Dynamic Source Research Articles

High throughput dynamic dual entropy source true random number generator based on FPGA

True Random Number Generator (TRNG), a primitive language of hardware security, plays a significant role in key generation, data encryption, initialization vectors, and other situations. To address the problem of incompatibility between low resource overhead and high throughput of TRNG, a dual entropy source TRNG architecture based on Field Programmable Gate Array (FPGA) is proposed, whose entropy sources include clock jitter and metastability. Different from other TRNGs that use multiplexers (MUX), it also uses the ring oscillators and path delay differences to dynamically convert the oscillation frequency of the multiplexer ring oscillator (MRO), thus increasing the randomness of the entropy source. This architecture is implemented on Xilinx Artix-7 and Kintex-7 FPGAs and has been tested with NIST tests, AIS-31 tests, as well as voltage and temperature tests, and has been compared with other up-to-date TRNGs. The experimental results show that the MRO-TRNG only requires 10 LUTs, 2 DFFs, and 1 MUX, providing a throughput of up to 300 Mbps.

Detecting potential cooperative network for tourist attractions in a destination using search data.

This study addresses the critical need for regional tourism integration and sustainable development by identifying cooperation opportunities among tourist attractions within a region. We introduce a novel methodology that combines association rule mining with complex network analysis and utilizes search index data as a dynamic and contemporary data source to reveal cooperative patterns among tourist attractions. Our approach delineates a potential cooperative network within the destination ecosystem, categorizing tourist attractions into three distinct communities: core, intermediary, and periphery. These communities correspond to high, medium, and low tourist demand scales, respectively. The study uncovers a self-organizing network structure, driven by congruences in internal tourist demand and variances in external tourist experiences. Functionally, there is a directed continuum of cooperation prospects among these communities. The core community, characterized by significant tourist demand, acts as a catalyst, boosting demand for other attractions. The intermediary community, central in the network, links the core and periphery, enhancing cooperative ties and influence. Peripheral attractions, representing latent growth areas within the destination matrix, benefit from associations with the core and intermediary communities. Our findings provide vital insights into the dynamics, systemic characteristics, and fundamental mechanisms of potential cooperation networks among tourist attractions. They enable tourism management organizations to employ our analytical framework for real-time monitoring of tourism demand and flow trends. Additionally, the study guides the macro-control of tourism flows based on the tourism network, thereby improving the tourist experience and promoting coordinated development among inter-regional tourist attractions.

Enso influence on water vapor transport and thermodynamics over Northwestern South America

The interannual variability of hydroclimatic conditions in Northwestern South America, especially precipitation, is mainly influenced by the El Niño–Southern Oscillation (ENSO). We explore potential mechanisms that affect precipitation occurrence in Northwestern South America during El Niño and La Niña events over the period 1980–2019, using data from the ERA5 reanalysis. We look at the atmospheric moisture contribution from different sources using the Dynamic Recycling Model to track water vapor trajectories. Interestingly, conditions with reduced precipitation during El Niño events can take place along with increased precipitable water. To understand this, we analyze thermodynamic conditions in the atmosphere that are necessary for precipitation to occur over the region, such as convective available potential energy, convective inhibition, lifting condensation level, and low-level relative humidity. With this approach, we find more favorable thermodynamic conditions for the occurrence of precipitation during La Niña events, even if the content of water vapor is equal or even less than during El Niño events. We also look at the structure of the regional Hadley and Walker circulation in both types of events and find that a weaker ascending motion during El Niño events also inhibits convection. This study provides an integral picture of how precipitation anomalies over Northwestern South America during ENSO events are related to dynamic and thermodynamic conditions and sources of atmospheric moisture.

Modeling of the Flow Field and Clad Geometry of a Molten Pool during Laser Cladding of CoCrCuFeNi High-Entropy Alloys

A flow field analysis was performed in this research using the ANSYS Fluent module, and a dynamic heat source employing UDF was constructed using the DEFINE_PROFILE macro. A VOF model was developed to track the volume fraction of each fluid throughout the computational domain as well as the steady-state or transient condition of the liquid–gas interface in the free liquid surface area. To determine the distribution state and regularity of the molten pool flow field, the flow field velocity was calculated iteratively by linking the Simple algorithm with the horizontal set method. The molten pool was concave, indicating that the key hole was distributed narrowly. Inserting cross-sections at different depths yielded the vector distribution of the molten pool flow velocity along the depth direction. We set up monitoring sites along the molten pool’s depth direction and watched the flow change over time. We investigated the effects of the process parameters on the flow field’s vector distribution.

