Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions to address these challenges. Research in this area has become crucial, as it contributes not only to improve mobility in urban areas, but also to positively impact the quality of life of its inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data was collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

The Covid-19 crisis has been one of the most relevant economic shocks in the history of the modern economy, mainly due to the restrictions on travel and social interaction taken by the authorities (quarantines, closure of schools, artistic activities, bars, and restaurants, capacity in stores), to reduce the transmission of the virus (Sars-Cov2), having consequences on the national productions of all countries (about 7% for the loss of GDP worldwide). In our study, we analyze, through clustering methods, the effects of the level of contagion, economic projections, and economic mitigation measures taken by governments for managing the pandemic. According to the analyses, it can be concluded that a low infection rate implies a lower loss of product and economic savings when applying economic mitigation measures, taking relevance in the management of sanitary measures to reduce the transmission of the virus (and the temporality of these).

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO₂ emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

In the interaction of the rolling stock and the upper structure of the railway track, intense dynamic loads occur. They have a destructive effect both on the parts of the rolling stock and on the elements of the superstructure of the track. In order to develop a durable, rational and reliably functioning design of cars and locomotives with good dynamic properties and good indicators of the impact of rolling stock on the railway track, along with theoretical computational studies, experimental studies are also required, which are usually the final stage in the design and implementation of rolling stock or modernization of existing ones. Locomotives and wagons in order to improve their strength and dynamic performance. The article presents the results of field tests to determine the dynamic performance of the type CKD6e diesel locomotive. The description of the preparation of the CKD6e shunting locomotive for testing is given. An analysis of the dynamic performance of a diesel locomotive during the passage of turnouts, on a straight section of the track and in a curve with a radius of 400 m, was carried out. The studies performed showed that the minimum value of the stability factor against wheel derailment on a straight section of the track is significantly higher than the standard value. The experimentally obtained ratio of frame forces to the static load from the wheelset on the rails, the coefficients of vertical dynamics of the first and second stages of suspension and the coefficient of stability against derailment of the wheel from the rail, registered on the track section in a curve with a radius of 400 m) meet the current requirements. A calculation scheme and equations of vertical oscillations are proposed, an analysis is carried out according to the graphs of movements of bogies and a locomotive body when moving along irregularities of different lengths at different speeds.

Simultaneous Localization and Mapping (SLAM) is considered as a challenge in environments with many moving objects. This paper proposes a novel LiDAR Inertial Odometry framework, ID-LIO, for dynamic scenes that builds on LIO-SAM. To detect the pointclouds on the moving objects, a dynamic point detection method is integrated, which is based on pseudo occupancy along the spatial dimension. Then, we present a dynamic points propagation and removal algorithm based on indexed points to remove more dynamic points in the local map along the temporal dimension and update the status of the point features in keyframes. In the LiDAR odometry module, a delay removal strategy is proposed for history keyframes and the sliding window-based optimization includes the LIDAR measurement with dynamic weights to reduce error from dynamic points in keyframes. We perform the experiments both on the public low dynamic and highly dynamic data set. The results show that the proposed method greatly increases the localization accuracy in high dynamic environments. And the ATE(Absolute Trajectory Error) average RMSE((Root Mean Square Error) of our ID-LIO can be improved by 67% and 85% in the UrbanLoco-CAMarketStreet dataset and UrbanNav-HK-Medium-Urban-1 dataset respectively when compared with LIO-SAM.

Modern computational biology makes widespread use of mathematical models of biological systems, in particular systems of ordinary differential equations, as well as models of dynamic systems described in other formalisms, such as agent-based models. Parameters are numerical values of quantities reflecting certain properties of a modeled system and affecting model solu-tions. At the same time, depending on parameter values, different dynamic regimes - stationary, oscillatory, established as a result of transient modes of various types - can be implemented in the system. Predicting changes in the solution dynamics type depending on changes of model parameters is an important scientific task. Nevertheless, this problem does not have an analytical solution for all formalisms in a general case. The routinely used method of performing a series of computa-tional experiments, i.e. solving a series of direct problems with various sets of parameters fol-lowed by expert analysis of solution plots is labor-intensive with a large number of parameters and a decreasing step of the parametric grid. In this regard, the development of methods allow-ing to obtain and analyze information on a set of computational experiments in an aggregate form is relevant. This work is devoted to developing a method for visualization and classification of various dy-namic regimes of a model using a composition of the dynamic time warping (DTW-algorithm) and principal coordinates analysis (PCoA) methods. This method allows to obtain a qualitative visualisation of the results of the set of solutions of a mathematical model and to carry out the correspondence between the values of the model parameters and the type of dynamic regimes of its solutions. This method has been tested on the Lotka-Volterra model and artificial sets of var-ious dynamics

The colossal depths of the deep neural network sometimes suffer from ineffective backpropagation of the gradients through all its depths. Whereas, The strong performance of shallower multilayer neural structures prove their ability to increase the gradient signals in the early stages of training which easily gets backpropagated for global loss corrections. Shallow neural structures are always a good starting point for encouraging the sturdy feature characteristics of the input. In this research, a shallow, deep neural structure called PrimeNet is proposed. PrimeNet is aimed to dynamically identify and encourage the quality visual indicators from the input to be used by the subsequent deep network layers and increase the gradient signals in the lower stages of the training pipeline. In addition to this, the layerwise training is performed with the help of locally generated errors which means the gradient is not backpropagated to previous layers, and the hidden layer weights are updated during the forward pass, making this structure a backpropagation free variant. PrimeNet has obtained state-of-the-art results on various image datasets, attaining the dual objective of (1) compact dynamic deep neural structure, which (2) eliminates the problem of backwards-locking. The PrimeNet unit is proposed as an alternative to traditional convolution and dense blocks for faster and memory-efficient training, outperforming previously reported results aimed at adaptive methods for parallel and multilayer deep neural systems.

This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the motion of the sprung mass and four degrees of freedom for the unsprung masses. For this model, the equations of motion were derived using Lagrange’s equation. To verify the model, the suspension deformations computed using the model were compared with the deformations measured through three actual vehicle driving tests which are the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic re-mapping, updating the map is recommended. However, in case of incorrect estimation of robot pose, updating the map can lead to errors that prevent the robot localisation and jeopardize map accuracy. In this paper, we propose a safe LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account the uncertainties in the estimation of the robot’s pose. The proposed approach allows robust long-term operations as it can recover the robot’s pose, even when it gets lost, to continue the map update process providing a coherent map. Moreover, the approach is robust also to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

In this work, we present an analytical model of dynamic power losses for enhancement-mode AlGaN/GaN high-electron-mobility transistor power devices (eGaN HEMTs). To build this new model, the dynamic on-resistance (Rdson) is first accurately extracted by our ex-traction circuit based on a double-diode isolation (DDI) method for a high operating frequency up to 1 MHz and a large drain voltage up to 600 V, thus the unique problem of an increase in the dynamic Rdson is presented. Then the impact of the current operation mode on the on/off transi-tion time is evaluated by a dual-pulse-current-mode test (DPCT) which including a discontinu-ous conduction mode (DCM) and a continuous conduction mode (CCM), thus the transition time is revised for different current mode. Afterward the discrepancy between the drain current and the real channel current is qualitative investigated by an external shunt capacitance (ESC) meth-od, thus the losses due to device parasitic capacitance are also taken into account. After these improvements, the dynamic model will be more compatible for eGaN HEMTs. Finally, the dynamic power losses calculated by this model are verified to be in good agreement with experimental results. Based on this model, we propose a superior solution with qua-si-resonant mode (QRM) to achieve lossless switching and accelerate switching speed.

In this paper, the research progress on dynamic behaviors of RC structural members is reviewed from experimental, theoretical and numerical perspectives. Firstly, the basic overview, measurement methods, main conclusions and current limitations of available dynamic loading tests are presented. Then the theoretical studies on the dynamic constitutive models of RC materials, the dynamic increase factor (DIF) model for concrete and reinforcing steel and the proposed modified models of dynamic behavior parameter at the structural member level are summarized. Finally, the available modelling approach and the method for incorporating the dynamic effect in numerical simulation of RC structures are reviewed. Moreover, a brief introduction is made to the dynamic hysteretic model established on the experimental data, which provides an alternative approach to consider the dynamic effect beside the commonly used DIF method. This paper provides valuable reference for experimental studies and numerical simulations on the dynamic behaviors of RC structures, and also puts forward some issues that need to be solved in the future research works..

Based on functional magnetic resonance imaging and multilayer dynamic network model, the quantified temporal stability of brain network has shown potentials in predicting altered brain functions. The present review focuses on summarizing current knowledge on the commonly-used measures of brain network’s temporal stability and the clinical research progress on them. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date that which measure is the best. The temporal stability of human brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, and data preprocessing/analyzing strategies, which should be considered in the clinical studies. Multiple common psychiatric diseases such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disease-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric diseases partly because of the divergent results, and further studies are warranted.

First, a brief overview is provided on humanoid robots, and also models for the dynamic behavior are discussed. As base for these models these two methods Denavit-Hartenberg and Newton-Euler are used. Main aim of this work is to investigate the stability of a biped robot developed from IHRT. There is currently the low base of robot - consisting of feet, legs, hips and upper part of robots body. This structure currently has ten degrees of freedom.

Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities like subgrade damages. The reason of the intensive void development lies in the mechanics of the sleeper and ballast bed interaction. The particularity of the interaction is a dynamic impact that occur due to void closure. Additionally, void zones cause inhomogeneous ballast pressure distribution between the void zone and fully supported neighbour zones. The present paper is devoted studying the mechanism of the sleeper-ballast dynamic impact in the void zone. The results of experimental in-situ measurements of rail deflections showed the significant impact accelerations in the zone even for light-weight slow vehicles. A simple 3-beam numerical model of track and rolling stock interaction has shown the similar to the experimental measurements dynamic interaction. Moreover, the model shows that the sleeper accelerations are more than 3 times higher than the corresponding wheel accelerations and the impact point appear before the wheel enters the impact point. The analysis of ballast loadings shows the specific impact behaviour in combination with the quasistatic part that is different for void and neighbour zones, which are characterised with high ballast pre-stressed conditions. The analysis of void sizes influence demonstrate that the impact loadings, wheel and sleeper maximal accelerations appear at certain void depth after which the values decrease. The ballast quasistatic loading analysis indicates more than twice increase of the ballast loading in neighbour zones for long voids and almost full quasistatic unloading for short length voids. However, the used imitation model cannot explain the nature of the dynamic impact. The mechanism of the void impact is clearly explained by the analytic solution using a simple clamped beam. A simplified analytical expression of the void impact velocity shows that it is linearly related to the wheel speed and loading. The comparison to the numerically simulated impact velocities shows a good agreement and the existence of the void depth with the maximal impact. An estimation of the long-term influences for the cases of normal sleeper loading, high ballast pre-stress and quasistatic loading in the neighbour zones and high impact inside the void are performed.

