Miracle-dz/UniFAR-FTUs huggingface dataset:
Near infrared spectral signature and their stability across environments.

Earlier work on food plants showed that NIRS applications appeared to be effective for authenticating varieties. In our work, we confirmed that result for coffee varieties, but we also showed that inter-variety relations are not stable from one harvest to the next. We put forward the hypothesis that the spectral signature is affected by environmental factors. The purpose of this work was to find a way of reducing environmental variance in order to increase measurement reliability and enable practical application in breeding. Spectral collections were obtained on ground green coffee samples from agronomy trials. Two harvests of bean samples from 11 homozygous introgressed lines, and the cv ‘Caturra’ as the control, supplied from 3 different sites, were compared. For each site, squared Mahalanobis distances between the 12 varieties were estimated based on NIR spectra. Matrix correlation coefficients between matrices were assessed by the Mantel test. The higher the coefficients, the more we concluded that inter-variety relations were identical from one environment to another. We showed that very good stability was obtained for inter-variety relations across the sites when using the two harvests data. At the same time, the spectrum was regarded as a succession of phenotypic variables, each resulting from an environmental and genetic effect. Heritabilities were calculated with confidence intervals. The stability of inter-variety relations was largely increased if only the most heritable zones of the spectrum were kept. It could therefore be considered that a coffee variety can be characterized by a NIRS signature acquired over a set of harvests. We indicated how this typical signature can be used in breeding to assist in selection.
Practical Emotion Recognition using Wearable Brain and Physiological Sensors

of Thesis Detecting user emotion will play an important role in bridging the gap between human and computers. Recently, brain and physiological signals have been employed to detect emotional cues of human subjects with the assumption that bodily signals would provide information of intrinsic emotions better than conventional approaches including computer vision and speech analysis. However, conventional devices for brain imaging and physiological recording featured in previous works tend to be obtrusive since they were originally designed for use in clinical and controlled environments. This drawback limits the practicability of emotion recognition systems. Recently, a variety of wearable brain and physiological sensors have been developed which demonstrate potential in the emotion detection domain but come with the significant challenges regarding signal quality and stability. Hence, the objective of this study is to improve the practicability of emotion recognition system by using wearable brain and physiological sensors without significantly degrading the performance. In particular, this study has two main focuses. Firstly, the study employs multiple wearable sensors, including electroencephalographic (EEG) headset, chest-attachable electrocardiogram (ECG) patch, and wrist-worn galvanic skin response (GSR) band, with the aim of improving the robustness of the system by implementing efficient multimodal integration. Hereby, this study proposes making use of the reliability information of each modality, quantified by signal quality and accelerometer data, to regulate the information ensemble. The empirical results from experiments with 30 subjects performing music-listening tasks demonstrate that the context-aware system significantly outperforms traditional approaches in arousal and valence classification. Secondly, this study addresses limitations of existing systems with regards to accommodating new users by minimizing the amount of calibration data required to make use of the system. Conventional generalized systems designed to detect emotions tend to suffer from degraded performance due to inter-subject variability in bodily signals, especially with regards to EEG. This necessitates collection of calibration data recordings which can be time-consuming, annoying and reducing the practicability of the entire system. To mitigate this shortcoming, an emerging technique called transfer learning is adopted. This technique can reduce calibration data requirements by allowing the use of information collected from other subjects to build a model for a new subject. This reduced data requirement streamlines the process of adding new users to the system, and empirical results also demonstrate the method’s potential for enabling subjectindependent emotion recognition. The proposed method may shed light on developing more practical emotion recognition systems for real-world applications.
Enhancement of photoluminescence signal from ultrathin layers with silicon nanocrystals

Using the model of oscillating dipoles, we simulated the photoluminescence intensity of a triple-layered structure where the silicon nanocrystals layer was enclosed by buffer and capping silicon dioxide layers. It was found that a structure with an optimized buffer layer thickness exhibited photoluminescence which was approximately 20 times more intense than that from the structure without a buffer layer. Theoretical simulations were verified by photoluminescence measurements for the corresponding structures with silicon nanocrystals fabricated by plasma enhanced chemical vapour deposition.
Performance Mechanized Set Tractor-Planter of Sugarcane Planting in Two Operation Shifts

