Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful Use of Leukocyte Platelet-Rich Fibrin in the Healing of Sinus Membrane Perforation: A Case Report

Purpose: The aim of this case report was to demonstrate the repair of a large sinus membrane perforation related to a sinus floor augmentation procedure, using leukocyte platelet-rich fibrin (L-PRF), for subsequent rehabilitation of a partially edentulous patient. Materials and Methods: The patient presented the absence of teeth numbers 18, 17, and 16, associated with insufficient bone height because of the maxillary sinus pneumatization. A maxillary inlay bone graft was proposed, however, during the sinus floor augmentation procedure, a large portion of the sinus membrane was ruptured. To avoid interruption of the surgical procedure, membrane mending was proposed using L-PRF and collagen membranes. Results: After 8 months, 2 external hexagon connection dental implants were placed in the 16 and 17 teeth regions, and a screw-retained implant-supported prosthesis was installed. Conclusion: The use of L-PRF associated with collagen membrane was efficient for the sealing of the sinus membrane perforation and enabled bone formation for subsequent implant installation.
TDM EPON Fronthaul Upstream Capacity Improvement via Traffic Classification and Sifting

Mobile Fronthaul (MF) is defined as the connection between Remote Radio Head (RRH) and Baseband Processing Unit (BBU) in a Cloud Radio Access Network (C-RAN). Dedicated MF connections between RRH and BBU would be very costly. Thus, Time Division Multiplexing Ethernet Passive Optical Network (TDM EPON) is a promising solution to reduce cost as it can enable multiplexing gain. Note that, even though mobile users transmit intermittently, in the upstream channel RRH is sampling radio signal all the time, limiting the achievable multiplexing gain. Our study enhances the conventional TDM EPON architecture by introducing traffic classification, sifting of useless data to avoid the transmission of unnecessary EPON frames, and hence increasing the multiplexing gain in the upstream channel. We also propose a Hybrid Bandwidth Allocation (HBA) scheme to exploit the traffic usefulness classification information. Simulation results show significant improvements in terms of load and number of connected RRHs that can be supported by same EPON, while keeping the end-to-end delay under 100 μs.
Energy Harvesting Aided Device-to-Device Communication in the Over-Sailing Heterogeneous Two-Tier Downlink

Device-to-Device (D2D) communication and heterogeneous networks have been considered as promising techniques for alleviating the demand both for increased spectral resources and for additional infrastructure required for meeting the increased tele-traffic. For the sake of improving both the bandwidth efficiency and the network capacity of heterogeneous cellular networks constituted by multiple tiers, a direct D2D communication is arranged between a pair of nearby devices without involving the base station (BS), whilst reusing the cellular resources. We aim for maximising the sum-rate of the energy harvesting (EH) aided D2D links in a two-tier heterogeneous network by superimposing their messages on the downlink resources of mobile users (MUs), which is achieved without unduly degrading MU’s throughput. Specifically, our optimization problem relies on the objective function of maximising the D2D sum-rate based on the joint assignment of both the resource blocks (RBs) and of the transmission power for both the EH aided D2D links and the MUs. This non-convex optimization problem, which is intractable in its original form, is then converted to a tractable convex problem, which is then analyzed by invoking the method of Lagrange multipliers of constrained optimization. As a result, an algorithmic solution defined as joint optimization of RB and power allocation (JORPA) is proposed, which jointly allocates the RBs and power for the D2D links, whilst relying on the results of Lagrangian constrained optimization, when the base stations (BSs) of different tiers obey one of the following regimes: (a) orthogonal; (b) co-channel; and (c) the proposed co-orthogonal channel deployments. We also propose low complexity heuristic methods for optimizing the D2D transmit power, while defining the D2D-MU matching heuristically and vice versa. The performance of both the JORPA algorithm as well as of the low-complexity heuristic algorithms is quantitatively analyzed using our simulation results for different channel deployments relying on diverse network parameter settings. As expected, orthogonal deployment performs best, followed by the co-orthogonal and co-channel deployments. Moreover, the throughput experienced by the MUs in presence of D2D communication is guaranteed by our co-orthogonal scheme as well as orthogonal scheme, while co-channel suffers a marginal degradation when compared with throughput threshold. We also demonstrate that our equal power allocation heuristic method is capable of achieving 96% of the sum-rate achieved by JORPA while other heuristic methods perform less well, implying that the optimization of the D2D-MU matching is indeed crucial for the system considered.
A first empirical look on internet-scale exploitations of IoT devices

