Miracle-dz/UniFAR-FTUs huggingface dataset:
NATURAL SOURCES SCREENING FOR ANTIMICROBIAL AGENT OF HERBS, SPICES, AND EXTRACTS: A SEMI-QUALITATIVE STUDY antimicrobial and

Indonesia has been recognized for its rich natural ingredients such as spices, herbs, and extracts for decades. Furthermore, these components also have been used as an herbal medicine for a long time ago. Meanwhile, the apparent capability comparison of several components on antimicrobial activity has not been updated yet. This study was conducting the comparison of antimicrobial activity for several materials such as noni ( Morinda citrifolia ), garlic ( Allium sativum ), celery ( Apium graveolens ), galangal ( Alpinia galangal ), ginger ( Zingiber officinale ), yellow turmeric ( Curcuma longa ), lime ( Citrus aurantifolia ), papaya ( Carica papaya ) leaf, betel ( Piper betle ) leaf, and cutcherry ( Kaempferia galangal ) using agar dilution method. Two types of bacteria are used for the test, namely gram-negative bacteria and gram-positive bacteria, with a total of seven bacteria. The media used were TSA (Trypticase Soy Agar) media for gram-negative bacteria and MRSA (Methicillin-Resistant Staphylococcus Aureus) media for gram-positive bacteria. This study had been conducted by using a fast screening method, which referred to a semi-qualitative method. Several components, such as noni, lime, and betel leaf, showed a significant result of antimicrobial activity. Otherwise, other compounds, surprisingly, could not suppress bacterial growth.
RED v2: Enhancing RED Dataset for Multi-Label Emotion Detection

RED (Romanian Emotion Dataset) is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts, containing single-label annotated tweets with one of the following emotions: joy, fear, sadness, anger and neutral. In this work, we propose REDv2, an open-source extension of RED by adding two more emotions, trust and surprise, and by widening the annotation schema so that the resulted novel dataset is multi-label. We show the overall reliability of our dataset by computing inter-annotator agreements per tweet using a formula suitable for our annotation setup and we aggregate all annotators’ opinions into two variants of ground truth, one suitable for multi-label classification and the other suitable for text regression. We propose strong baselines with two transformer models, the Romanian BERT and the multilingual XLM-Roberta model, in both categorical and regression settings.
Pulsar Searches at Effelsberg

We report on the progress of our search for highly dispersed pulsars near the Galactic Center at 5 GHz using the 100-m radio tele-scope in Effelsberg. We also present key aspects of our new survey for millisecond pulsars at 21 em in parts of the northern sky. This survey will greatly benefit from the L-band multibeam receiver and a new FFT-based backend which are currently under construction at the MPlfR.
Consumers’ Views and Responses Towards Sales Promotion Strategies in Pakistan: Fresh Evidence from Second Generational Approach

This study aims to analyze the responses and views of Pakistani consumers to different sales-promotion techniques used by companies. The methodological contribution of the current study used the second-generation technique (Smart PLS) as compared to OLS. In addition, the instrument used in this study is a fully structured questionnaire adapted, a total of 200 questionnaires is filled from the respondents. The findings of the current study show that different sales promotions tools have different effects on consumer buying behaviour. We observed significant evidence of an association between buy one get one free, coupon discount, price discount, and Pakistani consumer buying behaviour. We further revealed an insufficient association between free samples and buying behaviour. This study will be helpful for the marketing managers in understanding what attracts consumers most? It can be an efficient source of relevant information for marketing managers about the responses of consumers towards sales promotions.
Proceedings of the workshop on Analyzing Real Data with Formal Concept Analysis (RealDataFCA’2021)

COVID data are usually presented in a non-structured format and mainly focused on healthy issues (incidence, mortality, etc). At the same time, Governments have designed a set of measures to deal with the Pandemic. In addition, several institutions have studied the economical effects of the situation in each country. In this work, we combine these three data sources and illustrate how Formal Concept Analysis can become a useful tool to discover relationships among these three views of the situation: health, politics and economy. Our aim is to provide an implication-driven approach to discover knowledge behind the data.
SenseHandle : Investigating Human-Door Interaction Behaviour for Authentication in the Physical World

This work explores the integration of behavioural biometrics in the physical world. We developed SenseHandle , a system to unobtrusively measure users’ interactions with door handles, thus, enabling authentication on demand. Our system is based on consumer sensing technologies from related work for easy replicability and can be non-invasively integrated into existing environments with lever-style door handles. From an initial pilot test with four participants we compare the performance of the technologies we used and discuss possible improvements and applications beyond authentication
Sensitivity of precipitation forecast skill to the parameterisation of moist convection in COSMO-based ensemble systems

the of the than the In addition to this, the comparison of the Percentage of Outliers in the two systems shows a reduction in the fraction of observed points lying outside the maximum or minimum forecast value in Cleps20bt. These results suggest that the use of a probabilistic system in which a multiple moist convection formulation is used, provides the opportunity to have a more comprehensive description of the uncertainties in total precipitation forecast linked to the sub-grid cumulus representation.
Examining Relationships between Stability and Composition in the Tropopause Transition Layer

