Miracle-dz/UniFAR-FTUs huggingface dataset:
l-Lysine-Based Gelators for the Formation of Oleogels in Four Vegetable Oils

Supramolecular oleogel is a soft material with a three-dimensional structure, formed by the self-assembly of low-molecular-weight gelators in oils; it shows broad application prospects in the food industry, environmental protection, medicine, and other fields. Among all the gelators reported, amino-acid-based compounds have been widely used to form organogels and hydrogels because of their biocompatibility, biodegradation, and non-toxicity. In this study, four Nα, Nε-diacyl-l-lysine gelators (i.e., Nα, Nε-dioctanoyl-l-lysine; Nα, Nε-didecanoyl-l-lysine; Nα, Nε-dilauroyl-l-lysine; and Nα, Nε-dimyristoyl-l-lysine) were synthesized and applied to prepare oleogels in four kinds of vegetable oils. Gelation ability is affected not only by the structure of the gelators but also by the composition of the oils. The minimum gel concentration (MGC) increased with the increase in the acyl carbon-chain length of the gelators. The strongest gelation ability was displayed in olive oil for the same gelator. Rheological properties showed that the mechanical strength and thermal stability of the oleogels varied with the carbon-chain length of the gelators and the type of vegetable oil. The microstructure of oleogels is closely related to the carbon-chain length of gelators, regardless of oil type. The highest oil-binding capacity (OBC) was obtained in soybean oil for all four gelators, and Nα, Nε-dimyristoyl-l-lysine showed the best performance for entrapping oils.
Kajian Molekuler Layu Buah Muda Kakao (Theobroma cacao L.): Ekspresi TcPIN1 Like Gene

This experiment was carried out to evaluate the expression of TcPIN1 like gene in cocoa (Theobroma cacao L.). DNA and RNA were extracted from 7 weeks old of both healthy and cherelle wilt cocoa pods. PCR was done with two primer set based on PIN1 sequenced of Arabidopsis. Primer PIN1-1: Forward: taaggtgatgccaccaacaa; Reverse: gccatgaacaacccaagact. Primer PIN! -2: Forward: tttgtgtggagctcaagtgc; Reverse: ctgcgtcgttttgttgctta. RT-PCR was done with primer PIN1-2. The results showed that TcPIN1-2 like gene was found in healthy young pods, but not availabe in cherelle wilt pods of cocoa.
Concept Development and Field Testing of Wireless Outdoor Indicator System for Use in Monitoring Exposures at Work among Malaysian Traffic Police

Real-time exposure air monitoring is essential to protect the respiratory health of the Malaysian traffic police. However, the data from monitoring stations have been inadequate to provide accurate information about their exposure. This report describes the conceptual design of a wireless exposure indicator system, and then evaluates the field performance of the system by collocation. The study tested the accuracy of particulate matter size 2.5 (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2) by comparing the measurements from the prototype with the measurements from reference instruments. The field testing found that the data tested were significantly correlated with each other (PM2.5-rs = 0.207, p = 0.019; NO2-rs = 0.576, p = 0.02 and CO-rs = 0.545, p = 0.04). The prototype proved to be successful as it can compute and transmit real-time monitoring data on the level of exposure to harmful air.
BUSINESS DEVELOPMENT STRATEGY FOR NON-FINANCIAL PERSPECTIVE BALANCE SCORECARD METHODS TO INCREASE THE PERFORMANCE OF MSMEs IN INDONESIA DURING THE COVID-19 PANDEMIC

The COVID-19 pandemic has affected the performance of companies, especially MSMEs in Indonesia. The purpose of this study is to determine and analyze how much the company's business strategy has an influence on improving company performance, especially MSMEs. This research has its own peculiarities and is unique because at the time this research was conducted it was in a world situation that was experiencing a pandemic, including Indonesia. The research data was collected using quantitative techniques from primary data sources, which collected 173 questionnaires from 200 questionnaires distributed to several restaurants, canteens, and beverage outlets including small and medium businesses in the Jakarta, Bandung, and Surabaya areas. Based on the test results using multiple linear regression, it can be concluded that: (1) The customer perspective has a significant influence on the performance of MSMEs in Indonesia, (2) the Internal Business Process Perspective has no significant influence on the performance of MSMEs in Indonesia, and (3) The Learning and Growth Perspective has a significant influence on the performance of MSMEs in Indonesia.
Identifying differences in roadless areas in Canada based on global, national, and regional road datasets

