Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing Prevalence of Metabolic Syndrome in Korea

OBJECTIVE The number of people with metabolic syndrome is increasing worldwide, and changes in socioenvironmental factors contribute to this increase. Therefore, investigation of changes in metabolic syndrome and its components in South Korea, where rapid socioenvironmental changes have occurred in recent years, would be foundational in setting up an effective strategy for reducing this increasing trend. RESEARCH DESIGN AND METHODS We compared the prevalence and pattern of metabolic syndrome among participants in the Korean National Health and Nutrition Examination Surveys for 1998, 2001, 2005, and 2007. In each survey, stratified, multistage, probability–sampling designs and weighting adjustments were conducted to represent the entire Korean population. The revised National Cholesterol Education Program criteria were used as the definition of metabolic syndrome. All biochemical parameters were measured in a central laboratory. RESULTS A total of 6,907 (mean ± SE age 45.0 ± 0.2 years), 4,536 (45.5 ± 0.2), 5,373 (47.1 ± 0.2), and 2,890 (49.9 ± 0.3) Koreans over 20 years of age have participated in the studies in 1998, 2001, 2005, and 2007, respectively. The age-adjusted prevalence of metabolic syndrome increased significantly from 24.9% in 1998, 29.2% in 2001, and 30.4% in 2005 to 31.3% in 2007. Among the five components, the level of low HDL cholesterol increased the most, by 13.8% over the 10 years. Abdominal obesity and hypertriglyceridemia followed, with 8.7 and 4.9% increases, respectively. CONCLUSIONS Because dyslipidemia and abdominal obesity were major factors in increasing the prevalence of metabolic syndrome in Koreans for the past 10 years, lifestyle interventions should be conducted at the national level to reduce the burden and consequences of metabolic syndrome.
Understanding the Progression of Lewy Body Dementia with Longitudinal Data Modeling

Lewy Body Dementia is the second most common dementia type after Alzheimer's disease. The cognitive decline caused by Lewy Body Dementia affects the patients' performance in daily living and the ability to make decisions. It is critical to understand the course of the disease progression and estimate the cognitive decline trajectory for disease management and care planning. This study analyzed the factors that can be used for the Clinical Dementia Rating score estimation and developed a highly customized model using the data obtained from an existing Lewy Body Dementia database. The model will help the understanding of the course of the disease devel-opment and is proved to be robust and reliable in making estimation in cognitive decline for patients with Lewy Body Dementia.
TRANSFER LEARNING: INCEPTION-V3 BASED CUSTOM CLASSIFICATION APPROACH FOR FOOD IMAGES

Deep-learning approach has become more popular in the field of image processing. When it is concerned with health issues, there are lots of improvements in the applications of food image classification by deep learning methods. Transfer learning has become one of the popular techniques used in inception V3 for image classification, it is the reuse of a pre-trained model on a new model, where it uses a small amount of dataset to reduce the training time and increases the performance. In this paper, the Google Inception-V3 model is considered as a base, in top of that fully connected layer is built to optimize the classification process. In the model building process, convolution layers are capable to enough learn on its own convolution kernel to produce the tensor outputs. In addition, the separately obtained segmented features are concatenated with our custom model before the classification phase. It enhances the capability of important features and utilizes in the process of food classification. Here, the dataset of 16 class food images is considered and it contains thousands of images. The 96.27% classification accuracy has been obtained at the testing phase which is compared with different state-of-art techniques.
Comparing NLP Methods for Identifying Policy Decisions in Government Documents ∗

Advances in natural language processing (NLP) are making it easier to extract events from text, not only to analyze topics or tones, but to dissect who did what to whom. In political studies, these methods have mainly been applied to international conﬂict events as reported in news media. Much less work has been done to study internal government decisions in similar ways. Yet government policies and institutions also change dynamically, through decisions and actions that are recorded in laws and decrees. A major challenge is that existing event extraction systems do not extend well to new types of text or substantive domains. News-based event data projects rely heavily on pattern matching and supervised machine learning, using manually constructed event frames, entity taxonomies, and annotated corpora. Yet government documents may lend themselves well to less supervised forms of event identiﬁcation and extraction. In this paper, I explore and compare rule-based, supervised, and unsupervised computational approaches to labeling the contents of executive decrees, in order to produce data on diﬀerent types of policy actions and the entities they aﬀect. I apply these methods to executive decrees from Peru, as part of a broader study on presidential power in developing states. I present two diﬀerent forms of evaluation to determine the best methods for the research goals, including accuracy tests in relation to human coding, and usefulness in capturing relationships to other real-world phenomena.
NEOCLASSICAL TRANSPORT IN STELLARATORS – RESULTS FROM AN INTERNATIONAL COLLABORATION

