Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Security Exchange Scheduling Model for Business Workflow Based on Queuing Theory in Cloud Computing

With the rapid development of e-business, large volume of business processes need to be handled in a constrained time. There is always a security issue related to on-time completion in many applications in the economic fields. So, how to effectively manage and organize business processes became very important. By using cloud computing, instance-intensive processes can be handled more effectively by applying just-right virtual machines. Hence, the management of cloud resources became an important issue that many researchers focus on to fully utilize the advantage of cloud. In this paper, we mainly discuss the queuing theory and put forward our novel dynamic process scheduling model based on queuing theory, which is named M/G/k/l- for business processes. This model can solve the issue of allocating appropriate number of cloud resources based on the number of tasks and execution stages to ensure whether the numbers of cloud resources are sufficient and adequate or not, which can improve the security issue for business process. The service discipline in our model can provide a dynamic process by setting different priorities to improve the experience of users. Evaluations prove that the queuing model of M/G/k/l- can work very well for business workflow scheduling.
Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine

Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient training of the fault detection model, the incremental strategy based on the new incoming data and support vectors is proposed. The weight of the training sample is updated by the variations of the decision boundaries. Meanwhile, to increase the calculating speed of the fault detection model and reduce the redundant data, the decremental strategy based on the k-nearest neighbor (KNN) is adopted. Based on time series data stream, numerical simulations are conducted and the results validated the superiority of the proposed approach in terms of both the detection performance and robustness.
Diversity of freshwater macrophytic vegetation of six rivers of south West Bengal

Eighty-four species of macrophytes belonging to 73 genera and 34 families were recorded from the riverine beds of six major rivers (Dwarkeswar, Kansai, Keleghai, Rupnarayan, Shilabati and Subarnarekha) of South West Bengal from March 2001 to February 2002. Physicochemical parameters of water and soil of different study sites were estimated. Seasonal distributional patterns of different categories of vegetation (terrestrial, semiaquatic and aquatic) were studied along with the seasonal fluctuation of major ecological variables. Result on similarity indices showed that river Rupnarayan considerably differs from other rivers in terms of species composition and abundance of macrophytes. Resumen: Ochenta y cuatro especies de macrofitas pertenecientes a 73 generos y 34 familias fueron registradas en los lechos riberenos de seis rios principales (Dwarkeswar, Kansai, Keleghai, Rupnarayan, Shilabati y Subarnarekha) del sur de Bengala Occidental, entre marzo de 2001 y febrero de 2002. Se estimaron los parametros fisicoquimicos del agua y el suelo en diferentes sitios de estudio. Se estudiaron los patrones estacionales de distribucion de diferentes categorias de vegetacion (terrestre, semiacuatica y acuatica) junto con la fluctuacion estacional de las variables ecologicas principales. Los resultados de los indices de similitud mostraron que el rio Rupnarayan difiere considerablemente de los otros rios en terminos de la composicion de especies y la abundancia de macrofitas. Resumo: Oitenta e quatro especies de macrofitas pertencentes a 73 generos e 34 familias foram registados em leitos ribeirinhos de seis maiores rios (Dwarkeswar, Kansai, Kaleghai, Rupnarayan, Shilabati e Subarnarekha) do sudoeste de Bengala entre Marco de 20001 e Fevereiro de 2002. Os parâmetros fisico- quimicos da agua e do solo das diferentes estacoes de estudo foram estimados. Os padroes de distribuicao sazonal das diferentes categorias de vegetacao (terrestre, semi-aquatica e aquatica) foram estudados ao longo da flutuacao sazonal das principais variaveis ecologicas. Os resultados dos indices de semelhanca mostraram que o rio Rupnarayan difere consideravelmente dos outros rios em termos da composicao das especies e abundância das macrofitas.
Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.
3D printed intelligent scaffold prevents recurrence and distal metastasis of breast cancer

Rationale: Tumors are commonly treated by resection, which usually leads to massive hemorrhage and tumor cell residues, thereby increasing the risk of local recurrence and distant metastasis. Methods: Herein, an intelligent 3D-printed poly(lactic-co-glycolic acid), gelatin, and chitosan scaffold loaded with anti-cancer drugs was prepared that showed hemostatic function and good pH sensitivity. Results: Following in situ implantation in wounds, the scaffolds absorbed hemorrhage and cell residues after surgery, and promoted wound healing. In an in vivo environment, the scaffold responded to the slightly acidic environment of the tumor to undergo sustained drug release to significantly inhibit the recurrence and growth of the tumor, and reduced drug toxicity, all without causing damage to healthy tissues and with good biocompatibility. Conclusions: The multifunctional intelligent scaffold represents an excellent treatment modality for breast cancer following resection, and provides great potential for efficient cancer therapy.
Gravitational wave sensitivity curve of pulsar timing arrays affected by correlated noises

