Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary Fiber and Intestinal Health of Monogastric Animals

Animal performance, feed efficiency, and overall health are heavily dependent on gut health. Changes in animal production systems and feed regulations away from the use of antibiotic growth promoters (AGP) have necessitated the identification of strategies to optimize gut health in novel and effective ways. Among alternatives to AGP, the inclusion of dietary fibers (DF) in monogastric diets has been attempted with some success. Alternative feedstuffs and coproducts are typically rich in fiber and can be used in the diets to reduce feed costs and optimize gut health. DF are naturally occurring compounds with a diverse composition and are present in all plant-based feedstuffs. DF stimulate the growth of health-promoting gut bacteria, are fermented in the distal small intestine and large intestine to short-chain fatty acids and have beneficial effects on the immune system. Maternal DF supplementation is one novel strategy suggested to have a beneficial programming effect on the microbial and immune development of their offspring. One mechanism by which DF improves gut health is through maintenance of an anaerobic intestinal environment that subsequently prevents facultative anaerobic pathogens from flourishing. Studies with pigs and poultry have shown that fermentation characteristics and their beneficial effects on gut health vary widely based on type, form, and the physico-chemical properties of the DF. Therefore, it is important to have information on the different types of DF and their role in optimizing gut health. This review will provide information and updates on different types of DF used in monogastric nutrition and its contribution to gut health including microbiology, fermentation characteristics, and innate and adaptive immune responses.
Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection

A novel supervised machine learning system is developed to classify network traffic whether it is malicious or benign. To find the best model considering detection success rate, combination of supervised learning algorithm and feature selection method have been used. Through this study, it is found that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperform support vector machine (SVM) technique while classifying network traffic. To evaluate the performance, NSL-KDD dataset is used to classify network traffic using SVM and ANN supervised machine learning techniques. Comparative study shows that the proposed model is efficient than other existing models with respect to intrusion detection success rate.
High-Speed Visible Light Communication Based on a III-Nitride Series-Biased Micro-LED Array

Visible light communication (VLC) using III-nitride light-emitting diodes (LEDs) offers many advantages such as license-free operation, high spatial diversity and innate security. In particular, micro-LEDs (<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>LEDs) are strong candidates for VLC due to their high modulation bandwidths. However, the low optical power of a single <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>LED is a key factor limiting VLC performance. In this paper, we report an optimized series-biased <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>LED array to achieve higher optical power while retaining high modulation bandwidth for high-speed VLC. An example array consisting of 3 × 3 40 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m-in-diameter <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>LED elements is presented here. At a current density of 3200 A/cm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> in direct-current operation, the optical power and small signal 6-dB electrical modulation bandwidth of a blue-emitting series-biased <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>LED array are over 18.0 mW and 285 MHz, respectively. The data transmission capabilities of this <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>LED array are demonstrated by using <sc>on</sc>–<sc>off</sc> keying, pulse-amplitude modulation, and orthogonal frequency division multiplexing modulation formats over free space with the error-free data transmission rates of 1.95, 2.37, and 4.81 Gb/s, respectively.
Scenario-Based Economic Dispatch With Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: 1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints and 2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on: 1) 3-bus; 2) IEEE 14-bus system; and 3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers.
Bacterial Bioluminescence: Light Emission in Photobacterium phosphoreum Is Not Under Quorum-Sensing Control

Bacterial-bioluminescence regulation is often associated with quorum sensing. Indeed, many studies have been made on this subject and indicate that the expression of the light-emission-involved genes is density dependent. However, most of these studies have concerned two model species, Aliivibrio fischeri and Vibrio campbellii. Very few works have been done on bioluminescence regulation for the other bacterial genera. Yet, according to the large variety of habitats of luminous marine bacteria, it would not be surprising to find different light-regulation systems. In this study, we used Photobacterium phosphoreum ANT-2200, a piezophilic bioluminescent strain isolated from Mediterranean deep-sea waters (2200-m depth). To answer the question of whether or not the bioluminescence of P. phosphoreum ANT-2200 is under quorum-sensing control, we focused on the correlation between growth and light emission through physiological, genomic and, transcriptomic approaches. Unlike A. fischeri and V. campbellii, the light of P. phosphoreum ANT-2200 immediately increases from its initial level. Interestingly, the emitted light increases at much higher rate at the low cell density than it does for higher cell-density values. The expression level of the light-emission-involved genes stays constant all along the exponential growth phase. We also showed that, even when more light is produced, when the strain is cultivated at high hydrostatic pressure, no change in the transcription level of these genes can be detected. Through different experiments and approaches, our results clearly indicate that, under the tested conditions, the genes, directly involved in the bioluminescence in P. phosphoreum ANT-2200, are not controlled at a transcriptomic level. Quite obviously, these results demonstrate that the light emission of the strain is not density dependent, which means not under quorum-sensing control. Through this study, we point out that bacterial-bioluminescence regulation should not, from now on, be always linked with the quorum-sensing control.
An Extreme Value Theory Model of Cross-Modal Sensory Information Integration in Modulation of Vertebrate Visual System Functions

