Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation and properties of polyimide/Fe3O4 composite foams

Purpose In recent years, considerable attention has been devoted to the development of high-performance EMI shielding materials. In order to develop advanced polymer based EMI shielding materials with rather high temperature stability, good processability and moderate mechanical properties, we chose the polyimide (PI) foam as matrix and ferriferrous oxide (Fe3O4) as fillers to prepare the composite foams with lightweight and rather good EMI shielding performance. Design/methodology/approach Some polyimide nanocomposite foams with Fe3O4 as fillers have been prepared by in-situ dispersion and foaming with pyromellitic dianhydride (PMDA) and isocyanate (PAPI) as raw materials and water as foaming agent. By varying the Fe3O4 contents, a series of PI/Fe3O4 nanocomposite foams with fine microstructures and high thermal stability were obtained. The structure and performances of nanocomposite foams were examined and the effects of Fe3O4 on the microstructure and properties of composite foams were investigated. Fi.
Lightweight gossip-based distribution estimation

Monitoring the global state of an overlay network is vital for the self-management of peer-to-peer (P2P) systems. Gossip-based algorithms are a well-known technique that can provide nodes locally with aggregated knowledge about the state of the overlay network. In this paper, we present a gossip-based protocol to estimate the global distribution of attribute values stored across a set of nodes in the system. Our algorithm estimates the distribution both efficiently and accurately. The key contribution of our algorithm is that it has substantially lower overhead than existing distribution estimation algorithms. We evaluated our system in simulation, and compared it against the state-of-the-art solutions. The results show similar accuracy to its counterparts, but with a communication overhead of an order of magnitude lower than them.
An improved ASIFT algorithm for matching repeated patterns

Image matching is a well researched topic of computer vision. Several new algorithms have been developed in recent times to deal with repetitive pattern matching and affine invariant matching. This paper presents two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform (ASIFT) algorithm. The first improvement enables ASIFT to match repetitive patterns through the use of Graph Transformation Matching. The second increases the accuracy of matching by estimating the transformation between views more precisely. Results show that the proposed method is able to successfully match repetitive patterns such as the checkerboard. An increase in the number of matches can also be seen for matching views under severe affine transformations or projections.
Using inductive reasoning and reasoning about dynamic domains for automatic processing of claims

We report on the novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents so that we are uncertain about motivations and decisionmaking principles of some of these agents. Our reasoning setting for such domain includes the deductive and inductive components. The former component is based on situation calculus and describes the behavior of agents with complete information. The latter, machine learning-based inductive component (with the elements of abductive and analogous reasoning) involves the previous experience with the agent, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing the claims of unsatisfied customers. The task is to predict the future actions of a participating agent (the company that has upset the customer) to determine the required course of actions to settle down the claim. We believe our framework reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical and analogy-based reasoning.
Multidisciplinary molecular tumour board: a tool to improve clinical practice and selection accrual for clinical trials in patients with cancer

Background The complexity of delivering precision medicine to oncology patients has led to the creation of molecular tumourboards (MTBs) for patient selection and assessment of treatment options. New technologies like the liquid biopsy are augmenting available therapeutic opportunities. This report aims to analyse the experience of our MTB in the implementation of personalised medicine in a cancer network. Materials and methods Patients diagnosed with solid tumours progressing to standard treatments were referred to our Phase I unit. They underwent comprehensive next generation sequencing (NGS) of either tumour tissue or cell-free circulating tumour DNA (ctDNA) or both. The MTB expressed either a positive or negative opinion for the treatment of the patients with discovered druggable alterations inside a clinical trial, in an expanded access programme, with a compassionate use. Afterwards, discovered alterations were matched with OncoKB levels of evidence for the choice of alteration-specific treatments in order to compare MTB outcomes with a standardised set of recommendations. Results NGS was performed either on ctDNA or tumour tissue or in both of them in 204 patients. The MTB evaluated 173 of these cases. Overall, the MTB proposed alteration-specific targeted therapy to 72 patients (41.6%). 49 patients (28.3% of the total evaluated) were indicated to enter a clinical trial. In 29 patients with matched liquid biopsy NGS (lbNGS), tumour tissue NGS (ttNGS) and MTB evaluation, the MTB changed the treatment strategy coming from standardised recommendations based on lbNGS and ttNGS alone in 10 patients (34.5%), thanks to the evaluation of other clinical parameters. In our cohort, lbNGS was more likely, compared with ttNGS, to detect point mutations (OR 11, 95% CI 2.9 to 24.1, p<0.001) and all-type alterations (OR 13.6, 95% CI 5.5 to 43.2, p<0.001) from the same genes of matched patients. Conclusions Our MTB allows patients with refractory cancer to be included in clinical trials and improves the precision of clinical decisions compared with a standardised set of mutation-driven recommendations.
Monitoring total endotoxin and (1→3)-β-d-glucan at the air exhaust of concentrated animal feeding operations

