Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristic analysis of a novel segmental rotor axial field switched reluctance motor with single teeth winding

Switched reluctance machines with segmental rotors are investigated, base on the traditional radial field magnetic structure, a novel axial field SRM is prototyped. This paper presents a machine topology, which permits the use of short pitched windings placed around a single teeth stator, and with the segmental rotor design, the core loss and weight of the rotor will be reduced. This concept maintains the torque capability of a previous design, but uses much less copper volume. The finite element model is established base on the software of Maxwell 12- 3D, which is aimed at flux linkage, inductance, output torque, radial force and axial force. The theoretical basis for the machine configuration is developed and then the results for a working prototype machine are shown, illustrating static torque and flux linkage profiles. Results are presented for the machine operating as a drive, indicating the viability of the concept which is much suitable for the low speed and high torque application.
CEREBRAL PALSY IN CHILDHOOD AND ADOLESCENCE

Cerebral Palsy in Childhood and Adolescence is a good book for those interested in this subject. It will serve well as a supplementary monograph to the few textbooks already published. The uniqueness of the book is that it demonstrates how a small group of highly interested and competent specialists can discover answers to problems when efforts are made to study the problem carefully. The data presented concern the Eastern region of Scotland. After reviewing 301 suspected cases in patients under 21 years of age that were registered, the authors confirmed, through retrospective history data and actual examination, the diagnosis of cerebral palsy in 240 cases. The information was then itemized into general medical and neurological features. A detailed analysis of each type of cerebral palsy was done, and comparison was made with other studies elsewhere in the world. Orthopedic, ophthalmological, dental, psychological, educational, and social aspects were also studied in
The scutellum of germinated wheat grains undergoes programmed cell death: identification of an acidic nuclease involved in nucleus dismantling

Programmed cell death (PCD) is a crucial phenomenon in the life cycle of cereal grains. In germinating grains, the scutellum allows the transport of nutrients from the starchy endosperm to the growing embryo, and therefore it may be the last grain tissue to undergo PCD. Thus, the aim of this work was to analyse whether the scutellum of wheat grains undergoes PCD and to perform a morphological and biochemical analysis of this process. Scutellum cells of grains following germination showed a progressive increase of DNA fragmentation, and the TUNEL assay showed that PCD extended in an apical-to-basal gradient along the scutellum affecting epidermal and parenchymal cells. Electron-transmission microscopy revealed high cytoplasm vacuolation, altered mitochondria, and the presence of double-membrane structures, which might constitute symptoms of vacuolar cell death, whereas the nucleus appeared lobed and had an increased heterochromatin content as the most distinctive features. An acid- and Zn2+-dependent nucleolytic activity was identified in nuclear extracts of scutellum cells undergoing PCD. This nuclease was not detected in grains imbibed in the presence of abscisic acid, which inhibited germination. This nucleolytic activity promoted DNA fragmentation in vitro on nuclei isolated from healthy cells, thus suggesting a main role in nucleus dismantling during PCD.
Optimal content placement for peer-to-peer video-on-demand systems

In this paper, we address the problem of content placement in peer-to-peer systems, with the objective of maximizing the utilization of peers' uplink bandwidth resources. We consider system performance under a many-user asymptotic. We distinguish two scenarios, namely “Distributed Server Networks” (DSN) for which requests are exogenous to the system, and “Pure P2P Networks” (PP2PN) for which requests emanate from the peers themselves. For both scenarios, we consider a loss network model of performance, and determine asymptotically optimal content placement strategies in the case of a limited content catalogue. We then turn to an alternative “large catalogue” scaling where the catalogue size scales with the peer population. Under this scaling, we establish that storage space per peer must necessarily grow unboundedly if bandwidth utilization is to be maximized. Relating the system performance to properties of a specific random graph model, we then identify a content placement strategy and a request acceptance policy which jointly maximize bandwidth utilization, provided storage space per peer grows unboundedly, although arbitrarily slowly, with system size.
Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors' network structure by measuring the investors' communication patterns. Our results showed that the investors' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.
Event Extraction Entity Extraction and Linking Document Retrieval Entities Types coup detained Attack Arrest-Jail Events Types KaVD

Most previous efforts toward video captioning focus on generating generic descriptions, such as, “A man is talking. ” We collect a news video dataset to generate enriched descriptions that include important background knowledge, such as named entities and related events, which allows the user to fully understand the video content. We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. Then, given the video as well as the extracted events and entities, we generate a description using a Knowledgeaware Video Description network. The model learns to incorporate entities found in the topically related documents into the description via an entity pointer network and the generation procedure is guided by the event and entity types from the topically related documents through a knowledge gate, which is a gating mechanism added to the model’s decoder that takes a one-hot vector of these types. We evaluate our approach on the new dataset of news videos we have collected, establishing the first benchmark for this dataset as well as proposing a new metric to evaluate these descriptions.
Lexical access by L1 and L2 mono- and bidialectal listeners

