Miracle-dz/UniFAR-FTUs huggingface dataset:
Population estimates of photo-identified individuals using a modified POPAN model reveal that Raja Ampat’s reef manta rays are thriving

The 6.7-million-hectare Raja Ampat archipelago is home to Indonesia’s largest reef manta ray (Mobula alfredi) population and a representative network of nine marine protected areas (MPAs). However, the population dynamics of M. alfredi in the region are still largely unknown. Using our photo-identification database, we fitted modified POPAN mark-recapture models with transience and per capita recruitment parameters to estimate key demographic characteristics of M. alfredi from two of Raja Ampat’s largest MPAs: Dampier Strait and South East (SE) Misool. A total of 1,041 unique individuals were photo-identified over an 11-year period (2009–2019) from Dampier Strait (n = 515) and SE Misool (n = 536). In our models, apparent survival probabilities and per capita recruitment rates were strongly linked with El Niño–Southern Oscillation (ENSO) events. Our models also estimated high apparent survival probabilities and significant increases in (sub)population sizes in both MPAs over a decade. In Dampier Strait, the estimated population size increased significantly (p = 0.018) from 226 (95% CI: 161, 283) to 317 (280, 355) individuals. Likewise, the estimated population size in SE Misool increased significantly (p = 0.008) from 210 (137, 308) to 511 (393, 618) individuals. Regardless of variation in the percentage change in population size between years throughout the study, the estimated overall population change shows a compound growth of 3.9% (0.7, 8.6) per annum in Dampier Strait and 10.7% (4.3, 16.1) per annum in SE Misool. Despite the global decline in oceanic sharks and rays due to fishing pressure in the last five decades, our study demonstrates the positive impact of a suite of long-term conservation efforts, coupled with the influence of ENSO events, on increasing M. alfredi abundance in Raja Ampat MPAs. Our study also underscores the importance of long-term monitoring to evaluate the effectiveness of conservation management measures on manta ray populations. Our modification of the standard POPAN model by incorporating per capita recruitment and transience parameters represents an important advance in mark-recapture modelling that should prove useful when examining other manta ray populations and other highly migratory species that are likely to have a substantial percentage of transient individuals.
Causes of the male-female ratio of depression based on the psychosocial factors

Recently, depression has been a great concern of researchers, and the research on the male-female ratio of depression mainly covers the fields of epidemiology and clinical studies. This study is made to explore the causes of the male-female ratio of depression from a humanistic perspective, namely, the psychosocial factors, thus providing some support and reference for further research on depression. Thereby, the authors select and analyze word lists closely collocated with “depression” according to word score and frequency through the word sketch in enTenTen20 by the corpus tool “Sketch Engine. ” Meanwhile, the above words are further explored for the causes of the male-female ratio in depression from the biological, psychological, and social aspects based on the Biopsychosocial Model as well as some existing relevant medical research findings. Consequently, women are found to be more likely to suffer from emotional problems and depression than men under the influence of biological basis, general psychological characteristics, and social environment interference.
Adaptive Fixed-Time Cooperative Intercept Guidance Law with Line-Of-Sight Angle Constraint

In this paper, the cooperative interception issues with line-of-sight (LOS) angle constraints through multi-missile system are investigated. To enhance the stability of system, the target maneuvering is regarded as external disturbance, which is estimated by adaptive law and compensated in guidance law. Firstly, transform simultaneous attack issues into consensus problems. Then, a new distributed collaborative guidance law on the LOS direction has been deduced to ensure the impact time of all missiles can be consensus in fixed time. Furthermore, a novel integral sliding mode-based acceleration command in the vertical direction of LOS has been proposed to achieve the desired LOS angle within fixed time. In contrast to the finite-time cooperative guidance law, the convergence time of fixed-time cooperative guidance law is independent of initial states of missiles, which not only quickens the convergence speed, but also relaxes the constraint of the initial states. The validity of the given guidance law is evidenced through the simulated results illustrating a scenario of four missiles intercepting a maneuverable target.
Attacking IEC-60870-5-104 SCADA Systems

The rapid evolution of the Information and Communications Technology (ICT) services transforms the conventional electrical grid into a new paradigm called Smart Grid (SG). Even though SG brings significant improvements, such as increased reliability and better energy management, it also introduces multiple security challenges. One of the main reasons for this is that SG combines a wide range of heterogeneous technologies, including Internet of Things (IoT) devices as well as Supervisory Control and Data Acquisition (SCADA) systems. The latter are responsible for monitoring and controlling the automatic procedures of energy transmission and distribution. Nevertheless, the presence of these systems introduces multiple vulnerabilities because their protocols do not implement essential security mechanisms such as authentication and access control. In this paper, we focus our attention on the security issues of the IEC 60870-5-104 (IEC-104) protocol, which is widely utilized in the European energy sector. In particular, we provide a SCADA threat model based on a Coloured Petri Net (CPN) and emulate four different types of cyber attacks against IEC-104. Last, we used AlienVault's risk assessment model to evaluate the risk level that each of these cyber attacks introduces to our system to confirm our intuition about their severity.
Delay robust state feedback stabilization of an underactuated network of two interconnected PDE systems

