Miracle-dz/UniFAR-FTUs huggingface dataset:
Big data analytics in e-commerce logistics: Findings from a systematic review and a case study

Digital evolution has significantly changed consumer shopping habits and expectations resulting in a major growth of e-commerce. The immediate outcome of this growth was the creation of a dynamic and turbulent environment with increasing density of the distribution network. This environment consists of many delivery points, multiple delivery channels and last-mile delivery requirements. Due to its complexity and the proliferation of data challenges, e-commerce logistics is an area where the application of Big data analytics can be proven to be extremely fertile. Despite the growing interest, there are limited studies that investigate the role of Big data. By using a design science approach, we clarified the current e-commerce logistics practices as well as the envisioned ones that can, to a large extent, be supported by appropriate big data technologies. We concluded to a set of business requirements that express the needs towards the e-commerce logistics. Then, the requirements were translated into a set of use case scenarios to demonstrate how they could be supported by big data analytics. We conclude by proposing a conceptual architecture of a big data analytics artefact that could cover the e-commerce logistics requirements.
An Improved PRNG Based on the Hybrid between One- and Two- Dimensional Cellular Automata

simulation of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented. following:
Moments, Positive Polynomials And Their Applications

From a theoretical viewpoint, the GPM has developments and impact in var-ious area of Mathematics like algebra, Fourier analysis, functional analysis, operator theory, probabilityand statistics, to cite a few. In addition, and despite its rather simple and short formulation, the GPMhas a large number of important applications in various ﬁelds like optimization, probability, mathematicalﬁnance, optimal control, control and signal processing, chemistry, cristallography, tomography, quantumcomputing, etc.In its full generality, the GPM is untractable numerically. However when K is a compact basic semi-algebraic set, and the functions involved are polynomials (and in some cases piecewise polynomials orrational functions), then the situation is much nicer. Indeed, one can deﬁne a systematic numerical schemebased on a hierarchy of semideﬁnite programs, which provides a monotone sequence that converges tothe optimal value of the GPM. (A semideﬁnite program is a convex optimization problem which up toarbitrary ﬁxed precision, can be solved in polynomial time.) Sometimes ﬁnite convergence may evenocccur.In the talk, we will present the semideﬁnite programming methodology to solve the GPM and describein detail several applications of the GPM (notably in optimization, probability, optimal control andmathematical ﬁnance). R´ef´erences[1] J.B. Lasserre, Moments, Positive Polynomials and their Applications, Imperial College Press, inpress. [2] J.B. Lasserre, A Semideﬁnite programming approach to the generalized problem of moments,Math. Prog. 112 (2008), pp. 65–92.
Power Efficient Anomaly Detection Using Pattern at Nodes in Wireless Sensor Network

— W ireless Sensor Network (WSN) facilitate today’s world by offering large number of application in almost every field. These applications require the WSN to be work effectively even if it is deployed in harsh environment. The proper functioning of the sensor network mainly depends on the sensor nodes as they have power supply which make network alive. As the sensor network are deployed in harsh environment so there is great chance of interference in the communication among nodes. Hence there is a need of anomaly detection method but the key concern is to minimize the communication overhead and energy consumption while detecting anomalies. There is need of energy efficient protocol and recent studies shows that ZRP is energy efficient. To perform anomaly detection this paper proposed a protocol by modifying one of the most popular routing protocol ZRP. This power efficient anomaly detection ZRP (ZRP-PEAD) uses traditional ZRP at base to perform detection in WSN.
Arti�cial Intelligence Could Alert for Focal Skeleton/Bone Marrow Uptake in Hodgkin´s Lymphoma Patients Staged With FDG-PET/CT

Purpose: To develop an arti�cial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin´s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. Methods: The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake signi�cantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a prede�ned threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017-2018 with biopsy-proven HL were retrospectively included. Ten physicians classi�ed the 48 cases regarding focal skeleton/BMU. Results: The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25-0.80). Conclusion: An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.
Dynamical determination of the mass of the Kuiper Belt from motions of the inner planets of the Solar system

In this paper we determine dynamically the mass of the Kuiper Belt Objects by exploiting the latest least-squares determinations of the extra rates of perihelia of the inner planets of the Solar system. By modelling classical Kuiper Belt Objects as an ecliptic ring of ﬁnite thickness, we obtain 0.033 ± 0.115 in units of terrestrial masses. For resonant Kuiper Belt Objects, a two-ring model yields 0.018 ± 0.063. These values are consistent with recent determinations obtained using ground- and space-based optical techniques. Some implications for precise tests of Einsteinian and post-Einsteinian gravity are brieﬂy discussed.
Maternal and Fetal Hemodynamics of Chronic Pregnant Smokers: A Doppler Study

