Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Annealing on the Structural and Optical Properties of CuIn 1–x Al x S 2 thin Films

The effects of annealing of CuIn1–xAlxS2thin films for different temperature, which were grown by thermal evaporation, were investigated through X-ray diffraction (XRD), energy dispersive X-ray analysis (EDS) and optical absorption measurements. Post growth was used to modify the structural and optical properties of the CIAS thin films. Then it realized in three different temperatures 250, 300 and 400℃. It is found that significant difference of the transparent films in function of annealing temperature. The FWHM in the X-ray diffraction pattern is found to decrease with an increase in annealing temperature indicating that the crystalline nature of the CIAS improves with increase in annealing temperature. The position of the (112) peak and other peaks in the X-ray diffraction pattern has been observed to shift to higher values of 2θ with the increase of gallium concentration. We demonstrate a significant enhancement of the interdiffusion in the dot thin film.
Transgenerational Epigenetic Inheritance Under Environmental Stress by Genome-Wide DNA Methylation Profiling in Cyanobacterium

Epigenetic modifications such as DNA methylation are well known as connected with many important biological processes. Rapid accumulating evidence shows environmental stress can generate particular defense epigenetic changes across generations in eukaryotes. This transgenerational epigenetic inheritance in animals and plants has gained interest over the last years. Cyanobacteria play very crucial role in the earth, and as the primary producer they can adapt to nearly all diverse environments. However, few knowledge about the genome wide epigenetic information such as methylome information in cyanobacteria, especially under any environment stress, was reported so far. In this study we profiled the genome-wide cytosine methylation from a model cyanobacterium Synechocystis sp. PCC 6803, and explored the possibility of transgenerational epigenetic process in this ancient single-celled prokaryote by comparing the DNA methylomes among normal nitrogen medium cultivation, nitrogen starvation for 72 h and nitrogen recovery for about 12 generations. Our results shows that DNA methylation patterns in nitrogen starvation and nitrogen recovery are much more similar with each other, significantly different from that of the normal nitrogen. This study reveals the difference in global DNA methylation pattern of cyanobacteria between normal and nutrient stress conditions and reports the evidence of transgenerational epigenetic process in cyanobacteria. The results of this study may contribute to a better understanding of epigenetic regulation in prokaryotic adaptation to and survive in the ever changing environment.
Unsupervised translated word sense disambiguation in constructing bilingual lexical database

The performance of a machine translation system depends on the availability of bilingual lexical dictionary and completion of its word sense disambiguation performance. Word sense disambiguation plays a vital role in several applications such as machine translation, information retrieval and many other Natural Language Processing (NLP). In oder to construct a reliable machine translation system, not only consistent lexical database like WordNet (WN), but also word sense distinguished database, and bilingual lexical machine readable dictionary (MRD) are required to achieve an accurate translation. Since WN is for the English language, there have been several approcaches building WN like database for other languages, also known as multilingual WN, by linking and extending bilingual MRDs with synsets from WN. Lexical database like WN for Myanmar languae have been proposed in recent years which are bilingual lexical databases as the synsets from WN are merged with bilingual MRD. However, there are few limitations in constructing bilingual database based on MRD where thesauries are derived from WN yet there are no directed alignemnt of meanings between English and Myanmar language. In order to design a complete lexical database with correct parallel meanings for both languages, our proposed method composes a model which accurately matches the similar meanings between English words and Myanmar words by translating and comparing the word senses for both languages. The experimental result shows that the meanings for both languages are aligned as accurate as the manual alignment of the word senses for lexical database.
Theoretical construct of strategic control systems

