Miracle-dz/UniFAR-FTUs huggingface dataset:
An Insight into Preparatory Methods and Characterization of Orodispersible Film—A Review

Over the past few decades, researchers and companies have been trying to develop novel drug delivery systems to ensure safety, efficacy, compliance, and patient acceptability. Nowadays drug discovery and development are expensive, complex, and time-consuming processes, but trends are moving toward novel drug delivery systems. This delivery system helps to achieve drug response by local and systemic action through different routes. This novel approach of preparing orodispersible films (ODFs) provides benefits to paediatric, geriatric, and bedridden patients. This review paper aims to provide details on the preparation, characterization, and evaluation of ODFs; it also aims to focus on the positive and negative factors that affect film formulation and give an insight into potential drug candidates and polymers for use in ODFs. ODFs are effective, safe, and have good bioavailability as compared to fast-disintegrating tablets. The novel approach has various advantages because it provides instant effects in emergency situations and in schizophrenic and dysphasic patients without the need for taking water, the films disintegrating within a few seconds in the oral cavity. The solvent casting method is the most frequently used technique to develop ODFs, using film-forming polymers, which have a fast disintegration time, improved drug dissolution, and better drug contents.
Effects of Physical Activity Interventions on Health Outcomes among Older Adults Living with HIV: A Systematic Review and Meta-Analysis

There is a lack of evidence regarding the effects of exercise on older individuals living with HIV. This systematic review and meta-analysis examined previous studies on physical activity interventions for people living with HIV aged ≥50 years. The effectiveness of the interventions on various physical and psychological health outcomes was evaluated. Databases used for this review included PubMed, EMBASE, CINAHL, and Cochrane Library CENTRAL. Twelve randomized controlled trials on physical activity interventions for people ≥50 years and living with HIV were included. Standardized mean differences were calculated using random-effect models. All effect sizes were expressed using Cohen’s d values and their 95% confidence intervals (CIs). Physical activity interventions had a significant effect on walking capacity (Cohen’s d: 0.467; 95% CI [0.069, 0.865]). The effect sizes on cardiorespiratory fitness, weight, and health-related quality of life were not significant. These findings suggest that physical activity interventions for people living with HIV aged ≥50 years are effective for the improvement of walking capacity. Further larger and higher-quality studies are required to determine the full effects of physical activity interventions on various health outcomes among older adults with HIV.
Epithelial chemerin–CMKLR1 signaling restricts microbiota-driven colonic neutrophilia and tumorigenesis by up-regulating lactoperoxidase

Significance Epithelial antimicrobial defense is an important innate mechanism to maintain intestinal homeostasis and closely involved in the pathogenesis of inflammatory bowel disease (IBD). Chemerin, which is abundantly expressed in barrier tissues, regulates tissue inflammation via CMKLR1, its functional receptor. We herein reveal a role of epithelial chemerin–CMKLR1 signaling in sustaining epithelial antimicrobial defense, thereby conferring protection from microbiota-driven neutrophilic colitis and subsequent tumorigenesis following epithelial injury. Mechanistically, we identify lactoperoxidase (LPO), which is highly expressed in murine colonic epithelium, as the downstream effector of chemerin–CMKLR1 signaling to restrict the outgrowth and invasion of gram-negative bacteria, thereby preventing dysregulated CXCL1/2 production and pathological mucosal neutrophilia. Thus, targeting chemerin–CMKLR1/LPO axis may be beneficial for improving dysbiosis in IBD patients.
Current-Mode Electronically-Tunable Sinusoidal Oscillator Based on a Shadow Bandpass Filter

The design of a current-mode frequency and amplitude-controlled sinusoidal oscillator, is presented. The oscillator is based on a second order Gm-C bandpass filter made up of low-voltage current mirror cells. The frequency response of the filter can be modified by means of its biasing current. Besides, an automatic control circuit that ensures oscillation and leads to the desired amplitude of the output signal, has been included. The proposal has been designed and implemented in 180 nm CMOS technology to operate with a supply voltage of 1.8 V, resulting in a compact solution very appropriate for operation in low-voltage environments. The simulated results show high speed, with a maximum bandwidth of 2.75 MHz, and linear response, with a THD of around −40 dB for an output amplitude of 5 μA.
A piston detection method for segments via convolutional neural networks and its robustness analysis

