Miracle-dz/UniFAR-FTUs huggingface dataset:
Fatty Acid Composition, Phospholipid Molecules, and Bioactivities of Lipids of the Mud Crab Scylla paramamosain

The mud crab increases the yield of farming in Southeast Asian countries. Scylla paramamosain, one of four mud crab species belonging to the Scylla genus, is a rich nutrient source during its soft-shell moulting period. In this study, we analysed the total lipid content, fatty acid components, and phospholipid molecular species of the S. paramamosain mud crab. The total lipid content was 1.62 ± 0.08%, which is similar to that of S. serrata previously reported. Twenty-one fatty acids were identified in S. paramamosain. The composition and molecular forms of the phospholipids were identified using high-performance liquid chromatography-high-resolution mass spectrometry. Fifty-four different molecules belonging to six types of phospholipids were identified. Notably, phospholipids were made of fatty acids with C16 : 0; C18 : 0; C20 : 4; C20 : 5; and C22 : 6 main components. The anti-inflammatory and cytotoxic effects of crab lipids and phospholipids were investigated for the first time. The anti-inflammatory activity of the total and polar lipids had IC50 values of 71.5 and 68.6 μg/mL, respectively. The crab polar lipid fraction, which contained phospholipids, also presented high cytotoxic activity toward five cancer cell lines with IC50 values ranging from 85.4 to 95.8 μg/mL.
Research on Auto-Exposure Algorithm Based on Image Big Data and Information Entropy

Article history Received: 8 August 2019 Revised: 9 August 2019 Accepted: 10 October 2019 Published Online: 16 October 2019 An Auto-Exposure (AE) algorithm based on image big data and information entropy is proposed. On the basis of the traditional algorithm for automatic exposure adjustment based on image brightness, image big data analysis is introduced for the first time. Through the combination of ambient luminance evaluation and image information entropy, the dimension of information acquisition of the automatic exposure system is improved, thus improving the image effect and scene adaptability of the camera. Especially in high dynamic range scenes, compared with the traditional algorithm, the effect is significantly improved.
Supervised Methods Applied to the Construction of a Vision System for the Classification of Cocoa Beans in the Cut-Test

Supervised machine learning methods, also known as classification algorithms, have been widely used in the literature for many classification tasks. In this context, some aspects of these algorithms, as the used attributes used and the form they were built, have a direct impact in the system performance. Therefore, in this paper, we evaluate the application of classification algorithms, along with attribute selection, to propose an improved version of a vision system that performs the classification of cocoa beans. The main aim of this investigation is to improve the performance of a cocoa classification system that aims at helping farmers to classify the different cocoa beans based on images of these beans.
Sensitivity of modeled snow stability data to meteorological input uncertainty

Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty to modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e., the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Simulations were performed for a winter season, which was marked by a prolonged dry period at the beginning of the season. During this period, the snow surface layers transformed into layers of faceted and depth hoar crystals, which were subsequently buried by snow. The early-season snow surface was likely the weak layer of many avalanches later in the season. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during (a) the weak layer formation period, (b) the slab formation period, and (c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km or an elevation change of 200 m. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a, modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive to precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For cases b and c, the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e., deep snowpacks, the skier stability index decreased (became less stable). In contrast, the critical crack length increased with increasing precipitation (became more stable). With regard to spatial simulations of snow stability, the high sensitivity to precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.
“Yes to discuss different models of care between primary care physicians and diabetes-practice nurses, but not to complete implementation yet”: Explorative qualitative study at diabetes clinics in primary health care centres in Muscat, Oman

