Miracle-dz/UniFAR-FTUs huggingface dataset:
Formulation and in vitro Evaluation of Diacerein Loaded Transferosomal Topical Gel for the Effective Treatment of Osteoarthritis

Aim: The main objective of the current research work was to formulate and evaluate Diacerein-loaded transferosomal topical gel to overcome the drawbacks associated with oral administration of Diacerein in Osteo arthritis treatment. Study Design: Transferosomal topical gel Methodology: Transferosomes were developed using different ratios of Phospholipid® 90G as phospholipid and Span-60 as surfactant and cholesterol as fluidity buffer. The selected best formulation of transferosomes was further optimized as a topical gel using different rate controlling polymers such as hydroxylethyl cellulose (HEC), hydroxyl propyl methylcellulose (HPMC) and, carbopol. Transcutol P was used as a penetration enhancer. In vitro diffusion studies of developed formulations were carried out using Franz diffusion cell apparatus. Results: The formulations prepared with Hydroxy ethyl cellulose (TBG1, TBG2, TBG3), HPMC (TBG4, TBG5, TBG6) was shown rapid drug release when compared to formulations prepared with carbopol (TBG7, TBG8, TBG9) are showing controlled drug release.Where the formulation prepared with carbopol with 0.75% concentration has shown acceptable extended drug release for 12 hours with acceptable other physico-chemical properties. Conclusion: It can be concluded that Diacerein-loaded transferosomal gel can be administered topically for the treatment of osteoarthritis with reduced oral side effects.
Machine Learning Methods for Predicting League of Legends Game Outcome

The video game League of Legends has several professional leagues and tournaments that offer prizes reaching several million dollars, making it one of the most followed games in the Esports scene. This article addresses the prediction of the winning team in professional matches of the game, using only pregame data. We propose to improve the accuracy of the models trained with the features offered by the game application programming interface (API). To this end, new features are built to collect interesting information, such as the skills of a player handling a certain champion, the synergies between players of the same team or the ability of a player to beat another player. Then, we perform feature selection and train different classification algorithms aiming at obtaining the best model. Experimental results show classification accuracy above 0.70, which is comparable to the results of other proposals presented in the literature, but with the added benefit of using few samples and not requiring the use of external sources to collect additional statistics.
A Review of Alpha-1 Antitrypsin Binding Partners for Immune Regulation and Potential Therapeutic Application

Alpha-1 antitrypsin (AAT) is the canonical serine protease inhibitor of neutrophil-derived proteases and can modulate innate immune mechanisms through its anti-inflammatory activities mediated by a broad spectrum of protein, cytokine, and cell surface interactions. AAT contains a reactive methionine residue that is critical for its protease-specific binding capacity, whereby AAT entraps the protease on cleavage of its reactive centre loop, neutralises its activity by key changes in its tertiary structure, and permits removal of the AAT-protease complex from the circulation. Recently, however, the immunomodulatory role of AAT has come increasingly to the fore with several prominent studies focused on lipid or protein-protein interactions that are predominantly mediated through electrostatic, glycan, or hydrophobic potential binding sites. The aim of this review was to investigate the spectrum of AAT molecular interactions, with newer studies supporting a potential therapeutic paradigm for AAT augmentation therapy in disorders in which a chronic immune response is strongly linked.
Bandwidth-aware Scheduling Repair Technique in Erasure-coded Clusters Design and Analysis

