Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Computational Dynamic Trust Model for P2P Network

With today's multi-agent systems, security and privacy concerns are of utmost importance. Multi-agent systems are often open and dynamic by design. By offering protected communication, this nature undoubtedly creates an issue. Because of the rapid growth of multi-agent systems, security and privacy problems have taken on a fundamental importance. The majority of network applications, including ubiquitous computing, grid computing, and peer-to-peer (P2P) networks, may be thought of as open, anonymous, and dynamic multi-agent systems. These multi-agent system features create weaknesses and dangers for delivering encrypted communication. Evaluating the reliability and standing of the interacting agents is one practical technique to reduce the dangers. Several trust/reputation models have attempted to achieve this, but they fall short of accurately assessing trust when hostile actors begin to act in an unexpected manner. A further drawback of these models is their inability to react quickly to the fluctuating behavior of a hostile actor. The equitable allocation of workload across service-providing agents is another feature of multi-agent systems that is increasingly important for maintaining high service quality. This problem has not yet been solved by the majority of trust/reputation models. Current methods include context into the computation of trust. Their use is restricted to certain domains. The system presented an integrity belief model in this study. The suggested approach was evaluated against various trust models. The suggested approach performs better than alternative trust models in terms of efficiency. The suggested method separates trusting confidence in competence from that in integrity.
Nearly linear time encodable codes beating the Gilbert-Varshamov bound

We construct explicit nearly linear time encodable error-correcting codes beating the Gilbert-Varshamov bound. Our codes are algebraic geometry codes built from the Garcia-Stichtenoth function field tower and beat the Gilbert-Varshamov bound for alphabet sizes at least $19^2$. Messages are identified with functions in certain Riemann-Roch spaces associated with divisors supported on multiple places. Encoding amounts to evaluating these functions at degree one places. By exploiting algebraic structures particular to the Garcia-Stichtenoth tower, we devise an intricate deterministic nearly linear time encoding algorithm and nearly quadratic expected time randomized (unique and list) decoding algorithms.
Multi sensor data fusion for 6D pose estimation and 3D underground mine mapping using autonomous mobile robot

ABSTRACT Simultaneous localisation and mapping by using a robot is indeed a challenging and critically important problem for an accurate 3D underground mine mapping. Using 6D pose information of a robot (x, y and z coordinates and the yaw, pitch and roll angles), the robot vehicle can be tracked, which is very crucial for an accurate mine mapping. The estimation of 6D robot pose on the ground surface is easy and generally accomplished by either the Global Positioning System (GPS) or by putting some artificial visual landmarks. However, in the underground mining environment, the estimation of pose becomes difficult due to the unavailability of the GPS signal and the identification of landmarks (as surface wall of the underground mine is rough). In this paper, an attempt has been made to construct a 3D underground mine map using a prototype mine robot. For this purpose, a strategy for the underground mine robot pose estimation is devised with the help of multi-sensor data fusion techniques. A shaft-encoder (wheel odometry), Inertial Measurement Unit (IMU) and a rotated 2D Laser Scanner is used to improve and rectify the 6D robot pose as well as 3D mine map. An autonomous prototype robot is built up and allowed to navigate in a real underground mine gallery to validate our algorithm. Generated 3D map has been compared with the actual map available. The outputs of this study are highlighted in this paper.
Learning‐based containment control in the absence of leaders' information

This article considers the containment control problem of continuous‐time dynamic agents without leaders' state information. The leaders have first‐ and second‐order dynamics, respectively, while first‐order dynamics always govern the followers. Each agent has inherent nonlinear dynamics and can only measure the output information of its neighbors. The output of each leader is expressed as the product of an unknown coefficient and a position‐like state, while the output of each follower is equal to its position‐like state. To stabilize the position‐like states of the followers to the convex hull spanned by leaders, the unknown coefficients are asymptotically tracked by leveraging reinforcement learning based on the inherent dynamics and the output information. Two distributed learning‐based containment protocols are proposed, respectively. It is proved that if the directed communication topology has a spanning forest and certain conditions in terms of the inherent nonlinear dynamics are satisfied, then the proposed controllers with proper control gains solve the containment control problem asymptotically under arbitrary initial states. An exciting conclusion is that the learning algorithms' convergence rate plays an important role in achieving containment control. Numerical simulations are performed to validate the effectiveness of the obtained theoretical results.
Ballistic Behavior of Bioinspired Nacre-like Composites

