Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Sentinel Node Biopsy in Breast Cancer Patients Undergoing Skin Sparing Mastectomy and Immediate Autologous Reconstruction

Background: Intraoperative frozen section examination of the sentinel node in breast cancer patients is associated with a high number of incorrect negative results with the sentinel node becoming positive in the permanent examination and necessitating a secondary axillary lymph node dissection. A reoperation of the axilla following skin-sparing mastectomy and immediate autologous tissue reconstruction may compromise the vascular pedicle of the flap and should be avoided. Methods: Eighty breast cancer patients underwent skin-sparing mastectomy with immediate autologous reconstruction and sentinel node biopsy followed by axillary lymph node dissection irrespective of the result of the frozen section of the sentinel node. The goal of the study was to identify a subgroup of patients with incorrect negative sentinel node(s) in the frozen section who may forego a secondary axillary lymph node dissection due to a low risk of positive nonsentinel nodes. Results: Frozen section examination of the sentinel node was negative in 58 patients and positive in 22 patients. Permanent histologic examination revealed tumor in 13 of 58 (22.4 percent) sentinel node(s) found negative in the frozen section. None of these 13 patients showed positive nodes in the axillary specimen, whereas nine of 22 patients with their metastases in the sentinel node found through intraoperative frozen section examination had additional positive nonsentinel node(s) (p = 0.001). Conclusions: Patients with incorrect negative sentinel node(s) found in the frozen section examination had a significantly decreased risk for additional positive nonsentinel node(s) compared with patients with sentinel node metastases found in the frozen section. However, to avoid a secondary axillary lymph node dissection, the authors suggest performing sentinel node biopsy before mastectomy under local anesthesia to have the permanent result of the senti-nel node available before a planned reconstruction.
Promoting Computational Thinking Skills in Primary School Students to Improve Learning of Geometry

Computational thinking is a necessary skill for human life. However, promoting this competency in primary school students sometimes is hard and difficult for instructors. This article proposes a learning approach based on the use of a platform as part of a strategy of computational thinking to enhance learning of geometry. A case study was performed with primary school children from fourth grade at the Comfacor School (Montería-Colombia). The aim of the study was to analyze the effects of the approach in the children’s motivation and performance.
Novel Approach for Enhanced Reduction of SAR in a Mobile Phone Antenna Using High Impedance FSS

In this paper, we investigate the performance of periodic reflecting surfaces for reducing the energy absorption of human head from a mobile phone antenna. A six-layered human head model and a conventional planar inverted-F antenna (PIFA) designed to operate at the IMT-LTE band (1.8-2.3 GHz) are chosen for specific absorption rate (SAR) analysis. The reflector is placed between the human head model and the antenna to study the amount of SAR reduction. Initially, a monolayer frequency selective surface (FSS) is modelled on an FR4 substrate with overall dimensions of $0.084\lambda_{0}\times 0.084\lambda_{0}\times 0.011\lambda_{0}$ and an SAR reduction by 64.7% was observed. To overcome few disadvantages of these conventional reflecting surfaces, a high impedance surface (HIS) which is a special case of FSS is designed for SAR reduction with an overall dimensions of $0.21\lambda_{0}\times 0.21\lambda_{0} \times 0.011\lambda_{0}$. ($\lambda_{0}$ is the free space wavelength at 2.1 GHz). The proposed HIS showed 85.3% reduction in SAR, when compared with the SAR level observed in a conventional mobile phone and head phantom setup.
Project Based Learning: Predicting Bitcoin Prices using Deep Learning

Project based learning is the methodology in which projects drive knowledge and is used in dedicated subjects without negotiating the coverage of the required technical material. This paper discusses the scheme and delivery of project based learning in computer science engineering as major project which adopts undergraduate creativities and emphasizes on real-world, open-ended projects. These projects foster a wide range of abilities, not only those related to content knowledge or technical skills, but also practical skills. The goal for this innovative undergrad project is to show how a trained machine model can predict the price of a cryptocurrency if we give the right amount of data and computational power. It displays a graph with the predicted values. The most popular technology is the kind of technological solution that could help mankind predict future events. With vast amount of data being generated and recorded on a daily basis, we have finally come close to an era where predictions can be accurate and be generated based on concrete factual data. Furthermore, with the rise of the crypto digital era more heads have turned towards the digital market for investments. This gives us the opportunity to create a model capable of predicting crypto currencies primarily Bitcoin. This can be accomplished by using a series of machine learning techniques and methodologies.
Model Order Estimation using Ratio of Cumulative Sums of Eigenvalues

