Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandwidth Enhancement of RectangularArray Using Cross Slots DGS Pattern inGround Plane

A novel compact design for bandwidth enhancement of rectangular microstrip array is presented in this paper. The basis for achieving the bandwidth enhancement is through metamaterial structure. Two types of metamaterial namely EBG and DSG are studied. Metamaterial structures are used for further antenna performance improvement, which suppress surface waves and improve the design characteristics. In this paper two cross slots cuts in the ground plane, which enhance the bandwidth of reference antenna. This shape produces bandwidth ranging from 15.30 GHz to 17.66 GHz, gives impedance bandwidth of 2.3649 GHz, whereas reference antenna without DSG is having impedance bandwidth of 290 MHz.
Emerging Non-Antibiotic Options Targeting Uropathogenic Mechanisms for Recurrent Uncomplicated Urinary Tract Infection

Urinary tract infections (UTIs) are the most frequent bacterial infections in the clinical setting. Even without underlying anatomic or functional abnormalities, more than 40% of women experience at least one UTI in their lifetime, of which 30% develop recurrent UTIs (rUTIs) within 6 months. Conventional management with antibiotics for rUTIs may eventually lead to the development of multidrug-resistant uropathogens. Targeting of the pathogenicity of rUTIs, the evolution of uropathogenic Escherichia coli (UPEC), and inadequate host defenses by immune responses should be explored to provide non-antibiotic solutions for the management of rUTIs. The adaptive evolution of UPEC has been observed in several aspects, including colonization, attachment, invasion, and intracellular replication to invade the urothelium and survive intracellularly. Focusing on the antivirulence of UPEC and modulating the immunity of susceptible persons, researchers have provided potential alternative solutions in four categories: antiadhesive treatments (i.e., cranberries and D-mannose), immunomodulation therapies, vaccines, and prophylaxis with topical estrogen therapy and probiotics (e.g., Lactobacillus species). Combination therapies targeting multiple pathogenic mechanisms are expected to be a future trend in UTI management, although some of these treatment options have not been well established in terms of their long-term efficacy. Additional clinical trials are warranted to validate the therapeutic efficacy and durability of these techniques.
Study of τ Final States in Gauge Mediated Supersymmetry Breaking Models at ATLAS

In this thesis Supersymmetry models with Gauge Mediated Supersymmetry Breaking containing τ leptons in the final states are investigated using the ATLAS detector. A cut based selection is optimized with a typical GMSB signal to maximize the reduction of the Standard Model background with respect to the signal. For the first time an estimation of the discovery potential in the GMSB parameter space using τ leptons is done. In addition, the invariant mass distribution of two τ leptons is used to study the masses of the supersymmetric particles.
Machine Learning‐Derived Inference of the Meridional Overturning Circulation From Satellite‐Observable Variables in an Ocean State Estimate

The oceanic Meridional Overturning Circulation (MOC) plays a key role in the climate system, and monitoring its evolution is a scientific priority. Monitoring arrays have been established at several latitudes in the Atlantic Ocean, but other latitudes and oceans remain unmonitored for logistical reasons. This study explores the possibility of inferring the MOC from globally‐available satellite measurements via machine learning (ML) techniques, using the ECCOV4 state estimate as a test bed. The methodological advantages of the present approach include the use purely of available satellite data, its applicability to multiple basins within a single ML framework, and the ML model simplicity (a feed‐forward fully connected neural network (NN) with small number of neurons). The ML model exhibits high skill in MOC reconstruction in the Atlantic, Indo‐Pacific, and Southern Oceans. The approach achieves a higher skill in predicting the model Southern Ocean abyssal MOC than has previously been achieved via a dynamically‐based approach. The skill of the model is quantified as a function of latitude in each ocean basin, and of the time scale of MOC variability. We find that ocean bottom pressure generally has the highest reconstruction skill potential, followed by zonal wind stress. We additionally test which combinations of variables are optimal. Furthermore, ML interpretability techniques are used to show that high reconstruction skill in the Southern Ocean is mainly due to (NN processing of) bottom pressure variability at a few prominent bathymetric ridges. Finally, the potential for reconstructing MOC strength estimates from real satellite measurements is discussed.
Global Finite-Time Stabilization for Nonlinear Systems

This paper studies the problem of global finite-time stabilization of output feedback of a class of uncertain nonlinear system and the linearization of this system is not observable.By extending the adding-a-power-integration technique and a special observer,an output feedback controller is accurately designed and the stabilization of this system in finite time is realized.
Recent Trends on Mitigative Effect of Probiotics on Oxidative-Stress-Induced Gut Dysfunction in Broilers under Necrotic Enteritis Challenge: A Review