Erasure codes offer a storage-efficient redundancy mechanism for maintaining data availability guarantees in storage clusters, yet also incur high network traffic consumption and recovery time in failure repair. Extensive research has been carried out to reduce the recovery time. However, previous works either target specific erasure code constructions which are not commonly used in todays distributed storage clusters or neglect the bandwidth heterogeneous property in real network environments. Since erasure-coded clusters are typically composed of multi-node with heterogeneous bandwidth and accessed in parallel, the whole recovery time is mainly restricted by the low-bandwidth links. In this paper, we propose SMFRepair, a single-node multi-level forwarding repair technique that is designed to improve the performance in heterogeneous networks based on Reed-Solomon codes for general fault tolerance. SMFRepair carefully selects the helper nodes and uses idle nodes to bypass low-bandwidth links. Idle nodes have sufficient and unused network bandwidth. It also pipelines the repair links that are optimized by idle nodes. Furthermore, a multi-node scheduling repair technique, called MSRepair, is proposed. MSRepair carefully schedules the multi-node repair link to saturate the most unoccupied bandwidth and transfers data from as large-bandwidth links as possible, with the primary objective of minimizing the recovery time. Large-scale simulation and Amazon EC2 real experiments show that compared to state-of-the-art repair techniques, SMFRepair can accelerate the single-node recovery by up to 47.69%, and MSRepair can reduce the multi-node recovery time by 33.78%~67.53%.
Deep Learning-Based Fake Information Detection and Influence Evaluation

With the prevalence of the Internet, a large number of users have participated in OSN (Online Social Networks), which has gradually made it the mainstream way for obtaining news or information from the Internet. However, with the rapid development of the Internet, a large amount of fake information has also been spread on the Internet. Therefore, fake information detection is of great significance at the moment. A multimodal fake information detection method is proposed in this article, which has adopted the textual and visual contents in the piece of information to make the judgments. The textual feature representation vector is firstly obtained through the pretraining of the Bert model, and then the visual feature representation is obtained through the pretraining of the VGG-19 model. From the proposed method, two MCBP (Multimodal Compact Bilinear Pooling) modules are adopted. The first MCBP module is adopted to obtain the visual feature representation vector with attention, and the second MCBP module is adopted to join the visual feature with the attention mechanism and the textual feature vector. Then, the joined vector can be adopted for fake information detection. The proposed method in this article is compared with two baseline methods. The experimental results on the Twitter and Weibo datasets have proved that the proposed method in this article is better than the EANN method and the SpotFake method in terms of accuracy, precision, recall, and F1 score.
Hemorrhagic Transformation in Acute Ischemic Stroke: A Quantitative Systematic Review

The prevalence and risk factors of hemorrhagic transformation (HT) after acute ischemic stroke HT have not been adequately delineated. We performed a systematic review and meta-analysis to identify English-language prospective observational MEDLINE and EMBASE-listed reports of acute ischemic stroke with HT published from 1985–2017. Studies that used the ECASS-2 definitions of hemorrhagic transformation subtypes, hemorrhagic infarction (HI), and parenchymal hematoma (PH) were included. Patients treated with intravenous thrombolysis with tissue plasminogen activator (IV-tPA) were compared with those who did not receive thrombolysis. A total of 65 studies with 17,259 patients met inclusion criteria. Overall, HT prevalence was 27%; 32% in patients receiving IV-tPA vs. 20% in those without. Overall PH prevalence was 9%; 12% in IV-tPA treated patients vs. 5% in those without. HT was associated with a history of atrial fibrillation (OR 2.94) and use of anticoagulants (OR 2.47). HT patients had higher NIHSS (Hedge’s-G 0.96) and larger infarct volume (diffusion-weighted MRI, Hedge’s-G 0.8). In IV-tPA treated patients, PH correlated with antiplatelet (OR 3) and statin treatment (OR 4). HT (OR 3) and PH (OR 8) were associated with a poor outcome at 90-day (mRS 5–6). Hemorrhagic transformation is a frequent complication of acute ischemic stroke and is associated with poor outcome. Recognition of risk factors for HT and PH may reduce their incidence and severity.
Skills to Drive: Successor Features for Autonomous Highway Pilot

Reinforcement learning applications are spreading among different domains, including autonomous vehicle control. The diverse situations that can happen during, for instance, at a highway commute are infinite, and with labeled data, the perfect coverage of all use-cases sounds ambitious. However, with the complex tasks and complicated scenarios faced during an autonomous vehicle system design, the credit assignment problem arises. How to construct appropriate objectives for the artificial intelligence to learn and the preferences between the different goals also matter of the designer’s choice. This work attempts to tackle the problem by utilizing successor features and providing a possible decomposition of the reward functions, guiding the agent’s actions. This method makes the training easier for the agent and enables immediate, profound performance on new combined tasks. Furthermore, with the optimal composition, the desired behavior can be fine-tuned, and as an auxiliary gain, the decomposition empowers different driving styles and makes driving preferences rapidly changeable. We introduce the adaptation of FastRL algorithm to autonomous vehicle domain, meanwhile developing a stabilizing way of using Successor Features, namely DoubleFastRL. We compare our solution for a highway driving scenario with basic agents such as Q-learning having multi-objective training.
Control Strategies for Soft Robot Systems

