Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical structural behaviour of elastic flapping wings under the actuator effect

ABSTRACT The effect of inertial forces on the Structural Dynamics (SD) behaviour of Elastic Flapping Wings (EFWs) is investigated. In this regard, an analytical modal-based SD solution of EFW undergoing a prescribed rigid body motion is initially derived. The formulated initial-value problem is solved analytically to study the EFW structural responses, and sensitivity with respect to EFWs’ key parameters. As a case study, a rectangular wing undergoing a prescribed sinusoidal motion is simulated. The analytical solution is derived for the first time and helps towards a conceptual understanding of the overall EFW's SD behaviour and its analysis required in their designs. Specifically, the EFW transient and steady response in on-off servo condition is also attended.
‘She’s mine!’ – The lived experience of becoming parents through international adoption

Literature on the personal and intimate process of feeling the parent of an adopted child appears to be scant. The experience of adoptive parents’ emotional and psychological process to ‘feel a parent’ is often overshadowed by literature focused on the experience of loss especially when parents adopt following infertility, and by literature focusing on the experiences of children pre-and-post-adoption. This article presents the findings of an Interpretative Phenomenological Analysis (IPA) with adoptive parents, exploring the intimate emotional and psychological process of becoming a parent and ‘feeling a parent’ through international adoption. Nine semi-structured interviews with three couples were conducted where each couple was interviewed twice, both together and separately. Six main superordinate themes emerged: Holding the child in mind and in heart before the adoption; exceeding one’s expectations of connection with the child; special intimate moments consolidating the attachment process; being acknowledged as legitimate parents by the child, by their family and community; the story of how they became parents and how their child became theirs; and creating and embracing the child’s larger context. The findings indicate that a systemic perspective is important when working with adoptive parents as a pivotal part of the parents’ experience centred around being acknowledged by their family system as legitimate parents to their child. Practitioners also need to be sensitive to the idiosyncratic, often sensory-based attachment processes between the parents and the child in the process of becoming parents through international adoption.
Experimental Study of Soft Ballistic Packages with Embroidered Structures Fabricated by Using the Tailored Fiber Placement Technique

Textile ballistic shields are the basis of protection against bullets and fragments with low kinetic energy. They are usually made of para-aramid fabrics or unidirectional structure (UD) sheets of ultra-high molecular weight polyethylene (UHMWPE). The aim of the research presented in the article was to obtain ballistic packages made of embroidered structures and to compare their ballistic properties with those of woven structures in terms of deformation of the standardized ballistic substrate after impact with a 9 mm bullet at a velocity of 380 ± 3 m/s. Using the tailored fiber placement method, embroidered structures were fabricated by embroidering two sets of para-aramid threads at an angle of 90°. As the woven structures, the use of para-aramid fabric made of the same yarn and with a surface weight comparable to that of embroidered structures was adopted. Ballistic packages consisted of 26 layers in five variants, also taking into account the hybrid arrangement of woven and embroidered layers. Ballistic tests have shown that the best ballistic properties have hybrid packages made by folding 13 woven and then 13 embroidered layers, where the maximum deformation of the plasticine substrate is below 23 mm. The conducted research confirmed that embroidered structures in appropriate combination with woven structures can significantly improve the ballistic properties of textile packages.
Autonavi3at Software Interface to Autonomously Navigate on Urban Roads Using Omnidirectional Vision and a Mobile Robot

The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads.
Learning in Potential Games for Electric Power Grids: Models, Dynamics, and Outlook

Noncooperative game-theoretic methods have been widely utilized in system-level engineering applications as they are capable of aggregating interests, information, and behaviors of many independent, self-interested entities with conflicting objectives via payoffs. Another major advantage is the existence of adaptive learning dynamics that involve realistic, constrained decision making, and converge to Nash equilibrium. If players are not fully rational (e.g., constrained or regulated), such convergence is generally not guaranteed except in some special games, such as potential games (PGs). Within each PG, there exists a potential function that connects the global objective with players’ payoffs and actions such as optimizers of the global objective align with Nash equilibrium. Therefore, PGs have been widely utilized in many engineering domains and recently to power grids. This article summarizes the state-of-the-art existing literature on applying various PG models in power grids and provides an overlook of potential models and algorithms that have not received sufficient attention but possess unique capabilities to solve existing challenges in power grids.
An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and F1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.
Characteristics of Gas-centered Swirl Coaxial Injector for Staged Combustion Cycle Engines

