Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of Purchase Intention of Men's Fairness Cream: The Role of Electronic Word of Mouth (e-WoM) Communication in Formation of Product Specific Attitudes of Sri Lankan Millennials

At present male-niche cosmetic market is making radical growth signifying opportunities for all marketers in the industry worldwide. Major cosmetic companies have created dedicated product category in a male market, especially in the South-Asian context. Thus, the objective of the present study was to investigate the phenomenon with special emphasis on male Millennials in Sri Lanka as they are very much exposed to and engaged in social media platforms and this significantly influences the formation of their attitudes and perceptions towards products. The key purpose of the study was to explain how purchasing determinants together with electronic Word-Of-Mouth (e-WOM) communication affect the male Millennials' attitudes towards the purchase intention of fairness cream. Based on prior literature, a conceptual model was hypothesized. To achieve the objective of the study, data was collected from a sample of 105 males consisting of current users and potential users or non-users of . They are executives or above in their professional status aged between 18 to 35. Non-probability convenience sampling methods were adopted in this. The study was quantitative in nature where data collection was carried out based on structured selfadministrated questionnaires. The conceptual model and hypotheses were tested using simple Linear Regression models along with descriptive statistics. Findings revealed that both personal factors and product attributes have a significant impact on purchase intention whilst attitude towards consumption of the product category partially mediates the relationship between personal factors and purchase intention. Further, it is found that the presence of eWord-of-Mouth (eWoM) communication negatively moderates the relationship between attitudes both theory and practice.
Diet Variation and Prey Composition of Exotic Clown Featherback, Chitala ornata (Gray 1831) (Osteoglossiformes: Notopteridae) in Laguna De Bay, Luzon Island, Philippines

Diet composition and gut fullness of introduced clown featherback, Chitala ornata (Gray 1831) were assessed from three sampling sites (west, central, and east regions) of Laguna de Bay (Philippines). A total of 203 specimens (10.18–98.5 cm) were collected through the fish trap, seining and gillnet fishing from January to March 2014. Twelve stomachs were empty and excluded from the study. From the 191 stomachs, eleven prey items were identified, with Caridina sp. being the most important prey item by number (83.3 %) and frequency of occurrence (54.4 %). Teleosts prey, particularly an endemic Leiopotherapon plumbeus (Kner 1864) was the most important food item by volume (41.6 %). Index of Preponderance and Index of Relative Importance recognised Caridina sp. , and L. plumbeus as the two most important food items (in order of magnitude). Gut fullness was significantly different among geographical sites ( P < 0.01), with populations from the central region having the highest prey diversity and teleost prey consumption. An ontogenetic dietary shift was observed, where small-sized C. ornata (≤ 45 cm) consumed mainly on crustaceans, whilst teleosts were the main prey type for larger C. ornata (> 46 cm). The proportional weight of teleost-based diet and diversity of food items increased with C. ornata length.
First molecular-based contribution to the checklist of Lebanon macrofungi

The 13-page "First molecular-based contribution to the checklist of Lebanon macrofungi" by Sleiman, Bellanger, Richard, and Stephan may now be downloaded from Mycotaxon's mycobiota webpage. This well-documented annotated species list brings to 148 the number of free-access fungae now available on our website: http://www.mycotaxon. com/mycobiota/index.html
Neural Networks For Adaptive Shape Tracking

A practical approach to tracking aircraft with similar looking silhouettes while maintaining identification is proposed and simulated. Preprocessing is performed using moment invariants that are shown to be almost constant for images in which the object is translated in position, rotated, or changed in scale. These moment invariants are used to train a neural network to identify the aircraft for different aspect angles. Consequently, tracking of a specific aircraft is achieved no matter what the aspect angle, the position, rotation, or scale.
Examining the Metabolisable Protein system at ideal protein:energy ratios in dairy cows

Previous work at ADAS Bridgets aimed at evaluating the Metabolisable Protein (MP) system (AFRC 1992) has indicated that the efficiency of MP use for milk production may be too high, or that the amount of MP required for maintenance may be too low (Newbold et al 1994). This experiment was designed to indicate the true efficiency of MP use for milk protein production when metabolisable energy (ME) and MP supplies were balanced.
Effect of Absorptivity on Device Performance of Ratchet-Type Intermediate Band Solar Cells