C.D. Beidler1, S.V. Kasilov2, W. Kernbichler3, H. Maaßberg1, D.R. Mikkelsen4, S. Murakami5, V.V. Nemov2, M. Schmidt1, D.A. Spong6, V. Tribaldos7, A. Wakasa8, R.B. White4 1Max-Planck-Institut für Plasmaphysik, IPP–EURATOM Association, Greifswald, GERMANY 2Institute of Plasma Physics, NSC ‘Kharkov Institute of Physics and Technology’, Kharkov, UKRAINE 3Technische Universität Graz, ÖAW–EURATOM Association, Graz, AUSTRIA 4Princeton Plasma Physics Laboratory, Princeton NJ, USA 5Department of Nuclear Engineering, Kyoto University, Kyoto, JAPAN 6Oak Ridge National Laboratory, Oak Ridge TN, USA 7Laboratorio Nacional de Fusión, Asociación EURATOM–CIEMAT para Fusión, Madrid, SPAIN 8Graduate School of Engineering, Hokkaido University, Sapporo, JAPAN
Marnix Suilen Entropy Guided Decision Making in Multiple-Environment Markov Decision Processes

We study multiple-environment Markov decision processes (MEMDPs). MEMDPs form a set of standard Markov decision processes (MDPs) that share the same states and actions, and only differ in their transition functions. A MEMDP thus forms a set of potential models, called environments, of a real-world system. By assumption, exactly one of these environments is the true environment, meaning it captures the behavior of the real-world system precisely. A MEMDP can be seen as a partially observable MDP (POMDP) that exhibits a special structure. The belief update is now linear in the number of environments, versus quadratic in the number of states for arbitrary POMDPs. Furthermore, using the Shannon entropy as a measure of how much knowledge of the true environment we currently have, it is known that we cannot lose any information on average. We show under which conditions our knowledge of the true environment will strictly increase on average, and under which conditions this knowledge stays the same. We use this to construct an iterative, greedy learning algorithm to infer the true environment. Based on the entropy we choose the action that is expected to lead to the largest increase in knowledge at every step. We discuss potential drawbacks to this algorithm and present an optimized version that has a lower time complexity per iteration.
Organ-on-a-chip design for the small intestine modeling

In recent years, the ever-increasing scientific knowledge has clearly shown the limitation of the static two-dimensional (2D) and three-dimensional (3D), as well as animal models in predicting the pathophysiology of human disease. For this reason, microfluidic devices converging advances in microfabrication and tissue engineering became of great interest as emerging technologies offering promise as physiological models. In this thesis, we applied microfluidic technology to develop a small intestinal model or gut-on-a-chip, to accurately translate the human outcomes in intestinal physiology and digestive disease studies. The gut-on-a-chip device was designed to incorporate an extracellular matrix (ECM) mimic composed of hydrogel to form walls segmenting the lumen and side channels. Epithelial cells introduced into the lumen channel lined the hydrogel walls and were subjected to shear stress, which is generated by the circulating fluid to dynamically mimic the flow through the human intestine. A representation of the immune system was also integrated into the gut-on-a-chip device, with dendritic cells encapsulated within the hydrogel to allow biomimetic epitheliumimmune cross-talk. The synthetic PEG-NB has been selected as the proper ECM mimicked hydrogel. The device culturing position has been demonstrated in vertical position. The introduction of flow helped in replicating the small intestine morphology and producing a thicker mucus layer to protect the inner gut tissue.
SPAUTOREG: Stata module to estimate Spatial (Lag-Error-Durbin-SAC-SPGKS-SPGSAR-GS2SLS-GS3SLS-SPML-SPGS-SPIVREG-IVTobit)

