Institute of Electrical and Electronics Engineers (IEEE)

viii 2.3.3 Damping Control in the transmission path 2.3.4 Damping control at generation 2.4 Power System Stabilizer 2.4.1 Adaptive Power System Stabilizer 3 INTELLIGENT PSS CONTROL TECHNIQUE 3.1 3.7 Recurrent Neural Network 3.7.1 The Dynamic of General Recurrent Network 54 3.8.2 Application of Recurrent Network 55 3.8.3 Control Application 4 THE DESIGN OF RECURRENT NEURO FUZZY CONTROLLER POWER SYSTEM STABILIZER 58 4.1

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Compressive sensing (CS) is an alternative to the Shannon limit when the signal to be acquired is known to be sparse or compressible in some domain. Since compressed samples are non-hierarchical packages of information, this acquisition technique can be employed to overcome channel losses and restricted data rates. The quality of the compressed samples that a sensor can deliver is affected by the measurement matrix used to collect them. Measurement matrices usually employed in CS image sensors are recursive random-like binary matrices obtained using pseudo-random number generators (PRNG). In this paper we analyse the performance of these PRNGs in order to understand how their non-idealities affect the quality of the compressed samples. We present the architecture of a CMOS image sensor that uses class-III elementary cellular automata (ECA) and pixel pulse width modulation (PWM) to generate onchip a measurement matrix and high the quality compressed samples.

GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed.

Zero Trust Architecture (ZTA) is a fundamental cybersecurity framework necessitated by the intricacy of cyber threats and the security of commercial networks. Insider attacks, advanced persistent threats (APTs), mobility, and cloud computing render perimeter defenses inadequate for protecting critical data and infrastructure. ZTA redefines security with the principle of "never trust, always verify," acknowledging both network and external dangers. We address Zero Trust Architecture and microsegmentation of corporate networks. Networks are microsegmented to restrict attacker mobility and enhance security. ZTA designs must be microsegmented to fulfill security needs, implement network components, and oversee the network. The study investigates network identification, segmentation, policy enforcement, and microsegmentation oversight. ZTA's cybersecurity is underscored by prominent network security breaches. In these circumstances, Zero Trust Architecture and microsegmentation enhance network security and efficiency. These instances exemplify challenges in policy formulation, network intricacy, and the integration of security infrastructure. Best practices for the integration and administration of solution and microsegmented networks are discussed. Zero Trust Architecture (ZTA) and microsegmentation enhance network security visibility, control, and threat detection capabilities. Access restrictions and microsegmentation diminish lateral mobility and potential damage inflicted by attackers. The security and performance of microsegmentation are analyzed utilizing empirical deployment data. We analyze the scalability and flexibility of on-premises, cloud, and hybrid microsegmentation systems. Complex ZTA microsegmentation is advantageous. Examples encompass intricate security protocols, diminished network performance, and continuous maintenance. This article proposes microsegmented network administration to address these challenges. The management of microsegmented environments is streamlined by automation, orchestration, and pilot-to-scale deployment. Address increasing corporate network security requirements using Zero Trust Architecture and microsegmentation. Corporate security This page addresses case studies, best practices, and practical recommendations about ZTA architecture microsegmentation. Contemporary microsegmentation and Zero Trust Architecture may enhance network security and mitigate 561 Chetan Sasidhar Ravi et al. Beyond the Firewall: Implementing Zero Trust....

We calculate electron energy loss spectra (EELS) for composite plasmonic structures based on silicene and germanene. A continued-fraction expression for the effective dielectric function is used to perform multiscale calculations of EELS for both silicene and germanene-based structures on silver substrates. A distinctive change in plasmonic response occurs for structures with a germanene or silicene surface coating of more than three layers. These differences may be exploited using spectroscopic characterization in order to determine if a few-layer coating has been successfully fabricated.