In this paper, the ballistic performance of a multilayered composite inspired by the structural characteristics of nacre is numerically investigated using finite element (FE) simulations. Nacre is a natural composite material found in the shells of some marine mollusks, which has remarkable toughness due to its hierarchical layered structure. The bioinspired nacre-like composites investigated here were made of five wavy aluminum alloy 7075-T651 (AA7075) layers composed of ~1.1-mm thick square tablets bonded together with toughened epoxy resin. Two composite configurations with continuous layers (either wavy or flat) were also studied. The ballistic performance of the composite plates was compared to that of a bulk monolithic AA7075 plate. The ballistic impact was simulated in the 300–600 m/s range using two types of spherical projectiles, i.e., rigid and elastoplastic. The results showed that the nacre plate exhibited improved ballistic performance compared to the bulk plate and the plates with continuous layers. The structural design of the nacre plate improved the ballistic performance by producing a more ductile failure and enabling localized energy absorption via the plastic deformation of the tablets and the globalized energy dissipation due to interface debonding and friction. All the plate configurations exhibited a better ballistic performance when impacted by an elastoplastic projectile compared to a rigid one, which is explained by the projectile plastic deformation absorbing some of the impact energy and the enlarged contact area between the projectile and the plates producing more energy absorption by the plates.
Statistical properties of solar Hα flare activity

Magnetic field structures on the solar atmosphere are not symmetric distribution in the northern and southern hemispheres, which is an important aspect of quasi-cyclical evolution of magnetic activity indicators that are related to solar dynamo theories. Three standard analysis techniques are applied to analyze the hemispheric coupling (north-south asymmetry and phase asynchrony) of monthly averaged values of solar Hα flare activity over the past 49 years (from 1966 January to 2014 December). The prominent results are as follows: (1) from a global point of view, solar Hα flare activity on both hemispheres are strongly correlated with each other, but the northern hemisphere precedes the southern one with a phase shift of 7 months; (2) the long-range persistence indeed exists in solar Hα flare activity, but the dynamical complexities in the two hemispheres are not identical; (3) the prominent periodicities of Hα flare activity are 17 years full-disk activity cycle and 11 years Schwabe solar cycle, but the short- and mid-term periodicities cannot determined by monthly time series; (4) by comparing the non-parametric rescaling behavior on a point-by-point basis, the hemispheric asynchrony of solar Hα flare activity are estimated to be ranging from several months to tens of months with an average value of 8.7 months. The analysis results could promote our knowledge on the long-range persistence, the quasi-periodic variation, and the hemispheric asynchrony of solar Hα flare activity on both hemispheres, and possibly provide valuable information for the hemispheric interrelation of solar magnetic activity.
Plasma surface treatment to improve surface charge accumulation and dissipation of epoxy resin exposed to DC and nanosecond-pulse voltages

In this paper, deposition by non-thermal plasma is used as a surface modification technique to change the surface characteristics of epoxy resin exposed to DC and nanosecond-pulse voltages. The corresponding surface characteristics in both cases of DC and nanosecond-pulse voltages before and after the modification are compared and investigated. The measurement of the surface potential provides the surface charge distribution, which is used to show the accumulation and dissipation process of the surface charges. Morphology observations, chemical composition and electrical parameters measurements are used to evaluate the treatment effects. The experimental results show that, before the plasma treatment, the accumulated surface charges in the case of the DC voltage are more than that in the case of the nanosecond-pulse voltage. Moreover, the decay rate of the surface charges for the DC voltage is higher than that for the nanosecond-pulse voltage. However, the decay rate is no more than 41% after 1800 s for both types of voltages. After the plasma treatment, the maximum surface potentials decrease to 57.33% and 32.57% of their values before treatment for the DC and nanosecond-pulse voltages, respectively, indicating a decrease in the accumulated surface charges. The decay rate exceeds 90% for both types of voltages. These changes are mainly attributed to a change in the surface nanostructure, an increase in conductivity, and a decrease in the depth of energy level.
Blockchain-Based Partial Group Key Agreement Protocol for Intelligent Transportation Systems