Determination of model order is an important task in many problems such as sinusoidal parameter estimation. In this paper we consider the data matrix corresponding to the given sequence and introduce the Ratio of Cumulative Sums (RCS) of eigenvalues. We then exploit the structure of RCS to obtain a performance that is comparable to the Cumulative Sums based method of Shah and Tufts (ST). However the advantage is that we do not require knowledge of the noise level; moreover, our method is computationally cheaper. RCS can also be used in conjunction with existing methods to improve performance: we showcase improved results for Cheng and Hua's least-squares (LS) method when RCS information is used. When there are three closely spaced sinusoids, our method outperforms both the ST and LS methods.
A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art.
Binary Compressive Sensing and Super-Resolution With Unknown Threshold

We consider the problem of binary compressive sensing (CS), where random linear projections of a sparse signal are encoded using threshold-crossing information. The threshold used by the binary encoder for acquisition is unknown to the decoder and is estimated jointly with the signal. We cast the problem of signal reconstruction and threshold estimation as one of learning a hyperplane that separates the sampling vectors corresponding to the +1 and −1 measurements, and develop a reconstruction algorithm that entails iterative minimization of reweighted $\ell_{1}$-norm subject to a set of linear constraints that enforce measurement separability. The proposed algorithm leads to a reconstruction performance comparable with that obtained using a popular binary CS algorithm, namely binary iterative hard-thresholding, which assumes that the threshold is set to zero. We consider binary super-resolution as an application, where a signal consisting of point sources needs to be estimated from sign measurements of its blurred version. The proposed algorithm successfully recovers the locations and amplitudes of the point sources, even in the presence of significant blurring.
High-performance insulation materials from poly(ether imide)/boron nitride nanosheets with enhanced DC breakdown strength and thermal stability

Environmentally-friendly polymers which can be recycled and reused at the end of life are urgently needed for the development of advanced high-voltage direct current (HVDC) systems. Polymer nanocomposites have attracted considerable attention owing to their unique features arising from synergistic combination of inorganic fillers and polymer matrices. Here we describe thermoplastic poly(ether imide) (PEI) nanocomposites that contain boron nitride nanosheets (BNNSs). It is found that the polymer nanocomposites exhibit simultaneous improvements in multiple physical properties, such as the DC electrical breakdown strength, volume resistivity, mechanical strength and thermal stability, along with substantial reduction in loss tangent, compared with neat PEI. A great improvement in thermal conductivity has been attributed to the presence of BNNSs in the composites, which improves heat dissipation in comparison with neat polymers. The composite approach provides a promising solution for alleviating temperature-gradient distribution and addressing heat accumulation issues in HVDC equipment, and enables broader applications of insulation materials in HVDC systems operated under high voltages.
Micro-Transfer-Printing and Potential Process Optimizations by FEA

Micro-Transfer-Printing ($\mu$ TP) as an alternative micro-assembly technology opens up new possibilities in the integration and packaging of smart devices like processed III/V devices, optical filters and special sensors on CMOS and MEMS on wafer-level. The technology uses an elastomer stamp to manipulate multiple printable components at the same time that are difficult to handle because of their size or fragility. Nevertheless, the industrial application of this technology as well as the transfer and upscaling from laboratory scale is still challenging. In order to realize a reliable printing process with sufficient yield, the interaction of the components to be printed, their fixation by tether structures to the source wafer and the adhesion of the transfer stamp must be well adapted. Therefore, the presented work will deal with results of mechanical experiments and FEA-modelling in order to get a deeper understanding of the $\mu$ TP-process and will allow a defined tether layout and optimization of the processed source wafers.
Immunodeficiency-related Hodgkin Lymphoma and its Mimics

Classic Hodgkin lymphoma (CHL) in patients with underlying immunodeficiency disorders frequently differs from that in the immune competent population in terms of its clinical behavior and pathologic features. Moreover, differential from Hodgkin-like lymphoid proliferations may be problematic. Topics under review include: (a) CHL posttransplant lymphoproliferative disorders, (b) CHL in HIV/AIDS, (c) Hodgkin variant of Richter syndrome in chronic lymphocytic leukemia in association with fludarabine therapy, (d) CHL in other immunodeficiency states including methotrexate-associated lymphoproliferative disorder in patients with rheumatoid arthritis and primary immune deficiencies, and (e) Hodgkin-like lymphoid proliferations including senile Epstein-Barr virus+ B-cell lymphoproliferative disorder. Also under consideration is the pathogenesis of these disorders with an emphasis on the role of Epstein-Barr virus.
On the Excess Returns to Illiquidity

