Miracle-dz/UniFAR-FTUs huggingface dataset:
The Implementation of Eating Healthy Program in Early Childhood

Eating habits develop during the first years of a child's life, children learn what, when, and how much to eat through direct experience with food and by observing the eating habits of others. The aim of this study is to get a clear picture of the Eating program Healthy, starting from the planning, implementation, supervision, and evaluation as a case study of nutrition education; to get information about the advantages, disadvantages and effects of implementing a healthy eating program for children. This research was conducted through a case study with qualitative data analysed using Miles and Huberman techniques. Sample of children in Ananda Islāmic School Kindergarten. The results showed the Healthy Eating program could be implemented well, the diet was quite varied and could be considered a healthy and nutritious food. The visible impact is the emotion of pleasure experienced by children, children become fond of eating vegetables, and make children disciplined and responsible. Inadequate results were found due to the limitations of an adequate kitchen for cooking healthy food, such as cooking activities still carried out by the cook himself at the Foundation's house which is located not far from the school place; use of melamine and plastic cutlery for food; the spoon and fork used already uses aluminium material but still does not match its size; does not involve nutritionists.
Hybrid Power Quality Compensator Interfaced With Fuzzy for High-Speed Locomotive Systems

Implementation of fuzzy logic controller in place of classical controller attains good response, low THD values, voltage as feedback for significantly improving the dynamic performance of proposed HPQC module. With increase in traffic and speed railway became one of the major load on supply grid. AC-DC converters used in locomotive of traction system draws rich harmonics content current results in poor power quality and failure or missed operation of utility causing derating of grid a nd affect the reliability. A hybrid power quality compensator (HPQC) is proposed for comprehensive compensation under minimum dc operation voltage in high-speed traction power supplies. Reduction in HPQC operation voltage can lead to a decrease in the compensation device capacity, power consumptions, and installation cost. It is shown through simulation results that similar compensation performances can be provided by the proposed HPQC with reduced dc-link voltage level compared to the conventional railway power compensator. The co phase traction power supply with proposed HPQC is suitable for high-speed traction applications. In this study, the renewable energy sources are used as the supply to the proposed concept. For the high response and achieving the fast steady the system can be controlled the proposed concept with the fuzzy logic controller.
Detection Of Alzheimer’s Using Neural Network Models – CNN And VGG19

— The count of people suffering from Alzheimer’s has been gradually increasing over time. The disease is a bane to mankind and is also becoming one of major cause of deaths in recent years. A man with Alzheimer’s looses his cognitive ability over time and it makes his life difficult. To prevent this disease, it is important to detect in its early stage. There are many different approaches taken to diagnose this disease one such is through MRI scan images of brain. In this paper the approach of detection is through the same technique. Brain scan images are used as input to train neural network models like CNN and one of its advanced architectures VGG19. The performance metrics of each model is evaluated and the accuracy is presented as the result.
Inter-Color Context Classifier for High Performance Lossless Bayer Image Compression

Switched prediction algorithms are widely used for lossless image compression including Bayer image compression. All switched predictions algorithms have the same structure consisting of two separate functions working in tandem: A local pixel pattern function, or context classifier, and a set of pixel-value prediction functions. For each local context a different prediction function is selected. In this article we describe a new switched prediction algo-rithm specifically for lossless Bayer image compression. The new algorithm uses generic context classifier which may be used with any set of prediction functions. We show that using the generic context classifier we obtain a substantial improvement in loss-less Bayer image compression. The new context classifier is both simple and fast to implement with a low memory requirement.
ASSESSING THE IMPACT OF LAND USE CHANGES ON HYDROPOWER PRODUCTION AND EROSION IN THE COCA RIVER BASIN. A CONTRIBUTION TOWARDS INTEGRATED WATER RESOURCES MANAGEMENT IN ECUADOR. Zur Erlangung des akademischen Grades eines DOKTOR-INGENIEURS

