Miracle-dz/UniFAR-FTUs huggingface dataset:
New Skyrme interaction parameters for a unified description of the nuclear properties

The nucleon microscopic optical potential is obtained by calculating the mass operator of the one-particle Green function with an effective Skyrme interaction. The real and imaginary part potentials for finite nuclei are given by applying a local density approximation as usual, while the spin? orbit potential is further amended by using the Skyrme? Hartree? Fock approach and relativistic mean field results. The new variants of Skyrme interaction parameters are found by simultaneously fitting the characteristics of nuclear matter, the binding energy, charge radius and single-particle energy levels of double closed shell nuclei, the neutron induced reaction cross sections and polarization data. These data include the total cross sections, nonelastic sections, elastic scattering angular distributions and analyzing powers for the target mass range 24 ? A ? 209 with incident neutron energies below 100? MeV. Further, the Landau parameters are considered to describe the properties of an excited state. The obtained variants of Skyrme forces are used to predict the neutron? actinide reactions with incident energies below 100 MeV. It is found that the calculations can give a satisfactory description of the experimental data. Analogous calculations by the new Skyrme forces are performed to predict the proton induced reactions, which also yield reasonable results.
On The Implementation of Recursive Data Structures for Cache-Oblivious Algorithms

his paper presents implementation of two important recursive data structures. First data structure is k-funnel that can merge k sorted input lists into a single sorted list. The k-funnel is an integral part of Funnelsort. The second data structure is cache-oblivious search tree that using the van Emde Boas layout for mapping from the nodes in the tree to positions in memory. We focus on the challenges in implementing them. A clever implicit navigation method of Brodal is used in the search navigation process. We also experimentally compare cache-oblivious data structures with traditional RAM model data structures.
Adaptive Differences in Digestive Enzyme Activity in the Crab Neohelice Granulata in Relation to Sex and Habitat

Neohelice granulata (Dana, 1851) is a semi-terrestrial burrowing crab that inhabits estuaries and saltmarshes feeding on grasses or sediment depending on the microhabitat they occupy (vegetated saltmarsh or bare mudflat). Specific cellulolytic, amylolytic, and proteolytic enzyme activities were analyzed in midgut gland homogenates of males and females from each microhabitat fed in the laboratory with Spartina densiflora leaves and sediment, respectively, in order to detect sex, food and microhabitat related differences. The presence of β-1,4-glucosidase, endo-β-1,4-glucanase, α amylase, trypsin and chymotrypsin were confirmed. Specific cellulolytic activity was higher in crabs fed on leaves than in those fed on sediment or in no fed controls and variable differences between sexes were observed. Specific amylase activity of crabs fed on leaves was the lowest recorded. Trypsin and chymotrypsin specific activities were higher in saltmarsh crabs fed on leaves than in mudflat crabs fed on sediment. Different mechanisms of enzyme regulation to explain the observed differences among groups were suggested. Additionally, differences between sexes suggest different metabolic needs related to gonad maturation. It is concluded that N. granulata has the ability to adapt digestive enzyme production to support its physiological and metabolic needs based on the different food sources available at each microhabitat.
Nano Coat as an Effective Replacement for Bearing Liner

This work highlights an experimental investigation on the surface roughness and hardness of a nano composite, which can replace the liner of a journal bearing. There is a possibility of rotation of bearing liner, due to inconsistent rotation of the journal about its geometrical axis. At the same time, the liner may have sliding action inside the bearing over a period of time. The said drawbacks can be eliminated by replacing the bearing liner by a nano coat. Babbitt is the most widely used material for bearing liner. The Tin of Babbitt alloy has been replaced by cadmium due to its low coat and better mechanical and thermal properties. The results show that, Cadmium is offering better surface roughness and hardness in comparison with the Babbitt.
Detection of Melanoma Skin Cancer using Convolutional Neural Network algorithm