We have theoretically modeled ratchet-type intermediate band solar cells (IBSCs) and analyzed the effect of varying absorption coefficients on their performance. The main objective is to examine the impact of low absorptivity representing the low absorption of the practical nanostructured IBSCs. The devices are modeled using the detailed balance formalism at two bandgap combinations. Our results demonstrate that the introduction of small ratchet energy of 100 meV improves the efficiency of the IBSC. At one sun concentration, we show that using a ratchet mechanism provides steady enhancement over conventional IBSCs under different absorptivity. With an increase in light concentration, the performance of IBSCs with high absorptivity becomes close to the ratchet band solar cells (RBSCs). At the same time, the RBSCs continue to offer the enhancement at low absorptivity. These results show that the RBSCs at low to moderate sun concentration can provide promising results over the IBSCs even under low absorption conditions.
Extraction and Characterization of Biogenic Silica Obtained from Selected Agro-Waste in Africa

Increased amounts of available biomass residues from agricultural food production are present widely around the globe. These biomass residues can find essential applications as bioenergy feedstock and precursors to produce value-added materials. This study assessed the production of biogenic silica (SiO2) from different biomass residues in Africa, including cornhusk, corncob, yam peelings, cassava peelings and coconut husks. Two processes were performed to synthesize the biogenic silica. First, the biomass fuels were chemically pre-treated with 1 and 5% w/v citric acid solutions. In the second stage, combustion at 600 °C for 2 h in a muffle oven was applied. The characterization of the untreated biomasses was conducted using Inductively coupled plasma—optical emission spectrometry (ICP-OES), thermal analysis (TG-DTA) and Fourier-transform infrared spectroscopy (FTIR). The resulting ashes from the combustion step were subjected to ICP, nitrogen physisorption, Energy dispersive X-ray spectroscopy (EDX) as well as X-ray diffraction (XRD). ICP results revealed that the SiO2 content in the ashes varies between 42.2 to 81.5 wt. % db and 53.4 to 90.8 wt. % db after acidic pre-treatment with 1 and 5 w/v% acid, respectively. The relative reductions of K2O by the citric acid in yam peel was the lowest (79 wt.% db) in comparison to 92, 97, 98 and 97 wt. % db calculated for corncob, cassava peel, coconut husk and cornhusk, respectively. XRD analysis revealed dominant crystalline phases of arcanite (K2SO4), sylvite (KCl) and calcite (CaCO3) in ashes of the biomass fuels pre-treated with 1 w/v% citric acid due to potassium and calcium ions present. In comparison, the 5 w/v% citric acid pre-treatment produced amorphous, biogenic silica with specific surface areas of up to 91 m2/g and pore volumes up to 0.21 cm3/g. The examined biomass residues are common wastes from food production in Africa without competition in usage with focus application. Our studies have highlighted a significant end-value to these wastes by the extraction of high quality, amorphous silica, which can be considered in applications such as catalyst support, construction material, concrete and backing material.
Multi‐scale affinities with missing data: Estimation and applications

Many machine learning algorithms depend on weights that quantify row and column similarities of a data matrix. The choice of weights can dramatically impact the effectiveness of the algorithm. Nonetheless, the problem of choosing weights has arguably not been given enough study. When a data matrix is completely observed, Gaussian kernel affinities can be used to quantify the local similarity between pairs of rows and pairs of columns. Computing weights in the presence of missing data, however, becomes challenging. In this paper, we propose a new method to construct row and column affinities even when data are missing by building off a co‐clustering technique. This method takes advantage of solving the optimization problem for multiple pairs of cost parameters and filling in the missing values with increasingly smooth estimates. It exploits the coupled similarity structure among both the rows and columns of a data matrix. We show these affinities can be used to perform tasks such as data imputation, clustering, and matrix completion on graphs.
A multivariate view of cognitive performance reveals positive correlation in the Trinidadian Guppy (Poecilia reticulata)

