ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The Synergistic Effect of Urban Canyon Geometries and Greenery on Outdoor Thermal Comfort in Humid Subtropical Climates

Understanding the synergistic effect of multiple parameters is helpful to urban planners trying to design sustainable cities through a holistic approach. The objective of this research was to investigate how the street aspect ratio (HW), street orientation (AO), and greenery parameters, such as leaf area density (LAD) and aspect ratio of trees (ART), could affect the microclimate and outdoor thermal comfort of street canyons in a central business district under the local climate conditions of Chongqing city. To achieve this goal, a series of single- and multi-parameter simulations which followed an orthogonal design of experiments (ODOE) were conducted. The physiological equivalent temperature (PET) was adopted to assess the results of microclimate simulations for different urban models. The main findings are as follows: 1) The aspect ratio and orientation of urban canyons and ART play significant roles in influencing microclimate variables at the pedestrian level. 2) There is an inverse relationship between the street aspect ratio and T-mrt, and likewise for ART; the highest wind velocity was obtained when the aspect ratio of canyons was 2 and 3, which consequently developed the channeling phenomenon (when the domain wind is prevailing with street direction). 3) The East-West streets and canyons with an HW = 0.5 incur the warmest thermal conditions and longest extreme discomfort durations. 4) Results for the PET and meteorological parameters exhibit less significant variation obtained from different values of LAD than those observed in the other three parameters.
An introduction to block cipher cryptanalysis

Since the introduction of the Data Encryption Standard (DES) in the mid-1970s, block ciphers have played an ever-increasing role in cryptology. Because of the growing number of practical applications relying on their security, block ciphers have received, and are still receiving, a substantial amount of attention from academic cryptanalysts. This has led, over the last decades, to the development of several general techniques to analyze the security of block ciphers. This paper reviews the fundamental principles behind today's state of the art in block cipher cryptanalysis.
AED-Net: A Single Image Dehazing

In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases. Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information. This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images. We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images. Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset. Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics. Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity. Finally, AED-Net is more effective under complex real-world conditions.
A new approach for multiclass motor imagery recognition using pattern image features generated from common spatial patterns

This paper presents a new method that can classify multiple motor imageries and can be implemented in a realistic application because of its low computation time. The method proposes the use of pattern images, generated with the common spatial pattern (CSP) technique. The paper also suggests a new algorithm to determine the best frequency bands for optimal discrimination among the diverse motor imageries to classify. The pattern images and the state images, which represent the mental state of the user in a specific segment of time, are used to compute cross-correlation coefficients. Feature vectors, including characteristics obtained with CSP, and the mean and variance of the correlation coefficients were employed to design binary classifiers with support vector machines. In addition, the work includes a real-time simulation involving a sliding window technique. The proposed method was evaluated in four datasets: IVa, IVb and V from BCI Competition III and another provided by the software BCILAB, which compared with other state-of-the-art methods. The results that overcome surpassed the methods in these competitions and other state-of-the-art methods mentioned in this paper. The method also presents short computation time, robustness between subjects and capability to classify between multiple mental states.
Recent advances in mesoporous silica nanoparticle-based targeted drug-delivery systems for cancer therapy

Targeted drug-delivery systems are a growing research topic in tumor treatment. In recent years, mesoporous silica nanoparticles (MSNs) have been extensively studied and applied in noninvasive and biocompatible drug-delivery systems for tumor therapy due to their outstanding advantages, which include high surface area, large pore volume, tunable pore size, easy surface modification and stable framework. The advances in the application of MSNs for anticancer drug targeting are covered and highlighted in this review, and the challenges and prospects of MSN-based targeted drug-delivery systems are discussed. This review provides new insights for researchers interested in targeted drug-delivery systems against cancer.
Astersaponin I from Aster koraiensis is a natural viral fusion blocker that inhibits the infection of SARS-CoV-2 variants and syncytium formation

The continuous emergence of SARS-CoV-2 variants prolongs COVID-19 pandemic. Although SARS-CoV-2 vaccines and therapeutics are currently available, there is still a need for development of safe and effective drugs against SARS-CoV-2 and also for preparedness for the next pandemic. Here, we discover that astersaponin I (AI), a triterpenoid saponin in Aster koraiensis inhibits SARS-CoV-2 entry pathways at the plasma membrane and within the endosomal compartments mainly by increasing cholesterol content in the plasma membrane and interfering with the fusion of SARS-CoV-2 envelope with the host cell membrane. Moreover, we find that this functional property of AI as a fusion blocker enables it to inhibit the infection with SARS-CoV-2 variants including the Alpha, Beta, Delta, and Omicron with a similar efficacy, and the formation of syncytium, a multinucleated cells driven by SARS-CoV-2 spike protein-mediated cell-to-cell fusion. Finally, we claim that the triterpene backbone as well as the attached hydrophilic sugar moieties of AI are structurally important for its inhibitory activity against the membrane fusion event. Overall, this study demonstrates that AI is a natural viral fusion inhibitor and proposes that it can be a broad-spectrum antiviral agent against current COVID-19 pandemic and future outbreaks of novel viral pathogens.
Development of ZnO nanoflowers-assisted DNAzyme-based electrochemical platform for invertase and glucose oxidase-dominated biosensing

The investigation on invertase (INV) and glucose oxidase (GOx)-dominated biological process offers a new opportunity for the development of clinical diagnosis and prognostic treatment. Herein, a ZnO nanoflowers (ZnONFs)-assisted DNAzyme-based electrochemical platform for INV- and GOx-dominated biosensing is proposed by the change of pH in microenvironment. In this strategy, INV can usually catalyze the dissolution of sucrose to generate glucose, and glucose is then consumed by GOx to produce H2O2 and gluconic acid, in which ZnONFs can be effectively etched into free Zn2+ ions. Subsequently, the released Zn2+ ions have a shearing action for Zn2+-specific DNAzyme, thus triggering hybridization chain reaction along with the imbedding of methylene blue. The excellent electrochemical signals illustrate the method can be employed well for testing sucrose, INV and GOx with a low detection limit (0.019 μM, 0.047 mU/mL and 0.012 mU/mL, respectively). Finally, a series of basic and advanced logic gates (YES, AND, INHIBIT, and AND-AND-INHIBIT) in the biological process are constructed with different logic inputs, providing a valuable platform for the establishment of advanced molecular devices for bioanalysis and clinical diagnostics.
A fast arbitrary factor video resizing algorithm

We present a new algorithm for resizing video frames in the discrete cosine transform (DCT) space. We demonstrate that a frame resizing operation can be represented as multiplication by fixed matrices and propose a computation scheme which is applicable to any DCT-based compression method. The proposed approach is general enough to accommodate resizing operations with arbitrary factors conforming to the syntax of 16 x 16 macroblocks. The approach is shown to possess significant computational gain over the faster known state of the art algorithms while achieving similar picture quality.
Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification

Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.
Angiotensin II Regulates Mitochondrial mTOR Pathway Activity Dependent on Acyl-CoA Synthetase 4 in Adrenocortical Cells

