Miracle-dz/UniFAR-FTUs huggingface dataset:
Incentive Design for Flexibility Provisions From Residential Energy Hubs in Smart Grid

Transforming residential loads to residential energy hubs (REHs) and hence creating a flexibility in their energy consumption, can offer significant benefits to the power grid, such as peak reduction, congestion relief and capacity deferral. In this article a novel framework is proposed to simultaneously determine the optimal incentive paid by the local distribution company (LDC) to residential customers and their optimal level of participation in transforming to REHs, considering the economic benefits of both parties. The proposed framework models the relationship between customers’ participation and incentives offered by the LDC. A Monte Carlo simulation based optimization approach is employed to create a large dataset of the incentives (<inline-formula> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\rho ^{Inc}$ </tex-math></inline-formula>) payable by the LDC, and a desired internal rate of return (IRR) by the residential customer, so as to cover the wide range of possible IRRs the customers may have, and the impact of the incentives (<inline-formula> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\rho ^{Inc}$ </tex-math></inline-formula>) on each other. Numerical simulations considering a 33-bus distribution system show that the proposed framework is effective and can economically benefit the LDC and residential customers, while also achieving peak load reduction and improving distribution system operation.
Uncertainty analysis of ship shafting bearing load under the variational elevation based on stochastic finite element

Considering the hull deformation influence on the ship shafting, the uncertainty analysis of ship shafting bearing load under the variation of elevation based on stochastic finite element was proposed. The finite element and response surface models of shafting are established taking ship shafting as the research object. The Monte Carlo method was used to obtain the bearing elevation random samples, and the bearing load random distribution was calculated. The results show: bearing load obeys the normal distribution of (313.44, 4.6941), (42.107, 8.845), (151.98, 4.1509). Therefore, stochastic finite element can effectively analyze the uncertainty of bearing load under the bearing elevation changes randomly.
Intelligent Anti-Jamming Communication for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach

In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user anti-jamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.
Effect of negative gases admixture on the stability of beam-driven discharges

The influence of negative gas SF6 admixture on the stability of low-pressure beam-driven discharge is studied by self-consistent 1D particle-in-cell/Monte Carlo collisions model. We find that the plasma parameters as well as its stability are changed drastically when SF6 is added to argon gas. In an electropositive plasma we obtain the excitation of both fast and slow electrostatic plasma waves which is explained by the propagation of electron beam through the non-homogeneous bounded plasma. These waves lead to the heating of plasma electrons and ions. On the other hand, in electronegative plasma we also obtain a decay of the electron beam. However, since in electronegative beam-generated plasma the electron plasma density is homogeneous the beam is more stable. As a consequence, we obtain only the excitation of two fast electrostatic waves. One wave is excited due to two-stream instability and another wave is excited due to beam decay. The excitation of these fast waves does not influence the plasma homogeneity but influences the heating of plasma electrons.
A New Hybrid Three-Phase Multilevel Inverter Devoted to Electric Drive with Constant Volt per Hertz Control

In this paper, a novel hybrid three-phase multilevel inverter (MLI) is suggested in which the ratio of exploited switches versus the generated voltage levels is reduced. This ratio is mentioned as a critical factor due to its direct effects on the implementation costs, efficiency, and volume of the system. Each phase of the recommended MLI comprises the cascaded connection of the suggested basic module(s) and an auxiliary module, so the proposed topology is called a hybrid one. The auxiliary module is utilized to double the voltage levels in each phase. Furthermore, the presented MLI is allocated to control of the induction motor (IM). As a mature and simple method for controlling the speed of the IMs, the constant Volt per Hertz (CVH) is chosen and applied for the suggested inverter. The LS-PWM is utilized to reach this goal due to its controllability on the value and frequency of the output. Moreover, to validate the performance of the applied method and the feasibility of the presented circuit, a comprehensive simulation is done through the MATLAB/Simulink. Besides, the merits and superiorities of the suggested MLI over some other inverters are investigated.
Selenium and Zinc as Supplements to Extenders Frozen Semen for Improving Sperm Characteristics during Cryopreservation

