Phylogenetic and morphological significance of an overlooked flying squirrel(Pteromyini,Rodentia)from the eastern Himalayas with the description of a new genus

The flying squirrels(Pteromyini,Rodentia)are the most diverse and widely distributed group of gliding mammals.Taxonomic boundaries and relationships within flying squirrels remain an area of active research in mammalogy.The discovery of new specimens of Pteromys(Hylopetes)leonardi Thomas,1921,previously considered a synonym of Hylopetes alboniger,in Yunnan Province,China allowed a morphological and genetic reassessment of the status of this taxon.Phylogenetic reconstruction was implemented using sequences of two mitochondrial(12S ribosomal RNA and 16S ribosomal RNA)and one nuclear(interphotoreceptor retinoid-binding protein)gene fragments.Morphological assessments involved examinations of features preserved on skins,skulls,and penises of museum specimens,supplemented with principal component analysis of craniometric data.Together these assessments revealed that this taxon should be recognized not only as a distinct species,but should also be placed within a new genus,described here as Priapomys gen.nov.

Adaptive 3D Routing Protocol for Flying Ad Hoc Networks Based on Prediction-Driven Q-Learning

The routing protocols are paramount to guarantee the Quality of Service(QoS)for Flying Ad Hoc Networks(FANETs).However,they still face several challenges owing to high mobility and dynamic topology.This paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning(3DQ)based routing protocol to guarantee the packet delivery ratio and improve the QoS.In 3DQ routing,we propose a Q-Learning based routing decision scheme,which contains a link-state prediction module and routing decision module.The link-state prediction module allows each Unmanned Aerial Vehicle(UAV)to predict the link-state of Neighboring UAVs(NUs),considering their Three Dimensional mobility and packet arrival.Then,UAV can produce routing decisions with the help of the routing decision module considering the link-state.We evaluate the various performance of 3DQ routing,and simulation results demonstrate that 3DQ can improve packet delivery ratio,goodput and delay of baseline protocol at most 71.36%,89.32%and 83.54%in FANETs over a variety of communication scenarios.

Assessment of Ballast Flying in the National Railway Network of Ethiopia

One of the major problems in ballasted railroads is ballast flying, which is the projection of ballast particles from the at-rest position as the train passes over the track of a railway structure, mainly due to high speed. In this research, the possibility of railway ballast flying for the double track Addis-Adama section of the new Addis-Djibouti railway line is assessed by determining the major causes of ballast flying and applying Discrete Element Modeling (DEM) with the aid of Particle Flow Code (PFC3D) software. The analysis comprised of an impact load and ballast material behavior which were used to determine the vibrational speed of individual ballast particles. The governing result from the series of discrete element analyses performed by considering fouled ballast gradation with grain-size diameter of 22.4 mm gives rise to a ballast maximum vibrational speed of 0.014 m/s. Since the ballast vibrational speed for Addis Ababa-Adama line is less than 0.02 m/s that is recommended by the literature, no ballast flight is expected under the present traffic and ballast conditions.

A Cyber–Physical Routing Protocol Exploiting Trajectory Dynamics for Mission-Oriented Flying Ad Hoc Networks

As a special type of mobile ad hoc network(MANET),the flying ad hoc network(FANET)has the potential to enable a variety of emerging applications in both civilian wireless communications(e.g.,5G and 6G)and the defense industry.The routing protocol plays a pivotal role in FANET.However,when designing the routing protocol for FANET,it is conventionally assumed that the aerial nodes move randomly.This is clearly inappropriate for a mission-oriented FANET(MO-FANET),in which the aerial nodes typically move toward a given destination from given departure point(s),possibly along a roughly deterministic flight path while maintaining a well-established formation,in order to carry out certain missions.In this paper,a novel cyber–physical routing protocol exploiting the particular mobility pattern of an MO-FANET is proposed based on cross-disciplinary integration,which makes full use of the missiondetermined trajectory dynamics to construct the time sequence of rejoining and separating,as well as the adjacency matrix for each node,as prior information.Compared with the existing representative routing protocols used in FANETs,our protocol achieves a higher packet-delivery ratio(PDR)at the cost of even lower overhead and lower average end-to-end latency,while maintaining a reasonably moderate and stable network jitter,as demonstrated by extensive ns-3-based simulations assuming realistic configurations in an MO-FANET.