The studies on the operational performance of sugarcane machinery, in particular the mechanized planting system, are still incipient in Brazil, requiring greater efforts to increase the quality of agricultural operation. In this context, this study aimed to evaluate the operational performance of sugarcane mechanized planting in two operation shifts. The mechanized planting was conducted in the municipality area of Monte Alto – Sao Paulo (SP), Brazil. The statistical design was completely randomized, totaling 80 sampling points, from which 40 points for daytime operation and 40 points for nighttime operation. The variables evaluated were: displacement speed, engine rpm, engine oil pressure, engine water temperature, effective field capacity, and hourly and effective fuel consumption. The coefficient of variation is greater for the alignment of the tractor at night shift, while the coefficients of skewness and kurtosis are higher during day shift. Displacement speed, engine rpm, engine oil pressure, hourly and effective fuel consumption, and effective field capacity showed no influence over the shift operation.
Does livestock influence the diet of Iberian ibex Capra pyrenaica in the Peneda-Gerês National Park (Portugal)?

Abstract We carried out a pilot study about the influence of the domestic goat Capra hircus on the feeding ecology of the Iberian ibex Capra pyrenaica in the Peneda-Gerês National Park (PGNP, Portugal). We mapped the grazing areas of domestic goat flocks and determined their stocking rates in two mountains of PGNP, Gerês and Amarela, and we investigated the diets of both ungulates using fecal microhistology in an area where they partially overlapped during spring and summer. Diet composition, ligneous diversity, and ligneous trophic niche were compared seasonally. Although differences in ligneous components were observed, results revealed a considerable diet overlap mainly due to graminoids consumption, especially during autumn and spring. This suggests that direct or indirect foraging competition between the goat species should not be discarded if they spatially concur. Results also emphasized distinct summer feeding strategies of C. pyrenaica in the area, and the influence of cattle (Bos taurus) and horses (Equus caballus) were hypothesized. We recommend removing feral goats from the mountain environment and monitoring grazing areas of domestic flocks. Estimates of ecological carrying capacity and future diet studies should include cattle, horses, and improvement of herb identification.
A Novel Method for VANET Improvement using Cloud Computing

In this paper, we present a novel algorithm for VANET using cloud computing. We accomplish processing, routing and traffic control in a centralized and parallel way by adding one or more server to the network. Each car or node is considered a Client, in such a manner that routing, traffic control, getting information from client and data processing and storing are performed by one or more server in different bases. The procedure is about each client that receives its situation by GPS system and sends it online to the concerned server via GPRS networks. In order to perform routing and displaying data, each client sends a request to server. All processes and operations will be executable by the software which is inside the server. Finally, we propose an algorithm to work in server and make decision on route selection on the basis of three priorities of traffic, safety and shortest route. This algorithm is represented for the first time. The results indicate that by using this method VANET network is improved. By using this algorithm, we can communicate generally, not regionally, and improvement of processing and accurate statistics is achieved. In fact, the processing in the server is Fuzzy. It means that these priorities are Fuzzy.
Coordinates of a New Romanian Regional Policy - Identifying the Development Poles. A Case Study

The sustainable regional development is a crucial topic at the level of the EU nowadays. Therefore, the regional policies of the member states should be constructed in such a fashion, that they comply with the general European framework and maximize the potential of reaching the European goals. Following the general trend, Romania finds itself today in a complex project of rethinking its regional approach. The approach proposed in this paper will try, using statistical methods and aggregate data available in the Tempo database, to identify those counties that have the necessary characteristics in order to be taken in consideration when choosing the core county of each new region.
Young supernova remnants and the knee in the cosmic ray spectrum

It has recently been suggested that neutron stars inside the shells of young supernova remnants (SNR) are the sources of PeV cosmic rays and that the interaction of the particles with the radiation field in the SNR causes electron pair production, which has relevance to recent observations of "high" positron fluxes. Furthermore, the character of the interaction is such that the well-known knee in the cosmic ray energy spectrum can be explained. Our examination of the mechanism leads us to believe that the required parameters of SN and pulsars are so uncommon that the knee and positron fraction can only be explained if a single, local and recent SN - and associated pulsar - are concerned. In this case the mechanism can be valid.
Predicting Contemporary Volume with Historic Volume at Differential Price Level: Prospect Theory vs. Regret Aversion

Predicting Contemporary Volume with Historic Volume at Differential Price Level: Prospect Theoryvs. Regret Aversion This paper studies the behaviour of investors in the Amman Stock Exchange (ASE) through emphasising two behavioural theories: prospect theory and regret aversion theory. Prospect theory predicts disposition to sell winner stocks and ride loser stocks. Regret aversion, on the other hand, explains why investors may have difficulty in realising gains as well as losses. A new methodology is used to examine the relationship between volume at a given point in time and volume that took place in the past at different stock price level. The results support the prospect theory that investors show an inclination to sell shares for which price increased and keep shares for which price decreased.
Productivity and Qualitative Characteristics of Varieties of Beets

