ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Parallel query execution over encrypted data in database-as-a-service (DaaS)

The main challenge in database-as-a-service is the security and privacy of data because service providers are not usually considered as trustworthy. So, the data must be encrypted before storing into the database. Another challenge arises that the performance is degraded on the deployment of encryption algorithm on runtime. Furthermore, the connectivity through the Internet adds more delay. To tackle this, we have proposed parallel query execution methodology using multithreading technique up to 6 threads. We have conducted experiments up to 1000,000 (1 million) encrypted records. Our results are quite promising. For data encryption/decryption, we have used advance encryption standard with blocking length of 256 bits. We have designed our methodology in the context of parallel computation method proposed in the literature (Ho et al., in: Proceedings of the 2017 international conference on machine learning and soft computing, pp 47-52, 2017). We compared the results with state-of-art algorithms. The state-of-art algorithms execute the experiments on 10,120 encrypted records maximum which took about time of 1000 ms with 2 threads. But the proposed methodology is proved outstanding that executed the experiments which were performed on 100,000 encrypted records. It outperformed with 6 threads which took only 507 ms even with 2 threads, and the proposed methodology is much better which took only 994 ms. So, the efficiency and scalability of the proposed methodology are proved better as compared to state-of-the-art algorithms.
Gaussian mixture reduction based on fuzzy ART for extended target tracking

This paper presents a global Gaussian mixture (GM) reduction algorithm via clustering for extended target tracking in clutter. The proposed global clustering algorithm is obtained by combining a fuzzy Adaptive Resonance Theory (ART) neural network architecture with the weighted Kullback-Leibler (KL) difference which describes discrimination of one component from another. Therefore, we call the proposed algorithm as ART-KL clustering (ART-KL-C) in the paper. The weighted KL difference is used as a category choice function of ART-KL-C, derived by considering both the la divergence between two components and their weights. The performance of ART-KL-C is evaluated by the normalized integrated squared distance (NISD) measure, which describes the deviation between the original and reduced GM. The proposed algorithm is tested on both one-dimensional and four-dimensional simulation examples, and the results show that the proposed algorithm can more accurately approximate the original mixture and is useful in extended target tracking. (C) 2013 Elsevier B.V. All rights reserved.
Bridging the European Data Sharing Divide in Genomic Science

In this viewpoint, we argue for the importance of creating data spaces for genomic research that are detached from contexts in which fundamental rights concerns related to surveillance measures override a purpose-specific balancing of fundamental rights. Genomic research relies on molecular and phenotypic data, on comparing findings within large data sets, on searchable metadata, and on translating research results into a clinical setting. These methods require sensitive genetic and health data to be shared across borders. International data sharing between the European Union (EU) or the European Economic Area and third countries has accordingly become a cornerstone of genomics. The EU General Data Protection Regulation contains rules that accord privileged status to data processing for research purposes to ensure that strict data protection requirements do not impede biomedical research. However, the General Data Protection Regulation rules applicable to international transfers of data accord no such preferential treatment to international data transfers made in the research context. The rules that govern the international transfer of data create considerable barriers to international data sharing because of the cost-intensive procedural and substantive compliance burdens that they impose. For certain jurisdictions and select use cases, there exist practically no lawful mechanisms to enable the international transfer of data because of concerns about the protection of fundamental rights. The proposed solutions further fail to address the need to share large data sets of local and regional cohorts across national borders to enable joint analyses. The European Health Data Space is an emerging federated, EU-wide data infrastructure that is intended to function as an infrastructure bringing together EU health data to improve patient care and enable the secondary use of health-related data for research purposes. Such infrastructure is implementing new institutions to support its functioning and is being implemented in reliance on a new enabling law, the regulation on the European Health Data Space. This innovation provides the opportunity to facilitate EU contribution to international genomic research efforts. The draft regulation for this data space provides for a concept of data infrastructure intended to enable cross-border data exchange and access, including access to genetic and health data for scientific analysis purposes. The draft regulation also provides for obligations of national actors aimed at making data widely available. This effort is laudable. However, in the absence of further, more fundamental changes to the manner in which the EU regulates the secondary use of health data, it is reasonable to believe that EU participation in international genomic research efforts will remain impeded.
15-nW Biopotential LPFs in 0.35-mu m CMOS Using Subthreshold-Source-Follower Biquads With and Without Gain Compensation

