Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive n- and pMOSFET Channel Material Benchmarking and Analysis of CMOS Performance Metrics Considering Quantum Transport and Carrier Scattering Effects

Comprehensive channel material benchmarking for n- and pMOS are performed considering effects of quantum transport and carrier scattering. Various channel material options (Si, InAs, In<sub>0.7</sub>Ga<sub>0.3</sub>As, In<sub>0.53</sub>Ga<sub>0.47</sub>As, GaAs, and Ge for nMOS, Si and Ge for pMOS) are covered using hybrid simulation of quantum ballistic transport and semi-classical Monte Carlo. Current-voltage characteristics and performance metrics such as the capacitance and effective drive current (<inline-formula> <tex-math notation="LaTeX">$I_{eff}$ </tex-math></inline-formula>) are explored considering device parasitic components. For low power operation, III-V nMOS may deliver good performance while Ge n- and pMOS with different source/drain tip designs may give performance advantage over Si from low power to high performance operations. CMOS benchmarking results for <inline-formula> <tex-math notation="LaTeX">$I_{eff}$ </tex-math></inline-formula>, capacitance, and switching energy vs. delay (for gate capacitance loading vs. interconnect wire capacitance loading) are also presented for various homogeneous and hybrid combinations of n- and pMOS (Si CMOS, III-V hybrid CMOS, Ge hybrid CMOS, and Ge CMOS). Finally, sensitivity analysis is performed for <inline-formula> <tex-math notation="LaTeX">$I_{eff}$ </tex-math></inline-formula> on the parasitic resistance (<inline-formula> <tex-math notation="LaTeX">$R_{SD}$ </tex-math></inline-formula>) and contact resistivity (<inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\rho }} _{c}$ </tex-math></inline-formula>). Novel channel materials may relax the <inline-formula> <tex-math notation="LaTeX">$R_{SD}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\rho }} _{c}$ </tex-math></inline-formula> requirements to match the <inline-formula> <tex-math notation="LaTeX">$I_{eff}$ </tex-math></inline-formula> performance of Si reference. Comprehensive literature reviews of experimental <inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\rho }} _{c}$ </tex-math></inline-formula>’s of novel materials are also presented to discuss the effect of material-dependent <inline-formula> <tex-math notation="LaTeX">$R_{SD}$ </tex-math></inline-formula>.
Effect of a Product Containing Xyloglucan and Pea Protein on a Murine Model of Atopic Dermatitis

Atopic dermatitis (AD) is a chronic inflammatory disease of the skin, characterized by dryness and more or less severe itching. The etiology of AD is complex and has not been fully clarified, involving genetic susceptibility, immunological abnormalities, epidermal barrier dysfunction, and environmental factors. Xyloglucan (XG) and pea protein (PP) are two compounds of natural origin characterized by the ability to create a physical barrier that protects mucosae membranes, reducing inflammation. The aim of the present study was to evaluate the potential beneficial effects of XG + PP in both a mouse model of AD and Staphylococcus aureus (S. aureus) infection- associated AD. Mice were topically treated with 200 μL of 0.5% oxazolone on the dorsal skin three times a week for AD induction. Mice received XG and PP by topical administration 1 h before oxazolone treatment. In S. aureus infection-associated AD, to induce a superficial superinfection of the skin, mice were also treated with 5 μL of 108 of a culture of S. aureus for 2 weeks; mice superinfected received XG and PP by topical administration 1 h before oxazolone + S. aureus. Four weeks later, the skin was removed for histological and biochemical analysis. Our results demonstrated the protective barrier effects of XG and PP characterized by a reduction in histological tissue changes, mastocyte degranulation, and tight junction permeability in the skin following oxazolone treatment. Moreover, XG + PP was able to preserve filaggrin expression, a hallmark of AD. Our data also support the effectiveness of XG + PP to reduce the damage by superinfection post AD induced by S. aureus. In conclusion, a future product containing XG and PP could be considered as a potentially interesting approach for the treatment of AD.
SSSSS: Search for Social Similar Smart Objects in SIoT

