Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic and Thermal Characterization of Core-Shell Fe-Oxide@SiO2 Nanoparticles for Hyperthermia Applications

Nanoparticles for magnetic hyperthermia pose significant constraints in their size and composition to ensure cellular uptake and biocompatibility, while still requiring significant hysteresis losses exploitable at electromagnetic field values and intensities not exceeding safety limits for the human body. In this paper, core-shell Fe-oxide@SiO2 nanoparticles have been synthesized, and their size has been controlled so that the blocked-to-superparamagnetic transition is close to room temperature. Their size remains, therefore, as small as possible, while still displaying significant hysteresis losses in dynamic conditions (electromagnetic fields up to 48 kA/m at 100 kHz). Static loops measured by vibrating sample magnetometry and dynamic loops measured by a custom B-H tracer are used to characterize the paticles’ magnetic properties, as well as a custom-built, fully modeled, hyperthermia setup. The specific absorption rate is obtained either from static and dynamic loop areas or from direct hyperthermia measurements. Dynamic loops are shown to be a good estimator of specific absorption rate values.
An intraspinal extradural lipoma with spinal epidural lipomatosis: A case report and a review of literature

Background: Intraspinal extradural lipomas are very rare and should be differentiated from spinal epidural lipomatosis (SEL) and/or angiolipomas. Case Description: A 76-year-old male presented with left lower extremity radiculopathy. The magnetic resonance imaging (MRI) revealed hyperplasia of epidural fat at the L2–3 and L3–4 levels accompanied by a lipomatous L4–5 mass. Following resection of this mass and hyperplastic epidural fat, the histological examination was consistent with an intraspinal extradural lipoma and SEL. Conclusion: This case indicates that asymmetrical compression of the dural sac may be attributed to an intraspinal extradural lipoma vs. just SEL and/or an angiolipoma.
CD99 expression is strongly associated with clinical outcome in children with B‐cell precursor acute lymphoblastic leukaemia

Our study aimed to determine the expression pattern and clinical relevance of CD99 in paediatric B‐cell precursor acute lymphoblastic leukaemia (BCP‐ALL). Our findings demonstrate that high expression levels of CD99 are mainly found in high‐risk BCP‐ALL, e.g. BCR‐ABL1 and CRLF2Re/Hi, and that high CD99 mRNA levels are strongly associated with a high frequency of relapse, high proportion of positive for minimal residual disease at day 29 and poor overall survival in paediatric cohorts, which indicate that CD99 is a potential biomarker for BCP‐ALL.
Video Redundancy Coding in H.263+

: The forthcoming new version of ITU-T’s advanced video compression recommendation H.263 [1] includes several optional modes for the support of packet networks. This paper gives a brief description of those modes and discusses in detail a new method for temporal error resilience, called Video Redundancy Coding, which is one possible usage of one of the optional modes. In conjunction with spatial error resilience mechanisms, Video Redundancy Coding has been proven to be a superior method for achieving high quality video transmission over non guaranteed QoS packet networks that have packet loss rates as high as 20%, with a minimal additional coding overhead.
Dual Band VCO Based on a High-Quality Factor Switched Interdigital Resonator for the Ku Band Using 180-nm CMOS Technology

A dual band and low phase noise Ku-band voltage-controlled oscillator (VCO) using 180-nm CMOS technology is presented in this brief. The proposed VCO employs a switched notch filter that can operate in the low and high band, which depends on the state of nmos transistor and has a quality factor that is higher than that of a conventional inductor–capacitor (LC) resonator. The proposed resonator doubles the quality factor compared to LC in the technology and reduces the total die area. The first band is realized by the switched interdigital resonator when a nMOS transistor is in the off state. Furthermore, the second band is realized by turning nmos transistor to the on state, which is located between two fingers in the proposed resonator. The chip is implemented in 180-nm CMOS technology, and found that the proposed VCO operates from 15.5 to 16.7 Hz (low band) and 16.6 to 17.4 GHz (high band). At 1.8-V power supply, the power consumption of the oscillator core is 5.4 and 7.2 mW in the low- and high-frequency bands, respectively. The measured phase noise is −107 dBc/Hz at 1 MHz offset from 16.7-GHz carrier frequency.
Joint Mode Selection and Resource Allocation for Scalable Video Multicast in Hybrid Cellular and D2D Network

Scalable wireless video multicast has been widely studied as an efficient mechanism for real-time video transmission, and scalable video coding alongside adaptive modulation and coding is proposed to address the heterogeneity of networks and user capabilities in next generation mobile communication networks. This paper combines D2D technology with the traditional cellular network to form a hybrid network to improve video subscriber users’ experience. The proposed scheme is formulated into a joint mode selection, resource, and power allocation problem based on interference mitigation, which is proven to be NP-hard. Then, we divide it into two sub-problems, where the first one is model selection to maximize the number of D2D users who can access the systems, and the other is to assign appropriate MCS and resource blocks to each video layer. To solve these two problems, we present a greedy algorithm and a heuristic algorithm, respectively. Simulation results show that our hybrid cellular and D2D communications network can effectively increase the capacity of system users, and the mixed network video multicast model can improve the overall system throughput while providing better video quality.
Automated optimization of XCMS parameters for improved peak picking of liquid chromatography-mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics.

