Miracle-dz/UniFAR-FTUs huggingface dataset:
Li Storage Properties of Disordered Graphene Nanosheets

Graphene has aroused intensive interest because of its unique structure, superior properties, and various promising applications. Graphene nanostructures with significant disorder and defects have been considered to be poor materials because disorder and defects lower their electrical conductivity. In this paper, we report that highly disordered graphene nanosheets can find promising applications in high-capacity Li ion batteries because of their exceptionally high reversible capacities (794−1054 mA h/g) and good cyclic stability. To understand the Li storage mechanism of graphene nanosheets, we have prepared graphene nanosheets with structural parameters tunable via different reduction methods including hydrazine reduction, low-temperature pyrolysis, and electron beam irradiation. The effects of these parameters on Li storage properties were investigated systematically. A key structural parameter, Raman intensity ratio of D bands to G bands, has been identified to evaluate the reversible capacity. The gr.
Notiﬁcation and Prediction of Heap Management Pauses in Managed Languages for Latency Stable Systems

An increasing number of high-performance distributed systems are executed on top of runtime environments like Java Runtime Environment (JRE), such as Cassandra [1], Hadoop [2], Spark [3], Hazelcast [4], Alluxio [5], Hive [6], and Re-thinkDB [7]. The developers are attracted to a managed language like Java as it offers services like Garbage Collection (GC), run-time type checking, reference checking, and cross-platform execution. However, many applications running on the JVM (e.g., big data frameworks such as Hadoop, data stores such as Cassandra) suffer from long garbage collection (GC) time. The long pause time due to Stop-The-World (STW) during Garbage-Collection (GC) not only degrades application throughput and causes long latency, but also hurts overall system efﬁciency and scalability. To address these problems, we implement M IT M EM that provides JVM support to cut millisecond level tail latencies induced by GC. M IT M EM is a JVM drop-in replacement, requires no conﬁguration and can run off-the-shelf Java applications with a minimum modiﬁcation (e.g., adding 120 LOC to integrate with Cassandra). Our experiments indicate that the M IT M EM -powered Cassandra successfully reduces the tail latency up to 99%.
A Knowledgeable Itemset Recommendation Using Systolic Tree Structure

: Data is being created in great extend in the current era. The challenge is to handle the huge amount of data and to work with the same. The existing system provides a system to identify the frequent itemsets in the accurate manner but if the data is huge and for transactional databases it is difficult to identify the frequent itemsets. The proposed system uses the systolic tree mechanism in which the frequent pattern extraction process for the transaction database is done easily and accurately. Systolic tree based rule mining scheme is enhanced for weighted association rule mining (WARM) process. Automatic weight estimation scheme is used in the system. The proposed system improves the weight estimation process along with the utilized span
USING SIMPLE DYNAMIC ANALYTIC FRAMEWORK TO CHARACTERIZE AND FORECAST EPIDEMICS

Mathematical modeling provides a powerful analytic framework to investigate the transmission and control of infectious diseases. However, the reliability of the results stemming from modeling studies heavily depend on the validity of assumptions underlying the models as well as the quality of data that is employed to calibrate them. When substantial uncertainty about the epidemiology of newly emerging diseases (e.g. the generation interval, asymptomatic transmission) hampers the application of mechanistic models that incorporate modes of transmission and parameters characterizing the natural history of the disease, phenomenological growth models provide a starting point to make inferences about key transmission parameters, such as the reproduction number, and forecast the trajectory of the epidemic in order to inform public health policies. We describe in detail the methodology and application of three phenomenological growth models, the generalized-growth model, generalized logistic growth model and the Richards model in context of the COVID-19 epidemic in Pakistan.
Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

Many practical studies rely on hypothesis testing procedures applied to data sets with missing information. An important part of the analysis is to determine the impact of the missing data on the performance of the test, and this can be done by properly quantifying the relative (to complete data) amount of available information. The problem is directly motivated by applications to studies, such as linkage analyses and haplotype-based association projects, designed to identify genetic contributions to complex diseases. In the genetic studies the relative information measures are needed for the experimental design, technology comparison, interpretation of the data, and for understanding the behavior of some of the inference tools. The central difficulties in constructing such information measures arise from the multiple, and sometimes conflicting, aims in practice. For large samples, we show that a satisfactory, likelihood-based general solution exists by using appropriate forms of the relative Kullback--Leibler information, and that the proposed measures are computationally inexpensive given the maximized likelihoods with the observed data. Two measures are introduced, under the null and alternative hypothesis respectively. We exemplify the measures on data coming from mapping studies on the inflammatory bowel disease and diabetes. For small-sample problems, which appear rather frequently in practice and sometimes in disguised forms (e.g., measuring individual contributions to a large study), the robust Bayesian approach holds great promise, though the choice of a general-purpose"default prior"is a very challenging problem.
DiseaseImplications for Airway Inflammatory Via C-Type Lectin and Integrin Receptors: Allergen- Containing Pollen Starch Granules Alveolar Macrophages Bind and Phagocytose

2 † Recent studies suggest that IgE-independent mechanisms of airway inﬂammation contribute signiﬁcantly to the pathophysiology of allergic airway inﬂammatory diseases such as asthma. Such mechanisms may involve direct interactions between inhaled allergens and cells of the respiratory tract such as macrophages, dendritic cells, and epithelial cells. In this study, we investigated receptor-mediated interactions occurring between alveolar macrophages and allergen-containing pollen starch granules (PSG). We report here that PSG are released from a range of grass species and are rapidly bound and phagocytosed by alveolar macrophages. Human monocyte-derived dendritic cells also bound PSG but no internalization was observed. Phagocytosis of PSG was dependent on Mg 2 1 and Ca 2 1 and was inhibited by neo-glycoproteins such as galactose-BSA and N -acetylgalactose-BSA. Partial inhibition of phagocytosis was also seen with the Arg-Gly-Asp-Ser (RGDS) motif and with an anti-CD18 mAb (OX42). The combination of both neo-glycoprotein and anti-CD18 achieved the greatest degree of inhibition ( > 90%). Together, these data suggest a role for both C-type lectins and b 2 -integrins in the binding and internalization of PSG. The consequences of this interaction included a rapid up-regulation of inducible NO synthase mRNA and subsequent release of NO by alveolar macrophages. Thus, receptor-mediated recognition of inhaled allergenic particles by alveolar macrophages may represent a potential mechanism for modulating the inﬂammatory response associated with allergic airway diseases such as asthma. The Journal of Immunology, 2000, 164: 3878–3886.
Mobile Chatbot for Information Search

- A chatterbot or chatbot aims to make a conversation between both human and machine. It can also be built as a question answering system. A chatbot for student score searching is implemented in this system. To process the user requests and to provide efficient information the chatbot is developed with different services and skills. Watson Assistant service is the main service which acts as core of the chatbot. Watson Assistant service has the skeleton of conversations with intents and entities. Input is categorized with the intent and delivers output based on available entities. The entire knowledge of chatbot is stored in a SQL database which is known as knowledge base. The input from user can be fetched by Speech to Text service. Likewise, output is displayed in text as well as with voice by Text to Speech service. To develop intuitive and self-explanatory user interface for the user android studio is used with java language
Real-time RGBD-based Extended Body Pose Estimation

We present a system for real-time RGBD-based estimation of 3D human pose. We use parametric 3D deformable human mesh model (SMPL-X) as a representation and focus on the real-time estimation of parameters for the body pose, hands pose and facial expression from Kinect Azure RGB-D camera. We train estimators of body pose and facial expression parameters. Both estimators use previously published landmark extractors as input and custom annotated datasets for supervision, while hand pose is estimated directly by a previously published method. We combine the predictions of those estimators into a temporally-smooth human pose. We train the facial expression extractor on a large talking face dataset, which we annotate with facial expression parameters. For the body pose we collect and annotate a dataset of 56 people captured from a rig of 5 Kinect Azure RGB-D cameras and use it together with a large motion capture AMASS dataset. Our RGB-D body pose model outperforms the state-of-the-art RGB-only methods and works on the same level of accuracy compared to a slower RGB-D optimization-based solution. The combined system runs at 25 FPS on a server with a single GPU. The code will be available at saic-violet.github. io/rgbd-kinect-pose
Byzantine Fault-tolerant State-machine Replication from a Systems Perspective

Byzantine fault-tolerant (BFT) state-machine replication makes it possible to design systems that are resilient against arbitrary faults, a requirement considered crucial for an increasing number of use cases such as permissioned blockchains, firewalls, and SCADA systems. Unfortunately, the strong fault-tolerance guarantees provided by BFT replication protocols come at the cost of a high complexity, which is why it is inherently difficult to correctly implement BFT systems in practice. This is all the more true with regard to the plethora of solutions and ideas that have been developed in recent years to improve performance, availability, or resource efficiency. This survey aims at facilitating the task of building BFT systems by presenting an overview of state-of-the-art techniques and analyzing their practical implications, for example, with respect to applicability and composability. In particular, this includes problems that arise in the context of concrete implementations, but which are often times passed over in literature. Starting with an in-depth discussion of the most important architectural building blocks of a BFT system (i.e., clients, agreement protocol, execution stage), the survey then focuses on selected approaches and mechanisms addressing specific tasks such as checkpointing and recovery.
An Evaluation of Symmetries in Ground Reaction Forces during Self-Paced Single- and Dual-Task Treadmill Walking in the Able-Bodied Men

Gait is a complex autonomous activity that has long been viewed as a symmetrical locomotion, even when it adapts to secondary concurrent attention-demanding tasks. This study aimed to evaluate the symmetry of the three ground reaction forces (GRFs) in able-bodied individuals during self-paced treadmill walking with and without concurrent cognitive demands. Twenty-five male participants (age: 34.00 ± 4.44 years) completed two gait assessment sessions, each of whom were familiarized with the walking trials during their first session. Both sessions involved six-minute self-paced treadmill walking under three conditions: single-task walking and walking while concurrently responding to auditory 1-back and 2-back memory tasks. The symmetry of the GRFs was estimated using a nonlinear approach. Changes in the symmetry and walking speed across conditions in both sessions were assessed using inferential statistics. Results demonstrated that the three GRFs deviated from perfect symmetry by ≥10%. Engaging working memory during walking significantly reduced the symmetry of the vertical GRF (p = 0.003), and its detrimental effects on walking speed were significantly reduced in the second session with respect to the first session (p < 0.05). The findings indicate imperfect gait symmetry in able-bodied individuals, suggesting that common perceptions of gait symmetry should be reconsidered to reflect its objective importance in clinical settings.
Overcoming The Vanishing Gradient Problem Of Recurrent Neural Networks In The Iso 9001 Quality Management Audit Reports Classification

: Deep neural networks specifically the Recurrent Neural Networks (RNN) generally suffer from the vanishing gradient problem. However, with proper implementation of hyper parameters, activation functions and appropriate choose of text representation models which purposely fit for the recurrent neural networks it can optimize and stabilize the gradient and prevent it from being vanish. This paper, focus to resolve the issue of the vanishing gradient problem of the recurrent neural network models while also improving the quality audit procedures through the application of data mining and deep learning models that’s automatically classify an audit reports based on the ISO 9001 Quality Management System Requirements. A total of ninety (90) recurrent neural networks were developed to investigate the influence of the combined word representation model, combined activation functions and implementation of dropouts’ method on the learning ability of the Long Short-Term Memory (LSTM) and Bidirectional-LSTM recurrent neural networks. The highest average classification accuracy of LSTM is 85.27% while the highest average classification accuracy of the BLSTM is 88.01%
On the Validation of Chrono::Granular