Roads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Road‐free areas are decreasing globally, making accurate estimation of their location and size of great importance. Identification of such regions requires accurate data, but substantial variability exists in road network datasets created and maintained at different spatial scales. We compared estimates of road length, density, and roadless areas across Canada, which contains a high proportion of the world's remaining undisturbed and road‐free areas. Global‐ and national‐scale datasets included, on average, only 11%–14% of roads represented in regional‐scale data or volunteered geographic information (VGI), with the most pronounced differences in less‐developed areas. Regional‐scale datasets, with the lowest estimates of amount of roadless area and smallest mean roadless patch size, are likely the most complete road datasets but are not available for all jurisdictions, limiting their national‐scale utility. VGI provides a national‐scale alternative but still lacks many low‐use roads. Available global and national datasets have insufficient information for accurate assessments of roadless areas in Canada, which will require detailed, consistent subnational datasets assembled and maintained by each province and territory in a coordinated fashion to achieve national coverage.
Learning Programming Using Python: The Case of the DigiWorld Educational Game

Constructivist approaches have been demonstrated to contribute effectively to learning programming concepts. Game-based learning offers a novel and productive constructivist approach in this direction. Unfortunately, there is a lack of available educational games in the Greek language for teaching and learning programming using a programming language, especially Python. This paper describes the design and implementation of an educational game designed to teach programming through Python at an introductory level in the context of Greek upper secondary education. The game uses the Ren’Py visual novel creation toolbox, digital storytelling techniques and focuses on the narrative and the basic tenets of game-based learning to create a state of flow instead of simple gamification techniques. The outcome is a fully developed educational game, titled “DigiWorld”, which can be used in the classroom as an educational tool, as well as in non-formal and informal education. The results of the preliminary evaluation are encouraging regarding the motivation and user’s engagement as well as the educational approach followed through the challenges and feats designed.
Research on the innovation efficiency of listed companies based on data mining

In the context of global warming, how to improve the innovation efficiency in China’s renewable energy industry is of great significance. In this paper, we use the panel data of listed companies of China’s renewable energy industry from 2012 to 2018 as a sample and apply the distance output stochastic frontier model to measuring the investment (R&D input) efficiency by considering the factors affecting the innovation efficiency of China’s renewable energy industry. Conclusions show that the innovation capacity of China’s renewable energy industry is negatively correlated with economic development level, and positively correlated with the market structure, ownership structure, and R and D expenditure structure. From 2012 to 2018, the innovation capacity of most listed companies in the renewable energy industry is on the rise.
Ionization of heavy elements and the adiabatic exponent in the solar plasma

Context. The adiabatic exponent $\Gamma_1$ is studied as a thermodynamic quantity in the partially ionized plasma of the solar convection zone. Aims. The aim of this study is to understand the impact of heavy elements on the $\Gamma_1$ profile. We calculated $\Gamma_1$ with the SAHA-S equation of state for different chemical compositions of plasma, and we analyzed contributions of individual elements to $\Gamma_1$. Methods. We studied the decrease in $\Gamma_1$ due to the ionization of heavy elements in comparison with the value obtained for a pure hydrogen-helium plasma. These types of differences are denoted as"Z contributions", and we analyzed them for eight elements (C, N, O, Ne, Mg, S, Si, and Fe) as well as for a mixture of elements corresponding to the solar chemical composition. We compared linear combinations of individual Z contributions with the exact Z contribution. Applying a least-squares technique to the decomposition of the full Z contribution to a basis of individual-element contributions, we obtained the mass fractions of the heavy elements. Results. The Z contribution of heavy elements can be described by a linear combination of individual-element Z contributions with a high level of accuracy of 5e-6 . The inverse problem of estimating the mass fractions of heavy elements from a given $\Gamma_1$ profile was considered for the example of solar-type mixtures. In ideal numerical simulations, the mass fractions of the most abundant elements could be determined with a relative accuracy better than a few tenths of a percent. In the presence of random or systematic errors in the $\Gamma_1$ profile, abundance estimations become remarkably less accurate. If the amplitude of the errors does not exceed 1e-4, we can expect a determination of at least the oxygen abundance with a relative error of about 10%.
A Novel Multimodal Vehicle Path Prediction Method Based on Temporal Convolutional Networks

