ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Algorithmic Advancements and a Comparative Investigation of Left and Right Looking Sparse LU Factorization on GPU Platform for Circuit Simulation

Sparse LU factorization is a key tool in the solution of large linear set of algebraic equations encompassing a wide range of computing applications. Recent advances in this field exploit the massively parallel architecture of the GPUs via left-looking algorithm (LLA) and right-looking algorithm (RLA). In this paper, adaptive cluster mode is proposed to improve the state-of-the-art in LLA for GPU platforms. The proposed method takes into consideration of varying sparsity at different levels during cluster mode execution, to adaptively configure the GPU block size and the number of parallel columns. The new refinements for LLA are also integrated with the dynamic parallelism that is available in modern GPU architectures. The paper also provides a comprehensive performance comparison of the LLA and hybrid RLA along with state-of-the-art advances on the same GPU platform. The results indicate that, when implemented with similar refinements and on a same platform, LLA provides better performance compared to the hybrid-RLA. The results would be useful to the scientific community while making decision on adopting LLA or RLA algorithms for sparse LU factorization.
Robot-Assisted Surgery in Gynecology

The advancement of robotics-based procedures in the medical industry is the subject of this review article. The purpose of the surgical robot is to increase surgical abilities and address human shortcomings. The robot's success has been predicated on its ability to accurately and consistently repeat tasks. The following are a few objectives and quantifiable benefits of robotic technology improving surgical maneuverability and physical capabilities. In 2005, the FDA granted gynecological surgery approval for the Da Vinci surgical system. It has been quickly embraced and has already taken on a significant role at many of the locations where it is offered. It consists of a cart with robotic branches next to the patient and a high-resolution three-dimensional (3D) vision system. This study covers laparoscopy via robots in benign gynecological surgeries, myomectomy surgery, hysterectomies, endometriosis, tubal anastomosis, and sacrocolpopexy. The appropriate published studies were evaluated after a PubMed search was conducted. Additionally, procedures that may be used in the future are highlighted. In benign gynecological illness, most currently available research does not show a substantial benefit over traditional laparoscopic surgery. Robotics, however, does provide help in more complicated operations. Compared to laparoscopy, robotic assistance has a lower conversion rate to open procedures. Endo wrist movement of an automated machine during myomectomy surgery can perform proper and better suturing than traditional laparoscopy. The automated program is a noticeable improvement over laparoscopic surgery and, if price issues are resolved, can gain popularity among gynecological surgeons around the globe.
Inclusion of curcumin in b-cyclodextrin: a promising prospective as food ingredient

In the present work, the inclusion complexes between curcumin (CC) and β-cyclodextrin (β-CD) were obtained through co-precipitation at different times of magnetic stirring and simple mixing. The stoichiometric ratio between CC and β-CD was ascertained to be 1:2. The samples prepared by co-precipitation (24 h magnetic stirring and 24 h cold static precipitation) and by simple mixing, showed the best performance in terms of powder recovery (72 and 97%, respectively), encapsulation efficiency (72 and 95%, respectively) and tinting power, while no significant differences were revealed in terms of loading capacity (about 14%). Using either inclusion method, the complexation of CC into β-CD make the colorant 20-fold more water soluble than pure CC. The pigment stability was also enhanced under different storage conditions (high temperature and light exposure), either when applied in liquid or gel state. However, after 30 days under natural light exposure, an appreciable colour change from yellow to orange was revealed to an extent probably perceptible by an inexperienced observer.
Change of algal organic matter under different dissolved oxygen and pressure conditions and its related disinfection by-products formation potential in metalimnetic oxygen minimum

