Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation of the Ornithine- d -Aminotransferase cDNA and Effect of Salt Stress on Its Expression in Arabidopsis thaliana 1

To evaluate the relative importance of ornithine (Orn) as a precursor in proline (Pro) synthesis, we isolated and sequenced a cDNA encoding the Orn- d -aminotransferase ( d -OAT) from Arabidopsis thaliana. The deduced amino acid sequence showed high homology with bacterial, yeast, mammalian, and plant sequences, and the N-terminal residues exhibited several common features with a mitochondrial transit peptide. Our results show that under both salt stress and normal conditions, d -OAT activity and mRNA in young plantlets are slightly higher than in older plants. This appears to be related to the necessity to dispose of an easy recycling product, glutamate. Analysis of the expression of the gene revealed a close association with salt stress and Pro production. In young plantlets, free Pro content, D 1 -pyrroline-5-carboxylate synthase mRNA, d -OAT activity, and d -OAT mRNA were all increased by salt-stress treatment. These results suggest that for A.thaliana, the Orn pathway, together with the glutamate pathway, plays an important role in Pro accumulation during osmotic stress. Conversely, in 4-week-old A. thaliana plants, although free Pro level also increased under salt-stress conditions, the d -OAT activity appeared to be unchanged and d -OAT mRNA was not detectable. D 1 -pyrroline-5-carboxylate synthase mRNA was still induced at a similar level. Therefore, for the adult plants the free Pro increase seemed to be due to the activity of the enzymes of the glutamate pathway.
Edge-Based RNN Anomaly Detection Platform in Machine Tools

ABSTRACT With the rapid advances in machine learning algorithms and sensing technologies, machine prognostics and health management (PHM) via data-driven approaches has become a trend in sophisticated machine tool industry. The run-to-failure data are necessary for data-driven approaches. However, the average life of the machine is two to three years, the time of collecting data is extended. It is a big challenge to collect run-to-failure data and build a PHM model. Therefore, we propose an Edge-based RNN Anomaly Detection Platform (ERADP). ERADP builds the model based on healthy data and notify anomalies two hours in advance. The true alarm rate is up to 100%. Besides, ERADP can accelerate the training time almost 120 times faster than the traditional model. Graphical abstract We propose an Edge-based RNN Anomaly Detection Platform (ERADP) to solve the data-imbalance issue and demonstrate detect anomalies in real time for machinery industry. ERADP can make the true alarm rate up to 100% and speed up model training almost 120 times faster. Besides, we cooperate with TongTai, which is the biggest machine tool company in Taiwan. Equipped ERADP with machine tools, the cost of repairing and failure products can be intensively decreased. The price of machine tools can increase by 6%. The revenue of the machinery industry can increase by about 0.27 billion US dollars. ERADP can really make a significant impact on the machinery industry.
A Review Of Clustering Methods Based on Artificial Intelligent Techniques

: Due to the development in various areas of life, the development of the Internet, and the presence of many datasets, and in order to obtain useful information from the rapidly increasing volumes of digital data, there must be theories and computational tools to help humans extract the useful information they need from this data. Large data is collected from many different services and resources. Clustering is one of the most basic and well-known methods of data mining and extraction and obtaining useful information. The technique of recognizing natural groups or clusters within several datasets based on some measure of similarity is known as data clustering. Many researchers have introduced and developed many clustering algorithms based on the different methods of artificial intelligence techniques. Finding the right algorithms greatly helps in organizing information and extracting the correct answer from different database queries. This paper provides an overview of the different clustering methods using artificial intelligence and finding the appropriate clustering algorithm to process different data sets. We highlight the best-performing clustering algorithm that gives effective and correct clustering for each data set.
Estimating Driver’s Lane-Change Intent Considering Driving Style and Contextual Traffic

Estimating a driver’s lane-change (LC) intent is very important so as to avoid traffic accidents caused by improper LC maneuvers. This paper proposes a lane-change Bayesian network (LCBN) incorporated with a Gaussian mixture model (GMM), termed as LCBN-GMM, to estimate a driver’s LC intent considering a driver’s driving style over varying scenarios. According to the scores made by participates with a behavioral-psychological questionnaire, three driving styles are classified. In order to get more effective labeled LC and lane-keep (LK) data for model training, we propose a gaze-based labeling (GBL) method by monitoring a drivers’s gaze behavior, instead of using a time-window labeling method. The capability of LCBN-GMM to estimate a driver’s lane-change intent is evaluated in different LC scenarios and driving styles, in comparison to support vector machine and Naive Bayes. Data are collected in a seat-box-based driving simulator where 32 drivers, consisting of 9 aggressive, 15 neutral, and 8 conservative drivers, participated. Experimental results demonstrate that the LCBN-GMM with GBL achieves the best performance, estimating a driver’s LC intent an average of 4.5 s ahead of actual LC maneuvers with 78.2% accuracy considering both driving style and contextual traffic, compared with other approaches.
Updating a discriminant function in basis of unclassified data