Communication technologies have grown fast over the previous decade, resulting in substantial breakthroughs in Intelligent Transportation Systems (ITS) applications and services. ITS is distinguished by high node mobility and instability, making confidentiality, security, and privacy a difficult challenge to address in its implementation. Even though several researchers have suggested authenticated key agreement protocols for secure transmission, significant flaws can still be observed. Moreover, this research presents a unique blockchain-based group key agreement protocol to address these complex challenges. The proposed protocol can minimize processing latency, deployment costs, and queuing delay by utilizing RSU as a semi-trusted entity. Moreover, blockchain characteristics accomplish inter-regional authentication. We show that the proposed protocol is secure against known security threats, which we proved formally. In addition, the performance analysis demonstrates that the proposed protocol has considerably less communication and computational overhead than other existing protocols. Furthermore, the simulations are performed on NS-3 to demonstrate the practical aspects of the proposed protocol.
The dynamics of capital structure

The research explores the causality relationship between dividend distribution and capital structure among Chinese stock exchange-listed manufacturing firms. The research results confirm the pecking order theory and discover a non-linear U-shaped relationship between firm performances and dividend distributions. State-owned enterprises (SOEs) prefer more steady and regular dividends than private firms
Modeling SAR Observables by Combining a Crop-Growth Model With Machine Learning

In this article, our aim is to estimate synthetic aperture radar (SAR) observables, such as backscatter in VV and VH polarizations, as well as the VH/VV ratio, cross ratio, and interferometric coherence in VV, from agricultural fields. In this study, we use the decision support system for agrotechnology transfer (DSSAT) crop-growth simulation model to simulate parcel-level phenological and growth parameters for over 1500 parcels of silage maize in the Netherlands. The crop model was calibrated using field data, including silage maize phenological phases, leaf area index, and above-ground dry biomass (AGB). The simulations incorporate fine-resolution gridded precipitation data and soil parameters to model the interaction between soil–plant–atmosphere and genotype in DSSAT. The crop variables produced by DSSAT are then used as inputs to a support vector regression model. This model is trained to simulate SAR observables in 2017, 2018, and 2019, and its performance is evaluated using independent fields in each of these years. The results show a close fit between modeled and observed SAR C-band observables. The importance of vegetation variables in the estimation of SAR observables is assessed. The AGB showed significant importance in the estimation of backscatter. This study demonstrates the potential value of combining crop-growth simulation models and machine learning to simulate SAR observables. For example, the SVR model developed here could be used as an observation operator in an assimilation context to constrain vegetation and soil water dynamics in a crop-growth model.
Effect of omega-3 fatty acid supplementation during pregnancy or infancy on cardio-metabolic risk factors in childhood

vii Table of contents . xi List of figures . xv List of tables . xvii Manuscripts and Presentations . xix Acknowledgements . xxiii Abbreviations and acronyms . xxv Chapter 1 . 1 1.1 General introduction . 1 1.1.1 The shifting burden of non-communicable disease. 1 1.1.2 Developmental origins of health and disease . 3 1.1. 3 Nutrition and DOHaD . 4 1.1. 4 Dietary lipids – fatty acids . 6 1.1. 5 Effect of omega-3 LCPUFAs on cardio-metabolic risk factors . 9 1.2 Methods . 27 1.2. 1 Study Design . 27
Top and Side Lighting Induce Morphophysiological Improvements in Korean Ginseng Sprouts (Panax ginseng C.A. Meyer) Grown from One-Year-Old Roots