Cognitive variation is common among-individuals and can be consistent across time and context. From an evolutionary perspective, among-individual variation is important as a pre-requisite for natural selection and adaptive evolution. Selection is widely hypothesized to favor high cognitive performance but directional selection should erode variation over time, how then is cognitive variation maintained? As selection does not act on traits in isolation, covariance among specific cognitive traits and/or other aspects of phenotype (e.g. personality) could result in fitness trade-offs that are important in shaping evolutionary dynamics. Here we test this using Trinidadian guppies (Poecilia reticulata), using a multivariate approach by characterising the correlation structure among task-specific cognitive performance measures and a personality trait. We estimate the among-individual correlation matrix (ID) in performance across three cognitive tasks; colour association learning task; motor learning task; reversal learning task, and the personality trait ‘boldness’ measured as emergence time from a shelter. We found no support for trade-offs among performance in these tasks. Nor do we find evidence of hypothesised speed-accuracy trade-offs within the association learning task. Rather we find strong positive correlation structure in ID, with 57% of variation explained by the leading eigen vector. While noting that non-cognitive factors and assay composition may affect the structure of ID, we suggest our findings are consistent with the g-model of cognitive performance variation, in which a dominant axis of variation loads positively on all performance measures. Thus, we add to a growing body of support for general variation among individuals in animal cognitive ability.
An Immune Based Ontology Implementation of Hazard and Operability Analysis in Production Systems

In production systems, HAZOP is an example of such approaches that were suggested to identify, analyze, assess and control industrial risks. Unfortunately, HAZOP is not supported by methodological guidelines to implement it. Also, HAZOP does not rely on core concepts that allow the design of computerized toolkits that could be integrated within enterprise information systems to store and reuse knowledge stemming from the implementation of HAZOP projects. In this paper, we suggest an artificial immune based methodology to assist experts in implementing HAZOP projects. Through a case study, we show how immune concepts can provide both methodological guidance and computerized support in order to backbone expert efforts, and to capture risk related knowledge generated through each step of a HAZOP study.
Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Precipitation is a key component of the hydrological cycle and one of the most important variables in weather and climate studies. Accurate and reliable precipitation data are crucial for determining climate trends and variability. In this study, eleven different precipitation datasets are compared, six reanalysis and five observational datasets, including the reanalysis datasets ERA5 and WFDE5 from the ECMWF family, to quantify the differences between the widely used precipitation datasets and to identify their particular strengths and shortcomings. The comparisons are focused on the common time period 1983 through 2016 and on monthly, seasonal, and inter-annual times scales in regions representing different precipitation regimes, i.e., the Tropics, the Pacific Inter Tropical Convergence Zone (ITCZ), Central Europe, and the South Asian Monsoon region. For the analysis, satellite-gauge precipitation data from the Global Precipitation Climatology Project (GPCP-SG) are used as a reference. The comparison shows that ERA5 and ERA5-Land are a clear improvement over ERA-Interim and show in most cases smaller biases than the other reanalysis datasets (e.g., around 13% high bias in the Tropics compared to 17% for MERRA-2 and 36% for JRA-55). ERA5 agrees well with observations for Central Europe and the South Asian Monsoon region but underestimates very low precipitation rates in the Tropics. In particular, the tropical ocean remains challenging for reanalyses with three out of four products overestimating precipitation rates over the Atlantic and Indian Ocean.
X1: A Robotic Exoskeleton for In-Space Countermeasures and Dynamometry

Bone density loss and muscle atrophy are among the National Aeronautics and Space Administration's (NASA) highest concerns for crew health in space. Countless hours are spent maintaining an exercise regimen aboard the International Space Station (ISS) to counteract the effect of zero-gravity. Looking toward the future, NASA researchers are developing new compact and innovative exercise technologies to maintain crew health as missions increase in length and take humans further out into the solar system. The X1 Exoskeleton, initially designed for assisted mobility on Earth, was quickly theorized to have far-reaching potential as both an in-space countermeasures device and a dynamometry device to measure muscle strength. This lower-extremity device has the ability to assist or resist human movement through the use of actuators positioned at the hips and knees. Multiple points of adjustment allow for a wide range of users, all the while maintaining correct joint alignment. This paper discusses how the X1 Exoskeleton may fit NASA's onorbit countermeasures needs.
Predicting Blood Glucose Level in Malaysian Women Based on Glutathione and Anthropometric Parameters

