Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of Ontologies for E ective Knowledge Modelling and Information Retrieval

The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such di culties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems.
Resource Management for Asynchronous Mobile-Edge Computation Offloading

Mobile-edge computation offloading (MECO) is envisioned as a promising technique for enhancing mobiles' computation capabilities and prolonging their battery lives, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we investigate the energy-efficient resource-management policy for asynchronous MECO systems, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival- deadline orders. An optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource- management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained by using the block coordinate decent method. To gain further insights, we further study the special case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems to find the optimal scheduling order and then jointly optimize the data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to balance the defined effective computing power among offloading mobiles via time sharing.
Endocannabinoid-LTP Mediated by CB1 and TRPV1 Receptors Encodes for Limited Occurrences of Coincident Activity in Neocortex

Synaptic efficacy changes, long-term potentiation (LTP) and depression (LTD), underlie various forms of learning and memory. Synaptic plasticity is generally assessed under prolonged activation, whereas learning can emerge from few or even a single trial. Here, we investigated the existence of rapid responsiveness of synaptic plasticity in response to a few number of spikes, in neocortex in a synaptic Hebbian learning rule, the spike-timing-dependent plasticity (STDP). We investigated the effect of lowering the number of pairings from 100 to 50, and 10 on STDP expression, using whole-cell recordings from pyramidal cells in rodent somatosensory cortical brain slices. We found that a low number of paired stimulations induces LTP at neocortical layer 4–2/3 synapses. Besides the asymmetric Hebbian STDP reported in the neocortex induced by 100 pairings, we observed a symmetric anti-Hebbian LTD for 50 pairings and unveiled a unidirectional Hebbian spike-timing-dependent LTP (tLTP) induced by 10–15 pairings. This tLTP was not mediated by NMDA receptor activation but requires CB1 receptors and transient receptor potential vanilloid type-1 (TRPV1) activated by endocannabinoids (eCBs). eCBs have been widely described as mediating short- and long-term synaptic depression. Here, the eCB-tLTP reported at neocortical synapses could constitute a substrate operating in the online learning of new associative memories or during the initial stages of learning. In addition, these findings should provide useful insight into the mechanisms underlying eCB-plasticity occurring during marijuana intoxication.
Three Pteris species (Pteridaceae: Pteridophyta) reproduce by apogamy.

The ploidy levels, morphologies of gametophytes and young sporophytes, and reproductive modes of Pteris cretica, P. pellucidifolia, and P. wulaiensis were determined in this study. Pteris cretica and P. wulaiensis were diploid while P. pellucidifolia was triploid. Sporophytes of all three species produced 32-spored sporangia. Spore sizes of the triploid P. pellucidifolia were significantly larger than those of the other two diploid species. However, the frequencies of normal spores were lower in P. pellucidifolia (73%) than those of the other two species (95%-98%). Gametophytes of P. cretica and P. pellucidifolia produced normal antheridia but no archegonium. Although P. wulaiensis produced archegonia, those archegonia had lost their function. Every antheridium of these three species produced 32 active sperms. All of these three species apogamously produced sporophytes. Young sporophytes were produced directly on the gametophytes without fertilization. The first fronds of young sporophytes predominantly possessed midribs, with only a few of them lacking midribs. This study also suggests that the best way to infer the reproductive mode of ferns is to detect whether the embryo formed from the vegetative cells of gametophytes, although those other reproduction-nrelated traits may provide useful indicators.
The wideband printed monopole antenna using defected ground structure for S and C band applications

In this Paper, a wideband printed monopole antenna (PMA) using defected ground structure has been proposed for S and C band applications. The conventional PMA is designed with the center frequency of 4 GHz using low-loss dielectric substrate Glass Epoxy (FR4) with dielectric constant (εr)= 4.4 and substrate thickness of 1.575mm. The band width of this PMA is enhanced by etching the two square shaped slots in the ground plane. The dimensions and the positions of the DGS slots are finalized by thorough optimization. The simulation results demonstrate more than 4.3 dBi peak gain with wide bandwidth of 5.2 GHz and ideal monopole radiations. The prototypes are fabricated and the experimental validation of the simulation results is under process.
Design and manufacture of a biodegradable ureteral stent