The article presents the method of identification of dynamic models for different flight states of a rotary-wing UAV. Experimental flights were conducted to obtain data necessary for the identification process. Such models can later be implemented in simulations to represent the behavior of real-life objects. Simulation of UAV swarms is the first stage of developing a swarm system, where prototyping with physical models is problematic. Therefore, obtaining accurate models is crucial for the simulation process to be reliable. Also, verification of obtained models was performed to make sure that they were identified correctly. In particular, the presented method was proven effective and successfully used in some applications.

This report discusses the application of approximate dynamic programming (ADP) and Krylov subspace methods in solving sequential decision-making problems in machine learning. ADP is used when dealing with large state spaces or when the exact dynamics are unknown. The paper explores various ADP methods such as temporal difference learning and least-squares policy evaluation. The authors also focus on the use of Krylov subspace methods for solving the Bellman equation in ADP. The report provides insights into linear approximation, stochastic algorithms, and the Arnoldi algorithm for orthonormal basis. Overall, the paper highlights the theoretical foundation provided by ADP and its connection with Krylov subspace methods, shedding light on their application in computer science.

We have developed a self-consistent model for predicting velocity of 1/2[111] screw dislocation in binary iron--carbon alloys gliding by a high-temperature Peierls mechanism. The methodology of modelling includes: (i) Kinetic Monte-Carlo (kMC) simulation of carbon segregation in dislocation core and determination the total carbon occupancy of the core binding sites; (ii) Determination of kink-pair formation enthalpy of a screw dislocation in iron---carbon alloy; (iii) KMC simulation of carbon drag and determination of maximal dislocation velocity at which the atmosphere of carbon atoms can follow a moving screw dislocation; (iv) Self consistent calculation of average velocity of screw dislocation in binary iron--carbon alloys gliding by a high-temperature kink-pair mechanism under constant strain rate. We conduct a quantitative analysis of the conditions of stress and temperature at which screw dislocation glide in iron--carbon alloy is accomplished by a high-temperature kink-pair mechanism. We estimate the dislocation's velocity at which screw dislocation brakes away from the carbon cloud and thermally-activated smooth dislocation propagation is interrupted by sporadic bursts controlled by athermal dislocation activity.

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1 - considering the flexibility, modularity and portability - and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e. the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. 32 years' continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

Since the outbreak of 2019 novel coronavirus (2019-nCoV) at the hardest-hit city of Wuhan, the fast-moving spread has killed over three hundred people and infected more than ten thousands in China¹. There are more than one hundred cases outside of China, affecting a dozen of countries globally². The genome sequence of 2019-nCoV has been reported and fast diagnostic kits, effective treatment as well as preventive vaccines are rapidly being developed³. Initial fast-growing confirmed cases triggered lock-down of Wuhan as well as nearby cities in Hubei Province. Mathematical models have been proposed by scientists around the world to project the numbers of infected cases in the coming days ^4,5. However, major factors such as transportation and cultural customs have not been weighed enough. Our model is not set out for precise prediction of the number of infected cases, rather, it is meant for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. We hope that our model and simulation would provide more insights and perspective information to public health authorities around the globe for better informed prevention and containment solution.

Molecular dynamics simulations were carried out to investigate the atomic structure of a model Cu64Zr36 bulk metallic glass (BMG). It is found that the amount of icosahedral content of the system is significantly increased in a well relaxed structure. While we considered four connection types of vertex-, edge-, face-, and volume-sharing, the huge cluster in the relaxed samples mainly involve volume-type connection and exhibits a remarkable athermal plasticity that great stiffness and great yield strength compared to the as-quenched samples. In addition, the bond-angle distribution of annealed sample shows sharp peaks at specific bond angles which is an evidence of crystallized Laves-phase formed by icosahedral atoms, however the peaks are to be broaden after loading, which indicates decreasing amount of icosahedral content and their shape distortion. These results suggest that icosahedral content in a bulk metallic glasses plays a key role to determine the mechanical properties such as rigidity and maximum stress carrying capacity.

The study of values at European and global level has gained momentum along with the theory of values launched by Schwartz. Values are dormant, hidden, unobservable and impossible to measure directly. They exist somewhere within the human being, manifested by different attitudes, depending on their degree of generality. The paradigm of basic human values implies shared universal values that are encountered in cultures all over the world. The theory of values designed by Schwartz has refined the pool of 19 basic individual values representing principles in individuals and group, further describing the dynamic relationships among them. This study presents practical evidence of the dynamics between individual values, the dynamics theorized by Schwartz in 2012. Following the development of an online questionnaire made up of 46 items with online responses options on a Lickert scale from 1 to 6, where 1 represents less important and 6 represent very important, 220 young people from Western Romania voluntarily responded. By testing the hypothesis assuming the dynamic relationship between the two values through multiple regression analysis, the results demonstrate that in Model 1, which involves a linear relationship, health explains 42% of the variance in security with a F =161,215 significant at p <.01. In Model 2, which involves a curvilinear relationship, health explains 50% of the variance in security version with a F = 35,336 significant at p <.01. The incremental prediction optimization of 8% added by including the squared security accounts for the curve in the regression line, indicating the existence of a curvilinear relationship between security and health. The dynamic relationship demonstrates that upper limit and lower limit aspects of security, significantly influence the health value in a negative way. Normal levels of security trigger a high level of prioritization of health value. The implications of this type of relationship are discussed in explaining the value phenomenon at individual level. The study shows limits due to the selected sample, focusing exclusively on young people with higher education from the Western region of Romania. This sample was the target group of "The National Identity of Romanian Youth" project which designed this research.

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .

This paper studies a model of dynamic compensation and capital structure with managerial traits. We show that Optimistic manager perceives equity as more undervalued than debt, while, confident manager perceives debt as more undervalued than equity. Managerial risk aversion mitigates manager’s bias. The risk aversion of the optimistic manager has a convex effect on the optimal coupon. There exists the level of risk aversion eliminating the bias on the leverage. The managerial optimist has an ambiguous effect on the owner’s bankruptcy level. The risk aversion has a convex effect on the owner’s bankruptcy level. The optimistic/confident manager underestimates the credit spread. The risk aversion has convex effect on the credit spread. In contrast to rational manager, the optimistic/confident manger has higher level of effort. The risk aversion has a negative effect on the effort.

Piezoelectric cantilever harvesters have a large power output at their natural frequency, but in some applications the frequency of ambient vibrations is different from the harvester’s frequency and/or ambient vibrations are periodic with some harmonic components. To cope with these operating conditions harvesters with integrated trimming devices (ITDs) are proposed. Some prototypes are developed with the aid of an analytical model and tested with an impulsive method. Results show that a small trimming device can lower the main resonance frequency of a piezoelectric harvester of the same extent as a larger tip mass and moreover generates at high frequency a second resonance peak. A multi-physics numerical FE model is developed for predicting the generated power and for performing stress-strain analysis of harvesters with ITDs. The numerical model is validated on the basis of experimental results. Several configurations of ITDs are conceived and studied. Numerical results show that harvesters with ITDs are able to generate relevant power at two frequencies owing to the particular shape of the modes of vibration. The stress in the harvesters with ITDs is smaller than the stress in the harvester with a tip mass trimmed to the same frequency.

For better export marketing strategies (EMS), companies mobilize their internal resources, which are managerial commitment, firm experience, and product uniqueness. However, Small businesses with constrained resources cannot be well explained with this view. So, more research on how small business come up with EMS have been called for. To explain how resource-restricted firms which rely heavely on entrepreneur, this study adopted the concept of dynamic managerial capabilities (DMCs) and resource versatility to better explain small business exports. We analyzed small businesses in Mongolia with qualitative research methods, including interviews with entrepreneurs and support organizations, site visits, and group discussions. We suggest international dynamic marketing capabilities (IDMCs), which are entrepreneurial orientation, networking capability, and versatile dynamic capability for small business. Theoretical and managerial implications are discussed.

Digital twins are the foundation of autonomous off-road vehicles. Dynamic reduction methods are one of several ways to develop digital twins for off-road vehicles. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results of digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built control, electrical, and control systems.

5G heterogeneous network overlaid by millimeter-wave (mmWave) access employs mmWave meshed backhauling as a promising cost-efficient backhaul architecture. Due to the nature of mobile traffic distribution in practice which is both time-variant and spatially non-uniform, dynamic construction of mmWave meshed backhaul is prerequisite to support the varying traffic distribution. Focusing on such scenario of outdoor dynamic crowd (ODC), this paper proposes a novel method to control mmWave meshed backhaul for efficient operation of mmWave overlay 5G HetNet through Software-Defined Network (SDN) technology. Our algorithm is featured by two functionalities, i.e., backhauling route multiplexing for overloaded mmWave small cell base stations (SC-BSs) and mmWave SC-BSs’ ON/OFF status switching for underloaded spot. In this paper, the effectiveness of the proposed meshed network is confirmed by both numerical analyses and experimental results. Simulations are conducted over a practical user distribution modeled from measured data in realistic environments. Numerical results show that the proposed algorithm can cope with the locally intensive traffic and reduce energy consumption. Furthermore, a WiGig (Wireless Gigabit Alliance certified) device based testbed is developed for Proof-of-Concept (PoC) and preliminary measurement results confirm the proposed dynamic formation of the meshed network’s efficiency.

