Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Optimization and ARSs Selection in Uplink Cell-Free Multi-ARSs Communication Systems

Cell-Free (CF) is one of the techniques that play a crucial role in the evolution of current 5th Generation (5G) technology to satisfy the needs of emerging communication applications. In this paper, we analyze the uplink CF system utilizing multiple aerial relay stations (ARSs), where ARSs are defined as unmanned aerial vehicles functioning as relay stations. ARSs are designed with multiple antennas that concurrently serve a specific number of terrestrial users in a given area. In terms of signal processing, the system uses the time division duplex (TDD) protocol for communication and the minimum mean squared error (MMSE) method for channel estimation. We determine the uplink user throughput expression by applying the matched filtering (MF) technique and optimize pilot/data transmission power to enhance system quality using the successive convex approximation (SCA) method and the linear problem (LP), respectively. Additionally, we propose an ARS selection strategy based on the large-scale fading factor to reduce system load, computational complexity, and energy consumption for ARSs. The results indicate that addressing the power optimization problem improves user throughput. Furthermore, the findings also reveal that when a certain number of ARSs are selected, the ARS selection method outperforms the CF (no selection) technique in terms of the user throughput. Accordingly, we discover the best number of ARSs for the proposed system.
PCM-Based Reconfigurable Acoustic Resonators and Filters: For Potential Use in RF Front-End Applications

The worldwide explosion in mobile smartphone usage has been enabled by the development of piezoelectricity-based acoustic wave resonators and filters. Smartphones use acoustic filters because of their miniature sizes and unparalleled RF performance that meet the standard specified by telecom providers <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref>. The exclusivity of acoustic filters represents a multibillion-dollar segment in the RF front-end market.
Enhancing Restaurant Recommendations through User-Based Collaborative Filtering

In this study, a restaurant recommendation system based on user-based collaborative filtering was rigorously evaluated using the Yelp dataset. We developed a comprehensive methodology that involved data loading, matrix transformation, the normalization of user ratings and finding similarity. Overall, this study presents a disciplined approach to constructing a recommendation system that enhances user experiences by aligning suggestions with preferences, promising further advancements in restaurant recommendation systems and contributing to the culinary industry’s growth and enrichment. Our evaluation, measured by Root Mean Square Error (RMSE), revealed that considering the preferences of the top 5 nearest neighbors yielded the most accurate recommendations, highlighting the system’s robustness. The RMSE value of top 5 is 0.134096542. Interestingly, the system’s accuracy remained consistent across various neighbor-counting scenarios, ensuring users consistently received reliable restaurant suggestions.
Analysis and Design of a New Self-Packaged Wideband Balun Bandpass Filter With the Functionality of Impedance Transformation

In this paper, a new self-packaged wideband balun with the high selective bandpass filtering response and impedance transformation characteristic is presented. By virtue of circuit transformations, corresponding two-port equivalent circuit with asymmetrical port impedances is put forward. The analysis results of this two-port network exhibit that the proposed new design is able to achieve a quasi-elliptic equal-ripple response with three transmission poles and four transmission zeros. In addition, the frequency response can be directly determined according to the desired design specifications of impedance transformation ratio k, ripple fractional bandwidth (FBW), and in-band return loss RL. For validation purposes, a demonstrated wideband balun bandpass filter (BPF) working at 2.0 GHz with 50–100- $\Omega $ impedance transformation has been implemented by utilizing the multilayer liquid-crystal polymer (LCP) bonded printed circuit board (PCB) lamination technology. Theoretical, simulated, and measured results are recorded in good agreement, well verifying the design method.
Predicting the Performance of Electrical Machines using Machine Learning

Electrical machines play an important role in our day-to-day life. Electric machines like DC motors and 3- phase induction motors are essential systems and widely used in domestic, industrial and transportation systems. In order to operate the machines optimally and efficiently, in real time operations, it is required to predict the performance parameters at various loaded conditions. With the advancements in the field of predictive modelling and analytics, several researchers have applied in the area of energy consumption prediction, fault prediction, weather prediction, power grid management and so on. In this paper, the machine learning techniques are demonstrated that may be used to examine the performance of electrical machinery by forecasting performance characteristics like speed and efficiency. To validate the performance of the predictive model, an experiment was conducted at the laboratory on dc motor and 3-phase induction motor to generate the required dataset to train the regression algorithms. The model evaluation metrics such MSE and the R2 value showed that the model efficiently predicted the performance of the electrical machines.
Socially Aware Robot Navigation Framework: Automatic Detecting and Autonomously Approaching People in Unknown Dynamic Social Environments