The detection of gravitational waves (GWs) by pulsar timing arrays (PTAs) is not only a very important supplementation of the verification of general relativity but also a new window to study the evolution of supermassive binary black holes and the early universe. However, so far the detection sensitivity of PTAs is not good enough to catch signals of GWs due to the disturbance of various noises. In this paper we explore the influences of the correlated noises caused by the reference clock errors and solar system ephemeris errors in pulsar timing on the detection of stochastic gravitational waves background (GWB). We demonstrate the power-law integrated sensitivity curves of GWB detection under the impacts from these correlated noises. From the simulated data, we find that the influence of different reference time-scale is non-negligible, and the influence is even quite huge if one uses a very old version of solar system ephemeris. However, the impact from these correlated noises on the sensitivity curve is very limited for the real observational data released by international pulsar timing arrays (IPTA). We also calculate the signal-to-noise ratios based on the theoretical GW amplitude permitted by observations. Moreover, we study how the detection sensitivity increases with more pulsar number and longer observation.
Identification and utilization of qtls in crop improvement

Most of the traits of interest in plant breeding (e.g., height, yield etc.) are quantitative with continuous (normal) distribution, multifactorial or complex traits. A quantitative trait is a measurable trait that depends on the cumulative action of many genes and their interaction with the environment. A QTL is defined as “A region of the genome that is associated with an affect on a quantitative trait”. A QTL can be a single gene, or it may be a cluster of linked genes that effect the trait. Among all the available markers, AFLP and SSR markers are most commonly used markers in the development of new linkage maps and QTL studies. Use of QTL linkage for identification of a particular trait was first performed by Payne (1918) , where he demonstrated that several of the loci that responded to selection for high scutellar bristle number in Drosophila melanogaster were closely linked to known markers on the first and third chromosomes. Thereafter several QTLs from different crops were identified which includes yield traits, pest and disease resistance traits, quality improvement traits etc., which can be used for improving those crops. Hence one can say that QTLs play a great role in crop improvement. The process of constructing linkage maps and conducting QTL analysis is to identify genomic regions associated with traits which is known as QTL mapping (McCouch & Doerge, 1995). QTL mapping’ is based on the segregation of genes and markers via chromosome recombination (called crossing-over) during meiosis (i.e. sexual reproduction), thus allowing their analysis in the progeny (Paterson, 1996). Although there have been numerous QTL mapping studies for a wide range of traits in diverse crop species, relatively few markers have actually been implemented in plant breeding programs. This is due to lack of adoption is that the markers used have not been reliable in predicting the desired phenotype. New developments and improvements in marker technology, the integration of functional genomics with QTL mapping, and the availability of more high-density maps would greatly affect the accuracy and efficiency of QTL Mapping. It is expected that the development of high resolution maps will also facilitate the isolation of actual genes (rather than markers) via ‘mapbased cloning’ (also ‘positional cloning’).
Fauna of Protected Areas - 29: Orthopteran fauna of the Gibbon Wildlife Sanctuary, Assam

© Zoo Outreach Organisation; www.zoosprint. org Manuscript 1442a; Received 26 September 2005; Finally accepted 18 May 2006; Date of publication 21 July 2006 2347 August 2006 | ISSN 0973-2535 (Print edition); 0973-2551 (Online edition) ABSTRACT A checklist of 25 species of Orthoptera recorded from the Gibbon Wildlife Sanctuary is presented here along with a series of indices such as Simpson's, Hill's, Margalef's, Mehinick's and evenness. The order is comprised of 25 species of 21 genera and 12 families. This preliminary study indicates many more species yet to be recorded from the area.
Peroxisomal membrane contact sites in the yeast Hansenula polymorpha

Peroxisomes in methylotrophic yeast Hansenula polymorpha form intimate contacts with the ER (EPCONS) and vacuoles (VAPCONS) under peroxisome proliferation conditions. Recent data suggest that the simultaneous loss of EPCONS and VAPCONS, inhibits peroxisome membrane growth and results in peroxisome deficiency. Based on this model we hypothesized that in the absence of multiple EPCONS or VAPCONS components peroxisomes can still grow. Here we studied whether Pex11C, Pex29, Pex32 and Pex34 are involved in EPCONS or VAPCONS. Localization studies revealed that Pex11C and Pex34 are peroxisomal, whereas Pex29 and Pex32 are localized to the ER. Deletion of PEX11C and PEX29 has no apparent effect on peroxisome biogenesis or abundance, whereas deletion of PEX32 results in peroxisome deficiency and deletion of PEX34 in a major decrease in peroxisome numbers accompanied by an increase in organellar size.
Overview of the Three-dimensional Convolutional Neural Networks Usage in Medical Computer-aided Diagnosis Systems

