Miracle-dz/UniFAR-FTUs huggingface dataset:
The Efficacy of a Plant Based Diet in Reducing Metabolic Risk in Overweight or Obese Children

Background: Metabolic syndrome (MetS) is a group of risk factors associated with cardiovascular disease (CVD) and type II diabetes (T2DM). MetS has been found to be occurring with increasing frequency in younger populations, with overweight or obese children considered to be at greater risk. This special population may benefit from early intervention. The cornerstone of management in MetS is aggressive lifestyle modification typically consisting of diet and exercise. Although little is known regarding optimal dietary management in this population, recent studies suggest that there may be a role in advocating for consumption of plant based foods to reduce metabolic risk. The purpose of this systematic review is to evaluate the available literature and assess the viability of recommending a plant based diet to reduce metabolic risk in overweight or obese children. Methods: A systematic search of the available literature on MEDLINE-Ovid, CINAHL, and Web of Science databases was conducted using the following keywords: plant based diet, children, and metabolic risk. All articles were screened with eligibility criteria for interventions involving significant components of a plant based diet, measures relevant to risk factors for metabolic syndrome, and the use of overweight or obese children as subjects. The relevant studies were then assessed for quality using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. Results: The search of the literature resulted in two studies that met eligibility criteria for this systematic review. One was a randomized controlled study (RCT) and the other was a cross sectional study. The RCT reported multiple beneficial reductions from baseline in markers for CVD and T2DM in a group of obese children that were placed on a plant based diet (PBD). The cross sectional study found intake of vegetables to be associated with greater insulin sensitivity (SI), decreased hepatic fat fraction (HFF), and decreased visceral adipose tissue (VAT) in overweight or obese Latino children. Conclusion: The commonly accepted health benefits of a plant based diet (PBD) likely extend to overweight or obese children. Consumption of plant based foods may lead to substantial benefits for this population and are low risk when there is proper education and monitoring to ensure a balanced diet. Further studies regarding long-term health effects of a PBD on overweight or obese children are needed.
Synthesis of Nanoporous TiO 2 and Its Potential Applicability for Dye-Sensitized Solar Cell Using Antocyanine Black Rice

Nanoporous mesostructure TiO2 powders were synthesized by sol-gel method, with as a precursor in methanol solution. The Pluronic PE 6200 of block copolymer was used as the pores template. It was found from XRD measurements, both at C and C calcination temperatures, that the sol-gel technique yielded the nanoporous TiO2 with anatase phase. Based on adsorption characterization using BET method, the TiO2 samples have surface area of 108 /g and 88 /g for calcination temperatures of C and of C, respectively. From small-angle neutron scattering (SANS) patterns, TiO2 samples were observed to have nanoporous structures with pore sizes between 22–24 nm. The TiO2 also have order degree which depends on the calcination temperature. The potential applicability of the resulting TiO2 is confirmed for dye-sensitized solar cell (DSSC), composed of nanoporous anatase TiO2 and natural dye from antocyanine black rice. UV-Vis measurement of dye extracted from the black rice indicated that the antocyanine chelate can propagate into the TiO2 nanoporous network. The short circuit photocurrent density (Jsc) under 100 mWc reached 1.287 mAc with open circuit photovoltage (Voc) of 550 mV and the fill factor of 33.4%. The results show that the hybrid organic-inorganic structures are very attractive for future low-cost devices.
Point-to-Point Traffic in Wireless Mesh Networks

Although most work on Wireless Mesh Networks (WMNs) has focused on traffic flowing in and out of the network via gateways, traffic within the WMN may also be significant in many environments. This point-to-point (P2P) traffic between the nodes within WMN can be handled in different ways, particularly in WMNs containing multiple gateways. The approach used affects the performance of both the P2P flows and other traffic in the network. This work studies the impact of handling P2P traffic in the presence of gateways and gateway traffic. Through mathematical analysis of the resulting traffic patterns, along with extensive simulations, the need to route P2P traffic appropriately is demonstrated. While direct routing yields considerable performance improvements in small networks, it can actually decrease capacity in larger networks. Consequently, we extend the Hybrid Wireless Mesh Protocol (HWMP) proposed in IEEE 802.11s by adding two new message types to obtain more information useful for choosing the best route. Through simulations on different networks, HWMP shows better average delivery ratio and end-to-end delay than the original gateway-based and the P2P routing mechanisms in the simulation settings.
Detección de Fugas de Información Aplicando Estructura de Dinámica de Datos y Técnicas de Clasificación

