Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedded NMR Sensor to Monitor Compressive Strength Development and Pore Size Distribution in Hydrating Concrete

In cement-based materials porosity plays an important role in determining their mechanical and transport properties. This paper describes an improved low–cost embeddable miniature NMR sensor capable of non-destructively measuring evaporable water loss and porosity refinement in low and high water-to-cement ratio cement-based materials. The sensor consists of two NdFeB magnets having their North and South poles facing each other, separated by 7 mm to allow space for a Faraday cage containing a Teflon tube and an ellipsoidal RF coil. To account for magnetic field changes due to temperature variations, and/or the presence of steel rebars, or frequency variation due to sample impedance, an external tuning circuit was employed. The sensor performance was evaluated by analyzing the transverse magnetization decay obtained with a CPMG measurement from different materials, such as a polymer phantom, fresh white and grey cement pastes with different w/c ratios and concrete with low (0.30) and high (0.6) w/c ratios. The results indicated that the sensor is capable of detecting changes in water content in fresh cement pastes and porosity refinement caused by cement hydration in hardened materials, even if they are prepared with a low w/c ratio (w/c = 0.30). The short lifetime component of the transverse relaxation rate is directly proportional to the compressive strength of concrete determined by destructive testing. The r2 (0.97) from the linear relationship observed is similar to that obtained using T2 data from a commercial Oxford Instruments 12.9 MHz spectrometer.
Exceptional Point of Degeneracy in a Backward-Wave Oscillator with Distributed Power Extraction

We show how an exceptional point of degeneracy (EPD) is formed in a system composed of an electron beam interacting with an electromagnetic mode guided in a slow wave structure (SWS) with distributed power extraction from the interaction zone. Based on this kind of EPD, a new regime of operation is devised for backward wave oscillators (BWOs) as a synchronous and degenerate regime between a backward electromagnetic mode and the charge wave modulating the electron beam. Degenerate synchronization under this EPD condition means that two complex modes of the interactive system do not share just the wavenumber, but they rather coalesce in both their wavenumbers and eigenvectors (polarization states). In principle this new condition guarantees full synchronization between the electromagnetic wave and the beam's charge wave for any amount of output power extracted from the beam, setting the threshold of this EPD-BWO to any arbitrary, desired, value. Indeed, we show that the presence of distributed radiation in the SWS results in having high-threshold electron-beam current to start oscillations which implies higher power generation. These findings have the potential to lead to highly efficient BWOs with very high output power and excellent spectral purity.
Central Control Over Distributed Routing

Centralizing routing decisions offers tremendous flexibility, but sacrifices the robustness of distributed protocols. In this paper, we present Fibbing, an architecture that achieves both flexibility and robustness through central control over distributed routing. Fibbing introduces fake nodes and links into an underlying link-state routing protocol, so that routers compute their own forwarding tables based on the augmented topology. Fibbing is expressive, and readily supports flexible load balancing, traffic engineering, and backup routes. Based on high-level forwarding requirements, the Fibbing controller computes a compact augmented topology and injects the fake components through standard routing-protocol messages. Fibbing works with any unmodified routers speaking OSPF. Our experiments also show that it can scale to large networks with many forwarding requirements, introduces minimal overhead, and quickly reacts to network and controller failures.
BotHunter: Detecting Malware Infection Through IDS-Driven Dialog Correlation

We present a new kind of network perimeter monitoring strategy, which focuses on recognizing the infection and coordination dialog that occurs during a successful malware infection. BotHunter is an application designed to track the two-way communication flows between internal assets and external entities, developing an evidence trail of data exchanges that match a state-based infection sequence model. BotHunter consists of a correlation engine that is driven by three malware-focused network packet sensors, each charged with detecting specific stages of the malware infection process, including inbound scanning, exploit usage, egg downloading, outbound bot coordination dialog, and outbound attack propagation. The BotHunter correlator then ties together the dialog trail of inbound intrusion alarms with those outbound communication patterns that are highly indicative of successful local host infection. When a sequence of evidence is found to match BotHunter's infection dialog model, a consolidated report is produced to capture all the relevant events and event sources that played a role during the infection process. We refer to this analytical strategy of matching the dialog flows between internal assets and the broader Internet as dialog-based correlation, and contrast this strategy to other intrusion detection and alert correlation methods. We present our experimental results using BotHunter in both virtual and live testing environments, and discuss our Internet release of the BotHunter prototype. BotHunter is made available both for operational use and to help stimulate research in understanding the life cycle of malware infections.
Bottom‐Up Instructive Quality Control in the Biofabrication of Smart Protein Materials