Accurate peak picking and further processing is a current challenge in the analysis of untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) data. The optimization of these processes is crucial to obtain proper results. This study investigated and optimized the detection of peaks by XCMS, a widely used R package for peak picking and processing of high-resolution LC-MS metabolomics data by their coefficient of variation using neat standard solutions of drug like compounds. The obtained results were additionally verified by using fortified pooled plasma samples. Settings of the mass spectrometer were optimized by recommendations in literature to enable a reliable detection of the investigated analytes. XCMS parameters were evaluated using a comprehensive parameter sweeping approach. The optimization steps were statistically evaluated and further visualized after principal component analysis (PCA). Concerning the lower concentrated solution in methanol samples, the optimization of both mass spectrometer and XCMS parameters improved the median coefficient of variation from 24% to 7%, retention time fluctuation from 9.3 seconds to 0.54 seconds, and fluctuation of the mass to charge ratio (m/z) from m/z 0.00095 to m/z 0.00028. The number of parent compounds and their related species annotated by CAMERA increased from 88 to 113 while the total amount of features decreased from 3282 to 428. Optimized MS settings such as increased resolution led to a higher specificity of peak picking. PCA supported these findings by showing the best clustering of samples after optimization of both mass spectrometer and XCMS parameters. The results implied that peak picking needs to be individually adapted for the experimental set up. Reducing unwanted variation in the data set was most successful after combining high resolving power with strict peak picking settings.
Feedback control of a nonholonomic wheeled cart in Cartesian space

A preliminary study of the problem of the feedback control of mobile robots is presented. The robot considered is a two-wheel-driven nonholonomic cart. Despite the controllability of the system, pure state feedback stabilization of the cart's configuration around a given terminal configuration is not possible. However, feedback stabilization of the position of any point of the cart is possible. Extension to the problem of trajectory tracking in Cartesian space is then considered, and it is shown that stabilization of the cart's configuration around the configuration of a virtual reference cart becomes possible as long as the reference cart keeps moving. Several simple control laws are proposed, and simulation results are given. Connections with the path planning problem are pointed out. <<ETX>>
The Emergence of Silicon Photonics as a Flexible Technology Platform

In this paper, we present a brief history of silicon photonics from the early research papers in the late 1980s and early 1990s, to the potentially revolutionary technology that exists today. Given that other papers in this special issue give detailed reviews of key aspects of the technology, this paper will concentrate on the key technological milestones that were crucial in demonstrating the capability of silicon photonics as both a successful technical platform, as well as indicating the potential for commercial success. The paper encompasses discussion of the key technology areas of passive devices, modulators, detectors, light sources, and system integration. In so doing, the paper will also serve as an introduction to the other papers within this special issue.
Freehand 3D ultrasound breast tumor segmentation

It is very important for physicians to accurately determine breast tumor location, size and shape in ultrasound image. The precision of breast tumor volume quantification relies on the accurate segmentation of the images. Given the known location and orientation of the ultrasound probe, We propose using freehand three dimensional (3D) ultrasound to acquire original images of the breast tumor and the surrounding tissues in real-time, after preprocessing with anisotropic diffusion filtering, the segmentation operation is performed slice by slice based on the level set method in the image stack. For the segmentation on each slice, the user can adjust the parameters to fit the requirement in the specified image in order to get the satisfied result. By the quantification procedure, the user can know the tumor size varying in different images in the stack. Surface rendering and interpolation are used to reconstruct the 3D breast tumor image. And the breast volume is constructed by the segmented contours in the stack of images. After the segmentation, the volume of the breast tumor in the 3D image data can be obtained.
Evaluating the limits of network topology inference via virtualized network emulation