As Internet of Things (IoT) is overpopulated with multitude of objects, services and interactions, efficiently locating the most relevant object is emerging as a major obstacle. Over the last few years, the Social Internet of Things (SIoT) paradigm, where objects independently establish social relationships among them, has become more popular as it provides a number of exciting characteristics to boost network navigability and carryout reliable discovery approaches. Given a large scale deployment of socially connected objects, finding the shortest path to reach the service provider remains as a fundamental challenge. In most of the existing search techniques, the physical significance of the objects is not very well explained and the geographical location of mobile objects is not considered. In this paper, to improve the search performance over the SIoT, we propose a novel object search mechanism based on physical location proximity and social context of users in social communities. The results show an enhancement over the existing search technique in terms of average path length.
Deep Learning Assisted Predict of Lung Cancer on Computed Tomography Images Using the Adaptive Hierarchical Heuristic Mathematical Model

Lung cancer is known to be one of the most dangerous diseases which are the main reason for disease and death when diagnosed in primitive stages. Since lung cancer can only be detected more broadly after it spread to lung parts and the occurrence of lung cancer in the earlier stage is very difficult to predict. It causes a greater risk as radiologists and specialist doctors assess the existence of lung cancer. For this reason, it is important to build a smart and automatic cancer prediction system that is accurate and at which stage of cancer or to improve the accuracy of the previous cancer prediction that will help determines the type of treatment and treatment depth depending on the severity of the disease. In this paper, the Adaptive Hierarchical Heuristic Mathematical Model (AHHMM) has been proposed for the deep learning approach. To analyze deep learning based on the historical therapy scheme in the development of Non-Small Cell Lung Cancers (NSCLC) automated radiation adaptation protocols that aim at optimizing local tumor regulation at lower rates of grade 2 RP2 radiation pneumonitis. Furthermore, the system proposed consists of several steps including acquiring the image, preprocessing, binarization, thresholding, and segmentation, extraction of features and detection of deep neural network (DNN). Segmentation of the lung CT image is carried out to extract any significant feature of a segmented image, and a specific feature extraction method is implemented. The test evaluation showed that the model proposed could detect 96.67 % accuracy of the absence or presence of lung cancer.
A Three-Photo-Detector Optical Sensor Accurately Localizes a Mobile Robot Indoors by Using Two Infrared Light-Emitting Diodes

Indoor positioning systems are facing to the demand of large-scale industrial applications in mobile robotics. It is still challenging to create an indoor positioning system that is easily embeddable, accurate, robust and power efficient. We constructed an easily embeddable, low-power optical sensor named InLock without lens to localize a mobile robot indoors moving at <inline-formula> <tex-math notation="LaTeX">$0.20~m/s$ </tex-math></inline-formula> with an accuracy inferior to <inline-formula> <tex-math notation="LaTeX">$10cm$ </tex-math></inline-formula> for the position and 0.1rad for the heading by using only three photo-detectors (PDs) and two infrared Light-Emitting Diodes (LEDs). (i) We modelled the optical sensor based on only three photo-detectors and two infrared LEDs by taking into account radiometric properties. (ii) We constructed the optical sensor by optimizing the geometry of the beacon and the receiver. (iii) We implemented and validated online estimation algorithms for an operating range at a height up to <inline-formula> <tex-math notation="LaTeX">$3m$ </tex-math></inline-formula> by using an extended Kalman filter and a complementary filter. Our results showed that modelling the optical sensor so that it takes into account radiometric properties and it optimizes the geometry of the beacon can enhance the accuracy of the indoor positioning system.
Monotremes and the Biology of Mesozoic Mammals

Since the early XIXth century mammals were known to have existed during the Mesozoic Era. Only relatively recently, however, have major discoveries of these rare fossils opened new insights into the long history of mammals that preceded the appearance of modern forms during the Tertiary. The two purposes of my review are to place these discoveries in the context of a brief outline of Mesozoic mammalian
Fast SIFT Feature Matching Algorithm Based on Geometric Transformation