Gut health includes normal intestinal physiology, complete intestinal epithelial barrier, efficient immune response, sustained inflammatory balance, healthy microbiota, high nutrient absorption efficiency, nutrient metabolism, and energy balance. One of the diseases that causes severe economic losses to farmers is necrotic enteritis, which occurs primarily in the gut and is associated with high mortality rate. Necrotic enteritis (NE) primarily damages the intestinal mucosa, thereby inducing intestinal inflammation and high immune response which diverts nutrients and energy needed for growth to response mediated effects. In the era of antibiotic ban, dietary interventions like microbial therapy (probiotics) to reduce inflammation, paracellular permeability, and promote gut homeostasis may be the best way to reduce broiler production losses. The current review highlights the severity effects of NE; intestinal inflammation, gut lesions, alteration of gut microbiota balance, cell apoptosis, reduced growth performance, and death. These negative effects are consequences of; disrupted intestinal barrier function and villi development, altered expression of tight junction proteins and protein structure, increased translocation of endotoxins and excessive stimulation of proinflammatory cytokines. We further explored the mechanisms by which probiotics mitigate NE challenge and restore the gut integrity of birds under disease stress; synthesis of metabolites and bacteriocins, competitive exclusion of pathogens, upregulation of tight junction proteins and adhesion molecules, increased secretion of intestinal secretory immunoglobulins and enzymes, reduction in pro-inflammatory cytokines and immune response and the increased production of anti-inflammatory cytokines and immune boost via the modulation of the TLR/NF-ĸ pathway. Furthermore, increased beneficial microbes in the gut microbiome improve nutrient utilization, host immunity, and energy metabolism. Probiotics along with biosecurity measures could mitigate the adverse effects of NE in broiler production.
Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey

Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous driving. In recent years, more researchers have tried applying Deep Learning methods and techniques to VTP tasks. However, due to the black-box nature of Deep Learning, it cannot meet the interpretability and safety requirements of autonomous driving systems. Researchers have tried alleviating this problem by introducing driving knowledge in Deep Learning-based VTP. From the perspective of introducing driving knowledge, this paper systematically investigates the research status of DL-based VTP. First of all, this paper summarizes the research on VTP under three different problem formulations; secondly, this paper summarizes the application methods and application stages of driving knowledge in DL-based VTP; finally, this paper investigates and analyzes the VTP datasets and evaluation, and summarizes the knowledge contained in the datasets and its usage. Through the investigation and summary of problem formulation, knowledge usage, datasets, and evaluation of DL-based VTP, this paper analyzes the challenges and open questions of existing VTP research. It puts forward an outlook on future research directions.
A Perspective of Decarbonization Pathways in Future Buildings in the United States

The commitment of electrification and decarbonization goals in the United States (U.S.) will significantly change the performance of future buildings. To meet these goals, it is critical to summarize the existing research related to building electrification and decarbonization and discuss future research pathways. This paper provides a perspective on decarbonization pathways of future buildings in the U.S. A critical review of the existing research was conducted, which is divided into three closely linked categories: technologies, economic impacts, and code regulations. Technologies support investments and code regulations while marketing affects the design of building codes and standards. In the meantime, code regulations guide the development of technologies and marketing. Based on the review, future potential research directions for building decarbonization are then discussed. Due to the needs of building decarbonization, future research will be multidisciplinary, conducted at a large geographic scale, and involve a multitude of metrics, which will undoubtedly introduce new challenges. The perspective presented in this paper will provide policy-makers, researchers, building owners, and other stakeholders with a way to understand the impact of electrification and decarbonization of future buildings in the U.S.
Alcohol Septal Ablation in Patients with Hypertrophic Obstructive Cardiomyopathy: A Contemporary Perspective

Alcohol septal ablation is a minimally invasive procedure for the treatment of left ventricular outflow tract (LVOT) obstruction in patients with hypertrophic obstructive cardiomyopathy (HOCM) who remain symptomatic despite optimal medical therapy. The procedure causes a controlled myocardial infarction of the basal portion of the interventricular septum by the injection of absolute alcohol with the aim of reducing LVOT obstruction and improving the patient’s hemodynamics and symptoms. Numerous observations have demonstrated the efficacy and safety of the procedure, making it a valid alternative to surgical myectomy. In particular, the success of alcohol septal ablation depends on appropriate patient selection and the experience of the institution where the procedure is performed. In this review, we summarize the current evidence on alcohol septal ablation and highlight the importance of a multidisciplinary approach involving a team of clinical and interventional cardiologists and cardiac surgeons with high expertise in the management of HOCM patients—the Cardiomyopathy Team.
Iodine doping of CsPbBr3: toward highly stable and clean perovskite single crystals for optoelectronic applications

