ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems

The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part.
Exploiting Color Volume and Color Difference for Salient Region Detection

Foreground and background cues can assist humans in quickly understanding visual scenes. In computer vision, however, it is difficult to detect salient objects when they touch the image boundary. Hence, detecting salient objects robustly under such circumstances without sacrificing precision and recall can be challenging. In this paper, we propose a novel model for salient region detection, namely, the foreground-center background (FCB) saliency model. Its main highlights as follows. First, we use regional color volume as the foreground, together with perceptually uniform color differences within regions to detect salient regions. This can highlight salient objects robustly, even when they touched the image boundary, without greatly sacrificing precision and recall. Second, we employ center saliency to detect salient regions together with foreground and background cues, which improves saliency detection performance. Finally, we propose a novel and simple yet efficient method that combines foreground, center, and background saliency. Experimental validation with three well-known benchmark data sets indicates that the FCB model outperforms several stateof-the-art methods in terms of precision, recall, F-measure, and particularly, the mean absolute error. Salient regions are brighter than those of some existing state-of-the-art methods.
One stage lesion detection based on 3D context convolutional neural networks

Lesion detection from Computed Tomography (CT) scans is a challenge because non-lesions and true lesions always have similar appearances. Therefore, the performance of mainstream 2D image-based object detection algorithms is not promising since the texture and shape of inner-classes are always different. To detect lesions, we propose a novel deep convolutional feature fusion scheme, 3D Context Feature Fusion (3DCFF). Motivated by state-of-the-art object detection algorithms, we use a one-stage framework, rather than a Region Proposal Network, to extract lesions. In addition, because 3D context provides texture, contour, and shape information that are helpful for generating distinguishable lesion features, 3D context is used as the input for the proposed network. Furthermore, the network adopts a multi-resolution fusion scheme among different scales of feature maps. Results of experiments, conducted with the Deeplesion database, show that the proposed 3DCFF performs better and faster than state-of-the-art algorithms, such as Faster R-CNN, RetinaNet, and 3DCE. (C) 2019 Elsevier Ltd. All rights reserved.
Advances in numerical ditching simulation of flexible aircraft models

This paper deals with explicit numerical simulation of fixed-wing aircraft ditching using a coupled approach of Smoothed Particle Hydrodynamics and Finite Elements. Particular focus is put on recent advances toward simulation of flexible full aircraft models, which comprises significant challenges with respect to the numerical efficiency as well as the model complexity. First, the pursued numerical approach is briefly presented. In order to deal with aforementioned challenges, an automated modular tool, which generates numerical models and launches ditching simulations, is presented. The paper provides a brief explanation of state-of-the-art aircraft as well as fluid modelling techniques used and furthermore presents an integrated model that computes aerodynamic loads during the simulation. The developed tool is used to conduct various numerical studies. First, the improved efficiency of such simulations over the state of the art is shown. Next, results of parameter studies are presented, demonstrating the effects of impact conditions on the aircraft motion. Finally, the structural deformation experienced during ditching of a detailed finite element aircraft model is analysed and its effects on the aircraft motion are discussed.
RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement

Lesion volume segmentation in medical imaging is an effective tool for assessing lesion/tumor sizes and monitoring changes in growth. Since manually segmentation of lesion volume is not only time-consuming but also requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Although RECIST measurement is coarse compared with voxel-level annotation, it can reflect the lesion's location, length, and width, resulting in a possibility of segmenting lesion volume directly via RECIST measurement. In this study, a novel weakly-supervised method called RECISTSup is proposed to automatically segment lesion volume via RECIST measurement. Based on RECIST measurement, a new RECIST measurement propagation algorithm is proposed to generate pseudo masks, which are then used to train the segmentation networks. Due to the spatial prior knowledge provided by RECIST measurement, two new losses are also designed to make full use of it. In addition, the automatically segmented lesion results are used to supervise the model training iteratively for further improving segmentation performance. A series of experiments are carried out on three datasets to evaluate the proposed method, including ablation experiments, comparison of various methods, annotation cost analyses, visualization of results. Experimental results show that the proposed RECISTSup achieves the state-of-the-art result compared with other weakly-supervised methods. The results also demonstrate that RECIST measurement can produce similar performance to voxel-level annotation while significantly saving the annotation cost.
State-of-the-art of reverse osmosis desalination pretreatment

