Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Dose Volume and Excipient Dose on Luminal Concentration and Oral Drug Absorption

Excipient concentrations in the gastrointestinal luminal fluids can influence the absorption of poorly water-soluble drugs when dosed orally with solubilizing excipients, and poorly permeable drugs when dosed with permeation-enhancing excipients. This report examines how dose volume, excipient dose level, volume of water chaser, and gastric fluid volume influence luminal excipient concentrations, and how these could differ from preclinical species and humans. Gastric concentrations of excipient resulting immediately after dosing typical formulations containing a solubilizing excipient are estimated in preclinical species and humans. Examples of the effects of excipient dose and dose volume on drug absorption are illustrated using cases in the literature. When estimating human absorption potential of poorly soluble drug candidates from solubility data, in vitro dissolution models, physiologically based pharmacokinetic models, or preclinical pharmacokinetic data, it may be useful to consider the dose volume and excipient dose, and it is recommended to include estimated luminal excipient concentrations as a factor. There is a need for further studies collecting data on excipient concentrations in luminal fluids and evaluating the effects on drug absorption.
Massive Random Access in Massive MIMO via Opportunistic Thresholding

Non-orthogonal codes have been recently applied for massive IoT random access to a massive MIMO base station. Activity detection for this extension of on-off random access channel yields a jointly sparse multiple measurement vector (MMV) problem. However, no one investigated the regime when measurements per antenna are very limited but number of antennas is extremely large. Our contributions towards addressing this problem are as follows. Firstly, motivated by trivial pursuit which performs well with independent sensing matrices, we designate independent small-scale fading across antennas and users as a possible source of sensing matrix de-correlation. Secondly, two novel algorithms are proposed which exploit this partial de-correlation and collect sensing matrix diversity. Thirdly, probability of failure (PoF) for these methods are rigorously derived and corresponding measurement inequalities are presented. Fourthly, extensive simulations are conducted to confirm the superior performance of these methods versus state of the art in the aforementioned regime.
Increased rainfall variability and reduced rainfall amount decreases soil CO 2 flux in a grassland ecosystem

Predicted climate changes in the US Central Plains include altered precipitation regimes with increased occurrence of growing season droughts and higher frequencies of extreme rainfall events. Changes in the amounts and timing of rainfall events will likely affect ecosystem processes, including those that control C cycling and storage. Soil carbon dioxide (CO2) flux is an important component of C cycling in terrestrial ecosystems, and is strongly influenced by climate. While many studies have assessed the influence of soil water content on soil CO2 flux, few have included experimental manipulation of rainfall amounts in intact ecosystems, and we know of no studies that have explicitly addressed the influence of the timing of rainfall events. In order to determine the responses of soil CO2 flux to altered rainfall timing and amounts, we manipulated rainfall inputs to plots of native tallgrass prairie (Konza Prairie, Kansas, USA) over four growing seasons (1998‐ 2001). Specifically, we altered the amounts and/or timing of growing season rainfall in a factorial combination that included two levels of rainfall amount (100% or 70% of naturally occurring rainfall quantity) and two temporal patterns of rain events (ambient timing or a 50% increase in length of dry intervals between events). The size of individual rain events in the altered timing treatment was adjusted so that the quantity of total growing season rainfall in the ambient and altered timing treatments was the same (i.e. fewer, but larger rainfall events characterized the altered timing treatment). Seasonal mean soil CO2 flux decreased by 8% under reduced rainfall amounts, by 13% under altered rainfall timing, and by 20% when both were combined (Po0.01). These changes in soil CO2 flux were consistent with observed changes in plant productivity, which was also reduced by both reduced rainfall quantity and altered rainfall timing. Soil CO2 flux was related to both soil temperature and soil water content in regression analyses; together they explained as much as 64% of the variability in CO2 flux across dates under ambient rainfall timing, but only 38‐48% of the variability under altered rainfall timing, suggesting that other factors (e.g. substrate availability, plant or microbial stress) may limit CO2 flux under a climate regime that includes fewer, larger rainfall events. An analysis of the temperature sensitivity of soil CO2 flux indicated that temperature had a reduced effect (lower correlation and lower Q10 values) under the reduced quantity and altered timing treatments. Recognition that changes in the timing of rainfall events may be as, or more, important than changes in rainfall amount in affecting soil CO2 flux and other components of the carbon cycle highlights the complex nature of ecosystem responses to climate change in North American grasslands.
Iterative Doppler Frequency Offset Estimation in Low SNR Satellite Communications