Most biopotential readout front-ends rely on the g(m)-C lowpass filter (LPF) for forefront signal conditioning. A small g(m) realizes a large time constant (tau = C/g(m)) suitable for ultra-low-cutoff filtering, saving both power and area. Yet, the noise and linearity can be compromised, given that each g(m) cell can involve one or several noisy and nonlinear V-I conversions originated from the active devices. This paper proposes the subthreshold-source-follower (SSF) Biquad as a prospective alternative. It features: 1) a very small number of active devices reducing the noise and nonlinearity footsteps; 2) No explicit feedback in differential implementation, and 3) extension of filter order by cascading. This paper presents an in-depth treatment of SSF Biquad in the nW-power regime, analyzing its power and area tradeoffs with gain, linearity and noise. A gain-compensation (GC) scheme addressing the gain-loss problem of NMOS-based SSF Biquad due to the body effect is also proposed. Two 100-Hz 4th-order Butterworth LPFs using the SSF Biquads with and without GC were fabricated in 0.35-mu m CMOS. Measurement results show that the non-GC (GC) LPF can achieve a DC gain of -3.7 dB (0 dB), an input-referred noise of 36 mu V-rm (29 mu V-rms), a HD3@60 Hz of -55.2 dB (-60.7 dB) and a die size of 0.11 mm(2) (0.08 mm(2)). Both LPFs draw 15 nW at 3 V. The achieved figure-of-merits (FoMs) are favorably comparable with the state-of-the-art.
Early Detection of Failing Automotive Batteries Using Gas Sensors

Safety for automotive lithium-ion battery (LIB) applications is of crucial importance, especially for electric vehicle applications using batteries with high capacity and high energy density. In case of a defect inside or outside the cell, serious safety risks are possible including extensive heat generation, toxic and flammable gas generation, and consequently fire and explosion. New regulations (GB 38031-2020) require a warning for passengers at least five minutes before serious incidents. This regulation can hardly be fulfilled with state-of-the-art battery monitoring. In this study, gases produced during battery failure before and during a thermal runaway (TR) are investigated in detail and the use of different gas sensors as early detectors of battery incidents is tested and proposed. The response of several commercially available gas sensors is tested in four battery failure cases: unwanted electrolysis of voltage carrying parts, electrolyte vapor, first venting of the cell and the TR. The experiments show that battery failure detection with gas sensors is possible but depends highly on the failure case. The chosen gas sensor can detect H-2 produced by unwanted electrolysis and electrolyte vapor and gases produced by degassing of state-of-the-art LIBs. The results may contribute significantly to failure detection and improvement of battery safety.
Ultrasound Aided Vertebral Level Localization for Lumbar Surgery

Localization of the correct vertebral level for surgical entry during lumbar hernia surgery is not straightforward. In this paper, we develop and evaluate a solution using free-hand 2-D ultrasound (US) imaging in the operation room (OR). Our system exploits the difference in spinous process shapes of the vertebrae. The spinous processes are pre-operatively outlined and labeled in a lateral lumbar X-ray of the patient. Then, in the OR the spinous processes are imaged with 2-D sagittal US, and are automatically segmented and registered with the X-ray shapes. After a small number of scanned vertebrae, the system robustly matches the shapes, and propagates the X-ray label to the US images. The main contributions of our work are: we propose a deep convolutional neural network-based bone segmentation algorithm from US imaging that outperforms state of the art methods in both performance and speed. We present a matching strategy that determines the levels of the spinal processes being imaged. And lastly, we evaluate the complete procedure on 19 clinical data sets from two hospitals, and two observers. The final labeling was correct in 92% of the cases, demonstrating the feasibility of US-based surgical entry point detection for spinal surgeries.
Maximum Correntropy Criterion for Robust Face Recognition

In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l(1) norm-based sparse representation classifier ( SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we in particular impose nonnegativity constraint on the variables in the maximum correntropy criterion and develop a half-quadratic optimization technique to approximately maximize the objective function in an alternating way so that the complex optimization problem is reduced to learning a sparse representation through a weighted linear least squares problem with nonnegativity constraint at each iteration. Our extensive experiments demonstrate that the proposed method is more robust and efficient in dealing with the occlusion and corruption problems in face recognition as compared to the related state-of-the-art methods. In particular, it shows that the proposed method can improve both recognition accuracy and receiver operator characteristic (ROC) curves, while the computational cost is much lower than the SRC algorithms.
Enhanced Junction Selection Mechanism for Routing Protocol in VANETs

Routing in infrastructure less vehicular ad hoc networks is challenging because of the dynamic network, predictable topology, high speed of nodes, and predictable mobility patterns. This paper presents an enhanced routing protocol specifically designed for city environments. It uses vehicular speed and directional density for dynamic junction selection. Simulation results exhibit increased packet delivery ratio while decreased end-to-end delay when compared with state of the art protocols.
A case of lymphocytic myocarditis in a patient treated with an immune checkpoint inhibitor, a recent class of chemotherapy agents