Semantic segmentation of remote sensing urban scene imagery is a pixel-wise prediction, which is applied to identify the land-cover or land-use category. However, semantic segmentation demands huge computation cost. In order to reduce the huge computation cost, a common method is to introduce transformer and CNN hybrid method to have a good trade-off between accuracy and computation cost. However, recent CNN-transformer hybrid methods often capture local-global context and cross-window interaction information simultaneously. Then they introduce the local-global context to fuse the local feature, which repeat fuse the local and global feature and result in the additional computation cost. And previous methods often ignore to filter the local and global feature. To fix the problem, we design a lightweight decoder-EDDformer, which is Efficient Global Value Transformer with Dynamic Gatefusion module. Efficient global value transformer only response for extracting global feature. Dynamic Gatefusion module filter the local and global semantic feature and fuse them to capture the local-global context in one time. Extensive experiments reveal that our method not only runs faster but also achieves higher accuracy compared with other state-of-the-art lightweight models.

Japanese encephalitis virus (JEV) causes acute viral encephalitis in humans and reproductive disorders in pigs. JEV emerged during the 1870s in Japan and since that time, JEV has been transmitted exclusively throughout Asia, according to known reporting and sequencing records. A recent JEV outbreak occurred in Australia which affected commercial piggeries across different temperate southern Australian states and caused confirmed infections in humans. A total of 47 human cases and seven deaths were reported. The recent evolving situation of JEV needs to be reported due to its continuous circulation in endemic regions and spread to non-endemics areas. Here, we reconstructed the phylogeny and population dynamics of JEV using recent JEV isolates for the future perception of disease spread. Phylogenetic analysis shows the most recent common ancestor occurred about 3120 years ago (YA) (95% Highest posterior density [HPD], 2680 to 3715). Our results of the Bayesian skyline plot (BSP) demonstrates that JEV demography lacks fluctuations for the last two decades, but it shows that JEV genetic diversity has increased during the last ten years. This indicates the potential JEV replication in the reservoir host, which is helping it to maintain its genetic diversity, and to continue its dispersal into non-endemic areas. The continuous spread in Asia and recent detection from Australia further support these findings. Therefore, an enhanced surveillance system is needed along with precautionary measures such as regular vaccination and mosquito control to avoid future JEV outbreaks.

Cable-Driven Parallel Robots (CDPRs) are a special kind of parallel manipulator which use cables to control the position and orientation of the mobile platform or end-effector. The usage of cables instead of rigid links offer some advantages over their conventional rigid counterparts. As cables can only pull, but not push, the number of cables (n) required to command the end-effector is always n+1. This configuration is known as fully-constrained and it is the most extended configuration for CDPRs. Although CDPRs have many advantages, such as their ability to cover large working areas, one of their main problems is that their working area (workspace) is limited in comparison to its frame area (planar case), or frame volume (spatial case), due to the minimum and maximum allowed tensions. Depending of these tensions values the workspace can be notoriously decreased. In order to tackle this problem, lots of works focus on solving kinematics or dynamics problems for cable sagging, i.e., they take into account sagging when modelling the robot kinematic and include these poses inside the usable robot workspace. Taking into account phenomenons like this increases the mathematical complexity of the problem, and much more complex techniques are required. On the other hand, the lack of workspace problem can be tackled by adding active or passive elements to the robot design. In this sense, this paper proposes two mechanical modifications: to add passive carriages to the robot frame and to use a single cable loop to command the end-effector position and orientation. This work presents the kinematic, static and dynamic models of the novel design and shows the gain of workspace for a planar case while taking into account different parameters of the robot.

Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instance when any given region becomes active during the decision-making process. Here we try to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time. Thus, providing both spatial and temporal brain activation information. The activation information can then be further analyzed as per need and requirements. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way.

Large water bodies such as the Laurentian Great Lakes have significant influences on local and regional climate through their unique physical features. Due to the coarse spatial resolution of general circulation models (GCMs), the Great Lakes are geometrically ignored in most GCMs. Thus, the dynamical downscaling technique serves as a necessary and feasible solution to bridge the gap. The Weather Research and Forecasting model (WRF) with an updated lake scheme is employed to downscale from a GCM, GFDL-CM3. The WRF-Lake’s performance is evaluated against observations, the GCM, as well as 23 other GCMs. Results show that the coupled air-lake model, with a fine spatial resolution and realistic lake bathymetries, reproduced a more reasonable spatiotemporal climatology than GCMs, as well as the lake-induced characteristics that were missed in GCMs. With lakes present, the seasonal variability of air temperature was reduced in WRF-Lake relative to GFDL-CM3, especially in summer. A reduced air temperature trend, about 4.5 °C/100 year in the 21st century, was projected in WRF-Lake. The seasonal evolutions of lake surface temperature and lake ice coverage were well captured by the lake model. The lake surface temperature was projected to be warming by 3.5-4 °C and the lake ice diminishing by 58.9% - 86%. Those results brought by the WRF-Lake model suggest that a fine resolution of the topography and the incorporation of the lake-atmosphere interactions are crucial to improve the understanding of the climate and climate change in the Great Lakes region.

The sustainability of ecotourism is the backbone of tourism development of a country. Ecotourism can contribute to both conservation and development in which involves dynamic relationship between tourism, biodiversity and communities, facilitate by great management. The purpose of this study is to analyze the dimensions of the socio-economic and environmental impacts of ecotourism in communities surrounding the Tasik Kenyir in Terengganu, Malaysia. This study provides a framework for the development and evaluation of ecotourism. The framework will determine if the relationships between indicators are positively correlated which will result in positive contribution to the other by using System Dynamic. Socio-cultural and economic data will be collected through interviews and group discussions in selected communities in Tasik Kenyir. Data on wildlife will be extracted from secondary data from Kenyir Research Institute, Universiti Malaysia Terengganu. The data analysis will explore the socio-cultural and economic differences between and within different communities due to ecotourism development, the contribution of ecotourism to conservation activities, local support for conservation and ecotourism as well as the influence of tourist activities on the distribution of wildlife species in Tasik Kenyir. This study aims to contribute toward understanding the natural resource community-ecotourism inter-relationship and help to bridge the knowledge gap that hinders biodiversity conservation initiatives. The findings will be used as a base for further development of ecotourism and will recommend alternative management options where necessary for the study area.

Ransomware constitutes a distinctive category of pernicious software that sequesters a user's digital assets by encryption, holding them hostage until a sum is extorted from the victim. These incursions have escalated to become among the most prevalent and significant threats confronting both individuals and corporate entities. In combatting this virulent program, dynamic analysis has been established as the favored detection modality. Such analyses typically hinge on Windows API calls, the conduits through which programs requisition services from the operating system. Yet, the superfluous and unrelated Windows API calls interjected by adversaries into the execution stream of suspect binaries precipitate an excessively noisy behavioral sequence, which impairs the performance of counter-ransomware mechanisms. The research outlined herein introduces a novel non-signature-based detection paradigm that harnesses efficacious Windows API call sequences through supervised machine learning strategies. An innovative Enhanced Min Max (EmRmR) filter technique is proposed, aiming to purge noisy features and isolate the most indicative feature subset that encapsulates the ransomware's true behavior. The EmRmR method, diverging from the traditional Min Max approach, circumvents the superfluous calculations that are a hallmark of the conventional algorithms, thereby necessitating a reduced number of evaluations. Additionally, a refinement procedure has been integrated to contract the program's call trace volume by discarding those Windows API calls lacking a robust correlation with ransomware's pivotal behavior. Subsequent to rigorous experimental analyses and juxtaposition with extant behavior-based detection methodologies, the proposed strategy has demonstrated its efficacy in differentiating ransomware behavior, delivering high detection precision alongside a diminution in false-positive occurrences.

Leishmaniasis is a group of neglected infectious, non-contagious diseases caused by parasites of the Leishmania genus which affects millions of people worldwide. The drugs available for its treatment, especially in cases of visceral leishmaniasis, are old, outdated, have serious side effects and in many cases are ineffective due to the emergence of resistant strains. In this context, the search for new candidates for more effective and safer drugs which can become new therapeutic alternatives is a constant need. In this work, 10 new chalcones were synthesized in high yields through Claisen-Schmidt condensation and were evaluated in vitro against Leishmania infantum promastigotes and amastigotes. CP04 and CP06 compounds were the most promising, respectively showing IC50 values = 13.64 ± 0.25 and 11.19 ± 0.22 against promastigotes, and IC50 = 18.92 ± 0.05 and 22.42 ± 0.05 against amastigotes. The compounds did not show cytotoxicity in erythrocytes, showing selectivity indexes greater than 74 and 90. Molecular docking studies carried out under the sterol 14-alpha demethylase (CYP-51) (PDB: 3L4D) and trypanothione reductase (PDB: 5EBK) enzymes from L. infantum evidenced the great affinity of the CP04 molecule for these targets, showing Moldock score values of -94.0758 and -50.5692 KJ/mol-1, with these values being much lower than the co-crystallized ligand energies. Molecular dynamics simulations demonstrated great stability of CP04 in its targets, thus indicating a real interaction possibility. Our findings confirm the potential that chalcones have as candidates for anti-leishmania drugs, and suggest that the CP04 compound may become a promising drug candidate to design and develop novel chalcone-based analogs for leishmaniosis therapy.

The first appearance of viruses can date back to the late last century. As an effective form of malware, viruses reside in the permanent storage of target hosts. Before a virus can execute, that must load into memory from the persistent storage included in the associated file. Due to the reliable destructive power of viruses, many mechanisms have been developed to defend computer systems against these hazardous threats. Antivirus software is one of the most famous and popular among these mechanisms. Most antivirus software uses static analysis (signature-based) technology on files stored in permanent storage, such as hard disks or USB flashes, to detect viruses hidden in files. Fileless malware was developed to enhance the survivability of malware by circumventing detection. Fileless malware only exists in the target hosts’ memory, not files. Antivirus software cannot even access the fileless malware code, much less analyze it, since it may be performed in memory directly without needing to load it from a disk. As a result, it is difficult for an antivirus engine to defend a system against fileless malware attacks. This paper proposes a kernel-based solution called Check-on-Execution (CoE) to detect fileless malware on a Linux system. When a program is going to execute a piece of code in a writable and executable memory area of a process, CoE suspends the code execution first. Coe retrieves the code from memory, packs the code with an ELF header to create an ELF file, and uses VirusTotal to check the file to prevent a Linux system from executing fileless malware. Experimental results show that CoE noticeably enhances the ability of a Linux system to defend itself again fileless malware. CoE is also suitable for protecting a system from shell code injection attacks, such as buffer and heap overflow attacks. It is capable of handling even packed malware. But in this paper, we only focus on fileless malware.

