ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37 degrees C. The device features a limit of detection of 3.8 mu M, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.
Heat Management in Power Converters: From State of the Art to Future Ultrahigh Efficiency Systems

Thermal management is a key design aspect of power converters since it determines their reliability as well as their final performance and power density. Cooling technologies have been a research area in electronics since the 1940s and, in the last 15 years, the number of articles related to this field has grown significantly. At present, thermal management is essential in new disciplines and it is a critical enabling technology in the development of power electronic systems. This paper aims at presenting a review of the state-of-the-art technology and provides future design guidelines for high efficiency power electronic converters. The main design trends are focused on the need to develop cooling systems able to manage high local density heat fluxes due to two converging trends: higher power dissipation and smaller module size. Considering the latest advances in thermal management, as well as the huge improvement in power electronics in the last decades, a review and classification of the main thermal management techniques is presented. Besides, they are compared considering important parameters such as peak power dissipation, efficiency, cost/complexity, power density or technical maturity, and a design example for an ultrahigh efficiency converter is presented.
STATE-OF-ART, CHALLENGES, AND OUTLOOK ON MANUFACTURING OF COOLING HOLES FOR TURBINE BLADES

A cooling hole is important structure of turbine blades for high-performance aircraft engines. It is very challenging to manufacture cooling holes in superalloys including nickel-based and titanium alloys. This article aims to provide a critical assessment on the major types of machining processes for manufacturing cooling holes. The process mechanism, efficiency, form accuracy, and surface integrity of the state-of-art of four machining processes, i.e., mechanical drilling (MD), electrical discharge drilling (EDD), laser drilling (LD), and electrochemical drilling (ECD) have been thoroughly analyzed and compared in details. The future challenges and future potential research directions for the machining processes are also discussed.
SAKARYA SCIENCE AND ART CENTER NATURE EDUCATION PROGRAM

The aim of this study is to increase the awareness of the gifted and talented students about science and nature education according to their own learning properties. The study was carried out in July 2011, within the project 'Sakarya Arts and Sciences Centre Nature Education Program' with the code of 111B063 supported by the Scientific and Technological Research Counsil of Turkey (TUBITAK). A hundred and twenty gifted and talented students, each session composed of 30 students, have attended the research. In the study, pre-test, post-test, quasi-experimental design without control group was used. Also, nature education knowledge tests scrutinised by assessment and evaluation experts and instructors are used. Acquired data was analysed through statistical program for social sciences. It was observed that for all the students, the post-test scores are higher than the pre-test scores and a significant difference has been found as a result of the analyses. It is also determined that nature education program has a substantial impact on gifted and talented students awareness about nature education and science. The program prepared by considering the study results is believed to have enhanced the awareness to science and nature.
Assessment of the breadth of binding promiscuity of heme towards human proteins

Heme regulates important biological processes by transient interactions with many human proteins. The goal of the present study was to assess extends of protein binding promiscuity of heme. To this end we evaluated interaction of heme with &gt;9000 human proteins. Heme manifested high binding promiscuity by binding to most of the proteins in the array. Nevertheless, some proteins have outstanding heme binding capacity. Bioinformatics analyses revealed that apart from typical haemoproteins, these proteins are frequently involved in metal binding or have the potential to recognize DNA. This study can contribute for understanding the regulatory functions of labile heme.
Diel Periodicity in Males of the Navel Orangeworm (Lepidoptera: Pyralidae) as Revealed by Automated Camera Traps

Navel orangeworm, Amyelois transitella (Walker), is a key pest of walnuts, pistachio, and almonds in California. Pheromone mating disruption using timed aerosol dispensers is an increasingly common management technique. Dispenser efficiency may be increased by timing releases with the active mating period of navel orangeworm. Past work found that the peak time of sexual activity for navel orangeworm females is 2 h before sunrise when temperatures are above 18°C. Inference of male responsiveness from data collected in that study was limited by the necessity of using laboratory-reared females as a source of sex pheromone emission to attract males and the inherent limitations of human observers for nocturnal events. Here we used camera traps baited with artificial pheromone to observe male navel orangeworm mating response in the field over two field seasons. Male response to synthetic pheromone exhibited diel patterns broadly similar to females, i.e., they were active for a brief period of 2-3 h before dawn under summer conditions and began responding to pheromone earlier and over a longer period of time during spring and fall. But contrary to the previous findings with females, some males were captured at all hours of the day and night, and there was no evidence of short-term change of pheromone responsiveness in response to temperature. Environmental effects on the response of navel orangeworm males to an artificial pheromone source differ in important ways from the environmental effects on female release of sex pheromone.
A modular CNN-based building detector for remote sensing images