Abstract : The Internet measurement community is beset by a lack of ground truth, or knowledge of the real, underlying network in topology inference experiments. While better tools and methodologies can be developed, quantifying the effectiveness of these mapping utilities and explaining pathologies is difficult, if not impossible, without knowing the network topology being probed. In this thesis we present a tool that eliminates topological uncertainty in an emulated, virtualized environment. First, we automatically build topological ground truth according to various network generation models and create emulated Cisco router networks by leveraging and modifying existing emulation software. We then automate topological inference from one vantage point at a time for every vantage point in the network. Finally, we incorporate a mechanism to study common sources of network topology inference abnormalities by including the ability to induce link failures within the network. In addition, this thesis reexamines previous work in sampling Autonomous System-level Internet graphs to procure realistic models for emulation and simulation. We build upon this work by including additional data sets, and more recent Internet topologies to sample from, and observe divergent results from the authors of the original work. Lastly, we introduce a new technique for sampling Internet graphs that better retains particular graph metrics across multiple timeframes and data sets.
A Metamodel Enabled Approach for Discovery of Coherent Topics in Short Text Microblogs

Comprehending social media discussions in short text microblogs is fundamental for knowledge-based applications like recommender systems. Twitter, for example, provides rich real-time information in keeping with its streaming nature. Making sense of such data without automated support is not feasible due to its vast size and nature. The problem becomes more complex when the data in question have a low variance in terms of topical diversity. Therefore, an automatic method for understanding textual patterns in such topically constrained data needs to be developed. A major challenge to building such a system is in its ability to comprehend the nature of the data with regard to diversity of word structure correlations, vocabulary sparsity, and distinguishing factors in the generated topics. In this paper, we present a novel semi-supervised approach called metamodel enabled latent Dirichlet allocation to address this challenge. Compared to state-of-the-art approaches, our model incorporates a domain-specific metamodel. The metamodel is defined as a set of topic label vectors derived from long texts to guide the learning process in shorter texts.
Creating a contemporary corpus of similes in Serbian by using natural language processing

Simile is a figure of speech that compares two things through the use of connection words, but where comparison is not intended to be taken literally. They are often used in everyday communication, but they are also a part of linguistic cultural heritage. In this paper we present a methodology for semi-automated collection of similes from the World Wide Web using text mining and machine learning techniques. We expanded an existing corpus by collecting 442 similes from the internet and adding them to the existing corpus collected by Vuk Stefanovic Karadzic that contained 333 similes. We, also, introduce crowdsourcing to the collection of figures of speech, which helped us to build corpus containing 787 unique similes.
The Structure of Prejudice and Its Relation to Party Preferences in Belgium: Flanders and Wallonia Compared

We test two assumptions of the generalized prejudice literature. First, that the structure of generalized prejudice (i.e. how prejudices are interrelated) is dependent on the intergroup context. Second, that different types of prejudice have similar political consequences and run via the generalized prejudice component. We perform these tests in the two main regions of Belgium – Flanders and Wallonia – and investigate the influence of differences in the history of immigration, experience of the linguistic and autonomy conflict, and the separate party system and political discourse (i.e. the societal and intergroup context) on these premises. We make use of the Belgian Election Panel (BEP) data that included measures of prejudice toward multiple target groups (immigrants, Flemings, Walloons, homosexuals, and Jews) and voting propensities for the main political parties. Our results show that, regardless of the differences in intergroup experiences, the structure of prejudice is identical in Flanders and Wallonia. Flemings are, however, more tolerant toward homosexuals and immigrants than Walloons. The political context and the set of potential political outlets does play an important moderating role in the translation of prejudices to party preferences: While negative attitudes toward the other regional group seem to divide the electorate in Flanders, it does not affect voting intentions in Wallonia. Anti-immigrant prejudice is crucial in both regions, but affects voters in different ways at the right-side of the political spectrum.
Dual – Band Microtrip Quasi – Yagi Antenna Design for Free Band and 5G Mobile Communication

In this work, numerical and experimental analyses of a dual-band microstrip patch dipole-loop antenna are done. This design operates at amateur radio, amateur satellite (2.3-2.4 GHz) band and planned 5G mobile communication band; 3.4-3.8 GHz frequencies. There are two different radiating structures and regions in this antenna. One of the radiating structure is a rectangle shaped loop which is responsible for operation at lower frequency (2.3-2.4 GHz) and the other structure is a printed dipole which is responsible for operation of the upper (3.4-3.6 Hz) frequency. Apart from different operation frequencies, these structures also illustrates different radiation characteristics. The loop shaped region radiates bi-directional broadside radiation while the dipole part radiates with higher gain (peak gain 5 dBi). Proposed design is validated by High Frequency Structure Simulator (HFSS) and also measurements are carried out on fabricated prototype.
Unsupervised learning with sparse space-and-time autoencoders