i LIST . iv LIST OF FIGURES . vi LIST OF TABLES . ix NOMENCLATURE . x CHAPTER 1 INTRODUTION . 1 CHAPTER 2 DESIGN OF A GAS-CENTERED SWIRL COAXIAL INJECTOR . 7 2.1 Basic Conceptual Design for Thrust Chamber . 7 2.2 Injector Array in the Faceplate of the Mixing Head . 9 2.3 Characteristics of Swirl Injectors . 10 2.4 Design of Gas-Centered Swirl Coaxial Injectors . 12 2.4.1 Gas Injector Design . 14 2.4.2 Liquid Injector Design . 18 2.4. 3 Result of GCSC Injector Design . 21 CHAPTER 3 EXPERIMENTAL METHOD . 23 3.1 Spray Imaging . 23 3.2 Electrical Conductance Method . 24 3.3 Data Acquisition System . 28 CHAPTER 4 SPRAY CHARACTERISTICS OF GAS-CENTERED SWIRL COAXIAL INJECTORS: RECESS RATIO AND GAP THICKNESS . 31 4.1 Background and Objectives . 31 4.2 Experimental Methods . 33 4.2.1 Experimental Apparatus . 33 4.2.2 Experimental Conditions . 34
A Novel Trajectory Adjustment Mechanism-Based Prescribed Performance Tracking Control for Electro-Hydraulic Systems Subject to Disturbances and Modeling Uncertainties

This paper proposes a novel active disturbance compensation framework for exactly positioning control of electro-hydraulic systems (EHSs) subject to parameter deviations, unknown dynamics, and uncertain external load without velocity measurement mechanism. In order to accurately estimate and then actively compensate for the effects of these uncertainties and disturbances on the system dynamics, a combination between an extended sliding mode observer (ESMO) and a linear extended state observer (LESO) is firstly established for position control of EHSs. In addition, an inherited nonlinear filter-based trajectory planner with minor modifications is utilized to overcome the barriers of inappropriate desired trajectories which do not consider the system kinematic and dynamic constraints. Furthermore, for the first time, the command filtered (CF) approach and prescribed performance control (PPC) are successfully coordinated together and dexterously integrated into the backstepping framework to not only mitigate the computational cost significantly and avoid the “explosion of complexity” of the traditional backstepping design but also satisfy the predetermined transient tracking performance indexes including convergence rate, overshoot, and steady-state error. The stabilities of the observers and overall closed-loop system are rigorously proven by using the Lyapunov theory. Finally, comparative numerical simulations are conducted to demonstrate the advantages of the proposed approach.
Non-destructive models for estimating leaf area of guava cultivars

Leaf area is a commonly used measurement in many agronomic studies, but its assessment is generally destructive, and then simple, accurate and non-destructive methods are really appreciated. The objective of this study was to develop a non-destructive model that could be used to estimate the leaf area of four guava (Psidium guajava L.) cultivars by using leaf linear dimensions. Leaves from guava cultivars ‘Paluma’, ‘Sassaoka’, ‘Século 21’, and ‘Tailandesa’ were sampled randomly from an experimental orchard. Leaf length and maximum leaf width were measured with a ruler in 120 leaves from each cultivar. Leaf areas were also measured with a leaf area meter. Linear and power models relating leaf area to length, width, and length × width were fitted to the data. The most precise models were regressed again with a new data set to validate the proposed models. The power model (y = 0.61 x1.06) using the length × width was more precise and accurate to estimate the leaf area of all four cultivars evaluated herein, grown in field or greenhouse conditions. When only one leaf dimension was used, the power model (y = 1.81 x1.93) using the width was the best-performing model. Although models with only one leaf dimension (length or width) have shown good performance for estimating the guava leaf area, models based on the leaf length × width were more precise.
Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.
Anthropogenic Carbon Increase has Caused Critical Shifts in Aragonite Saturation Across a Sensitive Coastal System