Convolutional neural networks (CNNs) have resurged lately due to their state-of-the-art performance in various disciplines, such as computer vision, audio and text processing. However, CNNs have not been widely employed for remote sensing applications. In this paper, we propose a CNN architecture, named Modular-CNN, to improve the performance of building detectors that employ Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) in a remote sensing dataset. Additionally, we propose two improvements to increase the classification accuracy of Modular-CNN. The first improvement combines the power of raw and normalised features, while the second one concerns the Euler transformation of feature vectors. We demonstrate the effectiveness of our proposed Modular-CNN and the novel improvements in remote sensing and other datasets in a comparative study with other state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
Search for Compatible Source Code

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9-60.9% in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4-11% in Precision.
Use of Water-In-Salt Concentrated Liquid Electrolytes in Electrochemical Energy Storage: State of the Art and Perspectives

Batteries based on organic electrolytes have been raising safety concerns due to some associated fire/explosion accidents caused by the unusual combination of highly flammable organic electrolytes and high energy electrodes. Nonflammable aqueous batteries are a good alternative to the current energy storage systems. However, what makes aqueous batteries safe and viable turns out to be their main weakness, since water molecules are prone to decomposition because of a narrow electrochemical stability window (ESW). In this perspective we introduce aqueous batteries and then discuss the state-of-the-art of water-in-salt (WIS) electrolytes for aqueous energy storage systems. The main strategies to improve ESW are reviewed, including: (i) the use of fluorinated salts to make a solid electrolyte interphase (SEI); (ii) the use of cost-effective and highly soluble salts to reduce water activity through super concentration; and (iii) the use of hybrid electrolytes combining the advantages of both aqueous and non-aqueous phases. Then, we discuss different battery chemistries operated with different WIS electrolytes. Finally, we highlight the challenges and future technological perspectives for practical aqueous energy storage systems, including applications in stationary storage/grid, power backup, portable electronics, and automotive sectors.
Working memory is updated by reallocation of resources from obsolete to new items

Visual working memory (VWM) resources are limited, placing constraints on how much visual information can be simultaneously retained. During visually guided activity, stored information can quickly become outdated, so updating mechanisms are needed to ensure the contents of memory remain relevant to current task goals. In particular, successful deallocation of resources from items that become obsolete is likely to be critical for maintaining the precision of those representations still in memory. The experiments in this study involved presenting two memory arrays of coloured disks in sequence. The appearance of the second array was a cue to replace, rehearse, or add a new colour to the colours in memory. We predicted that successful resource reallocation should result in comparable recall precision when an item was replaced or rehearsed, owing to the removal of pre-replacement features. In contrast, a failure to update WM should lead to comparable precision with a condition in which a new colour was added to memory. We identified a very small proportion (∼5%) of trials in which participants incorrectly reported a feature from the first array in place of its replacement in the second, which we interpreted as a failure to incorporate the information from the second display into memory. Once these trials were discounted, precision estimates were consistent with complete redistribution of resources in the case of updating a single item. We conclude that working memory can be efficiently updated when previous information becomes obsolete, but that this is a demanding active process that occasionally fails.
Image compressive sensing via Truncated Schatten-p Norm regularization

Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. (C) 2016 Elsevier B.V. All rights reserved.
EIDM: deep learning model for IoT intrusion detection systems

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results.
Social Model Hospice Homes: Bridging the Gap in End-of-Life Care Delivery

Background: People prefer to die at home, if given a choice. However, data show that less than half of hospice patients get to do so, as many lack the social resources needed for end-of-life (EOL) care to be supported at home. The Social Model Hospice Home (SMHh) is an emerging model of care and offers an option for individuals whose EOL care cannot be fully supported by their available social network. An SMHh is a community-supported home staffed with round the clock caregivers dedicated to closing this social gap in EOL care delivery. Objective: This study aimed to enhance and clarify the reasons for successful outcomes of the SMHh concept. Methods: This study used a qualitative descriptive approach. Twenty-three semistructured interviews were conducted with the participants. Located throughout North America, participants consisted of staff, volunteers, guests, family members of previous guests, board members, and administrators of SMHh programs. Results: Over the course of the study, five major themes were developed: (1) physical attributes of the home, (2) volunteer base, (3) attention to detail, (4) structure of accountability, and (5) practice of compassionate honesty. These themes reveal the beginning of an explanatory theory of why the SMHh concept can support the delivery of effective, high-quality EOL care. Conclusions: The result of this study substantiates SMHh as a viable alternative to traditional in-home or institution-based EOL care. As the SMHh movement develops, more research is needed to explore and understand how this emerging model of care can be implemented on a larger scale.
A two-stage robust iterative learning model predictive control for batch processes

