Miracle-dz/UniFAR-FTUs huggingface dataset:
The Life History Significance of Human Breast Milk: Immune and endocrine factors as indicators of maternal condition and predictors of infant health and growth

This dissertation explores the relationships between maternal energetic condition, four bioactive compounds in milk, and infant health and growth outcomes through the lens of human life history theory. Research was conducted among the Toba of Barrio Namqom in Formosa, Argentina. This is among the first of studies to apply a life history biology lens to the dynamics of cortisol, insulin-like growth factor 1 (IGF-1), lactoferrin, and secretory immunoglobulin A (sIgA) in human milk. First, the role of maternal energetic condition as a predictor of the concentration of these four compounds in milk is explored. Several interesting relationships emerge, including significantly lower milk cortisol and IGF-1 among women with higher body mass index, and significantly higher milk cortisol among primiparous mothers. Next, the relationships between infant symptoms of illness and the two milk immunofactors, lactoferrin and sIgA, are investigated. Lactoferrin is found to exhibit a positive association with symptoms of illness, and sIgA shows a negative association. Finally, associations between concentration of milk bioactives and infant growth rate are tested, as well as associations between infant illness and growth rate, and maternal energetic condition and growth rate. Milk IGF-1 is found to positively associate with infant linear growth rate. Maternal parity is found to negatively associate with linear growth rate. First-born status is associated with significantly greater gains in
The most common eye disease in cat

Many eye problems in the cat are quite different from those found in the dog, or any other animals. Corneal sequestrum, for example, appears to be unique to the cat. Ocular manifestations of systemic disease are quite common in cats and routine examination may be crucial as an aid to diagnosis. Deficiencies of taurine and thiamine can change the appearance of the ocular fundus. Those alterations associated with taurine deficiency are usually considered pathognomic. Most commonly the diagnosis of infectious disease that is aided by ophthalmologic examination; causal agents include viruses such a feline herpes viruses, FIP, FIV, FeLV, parasites (Toxoplasma gondii), yeasts and fungi. Neoplasia in cats is generally more aggressive than that of other domestic mammals and the eye and adnexa may be primarily or secondarily involved (1, 2, 3).
Hybrid System to find Association using Genetic Algorithm and Fuzzy Logic

: Now a day, so many technologies are evolving around to find solution of the problem. Association rule mining is a well-known technology used to find the association among the different attributes in any field. ARM cannot work directly on numerical values, so concept of fuzzy logic will applied to assign Linguistic variable. And Genetic algorithm is an evolutionary algorithm to get the optimized result. GA is based on the theory of survival. Fittest chromosome has high probability of surviving. In this paper, author used the concept of genetic algorithm in association rule mining to get the frequent itemset. And before applying genetic algorithm, data has to be fuzzify to support ARM algorithm. With the help of this implementation rare and popular, both cases will be considered to derive the associations among the parameters which can be used further in any decision making strategy.
Study on Various Approaches used by Security as a Service Model in Cloud Computing

: Cloud computing is the way of using remote servers placed on the Internet for storing, managing, and processing data, rather than using a local server or a personal computer. In cloud computing, security and privacy are the major issues. However, there are many challenges for securing the cloud from various attacks. The main responsibility of the Security as a Service model is to provide flexible security to its users to prevent these attacks. The approaches used by the Security as a Service model are SLA based approach via SPECS and Crypto Coprocessor approach for providing flexible security to its users. In this paper, we do a study on various approaches used by the Security as a Service model and to propose a system which provides maximum security to the cloud users.
Two-Stage Distributionally Robust Optimization for Energy Hub Systems

Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This article proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This article proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where 1) the first stage optimizes EHS operation cost, and 2) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modeled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this article justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multienergy carriers.
Interference Steering to Manage Interference in IoT