Accurate and reliable prediction of future motions of the nearby agents and effective environment understanding will contribute to high-quality and meticulous path planning for the automated vehicles under uncertainty and guarantee traffic safety for future real-world deployments. This task becomes more challenging in highly dynamic and complex scenarios such as unsignalized intersections where no lights exist to control vehicles behavior, or there are not multiple lines for the vehicles to anticipate drivers’ future intentions based on the lane in which they are driving. In this study, we introduce a novel deep learning-based methodology to anticipate vehicles path at unsignalized intersections. The method provides multimodal outputs to take into account the inherited uncertainty and multimodality nature of vehicles behavior. Our proposed model works based on dilated convolutional networks in combination with a mixture density layer. We then cluster various existing mixes into possible paths that are ranked based on probability. We assess the performance and generalization capability of our vehicle path prediction model using several metrics over a large naturalistic dataset containing more than 23800 vehicle trajectories. The obtained results reveal the higher performance of our path prediction approach compared with several baselines and benchmarks.
Comparative Analysis of the pIgR Gene from the Antarctic Teleost Trematomus bernacchii Reveals Distinctive Features of Cold-Adapted Notothenioidei

The IgM and IgT classes were previously identified and characterized in the Antarctic teleost Trematomus bernacchii, a species belonging to the Perciform suborder Notothenoidei. Herein, we characterized the gene encoding the polymeric immunoglobulin receptor (pIgR) in the same species and compared it to the pIgR of multiple teleost species belonging to five perciform suborders, including 11 Antarctic and 1 non-Antarctic (Cottoperca gobio) notothenioid species, the latter living in the less-cold peri-Antarctic sea. Antarctic pIgR genes displayed particularly long introns marked by sites of transposable elements and transcription factors. Furthermore, analysis of T. bernacchii pIgR cDNA unveiled multiple amino acid substitutions unique to the Antarctic species, all introducing adaptive features, including N-glycosylation sequons. Interestingly, C. gobio shared most features with the other perciforms rather than with the cold-adapted relatives. T. bernacchii pIgR transcripts were predominantly expressed in mucosal tissues, as indicated by q-PCR and in situ hybridization analysis. These results suggest that in cold-adapted species, pIgR preserved its fundamental role in mucosal immune defense, although remarkable gene structure modifications occurred.
Use of information for problem resolution on construction projects

If construction managers are to carry out their responsibilities in the areas of construction and its support services, they need ready access to the right information [Lock 92]. Timely access to the right data, properly processed for decision making, can provide a competitive edge. And keeping track of information flow on a construction job site is a vital task that has a direct bearing on the timely and successful completion of a construction project [Rasdorf & Herbert 88]. Traditionally, in the construction industry data is collected at the departmental level, but there is very little exchange of information between the departments. Much less between companies. As a result, there is a great deal of duplication of effort and inconsistencies occur in data from different sources. The goal of this thesis is to analyze the information needs of project personnel and to develop a computer-based Project-Management Information Control System (PMICS) to enhance the problem-solving and information management abilities of construction-site management personnel. The use of the latest development in computer technology will be emphasized to manage all the information in such a way as to allow quick access to the right information at the right time and shared by various disciplines or parties. The PMICS builds upon the understanding of the functions and information needs of various personnel at construction projects, and the different documents used by them. The thesis consists of five steps: (1) an analysis of related literature and the current state-of-practice relating to project information; (2) the establishment of a matrix of document types and project
A Conserved Receptor-Binding Domain in the VP1u of Primate Erythroparvoviruses Determines the Marked Tropism for Erythroid Cells

Parvovirus B19 (B19V) is a human pathogen with a marked tropism for erythroid progenitor cells (EPCs). The N-terminal of the VP1 unique region (VP1u) contains a receptor-binding domain (RBD), which mediates virus uptake through interaction with an as-yet-unknown receptor (VP1uR). Considering the central role of VP1uR in the virus tropism, we sought to investigate its expression profile in multiple cell types. To this end, we established a PP7 bacteriophage-VP1u bioconjugate, sharing the size and VP1u composition of native B19V capsids. The suitability of the PP7-VP1u construct as a specific and sensitive VP1uR expression marker was validated in competition assays with B19V and recombinant VP1u. VP1uR expression was exclusively detected in erythroid cells and cells reprogrammed towards the erythroid lineage. Sequence alignment and in silico protein structure prediction of the N-terminal of VP1u (N-VP1u) from B19V and other primate erythroparvoviruses (simian, rhesus, and pig-tailed) revealed a similar structure characterized by a fold of three or four α-helices. Functional studies with simian parvovirus confirmed the presence of a conserved RBD in the N-VP1u, mediating virus internalization into human erythroid cells. In summary, this study confirms the exclusive association of VP1uR expression with cells of the erythroid lineage. The presence of an analogous RBD in the VP1u from non-human primate erythroparvoviruses emphasizes their parallel evolutionary trait and zoonotic potential.
Nash Mechanisms for Market Design Based on Distribution Locational Marginal Prices

