Miracle-dz/UniFAR-FTUs huggingface dataset:
Axial-Oblique Versus Standard Axial 3-T Magnetic Resonance Imaging for the Detection of Trochlear Cartilage Lesions: A Prospective Study

Background: Imaging of the femoral trochlea has been inherently difficult because of its convex anatomy. Purpose/Hypothesis: The purpose of this study was to compare the diagnostic utility of a standard axial magnetic resonance imaging (MRI) sequence with an axial-oblique MRI sequence of the knee for the detection of trochlear articular cartilage lesions on a high-field 3-T MRI scanner. We hypothesized that axial-oblique MRI scans of the knee obtained along the true axis of the trochlea would significantly improve the detection of high-grade cartilage lesions. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Patients who underwent MRI and subsequent surgery for any indication were prospectively enrolled into this study between June 2014 and February 2015. The articular cartilage of the trochlea was evaluated independently by 3 raters on axial and axial-oblique MRI and compared with arthroscopic findings (gold standard). The interrater and intrarater reliability of mild (International Cartilage Repair Society [ICRS] grades 1 or 2) and severe (ICRS grades 3 or 4) lesions on MRI were assessed as well as the sensitivity, specificity, positive predictive value, and negative predictive value. Results: A total of 99 knees in 96 patients were included in the study. Interrater and intrarater agreement for the identification of severe lesions were moderate to good on the proximal trochlea and fair to moderate on the distal trochlea. No significant differences in sensitivity were found between axial and axial-oblique scans for any grade of lesion (55% vs 51%, respectively; P = .700) or for severe lesions (61% vs 52%, respectively; P = .289). Similarly, specificity for detecting severe lesions was not significantly different between axial and axial-oblique scans (95% vs 87%, respectively; P = .219). Last, no significant differences in sensitivity or specificity were found between MRI sequences when separately evaluating proximal and distal trochlear lesions (all P > .05). Conclusion: The axial-oblique sequence was unable to improve the sensitivity of MRI in detecting articular cartilage lesions on the trochlea. Both conventional axial and axial-oblique sequences, reviewed independently of the complete MRI series, had low sensitivity in detecting trochlear articular cartilage lesions. For this reason, clinicians should utilize all MRI planes to evaluate the articular cartilage of the trochlea. Future studies should focus on improving MRI techniques for detecting and characterizing cartilage lesions of the trochlea.
Rotor Structure on Reducing Demagnetization of Magnet and Torque Ripple in a PMa-synRM With Ferrite Permanent Magnet

A rotor structure of Permanent Magnet (PM)-assisted synchronous Reluctance Machine (PMa-synRM) with ferrite PM is presented in this paper, in which the irreversible demagnetization of PM and torque ripple are reduced. The bypass rib is proposed and added to the flux barrier of the rotor. The path for the demagnetizing field of armature windings is provided and the torque ripple can also be reduced by the bypass rib. Based on the 2-D finite-element analysis (FEA), the location and dimensions of the bypass rib are investigated and improved. A PMa-synRM with the proposed rotor structure is designed and compared with that using a conventional rotor. The field distributions for different currents are studied to explain the principle of the bypass rib. The results show that the PMa-synRM with the proposed rotor achieves higher antidemagnetization ability and lower torque ripple.
Identification of Pulmonary Lung Nodules Ce ntroid on CT Scans Using Moment Analysis

Lung cancer becomes one of the diseases with a high mortality rate. The identification of lung nodules as a representation of lung cancer becomes a very important part. One method of identifying lung nodules is to segment the lung areas. Increased performance of segmentation is a thing that developed by experts. This research is a preliminary study that develops one of the lung nodule identification techniques by determining its centroid. This centroid will give information how accurate the segmentation method is. The best segmentation method so far gives the smallest value shift of centroid to the ground truth. In determining the central point of the lung nodule, lung cancer CT image is segmented using three different methods namely edge active contour, active base region contour and growing region. The segmented area is then analyzed using a moment analysis that expressed by the center of mass. Center of mass represents centroid coordinates. The coordinate shift between ground truth and suspected nodule coordinates expressed by the standard error value and euclidian distance in pixels. The smallest shift gives a smallest standard error and Euclidean distance. The results showed that the region growing method resulted in 0.004327182 for standard error value and 3.821609503 for a euclidian distance which is smallest than others segmentation methods.
Improving EDCA for Efficient Channel Access in Vehicular Communications