ii DEDICATION iv ACKNOWLEDGMENTS v LIST OF TABLES viii LIST OF FIGURES ix 1. LITERATURE REVIEW 1 1.1 Clinical Motivation 1 1.2 Anatomy of the Urinary System 1 1.3 Ureteral Stents 7 2. RATIONALE 22 3. MATERIALS AND METHODS 25 3.1 Fabrication of Novel Stent 25 3.2 Designing of Stent Sheath for Novel Anchoring Method 26 3.3 Characterization of the Novel Degradable Stent 27 3.4 Characterization of Processed PDO 28 3.5 Validation of Implantation Method 32 3.6 Radial Strength Testing 34 3.7 Statistical Analysis 36 4. RESULTS 37 4.1 Characterization of Stent Degradation 37 4.2 Physical Characterization of Stent 38 4.3 Mechanical Characteristics 47 5. DISCUSSION 53 5.1 Design Criteria and Innovative Features 54 5.2 Mechanical Characterization 56 5.3 Characterization of Stent Degradation 60 5.4 Physical Characterization of Stent 62 6. CONCLUSION 65 7. FUTURE WORK 66
Object storage in the cloud and multi-cloud: State of the art and the research challenges

The cloud has been one of the hottest topics of discussion in the information technology field, both in terms of research and on a commercial level, since its inception in the late 2000s. The ever increasing need for better, faster and more powerful computers to process the ever increasing amount of data has made the cloud an indispensable feature of modern IT. Due to its massive storage power the cloud has allowed corporate users, private users and researchers to centralize all their data and have access to it almost constantly so long as they have an internet connection. However, despite the obvious need for more storage capacity and the ever increasing reliance on storing files in the cloud no mainstream unified storage solution is available. This paper attempts to review and analyze the current cloud offerings on the market, the storage solutions provided both on a commercial level and on a theoretical level, the challenges of the multi-cloud and the challenges of any future research in this field.
A Comparative Study of Containers and Virtual Machines in Big Data Environment

Container technique is gaining increasing attention in recent years and has become an alternative to traditional virtual machines. Some of the primary motivations for the enterprise to adopt the container technology include its conveniency to encapsulate and deploy applications, lightweight operations, as well as efficiency and flexibility in resources sharing. However, there still lacks an in-depth and systematic comparison study on how big data applications, such as Spark jobs, perform between a container environment and a virtual machine environment. In this paper, by running various Spark applications with different configurations, we evaluate the two environments from many interesting aspects, such as how convenient the execution environment can be set up, what are makespans of different workloads running in each setup, how efficient the hardware resources, such as CPU and memory, are utilized, and how well each environment can scale. The results show that compared with virtual machines, containers provide a more easy-to-deploy and scalable environment for big data workloads. The research work in this paper can help practitioners and researchers to make more informed decisions on tuning their cloud environment and configuring the big data applications, so as to achieve better performance and higher resources utilization
Usage of Self-Talk in Competition by Athletes By Yuka Funatsu

Self-talk has frequently been studied in sport psychology to improve performance, increase mood, or improve confidence. Coaches and athletes focus on self-talk to perform better in competition. While a majority of the literature conducted were experimental studies on function of self-talk, only limited studies investigate self-talk dialogue in competition. This review will analyze the current literature on selftalk in competition to determine the important factors in future research. EBSCO Host database, including CINAHL Complete, Health Source: Nursing/Academic Edition, MEDLINE Complete, and SPORT Discus, were used to search for articles specific to self-talk in competition. The search was limited to articles published within 10 years and written in English. A total of seven articles focused on self-talk in competition: Four articles studied self-talk in competition with the effect on performance, and three articles studied elite athlete’s self-talk dialogue in competition. The review is limited due to lack of articles available on self-talk in competition. To unfold how athletes utilize self-talk, this review will introduce a sport-specific model of self-talk by Van Raalte, Vincent, and Brewer (2016). Additionally, it will compare the model with elite self-talk dialogue to explore the usage of self-talk in competition. Furthermore, future research to be conducted on self-talk dialogue of elite athletes will be recommended.
The Historical Perspective of Local Anesthetics

