Miracle-dz/UniFAR-FTUs huggingface dataset:
MUCOADHESIVE BUCCAL FILMS: A NOVEL APPROACH FOR THE DELIVERY OF ANTI-HYPERTENSIVE DRUGS

The buccal drug delivery system is a prominent route of administration for drug delivery through the buccal mucosa. It is rich in blood supply with more surface area for rapid absorption as well as provides direct entry of drugs from the site of application into the systemic circulation through the jugular vein. Buccal drug delivery systems consist of various approaches such as lozenges, wafers, gels, microparticles, patches or films from which mucoadhesive buccal film is an attractive dosage form in terms of flexibility and high systemic bioavailability. Since most of the antihypertensive drugs show first-pass metabolism which leads to less oral bioavailability generally up to 20–50%. Thus, incorporation of antihypertensive drugs in mucoadhesive buccal films using mucoadhesive polymers can provide higher systemic bioavailability. The films can be formulated using various techniques such as solvent casting method and hot extrusion melt method. These films can be evaluated based on various characteristics to determine their efficacy and performance such as tensile strength, mucoadhesion residence time, and kinetic release data analysis. They have various advantages over conventional solid oral dosage forms, hence are preferable for the preparation of antihypertensive drug-loaded buccal films. Mucoadhesive buccal films of antihypertensive drugs can also provide controlled drug delivery with enhanced bioavailability.
Performance Evaluation of Engineered Geothermal Systems Using Discrete Fracture Network Simulations

This paper summarizes the major conclusions of a panel commissioned by the US Department of Energy (DOE) to study the behavior of engineered geothermal systems (EGS) using discrete fracture network (DFN) approaches. Sandia National Laboratories (Sandia) and Lawrence Berkeley National Laboratory (LBNL) directed the study, and they in turn selected the Itasca Consulting Group (Itasca), Golder Associates Inc. (Golder), Lawrence Livermore National Laboratory (LLNL), and LBNL to develop numerical experiments using DFN models to determine the conditions under which EGS may be viable and to understand its critical parameters and sensitivities. The results of this study have recently been published in electronic form (Kennedy et al, 2021). The major conclusions included the following: (1) A successful EGS system will likely involve multiple fractures and multiple zones. These may be achieved either by multi-staged stimulations, as in unconventional oil and gas developments, or by stimulation methods that develop critical shearing stresses on natural fractures. (2) Optimizing well layouts to develop EGS by stimulating natural fractures needs to consider the geometry of the natural fractures and the in-situ stress field. (3) A multiple fracture or stimulation system will require either separate management of each stimulation to avoid premature breakthrough on the most transmissive pathways or will require an active management approach that shuts down the more permeable pathways as they experience thermal breakthrough at the production well. (4) Chemical effects are important particularly to reservoir longevity and demonstrations of a hybrid DFN-continuum approach provide a potential way forward. (5) Although most of the simulations in this report were two dimensional, , three- dimensional effects may be important for depth gradients of temperature and stress, which vary significantly with regional tectonic settings and influence the extent of EGS applicability within the USA.
Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.
Preliminary phytochemical screening and antibacterial activity of Samadera indica.

The plant Samadera indica is widely used as an antimicrobial agent from ancient times. According to literature, this plant has been less explored for its antimicrobial activity. In this study, the antibacterial activity of this plant was studied using hexane and toluene extracts by agar well diffusion method. Phytochemical study was carried out to find the presence of various phytoconstituents. Zone of inhibition was observed, which led to the conclusion that these extracts of Samadera indica possess antibacterial activity.
Convalescent Plasma: An Evidence-Based Old Therapy to Treat Novel Coronavirus Patients

Novel Coronavirus (nCoV-2019) is a highly infectious viral outbreak that has so far infected more than 110 million people worldwide. Fast viral transmission and high infection rates have severely affected the entire population, especially the old aged and comorbid individuals leaving significantly less time to find some effective treatment strategy. In these challenging times, convalescent plasma (CP) therapy came as a ray of hope to save humankind. It is a form of passive immunization that has been used to treat various infectious diseases since 1890, including the 1918 Spanish flu, 2002/03 SARS-CoV, 2009 H1N1, 2012 MERS-CoV, and 2014 Ebola outbreak. The transfusion includes administration of CP containing a high value of neutralizing antibodies against the virus in hospitalized patients. This chapter summarizes the potential outcome of CP therapy in the treatment of nCoV-2019 patients.
Pneumonia classification with capsule network by using x-ray images