ABSTRACT Caesium-based mixed halide perovskite single crystals, CsPbBr3-xIx, are shown to be good candidates for many optoelectronic applications under ambient conditions. The partial substitution of iodine into the bromine sites of CsPbBr3 single crystal makes attractive effects in optical and structural properties as well as their thermal and phase stability. Herein, the Inverse Temperature Crystallization (ITC) method has been utilised to grow high-quality, large, and single-phase single crystals of CsPbBr2.4I0. 6 perovskites. The iodine doping is found to result in improved purity, thermal, phase, and atmospheric stability of the single crystal and helped to tune the band gap. Further, there is a conspicuous decrease in the defect density with iodine doping. Thus the present study establishes iodine doping as a facile route to grow CsPbBr3 single crystals with overall stability and improved defect density with a broad absorption spectrum, that point toward extended optoelectronic and photovoltaic applications. GRAPHICAL ABSTRACT
Study on the Effect of Rural Low Carbon Logistics Industry Development on Rural Economic Growth

Development of the rural low-carbon logistics industry has become the main factor affecting the rapid development of China’s rural economy. From the perspective of a low-carbon economy, based on the analysis of the development level of rural low-carbon logistics, this study uses the theory and method of spatial measurement to analyze the interactive effect of rural low-carbon logistics development and rural economic construction in 31 provinces of China from 2002 to 2019 and then analyzes the mechanism of different factors on rural economic development. The results show that China’s low-carbon rural logistics development provinces have an obvious geographical agglomeration trend. The main distribution characteristics of rural low-carbon logistics development in China’s provinces and regions are “high-high” clustering and “low-low” clustering. Second, there is a long-term and sustainable interaction between the development of China’s low-carbon rural logistics industry and rural economic growth. Third, the income of rural residents in the eastern, central, and western regions has an important positive impact on rural economic growth. In the eastern region, financial industry growth has the largest effect on rural economic growth. In the central region, per capita income and education level of farmers have the greatest impact on rural economic growth. In the western region, the increase in income is the most important factor influencing low-carbon logistics in rural economic growth.
Study and application of orthogonal-genetic algorithm test method

To solve the problem of optimization design of parallel robot manipulators,an optimization method based on Orthogonal Experiment Method and Genetic Algorithm is proposed.On the basis of discussing the basic principles of the above two methods,compares in the aspects of the ways to hunt optimal solution and corresponding relationships among the parameters are conducted and then the method to construct the initial population of generic algorithm with orthogonal table is introduced.A method of "one generation" orthogonal-genetic algorithm test method that is applicable to greater design variables and unmanageable fit function is put forward.This method can be applied in structural optimization design of a new type of four DOF parallel robot manipulators and the optimal scheme of the mechanism's geometric dimensions can be obtained by taking the global condition index of mechanism as objective functions.Illustrations have demonstrated this method effective.
Image Segmentation Based upon Bounded Histogram and Its Application to Sonar Image Segmentation

Image segmentation is an important problem on pattern recognition and one of the classical difficult problems,and sonar image segmentation is still an important problem because of bad quality. The concept of the bounded histogram is developed from the concept of histogram. Otsu's method is a good thresholding method. The bounded histogram is applied to Otsu's method and an image segmentation method based upon the bounded histogram,is proposed.Its principle is expatiated on and its feasibility is demonstrated by an example of sonar image segmentation.
Une classe de variétés semi-kählériennes

Sur une variete C-analytique compacte M avec dim C = m, on donne des exemples de (m - 1, m - 1)-formes positives (au sens ou leur integrale sur un morceau d'hypersurface complexe est toujours positive) et d-fermee sur une variete C-analytique compacte qui est malgre tout d-exacte.
The Decentralized Guaranteed Cost Control of Descriptor System

This paper is concerned with the problem of decentralized guaranteed cost control for descriptor system. For a general decentralized control system of descriptor system, we construct a integral cost function, and present a decentralized guaranteed cost control law. By using Lyapunov method, we prove that the closed loop system of descriptor system is asymptotic stable and the limited condition is satisfied for the cost function, simultaneously, the least upper bound is given. At last, a sufficient condition of existence of decentralized guaranteed cost state feedback controllers is propose. Then we prove that the design of this controller can be transformed into a problem of finding a feasible solution of a set of linear matrix inequalities.
Bayesian nonparametric methods for non-exchangeable data