Most of the reservoirs or lakes will form a metalimnetic oxygen minimum (MOM) with the characterization of a substantial fraction of dissolved oxygen (DO) depleted below the epilimnion. The effect of intracellular organic matter (IOM) of algal cells transformed under MOM conditions is completely different from that of the original IOM on water quality. In this study, the IOM changes of Microcystic aeruginosa under different MOM conditions and its related disinfection by-products formation potentials (DBPFPs) were investigated by changing the pressure and DO concentration of MOM. Total Fmax increased slightly and then decreased under different pressure conditions, finally decreasing by no more than 22.0%. Under aerobic condition, dissolved organic carbon (DOC) and total Fmax decreased significantly, and decreased by 60.4% and 38.8% within the first 2 days. The results of specific UV absorbance (SUVA) and UV250/UV365 indicated that aromatic compounds and average molecular weight of IOM were gradually increased under different MOM conditions. The total DBPFPs increased firstly and then decreased under different pressure conditions, and finally decreased by 26.2%-33.1%. The decrease of total DBPFPs was significantly higher under aerobic condition than that under anoxic condition, which finally decreased by 64.5%. Redundancy analysis showed that the fluorescence parameter (protein-like and humic-like fluorescence) could be expected as an index to predict the DBPFPs. Moreover, the results revealed that with the decrease of DO, the activity and diversity of natural microbial consortium decreased, which prevented the further degradation and utilization of organic matter by natural microbial consortium. Therefore, lower DO was a key player for the deterioration of water quality under MOM conditions.
Postprocessor for five-axis machining of STL surfaces based on Nagata interpolation and optimization of rotation angles

This paper proposes a postprocessor which applies the appropriate rotation rules to reduce the kinematic error (KE). Our first contribution is an algorithm which minimizes the KE using the Hausdorff distance. The shortest path algorithm includes the constraints relevant to the limits of the linear and the rotation axis. The proposed procedure is compared with state-of-the-art methods. It demonstrates an advantage in terms of the kinematic error for test surfaces characterized by a sharp curvature. The second contribution is testing interpolation methods of the STL surfaces. The first step of this procedure is the evaluation of the tessellation error (TE), i.e., the difference between the actual surface and the local or global interpolation obtained from the STL file. The numerical results show that a simple local interpolation using normal vectors proposed by Nagata performs well. The method has been tested against several state-of-the-art interpolation procedures, demonstrating a significant advantage. The proposed elements are integrated into a postprocessor. The experiments have been performed on a virtual and the actual tilt-table five-axis machining center (Haas VF-2TR).
A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis

Modern wind turbines depend on their blade pitch systems for start-ups, shutdowns, and power control. Pitch system failures have, therefore, a considerable impact on their operation and integrity. Hydraulic pitch systems are very common, due to their flexibility, maintainability, and cost; hence, the relevance of diagnostic algorithms specifically targeted at them. We propose one such algorithm based on sensor data available to the vast majority of turbine controllers, which we process to fit a model of the hydraulic pitch system to obtain significant indicators of the presence of the critical failure modes. This algorithm differs from state-of-the-art, model-based algorithms in that it does not numerically time-integrate the model equations in parallel with the physical turbine, which is demanding in terms of in situ computation (or, alternatively, data transmission) and is highly susceptible to drift. Our algorithm requires only a modest amount of local sensor data processing, which can be asynchronous and intermittent, to produce negligible quantities of data to be transmitted for remote storage and analysis. In order to validate our algorithm, we use synthetic data generated with state-of-the-art aeroelastic and hydraulic simulation software. The results suggest that a diagnosis of the critical wind turbine hydraulic pitch system failure modes based on our algorithm is viable.
Molecular profiling identifies distinct subtypes across TP53 mutant tumors

Tumor protein 53 mutation (TP53mut) is one of the most important driver events facilitating tumorigenesis, which could induce a series of chain reactions to promote tumor malignant transformation. However, the malignancy progression patterns under TP53 mutation remain less known. Clarifying the molecular landscapes of TP53mut tumors will help us understand the process of tumor development and aid precise treatment. Here, we distilled genetic and epigenetic features altered in TP53mut cancers for cluster-of-clusters analysis. Using integrated classification, we derived 5 different subtypes of TP53mut patients. These subtypes have distinct features in genomic alteration, clinical relevance, microenvironment dysregulation, and potential therapeutics. Among the 5 subtypes, COCA3 was identified as the subtype with worst prognosis, causing an immunosuppressive microenvironment and immunotherapeutic resistance. Further drug efficacy research highlighted olaparib as the most promising therapeutic agents for COCA3 tumors. Importantly, the therapeutic efficacy of olaparib in COCA3 and immunotherapy in non-COCA3 tumors was validated via in vivo experimentation. Our study explored the important molecular events and developed a subtype classification system with distinct targeted therapy strategies for different subtypes of TP53mut tumors. These multiomics classification systems provide a valuable resource that significantly expands the knowledge of TP53mut tumors and may eventually benefit in clinical practice.
Visitors of two types of museums: A segmentation study