Being important information of both network conditions and energy trading, distribution locational marginal prices (DLMPs) are greatly useful for distribution network (DN) management. The goal of this paper is to develop a market mechanism that motivates price-making agents to trade active and reactive power at DLMPs. According to the optimal power flow problem based on second-order cone (SOC) relaxation, we first design the market rules that allow agents to strategically submit proposals of price and power generation/consumption. The market rules induce a noncooperative game between the DN operator and distributed generators. We prove that DLMP pricing can be implemented at any generalized Nash equilibrium (GNE) of the game. We also show that economic properties of strong budget balance, individual rationality, and system cost minimization can be achieved at any GNE. A decentralized algorithm is developed to reach a GNE of the game. Further, based on the convex-concave procedure, an extension of the market mechanism is proposed to cope with the situations that SOC relaxation is inexact. In simulation, the proposed market mechanism is compared with other game-theoretic models, and the economic properties are verified.
A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks

With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN’s monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.
4-Octyl Itaconate Prevents Free Fatty Acid-Induced Lipid Metabolism Disorder through Activating Nrf2-AMPK Signaling Pathway in Hepatocytes

Nonalcoholic fatty liver disease (NAFLD), characterized with oxidative stress and hepatic steatosis, is a serious threat to human health. As a specific activator of nuclear factor E2-related factor 2 (Nrf2), the 4-octyl itaconate (4-OI) has the beneficial effects in antioxidant and anti-inflammation; however, whether 4-OI can alleviate hepatic steatosis and its mechanism is still unknown. The present study was aimed at investigating the protective effects of 4-OI on free fat acid- (FFA-) induced lipid metabolism disorder and its potential molecular mechanism in hepatocytes. The results showed that 4-OI treatment markedly alleviated FFA-induced oxidative stress and excessive lipid accumulation in hepatocytes. Mechanistically, 4-OI significantly suppressed the overproduction of reactive oxygen species (ROS) through activation of Nrf2; the downregulation of ROS level induced a downregulation of AMP-dependent protein kinase (AMPK) phosphorylation level which finally ameliorated excessive lipid accumulation in FFA-stimulated hepatocytes. In general, our data demonstrated that 4-OI relieves the oxidative stress and lipid metabolism disorder in FFA-stimulated hepatocytes; and these beneficial effects were achieved by activating the Nrf2-AMPK signaling pathway. These data not only expand the new biological function of 4-OI but also provide a theoretical basis for 4-OI to protect against lipid metabolism disorders and related diseases, such as NAFLD.
KNOWING THE UNKNOWNS – FRESH INSIGHTS FROM AN UNKNOWN STOCK MARKET

The goal of our study is to examine the impact of natural disasters on the South Pacific Stock Exchange. We use daily time-series data for Fiji’s stock market for the period 2000-2019. Our empirical framework is based on three factor regression models, namely the market model, the Fama and French three-factor model, and the Fama and French five-factor model. We find evidence that natural disasters in Fiji reduce abnormal returns in the most relevant five-factor model. Additionally, we provide evidence that different types of natural disasters have heterogeneous effects on Fiji’s stock market. Our findings are further supported by a robustness check.
Lane-Changing Trajectory Prediction Modeling Using Neural Networks

Concerning autonomous driving, lane-changing (LC) is essential, particularly within complicated dynamic settings. It is a challenging task to model LC since driving behavior is complicated and uncertain. The present study adopts a dual-layer feed-forward backpropagation neural network involving sigmoid hidden neurons and linear output neurons for evaluating intrinsic LC complexity. Furthermore, the estimation and validation of the model were performed by large-scale trajectory data. Empirical LC data were obtained from the Next Generation Simulation (NGSIM) project for training and testing the neural network-based LC model. The findings revealed that the introduced model could make precise LC predictions of vehicles under small trajectory errors and satisfactory accuracy. The present work assessed LC beginning/endpoints and velocity estimates by analyzing the vehicles around. It was observed that the neural network model yielded almost the same predictions as the observational LC trajectories as well as following vehicle trajectories on the original and target lanes. Furthermore, for LC behavior characteristic validation, the neural network-produced LC gap distributions underwent comparisons to real-life data, demonstrating the characteristics of LC gap distributions not to differ from the real-life LC behavior substantially. Eventually, the introduced neural network-based LC model was compared to a support vector regression-based LC model. It was found that the trajectory predictions of both models were adequately consistent with the observational data and could capture both lateral and longitudinal vehicle movements. In turn, this demonstrates that the neural network and support vector regression models had satisfactory performance. Also, the proposed models were evaluated using new inputs such as speed, gap, and position of the subject vehicle. The analysis findings indicated that the performance of the proposed NN and SVR models was higher than the model with new inputs.