The Application of the Finite Elements Method in the Transient Measurements of Thermophysical Parameters

The finite elements method (FEM) is used to improve the extended dynamic plane source (EDPS) method to obtain more accurate values of the thermal conductivity and diffusivity of the specimen. A method of transformation of the FEM-calculated response into an analytical form, which can be used in fitting the measured temperature response, has been described and verified. Furthermore, a modification of the EDPS method designated as EDPS1 is designed, where only one piece of specimen is used. Since there is no analytical formula for the new method, the analytical formula for EDPS is used. Hence, the initial estimates of the specimen parameters for the EDPS1 method are very rough and the FEM numerical evaluations must be repeated several times until the estimates reach relative errors of around 1%.

Research on the Google Stock Price Prediction Based on SVR, Random Forest, and KNN Models

The prediction of stock prices is an intriguing and difficult study issue. Due to its frequent offers of quick rewards and low risk of loss, the stock market is currently regarded as a prestigious trading sector. Data mining and business researchers find the stock market to be an ideal environment due to its extensive and dynamic information sources. In this paper, the data of google stock price from 2018-1-2 to 2023-3-31 is imported. As our classification algorithms, the authors independently selected and contrasted super vector regression, random forest, and K-nearest neighbor. Following test, analysis, and comparison of these classifiers and calculate the mean square error of test data, the final result was reached. According to the result, the random forest has the smallest MSE value, so it is the best prediction and uses this method to predict the stock price of next ten days. After using random forest to do the stock price prediction, we find that the close price has the increasing trend for next ten days, which is good for the investors.

Cross-Layer Energy Efficient (CLEE) Routing Algorithm for Mobile Ad-Hoc Networks

Ad-hoc routing algorithms in Wireless Sensor Networks (WSN) rely on nodes’ position awareness by regularly updating routing data of neighbouring nodes. Meanwhile, the transmission energy usage is not optimised as a result of this repeated updates and routing table deployment. Therefore, it is very critical to consider techniques of optimising or conserving energy in the design of wireless sensor networks (WSNs) in order to prolong the lifetime of the individual nodes. One of these techniques is the Cross-layer design which, is considered as an efficient method for addressing this challenge with WSNs. In this paper, we propose a Cross-Layer Energy Efficient (CLEE) routing algorithm to establish an optimal route from the source node to the destination node by selecting candidate nodes from neighbouring nodes based on the distance between these nodes and the rate of energy consumption by a possible candidate node. Then to select a designated node from the candidate nodes, the algorithm further computes the Signal Strength Quality (SQS) and the Link Lifetime (LL) as well as the Throughput (TH) rate of selected nodes. The proposed algorithm was simulated with a network size of 600 X 600 on Network Simulator 3 (NS3) in order to analyse its performance. An evaluation of the performance of the proposed CLEE protocol with existing similar protocols such as Ad-hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) reveals that CLEE outperformes AODV and DSR by conserving about 44% of available energy.

Active Disturbance Rejection Control for Distributed Energy Resources in Microgrids

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER.

Estimation of pollutant source using source-receptor response matrix from maximum-length sequence pulse technique

Accurate estimation of pollutant sources in built environments is crucial for monitoring air quality and preventing harm to health and property. This study presented an improved method for estimating the dynamical pollutant source strength using the concentration signal monitored at the receptor point. We employed a built environment system subjected to a pollutant pulse signal in accordance with the maximum-length sequence (m-sequence) to acquire response concentration. Subsequently, the source-receptor response matrix was inversely identified based on the known pulse signal and the response concentration. Then, the unknown source release rate was inversely estimated by the monitored concentration signal and previously identified response matrix. In addition, the performances of different inverse algorithms and m-sequence orders were also analyzed. Here both simulation and application to a real experiment of a wind duct were implemented, and the results demonstrated the higher-order m-sequence method exhibited better noise resistance compared to the traditional unit pulse method. In both simulation and experiment, the m-sequence pulse method reduced the root mean square error (RMSE) by 39.9 % and 70.7 %, respectively, compared to the unit pulse method. In experiment, the simultaneous multiplicative algebraic reconstruction technique (SMART) combined with the Tikhonov regularization (TR) method showed relatively stable performance.

The anatomy of government bond yields synchronization in the Eurozone

Abstract We investigate the synchronization of the Eurozone’s government bond yields at different maturities. For this purpose, we combine principal component analysis with random matrix theory. We find that synchronization depends on yield maturity. Short-term yields are not synchronized. Medium- and long-term yields, instead, were highly synchronized early after the introduction of the Euro. Synchronization then decreased significantly during the Great Recession and the European Debt Crisis, to partially recover after 2015. We interpret our empirical results using portfolio theory, and we point to divergence trades as a source of the self-sustained yield asynchronous dynamics. Our results envisage synchronization as a requirement for the smooth transmission of conventional monetary policy in the Eurozone.

