ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Molecular insight into pyrolysis processes via reactive force field molecular dynamics: A state-of-the-art review

Molecular simulations based on reactive force-fields (ReaxFF) have been applied as a powerful tool for exploring the pyrolysis process of complex carbonaceous materials. It can describe the breakage and formation of bonds during the chemical reaction processes based on Bond-Order so as to explore the complex reaction mechanisms from microscale, which can offset experimental flaws. In this review, we attempt to provide an overview of the state-of-the-art advances in the pyrolysis process of coal, biomass, polymer and fuel at molecular level. First the major constituent elements, development, force field optimization, diverse application of ReaxFF MD is intro-duced. Then, the kinetics models, pyrolysis mechanisms and products reaction pathways of coal, biomass, polymer and fuel are discussed. In addition, the train and optimization of force field and post-processing of simulation are discussed, which can improve reaction analysis capability and provide new ideas to extract in-formation. We conclude the pyrolysis application using ReaxFF MD from microscopic level with an eye toward the future challenges and developments in those fields.
Image Restoration Using Gaussian Mixture Models With Spatially Constrained Patch Clustering

In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian distributions, i.e., the proposed statistical patch-based model provides a better goodness-of-fit to statistical properties of natural images. A novel approach for computing aggregation weights for image reconstruction from recovered patches is introduced which is based on similarity degree of each patch to the estimated Gaussian clusters. The results admit that in the case of image denoising, our method is highly comparable with the state-of-the-art methods, and our image interpolation method outperforms previous state-of-the-art methods.
Applying the AHP to Conflict Resolution: A Russia-NATO Case Study

In this paper, we apply the Analytic Hierarchy Process approach to conflict resolution in the context of the Russia-Ukraine conflict. We build models that illustrate the evaluation criteria, strategic and sub-criteria, and concessions for each party in this negotiation. Ratings are used to evaluate the degree to which concessions contribute or take away from successful resolution of the conflict. Afterwards, gain ratios are built to determine the benefit-cost scores so that concessions may be traded that result in equitable solutions. The approach presented here demonstrates for the first time why all concessions that parties to a conflict may offer might not trade all at once. A Max-Min optimization approach is used to maximize the gain to both parties of the conflict while minimizing the disparity in gain between the two.
Taphonomic Patterning of Cemetery Remains Received at the Office of the Chief Medical Examiner, Boston, Massachusetts

A sample of 49 cases of cemetery remains received at the Office of the Chief Medical Examiner, Massachusetts (OCME-MA), in Boston was compared with published taphonomic profiles of cemetery remains. The present sample is composed of a cross section of typical cases in this region that ultimately are derived from modern to historical coffin burials and get turned over to or seized by law enforcement. The present sample was composed of a large portion of isolated remains, and most were completely skeletonized. The most prevalent taphonomic characteristics included uniform staining (77.6%), coffin wear (46.9%), and cortical Exfoliation (49.0%). Other taphonomic changes occurring due to later surface exposure of cemetery remains included subaerial weathering, animal gnawing, algae formation, and excavation marks. A case of one set of skeletal remains associated with coffin artifacts and cemetery offerings that was recovered from transported cemetery fill is also presented.
Kaempferol: A potential agent in the prevention of colorectal cancer

Colorectal cancer (CRC) is the third most prevalent cancer in relation to incidence and mortality rate and its incidence is considerably increasing annually due to the change in the dietary habit and lifestyle of the world population. Although conventional therapeutic options, such as surgery, chemo- and radiotherapy have profound impacts on the treatment of CRC, dietary therapeutic agents, particularly natural products have been regarded as the safest alternatives for the treatment of CRC. Kaempferol (KMP), a naturally derived flavonol, has been shown to reduce the production of reactive oxygen species (ROS), such as superoxide ions, hydroxyl radicals, and reactive nitrogen species (RNS), especially peroxynitrite. Furthermore, this flavonol inhibits xanthine oxidase (XO) activity and increases the activities of catalase, heme oxygenase-1 (HO), and superoxide dismutase (SOD) in a wide range of cancer and non-cancer cells. Based on several studies, KMP is also a hopeful anticancer which carries out its anticancer action via suppression of angiogenesis, stimulation of apoptosis, and cell cycle arrest. Due to various applications of KMP as an anticancer flavonol, this review article aims to highlight the current knowledge regarding the role of KMP in CRC.
CNN weight sharing based on a fast accuracy estimation metric

