Miracle-dz/UniFAR-FTUs huggingface dataset:
Olympic Revolution: The Biography of Juan Antonio Samaranch

Juan Antonio Samaranch became President of the Olympic Committee in 1980. He took charge of a movement that was on the edge of collapse. The combined effects of terrorism, financial mismanagement and international boycotts, which had characterised the Games at Munich, Montreal and Moscow, had severely damaged the credibility of the Olympics. Twelve years later Samaranch presides over a highly successful organization, whose future is secure, and whose gross revenue between 1989 and 1992 will exceed $1.8 billion. David Miller has maintained close links with Samaranch throughout his IOC presidency, and with the help of many of Samaranch's colleagues and advisers, has produced a portrait of a man who has, in the course of his presidency, had to travel nearly two hundred million miles, meeting with many of the world's top political leaders, forging compromise and contributing to the stabilization of East-West sporting relations that ultimately produced a virtually boycott-free Seoul Olympics. This book takes the reader through the early days of deep and bitter internal political wrangling where Samaranch's diplomacy was tested to the limit - often reading like the machinations of a spy novel - the controversy surrounding the intense commercialism of the Los Angeles Olympics and the Ben Johnson drug incident.
Limitations to interorganizational knowledge acquisition: The paradox of corporate venture capital

By highlighting conditions under which viable interorganizational relationships do not materialize, we explore the limitations of interorganizational knowledge acquisition. In the empirical context of corporate venture capital (CVC), we analyze a sample of 1,646 start-up-stage ventures that received funding during the 1990s. Under a regime of weak intellectual property protection (IPP), an entrepreneur-CVC investment relationship is less likely to form when the entrepreneurial invention targets the same industry as corporate products. In contrast, under a strong IPP regime, industry overlap is associated with an increase in the likelihood of an investment relationship. Our findings suggest that many relationships do not form because the corporation will not invest unless the entrepreneur discloses his or her invention, and the entrepreneur may be wary of doing so, fearing imitation. To the extent that a CVC has greater capability and inclination to target same-industry ventures, such industry overlap would exacerbate imitation concerns under a weak IPP regime, yet facilitate an investment relationship under a strong IPP regime. Beyond CVC, this insight may explain patterns of other interorganizational relationships, including research and development alliances and technology licensing between start-ups and incumbents.
T Cells in Atherosclerosis: Key Players in the Pathogenesis of Vascular Disease

Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipid-rich plaques within arterial walls. T cells play a pivotal role in the pathogenesis of atherosclerosis in which they help orchestrate immune responses and contribute to plaque development and instability. Here, we discuss the recognition of atherosclerosis-related antigens that may trigger T cell activation together with additional signaling from co-stimulatory molecules and lesional cytokines. Although few studies have indicated candidates for the antigen specificity of T cells in atherosclerosis, further research is needed. Furthermore, we describe the pro-atherogenic and atheroprotective roles of diverse subsets of T cells such as CD4+ helper, CD8+ cytotoxic, invariant natural killer, and γδ T cells. To classify and quantify T cell subsets in atherosclerosis, we summarize current methods to analyze cellular heterogeneity including single cell RNA sequencing and T cell receptor (TCR) sequencing. Further insights into T cell biology will help shed light on the immunopathology of atherosclerosis, inform potential therapeutic interventions, and pave the way for precision medicine approaches in combating cardiovascular disease.
Mining and its Impact on the Environment

1. Introduction 2. Subsidence Due to the Partial Extraction of Stratiform Mineral Deposits 3. Longwall Mining and Subsidence 4. Metalliferous Mining and Subsidence 5. Subsidence Associated with the Abstraction of Fluids 6. Quarrying and Surface Mining 7. Waste Materials from Mining and Their Disposal 8. Mine Effluents and Acid Mine Drainage 9. Dereliction and Contamination Associated with Mining and Related Industries 10. Other Problems Associated with Mining
Predictors of relationship satisfaction during the COVID‐19 pandemic

