Miracle-dz/UniFAR-FTUs huggingface dataset:
Vertically Polarized Quasi-Yagi MIMO Antenna for 5G N78 Band Application

In this study, a three-port vertically polarized Quasi-Yagi Multiple-Input-Multiple-Output (MIMO) antenna system with pattern diversity is proposed for 5G N78 band application. All the three ports, which are sequentially rotated by 120°, are integrated together on a circle patch. Each antenna element is fed by a probe and an annular slot etched on the patch which works as a capacitor to counteract the inductance effect introduced by the feeding probe and the nearby shorting posts. A patch-like broadside radiation pattern is generated for each element. By introducing a novel <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>-shaped metal plate for each element, the radiation beam could be tilted and deviate from the z axis by around +30° to suit the diversity application demand. Good isolation is achieved by introducing a metal post between every two ports, which well suppresses the mutual coupling induced by the current between the ports. The working principle of the proposed MIMO antenna is theoretically illustrated in detail and experimentally verified. The Proposed antenna occupies a cylindrical volume of <inline-formula> <tex-math notation="LaTeX">$\pi \times 0.33^{2} \times 0.14\,\,\lambda _{0}^{3}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> represents the free space wavelength at the center frequency of the operating band. The measured overlapping bandwidth covers the N78 band (3.3-3.8 GHz) with <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert < -10$ </tex-math></inline-formula> dB for the three antenna elements. The proposed antenna offers attractive features including a shared radiation aperture, pattern diversity, good isolation (<inline-formula> <tex-math notation="LaTeX">$\le20$ </tex-math></inline-formula> dB) good gain (<inline-formula> <tex-math notation="LaTeX">$\ge8.2$ </tex-math></inline-formula> dBi), low field envelope correlation coefficient value (<inline-formula> <tex-math notation="LaTeX">$\le0.001$ </tex-math></inline-formula>), and high radiation efficiency (<inline-formula> <tex-math notation="LaTeX">$\ge74$ </tex-math></inline-formula>%).
Optimal Energy-Efficient Integration of Photovoltaic DG in Radial Distribution Systems for Various Load Models

The integration of renewable energy resources in distribution networks has become a necessity, due of the technical-economic and environmental advantages, as well as the ability to reduce the active power losses, improve the power quality and ensure the continuity of system. In this paper, optimal integration of Distributed Generation (DG) based on Photovoltaic (PV) solar panels in Radial Distribution System (RDS) is presented to maximize the technical, economic and environmental levels namely, the Active Power Loss Level (APLL), Net Saving Level (NSL), Short Circuit Level (SCL), Voltage Deviation Level (VDL) and Environmental Pollution Reduction Level (EPRL). This is achieved by using the Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) algorithm to find the optimal size and location of DG. In this paper, a comparative study is conducted for various load models, where the importance of considering different load models is projected. Therefore, four types of loads are used, which are constant, commercial, residential, and industrial loads. The results obtained show that the optimal allocation of PV-DGs in the distribution system gives a positive impact in terms of achieving better loss reduction and voltage profile enhancement.
A Framework for Embedded Model Predictive Control using Posits

This paper proposes a framework for the high-accuracy, low-precision, and memory-efficient embedded model predictive control (MPC) using the posit™ numbers and its implementation on the ARM-based embedded platform. A quadratic programming (QP) problem in posit-based linear MPC is solved by the active set method (ASM) with a Cholesky factorization-based linear solver. The main idea of this paper is to encode all data associated with the QP problem as posit numbers and employ posit number arithmetic to synthesis the ASM algorithm. We provide a detailed analysis of a posit number that acts as a memory-efficient replacement of the IEEE 754 floating-point standard numbers. We show the posit-based ASM algorithm employed in MPC and its implementation on STM32 Nucleo-144 development board with STM32F746ZG MCU. The results of hardware-in-loop (HIL) simulations with the detailed analysis of memory utilization and performance of the posit-based ASM algorithm is shown with two case studies. HIL results show that the proposed approach can reduce memory footprints by 50% to 75% without losing control accuracy and performance.
Almost Globally Stable Grid-Forming Hybrid Angle Control