Estuarine systems host a rich diversity of marine life that is vulnerable to changes in ocean chemistry due to addition of anthropogenic carbon. However, the detection and impact of secular carbon trends in these systems is complicated by heightened natural variability as compared to open‐ocean regimes. We investigate biogeochemical changes between the pre‐industrial (PI) and modern periods using a high‐resolution, three‐dimensional, biophysical model of the Salish Sea, a representative Northeast Pacific coastal system. While the seasonal amplitude of the air‐sea difference in pCO2 has increased on average since pre‐industrial times, the net CO2 source has changed little. Our simulations show that inorganic carbon has increased throughout the model domain by 29–39 mmol m−3 (28–38 µmol kg−1) from the pre‐industrial to present. While this increase is modest in a global context, the region's naturally high inorganic carbon content and the low buffering capacity of the local carbonate system amplify the resultant effects. Notably, this increased carbon drives the estuary toward system‐wide undersaturation of aragonite, negatively impacting shell‐forming organisms. Undersaturation events were rare during the pre‐industrial experiment, with 10%–25% of the domain undersaturated by volume throughout the year, while under present‐day conditions, the majority (55%–75%) of the system experiences corrosive, undersaturated conditions year‐round. These results are extended using recent global coastal observations to show that estuaries throughout the Pacific Rim have already undergone a similar saturation state regime shift.
A Reliable Self-Adaptive Scheduling Control Protocol for Cellular V2X Mode 4

The rapid development and deployment of the Cellular V2X (C-V2X) require a highly reliable support system for many vehicular key applications. However, the current standard sensing-based semi-persistent scheduling (SB-SPS) protocol yields poor performance in terms of reliability, due to the hidden terminal problem and the half-duplex issue. In this paper, we propose a reliable self-adaptive MAC protocol for C-V2X Mode 4 (autonomous sidelink). It utilizes frame information and RSSI to exclusively scheduling resource blocks in two-hop neighbors, and dynamically adjusts scheduling and frame length according to changes of the network environment. Simulation results in show that the proposed protocol achieves significantly higher PDR than SB-SPS and IEEE 802.11p, while it only causes a relatively small number of the packet collisions.
Circuit to be used in a wind power plant

The present invention particularly relates to a circuit for a wind power plant having a variable rotational speed, comprising one pair-fed asynchronous generator (DASM), a crowbar, an additional resistor (R15) and a converter. In order to meet the needs of the network provider, thereby Shidao specialized permanently connected to the network is guaranteed, so that the short-circuit voltage and the period after the fast switching can be started and stabilized, additional resistance may occur in the wind power plant in the network adjusting the assist mode, is taken by the short circuit occurs in the network can be temporarily switched off at least partially the transducer. Rotor current provisionally accepted the additional resistor, and disconnected after the rotor short-circuit current disappears, so that the converter can then be reconnected to the network and can provide a desired effective short circuit current.
Insight into the Epidemiology and Evolutionary History of Novel Goose Astrovirus-Associated Gout in Goslings in Southern China

A novel gout disease, characterized by visceral urate deposition with high-mortality, with outbreaks in goslings in China since 2016 was caused by a novel goose astrovirus (GoAstV) and resulted in serious economic loss. However, the epidemiology and variation of the GoAstV in goslings in southern China and its evolutionary history as well as the classification of the GoAstV are unclear. In the present study, systematic molecular epidemiology, and phylogenetic analyses of the GoAstV were conducted to address these issues. Our results showed that the GoAstV is widespread in goslings in southern China, and the genomes of six GoAstV strains were obtained. Two amino acid mutations (Y36H and E456D) were identified in capsid proteins in this study, which is the dominant antigen for the GoAstV. In addition, the GoAstV could be divided into two distinct clades, GoAstV-1 and GoAstV-2, and GoAstV-2 is responsible for gout outbreaks in goslings and could be classified into Avastrovirus 3 (AAstV-3), while GoAstV-1 belongs to Avastrovirus 1 (AAstV-1). Moreover, the emergence of GoAstV-2 in geese was estimated to have occurred in January 2010, approximately 12 years ago, while GoAstV-1 emerged earlier than GoAstV-2 and was estimated to have emerged in April 1985 based on Bayesian analysis. The mean evolutionary rate for the GoAstV was also calculated to be approximately 1.42 × 10−3 nucleotide substitutions per site per year. In conclusion, this study provides insight into the epidemiology of the GoAstV in goslings in southern China and is helpful for understanding the origin and evolutionary history as well as the classification of the GoAstV in geese.
Dynamic assessment of bridge deck performance considering realistic bridge-traffic interaction : research brief