Multi-season mobile monitoring campaign of on-road air pollution in Bengaluru, India: High-resolution mapping and estimation of quasi-emission factors

Mobile monitoring can supplement regulatory measurements, particularly in low-income countries where stationary monitoring is sparse. Here, we report results from a ~ year-long mobile monitoring campaign of on-road concentrations of black carbon (BC), ultrafine particles (UFP), and carbon dioxide (CO2) in Bengaluru, India. The study route included 150 unique kms (average: ~22 repeat measurements per monitored road segment). After cleaning the data for known instrument artifacts and sensitivities, we generated 30 m high-resolution stable ‘data only’ spatial maps of BC, UFP, and CO2 for the study route. For the urban residential areas, the mean BC levels for residential roads, arterials, and highways were ~ 10, 22, and 56 μg m−3, respectively. A similar pattern (highways being characterized by highest pollution levels) was also observed for UFP and CO2. Using the data from repeat measurements, we carried out a Monte Carlo subsampling analysis to understand the minimum number of repeat measures to generate stable maps of pollution in the city. Leveraging the simultaneous nature of the measurements, we also mapped the quasi-emission factors (QEF) of the pollutants under investigation. The current study is the first multi-season mobile monitoring exercise conducted in a low or middle -income country (LMIC) urban setting that oversampled the study route and investigated the optimum number of repeat rides required to achieve representative pollution spatial patterns characterized with high precision and low bias. Finally, the results are discussed in the context of technical aspects of the campaign, limitations, and their policy relevance for our study location and for other locations. Given the day-to-day variability in the pollution levels, the presence of dynamic and unorganized sources, and active government pollution mitigation policies, multi-year mobile measurement campaigns would help test the long-term representativeness of the current results.

Improving Broadcast System of Integrated Satellite-Terrestrial Network-based on Enhanced Ant colony Optimization

With the use of seamless high-speed worldwide network connectivity in future, it is anticipated that the integrated satellite-terrestrial network (ISTN) will be a possible option. Due to the inadequacy of topological data and the close coupling of data and control planes, deploying optimized rules on routers is difficult. The important factors in ISTN networks are routing rules and policies, link failure, and high-bandwidth communication. Software-defined networking (SDN) is an open innovation approach that enables programmability from a central location. The controller handles the complexity of the network, whereas the infrastructure layer devices relay the packets. Thus, we investigated the broadcast by dynamically adjusting routes for fault tolerance and energy efficiency of the ISTN using distance between nodes. In addition, using dynamic source routing, this study examines the competence functions of all managing nodes in a network. The routing design is distributed among all nodes to create numerous collective paths. For LEO satellite networks, the ant colony optimization-based routing algorithm is an improved version that considers the probability of faults and low-energy consumption. The proposed simulation ensures a seamless transition in the event of failures and avoids the requirement for an additional coordination service.

Point source modelling approach for sessile droplet evaporation

Evaporation of sessile droplets from unheated solid surfaces is a ubiquitous process in many practical applications. A reduced order, analytical point source model (PSM) for the axisymmetric diffusion-dominated evaporation of an isolated sessile droplet surrounded by non-saturated, quiescent air was developed. The droplet is modeled as a dynamic point mass source in the limit of an isothermal system. The model also incorporates the spatial variation in the evaporative flux across the droplet free surface. The model is capable of considering the mode of evaporation, i.e., constant contact angle or contract radius. The PSM was simulated using the finite difference method in MATLAB R2020a. The model determines the vapor concentration distribution in the surrounding environment, the instantaneous evaporative flux averaged across the droplet surface and the overall evaporation rate. Calculating the evaporation rate assuming a spatially uniform evaporative flux under-predicts the evaporation rate by up to an order of magnitude. The model results agreed with experimental data in literature and sufficiently captures the evaporation process phenomena. The versatility and accurate predictive power of the PSM allows it to be a robust and computationally inexpensive modeling tool for studying sessile droplet evaporation in a wide range of technical applications.

Optimal Sizing and Dynamic Energy Source Characteristics of Hybrid Electric Vehicles: A Comprehensive Review and Future Directions

Optimal Sizing and Dynamic Energy Source Characteristics of Hybrid Electric Vehicles: A Comprehensive Review and Future Directions

NeRC: Rendering Planar Caustics by Learning Implicit Neural Representations.