Accommodation of distributed generation (DG) units in the distribution power network (DPN) reduces the power losses (PL), improves the voltage profile (VP), and enhances the stability. The size and site for distribution generations have to be optimized to avail favorable results. Otherwise, the DPN may experience greater power losses, higher voltage deviation, and voltage instability issues. This article implements an optimization technique using a hunter-prey optimizer (HPO) algorithm to optimize single and multiple (two) DG units in the radial DPN to minimize total real power losses (RPL) and total voltage deviation (TVD). The effectiveness of the HPO algorithm is assessed on the IEEE benchmark 69-bus radial DPN and a real-world Cairo59 bus RDS. The simulation outcome after the optimized inclusion of DGs shown significant RPL reduction and considerable voltage enhancement. Furthermore, the optimized results of HPO algorithm were compared to the different algorithms and the comparison proved that the HPO can provide a more promising and authentic outcome than other algorithms.

Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such a task can be solved effectively using Graph Convolutional Networks (GCNs). However, a small number of users, socalled perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task. Existing works in this direction defend GCNs either by adversarial training or by identifying the attacker nodes followed by their removal. However, both of these approaches require that the attack patterns or attacker nodes be identified first, which is difficult in the scenario when the number of perturbator nodes is very small. In this work, we develop a GCN defense model, namely GraphLT 1 , which uses the concept of label transition. GraphLT assumes that perturbators' random activities deteriorate GCN's performance. To overcome this issue, GraphLT subsequently uses a novel Bayesian label transition model, which takes GCN's predicted labels and applies label transitions by Gibbs-sampling-based inference and thus repairs GCN's prediction to achieve better node classification. Extensive experiments on seven benchmark datasets show that GraphLT considerably enhances the performance of the node classifier in an unperturbed environment; furthermore, it validates that GraphLT can successfully repair a GCN-based node classifier with superior performance than several competing methods.

Three-dimensional (3D) object classification is crucial in various applications, including autonomous driving, robotics, and augmented reality, where precise detection of objects in 3D space is required. Traditional techniques for 3D object classification encompass voxel-based representations, multiview projections, and point cloud analysis. However, these approaches can be computationally intensive, and might not capture complete structure of the underlying object. This research introduces a novel lightweight model for 3D object classification based on point clouds that achieves higher accuracy while requiring minimal computing overhead. The proposed system incorporates adaptive sampling, an Attentive-Convolution Feature block, a Dualpath feature processing block, and a non-local attention mechanism to enhance feature representation and classification performance. To our knowledge, this is the first study to propose a lightweight TnetLight module as a transformation network that aligns and converts input point clouds, capturing subtle geometric variations and highlighting differences between objects to enhance shape recognition and differentiation accuracy. Additionally, the model integrates an Attentive-Convolution Feature extraction block that combines local geometric features with global contextual information, enhancing the network's ability to capture both detailed and broader characteristics for better differentiation between objects with slight variations. The network was evaluated on the ModelNet10 dataset, achieving an accuracy of 94.4%.

With the advancement of technology and the increasing demand on smart systems and smart applications that provide a quality-of-life improvement, there has been a surge in the demand of more conscious applications, Machine Learning (ML) is considered one of the driving forces behind implementing these types of applications, and one of its implementations is Speaker Identification (SID). Deep Neural Networks (DNNs) and also Recurrent Neural Networks (RNNs) are two main types of Deep Learning that are being used in the implementation of such applications. Speaker Identification is being utilized more and more on daily basis and is being focused on by the research community as a result of this demand. In this paper, a review will be conducted to some of the most recent researches that were conducted in this area and compare their results while discussing their outcomes.

