Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Gene Induction and Cytokine Production in Donor Care

Progress in Transplantation, Vol 13, No. 1, March 2003 During the past several years there has been an increased focus on the biochemical effects of neur o logical injury after traumatic brain injury (TBI). For many years similar investigations have identified potentially neurotoxic substances in the brain after regional ischemic stroke and global brain ischemia following cardiac arrest. Although these studies have most often been performed in animals, many observations in humans appear to confirm these findings from laboratory experimentation. In this article, I review evidence that, as part of an extended inflammatory response following neurological injury or ischemia, migratory cells entering the brain, resident macro phages, neuronal support cells, and other cells within and around the damaged area manufacture new protein molecules that may be helpful or harmful to the local process of recovery and healing. Genes within the nuclei of these cells are induced to begin the transcription of the genetic “code” provided by the nuclear DNA into a series of RNA intermediaries leading to a final protein product. The cyto kines, thus manufactured as part of the inflammatory reaction, have intense regional and systemic effects, both beneficial and injurious. They may significantly affect donor organs by reducing the donor’s cardiac output, stimulating programmed cell death, and sensitizing donor organs so that later rejection by the recipient is intensified. Although not yet possible, clinicians may eventually be able to influence this process, improve the condition of donor organs, and affect subsequent transplantation.
Association of apolipoprotein E epsilon 4 allele with sporadic late onset Alzheimer's disease

)p<0.001( )11.43% لباقم 35%( دهاشلا ةعومجم عم ينتوربلل 4 نوليسبا ليلأ راشتنلا ةيحيجرتلا ةبسنلا تناكو دهاشلا ةعومجمو ريماهزلاب ةباصلما تلاالحا ىدل يمحشلا افلتخم لماعلا اذه ناك دقلو .) 95%CI 3.44-4.61( 3.98 . ةيفارغلجا قطانلما ينب ليلأ ريثأت ديؤت يتلا ةيرظنلا يدعبلا ليلحتلا اذه تبثأ :ةتماخ ضربم ةباصلإا رطخ ةدايز ىلع يمحشلا ينتوربلل 4 نوليسبا 4 نوليسبا ليلأ مييقت نأ امك ،روهظلا رخأتلما يدارفلا ريماهزلا ىضرلما ةبقارلم ةمهم ةليسو دعي بوعشلا ىدل يمحشلا ينتوربلل . ةيحصلا ةياعرلا ينناوق عضوو
Herausforderungen für die Anonymisierung von Daten

Unternehmen, Wissenschaftler und staatliche Stellen haben ein großes Interesse, neue Erkenntnisse aus Daten zu gewinnen. Dabei müssen Datenschutzregeln eingehalten werden. Anonymisierung ist auf den ersten Blick eine attraktive Lösung, um Datenschutz und Analyseinteressen miteinander zu vereinbaren. Jedoch ist eine korrekte Anonymisierung, die jeglichen Personenbezug entfernt, kaum zu erreichen und schwerlich zu garantieren, wenn gleichzeitig möglichst viel des Informationsgehalts der Daten erhalten werden soll. Wir geben in diesem Aufsatz einen Überblick über den Stand der Technik der Anonymisierung für strukturierte und unstrukturierte Daten, arbeiten die bestehenden DeĄzite heraus und formulieren Herausforderungen, die auf dem Weg zu besseren Anonymisierungsverfahren gelöst werden müssen.
A Scalability Benchmark for a Virtual Audience Perception Model in Virtual Reality

In this paper, we describe the implementation and performance of a Virtual Audience perception model for Virtual Reality (VR). The model is a VR adaptation of an existing desktop model. The system allows a user in VR to easily build and experience a wide variety of atmospheres with small or large groups of virtual agents.The paper describes results of early evaluations for this model in VR. Our first scalability benchmark results demonstrated the ability to simultaneously handle one hundred virtual agents without significantly affecting there commended frame rate for VR applications.This research is conducted in the context of a classroom simulation software for teachers’ training.
Parking Lot Instance Segmentation from Satellite Imagery through Associative Embeddings