We propose a computational model of vision that describes the integration of cross-modal sensory information between the olfactory and visual systems in zebrafish based on the principles of the statistical extreme value theory. The integration of olfacto-retinal information is mediated by the centrifugal pathway that originates from the olfactory bulb and terminates in the neural retina. Motivation for using extreme value theory stems from physiological evidence suggesting that extremes and not the mean of the cell responses direct cellular activity in the vertebrate brain. We argue that the visual system, as measured by retinal ganglion cell responses in spikes/sec, follows an extreme value process for sensory integration and the increase in visual sensitivity from the olfactory input can be better modeled using extreme value distributions. As zebrafish maintains high evolutionary proximity to mammals, our model can be extended to other vertebrates as well.
Listening and Grouping: An Online Autoregressive Approach for Monaural Speech Separation

This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation. It exploits a causal temporal context in both mixture and past estimated separated signals and performs online separation that is compatible with real-time applications. The approach adopts a learned listening and grouping architecture motivated by computational auditory scene analysis, with a grouping stage that effectively addresses the label permutation problem at both frame and segment levels. Experimental results on the WSJ0-2mix benchmark show that the new approach can achieve better signal-to-distortion ratio and perceptual evaluation of speech quality scores than most of the state-of-the-art methods for both closed-set and open-set evaluations, even methods that exploit whole-utterance statistics for separation. It achieves this while requiring fewer model parameters.
Time-Course of Motor Involvement in Literal and Metaphoric Action Sentence Processing: A TMS Study

There is evidence that the motor cortex is involved in reading sentences containing an action verb (“The spike was hammered into the ground”) as well as metaphoric sentences (“The army was hammered in the battle”). Verbs such as ‘hammered’ may be homonyms, with separate meanings belonging to the literal action and metaphoric action, or they may be polysemous, with the metaphoric sense grounded in the literal sense. We investigated the time course of the effects of single-pulse transcranial magnetic stimulation to primary motor cortex on literal and metaphoric sentence comprehension. Stimulation 300 ms post-verb presentation impaired comprehension of both literal and metaphoric sentences, supporting a causal role of sensory-motor areas in comprehension. Results suggest that the literal meaning of an action verb remains activated during metaphor comprehension, even after the temporal window of homonym disambiguation. This suggests that such verbs are polysemous, and both senses are related and grounded in motor cortex.
Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load

The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student self-reports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.
Coordinated Voltage Control of a Grid Connected Ring DC Microgrid With Energy Hub

This paper presents a supervisory control scheme for the mitigation of voltage regulation issues in a ring dc microgrid with energy hub (EH). The EH is installed with battery banks, proton exchange membrane based fuel cell and electrolyzer stacks representing an energy source-storage unit. In a ring dc system, power injection and absorption at different buses can cause significant voltage deviations throughout the system. This situation is further aggravated due to the intermittent nature of PV power generation. To resolve this issue, a supervisory control scheme is proposed which utilizes a centralized communication system to estimate the network voltage profile and generates desired voltage and current references for the controllable sources. The proposed system consists of dispatchable and non-dispatcable energy sources. Conventionally, in a grid connected system, a bidirectional voltage source converter (VSC) maintains the network voltage while the energy storage system remains at standby/fully charged. This paper suggests to utilize both VSC and EH in a coordinated manner, so as to maintain the voltage at the load terminals within a tolerance band. The proposed ring dc microgrid model is simulated in MATLAB/Simulink environment and the simulation results validate the effectiveness of the supervisory control under wide variations in system operating conditions.
Effect of Plasma Fluorination in p-Type SnO TFTs: Experiments, Modeling, and Simulation

With experimental and numerical simulation, we report the origin of the performance enhancement in p-type SnO thin-film transistors (TFTs) due to fluorine plasma treatment (FPT). To study the effect of fluorination, using the reactive-ion-etching process, the devices are treated at various plasma powers upto 60 W. It is observed, through X-ray photoelectron spectroscopy and XRD measurements, that the plasma fluorination modifies the defect/trap states of SnO channel and SnO/HfO<sub>2</sub> interface. These effects are introduced through density of state (DOS) model for SnO in numerical simulations, to understand the routes of electrical performance improvement. It is observed that the attributes of donor-like band-tail state and acceptor-like Gaussian defect states (Sn vacancies) are modified in overall DOS due to plasma fluorination. The treated device shows excellent electrical performances with high <inline-formula> <tex-math notation="LaTeX">${I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}}$ </tex-math></inline-formula> ratio of ~10<sup>7</sup> and low substhreshold swing of ~ 100 mV/dec and field-effect mobility (<inline-formula> <tex-math notation="LaTeX">$\mu _{\text {FE}}$ </tex-math></inline-formula>) of 2.13 cm<sup>2</sup>V<sup>−1</sup>s<sup>−1</sup>.
Sparsity-Driven GMTI Processing Framework With Multichannel SAR