The objective of this study was to evaluate the physicochemical characteristics, biometric and productivity of beet cultivars. The experiment was conducted in random blocks with four repetition. The treatments were six beet cultivars: Maravilha, Merlot, Kestrel, Itapua 202, Chata do Egito and Tall Top Early Wonder. These cultivars were evaluated for plant height, leaf number, diameter and length of root, average mass of root and fresh weight of shoot, the root shape index, productivity, the soluble solids, titratable acidity, ratio, pH and anthocyanin content. The highest average root masses and productivity were observed for the cultivars Maravilha e Tall Top Early Wonder, being similar to Itapua 202 cultivars and the Boring Egypt. For quality characteristics there was no difference between cultivars for the soluble solids and pH, this was not the same behavior for titratable acidity and ratio. The highest anthocyanin content were observed in cultivars Merlot, Kestrel and Chata do Egito. The cultivars Chata do Egito presented the best productivity and quality characteristics, for the factors and elements of the elapsed year climate.
Ocean Bottom Seismic Acquisition via Jittered Sampling

We present a pragmatic marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing airguns at—jittered source locations and instances in time. Following the principles of compressive sensing, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for two source vessels.
Maternal Smoking, Misclassification, and Infant Health

Identifying the causal effect of prenatal maternal smoking on infant health is complicated by unobservable maternal characteristics and behaviors which are plausibly related to both birth outcomes and a mother’s propensity to smoke. Previous studies have addressed the omitted variables problem using instrumental variables (IV) techniques. However, with self-reported data on maternal smoking, misreporting can induce more severe biases in IV estimates than in OLS. This paper proposes an approach based on parametric methods for misclassified binary dependent variables that simultaneously addresses the endogeneity and measurement error problems. The relationship between infant health and maternal smoking is then re-examined using US Birth Records. I find that roughly 30% of smoking mothers are misclassified as non-smokers. As a result, conventional IV estimates deliver implausibly large birth weight losses (upwards of 1,000 grams among African Americans). Accounting for misclassification yields estimates that are considerably smaller in magnitude and more consistent with experimental evidence.
Study of Extraction and Characterization of Ultimate Date Palm Fibers

Natural fibers are generally hydrophilic in nature, as they are in fact derived from lignocellulose, which contains strongly polarized hydroxyl groups. During the last few decades, cellulose has been one of the most abundant, inexpensive, non-toxic, and renewable biomacromolecules in nature and has been widely applied in diverse fields. In this study, we choose to optimize the extraction process of date palm in order to obtain ultimate fibers with minimum rate lignin, minimum degradation, a high yield and degree of whiteness. To characterize these fibers infrared spectroscopy (IR), XRD, EDX, SEM and XPS were used.
Verification of bee algorithm based path planning for a 6DOF manipulator using ADAMS

In this article the end effector displacement control for a manipulator robot with 6 rotational joints on a predetermined 3-dimensional trajectory is investigated. Since for any end effector position there are more than a single set of answers, regarding to robot parts orientation, finding a method which gives the designer all existing states will lead to more freedom of action. Hence two different methods were applied to solve robot inverse kinematic issue. In the first method ADAMS software was considered, which is a well-known software in the field of solving inverse kinematic problems, and after that BA algorithm is used as an intelligent method. This method is one of the fastest and most efficient methods among all existing ones for solving non-linear problems. Hence problem of inverse kinematic solution is transformed into an affair of optimization. Comparison of results obtained by both models indicates the reasonable performance of BA because of its capability in providing the answers from all existing states along with the privilege of no need to 3D modeling.
Ion-neutral friction and accretion-driven turbulence in self-gravitating filaments

Recent Herschel observations have confirmed that filaments are ubiquitous in molecular clouds and suggest, that irrespective of the column density, there is a characteristic width of about 0.1 pc whose physical origin remains unclear. We develop an analytical model that can be applied to self-gravitating accreting filaments. It is based on the one hand on the virial equilibrium of the central part of the filament and on the other hand on the energy balance between the turbulence driven by accretion onto the filament and dissipation. We consider two dissipation mechanisms, the turbulent cascade and the ion-neutral friction. Our model predicts that the width of the inner part of the filament is almost independent of the column density and leads to values comparable to what is inferred observationally if dissipation is due to ion-neutral friction. On the contrary, turbulent dissipation leads to a structure that is bigger and depends significantly on the column density. Our model provides a reasonable physical explanation which could explain the observed filament width when they are self-gravitating. It predicts the correct order or magnitude though hampered by some uncertainties.
Second Language Vocabulary Research : 2004 (中宮光隆教授、横山利枝教授退職記念号)