With the development of artificial intelligence, IoT, and high-performance computing, modern vehicles are becoming more and more connected and autonomous, which is deeply dependent on wireless communications for information exchange and sharing between the vehicle and the surrounding environment. IEEE 802.11p is a standard protocol designed for low latency and high mobility of vehicular communications. However, the Enhanced Distributed Channel Access (EDCA) of 802.11p with static access category queue allocation and fixed backoff parameter setting cannot meet the requirement of efficient vehicular channel access. In light of this, we improve EDCA by prioritizing messages and adjusting backoff window values dynamically to guarantee the timely delivery of real-time messages. Simulation results show that the proposed mechanism has higher throughput and lower transmission delay for efficient vehicular communications.
Accurate and Scalable Image Clustering Based on Sparse Representation of Camera Fingerprint

Clustering images according to their acquisition devices is a well-known problem in multimedia forensics, which is typically faced by means of camera sensor pattern noise (SPN). Such an issue is challenging since SPN is a noise-like signal, hard to be estimated, and easy to be attenuated or destroyed by many factors. Moreover, the high dimensionality of SPN hinders large-scale applications. Existing approaches are typically based on the correlation among SPNs in the pixel domain, which might not be able to capture intrinsic data structure in the union of vector subspaces. In this paper, we propose an accurate clustering framework, which exploits linear dependences among SPNs in their intrinsic vector subspaces. Such dependences are encoded under sparse representations, which are obtained by solving an LASSO problem with non-negativity constraint. The proposed framework is highly accurate in a number of clusters’ estimation and image association. Moreover, our framework is scalable to the number of images and robust against double JPEG compression as well as the presence of outliers, owning big potential for real-world applications. Experimental results on Dresden and Vision database show that our proposed framework can adapt well to both medium-scale and large-scale contexts and outperforms the state-of-the-art methods.
Exploiting Posit Arithmetic for Deep Neural Networks in Autonomous Driving Applications

This paper discusses the introduction of an integrated Posit Processing Unit (PPU) as an alternative to Floating-point Processing Unit (FPU) for Deep Neural Networks (DNNs) in automotive applications. Autonomous Driving tasks are increasingly depending on DNNs. For example, the detection of obstacles by means of object classification needs to be performed in real-time without involving remote computing. To speed up the inference phase of DNNs the CPUs on-board the vehicle should be equipped with co-processors, such as GPUs, which embed specific optimization for DNN tasks. In this work, we review an alternative arithmetic that could be used within the co-processor. We argue that a new representation for floating point numbers called Posit is particularly advantageous, allowing for a better trade-off between computation accuracy and implementation complexity. We conclude that implementing a PPU within the co-processor is a promising way to speed up the DNN inference phase.
Lightweight, Integrated Data Deduplication for Write Stress Reduction of Mobile Flash Storage

Recently, smartphone users are becoming reluctant to replace their devices, so providing reliable data storage throughout the increasingly long smartphone replacement cycle is an imminent challenge. We advocate an integrated approach to data deduplication, which involves the host I/O stack and the storage firmware, to reduce the write stress of mobile storage for lifespan extension. First, we identified that the SQLite library produces a large amount of copy-induced duplicate data, which can be fully revealed to the mobile storage through proper file format alignment. Second, inspired by the short flash read latency, we present a firmware-based method to efficiently identify duplicate data without any hardware support. Third, we enhance the flash garbage collector with the awareness of data deduplication. Based on a selection of popular mobile applications, our simulation results show that prior methods, which employ data compression or differential logging, are ineffective for encrypted mobile storage. Compared to flash management without deduplication, our lightweight, integrated approach reduced the total flash-block erase count by 48.7%, average write response time by 43.8%, and total energy consumption by 25.1% on average under the workloads of all the selected mobile applications.
The Relevance of Container Monitoring Towards Container Intelligence

The Docker platform has become the leading containerization-enablement solution these days. Containers emerge as the most appropriate way for agile software development, packaging, shipping and deployment across heterogeneous IT environments (local servers, cloud servers, personal devices, etc.). The software portability being achieved through the containerization movement is being applauded by business houses and IT industry professionals. Containers are being touted as the viable alternative to virtual machines, which are verbose and bulky. The faster provisioning, the real-time scalability, and quick transmission of application containers are being pronounced as the key differentiators of the containerization paradigm. With a growing array of enabling tools from various product vendors, the realization of containerized cloud environments is picking up fast. The DevOps concept gets strengthened with the faster maturity and stability of the Docker platform. As a result, IT can ship faster and run the same application, unchanged, on laptops, data centre VMs, and any cloud. In this paper, the integration of Docker containers with the monitoring tool “Sysdig” (https://sysdig.com/) is explained. This integration simplifies and streamlines the container monitoring, measurement and management.
Towards the New Concept of Smart Roads: Regulatory Framework and Emerging Projects Overview

The evolution of the transportation sector enabled by the diffusion of smart roads represent a valuable opportunities for all the stakeholders involved, starting from and users (i.e. drivers) themselves and companies at the different stages of the value chain. Such evolution is driven not only by technology development, but also by the regulatory framework at national and supranational level. The analysis of some first experiences related to the implementation of smart roads concepts shows a certain level of heterogeneity among analyzed projects, none of them realized in Italy. In order to exploit such an opportunity, the Italian regulatory framework regarding transportation systems in Italy has been analyzed. It emerges that some improvements are required compared to the European context, e.g. the definition of technological standards.
Research for Enhancing Weak Multi-Target's Tracks Based on Radon-Clean Algorithm

Due to the complexity of oceanic channel, the bearing-time record of weak targets in passive sonar is vague and difficult to observe effectively. In this paper, an algorithm of enhancing multi-Target's Tracks based on Radon transform and Clean algorithm is proposed. Radon-Clean The algorithm detects the peaks in the Radon transform domain one by one until all the peaks in the image are detected and then the inverse Radon transform is used to obtain an enhanced tracks, so this algorithm has strong noise suppression capability, the algorithm of direct threshold filtering in the Radon transform domain has the defect that if the threshold is chosen to be larger, the weaker target tracks will be filtered out, if the threshold is chosen to be smaller, a large amount of noise will be introduced. Algebraic Radon transform using FFT and matrix operations to overcome the large amount of computational back-projection Radon transform is conducive to real-time implementation of the sonar system. The results of simulation and the sea trial data show that this algorithm can enhance the tracks of multiple weak targets in the low signal-to-noise ratio bearing-time record which has strong interference.
Six Years (2012-2018) of Researches on Catalytic EZH2 Inhibitors: The Boom of the 2-Pyridone Compounds.

Enhancer of zeste homolog 2 (EZH2), the catalytic subunit of the Polycomb repressive complex 2 (PRC2), catalyzes the methylation of lysine 27 of histone H3 (H3K27) up to its trimethylated form (H3K27me), inducing by this way block of transcription and gene silencing. High levels of H3K27me3 have been found in both hematological malignancies and solid cancers, due to EZH2 overexpression and/or EZH2 mutation. From 2012, a number of highly potent and selective catalytic inhibitors of EZH2 have been reported, almost all bearing a 2-pyridone group in their structure. Typically, 2-pyridone inhibitors are selective for EZH2 over other methyltransferases, and some of them are specific for EZH2 over EZH1, others behave as dual EZH2/EZH1 inhibitors. The 2-pyridone moiety was crucial for the enzyme inhibition, as revealed later by crystallographic studies because it occupies partially the site for the co-substrate SAM (or the by-product, SAH) in the binding pocket of the enzyme, accounting for the SAM-competitive mechanism of action displayed by all the 2-pyridone inhibitors. The 2-pyridone warhead is linked to a support substructure, that can be either a bicyclic heteroaromatic ring (such as indazole, see for instance EPZ005687 and UNC1999, or indole, see for instance GSK126, EI1, and the more recent CPI-1205) or a simple monocyclic (hetero) aromatic ring (tazemetostat, MC3629, (R)-OR-S1/2), eventually annulated with the amide chain carrying the 2-pyridone group (3,4-dihydroisoquinoline-1(2H)-ones). Different substitutions at the support moiety influence the pharmacokinetics and pharmacodynamics of the compounds as well as their water solubility. In cancer diseases, the first reported 2-pyridone inhibitors displayed high antiproliferative effects in vitro and in vivo in lymphomas characterized by mutant EZH2 (such as Y641N), but the most recent compounds exert their anticancer activity against tumors with wild-type EZH2 as well. The dual EZH2/1 inhibitors have been recently reported to be more effective than EZH2 selective inhibitors in specific leukemias including leukemias cancer stem cells.
Accuracy of Electrical Feeder Models for Distribution Systems Analysis

Low voltage distribution systems are coming to the fore mainly due to the massive presence of distributed generation. The challenges resulting from these new actors demand from reliable network models, which implies knowing detailed parameters not usually provided by power and electrical manufacturers or utility designers at these voltage levels. This paper performs a comparative assessment of the accuracy provided by several models customarily adopted for low voltage feeders in the analysis of distribution systems. The models are evaluated for different levels of unbalance and distributed generation penetration, which are among the most critical factors potentially affecting their accuracy. Simulation results show that the choice of a sufficiently accurate model, as well as the influence of the neutral grounding system (isolated, impedance earthed or solidly earthed neutral) may become critical in certain cases. The main conclusion is that the lack of detailed feeder models can avoid identifying voltages and currents out of limits.
Blog Response Volume Prediction Using Adaptive Neuro Fuzzy Inference System

