Miracle-dz/UniFAR-FTUs huggingface dataset:
A role for ARF19 in auxin and ethylene signaling in Arabidopsis

Although auxin response factors (ARFs) are the first well-characterized proteins that bind to the auxin response elements (AuxREs), elucidation of the roles of each ARF gene in auxin responses and plant development has been challenging. Here we show that ARF19 and ARF7 not only participate in auxin signaling, but also play a critical role in ethylene responses in Arabidopsis roots, indicating that the ARFs serve as a cross-talk point between the two hormones. Both arf19 and arf7 mutants isolated from our forward genetic screens are auxin resistant and the arf19arf7 double mutant had stronger auxin resistance than the single mutants and displayed phenotypes not seen in the single mutants. Furthermore, we show that a genomic fragment of ARF19 not only complements arf19 , but also rescues arf7 . We conclude that ARF19 complements ARF7 at the protein level and that the ARF7 target sequences are also recognized by ARF19. Therefore, it is the differences in expression level / pattern and not the differences in protein sequences between the two ARFs that determines the relative contribution of the two ARFs in auxin signaling and plant development. In addition to being auxin resistant, arf19 has also ethylene insensitive roots, and ARF19 expression is induced by ethylene treatment. This work provides a sensitive genetic screen for uncovering auxin resistant mutants including the described arf mutants. This study also provides a likely mechanism for coordination and integration of hormonal signals to regulate plant growth and development.
Gender and teamwork: an analysis of professors’ perspectives and practices

ABSTRACT Teamwork is increasingly seen as an important component of engineering education programmes. Yet, prior research has shown that there are numerous ways in which teamwork is gendered, and can lead to negative experiences for women students. This article presents the first interview findings on professors’ perspectives on gender and teamwork. Semi-structured interviews were conducted with 39 engineering professors to determine what and how they thought about gender in engineering and engineering education. For this article, the parts of the interviews about teamwork are analysed. We conclude that professors need tools to help them facilitate gender-inclusive teamwork, and those tools must address the beliefs that they already hold about teamwork. The findings raise questions about the adoption of evidence-based instructional practices and suggest current teamwork practices may exacerbate gender inequalities in engineering.
Detection of Vehicular Number Plate System using Deep Learning Approach

: An automated system for recognizing vehicles’ license plates are a growing need in order to improve security and for traffic control applications, particularly in major urban areas. Automatic Number Plate Recognition (ANPR) is a type of an Intelligent Transport System. While numerous studies on plate identification, character segmentation and character recognition have been performed, several challenges still remain. An efficient Vehicle Detection System is necessary to ensure traffic monitoring. In the last 4-5 years, several image processing and learning methods have been developed such as Optical Character Recognition (OCR) technique. The aspect of object detection, though, hasn’t been exploited for ANPR framework in the previous researches focused on object detection. This research uses deep learning to leverage these object detection algorithms. You-Only-Look-Once (YOLO) and Convolutional Neural Network (CNN) have shown themselves to be the most efficient methods with regard to both supervised and unsupervised learning. This work studies several algorithms for object detection and deep learning, and compares their performance. The custom dataset was used to identify and recognize the license plate to ensure successful traffic management. We studied Deep Learning algorithms based on Image Segmentation using object detection algorithm through YOLO object detection technique in darkflow frameworks and character recognition based on CNN and also trained a model as base learner. This study establishes a method for real-time detection and identification of license plate, and drawing useful conclusions. The results of the simulation show that the deep learning methodology is more effective when detecting vehicle plates.
Density Functional Theory Study on the Electrical Properties of α-CsPbX 3 (X=I, Cl, Br)

All-inorganic perovskite solar cells have become more important in the commercialization of the photovoltaic devices. In this study the structural, electronic properties of inorganic metal halide cubic perovskites CsPbX 3 (X = I, Br, Cl) for perovskite solar cells are simulated using first-principles Density Functional Theory (DFT). The newly adjusted parameters make the calculations more accurate. These compounds are semiconductors with direct band gap energy. Results suggest that the α-CsPbX 3 (X=I, Cl, Br) have a wide bandgap adjustment range with potential application in solar cells and other optoelectronic energy devices. On the basis of the electronic properties, one can expect that the α-CsPbI 3 would be a better used to perovskite solar cell. α -CsPbCl 3 and α-CsPbBr 3 better suitable for others photovoltaic device.
Jump down to document THE ARTS CHILD POLICY CIVIL JUSTICE EDUCATION ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INTERNATIONAL AFFAIRS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY SUBSTANCE ABUSE TERRORISM AND HOMELAND SECURITY TRANSPORTATION AND INFRASTRUCTURE WORKFO