Skin Cancer, a health issue which might cause severe consequences if not detected and controlled properly. Since there is a huge evolution in the health sector because of development in computer technologies, it is possible to analyze images efficiently and make correct decisions. Deep learning algorithms can be used for analyzing dermoscopic images by learning features of images in an incremental manner. Aim of our proposed method is to categorize skin lesion image as Benign or Melanoma and also to study the performance of Convolutional Neural Network algorithm using data augmentation technique and without data augmentation technique. The proposed method uses dataset from ISIC archive 2019. Steps involved in the proposed method are Image Pre-Processing, Image Segmentation and Image Classification. Initially, Image Pre-Processing algorithm is performed on skin lesion image. Image Segmentation algorithm is used to obtain Region of Interest (ROI) from pre-processed image. Then, Convolutional Neural Network algorithm classifies image as melanoma or benign. The Proposed method can rapidly detect melanoma skin cancer which aids in starting the treatment process without delay.
Innovation, Artificial Intelligence in Contingent Work-Force Management

In recent years, the global use of contingent workers is rapidly increasing despite the increasing quantity of artificial intelligence applications in business. The question is "how these companies leverage the use of artificial intelligence to enhance contingent workforce's management? ". The ideal goal of this paper is to develop a purely conceptual application of innovation, artificial intelligence (AI) adjacent to contingent workforce management(CWM). The researcher used qualitative information gathered from various authors and observations to reinforce the usage of AI. One of the critical tools to integrate with contingent workforce management for reduction of time spent on human resource administrative tasks is AI. There must be a transformation of thinking, accepting positive organizational change, utilization of technology and openness to new technology to foster AI. Along with that, integrating contingent workforce management with AI reduces risks and costs, increases efficiency and quality of work. Innovation and Artificial intelligence have been used in five pillars performance of contingent workforce management to mitigate the challenges associated with it.
Hedgerows and Their Role in Agricultural Landscapes

Abstract Hedgerows, rows of trees or shrubs enclosing or separating fields, are man made. They are part of rural landscapes and should not be studied without considering the farming systems and the rural society as a whole. Their species composition depends on the history of the countryside, as well as current farming practices. Land use and boundary management play a major role in determining the floristic composition of hedgerows, and are strongly related to the grain size of the field's mosaic. Hedgerow networks, due to their rectilinear shape, and with their high degree of connection, enhance or interrupt many fluxes in the landscape. Hedgerows act as a corridor of movement and dispersal for many forest species, such as carabids, small mammals, and plants. On the other hand, hedgerows interrupt or slow down air fluxes and the propagules they carry. In the heterogeneous mosaic of fields and wooded patches, hedgerows often increase the functional connectivity of rural landscapes. The future of hedgerows.
Appendix of Learning to Select Base Classes for Few-shot Classification 1. Proof of the Main Theories

1.1. Proof for Corollary 4.1 Lemma 1. ∀n ∈ N, gn : 2u → R≥0, gn(U) := M(f(cn, {cBs , cU})) is a submodular function. Proof. ∀A ⊆ B ⊆ BU , let u ∈ BU\B, define the top-K similar classes with class n in Bs ∪A and Bs ∪B are KA and KB separately, we also define that after adding class u to both A and B, the top-K similar classes become K A and K u B . Next, we discuss four cases: (1) u ∈ K A but u ∈ K B: In this case, gn(A + u) − gn(A) = f(cn, cu) − min x∈KA f(cn, cx) and gn(B + u) − gn(B) = f(cn, cu) − min x∈KB f(cn, cx). As (Bs ∪ A) ⊆ (Bs ∪ B), there must be min x∈KA f(cn, cx) ≤ min x∈KB f(cn, cx). Thus we have gn(A+ u)− gn(A) ≥ gn(B + u)− gn(B). (2) u ∈ K A but u / ∈ K B : In this case, easy to show that gn(A+ u)− gn(A) > 0 = gn(B + u)− gn(B). (3) u / ∈ K A but u ∈ K B: This case will not exist, as it represents that f(cn, cu) ≤ min x∈KA f(cn, cx) and f(cn, cu) ≥
Differentiation in a single-tube PCR between Leishmania mexicana and Leishmania braziliensis in clinical samples.