The computational workload involved in CNNs is typically out of reach for low-power embedded devices. The Approximate Computing paradigm can be exploited to reduce the CNN complexity since it improves performances and energy-efficiency by relaxing the need for fully accurate operations. In this work, we target weightsharing as an approximate technique to reduce the memory footprint of a CNN. More in detail, we prove that optimizing the number of shared weights can enable significant network memory compression without noticeable accuracy loss without retraining or fine-tuning steps. However, we observe that the exploration time can easily explode in state-of-the-art CNNs. We thus propose the use of a fast accuracy estimation metric to guide the design space exploration and drastically reduce the exploration time up to 12x. Compared with state-of-the-art CNN approximation methods, we obtained more than 4x compression on GoogleNet on the ImageNet dataset with less than 1% accuracy loss in less than 5 h and without any retraining step.
Urinary phthalate metabolite concentrations and hot flash outcomes: Longitudinal associations in the Midlife Women's Health Study

Midlife in women is an understudied time for environmental chemical exposures and menopausal outcomes. Recent cross-sectional research links phthalates with hot flashes, but little is known regarding such associations over time. Our objective was to estimate longitudinal associations between repeated measures of urinary phthalate metabolite concentrations and hot flash outcomes in midlife women. Using data from the Midlife Women's Health Study (MWHS), a prospective longitudinal study, we fit generalized linear mixed-effects models (GLMMs) and Cox proportional hazards regression models to repeated measures over a 4-year period. Recruitment occurred in Baltimore and surrounding counties, Maryland, USA between 2006 and 2015. Participants were premenopausal/perimenopausal women (n = 744) aged 45-54 years, who were not pregnant, not taking menopausal symptom medication or oral contraceptives, did not have hysterectomy/oophorectomy, and irrespective of hot flash experience. Baseline mean (SD) age was 48.4 (2.45), and 65% were premenopausal. Main outcome measures included adjusted odds ratios (ORs) for 4 self-reported hot flash outcomes (ever experienced, past 30 days experience, weekly/daily, and moderate/severe), and hazard ratios (HRs) for incident hot flashes. We observed mostly increased odds of certain hot flash outcomes with higher concentrations of metabolites of di (2-ethylhexyl) phthalate (DEHP), monoisobutyl phthalate (MiBP), and a molar summary measure of plasticizer phthalate metabolites (DEHP metabolites, mono-(3-carboxypropyl) phthalate (MCPP), monobenzyl phthalate (MBzP)). Some associations between exposures and outcomes indicated decreased odds. In conclusion, phthalate metabolites were associated with certain hot flash outcomes in midlife women. Midlife may be a sensitive period for higher phthalate metabolite concentrations with respect to menopausal symptoms.
Highly Undersampled Magnetic Resonance Image Reconstruction Using Two-Level Bregman Method With Dictionary Updating

In recent years Bregman iterative method (or related augmented Lagrangian method) has shown to be an efficient optimization technique for various inverse problems. In this paper, we propose a two-level Bregman Method with dictionary updating for highly undersampled magnetic resonance (MR) image reconstruction. The outer-level Bregman iterative procedure enforces the sampled k-space data constraints, while the inner-level Bregman method devotes to updating dictionary and sparse representation of small overlapping image patches, emphasizing local structure adaptively. Modified sparse coding stage and simple dictionary updating stage applied in the inner minimization make the whole algorithm converge in a relatively small number of iterations, and enable accurate MR image reconstruction from highly undersampled k-space data. Experimental results on both simulated MR images and real MR data consistently demonstrate that the proposed algorithm can efficiently reconstruct MR images and present advantages over the current state-of-the-art reconstruction approach.
Creative Mural Landscapes, Building Communities and Resilience in Uruguayan Tourism

The purpose of this research was to analyze open-air mural painting museums in Uruguay as a model of tourism resilience, sustainability, and social development, being one of the first Latin American examples to demonstrate the ability to adapt to change and overcome external shocks through the creation of creative community landscapes. To do so, documentary research, photographic documentation, and field research were carried out in order to explore the opportunities of mural tourism in small locations in Uruguay. In the nineties, a new type of artistic production was created in Uruguay, initially characterized by its decentralization. This was somewhat of a revolution in the muralist field as, until this time, Montevideo had been the center of cultural tradition, considered the intellectual focus of the country, and had concentrated the largest number of murals. For this reason, the birth of new muralist nuclei in small rural enclaves, which traditionally had not had much access to culture and no link to muralism, is remarkable. Secondly, this new movement sought to diversify economic activity given the consequences of the severe economic crises and environmental catastrophes that were and are still prevalent in these areas. Therefore, these new creative landscapes were conceived as important examples of the resilience of cultural tourist destinations. The results emphasize that, until now, the idea of giving muralism a new use as a tool for local economic development had not been envisaged with reference to mural art in Uruguay. This new rethinking has given rise to the so-called Regionalization Processes of Uruguayan wall production. The most relevant cases are those developed in the municipalities of San Gregorio de Polanco (1993), Rosario (1994), and Pan de Azucar (1998).
Sign Detection and Number Comparison on RNS 3-Moduli Sets {2(n)-1, 2(n+x), 2(n)+1}