Satellite communication systems usually work in low signal-to-noise ratio (SNR) circumstances owning to the limited satellites' link budgets. Large doppler frequency offset in low-SNR satellite communication systems severely influences the performance of frequency synchronization, while the frequency offset correction still remains an open problem under low SNR condition especially for short burst transmission. To solve such a problem in satellite communications, we present a novel method named GP-MASO-MLE, which comprises a coarse correction based on the objective function with Gaussian Process (GP) search and a fine correction based on Maximum Likelihood Estimation (MLE) jointly with turbo iterations. Specifically, the proposed method is appropriate for non-data-aided frequency offset correction in satellite communication systems. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal frequency offset correction within 0.1 dB, moreover, the proposed algorithm has lower computational complexity compared with traditional multi-step search algorithms.
Using Small-Scale History Data to Predict Large-Scale Performance of HPC Application

Performance modeling is an important problem in high-performance computing (HPC). Machine Learning (ML) is a powerful approach for HPC performance modeling. ML can learn complex relations between application parameters and the performance of HPC applications from historical execution data. However, extrapolation of large-scale performance with only small-scale execution data using ML is difficult, because the independent and identically distributed hypothesis (the basic hypothesis of most ML algorithms) does not hold in this situation. To solve the extrapolation problem, we propose a two-level model consisting of interpolation level and extrapolation level. The interpolation level predicts small-scale performance with small-scale execution. The extrapolation level predicts the large-scale performance of the fixed input parameter with its small-scale performance predictions. We use the random forest to build interpolation models to predict small-scale performance in the interpolation level. In the extrapolation level, to reduce the negative influence of interpolation errors, we employ the multitask lasso with clustering to construct the scalability models to predict large-scale performance. To validate the utility of our two-level model, we conduct experiments on a real HPC platform. We build models for two HPC applications using our two-level model. Compare with existing ML methods, our method can achieve higher prediction accuracy.
Thermal Control Compensation of Induction Motor Drive in Electrified Powertrain

The increase in operating as well as environmental temperature of an electric vehicle (EV) motor drive causes significant variations in its performance parameters such as rotor, stator resistance and mutual inductance. This variation results in the overall performance degradation of electrified powertrain in the form of excessive fuel (battery) consumption and inability to meet the desired terminal characteristics such as speed, torque and flux. To mitigate this issue, a robust linear parameter varying (LPV) control incorporated with linear matrix inequalities (LMIs) is presented in this work that ensures L2 gain bound and closed-loop control system stability. A comparison of sliding mode control (SMC) is made with the proposed controller to validate its robustness. In order to verify the efficacy of LPV controller, its performance is tested against a New European Driving Cycle (NEDC) in MATLAB Simulink environment. The nonlinear simulation results gurantee the excellence of LPV performance.
Tailored Deep Learning based Architecture for Smart Agriculture

Disease detection in a plant or tree using traditional ways such as the farmers expert naked eyes is both time and resource consuming and may engender tremendous crop losses. Thus, the early diagnosis and treatment of these diseases can minimize the losses in the whole crop and can improve quality and diversity for the consumer later. With the recent advances in Deep Learning, powerful approaches are developed for both detection and classification that can cope with complex environments. In this paper, we propose an efficient deep learning-based architecture for object detection in the context of Smart Agriculture. The proposed solution combines deep learning and tweaked transfer learning models for object detection with balanced data for every class of images. It can operate in a more complex environment and takes into consideration the state of the input. Its aim is to automatically detect damages in leaves and fruits, locate them, classify their severity levels, and visualize them by contouring their exact locations. Numerical results reveal that the proposed solution, based on Mask-RCNN achieves higher performances in features extraction and damage detection/localization compared to other pre-trained models such as VGG16 and VGG19.
Autonomous Identification of IoT Device Types based on a Supervised Classification

A wide diversity of IoT devices are connected in various smart homes with an extremely rapid growth. Identifying IoT devices as they connect to the network enables better devices and services management. However, autonomous identification is very challenging in such heterogeneous environments. In this paper, we present a near-real time classification approach, based on network features that are extracted both from characteristics of traffic flows and from the payloads of the packets, to discriminate device types. Our solution automatically identifies a newly connected device to the home network. Furthermore, we evaluate the performance of our method using a representative and heterogeneous set of real IoT devices. Our results show autonomous recognition with 97% average accuracy, based on decision tree models using the one-vs.-all method.
Toward a mechanistic understanding of contaminant‐induced changes in detritus processing in streams: Direct and indirect effects on detritivore feeding