Article history: Received April 17, 2012 Accepted 11 June 2012 Available online June 12 2012 This paper presents the development of an instrument for measuring strategic control systems (SCS). Although SCS has received considerable attention in the literature, most of the discussions, on the elements that constitute SCS, are limited to descriptive statements. Thus, the theoretical construct of SCS suffers from lack of extensive statistical validation procedures. However, there are a few authors that had initiated the development of an instrument to measure the SCS. Their small effort had also been criticized due to the inconsistency among authors in defining SCS, which would likely affect the validity of the instrument being developed. Given this lacuna, this paper has developed and validated an instrument for conceptualizing SCS, specifically under the environment of Quality Management strategy. Based on a careful and systematic process of instrument development, this paper revealed that SCS consists of two important dimensions, namely strategy implementation and strategic resource allocation. In developing this instrument, a total number of 205 respondents were involved. The findings reported in this paper would benefit future researchers in measuring SCS, particularly for survey-based research. © 2012 Growing Science Ltd. All rights reserved.
Robust single stage detector based on two-stage regression for SAR ship detection

Automatic ship detection in SAR imagery plays an indispensable role in the surveillance of maritime activity. With the spring of the deep neural network and the rapid development of SAR imaging technique, SAR ship detection based on deep convolutional neural networks has attracted increasing attention in remote sensing imagery interpretation. However, the ships of variant sizes (multi-scale) and complicated background result in the essential challenge of using deep learning based methods. The initial motivation of this paper is to design a structure that can achieve better accuracy as well as maintain compatible time efficiency on small object detection. Aiming at solving this problem, we develop anelaborately designed network based on single-shot detector and two-stage regression called Robust two-stage regression network (R2RN). Our network comprises an anchor modified first module and an object detection second module as well as the connective block between them which inherits the essence of feature pyramid. Experiments on SAR ship dataset have demonstrated the effectiveness and efficiency of the proposed method.
Comparing alternative goal model visualizations for decision making: an exploratory experiment

Decision making is an important part of early requirements analysis. Analysts are faced with the task of describing a large number of solutions to stakeholder problems and assess each of them with respect to high-level objectives. Goal models are regarded to be a useful tool for assisting this decision making activity through explicating relationships among problems, criteria and solutions. To visualize goal models, box-and-line diagrams have traditionally been used. But are diagrams the best way to visualize decision problems within goal models? In this paper, we consider two alternative ways: tree-maps and common pie-charts combined with bar-charts. In an experimental study we evaluate each with respect to whether they can help participants identify optimal alternatives accurately, quickly and confidently. We find that, for at least one of the introduced visualizations, participants tend to respond faster, more confidently and more accurately than when using traditional goal diagrams. The result calls for rethinking the way we visually present goal models and for further experimentation on the matter.
FP-LMTO Calculations of Structural and Electronic Properties of Alkaline-Earth Chalcogenides Alloys AY:A = Ca, Sr, Ba; Y = S

The structural and the electronic properties of the ternary SrxCa1-xS, BaxCa1-xS and BaxSr1-xS alloys have been calculated using the full-potential linear muffin-tin-orbital (FP-LMTO) method based on density functional theory, within both local density approximation (LDA) and generalized gradient approximation (GGA). The calculated equilibrium lattice constants and bulk modulus are compared with previous results. The concentration dependence of the electronic band structure and the direct and indirect band gaps are investigated. Using the approach of Zunger and co-workers, the microscopic origins of the band gap bowing are investigated also. A reason is found from the comparison of our results with other theoretical calculations.
Data locality and VM interference aware mitigation of data skew in hadoop leveraging modern portfolio theory

Data skew, which is the result of uneven distribution of data among tasks in big data processing frameworks such as MapReduce, causes significant variation in the execution time of tasks and makes their placement on computing resources more challenging. Moreover, with the proliferation of big data processing in the cloud, the interference among virtual machines co-located on the same physical machine exacerbates the aforementioned variation. To tackle this challenge, we propose Locality and Interference aware Portfolio-based Task Assignment (LIPTA) approach. LIPTA leverages the modern portfolio theory to mitigate the variation in execution time of tasks while considering the interference of virtual machines and locality of input data. It selects and assigns groups of tasks (the portfolio) to each machine such that variation of their total execution time is reduced due to portfolio effect. Experimental results using real-world workload logs demonstrate the effectiveness of our LIPTA approach. It can reduce the total execution time of workloads by up to 46.7% compared with several variation-oblivious approaches.
Biosynthesis of Colabomycin E, a New Manumycin‐Family Metabolite, Involves an Unusual Chain‐Length Factor

