Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Centric Cloud RAN: An Analytical Framework for Optimizing Area Spectral and Energy Efficiency

In this article, we develop a statistical framework to quantify the area spectral efficiency (ASE) and the energy efficiency (EE) performance of a user-centric cloud based radio access network (UC-RAN) downlink. We propose a user-centric remote radio head (RRH) clustering mechanism, which: 1) provides significant improvement in the received signal-to-interference-ratio through selection diversity; 2) enables efficient interference protection by inducing repulsion among scheduled user-centric RRH clusters; and 3) can self-organize the cluster radius to deal with spatio–temporal variations in user densities. It is shown that under the proposed user-centric clustering mechanism, the ASE (bits/s/Hz/m2) maximizes at an optimal cluster size. It is observed that this cluster size is sensitive to changes in both RRH and user densities and, hence, must be adapted with variations in these parameters. Next, we formulate the cost paid for the UC-RAN capacity gains in terms of power consumption, which is then translated into the EE (bits/s/Joule) of the UC-RAN. It is observed that the cluster radius which maximizes the EE of the UC-RAN is relatively larger as compared with that which yields maximum ASE. Consequently, we notice that the tradeoff between the ASE and the EE of UC-RAN manifests itself in terms of cluster radius selection. Such tradeoff can be exploited by leveraging a simple two player cooperative game. Numerical results show that the optimal cluster radius obtained from the Nash bargaining solution of the modeled bargaining problem may be adjusted through an exponential weightage parameter that offers a mechanism to utilize the inherent ASE-EE tradeoff in a UC-RAN. Furthermore, in comparison with existing state-of-the-art non user-centric network models, our proposed scheme, by virtue of selective RRH activation and non overlapping user-centric RRH clusters, offers higher and adjustable system ASE and EE, particularly in dense deployment scenarios.
Patient specific numerical modelling for the optimisation of HCC selective internal radiation therapy an image based approach

Selective internal radiation therapy using Yttrium-90 loaded glass microspheres injected in the hepatic artery is an emerging, minimally invasive therapy of hepatocellular carcinoma, which is the fourth cause of mortality in the world. Currently, microspheres distribution can be only approximately predicted by the injection of a radiotracer, whose behaviour may be different. A personalised intervention can lead to high concentration dose in the tumour, while sparing the surrounding parenchyma. This work is concerned with the development of a patient-specific numerical model for the simulation of microspheres tra- jectories and treatment optimisation. Clinical imaging protocol is utilised and optimised in order to extract patient’s specific data such as liver, tumours, hepatic artery and blood flow. Normal and malignant hepatic arterial vasculature and tissues are simulated down to a vessels diameter of 0.05 mm. A preliminary simulation of microspheres distribution in liver tissue is proposed and validated against post-treatment scintigraphy. Microspheres are here supposed to distribute proportionally to blood flow, which is computed based on Poiseuille’s law. More precise computational fluid dynamics (CFD) simulations of blood flow in the patient’s segmented arteries are performed. The Finite Volume Method (Ansys Fluent) and the Lattice Boltzmann Method (in-house developed software) are used to this purpose and their efficacy is compared. Microspheres transport is simulated in the patient’s hepatic artery using the FVM, and in a representative geometry using the LBM method. A phase contrast MRI sequence has been optimised in order to extract blood velocity from the hepatic artery and validate CFD simulations.
Effects of disodium fumarate on in vitro rumen microbial growth, methane production and fermentation of diets differing in their forage:concentrate ratio