Soft robots have recently attracted increased attention because their characteristics of low‐cost fabrication, durability, and deformability make them uniquely suited for applications in bio‐integrated systems. Being fundamentally different from traditional rigid robots, soft robots exhibit properties of infinite degrees of freedom (DOF) and nonlinear materials properties that require innovations in control systems. With the rapid development of materials science, robotics, and artificial intelligence, the diversification of actuator mechanisms and algorithms has enabled a wide range of unique control strategies. This review summarizes the basics of actuator mechanisms and control strategies, including open‐loop control, closed‐loop control, and autonomous control, and discusses their implementation from diversified perspectives. Control strategies are evaluated based on their compatibility with materials sets, application goals, and implementation route. The emerging directions are forecasted from the perspectives of interfacing between controller and actuator, underactuated control strategies, and implementation of artificial intelligence (AI).
Influence of Different Nanometals Implemented in PMMA Bone Cement on Biological and Mechanical Properties

Cemented arthroplasty is a common process to fix prostheses when a patient becomes older and his/her bone quality deteriorates. The applied cements are biocompatible, can transfer loads, and dampen vibrations, but do not provide antibacterial protection. The present work is aimed at the development of cement with antibacterial effectivity achieved with the implementation of nanoparticles of different metals. The powders of Ag, Cu with particles size in a range of 10–30 nm (Cu10) and 70–100 nm (Cu70), AgCu, and Ni were added to PMMA cement. Their influence on compression strength, wettability, and antibacterial properties of cement was assessed. The surface topography of samples was examined with biological and scanning electron microscopy. The mechanical properties were determined by compression tests. A contact angle was observed with a goniometer. The biological tests included an assessment of cytotoxicity (XTT test on human cells Saos-2 line) and bacteria viability exposure (6 months). The cements with Ag and Cu nanopowders were free of bacteria. For AgCu and Ni nanoparticles, the bacterial solution became denser over time and, after 6 months, the bacteria clustered into conglomerates, creating a biofilm. All metal powders in their native form in direct contact reduce the number of eukaryotic cells. Cell viability is the least limited by Ag and Cu particles of smaller size. All samples demonstrated hydrophobic nature in the wettability test. The mechanical strength was not significantly affected by the additions of metal powders. The nanometal particles incorporated in PMMA-based bone cement can introduce long-term resistance against bacteria, not resulting in any serious deterioration of compression strength.
A Review of Finite Element Studies in String Musical Instruments

String instruments are complex mechanical vibrating systems, in terms of both structure and fluid–structure interaction. Here, a review study of the modeling and simulation of stringed musical instruments via the finite element method (FEM) is presented. The paper is focused on the methods capable of simulating (I) the soundboard behavior in bowed, plucked and hammered string musical instruments; (II) the assembled musical instrument box behavior in bowed and plucked instruments; (III) the fluid–structure interaction of assembled musical instruments; and (IV) the interaction of a musical instrument’s resonance box with the surrounding air. Due to the complexity and the high computational demands, a numerical model including all the parts and the full geometry of the instrument resonance box, the fluid–structure interaction and the interaction with the surrounding air has not yet been simulated.
Update on Management of Cardiovascular Diseases in Women

Cardiovascular diseases (CVD) have a lower prevalence in women than men; although, a higher mortality rate and a poorer prognosis are more common in women. However, there is a misperception of CVD female risk since women have commonly been considered more protected so that the real threat is vastly underestimated. Consequently, female patients are more likely to be treated less aggressively, and a lower rate of diagnostic and interventional procedures is performed in women than in men. In addition, there are substantial sex differences in CVD, so different strategies are needed. This review aims to evaluate the main gender-specific approaches in CVD.
Study on the Perception Mechanism of Utricles Based on Bionic Models