In this study, we propose a socially aware navigation framework that enables a mobile robot to automatically detect humans and navigate autonomously and safely to approach the detected humans. To do that the proposed framework make uses of a real-time appearance-based mapping (RTAB-Map) technique, which is RGB-D SLAM approach. The RTAB-Map method is based on the global loop closure detector with real-time constraints to generate 3D point clouds of the surrounding environment and create a 2D occupancy grid map for navigation process. The proposed framework also applies a frontier-based exploration approach, consisting of a flexible framework to decide a proper goal for mapping and exploring. Finally, the YOLO model is adopted to detect the human during the exploration process of the mobile robot. The simulation result illustrates that the mobile robot equipped with the proposed framework is able to automatically detect the human and navigate autonomously and safely to the detected humans in unknown dynamic environments
Reinforcement learning algorithms for exploiting flexibility within a Net-Zero Energy Factory

Designing Net-Zero Energy Factories is a significant step towards sustainable industrial operations. Within industrial systems, the identification and exploitation of flexibility is a challenging task. This paper presents a unique approach to energy management, leveraging reinforcement learning to operate a manufacturing system (carpentry) as a Net-Zero Energy Factory. The plant operates on custom orders, allowing for flexibility in machinery operation, including options to postpone or anticipate tasks. In addition to real-time energy consumption data from various machines and energy production data from a 126 kW Photovoltaic (PV) system installed on the plant’s roof, the plant also has an integrated battery system. This additional feature enhances the plant’s energy management flexibility, enabling it to store excess energy or supply it when needed. We introduce an algorithm that uses these various data sources to suggest whether to continue, postpone, or adjust current energy consumption, as well as managing battery storage and discharge based on the energy demands and production. The study also compares three reinforcement learning algorithms, providing a comprehensive evaluation of our approach. The results highlight the potential of reinforcement learning in managing energy efficiently, operating the plant towards Net-Zero Energy Factory. This work contributes to the understanding of renewable energy usage optimization in the manufacturing sector, particularly in harnessing battery systems for energy storage, offering a promising pathway to sustainable manufacturing and energy management.
Optimizing Computational Efficiency in Feature Selection for Machine Learning Models: A Study Crime Detection Based on Criminal Data

In this study, we embarked on the journey to optimize the computational efficiency of feature selection techniques for machine learning models, with a focused case study on crime detection using criminal data. Through a comprehensive analysis of various aspects of the data, from preprocessing to feature selection and model evaluation, valuable insights were gained. The results of our investigation underscored the significance of feature selection in enhancing both the accuracy and computational time of machine learning models. By comparing three classification models—Random Forest, Naïve Bayes, and AdaBoost. We observed that the performance varied significantly across different models and feature subsets. This research aims to explore the impact of feature selection on the accuracy and computational efficiency of machine learning models applied to crime detection tasks. By leveraging a case study approach, we seek to uncover the interplay between various feature selection techniques and classification models. The research encompasses a comprehensive five-step methodology, including data collection, preprocessing, feature selection, model classification, and performance evaluation. We adopt mutual information-based feature selection to distill relevant attributes from the dataset. The results of our research show that the random forest model has the best accuracy value among other models when using feature selection which is 0.99. In terms of computation time, random forest has a time of 66 seconds.
Low-Capacitance Cascaded H-Bridge STA TCOM with Enhanced Control Scheme under Unbalanced Grid

Preserving the stable operation of STATCOMs under grid voltage sags has been a primary control challenge. This challenge is even more detrimental to the control system in the Low-Capacitance STATCOMs due to the relatively large capacitor voltage ripples. By utilizing the cluster voltage balancing control based on zero-sequence voltage injection, this paper proposes an optimal control method for the Low-Capacitance STATCOM to balance the capacitor voltages in the presence of grid voltage faults. In addition, the destructive effects of unbalanced capacitor ripples are canceled in the control system to maintain the proper current quality and power flow of the converter. The paper verifies the investigated analysis by providing simulation results under various grid voltage sag conditions.
Accurate Association of Radar-Vision Measurements Based on Weighted Euclidean Distance

Associating radar measurements with vision measurements is essential for radar-vision fusion. Most radar-vision association methods rely on Euclidean distance. However, these methods have overlooked measurement errors, leading to a significant amount of erroneous associations. To address the limitations of existing methods, we propose an association method based on weighted Euclidean distance between radar measurements and vision measurements. This paper analyzes the measurement errors of radar and vision and uses this analysis as the basis for calculating weighted Euclidean distance, to make weighted Euclidean distance sensitive to measurement errors and reduce the occurrence of association mistakes caused by such inaccuracies. The obtained weighted Euclidean distance is then used as a cost matrix to perform association using the Hungarian algorithm. The proposed method is validated through simulations, and the experiments demonstrate that our approach achieves higher accuracy.
A PVT Variation Dependencies of VCO in Frequency Locked Loop