The effects of disodium fumarate on microbial growth, CH4 production and fermentation of three diets differing in their forage content (800, 500 and 200 g/kg DM) by rumen micro-organisms in vitro were studied using batch cultures. Rumen contents were collected from four Merino sheep. Disodium fumarate was added to the incubation bottles to achieve final concentrations of 0, 4 and 8 mm-fumarate, and 15N was used as a microbial marker. Gas production was measured at regular intervals from 0 to 120 h of incubation. Fumarate did not affect (P>0·05) any of the measured gas production parameters. In 17 h incubations, the final pH and the production of acetate and propionate were increased linearly (P<0·001) by the addition of fumarate. Fumarate tended to increase (P=0·076) the organic matter disappearance of the diets and to decrease (P=0·079) the amount of NH3-N in the cultures. Adding fumarate to batch cultures tended (P=0·099) to decrease CH4 production, the mean values of the decrease being 5·4 %, 2·9 % and 3·8 % for the high-, medium- and low-forage diet, respectively. Fumarate tended to increase (P=0·082) rumen microbial growth for the high-forage diet, but no differences (P>0·05) were observed for the other two diets. These results indicate that the effects of fumarate on rumen fermentation depend on the nature of the incubated substrate, the high-forage diet showing the greatest response.
An efficiency measure for road transportation networks with application to two case studies

Enabling efficient transportation is a major challenge for large cities, as the transportation need is increasing, while the environmental impact has to be minimized. In this paper, we define an efficiency measure that shows how much of the current transportation mileage that is really necessary to meet all the transportation assignments. We show that the efficiency measure can be computed efficiently as a minimum cost flow, and we apply it on two case studies. The first case demonstrate the efficiency measure on a freight transportation system, and the second case computes the measure for a large real-world data set from the New York City taxis.
Microfluidic biosensing systems based on enzymes, antibodies and cells

The rapid developments within the life science and biotechnology areas put up ever-new demands on more sophisticated techniques and methods for chemical and biological analysis to reach deeper insight into the events at molecular level. Miniaturisation of assays and systems on microchips is one way to increase throughput and advance through parallel analysis lines, multiplexing and integration. This thesis shows some aspects on the use of silicon microchips as a platform for immobilisation of enzymes, antibodies and cells for chemiluminometric biosensing assays. For antibody- and enzyme-based systems, special attention was paid to the immobilisation chemistry due to its influence on stability and activity of the attached biomolecules. In general it was found that surface coatings with a layer of polymer, e.g., polyethylenimine or dextran, increased both stability and signal intensity of the systems, compared to ordinary silanisation-based attachment chemistry. Furthermore, two cell-based monitoring systems, using either yeast cells or a reporter gene modified human (HeLa) cell line, were developed. These systems demonstrated the usefulness and advantages of microfluidics, e.g. in continuous monitoring of cellular events in real time, which stands in great contrast to the common end-point microtiter plate assays. The demands on yeast compared to human cells are however quite different, where the latter has highly specialised requirements on the physical and chemical environment. Special attention was thus paid on investigating different factors that can lead to unspecific and stress-related expression of the reporter gene, hence affecting the quality of the data and overall performance of the system. In conclusion, the main focus of this thesis has been to develop and apply analytical techniques and methods on microchips on flow-format. During the period the which this research was conducted, many interesting and elegant microfluidic analytical systems have been reported in the literature, however, the attention is often drawn mainly to the hardware set-up and construction. The systems developed in this thesis (paper I to V) are technically simple but show real applications in which biological elements, such as living cells, enzymes and antibodies, are handled on silicon microchips.
Shape Recognition in 3D Point-Clouds

Currently, tasks as basic as selecting all windows in a 3d laser scan of a house currently require a disproportional amount of user interaction. We present a flexible framework for the rapid detection of such semantically motivated shapes in large point clouds. Semantic entities are represented as constrained graphs that describe configurations of primitive shapes. Experimental results in various scenarios related to the architectural domain demonstrate the feasibility of the approach.
Cutaneous botryomycosis in a crossbred domestic pig