Blood glucose is conventionally determined by the level of sugar present in our blood. Lesser known to the public that antioxidants in our body are also said to influence the level of blood glucose. Glutathione (GSH) as the main antioxidant parameter in our body helps in reducing the production of oxidative stress caused by a high blood glucose level. Particularly in women, high antioxidant activities are reported due to the presence of oestrogen hormone. However, in Malaysia limited study was done on the significance of GSH in influencing the blood glucose level. Thus, this study focuses on finding the significance of GSH and some other health predictors in affecting the blood glucose level of women volunteers. This study was carried out on 118 Malaysian women volunteers and blood samples were collected for GSH analysis and blood glucose. All data were trained and tested for the development of prediction models in classifying the blood glucose into normal and abnormal levels. The model construction is using three different classifiers: namely logistic regression, k-nearest neighbour classifier and decision tree. Five predictors that were used are GSH, weight, body mass index (BMI), waist-hip ratio (WHR) and groups (oral supplementation dosage). Results showed all predictors are significantly correlated with the blood glucose level at p < 0.10. The model with a combination of GSH, BMI, WHR, weight and supplementation dosage (groups) as predictors gave the best performance. The k-nearest neighbour classifier model displays the best accuracy (84.7%) in predicting the normal and abnormal level of blood glucose. This finding shows that by altering the amount of GSH via oral supplementation and other significant predictors in women, there are chances to modify the blood glucose level from abnormal to normal
Applying The Cores Requirements Development Method For Building It Tools For Urban Management Systems: The Urbanapi Project

Gathering and managing requirements from stakeholders is an essential task for the successful development of IT tools for any application domain. However, specifying stakeholders’ requirements for a collaborative research and development project in the urban management domain is especially challenging. The reason for this is that stakeholders are drawn from different professional, scientific and national backgrounds and contexts, and therefore have various and differing research, technological and application domain-specific objectives. These challenges make it difficult to identify commonalities in requirements that can result in the development of generic IT tools which can be applied in other cities. These various challenges make it highly desirable therefore to identify and apply a coherent methodological approach to the management of the requirements gathering process and stakeholder engagement more generally. In this paper, we introduce a coherent methodology and a generic requirements engineering process, namely – ‘CoReS – Collaborative Requirements Engineering and Stakeholder engagement’. This process is applied to the UrbanAPI-project - a collaborative research project, in which eleven partners from six European countries are collaborating to develop IT tools to support policy making, urban planning and participatory governance at different urban scales. The CoReS method results in the identification of commonalities in stakeholder requirements from four major cities located in different EU member states with the objective to develop generic IT tools and applications. More specifically, this paper reports on the urban planning issues and needs for generic IT tools. In addition, it presents strengths and weaknesses of the CoReS method applied in the UrbanAPI project. Furthermore, it is argued that this experience can effectively support the specification of a roadmap in defining the requirements for the development of IT tools for decision support in the urban planning domain, that can be applied to a wide range of cities throughout Europe.
Rehabilitation promotes the recovery of structural and functional features of healthy neuronal networks after stroke

Rehabilitation is the most effective treatment for promoting the recovery of motor deficits after stroke. One of the most challenging experimental goals is to unambiguously link brain rewiring to motor recovery prompted by rehabilitative therapy. Here, we investigated which aspects of cortical remodeling are induced by rehabilitation by combining optical imaging and manipulation tools in a mouse model of stroke. We revealed that the stabilization of peri-infarct synaptic contacts fostered by rehabilitation goes along with increased vascular density induced by angiogenesis. Furthermore, we showed the progressive formation of a new motor representation in the peri-infarct area where temporal and spatial features of cortical activation recovered towards pre-stroke condition. In the same animals we observed the reinforcement of inter-hemispheric connectivity after rehabilitation. The present work provides the first evidences that rehabilitation promotes the combined recovery of structural and functional features distinctive of healthy neuronal networks.
Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.
Application of AHP for the Weighting of Sustainable Development Indicators at the Subnational Level

This article presents an indicator weighting method for constructing composite indices to assess sustainable development at the subnational level. The study uses an analytic hierarchy process (AHP), which is considered relevant, since it establishes links between the indicators that make up the different sustainable development goals (SDG). For this purpose, 28 indicators defined by experts constitute the base to evaluate the progress towards sustainable development of the Aburrá Valley region, located in Antioquia, Colombia. The results show that health, employment, and education indicators obtained higher weights, while environmental indicators received the most reduced weights. Likewise, the model proves to be consistent using a consistency ratio, which generates the possibility of replicating this model at different subnational levels.