Miracle-dz/UniFAR-FTUs huggingface dataset:
ELECTRIC VEHICLE ONBOARD CHARGING VIA HARRIS HAWKS OPTIMIZATION-BASED FRACTIONAL-ORDER SLIDING MODE CONTROLLER

Electric vehicles (EVs) have become more popular due to their excellent efficiency and pollution-free benefits. The technology requirements for onboard chargers are increasing as the number of electric vehicles increases. This research proposes a fractional-order sliding mode controller (FOSMC) for power converters to improve the efficiency of the onboard battery charger. The Harris Hawks optimization (HHO) algorithm chooses the FOSMC parameters. Independent controllers are used in a two-stage charging scheme. The grid-side ac–dc converter helps to smooth the current and voltage in the dc bus while reducing the harmonic frequency in the grid. A dc-dc converter with a constant current–constant voltage curve regulates the charging parameters of the battery on the battery side. Experiments show that HHO-based FOSMC improves the overall dynamic response of the onboard battery charger. Moreover, the proposed method performs with a current total harmonic distortion (THD) of less than 2 %. The proposed method improves 98% efficiency than existing methods such as SSA-PID and SSA- FOAFPIDF controllers.
Link-Level Performance Evaluation of Sidelink Synchronization Signal Block for 5G V2X

In this paper, we evaluate the performance of sidelink synchronization signal block (SL-SSB) for 5G vehicular-to-everything (V2X) communication systems. The SLSSB serves to establish communication link by synchronizing signals between the vehicles. We first introduce the transmission structure of SL-SSB and related synchronization procedures based on the standard documents presented by 3rd Generation Partnership Project (3GPP). Then, we develop a link level simulator (LLS), and evaluate the SL identity (ID) detection performance based on the SL-SSB and block error rate (BLER) performance for the physical sidelink broadcast channel (PSBCH).
CNN-Based DMRS Pattern Optimization for 5G Vehicle-to-Everything Communications

In this paper, we optimize the demodulation reference signal (DMRS) pattern of the physical sidelink shared channel (PSSCH) based on a deep learning approach to maximize the channel estimation performance for 5G vehicle-to-everything (V2X) communication systems. For the DMRS optimization, we design a convolutional neural network (CNN) which classifies the optimal DMRS pattern index for the given received signals. It is shown that the proposed CNN structure achieves 91% accuracy for the optimal DMRS pattern index. The simulation results verify that the proposed scheme achieves 4 dB performance gain in terms of mean square error (MSE) compared to the legacy DMRS pattern.
Detection of Denial-of-Service Attack in Wireless Sensor Networks: A Lightweight Machine Learning Approach

The characteristics and performance of wireless sensor networks (WSNs) are the main reasons for their rapid expansion in various fields. However, these networks are extremely susceptible to multiple security assaults, including denial-of-service (DoS) attacks, which are among the most prevalent in these networks. This study sheds light on WSN restrictions, weaknesses, and security threats with a focus on DoS attacks. Recent techniques for DoS attack detection have been investigated thoroughly, highlighting their achievements and limitations. This provides valuable insight into the current state of recent research in this field. Accordingly, this study proposes a lightweight machine learning detection approach based on a decision tree (DT) algorithm with the Gini feature selection method to detect DoS attacks in WSNs. An enhanced version of the WSN-DS dataset, developed by the author, was used to train and test the proposed approach. The proposed approach has shown good performance by achieving an accuracy rate of 99.5% with minimum overhead compared to random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN) classifiers. It only takes 9.7%, 13%, and 2% of the processing time required by FR, XGBoost, and KNN respectively, which indicates that our proposed approach significantly outperforms these classifiers in terms of processing time. It is noteworthy that RF achieved an accuracy that was somewhat superior; however, the proposed approach greatly surpassed RF by taking only 9.7% of the RF processing time, which is an important factor in meeting WSN constraints.
A Parallel-Path Amplifier for Fast Output Settling