Pain is a phenomenon wisely initiated by nature as a warning sign of a condition that may be detrimental to our bodies. The efforts of humankind to find the means to control pain presents as one of the greatest challenges in medicine. The main aim of this article is to list out the forgotten history and evolution of local anesthesia. The comfortable use of today’s local anesthetics was a gift from the past, where like in all areas of studies is a process of many trial and errors perfecting the techniques and compounds used.
Colloquium The future of coral reefs

Coral reefs, with their millions of species, have changed profoundly because of the effects of people, and will continue to do so for the foreseeable future. Reefs are subject to many of the same processes that affect other human-dominated ecosystems, but some special features merit emphasis: (i) Many dominant reef builders spawn eggs and sperm into the water column, where fertilization occurs. They are thus particularly vulnerable to Allee effects, including potential extinction associated with chronic reproductive failure. (ii) The corals likely to be most resistant to the effects of habitat degradation are small, short-lived ‘‘weedy’’ corals that have limited dispersal capabilities at the larval stage. Habitat degradation, together with habitat fragmentation, will therefore lead to the establishment of genetically isolated clusters of inbreeding corals. (iii) Increases in average sea temperatures by as little as 1°C, a likely result of global climate change, can cause coral ‘‘bleaching’’ (the breakdown of coral–algal symbiosis), changes in symbiont communities, and coral death. (iv) The activities of people near reefs increase both fishing pressure and nutrient inputs. In general, these processes favor more rapidly growing competitors, often fleshy seaweeds, and may also result in explosions of predator populations. (v) Combinations of stress appear to be associated with threshold responses and ecological surprises, including devastating pathogen outbreaks. (vi) The fossil record suggests that corals as a group are more likely to suffer extinctions than some of the groups that associate with them, whose habitat requirements may be less stringent.
Decoding speech prosody in five languages

Abstract Twenty English-speaking listeners judged the emotive intent of utterances spoken by male and female speakers of English, German, Chinese, Japanese, and Tagalog. The verbal content of utterances was neutral but prosodic elements conveyed each of four emotions: joy, anger, sadness, and fear. Identification accuracy was above chance performance levels for all emotions in all languages. Across languages, sadness and anger were more accurately recognized than joy and fear. Listeners showed an in-group advantage for decoding emotional prosody, with highest recognition rates for English utterances and lowest recognition rates for Japanese and Chinese utterances. Acoustic properties of stimuli were correlated with the intended emotion expressed. Our results support the view that emotional prosody is decoded by a combination of universal and culture-specific cues.
Precise position control using shape memory alloy wires

Shape memory alloys (SMAs) are active metallic “smart” materials used as actuators and sensors in high technology smart systems [1]. The term shape memory refers to ability of certain materials to “remember” a shape, even after rather severe deformations: once deformed at low temperatures, these materials will stay deformed until heated, whereupon they will return to their original, pre-deformed “learned” shape [2]. This property can be used to generate motion and/or force in electromechanical devices and micro-machines. However, the accuracy of SMA actuators is severely limited by their highly nonlinear stimulus-response characteristics. In this work, modeling, simulation, and experimental efforts to precisely control the position of a Ni-Ti based shape memory alloy wire is presented. In this content three separate control strategies are tried and very good positioning accuracies are obtained.
Perpendicular magnetic tunnel junction and its application in magnetic random access memory

Recent progresses in magnetic tunnel junctions with perpendicular magnetic anisotropy (PMA) are reviewed and summarized. At first, the concept and source of perpendicular magnetic anisotropy (PMA) are introduced. Next, a historical overview of PMA materials as magnetic electrodes, such as the RE—TM alloys TbFeCo and GdFeCo, novel tetragonal manganese alloys Mn—Ga, L10-ordered (Co, Fe)/Pt alloy, multilayer film [Co, Fe, CoFe/Pt, Pd, Ni, Au]N, and ultra-thin magnetic metal/oxidized barrier is offered. The other part of the article focuses on the optimization and fabrication of CoFeB/MgO/CoFeB p-MTJs, which is thought to have high potential to meet the main demands for non-volatile magnetic random access memory.
A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.
A Configurable Cloud-Scale DNN Processor for Real-Time AI