v German Abstract . vii CHAPTER 1: Introduction . 1 1.1General overview . 1 1.2. Objectives . 10 1.3. Thesis outline . 11 1.4. Study area . 12 1.5. Integrated Water Resources Management – IWRM . 18 1.5. 1.IWRM Process. 19 CHAPTER 2. Data and Methods . 21 2.1. Available data . 21 2.1. 1.Digital Elevation Model (DEM) . 22 2.1.2.Soil map and soil data . 22 2.1.3.Land-Use data . 23 2.1.4.Meteorological and Hydrological Data. 23 2.1.5.Data for LU/LUC analysis . 26 2.2. Uncertainty in IWRM . 27 2.2.1.Uncertainties and the Modeling role inside the IWRM Process. 27 2.2.2.Modeling uncertainty sources . 28 2.2.3.Methodologies for uncertainty analysis in model-based IWRM process . 29 2.2. 4.Methodology selection for uncertainty analysis. 29 2.2. 5.Input and output uncertainties throughout the study area modeling process . 30 2.3. Methods for analyzing socioeconomic aspects and creating scenarios . 31 2.3. 1.Historical Land-use Change analysis . 32 2.3. 2.Markov Chain Model . 32 2.3. 3.Cellular Automata Model (CA) . 33 2.3. 4.The CA_Markov Chain Model . 33 2.3. 5. CA_Markov parameter selection and modeling procedure . 34 2.3. 6.Land-use modeling evaluation . 38
Factorizing a Quadratic Expression with Reasoning

It is not popularly realized that the factorization of a general quadratic expression basically requires solution to a hyperbola and a line. This fact is conspicuously pointed out and a few solutions to the problem are demonstrated that are scattered in the literature. In the high school level, the coefficients of a quadratic expression are mostly integers, and factorization is performed by the popular method of decomposition of the middle term. In this expository note, we have presented it in a simpler way that shows both insight and reasoning into the problem. Some other methods, namely, Linear Method, Average Method and Difference of two Squares Method are also discussed. Depending on the background, a reader may prefer a particular method to the others. The expository nature and artistry in presentation of the paper are expected to make the learning of the topic amusing and instructive. GANITJ. Bangladesh Math. Soc.41. 1 (2021) 15-25
AGRICULTURAL AND FOOD SCIENCE

Future increase in precipitation in Scandinavia may exacerbate the dilemma of spring fieldwork that farmers have, concerning topsoil compaction versus delayed sowing on autumn ploughed soil. The former may lead to soil physical degradation, while the latter may lead to a shorter growing season, both with consequential loss of cereal yield potential. In order to enable farmers to adapt their spring fieldwork to climate change, research needs to include seedbed preparation at higher soil moisture conditions. A split-plot experiment in southeastern Norway in 2014– 2017 explored the effects of timing (early, medium, late) and traffic intensity (zero, one, two or three additional wheelings) of spring fieldwork on soil physics and yield. Early spring fieldwork in the unfavourably wet conditions of 2016 gave rise to larger and stronger aggregates, higher penetration resistance, changed pore characteristics and reduced yields. Increased penetration resistance persisted until autumn. The small effect of traffic intensity was explained by location, soil type and intensity range involved. In this context of spring fieldwork timeliness, the proportion of 2–6 mm aggregates and penetration resistance were the properties most strongly correlated with other soil physical properties and cereal yield.
A Region Growing controlled by Edge Map technique (RGCE) for Automatic Registration of Satellite Images

-Image registration is a basic and troublesome assignment where managing remotely sensed satellite images. The registration of different platform images has turned into a critical issue with an expanding number of images gathered each day from various satellites. There exist an extensive variety of registration strategies for various kinds of utilizations and information sources, however no calculation is exact for enrolling multi-source images reliably. This exploration tends to this issue by examining the advancement of a completely automatic registration system for an extremely confused issue of multi-sensor remote detecting images particularly Synthetic aperture radar (SAR) and electro-optical images. The development of this automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking, pattern extraction and pattern matching to collect automatically the Ground Control Points (GCPs) required to the image registration. The application of the developed automatic image registration model to different SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.
The potential for structural errors in emergent constraints