Structure from Motion (SfM) is a series of methods for reconstructing scene structure (i.e.three-dimensional space points) and camera motion (i.e.camera pose) from an image set. In this paper, aimed at the low accuracy of the global reconstruction, the robustness to external points and the time-consuming incremental reconstruction, a fast, closed-loop and high precision reconstruction method is proposed. The method is based on the SIFT matching algorithm GeoMatch, which is constrained by geometric structure of the scene and by numerical and statistical characteristics of feature invariant scale transformation. Experiments show that GeoMatch outperforms both traditional tree-based and hash-based matching methods in terms of time and accuracy.
Lyapunov Function based Nonlinear Control of EGR-VVT Dual Loop in IC Engines

This study presents a control Lyapunov function (CLF) based nonlinear control design method for the intake and exhaust manifold pressure tracking problem with three control input: throttle valve opening, Exhaust Gas Recirculation (EGR) and Variable Valve Timing (VVT). Firstly, the optimal set-points are designed by solving the stationary optimization problem of minimize the fuel consumption and meanwhile to satisfy the coefficient of variation of IMEP (Indicated Mean Effective Pressure) constraint. Secondly, the Recursive Least Square (RLS) algorithm and Polynomial Fitting Method (PTM) are adopted to obtain an optimal estimation for the model parameters. Finally, a CLF based nonlinear control design method is applied to ensure the fast pressure tracking performance, and meanwhile to guarantee the combustion stability. Finally, the model and controller has been tested in on a real gasoline engine, and the experiment results demonstrate the performance of the control- oriented models.
Research on Radar Plot Classification Based on Fully Connected Neural Network

Clutter is the inherent environment of radar signal detection and processing. In this paper, the full connected neural network is used to study the classification of radar clutter and real targets. First, according to the engineering experience, the multi-dimensional feature index of radar plot is given, and the fully connected neural network is built in the Tensorflow architecture. According to the application scenarios in this paper, the effect evaluation indexes of the training model were defined, and the classical loss function was optimized. Finally, the training results were given.
Plesiomonas shigelloides - An Aquatic Food Borne Pathogen: A Review of its Characteristics, Pathogenicity, Ecology, and Molecular Detection

Plesiomonas shigelloides is a unique Gram-negative polarly flagellated pathogenic bacterium native to aquatic animals and environments. The genus Plesiomonas consists of a single homogeneous species. Its metabolism is similar to that of the genus Vibrio in that sugars are fermented with acid production but no gas. 5S rDNA sequencing has indicated the organism to be closely related to the genus Proteus. Diarrhea is the major symptom although extra intestinal infections, including septicemia, are known to occur with predisposed individuals. Oysters are the major food incriminated in outbreaks in the United States. A temperature of 42–44°C is recommended for isolation to eliminate aeromonads. The utilization of inositol with acid production is a unique characteristic of the organism that is exploited with several agar media developed for its selective and differential isolation. The organism is β-hemolytic and produces a cholera-like (CL) enterotoxin in addition to a thermostabile (TS) and a thermolabile labile (LT) enterotoxin. A large plasmid (>120 mdalton) has also been found to facilitate invasion. A variety of factors have been found to influence results obtained from application of the polymerase chain reaction (PCR) to the DNA of the organism.
BWatch: A Non-invasive Blood Leakage Detection Device using Robust Reflectance-based Sensing*