To achieve a diffraction-limited imaging, the piston errors between the segments of the segmented primary mirror telescope should be reduced to λ/40 RMS. The piston detection method using convolutional neural network (CNN) is an advanced technology with high precision and simplicity. However, such methods based on the deep learning strategy usually have generalization problems, that is, the network prediction precision will inevitably decrease if there is a certain difference between the test image and training set used in the network. This will directly affect the scope of application of the method. In this letter, we propose a CNN-based high-precision piston detection method and analyze its robustness. The point spread function (PSF) images acquired under the wide-spectrum light source are used to construct the dataset to overcome 2π ambiguity. In addition, a set of neural networks system including the classification CNN and the regression CNN with good generalization ability is designed to extract the piston value directly from the PSF image. Under the ideal condition, the piston detection precision can reach about 8.4 X 10-4 λοRMS in the capture range of the interference length of the operating light. Finally, we focus on testing the effect degree of the main disturbance factors in the actual system on the accuracy of the method, such as surface error, residual tip-tilt error, and CCD noise, so as to evaluate the robustness of the method. This method is robust and does not require complex hardware. It can be widely applied in segmented and deployable primary mirror telescopes. We believe that the study in this letter will contribute to the applications of the CNN-based technique for piston sensing.
Compact size of the hybrid branch‐line coupler using π‐type transmission‐line

In this article, the new of the transmission‐line (TL) using π‐type parameter is suggested and it applied to the design of a hybrid branch‐line coupler (HBC). The new HBC is small size than conventional HBC and the designed HBC is used by entire distributed elements. Then, the sizes of the new and conventional HBCs are 9.86 × 11.1 mm2 and 28.2 × 28.6 mm2, respectively. The advantage of the proposed HBC structure has very simple structure and low cost. The proposed HBC using π‐type TL can be applied to mobile communication system.
Implementation of Ultra-Fast Polar Decoders

Polar codes are used in the 5G standard, and due to their low-complexity decoding algorithm and the ability to achieve symmetric channel capacity, they are receiving increased research interest for beyond 5G networks as well. In our recent work, we have introduced new subcodes and their decoding algorithms for fast decoding of polar codes with short to moderate blocklengths. In this paper, we study the algorithms from a hardware implementation point of view. Moreover, some new subcodes are also introduced to further prune the binary decoder tree. A hardware architecture of the algorithms using resource sharing and multiplexing techniques is presented. The FPGA implementation results show that a polar code of length $N=102A$, rate $\mathcal{R}=1/2$ with two Processing Element $(P_{\mathrm{e}})$ values of 128 and 256 achieves 42.5% and 47.6% lower latency comparing to the original Fast-SSC algorithm. The proposed decoder architecture offers an information throughput of 393 Mbps for the same code with $P_{e}=128$.
Seepage Safety Assessment of Concrete Gravity Dam Based on Matter-Element Extension Model and FDA

As an important infrastructure project, the concrete gravity dam plays an extremely important role in hydropower generation, irrigation, flood control, and other aspects. Seepage is an important factor affecting the stability of concrete gravity dams. Seepage safety assessment is of great significance to the safe operation of the dams. However, the existing seepage safety assessment models are not dynamic, and the correlation among indicators is often neglected and the overall seepage safety of the concrete gravity dams has not been considered. To solve these problems, this research proposes a dynamic matter-element extension (D-MEE) model. First, the D-MEE model is established through adroit integration of the matter-element extension (MEE) model and functional data analysis (FDA). Second, a dynamic criteria importance through the intercriteria correlation (D-CRITIC) method that can effectively consider the correlation among indicators is proposed to determine the weights. Third, the influence of different dam blocks on the overall seepage safety status is considered by constructing a spatial weight matrix. Finally, the proposed method is applied to the concrete gravity dam X in southwest China. The results show that the proposed method is effective and superior to the existing evaluation methods of seepage safety.
Attitudes and Action in International Refugee Policy: Evidence from Australia

Abstract Do citizens care whether their government breaches international law, or are other imperatives more influential? We consider this question in the human rights arena, asking whether and how it matters how abuses are framed. In a novel survey experiment, we ask Australians about their attitudes toward restrictive immigration policy, holding the underlying breaches constant but varying how they are framed. We find that people most strongly oppose policy that violates international law. Emphasizing moral considerations has smaller but still notable impacts on attitudes, whereas reputational frames have the weakest effects. We also find that translating attitudes into political action is challenging: most who learn of current policy's legal, moral, or reputational dimensions and in turn become more critical do not subsequently express greater interest in trying to do something about it. Nonetheless, there are interesting differences across frames. Appealing to international law or moral considerations is more effective at spurring mobilization than emphasizing reputational harm, though via different mechanisms. Framing this debate in international reputational terms consistently has the weakest impacts on interest in political action, and may be worse than saying nothing at all.
Filtering Over Non-Gaussian Channels: The Role of Anytime Capacity