La fuga de informacion es un problema que esta presente en instituciones publicas y privadas alrededor del mundo. El principal problema que se presenta es identificar de forma eficiente el filtrado de la informacion. Para solucionar este problema en el presente trabajo desarrolla una estructura de datos adaptable al comportamiento humano, utilizando como base las actividades ejecutadas dentro del sistema informatico. Al aplicar esta estructura se modela un comportamiento NORMAL de cada uno de los usuarios y de esta manera detecta cualquier comportamiento ANO- MALO en tiempo real. Ademas, permite la aplicacion de varias tecnicas de clasificacion como los arboles de decision (C4.5), UCS y Naive Bayes las cuales han demostrado un eficiente resultado en la deteccion de intrusiones. Para probar este modelo se ha disenado un escenario que sirve para demostrar la validez de la propuesta con informacion real de una institucion gubernamental y para acreditar lineas futuras de trabajo.
Green Supply Chain Management in Malaysian Aero Composite Industry

Green supply chain management (GSCM) is a concept that gaining popularity in the most region in the world. For many organisations, it is a way to demonstrate their sincere commitment to environment sustainability. This paper is then, to investigate GSCM practices in aero composite manufacturing companies, as an initiative for environmental enhancement of green management programme in Malaysia which has the potential to offer greater economic value especially in manufacture of composites material components and sub-assemblies for aircraft application. Two major companies in the sector were chosen as the location of the study. The findings show that GSCM practices in the two companies are currently moderate; and, most of the practices have significant relationship to each other. Lean manufacturing system was became most preferable approach to support the development of green supply chain practices.
A method for real-time memory efficient implementation of blob detection in large images

In this paper we propose a method for real-time blob detection in large images with low memory cost. The method is suitable for implementation on the specialized parallel hardware such as multi-core platforms, FPGA and ASIC. It uses parallelism to speed-up the blob detection. The input image is divided into blocks of equal sizes to which the maximally stable extremal regions (MSER) blob detector is applied in parallel. We propose the usage of multiresolution analysis for detection of large blobs which are not detected by processing the small blocks. This method can find its place in many applications such as medical imaging, text recognition, as well as video surveillance or wide area motion imagery (WAMI). We explored the possibilities of usage of detected blobs in the feature-based image alignment as well. When large images are processed, our approach is 10 to over 20 times more memory efficient than the state of the art hardware implementation of the MSER.
INFLUENCE OF SKY CONDITIONS ON ESTIMATION OF PHOTOSYNTHETIC PHOTON FLUX DENSITY FOR AGRICULTURAL ECOSYSTEM

Abstract. Photosynthetic photon flux density (PPFD: µmol m−2 s−1) is indispensable for plant physiology processes in photosynthesis. However, PPFD is seldom measured, so that PPFD has been estimated by using solar radiation (SR: W m−2) measured in world wide. In method using SR, there are two steps: first to estimate photosynthetically active radiation (PAR: W m−2) by the fraction of PAR to SR (PF) and second: to convert PAR to PPFD using the ratio of quanta to energy (Q / E: µmol J−1). PF and Q/E usually have been used as the constant values, however, recent studies point out that PF and Q / E would not be constants under various sky conditions. In this study, we use the numeric data of sky-conditions factors such cloud cover, sun appearance/hiding and relative sky brightness derived from whole-sky image processing and examine the influences of sky-conditions factors on PF and Q / E of global and diffuse PAR. Furthermore, we discuss our results by comparing with the existing methods.
Understanding and Criticizing Dynamic Capabilities Models

There are at least four researchers who discussed the dynamic capabilities creation model, including Zollo & Winter (2002), Helfat (2007), Teece (2007) and Mc Carthy & Gordon (2010). This paper aims to understand and criticize the models proposed by the four researchers mentioned above. The method used is literature study. Therefore this study can be used as a reference in formulating dynamic capabilities creation model capable of enduring organization environment dynamics. The resulting model understanding and criticism exhibits several important discussion that could be used as guidelines in designing a more comprehensive dynamic creation model.
Effect of moisture stress on morphological and yield attributes of four sorghum varieties

Drought adversely affects growth and yield of crops to various extents. Growing of drought tolerant crops is a good option to obtain economical yields from water stress areas for which quick method to screen drought tolerant plants, particularly in early stages of their growth is important. For this reason, a field experiment was conducted to evaluate the genetical potential to drought tolerance of four sorghum varieties viz Hybrid Sorgo, Safal, BD 731, BD 740. The experiment was laid out in a split plot design with three replications. The plants were grown under three drought levels viz. 100% FC (control), 70% FC and 40% FC. Growth and yield of the Sorghum varieties were found to be decreased gradually with gradual increase in drought levels as compared to the control. This reduction was associated with decreased yield components. Water stress also decreased protein and starch content. The variety BD 740 followed by BD 731 showed the better performance in respect of no. of grains panicle -1 , panicle dry weight, 1000-grain weight, grain yield hill -1 , protein content and starch content than Hybrid Sorgo and Safal at same water stress condition. Therefore Bangladeshi varieties were found more tolerant than Hybrid Sorgo variety to water stress. Findings of this research will be helpful for the farmers about the beneficial use of resource in terms of managing limited water as well as increasing crop productivity. Finally these research findings would certainly contribute food security in Bangladesh. Progressive Agriculture 27 (3): 265-271 , 2016
A Behavioral Economic Study of Tax Rate Selection by the Median Voter: Can the Tax Rate Be Influenced by the Name of the Publicly Provided Private Good?