Botryomycosis is a rare, chronic, inflammatory response of the skin or internal organs to bacterial infection. This disease was first diagnosed in the lungs of a horse at the University of Veterinary Medicine, Vienna, Austria, in 1869 (Bollinger 1870) and was initially thought to be of fungal aetiology, with the true bacterial cause only discovered in 1919 (Smiet et al. 2012). Botryomycosis has since been described in humans and various animal species. Of these the most well characterised veterinary syndromes are scirrhous cord in geldings (Wolstenholme 1896; Pollock 2012), and chronic staphylococcal mastitis in cows (Blackburn 1959). Botryomycosis can be caused by a variety of non-filamentous bacteria including; Staphylococcus aureus, Pseudomonas spp. , Proteus spp. , Streptococcus spp. and Actinobacillus spp. The inflammatory reaction involves the formation of micro-abscesses within the tissues and the infection can be classified as either visceral or cutaneous depending on its location (Kvist et al. 2002; Smiet et al. 2012).
The potential prognostic and predictive roles of programmed cell death protein 1 expressed by tumor-infiltrating lymphocytes in solid tumors: a meta-analysis

Aim: Several previous studies have evaluated the potential role of programmed cell death protein 1 (PD-1) expressed by tumor-infiltrating lymphocytes (TILs) in various solid tumors and performed its prognosis role in patients’ survival with inconsistent results. This study aims to further systematically evaluate the association of PD-1 by TILs with clinicopathological parameters and clinical outcomes in solid tumor patients. Methods: A comprehensive search was conducted in PubMed, Embase, Web of Science, CNKI and Wanfang databases for relevant studies. The potential prognostic and predictive roles of PD-1 were assessed by pooled hazard ratio (HR), odds ratio (OR) and 95% confidence intervals (CI). A total of 1863 patients were selected for in-depth analysis. Results: The results demonstrated that PD-1 by TILs was correlated to overall survival for ovarian cancer (HR = 0.40, 95% CI: 0.26-0.61, P < 0.00001). Higher PD-1 expression was associated with lymph node metastasis (OR = 2.55, 95% CI: 1.22-5.29, P = 0.01) and tumor grade (OR = 3.08, 95% CI: 2.07-4.57, P < 0.00001). Conclusion: The prognostic role of PD-1 by TILs is variant in different tumor types, which highlights the role of PD-1 by TILs as a potential predictive and prognostic biomarker and the development of strategies against the PD-L1/PD-1 axis would be a promising therapeutic target for some solid tumors.
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

Relation extraction is the problem of classifying the relationship between two entities in a given sentence. Distant Supervision (DS) is a popular technique for developing relation extractors starting with limited supervision. We note that most of the sentences in the distant supervision relation extraction setting are very long and may benefit from word attention for better sentence representation. Our contributions in this paper are threefold. Firstly, we propose two novel word attention models for distantly- supervised relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based word attention model (BGWA), (2) an entity-centric attention model (EA), and (3) a combination model which combines multiple complementary models using weighted voting method for improved relation extraction. Secondly, we introduce GDS, a new distant supervision dataset for relation extraction. GDS removes test data noise present in all previous distant- supervision benchmark datasets, making credible automatic evaluation possible. Thirdly, through extensive experiments on multiple real-world datasets, we demonstrate the effectiveness of the proposed methods.
Plantar Pressure Differences between Nordic Walking Techniques

Abstract High plantar pressure has been associated with increased risk of injury. The characteristics of each physical activity determine the load on the lower limbs. The influence of Nordic Walking (NW) technique on plantar pressure is still unknown. The aim of this study was to analyze the differences between plantar pressure during NW with the Diagonal technique (DT) versus Alpha technique (AT) and compare them with the pressure obtained during normal walking (W). The normality and sphericity of the plantar pressure data were checked before performing a two-way repeated measures ANOVA in order to find differences between speeds (preferred, fast) and the gait (NW, W) as within-subject factors. Then, a t-test for independent measures was used to identify the specific differences between NW techniques. The strength of the differences was calculated by means of the effect size (ES). The results demonstrated that during NW with AT at preferred speed the pressure was lower under the Calcaneus, Lateral Metatarsal and Toes compared to the DT group (p = 0.046, ES = 1.49; p = 0.015, ES = 1.44; p = 0.040, ES = 1.20, respectively). No differences were found at the fast speed (p > 0.05). Besides the increase in walking speed during NW (p < 0.01), both technique groups showed lower pressure during NW compared to W under the Hallux and Central Metatarsal heads (F = 58.321, p = 0.000, ES = 2.449; F = 41.917, p = 0.012, ES = 1.365, respectively). As a practical conclusion, the AT technique may be the most effective of the NW techniques at reducing plantar pressure while allowing NW practitioners to achieve the physiological benefits of NW.
Diagnostic Hysterolaparoscopy for Evaluation of Infertility: Our Experience in a Tertiary Care Hospital