This paper investigates lexical access in English in 36 listeners from four different participant groups. The groups vary in English language background (L1 vs. L2 [native Russian]), and in terms of English dialect exposure (D1 [monodialectal Australian] vs. D2 [bidialectal American / Australian]), providing the four groups: L1D1, L1D2, L2D1, and L2D2. We analyze accuracy in a lexical decision task in terms of the lexical item’s dialect origin (e.g. American faucet vs. Australian tap), and the speaker’s accent (Australian vs. American). Results reveal that L1 lexical decision is significantly more accurate on items with congruent word dialect and speaker accent (e.g. faucet in an American rather than Australian accent) than on incongruent ones. However, (in)congruency has no significant effect on accuracy during L2 lexical decision.
Distributed Online Optimization of Fog Computing for Selfish Devices With Out-of-Date Information

By performing fog computing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fog computing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent effective cooperations beyond one hop. This paper presents a new approach to enable cooperations of $N$ selfish devices over multiple hops, where selfish behaviors are discouraged by a tit-for-tat mechanism. The tit-for-tat incentive of a device is designed to be the gap between the helps (in terms of energy) the device has received and offered; and indicates how much help the device can offer at the next time slot. The tit-for-tat incentives can be evaluated at every device by having all devices broadcast how much help they offered in the past time slot, and used by all devices to schedule task offloading and processing. The approach achieves asymptotic optimality in a fully distributed fashion with a time-complexity of less than $\mathcal {O}(N^{2})$ . The optimality loss resulting from multi-hop signaling delays and consequently outdated tit-for-tat incentives is proved to asymptotically diminish. Simulation results show that our approach substantially reduces the time-average energy consumption of the state of the art by 50% and accommodates more tasks, by engaging devices hops away under multi-hop delays.
Applying Data Mining Techniques and Extended RFM Model in Customer Loyalty Measurement

This paper proposes a loyalty measurement model of individual customer for the benefit in creating of marketing campaign and activities as well as the suitable products and services for customers and establishment of good customer relationship. This study adapts the concept of RFM (RecencyFrequency-Monetary) model and applies to database of customer purchases and the customer type. The business type of selected organization is commercial business. To apply the RFM concept to find customer loyalty according to type of customer, the customer loyalty is partitioned into 5 classes using k-means clustering algorithm and is heuristically assigned customer types: Platinum, Gold, and Silver. Type of customers is then brought into consideration the extending of the RFM Model with customer analytics to make it even better customer classification performance. Finally, the classification system generates decision rules to find out the loyalty of new future customers using C4.5 decision tree algorithm. 
Dynamics of human protein kinases linked to drug selectivity

Protein kinases are major drug targets, but the development of highly-selective inhibitors has been challenging due to the similarity of their active sites. The observation of distinct structural states of the fully-conserved Asp-Phe-Gly (DFG) loop has put the concept of conformational selection for the DFG-state at the center of kinase drug discovery. Recently, it was shown that Gleevec selectivity for the Tyr-kinases Abl was instead rooted in conformational changes after drug binding. Here, we investigate whether protein dynamics after binding is a more general paradigm for drug selectivity by characterizing the binding of several approved drugs to the Ser/Thr-kinase Aurora A. Using a combination of biophysical techniques, we propose a universal drug-binding mechanism, that rationalizes selectivity, affinity and long on-target residence time for kinase inhibitors. These new concepts, where protein dynamics in the drug-bound state plays the crucial role, can be applied to inhibitor design of targets outside the kinome. eLife digest The Ser/Thr kinase Aurora A is an important target for the development of new anticancer therapies. A longstanding question is how to specifically and effectively inhibit only this kinase in a background of over 550 protein kinases with very similar structures. To this end, understanding the inhibition mechanism of Aurora A by different drugs is essential. Here, we characterize the kinetic mechanism of three distinct kinase drugs, Gleevec (Imatinib), Danusertib (PHA739358) and AT9283 (Pyrazol-4-yl Urea) for Aurora A. We show that inhibitor affinities do not rely exclusively on the recognition of a specific conformation of the Asp-Phe-Gly loop of the kinase. Our quantitative kinetics data put forward an opposing mechanism in which a slow conformational change after drug binding (i.e., induced-fit step) dictates drug affinity.
The effect of blanching on antioxidant activity and glycosides of white saffron (Curcuma mangga Val.).