Internet of Things (IoT) is a new paradigm that involves the interconnection of thousands of devices and home appliances. Due to the scarcity of the spectrum suitable for wireless electromagnetic transmission, many communication systems and devices are close to each other, or even overlapping in spectrum, thus incurring complicated interference situations. Therefore, interference in IoT is worthy of thorough investigation and should be well addressed. There have been numerous interference management (IM) proposals at the interfering transmitter or the interfered transmitter/receiver separately or cooperatively. Moreover, the existing IM schemes rely mainly on the use of channel state information (CSI). However, in some communication scenarios, the option to adjust the interferer is not available, and, in the case of downlink transmission, it is always difficult or even impossible for the interfered receiver to acquire necessary information for IM. Based on the above observations, we first propose a novel IM technique, called interference steering (IS). By making use of both CSI with respect to and data carried in the interfering signal, IS generates a signal to modify the spatial feature of the original interference, so that the steered interference at the interfered receiver is orthogonal to its intended signal. We then apply IS to a wireless local area network (WLAN)-based IoT in which the same frequency band is reused by adjacent basic service sets (BSSs) with overlapping areas. With IS, multiple nearby access points (APs) could simultaneously transmit data on the same channel to their mobile stations (STAs), thus enhancing spectrum reuse. Our in-depth simulation results show that IS significantly improves network SE over existing IM schemes.
Counterfeit IC Countermeasure with 4T Cell Based Authentication Circuit

A simple and compact authentication circuit based on a 4T bit cell is proposed for counterfeit detection and avoidance. The PUF-based authentication circuit has been designed to reduce or eliminate the reluctance of COTs manufacturers to include on-chip fingerprints by not requiring additional die area, I/O pins, or read-out circuits and by using a deep-sleep mode during normal circuit operation so that it does not interfere with operation of the main IC. Keywords— PUF circuit; counterfeit countermeasure; 4T cells; trusted electronics; hardware security
The rat spinal cord following traumatic injury: An anatomical and behavioural study examining NADPH-d and fos

The general aim of this current work was to examine spinal cord injury (SCI), and in particular to examine the pathology of injury as it relates to changes in sensory transmission. Due to the limited possibilities for experimentation in humans, a range of animal models of SCI have been developed and are reviewed here. The weight drop SCI model is the most similar to the clinical presentation of SCI in humans and has been widely used in the rat. It was selected for the series of experiments reported in this thesis. Many of the functional deficits produced by SCI result from a cascade of biochemical events set into motion by the injury. Included amongst these is the activation of the enzyme nitric oxide synthase which produces the gaseous neuromodulator, nitric oxide (NO). NO is amongst the most widely distributed and widely utilised molecule in virtually all living organisms, and it is an important signalling molecule in the nervous system. One of the major functions performed by NO appears to relate to sensory transmission, and thus alterations in sensory transmission observed as a result of SCI may involve alterations to NO synthesis. One of the principal aims of this thesis was to examine the effect of SCI on the NO producing cells of the spinal cord and to consider what any changes in NO synthesis may suggest in regards to sensation. NO producing cells were examined using NADPH diaphorase (NADPH-d) histochemistry. As the symptoms of SCI such as motor loss and changes in sensory processing are functional changes, it was also useful to examine changes in neuronal function as a result of SCI. Widespread neuronal function was examined via immunohistochemical detection of the gene product of the immediate early gene, c-fos. It is not known how extensive the biochemical changes resulting from SCI may be, thus another of the aims of the present thesis was to examine the effects of SCI on NO synthesis not only at the level of injury, but also distant to the injury. Findings of the present thesis indicated that traumatic SCI resulted in a decrease in the number of NADPH-d positive cells from the superficial dorsal horn (SDH) of the spinal cord, while the number of these cells are increased in the ventral horn. These changes were restricted to spinal segments adjacent to
Nonlinear Systems in Healthcare towards Intelligent Disease Prediction

Healthcare is one of the key fields that works quite strongly with advanced ana-lytical techniques for prediction of diseases and risks. Data being the most important asset in recent times, a huge amount of health data is being collected, thanks to the recent advancements of IoT, smart healthcare, etc. But the focal objective lies in making sense of that data and to obtain knowledge, using intelligent analytics. Nonlinear systems find use specifically in this field, working closely with health data. Using advanced methods of machine learning and computational intelligence, nonlinear analysis performs a key role in analyzing the enormous amount of data, aimed at finding important patterns and predicting diseases. Especially in the field of smart healthcare, this chapter explores some aspects of nonlinear systems in predictive analytics, providing a holistic view of the field as well as some examples to illustrate such intelligent systems toward disease prediction.
Social Media Analytics: Data Utilization of Social Media for Research