Pushing CMOS technology to the nanometer range is detrimental to analog circuits’ performance due to the reduction of gain and slew rate of amplifiers, so the classical approaches need to be revisited for adjustment in advanced nodes. This paper presents a parallel-path amplifier used as a switched-capacitor (SC) amplifier. The proposed amplifier includes a high bandwidth and slewing path parallel to a high gain path. The high bandwidth and slewing path, named the feedforward path, provides high charging/discharging currents to decrease the slewing time of the amplification phase, significantly $(\geq 60$%). In parallel, the high gain path provides sufficient open-loop DC gain for final settling (59 dB). The feedforward path is enabled/disabled by control signals provided through a hysteresis detector and by considering the status of the feedback voltage. The proposed amplifier is designed and fabricated in 65nm CMOS technology as a multiplying digital-to-analog converter (MDAC) in a pipeline ADC. The chip is under fabrication, and this paper covers post-layout performance of the proposed amplifier. The results reveal that enabling the feedforward path guarantees the amplifier to have a constant error $(\lt2$ mV) for an extensive range of input voltages (300 mV $\leq$ Vin $\leq 900$ mV) compared to its standalone high gain path. At the same time, the static current of the feedforward path is minimal $(\lt 100 \mu \mathrm{A})$, and it can drive large load capacitors.
Deep Learning-Based Traffic Sign Identification Using Multi-Scale Feature Fusion

Roadside traffic signs are used to enforce vehicle safety laws, appropriately convey traffic conditions, and aid in safe driving. Therefore, accurate traffic sign identification is essential for automated driving. In this article, we suggest a simple and reliable one-stage object detection model for detecting and identifying traffic signs by using ResNet50 and a Feature Pyramid Network (FPN). We evaluate our network using the Tsinghua-Tencent 100K (TT100K) benchmark for traffic sign recognition, and the results demonstrate that the detection network can obtain overall Mean Average Precision (mAP) of 46.012%.
Capture-based aquaculture. Global overview

Capture-based aquaculture makes use of wild fish and fishery resources in its production systems. Focusing on this subject, this document addresses selected key issues of sustainability in relation to current global aquaculture practices and developments. It includes two review papers on the use of wild resources, one covering social and economic aspects and the other on environmental and biodiversity issues, as well as ten papers on selected marine and freshwater species used in capture-based aquaculture.
INFLUENCE OF THE SAMPLING FREQUENCY ON VARIOUS MAXIMUM POWER POINT TRACKING COMMANDS

The present work continues the previous article published in the International Journal of Energy (Elsevier, 2019). Our previous study aimed to develop a new and innovative method based on neural network algorithms to predict an instantaneous command. A new control strategy for photovoltaic systems was presented in [1]. This command is based on the neuronal network (NN) technique. To the best of our knowledge, this technique has never been used in this field for that objective. The authors of this work used it to synthesize control laws for electronic power converters.It should be noted that the newly designed algorithm based on neural networks is expected to be more robust with a good performance concerning tracking speed and precision. Moreover, the present research work aims at providing a robust neural structure against noisy empirical data, thus allowing the prediction of a new command. Indeed, in the present work, we will examine the parameters affecting four MPPT controls in addition to the new neural network-based algorithm developed in [1].
Mexico - Estudio sobre la disminucion de emisiones de carbono

One of the most compelling reasons for pursuing low-carbon development is that the potential impacts of climate change are predicted to be severe, for both industrial and developing countries, and that reducing greenhouse gas emissions can reduce the risk of the most catastrophic impacts. The challenge of reducing emissions is sobering: leading scientific models indicate that limiting the rise in global mean temperatures to less than two degree Celsius will require that global greenhouse gas emissions peak within the next 10-15 years and then fall by 2050 to levels about 50 percent lower than in 1990. Although many countries recognize the need to curtail carbon emissions, there is considerable uncertainty about how much this will cost in individual countries, what measures can be undertaken in both the short and longer term, and how cost-effective specific interventions are in reducing emissions. This study analyzes a range of energy efficiency options available in Mexico, including supply-side efficiency improvements in the electric power and oil and gas industries, and demand-side electricity efficiency measures addressing high-growth energy-consuming activities, such as air conditioning and refrigeration. It also evaluates a range of renewable energy options that make use of the country's vast wind, solar, biomass, hydro, and geothermal resources.
Corilagin Induces ROS-mediated Apoptosis and Triggers Cell-Cycle Arrest at G0/G1 Stage in Osteosarcoma Cells