This paper presents a processing framework to separate moving targets from the clutter, under multichannel synthetic aperture radar (SAR) scenarios, and addresses the moving target imaging and velocity estimation problems for ground moving target indication (GMTI) applications. A practical implementation is introduced to break the SAR/GMTI problem into two processing stages, and the sparsity of the moving targets in the observed scene is exploited throughout the stages. The two-stage process extracts the moving targets from the monitored region via a sparsity-based iterative decomposition algorithm and subsequently estimates the complete velocity vectors of moving targets by enforcing sparsity constraints. The model is sufficiently versatile to incorporate digital elevation map information, which further improves the moving target relocation accuracy. The effectiveness of the presented framework is demonstrated using the Air Force Research Laboratory Gotcha GMTI challenge data.
Bilateral Contract Networks for Peer-to-Peer Energy Trading

This paper proposes bilateral contract networks as a new scalable market design for peer-to-peer energy trading. Coordinating small-scale distributed energy resources to shape overall demand could offer significant value to power systems, by alleviating the need for investments in upstream generation and transmission infrastructure, increasing network efficiency and increasing energy security. However, incentivising coordination between the owners of large-scale and small-scale energy resources at different levels of the power system remains an unsolved challenge. This paper introduces real-time and forward markets, consisting of energy contracts offered between generators with fuel-based sources, suppliers acting as intermediaries and consumers with inflexible loads, time-coupled flexible loads and/or renewable sources. For each type of agent, utility-maximising preferences for real-time contracts and forward contracts are derived. It is shown that these preferences satisfy full substitutability conditions essential for establishing the existence of a stable outcome—an agreed network of contracts specifying energy trades and prices, which agents do not wish to mutually deviate from. Important characteristics of energy trading are incorporated, including upstream–downstream energy balance and forward market uncertainty. Full substitutability ensures a distributed price-adjustment process can be used, which only requires local agent decisions and agent-to-agent communication between trading partners.
Improved Human–Robot Collaborative Control of Redundant Robot for Teleoperated Minimally Invasive Surgery

An improved human–robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on a hierarchical operational space formulation of a seven-degree-of-freedom redundant robot. Redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a compliant behavior for the medical staff. Based on the implemented hierarchical control framework, an RCM constraint and a safe constraint are applied to the null-space motion to achieve the surgical tasks with human–robot interaction. Due to the physical interactions, safety and accuracy of the surgery may be affected. The control framework integrates an adaptive compensator to enhance the accuracy of the surgical tip and to maintain the RCM constraint in a decoupled way avoiding any physical interactions. The system performance is verified on a patient phantom. Compared with the methods proposed in the literature, results show that the accuracy of both the RCM constraint and the surgical tip is improved. The compliant swivel motion of the robot arm is also constrained in a defined area, and the interaction force on the abdominal wall becomes smaller.
Cross-Platform Analytics: A step towards Personalization and Adaptation in Education

Learning analytics are used to track learners' progress and empower educators and learners to make well-informed data-driven decisions. However, due to the distributed nature of the learning process, analytics need to be combined to offer broader insights into learner's behavior and experiences. Consequently, this paper presents an architecture of a learning ecosystem, that integrates and utilizes cross-platform analytics. The proposed cross-platform architecture has been put into practice via a Java programming course. After a series of studies, a proof of concept was derived that shows how cross-platform analytics amplify the relevant analytics for the learning process. Such analytics could improve educators' and learners' understanding of their own actions and the environments in which learning occurs.
Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.
Experimental Pharmacology in Transgenic Rodent Models of Alzheimer’s Disease

This Mini Review discusses the merits and shortfalls of transgenic (tg) rodents modeling aspects of the human Alzheimer’s disease (AD) pathology and their application to evaluate experimental therapeutics. It addresses some of the differences between mouse and rat tg models for these investigations. It relates, in a condensed fashion, the experience of our research laboratory with the application of anti-inflammatory compounds and S-adenosylmethionine (SAM) at the earliest stages of AD-like amyloid pathology in tg mice. The application of SAM was intended to revert the global brain DNA hypomethylation unleashed by the intraneuronal accumulation of amyloid-β-immunoreactive material, an intervention that restored levels of DNA methylation including of the bace1 gene. This review also summarizes experimental pharmacology observations made in the McGill tg rat model of AD-like pathology by applying “nano-lithium” or a drug with allosteric M1 muscarinic and sigma 1 receptor agonistic properties (AF710B). Extremely low doses of lithium (up to 400 times lower than used in the clinic) had remarkable beneficial effects on lowering pathology and improving cognitive functions in tg rats. Likewise, AF710B treatment, even at advanced stages of the pathology, displayed remarkable beneficial effects. This drug, in experimental conditions, demonstrated possible “disease-modifying” properties as pathology was frankly diminished and cognition improved after a month of “wash-out” period. The Mini-Review ends with a discussion on the predictive value of similar experimental pharmacological interventions in current rodent tg models. It comments on the validity of some of these approaches for early interventions at preclinical stages of AD, interventions which may be envisioned once definitive diagnosis of AD before clinical presentation is made possible.