spautoreg module to run Spatial (Lag-Error-Durbin-SAC-GS2SLS-GS3SLS-SPML-SPGS-SPIVREG-IVTobit) Regression for cross section data either dependent variable is continuous or truncated and calculate Spatial Autocorrelation LM tests, Jarque-Bera Normality LM Test, Breusch-Pagan Heteroscedasticity LM Test, Total, Direct, and InDirect Marginal Effects and Elasticities, Regression Specification Error Tests (Ramsey RESET), and Functional Form White LM Test. spautoreg can fit continuous and truncated dependent variables models, also fits Autocorrelation (serial correlation) and Heteroscedasticity models in the disturbance term.
A perception-aware NMPC for collision avoidance and control of a multi-rotor UAV with visual localization constraints

Multirotor Unmanned Aerial Vehicles (UAVs) are an extremely versatile and practical platform that can be used for automatization of many real-world tasks. Special interest at present time is shown to solutions requiring collaboration of multiple agents, which require precise relative localization and reactive control to avoid collisions, while performing high-level mission tasks (e.g., autonomous inspection, human-robot interaction) when UAVs are moving in close proximity with each other. In this work, a perception-aware NMPC (Nonlinear Model Predictive Control) scheme for controlling a multi-rotor UAV while avoiding dynamic obstacles and maintaining visibility coverage of a target is set up. The proposed approach is meant to directly produce the rotor-level (torque) inputs of the platform motors, hence it does not require an intermediate unconstrained controller to work. It is also meant to be generic, by covering standard coplanar quad-rotors as well as tilted-propeller multi-rotors. A generic onboard sensor is taken into account to retrieve the 3-D pose of the target. The model can be applied to various types of sensors equipped on aerial vehicles, such as monocular cameras or laser scanners. Further, real actuator limitations are modelled to simulate platforms physical constraints. Numerical simulations in MATLAB extensively test the proposed framework showing its validity and effectiveness.
THE CORRECTION GAME OR, HOW PRE-EVOLVED COMMUNICATIVE DISPOSITIONS MIGHT AFFECT COMMUNICATIVE DISPOSITIONS

How might pre-evolved communicative dispositions aﬀect how individuals learn to communicate in a novel context? I present a model of learning that varies the reward for coordination in the signalling game framework under simple reinforcement learning as a function of the agents’ actions. The model takes advantage of a type of modular compositional communicative bootstrapping by which the sender and receiver use pre-evolved communicative dispositions—a “yes/no” command—to evolve new dispositions.
Population dynamics in a Floquet realization of the Harper-Hofstadter Hamiltonian

We study a Floquet realization of the Harper-Hofstadter model that has recently been implemented in a gas of cold bosonic atoms. We study in detail the scattering processes in this system in the weakly interacting regime due to the interplay of particle interactions and the explicit time dependence of the Floquet states that lead to band transitions and heating. We focus on the experimentally used parameters and explicitly model the transverse confining direction. Based on transition rates computed within the Floquet-Fermi golden rule we obtain band population dynamics which are in agreement with the dynamics observed in experiment. Finally, we discuss whether and how photon-assisted collisions that may be the source of heating and band population dynamics might be suppressed in the experimental setup by appropriate design of the transverse confining potential. The suppression of such processes will become increasingly important as experiments progress into simulating strongly interacting systems in the presence of artificial gauge fields.
Detecting Pneumonia from X-Ray images: Machine Learning Approaches

: Detecting pneumonia from X-Ray images is a challenging task because of high variability amongst experts, time and cost. Automatic detection of pneumonia from Chest X-ray images is required to ensure scalability of such a process. In this project, we explore various methods to detect pneumonia presence from images. We start from classical computer vision features like LBP and SURF features to state of the art features derived from Deep Convolutional Neural Networks and Inception Networks. We also use linear decision boundary classifiers like Logistic Regression and Support Vector Machines to deep nets like Inception Net and Residual Neural Network. In this report, we will show the high benefits of using deep features with near perfect precision and recall scores for our deep learning methods.
A Case Report of Apical Necrosis in a Mango (Mangifera indica L.) Plantation Established under Colombian Dry Caribbean Conditions