Background Osteosarcoma (OS) is an extremely aggressive primary bone cancer (BC) malignancy. A variety of malignancies can develop in the bones, including BC. Primary BCs are tumors that start in the bone. Bones can also become affected by tumors that start in the body’s organs or other tissues. Objectives Several reports suggested that corilagin (CL) exerts anticancer properties on various kinds of tumor cells; however, its molecular action on OS cells remains undefined. Materials and Methods The CL activity of OS cells’ cytotoxicity, intracellular reactive oxygen species (ROS), mitochondrial membrane potential (MMP), apoptosis, and cell-cycle distribution was evaluated by 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assay, dichloro-dihydro-fluorescein diacetate (DCFH-DA), Rh-123, acridine orange and ethidium bromide (AO/EB), 4′,6-diamidino-2-phenylindole (DAPI), and flow cytometry analysis. Results Results revealed that CL could suppress OS cell proliferation via enhanced intracellular ROS, and MMP loss, and triggered apoptosis in a dose-dependent has an inferior manner. Our findings demonstrated that CL alleviates U2OS and MG-63 cell proliferation by the ROS-mediated apoptosis, which triggers G0/G1 cell-cycle arrest. Conclusion Thus, CL might be a protective therapeutic agent against BC.
Effects of Tai Chi combined with tDCS on cognitive function in patients with MCI: a randomized controlled trial

Background Mild cognitive impairment (MCI) is a critical stage of dementia. Previous reviews have suggested that physical exercise combined with non-invasive brain stimulation is more beneficial for improving cognitive function. However, no targeted studies have confirmed the effect of Tai Chi combined with transcranial direct current stimulation (tDCS) on the improvement of cognitive function in patients with MCI. Thus, this randomized trial was conducted to assess the effect of Tai Chi combined with tDCS on the cognitive performance of patients with MCI. Methods From April 2018 to February 2020, a randomized, single-blind clinical trial was conducted, involving 180 participants with MCI who were divided into four intervention groups: Tai Chi combined with tDCS (TCT), Tai Chi combined with sham tDCS (TCS), walking combined with tDCS (WAT), and walking combined with sham tDCS (WAS). All participants were assessed at baseline and 12 weeks for global cognitive function, memory, attention, and executive function. Results At baseline, there were no significant differences in age, gender, education duration, body mass index, or the Baker Depression Inventory among the four groups (P ≥ 0.05). After 12 weeks of intervention, the TCT group showed greater improvements in MOCA scores, memory quotient scores, and digit-symbol coding task reaction time compared to the TCS, WAS, and WAT groups (P < 0.05). The TCT group also had a shorter Stroop test color reaction time compared to the WAS and WAT groups (P < 0.05), a higher increase in Auditory Verbal Learning Test-immediate recall than the TCS and WAT groups (P < 0.05), a shorter visual reaction time than the TCS group (P < 0.05), and a shorter sustained attention time compared to the WAT group (P < 0.05). Conclusion Tai Chi combined with tDCS effectively improves global cognitive performance, memory, execution function, and attention in patients with MCI. These findings suggest the potential clinical use of Tai Chi combined with tDCS as a physical exercise combined with a non-invasive brain stimulation intervention to improve cognitive function in older adults with MCI. Clinical trial registration ChiCTR1800015629.
Evolution of eclogite facies metamorphism in the St. Cyr klippe, Yukon-Tanana terrane, Yukon, Canada