This paper argues that the high expected returns observed on illiquid assets should be expected theoretically, but are not actually a premium for illiquidity, per se. Instead, illiquidity, like size, is a proxy for any unobserved risk. Liquidity should therefore have explanatory power in any asset pricing model that is not perfectly specified, with low measured liquidity forecasting high expected returns.
Escherichia coli O 157 : H 7 SURVIVAL IN TRADITIONAL AND LOW LACTOSE YOGURT DURING FERMENTATION AND COOLING PERIODS SOBREVIVÊNCIA DA Escherichia coli O 157 : H 7 EM IOGURTE NATURAL

The purpose of this study was to evaluate the behavior of E. coli O157:H7 during lactose hydrolysis and fermentation of traditional and low lactose yogurt. It also aimed to verify E. coli O157:H7 survival after 12 h of storage at 4 °C ±1 °C. Two different types of yogurts were prepared, two with whole milk and two with pre-hydrolyzed whole milk; in both groups one yogurt was inoculated with E. coli O157:H7 and the other one was not inoculated. The survival of E. coli and pH of yogurt were determined during fermentation and after 12-h refrigeration. The results showed that E. coli O157:H7 was able to grow during the fermentation period (from 4.34 log CFU.mL-1 to 6.13 log CFU.mL-1 in traditional yogurt and 4.34 log CFU.mL-1 to 6.16 log CFU.mL-1 in low lactose yogurt). The samples with E. coli O157:H7 showed gas formation and syneresis. Thus, E. coli O157:H7 was able to survive and grow during fermentation of traditional and low lactose yogurts affecting the manufacture technology. Moreover, milk contamination by E. coli before LAB addition reduces the growth of L. bulgaricus and S. thermophilus especially when associated with reduction of lactose content
Aerosolized Antimicrobial Therapy in Acutely Ill Patients

Recent data are sparking renewed interest in therapy with aerosolized antimicrobials in critically ill patients as well as other populations such as those with neutropenia, human immunodeficiency virus infection, and cystic fibrosis. Pneumonia is a common complication in these patients and is associated with substantial morbidity and increased mortality. Clinical trials evaluated aerosolized antimicrobials for the prevention and treatment of pneumonia in hospitalized patients. In addition, factors that affect the pulmonary deposition of aerosolized drugs in mechanically ventilated patients were identified.
Numerical Modeling of Shear Banding Formation in Rheometry

The paper is dedicated to the numerical modeling of materials, such as complex fluids, which exhibit intrinsic material instability in viscometric flows. Some notorious examples of complex fluids are polymer solutions, colloids, solid dispersions in liquids and soft solids (pastes, creams, greases). Most of these materials disclose structure instability beyond critical deformations and stresses, being characterized by the existence of a non-monotonic steady flow curve and the shear banding formation. Numerical simulations performed with a generalized Newtonian model confirm the existence of the shear banding in the gap of the rheometers, as a consequence of the coexisting asymptotic steady shear rates of the velocity distribution under constant shear stress. The present findings are consistent with the previous results published in literature and confirmed by experiments performed in our laboratory.
Hosting Capacity Estimation of Underground Distribution Feeder in Urbain Areas

Integrating new plants of renewable energies for electrical production in a distribution network presents challenges for the grid operators. However, the power systems are not designed to receive these intermittent renewable energy sources. The distributed generation (DG) integration causes several problems such as overvoltage, lines overload, transformers overload, protection issues, and exceeding network limits. The hosting capacity approach (HCA) defined the hosting capacity (HC) as the maximum power that will be injected into the gird without causing any problem in the electrical system. In this paper, we propose a new method to calculate the HC of distributed generation (DG) facilities. Therefore, the variety of distribution line configurations and cross admittance are considered in the calculation and the overvoltage as a limit of HC. The proposed method is applied to an underground medium voltage grid. The simulation is carried out using Matlab and Etap.
IOVTee: A Fast and Pragmatic Software-based Zero-copy/Pass-through Mechanism for NFV-nodes