Chemotherapy-associated cardiotoxicity is a common adverse event. Immune checkpoint inhibitors (ICI) - a new class of monoclonal antibodies - have revolutionized the management of various diseases. Their use is expected to increase in the near future and their cardiac side effects have been increasingly recognized. CLINICAL CASE: We describe a case of a 67-year-old female patient with urothelial carcinoma undergoing treatment with pembrolizumab who presented to the emergency department with progressive fatigue, retrosternal pain and palpitations for three days. On admission she was diagnosed with acute heart failure (HF). The electrocardiogram revealed a right bundle branch block and ventricular bigeminy. Blood tests showed elevated troponin I, while transthoracic echocardiography revealed severe left ventricular dysfunction. Coronary angiography excluded coronary artery disease. Cardiac magnetic resonance revealed moderate left ventricular dysfunction and late gadolinium enhancement typical of myocarditis. Endomyocardial biopsy confirmed the diagnosis of lymphocytic myocarditis. In the first 48h of hospitalization, she developed transient complete AV block. Corticoid and HF therapy were initiated, leading to symptom improvement and disappearance of the rhythm disturbances. She was discharged on the 12th day, maintaining moderate LV dysfunction, which improved only mildly at a subsequent outpatient assessment. She died suddenly 35 days after discharge. CONCLUSION: Lymphocytic myocarditis is a serious cardiac side effect of ICI therapy. Pembrolizumab is increasingly used, so it is important to be aware of its effects, in order to perform an early diagnosis and provide adequate treatment. Corticosteroid therapy seems to be crucial in preventing disease progression and enabling ventricular remodeling.
Toward Understanding the Effect of Fluoride Ions on the Solvation Structure in Lithium Metal Batteries: Insights from First-Principles Simulations

Regulating the structure and composition of the lithium-ion (Li+) solvation shell is crucial to the performance of lithium metal batteries. The introduction of fluorine anions (F-) into the electrolyte significantly enhances the cycle efficiency and the interfacial stability of lithium metal anodes. However, the effect of dissolved F- on the solvation shell is rarely touched in the literature. Herein, we investigate the evolution processing of the fluorine-containing solvation structure to explore the underlying mechanisms via first-principles calculations. The additive F- is found to invade the first solvation shell and strongly coordinate with Li+, liberating the bis(trifluoromethanesulfonyl) imide anion (TFSI-) from the Li+ local environment, which enhances the Li+ diffusivity by altering the transport mode. Moreover, the fluorine-containing Li+ solvation shell exhibits a higher lowest unoccupied molecular orbital energy level than that of the solvation sheath without F- additives, suggesting the reduction stability of the electrolyte. Furthermore, the Gibbs free energy calculations for Li+ desolvation reveal that the energy barrier of the Li+ desolvation process will be reduced because of the presence of F-. Our work provides new insights into the mechanisms of electrolyte fluorinated strategies and leads to the rational design of high-performance lithium metal batteries.
LCAart: Communicating industrial ecology at a human scale

This Forum piece describes a collaborative project between engineering and architecture to visualize some of the most influential results from industrial ecology using human-scale, photorealistic images that are quantitatively accurate. Our goal was to apply visualization theories and practices from art and architecture to address a major communication problem in our field: though inspirational in concept, in practice much industrial ecology research is difficult to comprehend for the average person. Models are large and complex, metrics are esoteric, and results are often reported on a scale that is devoid of personal meaning. Our strategy was to place hidden flows and embodied emissions in plain sight, creating images that show the environmental implications of consumption as absurd insertions into scenes of daily life, at a scale that is relatable and personally meaningful. We also compare with and discuss other artistic efforts around the world in the oeuvre of "Consumption Art," providing historical context. Industrial ecology envisions a world where production systems can incorporate social and environmental implications in real-time, where policy is informed by our best understanding of trade-offs and inequities, and where the public has an appreciation for what actions are meaningful, all with the goals of improving quality of life for all while safeguarding the environment and human health. Effective communication of our research is vital to build consensus for policy and action toward this vision, and one under-appreciated aspect of communication in our field is the sympathetic power of Art.
A Direct Thrust Control Scheme for Linear Permanent Magnet Synchronous Motor Based on Online Duty Ratio Control

Linear permanent magnet synchronous motors, especially those with low inductance and short pole pitch, exhibit significant ripple in flux and thrust force under conventional direct thrust force control. Aimed at reducing ripple in the flux and the force, a duty ratio control scheme for direct thrust force control of linear permanent magnet synchronous motors is proposed. The effect of inverter voltage vectors on the flux and the thrust force is analyzed. Subsequently, a precise expression for the online computation of the required duty ratio is derived. Compared to the state-of-the-art duty ratio control, the proposed approach eliminates the need to tune gains. The proposed online duty ratio calculation is based on the selected voltage vector from the switching table and takes into account the mover's speed. The proposed approach retains the characteristics of the conventional direct thrust control in terms of fast transient and steady-state responses for both flux and thrust force. Experimental results demonstrate a faster transient response and negligible steady-state error for flux, force, and speed responses under the proposed approach when compared to the state of the art.
Blind symbol packing ratio estimation for faster-than-Nyquist signalling based on deep learning