Abstract. When implementing a DC distribution network, it is much easier to integrate distributed energy sources than it is with an ac grid.
Furthermore, the efficiency and reliability of DC distribution networks outperform those of AC systems. The protection of the DC distribution
networks, particularly the interruption and isolation of short-circuit fault currents, is still a major problem. Traditional mechanical and hybrid circuit-
breakers for DC fault protection have the disadvantage of being sluggish to operate, necessitating the use of high-power equipment. The Solid-State
Circuit Breaker is the best choice for quick fault interruption. Since they employ thyristors, Impedance-Source Circuit Breakers are among those that
provide automated fault detection and clearing. In this work, a new DC circuit breaker based Δ-impedance source configuration is proposed for
medium and low voltage DC distribution networks to provide the bidirectional operation which has also become the general requirement for the
modern power system. The proposed topology uses three-coupled windings with one capacitor and four SCR thyristors to facilitate the bidirectional
operation. MATLAB/Simulink environment is used to analyze and evaluate the performance of the proposed DC circuit breaker to protect the 240V
DC microgrid configuration with different fault conditions and locations. The results obtained prove that the proposed DC circuit breaker has a good
performance in protecting the DC distribution networks.
Keywords: DC microgrid protection; Coupled inductor; DC circuit breaker; Bidirectional operation; Impedance source circuit breaker

Introdução: A Hanseníase ou Mal de Hansen (MH) é uma doença infectocontagiosa que afeta os troncos nervosos periféricos em decorrência da invasão das células neurais pelo Mycobacterium leprae e da resposta do organismo ao processo infeccioso. O Brasil está entre os países que apresentam as taxas mais elevadas de detecção da doença, sendo mais de 10 casos novos por 100.000 habitantes, quando comparados com países europeus, em que apresentam taxas mínimas (>1 caso por 1000.000 habitantes) de ocorrência da doença Objetivos: O estudo buscou descrever e analisar o tratamento de úlcera hansênica em paciente atendido no ambulatório de feridas complexas. Materiais e Métodos: Estudo de caso clínico com aplicação da Sistematização da Assistência de Enfermagem, realizado no período de julho a agosto de 2016 na unidade de referência especializada de Santarém. Resultados: Paciente em tratamento apresentando uma lesão na região plantar direita (SIC: há 14 anos), sexo masculino, aposentado, por...

Next-generation firewalls are available and use machine learning and generative modeling to enhance the detection of hard-to-detect cyber threats. These systems incorporate advanced security controls, policies, and protocols with Layer 7 of the OSI model. This chapter updates these steep AI-based protection systems and applications. We present a taxonomy of machine learning solutions for cybersecurity and outline a family of next-gen firewalls that incorporate intelligent AI-based technologies, including deep learning, generative adversarial networks, and convolutional neural networks. We present extensive experimental results that show how deep generative Gaussian models effectively identify hard-to-detect threats of particular interest. Then we conclude with recommendations and further R&D directions within the next-generation firewall ecosystem that benefits significantly from AI and machine learning. Next-generation (next-gen) firewalls are already in the cybersecurity market, delivering crucial advancements previously not seen in traditional firewalls, in addition to incorporating the advanced security controls, policies, and protocols with Layer 7 of the OSI model. In general, next-gen firewalls leverage Artificial Intelligence (AI) and machine learning resources to keep up with the ongoing evolution of cyber threats as the systems develop and learn from accurate and incident data conditions. However, a shortcoming of some of the most expensive and advanced next-gen firewalls available is their reliance on supervised learning, which may require shared sensitive information from industries and extra difficulties.

The integration of artificial intelligence (AI) into
space exploration has ushered in a new era of discovery and
innovation. While AI offers immense potential for autonomous
decision-making, data analysis, and adaptive problem-solving, its
deployment in the harsh space environment poses significant
risks. This research paper investigates the multifaceted challenges
associated with AI in space, including the impact of cosmic
radiation, extreme temperatures, and communication disruptions
on AI system reliability and resilience. Through a comprehensive
review of literature, controlled experiments, and preliminary
findings, the paper highlights the need for specialized AI
development tailored to the unique demands of the cosmic
frontier. By understanding and mitigating these risks, we can
ensure the safe and effective utilization of AI in future space
missions, paving the way for continued exploration and
scientific advancement.