We detail in this article the development of a delay-robust stabilizing state feedback control law for an underactuated network of two systems of two heterodirectional linear first-order hyperbolic Partial Differential Equations interconnected through their boundaries. Only one of the two subsystems is actuated. The proposed approach combines successive backstepping transformations which allow to rewrite the original network system as a simple neutral system with distributed delays for which the control design is easy. The proposed feedback law is finally proved to be robust to small delays in the actuation.
A Multiple Working Mode Approach to Hammering with a Modular Reconfigurable Robot

Robot joints could be damaged by impulsive forces due to impact when the robot performs hammering operations. This paper presents a strategy that allows a modular and reconfigurable robot to safely perform nail hammering in 3D space without hardware enhancements. To do this, a multiple working mode approach is applied to switch the selected joint(s) to passive mode with friction compensation to allow free rotation during impact. Analytic impulse models are used to predict joint impulses which can be computed offline or online and serve as criteria for mode switching. End-effector pose, and link momentum are proposed as criteria to track and time the switching. The strategy assumes multiple swings are performed with the last swing resulting in a collision with the board or wall. Joint impulses are constrained in case the hammer collides with such an object of infinite effective mass. Advantages of the proposed approach may include savings on space, weight, costs, and complexity for a limited range of nail/board environments. An experimental study validates analytic models of hammering and effectiveness of multiple working mode approach.
Affine Formation Maneuver Control of Multi-Agent Systems with Triple-Integrator Dynamics

This paper addresses the affine formation maneuver control of multi-agent systems with triple-integrator dynamics for both continuous-time and sampled-data settings. In affine formation control, the agents are supposed to form a desired geometric pattern and simultaneously achieve required maneuvers, such as rotation, scaling and translation. In existing literature, it is understood that this can be achieved for systems with double-integrator dynamics where the inter-agent communications occur continuously in time. However, agents may only be able to communicate in periodic time intervals and a broad range of systems have complex dynamics requiring higher-order dynamics in some practical situations. In this paper, we propose novel maneuver control laws using triple-integrator dynamics. The proposed approach is based on stress matrix, which allows the communication graph weights to be positive or negative, and it can be considered as a generalized Laplacian matrix of a graph. Under the proposed control laws, the group of agents are able to track time-varying targets that are affine transformations of a given nominal formation, and the desired formation maneuvers are only known by the leaders.
Fitting Cornering Speed Models with One-Class Support Vector Machines

This paper investigates the modelling of cornering speed using road curvature as a predictive variable, which is of interest for advanced driver assistance system (ADAS) applications including eco-driving assistance and curve warning. Such models are common in the driver modelling and human factors literature, yet lack reliable parameter estimation methods, requiring an ad-hoc evaluation of the upper envelope of the data followed by linear regression to that envelope. Considering the space of possible combinations of lateral acceleration and cornering speed, we cast the modelling of cornering speed as an ‘outlier detection’ problem which may be solved using one-class Support Vector Machine (SVM) methods from machine learning. For an existing cornering model, we suggest a fitting method using a specific choice of kernel function in a one-class SVM. As the parameters of the cornering speed model may be recovered from the SVM solution, this provides a more robust and reproducible fitting method for this model of cornering speed than the existing envelope-based approaches. In addition, this gives comparable outlier detection performance to generic SVM methods based on Radial Basis Function (RBF) kernels while reducing training times by a factor of 10, indicating potential for use in adaptive eco-driving assistance systems that require retraining either online or between drives.
Robust Adaptive Tube Model Predictive Control

An adaptive Model Predictive Control (adaptive MPC) strategy is proposed for linear systems with constant unknown model parameters, bounded additive disturbances and state and control constraints. By combining online set-based identification and robust tube MPC, the proposed controller reduces the conservativeness of constraint handling, guarantees recursive feasibility and provides asymptotic bounds on the closed loop system state that depend explicitly on the the identified parameter set. Computational tractability is ensured by using fixed complexity polytopic sets to bound the model parameters and predicted states. Convex conditions for persistence of excitation are considered. The results are illustrated by a numerical example.
Amnion and chorion matrix maintain hMSC osteogenic response and enhance immunomodulatory and angiogenic potential in a mineralized collagen scaffold