Bayesian nonparametric methods have become increasingly popular in machine learning for their ability to allow the data to determine model complexity. In particular, Bayesian nonparametric versions of common latent variable models can learn an effective dimension of the latent space. Examples include mixture models, latent feature models and topic models, where the number of components, features, or topics need not be specified a priori. A drawback of many of these models is that they assume the observations are exchangeable, that is, any dependencies between observations are ignored. This thesis contributes general methods to incorporate covariates into Bayesian nonparametric models and inference algorithms to learn with these models. First, we will present a flexible class of dependent Bayesian nonparametric priors to induce covariate-dependence into a variety of latent variable models used in machine learning. The proposed framework has nice analytic properties and admits a simple inference algorithm. We show how the framework can be used to construct a covariate-dependent latent feature model and a time-varying topic model. Second, we describe the first general purpose inference algorithm for a large family of dependent mixture models. Using the idea of slice-sampling, the algorithm is truncation-free and fast, showing that inference can be done efficiently despite the added complexity that covariate-dependence entails. Last, we construct a Bayesian nonparametric framework to couple multiple related latent variable models and apply the framework to learning from multiple views of data.
Incremental Control Synthesis for Robotics in the Presence of Temporal Logic Specifications

This thesis presents methods for incrementally constructing controllers in the presence of uncertainty and nonlinear dynamics. The basic setting is motion planning subject to temporal logic specifications. Broadly, two categories of problems are treated. The first is reactive formal synthesis when so-called discrete abstractions are available. The fragment of linear-time temporal logic (LTL) known as GR(1) is used to express assumptions about an adversarial environment and requirements of the controller. Two problems of changes to a specification are posed that concern the two major aspects of GR(1): safety and liveness. Algorithms providing incremental updates to strategies are presented as solutions. In support of these, an annotation of strategies is developed that facilitates repeated modifications. A variety of properties are proven about it, including necessity of existence and sufficiency for a strategy to be winning. The second category of problems considered is non-reactive (open-loop) synthesis in the absence of a discrete abstraction. Instead, the presented stochastic optimization methods directly construct a control input sequence that achieves low cost and satisfies a LTL formula. Several relaxations are considered as heuristics to address the rarity of sampling trajectories that satisfy an LTL formula and demonstrated to improve convergence rates for Dubins car and single-integrators subject to a recurrence task.
Assessment of satisfaction in peritoneal equilibration test: a study on the validity and reliability of the peritoneal equilibration satisfaction scale

Aim: This study has been designed to develop an assessment tool to be used in determining the patients’ satisfaction level with the peritoneal equilibration test (PET) procedure. Materials and Methods: The development and validation of the peritoneal equilibration test Satisfaction Scale (PETSS) was completed in two phases. Phase I focused on instrument construction and included item development and establishment of concurrent validity. Phase II included the factor analysis and psychometric assessment of the scale. In statistical evaluation of the data descriptive statistics and non-paratmetric tests were used. Results: The first version of the scale that has 3.62 Content Validity Index value was composed of 20 items. It was found that the latest version of the scale that has 14 items explained 46% of the variance. It was found that the Cronbach alfa value of this scale, which has 0.52-0.89 coefficient of item-total correlation was 0.96. Psychometric assessment of the scale revealed that except for type of the PET application, none of the demographic and clinical characteristics effect patients level of satisfaction during the PET application. Conclusion: This preliminary study showed that PETSS was a valid and reliable scale that can be used for determining satisfaction level of patients during PET application.
In-orbit performance of SXT aboard AstroSat

A soft X-ray focusing Telescope (SXT) was launched in a near Earth, near equatorial orbit aboard the AstroSat on September 28th, 2015. The SXT electronics was switched on within 3 days of the launch and the first light was seen on October 26th, 2015 after a sequence of operations involving venting of the camera, cooling of the CCD, opening of the telescope door followed by the opening of the camera door. Several cosmic X-ray sources have been observed since then during the Performance Verification phase. A few near-simultaneous observations have also been carried out with the Swift observatory. The in-orbit performance of the SXT based on these observations is presented here.
Whatsapp in Sierra Leone Politics: Burning Bridges or Building Them?

ABSTRACT:The text messaging application WhatsApp has been heavily criticized for acting as a vehicle for the spread of misinformation and unsubstantiated rumor, leading, in some parts of the world, to violence and even death. But the closed nature of WhatsApp groups has presented a structural problem as a subject for credible social science research. A collaborative project between the Universities of Bedfordshire (UK) and Sierra Leone has tracked messaging in an experimental student WhatsApp group using critical discourse analysis in order to generate a deeper understanding of discursive influences in a fragile society. It asks whether the affordance of a WhatsApp group necessarily amplifies offline polarizations and explores routes to consensuality in a divided post-conflict state. It concludes that more robust interventions by group administrators could foster free speech while avoiding the need for intrusive regulation from outside agencies.
Simple Tuning of Discrete PI and PID Controllers

The paper investigates the effectiveness of a simple procedure for tuning discrete PI and PID controllers applied to processes subject to set point changes and load disturbances. Previous work is extended to obtain closed-loop stability properties and a complete set of graphical tuning relationships to minimise the ITAE performance index when the process under control can be represented adequately by a single time constant and time delay. In addition, the simple tuning procedure is shown to exhibit satisfactory performance when applied to the control of a process represented by an eighth order transfer function.