Background: Globally, many models of care through which the way health services are delivered have been adopted within team-based primary health care. Although these models have aimed to solve some of the health care challenges related to physician’s shortages in clinics and further acceptance of non-physician-led clinics, their application is usually determined by a range of factors, such as preparedness of the health care providers, preparedness of patients and support from higher authorities. Objective: The study was designed to explore health care providers’ perceptions for changes in models of care in diabetes clinics at primary health care in Muscat, Oman. Methods: A total of 27 semi-structured interviews were conducted with health care providers involved in diabetes clinics at five purposively selected primary health care centres in Muscat. The interviewees included the core members of the diabetes management team and other supportive members available at the centres, and were of mixed genders, nationalities and professions. Qualitative thematic analysis was applied. Results: The analysis resulted in one main theme, which captured positive responses towards task-sharing model, but revealed worries and requirements for complete implementation. Nurses’ competences and diabetic patients’ acceptance were among the main concerns. Health care providers revealed that for complete implementation of the model, nurses’ involvement in the team could be improved through updating their knowledge and through the provision of support from higher authorities, while diabetic patients’ acceptance could be improved through understanding of their perceived knowledge towards the model which could promote nurse-led clinics. Conclusion: Task-sharing within the discussed possibilities could provide many positive outcomes and a rewarding future for diabetes clinics at primary health care centres. Omani culture could play a role in its implementation; therefore, if successful implementation is desired, carefully considered steps must be applied by the government and the community.
TRAINING AND DEVELOPMENT PROCESS AND EMPLOYEES’ PERFORMANCE IN THE “CHOP BAR” INDUSTRY

Extant literature on training and development process and its relationship with employees’ performance are silent. We explore this vacuity in the chop bar industry by interviewing 700 employees in two sub-metropolitan assemblies in Kumasi, Ghana. We propose that the stages of training and development process influence employees’ performance. The results support our proposition and also produce five stages which constituting the stages of the training and development process. In addition, the design stage emerged as the strongest predictor of employees’ performance---the most important stage of the training and development process in the industry.
Why rare tropical cyclone formation after maturity of super El Niño events in the western North Pacific?

Using Gray’s “seasonal genesis parameter” (SGP) as a metric, we investigate the large-scale environmental factors and the associated circulation changes attributing to rare tropical cyclone (TC) formation in the western North Pacific (WNP) in the first-half years of 1983, 1998, and 2016, which are known to be the decaying phase of the 1982/83, 1997/98, and 2015/16 super El Niño events. A budget analysis of SGP shows that decreases in relative vorticity and specific humidity, due to the existence of a Philippine Sea anticyclonic anomaly, are the major factors attributing to strong suppression of TC formation mentioned above. From a broader basin-scale view, we demonstrate that this Philippine Sea anticyclone could also be a response resulting from the descending branch of a northwest-and-southeast tilting anomalous overturning (Walker) circulation forced by asymmetric SST anomalies typical of the Cold Tongue El Niño. Article history: Received 13 January 2019 Revised 17 June 2019 Accepted 23 June 2019
The Effect of Tax Differences Book on Income Growth (Empirical Study of Manufacturing Companies Listed on the Indonesia Stock Exchange in 2014–2018)

This study aims to find empirical evidence of the effect of book-tax differences on earnings growth. Earnings growth is measured using changes in net profit after tax. The independent variables used in this study are temporary differences and permanent differences, while the dependent variable is earnings growth for the next period. The sample is determined based on the purpose sampling method of manufacturing companies listing on the Indonesia Stock Exchange in the 2014-2018 period with a total of 150 companies. The sample was determined based on the purposive sampling method, then the number of samples obtained in this study amounted to 39 companies, so that the total observation in this study was 195 observations. The data used in this study were secondary data, in the form of financial reports and annual reports downloaded via the official IDX website: www.idx. co.id. Data were analyzed using panel data regression analysis which was processed using Eviews software version 10.0. Hypothesis testing using statistical tests t. The results obtained are permanent variable differences from book-tax differences and temporary differences from book-tax differences do not affect earnings growth.
Analysis of “Halal” Word in Social Media Using Text Mining and Word Networking