The insertion and removal of central venous catheter are associated with potentially fatal bleeding risk. This paper presents the design and development of BWatch, an effective stand-alone electronic device that continuously monitors the catheter insertion and extraction site for critical re-bleeding, that alerts medical staff immediately for proper care. The device employs novel non-invasive methods for blood detection. A capacitive circuit was designed to detect the presence of the moisture on the bandage. Exploiting the unique light absorption spectra of haemoglobin, the device subsequently illuminates and measures the absorbance of green light for accurate detection. Principal Component Analysis (PCA) was used to derive a suitable green LED light intensity and a single-value threshold from a triple-channel RGB light sensor for robust detection across medium. Finally, an experimental bench test was conducted in Changi General Hospital (CGH) and BWatch achieved 100% detection accuracy across 10 experimental run with real blood samples.
Effect of oil-enriched diets on the milk yield and composition, and on the composition and physical properties of the milk fat, of dairy cows receiving a basal ration of grass silage

Summary The effects of supplementing a basal diet of grass silage with various oil-enriched mixtures on milk yield and composition, fatty acid composition and the melting characteristics of the milk fats are reported. In the first experiment, various dietary fats (soya oil, tallow and a palm oil/palmitic acid mixture) were fed at a fixed level (10% by weight of the concentrate ration). Soya oil had no effect on the proportion or yield of milk fat, and increased the proportion of low melting material in the milk fat; tallow increased the proportion of milk fat, and had little effect on the melting characteristics of the milk fat; the palm oil/palmitic acid mixture increased both the content and the yield of milk fat, and increased the proportion of high-melting material in the milk fat. In the second experiment, soya oil was fed at 3 levels (10, 15 and 20% by weight of the concentrate ration). No statistically significant effect on the yield of milk or of its constituents was observed; the proportion of low-melting material in the milk fat increased progressively with increasing intake of soya oil.
Multi-Dimensional Data Preparation: A Process to Support Vulnerability Analysis and Climate Change Adaptation

Agriculture is the backbone of a country’s economic system, considering that it not only provides food and raw materials but also employment opportunities for a large percentage of the population. In this way, determining the degree of agricultural vulnerability represents a guide for sustainability and adaptability focused on changing future conditions. In many cases, vulnerability analysis data is restricted to use by authorized personnel only, leaving open data policies aside. Furthermore, data in its native format (raw data) by nature tend to be diverse in structure, storage formats, and access protocols. In addition, having a large amount of open data is important (though not sufficient) to obtain accurate results in data-driven analysis. These data require a strict preparation process and having guides that facilitate this process is becoming increasingly necessary. In this study, we present the step by step processing of several open data sources in order to obtain quality information for feedback on different agricultural vulnerability analysis. The data preparation process is applied to a case study corresponding to the upper Cauca river basin in Colombia. All data sources in this study are public, official and are available from different web platforms where they were collected. In the same way, a ranking with the importance of variables for each dataset was obtained through automatic methods and validated through expert knowledge. Experimental validation showed an acceptable agreement between the ranking of automatic methods and the ranking of raters. The result of this study corresponds to 16 processed data sources ready to feed data-driven systems, as well as agricultural vulnerability methodologies.
A Diode-Enhanced Scheme for Giant Magnetoresistance Amplification and Reconfigurable Logic

Magnetoresistance (MR) effects have been intensively investigated and widely recognized as an effective path for realizing information sensing, storage, and processing. In particular, giant MR (GMR) effect discovered in ferromagnetic/nonmagnetic multilayers or junctions exhibits high magnetic-field sensitivity and has been successfully applied in magnetic sensors and hard disk drive (HDD) read heads. However, the relatively small MR ratio becomes the Achilles’ Heel for its further application in high-reliability electronic systems. In this paper, we propose a scheme to amplify the GMR effect of a multilayer strip by utilizing the nonlinear transport property of a diode. MR ratio up to 6947% is obtained with a magnetic field as small as 50 Oe. A theoretical model is established to describe the amplification behavior and various factors influencing MR ratio are experimentally analyzed. Based on this scheme, reliable logic functions have been carried out, which can be reconfigured by changing the working current. Our work can be extended to enhance the MR effect of any two-terminal MR device and has the potential to build emerging high-performance computing systems.
SqueezeNet with Attention for Remote Sensing Scene Classification