Background: The relationship between utricle diseases and structural lesions is not very clear in the clinic due to the complexity and delicacy of the utricle structure. Therefore, it is necessary to study the perception mechanism of the utricle. Methods: Imitating the sensory cells in the macula of the utricle, a symmetrical metal core PVDF fiber (SMPF) was designed as a bionic hair sensor to fabricate a bionic macula (BM), a bionic macula with sand (BMS) and a bionic utricle (BU). Then experiments were carried out on them. Results: This indicated the SMPF sensor can sense its bending deformation, which was similar to the sensory cell. The amplitude of the output charges of the SMPF in BMS and BU were significantly improved. The SMPF, whose electrode boundary was perpendicular to the impact direction, exhibited the largest output charges. Conclusion: The presence of otoliths and endolymph can improve the sensing ability of the utricle. The human brain can judge the direction of head linear accelerations based on the location of the sensory cell in the macula that produces the largest nerve signals. This provides a possibility of studying utricle abnormal functions in vitro in the future.
Molecular Testing of Atypical Thyroid Nodules with Corresponding Surgical Correlation: Five-Year Retrospective Review in Veterans Population

Objective We report the results of a retrospective five-year study within a veteran population aimed at correlating abnormal thyroid fine-needle aspiration (FNA) diagnosis with associated molecular testing to the histology of the surgical resection. Methods A retrospective analysis of abnormal thyroid FNAs with associated molecular testing and surgical outcome was conducted from January 1, 2015 to December 31, 2020. Aspirates were classified using the Bethesda system for reporting thyroid cytopathology, including atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), follicular neoplasm/suspicious for follicular neoplasm (FN/SFN), suspicious for malignancy (SM), and malignant. Pertinent data, including patient demographics, imaging, and ancillary testing were reviewed. A thyroid cancer mutation panel assessing the most common mutations and rearrangements associated with neoplasia was utilized. The results of molecular testing were directly compared and correlated with final cytological and histological diagnosis. Results A total of 1850 thyroid aspirates were performed, 200 of which were given an abnormal cytologic diagnosis. Thirty-six samples were submitted for molecular testing and subsequent surgical follow-up. Four were called malignant on cytology. 32 were placed in an indeterminate category (89%). Within indeterminate cases: 53% exhibited positive molecular mutations (n=17), 34% no mutation detected (n=11), and 13% insufficient quantity for testing (n=4). Upon surgical resection in the mutation-positive group: 18% had no malignancy (n=3), and the remaining 82% were positive for malignancy (n=14). Mutations in the histologically malignant group included: 57% BRAF (n=8), 21% NRAS (n=3), 7% HRAS (n=1), 7% KRAS (n=1), and 7% PAX8/PPAR gamma (n=1). In indeterminate cases with no mutation detected, 10 cases were found to be benign, and one case of malignancy was diagnosed. The probability of indeterminate diagnosis in combination with no mutation yielded a 91% chance of benign entity and 9% chance of malignancy. We demonstrated 93% sensitivity and 91% negative predictive value (NPV) for the risk of malignancy in indeterminate cytology specimens with ancillary molecular testing. There was 77% specificity and 82% positive predictive value (PPV) for our data set. Conclusions In indeterminate samples, the detection of a mutation was highly predictive of malignancy and a strong indicating factor for surgery with a high sensitivity and NPV. Molecular testing refined or established the diagnosis in 89% of the cases. Our results indicate that molecular testing of thyroid nodules enhances the accuracy of FNA cytology and the subsequent surgical outcome.
Forcing faults diagnosis in K steps in discrete event systems