— In this paper, a single switch transformer-less DC-DC converter with continuous input current for photovoltaic applications is proposed. The suggested configuration utilizes a CL1C2D2 structure to achieve a high voltage gain and alleviate voltage pressure on the semiconductor components. This design enables a significant increase in voltage output with a minimal turns ratio, leading to reduced size, weight, and cost of the converter system. The reduction in turns ratio of the coupled inductor results in lowered voltage stress on the semiconductor elements. Additionally, employing just one power switch in the converter simplifies control and reduces expenses. With its continuous input current, this converter is particularly well-suited for integration in photovoltaic systems. Simulation results conducted using PSCAD/EMTDC software validate the efficacy of the proposed power converter. Furthermore, the maximum power output of the photovoltaic module through an MPPT (Maximum Power Point Tracking) controller under varying irradiance levels is determined, and simulations are executed using PSCAD/EMTDC.

Background: The Altmetric score (AS) is a novel measure of publication impact that is calculated by the number of mentions across various social media websites. This method may have advantages over traditional bibliometrics in the context of research by medical students. Objective: This study aimed to determine whether dermatology matriculants who graduated from higher-ranked medical schools published more articles with greater impact (ie, a higher AS) than those from lower-ranked institutions. Methods: A PubMed search for articles published by dermatology residents who started medical school in 2020 was conducted. Demographic information and Altmetric data were collected, and medical schools were sorted according to US News' top-25 and non-top-25 categories. Results: Residents who completed their medical training at a top-25 institution published more papers (mean 4.93, SD 4.18 vs mean 3.11, SD 3.32; P<.001) and accrued a significantly higher total AS (mean 67.9, SD 160 vs mean 22.9, SD 75.9; P<.001) and average AS (mean 13.1, SD 23.7 vs mean 6.71, SD 32.3; P<.001) per article than those who graduated from non-top-25 schools. Conclusions: Our results indicate that students in top-25 schools may have greater access to research resources and opportunities. With a pass/fail United States Medical Licensing Examination Step 1 exam that may increasingly shift focus toward scholarly output from medical students, further discussion on how to create a more equitable dermatology match is essential.

Background: The Altmetric score (AS) is a novel measure of publication impact that is calculated by the number of mentions across various social media websites. This method may have advantages over traditional bibliometrics in the context of research by medical students. Objective: This study aimed to determine whether dermatology matriculants who graduated from higher-ranked medical schools published more articles with greater impact (ie, a higher AS) than those from lower-ranked institutions. Methods: A PubMed search for articles published by dermatology residents who started medical school in 2020 was conducted. Demographic information and Altmetric data were collected, and medical schools were sorted according to US News' top-25 and non-top-25 categories. Results: Residents who completed their medical training at a top-25 institution published more papers (mean 4.93, SD 4.18 vs mean 3.11, SD 3.32; P<.001) and accrued a significantly higher total AS (mean 67.9, SD 160 vs mean 22.9, SD 75.9; P<.001) and average AS (mean 13.1, SD 23.7 vs mean 6.71, SD 32.3; P<.001) per article than those who graduated from non-top-25 schools. Conclusions: Our results indicate that students in top-25 schools may have greater access to research resources and opportunities. With a pass/fail United States Medical Licensing Examination Step 1 exam that may increasingly shift focus toward scholarly output from medical students, further discussion on how to create a more equitable dermatology match is essential.

State of the art communication system pave way for microstrip patch antennas to experience rapid development. Nowadays, patch antennas are becoming increasingly popular due to their light weight and low profile making them easy to fabricate and integrate into the feeding network. This paper presented a single feed dualband band antenna for 5G application operating in the 28 and 38 GHz millimeter wave band with an improved efficiency. The antenna is designed and simulated on Computer Simulation Technology (CST) platform using FR-4 substrate with 0. 8 mm height, 4.67 dielectric constant and 0.002 loss tangent. The total size of the antenna is 8 × 8 mm 2 , the rectangular radiator of the antenna is 3.4 × 3.4 mm 2 in size, where an inverted-L is introduced into the radiator to achieve dual-band capability, The antenna is fed through 50 Ω feed line probe of about 2.3 × 0.4 mm 2 in dimension. The results of the simulation shows that the antenna achieved wide bandwidth in the upper band (38 GHz) of about 3.54 GHz (35.56 GHz-39.12 GHz) with over 6 dB gain and the lower band (28 GHz) produce a bandwidth of about 1430 MHz (27.27 GHz-28.70 GHz) with 2.7 dB gain suitable for 5G applications.