Medical computer-aided diagnosis systems are essential applications that help doctors speed up, standardize, and improve disease prediction quality. Nevertheless, it is hard to implement a high-accuracy diagnosis system due to complex medical data structures that are hard to interpret even by an experienced radiologist, lack of the labeled data, and the high-resolution three-dimensional nature of the data. Meanwhile, modern deep learning methods achieved a significant breakthrough in various computer vision tasks. Thus, the same methods began to gain popularity in the community that works on the computer-aided systems implementation. Most modern diagnosis systems work with three-dimensional medical images that cannot be processed by traditional two-dimensional convolutional neural networks to get high enough prediction results. Hence, medical research introduced new methods that use three-dimensional neural networks to work with medical images. Even though these networks are usually an adapted version of state-of-the-art two-dimensional networks, they still have their specifics and modifications that help achieve human-level accuracy and should be considered separately. This article overviews the three-dimensional convolutional neural networks and how they are different from their two-dimensional versions. Moreover, the article examines the most influenced systems that achieve human-level accuracy in predicting the specific disease. The networks discussed in the perspective of two basic tasks: segmentation and classification. That is because the simple end-to-end classification neural networks usually do not work well on the available amount of data in the medical domain.
High-Performance Tree Indices: Locality matters more than one would think

In-memory data management systems have become the state-of-the-art, which leads to the development of highly-eﬃcient index structures. With the elimination of most sources of software overhead, such as interpretation, the impact of low-level hardware parameters becomes more pronounced. Input data locality is such a parameter: It is well known, data locality plays a signiﬁcant role in indices like B-trees, to our surprise, we found that the impact of data locality on the performance of high-performance indices has not been studied. In this paper, we study the eﬀect of locality on two of the most prominent high-performance index structures: Bw-Trees and Adaptive Radix Trees (ARTs). In our experiments, we ﬁnd that Bw-trees are highly aﬀected by the locality on inserts (performance diﬀerence exceeding a factor two) but fairly robust on lookups. ARTs, in contrast, are moderately aﬀected in their lookup performance (roughly 60%) but robust during insert.
Summer valley-floor snowfall in Taylor Valley, Antarctica from 1995–2017

Abstract. In polar, coastal areas like Taylor Valley, snowfall is predicted to increase under warming conditions as reduced sea ice increases open water area and evaporation potential, thereby creating conditions that would facilitate precipitation. Taylor Valley is a mosaic of glaciers, valley-bottom ice-covered lakes, ephemeral streams and dark, rocky soils. Ecosystems are both light- and nutrient-limited and rely on seasonally available liquid water. Although Taylor Valley receives minimal snowfall annually, light snow cover during summer months reduces radiation for primary productivity and slows melting by increasing the local albedo. Snowfall has been measured at four sites in Taylor Valley since 1995. Daily photographs at the Lake Hoare station in the central portion of the valley record snow cover since 2007 and augment the automated precipitation measurements. Here, we focus on valley-floor snowfall due to its effect on ecosystems in the valley-bottom lakes and streams. Precipitation increased by 3 mm water equivalent (w.e.) a−1 from 1995 to 2009, then decreased by 1 mm w.e. a−1 through 2017. Since 2009, annual snowfall in Taylor Valley ranges from 1 to 30 mm w.e. High snowfall during the Spring near the coast is indicative of high summer snowfall at the more inland Lake Bonney station (r2 = 0.66; p
DEVELOPMENT OF BEAM DYNAMICS APPLICATIONS WITHIN A CORBA FRAMEWORK AT THE SLS

A distributed client-server model, based on the Common Object Request Broker Architecture (CORBA), has been established to interface beam dynamics applications at the Swiss Light Source (SLS) to essential software packages such as the accelerator physics package TRACY and the Common DEVice (CDEV) control library. Within this model remote clients can invoke computer intensive meth-ods, such as beam orbit correction procedures, on a dedicated server. Access to the SLS accelerator devices is achieved through a dedicated C++ CDEV server. A status report on application development within the established CORBA framework is given.
Effects of Larval Rearing Density and Food Availability on Adult Size and Coloration in Monarch Butterflies (Lepidoptera: Nymphalidae)