In this paper, we apply the technique of instance segmentation through associative image embeddings using stacked hourglass networks to the problem of parking lot instance segmentation in satellite imagery. We sought an instance segmentation method that, unlike other common methods such as Mask R-CNN, was independent of object classification and robust to missing labeled instances. We demonstrate how associative image embeddings can be created, which can provide instance segmentation for use with any semantic segmentation map.
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory

Building Reinforcement Learning (RL) algorithms which are able to adapt to continuously evolving tasks is an open research challenge. One technology that is known to inherently handle such non-stationary input patterns well is Hierarchical Temporal Memory (HTM), a general and biologically plausible computational model for the human neocortex. As the RL paradigm is inspired by human learning, HTM is a natural framework for an RL algorithm supporting non-stationary environments. In this paper, we present HTMRL, the first strictly HTM-based RL algorithm. We empirically and statistically show that HTMRL scales to many states and actions, and demonstrate that HTM's ability for adapting to changing patterns extends to RL. Specifically, HTMRL performs well on a 10-armed bandit after 750 steps, but only needs a third of that to adapt to the bandit suddenly shuffling its arms. HTMRL is the first iteration of a novel RL approach, with the potential of extending to a capable algorithm for Meta-RL.
Effective capacity-aware resource allocation for 5G ultra dense network with hybrid access mode

In the 5G ultra dense networks, cross-tier and co-tier are seriously infected interferences. To mitigate those interferences, we propose a water-filling algorithm for sub channel and power allocation and an ant colony optimization based enhanced algorithm then heuristic algorithm to clustering small cells into disjoint clusters and allocate the sub channel and power for smallcell users (SUEs) gradually. Then, we proposed an algorithm for spectrum efficiency to meet the data rate requirements of SUEs.
Development of MirrorShape: High Fidelity Large-Scale Shape Rendering Framework for Virtual Reality

Today there is a high variety of haptic devices capable of providing tactile feedback. Although most of existing designs are aimed at realistic simulation of the surface properties, their capabilities are limited in attempts of displaying shape and position of virtual objects. This paper suggests a new concept of distributed haptic display for realistic interaction with virtual object of complex shape by a collaborative robot with shape display end-effector. MirrorShape renders the 3D object in virtual reality (VR) system by contacting the user hands with the robot end-effector at the calculated point in real-time. Our proposed system makes it possible to synchronously merge the position of contact point in VR and end-effector in real world. This feature provides presentation of different shapes, and at the same time expands the working area comparing to desktop solutions. The preliminary user study revealed that MirrorShape was effective at reducing positional error in VR interactions. Potentially this approach can be used in the virtual systems for rendering versatile VR objects with wide range of sizes with high fidelity large-scale shape experience.
An Exploratory Investigation of Quality of Life in Adult Liver Transplant Recipients

come measure in recent years for evaluating the value and impact of healthcare. Healthcare providers are concerned with QOL because it may affect decisions regarding prescribing habits, treatment regimens, and decisions to cease treatment. Studies of QOL offer greater understanding into the impact of illness and treatment and help healthcare providers determine whether a given intervention is beneficial in terms of clinical changes, individual perceptions, or economic benefits.
Coralline Hydroxyapatite Bone Graft Substitute: A Review of Experimental Studies and Biomedical Applications

A review of the various coral bone graft substitutes currently available for experimental and biomedical applications and ongoing investigations of coral derived bone replacement materials is presented here. Natural and synthetic graft materials that have been studied in vitro and in vivo and used in different medical procedures in osseous tissue have focused mainly on freeze-dried bone, hydroxyapatite (HA), tricalcium phosphate (TCP) and coral. Coralline hydroxyapatite (CHA) is manufactured from marine coral, which has a natural trabecular structure similar to that of bone, by the hydrothermal conversion of the calcium carbonate skeleton of coral to hydroxyapatite, a calcium phosphate. While many studies have demonstrated promising biocompatible properties and osteogenic results, as a bone graft substitute and bone void filler, the use of CHA may be limited owing to its inherent mechanical weakness and reduced biodegradation. The benefits of CHA as bone graft are predominantly its safety, biocompatibility and osteoconductivity so that it can be used as a substitution biomaterial for bone in many indications clinically. CHA can also be used as an efficient carrier system for the local delivery of growth factors to enhance osteointegration and implant fixation into peri-implant osseous tissue. (Journal of Applied Biomaterials & Biomechanics 2004; 2: 65-73)
HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments

Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.
Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning (GIS Cup)

Collecting spatio-temporal resources is an important goal in many real-world use cases such as finding customers for taxicabs. In this paper, we tackle the resource search problem posed by the GIS Cup 2019 where the objective is to minimize the average search time of taxicabs looking for customers. The main challenge is that the taxicabs may not communicate with each other and the only observation they have is the current time and position. Inspired by radial transit route structures in urban environments, our approach relies on round trips that are used as action space for a downstream reinforcement learning procedure. Our source code is publicly available at https://github.com/Fe18/TripBanditAgent.
Maximum Likelihood Channel Estimation for Millimeter-Wave MIMO Systems with Hybrid Beamforming

In a millimeter-wave massive multiple-input multiple-output system, the accurate estimation of the angle of departure is crucial to benefit from a high array gain. In this paper, a novel algorithm is proposed for the maximum likelihood estimation of angles of departure in the multiuser downlink scenario. The proposed approach is based on discrete Fourier transform beamforming vectors to estimate the angle of departure. The discrete Fourier transform matrix can efficiently be implemented in the analog domain by a so-called Butler matrix. Finally, we derived the Cramer-Rao lower bound to assess the performance of our proposed algorithm.
Characterization of Prepared Corn Starch-Based Hydrogel Membranes

The design and development of novel hydrogel membranes prepared by crosslinking polyvinyl alcohol with heattreated cornstarch (CS) and CS suspensions was attempted. Degradation studies of the hydrogel membranes were conducted using α-amylase. The mechanical property of the hydrogel membranes was characterized by tensile tests. The swelling behavior of the membranes was also evaluated. Methyl tetrazolium assay of the membranes was performed using mice splenocytes and the L929 cell line to establish cytocompatibility. The membranes had sufficient strength and water holding capacity. The membrane was found to be biocompatible with L929 fibroblast cells and mice splenocytes. (Journal of Applied Biomaterials & Biomechanics 2006; 4: 38-44)
OAP-WMN: optimised and secure authentication protocol for wireless mesh networks

Wireless mesh networks (WMN) proposes many attractive features to mobile networking area such as: self-reconfiguration and self-organisation which make it more flexible, easy deployed and not expensive. But, these features make it constantly overwhelmed with different types of security threats. In this article, we propose an optimised and secure authentication and re-authentication schemes based on the extensible authentication protocol (EAP) mechanism. The proposed solution ensures security of WMN handoff and with a better QoS. It achieves this by reducing the number of exchanging messages and computations in the proposed authentication and re-authentication processes. Besides, we choose to secure the link layer that makes the authentication process more efficient and optimised. For performance evaluation, we use the OPENSSL tool to compare our work with some related work and the result is good. Moreover, the security of our authentication scheme has been affirmed with the AVISPA tool.
Psychological Feasibility of a Virtual Cycling Environment for Human-in-the-Loop Experiments

Vulnerable Road Users (VRUs) are moving into the focus of road safety solutions, be it Advanced Driver Assistance Systems (ADAS) deployed in cars or enhanced guidance systems in smart cities. We concentrate on bicyclists and study means for assessing the impact of such safety solutions on the bicyclists’ behavior. We just recently developed the Virtual Cycling Environment (VCE), which allows a bicyclist to ride in a virtual reality environment while sitting on a real bike connected with a simulated environment. In this paper, we present a method for measuring the cyclists’ visual attention. This is a fundamental requirement for advanced studies of VRU safety systems including their performance as well as acceptance. In a small empirical study, we assess the feasibility of our approach for conducting psychological experiments in a human-in-the-loop setup.
Multi-parametric MRIs based assessment of Hepatocellular Carcinoma Differentiation with Multi-scale ResNet

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.
A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

Purpose The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.
A Scalable Logo Recognition Model with Deep Meta-Learning