This paper discusses the dependencies of PVT (process, voltage, temperature) variation in a frequency locked loop (FLL). The FLL has good frequency stability. However, the available supply voltage range and temperature range are limited in the actual design because of the PVT variation of the VCO. In particular, PVT variation becomes a problem when the output frequency of FLL is close to the upper and lower limits of the frequency range of VCO. In this paper, we propose a VCO based on an adjustable Schmitt trigger and design the FLL at a frequency close to the lower limit of the VCO. The VCO with a wide frequency range or a high PVT variation tolerance can realize the FLL with low PVT variation dependencies. The PVT variation in FLL is dominated by the PVT variation in the VCO according to the experimental results.
Multimodal Biometric Recognition Neural Networks with Branch Attention Mechanisms

It is generally known that compared to unimodal systems, the multi-modal biometric systems can improve recognition accuracy by utilizing the complementary features of multiple biometric. Nevertheless, the modalities used may not always play equally important roles in multi-modal recognition. Especially, in practical application scenarios, it often occurs that some of the biometric samples are contaminated by noise and corruption. In this context, the multi-modal recognition systems should pay more attention to the high-quality modality than the ones with low-quality samples, so as to maintain high accuracy. In this paper, we proposes a multi-branch deep learning network for multi-modal recognition, each branch using one biometric sample as input. To highlight the more important biometric modalities, we propose to embed a branch attention module between branches before multi-modal feature fusion. The branch attention module includes fuse, select, and calibrate steps, which can dynamically calculate the weight of each branch based on its importance. The proposed multi-modal recognition model is tested on a chimeric bimodal dataset with the face and the ear modalities. Our proposed method still achieves 92.76% of rank-1 on the most contaminated dataset; the rest of the experimental results are shown in the Experiment and Performance Analysis section. Our experimental results clearly demonstrate that the proposed branch attention method can improve the multi-modal recognition performance.
Deep Learning-based Thickness Measurement With Pulse Eddy Current Testing

The Pulse Eddy Current (PEC) technique represents an innovative approach within the field of eddy current testing (ECT), primarily employed to assess the structural integrity of engineering components. However, this method typically encounters challenges stemming from variations in lift-off distances and necessitates a manual calibration procedure prior to operation. In this research paper, we introduce a novel approach for thickness measurement that leverages deep learning techniques to enable automatic and highly precise thickness assessment. Our proposed solution encompasses an Auto-Encoder model, incorporating convolutional layers and long short-term memory layers, designed for effectively learning raw PEC signals and predicting thickness values across varying lift-off conditions. To validate this approach, we conducted experiments focusing on the measurement of thickness in a Mild Carbon Steel specimen. Our results demonstrate that the PEC system successfully predicts thickness values within the range of 1 mm to 10 mm, exhibiting a mean and standard deviation error of the thickness of 0.026 and 0.038 mm, respectively, even when confronted with lift-off variations ranging from 0.5 mm to 2.0 mm.
A Reconfigurable Microstrip Antenna Based on Photoconductive Switches

This paper presents a novel reconfigurable antenna design using silicon as photoconductive switches. The conductivity of switches increases sharply under illumination with light of an adequate wavelength. This allows the antenna to switch between the two states rapidly and without interference from the control circuitry compared to other reconfiguration methods. Full-wave simulations are conducted to demonstrate the performance of the antenna. The return loss and the maximum realized gain of the antenna increase by 15.36 dB and 5.89 dB with the silicon switches turned on under the pump of light.
Epitaxial Lift-Off Derived High Performance Ferroelectric Thin Film Bulk Acoustic Resonator

In this article, we develop a simple and efficient method to fabricate a thin film bulk acoustic resonator (FBAR). As developed epitaxial lift-off technology not only solves the problem of poor crystal quality of piezoelectric film directly grown on bottom electrode, but also obtains thinner piezoelectric film to increase operating frequency of the device compared to Smart $\text{Cut}^{\mathrm{T}\mathrm{M}}$ process. Benefitting from the high-quality ferroelectric PbZro.2Tio. 803 (PZT) thin film, a resonant frequency up to 8.00 GHz, and electromechanical coupling coefficient $(\mathrm{k}_{\mathrm{t}^{2}})$ of 17.2% were achieved, maintaining good balance between high frequency and wide bandwidth for filter components. These results offer a simple yet promising solution for high performance ferroelectric FBAR, and show great potential of epitaxial lift-off technique for more applications in passive RF devices.
Influence of External Transverse Magnetic Field on Post-arc Plasma Dissipation in Vacuum Interruption