RL, EQR RL, study ABSTRACT Aim: To assess the usefulness of a single-tube PCR end point for differentiating between Leishmania and Viannia subgenera in clinical samples of cutaneous leishmaniasis. Methodology: Impression smears of 65 patients from Campeche State, Mexico with leishmaniasis-like lesions were analyzed by conventional Giemsa staining and a new simple PCR end point with primers named LU-5A designed by [Harris et al.1998] and LME (designed for this study and based on a mini-exon of L.(L) mexicana ,GenBankX64317.1). This PCR generates an amplicon of 210 and BMRJ, 6(4): 270 bp with L. (L) mexicana (MHOM/MX/84/SETGS) and 230 bp with L. (V) braziliensis (MHOM/BR/75/M2903). Results: Out of 65 impression smears of clinical samples 50 were positive according to PCR (76.9%) and 29 were positive according to microscopic observation (44.6%). The primers did not hybridized with human, Trypanosoma cruzi, Staphylococcus aureus, Staphylococcus epidermidis or Pseudomona aeruginosa DNA. Of the positive smears, 84% (42/50) corresponded to L. (L) mexicana and 16% (8/50) to L. (V) braziliensis. Conclusion: A one-step PCR with primers LU-5A and LME improves the diagnostic capacity to differentiate between Leishmania and Viannia subgenera in parallel with conventional tests.
Evaluation of security function of Flipora plug-in on browsers

In this research, Flipora plug-in was installed on different browsers. Security evaluation and analysis were performed on the function performance of this plug-in in upon. User traffics that were sent from the browser when during visiting and opening various web pages were evaluated and analyzed. In our simulation, we show that what confidential information will be sent to the intended destination by Flipora plug-in that which was installed on Firefox browser. This information includes the content of visited web pages, the user's session, titles and addresses of these websites. Outgoing traffics were on port 80 and http protocol was selected. In the Software security Lab, we show that in addition to the title and URL of visited websites, all contents of the visited pages, along with the user's session are sent to the address of the Flipora server. Next furthermore, other plug-ins were studied partially and security suggestions were provided in using the plug-ins installed on the browsers.
A Flexible Camera Calibration System for Mobile Platform

We propose a flexible camera calibration system for mobile platform to calibrate the camera's intrinsic parameters which based on the geometrical property of the vanishing points determined by two perpendicular groups of parallel lines. The system only requires the camera to observe a rectangle card show at a few(at least four)different orientation. The experimental results of the real images show the proposed calibration system in this paper is easy to use and robust
Novel Techniques for Noise-Tolerance in Combinational Critical-Path Circuit Components

The continued scaling of the device and interconnect in the deep submicron jurisdiction of the complementary metal oxide semiconductor (CMOS) very large scale design (VLSI) has brought many new design challenges and exposed the limitations of the traditional VLSI design. One of the most severe problems in the deep submicron is that the circuit tend to malfunction by producing incorrect outputs in the event of inputs that have glitch. Such noise problem has emerged as the critical reliability problem in the deep submicron, in addition to the power dissipation problem. In this proposal, new research is proposed to counter the noise problem through novel circuit design techniques and methodologies. As we continue in deep submicron, the reliability of such designs is reduced as the output levels of such circuit suffer because of voltage scaling. We present our research along with the results and then describe the further proposed research. The research techniques are described using the combinatorial gates which serve as the critical path component in many designs. Also, an efficient flip-flop CD, that is conditionally discharged when there is no input changes and the input remains high to high, is proposed. This new flip-flop reduces the switching state activity, and is almost glitch-less at the output. The results from our proposed techniques demonstrate at least 2.3x the noise-immunity over the best known results in the literature.
Big Data Based Diabetes and Heart Disease Prediction System by Employing Supervised Learning Algorithm