This Letter proposes a blind symbol packing ratio estimation for faster-than-Nyquist (FTN) signalling based on state-of-the-art deep learning technology. The symbol packing ratio (also named speeding parameter, time packing parameter etc.) is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by calculating the intersymbol interference. To the best of the authors' knowledge, this is the first effective estimation approach for symbol packing ratio in FTN signalling and has shown its fast convergence and robustness to signal-to-noise ratio by numerical simulations. Benefiting from the proposed blind estimation, the packing-ratio-based adaptive FTN transmission without dedicate channel or control frame becomes available. Also, the secure FTN communications based on the secret symbol packing ratio can be easily cracked.
CCLS: An Efficient Local Search Algorithm for Weighted Maximum Satisfiability

The maximum satisfiability (MAX-SAT) problem, especially the weighted version, has extensive applications. Weighted MAX-SAT instances encoded from real-world applications may be very large, which calls for efficient approximate methods, mainly stochastic local search (SLS) ones. However, few works exist on SLS algorithms for weighted MAX-SAT. In this paper, we propose a new heuristic called CCM for weighted MAX-SAT. The CCM heuristic prefers to select a CCMP variable. By combining CCM with random walk, we design a simple SLS algorithm dubbed CCLS for weighted MAX-SAT. The CCLS algorithm is evaluated against a state-of-the-art SLS solver IRoTS and two state-of-the-art complete solvers namely akmaxsat_ls and New WPM2, on a broad range of weighted MAX-SAT instances. Experimental results illustrate that the quality of solution found by CCLS is much better than that found by IRoTS, akmaxsat_ls and New WPM2 on most industrial, crafted and random instances, indicating the efficiency and the robustness of the CCLS algorithm. Furthermore, CCLS is evaluated in the weighted and unweighted MAX-SAT tracks of incomplete solvers in the Eighth Max-SAT Evaluation (Max-SAT 2013), and wins four tracks in this evaluation, illustrating that the performance of CCLS exceeds the current state-of-the-art performance of SLS algorithms on solving MAX-SAT instances.
The Effects of Group Art Therapy on the Primary Family Caregivers of Hospitalized Patients with Brain Injuries in South Korea

This study examined the effects of group art therapy on depression, burden, and self-efficacy in primary family caregivers of patients with brain injuries. This was a quasi-experimental, nonequivalent control group and a pre- and post-test design. This study was carried out in one national rehabilitation hospital targeting 41 primary family caregivers of patients with brain injuries. Group art therapy intervention was carried out three days per week comprising 12 sessions over four consecutive weeks. The experimental group (n = 20) received group art therapy, whereas the control group (n = 21) did not. We used a time difference method to minimize the risk of contaminating the control group by sampling sequentially. For depression, although there was a significant difference after the intervention (t = 3.296, p = 0.004), the mean difference score was not statistically significant between the experimental group and the control group (t = 0.861, p = 0.395). The experimental group showed a significantly greater decrease in burden (t = 2.462, p = 0.020) and significantly greater improvement in self-efficacy (t = -6.270, p < 0.001) than the control group. Group art therapy may be an effective nursing intervention for primary family caregivers of patients with brain injuries.
Deep Residual Autoencoder for Blind Universal JPEG Restoration

We propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) leveraging both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline. The proposed method is blind and universal, consisting of a unique model that effectively restores images with any level of compression. It operates in the YCbCr color space and performs JPEG restoration in two phases using two different autoencoders: the first one restores the luma channel exploiting 2D convolutions; the second one, using the restored luma channel as a guide, restores the chroma channels exploiting 3D convolutions. Extensive experimental results on four widely used benchmark datasets (i.e. LIVE1, BDS500, CLASSIC-5, and Kodak) show that our model outperforms state of the art methods, even those using a different set of weights for each compression quality, in terms of all the evaluation metrics considered (i.e. PSNR, PSNR-B, and SSIM). Furthermore, the proposed model shows a greater robustness than state-of-the-art methods when applied to compression qualities not seen during training.
Implementation of energy-efficient fast convolution algorithm for deep convolutional neural networks based on FPGA

The state-of-the-art convolutional neural networks (CNNs) have been widely applied to many deep neural networks models. As the model becomes more accurate, both the number of computation and the data accesses are significantly increased. The proposed design uses the row stationary with network-on-chip and the fast convolution algorithm in process elements to reduce the number of computation and data accesses simultaneously. The experimental evaluation which using the CNN layers of VGG-16 with a batch size of three shows that the proposed design is more energy-efficient than the state-of-the-art work. The proposed design improves the total GOPs of the algorithm by 1.497 times and reduces the on-chip memory and off-chip memory accesses by 1.07 and 1.46 times than prior work, respectively.