The objective of this paper is to determine the influence of external transverse magnetic field (TMF) on the plasma dissipation process after current zero in vacuum interruption. A two-dimensional (2D) particle-in-cell (PlC) model was established. The results of 0~400mT TMF are studied. The results show that during the dissipation process of plasma, TMF enhances the particle collisions and produces more particles, which is not benefit to plasma dissipation. In addition, TMF increases the velocity of particles, which is benefit to the dissipation of plasma. Finally, under the influence of TMF on particle collisions and velocity, the time spent for post-arc plasma dissipation process decreases first and then increases with the increase of magnetic field.
Online Versus Face-to-Face Learning: Students’ Preferences During Crisis Times

In March 2020, the world faced an abrupt global health crisis as the pandemic rapidly spread, leading to widespread school closures. Our research explored students' online learning preferences during this crisis, considering a range of variables including age, gender, and personal characteristics. We used a quantitative approach to gather data through six online questionnaires covering demographic details, personality traits, self-efficacy, attitude toward technology, parental support, and learning preferences. Findings reveal that students who held a more favorable attitude towards technology, perceived higher levels of academic achievements and parental support, were inclined to favor online platforms to a greater extent.
Ammi Biplot Analysis of Genotype × Environment Interaction in Wheat in Egypt = تحليل التأثير الرئيسي المضيف و التفاعل المضاعف لتفاعل التركيب الوراثي و البيئة للقمح في مصر

The genotype by environment (G×E) interaction has an impact on the selection of genotypes. The yield productivity of 49 wheat (Triticum aestivum L.) genotypes was evaluated using additive main effects and multiplicative Interaction (AMMI) model. Experiments were implemented in four locations (Barrani, Matrouh, Assiut and ElKharga) across Egypt using a randomized complete block design with three replications. Analysis of variance for grain yield (g/m) showed that the sum of squares of locations accounted for 86% of total sum of squares. The AMMI analysis of variance showed that two interaction principal components (IPCs) out of three were highly significant (P < 0.01). The IPC1 and IPC2 accounted for 55.4% and 31.4% of the G×E interaction, respectively. Genotypes 40 and 48 were well adapted to Assiut location whereas genotype 18 was adapted to Barrani location. The AMMI model identified the best combinations of genotypes and environments for yield.
Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic

We study the causal effects of lockdown measures on uncertainty and sentiment on Twitter. To this end, we exploit the quasi-experimental framework created by the first COVID-19 lockdown in a high-income economy--the unexpected Italian lockdown in February 2020. We measure changes in public sentiment using deep learning and dictionary-based methods on the text of daily tweets geolocated within and near the locked-down areas, before and after the treatment. We classify tweets into four categories--economics, health, politics, and lockdown policy--to examine how the policy affected emotions heterogeneously. Using a staggered difference-in-differences approach, we show that the lockdown did not have a significantly robust impact on economic uncertainty and sentiment. However, the policy came at the price of higher uncertainty on health and politics and more negative political sentiments. These results, which are robust to a battery of robustness tests, show that lockdowns have relevant non-health related implications.
SERCA2a gene therapy restores microRNA-1 expression in heart failure via an Akt/FoxO3A-dependent pathway

Aims Impaired myocardial sarcoplasmic reticulum calcium ATPase 2a (SERCA2a) activity is a hallmark of failing hearts, and SERCA2a gene therapy improves cardiac function in animals and patients with heart failure (HF). Deregulation of microRNAs has been demonstrated in HF pathophysiology. We studied the effects of therapeutic AAV9.SERCA2a gene therapy on cardiac miRNome expression and focused on regulation, expression, and function of miR-1 in reverse remodelled failing hearts. Methods and results We studied a chronic post-myocardial infarction HF model treated with AAV9.SERCA2a gene therapy. Heart failure resulted in a strong deregulation of the cardiac miRNome. miR-1 expression was decreased in failing hearts, but normalized in reverse remodelled hearts after AAV9.SERCA2a gene delivery. Increased Akt activation in cultured cardiomyocytes led to phosphorylation of FoxO3A and subsequent exclusion from the nucleus, resulting in miR-1 gene silencing. In vitro SERCA2a expression also rescued miR-1 in failing cardiomyocytes, whereas SERCA2a inhibition reduced miR-1 levels. In vivo, Akt and FoxO3A were highly phosphorylated in failing hearts, but reversed to normal by AAV9.SERCA2a, leading to cardiac miR-1 restoration. Likewise, enhanced sodium–calcium exchanger 1 (NCX1) expression during HF was normalized by SERCA2a gene therapy. Validation experiments identified NCX1 as a novel functional miR-1 target. Conclusion SERCA2a gene therapy of failing hearts restores miR-1 expression by an Akt/FoxO3A-dependent pathway, which is associated with normalized NCX1 expression and improved cardiac function.