Completing development to a reproductively mature adult is energetically expensive, and environmental stress can impose substantial fitness tradeoffs during developmental processes. We tested the prediction that high larval rearing density and food stress should compromise monarch, Danaus plexippus L. (Lepidoptera: Nymphalidae), larval growth and the production of melanin or other pigments in the adult wings. We reared focal larvae on caged, potted plants in control, food stress and high density treatments. Food stressed larvae experienced food shortages but no crowding (n = 1 larva per plant), whereas high density larvae were crowded (n = 5 larvae per plant) but had constant access to food. We used digital image analysis to measure the size and coloration (black and orange) of the resulting adult forewings. Larval rearing treatment affected adult size, but not wing coloration or development time. Adults emerging from the high density treatment were significantly larger than adults from the control or food stress treatments. This suggests that larval crowding may stimulate monarch larvae to increase their feeding rate, producing larger body size when there is not a subsequent food shortage caused by the crowding. Food stress did not affect adult size, wing coloration or development time, which suggests that monarch larvae can overcome moderate levels of food stress without experiencing compromised development. Sibling group affected forewing area, development time, and several aspects of wing coloration, which is consistent with previous research that has demonstrated a heritable component to size and melanism in monarch butterflies.
Rates and Ratios of Nitrogen and Phosphorus Released by a Bacterivorous Flagellate.

Rates and elemental ratios of nutrient released by a bacterivorous flagellate (Spumella sp.) were examined during the exponential and stationary growth phases of the flagellate. Two types of an isolated bacterium were used as prey : one was phosphorus-rich (C: N : P molar ratio=59 : 13: 1) and the other was phosphoruspoor (C: N : P=79: 17: 1). Specific release rates by the flagellate of ammonium, dissolved inorganic and organic phosphorus (DIP and DOP) were much higher during the exponential growth phase than during the stationary one. Ammonium and DIP release rates in the experiment using the P-rich bacterium (Experiment 1) were not so remarkably different from those using the P-poor bacterium (Experiment 2) during both phases. However, DOP release rates were much higher in Experiment 1 than in Experiment 2 during both phases. Ratios of ammonium released to total dissolved phosphorus released were significantly lower in Experiment 1 than those in Experiment 2 during both phases. Further, these ratios were higher than bacterial N : P ratios in both experiments. N : P ratios of nutrients released by the flagellate could have been determined by bacterial N : P ratios, but the release appeared more efficient for nitrogen than for phosphorus.
Fuzzy Optimization and Modelling

This paper considers the rank of set efficient units in Data envelopment analysis (DEA). DEA measures the efficiency of decision making units (DMUs) within the range of less than or equal to one. The corresponding efficiencies are referred to as relative efficiencies, which describe the best performances of DMUs, and these efficient units determine efficiency frontier. This research proposes an extended on a current research by a technique for order preference by similarity to an ideal solution (TOPSIS) method. Therefore, in this paper, we first introduce two methods namely regular polygon area (RPA) and TOPSIS. Then using common set of weights in order to all efficient units obtained from DEA models, they are projected into two-dimensional plane. Finally, the units are ranked by RPA and TOPSIS methods. Also, with the numerical example, our method is compared with other methods. The obtained results of numerical example show that they are almost close to each of several methods.
In vivo effects of the CB1 allosteric modulator LDK1258, a structural analog of ORG-27569

7 Chapter I: General introduction. 10 Chapter II: In vivo evaluation of the CB1 allosteric modulator LDK1258 reveals CB1 receptor independent behavioral effects. 25 Chapter III: Conclusions and discussion. 47 List of References. 56 Vita.65
- 2017 : Country and University Level Perspective Conference or Workshop Item

Each year the number of Open Access (OA) papers is gradually increasing. We carried out a study investigating 400 universities from 8 countries to examine: i) the total number of OA papers per country, ii) proportion of OA papers published by representative universities in each country classified into three tiers of research quality: high, middle and low, iii) how universities within the same country compare to each other and iv) the growth of OA papers in countries per year. We conclude that among the analysed countries the UK and USA rank first and second respectively, while Russia and India are positioned towards the bottom of the list. We observe no link between the proportion of OA papers published by authors at a university and the university ranking, with some universities in the middle university rank tier having a larger proportion of OA papers than those in the high tier.
Supplementary Material: Controlling Perceptual Factors in Neural Style Transfer

As described in the main text, due to increasing stride and receptive field sizes as well as the quadratic shape of the receptive fields, spatial resolution in the upper layers of the CNN is limited. Therefore care needs to be taken when propagating an accurate spatial mask defined on the image pixels into a spatial mask defined on a feature map in the network. We found three strategies generally useful for this propagation: