Miracle-dz/UniFAR-FTUs huggingface dataset:
3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings

7 Tesla (7T) magnetic resonance imaging holds great promise for improved visualization of the human brain for clinical purposes. To assess whether 7T is superior regarding localization procedures of small brain structures, we compared manual parcellations of the red nucleus, subthalamic nucleus, substantia nigra, globus pallidus interna and externa, created on a commonly used clinical anisotropic clinical 3T with an optimized isotropic (o)3T and standard 7T scan. While the clinical 3T MRI scan did not allow delineation of an anatomical plausible structure due to its limited spatial resolution, optimized 3T and 7T parcellations could be directly compared. We found that 7T outperformed the o3T MRI as reflected by higher Dice scores, which were used as a measurement of interrater agreement for manual parcellations on quantitative susceptibility maps. This increase in agreement was associated with higher contrast to noise ratios for smaller structures, but not for the larger globus pallidus segments. Additionally, control-analyses were performed to account for potential biases in manual parcellations by assessing semi-automatic parcellations. These results did not confirm increased Dice scores for 7T, although they revealed a higher consistency for structure volumes for 7T compared to optimized 3T, potentially indicative of more agreement with increased field strength. Together these results indicate the importance of the use of isotropic voxels with both 3T and 7T, and the potential for clinical applications.
VRK-1 extends life span by activation of AMPK via phosphorylation

A conserved protein kinase VRK-1 increases life span by activating a key energy sensor AMPK in the roundworm C. elegans. Vaccinia virus–related kinase (VRK) is an evolutionarily conserved nuclear protein kinase. VRK-1, the single Caenorhabditis elegans VRK ortholog, functions in cell division and germline proliferation. However, the role of VRK-1 in postmitotic cells and adult life span remains unknown. Here, we show that VRK-1 increases organismal longevity by activating the cellular energy sensor, AMP-activated protein kinase (AMPK), via direct phosphorylation. We found that overexpression of vrk-1 in the soma of adult C. elegans increased life span and, conversely, inhibition of vrk-1 decreased life span. In addition, vrk-1 was required for longevity conferred by mutations that inhibit C. elegans mitochondrial respiration, which requires AMPK. VRK-1 directly phosphorylated and up-regulated AMPK in both C. elegans and cultured human cells. Thus, our data show that the somatic nuclear kinase, VRK-1, promotes longevity through AMPK activation, and this function appears to be conserved between C. elegans and humans.
A Dual Circularly Polarized Patch Antenna With High Isolation for MIMO WLAN Applications

A dual circularly polarized (CP) stacked patch antenna for Multiple-Input Multiple-Output (MIMO) WLAN applications (2.4-2.485 GHz) is presented, which exploits a square ring slot feeding technique. The antenna occupies an overall area of 96 mm <inline-formula> <tex-math notation="LaTeX">$\times96$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times39$ </tex-math></inline-formula> mm, when also including the metal reflector. The 7-dBic gain antenna prototype presents an axial ratio lower than 2.5 dB as well as a port isolation higher than 25 dB. An envelope correlation coefficient (ECC) lower than 0.02 is also achieved among the two ports, thanks to the polarization orthogonality. Moreover, the stacked patch has been duplicated and used in a two-antenna system for a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> MIMO WLAN which fits an overall volume of 157 mm <inline-formula> <tex-math notation="LaTeX">$\times96$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times39$ </tex-math></inline-formula> mm. Antenna performance in terms of S-Parameters, gain, axial ratio, ECC and channel capacity are compared and discussed, together with the effect of the inter-element distance.
LVQ Neural Network for Online Identification of Power System Network Branch Events