In this paper, an efficient methodology to retargeting and reuse of embedded mixed-signal blocks is presented. Parametrized layout templates, accurate behavioral modeling of mixed-signal blocks, and appropriate mechanisms for tuning sized circuits to new sets of specs are its key components.

In the last decade, training recurrent neural networks (RNN) using techniques from the area of reservoir computing (RC) became more attractive for learning sequential data due to the ease of network train- ing. Although successfully applied in the language and speech domains, only little is known about using RC techniques for dynamic gesture recog- nition. We therefore conducted experiments on command gestures using Echo State Networks (ESN) to investigate both the eect of dierent ges- ture sequence representations and dierent parameter congurations. For recognition we employed the ensemble technique, i.e. using ESN as weak classiers. Our results show that using ESN is a promising approach for dynamic gesture recognition and we give indications for future experi- ments.

One of the main aims of this paper is to draw the reader's attention to some problems and results from Structural Geometry which can be easily reformulated in terms familiar to the researches in Computational Kinematics, thus establishing connecting threads between both disciplines. In particular, we herein study the problem of nding the spatial interpretation of polyhedral projections using the correspondence of this problem with that of analysing the instantaneous kinematics of panel-and-hinge frameworks. A robust algorithm to solve this problem is presented, based on the computation of the singular value decomposition of the rigidity matrix associated with such frameworks.

This systematic review and meta-analysis evaluated the performance of machine learning models in predicting prostate cancer progression using lifestyle factors as predictive biomarkers to improve prognostic accuracy. Various models were analyzed, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN). These models were applied to identify diagnostic and prognostic biomarkers and to enhance the forecasting of prostate cancer progression. The meta-analysis demonstrated high predictive effectiveness across models, with mean performance metrics of 0.901 AUC (Area Under the Curve), 0.914 F1 Score, 0.889 accuracy, and 0.914 sensitivity. Among the models, the Multi-Layer Perceptron (MLP) emerged as the most effective, achieving 97% accuracy and an AUC of 95.8%. These findings underscore the potential of machine learning to integrate lifestyle factors as predictive biomarkers, advancing precision oncology in prostate cancer care.

IT is evolving at an incredible pace, and this has greatly changed the perimeter of the network that is distributed. As such, the traditional concepts of security are not enough. Today, port-based firewalls have become virtually useless because, as more and more businesses use analytics, cloud computing, and other forms of automation to speed up their progress in creating new products and services to meet the public’s demand, smart cyber threats are being created to exploit the gaps left behind by traditional security tools. To counter such challenges, there has been the development of advanced firewalls referred to as the next generation firewalls (NGFWs) and Web Applications firewalls (WAFWs). The aim of this paper is to describe how NGFWs developed and merged with cloud solutions, focusing on additional capabilities like DPI, application filtering, and implementation of AI. Pursuant to that, this research compares AI-based firewalls as it assesses their performance in meeting current cyber threats using sophisticated methods like machine learning and deep learning. The paper focuses on the cloud applicability of AI-based firewalls and examines their capacity to deliver constant performance in dynamic networks. This research does not provide theoretical simulation but shows the actual performance of different AI-based firewall architectures, their ability to detect threats, false positives, and time consumption. The paper also looks into the issues related to the use of these advanced firewalls in cloud environments and discusses possible drawbacks and modifications that may be done. Therefore, this research offers a literature review of the NGFWs and WAFWs and their applicability in defending the current complex enterprise environments, including the use of cloud services. The findings add to the current knowledge of cyber security management, suggesting the potential of AI to be a key enabler of detection, growth, and resource management within the cloud security paradigm.