Healthcare systems generate bytes and bytes of data and the data growth is exponential. The voluminous data can be analysed effectively, only when the data organization is efficient. Additionally, data retrieval must also be made simpler, such that the healthcare professional can compare and contrast the test sample with the database of health records. This makes it possible to achieve better disease prediction and this work presents a big data based disease prediction system with the help of supervised learning. The proposed approach clusters the related health records, based on every medical attribute followed by which the disease is predicted by SVM classifier. The performance of the proposed disease prediction system is observed to be satisfactory in terms of accuracy, precision, recall, F-measure, while consuming reasonable period of time.
DFWC: Discriminative Feature Weight Correlation based Sentiment Analysis of Social Networks

: Sentiment analysis is the buzz in current era of the social network-based opinion sharing about products, events, politics and many more.The lexicon analysis and machine learning are the major strategies to perform sentiment analysis. The phenomenal escalation of available data in social networks is indeed evincing the intensification of dimensions inportraying the opinion. Hence, the machine learning is the much effective than the lexicons analysis. However, the escalation of the dimensions in projection of the sentiment, indicating the need of the optimal learning methods enables to learn from the correlation between the features of vivid dimensions, which is the crucial requirement for available labeled data with multidimensional features. This manuscript portrayed a novel method that learns from the discriminative feature weight correlation to train the classifier in regard to identify the polarity of the sentiment to the given opinion about the target event. In this regard, the feature weights are used to identify the impact of the corresponding feature towards positive or negative polarity. The experimental study evincing the performance advantage of the proposal that compared to other contemporary models.
Microaneurysms Detection in Retinal Fundus Images

Diabetic retinopathy (DR) is accepted to be the vision threatening disease in most of the developing countries. The anomalies caused by Diabetic retinopathy are identified through Micro aneurysms and Hemorrhages. Manual detection of abnormalities like Micro aneurysm and Hemorrhages in color fundus images is a time consuming process and hence there is a need of an automated system to grade the level of diabetic retinopathy. The proposed method is based on profile based features, local features and SVM classifier to detect microaneurysm in retinal fundus image. The microaneurysm detection accuracy of the proposed method is about 93.73% using SVM classifier and 90.71% for KNN classifier.
Emerging Subspace Discovery from Daily Load Consumption Data

Customers of different electricity consumer types have different daily load shapes in the manner of different characteristics. Therefore, maximally capture load shape variability are desirable in load flow analysis. And most of time, such load shape variability can be found in the particular subspace of load diagrams. Therefore, in this paper, we are using subspace projection method to capture the emerging subspaces of load diagrams which maximize the difference between particular load shapes in different group of customers. As the result, subspace projection method can be used in load profiling and the performance is good as traditional approaches
Cornea-PAMPA as an Orthogonal in Vitro Physicochemical Model of Corneal Permeability

The present study was aimed to investigate the relationships between permeability and membrane retention values (logPe and MR) of the in vitro non-cellular permeability assay, corneal-PAMPA in comparison with experimental Caco-2 permeability data and calculated physicochemical properties (MW, clogP, clogD7.4 , TPSA). For the investigation, 50 structurally and physicochemically diverse drugs were selected and measured in PAMPA model optimized for corneal permeability. The results showed corneal-PAMPA model's orthogonality in terms of passive diffusion to the FDA approved Caco-2 as a gastrointestinal absorption model, while the comparison with physicochemical properties revealed trends between logPe , MR and the lipophilicity descriptors and TPSA.
COSMETICS APPLICATION USING COMPUTER VISION