The interface separating the upper troposphere from the lower stratosphere is called the tropopause, a com- plex transition layer contributing to many dynamical aspects of the atmosphere. In this study, observational data is obtained from the ESRL Ozonesonde Data Archive with a multidecadal time-span from 1967-2021 and 1982-2021. Locations in the tropical, polar, and midlatitude regions are examined due to their variation in latitude, climate, and season. After analyzing the tropopause from a composition and stability perspective at each of these areas, the World Meteorological Organization (WMO) lapse-rate deﬁnition of 2°C km − is evaluated in comparison. Upon investigation, using a 9 K km − 1 potential temperature gradient threshold as a stability identiﬁer has proven to be ideal in determining the location of the tropopause in all regions and seasons. The WMO deﬁnition and stability threshold are applied to individual proﬁles and compared to vertical proﬁles of ozone for evaluation. The stability threshold tends to always mark a tropopause layer directly below the WMO deﬁnition in instances where the temperature lapse rate is not met, while the altitude difference between the two vary based on time of year, location and potentially dynamical impacts. Although there are many different deﬁnitions that can be used to ﬁnd the tropopause, the purpose of this study is to investigate the accuracy and consistency of using the stability threshold, especially in situations where the WMO tropopause may not perform well.
Deep Learning for Classification of Brain Tumor Histopathological Images

Histopathological image classification has been at the forefront of medical research. We evaluated several deep and non-deep learning models for brain tumor histopathological image classification. The challenges were characterized by an insufficient amount of training data and identical glioma features. We employed transfer learning to tackle these challenges. We also employed some state-of-the-art non-deep learning classifiers on histogram of gradient features extracted from our images, as well as features extracted using CNN activations. Data augmentation was utilized in our study. We obtained an 82% accuracy with DenseNet-201 as our best for the deep learning models and an 83.8% accuracy with ANN for the non-deep learning classifiers. The average of the diagonals of the confusion matrices for each model was calculated as their accuracy. The performance metrics criteria in this study are our model’s precision in classifying each class and their average classification accuracy. Our result emphasizes the significance of deep learning as an invaluable tool for histopathological image studies. INDEX WORDS: Histopathological images, Deep learning model, Non-deep learning model, Brain tumor, Machine learning classifier, Histogram of gradient Copyright by Ifeanyi Austin Ezuma 2022 Deep Learning for Classification of Brain Tumor Histopathological Images
Accelerating Digital Transformation during the COVID-19 Pandemic: A Model Design for Indonesian MSMEs

The acceleration of digital transformation and business model innovations fundamentally improves business growth and performance. However, the occurrence of the COVID-19 pandemic has a significant influence on the insecurity of many business actors, especially Micro Small Medium Enterprises (MSMEs). There are many efforts to recover the national economy by accelerating digital transformation for MSMEs programs by the government of the Republic of Indonesia. However, the problem arises when the process of accelerating digital transformation in Indonesian MSMEs is considered slow to encourage Indonesia’s National Economic Recovery (PEN as shortened in Indonesian) during the COVID-19 pandemic. The research aimed to present a design model for accelerating digital transformation during the COVID-19 pandemic for Indonesian MSMEs. The research applied a descriptive-analytic approach with a qualitative method. Then, the analysis reviewed the content analysis of previous research. The two research models were analyzed. One was from Indonesia, while another one was from outside Indonesia. The results of the model design reveal that the acceleration of digital transformation is influenced by business driving factors and impacts the gaining value from business model innovations. The model of accelerating digital transformation for MSMEs is expected to contribute significantly to supporting government programs and strengthening National Economic Recovery policies during the COVID-19 pandemic.
Ultra-low-voltage GDI-based hybrid full adder design for area and energy-efficient computing systems

In recent years, ultra-low-voltage (ULV) operation is gaining more importance for achieving minimum energy consumption. Full adder is the basic computational arithmetic block in many of the computing and signal/image processing applications. Here, a new hybrid 1-bit full adder circuit which employs both Gate Diffusion Input (GDI) logic and multi-threshold voltage (MVT) transistor logic is reported. The main objective of the proposed MVT-GDI-based hybrid full adder design is to provide minimum energy consumption with less area. The proposed hybrid design is simulated using standard 45 nm CMOS process technology at an ULV of 0.2 V. The post-layout simulation results have shown that the proposed design achieved significant improvements in comparison with the other reported designs by achieving >57%, 92% savings in the Energy and EDP, respectively, with only 14 transistors. Monte–Carlo simulations have also been performed and is found that the proposed design methodology yields full functionality and robustness against local and global process variations. Normalised energy metrics to 32 and 22 nm technologies shows that the proposed design achieves >57% energy savings in prior to the recent works.
Morpheme combinatorics of compound words through Box Embeddings