Introdução: Introdução: A hanseníase configura-se como um problema de saúde pública no Brasil juntamente com outros países, pois ainda não se conseguiu alcançar as taxas alvo de erradicação preconizada pela Organização Mundial da Saúde. O diagnóstico da forma neural pura representa um grande desafio, pois não apresenta as lesões de pele características da doença e demanda ampla e demorada investigação, uma vez que não há exame laboratorial confirmatório rápido, fácil e universalmente acessível. Objetivos: Objetivos: Relatar oito casos de Hanseníase Neural Pura diagnosticados e tratados no Núcleo de Medicina Tropical da Universidade Federal do Pará (NMT/UFPA) com ênfase no tempo entre a queixa e o diagnóstico no âmbito do SUS Materiais e Métodos: Metodologia: Trata-se de estudo descritivo de 8 casos de Neural Pura da Hanseníase, considerando idade, sexo, procedência, tempo de surgimento dos sintomas até o diagnóstico, número de troncos nervosos afetados, grau de incapacidade e tratam...

Highways are an important component of any country. However, some highways in Indonesia endanger users while maintaining road safety. Crack detection early in the deterioration process can prevent further damage and lower maintenance costs. A recent study sought to develop a method for detecting road damage by combining the road damage detection (RDD) dataset with generative adversarial network technology and data augmentation to improve training. The current study aims to broaden the you only look once (YOLO) framework by incorporating the Swin Transformer into the chiral stationary phases (CSP) component of YOLOv7, with the goal of improving object detection accuracy in a variety of visual scenarios. The study compares the performance of various object detection models with varying parameters and configurations, such as YOLOv5l, YOLOv6l, YOLOv7-tiny, YOLOv7, and YOLOv7x. YOLOv5l has 46 million parameters and 108 billion floating point operations per second (FLOPS), whereas YOLOv6l has 59.5 million parameters and 150 billion FLOPS. With 31 million parameters and 140 billion FLOPS, the YOLOv7-swin model performs best with mean average precision (mAP), mAP_0.50 of 0.47. and mAP_0.5:0.95 of 0.232. The experimental results show that our YOLOv7-swin model outperforms both YOLOv7x and YOLOv7-tiny. The proposed model significantly improves object detection accuracy while keeping complexity and performance in balance.

This paper presents an optimization algorithm called KGCS suitable to be used for different engineering problems. The efficiency of the algorithm is given by the aggregation between the particularities of the Cuckoo Search algorithm and of the Knowledge Gradient policy. The paper also presents the testing of the KGCS algorithm on benchmark engineering problems, such as the optimal design of welded beams, speed reducers or four bar trusses. The results of the tests are compared to the ones reported in the literature for other evolutionary algorithms to prove the high performance of the KGCS algorithm. Key words: evolutionary algorithm, optimization, engineering design, welded beam, speed reducer, four bar truss.

During the fast progress of digital technologies, business intelligence (BI) has experienced a significant change, leading to new chances for making data-informed decisions and gaining strategic insights. Businesses are currently producing large amounts of data from various origins, requiring sophisticated analytics to obtain valuable insights. The merging of Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) has emerged as a crucial factor, boosting operational effectiveness and competitive edge. This study investigates how the combination of Big Data, AI, and IoT in BI improves data analytics and decision-making processes. A thorough examination of the literature and analysis of keyword co-occurrence and cluster reveal important concepts and their connections within the subject area. Big Data analytics reveals concealed patterns and trends, AI offers advanced predictive algorithms, and IoT supplies immediate data from interconnected devices, enhancing the detail and timeliness of insights. We suggest a thorough structure for combining these technologies to enhance BI capabilities. This structure includes gathering information from IoT devices, handling it with Big Data technologies, and utilizing AI for examination. Edge computing decreases latency for immediate decision-making, while blockchain guarantees the confidentiality and accuracy of data. The insights are utilized by the application layer to power BI applications such as dynamic dashboards and automated workflows. The research has made contributions by pinpointing central themes in the field, illustrating connections between important ideas, and creating a comprehensive framework to improve BI. By making use of these cutting-edge technologies, companies can acquire a more profound understanding, forecast future results, and establish more effective procedures. This method helps organizations maneuver through the challenges of today's data environment, preparing them for long-term development and achievement in a world that relies heavily on data. As technology advances, incorporating Big Data, AI, and IoT will enhance BI's abilities, creating fresh possibilities for innovation and a competitive edge.

