ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
An Accurate Process-Induced Variability-Aware Compact Model-Based Circuit Performance Estimation for Design-Technology Co-Optimization

In sub-10-nm fin field-effect transistors (FinFETs), line-edge roughness (LER) and metal-gate granularity (MGG) are the two most dominant sources of variability and are mostly modeled semi-empirically. In this work, compact models of LER and MGG are used. We show an accurate process-induced variability (PIV)-aware compact model-based circuit performance estimation for design-technology co-optimization (DTCO). This work is carried out using an experimentally validated Berkeley Short-channel IGFET Model-Common MultiGate (BSIM-CMG) model on a 7-nm FinFET node. First, we have shown performance benchmarking of LER and MGG models with the state of the art and shown similar to 4x (similar to 2.3x) accuracy improvement for nMOS (pMOS) in the estimation of device figure of merits (DFoMs). Second, ring oscillator (RO) and static random-access memory (SRAM) circuit's performance estimation is carried out for LER and MGG variability. Furthermore, similar to 22% more optimistic estimate of (sigma/mu)(SHM) (static hold margin) compared to the state-of-the-art model with V-DD variation is shown. Finally, we demonstrate our improved DFoM accuracy translated to more accurate circuit figure of merits (CFoMs) performance estimation. For worst-case SHM (3(sigma/mu)(SHM)@V-DD = 0.75 V) compared to state of the art, dynamic (standby) power reduction by similar to 73% (similar to 61%) is shown. Thus, our enhanced variability model accuracy enables more credible DTCO with significantly better performance estimates.
M/G/1-type Markov processes: A tutorial

M/G/1-type processes are commonly encountered when modeling modern complex computer and communication systems. In this tutorial, we present a detailed survey of existing solution methods for M/G/1-type processes, focusing on the matrix-analytic methodology. From first principles and using simple examples, we derive the fundamental matrix-analytic results and lay out recent advances. Finally, we give an overview of an existing, state-of-the-art software tool for the analysis of M/G/1-type processes.
A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems

Lenders, such as banks and credit card companies, use credit scoring models to evaluate the potential risk posed by lending money to customers, and therefore to mitigate losses due to bad credit. The profitability of the banks thus highly depends on the models used to decide on the customer's loans. State-of-the-art credit scoring models are based on machine learning and statistical methods. One of the major problems of this field is that lenders often deal with imbalanced datasets that usually contain many paid loans but very few not paid ones (called defaults). Recently, dynamic selection methods combined with ensemble methods and preprocessing techniques have been evaluated to improve classification models in imbalanced datasets presenting advantages over the static machine learning methods. In a dynamic selection technique, samples in the neighborhood of each query sample are used to compute the local competence of each base classifier. Then, the technique selects only competent classifiers to predict the query sample. In this paper, we evaluate the suitability of dynamic selection techniques for credit scoring problem, and we present Reduced Minority k-Nearest Neighbors (RMkNN), an approach that enhances state of the art in defining the local region of dynamic selection techniques for imbalanced credit scoring datasets. This proposed technique has a superior prediction performance in imbalanced credit scoring datasets compared to state of the art. Furthermore, RMkNN does not need any preprocessing or sampling method to generate the dynamic selection dataset (called DSEL). Additionally, we observe an equivalence between dynamic selection and static selection classification. We conduct a comprehensive evaluation of the proposed technique against state-of-the-art competitors on six real-world public datasets and one private one. Experiments show that RMkNN improves the classification performance of the evaluated datasets regarding AUC, balanced accuracy, H-measure, G-mean, F-measure, and Recall. (C) 2020 Elsevier Ltd. All rights reserved.
A comprehensive approach to winery wastewater treatment: a review of the state-of the-art

Winery industries generate large volumes of high-strength wastewater whose characteristics greatly vary depending on either seasons, production technologies or scale of the wineries. Winery wastewater (WW) is persistent to degrade by means of the conventional activated sludge process because of the high organic loading and polyphenolic content especially during vintage. To face this situation, a number of processes have recently been attempted as alternatives or integrative to biological treatments. However, there is still no agreement on the best practice to treat WW. Despite even more stringent standards, untreated or partially treated effluents continue to be improperly discharged into aquatic or soil matrixes, influencing microbial communities and physicochemical soil properties. This work presents a review on the state-of-the-art of management of wastewater originated from winery industries. Advantages and drawbacks of the treatment technologies at bench-, pilot-, and full-scale applications in the scientific literature have been considered to draw out a sustainable management scheme.
Pareto Optimal Design for Synthetic Biology

Recent advances in synthetic biology call for robust, flexible and efficient in silico optimization methodologies. We present a Pareto design approach for the bi-level optimization problem associated to the overproduction of specific metabolites in Escherichia coli. Our method efficiently explores the high dimensional genetic manipulation space, finding a number of trade-offs between synthetic and biological objectives, hence furnishing a deeper biological insight to the addressed problem and important results for industrial purposes. We demonstrate the computational capabilities of our Pareto-oriented approach comparing it with state-of-the-art heuristics in the overproduction problems of i) 1,4-butanediol, ii) myristoyl-CoA, iii) malonyl-CoA, iv) acetate and v) succinate. We show that our algorithms are able to gracefully adapt and scale to more complex models and more biologically-relevant simulations of the genetic manipulations allowed. The Results obtained for 1,4-butanediol overproduction significantly outperform results previously obtained, in terms of 1,4-butanediol to biomass formation ratio and knock-out costs. In particular overproduction percentage is of +662.7%, from 1.425 mmolh(-1) gDW(-1) (wild type) to 10.869 mmolh(-1) gDW(-1), with a knockout cost of 6. Whereas, Pareto-optimal designs we have found in fatty acid optimizations strictly dominate the ones obtained by the other methodologies, e.g., biomass and myristoyl-CoA exportation improvement of +21.43% (0.17 h(-1)) and +5.19% (1.62 mmol h(-1)gDW(-1)),respectively. Furthermore CPU time required by our heuristic approach is more than halved. Finally we implement pathway oriented sensitivity analysis, epsilon-dominance analysis and robustness analysis to enhance our biological understanding of the problem and to improve the optimization algorithm capabilities.
Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study

The automation of robotic processes has been experiencing an increasing trend of interest in recent times. However, most of literature describes only theoretical foundations on RPA or industrial results after implementing RPA in specific scenarios, especially in finance and outsourcing. This paper presents a systematic mapping study with the aim of analyzing the current state-of-the-art of RPA and identifying existing gaps in both, scientific and industrial literature. Firstly, this study presents an in-depth analysis of the 54 primary studies which formally describe the current state of the art of RPA. These primary studies were selected as a result of the conducting phase of the systematic review. Secondly, considering the RPA study performed by Forrester, this paper reviews 14 of the main commercial tools of RPA, based on a classification framework defined by 48 functionalities and evaluating the coverage of each of them. The result of the study concludes that there are certain phases of the RPA lifecycle that are already solved in the market. However, the Analysis phase is not covered in most tools. The lack of automation in such a phase is mainly reflected by the absence of technological solutions to look for the best candidate processes of an organization to be automated. Finally, some future directions and challenges are presented.
What Drives the Assembly of Plant-associated Protist Microbiomes? Investigating the Effects of Crop Species, Soil Type and Bacterial Microbiomes

In a field experiment we investigated the influence of the environmental filters soil type (i.e. three contrasting soils) and plant species (i.e. lettuce and potato) identity on rhizosphere community assembly of Cercozoa, a dominant group of mostly bacterivorous soil protists. Plant species (14%) and rhizosphere origin (vs bulk soil) with 13%, together explained four times more variation in cercozoan beta diversity than the three soil types (7% explained variation). Our results clearly confirm the existence of plant species-specific protist communities. Network analyses of bacteria-Cercozoa rhizosphere communities identified scale-free small world topologies, indicating mechanisms of self-organization. While the assembly of rhizosphere bacterial communities is bottom-up controlled through the resource supply from root (secondary) metabolites, our results support the hypothesis that the net effect may depend on the strength of top-down control by protist grazers. Since grazing of protists has a strong impact on the composition and functioning of bacteria communities, protists expand the repertoire of plant genes by functional traits, and should be considered as 'protist microbiomes' in analogy to 'bacterial microbiomes'.
An Empirical Comparison of Combinatorial Testing, Random Testing and Adaptive Random Testing

We present an empirical comparison of three test generation techniques, namely, Combinatorial Testing (CT), Random Testing (RT) and Adaptive Random Testing (ART), under different test scenarios. This is the first study in the literature to account for the (more realistic) testing setting in which the tester may not have complete information about the parameters and constraints that pertain to the system, and to account for the challenge posed by faults (in terms of failure rate). Our study was conducted on nine real-world programs under a total of 1683 test scenarios (combinations of available parameter and constraint information and failure rate). The results show significant differences in the techniques' fault detection ability when faults are hard to detect (failure rates are relatively low). CT performs best overall; no worse than any other in 98 percent of scenarios studied. ART enhances RT, and is comparable to CT in 96 percent of scenarios, but its computational cost can be up to 3.5 times higher than CT when the program is highly constrained. Additionally, when constraint information is unavailable for a highly-constrained program, a large random test suite is as effective as CT or ART, yet its computational cost of test generation is significantly lower than that of other techniques.
Horseshoe Kidney With a Documented Giant Calculi: A Case Report