In this study I probe the combinatoric properties of Japanese morphemes that participate in compounding. By representing morphemes through box embeddings (Vilnis et al., 2018; Patel et al., 2020; Li et al., 2019), a model learns preferences for one morpheme to combine with another in two-member compounds. These learned preferences are represented by the degree to which the box-hyperrectangles for two morphemes overlap in representational space. After learning, these representations are applied to test how well they encode a speaker’s knowledge of the properties of each morpheme that predict the plausibility of novel compounds in which they could occur.
Question Modifiers in Visual Question Answering

Visual Question Answering (VQA) is a challenge problem that can advance AI by integrating several important sub-disciplines including natural language understanding and computer vision. Large VQA datasets that are publicly available for training and evaluation have driven the growth of VQA models that have obtained increasingly larger accuracy scores. However, it is also important to understand how much a model understands the details that are provided in a question. For example, studies in psychology have shown that syntactic complexity places a larger cognitive load on humans. Analogously, we want to understand if models have the perceptual capability to handle modifications to questions. Therefore, we develop a new dataset using Amazon Mechanical Turk where we asked workers to add modifiers to questions based on object properties and spatial relationships. We evaluate this data on LXMERT which is a state-of-the-art model in VQA that focuses more extensively on language processing. Our conclusions indicate that there is a significant negative impact on the performance of the model when the questions are modified to include more detailed information.
Multivariate Time Series Classiﬁcation Using Inter-leaved Shapelets

We propose an approach for classifying multivariate time series based on extracting shapelets , which are discriminative subsequences within time series capable of differentiating between different classes. While univariate shapelet extraction methods are well established, there has been little effort towards extending them for the multivariate scenario. The few approaches that do exist for multivariate shapelet-based classiﬁcation make the assumption that the data from individual sensors is independent of each other. In industrial processes such as manufacturing, we can observe the effect of a change in one sensor affect the data from another sensor, as all the sensors record data synchronized in time. We attempt to incorporate these temporal dependencies across sensors through a simple inter-leaving heuristic, and evaluate our approach on a real silicon wafer manufacturing dataset.
A NEW APPROACH TO CYCLOTRON DESIGN

Cyclotrons are the oldest type of circular accelerators, with many applications, design of the majority of cyclotrons nowadays follow has become a standard for most of developers, and there is a clear trend for switching towards superconducting magnets to increase the magnet field level and decrease the size and weight. A new approach, de-scribed in this paper allowed the author to design a line-up of cyclotrons from 15 to 230 MeV as compact and power efficient as superconducting cyclotrons, but using resistive copper coil.
Neutralization of Naja naja and Daboia russelii snake venoms by aqueous plant extracts

Traditional Indian literature enlists many methods and agents to treat snake envenomation, but most of these methods are not pharmacologically investigated. This study evaluates snake venom-antagonizing properties of the aqueous extracts of five plants, viz. Sapindus laurifolius , Spondias pinnata , Plumeria lutea , Woodfordia fruticosa , and Croton roxburghii , against the venoms of the spectacled cobra and Russell’s viper. Inhibitions of venom lethality, hemorrhage, coagulation, and enzymes were studied to determine the action of the plant extracts. Median lethal dose values of Naja naja and Daboia russelii venoms in mice were 0.625 and 4.0 mg/kg, respectively, when determined as per the modified guidelines by Organisation for Economic Co-operation and Development (OECD). Woodfordia fruticosa presented ~20- and ~5-fold neutralization capacities in vitro and in vivo , respectively. Plant extracts under investigation presented changes in hemorrhagic and coagulant activities in varying degrees. Spondias pinnata and W. fruticosa neutralized the activity of phospholipase A2 and acetylcholinesterase almost completely in vitro . Woodfordia fruticosa presented relatively fair results among the studied plants, and molecular investigations of its isolates can lead us to new molecules. Easy-to-administer drugs with better safety margins can be great lifesavers in remote places where the chances of a snake bite are higher and medical resources are sparse.
Implementation of the Principal's Strategy in Development Talents and Interests of Students (Multi-Site Study at MTsN 3 Nganjuk and MTsN 2 City)