The transmission network is the backbone of the power system. Fast power system network branch event identification is required to maintain security of the power system. Phasor measurement units (PMUs) provide synchronized voltage and current phasor measurements to a centralized phasor data concentrator (PDC). Thus, PMU measurements can be used for online branch event identification. A fast and efficient approach is required to ease the computational load. Learning vector quantization (LVQ) neural network algorithm is presented in this paper as a solution to identify branch events. The power system is highly distributed. Thus, a cellular computational network (CCN) is utilized to distribute a single large LVQ's computational load. The IEEE 12-bus benchmark power system is used to illustrate the CCN-LVQ approach. The power system is simulated on a realtime digital simulator. CCN-LVQ approach is shown to be fast, efficient and comparatively accurate for transmission network branch event identification.
An Approach for Estimation of Packet Delivery Time on SpaceFibre Networks

The packet delivery time (minimal, average, maximal) is one of important parameters for onboard aerospace networks. The SpaceFibre standard developed specially for onboard aerospace networks, support guaranteed Quantity of Service (QoS) such as priorities, reserved bandwidth, scheduling. These mechanisms implemented on Data Link. They provide division of physical link resources between Virtual Networks. The wormhole routing is used on the Network Layer. In this paper we considered existing methods for estimating packet delivery time in networks with wormhole routing. We considered specific of the SpaceFibre standard because of which these method can not be directly used for this standard. In the paper we proposed the evaluation method adopted for the SpaceFibre standard
Hybrid Reinforcement Learning Control for a Micro Quadrotor Flight

This letter presents a combination of reinforcement learning (RL) and deterministic controllers to learn a quadrotor control. Learning the quadrotor flight in a standard RL approach requires many iterations of trial and error, which may bring about risky exploration and battery consumption. In this letter, we integrate a classical controller such as PD (proportional and derivative) or LQR (linear quadratic regulator) with a RL policy using their linear combination. The proposed method is not only simple to use, but also fast in learning convergence. When the algorithm is evaluated for a quadrotor trajectory tracking by means of a velocity control for both simulation and experiment, it demonstrates the faster convergence rate and better control performance in comparison with an existing rapid model-based RL method.
Comparative cytogenetics of three populations from the Rhamdia quelen species complex (Siluriformes, Heptapteridae) in two Brazilian hydrographic basins

Abstract Among the more than 30 families of the order Siluriformes, Heptapteridae is composed of 189 species distributed into 24 genera. Rhamdia, which has wide distribution throughout the Neotropical region, presents only 11 valid species, with 8 being in the Brazilian territory. Rhamdia quelen is the only species considered as widely distributed in almost all Brazilian hydrographic basins. It is a Neotropical fish species with a confusing taxonomical history. Classic and molecular cytogenetics data for three populations of this species from two large Brazilian hydrographic basins (Paraná and Araguaia) are presented here. The diploid number found for the three analyzed populations was 58 chromosomes, but with distinct karyotypic formulae. The presence of B chromosomes was detected in the two Araguaia River populations with intra- and interindividual variation. C-banding evidenced little heterochromatin in the three analyzed populations. FISH with 18S rDNA probes evidenced a single chromosome pair bearing this site, confirming the presence of simple NORs, as visualized through silver nitrate staining. The site of 5S rDNA was observed in only one pair of chromosomes, but differing in the marked pair and their location. Based in the differences of the karyotypic formulae and rDNA 5S found between populations on this study and many others available in the literature, it is suggested that this group represents a species complex, and that a new and detailed taxonomical review is necessary.
Towards Smart City through Virtualized and Computerized Car parking System using Arduino in the Internet of Things

Nowadays, IoT offering many smart systems to internet users. This smart system has played a vital role in many real-time applications like medicine, cultivation, and security. This paper proposes a new architecture with an algorithm for better to park the vehicle further in this document, examine the parking availability status of the registered and / or reserved vehicle to park the vehicle and examine the sensor-based intelligent parking system. Using mobile applications, the system will help the user detect the parking space in the user’s current arena. The IoT-based smart car hosts heterogeneous sensors, each of which has unique assiduously. The smart parking system requires an Arduino microcontroller, a cloud server, an ultrasonic sensor, and an IR sensor, enough to efficiently park a vehicle in the assigned slot. The ultrasonic sensor can check the availability of the slot in the parking area. Similarly, the IR sensor is used to control the entry and exit of the vehicle. The main intention of the car parking system is to distribute the slot in the vehicle (user) in the parking area without becoming rigid for vehicle parking. At last, this paper explains and also shows a general description of the intelligent system.
Spectrum-Based Fault Localization for Concurrent Programs

Traditional fault localization techniques are well equipped for identifying faults in sequential programs. Faults in concurrent programs are caused due to interleaving of the threads during run-time. Finding the faults in concurrent programs are more difficult in comparison with sequential programs. In this paper, we have proposed a fault localization framework based on spectrum-based fault localization for concurrent programs to find the faults occurs during execution of multiple threads. The technique finds the most suspicious function or branch for effective fault localization. We have tested the proposed the technique with two different concurrent example programs to check the accuracy of the technique.
A Mobility Stress Response Model of FinFET: Silicon vs Germanium

FinFET, which could be applied to sub 22 nm technology nodes, is the novel 3D transistor device structure to effectively inhibit short channel effect for the continuous microminiaturization of transistor components. This study focuses on the comparison of silicon and germanium, as the channel material, to FinFET for calculating carrier distribution and carrier mobility on inversion layer as well as the stress response. A mobility stress response model (piezomobility model) which could rapidly and accurately predict carrier mobility changes under different stress conditions is also investigated.
Novel Approach Using Deep Learning for Intrusion Detection and Classification of the Network Traffic

A variety of challenges are being faced nowadays of network intrusion which is continually increasing. These are due to vulnerabilities in software, hardware, and network protocols. Therefore, stronger IDS is required; ML and DM have further strengthened the IDS technology. At the same time threat has also become more sophisticated. Now overfitting and structured optimization techniques are used in IDS. In this paper, we proposed a deep neural network-based IDS. The DL based system monitors the traffic coming from authentic and non-authentic sources. It classifies and segregates malicious traffic with accuracy up to 99.78used for experimentation and comparative analysis with previous techniques shows encouraging results.
Design, Verification, Test and In-Field Implications of Approximate Computing Systems

Today, the concept of approximation in computing is becoming more and more a “hot topic” to investigate how computing systems can be more energy efficient, faster, and less complex. Intuitively, instead of performing exact computations and, consequently, requiring a high amount of resources, Approximate Computing aims at selectively relaxing the specifications, trading accuracy off for efficiency. While Approximate Computing gives several promises when looking at systems' performance, energy efficiency and complexity, it poses significant challenges regarding the design, the verification, the test and the in-field reliability of Approximate Computing systems. This tutorial paper covers these aspects leveraging the experience of the authors in the field to present state-of-the-art solutions to apply during the different development phases of an Approximate Computing system.
Zur polychaeten‐fauna von peru

Zusammenfassung Der vorliegende Bericht enthalt die ersten systematischen Notizen fur eine umfangreiche Arbeit uber die Okologie der Polychaeten Perus mit besonderer Berucksichtigung des Humboldt‐Stromes, die bereits begonnen und im Humboldt‐Jahr 1959 fortgefuhrt werden soll. Das hier bearbeitete Material besteht aus 16 Gattungen und 19 Arten, von denen eine Gattung und 16 Arten neu fur die Wissenschaft sind. Die Typen und Paratypoide der beschriebenen Arten finden sich in der Sammung der Verfasserin.
Automatic Keywords Extraction Based on Co-Occurrence and Semantic Relationships Between Words

Automatic keywords extraction is a method that extracts words or phrases from a document which can express the main idea of the document. In this paper, we propose an unsupervised keywords extraction framework for individual documents, which improves the keywords extraction from two aspects. In the step of candidate keywords selection, we use the methods of removing the stopwords, regular matching, and length filtering to reduce the number of candidate keywords, but improve the quality. In the step of scoring words, we use word co-occurrence, semantic relationships (WordNet, Word Embedding, Normalized Google Distance), and three ways to combine word co-occurrence and semantic relationships to measure the weight of edges in the graph model. In experiments, we use Precision, Recall, and F1-measure values as evaluation criteria to compare all keywords extraction methods we proposed with other strong baseline methods in two datasets. According to the results of experiments, methods under our proposed framework achieve good results. We verify that the methods of using both word co-occurrence and semantic relationships have a better effect on keywords extraction than using co-occurrence or semantic relationships only. At the same time, we also find that for the keywords extraction of individual documents, the method of using co-occurrence between words has a better effect than semantic relationships.
Sub-THz Circularly Polarized Horn Antenna Using Wire Electrical Discharge Machining for 6G Wireless Communications

A novel compact high-efficient circularly polarized conical horn antenna is realized with Wire Electrical Discharge Machining (EDM) technique operating at 300 GHz for 6G wireless communications is presented. The proposed antenna structure consists of three main components; a waveguide feed, a circular polarizer disk, and a conical horn. The waveguide feed is a standard WR-03 rectangular waveguide which feeds the whole structure. The circular polarizer disk is comprised of a crossed slot with unequal lengths which are perpendicular to each other. By adjusting the lengths of both slots, two orthogonal modes are excited in the antenna to produce circular polarization. The conical horn is mounted on the circular polarizer disk. The proposed antenna was designed and optimized using CST Microwave Studio and is validated experimentally by an inexpensive direct Wire-cutting EDM technique over the WM-860 frequency band. The fabricated circularly polarized horn antenna achieved a wide impedance bandwidth of 20 % from 270 GHz to 330 GHz with a reflection coefficient ≤ −15 dB. The measured results show that the 3 dB axial ratio of the fabricated antenna prototype has a bandwidth of 7 GHz from 309 GHz to 316 GHz with a minimum axial ratio of 1.15 dB at 312 GHz. The measured normalized radiation patterns show good agreement with simulated results for different frequencies in both the elevation and azimuth planes at broadside direction with good symmetry in both planes.
Prolactinomas: Prognostic Factors of Early Remission After Transsphenoidal Surgery

Background and Objective: Most patients with prolactinomas receive pharmacological treatment only, resulting in limited research on the predictors of successful prolactinoma surgery. In this study, we analyzed whether early postoperative serum prolactin concentrations and selected tumor characteristics could predict early, hormonal remission after removal of prolactinomas. Methods: We prospectively enrolled 48 consecutive patients with prolactinomas who underwent transsphenoidal resection performed by the same surgeon. Early remission, defined as a lack of hyperprolactinemia symptoms and normalization of serum prolactin concentration, was ascertained in all patients at 3 months. We evaluated the invasiveness of prolactinomas on the Knosp grading scale and measured serum prolactin concentrations on the first postoperative day. Routine immunohistochemical analysis, evaluation for plurihormonality, and assessment of the Ki-67 proliferation index (<3 or ≥3% of positive nuclei) were performed in all tumor samples. Results: Of 48 patients, 38 (79%) achieved early biochemical remission at 3 months. Patients in early remission at 3 months had lower serum prolactin concentrations on the first postoperative day than patients with recurrent or persistent hyperprolactinemia (p < 0.001). Using univariate logistic regression, larger maximum tumor diameter (p = 0.014), higher Knosp grade (p < 0.001), and plurihormonality predicted remission at 3 months (p = 0.021). However, using multivariate stepwise logistic regression, only the Knosp grade remained significant (p < 0.001). Conclusions: Radiological assessment of prolactinoma invasiveness (Knosp grades) and early postoperative serum prolactin concentrations are important predictors of early remission following transsphenoidal prolactinoma resection.
Unsupervised Genre-Based Multidomain Sentiment Lexicon Learning Using Corpus-Generated Polarity Seed Words