Prior work and theory suggest many vulnerabilities, stressors, and adaptive processes shape relationship satisfaction. In the current research, we used machine learning to understand which constructs have greater predictive importance for perceived changes in satisfaction since the pandemic began and satisfaction over the prior week. In a large sample collected at the beginning of the pandemic (N = 1873; Study 1), relationship processes were most predictive, explaining up to 70% of variance in satisfaction. Feeling appreciative of one's partner and being satisfied with quality time spent with one's partner were consistently top predictors of satisfaction. We also examined whether these important predictors were associated with changes in relationship satisfaction across the first year of the pandemic in a longitudinal subsample (N = 618; Study 2). Appreciation and satisfaction with quality time were associated with high and relatively stable relationship satisfaction over time.
Limiting the Spread of Fake News on Social Networks by Users as Witnesses

In this paper, we study how users can act as witnesses to limit the spread of fake news. We present a new technique based on consulting a set of users, called witnesses. Before resharing a content <tex>$M$</tex> by a user <tex>$A$</tex>, a set of <tex>$k$</tex> users (witnesses) are selected randomly, by the system, from the friends set of <tex>$A$</tex>. Witnesses are asked to validate the content <tex>$M$</tex> after allowing the user <tex>$A$</tex> to re-share it. If there is an authenticated user <tex>$AU$</tex>, among this set of witnesses that validating <tex>$M$</tex>, the user <tex>$A$</tex> is allowed to re-share it without considering the others <tex>$(k-1)$</tex> responses. In the case where all witnesses are non-authenticated, if at least one witness rejects the content <tex>$M$</tex>, the user <tex>$A$</tex> is not allowed to re-share it. Note that <tex>$A$</tex> has no knowledge about the set of selected witnesses.
A Study of Systemic Approaches to Treating Patients with Physical Illness or Disability in Turkey

ABSTRACT Medical problems can affect a person’s physical health, mental health, relationships, quality of life, and the people with whom the patient is connected. Although we presume mental health professionals in Turkey work with individuals coping with health issues, there has been no prior published research. This study sought to determine how mental health professionals in Turkey approached their patients currently coping with a physical illness or disability and to what degree they collaborated with other care providers. Results revealed limited education on how to work with patients and their families currently coping with illness or disability, low interdisciplinary collaboration, and low family involvement in mental healthcare related to medical issues. The authors discuss how mental healthcare providers can use the findings of this study to advance systemic training and improve the use of systemic approaches for patients and their families coping with illness and disability in Turkey.
Bioinformatics analysis of key pharmacological pathways of proanthocyanidins combined with allicin against atherosclerosis

Objectives: By the ways of Network Pharmacology and Molecular Docking Technology，researching the potential targets and the mechanism of action of proanthocyanidins combined with Allicin against atherosclerosis(AS). Methods: Searching in the Genecards database, collect the targets of Proanthocyanidins, Allicin and AS and try to analyse the common targets of them. Import the common targets of Proanthocyanidins, Allicin and AS into the web platform called Venny2.1. 0 and get the Venn diagram; then after importing the intersecting targets that we got into the STRING database to build up the PPI net, beautify the PPT net by the Cytoscape software which could lead to the target nets of “Medicine and Disease. ” Import the targets of how the medicines is used to treat the AS which is analyzed before into the web platform Metascape to perform GO and KEGG enrichment of analysismedicines and diseases. Make use of vina, pymol, ligplot and GROMACS software to perform the Docking validation and Molecular dynamics studies about the affinities among the medicine ingredient targets and core AS targets. Results: The key targets of Proanthocyanidins combined with Allicin against the relevant proteins of AS include Jun proto-oncogene(JUN), epidermal growth factor receptor(EGFR), mitogen-activated protein kinase 3(MAPK3), tumor necrosis factor (TNF), interleukin 6(IL6) and vascular endothelial growth factor A (VEGFA), etc. According to the enrichment analyses of GO, we find 2 pathways: GO:0000302 response to reactive oxygen species and GO:0009725：response to hormone; According to the enrichment analyses of KEGG, we find that AS closely relates to cancer, Lipids and Fluid shear stress. Docking validation and Molecular dynamics studies show that Proanthocyanidins combined with Allicin are capable to stably combine with eNOS and TNF-α. Conclusion: Proanthocyanidins combined with Allicin can fight against AS by IL-6, TNF, VEGF and JUN and other core targets. Meanwhile, by the pathways of Cancers, Lipids and Fluid shear stress.
On the role of polymeric hydrogels in the thermal response of gold nanorods under NIR laser irradiation