Number comparison, sign identification and overflow detection are important operations, especially for digital signal processing, but hard to perform using the residue number system (RNS). In this paper, a new method is proposed for sign identification and number comparison based on an optimized version of the mixed radix conversion for the augmented 3-moduli sets . Notably, most of the computations are directly performed on the moduli channels, thus allowing to easily adapt this new method to any RNS processor. Accordingly, this paper proposes an efficient unified very large scale integration architecture based on the presented methodology, which can be used not only to design application specific integrated circuits (ASICs) but also to configure field-programmable gate arrays (FPGAs). The implementation results that were obtained using CMOS technologies show that the proposed architecture provided comparators that are more efficient than the related state of the art, by considering as a figure of merit the area time product. More specifically, the considered ASIC and FPGA implementations provide relative improvements in the efficiency of up to 57 and , respectively. The experimental assessment also shows that the power consumption of the proposed circuits is significantly lower than the related state of the art, with relative reductions of up to .
Connected Bicycles-State-of-the-Art and Adoption Decision

This paper provides a snapshot of the current state-of-the-art on connected bicycles as discussed in the published literature as well as real-world implementations. We discuss several facets of connected bicycles, including those associated with quantified-self, synergies with social media, network effect, environmental sustainability, big data, among others. We then consider extensions and what might be possible in this general area based on existing and proposed systems as well as extant literature. We also discuss some of the issues and challenges that are associated with connected bicycles. A connected bicycle's utility increases with the number of other connected bicycles in its immediate vicinity. Through a Hotelling model, we consider the price and profit dynamics for a connected bicycle retailer. Our results indicate that the equilibrium price of a connected (regular) bicycle increases (decreases) with associated connected bicycle network effects. The unit connectivity cost for a connected bicycle has a direct effect on regular bicycle price. We derive expressions on when it is profitable for a retailer to switch from regular to connected bicycles as well as when the lowest profit levels are reached.
Ketones: the double-edged sword of SGLT2 inhibitors?

Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of medications used by individuals with type 2 diabetes that reduce hyperglycaemia by targeting glucose transport in the kidney, preventing its reabsorption, thereby inducing glucosuria. Besides improving HbA1c and reducing body weight and blood pressure, the SGLT2 inhibitors have also been demonstrated to improve cardiovascular and kidney outcomes, an effect largely independent of their effect on blood glucose levels. Indeed, the mechanisms underlying these benefits remain elusive. Treatment with SGLT2 inhibitors has been found to modestly increase systemic ketone levels. Ketone bodies are an ancillary fuel source substituting for glucose in some tissues and may also possess intrinsic anti-oxidative and anti-inflammatory effects. Some have proposed that ketones may in fact mediate the cardio-renal benefits of this drug category. However, a rare complication of SGLT2 inhibition is ketoacidosis, sometimes with normal or near-normal blood glucose concentrations, albeit occurring more frequently in patients with type 1 diabetes who are treated (predominately off-label) with one of these agents. We herein explore the notion that an underpinning of one of the more serious adverse effects of SGLT2 inhibitors may, in fact, explain, at least in part, some of their benefits-a potential 'double-edged sword' of this novel drug category.
Unification of Blockchain and Internet of Things (BIoT): requirements, working model, challenges and future directions