On Connectivity of Flying Ad Hoc Networks in the Presence of Ground Terminal

The Unmanned Aerial Vehicle(UAV)technologies are envisioned to play an important role in the era of Air-Space-Ground integrated networks.In this paper,we investigate the connectivity of a Flying Ad hoc Network(FANET)in the presence of a groundbased terminal.In particular,the connected probability of the UAV-to-UAV (U2U) link as well as that of the UAV-to-Ground (U2G) link in a three dimensional (3D) space are analyzed.Furthermore,to mitigate the aggregate interference from UAV individuals,a priority based power control scheme is implemented for enhancing the connectivity of both U2U and U2G links.Numerical results illustrate the effectiveness of the proposed analysis.

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)

The paper presents a new protocol called Link Stability and Transmission Delay Aware(LSTDA)for Flying Adhoc Network(FANET)with a focus on network corridors(NC).FANET consists of Unmanned Aerial Vehicles(UAVs)that face challenges in avoiding transmission loss and delay while ensuring stable communication.The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network.The protocol uses a Red-Black(R-B)tree to achieve maximum channel utilization and an advanced relay approach.The paper evaluates LSTDA in terms of End-to-End Delay(E2ED),Packet Delivery Ratio(PDR),Network Lifetime(NLT),and Transmission Loss(TL),and compares it with existing methods such as Link Stability Estimation-based Routing(LEPR),Distributed Priority Tree-based Routing(DPTR),and Delay and Link Stability Aware(DLSA)using MATLAB simulations.The results show that LSTDA outperforms the other protocols,with lower average delay,higher average PDR,longer average NLT,and comparable average TL.

An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

An Overview of the Achievements of Modern Research on the Flying Shadow Pavilion Pictorial

The current academic research on the Dian Shi Zhai Pictorial founded in 1884 has been perfected,but there is very little research on the Flying Shadow Pavilion Pictorial,which can be said to be derived from Dian Shi Zhai Pictorial terms of both content and form,and which was founded by the Haiist painter Wu Youru in 1890 in order to reward the increasing number of admirers,but it is still in the initial stage.Flying Shadow Pavilion Pictorial consisted of four parts:pictures of ladies in Shanghai costumes,news about current affairs,pictures of animals,and compilations of women,each of which was accompanied by the then popular notebooks,which were popular at the time among the then readers because of the matching of pictures with text and the mixing of narratives and discussions.This paper takes Flying Shadow Pavilion Pictorial as the main object,summarizes the existing literature about Flying Shadow Pavilion Pictorial,points out the deficiencies of the current research on this basis,and points out the development trend of Flying Shadow Pavilion Pictorial in the future research.

Kinetics of SiO_2 leaching from Al_2O_3 extracted slag of fly ash with sodium hydroxide solution

Kinetics of SiO2 leaching from Al2O3 extracted slag of fly ash with sodium hydroxide solution was studied.The effect of leaching temperature,mass ratio of NaOH to SiO2 and stirring speed on SiO2 leaching rate was investigated.The results show that increasing leaching temperature,mass ratio of NaOH to SiO2 and stirring speed increases SiO2 leaching rate.The SiO2 leaching rate is 95.66%under the optimized conditions.There are two stages for the SiO2 leaching process,and the leaching reaction is very rapid in the first stage but quite slow in the second stage.The whole leaching process follows the shrinking core model,and the outer diffusion of no product layer is the rate-controlling step.The activation energies of the first and second stages are calculated to be8.492 kJ/mol and 8.668 kJ/mol,respectively.The kinetic equations of the first and the second stages were obtained,respectively.