Interactive AI-powered services require low-latency evaluation of deep neural network (DNN) models—aka ""real-time AI"". The growing demand for computationally expensive, state-of-the-art DNNs, coupled with diminishing performance gains of general-purpose architectures, has fueled an explosion of specialized Neural Processing Units (NPUs). NPUs for interactive services should satisfy two requirements: (1) execution of DNN models with low latency, high throughput, and high efficiency, and (2) flexibility to accommodate evolving state-of-the-art models (e.g., RNNs, CNNs, MLPs) without costly silicon updates. This paper describes the NPU architecture for Project Brainwave, a production-scale system for real-time AI. The Brainwave NPU achieves more than an order of magnitude improvement in latency and throughput over state-of-the-art GPUs on large RNNs at a batch size of 1. The NPU attains this performance using a single-threaded SIMD ISA paired with a distributed microarchitecture capable of dispatching over 7M operations from a single instruction. The spatially distributed microarchitecture, scaled up to 96,000 multiply-accumulate units, is supported by hierarchical instruction decoders and schedulers coupled with thousands of independently addressable high-bandwidth on-chip memories, and can transparently exploit many levels of fine-grain SIMD parallelism. When targeting an FPGA, microarchitectural parameters such as native datapaths and numerical precision can be "synthesis specialized" to models at compile time, enabling atypically high FPGA performance competitive with hardened NPUs. When running on an Intel Stratix 10 280 FPGA, the Brainwave NPU achieves performance ranging from ten to over thirty-five teraflops, with no batching, on large, memory-intensive RNNs.
Near-Infrared Activated Release of Doxorubicin from Plasmon Resonant Liposomes

Precise control of drug release from nanoparticles can improve efficacy and reduce systemic toxicity associated with administration of certain medications. Here, we combined two phenomena, photothermal conversion in plasmon resonant gold coating and thermal sensitivity of liposome compositions, to achieve a drug delivery system that rapidly releases doxorubicin in response to external stimulus. Methods: Thermosensitive liposomes were loaded with doxorubicin and gold-coated to produce plasmon resonant drug delivery system. Plasmon resonance facilitates release of contents upon near-infrared laser illumination, thus providing spatial and temporal control of the process. This controlled delivery system was compared to thermosensitive liposomes without gold coating and to the FDA-approved Doxil that was gold-coated to create a plasmon resonant coating. Release of doxorubicin from the gold-coated thermosensitive liposomes was further confirmed by tests of cell viability. Results: Upon laser illumination at 760 nm and 88 mW/cm2 power density, permeability of plasmon resonant liposomes increased by three orders of magnitude, from 70×10-12 to 60,000x10-12 cm/s. In control experiments, mild hyperthermia (42°C) increased permeability of these thermosensitive liposomes to just 3,700×10-12 cm/s. Neither hyperthermia nor laser illumination elicit content release from Doxil or plasmon resonant Doxil obtained by gold coating. Laser-induced release of doxorubicin from plasmon resonant thermosensitive liposomes resulted in the loss of cell viability significantly greater than in any of the control groups. Conclusion: Combination of thermosensitive liposomes with plasmon resonant coating enables rapid, controlled release, not currently available in pharmaceutical formulations of anticancer drugs.
Robust Quality Metric for Scarce Mobile Crowd-Sensing Scenarios

This paper proposes a novel quality of source metric for Mobile Crowd-Sensing systems (MCS), for systems with scarce participant availability due to small sample sizes in each sensing cycle. We introduce a controlled quality metric that is based on the difference between centrality estimates, the trimmed mean, and the Median Absolute Deviation (MAD) filtered mean. Our metric permits outlier detection, and therefore allows the estimation of quality under the stringent conditions of small sample sizes. The proposed algorithm also introduces a parameter that allows MCS administrators to control the accuracy of the metric, and therefore control the range of accepted values. Such control is achieved by means of introducing the MAD mean, which deliberately widens error terms, and therefore affects the perception of quality. We mathematically develop the proposed metric, while showing the impact of all MCS design parameters in it, in a closed-form expression, and we compare it to computer simulations.
Verification and validation of a simulation model for energy use in buildings

Different modeling approaches address the problem of energy efficiency in the building sector, being a multi-parametric problem. Physical or white-box approaches describe physical phenomena with sets of equations, and offer high interpretability in physical terms. Present work deals with a calculation-based monthly quasi-steady state simulation model for energy use in buildings based on ISO 13790 standard methodologies and presents its implementation. Model verification and validation is performed according to procedures of EN 15265 standard and against a real hospital usecase.