Pneumonia, an infectious disease that can influence the lungs, is a severe medical field topic. Therefore, how to correctly classify images of pneumonia is very important. The limitations of traditional machine learning algorithms and the significant improvement of computing performance make deep learning widely used. At present, using a convolutional neural network to classify pneumonia is still the mainstream method. This paper provides a modified capsule network to detect and classify pneumonia by using X-ray pictures. The model consists of two parts: encoder and decoder. Encoder contains convolutional layer, primary capsule layer, and digital capsule layer. The primary capsule layer and digital capsule layer convert a scalar into a vector and then try to cluster vectors of the same category by dynamic routing. The decoder contains a deconvolutional layer. The image is reconstructed by up-sampling the vector generated by the encoder, and the reconstructed image is compared with the original image to make the features extracted by the encoder more representative. The training and testing process takes place on the dataset "Labeled Optical Coherence Tomography (OCT) and Chest XRay Images for Classification. " This dataset contains a total of 5856 pictures. We divide the images into the training set and testing set at a ratio of 8:2. The accuracy rate on this dataset is 98.6%. This model has a more straightforward structure and fewer parameters than other popular models, which means that it can be more easily deployed in various conditions in practical applications.
A dataset to evaluate Hindi Word Embeddings

The current trend to solve different challenges of Natural Language Processing utilizes various online crawling methods to fetch the data and applying different shallow or deep learning methods to develop models for the respective tasks on this data. Word vectors generated using such methods are being applied for several NLP challenges and such vectors are being evaluated on word similarity task. Not only huge data is available but also multiple datasets are available for the English language to evaluate the performance of the developed models. However, the scenario is not the same for Indian languages specifically for Hindi. Focusing this challenge, we propose a dataset to check word similarity in Hindi. The construction process and afterwards annotation process are described in details. To construct this dataset, first, 353 word-pairs from the most popular English dataset are selected and translated. Their translations are verified by Hindi Experts. These word pairs are finally annotated independently by 11 native Hindi speakers. Multiple criteria have been set to select the annotators for this task. The final dataset has been evaluated on CBOW and Skip-gram models.
Encapsulation of Natural Flavors in Cyclodextrins: Free Volume Studies by PALS

Positron annihilation lifetime spectroscopy (PALS) is used to study the free-volume in β-cyclodextrin (CD) with the encapsulation of two different essential oils: garlic (Allium sativum) and marjoram (Origanum majorana). The data reveal the presence of a long lifetime component related to the o-Ps. It is observed that the addition of garlic (or marjoram) oil to β-CD results in a decrease of the o-Ps lifetime which was ascribed to a reduction of the free volume holes from 83.6 to 67.7 Å3 (79.1 to 72.6 Å3). Different mechanisms for the encapsulation of garlic and marjoram oils in β-CD are suggested by the PALS parameters.
Modified grey wolf optimization for global maximum power point tracking under partial shading conditions in photovoltaic system

In the study of photovoltaic (PV) system, power‐voltage (P‐V) curves exposed to view several peaks under partial shaded condition (PSC), which brings about muddled and most extreme maximum power point tracking (MPPT) process. Under uniform weather conditions, regular MPPT algorithms such as perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC) work in an effective manner. However, these conventional methods are unable to track global peak successfully under PSC. In this context, the evolutionary algorithms such as grey wolf optimization (GWO) perform better than conventional algorithms. However, the conventional GWO is not sufficient for exploration point of view to locate global best particles; and moreover, GWO deteriorates the convergence process. To overcome these drawbacks, a modified GWO (MGWO) is proposed in this paper to track global best particle, which improves the convergence process under static condition and as well as re‐initialization of parameters under dynamic conditions. The proposed method is verified using simulations as well as using experimental results. The obtained results demonstrate superiority compared to conventional GWO and HC methods under partial shaded patterns of PV array.
Indigenous Environmental Justice within Marine Ecosystems: A Systematic Review of the Literature on Indigenous Peoples’ Involvement in Marine Governance and Management