The amount of production data generated by social media opportunities that can be exploited by various parties, both government and private sectors to produce the information. Social media data can be used to know the behavior and public perception of the phenomenon or a particular event. To obtain and analyze social media data needed depth knowledge of Internet technology, social media, databases, data structures, information theory, data mining, machine learning, until the data and information visualization techniques. In this research, social media analysis on a particular topic and the development of prototype devices software used as a tool of social media data retrieval or retrieval of data applications. Social Media Analytics (SMA) aims to make the process of analysis and synthesis of social media data to produce information can be used by those in need. SMA process is done in three stages, namely: Capture, Understand and Present. This research is exploratorily focused on understanding the technology that became the basis of social media using various techniques exist and is already used in the study of social media analytic previously.
Limitations of wireless power transfer technologies for mobile robots

Advances in technology have seen mobile robots becoming a viable solution to many global challenges. A key limitation for tetherless operation, however, is the energy density of batteries. Whilst significant research is being undertaken into new battery technologies, wireless power transfer may be an alternative solution. The majority of the available technologies are not targeted toward the medium power requirements of mobile robots; they are either for low powers (a few Watts) or very large powers (kW). This paper reviews existing wireless power transfer technologies and their applications on mobile robots. The challenges of using these technologies on mobile robots include delivering the power required, system efficiency, human safety, transmission medium, and distance, all of which are analyzed for robots operating in a hazardous environment. The limitations of current wireless power technologies to meet the challenges for mobile robots are discussed and scenarios which current wireless power technologies can be used on mobile robots are presented.
The Public Health Primer

PART ONE: THE DEFINITION, DEVELOPMENT AND ORGANIZATION OF PUBLIC HEALTH History and Development The Current US Public Health System Public Health Practice Assessment, Assurance and Public Development PART TWO: BASIC CONCEPTS AND ANALYTIC TOOLS IN PUBLIC HEALTH Epidemiology and Determinants of Disease Basic Measurements and Statistics Makng Inferences from Observations Surveillance and Monitoring the Health of Populations PART THREE: PUBLIC HEALTH INTERVENTIONS AND APPLICATIONS Health Promotion and Health Protection Personal Health Care Services Health Planning Global Health
Disentangled Spectrum Variations Networks for NIR–VIS Face Recognition

Surveillance cameras often capture near infrared images since it provides a low-cost and effective solution to acquire high-quality images under low-light environments. However, visual versus near infrared (VIS-NIR) heterogeneous face recognition (HFR) is still a challenging issue in computer vision community due to the gap between sensing patterns of different spectrums as well as the lack of sufficient training samples. To solve the above problem, in this paper, we present an effective Disentangled Spectrum Variations Networks (DSVNs) for VIS-NIR HFR. Two key strategies are introduced to the DSVNs for disentangling spectrum variations between two domains: Spectrum-adversarial Discriminative Feature Learning (SaDFL) and Step-wise Spectrum Orthogonal Decomposition (SSOD). The SaDFL consists of Identity-Discriminative subnetwork (IDNet) and Auxiliary Spectrum Adversarial subnetwork (ASANet). On the one hand, the IDNet is composed of a generator <inline-formula><tex-math notation="LaTeX">$G_H$</tex-math></inline-formula> and a discriminator <inline-formula><tex-math notation="LaTeX">$D_U$</tex-math></inline-formula> for extracting identity-discriminative feature. On the other hand, the ASANet is built by a generator <inline-formula><tex-math notation="LaTeX">$G_H$</tex-math></inline-formula> and a discriminator <inline-formula><tex-math notation="LaTeX">$D_M$</tex-math></inline-formula> for eliminating modality-variant spectrum information under the guidance of the discriminator <inline-formula><tex-math notation="LaTeX">$D_M$</tex-math></inline-formula>. The identity-label and modality-label HFR datasets are used to train the DSVNs with triplet loss. Both IDNet and ASANet can jointly enhance the domain-invariant feature representations via an adversarial learning. Furthermore, to disentangle spectrum variations effectively as well as making identity information and modality information unrelated to each other, we present a new topology of connection block called Disentangled Spectrum Variations (DSV). An orthogonality constraint is imposed to DSV at the convolution level for channel-wise orthogonal decomposition between the modality-invariant identity information and modality-variant spectrum information. In particular, the SSOD is built by stacking multiple modularized mirco-block DSV, and thereby enjoys the benefits of disentangling spectrum variation step by step. Moreover, we investigate the similarity calculation method to further improve the HFR performance. To sum up, the designed DSVNs leads to a purification of identity information as well as an elimination of modality information. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