Iterative learning model predictive control (ILMPC) has been considered as potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along batches and ensure system stability within batches. However, as a model-based control method, the control performance of the ILMPC algorithm deteriorates when exists model parameter uncertainty. Therefore, guaranteeing system tracking performance in the case of model parameter uncertainty is a challenging task in the framework designing of ILMPC method. To this end, we develop a two-stage robust ILMPC strategy for batch processes, which integrates the robust iterative learning control (ILC) in the domain of batch-axis and robust model predictive control (MPC) in the domain of time-axis into one comprehensive control scheme. The integrated control law of the developed two-stage robust ILMPC algorithm is obtained by solving two convex optimization problems. As a result, the developed control method obtains faster convergence speed and better tracking performance in the case of model parameter uncertainty. Moreover, the convergence analysis of the system is presented. Finally, comparative simulations are provided to verify the superiority of the developed control algorithm.
Life cycle impacts and costs of photovoltaic systems: Current state of the art and future outlooks

The photovoltaic energy sector is rapidly expanding and technological specification for PV has improved dramatically in the last two decades. This paper sketches the current state of the art and drafts three alternative scenarios for the future, in terms of costs, market penetration and environmental performance. According to these scenarios, if economic incentives are supported long enough into the next ten to twenty years, PV looks set for a rosy future, and is likely to play a significant role in the future energy mix, while at the same time contributing to reduce the environmental impact of electricity supply. (C) 2009 Elsevier Ltd. All rights reserved.
Camera-Vision-Based Water Level Estimation

This letter presents a camera-vision-based water level estimation method that uses a single image. Existing state-of-the-art multiple-image and airborne ultrasonic sensor-based water level estimation methods rely on a large number of image frames to stabilize the results given the heavy background jitter. In real environments, our proposed two-step histogram-based method is superior to performance of previous image and sensor-based methods in terms of efficiency, robustness, and accuracy.
Advanced and recurrent endometrial cancer: State of the art and future perspectives

Patients with primary metastatic/recurrent endometrial cancer have poor prognosis and available therapeutic options are limited. Current treatment is mainly based on platinum-based chemotherapy. Recently, the Food and Drug Administration (FDA) granted approval for the combination of pembrolizumab and lenvatinib for endometrial cancer patients without microsatellite instability (MSS) progressing on a previous line of therapy while European Medicines Agency (EMA) approved the combination for all comers patients failing previous platinum treatment. Anti programmed cell death protein-1 (PD-1) dostarlimab (TSR-042) was approved as monotherapy in patients with advanced, microsatellite instable (MSI) endometrial cancer progressing to platinum treatment. Phase II-III clinical trials in metastatic endometrial cancer are mainly focused on target therapies and immunotherapy as single agents or in combination. Unfortunately, most of these trials are lacking of predictive biomarkers of response to select patients most or at least likely to benefit from those treatments.
Post-processing approaches for improving people detection performance

People detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. We address one of the main problems of people detection, in video sequences: every people detector from the state of the art must maintain a balance between the number of false detections and the number of missing pedestrians. This compromise limits the global detection results. In order to reduce or relax this limitation and improve the detection results, we evaluate two different post-processing subtasks. Firstly, we propose the use of people-background segmentation as a filtering stage in people detection. Then, we evaluate the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results. And, finally, we evaluate the successive application of both post-processing approaches. Experiments have been performed on two extensive datasets and using different people detectors from the state of the art: the results show the benefits achieved using the proposed post-processing techniques. (C) 2014 Elsevier Inc. All rights reserved.
Sinus of Valsalva aneurysm: Defining the optimal approach

Sinus of Valsalva aneurysm (SVA) is relatively rare, especially in Western countries, and reports on long-term results after surgical SVA repair in a sizable patient cohort are scarce. In this issue of the Journal of Cardiac Surgery, Chaganti and colleagues publish their surgical experience over the past 30 years in 216 patients with SVA. SVAs were closed via a dual approach, with (1) patch closure (80%) or direct closure (20%) of the base of the fistula through aortotomy and (2) direct closure of the ruptured tip through the chamber of rupture. Aortic valve replacement (9.7%) or repair (6.5%) was performed for moderate to severe aortic regurgitation (AR). There was no hospital mortality. During a mean follow-up of 10 years, no patient had residual/recurrent shunting. The actual survival at 10 years was 99%, with only two deaths. Freedom from moderate or severe AR was 98.5% at 10 years. Early and long-term results after surgical repair of SVA were excellent in their 216 patients with a mean follow-up of 10 years. Their dual approach for SVA was effective in preventing residual/recurrent shunting. The need for AVR in 10% of the patients speaks to the importance of follow-up. The current report provides strong support for surgical repair being the preferred management for SVA.
State-of-the-art cerium nanoparticles as promising agents against human viral infections

The world is faces a significant global health challenge in the form of viral infections, particularly the emergence of viral strains that are resistant to effective antiviral therapies. This underscores the urgent need for the development of effective and safe antiviral agents. Nanoscale materials are now being used as novel antiviral agents. Cerium nanoparticles have unique chemical and physical properties that make them particularly promising for viral infections. These particles reduce inflammation and the autoimmune response. Cerium nanoparticles, in addition to their antiviral properties, have many other advantages that are highly sought after for various aspects of biomedical applications. This review focuses on the various properties of cerium nanoparticles as a novel agent against viral infections.