Discrete Event Systems occur naturally in engineering practice and include industrial processes, production systems, robotics, among others. So, its diagnosis is important. The aim of this work is to present a new approach to enforce faults diagnosis in Discrete Event Systems modeled by Interpreted Petri nets without limited to much its language. This approach is based on a Regulation Circuit that reduces the relative distance between any two pair of transitions that prevent the detection of the firing of transitions whose occurrence indicates that a fault occurred. By modifying the initial marks of the net that make up the Regulation Circuit, language limitation is avoided, and the diagnosis is established in k steps. First, the terminology used in the Interpreted Petri nets and the diagnosis are established, then the relationship between theses nets and the Regulation Circuit is reviewed to determine the characterization of nets where they can be added, they are analysed and then the proposal is made. The proposal is an option to force faults diagnosis in binary Interpreted Petri nets that are not diagnosable without limiting so much its language that represents their behavior.
Utilization of Palm Kernel Oil (PKO) as Vegetable Oil in Making Mayonnaise with the Addition of Virgin Coconut Oil (VCO) and Palm Cooking Oil (PCO)

Palm kernel oil (PKO) is as oil to make mayonnaise. Mayonnaise is a mixture of vegetable oil with lime, sugar, salt, and egg yolk as an emulsifier that will form an emulsion system. This study aimed to determine the effect of adding vegetable oil to the quality of mayonnaise from PKO. The analysis carried out on the PKO is the analysis of free fatty acids, levels of impurities, and water content. Furthermore, PKO can be used in the manufacture of mayonnaise. Therefore, making mayonnaise with PKO can be analyzed by analyzing fat, protein, water, and sensory/organoleptic tests. The results showed that the addition of various types of vegetable oil: virgin coconut oil (VCO) and palm cooking oil (PCO), to based mayonnaise affected the water content, fat content, protein content, and sensory (color, aroma, taste, texture) of mayonnaise. Mayonnaise composition based on panelists assessment was obtained by mayonnaise with a composition of PKO + VCO (1:1) which contains 76.36% fat content, 8.27% water content, and 1.613% protein content.
Design of compact microstrip-fed triple-band slot antenna with defected ground structure for wireless communications

This article presents the design of a compact microstrip-fed triple-band slot antenna for WLAN/WiMAX/C-band downlink satellite communications. The newly designed proposed simple structure has a considerably high-center frequency ratio and can significantly suppress cross-polarization levels. This antenna consists of a double annular-ring slot (DARS), two L- and one I-shaped slot on the radiator, and an ellipse-shaped-slit (ESS) based defective ground structure (DGS) is imposed. Furthermore, to achieve a better impedance bandwidth and impedance matching, an insert-fed has been implemented on the proposed structure. The proposed antenna has the advantages of the easy tuned of individual resonant frequency and relatively simple antenna structure. The fabricated prototype resonates at 11.13% (2.257–2.523 GHz), 5.63% (3.455–3.655 GHz) and 6.05% (4.012–4.262 GHz) and gives a gain of 4.05, 2.52 and 2.15 dBi, respectively, and cross-polarization nearly −38.85 dB. The transmission line model of the proposed antenna is presented, and it shows the antenna behavior based on the effect of each element. It is observed that the characteristics of the RLC circuit model result is close to the simulated results using the CST simulator.
Design of flexible pavement thickness using stabilized high plastic soil by means of fly ash-based geopolymer

ABSTRACT This paper presents an approach for stabilising high plastic soil (HPS) with fly ash-based geopolymer and its application in flexible pavement. The mechanical performance of HPS with various proportions of fly ash (FA) (10, 15, 20, 25, and 30%) based geopolymer were evaluated through a series of experimental investigations. The soil was mixed with sodium hydroxide of 6 and 8 molar concentrations (6M & 8M) and a sodium silicate mixture of ratio 1.5 was used as alkaline activators for (FA). The results clearly indicate that the mechanical performance of (FA)-based geopolymer stabilised HPS increases up to 25% (FA) content and was less prone to swelling and shrinkage behaviour. Also, the microstructure of HPS was studied through the Scanning Electronic Microscope (SEM) with energy dispersive x-ray (EDX), X-ray Diffraction (XRD), and Fourier Transformed Infrared Spectra (FTIR), and results showed significant alteration of the microstructure of HPS due to the formation of geopolymeric gel. The application of stabilised HPS is demonstrated for the design of flexible pavement thickness for low and high-volume roads. It has been observed that there is a significant reduction in pavement thickness for stabilised HPS compared to HPS. Thus, it can be used as a sustainable alternative to conventional stabilisers.