Due to the wide streaming blogs over the social media, blog volume automation has become indispensable for the analysis of blog popularity. As a rule base driven method, Adaptive Neuro Fuzzy Inference System has gained popularity in various prediction tasks for its efficiency and ease of implementation. In this paper, ANFIS model have been explored to predict the volume of blog responses. With the help of this rule based machine learning technique, an accurate non-linear regression model is developed which outperforms other state-of-art techniques such as ANN, SVM and Random Forest using UCI BlogFeedback dataset.
Phenotyping Ex-Combatants From EEG Scalp Connectivity

Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this paper, we propose a support vector machine-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.
CT-Blocks Analyser: Analysing CT-Blocks Projects

This paper introduces CT-Blocks Analyser which is used for analysing computational thinking concepts of CT-Blocks project activities. This uses the emerging techniques of educational data mining and learning analytics to automate the instructional approach for CT-Blocks projects. CT-Blocks aims to infuse computational thinking within K-12 curriculum. The big data toolkit called, CT-Blocks Analyser which is specifically developed for CT-Blocks, a visually appealing programming environment for beginners; a block based programmable platform interconnected with more features in which students enjoy the learning process and it tries to make the users master in computational thinking.
Assessment of neuropsychological performance in Mexico City youth using the Cambridge Neuropsychological Test Automated Battery (CANTAB)

ABSTRACT Background: The Cambridge Neuropsychological Test Automated Battery (CANTAB) is a computerized tool used to measure cognitive function in diverse populations and is sensitive for assessing developmental changes in children. Although CANTAB has been used in several countries, its applicability in a Mexican child population is unknown. This study examined developmental trends on CANTAB in a large sample of urban Mexico City youth and tested the hypothesis that their performance would be similar to a large US normative sample. Method: As part of a birth cohort, Early Life Exposures in Mexico to Environmental Toxicants, 826 children, ages 5–15 years, completed CANTAB. Subtests measured planning (Stockings of Cambridge; SOC), short-term memory (Delayed Matching to Sample; DMS), sustained attention (Rapid Visual Information Processing; RVP), ability to match visual stimuli (Match to Sample Visual Search; MTS), flexibility (Intra-extra Dimensional Set Shift; IED), and response inhibition (Stop Signal Task; SST). Determinants of performance on the CANTAB, including age, maternal/child IQ, and sex, were analyzed using Spearman correlation and Welch t tests. Equivalence testing compared performance with existing norms. Results: Performance improved with age on all measures. Child IQ was mildly associated with measures of memory and attention but not executive functioning, consistent with US norms. Maternal IQ was not associated with any outcomes, and males performed better on IED. Mexican norms were comparable to US norms on almost all outcomes, with the exception of a short-term visual recognition memory task (DMS). Conclusions: This study provides the largest normative data for CANTAB performance in a community sample of Mexican youth. Findings demonstrate the expected maturational effects of executive function, specifically in cognitive shifting and inhibition. Levels of executive function performance demonstrated by a Mexican sample were consistent with normative values reported in US youth. These findings, as well as expected associations with child IQ, indicate high applicability of CANTAB for Mexican youth in neurobehavioral studies.
The Impact of Web-Based Video Lectures on Learning in Nursing Education: An Integrative Review

AIM The purpose of this review was to explore what is known about the efficacy of web-based video lectures (WBVLs). BACKGROUND Nurse educators have embraced innovations such as online, flipped, and blended learning. Such innovations are enhanced by WBVLs, but there has been limited research on effective design. METHOD A systematic search was conducted to identify studies of the efficacy of WBVL in nursing education between 2005 and 2017. An established framework was used to analyze the quality of studies. RESULTS Findings from 11 studies revealed that students were satisfied with WBVL as supplement to or replacement for in-person lectures. Studies were hampered by lack of theory, methodological concerns, and an absence of experimental trials. CONCLUSION Findings suggest that the efficacy of WBVL may be equivalent to or better than in-person lectures. Future studies should be guided by pedagogical theory and focus on comparing design practices for WBVL.
CORGIDS: A Correlation-based Generic Intrusion Detection System

Cyber-physical systems (CPS) consist of software and physical components which are knitted together and interact with each other continuously. CPS have been targets of security attacks due to their safety-critical nature and relative lack of protection. Specification based intrusion detection systems (IDS) using data, temporal, data temporal and time, and logical correlations have been proposed in the past. But none of the approaches except the ones using logical correlations take into account the main ingredient in the operation of CPS, namely the use of physical properties. On the other hand, IDS that use physical properties either require the developer to define invariants manually, or have designed their IDS for a specific CPS. This paper proposes CORGIDS, a generic IDS capable of detecting security attacks by inferring the logical correlations of the physical properties of a CPS, and checking if they adhere to the predefined framework. We build a CORGIDS-based prototype and demonstrate its use for detecting attacks in the two CPS. We find that CORGIDS achieves a precision of 95.70%, and a recall of 87.90%, with modest memory and performance overheads.