Mitigation of bioaerosol emissions from concentrated animal feeding operations (CAFOs) demands knowledge of bioaerosol concentrations feeding into an end-of-pipe air treatment process. The aim of this preliminary study was to measure total endotoxin and (1→3)-β-glucan concentrations at the air exhaust of 18 commercial CAFOs and to examine their variability with animal operation type (swine farrowing, swine gestation, swine weaning, swine finishing, manure belt laying hen, and tom turkey) and season (cold, mild, and hot). The measured airborne concentrations of total endotoxin ranged from 98 to 23,157 endotoxin units (EU)/m3, and the airborne concentrations of total (1→3)-β-d-glucan ranged from 2.4 to 537.9 ng/m3. Animal operation type in this study had a significant effect on airborne concentrations of total endotoxin and (1→3)-β-d-glucan but no significant effect on their concentrations in total suspended particulate (TSP). Both endotoxin and (1→3)-β-d-glucan attained their highest airborne concentrations in visited tom turkey buildings. Comparatively, season had no significant effect on airborne concentrations of total endotoxin or (1→3)-β-d-glucan. Endotoxin and (1→3)-β-glucan concentrations in TSP dust appeared to increase as the weather became warmer, and this seasonal effect was significant in swine buildings. Elevated indoor temperatures in the hot season were considered to facilitate the growth and propagation of bacteria and fungi, thus leading to higher biocomponent concentrations in TSP. Implications: This study monitored total endotoxin and (1→3)-β-d-glucan concentrations at the air exhaust of 18 commercial animal buildings during multiple seasons. The airborne concentrations of total endotoxin and (1→3)-β-d-glucan differed significantly with animal operation type but showed no significant effect by season. Given that animal buildings in general have the highest ventilation rates in summer, these findings suggest that endotoxin and (1→3)-β-d-glucan may attain their maximum emission rates in summer. It is therefore recommended that particular attention should be paid to further investigating summertime bioaerosol emissions from animal feeding operations and the emissions’ impact on neighboring communities.
Mesh refinement study and experimental validation for stretch bending of sheet metals

For sheet metal parts with small radii and large bending angles, the sheet metal forming simulation reaches their application limits. Alternatives are complex shell formulations and volume elements. For volume elements, the necessary number of elements over the thickness is important. Valid values are not available depending on discrete radii. Therefore in this work, a convergence study is performed using the example of an angular stretch bend test with a radius to thickness ratio of 1. For various states of mesh refinement, simulations are performed, various results are presented, analysed and discussed with regard to convergence behaviour to the necessary number of elements in thickness direction. Recommendations for suitable validation variables are derived. Based on the refinement study, a simulation model for an experimental validation is developed. The experiments are carried out in a sheet metal forming machine. Experimental angular stretch bend test with a punch radius of 1 mm are performed until failure and the strain distribution on the top side of the sheet is measured. Finally, simulation and experiments are compared based on the surface strain.
A circuit SAT solver with signal correlation guided learning

Boolean Satisfiability has attracted tremendous research effort in recent years, resulting in the developments of various efficient SAT solver packages. Based upon their design architectures, researchers have tried to develop better heuristics to further improve its efficiency, by either speeding up the Boolean Constraint Propagation (BCP) procedure or finding a better decision ordering (or both). In this paper, we propose an entirely different SAT solver design concept that is circuit-based. Our solver is able to utilize circuit topological information and signal correlations to enforce a decision ordering that is more efficient for solving circuit-based SAT problem instances. In particular, for unsatisfiable circuit examples, our solver is able to achieve from 2x up to more than 75x speedup over a state-of-the-art SAT solver.
A Value Analysis for C Programs

We demonstrate the value analysis of Frama-C. Frama-C is an Open Source static analysis framework for the C language. In Frama-C, each static analysis technique, approach or idea can be implemented as a new plug-in, with the opportunity to obtain information from other plug-ins, and to leave the verification of difficult properties to yet other plug-ins. The new analysis may in turn provide access to the data it has computed. The value analysis of Frama-C is a plug-in based on abstract interpretation. It computes and stores supersets of possible values for all the variables at each statement of the analyzed program. It handles pointers, arrays, structs, and heterogeneous pointer casts. Besides producing supersets of possible values for the variables at each point of the execution, the value analysis produces run-time-error alarms. An alarm is emitted for each operation in the analyzed program where the value analysis cannot guarantee that there will not be a run-time error.
Design of the Hydraulically Actuated, Torque-Controlled Quadruped Robot HyQ2Max