Keywords: Business, Data Analytics, Big Data Analytics, Artificial Intelligence, Internet of Things, Machine Learning.

Blockchain technology can play a vital role in the space industry and exploration. This magic technology can provide decentralized and secure techniques for processing and manipulating space resources as space digital tokens. Tokenizing space resources such as orbits, satellites, spacecraft, orbital debris, asteroids, and other space objects in the form of blockchain-based digital tokens will reflect plenty of various applications in the space mining industry. Moreover, Blockchain algorithms based on smart contracts can be utilized for tracking all space transactions and communications in a transparent, verifiable, and secure manner. This paper is one of the first attempts towards conceptually investigating adopting blockchain theory in the space industry based on space digital token concept. A new conceptual blockchain in space industry framework is proposed, and new models are created for introducing proposed solutions for some major challenges in the space industry and exploratio...

YOLOv5 represents a significant advancement in the field of real-time object detection, building upon the YOLO (You Only Look Once) series' legacy. This paper provides a comprehensive review of YOLOv5, examining its architecture, innovations, performance benchmarks, and applications. We also compare YOLOv5 with previous YOLO versions and other state-of-the-art object detection models, highlighting its strengths and limitations. Through this review, we aim to offer insights into the evolution of YOLOv5 and its impact on the field of computer vision.

Open-vocabulary keyword spotting (KWS) aims to detect arbitrary keywords from continuous speech, which allows users to define their personal keywords. In this paper, we propose a novel location guided end-to-end (E2E) keyword spotting system. Firstly, we predict endpoints of keyword in the entire speech based on attention mechanism. Secondly, we calculate the existence probability of keyword by fusing the located keyword speech segment and text with local attention. The results on Librispeech dataset and Google speech commands dataset show our proposed method significantly outperforms the baseline method and the latest small-footprint E2E KWS method.

Open-vocabulary keyword spotting (KWS) aims to detect arbitrary keywords from continuous speech, which allows users to define their personal keywords. In this paper, we propose a novel location guided end-to-end (E2E) keyword spotting system. Firstly, we predict endpoints of keyword in the entire speech based on attention mechanism. Secondly, we calculate the existence probability of keyword by fusing the located keyword speech segment and text with local attention. The results on Librispeech dataset and Google speech commands dataset show our proposed method significantly outperforms the baseline method and the latest small-footprint E2E KWS method.

Message oriented and robotics middleware play an important role in facilitating robot control, abstracting complex functionality, and unifying communication patterns between sensors and devices. However, using multiple middleware frameworks presents a challenge in integrating different robots within a single system. To address this challenge, we present Wrapyfi, a Python wrapper supporting multiple message oriented and robotics middleware, including ZeroMQ, YARP, ROS, and ROS 2. Wrapyfi also provides plugins for exchanging deep learning framework data, without additional encoding or preprocessing steps. Using Wrapyfi eases the development of scripts that run on multiple machines, thereby enabling cross-platform communication and workload distribution. We finally present the three communication schemes that form the cornerstone of Wrapyfi's communication model, along with examples that demonstrate their applicability.

Space-time focusing of phase-stabilized nanojoulelevel 2.5-cycle pulses to peak intensities >3x10[superscript 13] w/cm[superscript 2] at 80 mhz The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Miranda, M.N. et al. "Space-time focusing of phasestabilized nanojoule-level 2.5-cycle pulses to peak intensities #3×10[superscript 13] W/cm[superscript 2] at 80 MHz.