Hydrogels are 3D cross-linked networks of polymeric chains designed to be used in the human body. Nowadays they find widespread applications in the biomedical field and are particularly attractive as drug delivery vectors. However, despite many good results, their release performance is sometimes very quick and uncontrolled, being forced by the high in vivo clearance of body fluids. In this direction, the development of novel responsive nanomaterials promises to overcome the drawbacks of common hydrogels, inducing responsive properties in three-dimensional polymeric devices. In this study, we synthesized and then loaded gold nanorods (Au NRs) within an agarose-carbomer (AC)-based hydrogel obtained from a microwave-assisted polycondensation reaction between carbomer 974P and agarose. The photothermal effect of the composite device was quantified in terms of maximum temperature and spatial–temporal temperature distribution, also during consecutive laser irradiations. This work shows that composite Au NRs loaded within AC hydrogels can serve as a stable photothermal treatment agent with enhanced photothermal efficiency and good thermal stability after consecutive laser irradiations. These results confirm that the composite system produced can exhibit an enhanced thermal effect under NIR laser irradiation, which is expected to lead to great therapeutic advantages for the localized treatment of different diseases.
Design of a Broadband Flat Metasurface Lens by Using One-Dimensional Meander Microstrip-Line Structures

We design a broadband flat metasurface lens whose thickness is much thinner than the wavelength. The concept with one-dimensional (1-D) transmission-line model is recalled and 1-D meander microstrip-line structures are used for the design. Determining the specification of the lens, the line length is calculated based on the design formula. The eigenfrequency analysis is carried out to modify the error of electrical length from the theoretical value, and the line length is optimized. A metasurface lens is constituted with the structures and full-wave simulations are performed to confirm the broadband operation. It is seen from the results that the designed lens can make a focus at any frequency.
Screening the Extract of Laportea bulbifera (Sieb. et Zucc.) Wedd. Based on Active Component Content, Its Antioxidant Capacity and Exploration of Hepatoprotective Activity in Rats

Laportea bulbifera (Sieb. et Zucc.) Wedd. , a plant with a long history of medicinal use, possesses uncertainly defined medicament portions while its antioxidant capacity remains largely unexplored. To gain a better understanding of its medicinal value, this study focused on investigating the Laportea bulbifera aboveground part (LBAP) and the Laportea bulbifera root (LBR). Through an assessment of the bioactive compound content, a significant finding emerged: the LBR exhibited notably higher levels of these bioactive phytochemicals compared to the LBAP. This observation was further reinforced by the antioxidant assays, which demonstrated the superiority of the LBR’s antioxidant capacity. The experimental results unequivocally indicate that the root is the optimal medicament portion for Laportea bulbifera. Furthermore, it was discovered that the presence of alcohol in the extraction solvent significantly enhanced the extraction of active ingredients, with the methanol extract of LBR performing the best among the extracts tested. Consequently, this extract was selected for further research. Leveraging cutting-edge UHPLC-ESI-Q-TOF-MS technology, the methanol extract of LBR was meticulously analyzed, revealing the presence of 41 compounds, primarily belonging to the phenolics and fatty acids. Remarkably, stability experiments demonstrated that the phenolics in the methanol extract maintained their stability across various pH values and during in vitro simulations of the human digestive system, albeit showing gradual degradation under high temperatures. Furthermore, the oxidative stability tests conducted on oils revealed the potential of the methanol extract as a stabilizer for olive oil and sunflower oil. Moreover, oral acute toxicity studies confirmed the low toxicity of the methanol extract, further supporting its safe use for medicinal purposes. Of particular note, histopathological examination and biochemical analysis affirmed the remarkable protective effects of the methanol extract against d-galactosamine-induced liver damage. These findings underscore the therapeutic potential of the methanol extract from the LBR in the treatment of diseases associated with oxidative imbalance.
Development and evaluation of new algorithms for the retrieval of wind and internal wave parameters from shipborne marine radar data