: Computer vision is a new emerging field of artificial intelligence using which machines are trained to make sense out of digital images and videos. An end solution which is directly going to benefit the producer and consumer in the cosmetics industry is planned to be developed with the help of state of art technologies like Computer Vision and Flask Framework. The solution planned is based on Image processing and comes under the banner of augmented reality. It is going to augment the cosmetics decided by the user and will apply on the face of the end user. It is beneficial for the user because it bypasses the need of physically applying the cosmetics on the face and instead can do it virtually with this solution. On the other hand it is also going to benefit the Producer because the producer doesn’t have to waste its cosmetic’s material for testing and instead can lend the consumer this product for trial of the cosmetics. In order to develop this solution the likes of Computer Vision in Python and Flask Framework for Web development have to be used .
Between Hard Rock and Open Space: Constraints and Freedom of Finnish Paleontology

Palaeontology is the science which treats of the life that has existed on the globe during former geological periods. It deals with all questions concerning the properties, classification, relationships, descent, conditions of existence, and the distribution in space and time of the ancient inhabitants of the earth, as well as with those theories of organic and cosmogonic evolution which result from such inquiries.
Ornithol Sci 6: 107–119 (2007)

Most studies on avian vocal ontogenesis have focused on taxa exhibiting some kind of vocal learning. This study provides a detailed analysis of vocal processes through early and late ontogenesis in 17 chicks of the Red crowned Crane Grus japonensis, a species lacking vocal learning. Three basic structural classes: trills, PE-chirps and PS-chirps and their transitional forms are described. Trends in call parameter values are presented for 10 age classes in the course of a period from birth to 9.5 months. We discuss our vocal classification with those reported for other crane species, relate the revealed stages of vocal ontogenesis in the Red-crowned Crane to biologically relevant life stages in this species and advance a hypothesis for the proposed function of retaining high juvenile frequencies in adolescent cranes for a prolonged period, up to voice breaking (a rapid significant decrease in fundamental frequency). We conclude, that voice breaking is universal for both sexes and that the retained high call frequencies may represent an infantile characteristic, essential in evoking care from the parents towards the growing chick and may also act as a mechanism to reduce aggression from conspecifics.
Select, Supplement and Focus for RGB-D Saliency Detection

Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction. However, RGB-D saliency detection methods are also negatively influenced by randomly distributed erroneous or missing regions on the depth map or along the object boundaries. This offers the possibility of achieving more effective inference by well designed models. In this paper, we propose a new framework for accurate RGB-D saliency detection taking account of local and global complementarities from two modalities. This is achieved by designing a complimentary interaction model discriminative enough to simultaneously select useful representation from RGB and depth data, and meanwhile to refine the object boundaries. Moreover, we proposed a compensation-aware loss to further process the information not being considered in the complimentary interaction model, leading to improvement of the generalization ability for challenging scenes. Experiments on six public datasets show that our method outperforms18state-of-the-art methods.
Predicting Student Performance using Classification and Regression Trees Algorithm

Now a days Internet and Web technologies providing students opportunities for flexible interactivity with study materials, peers and instructors. And also generating large amounts of usage data that can be processed and reveal behavioral patterns of study and learning. In this paper, to predict course performance we extracted data from a Moodlebased blended learning course and build a student model. Classification and Regression Trees (CART) decision tree algorithm was used to classify students and predict those at risk, based on the impact of four online activities: message exchanging, group wiki content creation, course files opening and online quiz taking. The correct classifications in results prove that the model is sensitive to categorize very specific groups at risk.
Road Dust Emission Modelling

Abstract Atmospheric particulate matter (PM) is a well known risk to human health. Vehicular traffic is one of the major sources of particulates in an urban setting. Here we study a problem of road dust dispersion. Using CFD solver based on RANS equations, we investigate the effect of a vegetation barrier on the concentration of airborne PM induced by road traffic. Simplified 2D model of a porous obstacle adjacent to a road source of PM10 serves as an idealization of a real-world situation. Importance of several model parameters is estimated using direct sensitivity approach.