Objectives: To evaluate the influence of chronic smoking on maternal-fetal hemodynamics through Doppler study of maternal uterine (UtA), fetal umbilical (UmA) and fetal middle cerebral (MCA) arteries compared to pregnant nonsmokers. The effect of the degree of nicotine addiction on this hemodynamics was also analyzed. Methods: Observational cross-sectional study conducted in 98 chronic pregnant smokers and 102 pregnant non-smokers. Doppler study was performed for measuring Pulsatility Index (PI) and Resistance Index (RI) of UtA, UmA and MCA. Fargeström and exhaled Carbon Monoxide (COex) tests were analysed. A multivariate general linear model with re sampling was applied. The significance level of 95% and 95% confidence intervals were adopted. Results: Comparison of Doppler indexes between the two groups showed that only UtA RI and UmA PI had significant effect (p<0.001 and p=0.032, respectively), with higher values in pregnant smokers than non-smokers, indicating vasoconstriction and increase impedance in the studied vessels. Smoking had a positive effect for PI (p=0.045) and RI (p=0.007) of MCA, with significantly decreased indexes in smokers with high and very high dependence compared to low and moderate dependence. COex analysis showed elevated Um PI in moderate vs light smokers (p=0.035) whereas MCA RI was higher in light vs heavy smokers (p=0.024). Conclusions: Chronic smoking interfered negatively on maternal-fetal vascular hemodynamics, as demonstrated by increased impedance in the maternal uterine and fetal umbilical arteries. Moreover, the greatest exposure to tobacco caused vasodilation effect in the fetal MCA, demonstrating that higher nicotine dependence, the greater the maternal-fetal hemodynamic changes. The mechanisms of action and damage caused by the cigarette consumption during pregnancy are complex, since it is the result of inhalation of a number of vasoactive substances, which have a range of effects that can be complementary or antagonistic at various levels within the vascular tree [10]. Several studies in the have used the Doppler method for analyzing the flow patterns in the maternal and fetal arteries of pregnant smokers with some divergent results The present study becomes relevant by analyzing the pulsatility and resistance indexes of the uterine, and in the of specific effect is a
CONTINUOUS ADJOINT SHAPE OPTIMIZATION OF INTERNALLY COOLED TURBINE BLADE

This paper presents an adjoint-based shape optimization framework and its demonstration in a Conjugate Heat Transfer problem in a turbine blading. The gradient of the objective function is computed based on the continuous adjoint method which also includes the adjoint to the turbulence model. The developed software was used to optimize both the blade shape of the internally cooled linear C3X turbine blade and the position of cooling channels aiming at (a) minimum total pressure drop of the hot gas ﬂow and (b) minimum highest temperature within the blade. A free-form parameterization tool, based on volumetric NURBS, controls the blade airfoil contour while the cooling channels are free to move inside the blade, being controlled by the coordinates of their centers. Geometric and ﬂow constraints are included in the performed optimizations keeping the cooling channels away from the airfoil sides and retaining the turbine inlet capacity and ﬂow turning.
Multitenant Cloud VM Security Using HyperCoffer and HSVM

: Security of tenant’s data mainly based upon infrastructure of multitenant cloud environment. However both hardware and software is combined being processed and controlled, there is no any surprise, leakage of data or even malicious cloud operator occurring. Challenge is to secure the virtual machine (VM). Unfortunately, none can make a solution to cloud platform but we can give protection to our VM by against controlling the physical machine. In this paper we analyzed the challenge of virtual machine by using both hardware and software stack processor. We introduce the HyperCoffer, hardware-software framework that used to protect the user data. HyperCoffer doesn’t make any security assumption and only trust the processor chip. HyperCoffer use a VMShim that run in between guest VM and hypervisor. Another hardware virtualization security is used and it’s called as hardware-assisted secure virtual machine (H-SVM) that uses PIC16f877a microcontroller which is inserted in between hypervisor and hardware. H-SVM that is used to protect the hardware data is a server side processor. In this paper we have implemented a prototype of HyperCoffer by QEMU-based full-system emulator and VMShim mechanism then HSVM by USART and pic microcontroller. By this process we also improve the resource management, performance ratio (Performance measurement using trace-based simulation), reliability.
A Distributed Reputation System for Super-Peer Desktop Grids