This paper presents the design of the hydraulically actuated quadruped robot HyQ2Max. HyQ2Max is an evolution of the 80 kg agile and versatile robot HyQ. Compared to HyQ, the new robot needs to be more rugged, more powerful and extend the existing locomotion skills with self-righting capability. Since the robot's actuation system has an impact on many aspects of the overall design/specifications of the robot (e.g., payload, speed, torque, overall mass, and compactness), this paper will pay special attention to the selection and sizing of the joint actuators. To obtain meaningful joint requirements for the new machine, we simulated seven characteristic motions that cover a wide range of required behaviors of an agile rough terrain robot, including trotting on rough terrain, stair climbing, push recovery, self-righting, etc. We will describe how to use the obtained joint requirements for the selection of the hydraulic actuator types, four-bar linkage parameters, and valve size. Poorly sized actuators may lead to limited robot capabilities or higher cost, weight, energy consumption, and cooling requirements. The main contributions of this paper are: 1) a novel design of an agile quadruped robot capable of performing trotting/crawling over flat/uneven terrain, balancing, and self-righting; 2) a detailed method to find suitable hydraulic cylinder/valve properties and linkage parameters with a specific focus on optimizing the actuator areas; and 3) to the best knowledge of the authors, the most complete review of hydraulic quadruped robots.
Reversibility of the Other-Race Effect in Face Recognition During Childhood

Early experience with faces of a given racial type facilitates visual recognition for this type of face relative to others. To assess whether this so-called other-race effect can be reversed by subsequent experience with new types of faces, we tested adults of Korean origin who were adopted by European Caucasian families when they were between the ages of 3 to 9. The adoptees performed a face recognition task with photographs of Caucasian and Asian faces. They performed exactly like a control group of French participants, identifying the Caucasian faces better than the Asiatic ones. In contrast, a control group of Koreans showed the reverse pattern. This result indicates that the face recognition system remains plastic enough during childhood to reverse the other-race effect.
Frame Rate Adaptive Rate Model for Video Rate Control

This paper analyzes in theory the relationship of the coding bit-rate with the frame rate and the quantization step respectively, using the quadratic rate model. Based on the theoretical analysis, a frame rate adaptive rate model is proposed for video rate control. Extensive experimental results have demonstrated the effectiveness of the theoretical analysis and the corresponding model. The proposed model is well suited for advanced rate control in video coding.
Nanocomposites based on chain extended poly(l-lactic acid)/carboxylated carbon nanotubes: Crystallization kinetics and lamellar morphology

Isothermal and nonisothermal crystallization kinetics of poly(l-lactide) and its nanocomposites with different multiwalled carbon nanotubes (i.e., pristine and functionalized) were investigated. The effects of chain extension and nanofiller content on the crystallization kinetics were also explored. The Avrami analysis was applied as a useful tool for the study of isothermal crystallization kinetics, which was also supported by optical microscopy observations. In addition, in nonisothermal crystallization process, the Avrami and Mo methodologies were used to study the crystallization kinetics as well as the evaluation of activation energy from isoconversional methods. The activation energy was always negative for the process driven by the secondary nucleation. Great differences were found between the studied samples, but activation energy values indicated a favored crystallization (i.e., less negative energies) when functionalized multiwalled carbon nanotubes were added at a concentration of 0.2 wt%. Cold crystallization was also influenced by the multiwalled carbon nanotube content due to the nucleating effect and the existence of geometrical constrictions that limited the size of crystalline domains. Small-angle X-ray scattering studies showed that the size of crystal lamellae after cold crystallization could vary by a magnitude of 3 nm depending on the way as the sample was previously cooled (i.e., by the presence of incipient crystalline domains).
Transmission of a 13–15/21 translocation in six families

The genealogical, cytogenetical and blood‐group data available for six families with an inherited translocation between a chromosome of the 13–15 group and chromosome no. 21 are presented. In two families the translocation was transmitted through three generations, in two families it occurred de novo in the carrier parent, and in two families it could not be decided if the translocation was inherited from a further generation or originated in the gametogenesis of generation I. In five families the transmission to a patient with Down's syndrome was maternal, in one family it was paternal. Meiotic studies of a testicular biopsy from a male carrier, the father of a child with Down's syndrome, revealed trivalent formation in diakinesis.
Learning Constraint Grammar-style Disambiguation Rules using Inductive Logic Programming

This paper reports a pilot study, in which Constraint Grammar inspired rules were learnt using the Progol machine-learning system. Rules discarding faulty readings of ambiguously tagged words were learnt for the part of speech tags of the Stockholm-Umea Corpus. Several thousand disambiguation rules were induced. When tested on unseen data, 98% of the words retained the correct reading after tagging. However, there were ambiguities pending after tagging, on an average 1.13 tags per word. The results suggest that the Progol system can be useful for learning tagging rules of good quality.
Raised TSH is associated with endothelial dysfunction in Metabolic Syndrome: A case control study