The problem of updating a discriminant function on the basis of data of unknown origin is studied. There are observations of known origin from each of the underlying populations, and subsequently there is available a limited number of unclassified observations assumed to have been drawn from a mixture of the underlying populations. A sample discriminant function can be formed initially from the classified data. The question of whether the subsequent updating of this discriminant function on the basis of the unclassified data produces a reduction in the error rate of sufficient magnitude to warrant the computational effort is considered by carrying out a series of Monte Carlo experiments. The simulation results are contrasted with available asymptotic results.
Self-organization in walker experiments

Microscopic simulation models predict different forms of selforganization in pedestrian flows, such as the dynamic formation of lanes in bi-directional pedestrian flows. This paper proposes a theory predicting selforganization, as well as results from experimental research that provides more insight into these dynamic phenomena. Groups of pedestrians that are homogeneous in terms of desired walking speeds and direction appear to form structures consisting of overlapping layers. This basic pattern forms the basis of other more complex patterns emerging in multi-directional pedestrian flow: in a bi-directional pedestrian flow, dynamic lanes are formed which can be described by the layer structure. Diagonal patterns can be identified in crossing pedestrian flows. This paper both describes these structures, as well as the implications for theory and modeling of pedestrian flows.
Role of the Complement System in the Modulation of T-Cell Responses in Chronic Chagas Disease

Chagas disease, caused by the intracellular pathogen Trypanosoma cruzi, is the parasitic disease with the greatest impact in Latin America and the most common cause of infectious myocarditis in the world. The immune system plays a central role in the control of T. cruzi infection but at the same time needs to be controlled to prevent the development of pathology in the host. It has been shown that persistent infection with T. cruzi induces exhaustion of parasite-specific T cell responses in subjects with chronic Chagas disease. The continuous inflammatory reaction due to parasite persistence in the heart also leads to necrosis and fibrosis. The complement system is a key element of the innate immune system, but recent findings have also shown that the interaction between its components and immune cell receptors might modulate several functions of the adaptive immune system. Moreover, the findings that most of immune cells can produce complement proteins and express their receptors have led to the notion that the complement system also has non canonical functions in the T cell. During human infection by T. cruzi, complement activation might play a dual role in the acute and chronic phases of Chagas disease; it is initially crucial in controlling parasitemia and might later contributes to the development of symptomatic forms of Chagas disease due to its role in T-cell regulation. Herein, we will discuss the putative role of effector complement molecules on T-cell immune exhaustion during chronic human T. cruzi infection.
Enhanced Multicore Performance Using Novel Thread-Aware Cache Coherence and Prefetch-Control Mechanism

We propose a hardware technique for cache coherence over the existing approaches that ensure that shared and less frequently used cache blocks bypass private caches of multiple cores. Furthermore, this manuscript proposes a mechanism to tune the aggressiveness of a data prefetcher. Increased cache hit rate and improved performance have been observed since coherence management and prefetching delays are avoided using the proposed bypassing and thread progress-aware prefetch controlling mechanism. Our approach shows around 19% improvement in cache hit rate and 29% average performance improvement over existing state-of-the-art techniques for Parsec & Splash-2 multithreaded benchmarks.
Intra- and Inter-individual Differences in the Human Intestinal Microbial Conversion of (-)-Epicatechin and Bioactivity of Its Major Colonic Metabolite 5-(3′,4′-Dihydroxy-Phenyl)-γ-Valerolactone in Regulating Nrf2-Mediated Gene Expression