Two well-known protein complexes in mammalian cells, mTOR type 1 and type 2 (mTORC1/2) are involved in several cellular processes such as protein synthesis, cell proliferation, and commonly dysregulated in cancer. An acyl-CoA synthetase type 4 (ACSL4) is one of the most recently mTORC1/2 regulators described, in breast cancer cells. The expression of ACSL4 is hormone-regulated in adrenocortical cells and required for steroid biosynthesis. mTORC1/2 have been reported to be crucial in the proliferation of human adrenocortical tumor cells H295R and interestingly reported at several subcellular locations, which has brought cell biology to the vanguard of the mTOR signaling field. In the present work, we study the regulation of mTORC1/2 activation by angiotensin II (Ang II)-the trophic hormone for adrenocortical cells-the subcellular localization of mTORC1/2 signaling proteins and the role of ACSL4 in the regulation of this pathway, in H295R cells. Ang II promotes activation by phosphorylation of mTORC1/2 pathway proteins in a time-dependent manner. Mitochondrial pools of ribosomal protein S6, protein kinase B (Akt) in threonine 308, and serine 473 and Rictor are phosphorylated and activated. Glycogen synthase kinase type 3 (GSK3) is phosphorylated and inactivated in mitochondria, favoring mTORC1 activation. Epidermal growth factor, a classic mTORC1/2 activator, promoted unique activation kinetics of mTORC1/2 pathway, except for Akt phosphorylation. Here, we demonstrate that ACSL4 is necessary for mTORC1/2 effectors phosphorylation and H295R proliferation, triggered by Ang II. Ang II promotes activation of mitochondrial mTORC1/2 signaling proteins, through ACSL4, with a direct effect on adrenocortical cellular proliferation.
Arctic Arts with Pride: Discourses on Arctic Arts, Culture and Sustainability

There has been growing interest in Arctic arts and culture as well as in sustainability among artists, researchers, and policy makers. However, until recently, the comprehension of Arctic arts and culture within the framework of sustainable development has remained vague. In this study, by analysing diverse debates from the Arctic Arts Summit 2019 in Rovaniemi, we investigate how the arts and culture sector promotes Arctic sustainability. An analysis of abstracts, conclusions, blogs and newspaper articles reflecting the presentations, art events, exhibitions and dialogues showed that the discourse on sustainability is organised around five themes: (1) global politics and ecological crises as part of the cultural politics of the Arctic; (2) indigenous and non-indigenous Arctic arts and culture; (3) 'handmade' and the material culture of the Arctic; (4) place-making, revitalisation and regional development; and (5) economy and sustainability. These partly interlinked themes have relevance for policy making, defining principles for arts and culture funding, artistic practice and research on the Arctic. In addition, education and artistic training are important for all of the five themes; therefore, resources for educational institutions are crucial for the sustainable future of the Arctic. Arts, culture and education have the potential to empower people in the Arctic, increase cultural pride, educate and inform global audiences and create connectedness between the past, present and future. Arts, culture and education contribute to Arctic sustainability.
Evaluation of the automated reference toolset as a method to select reference plots for oil and gas reclamation on Colorado Plateau rangelands

Rangelands are typically characterized by low precipitation and low biomass which makes them susceptible to disturbance and difficult to reclaim. These characteristics become a management issue when considering the widespread and significant impact of oil and gas development on rangelands. Reclamation from this land use involves the complexities of dealing with multiple state and federal agencies, private landowners, and their sometimes conflicting rules. Reference plots (e.g., nearby undisturbed sites) can help with these issues by providing an objective context for reclamation planning. They are selected to provide a comparison that is similar to a reclamation site in most aspects except for the disturbance activity. This allows for the relative condition of the reclamation site to be determined. Because selection of reference plots is normally expert-driven on a site-by-site basis, it can be time consuming and thus ineffective in helping to meet reclamation goals over large landscapes. The Automated Reference Tool (ART) was developed to improve the efficiency and efficacy of reference plot selection. The ART improves reference plot selection through remote sensing and indicators of land potential by selecting reference plots of similar land potential to the reclamation site based on soil texture, topography, and geology. We evaluated the ART in the context of well-pad reclamation to determine if ART-selected plots were appropriate to use as reference when compared to an existing reference plot network. We applied the ART to reclamation sites managed by the Bureau of Land Management's (BLM) White River Field Office, Colorado which had existing expert-selected reference plots. We found that the ART-selected reference plots and their matching expert-selected reference plot had similar large-scale vegetative cover characteristics (total foliar: R-2 = 0.34, p-value = 0.0012) and dissimilar finer-scale cover characteristics (plant diversity: R-2 = 0.079, p-value = 0.15). In addition, we detected similarities in their soil water content (R-2 = 0.43, p-value<0.001), depth to restricting layer (RMSD = 21.90), and rock fragment (RMSD = 19.99). These results demonstrate that ART could be a useful tool for managers to help meet their reclamation goals over large landscapes, but it is not a complete automation of the reference selection process.
Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO With One-Dimensional Antenna Array