White saffron (Curcuma mangga Val.) rhizome grows well in Indonesia. The objectives of this research were to determine antioxidant activity and to identify and quantify glycoside and aglicone substances in fresh and blanched white saffron. Sample of white saffron rhizome was extracted with methanol, rota-evaporated, and freeze dried. Dried extract was identified and quantified for its glycoside and aglicone content by High Performance Liquid Chromatography (HPLC). One of the identified glycoside compound was quercetin-3rutinoside. The antioxidant activity and quercetin content of blanched white saffron extracts were significantly higher (p ≤ 0.05) than that of fresh white saffron. Blanching of white saffron with citric acid and distilled water hydrolyzed quercetin-3-rutinoside into quercetin.
Fusion Model for Speech Emotion Recognition with Low Level Descriptor Features

Speech emotion recognition is one of the most challenging speech processing tasks, with many applications in the field of Human-Machine Interaction (HMI). Traditional works have been using Gaussian Mixture Models (GMM) for classification. In recent researches, Deep Neural Networks (DNN) have shown strong ability in feature learning and modeling in many tasks. In this paper, we present to utilize the DNN to learn extra features from Low Level Descriptor (LLD) features for other classifiers, and propose a decision-level fusion model for speech emotion recognition. We carry out experiments and evaluations on a public German corpus called FAU-AIBO with five emotions. And our experimental results using LLD features demonstrate that our proposed approach improves the recognition performance on unweighted results and outperforms the baseline using GMM significantly.
Fast field‐cycling nuclear magnetic resonance spectrometer

We describe here the design and construction of a modern, state‐of‐the‐art nuclear magnetic resonance (NMR) field‐cycling instrument. Fourier transform NMR spectra of both liquid and solid samples can be measured, and spin‐lattice relaxation times (T1Z) investigated over a broad range of magnetic field strengths ranging from 0 to 2 T. The instrument is based upon an existing personal computer‐based NMR spectrometer [C. Job, R. M. Pearson, and M. F. Brown, Rev. Sci. Instrum. 65, 3354 (1994)] which has been expanded into a fully computer‐controlled field‐cycling instrument. The magnetic field cycling is accomplished electronically by utilizing fast switching thyristors and a storage capacitor based on the Redfield energy storage concept. Unique aspects of the design include the field‐cycling magnet, which can reach fields as high as 2 T; the personal computer‐based NMR spectrometer and associated waveform electronics; and the use of a commercially available pulse width modulation switching current amplifier.
Conifer: Centrally-Managed PKI with Blockchain-Rooted Trust

Secure naming systems, or more narrowly public key infrastructures (PKIs), form the basis of secure communications over insecure networks. All security guarantees against active attackers come from a trustworthy binding between user-facing names, such as domain names, to cryptographic identities, such as public keys. By offering a secure, distributed ledger with highly decentralized trust, blockchains such as Bitcoin show promise as the root of trust for naming systems with no central trusted parties. PKIs based upon blockchains, such as Namecoin and Blockstack, have greatly improved security and resilience compared to traditional centralized PKIs. Yet blockchain PKIs tend to significantly sacrifice scalability and flexibility in pursuit of decentralization, hindering large-scale deployability on the Internet. We propose Conifer, a novel PKI with an architecture based upon CONIKS, a centralized transparency-based PKI, and Catena, a blockchain-agnostic way of embedding a permissioned log, but with a different lookup strategy. In doing so, Conifer achieves decentralized trust with security at least as strong as existing blockchain-based naming systems, yet without sacrificing the flexibility and performance typically found in centralized PKIs. We also present our reference implementation of Conifer, demonstrating how it can easily be integrated into applications. Finally, we use experiments to evaluate the performance of Conifer compared with other naming systems, both centralized and blockchain-based, demonstrating that it incurs only a modest overhead compared to traditional centralized-trust systems while being far more scalable and performant than purely blockchain-based solutions.
INPUT–OUTPUT ANALYSIS AND CARBON FOOTPRINTING: AN OVERVIEW OF APPLICATIONS

This article provides an overview of how generalised multi-regional input–output models can be used for carbon footprint applications. We focus on the relevance and suitability of such evidence to inform decision making. Such an overview is currently missing. Drawing on UK results, we cover carbon footprint applications in seven areas: national emissions inventories and trade, emission drivers, economic sectors, supply chains, organisations, household consumption and lifestyles as well as sub-national emission inventories. The article highlights the multiple uses of generalised multi-regional input–output models for carbon footprinting and concludes by highlighting important avenues for future research.
Additive Manufactured Microstructures and Designs for High Heat Flux Dissipation During Pool Boiling