—The increasing of Islam news in internet makes some perceptions for many people in the world. One of them is about “Halal” concepts as the Islam standard for muslim belief of God. Halal product is the product that created based on Islamic standard law. In every product, it is necessary to ensure its safety by conducting halal certification from the Indonesian Ulama Council (MUI). Then, based on the explanation, this research aims to analysis what internet user say about halal by using social media Twitter. This research uses text mining, word networking and word networking matrix to understanding the use of halal word. This research shows that “halal” has a large network and having relationship with many of word such as: food, meat, certification, restaurant, slaughter, animal, and industry. Then, from the network shows that “food” is the biggest word matrix in the “halal” networking. It word define degree centrality 75, betwenness centrality 20.001,38, and closeness centrality 0.473064 which mean that “halal” word have strong relationship with “food”.
Testing the efficacy of unsupervised machine learning techniques to infer shark behaviour from accelerometry data

Biologging is becoming a powerful tool in the study of free-ranging animal behaviour. Accelerometers play an important role particularly for cryptic aquatic species by facilitating the measurement of animal body movement and thus, behaviour. However, our ability to collect large and complex data sets is surpassing our ability to analyse them, prompting a need to develop methodologies for automated behavioural classification. Unsupervised machine learning is particularly useful for behavioural classification where direct observations to link patterns of acceleration to animal behaviour are not always attainable. We tested the ability of unsupervised machine learning to classify shark behaviour by applying two common unsupervised approaches, K-means clustering and Hidden Markov models (HMM), to ground-truthed accelerometry data collected from captive juvenile lemon sharks (Negaprion brevirostris). Although K-means clustering demonstrated low classification performance, the HMM performed well in distinguishing broad categories in behaviour (resting vs swimming), but generally had poor performance in rare and more complex behaviours (e.g. prey handling or burst swimming). This study is one of the first to validate the use of common unsupervised machine learning algorithms and lends further support to their use in the study of behaviour in free-ranging animals, while also showing limitations in their ability to discern complex behaviours.
Gelation of Anisotropic Colloids with Short-Range Attraction

Colloidal gels are space-spanning networks that form solids at dilute particle volume fractions. The kinetic process of gelation is central to understand the flow of complex fluids. Here, we report a simulation study of colloidal gelation of anisotropic colloids with attractive LennardJones potential. These forces quasi-model the critical Casimir effect far from the critical solvent fluctuations acting on colloidal patches. By tuning the depths of the patch-to-patch particle interactions and the selected colloidal patches, we dynamically arrest the colloids to form gels. We find that thermal density fluctuation is the key factor to activate colloidal cluster space spanning: the balance between clustering and break-up mechanism is important for the gelation process of anisotropic systems. These results open new opportunities for studying the structural modifications of colloidal gels formed by anisotropic particles, and shed light on non-equilibrium behavior of anisotropic colloidal building blocks.
VERIFICATION OF SATISFACTION AND CLIMATE MODELS IN A TOURIST SERVICE ORGANIZATION

In this research study climate and satisfaction models were analyzed in a tourist service organization. More specifically, the aim was to verify Hackman and Oldham’s (1980) Job Characteristics Model and Avallone and Paplomatas’s (2005) Multidimensional Organizational Health Questionnaire. The research involved 13 hotels from an Italian chain for a total of 440 individuals. The Job Characteristics Model was totally confirmed. As regards wellbeing, perception of people in charge associated with perception of colleagues and increased by fairness are the foundations on which to base a healthy enterprise.
Effects of potassium chloride and nitric oxide on growth and physiological characteristics of winter wheat under salt stress

A hydroponic culture was conducted to evaluate the effects of KCl and sodium nitroprusside (SNP; a nitric oxide donor) in wheat seedlings under salt stress. Exposure to 100 mM NaCl for 7 d decreased biomass of wheat seedlings, root activity and H+-ATPase activity, significantly increased free proline content, reactive oxygen species (ROS) accumulation and lipid peroxidation, and suppressed the activity of superoxide dismutase (SOD). Moreover, NaCl stress significantly decreased the K+ and increased the Na+ content. Addition of KCl or SNP led to the increase in root activity and soluble protein content, stimulated the activity of SOD, and decreased free proline content, superoxide anion radical generation rate, and lipid peroxidation. The increased K+ and decreased Na+ content in the leaves of treated seedlings indicated that suitable KCl and NO addition stimulated the selective transport of K+ and Na+ to the maintain K+/Na+ homeostasis.
A Method of Distinction Between Momentum Chasing or Contrarianism Trading