The impact of cell factory quality control on material properties is a neglected but critical issue in the fabrication of protein biomaterials, which are unique in merging structure and function. The molecular chaperoning of protein conformational status is revealed here as a potent molecular instructor of the macroscopic properties of self-assembling, cell-targeted protein nanoparticles, including biodistribution upon in vivo administration.
CORRECTING VELOCITY DISPERSIONS OF DWARF SPHEROIDAL GALAXIES FOR BINARY ORBITAL MOTION

We show that the measured velocity dispersions of dwarf spheroidal galaxies from about 4 to 10 km s−1 are unlikely to be inflated by more than 30% due to the orbital motion of binary stars and demonstrate that the intrinsic velocity dispersions can be determined to within a few percent accuracy using two-epoch observations with 1–2 yr as the optimal time interval. The crucial observable is the threshold fraction—the fraction of stars that show velocity changes larger than a given threshold between measurements. The threshold fraction is tightly correlated with the dispersion introduced by binaries, independent of the underlying binary fraction and distribution of orbital parameters. We outline a simple procedure to correct the velocity dispersion to within a few percent accuracy by using the threshold fraction and provide fitting functions for this method. We also develop a methodology for constraining properties of binary populations from both single- and two-epoch velocity measurements by including the binary velocity distribution in a Bayesian analysis.
Sucralose administered in feed, beginning prenatally through lifespan, induces hematopoietic neoplasias in male swiss mice

Background: Sucralose is an organochlorine artificial sweetener approximately 600 times sweeter than sucrose and used in over 4,500 products. Long-term carcinogenicity bioassays on rats and mice conducted on behalf of the manufacturer have failed to show the evidence of carcinogenic effects. Objective: The aim of this study was to evaluate the carcinogenic effect of sucralose in mice, using a sensitive experimental design. Methods: Five groups of male (total n = 457) and five groups female (total n = 396) Swiss mice were treated from 12 days of gestation through the lifespan with sucralose in their feed at concentrations of 0, 500, 2,000, 8,000, and 16,000 ppm. Results: We found a significant dose-related increased incidence of males bearing malignant tumors (p < 0.05) and a significant dose-related increased incidence (p < 0.01) of hematopoietic neoplasias in males, in particular at the dose levels of 2,000 ppm (p < 0.01) and 16,000 ppm (p < 0.01). Conclusions: These findings do not support previous data that sucralose is biologically inert. More studies are necessary to show the safety of sucralose, including new and more adequate carcinogenic bioassay on rats. Considering that millions of people are likely exposed, follow-up studies are urgent.
Colour Based Image Segmentation Using Fuzzy C-Means Clustering

Mostly due to the progresses in spatial resolution of satellite imagery, the methods of segment- based image analysis for generating and updating geographical information are becoming more and more important. This work presents a new image segmentation based on colour features with Fuzzy c-means clustering unsupervised algorithm. The entire work is divided into two stages. First enhancement of color separation of satellite image using decorrelation stretching is carried out and then the regions are grouped into a set of five classes using Fuzzy c-means clustering algorithm. Using this two step process, it is possible to reduce the computational cost avoiding feature calculation for every pixel in the image. Although the colour is not frequently used for image segmentation, it gives a high discriminative power of regions present in the image. In remote sensing, the process of image segmentation is defined as: "the search for homogenous regions in an image and later the classification of these regions". It also means the partitioning of an image into meaningful regions based on homogeneity or heterogeneity criteria. Image segmentation techniques can be differentiated into the following basic concepts: pixel oriented, Contour-oriented, region-oriented, model oriented, color oriented and hybrid. Color segmentation of image is a crucial operation in image analysis and in many computer vision, image interpretation, and pattern recognition system, with applications in scientific and industrial field(s) such as medicine, Remote Sensing, Microscopy, content based image and video retrieval, document analysis, industrial automation and quality control. The performance of color segmentation may significantly affect the quality of an image understanding system . The most common features used in image segmentation include texture, shape, grey level intensity, and color. The constitution of the right data space is a common problem in connection with segmentation/classification. In order to construct realistic classifiers, the features that are sufficiently representative of the physical process must be searched. In the literature, it is observed that different transforms are used to extract desired information from remote-sensing images or biomedical images. Segmentation evaluation techniques can be generally divided into two categories (supervised and unsupervised). The first category is not applicable to remote sensing because an optimum segmentation (ground truth segmentation) is difficult to obtain. Moreover, available segmentation evaluation techniques have not been thoroughly tested for remotely sensed data. Therefore, for comparison purposes, it is possible to proceed with the classification process and then indirectly assess the segmentation process through the produced classification accuracies. For image segment based classification, the images that need to be classified are segmented into many homogeneous areas with similar spectrum information firstly, and the image segments' features are extracted based on the specific requirements of ground features classification. The color
Ten-Minute Silence: A New Notification UX of Mobile Instant Messenger