The horseshoe kidney is the most frequent genitourinary fusion abnormality. The horseshoe kidney is a combination of the anatomical abnormalities of ectopia and malrotation. Along with other anomalies, it is linked to malrotations, fluctuating blood flow, high ureter insertion, a tendency to establish a ureteropelvic junction, and blockage in up to one-third of patients, and these are all symptoms of this condition. Kidney calculus and pelvic ureteric junction (PUJ) obstruction are one of horseshoe kidneys' most prevalent side effects and are seen in approximately one-third of the patients. In our case report, we discuss the treatment of a 61-year-old male patient who had been complaining of abdominal pain for the past few years, was found to have a horseshoe kidney, a history of recurrent renal calculi with a non-functioning right side portion, and recurrent urinary tract infections (UTI) treated with open surgery. The patient symptomatically alleviated his symptoms at the three-month follow-up after open surgery; there were no complaints of discomfort or abdominal fullness, and the patient resumed daily routines.
Extracellular Vesicles in Cancer Drug Resistance: Roles, Mechanisms, and Implications

Extracellular vesicles (EVs) are cell-derived nanosized vesicles that mediate cell-to-cell communication via transporting bioactive molecules and thus are critically involved in various physiological and pathological conditions. EVs contribute to different aspects of cancer progression, such as cancer growth, angiogenesis, metastasis, immune evasion, and drug resistance. EVs induce the resistance of cancer cells to chemotherapy, radiotherapy, targeted therapy, antiangiogenesis therapy, and immunotherapy by transferring specific cargos that affect drug efflux and regulate signaling pathways associated with epithelial-mesenchymal transition, autophagy, metabolism, and cancer stemness. In addition, EVs modulate the reciprocal interaction between cancer cells and noncancer cells in the tumor microenvironment (TME) to develop therapy resistance. EVs are detectable in many biofluids of cancer patients, and thus are regarded as novel biomarkers for monitoring therapy response and predicting prognosis. Moreover, EVs are suggested as promising targets and engineered as nanovehicles to deliver drugs for overcoming drug resistance in cancer therapy. In this review, the biological roles of EVs and their mechanisms of action in cancer drug resistance are summarized. The preclinical studies on using EVs in monitoring and overcoming cancer drug resistance are also discussed.
Development trends and review of free-machining steels

This work presents the technologies more widely used to improve the machinability of metal materials, mainly focusing on the free-machining steels. A wide literature and patent review are included with the purpose of presenting the state of the art on free-machining steels.
Degradation Mechanisms of High-Power LEDs for Lighting Applications: An Overview

This paper reports on the degradation mechanisms that limit the reliability of high-power light-emitting diodes (LEDs) for lighting applications. The study is based on the experimental characterization of state-of-the-art LEDs fabricated by leading manufacturers. We demonstrate that, despite high potential reliability, high-power LEDs may suffer from a number of degradation mechanisms that affect the stability of the blue semiconductor LED chip and of the phosphor layer used for the generation of white light. More specifically, we describe the following relevant mechanisms: 1) the optical degradation of LEDs, due to an increase in the nonradiative recombination rate, which can be correlated to modifications in the forward-bias current-voltage characteristics; 2) the variation in forward voltage, due to the increase in series resistance; 3) the optical degradation of phosphor layers used for blue-to-white light conversion; and 4) the failure of LEDs submitted to "hot plugging," which is the direct connection of an LED chain to an energized power supply, due to the generation of high current spikes. Results provide an overview on the failure mechanisms that limit the reliability of state-of-the-art LEDs and on the role of current and temperature in determining the failure of the devices.
Single Image Super-Resolution Based on Wiener Filter in Similarity Domain

Single image super-resolution (SISR) is an ill-posed problem aiming at estimating a plausible high-resolution (HR) image from a single low-resolution image. Current state-of-the-art SISR methods are patch-based. They use either external data or internal self-similarity to learn a prior for an HR image. External data-based methods utilize a large number of patches from the training data, while self-similarity-based approaches leverage one or more similar patches from the input image. In this paper, we propose a self-similarity-based approach that is able to use large groups of similar patches extracted from the input image to solve the SISR problem. We introduce a novel prior leading to the collaborative filtering of patch groups in a 1D similarity domain and couple it with an iterative back-projection framework. The performance of the proposed algorithm is evaluated on a number of SISR benchmark data sets. Without using any external data, the proposed approach outperforms the current non-convolutional neural network-based methods on the tested data sets for various scaling factors. On certain data sets, the gain is over 1 dB, when compared with the recent method A+. For high sampling rate (x4), the proposed method performs similarly to very recent state-of-the-art deep convolutional network-based approaches.
The Cost of Resistance to Diamide Insecticide Varies With the Host Plant in Spodoptera frugiperda (Lepidoptera: Noctuidae)