Nowadays, not only the roots, but also leaves and flowers of ginseng are increasingly popular ingredients in supplements for healthcare products and traditional medicine. The cultivation of the shade-loving crop, ginseng, is very demanding in terms of the light environment. Along with the intensity and duration, light direction is another important factor in regulating plant morphophysiology. In the current study, three lighting directions—top (T), side (S), or top + side (TS)—with an intensity of 30 ± 5 μmol·m−2·s−1 photosynthetic photon flux density (PPFD) were employed. Generally, compared with the single T lighting, the composite lighting direction, TS, was more effective in shaping the ginseng with improved characteristics, including shortened, thick shoots; enlarged, thick leaves; more leaf trichomes; earlier flower bud formation; and enhanced photosynthesis. The single S light resulted in the worst growth parameters and strongly inhibited the flower bud formation, leading to the latest flower bud observation. Additionally, the S lighting acted as a positive factor in increasing the leaf thickness and number of trichomes on the leaf adaxial surface. However, the participation of the T lighting weakened these traits. Overall, the TS lighting was the optimal direction for improving the growth and development traits in ginseng. This preliminary research may provide new ideas and orientations in ginseng cultivation lodging resistance and improving the supply of ginseng roots, leaves, and flowers to the market.
A Modular Biological Neural Network-Based Neuro-Robotic System via Local Chemical Stimulation and Calcium Imaging

Embodying in vitro biological neural networks (BNNs) with robots to explore the rise of intelligence in these simpler models and to endow robots with biological intelligence has been attracting increasing attention in the fields of neuroscience and robotics. However, current research suffers from unstable sensory-motor mapping due to the random wiring of neurons seeded on multi-electrode arrays (MEAs). Therefore, here we propose a modular BNN (mBNN)-based neuro-robotic system via local chemical stimulation and calcium recording. In this system, reliable evoked sensory-motor mapping (success rate $> $ 89%) from the sensory to the motor area in the mBNN was demonstrated. It is achieved in the mBNNs by combining global chemical modulation (for suppressing spontaneous signal transmission) and local chemical stimulation (for inducing the evoked signal transmission). The neural signals of the motor area of the BNN are recorded by calcium imaging, analyzed, and decoded to control the motion state of the mobile robot in real-time. The sensory signals of the robot are encoded and transmitted to the sensory area of the BNN, closing the loop. This system presents a platform to investigate how information is processed and transmitted in mBNNs, and also to examine the influence of local and global chemical modulation on within-network signal transmission.
Symmetry analysis for a Fisher equation with exponential diffusion

In this paper, we consider a generalized Fisher equation with exponential diffusion from the point of view of the theory of symmetry reductions in partial differential equations. The generalized Fisher‐type equation arises in the theory of population dynamics. These types of equations have appeared in many fields of study such as in the reaction‐diffusion equations, in heat transfer problems, in biology, and in chemical kinetics. By using the symmetry classification, simplified by equivalence transformations, for a special family of Fisher equations, all the reductions are derived from the optimal system of subalgebras and symmetry reductions are used to obtain exact solutions.
Attitude Estimation Using Iterative Indirect Kalman With Neural Network for Inertial Sensors

In this article, an iterative indirect Kalman filter is proposed to realize motion estimation based on inertial sensors. In the fusion of gyroscope, accelerometer, and magnetometer measurements, it is vital to decrease the impact of linear acceleration (LA) and external magnetic disturbances (EMAs) on the estimates. To this end, the proposed filter in this article performs first-order Gauss–Markov modeling for LA and EMA, respectively. Instead of simply adjusting the measurement noise covariance online, an iterative measurement strategy is presented to separate external disturbances based on the a posteriori estimation of the state during the measurement update. Moreover, a long short-term memory (LSTM) network is designed to assist the filter in the disturbance estimation process. It matches the process noise covariance to the strength of perturbation and adapts to the disturbance noise covariance. The experimental outcomes indicate that the proposed algorithm provides better accuracy and adaptive ability than some state-of-the-art results.
Phase Angle, Inflammation, and Sarcopenia in Late Postoperative Roux-En-Y Gastric Bypass

