Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Principles for Route Optimization Business Models: A Grounded Theory Study of User Feedback

The article generates design principles for business models offering route optimization software. Route optimization is a widely relevant activity in logistics, as it is its purpose to optimize the amount of time and the associated cost that a vehicle needs to reach its intended destination. The article presents a Grounded Theory study of user reviews drawn from the comparison portal Capterra to uncover possible design principles for these business models. User reviews are suitable data for that purpose as they are a rich source to derive basic requirements for a product. The data are analyzed qualitatively, adhering to established coding guidelines, and aligned with established business model components to achieve a link between empirical research and the underlying knowledge base. The synthesis of the data to design principles enables practitioners to adequately process and instantiate them into real-life design decisions that enhance the possibility of designing a successful business model.
BotFP: FingerPrints Clustering for Bot Detection

Efficient bot detection is a crucial security matter and has been widely explored in the past years. Recent approaches supplant flow-based detection techniques and exploit graph-based features, incurring however in scalability issues in terms of time and space complexity. Bots exhibit specific communication patterns: they use particular protocols, contact specific domains, hence can be identified by analyzing their communication with the outside. To simplify the communication graph, we look at frequency distributions of protocol attributes capturing the specificity of botnets behaviour. In this paper, we propose a bot detection technique named BotFP, for BotFinger-Printing, which acts by (i) characterizing hosts behaviour with at-tribute frequency distribution signatures, (ii) learning behaviour of benign hosts and bots through a clustering technique, and (iii) classifying new hosts based on distances to labelled clusters. We validate our solution on the CTU-13 dataset, which contains 13 scenarios of bot infections, connecting to a Command-and-Control (C&C) channel and launching malicious actions such as port scanning or Denial-of-Service (DDoS) attacks. Our approach applies to various bot activities and network topologies. The approach is lightweight, can handle large amounts of data, and shows better accuracy than state-of-the-art techniques.
Prevalence of Fetal Alcohol Spectrum Disorders in 4 US Communities

Importance Fetal alcohol spectrum disorders are costly, life-long disabilities. Older data suggested the prevalence of the disorder in the United States was 10 per 1000 children; however, there are few current estimates based on larger, diverse US population samples. Objective To estimate the prevalence of fetal alcohol spectrum disorders, including fetal alcohol syndrome, partial fetal alcohol syndrome, and alcohol-related neurodevelopmental disorder, in 4 regions of the United States. Design, Setting, and Participants Active case ascertainment methods using a cross-sectional design were used to assess children for fetal alcohol spectrum disorders between 2010 and 2016. Children were systematically assessed in the 4 domains that contribute to the fetal alcohol spectrum disorder continuum: dysmorphic features, physical growth, neurobehavioral development, and prenatal alcohol exposure. The settings were 4 communities in the Rocky Mountain, Midwestern, Southeastern, and Pacific Southwestern regions of the United States. First-grade children and their parents or guardians were enrolled. Exposures Alcohol consumption during pregnancy. Main Outcomes and Measures Prevalence of fetal alcohol spectrum disorders in the 4 communities was the main outcome. Conservative estimates for the prevalence of the disorder and 95% CIs were calculated using the eligible first-grade population as the denominator. Weighted prevalences and 95% CIs were also estimated, accounting for the sampling schemes and using data restricted to children who received a full evaluation. Results A total of 6639 children were selected for participation from a population of 13 146 first-graders (boys, 51.9%; mean age, 6.7 years [SD, 0.41] and white maternal race, 79.3%). A total of 222 cases of fetal alcohol spectrum disorders were identified. The conservative prevalence estimates for fetal alcohol spectrum disorders ranged from 11.3 (95% CI, 7.8-15.8) to 50.0 (95% CI, 39.9-61.7) per 1000 children. The weighted prevalence estimates for fetal alcohol spectrum disorders ranged from 31.1 (95% CI, 16.1-54.0) to 98.5 (95% CI, 57.5-139.5) per 1000 children. Conclusions and Relevance Estimated prevalence of fetal alcohol spectrum disorders among first-graders in 4 US communities ranged from 1.1% to 5.0% using a conservative approach. These findings may represent more accurate US prevalence estimates than previous studies but may not be generalizable to all communities.
What mathematical models can or cannot do in glioma description and understanding