Macroinvertebrate feeding is a major rate‐limiting step in the processing of detritus in streams. Environmental contaminants can reduce detritus processing by decreasing the abundance or feeding activity of detritivores. Contaminant‐induced reductions in detritus processing can have important consequences for the structure and functioning of the stream community. Here we investigate the mechanistic basis for reduced leaf processing in a stream contaminated with motorway (superhighway) runoff. In situ and laboratory studies were used and the work focused on Gammarus pulex (Amphipoda), the dominant detritivore at the site. The in situ feeding rate of G. pulex was significantly reduced downstream of the motorway discharge. Reductions in feeding rate could be the result of contaminants in the discharge having a direct effect on the animal or the discharge may affect the animal's feeding indirectly, by reducing food quality. Food quality may be reduced by contaminant accumulation or changes in microbial conditioning. Laboratory studies demonstrated that the main mechanism responsible for the reduction in feeding was direct toxicity and that this was most severe when animals were in direct contact with contaminated sediments. Neither contaminant accumulation nor differences in microbial conditioning affected the choice or consumption of leaf material by G. pulex over short exposure periods (i.e., < 14 d). However, contaminant‐induced reductions in microbial conditioning were important over longer exposure periods (i.e., ≥27 d).
Low-Voltage Operating Ferroelectric FET with Ultrathin IGZO Channel for High-Density Memory Application

We have fabricated and demonstrated ultrathin In-Ga-Zn-O (IGZO) channel ferroelectric HfO2 field effect transistor (FET) with memory operation. Ultrathin-body IGZO ferroelectric FET (FeFET) shows high mobility and nearly ideal subthreshold slop with minimum 8 nm channel thickness, thanks to the properties of IGZO material, junctionless FET operation, nearly-zero low-k interfacial layer on metal-oxide channel and effective capping for realizing ferroelectric phase formation with HfZrO2 (HZO). The controllable memory operations are achieved with the use of back gate. The design guideline of IGZO FeFET is proposed by discussing the thickness of front gate oxide HZO and back gate oxide SiO2 using TCAD simulation. The material and electrical properties of metal/HZO/IGZO/metal capacitor are also investigated. Metal/HZO/IGZO/metal capacitor has up to 108 endurance and over one-year retention. IGZO FeFET shows a potential for high-density and low-power memory application.
Enacting Accountability in IS Research after the Sociomaterial Turn(ing)

Sociomateriality represents an emergent philosophical stance that instantiates an ontological turn towards relationality and materiality in information systems (IS) research. As an emergent perspective or way of seeing, sociomateriality has significant implications for researchers and the practices they employ. If we accept that the ontological, epistemological, and methodological assumptions we enact in our research shape the realities we perceive and create, questions around researchers’ accountability for the realities they produce need to be addressed. The sociomaterial turn(ing) in IS challenges our deeply held assumptions about what constitutes reality. What are these challenges, and how are they being addressed in sociomaterial research? And what implications for accountability in IS research more generally does a turn towards relationality and materiality hold? The objectives of this editorial are: (1) to sensitize IS researchers, irrespective of their ontological and epistemological persuasions, to the field’s turn(ing) toward relationality and materiality; (2) to provide insight into the practices of data generation, analysis, and presentation through which this turn(ing) is being enacted in sociomaterial theorizing; and (3) to contemplate the implications of this turn(ing) for the accountability of IS research more generally.
Load Balancing Model against Multiple Controller Failures in Software Defined Networks

This paper proposes a preventive priority setting model to handle load balancing against multiple controller failures in software defined networks. For each switch, a set of controllers can control it, where only one master controller controls the switch and others are slave controllers. We introduce a priority for each controller to become the master controller. At any time, the controller which does not fail and has the highest priority works as the master controller. Once the existing master controller fails, the assignment of new master controller is automatically obtained according to the priority setting to promptly recover the control. The priority set is decided at the network operation start time to minimize the maximum utilization ratio among controllers in the worst-case of failure patterns. We formulate the proposed model in two different forms, which are an integer linear programming formulation and a min-max formulation. We prove that the preventive priority setting problem is NP-hard. A heuristic algorithm is introduced based on the min-max formulation. The numerical results reveal that the proposed model obtains the maximum utilization ratio comparable to those obtained by two baselines; it provides a faster recovery compared to one baseline and reduces the instability of network operation compared to the other.
Efficient sampling and robust 3D diffusion magnetic resonance imaging signal reconstruction