The St. Cyr klippe hosts well preserved to variably retrogressed eclogites found as sub-meter to hundreds of meter scale lenses within quartzofeldspathic schists in Yukon, Canada. The St. Cyr area consists of structurally imbricated, polydeformed, and polymetamorphosed units of continental arc crust and oceanic crust. The eclogitebearing quartzofeldspathic schists form a 30 by 6 km thick, northwest-striking, coherent package. The schists comprise metasedimentary rocks and felsic intrusives that are intercalated on the meter to tens of meter scale. Ultramafic rocks, serpentinites, and associated greenschist facies gabbros form imbricated tectonic slices within the eclogitebearing quartzofeldspathic unit, which led to a previously held hypothesis that the eclogites were exhumed within a tectonic mélange. The presence of phengite and Permian age zircon crystallized under eclogite facies metamorphic conditions in the quartzofeldspathic host rocks indicate that the eclogite was metamorphosed in situ within a coherent unit. This indicates that the eclogites and their host schists were derived from arc crust of the Yukon-Tanana terrane, rather than the subducting oceanic crust of the Slide Mountain Ocean terrane. Petrological, geochemical, geochronological, and structural relationships link the St. Cyr eclogites to other high-pressure localities Yukon, indicating the high-pressure assemblages form a larger lithotectonic unit within the Yukon-Tanana terrane. Introduction High-pressure (HP) metamorphic assemblages formed in Pacific-type convergent margins are found along the western North American margin from Alaska to Mexico
Cluster Satellite Relative Navigation with Angularly Oriented and Colored Light Source Image Processing

The study proposes a new method for determining the relative states of a target satellite using angularly directed and coloured LED lights. This visual-based relative satellite navigation system is inspired by the PAPI lights commonly used in aviation and involves mounting LED lights on the target satellite while the tracker satellite carries out camera tracking manoeuvres. The study compares different versions of relative state estimation techniques and evaluates their performance in test settings and simulation scenarios. The proposed visual-based approach was tested in a simulated space environment with background starlight, and the outcomes obtained were compared. The study suggests that the directed and coloured light source strategy can help address visual-based sensor challenges while avoiding computational burdens and line-of-sight vector concerns. The methodology proposed in this study can guide future cluster satellite configurations and creative approaches to spacecraft docking issues. This study presents a novel approach to satellite navigation and relative state estimation that could have important implications for space exploration and technology development.
Short and Long Term Volcano Instability Studies at Concepción Volcano, Nicaragua

Concepción is the most active composite volcano in Nicaragua, and is located on Ometepe Island, within Lake Nicaragua. Moderate to small volcanic explosions with a volcanic explosivity index (VEI) of 1–2 have been characteristic of this volcano during the last four decades. Although its current activity is not violent, its volcanic deposits reveal stages of violent activity involving Plinian and sub-Plinian eruptions that deposited vast amounts of volcanic tephra in the Atlantic Ocean. These observations, together with the 31,000 people living on the island, make Concepción volcano an important target for volcanological research. My research focuses on the investigation of the stability of the volcano edifice of Concepción, using geophysical data such as gravity, geodetic global positioning system (GPS), sulphur dioxide (SO2) flux, real-time seismic amplitude (RSAM), and satellite remotely-sensed data. The integration of these data sets provides information about the short-term behavior of Concepción, and some insights into the volcano’s long-term behavior. This study has provided, for the first time, information about the shallow dynamics of Concepción on time scales of days to weeks. I furnish evidence that this volcano is not gravitationally spreading in a continuous fashion as previously thought, that its bulk average density is comparable to that of a pile of gravel, that the volcano edifice is composed of two major distinctive lithologies, that the deformation field around the volcano is recoverable in a matter of days, and that the deformation source is located in the shallow crust. This source is also degassing through the relatively open magmatic conduit. There are, however, several remaining questions. Although the volcano is not spreading continuously there is the possibility that gravitational spreading may be taking place in a stick-slip fashion. This has important implications for slope stability of the volcano, and the associated hazards. The factors influencing the long term slope stability of the volcano are still
Padrões silábicos no Português de São Tomé e Príncipe: relações complexas