Our goal is to create a deep learning (DL) logo classifier that is widely applicable for conferences. We train this classifier prior to a conference for logos of organisations that will be present. However, DL models typically require many samples to learn such a classification task, whereas deep meta-learning models can learn different but related tasks with 1-10 training samples per class. This reduces data collection costs and increases deployment speed. In this thesis [1], we create a meta-learning classifier that can learn different sets of 35 logo classes with only five training samples per class.
Oversampling based Channel Estimation for 1-bit Large-Scale Multiple-antenna Systems

In this paper, we propose an oversampling based low-resolution aware least squares channel estimator for large-scale multiple-antenna systems with 1-bit analog-to-digital converters on each receive antenna. To mitigate the information loss caused by the coarse quantization, oversampling is applied at the receiver, where the sampling rate is faster than the Nyquist rate. We also characterize analytical performances, in terms of the deterministic Cramer-Rao bounds, on estimating the channel parameters. Based on the correlation of the filtered noise, both the Fisher information for white noise and a lower bound of Fisher information for colored noise are provided. Numerical results are provided to illustrate the mean square error performances of the proposed channel estimator and the corresponding Cramer-Rao bound as a function of the signal-to-noise ratio.
PARCo: A Knowledge-Based Agent for Context-Sensitive Reasoning and Decision-Making Regarding Privacy

Substantial technological developments are pushing digital devices into virtually all spheres of humans’ lives. While these devices exploit users’ generated data to offer a number of valuable services, the exchange of such information has increasingly raised concerns regarding users’ privacy. In particular, privacy concerns are likely to arise due to massive and uncontrolled data exchange among interconnected devices in the scope of the Internet of Things (IoT). Traditional legal approaches, which to date are still used to settle privacy disputes, such as the US Constitution and the GDPR, have obtained particularly little success with respect to dynamic scenarios like Online Social Networks and the IoT. These environments share the common characteristic of allowing privacy violations to occur implicitly. With respect to implicit privacy violations however, studies show that the dichotomy distinguishing between private and not private information, mainly used by legal approaches, provides no satisfactory tool to prevent violations. Alternatively, Contextual Integrity has been advanced as another account to privacy [3]. This definition focuses on the appropriateness of information exchange with respect to contextual norms. In other words, privacy is maintained when information exchange respects the appropriateness and distribution norms related to a certain context. Such concept has inspired a number of scholars to provide privacy-preserving approaches in the environment of OSNs and the IoT. These approaches share the common intuition of the need of a context concept to be used towards achieving appropriate information exchange. Nevertheless, to date, there is no established privacy preserving approach applicable to Online Social Networks nor for the Internet of Things environments. In particular, due to its scale and heterogeneity, the IoT environment in particular, has been addressed by a limited number of efficient methods. The literature suggests that available approaches would benefit from a definition of contexts which can capture contexts’ relations, allowing contexts’ inference from fragmentary information. Furthermore, these approaches should display decision-making capabilities in partially observable and incomplete information environments.
SRC

This study investigates human activity community in a city by conceptualizing it as a network embedding problem. In order to learn the latent representations of activity-travel patterns from individual daily trajectories, network embedding learns a vector space representation for each type of activity place as a node connected by movement links to preserve the structure of individual activities. The proposed approach is applied to mobile positioning data at the individual level obtained for a weekday from volunteers at Guangzhou City. Assessments are conducted to validate individual decision making for several types of activities by a field survey. This study contributes to a general framework for discovering individual activity-travel patterns from human movement trajectories.
An indoor environmental monitoring system for large buildings based on LoRaWAN

Indoor environmental not only educes the overall air quality, but also poses a great threat to people's life safety. Therefore, indoor environmental problems are serious problems. However, at present, the development of monitoring and treatment technology is still immature. Many areas still use manual sampling and laboratory analysis to monitor, which is not only inefficient, but also costly and time consuming. How to use LoRa technology to build indoor environmental monitoring system is a research topic. This paper focuses on the characteristics and advantages of LoRa technology, studies the indoor environment monitoring system based on LoRaWAN, the system architecture. We also try to reduce the air transmission time of RF signals and improve the communication efficiency of the system.