Masters of Science by Austin Hayes Heat dissipation is vital in industries requiring predictable operating temperatures while also producing large heat fluxes. These industries include electronics and power generation. For electronics, as more devices fit on a smaller area, the heat flux increases dramatically. Pool boiling offers a solution to electronic cooling due to extremely high heat transfer with a low temperature change. Previous research has focused on coatings and precision manufacturing to create microchannels and features for boiling augmentation. However, this is limited to designs for subtractive processes. The use of additive manufacturing (AM) offers a novel way of thinking of design for boiling enhancement. 3-D boiling structures are fabricated out of aluminum using the Vader System’s magnetojet printer. Three generations of geometric structures are created: a volcano-with-holes, a miniaturized volcano-with-holes, and a modular volcano-with-holes. These designs are not easily manufactured using standard techniques. As such, three-dimensional bubble dynamics are currently being explored using high speed imaging and particle image velocimetry. By printing a volcano shape with base holes, the liquid and vapor phases are physically separated in a process termed macroscale liquid-vapor pathways. The singular volcano-with-holes chips achieved a maximum heat flux of 217.3 W/cm2 with a maximum heat transfer coefficient (HTC) of 97.2 kW/m2K (81% improvement over plain). By producing four volcanoes on a single chip, the liquid flow length inside the volcano, which acts as the entrance length, is reduced by 25% and the HTC greatly increased. The highest performing miniaturized volcano-with-holes chip reached a maximum heat flux of 223.1 W/cm2 with a maximum HTC of 139.1 kW/m2K (150% improvement over plain). Additionally, the highest performing miniaturized chip was printed on top of a microchannel array. This resulted in combined enhancement from both microchannel and bubble dynamics resulting in a maximum heat flux of 228.4 W/cm2 with a HTC of 339.6 kW/m2K (533% improvement over plain). Finally, a modular structure was created to determine the individual influence of conduction and bubble dynamic augmentation on boiling enhancement. The modular designs show an 83% improvement in CHF (202.4 W/cm2) over plain copper chips and a 83% improvement in HTC(139.0 kW/m2K). This indicates boiling enhancement arises from three-dimensional control over bubble dynamics, resulting in macroscale separate liquid-vapor pathways.
Convolutional Neural Networks for Predicting Skin Lesions of Melanoma

Diagnosis of an unknown skin lesion is crucial to enable proper treatments. While curable with early diagnosis, only highly trained dermatologists are capable of accurately recognize melanoma skin lesions. Expert dermatologist classification for melanoma dermoscopic images is 65-66%. As expertise is in limited supply, systems that can automatically classify skin lesions as either benign or malignant melanoma are very useful as initial screening tools. Towards this goal, this study presents a convolutional neural network model, trained on features extracted from a highway convolutional neural network pretrained on dermoscopic images of skin lesions. This requires no lesion segmentation nor complex preprocessing. Further, it doesn’t cost much computational power to train the model. This proposed approach achieves a favorable training accuracy of 98%, validation accuracy of 64.57% and validation loss 0.07 in the model with 46% sensitivity and 64% classification accuracy in testing data.
Complexity Reduction Techniques for the Compression of High-Definition Video DOI: 10.14209/jcis.2009.1

The state-of-the-art video compression standard, the hybrid predictive-transform H.264/AVC codec, has lead to substantial performance improvement compared to other existing standards. Performing predictions is a computational demanding task and optimizing this stage may result in substantial encoding speedup. In this paper, we propose more efficient approaches to implement the H.264/AVC prediction stage. The first idea is to use original data rather than reconstructed ones to perform prediction tests before choosing the best mode. The residue, however, is evaluated using previously decoded data in order to avoid drifting. The second approach is to employ a subset of dominant prediction modes instead of testing all modes recommended by the H.264/AVC standard. The subset is updated frame-by-frame using macroblock sampling. Results for high definition sequences show that the quality loss is negligible allowing us not only to parallelize the inter-prediction stage but also to reduce total complexity.
Age Assessment in Forensic Cases : Anthropological , Odontological and Biochemical Methods for Age Estimation in the Dead

Mathews Journal of Forensic Research Age Assessment in Forensic Cases: Anthropological, Odontological and Biochemical Methods for Age Estimation in the Dead Joe Adserias-Garriga*1, Sara C. Zapico2 1Fundació Universitat de Girona: Innovació i Formació, Universitat de Girona, Spain. 2Department of Chemistry and Biochemistry and International Forensic Research Institute. Florida International University. Corresponding Author: Joe Adserias-Garriga, Fundació Universitat de Girona: Innovació i Formació, Universitat de Girona, Spain, Tel: (+34) 649 664 437; Email: mjadserias@hotmail.com