This paper presents a new grid-forming converter control strategy, termed hybrid angle control (HAC) that ensures the almost global closed-loop stability. HAC combines the dc-based matching control with a novel nonlinear angle control that resembles the droop control. The design of HAC is inspired by ideas from direct angle control and nonlinear damping injection technique. The proposed HAC is applied to a detailed nonlinear converter model that is connected to an infinite bus via a dynamic line. We provide insightful parametric conditions for existence, uniqueness, and almost global stability of closed-loop equilibria. Unlike in related stability certificates, our parametric conditions do not demand strong physical damping, but they can be met by appropriate choice of ac and dc converter control gains. Finally, we present an implementation of the HAC and illustrate the behavior of the closed-loop system with a publicly available numerical example.
Reducing Modern Slavery Using AI and Blockchain

The Modern Slavery Act of the UK has failed in reducing modern slavery significantly in UK organizations and their supply chains. One of the critical reasons for the failure is that these organizations utilize human-centered modern slavery due diligence processes, which have significant weaknesses in identifying modern slavery offenses. This paper aims to propose modern slavery due diligence process which leverages the self-sovereign digital identity component, an AI-based modern slavery offense monitoring component, and a modern slavery offense blockchain component designed to help improve the identification of modern slavery offenses which would otherwise remain hidden. This process enables managers to identify and act on more modern slavery offenses due to low-cost continuous monitoring instead of high-cost sample-based monitoring. This proposed solution can significantly reduce modern slavery offenses and generate tangible business benefits
On the Deep Learning-based Age Prediction of Color Fundus Images and Correlation with Ophthalmic Diseases

Color fundus imaging is an important modality used for ophthalmic disease screening and provides a non-invasive invivo method for assessing body condition. We aimed to assess deep learning models for age prediction from fundus images of normal patients and patients with ophthalmic diseases. In addition, we sought to investigate interpretable clues regarding the salient regions between normal and pathological changes as determined by deep learning models during age prediction. In this study, we used a convolutional neural network model for age prediction and evaluated it on an in-house database of fundus images of the Chinese population. The results of the experiment revealed some conclusions as follows: (1) deep learning-based classification models have better age prediction performance than deep learning-based regression models of fundus images; (2) deep learning-based models tend to use holistic information of the fundus for age prediction; (3) ophthalmic diseases that cause damage to the structure of the fundus and change its appearance will result in a decline in age prediction performance.
The Inventory of Spare Parts Prediction based on LSTM for Large Data and a Short Replacement Cycle

Large variations in the replacement cycle and data size of different auto parts present a challenge to auto part companies. To predict the inventory of spare parts, an prediction method based on long short-term memory (LSTM) is proposed. The LSTM-based predictive model is mainly adopted for auto parts with large data and a short replacement cycle. We verified by experimentation that our prediction method based on LSTM can accurately forecast the inventory of spare parts with large data and a short replacement period. The method provides an efficient solution for improving the inventory performance of auto parts companies.
Development of Temperature and Humidity Control System in Internet-of-Things based Oyster Mushroom Cultivation

Oyster mushrooms are a kind of mushrooms that have high nutritional content and medicinal properties. This plant can be cultivated using planting media with the proper composition and the certain temperature as well as humidity. This paper describes the design and implementation of a prototype of temperature and humidity control system in the oyster mushroom cultivation based on the internet-of-things framework to obtain the good quality mushrooms. Controlling is carried out by utilizing DHT-11 sensor, Arduino Uno microcontroller, MCU ESP8266, and the internet-of-things (IoT) platforms which are called Cayenne. The temperature is maintained between 22°C-28°C and humidity of 60%-80% through continuous monitoring remotely. The Cayenne application installed on the desktop computer or an Android-based mobile phone provides data on temperature and humidity at all times. Experimental results show that monitoring and controlling temperature and humidity can be done well through the Cayenne application so that the whole system realizes the concept of IoT.
Challenges Faced by Electric Vehicle Motors and Their Solutions

This paper reviews motor techniques for reducing the cost of electric vehicles (EVs) and improving their range. In recent years, environmental issues, energy crises and the greenhouse effect have urged the popularization of clean energy EVs. In order to achieve this goal, it is necessary to overcome technical difficulties in vehicle cost and range. As a key component of an EV, the motor occupies a large proportion of the overall vehicle cost, and its efficiency directly affects the mileage. In this context, this article discusses the merits and challenges of three mainstream EV motors: permanent magnet synchronous motor (PMSM), induction motor (IM), and switched reluctance motor (SRM) in terms of vehicle cost and range. Then this paper compares the advanced techniques of these motors in terms of topology, material applications and control strategies. Finally, the development trends and opportunities of the three motors in EVs are predicted.
Time‐ and wavelength‐resolved spectroscopy in two‐photon excited fluorescence microscopy