6 Jump down to document THE ARTS CHILD POLICY CIVIL JUSTICE EDUCATION ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INTERNATIONAL AFFAIRS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY SUBSTANCE ABUSE TERRORISM AND HOMELAND SECURITY TRANSPORTATION AND INFRASTRUCTURE WORKFORCE AND WORKPLACE The RAND Corporation is a nonprofit research organization providing objective analysis and effective solutions that address the challenges facing the public and private sectors around the world.
The Determining of Nutritive Value of Sallow and Service Leaves Using Nylon Bags and Gas Production Techniques

: This study was carried out to the determination of nutritive value of sallow and service leaves using nylon bag and gas production techniques in Gizel sheep. Two fistulated Gizel sheep with average BW 35±1.8 kg are used in a complete randomized design. The gas production was measured at 2, 4, 6, 8, 12, 16, 24, 36, 48, 72 and 96 h and ruminal DM and CP disappearance were measured at 0, 2, 4, 6, 8, 12, 16, 24, 36, 48, 72 and 96 h. Dry matter degrade abilities of service and sallow leaves at 96 h were 67.92 and 73.40%, respectively. Crude protein degradabilities of service and sallow leaves at 96 h were 79.91 and 82.02%, respectively. The gas production of service and sallow leaves at 96 h were 138.324 and 212.566 ml/g DM. The relationship between dry matter and gas production values for service and sallow leaves obtained about 0.99652 and 0.99039 and this parameter for crude protein and gas production achieved 0.98817 and 0.99443, respectively. High correlation between in situ and gas production techniques indicated that digestibility values can be predicted from gas production data.
Checkpointing Workflows à la Young/Daly Is Not Good Enough

This article revisits checkpointing strategies when workflows composed of multiple tasks execute on a parallel platform. The objective is to minimize the expectation of the total execution time. For a single task, the Young/Daly formula provides the optimal checkpointing period. However, when many tasks execute simultaneously, the risk that one of them is severely delayed increases with the number of tasks. To mitigate this risk, a possibility is to checkpoint each task more often than with the Young/Daly strategy. But is it worth slowing each task down with extra checkpoints? Does the extra checkpointing make a difference globally? This article answers these questions. On the theoretical side, we prove several negative results for keeping the Young/Daly period when many tasks execute concurrently, and we design novel checkpointing strategies that guarantee an efficient execution with high probability. On the practical side, we report comprehensive experiments that demonstrate the need to go beyond the Young/Daly period and to checkpoint more often for a wide range of application/platform settings.
Multi Targets Detecting Based on Glucose Monitoring Platform

The enzyme sensor field has grown greatly since 1962. Today’s biosensor market is dominated by glucose electrodes which are used for the rapid detection of blood sugar levels by diabetes sufferers. In this paper we take a comprehensive review on the inception, growth and development of the enzyme sensor field from a commercial viewpoint. The current status of the technology is evaluated and future trends that multi targets enzyme sensor i.e. cholesterol, lactate and ethanol enzyme sensors in this dynamic and fast moving field are also discussed. For glucose meter and glucose is the most successful monitoring system for diabetic products, which approximately 95% of the current Chinese market for biosensors, and been at approximately $120–150 million. The why the blood glucose market was receptive numerous, the biggest is the prevalence of diabetes in China. 140 million adults with diabetes were confirmed and a further 230 million adults were also found with pre-diabetes (120 million men and 110 million women). These results indicate that diabetes has a major public health problem in China, and national strategies aimed at preventing, detecting, and treating diabetes are urgently needed. In the absence of a cure for diabetes, home blood glucose monitoring will need to continue, and the current commercial dominance of mediated electrochemical biosensors will not be easily replaced. Since diabetic patients need to control their blood glucose levels the importance of self-monitoring of blood glucose as an effective method of measuring blood sugar not in clinics at home in the
: Alternation in Disguise (Extended Version)

: Wireless parallel Turing machine (WPTM) is a new computational model introduced recently by the authors. Its design has been inspired by wireless mobile networking. The WPTM is an entirely deterministic model of parallel computations where the processes communicate wirelessly via dynamically tuned broadcasting channels. We show a tight connection between the computations of WPTMs and those of the classical alternating Turing machines. Namely, we prove that time-space simultaneously bounded computations on WPTMs correspond exactly to similarly bounded computations of alternating Turing machines. This result leads not only to an alternate characterization of alternation, but it also oﬀers an alternate view of bounded uniform circuit families, such as NC i and NC .
Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