Technological advances and innovative business models led to the modernization of the cyber-physical concept with the realization of the Internet of Things (IoT). While IoT envisions a plethora of high impact benefits in both, the consumer as well as the control automation markets, unfortunately, security concerns continue to be an afterthought. Several technical challenges impede addressing such security requirements, including, lack of empirical data related to various IoT devices in addition to the shortage of actionable attack signatures. In this paper, we present what we believe is a first attempt ever to comprehend the severity of IoT maliciousness by empirically characterizing the magnitude of Internet-scale IoT exploitations. We draw upon unique and extensive darknet (passive) data and develop an algorithm to infer unsolicited IoT devices which have been compromised and are attempting to exploit other Internet hosts. We further perform correlations by leveraging active Internet-wide scanning to identify and report on such IoT devices and their hosting environments. The generated results indicate a staggering 11 thousand exploited IoT devices that are currently in the wild. Moreover, the outcome pinpoints that IoT devices embedded deep in operational Cyber-Physical Systems (CPS) such as manufacturing plants and power utilities are the most compromised. We concur that such results highlight the wide-spread insecurities of the IoT paradigm, while the actionable generated inferences are postulated to be leveraged for prompt mitigation as well as to facilitate IoT forensic investigations using real empirical data.
A real-time system for audio source localization with cheap sensor device

We propose an architecture for real-time audio source localization based on the integration of localization methodologies within a framework that employs a cheap acquisition sensor. The architecture that we present takes as input the audio signals from two calibrated microphones. Then, it computes biological-inspired features of the sound signal and estimates its direction by means of a Gaussian Mixture Model estimator. We carried out an extensive experimental analysis on four data sets, one of which we realized and made publicly available. We evaluated several characteristics of the sound localization architecture and its use in real scenarios.
Secure two-party computations in ANSI C

The practical application of Secure Two-Party Computation is hindered by the difficulty to implement secure computation protocols. While recent work has proposed very simple programming languages which can be used to specify secure computations, it is still difficult for practitioners to use them, and cumbersome to translate existing source code into this format. Similarly, the manual construction of two-party computation protocols, in particular ones based on the approach of garbled circuits, is labor intensive and error-prone. The central contribution of the current paper is a tool which achieves Secure Two-Party Computation for ANSI C. Our work is based on a combination of model checking techniques and two-party computation based on garbled circuits. Our key insight is a nonstandard use of the bit-precise model checker CBMC which enables us to translate C programs into equivalent Boolean circuits. To this end, we modify the standard CBMC translation from programs into Boolean formulas whose variables correspond to the memory bits manipulated by the program. As CBMC attempts to minimize the size of the formulas, the circuits obtained by our tool chain are also size efficient; to improve the efficiency of the garbled circuit evaluation, we perform optimizations on the circuits. Experimental results with the new tool CBMC-GC demonstrate the practical usefulness of our approach.
StructureMapper: a high-throughput algorithm for analyzing protein sequence locations in structural data

Motivation StructureMapper is a high‐throughput algorithm for automated mapping of protein primary amino sequence locations to existing three‐dimensional protein structures. The algorithm is intended for facilitating easy and efficient utilization of structural information in protein characterization and proteomics. StructureMapper provides an analysis of the identified structural locations that includes surface accessibility, flexibility, protein‐protein interfacing, intrinsic disorder prediction, secondary structure assignment, biological assembly information and sequence identity percentages, among other metrics. Results We have showcased the use of the algorithm by estimating the coverage of structural information of the human proteome, identifying critical interface residues in DNA polymerase &ggr;, profiling structurally protease cleavage sites and post‐translational modification sites, and by identifying putative, novel phosphoswitches. Availability and implementation The StructureMapper algorithm is available as an online service and standalone implementation at http://structuremapper.uta. fi.
Price-Taker Offering Strategy in Electricity Pay-as-Bid Markets

The recent increase in the deployment of renewable energy sources may affect the offering strategy of conventional producers, mainly in the balancing market. The topics of optimal offering strategy and self-scheduling of thermal units have been extensively addressed in the literature. The feasible operating region of such units can be modeled using a mixed-integer linear programming approach, and the trading problem as a linear programming problem. However, the existing models mostly assume a uniform pricing scheme in all market stages, while several European balancing markets (e.g., in Germany and Italy) are settled under a pay-as-bid pricing scheme. The existing tools for solving the trading problem in pay-as-bid electricity markets rely on nonlinear optimization models, which, combined with the unit commitment constraints, result in a mixed-integer nonlinear programming problem. In contrast, we provide a linear formulation for that trading problem. Then, we extend the proposed approach by formulating a two-stage stochastic problem for optimal offering in a two-settlement electricity market with a pay-as-bid pricing scheme at the balancing stage. The resulting model is mixed-integer and linear. The proposed model is tested on a realistic case study against a sequential offering approach, showing the capability of increasing profits in expectation.
An Energy-Aware Model for the Logic Synthesis of Quantum-Dot Cellular Automata