Microstructure Evolution and Mechanism of Strength Development of Fly Ash Paste

Three types of activators such as sodium hydroxide,calcium oxide and triethanolamine(TEA)are used to establish different activation environments to address the problems associated with the process of activating fly ash paste.We conducted mechanical tests and numerical simulations to understand the evolution of microstructure,and used environmental scanning electron microscopy(ESEM)and energy dispersive spectroscopy(EDS)techniques to analyze the microenvironments of the samples.The mechanical properties of fly ash paste under different activation conditions and the changes in the microstructure and composition were investigated.The results revealed that under conditions of low NaOH content(1%-3%),the strength of the sample increased significantly.When the content exceeded 4%,the rate of increase in strength decreased.Based on the results,the optimal NaOH content was identified,which was about 4%.A good activation effect,especially for short-term activation(3-7 d),was achieved using TEA under high doping conditions.The activation effect was poor for long-term strength after 28 days.The CaO content did not significantly affect the degree of activation achieved.The maximum effect was exerted when the content of CaO was 2%.The virtual cement and concrete testing laboratory(VCCTL)was used to simulate the hydration process,and the results revealed that the use of the three types of activators accelerated the formation of Ca(OH)_(2) in the system.The activators also corroded the surface of the fly ash particles,resulting in a pozzolanic reaction.The active substances in fly ash were released efficiently,and hydration was realized.The pores were filled with hydration products,and the microstructure changed to form a new frame of paste filling that helped improve the strength of fly ash paste.

Interpretable Machine Learning Method for Compressive Strength Prediction and Analysis of Pure Fly Ash-based Geopolymer Concrete

In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.

Warriors on Wings

In an era when Chinese-American relations face challenges,an organization is promoting the shared“Flying Tigers”legacy as a reminder of what can be achieved with solidarity.

Avoidant Learning Ability in Free Flying Housefly (Aldrichina grahami) by Electric Shock

Previous studies have confirmed that both honeybee and Drosophila are capable of learning and memory. This study aimed to investigate whether the house fly （Aldrichina grahami）, with strong instincts to adapt their living environment, have the learning ability to associate odor stimulus to avoid electric shock in free flying state using a device developed by the authors. The result showed the learning ability ofA. grahami at the electric shock voltages of 5 V, 25 V and 45 V AC. When 60 V was used, the flies were frequently injured. Our results indicate that A. grahami is a good model to study the neural mechanism of learning and memory. The paradigm in this study has some advantages that can be used in future studies of free insects.

A comparison of fishery biology of jumbo flying squid, Dosidicus gigas outside three Exclusive Economic Zones in the Eastern Pacific Ocean

Although many studies on the fishery biology of jumbo flying squid, Dosidicus gigas, have been conducted in the coastal areas within Exclusive Economic Zones （EEZs） of various countries due to its commercial and ecological importance, limited biological information is available from waters outside these EEZs. In this paper, we examined D. gigas fishery biology from waters outside Chilean, Peruvian and Costa Rican EEZs, based on the fishery data collected by Chinese jigging vessels during 2006 to 2010. The dominant mantle lengths olD. gigas were 350-450 mm, 250-400 mm and 250-350 mm outside Chilean, Peruvian and Costa Rican EEZs, respectively. Size structure analysis show that a medium-sized group existed mostly in the waters outside the Chilean and Peruvian EEZs, whereas a small-sized group occurred mainly in the waters outside the Costa Rican EEZ. The longevity of the squid outside the Costa Rican EEZ was less than 10 months, while most of those outside Chilean and Peruvian EEZs were about 1-1.5 years and very few large individuals were 1.5-2 years old. A higher percentage of mature individuals existed outside Costa Rican EEZ implying the region as a potential spawning ground, while lower proportions of mature squid outside the Peruvian and Chilean EEZs indicated that spawning may be occurring outside our study area. Spatial differences in sizes at maturity of the squid are thought to be result from different environmental factors especially different temperature and nutrition among the three areas. Stomach-content analysis showed that cannibalism was important in the diet of D. gigas. Stress generated by jigging may increase the incidence of cannibalism.