This paper explains a method of making distinction between momentum chasing and contrarianism trading. First, we randomly select a time series of some security in futures market, and the time frequency is one minute. Second, we convert the close price into index price, and calculate thirty-minute moving average of index price. Third, after curving fitting and derivation, we found all the extremum points. We study the continuous rising or falling percentage after a extremum point is confirmed, in order to study momentum chasing and contrarianism trading. The result shows that, setting 0.1% as condition, momentum chasing is major trading way when continuous rising or falling 0.1%, while contrarianism trading is major trading way when continuous rising or falling 1%.
Investigating ENSO and its teleconnections under climate change in an ensemble view – a new perspective

Abstract. The changes in the El Nino–Southern Oscillation (ENSO) phenomenon and its precipitation-related teleconnections over the globe under climate change are investigated in the Community Earth System Model Large Ensemble from 1950 to 2100. For the investigation, a recently developed ensemble-based method, the snapshot empirical orthogonal function (SEOF) analysis, is used. The instantaneous ENSO pattern is defined as the leading mode of the SEOF analysis carried out at a given time instant over the ensemble. The corresponding principal components (PC1s) characterize the ENSO phases. By considering sea surface temperature (SST) regression maps, we find that the largest changes in the typical amplitude of SST fluctuations occur in the June–July–August–September (JJAS) season, in the Nino3–Nino3.4 (5 ∘ N–5 ∘ S, 170–90 ∘ W; NOAA Climate Prediction Center) region, and the western part of the Pacific Ocean; however, the increase is also considerable along the Equator in December–January–February (DJF). The Nino3 amplitude also shows an increase of about 20 % and 10 % in JJAS and DJF, respectively. The strength of the precipitation-related teleconnections of the ENSO is found to be nonstationary, as well. For example, the anticorrelation with precipitation in Australia in JJAS and the positive correlation in central and northern Africa in DJF are predicted to be more pronounced by the end of the 21th century. Half-year-lagged correlations, aiming to predict precipitation conditions from ENSO phases, are also studied. The Australian and Indonesian precipitation and that of the eastern part of Africa in both JJAS and DJF seem to be well predictable based on the ENSO phase, while the southern Indian precipitation relates to the half-year previous ENSO phase only in DJF. The strength of these connections increases, especially from the African region to the Arabian Peninsula.
Effect of Carbon Black on the Dynamic Mechanical Properties of Magnetorheological Elastomers

In this work, dynamic properties of magnetorheological elastomers (MREs) based on iron sand and natural rubber, with different contents of carbon black filler (30 and 50 phr) were investigated. Tan δ was measured using dynamic mechanical analysis (DMA) over a range of frequency (0.01–130Hz) and strain amplitude (0.1–4.5%). Tan δ was found to be higher for anisotropic MREs with additions of carbon black, with 20-40% improvement over the whole frequency range explored and 6-15% improvement over the strain amplitude range explored. The results exhibited the advantages of carbon black in improving the damping performance of the MREs. The morphological characteristics of the MREs were also examined with scanning electron microscopy.
Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.
ANTI POACHING OF TREES AND WILDLIFE MONITORING

Depleting natural habitats for animals and thereby creating an imbalance in the nature is very much visible in the current scenario and one of the prime reasons is due to smuggling of trees in forested areas. This not only leads to increased risk of the priceless natural resource towards being extinguished, but also affects the lively hood of people in these habitats. This project proposes a solution to the same through a micro-controller this system employees a WSN protocol which is empowered to monitor vibrations of the trees through a MEMS accelerometer/flex sensor. A low power msp430f5528 microcontroller is used along with 2.4 GHz zigbee to communicate to the Central Base/Monitoring Station from a geographical location where activity is detected. WSN is used by the monitoring application, alongside reduces the limitation of transmitting capacity of a communication module and optimizes battery consumption. The embedded system architecture and the hardware/software designs are described in detail. Index Terms – Embedded system, WSN (wireless network), ZigBee.