Craniomaxillofacial (CMF) bone injuries present a major surgical challenge and cannot heal naturally due to their large size and complex topography. We are developing a mineralized collagen scaffold that mimics extracellular matrix (ECM) features of bone. These scaffolds induce in vitro human mesenchymal stem cell (hMSC) osteogenic differentiation and in vivo bone formation without the need for exogenous osteogenic supplements. Here, we seek to enhance pro-regenerative potential via inclusion of placental-derived products in the scaffold architecture. The amnion and chorion membranes are distinct components of the placenta that each have displayed anti-inflammatory, immunomodulatory, and osteogenic properties. While potentially a powerful modification to our mineralized collagen scaffolds, the route of inclusion (matrix-immobilized or soluble) is not well understood. Here we compare the effect of introducing amnion and chorion membrane matrix versus soluble extracts derived from these membranes into the collagen scaffolds on scaffold biophysical features and resultant hMSC osteogenic activity. While inclusion of amnion and chorion matrix into the scaffold microarchitecture during fabrication does not influence their porosity, it does influence compression properties. Incorporating soluble extracts from the amnion membrane into the scaffold post-fabrication induces the highest levels of hMSC metabolic activity and equivalent mineral deposition and elution of the osteoclast inhibitor osteoprotegerin (OPG) compared to the conventional mineralized collagen scaffolds. Mineralized collagen-amnion composite scaffolds elicited enhanced early stage osteogenic gene expression (BGLAP, BMP2), increased immunomodulatory gene expression (CCL2, HGF, and MCSF) and increased angiogenic gene expression (ANGPT1, VEGFA) in hMSCs. Mineralized collagen-chorion composite scaffolds promoted immunomodulatory gene expression in hMSCs (CCL2, HGF, and IL6) while unaffecting osteogenic gene expression. Together, these findings suggest that mineralized collagen scaffolds modified using matrix derived from amnion and chorion membranes represent a promising environment conducive to craniomaxillofacial bone repair.
Design and Optimization of Squeeze Film Effect based Tactile Display System*

The friction between the fingertip and a plate will be decreased with the squeeze film effect caused by ultrasonic vibration, which will lead to the lateral force changes, and can be used to implement tactile display system through providing the illusions of fine texture surfaces. To improve system performance of this friction based tactile rendering, homogenous and high-amplitude of the tactile plate is needed. This paper presents a method for the optimization of the tactile device configuration, which includes the parameters of geometric dimensioning and the layout of the ultrasonic tactile plate and the piezoelectric actuators. First, the relationship between the various parameters and the vibration mode of the plate is studied through the FEM (finite element method) with ANSYS. Then, a function combining average vibration amplitude μ and its standard deviation σ is proposed for evaluating the various configuration of the tactile display system. Finally, contrast experiments are conducted to verify the efficacy of the proposed method with different system configuration. Furthermore, we demonstrated the proposed method on a tactile display system. The simulation data of optimization method is accordant with the practical experiment results. The proposed optimization method can be applied to general tactile display system designing.
Application of Deep Learning for Fault Diagnostic in Induction Machine’s Bearings

Recent developments in sensor technologies and advances in communication systems have resulted in deployment of a large number of sensors for collecting condition monitoring (CM) data in order to monitor health condition of a manufac-tring/industrial system. Efficient utilization of sensory data leads to highly accurate results in system fault diagnostics/prognostics. The exponential growth of CM data poses significant analytical challenges, due to their high variety, high dimensionality and high velocity rendering conventional health monitoring tools impractical. In this regard, the paper proposes a deep learning-based framework for fault diagnosis of an induction machine’s bearing based on real data set provided by Case Western Reserve University bearing data center. In particular, we focus on deep bidirectional long short-term memory (BiD-LSTM) networks fed with raw signals for fault diagnosis to address drawbacks of conventional machine learning (ML) solutions such as support vector machines. A numerical example is provided to illustrate the complete procedure of the proposed framework, which shows the great potentials of the BiD-LSTM for detection of different types of the bearing fault with high accuracy. The effectiveness of the proposed model is demonstrated through a comparison with a recently developed deep neural network (DNN) that considers temporal coherence for the same data set. The results indicate that the proposed framework provides considerably improved performance in comparison to its counterparts.
Dynamic Data Routing Decisions for Compliant Data Handling in Service-and Cloud-Based Architectures: A Performance Analysis