Concrete bridge decks are directly exposed to daily traffic loads and may experience some surface cracking caused by excessive stress or fatigue accumulation, which requires repair or replacement. Among typical bridges in North America, bridge decks are considered the most expensive component to construct and repair, not only because of direct repair cost repair, but also due to the indirect cost from the traffic disruption during the repair action. In order to rationally predict the bridge deck response and the damage risk, some appropriate analytical tools are needed to consider the dynamic effects between the bridge and multiple vehicles moving in a realistic way. Such an analytical tool should be able to provide rational prediction of the bridge global and local responses, such as displacement and stress on bridge decks in addition to other members, which can be used for fatigue or other damage assessment. In the present study, a hybrid dynamic analytical approach is developed for a typical multi-span concrete bridge and stochastic traffic flow by considering the excitation from road roughness. Based on the dynamic response results, the fatigue assessment is conducted with a focus on providing insights on vulnerable locations and the impacts from varied traffic and road roughness conditions.
Comparative Study of Pavement Rehabilitation Using Hot in-Place Recycling and Hot-Mix Asphalt: Performance Evaluation, Pavement Life Prediction, and Life Cycle Cost Analysis

The use of hot in-place recycling (HIR) for pavement surface rehabilitation has gained increasing attention in recent years with the aim of achieving sustainable pavement. For the purpose of cost saving and the consumption of 100% reclaimed asphalt pavement (RAP), an in-place HIR train has been developed to combine various units including preheating, milling, rejuvenating, and compacting. Compared with the conventional hot-mix asphalt (HMA) surface treatment, the HIR technique might result in different pavement performances as a result of the low mixing temperature and insufficient quality control. This study aimed to conduct a comparative analysis of pavement surface rehabilitation using HIR and HMA, including the performance evaluation, pavement life prediction, and life cycle cost analysis (LCCA). HIR and HMA mixes were collected from construction areas and the nearby asphalt plant, respectively. The loose mixes were reheated and compacted for performance testing, including dynamic modulus tests, Superpave IDT tests, and moisture susceptibility tests. The AASHTOWare Pavement ME Design software was adopted to predict the pavement life with two rehabilitation techniques, followed by the LCCA with regard to the prediction results. Test results showed that pavement surface rehabilitation with HIR achieves acceptable performance and economic benefits. However, the HIR mixes are brittle and more susceptible to cracking and, therefore, have shorter pavement service lives than HMA.
The family Plagiochilaceae (Marchantiophyta) in Batang Toru Forest, North Sumatra, Indonesia

Abstract. Damanik R, Pasaribu N, Siregar ES, Syamsuardi. 2022. The family Plagiochilaceae (Marchantiophyta) in Batang Toru Forest, North Sumatra, Indonesia. Biodiversitas 23: 3127-3134. Investigation on the liverwort family Plagiochilaceae in Batang Toru Forest, North Sumatra, Indonesia resulted in ten taxon of Plagiochilaceae namely Plagiochilion oppositum, Plagiochila bantamensis, Plagiochila dendroides, Plagiochila denticulate, Plagiochila frondescens, Plagiochila junghuhniana, Plagiochila propinqua, Plagiochila sciophila, Dinckleria singularis, Plagiochila sumatrana, distributed under three genera Dinckleria (1 species), Plagiochila (8 species) and Plagiochilion (1 species). Plagiochila junghuhniana was the most observed species, while Plagiochila sciophilia was the least frequently encountered species in the forest.
Bidirectional DC–DC Converter for Modular Residential Battery Energy Storage Systems

A novel bidirectional dc–dc converter based on the quasi-Z-source (qZS) topology is presented in this paper. During battery discharge, it operates as the conventional qZS full-bridge converter with a synchronous voltage doubler rectifier. During battery charging, it operates as the half-bridge converter with a synchronous full-bridge rectifier and LC filter. A relay is used for reconfiguration between those two modes. The operation principle is explained, and design guidelines are provided. A prototype with a nominal power of 300 W is used for verification of steady-state regulation characteristics and efficiency measurements in the input voltage range compatible with an eight-cell LiFePO4 battery. Closed-loop control system for the converter application in dc microgrids is presented and tested in two control scenarios: dc-bus signaling and direct reference defined by a master controller through a communication channel.