(-)-Epicatechin (EC) is one of the most popular polyphenols present in various food products in daily life. Upon intake, it is intensively metabolized by microbiota in the large intestine. In the present study, intra- and inter-individual variations in this gut microbial conversion of EC and the concomitant formation of its major metabolites, including 5-(3′,4′-dihydroxy phenyl)-γ-valerolactone (3,4-diHPV), were identified and quantified via liquid chromatography triple quadrupole mass spectrometry (LC-TQ-MS) in anaerobic fecal incubations. In addition, the bioactivity of EC and 3,4-diHPV in activating Nrf2-mediated gene expression was tested quantifying their effects in the U2OS Nrf2 CALUX assay (a reporter gene assay that is used to test the potency of chemicals in activation of Nrf2 signaling), and on the expression levels of Nrf2-related proteins in Hepa1c1c7 and Caco-2 cells via nanoLC-MSMS. A quantitative real-time polymerase chain reaction (RT-qPCR) was carried out to confirm selected Nrf2-regulated gene expressions at the mRNA level. Results obtained show that both intra- and inter-individual differences exist in human gut microbial EC degradation and 3,4-diHPV formation, with inter-individual differences being more distinct than intra-individual differences. The metabolite, 3,4-diHPV, showed higher potency in the U2OS Nrf2 CALUX assay than EC itself. Among the obviously altered Nrf2-related proteins, 14 and 10 Nrf2-associated proteins were upregulated to a higher extent upon 3,4-diHPV treatment than in the EC treated group for Hepa1c1c7 and Caco-2 cells, respectively. While only three and four of these Nrf2-associated proteins were induced at a higher level upon EC than upon 3,4-diHPV treatment for Hepa1c1c7 and Caco-2 cells, respectively. RT-qPCR results showed that indeed Nrf2-mediated genes (e.g., Nqo1 and Ugt1a) were only induced significantly in 3,4-diHPV treated and not in EC treated Hepa1c1c7 cells. Taken together, the results suggest that the major colonic EC metabolite, 3,4-diHPV, was more capable of inducing Nrf2-mediated gene expression than its parent compound EC. This implies that the evident inter- and intra-individual differences in the microbial conversion of EC to this major metabolite 3,4-diHPV may affect the overall health-promoting effects of EC consumption related to the Nrf2 pathway activation.
THE DEFICIENT HARVESTING RAINWATER SYSTEM IN KUALA LUMPUR AND THE HUMAN-MADE WATER POLLUTION CONDITIONS

Rainwater harvesting is a common method to collect water from rain, in diverse forms such as direct from the roof, in tanks deposits, ponds or even artificial lakes. The city of Kuala Lumpur (with now 1.7 million inhabitants only in the urban area) has this process regulated by the Department of Irrigation and Drainage Malaysia, by enforcing the MSMA (Manual of Urban Storm water Management). The process has in consideration flash flood, river pollution, soil erosion, development in the highlands and lowlands. With an average annual rainfall of 2486 mm, and the buildup area that has reached 1663.23 km2 in 2014 while the area of forest has suffered a reduction of 8.3% in 2014, Kuala Lumpur requires a smart and sustainable rainwater harvesting system. The paper examines the conditions that exert pressure in the infrastructure of Kuala Lumpur whose center has experienced an increase in the number of violent flooding yet at the same time prevent an effective harvesting of storm water. This resulted in a high volume of water wasted without reuse and the limited capacity of the recollection of the rainwater. The primary methodology used is the consultation of the available literature, journals, published reports, interview with experts and a survey at impacted neighborhoods. A secondary source of information is the observation in situ to help to support the validation of the conclusions. The deficiency in the control of the flooding system is a negative correlation to the city’s development. This fact is related to the high rate of urbanism, insufficient drainage system, narrowed stretches of the rivers, low prices of water and the lack of an adequate implementation of incentive policies and the low volume of information to the population. Key words: harvesting, storm water, flooding, deficiency, erosion. DOI: https://doi.org/10.3846/enviro.2017. 070
Truck queuing analysis at landfill sites in a waste collection vehicle routing problem

This paper presents an analysis of a truck queuing problem at landfill sites due to a static rest time of truck drivers in a waste collection vehicle routing problem. The analysis is based on a solution of two datasets from a benchmark waste collection problem using the nearest greedy heuristic algorithm.In this problem, all drivers need to have a one-hour rest break during their collection.The rest break can start from 11.00 a.m. to 12.00 p.m. However, based on the solution from a previous study, all drivers have their rest break almost at the same time. Consequently, all drivers travelled to landfill sites to unload waste approximately at the same time.Based on the truck queuing analysis presented in this paper, there is a long queue at landfill site 3 (ten trucks arrived between 2.52 p.m. to 2.53 p.m.) for dataset 1; whereas for dataset 2, 13 trucks arrived at landfill site 1 between 4.10 p.m. to 4.55 p.m. Thus, it may be concluded that the static rest time period of truck drivers can cause truck queuing at landfill sites. Long queues at landfill sites will increase the service time at landfill sites as well as affect the drivers’ arrival time at customers. This paper suggests that future works need to consider a dynamic rest time period in solution techniques to overcome the truck queuing problem at landfill sites for solving the waste collection vehicle routing problem.
The Effect of Temperature and pH on FeCoNi Film Electrodeposition

This paper reports the effect of temperature and electrolyte pH on the electrodeposition of FeCoNi film. The FeCoNi electrodeposition was carried out from sulfate solution using the potentiostatic technique. The higher temperature increased deposition rate, thickness of the deposit, and size of the FeCoNi crystallites. The obtained FeCoNi films were characterized by X-ray diffractometer (XRD) and energy dispersive X-Ray (EDX). XRD analysis confirmed typical diffraction patterns of awaruite phase. EDX evaluation indicated that the relative weight increases in pH from 2.5 to 4, the composition of wt% Fe deposited increased from 39.2% to 47.9%, while wt% Co and wt% Ni decreased from 36.2% to 33.5 % and 24.6% to 18.6%.
Balancing Art & Business: Performance Goals and Measurement Systems in Commercial Art Galleries