Oleanolic acid (OA) being the ubiquitous compound in the plant kingdom is studied for both the neuroprotective and neurotoxic properties. The mechanism of acetylcholinesterase (AChE) inhibitory potential of OA is investigated using the molecular dynamic simulations (MD) and docking as well as membrane-like and in vitro tests. There are also applied SH-SY5Y human neuroblastoma cells as well as in vivo on the zebrafish tests. The inhibitory potential towards the AChE enzyme is examined using the TLC-bioautography assay (the IC50 value is 9.22 μM). The CH- interactions between the central fragment of the ligand molecule and the aromatic cluster created by the His440, Phe288, Phe290, Phe330, Phe331, Tyr121, Tyr334, Trp84 and Trp279 side chains are observed. The in vitro tests carried out on the SH-SY5Y cells indicates that the viability rate is reduced to 71.5%, 61%, and 43% at the concentrations of 100 µg/mL, 300 µg/mL, and 1000 µg/mL, respectively after 48 hours of incubation whereas cytotoxicity against the tested cell line with the IC50 value is 714.32 ± 32.40 µg/mL. The in vivo tests on the zebrafish prove that there is no difference between the control and experimental groups as regards the mortality rate and morphology (P>0.05).

The traditional Simultaneous Localization and Mapping (SLAM) systems are based on the 1 strong static assumption, and their performance will degrade significantly due to the presence of 2 dynamic objects located in dynamic environments. To decrease the effects brought by the dynamic 3 objects, based on ORB-SLAM2 system framework, a novel dynamic semantic SLAM system called 4 MOR-SLAM is presented using mask repair method, which can accurately detect dynamic objects 5 and realize high-precision positioning and tracking of the system in dynamic indoor environments. 6 First, an instance segmentation module is added to the front end of ORB-SLAM2 to distinguish 7 dynamic and static objects in the environment and obtains a preliminary mask. Next, to overcome the 8 under-segmentation problem in instance segmentation, a new mask inpainting model is proposed to 9 ensure that the integrity of object masks, which repairs large objects and small objects in the image 10 with depth value fusion method and morphological method respectively. Then, a reliable basic matrix 11 can be obtained based on the above repaired mask. Finally, the potential dynamic feature points 12 in the environment are detected and removed through the reliable basic matrix, and the remaining 13 static feature points are input into the tracking module of the system to realize the high-precision 14 positioning and tracking in dynamic environments. The experiments on the public TUM dataset 15 show that, compared with ORB-SLAM2, the MOR-SLAM improves the absolute trajectory accuracy 16 by 95.55%. In addition, compared with DynaSLAM and DS-SLAM on the high-dynamic sequences 17 (fr3/w/rpy and fr3/w/static), the MOR-SLAM improves the absolute trajectory accuracy by 15.20% 18 and 59.71%, respectively

The purpose of this study was to compare Electromyographic (EMG) with Mechanomyo- 1 graphic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. 2 Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and 3 submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants 4 then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, 5 at 25%, 50% and 75% of their maximum activity (based on MVC). The low frequency part of the 6 accelerometer signal (< 5 Hz) was used to segment the 6 phases of the motion. EMG and MMG were 7 both recorded during the entire experimental procedure. Root mean square (RMS) and mean power 8 frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG 9 and MMG exhibited similar repeatability scores. They also shared similar RMS vs force relationship, 10 with RMS increasing to 75% MVC and plateaued to 100%. MPF decreased with increasing force to 11 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than 12 RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional 13 activities outside the laboratory. It opens new perspectives for future applications in sports science, 14 ergonomics and human-machine interfaces conception.

The process of identifying and approving a new drug is a time-consuming and expensive procedure. One of the biggest issues to overcome is the risk of hepatotoxicity, which is one of the main reasons for drug withdrawal from the market. While animal models are the gold standard in preclinical drug testing, the translation of results into therapeutic intervention is often ambiguous due to interspecies differences in hepatic metabolism. The discovery of human induced Pluripotent Stem Cells (hiPSCs) and their derivatives has opened new possibilities for drug testing. We used mesenchymal stem cells and hepatocytes both derived from hiPSC, together with endothelial cells, to miniaturize the process of generating hepatic organoids. These organoids were then cultivated in vitro using both static and dynamic cultures. Additionally, we tested spheroids composed solely by induced hepatocytes. By miniaturizing the system, we demonstrated the possibility of maintaining the organoids, but not the spheroids, in culture for up to 15 days. This timeframe may be sufficient to carry out a hypothetical pharmacological test or screening. In conclusion, we propose that the hiPSC-derived liver organoids model could complement or, in the near future, replace the pharmacological and toxicological tests conducted on animals.

This paper has designed a four-channel wide dynamic microwave rectifier circuit for the shortcomings of uneven output voltage of each branch of the multi-channel rectification technology. A voltage-doubling rectifier circuit with identical structure was used in each branch. The four rectifier branches shared the same load at the output terminal, allowing the rectifier circuit to have an RF-DC conversion efficiency greater than 50% over the input power range from 12mW to 274mW, widened by 274% compared to a single channel. Not only the advantages of multi-channel technology with wide dynamics were retained, but also the output voltage was highly consistent and could be directly output in parallel without complex power electronics conversion.

In this paper is shown the dynamic modeling and design of a passivity based controller for the RV-3SB robot. Firstly, the dynamic modeling of the of the Mitsubishi Rv-3SB robot is done by the Euler-Lagrange formulation in order to obtain a decoupled dynamic model considering the actuators orientation besides the position of this analyzed robot. It is important to remark that the dynamic model of the RV-3SB robot is done based in the kinematic model obtention in which is developed by the implementation of screw theory. Then the passivity based controller is obtained by separating the end effector variables and the actuator variables by making an appropriate coordinate transformation. The passivity based controller is obtained by selecting an appropriate storage function and by the Lyapunov theory the passivity based control law is obtained in order to drive the error variaable, which is the difference between the measured end effector position variable and the end effector desired position variable. The passivity based controller makes that the error variable reach the origin in finite time taking into consideration the dissipation properties of the proposed controller in order to stabilizes the desired end effector position. A numerical simulation experiement is performed in order to validate the theoretical results obtained in this research study. It is verified by numerical experimentation that the proposed control strategy is efficient and effective in order to drive the error variable to the origin in comparison with other modified techniques found in the literature. Finally, the appropriate discussion and conclusion of this research study are provided.

The study considers building a dynamic model of profit maximization for the car-sharing system and its verification on the example of implementing such a system in Astana (Republic of Kazakhstan). The region, bounded by the administrative boundaries of Astana, was divided into subregions by covering the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of trips in the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific sub-region, for which the quantitative parameters necessary for solving the profit maximization problem in the car-sharing system are calculated. Stations with cars will be placed in the cells of the grid, which will be available to car-sharing service customers. Based on the collected data, a dynamic (four periods per day) and static profit maximization model in the car-sharing system was built. Modeling was carried out based on the built models in the example of Astana. It was established that using a dynamic model of profit maximization in the car-sharing system increases the profit by 3.7%.

The heterogeneous structure implies that a few nodes may be crucial in maintaining network structural and functional properties. Identifying these crucial nodes correctly and quickly is a primary issue as contemporarily may face the mushrooming of large-scale datasets. Besides, the ‘weight issue’ is always ignored in this field which edge weight may play a positive/negative role in contributing to the node importance in different weighted networks. This paper provides a novel algorithm, Weighted Expectation Algorithm (WEA), which aims to ensure accuracy and speed of computation by taking advantage of dynamic programming to better handle the task of large-scale networks. Additionally, the weight issue that edge weights may contribute differently is addressed by a simply quantitative definition. Two standard experiments show WEA can maintain the network structure in connectivity well (by the lowest average robustness 0.192) and identify the node importance better in the spreading function test of spreading dynamics (by the highest average Kendall’s tau-b 0.678). In addition, the time complexities of different algo-rithms are evaluated, and their time-consuming are tested, proving that WEA consumes a rela-tively short time.

A proxy war, between a coalition of countries, BLUE and a country, RED, is considered. RED wants to increase the size of the RED territory. BLUE wants to involve more regions in trade and other types of cooperation. GREEN is a small and independent nation that wants to become a member of BLUE. RED attacks GREEN and tries to invade. BLUE decides to give optimal arms support to GREEN. This support can help GREEN in the war against RED and simultaneously reduce the military power of RED, which is valuable to BLUE, also outside this proxy war, since RED may confront BLUE also in other regions. The optimal control problem of dynamic arms support, from the BLUE perspective, is defined in general form. The objective function is a weighted sum of the present value of the free GREEN territory and the present value to BLUE of the net loss of military resources in the RED army. The net loss of RED at a particular point in time is a function of the location of the front line and the size of the mobile GREEN forces behind the RED line. First, it is assumed that the expected net loss is proportional to the force ratio behind the red front line. It is proved that the net loss function is a strictly concave quadratic function of x, the location of the front line. It is also proved that the unique maximum of the net loss function occurs at the same front location, also if the net loss function is proportional to the strength ratio behind the RED lines, raised to any strictly positive exponent. From the BLUE perspective, there is an optimal position of the front. This position is a function of the weights in the objective function and all other parameters. Optimal control theory is used to determine the optimal dynamic BLUE strategy, conditional on a RED strategy, which is observed by BLUE military intelligence. The optimal arms support strategy for BLUE is to initially send a large volume of arms support to GREEN, to rapidly move the front to the optimal position. Then, the support should be almost constant during most of the war, keeping the war front location stationary. In the final part of the conflict, when RED will have almost no military resources left and tries to retire from the GREEN territory, BLUE should strongly increase the arms support and make sure that GREEN rapidly can regain the complete territory and end the war

Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that the vehicle operates in its true optimal operating region given a variety of constraints such as road grade, load, gear shifts, Battery State of charge (for hybrid vehicles), etc. In this paper a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line haul truck. The problem is solved using a combination of dynamic programming with back tracking and model predictive control. The specific fuel saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features are a function of road grade. The result and analysis shows significant improvement in fuel savings along with NOx benefits. Out of the control features dynamic cruise (predictive) control and dynamic coasting showed the most benefits while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency.