Neste artigo discutimos a silabificação no português urbano de São Tomé e Príncipe (PSTP), com base no exame de alguns processos fonológicos: apagamentos de clusters, coda e núcleo; ensurdecimento vocálico; vocalização, nasalização e posteriorização do rótico. Tais fenômenos, embora tratados, comumente, de forma independente pela literatura, quando analisados em conjunto podem refletir padrões silábicos diversos, trazendo uma visão ampla e complexa sobre o sistema em foco. De fato, o levantamento dos fenômenos no PSTP indica a atuação de duas trajetórias gramaticais paradoxais: a prevalência de sílabas CV fomentada por apagamentos e lenições, em que sílabas fechadas se adaptam em sílabas abertas, e a emergência de estruturas complexas, promovida, sobretudo, pela ressilabificação de sibilantes e pelo apagamento vocálico. Essas trajetórias contrastantes refletem a dificuldade de propostas tradicionais em explanar a variação observada no PSTP. Ressaltamos, a partir dos processos discutidos, a necessidade, em trabalhos futuros, em abordar a questão por modelos pautados no uso, que acomodem a variação, e possam abarcar a complexidade dos sistemas linguísticos.
Random and Static Phase Errors in a PLL Array for Millimeter-Wave Frequency Generation

The phase noise behavior of a PLL array driven by the same reference is investigated. The phase variance at the output and the cross-correlation between different output phases are calculated. The phase jitter of a primary radar system using this PLL array is discussed. Design suggestions for obtaining a small rms phase error of the IF radar signal are derived. A closed-form expression for the signal-to-noise ratio of a long-range primary radar system is presented. The static phase error between different PLL signals can be minimized by controlling the average charge pump (CP) duty cycle. Digital control of the CP duty cycle is discussed including an estimation of the static phase error.
Comprehensive Approach to Multi Model Speech Emotion Recognition System

Human-Robot Interfaces have made it possible for robots to learn how to communicate with people and understand their feelings. Robotic dogs, for instance, should be able to comprehend not just verbal orders, but also other information, such as the emotional and physical state of its human commander, and adjust their behavior appropriately. The goal of this article is to learn how to recognize the feelings conveyed by a speaker's tone of speech. In today's world, detecting emotions has become an important task. When people speak in fearful, angry, or joyful emotions, their pitch range tends to be higher and broader, while their pitch range is lower when expressing these emotions. To learn both short-and long-term correlations in the log, a convolutional neural network (CNN) with four local feature-learning blocks and an LSTM layer can be utilised. This deep learning model is built using the Mel-spectrogram of the input audio recordings [1]. The audio characteristics MFCC, MEL, Chroma, and Tonnetz, as well as the Support Vector Machine and Multilayer Perception, were used in this study to identify different emotional states [4]. The audio files used by the algorithm are those found in the RAVDESS dataset, which is publicly available online. The system's effectiveness, which is the desired Outcome, will next be determined by analyzing the audio recordings' spectrograms in WAV format. [5].
Form, function and matter: crossing the border of materiality

In this chapter, I examine whether and to what degree the physical or material substratum of technologies and technological objects affect the shaping of their functionalities, and the social relationships that develop around their conception and use (Pinch, 2008). Straightforward as such an objective may seem, actually achieving it is elusive. Even simple objects such as a chair or a knife entail much more than the materials of which they are made. Do the functions objects fulfill in social settings stem from their material makeup? A chair, for instance, is so shaped as to serve sitting rather than cutting and yet invoking the function of sitting is a purposeful (meaningful) act. To the degree that function is a modality of meaning, a particular way through which beliefs or intentions are expressed with respect to technological objects, it could be seen as predominantly nonmaterial (Faulkner and Runde, 2011; Searle, 1995).