Sarcopenic obesity is characterized by a disproportion between the amount of muscle to fat. Contrary to most studies evaluating parameters related to sarcopenic obesity in the elderly, this study aims to evaluate the phase angle (PhA) and sarcopenia in young individuals pre- and post-Roux-en-Y gastric bypass. A total of 69 volunteers (46 women and 23 men; 38.5 ± 8.1 years) participated in this study. Body composition and PhA were assessed using BIA. Sarcopenia was assessed using a handgrip strength test (HGS) and gait speed (GS), and appendicular lean mass (ALM) was assessed using Dual Energy X-ray Absorptiometry (DXA). The PhA was significantly lower (p < 0.0007) and the resistance (R) significantly higher (p = 0.0026) in the postoperative group. HGS was negatively correlated with R (r = −0.63669; p < 0.0001), hs-CRP (r = −0.45436; p = 0.0197), and leptin (r = −0.46505; p = 0.0043). GS was negatively correlated with R (r = −0.36220; p = 0.0254), and ALM was negatively correlated with reactance (r = −0.49485; p = 0.0034) and R (r = −0.65797; p ≤ 0.0001). PhA and other components of BIA provide a good correlation with sarcopenia, especially regarding the reduction in muscle function, in an early form, in individuals in the pre- and postoperative period of gastric bypass.
The model of the kernel of the Lindley integral equation based on selective functions

Here considered a model of the kernel of the Lindley integral equation for a queuing system G/G/1. An exact solution of Lindley equation can be obtained using the Fourier transform under analytic continuation to the half-plane of the complex function originally defined on the real axis. However, it is quite difficult to obtain such solution, therefore, one resorts to finding approximate solutions based on the procedure of “degeneration” of the kernel when the kernel is factorized according to its variables. The method based on the use of selective functions is used. As an example, a special case is considered when the arrival time intervals have a gamma distribution, and the service time is constant. The result is shown for the kernel with the exponential form.
Initial State Stabilities and Inverse Engineering in Conflict Resolution

Two original contributions are made which extend the GraphModel for Conflict Resolution: one is a new family of solution concepts, while the other is a novel methodological approach. In addition to theoretical contributions, applications to complex energy problems are demonstrated; in particular, the consideration of the ongoing Trans Mountain Expansion Project is the first of its kind. The family of solution concepts, called initial state stabilities, is designed to complement existing solution concepts within the GraphModel framework by modelling both risk-averse and risk-seeking decision-makers. The comparison which underpins these concepts examines the consequences of moving from a given starting state to those of remaining in that state. The types of individuals modelled by these stability concepts represent a new class of decisionmakers which, up until now, had not been considered in the GraphModel paradigm. The innovative methodology presented is designed to "inverse engineer" decision-makers’ preferences based on their observable behaviour. The algorithms underlying the inverse engineering methodology are based on the most commonly used stability concepts in the GraphModel for Conflict Resolution and function by reducing the set of possible preference rankings for each decision-maker. The reduction is based on observable moves and countermoves made by decision-makers. This procedure assists stakeholders in optimizing their own decision-making process based on information gathered about their opponents and can also be used to improve the modelling of strategic interactions. In addition to providing decision-makers and analysts with up-to-date preference information about opponents, the methodology is also equipped with an ADVICE function which enriches the decision-making process by providing important information regarding potential moves. Decision-makers who use the methods introduced in this thesis are provided with the expected value of each of their possible moves, with the probability of the opponent’s next response, and with the opponent reachable states. This insightful data helps establish an accurate picture of the conflict situation and in so doing, aids stakeholders in making strategic decisions. The applicability of this methodology is demonstrated through the study of the conflict surrounding the Trans Mountain Expansion Project in British Columbia, Canada.