Our aim in this article is not to provide a review of the existing literature but to make a critical analysis on glioma behavior mathematical modeling. We present here mathematical modeling history, interests, modalities and limitations on the study of glioma growth. We also make a point on model consideration according to glioma comportments. We finally introduce medical imaging coupled with in silico models as the next step in glioma research. We do not claim completeness of the bibliography but we tried to cover a large amount of mathematical considerations for glioma behavior illustrated with selected representative papers.
Avian TAP1 and TAP2 genes: polymorphisms and haplotypes.

Statistical Genetics and Bioinformatic Unit, Parco Tecnologico Padano – CERSA, 26900 Lodi, Italy. *Corresponding author: laura.sironi@tecnoparco.org TAP1 and TAP2 are the two subunits of the transporter associated with antigen processing (TAP). TAP role is the transport of peptides, that will be specifically loaded onto the Major Histocompatibility Complex (MHC) class I molecules for the antigen presentation, from the cytoplasm into the endoplasmic reticulum. Chicken TAP1 and TAP2 genes are located inside the MHC, between the two class I genes. TAP1 includes 11 exons and TAP2 includes 9 exons. Aim of the present work is the discovery and validation of Single Nucleotide Polymorphisms (SNPs) within these two genes and the study of the haplotypes. Chicken and turkey sequences of the regions spanning from TAP1 exon 6 to exon 7 and from TAP2 exon 4 to exon 6 were obtained by direct sequencing of specifically amplified fragments. Sequences were aligned to investigate the intra- and inter-species polymorphisms and the SNP discovery was carried out using the PolyBayes program. Eighteen TAP1 putative SNPs were identified in chicken and seven in turkey. Twenty-four TAP2 putative SNPs were detected in chicken and eleven in turkey. The analysis of chicken polymorphisms showed that most of the SNPs did not have a breed specific pattern. Only one breed presented a specific SNP, that was not identified in the other breeds. A second specific SNP was identified in a small number of broilers. On the other hand, turkey sequences showed higher nucleotide substitution between the commercial line and the local breeds in both TAP1 and TAP2 genes. Polypeptide sequences were inferred from nucleotide sequences and aligned to identify synonymous and non-synonymous substitutions. The polymorphic sites identified in this work were further used for the identification of haplotypes for these two genes. Haplotypes were reconstructed using the Arlequin software. Differences among haplotype frequencies in different species and breeds were tested for significance. No significant differences were found for TAP1. Significant differences (P<0.05) were obtained for TAP2 among chicken breeds and turkey, with a few specie-specific haplotypes. The putative chicken SNPs detected in the present study are under validation in a commercial pedigree. Keywords: Major Histocompatibility Complex; TAP1; TAP2; SNPs; Haplotypes.
Construction of Virtual Datasets for Bin Picking

Bin Picking is a typical problem for identifying and sorting complex stacked objects, the difficulty of which lies in the identification of objects. This paper uses deep learning method to identify objects preliminarily. Because the deep learning method requires high-quality datasets, the traditional manual annotation method is too difficult to complete under the industrial production environment. Therefore, this paper proposes a method to build virtual datasets based on virtual simulation technology. It uses OpenGL technology and Bullet physics engine to build virtual scenes. Then through camera loading and scene drawing, the datasets including RGB images, segmentation annotation images and so on are generated. Experimental results show that the model trained on virtual datasets by deep learning can achieve good accuracy, so the feasibility and practical application value of the method are verified.
Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability

Internet-of-Things (IoT) environment has a dynamic nature with high risks of confidentiality, integrity, and availability violations. The loss of information, denial of access, information leakage, collusion, technical failures, and data security breaches are difficult to predict and anticipate in advance. These types of nonstationarity are one of the main issues in the design of the reliable IoT infrastructure capable of mitigating their consequences. It is not sufficient to propose solutions for a given scenario, but mechanisms to adapt the current solution to changes in the environment. In this article, we present a multicloud storage architecture called WA-MRC-RRNS that combines the weighted access scheme, threshold secret sharing, and redundant residue number system with multiple failure detection/recovery mechanisms and homomorphic ciphers. We provide a theoretical analysis of the probability of information loss, data redundancy, speed of encoding/decoding, and show how to dynamically configure parameters to cope with different objective preferences, workloads, and cloud properties. We propose a multiobjective optimization mechanism to adjust redundancy, encryption–decryption speed, and data loss probability. Comprehensive experimental analysis with real data shows that our approach provides a secure way to mitigate the uncertainty of the use of untrusted and not reliable IoT infrastructure.
Metadata model for an archeological data lake

The HyperThesau project was initiated by a multidisciplinary team consisting of two research laboratories of archaeology and computer science, respectively, a digital library, two archeological museums and a private company. This project has two main objectives: 1) the design and implementation of an integrated platform to host, search, share and analyze archaeological data; 2) the design of a domain-specific thesaurus taking the whole archaeological data lifecycle into account, from data creation to publication. Archeological data may bear many different types, e.g., textual documents, images (photos, drawings.), sensor data, etc. Moreover, similar documents, e.g., excavation reports, are often created by various software tools that are not compatible with each other. The description of an archaeological object also differs with respect to users, usages and time (De Luca et al., 2016). Such variety of archeological data induces many scientific challenges related to storing heterogeneous data in a centralized repository, guaranteeing data quality, cleaning and transforming the data to make them interoperable, finding and accessing data efficiently and cross-analyzing the data by taking their spatial and temporal dimensions into account. To overcome all these challenges, we exploit the concept of data lake (Dixon, 2010). A data lake is a centralized repository that helps store all types of data without having any predefined structure (Hai et al., 2016). To avoid a data lake turning into an inoperable data swamp, an efficient metadata management system is essential to catalogue, search and access the data (Alrehamy & Walker, 2015; Hai et al., 2016). Note that we do not propose a new archaeological data model nor new data formats for data archiving here. Our approach aims to collect all types of archaeological data, save them inside a data lake and propose metadata for better organizing data and for allowing users to easily find data for analysis purposes. In this article, we first present a data lake prototype that is architectured in nine layers such as data ingestion, data storage, data application, data governance, data security, etc. Each layer is implemented with one or more frameworks of the Hadoop ecosystem, e.g., Atlas, HDFS, HIVE, OpenLdap, Spark, etc. This prototype is operational and currently hosts the data of two archeological research facilities. Secondly, we present our metadata management system and the metadata model we use to manage archeological data inside the data lake. We actually instantiate the MEtadata model for Data Lakes (MEDAL), which adopts a graph model (Sawadogo et al., 2019). Eventually, our metadata management system is implemented with Apache Atlas, which can host not only descriptive metadata, but also several thesauruses. With the help of a search engine, i.e., Solr, users can find data through descriptive metadata, a thesaurus or the data lineage.
Dynamics of Some Routine Immunological Parameters During Anti - TNF Therapy in Patients with Crohn’s Disease