This article is a theoretical contribution to mental model research, which currently has different threads. Whereas some researchers focus on the perceived causal structure, others also include decision policies and decisions. We focus on the link between recognized causal structure (“mental models of dynamic systems”) and policies, proposing Johnson-Laird’s theory of mental models as the link. The resulting framework hypothesizes two types of systematic mental model errors: (1) misrepresentation of the system’s structure and (2) failure to deploy relevant mental models of possibilities. Examination of three experiments through this lens reveals errors of both types. Therefore, we propose that the cognitive theory of mental models opens a path to better understand how people construct their decision policies and develop interventions to reduce such mental model errors. The article closes by raising several questions for empirical studies of the reasoning process leading from mental models of dynamic systems to decision policies.

Oncogenic protein E6 from Human Papilloma Virus 16 (HPV-16) mediates the degradation of Membrane-associated guanylate kinase with inverted domain structure-1 (MAGI-1), throughout the interaction of its protein binding motif (PBM) with the Discs-large homologous regions 1 (PDZ1) domain of MAG1-1. Generic variation in the E6 gene that translates to changes in the protein’s amino acidic sequence modifies the interaction of E6 with the cellular protein MAGI-1. MAGI-1 is a scaffolding protein found at tight junctions of epithelial cells, where it interacts with a variety of proteins regulating signaling pathways. MAGI-1 is a multidomain protein containing two WW (rsp-domain-9), one guanylate kinase-like, and six PDZ domains. PDZ domains played an important role in the function of MAGI-1 and served as targets for several viral proteins including the HPV-16 E6. The aim of this work was to evaluate, with an in silico approach, employing molecular dynamics simulation and protein-protein docking, the interaction of the intragenic variants E-G350 (L83V), E-C188/G350 (E29Q/L83V), E-A176/G350 (D25N/L83V), E6-AAa (Q14H/H78Y/83V) y E6-AAc (Q14H/I27RH78Y/L83V) and E6-reference of HPV-16 with MAGI-1. We found that variants E-G350, E-C188/G350, E-A176/G350, AAa and AAc increase their affinity to our two models of MAGI-1 compared to E6-reference.

Objectives: To investigate the value of early lactate dynamic monitoring index in predicting prognosis of patients with sepsis and septic shock. Methods: We performed our test on 50 patients. Out of 50 patients, 28 are male, and 22 are female. Prospectively studied pediatric patients with septic shock were performed. Vital signs, Lactate clearance, were obtained at presentation 6 h, 12 h, 24 h over the first 48 h of hospitalization. The therapy received, outcome parameters of mortality and duration of hospitalization were recorded. Results: The statistical data and comparative analysis showed that an average of 16.88 days after admission, 5 patients have died, 17 patients are poorly prognosis leaves the hospital, and the remaining 28 are recovered and discharged. The primary outcome variable of mean 16 days hospitalization mortality rate was 10%. Poor prognosis 34% and fully recovery 56 % were observed. In this retrospective cohort study, a lactate level of more than 2.5mmol/L was the best threshold to predict 28-day mortality among severe sepsis and septic shock patients. In our research, we found mean LC 6 h 3.08mmol/L, and after 48 h mean it is 1.79mmol/L. Significant LC 6 h found, which is 8.08mmol/L in the death group patient where 48 h mmol/L shows significant high. Poor prognosis also presents a clinical increase of lactate level high in the LC 6 h analysis, which is 3.32mmol/L. Recovered patients showed a significant improvement after administering treatment depending on the patient organ involvement and good decrease of lactate reports achieved, which is 1.20mmol/L, where admission reports show it was 1.91mmol/L in LC 6 h. Mean Heart rate 94/51mmhg, pulse 119, temperature 39℃, respiratory rate 32.26, and urine output 456 ml recorded during our study. Death patient shows a remarkable detonation of those reports but has a significant clinical report with the recovered patients. Conclusion: The early lactate dynamic monitoring index has a high value in predicting sepsis and septic shock patients' prognosis, thus worth popularizing.

It is challenging to apply heat flow through a thermal bridge, which requires the analysis of 2D or 3D heat transfer to building energy simulation(BES). Research on the dynamic analysis of thermal bridges has been underway for many years, but their utilization remains low in BESs. This paper proposes a thermal bridge modeling and a dynamic analysis method that can be easily applied to BESs. The main idea begins with an analogy of the steady-state analysis of thermal bridges. As with steady-state analysis, the proposed method first divides the thermal bridge into a clear wall, where the heat flow is uniform, and the sections that are not the clear wall (the thermal bridge part). For the clear wall part, the method used in existing BESs is applied and analyzed. The thermal bridge part (TB part) is modeled with the linear time-invariant system (LTI system) and the system identification process is performed to find the transfer function. Then, the heat flow is obtained via a linear combination of the two parts. This method is validated by comparing the step, sinusoidal and annual outdoor temperature response of the finite differential method(FDM) simulation. When the thermal bridge was modeled as a third-order model, the root mean square error(RMSE) of annual heat flow with the FDM solution of heat flow through the entire wall was about 0.1W.

In this paper, drug treatment and vaccination are compared healthy and economically. In this comparison, in addition to comparing economic costs, it has paid attention to patients' recovery. To reach the goal, the prevalence of Covid-19 virus in New York is modeled by using the SEIR dynamic model. The SEIR dynamic model is used to model the period of epidemic diseases. Then, three disease control scenarios are evaluated economically. Treatment and vaccination are two important issues in controlling epidemic diseases. To control the disease, each of these two methods has economic costs and benefits. The main purpose of this paper is to compare the two methods of vaccination and treatment economically. Although the results of the study show that vaccination can be effective in preventing the epidemic of the disease than treatment, but due to the high cost and genetic mutation of the virus, vaccination is not the optimal method for both health and the economy in the long term.

China is not only short of fresh water resources per capita, but also faces a serious problem of water pollution in recent years, with 190 million people suffering from excessive levels of harmful substances in their drinking water. Such as arsenic poisoning and fluorosis and other endemic water diseases high incidence. As a series of water pollution prevention plan of action by the Chinese government announced that, this paper uses the modified Undesirable Dynamic Network model empirical analysis of China's 31 provincial administrative region economic growth, wastewater treatment, and water disease control efficiency between 2013 and 2017. The results show that the efficiency of water pollution disease in all four regions of China and the total efficiency in the three regions of east, west and central China all show a decreasing trend, and the efficiency scores and rankings of all provinces and cities within the region fluctuate greatly. The eastern region with the most developed economy has the best overall performance, with higher efficiency in Water consumption and Water disease control. However, the efficiency of wastewater treatment in northeast China is stable and better. Given the high level of economic development in China and the results of the above mentioned efficiency in water pollution and water diseases, improving the efficiency and quality of wastewater treatment in China is regarded as an important factor in achieving the strategic goal of green growth.

This paper puts forward an alteration for Tensor Calculus utliized in a coordinate system which is under a dynamic distortion drawing inspiration from similar fields such as the Calculus of Moving Surfaces (CMS). The paper establishes transformation laws for Tensors within these regions and establishes Operators such as the Tensorial Field Derivative which enforce a Tensorial Transformation on Tensors defined within a Dynamically Moving coordinate system. This variation of Tensor Calculus can be utilized to observe how disciplines such as QFT and Continuum Mechanics would change to accomodate for a distorting coordinate system and can be utliized to develop new theoretical models which account for this temporal distortion particularly within Biological Settings.

This paper presents performed Ansys numerical simulation on WindFloat structures. It contributes to the methodology of calculation based on CFD instruments to study the movements of the waves on semi-submersible stationary structures and highlights the importance of using RAO functions. RAO numerical approach developed can be applied to design semisubmersible WindFloat structures in order to fulfill essential requirements regarding operational safety and design. Numerical methodology was designed in accordance marine loads and the I.T.T.C. recommendations. The numerical RAO results for regular wave movements include many elements for the semi-submergible structure. Close results to open sea operation can be achieved by constant improvement in numerical simulation methodologies. The work opens the way for further hydrodynamic development on irregular waves for realistic hydrodynamic and structural response of the actual semi-submersibles at sea. The results demonstrate the accuracy of RAO function approach for specific WindFloat in Jonswap spectrum.

Here, we present a series of novel block copolymers (BCP) from bio-derived monomers, poly(lactic acid)-block-poly(2,5-furandimethylene succinate) (PLA-b-PFS), in which the furan groups from PFS block can be crosslinked with bis(maleimido) triethylene glycol (M2) through a Diels-Alder reaction. This dynamic crosslinking reaction leads to a network structure for enhancing the mechanical properties compared to their linear BCP analogous. Decreasing the crosslinking density leads to a decrease of glass transition temperature of BCPs and a transition from glassy to rubbery-like behavior at room temperature. This allows a wide tunablity of both elastic moduli and yields of the materials. For the lowest crosslinking density. the material exhibits an over 50% self-healing efficiency at room temperature after five days, attributed to the low Tg (15.2 C) from the introduction of PFS block, allowing sufficient chain mobility for structure re-organization. Moreover, with the appropriate selection of crosslinking density (PLA-b-PFS/M2 (6/1)), it also shows an excellent shape memory property with a high recovery rate of 96.3% and a fixity rate of 97.3%. The permanent shape can be rewriteable due to the reversibility of Diels-Alder reaction. With these advanced functionalities and ease in large-scale fabrication, the PLA-b-PFS/M2 shows great promises for self-healing coatings or films with shape memory properties in a wide variety of applications such as packaging materials

The excessive vibration caused by crowds walking across footbridges has attracted great public concerns in the past few decades. This paper presents, from considering the dynamic characteristics of the bipedal crowd model, a new stability limit criterion based on the bipedal excitation model. The stability limit can be used to estimate the upper boundary of crowd size. In addition, the dynamic stable performances of a structure, under a certain walking crowd size, can be predicted by the stability criterion. This proposed mechanism provides an alternative comprehension how crowd excite the excessive sway motion with a large-span structure.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some on-line resource, new information is added to the user profile. Exposing private data does not only reveal information about single users' preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident on \emph{social networks} where users receive suggestions based on their friends' activity. We propose an information theoretic approach to measure the differential update of the anonymity risk for time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third party services get to know about the users when a new activity is shared. We use real Facebook data to show how our model can be applied on a real world scenario.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