This is a review article on second language vocabulary research. Articles published in leading international research journals in 2002 are the scope of this investigation. The present review is composed of the following key themes: the use of emotion vocabulary in a second language, the relationship between vocabulary knowledge and academic reading performance, the effect of reading purpose on incidental word learning from context, supporting listening comprehension and vocabulary acquisition with the help of multimedia annotations, learners' ability to produce appropriate derivative word forms, densities of first language and second language lexical networks, and examining lexical diversity indices.
An Overview Profile and Green Purchasing Behavior of Consumers in the Northern Region of Malaysia

Green purchasing has been an important topic since the introduction of green products in the late 1980s which led in later years to a new market segment known as green consumers, and since then governments and rules regulators became part of this wave which encouraged the growth of green products. Therefore, this study aims to investigate an overview of profile of Malaysians and the level of green purchasing behavior. The paper reports and analyses the findings of 383 questionnaires which were collected from selected states in north Malaysia namely: Penang, Kedah and Perlis. The paper illustrates an overview consumer profile among Malaysians. Furthermore, the results show that green purchasing level among Malaysian is at a moderate level. Discussion and implication of this study are introduced in the last section of this paper.
Upregulated TSG-6 Expression in ADSCs Inhibits the BV2 Microglia-Mediated Inflammatory Response

Objectives The microglial cells are immune surveillance cells in the central nervous system and can be activated during neurological disorders. Adipose-derived stem cells (ADSCs) were reported to inhibit the inflammatory response in microglia by secreting proteins like tumor necrosis factor-inducible gene 6 protein (TSG-6). We aim to explore the mechanisms and the associated microRNAs. Methods ADSCs were cultured and TSG-6 expression was evaluated. ADSCs were cocultured with lipopolysaccharide- (LPS-) induced BV2 microglia and the supernatant was harvested for detecting cytokines. The total RNA was extracted and sequenced by high-throughput sequencing. MicroRNA profiles were compared between two treatment groups of ADSCs. A comprehensive bioinformatics analysis and confirmation experiments were performed to identify the microRNAs targeting at TSG-6. Results We found that ADSCs could secrete TSG-6 to inhibit the proinflammatory cytokines, including interleukin-1 beta and interleukin-6, and tumor necrosis factor alpha (TNFα), produced by LPS-induced microglia-mediated inflammatory response. Bioinformatics analysis showed a total of 35 microRNAs differentially expressed between the two groups of ADSCs, and miR-214-5p was identified as a regulator of TSG-6 mRNA. Conclusion Following a treatment with TNFα, ADSCs can regulate the inflammatory response in LPS-activated BV2 microglia by upregulating TSG-6 expression, which itself is under the negative control of miR-214-5p.
A survey of evolutionary algorithms for production and logistics optimization

The main objective of this paper is to present heuristic methods based on evolutionary algorithms to address the production and logistic problem. The focus is brought on problems related to the design, organization, and management of the supply network. From the recent published literature, the author has identified the following types of problems as the most addressed: cell formation, facility layout and optimization of the workshop configuration, choice of locations for distributions centers, assembly line balancing, lot-sizing, production planning and scheduling, and configuration of the supply network. In addition, the author proposes a new approach to the distributed scheduling in industrial clusters which uses a modified genetic algorithm.
A Novel Innovation to Statistical Analysis Using Structural Equation Modeling on Management Strategies

This report was intended to determine what factors affect online shoppers’ purchase intention in the e-business environment and to verify how organizations’ internal and external dynamics may underlie the success of e-commerce companies. Although the technology-acceptance model is widely accepted in research of e-commerce topics, the present study went beyond technology and targeted other factors that might have dramatic influence on online shoppers’ purchasing intention as well, a conceptual model and a number of hypotheses were proposed. The factor analysis and structural equation modeling (SEM) were adopted for statistical and empirical analyses. The results showed positive correlations among the identified factors indicating a great influence of innovative performance in different areas of management strategies on e-purchase intention; they also demonstrated the great impact from the awareness of sustainability development in e-commerce companies.