Aim of this study is to evaluate the effect of adding different levels of Zinc and Selenium on extender as antioxidants to improve Friesian-bull semen quality after cryopreservation. Semen was collected, pooled, and extended with five treatment extenders, including control without (E1), 0.3 mg/100ml Zn (E2), 0.6 mg/100ml Zn (E3), 0.2 mg/100ml Se (E4), and 0.5 mg/100ml Se (E5). Semen was evaluated for visual parameters after dilution, equilibration, and thawing. Also, semen was evaluated by CASA only after thawing. Results showed that there are no effects of all additives on sperm characteristics after dilution and thawing. Only visual progressive motility percentage increased (P<0.05) post-equilibration by E5. Percentages of acrosome integrity were enhanced by E4 and E5. Semen analysis by CASA revealed that non-motility, and total and head abnormalities percentages were decreased (P<0.05), while vitality, and total and rapid progressive motility percentages were increased (P<0.05) by E5. Dynamic sperm parameters straightness and wobble) were increased (P<0.05) E5. In conclusion, adding selenium at a level of 0.5 mg/100ml to extender of cryopreserved Friesian-bull semen ha beneficial effects on maintaining sperm parameters and improved sperm freezability. This may be used as a tool for improving cryopreserved semen in artificial insemination centers.
Use of Deep Learning Models to detect COVID-19 from Chest X-Rays

The COVID 19 pandemic has spread rapidly across the globe in the last one year and the global response to it especially from the artificial intelligence community has been tremendous. It was seen from chest CT scans and chest X-rays that the respiratory system is hugely affected by the corona virus. In developing countries like India, a rapid and low-cost diagnostic tool is of prime importance for mass screening of population. Given the recent success of deep learning techniques in solving real-time problems, this paper presents application of deep learning models to detect and classify COVID 19 from chest radiographs. Dataset of 2727 images was constructed for this study and analysis from various open source resources like Kaggle and Github. Several pre-trained models have been used for experimentation. Among all the models, VGG-16 model achieved an impressive classification accuracy of 98.9% and F1-score of 0.984 with high sensitivity and specificity as well.
A wide soft switching range Power factor correction Converter

In this paper, a totem-pole power factor correction converter with low THD, high efficiency and wide soft switching range in the entire power grid voltage cycle, is presented. The converter has a proper efficiency at high frequencies due to the energy recovering of the snubber capacitor and the switches parasitic capacitor. Due to the DCM operation, this converter has an inherent power factor correction and does not need a control circuit to form the input current. Also, due to its structure, the main switches are pulsed simultaneously regardless of the input voltage positive or negative half cycle, which simplifies the control circuit. In this paper, the principles of circuit operation and analysis of the proposed converter are presented and verified by simulation results.
Spectrum Management Analysis for Cognitive Radio IoT

Recently, several Internet of Things Tools have been created, contributing to growing network loads. To refrain from IoT should use the idea of cognitive radio networks because of the lack of bandwidth. This article presents much of the research discusses the distribution of channels and preparation of packets when combining cognitive radio networks with IoT technology and we are further discussing the spectrum-based Features and heterogeneity in cognitive IoT Security. Surveying the research performed in this field reveals that the work performed is still developing. A variety of inventions and experiments are part of its initial phases.
Research on Sina Weibo Altmetrics Index about Sentiment Analysis

[Purpose/significance] In order to explore the social public’s emotional tendency towards academic achievements on social platforms and whether academic information will cause certain social risks to scholars when facing the public with great differences. [Method/process] Links to original texts of all academic information related to Sina Weibo provided by Altmetric company from October 2011 to October 2017 have been selected. On this basis, python was used to crawl Sina Weibo comment data, and a total of 53826 valid comment data were obtained, which constituted the basic data set of this study for emotional analysis. [Result/conclusion] The research finds that different scholars and the public have a greater acceptance of networked academic information, and the emotional tendency of Sina Weibo data comments is generally positive. However, if the original Sina Weibo has ambiguous or extreme comments, the public may have negative emotions due to misunderstanding of the original text and attacks on these extreme comments. This paper also tries to introduce The Schwartz value Scale from the micro perspective and put forward the idea of emotional evaluation of Sina Weibo academic information review based on values, in order to provide more detailed thinking of emotion dimension for the study of Altmetrics index from the perspective of emotion analysis.
Surfactant adsorption and water desorption on thin cellulose films monitored by in-situ ATR-FTIR spectroscopy

The adsorption of surfactants and formation of surface layers play an important role in many industrial processes, e.g. the production and finishing of fibres or surface treatment of polymers. This work deals with investigations into the formation of layers of surface-active substances on the cellulose surface by ATR-FTIR spectroscopy. These results were supported by quantitative adsorption measurements, i.e. determination of the difference between the starting and the residual concentration. It was shown that the formation of multilayers of cationic substances on cellulose is chelated to partial dehydration of the surface region.
Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm

Path planning is a global optimization problem aims to program the optimal flight path for Unmanned Aerial Vehicle (UAV) that has short length and suffers from low threat. In this paper, we present a Mixed-Strategy based Gravitational Search Algorithm (MSGSA) for the path planning. In MSGSA, an adaptive adjustment strategy for the gravitational constant attenuation factor alpha ( $\alpha$ ) is presented firstly, in which the value of $\alpha $ is adjusted based on the evolutionary state of the particles. This helps to adaptively balance the exploration and exploitation of the algorithm. In addition, to further alleviate the premature convergence problem, a Cauchy mutation strategy is developed for MSGSA. In this strategy, only when the global best particle cannot be further improved for several times the mutation is executed. In the MSGSA based path planning procedure, we construct an objective function using the flight length cost, threat area cost, and turning angle constraint to decrease the flight risk and obtain the smoother path. For performance evaluation, the MSGSA is applied to two typical simulated flight missions with complex flight environments, including user-defined forbidden flying areas, Radar, missile, artillery and anti-aircraft gun. The obtained flight paths are compared with that of the standard Gravitational Search Algorithm (GSA) and two improved variants of GSA, i.e. gbest-guided GSA (GGSA), and hybrid Particle Swarm Optimization and GSA (PSOGSA). The experimental results demonstrate the superiority of the MSGSA based method in terms of the solution quality, robustness, as well as the constraint-handling ability.
A Non-isolated High Step-Up DC-DC Converter Recommended for Photovoltaic Systems

This paper introduces a new non-isolated high gain dc-dc topology with magnetic coupling and voltage multiplier technique (VM). The high voltage conversion ratio can be achieved by selecting the suitable turn’s relation of the magnetic coupling. The VM circuit has been utilized for further extending the voltage rate. Also, the voltage spike through the switch can be clamped by VM circuit which leads the decrease the peak voltage through the switch. Moreover, there is only single switch, so the control circuit will be simple and a switch with lower conducting resistance can be selected. For validating the proficiency of the proposed structure, technical analysis and simulation results with 200W power level and 25 KHz operating frequency is provided. Furthermore, the comparison survey with other topologies are prepared for demonstrating the effectiveness of the recommended converter.
Factors Affecting Internet of Things (IoT) Adoption in the Yemeni Oil and Gas Sector

The Internet of Things (IoT) is defined as a potentially effective means of integrating multiple technologies to support the oil and gas (O&G) sector as a network of physical objects connected to the Internet. In Yemen, O&G organizations can embedded vehicles, equipment, buildings, fire siren and wells with electronics, software, sensors, and network connectivity that is now evolving. It is assumed that in one way or another, everything should be connected to one network to ease processes of business. To make IoT a reality, and not only remain academic, O&G sector must be able to engage in and pick up this technology in its day-to-day activities. Several studies have explored IoT’s potential for numerous organizations. However, IoT remains inadequately applied by organizations, including O&G sectors in developing countries. This article attempts to identify the factors that influence users to adopt IoT in the O&G sector in Yemen. Based on a study of prior tests, drivers were identified. Furthermore, a framework is proposed based on information system adoption theory which is the technology-organization-environment (TOE). Partial least squares structural equation modeling (PLS-SEM) has become a popular tool for analyzing such relationships is applied to a survey of 390 IoT users, with results indicating that the factors selected in this study significantly affected the IoT adoption in the Yemeni O&G sector. This study is enabling O&G organizations to understand the factors of IoT adoption, to improve their framework of business and investment in IoT and to inspire researchers to continue research into other IoT adoption or implementation factors.
An Edge-Cloud Framework Equipped with Deep Learning Model for Recyclable Garbage Detection

To identify recyclable garbage via the garbage classification is an effective countermeasure for protecting the environment. An automatic classification system supported by image recognition technologies is able to significantly reduce huge human labors of recycling tasks. However, performing the real-time and accurate garbage detection is not a trivial task. In this paper, we present an Edge-Cloud framework equipped with deep learning model for recyclable garbage detection. Specifically, we propose to use the deep convolutional neural network for garbage images classification, and thus design the collaborative mechanism between edge devices and the cloud server. As a result, we design and develop a novel recyclable garbage detection system, where scanning garbage images and thus detecting recyclable ones can be completed in real-time. We validate the performances of the proposed recyclable garbage detection system on 1000 real-life household garbage images. Experimental results show the recyclable garbage detection rate is over 85%, and the time for detection is less than 500 milliseconds.
Exploring the Limits of a Redundant Actuation System Through Co-Design