Fitness costs associated with insect resistance to insecticides can be exploited to implement resistance management programs. However, most of these studies are restricted to evaluating biological traits on artificial diets. Here, we investigated the fitness cost associated with chlorantraniliprole in Spodoptera frugiperda (J.E. Smith) feeding on corn, soybean, and cotton plants. We used a near-isogenic strain of S. frugiperda resistant to chlorantraniliprole (Iso-RR), a susceptible strain (SS), and heterozygotes strains (H1 and H2) to evaluate several biological and population growth parameters. Larval survival of the Iso-RR strain was on average 90% on corn, 65% on soybean, and 57% on cotton plants. Development time of the larval stage also differed among host plants, Iso-RR strain took on average 14, 17, and 26 days to reach the pupal stage on corn, soybean, and cotton plants respectively. Net reproductive rate, intrinsic rate of population increase, and finite rate of population increase were higher for Iso-RR strain feeding on corn plants than other host plants. The relative fitness, based on the intrinsic rate of population increase, of S. frugiperda resistant strain on corn, soybean, and cotton plants were 1.04, 0.85, and 0.88, respectively. Therefore, no fitness cost was observed for S. frugiperda feeding on corn plants, but a significant fitness cost was observed when this pest fed on soybean and cotton plants. We showed that the food source influences the fitness cost of S. frugiperda resistant to diamide. Such information may help to implement resistance management strategies based on each crop.
Genotoxic effect of two inks used in the art of tattoo

The present study provides answers about the genotoxic effect of two inks used in the art of tattooing. Currently, there is no information on the genotoxic effects that some of the chemical compounds in this inks can produce on human health. The objective of this study was to know the genotoxic effects of black and white ink, mostly used in tattoo art. In this investigation, the micronucleus test was applied in peripheral blood cells of CD-1 mice and the structural chromosomal aberration test in human lymphocyte cultures. The results obtained indicated that the average values of both the dividing cells (% IM), the frequency of micronuclei (% MCN) in the peripheral blood cells of the mice and the average number of structural chromosomal aberrations in human lymphocyte cells showed significant differences, therefore they were subjected to Dunnett's multiple comparison test analysis (p < 0.05). It was concluded that the chemical components of the white and black inks have cytotoxic effects in peripheral blood cells of the CD-1 mouse and human lymphocyte cells, while the chemical compounds of the two inks produce genotoxic effects in peripheral blood cells of CD-1 mice and only the white ink had genotoxic effects on human lymphocyte cells at a 10 mg/ml concentration.
Large-scale online learning of implied volatilities

Online-learning-based approaches do not suffer from generalization errors because the data are used once anddiscarded rather than reused. This characteristic enables incredible accuracy in estimating implied volatilitiesover a wide input area, outperforming existing state-of-the-art studies. In addition, an iterative method furtherimproves the estimates. The iterative method takes the estimate as an initial guess and corrects it, quicklygiving a virtually true value. Additionally, optimizing the network structure with TensorRT significantlyreduces the time spent on these processes
Management of patients with lymphoma and COVID-19: Narrative review and evidence-based practical recommendations

Patients with hematologic malignancies can be immunocompromized because of their disease, anti-cancer therapy, and concomitant immunosuppressive treatment. Furthermore, these patients are usually older than 60 years and have comorbidities. For all these reasons they are highly vulnerable to infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and have an increased risk of developing severe/critical Coronavirus disease 2019 (COVID-19) compared to the general population. Although COVID-19 vaccination has proven effective in reducing the incidence of severe/critical disease, vaccinated patients with lymphoma may not be protected as they often fail to develop a sufficient antiviral immune response. There is therefore an urgent need to address the management of patients with lymphoma and COVID-19 in the setting of the ongoing pandemic. Passive immunization with monoclonal antibodies against SARS-CoV-2 is a currently available complementary drug strategy to active vaccination for lymphoma patients, while monoclonal antibodies and antiviral drugs (remdesivir, ritonavir-boosted nirmatrelvir, and molnupiravir) have proven effective in preventing the progression to severe/critical COVID-19. In this narrative review we present the most recent data documenting the characteristics and outcomes of patients with concomitant lymphoma and COVID-19. Our ultimate goal is to provide practice-oriented guidance in the management of these vulnerable patients from diagnosis to treatment and follow-up of lymphoma. To this purpose, we will first provide an overview of the main data concerning prognostic factors and fatality rate of lymphoma patients who develop COVID-19; the outcomes of COVID-19 vaccination will also be addressed. We will then discuss current COVID-19 prophylaxis and treatment options for lymphoma patients. Finally, based on the literature and our multidisciplinary experience, we will summarize a set of indications on how to manage patients with lymphoma according to COVID-19 exposure, level of disease severity and former history of infection, as typically encountered in clinical practice.