The proliferation of ransomware poses a significant threat to global cybersecurity. This study presents a comprehensive review of the methodologies employed in the detection and analysis of ransomware, emphasizing the dichotomy between static and dynamic analysis approaches. It introduces the historical context and the necessity for robust cybersecurity measures, followed by an outline of the methodological framework used to evaluate existing ransomware analysis techniques. The results detail the effectiveness and limitations of various analysis strategies, identifying key features and patterns that aid in the detection and classification of ransomware threats. The study concludes by summarizing the primary achievements, including the identification of gaps in current research and proposing future research directions aimed at enhancing ransomware detection and mitigation strategies. The synthesis provided in this survey offers a consolidated view of the state-of-the-art in ransomware threat analysis and serves as a resource for cybersecurity professionals and researchers.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

Porous metals possess low bulk density, large specific strength, high stiffness and special thermal, electromagnetic and acoustic conductivity, so that porous metals are widely used in filtration and separation, flame retardant explosion-proof, biomedical application, etc. Compared with its corresponding dense metal, the presence of porous structures also leads to different corrosive performances of porous metal. So, some researches have utilized weight loss method, electrochemical impedance to evaluate porous metals’ corrosion behavior; however, the influence of pore structure on metal corrosion is still controversial, and present methods used for analyses of porous metals’ corrosion are statistical averages of the corrosion behavior of the entire porous material, which cannot accurately reflect the corrosion behavior inside the pore structure. Herein, we prepare porous copper samples with 0, 24, 72, 96 pores using mechanical process, and employ scanning kelvin probe combined with electrochemical polarization curve and impedance spectroscopy to test the corrosion performance of the porous copper in static and dynamic NaCl solution. The relevant results indicate that in static solution, the corrosion resistance of samples gradually increases with the rise of the number of pores. By contrast, the sample with 24 pores exhibits more susceptible to corrosion than the sample without pore.

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a computer data capture and visualization platform foundation for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design theory based on extensive data analysis. Additionally, the system presents information on occupants' behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human-machine communication and enabling timely adjustments to design strategies for new buildings and operation and maintenance strategies for existing buildings.

We propose a method of cooling nuclear spin systems of solid-state nanostructures by application of a time-dependent magnetic field synchronized with spin fluctuations. Optical spin noise spectroscopy is considered as the method of fluctuation control. Depending on the mutual orientation of the oscillating magnetic field and the probe light beam, cooling might be either provided by dynamic spin polarization in an external static field or result from population transfer between spin levels without build-up of a net magnetic moment (“true cooling”).

Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. This work examines through extensive experimentation methods and procedures suitable for assessing the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, focusing on the dynamic branch predictor (DBP). The investigation relies on the Running Average Power Limit (RAPL) interface from Intel, a software tool for credibly reporting the power and energy based on instrumentation inside the CPU. We use well-known microbenchmarks under various run conditions to explore potential pitfalls and to develop precautions to raise the precision of the measurements obtained from RAPL for more reliable power estimation. The authors discuss the factors that affect measurements and share the difficulties encountered and the lessons learned.

As we all know, many more dynamic objects appear almost continuously in real world, which are immensely able to impair the performance of the majority of vision-based SLAM systems that are based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in the high dynamic environment, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken influences of dynamic content, the moving object detection method was put forward in our visual odometry, then the semantic segmentation network was combined into our stereo SLAM to extract pixel-level contours of dynamic objects. Then influences of dynamic objects were extremely weaken and the performance of our system was increased markedly in dynamic, complex and crowed city spaces. Experiment with both on KITTI Odometry dataset and in a real-life scene, the results show that our method can dramatically decrease the tracking error or drift, improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.

Considering the dynamic stall effects in engineering calculations is essential for correcting the aerodynamic loads acting on wind turbines, both during power production and stand-still cases, and impacts significantly the turbine aeroelastic stability. The employed dynamic stall model needs to be accurate and robust for a wide range of airfoils and range of angle of attack. The present studies are intended to demonstrate the performance of a recently implemented "IAG dynamic stall" model in a wind turbine design tool Bladed. The model is transformed from the indicial type of formulation into a state-space representation. The new model is validated against measurement data and other dynamic stall models in Bladed for various flow conditions and airfoils. It is demonstrated that the new model is able to reproduce the measured dynamic polar accurately without airfoil specific parameter calibration and has a superior performance compared to other models in Bladed.

In recent years, the production of renewable energy represents one of the main challenges of modern and future society. The new cultivation method called Agrivoltaic (hereafter AV), combines and associates agricultural production and renewable energy (photovoltaic) production in a collaborative synergy from which both benefit. In this study, the dry matter and essential oils yield of six medicinal species (Salvia officinalis L., Origanum vulgare L., Rosmarinus officinalis L., Lavendula angustifolia L., Thymus citriodorus L. and Mentha spicata L.) grown underneath a dynamic AV system in comparison to those in full sun (T), were evaluated. In particular, below the dynamic AV system, due to the rotation of the panels, two plot areas on the ground were distinguished: one always covered by the panels (UP), and another one covered alternatively (BP). During the aromatic crop cycles, the solar radiation, infrared leaf temperature and air temperatures in UP, BP and T plot, were measured. In addition, at harvest time, weed survey was carried out. The results showed that, in general, the solar radiation and infrared leaf temperature were lower in UP plot than in either the BP and T plots, while the air temperatures were similar in all compared plots. Also the weed cover percentage was lower in the UP plot than other ones. The essential oils yield in the sage, thyme, mint and rosemary plants were higher in both UP and BP plots than for T plot. Thus, shading and/or less weed infestation may have been the main factors favorably influencing the performance of medicinal crops under a dynamic AV system.

Dynamic Realistic Failure Process Analysis (DRFPA) is a numerical software based on FEM it is firstly validated by comparing with the experimental test. Such validation is carried out under two consistent concrete specimens under uniaxial compression. After validation, DRFPA is used to study the influence of two parallel pre-existing crack inclination angles on the fracture of concrete under dynamic loads. The evolution of wing cracks and secondary cracks is investigated by observing the initiation, coalescence, and propagation of them. Meanwhile, the relationship between failure mode and inclination angles is discussed via analyzing the acoustic emission events. Finally, it is concluded the relationship between inclination angles of pre-cracks and performance/stability of concrete structure.

Human behaviour was tipped as the mainstay in the control of further SARS-CoV-2 (COVID-19) spread especially after the lifting of restrictions by many countries. Countries in which restrictions were lifted soon after the first wave, had subsequent waves of the COVID-19 infections and it remains to be seen whether there will be more waves. In this paper, we formulate a deterministic model for COVID-19 incorporating dynamic non-pharmaceutical interventions, dubbed social dynamics. The model steady states are determined and their stability analysed. Numerical simulations are carried out to determine the pack of various parameters that influence the social dynamics. In South Africa, the first wave was the only wave in which the only interventions rested solely on human behavior. The model is thus fitted to COVID-19 data on the first wave in South Africa. The results presented in this paper have implications on the trajectory of the pandemic in the presence of changing social processes.

One of the central criteria for free will is “Could I have done otherwise?” But because of a temporal asymmetry in human choice, the question makes no sense. The question is backward-looking, while human choices are forward-looking. At the time when any choice is actually made, there is as of yet no action to do otherwise. Expectation is the only thing to contradict (do other than). So the ability to do something not expected by the ultimate expecter, Laplace’s demon, is a better criterion for free will. If human action is fundamentally unpredictable, then we have free will. Scientists have studied a form of fundamental unpredictability, known as undecidability. The features that make a system capable of undecidable dynamics have been identified: program-data duality; potential to access an infinite computational medium; and the ability to implement negation. Humans have all three of these features, so we very likely are fundamentally unpredictable, so we have free will.

The fault diagnosis of the gearbox is primarily based on the analysis of the harmonic response. However, the generation mechanism of the harmonic response of the gear pair is still not distinctly explained in a healthy gearbox. Therefore, this paper is dedicated to establishing a dynamic model of the gearbox for this issue. In this model, the contact force of the bearing and the meshing stiffness of the gear pair are determined by the Hertz theory and the potential energy method, then the shaft motion is used as the connection to establish the coupling relationship between the gear pair and the bearing. After this dynamic model is confirmed by experiments and existing models, the coupling relationship inside the gearbox and the harmonic response of the gear pair are studied using this model. The result demonstrates that a closed control loop is formed due to the vibration transmission between the gear pair, the bearing and the shaft, and there is a strong coupled vibration between the gear pair and the bearing in the gearbox. Due to the generation of the center distance error, misalignment angle and disorganized meshing process of the gear pair, the meshing stiffness is altered, which excites the harmonic response of the gear pair. When the speed of the gearbox is increased, although the vibration amplitude of the gearbox is increased, the system predominantly vibrates at the fundamental frequency, and the harmonic of the gear pair is also shifted to the low frequency band.

Emergence of multi drug resistant (MDR) and extensively drug resistant (XDR) Mycobacterium tuberculosis poses a serious threat to TB control as the available treatment options are less effective in these cases. Therefore, new strategies are required for identification of new drugs and drug targets. The M. tuberculosis dihydrodipicolinate synthase is a putative drug target with no potent inhibitor. Here in this study, we have used a comprehensive computational approach to identify small molecule inhibitors and validated them in vitro. As similar structures tend to have similar functions, therefore 3 approaches, namely, (a) known inhibitor-based approach, (b) substrate analogues, (c) product analogues were used for compound screening from various chemical libraries. The fourth approach we have taken is fragment library screening.In vitro studies revealed that β-hydroxypyruvic acid tartronic acid are inhibitors of Mtb-rDapA with maximum inhibition 48% and 51% at 500µM concentrations respectively. We have also observed that β-hydroxypyruvic acid is a time dependent inhibitor of Mtb-rDapA. Further, thermal shift assays showed that β-hydroxypyruvic acid interacts with Mtb-rDapA and the thermal stability of Mtb-rDapA shifted by 3˚C. Among the product analogues of Mtb-DapA, dipicolinic acid showed to bind to Mtb-rDapB shifting thermal stability of Mtb-rDapB by 0.5˚C in binding. Additionally, we have designed a new approach by combining substrate and product analogues to achieve better inhibitions. In vitro assays validated that this combination approach increased maximum inhibition up to 100%. We envision that these compounds would serve as potent leads for tuberculosis drug discovery.