We use spatially-sparse two, three and four dimensional convolutional autoencoder networks to model sparse structures in 2D space, 3D space, and 3+1=4 dimensional space-time. We evaluate the resulting latent spaces by testing their usefulness for downstream tasks. Applications are to handwriting recognition in 2D, segmentation for parts in 3D objects, segmentation for objects in 3D scenes, and body-part segmentation for 4D wire-frame models generated from motion capture data.
Orbiting of bacteria around micrometer-sized particles entrapping shallow tents of fluids

Hydrodynamics and confinement dominate bacterial mobility near solid or air-water boundaries, causing flagellated bacteria to move in circular trajectories. This phenomenon results from the counter-rotation between the bacterial body and flagella and lateral drags on them in opposite directions due to their proximity to the boundaries. Numerous experimental techniques have been developed to confine and maneuver motile bacteria. Here, we report observations on Escherichia coli and Enterobacter sp. when they are confined within a thin layer of water around dispersed micrometer-sized particles sprinkled over a semi-solid agar gel. In this setting, the flagellated bacteria orbit around the dispersed particles akin to planetary systems. The liquid layer is shaped like a shallow tent with its height at the center set by the seeding particle and the meniscus profile set by the strong surface tension of water. The tent-shaped constraint and the left handedness of the flagellar filaments result in exclusively clockwise circular trajectories. The thin fluid layer is resilient due to a balance between evaporation and reinforcing fluid pumped out of the agar. The latter is driven by the Laplace pressure caused by the curved meniscus. This novel mechanism to entrap bacteria within a minimal volume of fluid is relevant to near surface bacterial accumulation, adhesion, biofilm growth, development of bio-microdevices, and cleansing hygiene.
A fast and robust algorithm for face detection and localization

This paper presents a fast and robust face detection and localization approach. The major techniques employed in method are the support vector machine (SVM) and the generalized symmetric map. The proposed face detection system consists of 3 stages. In the first stage, a feature detection technique is used to hypothesize candidate regions by looking for gross features. The facial features of the concern are the pair of eyes. By making use of the symmetrical property of eyes, a generalized symmetry approach is used. The exhaustive pairing of eyes gives the positions of potential face candidates. In the second stage, a view-based approach is used to verify the face candidates obtained in the first stage. A SVM is trained to do the face candidate verification. The last stage of the system precisely locates the eye positions. The proposed detection system was evaluated using the Bern face database. A detection rate of 96.67% was obtained.
Steady-State Non-Line-Of-Sight Imaging

Conventional intensity cameras recover objects in the direct line-of-sight of the camera, whereas occluded scene parts are considered lost in this process. Non-line-of-sight imaging (NLOS) aims at recovering these occluded objects by analyzing their indirect reflections on visible scene surfaces. Existing NLOS methods temporally probe the indirect light transport to unmix light paths based on their travel time, which mandates specialized instrumentation that suffers from low photon efficiency, high cost, and mechanical scanning. We depart from temporal probing and demonstrate steady-state NLOS imaging using conventional intensity sensors and continuous illumination. Instead of assuming perfectly isotropic scattering, the proposed method exploits directionality in the hidden surface reflectance, resulting in (small) spatial variation of their indirect reflections for varying illumination. To tackle the shape-dependence of these variations, we propose a trainable architecture which learns to map diffuse indirect reflections to scene reflectance using only synthetic training data. Relying on consumer color image sensors, with high fill factor, high quantum efficiency and low read-out noise, we demonstrate high-fidelity color NLOS imaging for scene configurations tackled before with picosecond time resolution.
Highlights on the Application of Genomics and Bioinformatics in the Fight Against Infectious Diseases: Challenges and Opportunities in Africa

Genomics and bioinformatics are increasingly contributing to our understanding of infectious diseases caused by bacterial pathogens such as Mycobacterium tuberculosis and parasites such as Plasmodium falciparum. This ranges from investigations of disease outbreaks and pathogenesis, host and pathogen genomic variation, and host immune evasion mechanisms to identification of potential diagnostic markers and vaccine targets. High throughput genomics data generated from pathogens and animal models can be combined with host genomics and patients’ health records to give advice on treatment options as well as potential drug and vaccine interactions. However, despite accounting for the highest burden of infectious diseases, Africa has the lowest research output on infectious disease genomics. Here we review the contributions of genomics and bioinformatics to the management of infectious diseases of serious public health concern in Africa including tuberculosis (TB), dengue fever, malaria and filariasis. Furthermore, we discuss how genomics and bioinformatics can be applied to identify drug and vaccine targets. We conclude by identifying challenges to genomics research in Africa and highlighting how these can be overcome where possible.