Pretreatment plays the critical role of removing source water constituents, like sediment and microbes, which could hinder the downstream reverse osmosis (RO) desalination process. While some source waters require negligible pretreatment, others like surface waters, require rigorous treatment to protect the RO process operation. The RO industry grew rapidly between 1995 and 2010. Growth pushed the industry to find cost-effective and robust large-scale pretreatment solutions. Over the last two decades, RO manufacturers have also developed membranes with greater fouling resistance and advocated system designs that reduce fouling potential. As a result, the state of the art in pretreatment has progressed significantly since the mid 1990s. Many of the improvements in pretreatment were enabled by a better understanding of fouling processes. Because fouling is complex and dynamic, with biofouling contributing to its complexity, significant research and development have been necessary to identify improvements. This paper provides a basis to understand the various fouling mechanisms found in RO systems and to describe the current state-of-the-art of the pretreatment technologies for fouling control. The paper addresses pretreatment of the myriad water sources in which RO technology is applied, with greater emphasis on seawater RO SWRO pretreatment as the largest single pretreatment market segment. (C) 2014 Elsevier B.V. All rights reserved.
fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs

Since neural networks renaissance, convolutional neural networks (ConvNets) have demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can provide a potential platform that can be tailored to application-specific requirements. However, with the complexity of ConvNet models increasing rapidly, the ConvNet-to-FPGA design space becomes prohibitively large. This paper presents fpgaConvNet, an end-to-end framework for the optimized mapping of ConvNets on FPGAs. The proposed framework comprises an automated design methodology based on the synchronous dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently navigate the architectural design space. By proposing a systematic multiobjective optimization formulation, the presented framework is able to generate hardware designs that are cooptimized for the ConvNet workload, the target device, and the application's performance metric of interest. Quantitative evaluation shows that the proposed methodology yields hardware designs that improve the performance by up to 6.65x over highly optimized graphics processing unit designs for the same power constraints and achieve up to 2.94x higher performance density compared with the state-of-the-art FPGA-based ConvNet architectures.
Single-Event Multiple Effect Tolerant RHBD14T SRAM Cell Design for Space Applications

Static Random Access Memory (SRAM) is primarily used as a memory storage element, which is susceptible to radiation-induced Single Event Upsets (SEUs). Hence, a robust SRAM bit-cell design is primarily a difficult task to address the space radiation environment. Furthermore, as the transistor's size moves into nanometer regimes, a new challenge like Single Event Multiple Effects (SEME's) evolved in SRAMs. SEME's make the design of SRAM a serious challenge. In this article, a novel Radiation Hardened By Design (RHBD) SRAM bit-cell is proposed based on the polarity upset mechanism of SEUs. This work shows that the proposed RHBD14T SRAM bit-cell is SEU immune and delivers higher SEME critical charge than state-of-the-art RHBD SRAM bit-cells. The Monte Carlo (MC) simulations further show that the proposed RHBD14T SRAM delivers the lower Probability of Failure when compared to reported RHBD SRAM cells. Consequently, the proposed bit cell's sensitive area is 128% lower with respect to the recently reported state-of-the-art RHBD RSP14T SRAM bit-cells.
A framework for advancing sustainable magnetic resonance imaging access in Africa

Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low-field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low-resource settings.
Deep Spatial Interpolation of Rain Field for UK Satellite Networks

This article presents two new state-of-the-art spatial rain field interpolation convolutional neural networks (SRFICNNs), referred to as learned deviation (LD) and learned interpolation (LI) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high-resolution (HR) rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models' perfor-mances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and structural SIMilarity (SSIM) in rainfall field reconstruction if the network depth falls in the range of 15-25 weight layers. The final model uses 20 layers for HR point rain rate prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this article compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimate of these two indices with the corresponding root mean square (rms) error of 5.1709 x 10(-4) and 0.0013, respectively.
The state of the art of electric and hybrid vehicles

In a world where environment protection and energy conservation are growing concerns, the development of electric vehicles (EV) and hybrid electric vehicles (HEV) has taken on an accelerated pace. The dream of having commercially viable EVs and HEVS is becoming a reality, EVs, and HEVs are gradually available in the market. This paper will provide an overview of the present status of electric and hybrid vehicles worldwide and their state of the art, with emphasis on the engineering philosophy and key technologies. The importance of the integration of technologies of automobile, electric motor drive, electronics, energy storage, and controls and also the importance of the, integration of society strength from government, industry, research institutions, electric power utilities, and transportation authorities are addressed. The challenge of EV commercialization is discussed.
Sparsity-Based Audio Declipping Methods: Selected Overview, New Algorithms, and Large-Scale Evaluation