Abstract. Studies of emergent constraints have frequently proposed that a single metric can constrain future responses of the Earth system to anthropogenic emissions. Here, we illustrate that strong relationships between observables and future climate across an ensemble can arise from common structural model assumptions with few degrees of freedom. Such cases have the potential to produce strong yet overconfident constraints when processes are represented in a common, oversimplified fashion throughout the ensemble. We consider these issues in the context of a collection of published constraints and argue that although emergent constraints are potentially powerful tools for understanding ensemble response variation and relevant observables, their naïve application to reduce uncertainties in unknown climate responses could lead to bias and overconfidence in constrained projections. The prevalence of this thinking has led to literature in which statements are made on the probability bounds of key climate variables that were confident yet inconsistent between studies. Together with statistical robustness and a mechanism, assessments of climate responses must include multiple lines of evidence to identify biases that can arise from shared, oversimplified modelling assumptions that impact both present and future climate simulations in order to mitigate against the influence of shared structural biases.
Machine translation status and its effect on business

The globe has become like a town which is inhibited by people speaking more than two thousands languages. The role of Machine Translation (MT) became essential. For global corporations, MT is providing a viable answer to their needs by supplying better control over translation volume, speed and brand fidelity. This article provides an overview for the state of the art of MT systems, the languages covered by MT systems and the used technologies. Furthermore, Pros and Cons of using MT in business and the current challenges are explained. The main MT systems involving Arabic language are presented and the need for post-editing is recognized. MT evaluation methods, both human based or Automated, are discussed. Speech-to-Speech translation technology appears to have finally become a reality; this article outlines the current state, challenges and opportunities of speech translation.
Pyrenyl substituted 1,8-naphthalimide as a new material for weak efficiency-roll-off red OLEDs: a theoretical and experimental study

Based on the theoretical calculations of excited states and semiconducting properties, a new 1,8-naphthalimide derivative having an electron-donating 1-pyrenyl group at the C-4 position was designed and synthesized. This derivative exhibited an excellent thermal stability and bipolar charge carrier transport ability. It was successfully utilized as a host in red phosphorescent organic light-emitting diodes showing an efficient energy transfer from the host to the phosphorescent emitter. The derivative may be a single material electroplex-forming host for PhOLEDs. The best fabricated red emitting device demonstrated maximum current, power, and external quantum efficiencies of 10.8 cd A−1, 7 lm W−1, and 13.6%, respectively. The best device exhibited a high brightness of 15 300 cd m−2 and weak efficiency-roll-off.
An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings

: The heterogeneity of Knowledge Graphs is problematic for conventional data integration frameworks. A possible solution to this issue is using Knowledge Graph Embeddings (KGEs) to encode entities into a lower-dimensional embedding space. However, recent ﬁndings suggest that KGEs suffer from the so-called hubness phenomenon. A dataset that suffers from hubness has a few popular entities that are nearest neighbors of a highly disproportionate amount of other entities. Because the calculation of nearest neighbors is an integral part of entity alignment with KGEs, hubness reduces the accuracy of the matching result. We therefore investigate a variety of hubness reduction techniques and utilize approximate nearest neighbor (ANN) approaches to offset the increase in time complexity stemming from the hubness reduction. Our results suggest, that hubness reduction in combination with ANN techniques improves the quality of nearest neighbor results signiﬁcantly compared to using no hubness reduction and exact nearest neighbor approaches. Furthermore, this advantage comes without losing the speed advantage of ANNs on large datasets.
An age–period–cohort approach to disentangling generational differences in family values and religious beliefs: Understanding the modern Australian family today