Virtualization of various network equipment has been already seen in modern telco and cloud networks. Their mission-critical virtualized devices commonly run on ASIC-powered white-box switches instead of Commercial Off-The-Shelf (COTS) x86 servers for performance reasons. In fact, many studies have revealed insufficient throughput/latency of virtual machine/container-based virtual network functions (VNFs) on x86 servers, and virtual network I/O implementations have been thought to be major performance bottlenecks. Zero-copy and pass-through are well-known techniques to reduce the I/O overhead between a host and a guest; however, existing implementations incur pragmatic issues including security, compatibility, management, and inexplicable performance characteristics. In this paper, we propose a fully software-based SR-IOV-like zero-copy/pass-through technique, Tee-type I/O Virtualization (IOVTee), to resolve the problems. IOVTee takes advantage of SR-IOV and NetVM under the framework of de-facto standard DPDK/vhost-user. Inbound packet "data" are directly stored into VM’s memory space like SR-IOV, but the hypervisor’s virtual switch can handle the packets before the VNF’s processing like NetVM. We evaluated the performance of IOVTee in 100 GbE environment, and the results showed that an IOVTee-accelerated single core VNF achieved about 19 Mpps (64 bytes) and 90 Gbps (1518 bytes) throughputs.
Rearrangeability of Wavelength-Space-Wavelength Switching Fabric Architecture for Elastic Optical Switches

In this paper, we consider the necessary and sufficient rearrangeability conditions for the wavelength-space-wavelength switching fabric, called the WSW2 switching fabric, for elastic optical network nodes. We derive and prove the lower bound of the necessary conditions for such switching fabrics to be rearrangeable. We also show the upper bound of the sufficient conditions for the rearrangeability of the switching fabric with two input and two output switches. For simultaneous connection routing, we propose a routing algorithm that always ends with success in switching fabrics, which fulfill the sufficient conditions. Finally, we extend the proposed algorithm to the switching fabric with <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> input and <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> output switches, and we derive the conditions under which this routing algorithm always ends in success. The required number of center-stage switches is significantly lower than that in the strict-sense nonblocking switching fabrics. To our knowledge, the proposed routing algorithm is the first one which ends with success for any connection set, within the provided sufficient conditions.
Secure Bitcoin Transaction and IoT Device usage in Decentralized Application

In the recent years, there has been a boom in the number of connected devices due to developments in the field of Internet of things. This has also increased the requirements of security specification. The proposed method is introducing a secure information transmission system by using Blockchain technology. Blockchain is a relatively new technology which was introduced by stoshi nakamoto, which was also the basis for developing crypto currency [bitcoin]. Crypto currencies are made transparent and secure using their network architecture, which is a combo of a decentralized and distributed network. In this paper is try to exploit the same methodology used in crypto currencies to develope an IOT network, where the devices can talk to their peers in a secure manner. They explored all the different networks and features of developing a Decentralized application that is named as Dapp.
How to Get a Badge? Unlock Your Mind : Motivation through Student Empowerment

The introduction of gamification principles into the classroom may enhance and extend the student engagement in learning activities, and therefore, contribute to a more efficient knowledge acquisition. In this paper, we report a case study whose methodology is based on awarding merit badges as a result of the successful completion of learning activities focused on either soft and hard skills. This initiative was implemented in two universities, involving 221 participants, aimed at assessing how the proposed model may contribute to students’ motivation, appealing for its participation in the classroom, and measuring their stimulation for continuous study. Early stage results are promising and suggest not only a higher acceptance of the system and its effectiveness, but also, the suitability of gamification and reward systems on education. Further studies should be implemented in order to determine the effect of the reward system on students’ grades.
Achievement, Engagement and Student Satisfaction in a Synchronous Online Course

This paper compares academic achievement, engagement and student satisfaction between courses in online synchronous (LD) and face to face (F2F) methods with the objective of determining the best way to deliver content. The course was delivered to the students using the same content structure and activities. Academic achievement was measured through exams, study cases, evaluation rubrics, evidence portfolio, and a final exam. The engagement was measured considering the number of absences, assignments not delivered and the number of drop out students. Motivation was measured by applying a survey measuring connection with the teacher and an understanding of the course. Satisfaction was measured with a survey designed, validated and applied to the students in both groups. The study sample was integrated by two groups on the subject Material Balance.
Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond

Blockchain and AI are promising techniques for next-generation wireless networks. Blockchain can establish a secure and decentralized resource sharing environment. AI can be explored to solve problems with uncertain, time-variant, and complex features. Both of these techniques have recently seen a surge in interest. The integration of these two techniques can further enhance the performance of wireless networks. In this article, we first propose a secure and intelligent architecture for next-generation wireless networks by integrating AI and blockchain into wireless networks to enable flexible and secure resource sharing. Then we propose a blockchain empowered content caching problem to maximize system utility, and develop a new caching scheme by utilizing deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.