Two‐photon excited fluorescence spectroscopy has been performed at a microscopic scale in combination with normal, white‐light microscopy. This gave simultaneously a spectral resolution of 20 nm and a temporal resolution of 20 ps, from a volume element less than 5 μm in all three dimensions. The sample was excited with the light from a continuously mode‐locked Ti: sapphire laser that was focused on the sample in a fluorescence microscope. A polychromator and a streak‐camera were used for detection. The method has been used on tissue, plant and paper samples. It has also been demonstrated how substances naturally occurring in the samples can be identified from their spectroscopic properties and the spatial distribution of these substances can be observed.
Wideband Phase Shifters at 22.5, 45 and 67.5 Degrees

The article presents the results of a three-dimensional full-wave computer simulation of broadband phase shifters designed to provide phase delays at 22.5°, 45° and 67.5°. The devices operate in a frequency band of 2.2±0.8 GHz, phase delays errors do not exceed 1.5°, the minimum attenuation value in the lines of the strip phase shifters was −0.32 dB. The phase shifters are implemented on an FAF-4D dielectric film with a material thickness of 0.23 mm, a dielectric constant of 2.5, and a metallization thickness of 35 µm. The considered strip phase shifters can be used as an element base for various microwave devices.
WLAN Based Indoor Localization System for Evacuation of Victims in a Building

Severe damage to a building can trap victims inside and cause human fatalities. One of the problems that emerge during the victims' evacuation process is detecting the number of victims and their location. An indoor localization system can be a solution to this problem. Indoor localization systems can utilize WLAN Access Point (AP) devices, which are generally available in most buildings that provide internet access. Therefore, this research aims to collect fingerprinting data, analyze the accuracy of victims' position using the Euclidean Distance (ED) algorithm, and build an Android mobile application to detect several victims trapped in a building WLAN network, analyze the functionalities and the usability of the application. The fingerprinting method is collected using an Android application designed explicitly for Received Signal Strength (RSS) collecting. This mapping process collects signal data from 1 and 3 sources of AP for every 2 meters in four directions. The training data is used to compare the user's current position with the actual point using the ED algorithm. The use of KNN classification gives a better result than no classification method. Also, the accuracy of point and room prediction using 3 APs is higher than using 1 AP. The accuracy of room prediction is 87% by using 3 APs. The Android application consists of regular user and rescuer applications. Both applications passed the functional testing with the Black Box method and tested for usability using the USE questionnaire method. Based on the result obtained, the application has met all of the usability requirements.
District Heating Simulation Model Development to Solve Optimization Problems in the Market Conditions

The paper reviews recent attempts to create and use district heating simulation by district heat companies to solve district heat network optimization problems in the market conditions. Today's market conditions determine a higher quality product to the consumer at the lowest possible prices. In order to fulfill such requirements, it is necessary to make accurate calculations, planning, and realization in life. District heat network model conformity level to reality determines the precision of planning and implementation of optimization solutions of district heat network in life. Creating of working district heat network model for simulations is a complex task where many parameters should be taken into account. In this paper, the authors examine mistakes and solutions of case study - district heat network model structure and it's conformity to the real object (5 biggest Latvian cities). Based on case study authors developed and offered methodology that provides minimal investment solutions to correctly create a model with the level of accuracy where the model can be used for correct simulations to perform a feasibility study for new consumer connection, heat load distributing between heat sources and implementing energy efficient events in interchangeability of heat supply/power supply/gas supply systems. By using the authors methodology it is possible to develop the correct district heating simulation model, analyze the conformity of the developed model to life, and determine the level of accuracy to perform the feasibility study.
Low Power OTA with Series Parallel Current Mirror for Low frequency Gm-C Filters

Operational transconductance amplifier are the most frequently used analog block in analog front end of wearable, portable and implantable biomedical devices. A low power OTA with low transconductance is designed based on series parallel current mirror and current cancellation technique with bulk driven input differential pair. The designed OTA is simulated using Cadence virtuoso tool in gpdk 180 nm CMOS process. It has DC gain of 44.5 dB and UGBW of 22 kHz with 0.4 V power supply consuming 40.86 nW of power. Also, a Gm-C low pass filter is designed using the proposed OTA and is suitable for low frequency applications as it has cut-off frequency in the range 32 Hz - 263 Hz.
A multi-task learning method for analyzing microbiota as cancer immunotherapy signal

Researches have found that tumor immunotherapy can only work for some patients, and the intestinal microbiota is one of the important factors affecting the responses of patients with cancer to immune checkpoint blockade therapy. It is highly desirable to develop computational methods that can predict whether a patient with cancer will have positive effects on cancer immunotherapy by analyzing intestinal microorganisms of the patient. In this study, a multi-task model is introduced to predict the efficacy of cancer immunotherapy on a patient who suffered from non-small cell lung cancer or renal cell carcinoma. The results demonstrate the multi-task model outperforms several single-task methods. Therefore, we believe the multi-task idea can be used to predict the efficacy of cancer immunotherapy based on the gut microbe, which would be important to cancer patients.
Efficient Resolution of Enantiomers by Coupling Preferential Crystallization and Dissolution. Part 2: A Parametric Simulation Study to Identify Suitable Process Conditions

For pairs of enantiomers crystallizing as a conglomerate, several process strategies can be used to resolve them by preferential crystallization (PC). Usually, a main limitation is the nucleation of the counter enantiomer, which restricts productivities. The performance of PC can be strongly increased, when crystallization and selective dissolution are combined by coupling two crystallizers via the liquid phase. In doing so, one crystallizer contains a saturated racemic suspension, while the other is in a supersaturated state and initially seeded with pure enantiomer. The crystallization of the seeds in the corresponding tank leads to a transient undersaturation in the other tank and, thus, to a selective dissolution from the racemate. Due to the exchange of solution, crystallization is accelerated, while the solid racemate is purified.The process described, has been investigated in detail using a well-established population balance model, which was shown to be in good agreement with experimental results .
Near-Infrared Spectroscopy for Predicting Structural Stability of Light-Harvesting Complex 2 from the Purple Bacteria

To investigate the feasibility of using near-infrared absorption to predict stability of light-harvesting complex 2 from the purple bacteria, we compared the structural stability of light-harvesting complex 2 with different absorption properties. The spectral changes showed that light-harvesting complex 2 from Rhodobacter azotoformans (absorption maxima at 799 and 847 nm) was more susceptible to both sodium-dodecylsulfate and acid than light-harvesting complex 2 from Rhodopseudomonas palustris (absorption maxima at 803 and 857 nm). It is concluded that a relatively redshifted near-infrared absorption tends to indicate a more stable structure of light-harvesting complex 2.
Topology for Robust RF/FSO Backhauling under Random Jamming Attacks and Adversary Weather Conditions

Backhauling in future 5G systems is expected to depend on emerging technologies such as Free Space Optical (FSO) communication, due to its wide unlicensed spectrum, alongside Radio Frequency (RF) communication. Wireless links are prone to jamming attacks from malicious nodes. Also, FSO links are susceptible to the changing weather conditions. Therefore backhaul networks should be designed to remain functional under such adversary operating conditions. In this paper, we design an RF/FSO backhaul topology that is robust to jamming attacks from random unknown malicious nodes and to changing weather conditions that affect the reliability of FSO communication. The design problem finds the required minimum number of links and specifies their types and locations in order to achieve a pre-specified degree of robustness. To the best of our knowledge, this is the first work in literature that formulates and considers the effect of random jamming and variable weather conditions when designing the backhaul topology. We also compare the numerical results of three different regions in Japan (urban, basin, and mountain) with different statistical weather conditions, and we show how this affects the number and type of the selected links.
Verify: a toolbox for deterministic verification of computational models

The application of Agent Based Models (ABMs) in biology and immunology has recently come to the fore, thanks to their ability to accurately describe complex biological behaviors, rules, and interactions, without the need to use complex mathematical formalisms. However, even if there is a growing interest in applying such methodologies to improve and speed up the research of novel pharmaceutical products, verification and validation procedures voted at assessing ABMs credibility are far from being well-established. We present Verify, the first toolbox of instruments selected and designed for the verification of discrete-time models, with a focus on agent-based approaches. The toolbox has a friendly GUI, does not require the installation of any additional software, and can easily find possible numerical errors and incongruences that may affect such models.
Estimation of Heteroscedastic Multilinear Systems

In this paper, we propose an estimation method for heteroscedastic multilinear systems. The system consists of a multilinear map of latent functions and an input-dependent noise process. We assume Gaussian-process priors on the unknowns to embed non-parametric models. This leads to a hierarchical model called heteroscedastic multilinear Gaussian processes which do not admit closed-form posterior and predictive distributions. The model is treated in an empirical Bayes fashion where the hyperparameters are estimated by maximizing the marginal distribution. To achieve that, we use a Monte Carlo expectation maximization method based on a Gibbs sampling algorithm. The predictive inference is also introduced where the mean is used as an approximation of the unknown functions. The performance of the proposed method is illustrated in a simulation study.