This report summarizes a set of validation tests for Chrono::Granular, a module that is part of the Chrono dynamics simulation engine [1, 2]. These tests range from simple one-sphere simulations, to complex scenarios in which many spheres interact with external objects represented as meshes. The Chrono::Granular results are compared against analytical results or data published in the literature. No non-physical behavior is observed; the results match the expected outcomes.
EVALUATION OF THE EFFICIENCY OF AQUEOUS AND ALCOHOLIC EXTRACT OF THEVETIA (THEVETIA PERUVIANA) AND ITS COMPARISON WITH PESTICIDE ORTUS IN CONTROLLING THE BROAD MITE POLYPHAGOTARSONEMUS LATUS (BANKS) (ACARI: TARSONEMIDAE), ON KARISMA PEPPER IN GREENHOUSE

The experiment was conducted in one of the greenhouses of the Plant Protection Department / College of Agricultural Engineering Sciences / University of Baghdad / Jadiriya to evaluate the efficacy of the alcoholic and aqueous extract of the plant Thevetia (Thevetia peruviana )and compare it with the pesticide Ortus on the stages of the broad mite (Polyphagotarsonemus latus) on sweet pepper Karisma. The results showed that the alcoholic extract was superior to the aqueous extract against all stages of the broad mite after three weeks of treatment. The relative efficiency of the alcoholic and aqueous extracts was 80.38% and 75.94% respectively on egg stage, whereas the pesticide Ortus was 87.5% at the larvae stage a relative efficiency of 86.17%, 78.63% for the alcoholic and aqueous extracts respectively and 90.33% for the pesticide. The relative efficiency of the nymphs was 86.94% and 80.39% for the alcoholic and aqueous extracts respectively, whereas the pesticide Ortus was 91.72%. The relative efficacy of adult was 87.22%, 80.22% and 91.78% for alcoholic, aqueous and Ortus extracts respectively.
ÖĞRETMENLERİN ÖRGÜTSEL ADALET KONUSUNDAKİ ALGILARININ ÖRGÜTSEL BAĞLILIKLARI ÜZERİNDEKİ ETKİSİ THE EFFECTS OF TEACHERS’ ORGANIZATIONAL JUSTICE PERCEPTIONS ON ORGANIZATIONAL COMMITMENT

Organizational justice perception enables the individual to determine whether he or she, in an organization, is treated fairly or not. As a consequence of this perception, positive or negative attitudes towards work are acquired. This study based on relational screening model, aimed to identify the level of teacher’s organizational justice perception and the variables affecting this perception. It also intended to explain to what extend does this concept of justice effect their organizational commitment. A sample of 225 teachers and school administrators, working in secondary level schools under TRNC Ministry of Education, Youth and Sports, are involved in the study. Niehoff and Moorman’s (1993) “Organizational Justice Perception Scale” and “Organizational Commitment Scale” (Allen and Meyer, 1990) were used. In addition, Cronbach’s Alpha Test has been performed in order to test the internal consistency. This study tried to reflect if there existed a missing notion of justice within these institutions and investigated the sources for lack of communication or schism faced by teachers.
Assessing the Impact of Heterogeneous Traffic on Highways via Agent-Based Simulations

Rules that govern highways are very different across countries. For instance, in the USA, most of the highway traffic is comprised of cars and trucks; whereas in India, there are more types of vehicles that use the highway. Each vehicle type has its characteristics and capabilities, thus causing variation in driver preferences, especially the preferred-speed. We investigate if this heterogeneity leads to an increase in the number of lane changes, which could potentially lead to an increase in accidents. We use Agent-Based Modeling to compare the interaction between vehicles in two simulations, one representative of the traffic in the USA and the second representative of traffic in India. The results show that increased heterogeneity in vehicle types causes a significant increase in the number of lane changes. These results have broader implications for traffic policy-making and bring into focus the need for minimum-speed limits and dedicated lanes for slower vehicles.
WEED MANAGEMENT IN TRANSPLANTED ONION (ALLIUM CEPA L.) THROUGH EARLY POST-EMERGENCE HERBICIDES