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior gives a severe challenge to distinguish the inliers and outliers, and wrong loop closures can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, which does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. The traditional high-dimensional consistency matrix is decomposed into the sub-matrix blocks corresponding to the overlapping trajectory area. Building on this logic, a clustering method involving topology correctness of inter-robot loop closures is introduced to split the entire measurement set into multiple clusters. We define the weight function to find the maximum cardinality subset with topologically correct and consistent, then convert the weight function to a maximum clique problem in the graph and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

The optoelectronic properties of free standing monolayer (ML) hexagonal boron nitride (h-BN) is investigated for potential solar energy conversion applications using the Density Functional Theory (DFT) full potential linearized augmented plane wave (FP-LAPW) method. In addition, the bulk optical properties have also been calculated for the sake of comparison. The dielectric functions, optical conductivities and the optical constants are evaluated using the relaxed structures from electronic total energy pseudopotential calculations. The results reinforce previous research on h-BN DUV optoelectronics and demonstrate the suitability of its use as a component in deep ultraviolet (DUV) and energy conversion devices.

Purpose: While resultant maximal voluntary contraction (MVC) is commonly used to assess muscular performance, the simultaneous activation of antagonist muscles could dramatically underestimate the strength of the agonist muscles. While quantification of antagonist torque has been performed in plantar- (PF) and dorsi-flexion (DF) joint in isometric conditions, it has yet to be determined in anisometric (concentric and eccentric) conditions. Methods: The experiment was performed in 9 participants through 2 sessions (reliability). The MVCs in DF and PF were measured in isometric, concentric and eccentric conditions (10°.s^-1). Electromyographic (EMG) activities from the soleus, gastrocnemius medialis and lateralis, and tibialis anterior muscles were simultaneously recorded. The EMG biofeedback method was used to quantify antagonist torque, where participants were asked to maintain a level of EMG activity, corresponding to antagonist EMG activity and related to the muscle contraction type, according to a visual EMG bio-feedback displayed on a screen. Results: Resultant torque significantly underestimated agonist torque in DF MVC (30-65%) and to a lesser extent in PF MVC (3%). Triceps surae antagonist torque was significantly modified with muscle contraction type, showing higher antagonist torque in isometric (29 Nm) than eccentric (23 Nm, p < 0.001) and concentric (14 Nm, p < 0.001) conditions and resulting in modification of the DF MVC torque-velocity shape. The difference between DF eccentric and concentric MVC was attenuated when considered agonist torque (12%) rather than resultant torque (45%). Conclusion: Estimation of the antagonist torque in isometric or anisometric condition brings new insights to assessment of muscular performance and could result in costly misinterpretation in strength training and/or rehabilitation programs.

Based on the recorded watershed characteristics, the future conditions on the basin system can be predicted using a different method. In this study, dynamic land-use change and its impacts on the streamflow for the Dabus watershed were predicted using ANN-CA based method. The model performance for accurate prediction of the future land-use change on the Dabus River watershed has been checked by validation of the simulated value with the actual value, hence the overall kappa value (k) = 0.83 for the simulated 2016-LULC validated with actual 2016-LULC. Then, 2026-LULC was predicted based on the 2004 and 2009-LULC. The streamflow for the case of 2004 and 2009-LULC has been simulated using the SWAT model. The value of NSE = 0.87 and 0.90 was attained during validation of simulated streamflow for 2004 and 2009-LULC data cases, respectively. The agreement of simulated value of streamflow with the observed data is indicated as R2 = 0.91 and 0.96 for 2004-LULC and 2009-LULC. The effects of the dynamic land-use change on streamflow for the predicted land use(2026-LULC) catchment were evaluated by T-test analysis. Hence, T-stat =0.04 and -0.002 in the case of simulated streamflow used 2004-LULC and 2009-LULC, respectively compared with simulated value using 2026-LULC.

This review describes the subjective experience of visual aura in migraine, outlines theoretical models of this phenomenon, and explores how these may be linked to neurochemical, electrophysiological and psychophysical differences in sensory processing that have been reported in migraine with aura. Reaction-diffusion models have been used to model the hallucinations thought to arise from cortical spreading depolarisation and depression in migraine aura. One aim of this review is to make the underlying principles of these models accessible to a general readership. Cortical spreading depolarisation and depression in these models depends on the balance of the diffusion rate between excitation and inhibition, and the occurrence of a large spike in activity to initiate spontaneous pattern formation. We review experimental evidence, including recordings of brain activity made during the aura and attack phase, self-reported triggers of migraine, and psychophysical studies of visual processing in migraine with aura, and how these might relate to mechanisms of excitability that make some people susceptible to aura. Increased cortical excitability, increased neural noise, and fluctuations in oscillatory activity across the migraine cycle are all factors likely to contribute to the occurrence of migraine aura. There remain many outstanding questions relating to the current limitations of both models and experimental evidence. Nevertheless, reaction-diffusion models, by providing an integrative theoretical framework, support the generation of testable experimental hypotheses to guide future research.

This work investigates the influence of strain rate on the stress/strain behaviour of Scalmalloy. This material is an aluminium-scandium-magnesium alloy, specifically developed for additive manufacturing. The bulk yield stress of the material processed by Selective Laser Melting is approximately 340 MPa which can be increased by heat-treating to approximately 530 MPa. These numbers, combined with the low mass density of 2.7 g/cm3, make Scalmalloy an interesting candidate for lightweight crash-absorbing structures. As this application is inherently dynamic, it is of interest to study the loading rate sensitivity, which is difficult to predict: Al-Sc alloys exhibit classic strain rate sensitivity with an increased yield stress at elevated strain rates. However, Al-Mg alloys are known to show the contrary effect, they exhibit less strength as strain rate is increased. To answer the question how these effects combine, we study the dynamic behaviour at four different strain rates ranging from 10−3 /s to 1000 /s using servo-hydraulic and Split-Hopkinson testing methods. The resulting data is analysed in terms of strain rate sensitivity of tensile strength and failure strain. A constitutive model based on a simplified Johnson-Cook approach is employed to simulate the tensile tests and provides good agreement with the experimental observations.

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

Environmental decentralization (ED), or the allocation of environmental protection affairs and responsibilities among various administrative authorities, affects the effectiveness of environmental regulation in promoting green total factor productivity (GTFP). Based on panel data of 30 Chinese provinces from 2001 to 2015, this paper employs dynamic panel models to test the effects of environmental regulations (environmental protection investment, ENV; pollutant discharge fees, PDF) on GTFP, with or without being influenced by ED. Without the impact of ED, GTFP is significantly inhibited by ENV while significantly promoted by PDF. Considering the impact of ED, with the strengthening of ED, the negative effects of ENV on GTFP is significant; contrarily, the positive effects of PDF on GTFP is significant; improving provincial ED adds negative effects of ENV, while reduces the positive effects of PDF; increasing prefectural ED reduces negative effects of ENV; expanding county-level ED adds the positive effects of PDF. Therefore, to boost GTFP growth, prefectural environmental protection authorities should have more autonomy in ENV, while the county-level should have more autonomy in PDF.

Neuromuscular disorders cause progressive muscular weakness, which limits upper extremity mobility and performance during activities of daily life. A dynamic arm support can improve mobility and quality of life. However, their use is often discontinued over time for unclear reasons. This study aimed to evaluate whether users of dynamic arm supports demonstrate and perceive quantifiable mobility benefits over a period of two months. Nine users of dynamic arm supports were included in this observational study. They had different neuromuscular disorders and collectively used four different arm supports. They were observed for three consecutive weeks during which they were equipped with a multi-sensor network of accelerometers to assess the actual use of the arm support and they were asked to provide self-reports on the perceived benefits of the devices. Benefits were experienced mainly during anti-gravity activities and the measured use did not change over time. The self-reports provided contextual information in domains such as participation to social life, in addition to the sensor system. However self-reports overestimated the actual use by up to three-fold compared to the accelerometer measures. A combination of objective and subjective methods is recommended for meaningful and quantifiable mobility benefits during activities of daily life.

Biofuels and biochemicals play significant roles in the transition towards a fossil-free society. However, large-scale biorefineries are not yet cost-competitive with their fossil-fuel counterparts, and it is important to identify biorefinery concepts with high economic performance. For evaluating early-stage biorefinery concepts, one needs to consider not only the technical performance and process costs but also the economic performance of the full supply chain and the impacts on feedstock and product markets. This article presents and demonstrates a conceptual interdisciplinary framework that can constitute the basis for evaluations of the full supply-chain performance of biorefinery concepts. This framework considers the competition for biomass across sectors, assumes exogenous end-use product demand, and incorporates various geographical and technical constraints. The framework is demonstrated empirically through a case study of a sawmill-integrated biorefinery producing liquefied biomethane from forestry and forest industry residues. The case study results illustrate that acknowledging biomass market effects in the supply chain evaluation implies changes in both biomass prices and the allocation of biomass across sectors. The proposed framework should facilitate the identification of biorefinery concepts with a high economic performance which are robust to feedstock price changes caused by the increase in biomass demand.

Many classic visual monocular SLAM systems have been developed over the past decades, however, most of them will fail when dynamic scenarios dominate. DM-SLAM is proposed for handling dynamic objects in environments based on ORB-SLAM. The article mainly concentrates on two aspects. Firstly, DLRSAC is proposed to extract static features from the dynamic scene based on awareness of nature difference between motion and static, which is integrated into initialization of DM-SLAM. Secondly, we design candidate map points selection mechanism based on neighborhood mutual exclusion to balance the accuracy of tracking camera pose and system robustness in motion scenes. Finally, we conduct experiments in the public dataset and compare DM-SLAM with ORB-SLAM. The experiments verify the superiority of the DM-SLAM.