Quantum-dot cellular automata (QCA) are an emerging field-coupled nanotechnology with remarkable performance and energy efficiency. In order to enable the exploration of this technology, we propose a model for the logic synthesis of QCA circuits that, for the first time, considers and abstracts all main physical aspects—in particular, energy dissipation. To this end, we review in detail how energy is dissipated in QCA cells and present a corresponding environment that allows for the estimation of the energy dissipation with respect to any specific set of technology parameters. Based on that, we derive a model for logic synthesis. A case study confirms the accuracy of the proposed model and reveals that interconnections have a significant impact in this technology—motivating a more rigorous consideration. These findings eventually provide the basis for a new generation of synthesis approaches at the logic level that are explicitly dedicated to QCA systems.
The Neonatal Behavioral Observation System: A Tool to Enhance the Transition to Motherhood

Background: Shorter hospitalisation after childbirth raises new challenges in the transition to parenthood. Aim: This paper reports a study designed to identify first-time mothers' experience with The Neonatal Behavioral Observation System (NBO) as a guiding model. Method: A phenomenological qualitative study sampling four first-time mothers who received guidance from the NBO-specialist nurse two days after delivery. Interviews were conducted using a semi-structured interview guide. Findings: Analysis revealed three themes describing the mothers positive experience of the guidance model: a) a new understanding of the baby's communication abilities, b) an increased feeling of competence and confidence in dealing with challenges in caretaking issues, and c) being treated as individuals encouraged to make their own decisions. Conclusion: The NBO system can be used in clinical practice to enhance the transition to motherhood and family life and to provide support during the post-partum period. More research on the impact of NBO that includes fathers as well as different groups of at-risk parents is recommended.
Predicting suicide following self-harm: systematic review of risk factors and risk scales.

BACKGROUND People with a history of self-harm are at a far greater risk of suicide than the general population. However, the relationship between self-harm and suicide is complex. AIMS To undertake the first systematic review and meta-analysis of prospective studies of risk factors and risk assessment scales to predict suicide following self-harm. METHOD We conducted a search for prospective cohort studies of populations who had self-harmed. For the review of risk scales we also included studies examining the risk of suicide in people under specialist mental healthcare, in order to broaden the scope of the review and increase the number of studies considered. Differences in predictive accuracy between populations were examined where applicable. RESULTS Twelve studies on risk factors and 7 studies on risk scales were included. Four risk factors emerged from the meta-analysis, with robust effect sizes that showed little change when adjusted for important potential confounders. These included: previous episodes of self-harm (hazard ratio (HR) = 1.68, 95% CI 1.38-2.05, K = 4), suicidal intent (HR = 2.7, 95% CI 1.91-3.81, K = 3), physical health problems (HR = 1.99, 95% CI 1.16-3.43, K = 3) and male gender (HR = 2.05, 95% CI 1.70-2.46, K = 5). The included studies evaluated only three risk scales (Beck Hopelessness Scale (BHS), Suicide Intent Scale (SIS) and Scale for Suicide Ideation). Where meta-analyses were possible (BHS, SIS), the analysis was based on sparse data and a high heterogeneity was observed. The positive predictive values ranged from 1.3 to 16.7%. CONCLUSIONS The four risk factors that emerged, although of interest, are unlikely to be of much practical use because they are comparatively common in clinical populations. No scales have sufficient evidence to support their use. The use of these scales, or an over-reliance on the identification of risk factors in clinical practice, may provide false reassurance and is, therefore, potentially dangerous. Comprehensive psychosocial assessments of the risks and needs that are specific to the individual should be central to the management of people who have self-harmed.
Effect of p-GaN layer doping on the photoresponse of GaN-based p-i-n ultraviolet photodetectors

We report on two-dimensional (2D) numerical simulations of photoresponse characteristics for GaN based p-i-n ultraviolet (UV) photodetectors. Effects of doping density of p-GaN layer on the photoresponse have been investigated. In order to accurately simulate the device performance, the theoretical calculation includes doping-dependent mobility degradation by Arora model and high field saturation model. Theoretical modeling shows that the doping density of p- GaN layer can significantly affect the photoresponse of GaN based p-i-n UV photodetectors, especially at schottky contact. We have to make a suitable choice of the doping in the device design according to the simulation results.
Mining tourist routes using flickr traces

This paper is about a new methodology to automatically rebuild main paths from Flickr's traces. Our geotagged image metadata's corpus allows the construction of each photographer's timeline. The tourist's itinerary in the destination can then be reconstructed considering two modes: the fastest path and the most likely path. Major paths are directly extracted from the fusion of all itineraries. To illustrate the multi-scale efficiency of this work, several fields of study are presented : Berlin, the Loire Valley and the Palace of Versailles.
Neural-based predictive learning control