In order to improve the accuracy of wind power prediction (WPP), we propose a WPP based on multivariate phase space reconstruction (MPSR) and multivariate linear regression (MLR). Firstly, the multivariate time series (TS) are constructed through reasonable selection of wind power and weather factors, which are closely associated with wind power. Secondly, the phase space of the multivariate time series is reconstructed based on the chaos theory and C-C method. Thirdly, an auto regression model for multivariate phase space is created by regarding phase variables as state variables, and the very-short-term wind power is predicted by using a multi-linear regression algorithm. Finally, a parallel algorithm based on map/reduce is presented to improve computing speed. A cloud computing platform, Hadoop consisting of five nodes, is established as a matter of convenience, followed by the prediction of wind power of a wind farm in the Hunan province of China. The experimental results show that the model based on MPSR and MLR is more accurate than both the continuous method and the simple approximation method, and the parallel algorithm based on map/reduce effectively accelerates the computing speed.
Clicks Prediction with L1 Regularized Logistic Regression and A Study on Poisson Factorization Recommender Evaluation !

The key task for a search engine advertising system is, for each query that the search engine receives, to choose what advertisement should be displayed, and in what order. This ranking order has a strong impact on the revenue the search engine receives from the ads. Meanwhile, showing the user an advertisement that they prefer to click on improves user satisfaction. Therefore, it is reasonable to set up click-through rate (CTR) as a weighted ranking criteria. In this project, we aim to develop a model capable of accurately predicting CTR of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. Combining logistic regression model with l1 regularization we are able to predict CTR for new ads based on self-deﬁned features. The ultimate goal is to improve the convergence and performance of our advertising system, consequently increasing both revenue and user satisfaction.
Adaptive Object-Models: a Research Roadmap

—The Adaptive Object-Model (AOM) is a meta-architectural pattern of systems that expose an high-degree of runtime adaptability of their domain. Despite there being a class of software projects that would directly beneﬁt by being built as AOMs, their usage is still very scarce. To address this topic, a wide scope of concepts surrounding to Adaptive Object-Models need to be understood, such as the role of incompleteness in software, and its effects on system variability and adaptability, as well as existing metamodeling and metaprogramming techniques and how do they relate to software construction. The inherent complexity, reduced literature and case-studies, lack of reusable framework components, and fundamental issues as those regarding evolution, frequently drive developers (and researchers) away from this topic. In this work, we provide an extensive review of the state-of-the-art in AOM, as well as a roadmap for empirical validation of research in this area, which underlying principles have the potential to alter the way software systems are perceived and designed.
Simultaneous determination of sulfamethoxazole, trimethoprim and bromhexine in veterinary formulation using high performance liquid chromatography (HPLC)

Aim: To develop and to validate a simple, sensitive and fast method for the simultaneous determination of sulfamethoxazole (SMT), trimethoprim (TMP) and bromhexine (BMX) in veterinary formulation by high performance liquid chromatography according to the guidelines of the Validation and Control Guide for analytical quality of medicines in food products and veterinary medicines, RDC 166/2017 and international guides International Conference on Harmonization and International Association of Official Analytical Chemists. Materials and methods:The separation was performed on a ThermoScientific® C18 AcclaimTM120 analytical column (4.6 × 250 mm, 5 µm), with a flow rate of 0.7 mL min-1 and detection at 245 nm, 265 nm and 271 nm, for BMX, SMT and TMP, respectively. All measurements were performed in methanol: water (84:16 v/v; pH 3.0). The analytical curves were linear (r > 0.9997) in the concentration range of 15.0 to 30.0 μg·mL-1 for SMT, 3.0 to 9.0 μg·mL-1 for TMP and 0.5 to 2.0 μg·mL-1 for BMX. Results: The method proved to be accurate, with coefficients of variation below the maximum limit of 2.0%, robust, without significant influence of the variations used in the analysis, exact (recovery >99%) and selective, in the assessment of interference from adjuvants. Conclusion:Therefore, the developed method proved to be suitable for routine quality control analyzes for the simultaneous determination of SMT, TMP and BMX in pharmaceutical formulations.
The Concept of Wahdah Al-Wujud : Comparative Study on Sheikh Siti Jenar and Mulla Shadra’s Perpertives

This article explains the comparison of the concept of wahdah al-wujud between Siti Jenar and Mulla Sadra. Siti Jenar is famous for the idea of her divine ontology, which is always interesting to discuss; Manunggaling Kawula Gusti. In contrast, Mulla Sadra is famous for the concept of his ontology called al-Hikmah al-Muta’aliyyah. This study uses a type of library research with descriptive analytical methods. The results show that the concept of wahdah al-wujud between Siti Jenar and Mulla Sadra has similarities and differences. The similarities and differences between the two focus on the form of God and His creatures. As for the similarities, both assume the form of God and His creatures are the same. However, the difference lies in the cause of being "the same" of God and His creatures.
Determinant factors of innovation management in the manufacturing industry of Pichincha, Ecuador