People receive a tremendous number of messages through mobile instant messaging (MIM), which generates crowded notifications. This study highlights our attempt to create a new notification rule to reduce this crowdedness, which can be recognized by both senders and recipients. We developed an MIM app that provides only one notification per conversation session, which is a group of consecutive messages distinguished based on a ten-minute silence period. Through the two-week field study, 20,957 message logs and interview data from 17 participants revealed that MIM notifications affect not only the recipients' experiences before opening the app but also the entire conversation experience, including that of the senders. The new notification rule created new social norms for the participants' use of MIM. We report themes about the changes in the MIM experience, which will expand the role of notifications for future MIM apps.
A New Uncertainty Framework for Stochastic Signal Processing

The fields of signal processing and information theory have evolved with the goal of developing formulations to extract intrinsic information from limited amount of data. When one considers the modeling of unpredictably varying processes and complex dynamical signals with a large number of unknowns (such as those encountered in the fields of finance, NLP, communications, etc.), there is a need for algorithms to have increased sensitivity with short spans of data, while maintaining stochastic generalization ability. This naturally calls for an increased focus on localized stochastic representation. So far, most metrics developed for characterizing signals envision data from entropic and probabilistic points of view that lack sensitivity towards quick changes in signal dynamics. We hypothesize that models that work with the intrinsic uncertainties associated with local data induced metric spaces would be significantly more sensitive towards signal characterization. To this end, we develop a new framework for stochastic signal processing that is based on decomposing the local metric space of the signal in a Gaussian Reproducing Kernel Hilbert Space (RKHS). A major advantage of our framework is that we are able to implement this decomposition on a sample-by-sample basis. The key aspects of our framework are the following: (1) We use a data defined metric related to Parzen density estimation for quantifying the local structure of data in the Gaussian RKHS. (2) We use a quantum description of this metric which consequently introduces uncertainty in the structure of the local kernel space. Since the RKHS has been well established and known for providing universal data fitting capabilities, we submit that local quantifiers of the kernel space data projection could significantly improve acquisition of signal information.
DELAYED NEUTRINO-DRIVEN SUPERNOVA EXPLOSIONS AIDED BY THE STANDING ACCRETION-SHOCK INSTABILITY

We present two-dimensional hydrodynamic simulations of stellar core collapse and develop the framework for a detailed analysis of the energetic aspects of neutrino-powered supernova explosions. Our results confirm that the neutrino-heating mechanism remains a viable explanation of the explosion of a wider mass range of supernova progenitors with iron cores, but the explosion sets in later and develops differently than thought so far. The calculations were performed with an energy-dependent treatment of the neutrino transport based on the “ray-by-ray plus” approximation, in which the neutrino number, energy, and momentum equations are closed with a variable Eddington factor obtained by iteratively solving a model Boltzmann equation. We focus here on the evolution of a 15 M☉ progenitor and provide evidence that shock revival and an explosion are initiated at about 600 ms after core bounce, powered by neutrino energy deposition. This is significantly later than previously found for an 11.2 M☉ star, for which we also present a continuation of the explosion model published by Buras et al. The onset of the blast is fostered in both cases by the standing accretion-shock instability. This instability exhibits highest growth rates for the dipole and quadrupole modes, which lead to large-amplitude bipolar shock oscillations and push the shock to larger radii, thus increasing the time accreted matter is exposed to neutrino heating in the gain layer. As a consequence, also convective overturn behind the shock is strengthened, which otherwise is suppressed or damped because of the small shock stagnation radius. When the explosion sets in, the shock reveals a pronounced global deformation with a dominant dipolar component. In both the 11.2 M☉ and 15 M☉ explosions long-lasting equatorial downflows supply the gain layer with fresh gas, of which a sizable fraction is heated by neutrinos and leads to the build-up of the explosion energy of the ejecta over possibly hundreds of milliseconds. A “soft” nuclear equation of state that causes a rapid contraction, and a smaller radius of the forming neutron star and thus a fast release of gravitational binding energy, seems to be more favorable for the development of an explosion. Rotation has the opposite effect because in the long run it leads to a more extended and cooler neutron star and thus lower neutrino luminosities and mean energies and overall less neutrino heating. Neutron star g-mode oscillations, although we see their presence, and the acoustic mechanism play no important role in our simulations. While numerical tests show that our code is also well able to follow large-amplitude core g-modes if they are instigated; the amplitude of such oscillations remains small in our supernova runs and the acoustic energy flux injected by the ringing neutron star and by the deceleration of supersonic downflows near the neutron star surface is small compared to the neutrino energy deposition.
Quality classification of tandem mass spectrometry data