—Desktop Grids leverage otherwise unused resources of idle desktop computers, providing vast amounts of cumulative computational power. However, resource sharing in Peer-to-Peer environments with selﬁsh participants suffers from the free-riding phenomenon unless the environment provides appropriate countermeasures. In Peer-to-Peer-based Desktop Grids, cooperative participants require protection against free-riding job distributors. In this article, we present a decentralized shared-history reputation mechanism designed for use with Desktop Grids built on dynamic super-peer structures. Embedded in a distributed Desktop Grid workﬂow model, our concept promotes reciprocity and discourages free-riding. In simulations based on real-world network delay and workload information, we show that our concept offers a considerable speedup over non-distributed computation while effectively thwarting free-riding and maintaining the system’s commitment to robustness and scalability
Global beta measurement from two perturbed closed orbits

A simple algorithm is presented which transforms two closed orbits observed at beam position monitors around a ring into {beta} and {phi} values at the monitors. The procedure assumes the prior use of a second algorithm to measure {beta}{sub c} and {phi}{sub c} at the two dipole correctors used to excite the perturbed closed orbits. Test results from the program BETA, written to measure {beta} around the Tevatron, are shown. The sensitivities of the measurement to monitor digitisation and to quadrupole errors between the reference correctors are estimated. 4 figs.
An Explicit Fractal Interpolation Algorithm for Reconstruction of Seismic Data

基于我们的以前的调查和另外的研究人员，的开创的工作一个新奇明确的分数维的插值方法基于仿射的变换被建议，在哪个我们接近由局部地明确的表示的垂直可伸缩因素。 数字实验显示在波浪方程上的地震侧面和收益 signficant 改进的重建的明确的分数维的插值方法表演伟人精确性基于踪迹插值方法(统一途径) 。
Factors Affecting the Adoption of e-Learning: A Meta-analysis of Existing Knowledge

— There are different factors that can influence a learner’s decision on how and when they will use a particular, innovative e-learning technology. In the existing literature, there are many studies that deal with the identification of factors and their impact on a user’s acceptance of e-learning technology. This paper demonstrates the results of a meta-analysis – a statistical synthesis method that provides the opportunity of viewing the research context by combining and analyzing the quantitative results of several independent empirical studies. The meta-analysis was conducted on the basis of empirical data gathered from 28 independent empirical studies in the field of e-learning acceptance. The meta-analysis provided strong evidence that perceived usefulness is the strongest predictor of a learner’s adoption of an e-learning technology.
Cochlear Implant in Labyrinthitis Ossificans

Introduction: Labyrinthitis ossificans is a pathologic ossification in the otic capsule due to an inflammatory or destructive process. The main causes are meningitis, trauma and otosclerosis. A Cochlear ossification result in sensorineural hearing loss, and its presence is associated with technical difficulties and poorer functional results. Advances in surgical techniques and bioengineering provided the development of special electrodes for this condition. This study aimed to evaluate the functional results with partial standard and double-array cochlear implantation in ossified cochlea and describe the performance using the device. Methods: A retrospective and transversal analysis of data on demographic aspects, auditory performances and surgical findings was made. A transorbital radiograph was used to evaluate electrode migration and insertion depth and the position of the internal component. All patients who underwent either partial insertion of a standard electrode array or double-array electrode insertion for their cochlear implantation in a quaternary centre in the last five years were included. Results: Eight patients (six adults and two children) were included. The cause of labyrinthitis ossificans was bacterial meningitis in six cases, Cogan’s syndrome in one patient and trauma in another patient. Most patients were implanted in adulthood and the average duration of deafness was 104.5 months. Displacement in the basal electrode was observed in one patient, who went through a new surgical procedure with successfully repositioning. Discussion: Bacterial meningitis was the most common cause of cochlear ossification in the sample. In adult patients, meningitis acquired in childhood resulted in a longer deafness than in children, which may explain poorer auditory results. Conclusion: Patients with ossified cochlea benefits significantly from double-array compressed short array cochlear implantation.
A Study on Context–based Retrieval Trends and Techniques