The Internet of Things (IoTs) enables coupling of digital and physical objects using worthy communication technologies and introduces a future vision where computing systems, users and objects cooperate for convenience and economic benefits. Such a vision requires seamless security, data privacy, authentication and robustness against attacks. These attributes can be introduced by blockchain, a distributed ledger that maintains an immutable log of network transactions. In this paper, we present a comprehensive review on how to remodel blockchain to the specific IoT needs in order to develop Blockchain based IoT (BIoT) applications and aim to shape a coherent picture of the current state-of-the-art efforts in this direction. After describing the basic characteristics and requirements of IoT, evolution of blockchain is presented. In this regard, we start with the fundamental working principles of blockchain and how such systems achieve auditability, security and decentralization. Further, we describe the most relevant BIoT applications, its architecture design and security aspects. From there, we build our narrative on the centralized IoT challenges followed by recent advances towards solving them. Finally, some future directions are enumerated with the aim to guide future BIoT researchers on challenges that needs to be considered ahead of deploying the next generation of BIoT applications.
Multiple feature kernel hashing for large-scale visual search

Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved.
Insights into the characteristics and mechanism of vacuum drying technology for municipal sludge processing

Vacuum drying is an effective approach for sludge treatment and valorization. However, the vacuum drying of sludge has not been industrialized at present. The objective of this study was to elucidate the vacuum drying characteristics of static sludge and crack initiation mechanism. Our results indicate that crusting on the sludge surface under a high vacuum inhibited drying by reducing major cracks at sludge thicknesses of 13.6 and 10.2 mm. The inhibition effect weakened with decreasing sludge thickness. At 6.8 mm, the mean drying rate (VM) was the lowest at 0.08 MPa, while VM decreased with increasing vacuum degree at thicknesses of 13.6 and 10.2 mm. The decrease in drying rate could be attributed to rapid evaporation on the sludge surface under a high vacuum, leading to crusting, which inhibited crack initiation. VM was raised by 67.9-162.2% from 10.2 to 6.8 mm because the suction force of vacuum on water was much higher than the resistance to water diffusion of small isolation piles at 6.8 mm. Additionally, this study provided essential information to improve existing sludge treatment methods.
Modeling uncertain data using Monte Carlo integration method for clustering

Nowadays, data clustering is an important task to the mining research community since the availability of uncertain data is increasing rapidly in many applications such as weather forecasting, business information management systems. In this work, proposed Monte Carlo integration based uncertain objects modeling technique is compared with three state-of-the-art methods namely, kernel density estimation, Dempster-Shafer, and Monte Carlo simulation. Then Kullback-Leibler and Jeffrey divergences are used to measure the similarity between uncertain objects and merge them with modified DBSCAN and k-medoids clustering algorithms. A heuristic algorithm is proposed to find the optimum radius, which is one of the inputs of DBSCAN. All the experiments are performed on one synthesized dataset and three real datasets namely, weather data, Japanese vowels and activity of daily living data. Five performance measures namely, accuracy, precision, recall, F-score, and Jaccard index are considered for comparing proposed method with state-of-the-art methods. Two non-parametric tests namely, Wilcoxon rank sum and sign test are also conducted. These results denote the effectiveness and efficiency of the proposed method over state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
Double trouble: A case of fraternal twins with iron-refractory iron-deficiency anemia

Iron-refractory iron-deficiency anemia (IRIDA) is a rare autosomal recessive disease that presents in childhood. We report the case of fraternal twins presenting with severe hypochromic microcytic anemia and hypoferritinemia. Two missense mutations affecting the TRMPSS6 gene were identified, consistent with IRIDA. Subsequent parenteral iron therapy improved clinical and blood parameters.
Impact of Virtual Learning on Mothers With Children in Elementary School: A Psychosocial Viewpoint

Background Measuring the impact of virtual learning (VL), specifically psychosocially, and its consequences have been poorly studied because VL has never been implemented in this way before worldwide. To our knowledge, no studies in Saudi Arabia have addressed this topic, with very limited available literature internationally. This study aims to evaluate the psychosocial effects of VL on mothers of children in elementary school and its relation to psychosocial factors. Methodology Using an anonymous online questionnaire posted on social media, a quantitative, observational, cross-sectional study was conducted from May through December 2021 in Riyadh, Kingdom of Saudi Arabia. A total of 460 mothers consented to complete the study questionnaire. The questionnaire consisted of a socioeconomic section and collected information on perceived stress via the Perceived Stress Scale (PSS-14). Results The overall PSS-14 score showed a moderate stress level. Our results showed that as age groups tended to rise, stress scores tended to decline. Diabetes was a significant variable contributing to high stress. Verbal abuse toward a child essentially reflects an increase in stress. Mothers with familial conflicts were more prone to high stress. Conclusions The consequences of VL on mothers and the whole family are alarming. Stress, verbal and physical abuse, and unhealthy family dynamics are strongly associated with such a way of learning. The impact of emotional and behavioral changes among this group of individuals needs to be further investigated.