A permanent challenge for business organizations is to transform, adapt to the environment and innovate, so knowing the factors that influence the ability to innovate is relevant information. In this sense, the objective of this article is to determine and conceptualize the decisive factors that interact in a systemic way in the management of innovation in the manufacturing industry. The research has a qualitative approach, with a des-criptive and transversal scope. In the first place, a review of the literature was carried out that allowed delimiting the management of innovation in the company in three main categories. Second, the categories were characterized, and subcategories and properties were identified, taking the manufacturing companies of Pichincha, Ecuador as the subject of study. This characterization was carried out through a qualitative study that takes the Grounded Theory as a reference, using research techniques: questionnaires, interviews, and documentary research. The results reveal that the relevant factors for the management of innovation in the company can be grouped into three main categories: knowledge management (KM), innovation capabilities (IC) and financial performance (FP). The subcategories that explain KM are policies and strategies, organizational structure, technology, people, incentive systems, organizational culture, and communication. The subcategories that explain IC are research and development capacity, management capacity, resource availability, human talent management, staff skills and technological capacity. The subcategories that explain the FP are sales and costs. This research contributes to the field of innovation management with new information and theory for action and emphasizes the systemic vision of innovation management and the key factors for the development of innovations in the Ecuadorian industrial sector, with the purpose of strengthening the theoretical and empirical advances of innovation management in the company.
Urban induced-displacement of informal settlement dwellers: A comparison of affected households ’ and planning officials ’ preferences for resettlement site attributes in Kigali, Rwanda

There is an increase of induced displacement of informal settlement dwellers in Kigali city due to the ongoing redevelopment of existing inner city areas and disaster risk mitigation actions in high-risk zones. Local authorities are currently much more interested in compensating the affected households in kind, namely by providing new homes in resettlement sites as a strategy to avoid the creation of new informal settlements. In this context, a resettlement site is an issue of fundamental concern for both the targeted communities and the policy makers. Understanding affected households ’ preferences regarding resettlement site attributes is crucially important if such relocation projects are to be successful in the long term. This study explores the preferences of affected households for resettlement attributes and compares them to the opinions of professional planning officials. Findings revealed similarities as well as significant differences between the two groups ’ opinions on what are the important resettlement sites ’ attributes. The paper further analyses the spatial implications of the two groups ’ preferences on the suitability of residential areas in the city. Differences in opinions led to different spatial suitability maps of the existing residential areas. Given the substantial spatial implications of the divergent views, selecting a resettlement site based on both stakeholder groups ’ views would be essential to contribute to more effective and conflict-free resettlement processes.
MULTISCALE STATISTICAL HYPOTHESIS TESTING FOR K-SAMPLE GRAPH INFERENCE

A connectome is a map of the structural and/or functional connections in the brain. This information-rich representation has the potential to transform our understanding of the relationship between patterns in brain connectivity and neurological processes, disorders, and diseases. However, existing computational techniques used to analyze connectomes are often insufficient for interrogating multi-subject connectomics datasets. Several methods are either solely designed to analyze single connectomes, or leverage heuristic graph invariants that ignore the complete topology of connections between brain regions. To enable more rigorous comparative connectomics analysis, we introduce robust and interpretable statistical methods motivated by recent theoretical advances in random graph models. These methods enable simultaneous analysis of multiple connectomes across different scales of network topology, facilitating the discovery of hierarchical brain structures that vary in relation with phenotypic profiles. We validated these methods through extensive simulation studies, as well as synthetic and real-data experiments. Using a set of high-resolution connectomes obtained from genetically distinct mouse strains (including the BTBR mouse—a standard model of autism—and three behavioral wild-types), we show that these methods uncover valuable latent information in multi-subject connectomics data and yield novel insights into the connective correlates of neurological phenotypes. The documentation and code for all analyses in this thesis are available at https://github.com/neurodata/MCC.
CryptoComparator: A Visual Analytics Environment for Cryptocurrencies Analysis

Cryptocurrencies are a novel phenomenon in the ﬁnance world that is gaining more attention from the general public, banks, investors, and lately also academic research. A characteristic of cryptocurrencies is to be the target of investments that, due to the volatility of most of the cryptocurrencies, tends to be at high risk and behave very differently from classic currencies. A way of reducing this risk is to look at the history of existing cryptocurrencies and compare them in order to spot promising trends for increased gain. This paper introduces CryptoComparator, a Visual Analytics tool designed for allowing analysis of correlations and trends of cryptocurrencies. The system exploits an initial proposal for a double elliptic graph layout, reconﬁgurable with three different ordering functions, in order to support fast visual search of cryptocurrencies by correlation strength. One use-case developed with a domain expert in cryptocurrency ﬁnancial activities demonstrates qualitatively the usage of the system.