Background: Fecal and immunological biomarkers can be used to diagnose and manage patients with Crohn’s disease (CD). Anti tumor necrosis factor (TNF) should be evaluated in addition to biomarkers to determine the response to therapy. Objectives: The current study aimed at following up fecal calprotectin (FC), perinuclear anti neutrophil cytoplasmic antibodies (pANCA), anti Saccharomyces cerevisiae antibodies (ASCA), and anti nuclear antibodies (ANA) in patients with CD on anti-TNF therapy. Methods: A total of 57 patients with CD and the mean age of 40 ± 15 years (ranged: 20 75) were monitored after initiation of anti TNFa treatment. Stool samples were tested for FC (Alegria automated the enzyme linked immunosorbent assay (ELISA) system), and serum samples for ANCA, ANA (indirect immunofluorescence IIF), and ASCA (ELISA) in the beginning and after six months on immunosuppressive therapy plus anti TNFa agents. Results: It was observed that all patients with CD had significantly decreased FC levels after anti TNFa therapy (963.97 mg/kg initially vs. 268.42 mg/kg after treatment; P = 0.043). Moreover, in 75% of patients, FC levels dropped below the cutoff value of 50 mg/kg. Positive for ASCA IgA/IgG were 17/24 tested patients, but no differences were observed regarding the application of anti TNFa therapy. However, the titers of pANCA decreased in four patients after anti TNFa treatment. Conclusions: Initial and follow up measurements of some immunological markers such as FC and pANCA could be of benefit for patients with CD in anti TNF therapy, whereas others such as ANA and ASCA were not useful to monitor the therapy.
Developing Business Simulator Media Application-Based to Improve Analytical Capability and Independent Business Decision Making for Business Management Students

Objective of this research to designing, building, and testing business simulator applications which have purpose as an effective learning media in term of training and increasing constructive and transformative capability of students to do business analysis and decision making for business management students. So, students able to understand what will be happened in industry and able to take a right business decision. This research designed have 4 research stages, which are preliminary research and collecting data stage, planning and creating business simulator media stage, preliminary testing and its revisions stage, and main field testing and its revisions stage. Based on testing result, it shows that this business simulator application is effective improving student’s ability to analysing business cases and make decision which profitable for their business. Researcher hope, this business simulator able to be a reference of constructive learning media applicationbased where students able to proactive in business problem solving.
Structural Design and Analysis of a High Reliability Multi-Turn Wound-Rotor Resolver for Electric Vehicle

Cost, durability and reliability are three critical issues in electrical/hybrid electrical vehicles (EV/HEV). In this regard, resolvers are often used as position and speed sensors in such applications. In this paper design and analysis of a novel multi-turn resolver is discussed to address all the mentioned issues. The proposed resolver has got one stator core and one rotor core. Double sets of signal windings on the stator and a special excitation winding on the rotor are designed to achieve both absolute and multi-speed operations. Mathematical model of the proposed resolver is verified using time stepping finite element analysis and experimental measurements.
A Combination of a Volatile‐Memristor‐Based True Random‐Number Generator and a Nonlinear‐Feedback Shift Register for High‐Speed Encryption

A true random‐number generator (TRNG) and a nonlinear feedback shift register (NFSR) are combined to create a new type of TRNG. This TRNG is based on the intrinsic stochasticity of threshold switching behavior in a Pt/HfO2/TiN memristor and an NFSR circuit. Considering the transition rate of the hopping process, the stochasticity of the delay time can be attributed to the phonon‐assisted hopping process. This novel TRNG passes all 15 National Institute of Standards and Technology randomness tests without post‐processing steps, proving its performance as a hardware security application. By combining the TRNG with the NFSR, the bit generation rate is further improved, allowing it to be used for high‐speed applications.
Intelligent Tomato Picking Robot System Based on Multimodal Depth Feature Analysis Method

In order to achieve monomer picking and string picking of tomato fruit. The tomato image was obtained by binocular camera, and the preliminary search of tomato fruit was carried out. Then the second close-up camera was used to obtain the accurate image of tomato fruit. The improved Deeplab model was used to complete the recognition and localization of tomato fruit, and the fruit was detected by Harris algorithm. The corner point of the fruit stem of the first fruit bifurcation point and the stem of the plant is determined as the position of the cut point of the tomato fruit stem. The robotic arm, the end effector and the mechanical knife are controlled to complete the picking of the individual tomato fruit and the string of tomato fruits, providing a solution for the manual picking of the replacement tomato.
Future Generation Computer Systems