Objective: The objective of this study is to analyze the role of diagnostic hysterolaparoscopy (DHL) for evaluation of infertility in a tertiary care hospital. Materials and Methods: This retrospective study was conducted from July 2014 to June 2016. Results: Out of 151 patients, 58.28% and 41.72% had primary and secondary infertility, respectively. In primary infertility group 37.5% and in secondary infertility group 49.2% had abnormal findings. Most common finding was adnexal adhesions (pelvic inflammatory disease) and laparoscopic findings were more common than the hysteroscopic ones. Conclusion: DHL was helpful in finding some reversible causes of infertility such as adnexal adhesions, tubal blockade, and uterine synechiae, etc.
FOR SEMI-SUPERVISED ACOUSTIC MODELING

Current approaches to semi-supervised incremental learning prefer to select unlabeled examples predicted with high confidence for model re-training. However, this strategy can degrade the classification performance rather than improve it. We present an analysis for the reasons of this phenomenon, showing that only relying on high confidence for data selection can lead to an erroneous estimate to the true distribution when the confidence annotator is highly correlated with the classifier in the information they use. We propose a new data selection approach to address this problem and apply it to a variety of applications, including machine learning and speech recognition. Encouraging improvements in recognition accuracy are observed in our experiments.
Development of wireless sensor node hardware for large-area capacitive strain monitoring

Conventional resistive-type strain sensing methods have limitations in large-area sensing due to their relatively small size. The soft elastomeric capacitive (SEC) sensor is a capacitance-based stretchable electronic strain sensor, which has shown distinct advantages for mesoscale sensing over conventional strain-based structural health monitoring (SHM) due to its wide surface coverage capability. While recent advances in wireless sensor technologies have provided an attractive alternative to wired and centralized SHM, the capacitive strain sensing methods have not benefitted from the wireless approaches due to the lack of appropriate hardware element. This study develops a wireless sensor board to use the SEC sensor in combination with a wireless sensor network for SHM by addressing key implementation challenges. An alternating current (AC)-based De-Sauty Wheatstone bridge circuit is employed, converting dynamic capacitance variation from the SEC sensor into analog voltage signal. A high-precision bridge balancer and two-step signal amplifiers are implemented to effectively apply for low-level structural strain vibrations. An amplitude modulation-demodulator has been designed to extract the baseband signal (i.e. strain signal) from the carrier signal (i.e. AC excitation for the Wheatstone bridge). And a dual-step shunt calibrator has been proposed to remove the parasitic capacitance effect of lead wires during on-board calibration process. The performances of the sensor board developed in this study have been validated via a series of lab tests, outperforming a conventional wired capacitance measurement system.
PROBABILITY AS METADATA: EVENT DETECTION IN MUSIC USING ICA AS A CONDITIONAL DENSITY MODEL

We consider the problem of detecting note onsets in music under the hypothesis that the onsets, and events in general, are essentially surprising moments, and that event detection should therefore be based on an explicit probability model of the sensory input, which generates a moment-by-moment trace of the probability of each observation as it is made. Relatively unexpected events should thus appear as clear spikes. In this way, several well known methods of onset detection can be understood in terms of an implicit probability model. We apply ICA to the problem as an adaptive non-Gaussian model, and investigate the use of ICA as a conditional probability model. The results obtained using several methods on two extracts of piano music are presented and compared. Finally, we tentatively suggest an information theoretic interpretation of the approach.
2D Transition Metal Dichalcogenides and Graphene-Based Ternary Composites for Photocatalytic Hydrogen Evolution and Pollutants Degradation