Study on relative orbital configuration in satellite formation flying

In this paper, the relative orbital configurations of satellites in formation flying with non-perturbation and J2 perturbation are studied, and an orbital elements method is proposed to obtain the relative orbital configurations of satellites in formation. Firstly, under the condition of non-perturbation, we obtain many shapes of relative orbital configurations when the semi-major axes of satellites are equal. These shapes can be lines, ellipses or distorted closed curves. Secondly, on the basis of the analysis of J2 effect on relative orbital configurations, we find out that J2 effect can induce two kinds of changes of relative orbital configurations. They are distortion and drifting, respectively. In addition, when J2 perturbation is concerned, we also find that the semi-major axes of the leading and following satellites should not be the same exactly in order to decrease the J2 effect. The relationship of relative orbital elements and J2 effect is obtained through simulations. Finally, the minimum relation perturbation conditions are established in order to reduce the influence of the J2 effect. The results show that the minimum relation perturbation conditions can reduce the J2 effect significantly when the orbital element differences are small enough, and they can become rules for the design of satellite formation flying.

Thermodynamics and kinetics of alumina extraction from fly ash using an ammonium hydrogen sulfate roasting method

A novel method was developed for extracting alumina （Al2O3） from fly ash using an ammonium hydrogen sulfate （NH4HSO4） roasting process, and the thermodynamics and kinetics of this method were investigated. The thermodynamic results were verified experi-mentally. Thermodynamic calculations show that mullite present in the fly ash can react with NH4HSO4 in the 298-723 K range. Process op-timization reveals that the extraction rate can reach up to 90.95% when the fly ash reacts with NH4HSO4 at a 1：8 mole ratio of Al2O3/NH4HSO4 at 673 K for 60 min. Kinetic analysis indicates that the NH4HSO4 roasting process follows the shrinking unreacted core model, and inner diffusion through the product layer is the rate-controlling step. The activation energy is calculated to be 16.627 kJ/mol;and the kinetic equation can be expressed as 1-（2/3）α-（1-α）2/3=0.0374t exp[-16627/（RT）], whereαis the extraction rate and t is the roasting temperature.

Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.

Compressive Strength, Pore Size Distribution and Chloride-ion Penetration of Recycled Aggregate Concrete Incorporating Class-F Fly Ash

The effects of fly ash on the compressive strength, pore size distribution and chloride-ion penetration of recycled aggregate concrete were investigated. Two series of concrete mb：tures were prepared. The concrete mixtures in series I had a water-to-binder ratio and a cement content of 0.55 and 410 kg/ m^3 , respectively. The concrete rnixtures in series II had a water-to-binder ratio and a cement content of 0.45 and 400 kg/ ml respectively. Recycled aggregate was ased as 20% , 50% , and 100% replacements of natural coarse aggregate in the concrete mixtures in both series. In cutdition, fly ash was used as 0% , 25% and 35% by weight replacements of cement. The results show that the compressive strengths of the concrete decreased as the recycled aggregate and the fly ash contents increased. The total porosity and average porosity diameter of the concrete increased us the recycled aggregate content increased. Furtherrruore , an increase in the recycled aggregate content decreased the resistance to chloride ion penetration. Nevertheless, the replacement of cement by 25% fly ush improved the resistance to chloride ion penetration and pore diameters and reduced the total porosity of the recycled aggregate concrete.

Joint Task Scheduling, Resource Allocation, and UAV Trajectory under Clustering for FANETs

This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members(CMs) within a cluster to its cluster head(CH). Then, we need to determine whether each CH’ tasks are executed locally or offloaded to one of the MEC UAVs for remote execution(i.e., task scheduling), and how much resources should be allocated to each CH(i.e., resource allocation), as well as the trajectories of all MEC UAVs.We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them,respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.