The research in this dissertation is motivated by the phenomenon of the development of quality educational institutions that have a good process to create high student achievement, design a good curriculum taught by qualified teachers and good products as evidenced by the students being able to compete with superior schools. inside and outside the city so that, with a good process, Madrasa are no longer underestimated and even become the community's main choice of superior schools. The research approach uses qualitative methods. Data collection techniques are participant observation, in-depth interviews and documentation. The data analysis techniques used are 1) single case data analysis, 2) cross-site analysis. From the results of this study, it can be concluded that: ( 1) The formulation of the madrasa principal's strategy in developing students' talents and interests at MTsN 3 Nganjuk and MTsN 2 Kediri City is carried out by : formulating the vision and mission of the madrasa, determining the goals of planning for the development of student talents and interests that have been carried out by the madrasa, mapping talents and interests based on abilities and fields of study . Recruitment of basic education students with implementation of strict selection assessment taking into account several assessments, namely the recruitment of the academic path through with report cards and Olympics. Non-academic selection recruitment is seen from the results of students' memorization of the Koran, sports, arts and so on . (2) The implementation of the madrasa principal's strategy in developing students' talents and interests at MTsN 3 Nganjuk and MTsN 2 Kediri City is carried out by: 1) Programs of extracurricular activities provided for all students according to their potential, interests, talents, and abilities, so that students can develop maximally. 2) Supporting activities through routine and spontaneous activities that are carried out optimally can develop students' talents and interests.
Increased spatial extent and likelihood of compound long-duration dry and hot events in China, 1961–2014

Abstract. Compound dry and hot events can cause aggregated damage compared with isolated hazards. Although increasing attention has been paid to compound dry and hot events, the persistence of such hazards is rarely investigated. Moreover, little attention has been paid to the simultaneous evolution process of such hazards in space and time. Based on observations during 1961–2014, the spatiotemporal characteristics of compound long-duration dry and hot (LDDH) events in China during the summer season are investigated on both a grid basis and a 3D event basis. Grid-scale LDDH events mainly occur in eastern China, especially over northeastern areas. Most regions have experienced a pronounced increase in the likelihood of LDDH events, which is dominated by increasing temperatures. From a 3D perspective, 146 spatiotemporal LDDH (SLDDH) events are detected and grouped into 9 spatial patterns. Over time, there is a significant increase in the frequency and spatial extent of SLDDH events. Consistent with the grid-scale LDDH events, hotspots of SLDDH events mainly occur in northern China, such as the Northeast China, North China and Qinghai clusters, which are accompanied by a high occurrence frequency and large affected areas greater than 300 000 km2.
Mild solutions of SPDE's driven by Poisson noise in infinite dimensions and their dependence on initial conditions

We investigate stochastic partial differential equations in an infinite dimensional Hilbert space H of the following form: dX(t)=[AX(t)+F(X(t))]dt+B(X(t),y)q(dt,dy), X(0)=g, where q(dt,dy) is a compensated Poisson random measure associated to a stationary Poisson point process on a [sigma]-finite measure space (U,C,m), A is the generator of a strongly continuous semigroup, F is a measurable mapping from H to H, B is a measurable mapping from the product space HxU to H and g is an H-valued random variable. We are interested in the existence and uniqueness of a so called mild solution. But, apart from the existence and uniqueness of mild solutions our main interest is directed towards their regularity w.r. t. the initial datum g. Under Lipschitz assumptions on the drift and the diffusion we show that for every initial condition g, which is a p-integrable random variable, there exists a unique mild solution in the space of predictable p-integrable processes, where p>=2. In the case that p equals two this is done by Banach's fixed point theorem and the help of the isometric property of the stochastic integral. The case p>2 is crucial for the analysis of the dependence on the initial condition. For the proof of the existence of a unique mild solution in the space of predictable p-integrable processes, where p>2, one needs an appropriate estimate for the p'th moment of the stochastic integral. In this paper we prove a generalized Burkholder-Davis-Gundy-inequality, for estimating the p'th moment of the stochastic integral. This inequality enables us to prove the existence of a unique mild solution in the space of predictable p-integrable processes, where p>2. Under the main assumption that the coefficients F and B are Gâteaux differentiable we prove the Gâteaux differentiability of the mild solution as a mapping from the space of square integrable random variables to the space of predictable square integrable processes. Already the Gâteaux differentiability enables us to give, under the additional assumptions that F is dissipative, the intensity measure m of q is finite and B( ,y) is constant, an [omega]-wise estimate for the Gâteaux derivative of the mild solution and gradient estimates for the resolvent corresponding to the mild solution. Under stronger assumptions than needed for the statement about the Gâteaux differentiability, we prove our main results: the first and second order Frechet differentiability of the mild solution. Under the main assumption that the coefficients F and B are Frechet differentiable, twice Frechet differentiable, respectively, we prove that the mild solution as a mapping from the space of q-integrable random variables to the space of predictable p-integrable processes is Frechet-differentiable if q>p>=2, twice Frechet-differentiable if q>4p>=8, respectively.