Market segmentation comprises a wide range of measurement tools that are useful for the sake of supporting marketing and promotional policies also in the sector of cultural economics. This paper aims to contribute to the literature on segmenting cultural visitors by using the Bagged Clustering method, as an alternative and effective strategy to conduct cluster analysis when binary variables are used. The technique is a combination of hierarchical and partitioning methods and presents several advantages with respect to more standard techniques, such as k-means and LVQ. For this purpose, two ad hoc surveys were conducted between June and September 2011 in the two principal museums of the two provinces of the Trentino-South Tyrol region (Bolzano and Trento), Northern Italy: the South Tyrol Museum of Archaeology in Bolzano (OTZI), hosting the permanent exhibition of the "Iceman" Otzi, and the Museum of Modern and Contemporaneous Art of Trento and Rovereto (MART). The segmentation analysis was conducted separately for the two kinds of museums in order to find similarities and differences in behaviour patterns and characteristics of visitors. The analysis identified three and two cluster segments respectively for the MART and OTZI visitors, where two OTZI clusters presented similar characteristics to two out of three MART groups. Conclusions highlight marketing and managerial implications for a better direction of the museums. (C) 2012 Elsevier Ltd. All rights reserved.
Theoretical Circuit Design of an Efficient Spintronic Random Number Generator With an Internal Postprocessing Unit

In this letter, a spintronic true random number generator (TRNG) is designed using the stochastic switching feature of the magnetic tunnel junction device in the subcritical current regime. The proposed structure consumes low power and occupies a small area. Also, to improve the quality of random numbers production and compensate for the impact of process variations on the quality of the random output, the proposed TRNG includes an internal postprocessing unit. Compared to state-of-the-art designs, using an internal postprocessing unit reduces the proposed generator's area overhead and power consumption. The simulation results show that the TRNG proposed in this letter consumes up to 68% less power and occupies up to 64% smaller area than the state-of-the-art design. Also, due to the existence of the efficient postprocessing unit, the proposed TRNG successfully passes the National Institute of Standards and Technology random number tests even in the presence of the fabrication process variations.
Hand gesture recognition using topological features

Hand Gestures Recognition (HGR) is one of the main areas of research for Human Computer Interaction applications. Most existing approaches are based on local or geometrical properties of pixels. Still, there are some serious challenges on HGR methods such as sensitivity to rotation, scale, illumination, perturbation, and occlusion. In this paper, we study HGR from graph viewpoints. We introduce a set of meaningful shape features based on a graph constructed by Growing Neural Gas (GNG) algorithm. These features are constructed from topological properties of this graph. Graph properties in conserving topological features improve stability against different deformations, scale, and noise. We evaluate our method on NTU Hand Digits dataset with state-of-the-art methods. We also prepared a comprehensive dataset (SBU-1) for different hand gestures containing 2170 images. This dataset includes many possible deformations and variations and some articulations. Most of the existing datasets don't capture these variations. We show the robustness of the algorithm to scale, rotation and noise, while preserving similar recognition rate in comparison with the state-of-the-art results.
First-time mothers' experiences of foetal reduction in pregnancy following assisted reproductive technology treatment in Taiwan: a qualitative study

Background Foetal reduction-removal of one or more foetuses to reduce the number of foetuses in multiple conceptions-is a procedure used for improving pregnancy outcomes following assisted reproductive technology (ART) treatment. While there is a recognition of the importance of understanding the experiences of women who undergo foetal reduction to offer appropriate help and support, studies that provide relevant insights are sparse. Our aim was to gain an in-depth understanding about first-time mothers' experiences of foetal reduction following ART treatment in Taiwan. Methods We adopted a qualitative design based on a phenomenological approach for this study. In-depth semi-structured interviews were conducted with seven first-time mothers who underwent foetal reduction following ART treatment at a fertility centre in Taipei, Taiwan. All interviews were recorded, transcribed and analysed using the Colaizzi strategy. Results The views and experiences relating to foetal reduction reflected five key themes: hesitation, ambivalence and distress; the guilt of knowingly terminating a life; rituals and ceremonies to ease the sense of guilt; persuading oneself to consider the 'big picture'; and wishing for a reunion in next pregnancy. Mothers often regretted that they took clinical advice to implant multiple embryos and then having had to resort to foetal reduction. There was a sense of hesitation, ambivalence and distress reflected in the views from all participants. They believed that they ended the fetuses' lives knowingly and expressed strong feelings of guilt. Mothers often tried to persuade themselves to look at foetal reduction within the 'big picture' of the overall pregnancy outcome. Losing their unborn babies was as an unforgettable incident for most mothers, and they wished for a reunion with the lost baby in the next pregnancy. Conclusion Findings indicate the need for ART providers to undertake a more sensitive approach that involves detailed discussions with women and their families to tailor the embryo transfer processes to suit individual needs. Women who undergo foetal reduction should be provided with tailored interventions towards enhancing their coping strategies before and after foetal reduction taking into account the cultural and religious context.
Do Discounts in Ticket Prices Induce Sustainable Profit to Performing Arts Suppliers?