Filtering over noisy channels is of interest in many network applications, in which a node infers a time-varying state by using messages received from another node that can observe such a state. This letter explores filtering with general models for state disturbance and communication channels by deriving a sufficient condition for which the estimation error is bounded. Specifically, the sufficient condition is expressed in terms of anytime capacity, a notion that characterizes the maximum sequential communication rate. The joint design of encoder and estimator with bounded estimation error is also presented.
Predicting locally manageable resource failures of high availability clusters

Critical services from domains as diverse as finance, manufacturing and healthcare are often delivered by complex enterprise applications (EAs). High‐availability clusters (HACs) are software‐managed IT infrastructures that enable these EAs to operate with minimum downtime. This article presents a novel Bayesian decision network model to improve the failure detection capabilities of the HACs components using a comprehensive set of characteristics for the analyzed component. The model then combines these characteristics to predict whether the failure of this component can be managed locally at the failed component level without propagating the failure to upper‐level components and causing a complete system failure. By improving the detection capabilities and predicting locally manageable failures, the model improves the decision‐making process of HACs, and has the potential to reduce the downtime and improve availability for the applications protected by HACs. The model uses the capabilities of the Bayesian decision networks, which combines Bayesian networks with the utility theory, to assign weights to different characteristics and consolidate the related variables to output the result. The model evaluation in a realistic testbed environment with three servers, an established HAC and a well‐known EA shows that the model can improve the area under the receiver operating characteristic curve for prediction of locally manageable failures by up to 9.05% compared to the baseline HAC results.
AD-Mol: A Dataset for Antibiotic Discovery

There is a growing need for new antibiotics, accelerated by the rapid emergence of 1 antibiotic-resistant bacteria. Recently, machine learning models have shown to be 2 important potential tools in the antibiotic discovery pipeline, useful in predicting 3 antibacterial activity for screening libraries of molecules for possible antibiotics. 4 However, there are no datasets of antibiotic-like structures that are large enough 5 for training generative models or other machine learning methods to directly 6 discover novel candidates. In order to help accelerate machine learning assisted 7 antibiotic discovery and as a new benchmark for molecular design– we introduce 8 AD-Mol, a new dataset of over 500K molecular structures that are likely inhibitors
Total Variation Based Rician Denoising and Deblurring Model

Total variation regularization is usually applied with L 2 data ﬁdelity assuming an additive white Gaussian noise model [24, 25]. However, better results are possible when the noise model accurately describes the noise distribution in the given image. Total variation denoising has already been developed with the Laplace noise model [2, 12] ( L 1 data ﬁdelity) and the Poisson noise model [19, 26]. In this paper, we develop two variational methods for total variation regularization using the Rician noise model. Our ﬁrst model uses the Rician probability distribution function directly in the ﬁdelity term, leading to a slightly nonconvex objective function. We give existence and comparison results for this model and investigate the validity of the model on three dimensional synthetic magnetic resonance imaging (MRI) data corrupted with Rician noise and Gaussian blur, both with known standard deviations. In this case, the numerical solutions are obtained using the L 2 and Sobolev H 1 gradient descent methods. In our second model, we develop a variational method for total variation regularization that closely approximates the Rician noise model. A key challenge is to ﬁnd a convex approximation of the slightly nonconvex objective function. The variational problem is eﬃciently solved using the split Bregman method [16], and numerical examples are performed on the MRI data described above. Comparison of both models is presented.
A gradient ﬂow approach to a free boundary droplet model

We consider a quasi-static droplet motion based on contact angle dynamics on a planar surface. Based on a gradient ﬂow structure of the problem we derive a natural time–discretization using the JKO–scheme, and discuss convergence in the continuum limit. The time discrete interface motion is described in comparison with barrier functions, which are classical sub- and super-solutions in a local neighborhood. This barrier property is diﬀerent from standard viscosity solutions since there is no comparison principle for our problem. In the continuum limit the barrier properties still hold in a modiﬁed sense.
ABM: active buffer management in datacenters

Today's network devices share buffer across queues to avoid drops during transient congestion and absorb bursts. As the buffer-per-bandwidth-unit in datacenter decreases, the need for optimal buffer utilization becomes more pressing. Typical devices use a hierarchical packet admission control scheme: First, a Buffer Management (BM) scheme decides the maximum length per queue at the device level and then an Active Queue Management (AQM) scheme decides which packets will be admitted at the queue level. Unfortunately, the lack of cooperation between the two control schemes leads to (i) harmful interference across queues, due to the lack of isolation; (ii) increased queueing delay, due to the obliviousness to the per-queue drain time; and (iii) thus unpredictable burst tolerance. To overcome these limitations, we propose ABM, Active Buffer Management which incorporates insights from both BM and AQM. Concretely, ABM accounts for both total buffer occupancy (typically used by BM) and queue drain time (typically used by AQM). We analytically prove that ABM provides isolation, bounded buffer drain time and achieves predictable burst tolerance without sacrificing throughput. We empirically find that ABM improves the 99th percentile FCT for short flows by up to 94% compared to the state-of-the-art buffer management. We further show that ABM improves the performance of advanced datacenter transport protocols in terms of FCT by up to 76% compared to DCTCP, TIMELY and PowerTCP under bursty workloads even at moderate load conditions.
Implementation and Parameter Selection for BV -Hilbert Space Regularizations