This paper presents the results of a behavioral economics study to test if the tax rates submitted to finance the public provision of a private good are influenced by changing the name of the private good. A revealed-preference laboratory decision-making experiment is used to test if participants choose significantly different tax rates to support provision of a private good named as a health care investment compared to an identical good named as a neutral monetary investment. Although some previous studies focusing on both framing and context effects find differences associated with health versus non-health environments, these studies have not involved voting over public provision of a private good. In our experimental environment, participants with different income endowments provide their preferred proportional tax rates for financing public provision of a private good in either a neutral or a health context. The implemented tax rate is the median preferred tax rate, and once the budget is determined, each participant receives the same quantity of the publicly provided private good. In each context, the payoff functions are the same. The only difference between the contexts is the name attached to the publicly provided private good, regardless of the name attached to the publicly provided private good, consuming it imposes no externalities. This controls for the positive externality characteristics of many health care goods, but not for preferences evoked by the merit good character of health care which factor into decisions about the public provision of health care. We find that the theoretical predictions of the median voter model are generally supported by the data. However, the conjecture that the implemented tax rate would be affected by context is not supported by the results.
Temporal variability of carbon recycling in coastal sediments influenced by rivers: assessing the impact of flood inputs in the Rhône River prodelta.

Abstract. River deltas are particularly important in the marine carbon cycle as they represent the transition between terrestrial and marine carbon: linked to major burial zones, they are reprocessing zones where large carbon fluxes can be mineralized. In order to estimate this mineralization, sediment oxygen uptake rates were measured in continental shelf sediments and river prodelta over different seasons near the outlet of the Rhone River in the Mediterranean Sea. On a selected set of 10 stations in the river prodelta and nearby continental shelf, in situ diffusive oxygen uptake (DOU) and laboratory total oxygen uptake (TOU) measurements were performed in early spring and summer 2007 and late spring and winter 2008. In and ex situ DOU did not show any significant differences except for shallowest organic rich stations. Sediment DOU rates show highest values concentrated close to the river mouth (approx. 20 mmol O2 m−2 d−1) and decrease offshore to values around 4.5 mmol O2 m−2 d−1 with lowest gradients in a south west direction linked to the preferential transport of the finest riverine material. Core incubation TOU showed the same spatial pattern with an averaged TOU/DOU ratio of 1.2±0.4. Temporal variations of sediment DOU over different sampling periods, spring summer and late fall, were limited and benthic mineralization rates presented a stable spatial pattern. A flood of the Rhone River occurred in June 2008 and delivered up to 30 cm of new soft muddy deposit. Immediately after this flood, sediment DOU rates close to the river mouth dropped from around 15–20 mmol O2 m−2 d−1 to values close to 10 mmol O2 m−2 d−1, in response to the deposition near the river outlet of low reactivity organic matter associated to fine material. Six months later, the oxygen distribution had relaxed back to its initial stage: the initial spatial distribution was found again underlining the active microbial degradation rates involved and the role of further deposits. These results highlight the immediate response of the sediment oxygen system to flood deposit and the rapid relaxation of this system towards its initial state (6 months or less) potentially linked to further deposits of reactive material.
Speaker adapted dynamic lexicons containing phonetic deviations of words

Speaker variability is an important source of speech variations which makes continuous speech recognition a difficult task. Adapting automatic speech recognition (ASR) models to the speaker variations is a well-known strategy to cope with the challenge. Almost all such techniques focus on developing adaptation solutions within the acoustic models of the ASR systems. Although variations of the acoustic features constitute an important portion of the inter-speaker variations, they do not cover variations at the phonetic level. Phonetic variations are known to form an important part of variations which are influenced by both micro-segmental and suprasegmental factors. Inter-speaker phonetic variations are influenced by the structure and anatomy of a speaker’s articulatory system and also his/her speaking style which is driven by many speaker background characteristics such as accent, gender, age, socioeconomic and educational class. The effect of inter-speaker variations in the feature space may cause explicit phone recognition errors. These errors can be compensated later by having appropriate pronunciation variants for the lexicon entries which consider likely phone misclassifications besides pronunciation. In this paper, we introduce speaker adaptive dynamic pronunciation models, which generate different lexicons for various speaker clusters and different ranges of speech rate. The models are hybrids of speaker adapted contextual rules and dynamic generalized decision trees, which take into account word phonological structures, rate of speech, unigram probabilities and stress to generate pronunciation variants of words. Employing the set of speaker adapted dynamic lexicons in a Farsi (Persian) continuous speech recognition task results in word error rate reductions of as much as 10.1% in a speaker-dependent scenario and 7.4% in a speaker-independent scenario.
Kerfing with Generalized 2D Meander-Patterns: Conversion of Planar Rigid Panels into Locally-Flexible Panels with Stiffness Control