This research investigates the performance goals of commercial art galleries and the performance measurement systems (PMS) they use. As one of the first examinations into the topic, this research provides a foundational understanding of performance measurement in for-profit arts organizations. Combining notions from traditional management, non-profits arts PMS, and cultural economics I develop a framework within which to analyze galleries’ performance goals and PMS. Through qualitative interviews with galleries in Los Angeles and the Netherlands, I find that previous explanations of performance goals in arts organizations are insufficient to explain the varying goals of galleries. I develop a gallery orientation matrix that defines four types of galleries (discoverers, developers, endorsers, and canonizers) based on their values which helps to account for the variation in galleries’ goals and measurement systems. I also find several factors which are shared across galleries (motivations, challenges, and business structure) which shape performance goals and measurement systems. Based on these factors, the different performance goals and PMS of galleries are outlined.
INTERNET OF THINGS BASED ZIGBEE SNIFFER FOR SMART AND SECURE HOME

The aim of this research is to determine the usage of energy or power with high spectrum allocation in ZigBee Protocol with the help of clustering in IoT. This research starts presenting an overview of the broadband network energy sector and the challenges that are facing. It is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group in order to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement. The desired segmentation is obtained by an iterative process, based on computational clusters calculation (using python programming language) and finalized by a post-clustering analysis applying visualization and statistical data mining technique to detect the energy consumption and reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared, giving the best prediction of accuracy 98.46% for all energy load profiles with high spectrum of 100GHz. The solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used as the base of the post-clustering stage to obtain the final energy consumption segmentation. Most of these methodologies use the absolute values in 100 kWh, as they were more focused on identify the users with higher energy savings potential. In this case, it allows personalizing energy savings recommendations according to the specific characteristics of ZigBee protocol, improving the consumer experience by being able to provide the adequate advice at the appropriate time, facts that increase the effectiveness of the energy efficiency advises’ service for future ZigBee protocol.
Imposter Syndrome Among Pre-service Educators and the Importance of Emotion Regulation

This study examined the prevalence and impact of imposter syndrome (IS) on a sample of pre-service educators. We report a majority of pre-service educators experience IS; 93% experience moderate levels and 54% had frequent or severe levels of imposter thoughts, and further that IS was negatively associated with educator well-being. We also investigated the effects of minority group membership on experiences of IS, and found that IS was more severe for women and queer minorities, but less severe for racial minorities. Lastly, we investigated the potential for healthy emotion regulation to mitigate the effects of IS on pre-service educator well-being and found that adaptive emotion regulation strategy use mitigates the effects of IS, which may provide a viable means for addressing this pervasive issue among educators, and specifically among those with minoritized identities. We discuss the implications of our findings for educational training and improving the experiences of pre-service educators.
BEING A PATIENT IN POLAND: IN THE OPINIONS OF PATIENTS’ AGENTS AND IN THE PROCEDURES OF THE PATIENTS’ OMBUDSMAN

The article presents the situation of patients in Poland, based on existing data (non reactive research), and individual interviews with Patients’ Agents in the given clinic or hospital and Patient’s Ombudsmen. It presents doctor-patient relationships in the daily routine of a health centre in the context of Polish law and procedures, and highlights the role of the Patients’ Ombudsmen in creating a patient-friendly environment. Due to the large number of hospitalisations, whose duration is continually shortened because of financial limitations, ensuring the patient’s well-being is becoming increasingly difficult.
Inventory of Attitudes toward Seeking Mental Health Services: Psychometric Properties among Adolescents

Mental health is an essential element of wellness; however, some populations are more vulnerable to mental health problems. Suicide is the second-highest cause of death in young people and help-seeking unwillingness is a significant obstacle to mental health interventions. Young people are especially reluctant to seek help and negative attitudes stand out as help-seeking barriers, highlighting the importance of evaluating these constructs. The Inventory of Attitudes toward Seeking Mental Health Services (IASMHS) is a measure that evaluates help-seeking attitudes; nevertheless, it lacks applicability in Portuguese youth. This study aims to explore the psychometric characteristics of the IASMHS in 750 Portuguese adolescents (52.3% female, Mage = 14.67), through exploratory and confirmatory factor analysis, and reliability and validity analyses. Results suggest that a three-factor structure was adequate, those factors being help-seeking propensity, indifference to stigma, and psychological openness. The IASMHS was shortened to 20 items and revealed satisfactory properties regarding internal consistency, convergent/discriminant validity, and temporal stability. These findings validate the IASMHS as an adequate tool in the assessment of young people’s help-seeking attitudes.