We develop and apply a systematic review methodology to identify and understand how the peer-reviewed literature characterises Indigenous peoples’ involvement in marine governance and management approaches in terms of equity and justice worldwide. We reviewed the peer-reviewed English-language research articles between January 2015 and September 2020 for examples of Indigenous peoples’ involvement in marine governance and management using the analytical lens of environmental justice. The majority of research studies highlighted that Indigenous peoples experienced some form of environmental injustice linked to existing marine governance and management, most notably in the context of inequitable decision-making procedures surrounding the establishment and operation of marine protected areas. However, there are significant gaps in the current literature, including a notable absence of studies exploring Indigenous women and other gender minorities’ involvement in marine planning and management and the limited number of studies about Indigenous peoples living throughout Asia, the Arctic, Russia, and Africa. More studies are needed to explore collaborative and intersectional approaches, including co-governance and co-management and ecosystem-based management, and critically evaluate what constitutes inclusive, equitable, and just marine governance and management processes, practices, and outcomes for different Indigenous peoples occupying diverse social–ecological systems.
Digitalization and new technologies for sustainable business models at the ship–port interface: a bibliometric analysis

ABSTRACT Drawing on the business model innovation theory and the resilience theory, the present study explores existing literature on the capacity of digitalization and new technologies for the sustainable and innovative development of shipping and seaports; more in details, it identifies the shipping and seaport business models that can meet environmental, economic and social goals through the digitalization of operational processes in the ship–port interface. This paper aims to investigate whether digitalization and new technologies can help in the creation of sustainable business models as set out in the United Nations (UN) 2030 Agenda for Sustainable Development Goals (SDGs) adopted by all UN member states in 2015. The methodology is based on a qualitative approach, conducting a systematic literature review followed by a bibliometric analysis of a database containing 132 items in English language publications between 1969 and 2020. The findings indicate that literature on the topic has concentrated mainly on the environmental externalities connected with operations at the ship–port interface. The study offers theoretical and practical contributions, outlining the main issues debated in the literature on digitalization and new technologies in the shipping and seaport industry, and analyses the key variables that will play a role in achieving the SDGs.
Facile synthesis of nitrogen-doped and boron-doped reduced graphene oxide using radio-frequency plasma for supercapacitors

Heteroatom doping is an effective method to improve the capacitive performance of graphene-based materials. In this work, a facile and efficient radio-frequency (RF) plasma treatment strategy has been employed to achieve simultaneous doping and reduction of graphene oxide (GO). As a result, boron-doped and nitrogen-doped reduced graphene oxide (denoted as B-rGO and N-rGO) have been synthesized rapidly under relatively low temperatures compared with conventional thermal methods. The B-rGO and N-rGO present significantly improved specific capacitances as high as 345 F g−1 and 365 F g−1 at 0.2 A g−1, respectively, exhibiting a fourfold increase compared to that of GO before plasma treatment. Interestingly, the N-rGO shows better rate capability than the B-rGO. Furthermore, the mechanism of simultaneous doping and reduction by RF plasma treatment is discussed based on the diagnosis of emission spectroscopy. The high energy electrons and plasma-excited ions and radicals render effective reduction, etching, and doping of GO at the same time. Compared with high-temperature carbonization and wet chemical methods, our plasma treatment method is more energy-saving and eco-friendly. We believe this rapid and straightforward plasma treatment method reported here can be extended to the incorporation of various heteroatoms into graphene lattice for broad applications.
Novel Approach to detect Anomalies using Defensive Algorithm in SDN flows

The exponential growth of network users has to lead to poor management of networks that use the traditional networking approach. Traditional networking approaches have become an overhead in terms of flexibility, innovations, complexity, and programmability among the network. SDN guarantees a holistic approach to network flexibility and programmability. Network visibility in SDN gives scope for rapid innovation. SDN being a new paradigm, less work has been done towards security. Security is one of the biggest concerns in SDN. Separation of control and data plane in SDN has to lead to the emergence of Denial of Service (DoS) attack. The centralized controller in SDN makes it the best target for attackers and acts as a single point of failure. Attacks on the SDN controller can bring the entire network down. This paper presents an approach to monitor traffic and we propose a novel method to mitigate these anomalies and attacks in the network. We believe that the DoS attack can be toned down using this new technique.
Towards the design of a quick and universal questionnaire to assess the intuitiveness of products