Abstract Introduction. Endothelial dysfunction has been considered as one of the important factors in pathogenesis of Metabolic Syndrome (Met S). Subclinical hypothyroidism (SCH) has also been reported to be associated with Met S. The aim of our study is to evaluate the association of raised TSH with mediators of endothelial dysfunction in Met S with Subclinical hypothyroidism as compared to healthy controls. Methods. Study population consisted of 100 subjects, out of which 50 were cases of Met S and 50 were healthy controls. Met S group were further divided into two, based on the presence & absence of SCH. Serum insulin, T3, T4, TSH were measured by chemiluminescence based immunoassay (CLIA). Serum nitric oxide (NO) levels were measured by Modified Griess’s method and serum endothelin-1 (ET-1) levels were measured by ELISA. Results. Out of 50 cases of Met S, SCH was diagnosed in 22. The mean serum TSH levels were significantly higher in Met S cases as compared to healthy controls (5.7 ± 1.2 μIU/mL vs. 2.3 ± 1.6 μIU/mL, P <0.0001). Mean serum NO levels were significantly lower in Met S cases as compared to healthy control (15.4 ± 10 μM vs. 21 ± 10 μM, p = 0.009). Mean serum ET-1 levels were significantly higher in Met S cases as compared to healthy controls (2.68 ± 1.7 fmol/mL vs. 2.1 ± 0.84 fmol/mL, p = 0.011). On Pearson’s correlation analysis, TSH showed positive correlation with ET-1 (r = 0.341, p = 0.001) and negative correlation with NO (r = −0.331, p = 0.001). Binary logistic regression analysis showed that TSH, NO and ET-1 has significant odd’s ratio for predicting Met S. Conclusion. Met S cases were screened for thyroid abnormalities and found to have 44% of SCH along with co-existing endothelial dysfunction. Raised TSH in SCH could cause endothelial dysfunction which may lead to Met S and associated co-morbidities. Present study gives new insight in linking endothelial dysfunction and raised TSH in Met S. Therefore, Met S cases should be screened for SCH and treated appropriately to attenuate endothelial dysfunction and associated comorbidities in Met S.
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)

Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment. SDN provides flexibility to program network devices for different objectives and eliminates the need for third-party vendor-specific hardware. We implement our system as a network application on top of an SDN controller. We use deep learning for feature reduction of a large set of features derived from network traffic headers. We evaluate our system based on different performance metrics by applying it on traffic traces collected from different scenarios. We observe high accuracy with a low false-positive for attack detection in our proposed system.
Cooking Up Problems for Babies

Background: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. Affymetrix GeneChips are widely used microarrays, where a variety of statistical algorithms have been explored and used for detecting significant genes in the experiment. These methods rely solely on the quantitative data, i.e., signal intensity; however, qualitative data are also important parameters in detecting differentially expressed genes. Results: AffyMiner is a tool developed for detecting differentially expressed genes in Affymetrix GeneChip microarray data and for associating gene annotation and gene ontology information with the genes detected. AffyMiner consists of the functional modules, GeneFinder for detecting significant genes in a treatment versus control experiment and GOTree for mapping genes of interest onto the Gene Ontology (GO) space; and interfaces to run Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. AffyMiner has been used for analyzing the GeneChip data and the results were presented in several publications. Conclusion: AffyMiner fills an important gap in finding differentially expressed genes in Affymetrix GeneChip microarray data. AffyMiner effectively deals with multiple replicates in the experiment and takes into account both quantitative and qualitative data in identifying significant genes. AffyMiner reduces the time and effort needed to compare data from multiple arrays and to interpret the possible biological implications associated with significant changes in a gene's expression. Background DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously [1]. Affymetrix GeneChips are widely used microarrays with a collection of 11 – 20 probe pairs called a probe set that measures the expression of each transcript. The probe from Symposium of Computations in Bioinformatics and Bioscience (SCBB06) in conjunction with the International Multi-Symposiums on Computer and Computational Sciences 2006 (IMSCCS|06) Hangzhou, China. June 20–24, 2006 Published: 12 December 2006 BMC Bioinformatics 2006, 7(Suppl 4):S26 doi:10.1186/1471-2105-7-S4-S26 <supplement> <title> <p>Symposium of Computations in Bioinformatics and Bioscience (SCBB06)</p> </title> <editor>Youping Deng, Jun Ni</editor> <note>Research</note> <url>http://www.biomedcentral. com/content/pdf/1471-2105-7-S4-info.pdf</url> </supplement> © 2006 Lu et al; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.