In many service-based applications, decisions about data routing need to be made at runtime, for instance to ensure compliant data handling. Different service-and cloud-based architectures to make dynamic data routing decisions exist including central entities, multiple dedicated dynamic router services, or using a sidecar for each involved service. These archi-tectures differ in various quality attributes including complexity, understandability, and changeability of the decision logic. Choosing the wrong architecture for decision-making at runtime may severely impact the performance of the software system. In this paper, we have evaluated the performance of three representative approaches for processing compliance rules concerned with data routing in service-and cloud-based architectures. The results show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. On the other hand, centralized solutions are easier to understand and change, but this strongly depends on the domain problem.
Trajectory Tracking Control of a nonlinear Autonomous Surface Vessel

This paper presents a nonlinear controller based on a double integrator system for trajectory tracking of a nonlinear autonomous surface vessel. Using a fixed-point on the vessel body frame as position output, we devise a simplified controller with only two error states, as opposed to the four required in a more straightforward and naive backstepping approach. The resulting controller is dynamically simple and easy to implement, and does not require higher than second-order plant dynamics and reference trajectory derivatives. The proposed approach mitigates typical problems arising during backstepping such as noise amplification and the need for a highly accurate plant model. Experimental results with an instrumented autonomous surface vessel are presented to corroborate the performance and robustness of the proposed controller.
BarryWhaptics: Towards Countering Social Biases Using Real-Time Haptic Enhancement of Voice

Studies suggest that imbalances in speaking opportunities during meetings often lead to sub-optimal meeting outcomes. These imbalances can be due to a variety of reasons, including people’s perception of speakers and their voice. Indeed, speakers with higher pitched voices were shown to be perceived as having lower leadership ability. In an attempt at countering such voice-pitch related biases, this work introduces BarryWhaptics, a real-time speech-to-haptics conversion system that leverages multimodal perception to alter the listener’s perception of a speaker. The system operates by augmenting human speech with vibration, applying more intense vibrations to voices that would ordinarily be considered low in dominance. Results from a pilot study assessing the influence of the system in a decision-making task demonstrate that it can meaningfully influence how users choose to follow instructions given by one speaker over another.
Robust Pole Placement: DR-regions for Discrete-time Systems

In this paper, novel convex approximations of pole regions corresponding to the prescribed damping and stability degree for uncertain discrete-time systems are introduced. A developed MATLAB tool for computation of matrices for the proposed new DR regions significantly simplifies the design procedure. The only inputs required by the developed tool are prescribed damping defined by the angle and stability degree.
Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment

The coordinated on-ramp merging is one of the most common but critical vehicular applications that require complex data transmission and low-latency communication in the Connected and Automated Vehicles (CAVs) environment. An effective way to address on-ramp merging is to leverage the edge computing to optimize the coordination among vehicles to achieve overall minimum vehicle travel time and energy consumption. In this study, we propose an Bi-level Optimal Edge Computing (BOEC) model for on-ramp merging in the CAVs environment to optimize both merge time and vehicle trajectory. The simulation results show that the proposed BOEC model achieves great benefits in vehicle mobility, energy saving and air pollutant emission reduction by providing an energy-efficient trajectory following the optimal merge time without compromising safety.
Back-Stepping Controller Design for Altitude subsysytem of Hypersonic Missile with Takagi-Sugeno Fuzzy Estimator

In this paper, guidance law is selected as desired flight path angle that derived from the sliding mode control method. The back-stepping technique is designed and analyzed for the altitude dynamics of hypersonic missile for maneuvering target. Hypersonic missile model is nonlinear, non-minimum phase, uncertain, and highly coupled. Takagi-Sugeno fuzzy estimator is used for estimating the uncertainty of model parameters. Lyapunove theorem is used to examine the stability of closed-loop systems. The simulation indicate that the proposed scheme has shown effectiveness of the control strategy, high accuracy, stability of states, and low-amplitude control inputs in the presence of uncertainties and external disturbance.
Driving Behavior Modeling Based on Hidden Markov Models with Driver's Eye-Gaze Measurement and Ego-Vehicle Localization

This paper presents a comparison of driving behavior modeling methods based on hidden Markov models (HMMs) with driver's eye-gaze measurement and ego-vehicle localization. Original HMMs are sometimes insufficient to model real-world scenarios. To overcome these limitations, extended HMMs have been proposed, e.g., autoregressive input-output HMMs (AIOHMMs). This paper first details AIOHMMs and presents ways to use them for driving behavior modeling. We compare the performance for behavior modeling and maneuver discrimination for six types of HMMs. The driving data for this work was gathered in our university campus with a car-like vehicle. Experimental results suggest that the hidden states can properly represent the average of the driving actions when the driving behaviors are accurately modeled by the HMMs. It is also suggested that surrounding and past information can be used to flexibly model the relationship between driving actions and related information.