Photocatalysis have attracted great attention due to their useful applications for sustainable hydrogen evolution and pollutants degradation. Transition metal dichalcogenides (TMDs) such as MoS2 and WS2 have exhibited great potential as cocatalysts to increase the photo-activity of some semiconductors. By combination with graphene (GR), enhanced cocatalysts of TMD/GR hybrids could be synthesized. GR here can act as a conductive electron channel for the transport of the photogenerated electrons, while the TMDs nanosheets in the hybrids can collect electrons and act as active sites for photocatalytic reactions. This mini review will focus on the application of TMD/GR hybrids as cocatalysts for semiconductors in photocatalytic reactions, by which we hope to provide enriched information of TMD/GR as a platform to develop more efficient photocatalysts for solar energy utilization.
Debugging OpenStack Problems Using a State Graph Approach

It is hard to operate and debug systems like OpenStack that integrate many independently developed modules with multiple levels of abstractions. A major challenge is to navigate through the complex dependencies and relationships of the states in different modules or subsystems, to ensure the correctness and consistency of these states. We present a system that captures the runtime states and events from the entire OpenStack-Ceph stack, and automatically organizes these data into a graph that we call system operation state graph (SOSG). With SOSG we can use intuitive graph traversal techniques to solve problems like reasoning about the state of a virtual machine. Also, using a graph-based anomaly detection, we can automatically discover hidden problems in OpenStack. We have a scalable implementation of SOSG, and evaluate the approach on a 125-node production OpenStack cluster, finding a number of interesting problems.
Effectiveness of brief intervention and contact for suicide attempters: a randomized controlled trial in five countries.

OBJECTIVE To determine whether brief intervention and contact is effective in reducing subsequent suicide mortality among suicide attempters in low and middle-income countries. METHODS Suicide attempters (n = 1867) identified by medical staff in the emergency units of eight collaborating hospitals in five culturally different sites (Campinas, Brazil; Chennai, India; Colombo, Sri Lanka; Karaj, Islamic Republic of Iran; and Yuncheng, China) participated, from January 2002 to October 2005, in a randomized controlled trial to receive either treatment as usual, or treatment as usual plus brief intervention and contact (BIC), which included patient education and follow-up. Overall, 91% completed the study. The primary study outcome measurement was death from suicide at 18-month follow-up. FINDINGS Significantly fewer deaths from suicide occurred in the BIC than in the treatment-as-usual group (0.2% versus 2.2%, respectively; chi2 = 13.83, P < 0.001). CONCLUSION This low-cost brief intervention may be an important part of suicide prevention programmes for underresourced low- and middle-income countries.
Pathogen and Toxin Entry - How Pathogens and Toxins Induce and Harness Endocytotic Mechanisms

Humans have been exposed to a plethora of pathogens (bacteria, viruses) ever since. Infectious diseases are among the leading causes of death worldwide. For example, in 2011, 1.34 million people died of tuberculosis, which is caused by an infection with Mycobacterium tuberculosis. Even more died of an infection by the human immunodeficiency virus (HIV; 1.78 million) or lower respiratory tract infection (3.46 million) [1]. In addition, recurring pandemic outbreaks of the influenza A virus, as in 2009, or an epidemic outbreak of enterohemorrhagic E. coli (EHEC) in Germany in 2011, show quite plainly that pathogens in the 21th century still are a severe health problem, not only in developing countries.
Reexamining the Market Value of Information Technology Events

The widespread use of announcement period returns to assess the financial impact of information technology (IT) events implicitly assumes that the market can completely price IT investments during the announcement period. However, some studies in strategy and information systems have suggested that long-term abnormal returns may be a more appropriate measure of the value of IT events. To reconcile these streams of research, we develop and test an exploratory framework involving the maturity and scope of an IT event to assess the suitability of short-versus long-term abnormal returns. We conceptualize event maturity in terms of diffusion of the technology or phenomenon, and scope as the extent of complementary organizational changes that need to be implemented and managed. We posit that because of lack of widespread knowledge of best practices and cases of success and failure in a period of low technological maturity, the market may find it difficult to price such an event completely during the announcemen.