In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propose a sparse channel representation with a super-resolution sampling grid and a hidden Markovian support. Based on this, we develop a novel approximate inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method.
Feature-Preserving Noise Removal

Conventional image restoration algorithms use transform-domain filters, which separate the noise from the sparse signal among the transform components or apply spatial smoothing filters in real space whose design relies on prior assumptions about the noise statistics. These filters also reduce the information content of the image by suppressing spatial frequencies or by recognizing only a limited set of shapes. Here we show that denoising can be efficiently done using a nonlinear filter, which operates along patch neighborhoods and multiple copies of the original image. The use of patches enables the algorithm to account for spatial correlations in the random field whereas the multiple copies are used to recognize the noise statistics. The nonlinear filter, which is implemented by a hierarchical multistage system of multilayer perceptrons, outperforms state-of-the-art denoising algorithms such as those based on collaborative filtering and total variation. Compared to conventional denoising algorithms, our filter can restore images without blurring them, making it attractive for use in medical imaging where the preservation of anatomical details is critical.
Local performing arts and recovery from the Great East Japan earthquake and tsunami: A descriptive qualitative study

The purpose of this study is to understand the role of local performing arts in disaster recovery after the 2011 Great East Japan earthquake and tsunami. A qualitative descriptive methodology was used to obtain insights into local people's experiences and perspectives. Three main methods were used to collect data: semi-structured group interviews with five traditional preservation societies including 13 participants, as well as interviews with two key informants, a questionnaire survey with 53 respondents, and field observation and visual data. The study setting was an area called Karakuwa, which is located in Miyagi Prefecture. Four themes with 11 associated sub-themes were generated: 1. Locality, 2. Connections with other areas, 3. Connections within the community, and 4. Psychosocial support. Findings indicated that local performing arts are embedded in people's lives and are based on knowledge and experiences that have been transmitted through generations. Although the people experienced the tsunami, they gradually began to perform again within the community as a means of regaining their identity and encouraging themselves and others. In addition, they began to perform to non-locals to show their appreciation for the assistance they had received and in attempts to increase the number of people involved in the activities. A key implication for policy is that, in disaster-recovery efforts, it is necessary to invest in local performing arts in order to help people restore their ways of life and promote ties among locals and connections with non-locals.
Hybrid absorbing boundary conditions based on fast nonuniform grid integration for nonconvex scatterers

Novel hybrid (local-global) absorbing boundary conditions (ABCs) art proposed for the truncation of computational domains infinite-method analysis of open-region problems. The pertinent boundary integration is accelerated by the nonuniform grid-interpolation scheme, resulting in a fast algorithm,for treating electrically large objects with largely concave outer boundaries. (C) 2004 Wiley Periodicals, Inc.
LDC: Lightweight Dense CNN for Edge Detection

This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC.
State-of-the art CAPEX data for water electrolysers, and their impact on renewable hydrogen price settings

Within the framework of the Hydrogen Implementing Agreement (HIA) of the International Energy Agency (IEA), a new Task 38 was started early 2016, entitled "Power-to-Hydrogen and Hydrogen-to-X: System Analysis of techno-economic, legal and regulatory conditions". Within this framework, a specific task force was set-up for the compilation of state-of-the-art technical and economical data on large-scale water electrolyser systems, both based on PEM and alkaline technology. The objectives set forward have been twofold. Firstly, to offer policy makers and industry with comprehensive trends and guidelines for further electrolyser cost reduction (CAPEX, in Euro/kW) into the MW-scale. Secondly, to provide objective technological & economic arguments for converging towards a realistic electrolytic (and hence renewable) H-2 market price (in Euro/kg). This should help water electrolysis to become competitive with SMR technology for (local) H-2 production, and hence to start making H-2 a competitive fuel. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.