This study investigates the effect of price promotions for musicals on consumer perception of fairness toward other similar musicals and the role of consumer involvement in performing arts as a moderating variable. A threshold regression estimation which was performed using data from a survey on South Korean consumers showed a decrease in consumers' fairness perception once they became aware of discounts on similar performing arts and, consequently, a decrease in their intent to purchase tickets at regular prices. The results also showed that the samples can be split into low- and high-involvement groups, with the effect of fairness perception on purchase intention statistically significant for both groups, however, larger for the high-involvement group; the high-involvement group is expected to consume more performing arts products than the low-involvement group. These results imply that consumer perception of unfairness regarding ticket price discounts may negatively affect the long-term profits of performing arts suppliers.
Constant-Parameter Interfacing of Induction Machine Models Considering Main Flux Saturation in EMTP-Type Programs

The state-of-the-art magnetically linear voltage-behind-reactance (VBR) and phase-domain induction machine models for nodal-analysis-based electromagnetic transients programs (EMTP-type) offer an excellent combination of numerical stability, accuracy, and efficiency. However, incorporation of magnetic saturation in these models renders their interfacing circuits dependent on the operating segment of the piecewise-linear saturation characteristics. Consequently, refactorization of the network's conductance matrix is required during simulations, which reduces the numerical efficiency of the overall solution and limits the models' range of application. This paper presents a new VBR squirrel-cage induction machine model that includes main flux saturation and possesses a saturation-independent constant-parameter interfacing circuit. Case studies in PSCAD/EMTDC demonstrate that the proposed model offers similar numerical stability and accuracy to the state-of-the-art models, while considerably increasing simulation speed for practical multimachine systems.
A preliminary study on the association between social media at night and sleep quality: The relevance of FOMO, cognitive pre-sleep arousal, and maladaptive cognitive emotion regulation

Research shows that nighttime social media is negatively associated with sleep quality and that it might be utilized to cope with aversive psychological states related to cognitive pre-sleep arousal (i.e., transdiagnostic psychopathology variables, referring to maladaptive repetitive thought), namely Fear of Missing Out (FoMO). The use of nighttime social media to cope with other aversive cognitive states (i.e., worry/rumination), their relationship with FoMO, and these variables' association with sleep are not fully understood. This study explored the relationships between nighttime social media, sleep quality, FoMO, cognitive pre-sleep arousal, and maladaptive cognitive emotion regulation (i.e., worry/rumination). The present correlational study followed a cross-sectional design. Participants were 525 university students, ranging in age from 18 to 64 (M = 22.39, SD = 5.62). Measures of sleep quality, morningness/eveningness, cognitive pre-sleep arousal, worry, rumination, FoMO, nighttime screen, and social media use were collected online or in pencil-paper format. Multiple linear regression analyses were performed. Nighttime social media and FoMO were associated with rumination, worry (H1) and cognitive pre-sleep arousal. Both FoMO and worry predicted higher levels of cognitive pre-sleep arousal (H2) and nighttime social media use (H3). Nighttime social media use independently predicted poor sleep quality (H4). These results suggest that worry and FoMO may potentially affect sleep quality by increasing cognitive pre-sleep arousal and nighttime social media. A possible explanation for these findings is that nighttime social media might be used as a strategy to cope with aversive cognitive states. These conclusions may contribute to improving sleep intervention in this population.
A Bayesian Inference Based Hybrid Recommender System

The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of model-based methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.