This paper presents novel single and multi-shell sampling schemes for diffusion MRI. In diffusion MRI, it is paramount that the number of samples is as small as possible in order that scan times are practical in a clinical setting. The proposed schemes use an efficient number of measurements in that the number of samples is equal to the degrees of freedom in the orthonormal bases used for reconstruction. Novel reconstruction algorithms based on smaller subsystems of linear equations, as compared to the standard regularized least-squares method, are developed for both single and multi-shells sampling schemes. The smaller matrices used in these novel reconstruction algorithms are designed to be well-conditioned, leading to improved reconstruction accuracy. Accurate and robust reconstruction is also achieved through incorporation of regularization into the novel reconstruction algorithms and using a Rician or non-central Chi noise model. We quantitatively validate our single and multi-shell schemes against standard least-squares reconstruction methods to show that they enable more accurate reconstruction when the number of samples is equal to the degrees of freedom in the basis. Human brain data is also used to qualitatively evaluate reconstruction
Locality-Improved Repairable Fountain Codes for Distributed Storage Systems

We propose systematic fountain codes over GF (q) with the maximum likelihood (ML) decoding for distributed storage systems (DSS), which is called locality-improved repairable fountain codes (LIRFC). A generator matrix is constructed by entry-wise sampling and row-discarding, where the parameters are carefully determined such that locality is reduced while the ML decoding performance is not degraded. The degree distribution of the proposed codes is represented as a truncated and scaled binomial distribution with an optimized truncation threshold. Compared to the repairable fountain codes (RFC) that have logarithmic locality, the proposed codes achieve 5.97% to 22.44% less average locality than RFC, while having equivalent decoding failure probability and higher availability.
The role of constraints and limitation in driving individual variation in immune response

Summary 1. Life history theory predicts that immunity should be plastic and reflect environmental contexts. However, individual variation in immune investment may arise not just because of individual adjustment, but because of developmental, physiological, genetic or immunological constraints which lead to non-adaptive responses by limiting or eliminating flexibility in immune investment. Constraints can arise because organisms are single integrated units with interconnected and interacting components, in which physiological and genetic control mechanisms may limit or constrain immunity. We review some of the key underlying genetic and physiological factors that may constrain the occurrence and intensity of immune responses. 2. A major part of individual variability may rest on variation in genetic background. Genetic-based constraints can limit or influence immune responses, particularly through pleiotropy and epistatic interactions. In addition, genetic variation, an important driver of variation in antigen recognition and immune system polarization, can be constrained through linkage disequilibrium and genetic drift. Epigenetic changes can also constrain or limit immune responses in future generations based on individual experience. 3. The immune system itself can influence individual flexibility in immune investment. Throughout development individuals face tradeoffs within the immune system that favour the expression of one trait at the expense of another. Ontogenetic differences can cause juveniles and adults to produce entirely different immune responses to the same pathogen. T-helper 1 (Th1)/T-helper 1 (Th2) polarization during infection also imposes constraints upon an individual’s immune responsiveness, with the consequence that hosts cannot simultaneously mount strong responses using both Th1 and Th2 cells. In addition, evidence suggests that flexibility in immune responses becomes constrained with age through accumulation of memory cells at the expense of naive cells, decreased function of cells involved in adaptive and innate immunity, and programming of HPA-immune interactions. 4. In summary, selection on a particular immune trait can have effects on other immune components or phenotypic characters, as revealed by artificial selection studies. In particular selection for increased investment in compartments of the immune system leads to decreased investment in other competing life history functions and/or marked changes in other immune components. The role of past experience, even the past experience of parents, may limit and constrain immune responses through influencing the ontogeny of immunity, as well as through epigenetic influences.
Exact Completion of Rectangular Matrices Using Ramanujan Bigraphs

In this paper, we study the matrix completion problem: Suppose $X \in {\mathbb{R}^{{n_r} \times {n_c}}}$ is unknown except for an upper bound r on its rank. By measuring a small number m ≪ nrnc of elements of X, is it possible to recover X exactly, or at least, to construct a reasonable approximation of X? At present, there are two approaches to choosing the sample set, namely probabilistic and deterministic. Probabilistic methods can guarantee exact recovery of the unknown matrix, but only with high probability. In this approach, samples are taken uniformly at random. Therefore we need to start sampling for every new matrix afresh. In the deterministic approach, sampling points can be kept fixed. At present, there are very few deterministic methods, and they mostly apply only to square matrices. In this paper, we present a deterministic method for selecting the sample set that can guarantee the exact recovery of the unknown matrix. This approach works for the recovery of rectangular as well as square matrices. We achieve this by choosing the elements to be sampled as the edge set of a Ramanujan bigraph. If samples are the edge set of a Ramanujan bigraph, then we can recover the unknown matrix from that sample set using nuclear norm minimization. A companion paper discusses the explicit construction of Ramanujan bigraphs. We provide a sufficient condition, that is if the samples taken are of the order of r3 then we can recover the unknown entries exactly if the unknown matrix satisfies some coherence condition. We believe this the first sufficient condition available using deterministic sampling technique and nuclear norm minimization.