We examine the general regularization model which is based on total-variation for the structural part and a Hilbert-space norm for the oscillatory part. This framework generalizes the ROF and OSV models and opens way for new regularizations methods, that have not been considered yet. We provide a straightforward numerical implementation, following Chambolle’s projection algorithm. In order for such schemes to be practical, a systematic method for automatic parameter selection is imperative. The ROF method for selecting the weight parameter according to the noise variance is reformulated in a Hilbert space sense. Moreover, we generalize a recent study of GSZ where the weight parameter is selected such that the denoised result is close to optimal, in the SNR sense. A broader deﬁnition of SNR, which is frequency weighted, is formulated in the context of inner products. A necessary condition for maximal SNR is provided. Lower and upper bounds on the SNR performance of ROF and GSZ type strategies are established, under quite general assumptions.
Blind uniform motion blur deconvolution for image bursts and video sequences based on sensor characteristics

Video sequences can be enhanced by a spatial deconvolution of any motion blur whose support does not exceed two pixels per frame. However, this deconvolution requires an accurate blur estimation and local deconvolution which is diﬃcult for multiple local motions. We provide a discrete temporal ﬁlter whose coeﬃcients are designed 1) to deconvolve blindly any uniform motion blur whose support does not exceed one pixel per frame, 2) to take into account for the sensor dead time between two consecutive frame (duty ratio/inter frame delay). The proposed ﬁlter enjoys optimality properties in terms of mean square error. In addition, it is demonstrated on real movies obtained from a smartphone and the Middlebury dataset.
The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift

Generalization to novel domains is a fundamental chal-lenge for computer vision. Near-perfect accuracy on bench-marks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that truly solve real-world prob-lems at global scale, we need benchmarks that fully capture real-world complexity, including geographic domain shift, long-tailed distributions, and data noise. We propose urban forest monitoring as an ideal testbed for studying and improving upon these computer vision challenges, while working towards filling a crucial environ-mental and societal need. Urban forests provide significant benefits to urban societies. However, planning and main-taining these forests is expensive. One particularly costly aspect of urban forest management is monitoring the ex-isting trees in a city: e.g., tracking tree locations, species, and health. Monitoring efforts are currently based on tree censuses built by human experts, costing cities millions of dollars per census and thus collected infrequently. Previous investigations into automating urban forest monitoring focused on small datasets from single cities, covering only common categories. To address these short-comings, we introduce a new large-scale dataset that joins public tree censuses from 23 cities with a large collection of street level and aerial imagery. Our Auto Arborist dataset contains over 2.5M trees and 344 genera and is >2 or-ders of magnitude larger than the closest dataset in the literature. We introduce baseline results on our dataset across modalities as well as metrics for the detailed analy-sis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
Smart Technology for “Clean Your Plate” Campaign against Wasting Food in University Canteens

Food security is becoming increasingly prominent due to the impact of the COVID-19 and extreme weathers. The Smart Project for “Clean Your Plate” Campaign has been designed and implemented to address food waste on campus. The project takes advantage of technologies such as IoT, dynamic QR code, and visual recognition. Smart devices for clear plate recognition can be installed in multiple locations in campus cafeterias and canteens. Users can login onto a public account and a Mini Program for points reward, exchange and sale. Combining offline clear plate recognition and online points exchange, the project encourages food saving and reduces human cost for university canteens.
Knowledge Structures Over Simulation Units

Modern cyber-physical applications, such as those adopting the Digital Twin paradigm, typically connect simulators with data-rich components and domain knowledge, both often formalized as knowledge graphs. Engineering such applications poses challenges to developers. This paper presents a language-based integration of knowledge graphs and simulators for object-oriented languages. We use Functional Mock-Up Objects (FMOs) as a programming layer to encapsulate simulators compliant with the FMI standard into OO structures and integrate FMOs into the class and type systems. We show how FMOs can be integrated into knowledge graphs by means of semantical lifting, and used to ensure structural properties of cyber-physical applications. We provide a prototype implementation of the proposed integration and discuss how it can be realized in other languages. Finally, the use of FMOs in practice is illustrated by two case studies.