Colabomycin E is a new member of the manumycin‐type metabolites produced by the strain Streptomyces aureus SOK1/5‐04 and identified by genetic screening from a library of streptomycete strains. The structures of colabomycin E and accompanying congeners were resolved. The entire biosynthetic gene cluster was cloned and expressed in Streptomyces lividans. Bioinformatic analysis and mutagenic studies identified components of the biosynthetic pathway that are involved in the formation of both polyketide chains. Recombinant polyketide synthases (PKSs) assembled from the components of colabomycin E and asukamycin biosynthetic routes catalyzing the biosynthesis of “lower” carbon chains were constructed and expressed in S. aureus SOK1/5‐04 ΔcolC11–14 deletion mutant. Analysis of the metabolites produced by recombinant strains provided evidence that in both biosynthetic pathways the length of the lower carbon chain is controlled by an unusual chain‐length factor supporting biosynthesis either of a triketide in asukamycin or of a tetraketide in colabomycin E. Biological activity assays indicated that colabomycin E significantly inhibited IL‐1β release from THP‐1 cells and might thus potentially act as an anti‐inflammatory agent.
Arbuscular Mycorrhizal Fungus Mediate Changes in Mycorrhizosphere Soil Aggregates

Many studies have shown that arbuscular mycorrhizal (AM) fungus has an important role in soil aggregate formation and stabilization. While most studies about the effects of AM fungus on soil aggregate have experimental set-ups in single pots or containers with two compartments, these studies cannot differentiate the effects of roots, mycorrhizal roots or hyphae. In this study we used containers with four compartments to split the roots and quantitatively compare the change of soil aggregate in the mycorrhizosphere soil, rhizosphere soil, hyphosphere soil and bulk soil. Our results demonstrate a significant positive correlation among hyphal length density, easily extractable glomalin (EEG) and aggregate mean weight diameter (MWD), geometric mean diameter (GMD) and percentage of soil macroaggregate with a diameter larger than 0.25 mm (R0.25). The GMD and MWD of R0.25 in the hyphal compartment were higher than those in the non-inoculated root compartment, but were lower than those in the mycorrhizal compartment. This suggests the mycorrhizal hyphae had a greater effect than the non-inoculated roots, but less of an effect than the mycorrhizal roots on the formation and stabilization of soil aggregate. The results reveal that plant roots, mycorrhizal roots and mycorrhizal hyphae contribute to aggregate stability in individual ways and that their effects are additive, creating a synergistic stabilizing effect.
Learning bayesian network structures with GOMEA

Bayesian networks (BNs) are probabilistic graphical models which are widely used for knowledge representation and decision making tasks, especially in the presence of uncertainty. Finding or learning the structure of BNs from data is an NP-hard problem. Evolutionary algorithms (EAs) have been extensively used to automate the learning process. In this paper, we consider the use of the Gene-Pool Optimal Mixing Evolutionary Algorithm (GOMEA). GOMEA is a relatively new type of EA that belongs to the class of model-based EAs. The model used in GOMEA is aimed at modeling the dependency structure between problem variables, so as to improve the efficiency and effectiveness of variation. This paper shows that the excellent performance of GOMEA transfers from well-known academic benchmark problems to the specific case of learning BNs from data due to its model-building capacities and the potential to compute partial evaluations when learning BNs. On commonly-used datasets of varying size, we find that GOMEA outperforms standard algorithms such as Order-based search (OBS), as well as other EAs, such as Genetic Algorithms (GAs) and Estimation of Distribution algorithms (EDAs), even when efficient local search techniques are added.
CodeCatch: Extracting Source Code Snippets from Online Sources