UNLABELLED Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.
TH 2-Polarized CD 4 þ T Cells and Macrophages Limit Ef fi cacy of Radiotherapy

Radiotherapy and chemotherapy following surgery are mainstays of treatment for breast cancer. Although multiple studies have recently revealed the significance of immune cells as mediators of chemotherapy response in breast cancer, less is known regarding roles for leukocytes as mediating outcomes following radiotherapy. To address this question, we utilized a syngeneic orthotopic murine model of mammary carcinogenesis to investigate if response to radiotherapy could be improved when select immune cells or immune-based pathways in the mammary microenvironment were inhibited. Treatment of mammary tumor–bearing mice with either a neutralizing mAb to colonystimulating factor-1 (CSF-1) or a small-molecule inhibitor of the CSF-1 receptor kinase (i.e., PLX3397), resulting in efficient macrophage depletion, significantly delayed tumor regrowth following radiotherapy. Delayed tumor growth in this setting was associated with increased presence of CD8þ T cells and reduced presence of CD4þ T cells, the main source of the TH2 cytokine IL4 in mammary tumors. Selective depletion of CD4þ T cells or neutralization of IL4 in combination with radiotherapy phenocopied results following macrophage depletion, whereas depletion of CD8þ T cells abrogated improved response to radiotherapy following these therapies. Analogously, therapeutic neutralization of IL4 or IL13, or IL4 receptor alpha deficiency, in combination with the chemotherapy paclitaxel, resulted in slowedprimarymammary tumor growth byCD8þT-cell–dependent mechanisms. These findings indicate that clinical responses to cytotoxic therapy in general can be improved by neutralizing dominant TH2-based programs driving protumorigenic and immune-suppressive pathways in mammary (breast) tumors to improve outcomes. Cancer Immunol Res; 3(5); 518–25. 2015 AACR.
Significant others’ experiences of the coming out process of a lesbian individual

of the study This study aims to explore and describe the experiences of significant others during the coming out process of two lesbian individuals. The study gathered more detail regarding how various individuals experience the coming out process in the hope of contributing to the theoretical base of literature concerning lesbian studies in South Africa. The literature review provides a survey of the coming out process as well as a discussion regarding significant others (family and friends). Some factors and experiences involved in the coming out process are also discussed. The qualitative case study is approached from the interpretive paradigm and systems theory was utilised as a theoretical framework throughout the study. Data was generated primarily through unstructured interactive/in-depth interviews and secondarily through stories, field texts, photographs and a research diary. Six individuals participated in the research study, the two lesbian individuals and four significant others as identified by them two mothers, a father and a close friend. The data analysis led to both commonalities in experiences and unique experiences identified by participants. The results of the study indicated that some form of suspicion or awareness was present in the significant others before the act of disclosure; emotional reactions from involved participants can be placed in a continuum ranging from positive to negative and the act of disclosure was viewed and experienced as both a crisis and as progressive by different participants. Various factors could influence the experience of the coming out process for significant others as well as the reactions to the act of disclosure. However more research is needed to investigate what these factors might be. Participants also mentioned various stages towards acceptance (individuals fall in a continuum and can move forward and backward). Both common and unique experiences were present and can assist others in understanding the coming out process from different perspectives.