This paper assesses the energy efficiency of a redundant actuation architecture combining Quasi-Direct Drive (QDD) motors and Series Elastic Actuators (SEAs) by comparing its energy consumption to Geared Motors (GMs) and SEAs alone. We consider this comparison for two robotic systems performing different tasks. Our results show that using the redundant actuation we can save up to 99% of energy with respect to SEA for sinusoidal movements. This efficiency is achieved by exploiting the coupled dynamics of the two actuators, resulting in a latching-like control strategy. We also show that these large energy savings are not straightforwardly extendable to nonsinusoidal movements, but smaller savings (e.g., 7%) are nonetheless possible. The presented results were obtained thanks to the framework of concurrent design (co-design), namely the simultaneous optimization of hardware parameters and control trajectories. This shows that the combination of complex hardware morphologies and advanced numerical co-design can lead to peak hardware performance that would be unattainable by human intuition alone.
Cooperative Optimization of Networked Microgrids for Supporting Grid Flexibility Services Using Model Predictive Control

The transition towards fully renewable energy-based power systems will require to increase the number of reserves at the System Operators’ (SOs) disposal to provide flexibility on the energy management process. The microgrid’s ability of integrating distributed energy resources, loads and energy storage systems (ESS) appears as a powerful flexibility tool. Nevertheless, the associated control problem of microgrids increases with the number of connected devices. A structuration of the distribution grids in networks of microgrids is proposed, focusing on their ability to provide flexibility services. The complexity of the associated optimization algorithm is faced using Distributed Model Predictive Control (MPC). The algorithm is divided in two steps. The first one is applied to the cooperative participation of microgrids in the day-ahead market. The second step covers the interaction with the SO offering flexibility services in exchange for a financial benefit. The financial benefit is optimally shared between the networked microgrids to satisfy the power profile requested by the SO at the lowest cost. As the proposed control algorithm presents both continuous and binary variables, its associated optimization problem is formulated using the Mixed Logic Dynamic (MLD) framework, which results in a Mixed Integer Quadratic Programming problem (MIQP).
The Proficient Nano Remote Sensor Network Using Energy Preserving Routing Strategy

In recent years developing and implementing sensor networks has been a lot of growth. Wireless Body Area Networks (WBANs) support the prior phase of tracking, detection, diagnosis, or clinical measures of medical services. The key problems have been found in the growth of wireless nano-sensor network protocols, such as nano-nodes with severe energy constraints, small processing capacity, high bandwidth with insufficient transmitting range, etc. This study suggests an enhanced nano communication efficiency scheme over high bands for the wireless network that makes it perfect for intelligent e-health applications. The proposed system consists of a new energy paradigm for wireless nano-network mining and utilization which also considering the accumulated effect of molecular absorption, failure distribution, and interning. The results show the reliability of the proposed solution by optimizing the use of energy in both single and multihop communications; multi-sensor fusion at the fusion node increases the transmitting link rate.
Roulette Wheel Balancing Algorithm With Dynamic Flowlet Switching for Multipath Datacenter Networks

Load balance is an important issue in datacenter networks. The flowlet-based algorithms can balance the traffic with fine granularity and does not suffer the packet mis-sequencing problem. But their performances are rather limited or require extra communication overhead. In this paper, we propose a local load-aware algorithm called Dynamic Roulette Wheel (DRW). In DRW, the roulette wheel is adopted to select a new path for the flowlet according to the local load. Each source of multipath balances the traffic to all its egress links without the communication overhead. Moreover, the granularity of flowlet can be dynamically tuned from a single packet to the whole flow. Finally, the Capacity Aggregation (CA) mechanism is designed for the case of link or switch failure. We prove in theory that DRW can achieve the optimal global load balancing. The simulation results also show that DRW provides almost the best delay performance and the least packet out-of-order proportion overall among all existing flowlet switching algorithms.
A New High Step-Up Interleaved LLC Converter

In this study, a new LLC resonant converter for high-voltage high-power applications is introduced. The introduced power converter is a two-phase interleaved full-bridge based that uses a transformer with secondary and tertiary windings to obtain higher output voltage. Zero voltage switching (ZVS) at MOSFETs turn on and zero current switching (ZCS) for all the output diodes at turn off are achieved for a wide range of input voltage (100 V–200 V) and output power (200 W–1500 W) variations. Simulation results show a 95% peak efficiency.