Reservoir sampling is widely employed to characterize large graph streams by producing edge samples. However, existing reservoir-based sampling methods mainly focus on counting triangles but perform poorly in analyzing topological characteristics reflected by node degrees. This paper proposes a new method, called triangle-induced reservoir sampling, or T-Sample, to count triangles and estimate node degrees simultaneously and efficiently. While every edge in a graph stream is processed only once by T-Sample, a dual sampling mechanism performing both uniform sampling and non-uniform sampling is carefully designed. Specifically, T-Sample’s uniform sampling is used to count triangles by a newly proposed method with smaller estimation variances than existing reservoir-based sampling methods; whereas, its non-uniform sampling ensures that edge samples are connected. Experimental results driven by real datasets show that T-Sample can count triangles with smaller estimation errors and variances than the state-of-the-art reservoir-based sampling methods while obtaining much more accurate information about node degrees at smaller time and memory costs.
Analytics Maturity Models: An Overview

This paper aims to review, characterize and comparatively analyze selected organizations’ analytics maturity models. Eleven various organizations’ analytics maturity models (AMMs) were characterized. The models’ characteristics were developed based on an academic literature review as well as reports and publications shared by analytics sector operators. Most of the analyzed models comprised five analytics maturity levels. Comprehensive descriptions of an organization’s analytics maturity levels were available for all models. However, no detailed description of the assessment process or criteria for placing an organization at a specific analytics development level were available in all cases. Selected analytics maturity models were described in such a detailed manner that their application in an independent assessment of an organization’s analytics maturity was possible. In the future, an increase is expected in both the number and availability of new analytics maturity models, in particular those personalized and dedicated to a specific sector or business, and the number of entities involved in an assessment of an organization’s analytics maturity and the implementation of data analytics in organizations. The article presents and summarizes selected features of eleven various organizations’ analytics maturity models. This is the firstever such extensive review of those models.
A 500-nW-to-1-mW Input Power Inductive Boost Converter With MPPT for RF Energy Harvesting System

This article discusses a novel RF energy-harvesting system for available power as low as 500 nW. The system includes a main RF rectenna (antenna followed by rectifier) for energy harvesting, an auxiliary RF rectenna for power detection, and a boost converter along with its control circuit. For maximum power point tracking (MPPT), a particular reference voltage <inline-formula> <tex-math notation="LaTeX">${V_{\text {ref}}}$ </tex-math></inline-formula> for a range of received RF power (from −11 to 3 dBm) is used to harvest maximum available power from the main rectenna by regulating its output voltage with the help of the boost converter. <inline-formula> <tex-math notation="LaTeX">${V_{\text {ref}}}$ </tex-math></inline-formula> for MPPT is provided by the auxiliary RF rectenna and the power-detector circuit and depends on the RF power. The complete system is designed, simulated, fabricated, and tested in the 180-nm mixed-mode CMOS technology. Measured results show that the boost converter can track the maximum power point for the input available power ranging from 500 nW to 1 mW. The efficiency of the boost converter is a function of the available input power and the equivalent source resistance of the RF energy harvester. For an equivalent source resistance of <inline-formula> <tex-math notation="LaTeX">${1~\text {k}\Omega }$ </tex-math></inline-formula>, the maximum efficiency of the boost converter is 91.6% at <inline-formula> <tex-math notation="LaTeX">${165~\mu \text {W}}$ </tex-math></inline-formula> of the available input power. As a potential application, a <inline-formula> <tex-math notation="LaTeX">${\Delta \Sigma }$ </tex-math></inline-formula> modulator designed in the 180-nm mixed-mode CMOS technology is powered by the proposed energy-harvesting system. The efficiency of the boost converter is approximately 76.6% while powering the <inline-formula> <tex-math notation="LaTeX">${\Delta \Sigma }$ </tex-math></inline-formula> modulator and 78.5% while powering the dc load at the RF power of <inline-formula> <tex-math notation="LaTeX">${-7\,\,\text {dBm}}$ </tex-math></inline-formula>.