Bacterial apical necrosis of mango ( Mangifera indica L.) is an acute disease reported mainly in Southern Europe, characterized by the generation of necrotic lesions in buds and leaves that spread through the petioles towards the stems. Its incidence results in significant economic losses due to a decrease in production. Known causative agents of this disease are the bacteria Pseudomonas syringae and Pantoea spp. , which survive as epiphytes on plant surfaces. In a two-year-old mango cv. Keitt orchard, established at the Colombian Corporation for Agricultural Research (AGROSAVIA), Motilonia Research Station, mango trees exhibited symptoms of necrosis in buds, leaves, and branches. The mango plants were affected downwards, causing their death 30 days after the first symptoms. Five samples of affected leaf tissue and petioles were taken to isolate the causative agent and make a diagnosis to manage the disease properly. The samples were superficially disinfected and incubated in a humid chamber and culture media for three days. After incubation, hyphae, exudates, and bacterial colonies were plated on solid culture media. The isolates obtained were characterized morphologically and biochemically. The results showed the presence of bacteria identified as Pantoea spp. by amplifying and sequencing the region coding for the 16S subunit of rRNA. In vitro pathogenicity tests preliminarily suggest Pantoea spp. as the causative agent of the disease. This work is the first report of apical necrosis by Pantoea spp. in mango for Colombia. CONCLUSION : The causal agent of apical necrosis noted in a mango crop cv. Keitt was caused by a bacterium, which, according to the evidence provided by microbiological, biochemical, and molecular tests performed, belongs to the genus Pantoea . This work is the first report of apical necrosis by Pantoea spp. in mango for Colombia.
COVID-19 market disruptions and food security: Evidence from households in rural Liberia and Malawi

We use data collected from panel phone surveys to document the changes in food security of households in rural Liberia and Malawi during the market disruptions associated with the COVID-19 lockdowns in 2020. We use two distinct empirical approaches in our analysis: (a) an event study around the date of the lockdowns (March to July 2020), and (b) a difference-in-differences analysis comparing the lockdown period in 2020 to the same months in 2021, in order to attempt to control for seasonality. In both countries, market activity was severely disrupted and we observe declines in expenditures. However, we find no evidence of declines in food security.
DETERMINANTS OFENSET PRODUCERS MARKET PARTICIPATION DECISION AND INTENSITY OF PARTICIPATION IN ENSETPRODUCT:THE CASE OF WONCHI DISTRICT, SOUTH WEST SHOA ZONE, OROMIA NATIONAL REGIONAL STATE, ETHIOPIA

SHELEME REFERA JEBESA Socio economics Research Team, Bako Agricultural Engineering Research Center, Bako, Ethiopia ABSTRACT In Ethiopia, enset is one of the indigenous root crops widely cultivated in the south and south western parts. Particularly in Wonchi district it is a major crop used as source of food and cash income for majority of smallholder farmers but there has been limited performance of producers in the enset product marketing. To this end, this study was intended to assess determinants of enset product (kocho) market participation decision and intensity of participation. Both primary and secondary data were used for the study. Primary data were collected from randomly selected 184 sample enset producers through stratified two stage sampling technique. The data were analyzed using both descriptive and econometric methods. A double hurdle model was used to identify determinants of enset producers’ market participation decision and intensity of participation. The first stage of double hurdle shows that participation decision in kocho market is positively and significantly related with availability of economically active labor in the family, land allocated for enset crop, decision making on kocho income utilization, ownership of transport facility and access to market information. In contrast, ownership of livestock and distance to the nearest market limit sampled respondents participation decision in kocho market. A second hurdle regression model result indicate that educational level of the household head, enset farming experience, land allocated for enset crop and decision making on kocho income utilization determine intensity of participation in kocho market positively and significantly. The study recommends spreading market information in chain actors, encouraging farmers to learn adult and formal education and rising experience producers through experience sharing on the enset production.
COMPUTATIONAL STUDY OF CONCERTED REACTION PATHWAYS FOR N-ALKYLANINE TRANSFORMATION TO ANILINE IN ACID MEDIA AND UNDER GAS PHASE