In this paper, a new control algorithm to combine neural-based learning with error predictor is developed for batch processes. First, the control is represented by a radial basis function (RBF) network within the time horizon. Next, to accommodate the advantages of model predictive control, the error predictor is designed based on the batch iteration direction. Finally, the learning algorithm is derived by guaranteeing the stability. To highlight the key features of the algorithm, an example is provided to demonstrate the performance in a batch process.
Compensating for geographic variation in detection probability with water depth improves abundance estimates of coastal marine megafauna

The probability of an aquatic animal being available for detection is typically <1. Accounting for covariates that reduce the probability of detection is important for obtaining robust estimates of the population abundance and determining its status and trends. The dugong (Dugong dugon) is a bottom-feeding marine mammal and a seagrass community specialist. We hypothesized that the probability of a dugong being available for detection is dependent on water depth and that dugongs spend more time underwater in deep-water seagrass habitats than in shallow-water seagrass habitats. We tested this hypothesis by quantifying the depth use of 28 wild dugongs fitted with GPS satellite transmitters and time-depth recorders (TDRs) at three sites with distinct seagrass depth distributions: 1) open waters supporting extensive seagrass meadows to 40 m deep (Torres Strait, 6 dugongs, 2015); 2) a protected bay (average water depth 6.8 m) with extensive shallow seagrass beds (Moreton Bay, 13 dugongs, 2011 and 2012); and 3) a mixture of lagoon, coral and seagrass habitats to 60 m deep (New Caledonia, 9 dugongs, 2013). The fitted instruments were used to measure the times the dugongs spent in the experimentally determined detection zones under various environmental conditions. The estimated probability of detection was applied to aerial survey data previously collected at each location. In general, dugongs were least available for detection in Torres Strait, and the population estimates increased 6–7 fold using depth-specific availability correction factors compared with earlier estimates that assumed homogeneous detection probability across water depth and location. Detection probabilities were higher in Moreton Bay and New Caledonia than Torres Strait because the water transparency in these two locations was much greater than in Torres Strait and the effect of correcting for depth-specific detection probability much less. The methodology has application to visual survey of coastal megafauna including surveys using Unmanned Aerial Vehicles.
On the Number of α-Labeled Graphs

Abstract When a graceful labeling of a bipartite graph places the smaller labels in one of the stable sets of the graph, it becomes an α-labeling. This is the most restrictive type of difference-vertex labeling and it is located at the very core of this research area. Here we use an extension of the adjacency matrix to count and classify α-labeled graphs according to their size, order, and boundary value.
Nanotube fibers for electromechanical and shape memory actuators

Carbon nanotubes are light, stiff and electroactive materials particularly promising in the field of actuating materials. Indeed, carbon nanotubes can expand and contract upon charge injection and be used for the development of electromechanical actuators. Carbon nanotubes can also be included in polymers to improve their properties and bring specific functionalities. When added to shape memory polymers, carbon nanotubes yield an improved stiffness and the possibility to heat the material through Joule’s heating. Nevertheless, spatial ordering of the nanotubes is a critical issue in all these classes of actively moving materials. It is shown in this article that assembling nanotubes under the form of pure or composite fibers is an effective approach to orient carbon nanotubes on a large scale along a well defined direction. Nanotube alignment achieved via fiber drawing allows the optimization of properties of shape memory polymer fibers and electrochemical actuators. In particular, the mechanical response of pure nanotube fibers to electrical stimulations is investigated in liquid electrolytes. It is observed that the fibers can generate a stress one order of magnitude greater than that achieved with unaligned assemblies of nanotubes. We also present the properties of shape memory polymer fibers loaded with carbon nanotubes. These fibers generate a very large stress when they recover their shape after they have been stretched and cooled under tensile load. Composite nanotube polymer fibers also exhibit a temperature memory behavior, which is still raising fundamental questions regarding its microscopic origin.
MSTM: A novel map matching approach for low-sampling-rate trajectories

Map matching is an important technique that matches user trajectories to the real road networks on a digital map. It is crucial and has been more and more widely used in various fields, such as route plan, traffic forecast, location-based services and so on. However, most existing algorithms are less effective when applied to low-sampling-rate trajectories. In this paper, we propose MSTM, a novel approach that can effectively match the low-sampling-rate trajectory to road networks. In MSTM, we first partition the trajectories into trajectory segments according to the stay points. Then we construct a map-searching tree by conditional extend and prune operations, which contains all the candidate paths. Finally, by considering the spatial and temporal information of trajectories, we evaluate each branch path in the map-searching tree and choose the one with the highest score as the result. Extensive experiments on real-world datasets show that, compared with two classic approaches, MSTM outperforms ST-Matching and IVMM in terms of matching accuracy as well as efficiency.