BACKGROUND Over the last few decades, Australian families have undergone profound changes, including fewer marriages, more divorces, and an increase in double-income families, resulting in a qualitative shift in understanding the family today. OBJECTIVE This paper investigates whether generational differences in family values and religious beliefs are at the core of changes to the family structure. METHODS Using the Household, Income and Labour Dynamics in Australia (HILDA) survey, we apply the Age–Period–Cohort Detrended (APCD) methodology to investigate generational differences in family values and religious beliefs. RESULTS Results show that changes in family values and religious beliefs are overwhelmingly generational. Cohorts born between 1946 and 1964 (commonly referred to as baby boomers) have significantly contributed to the revolutionary shift in family behaviours and attitudes. CONCLUSIONS The baby boomer generation has played a crucial role in supporting progressive views on marriage, children, gender roles, and religious beliefs. When compared to older and younger cohorts, baby boomers saw the largest shift in family behaviours and attitudes, having matured in a period of rapid economic prosperity and significant social change. 1 [Corresponding author] Melbourne Institute of Applied Economic and Social Research, Faculty of Business and Economics, the University of Melbourne. Email: e.veratoscano@unimelb.edu. au. ORCID iD: 0000-00020736-1381. 2 European Commission, Joint Research Centre (JRC), Ispra, Italy. Email: elena.meroni@ec.europa. eu. ORCID iD: 0000-0003-4904-9238. Vera-Toscano & Meroni: Understanding the modern Australian family today: An age–period–cohort approach 654 https://www.demographic-research.org The unique events that occurred during their formative years may have influenced these behaviours and attitudes, ultimately contributing to the qualitative shift in the understanding of family. CONTRIBUTION Recognising differences between cohorts is essential to our understanding of social change. The APCD models used in this study can detect birth cohort nonlinearities pertaining specifically to the cohort variable. We then search for appropriate explanations of these cohort fluctuations with contextual elements of cohort-specific socialisation and life conditions.
Lignin‐derived heteroatom‐doped porous carbons for supercapacitor and CO2 capture applications

The present study reports the economic and sustainable syntheses of functional porous carbons for supercapacitor and CO2 capture applications. Lignin, a byproduct of pulp and paper industry, was successfully converted into a series of heteroatom‐doped porous carbons (LHPCs) through a hydrothermal carbonization followed by a chemical activating treatment. The prepared carbons include in the range of 2.5 to 5.6 wt% nitrogen and 54 wt% oxygen in its structure. All the prepared carbons exhibit micro‐ and mesoporous structures with a high surface area in the range of 1788 to 2957 m2 g−1. As‐prepared LHPCs as an active electrode material and CO2 adsorbents were investigated for supercapacitor and CO2 capture applications. Lignin‐derived heteroatom‐doped porous carbon 850 shows an outstanding gravimetric specific capacitance of 372 F g−1 and excellent cyclic stability over 30,000 cycles in 1 M KOH. Lignin‐derived heteroatom‐doped porous carbon 700 displays a remarkable CO2 capture capacity of up to 4.8 mmol g−1 (1 bar and 298 K). This study illustrates the effective transformation of a sustainable waste product into a highly functional carbon material for energy storage and CO2 separation applications.
Brain criticality beyond avalanches: open problems and how to approach them

A homeostatic mechanism that keeps the brain highly susceptible to stimuli and optimizes many of its functions—although this is a compelling theoretical argument in favor of the brain criticality hypothesis, the experimental evidence accumulated during the last two decades is still not entirely convincing, causing the idea to be seemingly unknown in the more clinically-oriented neuroscience community. In this perspective review, we will briefly review the theoretical framework underlying such bold hypothesis, and point to where theory and experiments agree and disagree, highlighting potential ways to try and bridge the gap between them. Finally, we will discuss how the stand point of statistical physics could yield practical applications in neuroscience and help with the interpretation of what is a healthy or unhealthy brain, regardless of being able to validate the critical brain hypothesis.
Chemopreventive Potential of a Phytofabricated Silver Nanoformulation using Bark Extract of Saraca asoca in AML Mice In vivo and In vitro