In this paper, we present a kerfing (relief-cutting) method to turn rigid planar surfaces into flexible ones. Our kerfing method is based on a generalization of the 2D meander-pattern recently invented by Dujam Ivaniseviae. We have developed algorithms to obtain a large subset of all possible 2D meander-patterns with a simple remeshing process. Our algorithm can be applied to any polygonal mesh to produce 2D meander-patterns. The algorithm, when applied to regular (4,4) tiling pattern, in which every face is 4-sided and every vertex is 4-valence, provides the original 2D meander-pattern of Ivaniseviae. Moreover, since these meander-patterns are obtained by a remeshing algorithm, by changing parameters, we can control local properties of the pattern with intensity of images to obtain desired stiffness in any given region (See Fig.1). This approach provides a simple interface to construct desired patterns.
Decision-making of spare subsea trees with multi-restrictive factors in deepwater development

In order to quantify the influential factors of subsea trees’ maintenance proactively, multiple restrictive factors first are elaborated, such as locale meteorological conditions (i.e. weather), transport resources, heavy intervention vessels, maintenance technicians, spare trees and so on. Then, the focus is on three vital factors: weather, intervention vessel and spare trees. These restrictions dramatically impact the cost and accessibility of maintenance. For the inaccessible duration of significant wave height in weather model for computing non-feasibility days, we utilized the statistic data from the ERA Interim dataset. An analytical model is established to simplify the calculation of maintenance costs. As the predictive maintenances are seldom performed in subsea field, the built maintenance model only considers the corrective maintenance. Results show that hostile weather as well as the shortage of adequate spare subsea trees can induce severe downtime cost. The comparison of two contractual alternatives indicates that the better way to reduce the maintenance cost is to make the intervention vessel available enough. It is significant to provide quantitative views of subsea maintenance and to supply a method for the decision-making of spare subsea trees with multiple restrictive factors from the proposed model.
Life Cycle Assessment of a Concentrated Solar Power Plant for the Production of Enriched Methane by Steam Reforming Process

Please cite this article as: Piemonte V. , De Falco M. , Giaconia A. and Tarquini P., (2010), Life cycle assessment of a concentrated solar power plant for the production of enriched methane by steam reforming process, Chemical Engineering Transactions, 21, 25-30 DOI: 10.3303/CET1021005 Life cycle assessment of a concentrated solar power plant for the production of enriched methane by steam reforming process.
Carbon and water cycling in lake‐rich landscapes: Landscape connections, lake hydrology, and biogeochemistry

[1] Lakes are low-lying connectors of uplands and wetlands, surface water and groundwater, and though they are often studied as independent ecosystems, they function within complex landscapes. One such highly connected region is the Northern Highland Lake District (NHLD), where more than 7000 lakes and their watersheds cycle water and carbon through mixed forests, wetlands, and groundwater systems. Using a new spatially explicit simulation framework representing these coupled cycles, the Lake, Uplands, Wetlands Integrator (LUWI) model, we address basic regional questions in a 72-lake simulation: (1) How do simulated water and carbon budgets compare with observations, and what are the implications for carbon stocks and fluxes? (2) How do the strength and spatial pattern of landscape connections vary among watersheds? (3) What is the role of interwatershed connections in lake carbon processing? Results closely coincide with observations at seasonal and annual scales and indicate that the connections among components and watersheds are critical to understanding the region. Carbon and water budgets vary widely, even among nearby lakes, and are not easily predictable using heuristics of lake or watershed size. Connections within and among watersheds exert a complex, varied influence on these processes: Whereas inorganic carbon budgets are strongly related to the number and nature of upstream connections, most organic lake carbon originates within the watershed surrounding each lake. This explicit incorporation of terrestrial and aquatic processes in surface and subsurface connection networks will aid our understanding of the relative roles of on-land, in-lake, and between-lake processes in this lake-rich region.
Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks

Curriculum learning strategies are known to improve the accuracy, robustness and convergence rate for various language learning tasks using deep architectures (Bengio et al., 2009). In this work, we design and experiment with several training curricula for two tasks – word-level language detection and language modeling – for code-switched text data. Our study shows that irrespective of the task or the underlying DNN architecture, the best curriculum for training the code-switched models is to first train a network with monolingual training instances, where each mini-batch has instances from both languages, and then train the resulting network on code-switched data.