Mapping structural brain connectomes for living human brains typically requires expert analysis and rule-based models on diffusion-weighted magnetic resonance imaging. A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals. In this work, we explore a framework that facilitates applying learning algorithms to automatically extract brain connectomes. Using a tensor encoding, we design an objective with a group-regularizer that prefers biologically plausible fascicle structure. We show that the objective is convex and has unique solutions, ensuring identifiable connectomes for an individual. We develop an efficient optimization strategy for this extremely high-dimensional sparse problem, by reducing the number of parameters using a greedy algorithm designed specifically for the problem. We show that this greedy algorithm significantly improves on a standard greedy algorithm, called Orthogonal Matching Pursuit. We conclude with an analysis of the solutions found by our method, showing we can accurately reconstruct the diffusion information while maintaining contiguous fascicles with smooth direction changes.
Bayesian Inference of Gene Regulatory Network

Gene regulatory networks (GRN) have been studied by computational scientists and biologists over 20 years to gain a fine map of gene functions. With large-scale genomic and epigenetic data generated under diverse cells, tissues, and diseases, the integrative analysis of multi-omics data plays a key role in identifying casual genes in human disease development. Bayesian inference (or integration) has been successfully applied to inferring GRNs. Learning a posterior distribution than making a single-value prediction of model parameter makes Bayesian inference a more robust approach to identify GRN from noisy biomedical observations. Moreover, given multi-omics data as input and a large number of model parameters to estimate, the automatic preference of Bayesian inference for simple models that sufficiently explain data without unnecessary complexity ensures fast convergence to reliable results. In this chapter, we introduced GRN modeling using hierarchical Bayesian network and then used Gibbs sampling to identify network variables. We applied this model to breast cancer data and identified genes relevant to breast cancer recurrence. In the end, we discussed the potential of Bayesian inference as well as Bayesian deep learning for large-scale and complex GRN inference.
The Cloud Resolving Model Radar Simulator (CR-SIM) Version 3.2:
Description and Applications of a Virtual Observatory

Abstract. Ground-based observatories use multi-sensor observations to characterize cloud and precipitation properties. A challenge is how to design strategies to best use these observations to understand the atmosphere and evaluate atmospheric numerical prediction models. This paper introduces the Cloud resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution atmospheric models to emulate multi-wavelength, zenith-pointing, and scanning radar observables and multi-sensor (multi-radar and radar-lidar) integrated products. CR-SIM allows comparisons of the same variables between an atmospheric model simulation and remote sensing products using a forward modeling framework consistent with the microphysical assumptions used in the numerical model simulations. In this paper, we present several applications of CR-SIM for evaluation of a numerical model, quantification of retrieval uncertainty, and optimization of radar sampling strategy using observing system simulation experiments. These applications demonstrate that the application of CR-SIM as a virtual observatory operator on high-resolution model output helps interpret the differences between model results and observations and also improve under-standing of the representativeness errors due to the sampling limitations of the ground-based observatories. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are freely available to scientific community.
Augmented Cross-Entropy-Based Joint Temperature Optimization of Real-Time 3-D MPSoC Systems

3-D multiprocessor system-on-chip (MPSoC) systems can offer higher integration density, lower interaction cost, better bandwidth, and greater performance. However, vertically stacked silicon layers and limited heat dissipation paths result in high peak temperature and large temperature variation, which incur reliability reduction, lifetime decay, and performance degradation. In this article, we propose an offline augmented cross-entropy (CE)-based task scheduling strategy to jointly optimize peak temperature and temperature variation under the constraint of timeliness. Specifically, based on the conventional CE method, a heuristic iterative sampling method is designed to explore task-to-core assignment for balanced heat distribution between the top-layer and the bottom-layer cores. Subsequently, thermal characteristics of 3-D MPSoC systems are used to judiciously swap tasks between the two layers to improve the conventional CE-based task assignment and accelerate the iterative process. The peak temperature of individual cores is further reduced via sequencing, splitting, and slacking task execution. The experimental results demonstrate that compared to the existing state-of-the-art methods, the proposed scheme can reduce peak temperature by up to 8.02 °C and temperature variation by up to 24.78% without violating the timeliness of tasks.