Scene classification is an important problem in remote sensing (RS) since it is a prerequisite to other more intelligent analysis operations. Given an RS scene, not all of its parts are important for classification. Thus, using an attention mechanism that directs the classification system to focus on the parts that are important and ignore the irrelevant background should enhance the system’s accuracy. In this work we propose a deep CNN architecture based on the pre-trained SqueezeNet CNN. This CNN is composed of nine fire modules (fire 1 to fire 9) each consisting of Squeeze followed by expansion convolution layers. First, we improve the SqueezeNet CNN by introducing several modifications to the architecture. Then we introduce a separate branch to the network that implements an attention mechanism. Each neuron in this activation map of the fire 9 module covers a different receptive field in the original scene. An attention mechanism is applied to these neurons to learn the appropriate weighing scheme for merging the feature vectors corresponding to each neuron. Feature vectors that are assigned a higher weight indicate that the network has given more attention to the receptive field in the scene corresponding to that feature vector. Preliminary results are presenting on five popular scene datasets, namely UC Merced, KSA, AID, Whurs19, and Optimal31 datasets.
Future Trends and Current State of Smart City Concepts: A Survey

Intelligent systems are wanting for cities to cope with limited spaces and resources across the world. As a result, smart cities emerged mainly as a result of highly innovative ICT industries and markets, and additionally, they have started to use novel solutions taking advantage of the Internet of Things (IoT), big data and cloud computing technologies to establish a profound connection between each component and layer of a city. Several key technologies congregate to build a working smart city considering human requirements. Even though the smart city concept is an advanced solution for today’s cities, recently, more living spaces should be discovered, and the concept of a smart city could be moved to these alternative living spaces, namely floating cities. The concept of a floating city emerged as a novel solution due to rising sea levels and land scarcity in order to provide alternative living spaces for humanity. In this article, our main research question is to raise awareness on the current state of smart city concepts across the world by understanding the key future trends, including floating cities, by motivating researchers and scientists through new IoT technologies and applications. Therefore, we present a survey of smart city initiatives and analyze their key concepts and different data management techniques. We performed a detailed literature survey and review by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living. We also discuss multiple perspectives of smart floating cities in detail. With the proposed approach, recent advances and practical future opportunities for smart cities can be revealed.
Backward Compatible Physical Layer Protocol Evolution for ADS-B Message Authentication

The automatic dependent surveillance-broadcast system is a pillar of the future air traffic control system. It is a surveillance system in which air planes transmit their information (identity, position, velocity, etc.) in broadcast to any listener equipped with a receiver. Networks of ADS-B receivers have been installed all over the world, producing a clear traffic awareness for air traffic controllers. For historical reasons, this protocol does not have any authentication/encryption mechanism. The aim of this article is to improve the ADS-B system security by introducing a physical layer protocol modification for the introduction of an authentication scheme. The proposed evolution fully complies with the current standard and exploits the phase modulation to increase the number of bits that can be transmitted with a single packet of data. These additional bits can be used to carry authentication information, making the overall system more secure (and safe). The proposed protocol evolution is developed ensuring backward compatibility and respecting the ICAO and RTCA recommendations; it can be introduced and used together with the present standard in a mixed scenario, allowing a smooth transition from the present protocol to the new one. Protocol performances were evaluated by simulations and, moreover, its feasibility and compliance with recommendations were tested using a software-defined radio (as transmitter) and a laboratory ADS-B receiver.
Research on the Running-in Test System Based on a Certain Tank Transmission

With the improvement of China’s comprehensive national strength, the requirements for modernization of national defense have also increased. Tanks, as the king of land warfare, are facing many new technological changes in the new situation. The gearbox serves as a major component of the tank to provide safe, reliable shifting for the tank. The author puts forward the design project of the no-load test system for the tank gearbox of a certain assembly unit. The project was designed to create a new test system for detecting the tank’s gearbox operating well under no-load conditions, with or without faults and temperature at critical locations, replacing the under-completed fully manual test system.