Objectives: To evaluate the therapeutic potential of phytofabricated silver nanoparticle reduced by bark extract of Saraca asoca for Acute Myeloid Leukemia (AML). Methods: The efficacy of silver nanoformulation was tested in vivo secondary model of AML in comparison to chemotherapeutic drug doxorubicin and the cytotoxic and genotoxic potential of the nanoparticle in vitro in leukemic cells in comparison to normal lymphocytes. Results: Both these approaches proved that AgNPs were selectively cytotoxic towards leukemic lymphocyte cells through oxidative damage, which were comparable to the standard chemotherapeutic drug. Conclusions: This finding opens a new avenue for the biomedical application of environmentally sustainable green phyto-fabricated AgNPs in chemoprevention of AML.
Statistical properties of flux variations in blazar light curves at GeV and TeV energies

Despite numerous detections of individual flares, the physical origin of the rapid variability observed from blazars remains uncertain. Using Bayesian blocks and the Eisenstein-Hut HOP algorithm, we characterize flux variations of high significance in the $\gamma$-ray light curves of two samples of blazars. Daily binned long-term light curves of TeV-bright blazars observed with the First G-APD Cherenkov Telescope (FACT) are compared to those of GeV-bright blazars observed with the Large Area Telescope on board the $Fermi$ Gamma-ray Space Telescope ($Fermi$-LAT). We find no evidence for systematic asymmetry of the flux variations based on the derived rise and decay time scales. Additionally, we show that the daily-binned blazar light curves can be described by an exponential stochastic Ornstein-Uhlenbeck (OU) process with parameters depending on energy. Our analysis suggests that the flux variability in both samples is a superposition of faster fluctuations. This is, for instance, challenging to explain by shock-acceleration but expected for magnetic reconnection.
A Study on Image Search System using Semantics Based on Smartphone

Image semantic retrieval has been a crux to bridge "semantic gap" between the simple visual features and the abundant semantics delivered by a image. Effective image retrieval using semantics is one of the major challenges in image retrieval. We suggest a semantic retrieval and clustering method of image using image annotation user interface. And also design and implement a image semantic search management system that facilitates image management and semantic retrieval, which fully relies on the MPEG-7 standard as information base, and using a native XML database, which is Berkeley DB XML
Prediction and Analysis of COVID-19 with Probabilistic Graphical Models

Data analysis using machine learning, in particular probabilistic graphical models, can provide valuable information on the COVID-19 pandemic. Recently, the Mexican National COVID-19 Data Base has been made public, including more than 6.5 million cases of persons that have been registered with symptoms of COVID-19, in a single, free, open-access information system. In this work, we report an initial exploration of the Mexican COVID-19 data base with two types of models: (i) Bayesian network classiﬁers and (ii) causal graphical models. We obtained several Bayesian clas-siﬁers for predicting COVID-19 based on all the relevant attributes in the data base. Additionally, the structures obtained provide interesting information regarding the most relevant attributes for predicting COVID. We also applied causal discovery algorithms to uncover causal relationships between COVID and several factors, providing additional insights of this phenomena.
BARD: A structured method and application for collaboratively building Bayesian networks to support human reasoning and decisions

In many complex real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is notoriously prone to confusion and error [Kahneman et al., 1982]. One way to support better reasoning is to employ Bayesian networks (BNs) [Pearl, 1988] to model and represent uncertain situations clearly for the user, and make complex calculations quickly and accurately on demand. BNs have been deployed for this purpose in diverse domains such as medicine, education, engineering, surveillance, the law, weather forecasting, and the environment. We present our new methodology and application, Bayesian ARgumentation via Delphi (BARD), which combines BNs with a Delphi social process: a systematic method for combining multiple perspectives in a democratic, reasoned, iterative manner