Nowadays, developers rely on online sources to find example snippets that address the programming problems they are trying to solve. However, contemporary API usage mining methods are not suitable for locating easily reusable snippets, as they provide usage examples for specific APIs, thus requiring the developer to know which library to use beforehand. On the other hand, the approaches that retrieve snippets from online sources usually output a list of examples, without aiding the developer to distinguish among different implementations and without offering any insight on the quality and the reusability of the proposed snippets. In this work, we present CodeCatch, a system that receives queries in natural language and extracts snippets from multiple online sources. The snippets are assessed both for their quality and for their usefulness/preference by the developers, while they are also clustered according to their API calls to allow the developer to select among the different implementations. Preliminary evaluation of CodeCatch in a set of indicative programming problems indicates that it can be a useful tool for the developer.
"You Can't Just Say That the Only Ones Who Can Speak Are Those Who Agree with Your Position": Political Discourse in the Classroom.

In this article, Melinda Fine describes the classroom dynamics surrounding the discussion of controversial issues in a middle school classroom. Through observation and interviews, she creates a detailed portrait of the interactions among teachers and students, revealing that while discussions of emotionally charged social and political issues are often heated and difficult, they can still be constructive. Fine maintains that students are more resilient and able to handle disagreement than is often believed. She concludes by arguing that education in a democracy requires that teachers and students learn to deal constructively with political and social differences.
Participatory Evaluation of Productivity, Fertility Management, and Dissemination of Irrigated Exotic Vegetables in the Sahel, West Africa

We conducted on-farm participatory experiments and activities for 3 years at three sites in the Fakara district of western Niger to demonstrate, verify, and evaluate crop productivity due to fertilizer application, economic benefits, and the dissemination of crop technology. We tested combinations of manure and mineral fertilizer on 13 exotic vegetables: bell pepper, cabbage, carrot, chili, eggplant, lettuce, melon, onion, potato, pumpkin, sweet potato, tomato, and zucchini. Farmers’ selectivity was evaluated as the number of plots that farmers selected to carry out their own trials. The application of 110 kg·N/ha manure plus 13.7 kg·N/ha mineral fertilizer increased overall vegetable yields by 161% (P < 0.01). The improvement of soil fertility increased the yield of subsequent rainfed millet by 124% (P < 0.05). Less-experienced female farmers could afford to grow cabbage, onion, lettuce, potato, and pumpkin, which yielded 4.8 - 11.4 t/ha fresh weight. Daily management by women in the vegetable gardens gave regular opportunities to chat and thus disseminate the technology.
An Approach of Automatic Data Mining Algorithm for Intrusion Detection and Prevention System

The Network security system plays a major role in the communication world, here every one's need security and assurance for their communication. Normally the hacking, Intrusion software's are using threats, malicious to enter in to the network and they are keep recording or monitoring the communication for this action the hackers may use following techniques Rexd, Rsh, Defult account/Null password, Generic Recon Mechanisms, NIS (Formerly yp), and Rpc Portmapper. The normal firewalls can address only insider threats. The main focus of our research is applied on two different platforms, the first one is processing on TCP-Dump portion using KDDCUP99 data set and mining, the second one is finding of best algorithm for data mining Intrusion on UNIX. From the available kddcup'99 dataset two subsets are taken with the record size of 997 and 11438. For UNIX User data all instances are taken. The classification rate and the false negative rate are used as the performance criteria with 3 fold cross validation. It is found that PART, SMO, Hyper pipes, Filtered Classifier, Random forest, Naive Bayes Updateable, KStar are giving high classification rate with low false positive rate. For UNIX User data ZeroR is giving high performance. Real time data is also applied to finalize the best algorithm under each category of classifier. Later online implementation has to be done.
CARE: Criticality-Aware Data Transmission in CPS-Based Healthcare Systems