: With the tremendous growth of the Web, information “Big Bang” has been taken place on the Internet. Search Engines have become one of the most helpful tools for obtaining useful information from the “Big Bang”. Increasing growth of information volume in the Internet causes an increasing need to develop automatic methods for retrieval of documents and ranking them according to their relevance to the query. Although various methods for the efficient retrieval of information were presented, still the end users are struggling to get the desired result. At present the major issues in searching are (a) topic diffusion: results returned by a keyword based search, fall into multiple topic areas, which are not interested to users; (b) there is no effective scoring mechanism; so the users are forced to scan a large result set, which leads them to miss the important ones. To resolve these issues context – based retrieval is used. This paper explores the research trends and techniques by reviewing various approaches and lists the research issues in this field.
Evaluation of Antibacterial and Antioxidant Activities of the Crude Extracts of

Albizia ferruginea is a plant used in traditional medicine in Cameroon for the treatment of microbial and non-microbial diseases. Due to the lack of scientific data to support the ethnopharmacological use, the present work focused on investigating the antioxidant and antibacterial activities of stem and root barks' crude extracts of this plant. Phytochemical screening of different extracts has been performed and these extracts were evaluated for their antioxidant activities using ABTS , DPPH and FRAP assays, while the antibacterial assays were evaluated by diffusion method. The phytochemical screening showed the presence of coumarins, saponins, tanins, glycosides, lipids, sugars, alkaloids, resins and phenolic compounds . The results of the antioxidant tests revealed that whatever the solvent, the extract of the stem bark of A. ferruginea showed greater activity compared to the roots’ extract. Among all the extracts, hydroalcoholic extract of root (AFR3) was found most active against E. coli ATCC 25922, E. coli ATCC 11775, S. aureus BAA 997, S. aureus ATCC 25923 and K. pneumonia ATCC 700603, with average zone of inhibition of 20 mm. The ethyl acetate extract of root (AFR2) was also active against K. pneumonia ATCC 700603 (21 mm). In the case of stem extracts only hydroalcoholic extract (AFS3) exhibited higher activity against S. aureus ATCC 25923 (21 mm).
Privacy-Preserving Public Auditing for Shared Data in the Cloud

: Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a threshold proxy re-encryption, on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. While prior works on ensuring remote data integrity often lacks the support of either public audit ability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from cloud server. In our proposed work we use multiple servers form storing data to servers. We avoid the additional storage for multi cloud system using split server concept as for security.
Effectiveness of Dynamic Matrix Control algorithm with Laguerre functions

The paper is concerned with the presentation and analysis of the Dynamic Matrix Control (DMC) model predictive control algorithm with the representation of the process input trajectories by parametrised sums of Laguerre functions. First the formulation of the DMCL (DMC with Laguerre functions) algorithm is presented. The algorithm diﬀers from the standard DMC one in the formulation of the decision variables of the optimization problem – coeﬃcients of approximations by the Laguerre functions instead of control input values are these variables. Then the DMCL algorithm is applied to two multivariable benchmark problems to investigate properties of the algorithm and to provide a concise comparison with the standard DMC one. The problems with diﬃcult dynamics are selected, which usually leads to longer prediction and control horizons. Beneﬁts from using Laguerre functions were shown, especially evident for smaller sampling intervals.
Advanced Panel Splitting for Non-Uniform Illumination Images by using Transformation Technique

—Image enhancement is an important tool for processing image. There exits many enhancement algorithms, in which Retinex algorithm enhance details of an image and is widely used. Retinex algorithm can remove illumination but cannot maintain the range of reflectance, hence it is not essential for non-uniform illumination images. So to preserve naturalness along with enhancing the details we propose an algorithm named naturalness preserved enhancement panel splitting algorithm. This paper mainly contributes three main issues. First, splitting a non-uniform illumination image into three coloured panels. Secondly, decomposing each and every image in the panel for brightness. Third, applying a bilog transformation to have a balance between the details and naturalness of an image. Experimental results for the proposed algorithm gives more enhanced naturalness preserved image.
Meta Information Based Text Clustering and Classification with the Use of COATES and COLT Algorithms.

- Large amount of meta-information is available along with the text documents in many text mining applications. Such meta-information may be of different kinds, such as links in the document, user-access behaviour from web logs which can be useful for data mining. Tremendous amount of information can be found in unstructured attributes for clustering purposes. Therefore, the paper will use an approach which carefully ascertains the coherence of the clustering characteristics of the meta information with that of the text content. For improving the quality of the clustering both the text data and meta information will be helpful. In this paper, the design of an algorithm which combines classical partitioning algorithms with probabilistic models in order to create an effective clustering approach using meta information present in document will be perform. Then it shows how to extend the clustering approach to the classification problem. COATES and COLT algorithm for clustering and classification of text data along with the meta information are presented in this paper and it shows the advantages of using such an approach.