Abstract The goal of this research is to design a tool to assess intuitiveness of products. Existing scales such as INTUI (Diefenbach and Ullrich 2015) show some limitations when used by children and/or for evaluating non-digital products. We aim to obtain a more universal questionnaire tool. INTUI questionnaire measures the components of an intuitive interaction (Effortlessness, Gut feeling, Magical experience, Verbalizability and Intuitiveness) through 17 items. After removing or rephrasing several items, we tested a revised version (8 items) with 68 participants (children and adults) who performed a task (a drawing for children and subtractions for adults) with a digital device (tactile tablet for children and smartphone for adults) and a non-digital device (paper and pencil). The results led us to remove the “Verbalizability” and “Gut feeling” dimensions which were difficult to understand and inconsistent with the conceptual model. The final version of the questionnaire (5 items) including three dimensions (Effortlessness, Magical Experience and Intuitiveness) was tested with 69 adults to evaluate a Coffee Dispenser Machine. Both “Effortlessness” and “Magical experience” dimensions seem to reliably predict intuitiveness.
The Effect of Earnings Opacity and Tax Avoidance on Companies Value

Companies in Indonesia have carried out many earnings modification practices where earnings modification is the attitude of management to choose accounting policy with various purposes or motives, namely opportunistic motives and efficiency motives. Opportunistic motive if the modification aims to provide welfare for management, while the efficient motive is if it aims to increase the market value of the company. This study functions to analyze the effect of earnings opacity , tax avoidance , price earnings ratio, return on assets, and leverage on the value of food and beverage companies listed on theIDX in 2018-2020, with a sample of 15 companies based on the purposive sampling method. The research method used is a quantitative method. Analysis of the information used in this study is multiple regression analysis. The results prove that earnings opacity and tax avoidance have a negative effect on companies value
Immersion-based holographic wave front printer setup for volume holographic retinal projection elements

The recording of computer-generated holographic optical elements (HOEs) via the concept of holographic wave front printing has been a topic of rising interest in many research groups over the last years. Especially for applications in augmented reality (AR), holographic wave front printing has the potential to realize HOEs with complex optical transformations and high diffraction efficiencies while maintaining excellent transmittance. Here, we present a novel immersion-based holographic wave front printer setup, which allows the recording of reflection volume holographic optical elements (vHOEs) in both on-axis and off-axis configurations. HOEs fabricated via our wave front printing process are made up of individual sub-holograms, so called Hogels. Each sub-hologram is recorded via two phase-only reflective spatial light modulators (SLMs). Large-area vHOEs are achieved by adjacent recording of multiple Hogels in a step-wise fashion. Our immersion-based holographic printer setup ensures a high numerical aperture for the recording configuration, that is directly linked to a wide angular range in which recorded wave fronts can be replayed in air configuration. As a possible AR application, we demonstrate the recording of a holographic combiner for retinal projection. A single eye box is projected in the user's field of view (FOV) by means of a scanned laser projector source. Each Hogel of the holographic combiner performs an individual wave front transformation of large off-axis to on-axis angles, which contributes to the global holographic transfer function of the vHOE. Haze and clarity analysis of the recorded vHOE confirm high transmittance, which is crucial for AR applications.
Compact four port slot based MIMO antenna with isolation enhancement

ABSTRACT This paper presents the design of a compact arc shape slot antenna and its application in multiple inputs and multiple-output (MIMO) communications. The unit cell antenna has a footprint of 15 × 15 mm2. The antenna with arc shape stub loaded feedline provides broadband impedance matching extending from 3 GHz– 9.5 GHz. The antenna is made dual-polarised using orthogonal feed structure. A four-port, two-element MIMO antenna is constructed by replicating the unit cell design in a parallel fashion. Broadband isolation enhancement is achieved by using a modulated microstrip line embedded with circular patches of varying diameters. The four-port MIMO antenna provides 10 dB additional enhancement in comparison with the unmodulated microstrip line decoupling unit cell. The prototype antenna is fabricated and the simulation results are verified using experimental measurements. The antenna offers an average peak gain of 5.2 dBi and efficiency greater than 75%. The MIMO parameters such as envelope correlation coefficient, diversity gain, mean effective gain are evaluated and presented. The proposed antenna is one of the most compact 4 port solution reported in the literature.
Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.