In this paper, we propose a scheme, criticality-aware data transmission (CARE) in CPS-based healthcare systems, for increasing the processing rate of the sensed physiological parameters' value of any patient. The criticality of a patient may vary at any instant of time, and thus, continuous monitoring and quick processing of the physiological parameter value of a patient is essential. Therefore, in order to reduce the latency of data processing of a critical patient, we consider fog computing in our architecture. Based on the criticality value of physiological parameters, a Local Processing Unit (LPU) transmits the sensor data either to the fog aggregation node or cloud. We use a cooperative game theory-based Nash bargaining approach, where the LPUs bargain among themselves to decide whether the sensor data need to be transmitted to cloud or fog aggregation node. Based on the criticality index and the weight factor assigned to the LPU participating in the bargaining process, the utility of each LPU is computed. Analytical results show that the utility increases with the increase in the criticality index of any patient. Considering the total number of WBANs 5, 10, and 15, the average utility varies 75%-80%. Moreover, the data dissemination delay and power consumption are reduced by 23.39% and 31.089% respectively in the presence of fog node.
Anti-Proliferative Properties and Proapoptotic Function of New CB2 Selective Cannabinoid Receptor Agonist in Jurkat Leukemia Cells

Several studies demonstrated that cannabinoids reduce tumor growth, inhibit angiogenesis, and decrease cancer cell migration. As these molecules are well tolerated, it would be interesting to investigate the potential benefit of newly synthesized compounds, binding cannabinoid receptors (CBRs). In this study, we describe the synthesis and biological effect of 2-oxo-1,8-naphthyridine-3-carboxamide derivative LV50, a new compound with high CB2 receptor (CB2R) affinity. We demonstrated that it decreases viability of Jurkat leukemia cells, evaluated by Trypan Blue and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), but mainly induces a proapoptotic effect. We observed an increase of a hypodiploid peak by propidium iodide staining and changes in nuclear morphology by Hoechst 33258. These data were confirmed by a significant increase of Annexin V staining, cleavage of the nuclear enzyme poly(ADP-ribose)-polymerase (PARP), and caspases activation. In addition, in order to exclude that LV50 non-specifically triggers death of all normal leukocytes, we tested the new compound on normal peripheral blood lymphocytes, excluding the idea of general cytotoxicity. To characterize the involvement of CB2R in the anti-proliferative and proapoptotic effect of LV50, cells were pretreated with a specific CB2R antagonist and the obtained data showed reverse results. Thus, we suggest a link between inhibition of cell survival and proapoptotic activity of the new compound that elicits this effect as selective CB2R agonist.
Analysis on the impact of slot and DGS on bandwidth & gain of a pentagon patch antenna

Increase in the use of portable and wireless gadgets, demanding antennas that are compact and easy to integrate with RF circuits. Micro-strip antennas are useful for the above requirements. Generally, the gain and bandwidth of patch antennas are low. Many researchers had emphasized regular shapes like square, rectangle and circular patches. In this paper, a pentagonal patch antenna is considered and impact of introduction of slot and DGS on bandwidth and gain is analyzed. It is evident from the results that bandwidth and gain are modified with introduction of slot and DGS. Simulation is carried out using HFSS software. Fabricated antenna is tested for practical results using Vector Network Analyzer.
Polarity and Intensity: the Two Aspects of Sentiment Analysis

Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often been overlooked. As a measurement of opinions and affective states, a sentiment score generally consists of two aspects: polarity and intensity. We decompose sentiment scores into these two aspects and study how they are conveyed through individual modalities and combined multimodal models in a naturalistic monologue setting. In particular, we build unimodal and multimodal multi-task learning models with sentiment score prediction as the main task and polarity and/or intensity classification as the auxiliary tasks. Our experiments show that sentiment analysis benefits from multi-task learning, and individual modalities differ when conveying the polarity and intensity aspects of sentiment.