Recent advances in audio declipping have substantially improved the state of the art. Yet, practitioners need guidelines to choose a method, and while existing benchmarks have been instrumental in advancing the field, larger-scale experiments are needed to guide such choices. First, we show that the clipping levels in existing small-scale benchmarks are moderate and call for benchmarks with more perceptually significant clipping levels. We then propose a general algorithmic framework for declipping that covers existing and new combinations of variants of state-of-the-art techniques exploiting time-frequency sparsity: synthesis vs. analysis sparsity, with plain or structured sparsity. Finally, we systematically compare these combinations and a selection of state-of-the-art methods. Using a large-scale numerical benchmark and a smaller scale formal listening test, we provide guidelines for various clipping levels, both for speech and various musical genres. The code is made publicly available for the purpose of reproducible research and benchmarking.
Probe current determination in analytical TEM/STEM and its application to the characterization of large area EDS detectors

A simple procedure, which enables accurate measurement of transmission electron microscopy (TEM)/STEM probe currents using an energy loss spectrometer drift tube is described. The currents obtained are compared with those measured on the fluorescent screen to enable the losses due to secondary and backscattered electrons to be determined. The current values obtained from the drift tube allow the correction of fluorescent screen current densities to yield true current. They also enable CCD conversion efficiencies to be obtained, which in turn allows images to be calibrated in terms of electron fluence. Using probes of known current in conjunction with a NiO reference specimen enables the X-ray detector solid angle to be determined. The NiO specimen also allows a wide range of other EDS detector parameters to be obtained, including the presence of ice and carbon contamination. A range of performance characteristics are reported for two large area EDS detector systems. Many of the measurements reported herein have been automated via the use of freely available scripts for DigitalMicrograph.
Environmental performance of Cantabrian (Northern Spain) pelagic fisheries: Assessment of purse seine and minor art fleets under a life cycle approach

The perpetuation of fishing activity from an environmentally, socially and economically sustainable approach is essen-tial to guarantee not only the future of coastal populations, but also the supply of high-value seafood for society and the safeguarding of cultural heritage.This article aims to assess the environmental performance associated with fishing fleet operations in Cantabria (north-ern Spain) under a life cycle thinking from a holistic approach. Thus, the Life Cycle Assessment (LCA) methodology was applied under a 'cradle-to-port' approach, setting the functional unit as 1 kg of fresh fish landed. Inventory data on the main inputs and outputs were collected from a sample of 57 vessels covering for the first time the main tech-niques, purse seine and minor art fisheries.The results identified that the vessel use stage was the responsible of most of the impacts. In line with the literature, diesel consumption stood as the chief hotspot in six of the seven impact categories analysed. Purse seiners got a value of 0.25 kg of fuel per kg of fish landed, while the performance of the minor art fleet showed significantly lower consumption (0.07). Regarding impacts on climate change, this study found a quantity of 1.00 and 0.34 kg CO2 eq. per FU, for purse seine and minor arts, respectively. These figures were consistent with the expected results for pelagic fisheries. For the remaining indicators, purse seiners generally performed worse.The LCA methodology provided outcomes that allow the proposal of potential improvements and measures to foster the transition towards a more sustainable smart-fishing sector. Further research efforts should focus on the develop-ment and implementation of renewable energy and low-carbon vessel propulsion technologies.
Potential of paracetamol for reproductive disruption: molecular interaction, dynamics, and MM-PBSA based in-silico assessment

Paracetamol is generally recommended for pain and fever. However, as per experimental and epidemiological data, widespread and irrational or long-term use of paracetamol may be harmful to human endocrine homeostasis, especially during pregnancy. Some researchers suggest that prenatal exposure to paracetamol might alter fetal development and also enhance the risk of reproductive disorders. An imbalance in the levels of these hormones may play a significant role in the emergence of various diseases, including infertility. Therefore, in this study, the interaction mechanism of paracetamol with reproductive hormone receptors was investigated by molecular docking, molecular dynamics (MD) simulations, and Poisson-Boltzmann surface area (MM-PBSA) for assessing paracetamol's potency to disrupt reproductive hormones. The results indicate that paracetamol has the ability to interact with reproductive hormone receptors (estrogen 1XP9; 1QKM with binding energy of -5.61 kcal/mol; -5.77 kcal/mol; androgen 5CJ6 - 5.63 kcal/mol; and progesterone 4OAR -5.60 kcal/mol) by hydrogen bonds as well as hydrophobic and van der Waals interactions to maintain its stability. In addition, the results of the MD simulations and MM-PBSA confirm that paracetamol and reproductive receptor complexes are stable. This research provides a molecular and atomic level understanding of how paracetamols disrupt reproductive hormone synthesis. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), Radius of Gyration and hydrogen bonding exhibited that paracetamol mimic at various attribute to bisphenol and native ligand.
Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost

We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new metric learning approach for the latter. We also introduce an extension of the NCM classifier to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over 10(6) training images of 1,000 classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally, we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art while being orders of magnitude faster. Furthermore, we show how a zero-shot class prior based on the ImageNet hierarchy can improve performance when few training images are available.