As a special class of “green” elastomers, thermoplastic vulcanizates (TPVs) have been widely used in industries due to the combination of the excellent resilience of conventional elastomers and the easy recyclability of thermoplastics. Here, the morphology evolution of TPVs based on polyamide 6/ethylene-propylene-diene rubber (PA6/EPDM) blends was investigated by varying the content of the curing agent, phenolic resin (PF). With the incorporation of 6 wt% PF, the gel content of the EPDM phase reaches a high value of 49.6wt% and a typical sea-island structure is formed with EPDM domain in a micro-nano size. The dynamic rheology behaviors of TPVs showed that with the curing degree of EPDM phase increasing, a denser network of EPDM particles is formed in PA6 matrix. Also, a lower crystal degree and crystal peak temperature are observed, indicating that the presence of a growth restriction of PA6 crystal plate induced by a thinner plastic layer between the adjacent EPDM particles. However, the crystal form of PA6 is not changed with the increasing curing degree of the EPDM phase. This study provides an effective strategy to realize a new kind of TPVs, which can be easily introduced into industrial applications.

Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations have been used to accelerate and optimize bioprocess development. As implementation of fed-batch conditions, multiple options of process control and sample analysis are possible, these systems represent valuable screening tools for large-scale production. However, the dynamic behavior of cultivations has not yet been considered regarding data evaluation and decision making during high-throughput screening in mini-bioreactors. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed in 48 parallel fed-batch cultivations regarding 16 experimental conditions. Automated parallel process control, frequent sampling and analysis were implemented. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable and suitable for automatized process development reducing the experimental times and costs.

Comparison between hydraulic and hydrologic computational methods is conducted in this study, regarding prismatic open channels under unsteady subcritical flow conditions. One-dimensional unsteady flow continuity and momentum equations are solved using explicit and implicit finite difference schemes for a symmetrical trapezoidal cross section, where the flow discharge and depth are the dependent variables. The results have been compared to those derived from Muskingum-Cunge hydraulic/hydrologic method as well as the commercial software HEC-RAS. The results from explicit and implicit code compare well to those from commercial software and hydraulic/hydrologic methods for long prismatic channels, thus directing the hydraulic engineer to quick preliminary design of prismatic open channels for unsteady flow with satisfactory accuracy.

Stress relaxation tests in cantilever bending were performed on the C7025 and C7035 alloys at 298K and 393K，respectively. The effect of stress-relief treatments on stress relaxation properties was investigated. The structural changes associated with the stress relaxation process were examined using transmission electron microscope. The stress relaxation curve fits well to empirical formula σ*=[Kˊln(t+α₀)+C]^-n for stress relaxation. The curves can be split into two stages. The stress relax quickly in the first stage, slows down in the second stage, and tends to be a certain limit value after a long time. The curve and microstructure reveals that the C7035 alloy has a lower rate of stress relaxation and a higher anti-stress relaxation capacity than the C7025. The first reason is that the movement of vacancies required by spinodal decomposition is inhibited, and the quantity of cobalt -containing vacancies decreases dramatically in the C7035 alloy. The other reason is that the precipitated phases became uniformly diffused in the C7035 alloy. The precipitate phase uniformly distributes in the grain boundaries and the matrix, during the relaxed condition, and thus the dislocations moving is blocked by the precipitates.

The effects of psychological pressure on perceiving the height of a jump bar just before starting a high jump run was investigated. University students (N = 14) training for a high jump event performed 15 trials (3 practice, 6 pressure, and 6 non-pressure) in counterbalanced order in their daily practice environment. The height of the bar was judged as significantly higher on pressure trials compared to non-pressure trials. A regression analysis indicated that participants who reported increased subjective perceived pressure tended to judge the bar to be higher. There was no significant difference between pressure and non-pressure trials for the performance index, defined as the success rate. This study provides the first evidence that environmental perceptions prior to executing a motor task under pressure may make performance of the task appear to be more difficult.

Based on the fractional order of nonlinear system for love model with a periodic function as an external force, analyzed the characteristics of the chaotic dynamic in this study. The relationship between the chaotic dynamic of the fractional-love model with the external force and the fractional-order system was analyzed when the parameters are fixed. Further, we also studied the relationship between the chaotic systemic dynamic and the parameters when the fractional-order system is fixed. The results show that when the parameters are fixed, the fractional-order system exhibited segmented chaotic states for the different fractional orders of the system. When fixed the fractional-order system, the system exhibited the periodic and chaotic states as parameter changes.

Dynamic laser speckle is applied as a reliable sensor of activity in all sort of material. Traditional applications are based on a time rate that is usually higher than 10 frames-per-second (FPS). Even in drying processes, where there is a high activity in the first moments after the painting and a slow activity after some minutes or hours, the process is based on the acquisition of images in a time rate that is the same in both moments of high and low activity. In this work, we present an alternative approach to follow the drying of paint and the other processes related to restauration of paintings that takes long-term to reduce the activity. We illuminated, using three different wavelength lasers, an accelerator (Cyclododecane) and a varnish used in restauration of paintings and monitor them at long-term drying using an alternative fps, comparing the results to the traditional method. The work also presents a way to do the monitoring using a portable equipment. The results present the feasibility to use the portable device and show the improvement in the sensitivity of the dynamic laser speckle to sense long-term process regarding the drying of Cyclododecane and Varnish used in restauration.

Green supply chain management (GSCM) is fundamentally growing as a key component of sustainability, owing to its ability to improve supply chain competitiveness and performance within firms. The body of literature on GSCM within the manufacturing sector in developing countries such as South Africa is still limited. The study examined the relationships between GSCM practices, dynamic capabilities, and supply chain performance in the South African manufacturing sector. The study was quantitative, involving a sample of 402 supply chain professionals drawn from the manufacturing sector across the four provinces of the country. Hypotheses were tested using structural equation modelling based on the SMART PLS technique. Eco-design Green distribution and green training impacted positively on dynamic capabilities, the latter of which in turn is linked to improved agility, reliability, and reduced supply chain costs. The study validates the importance of GSCM as a driver of dynamic capabilities and performance in manufacturing environments within developing countries such as South Africa.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

Among the plethora of financial management tools available, stock investments stand out as the most recognizable and widely accepted option. However, investors often make buying and selling decisions based on unverified information provided by stock analysts. Since the credibility of this information remains uncertain, it frequently results in investment losses. Ordinary investors relying solely on rumors often miss out on prime opportunities for buying and selling stocks. In the current volatile investment landscape, various investment strategies cater to different situations. If individuals can effectively identify opportunities within this complex environment and dynamically adapt suitable investment strategies at any given time, substantial returns can be achieved. The ability of investors to promptly detect shifts in the current stock market environment and make accurate decisions regarding stock selection, timing, and capital allocation strategies is of utmost importance. This study presents a dynamic framework for investment decision support, which utilizes evolutionary strategies to determine the optimal timing for key strategic tools such as stock selection, timing, and capital allocation. By leveraging historical data for training and testing, this approach aims to assist investors in adapting to the ever-changing stock market environment. If investors can successfully identify and implement an appropriate investment strategy to aid in making accurate decisions, it is possible to achieve significant excess returns.

Numerical simulations of standard situations in the transition region from gas kinetics to fluid dynamics at small Mach numbers indicate a clear dependence of the simulation results on the underlying kinetic model (here: nonlinear and linearized Boltzmann collision operator vs. BGK relaxation model). We develop an improved mathematical framework (trace theory) to explain these differences. In particular we reveal certain deficiencies for the classical BKG system as well as for the standard Navier Stokes approach.

Fuzzy Rule Interpolation (FRI) offers an innovative methodology for inferring outcomes from data points that match no established rules in a sparse fuzzy rule base. Generally, a traditional fuzzy rule-based system suffers from the inability to perform inference while facing an unmatched observation, given a sparse fuzzy rule base that is derived from insufficient data or inadequate human experience and that fails to cover parts of a specific problem domain. Fortunately, starting with a sparse rule base, Fuzzy Rule Interpolation (FRI) can help to formulate interpolated rules specifically in instances where observations fail to activate any existing rule. An interpolated rule gives a result called consequent, which is related to the unmatched observation so that the inference system will not fail to generate an outcome corresponding to the observation as the output. Conventionally, these interpolated rules will be discarded after obtaining outcomes. However, valuable information may be potentially embedded within those discarded rules addressing the limitation regarding the coverage of the original sparse knowledge space. Moreover, without exploiting the potential value of the interpolated rules, the fuzzy inference system based on a sparse rule base will never improve its efficiency and robustness in the long run. Therefore, this underscores the necessity for Dynamic Fuzzy Rule Interpolation (D-FRI), which aims to collate interpolated rules that cover particular knowledge spaces absent in the sparse rule base. Intuitively, those frequently appeared interpolated rules are considered valuable because future observations are more likely to hit the respective problem areas. Promoting selected interpolated rules to the sparse rule base can enhance the overall coverage of the knowledge space and increase the inference efficiency over time. This paper introduces a framework for Dynamic Fuzzy Rule Interpolation that integrates the widely used Transformation-based Fuzzy Rule Interpolation (T-FRI) with an effective form of clustering of interpolated results. Experimental findings validate the effectiveness of this approach.

The algorithm now known as Kadane’s algorithm for the maximum subarray problem uses many of the ideas that Kadane proposed, but it is not the algorithm that Kadane intended. This paper compares the two algorithms in question. They are both linear in time, employ just a few words of memory, and use a dynamic programming structure. The upshot is two light-weight algorithms for the maximum subarray problem.

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net 1 Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, extreme weather periods are occurring in a cyclical manner, with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

One of the most important problems of geotechnical engineering is the design of pile foundations. Piles are generally designed for vertical and horizontal static loads. However, it is important to design piles against dynamic loads in regions with high seismicity. This study verified a centrifuge experiment obtained from the literature with a finite element program. With the kinematic interaction analyses made for this purpose, the effect of parameters such as layered soil, groundwater, pile stiffness, and earthquake acceleration were investigated numerically. According to the results obtained, it was understood that the earthquake magnitude is the most important parameter in kinematic interaction analyses. In addition, in layered soil conditions, the fact that 75% of the pile length is in the weak soil creates the most unfavorable situation. It has been observed that pile stiffness and groundwater level also have certain effects on the kinematic interaction.

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.