This paper presents a low-profile textile scorpion patch antenna for wearable applications. The modal behaviors of the proposed antenna are investigated by using the theory of characteristic modes analysis (CMA). Four characteristic modes with the same resonant frequencies and orthogonal current distributions are chosen for operation. The proposed antenna has a simple structure with an overall size of 0.74 λg × 0.74 λg × 0.06 λg (guided wavelength at 5.5 GHz). This work characterizes and estimates the final shape of the patch using the CMA method aimed at obtaining the required resonant frequency. The shape optimization is simplified, as the substrates' influence and excitation are first excluded. This optimizes the needed time and resources in the design process, in contrast to the conventional optimization approaches made using full-wave "trial and error" simulations on a complete antenna structure. A systematic investigation of the effects of bending applied on wearable patch antennas over cylindrical surfaces is presented via extensive simulations performed using FEKO software based on both the CMA and the Method of Moments (MoM). The measurement results show that the proposed antenna has-10 dB impedance bandwidth of from 4.8 to 5.59 GHz. Moreover, the simulation results indicate that the antenna gain of 3 dBi over the whole operating bandwidth.

High-utility itemset mining (HUIM) is an important data mining task with broad applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces a number of new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM trust on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to decreses the cost of database scans, EFIM come up with efficient database projection and transaction merging techniques. An extensive experimental study on various datasets appears that EFIM is in general two to three orders of magnitude faster and consumes up to eight times fever memory.

—Sentiment analysis of online doctor reviews helps patients to better evaluate and select the related doctors based on the previous patients' satisfaction. Although some studies are addressing this problem in the English language, only one preliminary study has been reported for the Persian language. In this study, we propose a new evolutionary deep model for sentiment analysis of Persian online doctor reviews. The proposed method utilizes both Persian reviews and their English translations as inputs of two separate deep models. Then, the outputs of the two models are combined in a single vector which is used for deciding the sentiment polarity of the review in the last layer of the proposed deep model. To improve the performance of the system, we propose an evolutionary approach to optimize the hyperparameters of the proposed deep model. We also compared three evolutionary algorithms, namely, Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Gray Wolf Optimization (GWO) algorithm, for this purpose. We evaluated the proposed model in two phases; In the first phase, we compared four deep models, namely, long shortterm memory (LSTM), convolutional neural network (CNN), a hybrid of LSTM and CNN, and a bidirectional LSTM (BiLSTM) model with four traditional machine learning models including Naïve Bayes (NB), decision tree (DT), support vector machines (SVM), and random forest (RF). The results showed that the BiLSTM and CNN models outperform other methods, significantly. In the second phase, we compared the optimized version of two proposed bi-lingual models in which either two BiLSTM or two CNN models were used in parallel for processing Persian and English reviews. The results indicated that the optimization of the CNN using ACO and the optimization of BiLSTM using a genetic algorithm can achieve the best performance among other combinations of two deep models and three optimization algorithms. In the current study, we proposed two deep models for bi-lingual sentiment analysis of online Persian doctor reviews. Moreover, we optimized the proposed models using ACO, genetic algorithm, and gray wolf optimization methods. The results indicated that the proposed bi-lingual model outperforms a similar model using only Persian or English reviews. Also, optimizing the parameter of proposed deep models using ACO or genetic algorithms improved the performance of the models.

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework facilitates the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. Our methodology provides a natural extension to decide what buses a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to active distribution networks.

The process of categorizing students' performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students' performance. However, this study entails the need to analyze the diverse attributes of students' information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students' online learning activities, utilizing a variety of classification algorithms.

Estimation of lateral inflows using data assimilation in the context of realtime flood forecasting for the Marne catchment in France. Estimation des apports latéraux via assimilation de données pour le bassin versant de la Marne dans un contexte opérationnel de prévision des crues.

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shamin...