A field experiment was conducted to evaluate different weed management practices in onion during rabi 2016-17 to 2017-18 at the Agricultural Farm, PalliSiksha Bhavana, Visva-Bharati, Sriniketan, West Bengal. Theexperiment was laid out in randomized block design having eleven treatments with three replications. The pooled results revealed that highest bulb yield (22.07 t ha1) was obtained from weed free treatment over the remaining treatments. Among herbicidal treatments, oxyfluorfen 20% DF at 3000 g ha-1 recorded significantly highest bulb yield (21.85 t ha-1) than oxyfluorfen 23.5% EC (850 ml ha-1), propaquizafop 10% EC (625ml ha-1) and pendimethalin 38.7% EC (1750 ml ha-1). However, bulb yield produced through the treatment oxyfluorfen 20% DF at 3000 g ha-1 was at par with other doses of oxyfluorfen 20% DF at 2000, 1000 and 750 g ha-1. Among herbicidal treatments the weed control efficiency was highest in oxyfluorfen 20% DF at 3000 g ha-1 than the rest of the treatments. Highest B:C ratio (1.638) was obtained with oxyfluorfen 20% DF at 1000 g ha-1 but it was very close to oxyfluorfen 20% DF at 750 g ha-1 (1.626). Yield loss due to weeds was to the extent of 30.68 %.
Simultaneous Differential Determination of Inorganic Acid and Nitrate in DMF Solution by Nonaqueous Conductometric Titration with Sodium Tetrahydroborate

Generally the degree of electrolytic dissociation of an aqua (protic solvent) is larger than that of a nonaqueous solvent (aprotic solvent). And the electric conductivity of an inorganic salt dissolved in a nonaqueous solvent is smaller than in an aqueous solution. Therefore the differential of electric conductivity was conspicuous because electric conductivity when dissolved with a solute is low in a nonaqueous solvent. The differential determination by conductometric titration of mixed solution of inorganic salts was possible in a nonaqueous solvent such as N,N-dimethylformamide (DMF). The simultaneous differential determination of nitrate and inorganic acid in solution was conducted by nonaqueous conductometric titration with sodium tetrahydroborate (STHB) in DMF. Inflection points appeared on conductometric titration curves of nitrate and inorganic acid solution in DMF, and were due to differences in reactivity and acidity. The inflection points indicated concentrations of nitrate and inorganic acid simultaneously. STHB and Al(NO3)3 reacted at a mole ratio of 1:3, and Co(NO3)2 reacted at 1:2. It was considered that the reaction mole ratio of STHB and nitrate was fixed by the valence of the metal ion. In order to investigate the reaction mechanism, ions in the DMF solution and precipitates were identified during titration by fourier transformation infrared spectroscopy (FT-IR), inductive coupling plasma emission spectrometry (ICP-AES) and the X-ray diffraction method (XRD). 10 % water in the sample solution did not interfere with the titration. The determination limit was 0.01-0.005 mol dm-3 with this method.
Comparative Study of Rural Macrocell (RMa) and Urban Macrocell (UMa) Propagations and their Power Delay Profile (PDP) for Millimeter Wave 5G Cellular Network in LOS Communication

– This paper analyses the performance of Rural Macrocell (RMa) and Urban Macrocell (UMi) propagations based on their power delay profiles (PDP) for some specific frequencies to make a proper judgement on the selection of these frequencies in Urban Macrocell (UMa) and Rural Macrocell (RMa) propagation. The overall judgement was done for Line of sight (LOS) communication for the directional propagation. The simulation procedure has performed in NYUSIM software developed by New-York University (NYU) USA which uses the MATLAB in the backend to perform. Later on, the performances have evaluated based on their directional power delay profiles (PDP). Some parameters like the Pathloss, Pathloss exponent, and Received power has played the key role to measure the performance. The results have shown with their characteristics curve generated for the frequencies like 16, 20, 28, 37, 48, 56, 60, 71, 82GHz through Origin software and finally, the outcomes have compared with each other both in UMa and RMa propagation and some suggestions are suggested for the use of millimeter wave frequency bands in 5G cellular communication.