Sentiment lexicon learning is of paramount importance in sentiment analysis. One of the most considerable challenges in learning sentiment lexicons is their domain-specific behavior. Transferring knowledge acquired from a sentiment lexicon from one domain to another is an open research problem. In this study, we attempt to address this challenge by presenting a transfer learning approach that creates new learning insights for multiple domains of the same genre. We propose an unsupervised sentiment lexicon learning methodology scalable to new domains of the same genre. Incremental learning and the methodology learn polarity seed words from corpora of multiple automatically selected source domains. This process then transfers its genre-level knowledge of corpus-learned seed words to the target domains. The corpus-learned seed words are used for sentiment lexicon generation for multiple target domains of the same genre. The sentiment lexicon learning process is based on the latent semantic analysis technique and uses unlabeled training data from the source and target domains. The experiment was performed using 24 domains of the same genre, i.e., consumer product review. The proposed model displays the best results using standard evaluation measures compared with the competitive baselines. The proposed genre-based unsupervised approach achieves a maximum accuracy of 86% and outperforms methods recently presented in the literature.
Next Generation Warehouse through disruptive IoT Blockchain

Internet of Things (IoT) based solutions bringing disruptions across many domains. Logistics particularly warehouse could be leveraged by the IoT. Warehouse has many operational activities like asset management, safety management, staff tracking, staff crowing detection, way finding, ULD entry and exit monitoring and location identification are few of the areas which would be optimized and realized using IOT technologies. Cloud centric solutions for IoT adding values to existing mobility and cloud centric applications. As the worlds are moving towards more connecting solutions, there is a need for more decentralized approach as compared to cloud centric solutions. This paper discusses how to address limitations and caveats of cloud based IoT solutions using blockchain, feature of edge computing to enable better decentralized approach and a hybrid model for end to end data warehouse solution. With better field devices like actuators/sensors and new protocols coming up, devices would be able to more independently. In this paper we would also provide a comparative study of various non-functional attributes like offline accessibility, performance, response time, reusability, security etc.
Single-Nucleotide Polymorphism Array Technique Generating Valuable Risk-Stratification Information for Patients With Myelodysplastic Syndromes

Background: Chromosomal abnormalities play an important role in the diagnosis and prognosis of patients with myelodysplastic syndromes (MDSs). The single-nucleotide polymorphism array (SNP-A) technique has gained popularity due to its improved resolution compared to that of metaphase cytogenetic (MC) analysis. Methods: A total of 376 individuals were recruited from two medical centers in China, including 350 patients and 26 healthy individuals. Among these patients, 200 were diagnosed with de novo MDS, 25 with myeloproliferative neoplasm (MPN), 63 with primary acute myeloid leukemia (AML), and 62 with idiopathic cytopenia of undetermined significance (ICUS). We evaluated the significance of abnormal chromosomes detected by SNP-A in the diagnosis and prognosis of MDS-related disorders. Results: (1) When certain chromosomal abnormalities could not be detected by conventional MC methods, these abnormalities could be detected more efficiently by the SNP-A method. With SNP-A, the detection rates of submicroscopic or cryptic aberrations in the MDS, MPN, and AML patients with normal MC findings were 32.8, 30.8, and 30%, respectively. (2) The chromosomal abnormalities detected by SNP-A had a very important value for the prognosis of patients with MDSs, especially in the low-risk group. The survival of patients with abnormal chromosomes detected by SNP-A was significantly lower than that of patients with no detected chromosomal abnormalities; this difference was observed in overall survival (OS) (P = 0.001) and progression-free survival (PFS) [24 months vs. not reach (NR); P = 0.008]. The patients with multiple chromosomal abnormalities detected by SNP-A had an inferior prognosis, and SNP-A abnormalities (≥3 per patient) were found to be an independent predictor of poor prognosis in patients with MDSs [hazard ratio (HR) = 2.40, P = 0.002]. (3) Patients with ICUS may progress to myeloid malignancies, but most patients often maintain a stable ICUS status for many years without progression. An ICUS patient found to have an MDS-related karyotype would be rediagnosed with MDS. SNP-A can efficiently detect chromosomal abnormalities, which would be important for assessing the evolution of ICUS. In our study, 17 ICUS patients with SNP-A-detected abnormalities developed typical MDSs. Conclusions: SNP-A can help evaluate the prognosis of patients with MDSs and better assess the risk of disease progression for patients with ICUS.