Abstract : The goal of this work is to develop and evaluate techniques for the retrieval of wind and internal wave (IW) information from marine X-band radar data. While ocean wind measurements are crucial for the transfer of energy and momentum across the air-sea interface, IWs play an important role in tidal energy transport. Marine radars work by transmitting microwave energy from a rotating antenna that also measures the backscatter. The radar backscatter from the sea surface is controlled by the wind-generated small ripple waves through the Bragg-scattering mechanism. Surface winds are thus the dominant factor for generating the radar backscatter. The varying surface current fields associated with IWs interact with the ripples, generating rough convergent and smooth divergent zones. Radars are capable of imaging such IW-induced surface signatures as bands of enhanced and weakened backscatter.
Impact of the COVID-19 Pandemic on Cancer Care and Quality of Life for Patients With Breast and Gynecologic Malignancies: A Single-Center Survey-Based Study

This study evaluated the mental health and cancer treatment-related impact of the first wave of the COVID-19 pandemic on patients with breast and gynecologic cancers. An 18-question survey was administered in June 2020 at a New York City-based cancer center to assess the quality of life (QOL) and overall health (OH) during both the pandemic time period from March 1, 2020, through June 30, 2020, and the pre-pandemic period (prior to March 1, 2020). Survey questions were answered on a 5-point Likert scale and a 7-point EORTC QLQ-C30 QOL scale. Differences in mean QOL and OH scores were evaluated using a paired t-test. QOL and OH were significantly worsened by the pandemic, with significant increases in anxiety, depression, and mood swings.
Inhibition of Urban Particulate Matter-Induced Airway Inflammation by RIPK3 through the Regulation of Tight Junction Protein Production

Urban particulate matter (UPM) is a high-hazard cause of various diseases in humans, including in the respiratory tract, skin, heart, and even brain. Unfortunately, there is no established treatment for the damage caused by UPM in the respiratory epithelium. In addition, although RIPK3 is known to induce necroptosis, its intracellular role as a negative regulator in human lungs and bronchial epithelia remains unclear. Here, the endogenous expression of RIPK3 was significantly decreased 6 h after exposure to UPM. In RIPK3-ovexpressed cells, RIPK3 was not moved to the cytoplasm from the nucleus. Interestingly, the overexpression of RIPK3 dramatically decreased TEER and F-actin formation. Its overexpression also decreased the expression of genes for pro-inflammatory cytokines (IL-6 and IL-8) and tight junctions (ZO-1, -2, -3, E-cadherin, and claudin) during UPM-induced airway inflammation. Importantly, overexpression of RIPK3 inhibited the UPM-induced ROS production by inhibiting the activation of iNOS and eNOS and by regulating mitochondrial fission processing. In addition, UPM-induced activation of the iκB and NF-κB signaling pathways was dramatically decreased by RIPK3, and the expression of pro-inflammatory cytokines was decreased by inhibiting the iκB signaling pathway. Our data indicated that RIPK3 is essential for the UPM-induced inflammatory microenvironment to maintain homeostasis. Therefore, we suggest that RIPK3 is a potential therapeutic candidate for UPM-induced pulmonary inflammation.
Ultrasound-Guided Injections of HYADD4 for Knee Osteoarthritis Improves Pain and Functional Outcomes at 3, 6, and 12 Months without Changes in Measured Synovial Fluid, Serum Collagen Biomarkers, or Most Synovial Fluid Biomarker Proteins at 3 Months

