion. When the serum ferritin level reached subnormal values there was an increase in serum receptor levels, which continued throughout the phlebotomy program. Functional
Drug Release Kinetics From Stent Device-Based Delivery Systems

In an effort to overcome the limitations of balloon-expandible intravascular metal stent-induced neointimal formation, drug-coated stent devices have been developed. The stent platform allows the local delivery of drugs to an injury site, thereby reducing the amount of drug exposure to the systemic circulation and other organs. The drug carrier matrix allows the release of the drug in a diffusion-controlled manner over an extended time period after the stent implant. The drugs are chosen such that the complex cascade of events that occurs after stent implantation that leads to smooth muscle cell proliferation and migration towards the intima are inhibited. The success of an antirestenotic drug therapy from a drug-coated stent is dependent, at least partially, on the extent of drug elution from the stent, the duration and rate of release, and accumulation of drug in the arterial wall in such a way that it covers the initiation and progression of vessel wall remodeling. The local vascular drug concentrations achieved are directly correlated with the biological effects and local vascular toxicity, and there is therefore a challenge in finding an optimum dose of drug to be delivered to tissues (ie, one that has the desired therapeutic effect without local adverse effects). There is increased focus on optimization of various factors that affect drug release from the stent system, including the physicochemical properties of the drugs, carrier vehicle formulation, and profile of elution kinetics. This review highlights the various factors involved in drug release kinetics, local vascular toxicity, carrier vehicle matrix, tissue deposition, and distribution through the arterial wall from stent-based drug delivery systems.
An Effective Handover Scheme Based on Antenna Selection in Ground–Train Distributed Antenna Systems

Due to frequent handovers in broadband wireless communications for the high-speed railway system, communication interruption caused by handover failures could seriously degrade the users' experience. Aiming at reducing the handover failure probability, this paper proposes an effective handover scheme based on the distributed antenna system (DAS) and mobile-relay-aided two-link architecture, i.e., ground-relay link and relay-onboard link, where the mobile relay is mounted on top of the moving train. In addition, an antenna selection scheme with optimal power allocation is proposed to improve handover performance. Our simulation and analysis results show that the DAS structure with macrodiversity can reduce the unnecessary handovers, as well as reduce the handover failure and link outage probabilities compared with the traditional cell without DAS. In addition, the proposed DAS with an antenna selection scheme performs better in terms of handover failure and link outage probabilities than that of DAS with a equal power allocation (EPA) scheme.
Saturation throughput analysis of IEEE 802.11e enhanced distributed coordination function

The IEEE 802.11 Task Group E will soon approve the 802.11e standard for medium access control (MAC) layer quality-of-service (QoS) enhancements to the 802.11 protocol, and it is widely believed that these enhancements will allow 802.11 technology to form the foundation of high-bandwidth vertically integrated networks. At the heart of 802.11e is a modified contention-based access mechanism, named the enhanced distributed coordination function (EDCF). In this paper, we propose and validate an analytical model for the saturation throughput of EDCF. Key to the accuracy of our model is a treatment of the postcollision period, which has been ignored by all previous 802.11 models. With results from the ns-2 simulator, we show that our model can accurately predict throughput over a wide range of scenarios, and thereby demonstrate its usefulness as a predictive tool for use in QoS provision. With context provided by our analytical model, we discuss the primary throughput differentiation mechanisms of EDCF.
Communication-Aware and Energy-Efficient Scheduling for Parallel Applications in Virtualized Data Centers

In this paper, we propose Peer VMs Aggregation (PVA) to enable dynamic discovery of communication patterns and reschedule VMs based on the determined communication patterns using VM migration. In the implementation, we consider that communication delays occur at the server (i.e., memory-bus) and at the data center network. To evaluate our approach, we modeled a network and a memory subsystem on CloudSim simulator. We then used NAS Parallel Benchmarks, which consists of six different applications as parallel applications. We thoroughly evaluated our proposed approach measuring several assessment metrics including VMs placement, performance degradation, and network utilization of each link. The results of the simulation show that our proposed approach significantly reduces the total amount of traffic in the network where it reduces the average of the network's utilization by 25%.
Checkpointing with Minimal Recover in Adhocnet based TMR

This paper describes two-fold approach towards utilizing Triple Modular Redundancy (TMR) in Wireless Adhoc Network (AdocNet). A distributed checkpointing and recovery protocol is proposed. The protocol eliminates useless checkpoints and helps in selecting only dependent processes in the concerned checkpointing interval, to recover. A process starts recovery from its last checkpoint only if it finds that it is dependent (directly or indirectly) on the faulty process. The recovery protocol also prevents the occurrence of missing or orphan messages. In AdocNet, a set of three nodes (connected to each other) is considered to form a TMR set, being designated as main, primary and secondary. A main node in one set may serve as primary or secondary in another. Computation is not triplicated, but checkpoint by main is duplicated in its primary so that primary can continue if main fails. Checkpoint by primary is then duplicated in secondary if primary fails too.
VMDV: A 3D visualization tool for modeling, demonstration, and verification

In the setting of automated theorem proving, the output of an automated theorem prover is usually presented in text format, which is often too heavy to be understood. In the setting of model checking, it would be helpful if one can observe, at the same time, both the model structure under consideration and the verification procedure. To address these problems, a 3D visualization tool for modeling, demonstration and verification (VMDV for short) is proposed in this paper. The facilities of VMDV are illustrated by applying it to an automated theorem prover.
Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data

The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and associated expression data. This article reports the application of SynTReN, an existing network generator that samples topologies from existing biological networks and uses Michaelis-Menten and Hill enzyme kinetics to simulate gene interactions. We illustrate the effects of different aspects of the expression data on the quality of the inferred network. The tested expression data parameters are network size, network topology, type and degree of noise, quantity of expression data, and interaction types between genes. This is done by applying three well-known inference algorithms to SynTReN data sets. The results show the power of synthetic data in revealing operational characteristics of inference algorithms that are unlikely to be discovered by means of biological microarray data only.
Proposition to distinguish machine-printed from handwritten Arabic and Latin words

In this work, we gathered some contributions to identify script and its nature. We successfully employed many features to distinguish between handwritten and machine-printed Arabic and Latin scripts at word level. Some of them are previously used in the literature, and the others are here proposed. The new proposed structural features are intrinsic to Arabic and Latin scripts. The performance of all extracted features is studied towards this paper. We also compared the performance of three classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN) and Decision Tree (J48), used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. We carried experiments using standard databases. Obtained results demonstrate used feature capability to capture differences between scripts. Using a set of 58 selected features and a Bayes-based classifier, we achieved an average identification rate equals to 98.72%, which considered a very satisfactory rate compared to some related works.
Texture Complexity Based Redundant Regions Ranking for Object Proposal

Object proposal has been successfully applied in recent visual object detection approaches and shown improved computational efficiency. The purpose of object proposal is to use as few as regions to cover as many as objects. In this paper, we propose a strategy named Texture Complexity based Redundant Regions Ranking (TCR) for object proposal. Our approach first produces rich but redundant regions using a color segmentation approach, i.e. Selective Search. It then uses Texture Complexity (TC) based on complete contour number and Local Binary Pattern (LBP) entropy to measure the objectness score of each region. By ranking based on the TC, it is expected that as many as true object regions are preserved, while the number of the regions is significantly reduced. Experimental results on the PASCAL VOC 2007 dataset show that the proposed TCR significantly improves the baseline approach by increasing AUC (area under recall curve) from 0.39 to 0.48. It also outperforms the state-of-the-art with AUC and uses fewer detection proposals to achieve comparable recall rates.