Experimentally the transformation of N-alkylaniline to aniline is assumed to proceed by S N 2 reaction pathway. New concerted reaction pathway was proposed. These concerted reaction pathways were studied computationally and compared using different alkyl substituents (methyl and isopropyl). The computational calculation was carried out with Density Functional Theory using B3LYP (Becke-style 3-Parameter Density Functional Theory, that use Lee-Yang-Parr correlation functional) and 6-311++G (d, p) basis set. The reaction pathways were followed and verified with Intrinsic Reaction Coordinate (IRC). It was found that, in accordance with the experimental assumption, S N 2 reaction pathway was the choice of the transformation.
Channel-Aware 5G RAN Slicing with Customizable Schedulers

This paper focuses on 5G RAN slicing, where the 5G radio resources must be divided across slices (or enterprises) so as to achieve high spectrum efficiency, fairness and isolation across slices, and the ability for each slice to customize how the radio resources are divided across its own users. Realizing these goals requires accounting for the channel quality for each user (that varies over time and frequency domain) at both levels – inter-slice scheduling (i.e. dividing resources across slices) and enterprise scheduling (i.e. dividing resources within a slice). However, a cyclic dependency between the inter-slice and enterprise schedulers makes it difficult to incorporate channel awareness at both levels. We observe that the cyclic dependency can be broken if both the inter-slice and enterprise schedulers are greedy. Armed with this insight, we design RadioSaber, the first RAN slicing mechanism to do channel-aware inter-slice and enterprise scheduling. We implement RadioSaber on an open-source RAN simulator, and our evaluation shows how RadioSaber can achieve 17%-72% better throughput than the state-of-the-art RAN slicing technique (that performs channel-agnostic inter-slice scheduling), while meeting the primary goals of fairness across slices and the ability to support a wide variety of customizable enterprise scheduling policies.
A Scale to Measure the Attitude of Beneficiaries and Non-beneficiaries of ARYA Project towards Poultry Farming

Krishi Vigyan Kendra, Banswara will be the locale of the study because initially ARYA project was implemented through KVKs in 25 states of the country. In Rajasthan, Banswara is the only district in which this project was initiated. Under, ARYA project poultry farming is one of the major aspects chosen for the present study. With this context, the present study has been carried out to know the attitude of beneficiaries and non-beneficiaries of ARYA project towards poultry farming. For measuring attitude, a scale was developed in Likert method with 24 statements. 12 positive and 12 negative statements were selected from poultry farming technologies and framed accordingly. The statements were pretested and selected based on the ‘t’ values from the item analysis. The attitude scale may be useful for policy makers and researcher working in the field of poultry farming.
Revisiting orbital-fluctuation-mediated superconductivity in LiFeAs: Nontrivial spin-orbit interaction effects on the band structure and superconducting gap function

Precise gap structure in LiFeAs (Tc = 18 K) given by ARPES studies offers us significant information to understand the pairing mechanism in iron-based superconductors. The most remarkable characteristics in LiFeAs gap structure would be that "the largest gap emerges on the tiny hole-pockets around Z point". This result had been naturally explained in terms of the orbital-fluctuation scenario (T. Saito et al., Phys. Rev. B 90, 035104 (2014)), whereas an opposite result is obtained by the spin-fluctuation scenario. In this paper, we study the gap structure in LiFeAs by taking the spin-orbit interaction (SOI) into account, motivated by the recent ARPES studies that revealed the significant SOI-induced modification of the Fermi surface topology. For this purpose, we construct the two possible tight-binding models with finite SOI by referring the bandstructures given by different ARPES groups. In addition, we extend the gap equation for multiorbital systems with finite SOI, and calculate the gap functions by applying the orbital-spin fluctuation theory. On the basis of both SOI-induced band structures, main characteristics of the gap structure in LiFeAs are naturally reproduced only in the presence of strong inter-orbital interactions between (xz/yz - xy) orbitals. Thus, the experimental gap structure in LiFeAs is a strong evidence for the orbital-fluctuation pairing mechanism.