Background: Prior studies have demonstrated improved efficacy when intra-articular (IA) therapeutics are injected using ultrasound (US) guidance. The aim of this study was to determine if clinical improvement in pain and function after IA hyaluronic acid injections using US is associated with changes in SF volumes and biomarker proteins at 3 months. Methods: 49 subjects with symptomatic knee OA, BMI < 40, and KL radiographic grade II or III participated. Subjects with adequate aspirated synovial fluid (SF) volumes received two US-guided IA-HA injections of HYADD4 (24 mg/3 mL) 7 days apart. Clinical evaluations at 3, 6, and 12 months included WOMAC, VAS, PCS scores, 6 MWD, and US-measured SF depth. SF and blood were collected at 3 months and analyzed for four serum OA biomarkers and fifteen SF proteins. Results: Statistical differences were observed at 3, 6, and 12 months compared to baseline values, with improvements at 12 months for WOMAC scores (50%), VAS (54%), and PCS scores (24%). MMP10 levels were lower at 3 months without changes in SF volumes, serum levels of C2C, COMP, HA, CPII, or SF levels of IL-1 ra, IL-4, 6, 7, 8, 15, 18, ILGFBP-1, 3, and MMP 1, 2, 3, 8, 9. Baseline clinical features or SF biomarker protein levels did not predict responsiveness at 3 months. Conclusions: Clinical improvements were observed at 12 months using US needle guidance for IA HA, whereas only one SF protein biomarker protein was different at 3 months. Larger studies are needed to identify which SF biomarkers will predict which individual OA patients will receive the greatest benefit from IA therapeutics.
Improved Opinion Mining for Unstructured Data Using Machine Learning Enabling Business Intelligence

—There has been an exponential increase in usage of social informatics in recent years. This makes opinion mining more complex, especially for unstructured data available online. Although a substantial amount of research has been conducted on the COVID pandemic, post-pandemic research is lacking. Our research focuses on design and implementation of opinion mining framework for unstructured data input for business intelligence dealing with post pandemic work environment in industries. In this paper, we implement opinion mining algorithm in combination with machine learning approaches providing a hybrid approach. Transformer architecture Bidirectional Encoder Representations from Transformers language model is implemented to obtain sentence level feature vector of the document corpus and t-distributed stochastic neighbor embedding is implemented for clustering experimental evaluation. In this work, performance evaluation is undertaken using the Intertopic Distance map. By applying a hybrid strategy of natural language processing and machine learning, the results of this study indicate efficient framework development and anticipated to contribute to the improvement of efficacy of opinion mining models compared to existing approaches. This research is significant and will benefit businesses in gaining valuable insights that will lead to improved decision-making and business insights.
DSC-FeedNet Based CSI Feedback in Massive MIMO OTFS Systems

Due to the data rate demand, reliability, and flexibility of 5G cellular systems, quest for new modulation schemes has gained importance. The high mobility use case family is one of the many new use case families for 5G and is one that presents technological difficulties. In the future, it is anticipated that there will be a demand in growth for mobile services in vehicles, high-speed trains as well as aircraft. Orthogonal Time Frequency Space (OTFS) is a recently proposed modulation scheme that is very well-suitable for high-speed scenarios where the Doppler shifts are quite high. To utilize spatial multiplexing, diversity gain, and downlink precoding computations, downlink channel responses need to be fed back to the base station in frequency division duplexing (FDD) operated systems. Acquiring accurate channel state information (CSI) in high mobility scenarios is extremely challenging. Conventional algorithms use compressed sensing but, the process of reconstruction is very slow. Hence, recent techniques use deep learning to compress the CSI with low dimensionality and the original channel matrix is recovered at the base station. This paper proposes a novel deep learning network named Doubly Selective Channel Feedback Network (DSC-FeedNet) which follows the concept of an autoencoder and inception block for reliable reconstruction of channel information. Simulation results show the superior performance of DSCFeedNet compared to the existing fast iterative shrinkage thresholding algorithm (FISTA) network and CSiNet. The proposed network provides an improvement of 11.5 dB, 14.7 dB, 19.55 dB, and 20.54 dB in NMSE over CSiNet for the reconstruction of the original channel matrix for the compression ratios 1/4, 1/8, 1/16, and 1/32 respectively.