Caustics are challenging light transport effects for photo-realistic rendering. Photon mapping techniques play a fundamental role in rendering caustics. However, photon mapping methods render single caustics under the stationary light source in a fixed scene view. They require significant storage and computing resources to produce high-quality results. In this paper, we propose efficiently rendering more diverse caustics of a scene with the camera and the light source moving. We present a novel learning-based volume rendering approach with implicit representations for our proposed task. Considering the variety of materials and textures of planar caustic receivers, we decompose the output appearance into two components: the diffuse and specular parts with a probabilistic module. Unlike NeRF, we construct weights for rendering each component from the implicit signed distance function (SDF). Moreover, we introduce the centering calibration and the sine activation function to improve the performance of the color prediction network. Extensive experiments on the synthetic and real-world datasets illustrate that our method achieves much better performance than baselines in the quantitative and qualitative comparison, for rendering caustics in novel views with the dynamic light source. Especially, our method outperforms the baseline on the temporal consistency across frames.

Сейсмотектонические деформации и сброшенные напряжения землетрясений Центрального Тянь-Шаня

Based on the seismic moment tensor (SMT) data of 270 earthquakes in the Central Tien Shan, which occurred from 1978 to 2021 and include 63 events from the Global Centroid Moment Tensor catalog and 207 events from the works of Aleksander D. Kostyuk and Naylya A. Sycheva, the focal parameters (seismotectonic deformations (STD), kinematic and dynamic parameters, source radius, and stress drop) were calculated. The STD calculation was based on the approaches proposed in the studies of Yuriy V. Riznichenko and Sergei L. Yunga. The area under consideration is characterized by such deformation modes as thrust, transpression, underpression, and oblique. The distribution of the Lode–Nadai coefficient was calculated and plotted. A significant part of the study area is characterized by simple compression deformation, dominated by simple compression and simple shear. To calculate the stress drop , the values of the scalar seismic moment M0, which are determined in the SMT calculation, and the source radii r, which are calculated on the basis of theoretical and experimental models of the dependence of the source radius on the moment magnitude, were used. The radii r and stress drops  were calculated for two models of earthquake sources: the Brune model and the Madariaga–Kaneko–Shearer model. A catalog of dynamic parameters was compiled. The comparison of the kinematic and dynamic parameters of earthquakes was carried out, and the relationship of the dropped stresses with the type of movement in the source, as well as with the Lode–Nadai coefficient, was established. The results obtained in the study can be useful for specialists in other fields of knowledge – geodesy, geology, and geophysics.

Northern Hemisphere extratropical cyclone biases in ECMWF subseasonal forecasts

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large‐scale flow. Biases in cyclone frequency and characteristics in medium‐range to subseasonal numerical weather prediction might therefore hinder exploitation of potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 20 years (2000–2020) of subseasonal ensemble reforecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. The reforecasts reproduce the climatology of cyclone frequency and life‐cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis, size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest regional deficit of cyclones of more than 30% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones reach too‐high intensities during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime might partly but not exclusively explain this intensity bias. While the cyclone bias patterns often appear in lead‐time weeks 1 and 2, their magnitudes typically grow further at subseasonal lead times, in some cases up to weeks 5 and 6. Most of the dynamical sources of these biases thus likely appear in the early medium range, but sources on longer timescales probably contribute to the further increase of biases with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large‐scale flow and in surface weather in subseasonal weather forecasts. Given the considerable biases during summer, when subseasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.

A Comment on: “Walras–Bowley Lecture: Market Power and Wage Inequality” by Shubhdeep Deb, Jan Eeckhout, Aseem Patel, and Lawrence Warren

A burgeoning literature in labor economics is focused on modeling employer labor market power, generally finding nontrivial estimates of monopsony power. A smaller literature also simultaneously incorporates product market power. Deb, Eeckhout, Patel, and Warren (2024) is an example of applying an oligopoly‐oligopsony model to the U.S. labor market, arguing for important effects on wage levels and inequality from rising market power. I support combining IO and labor as a fruitful way of studying wages and business dynamism, but argue for looking more broadly at (i) differential degrees of employer power in labor and product markets; (ii) investigating the dynamic sources of markups (e.g., through innovation), and (iii) considering wage bargaining models, not just wage posting models, which have some starkly different implications for wage setting.

On the Formation and Maintenance of the Interannual Variability of the North Atlantic Oscillation

Abstract Motivated by the observation that the interannual variability of the North Atlantic Oscillation (NAO) is associated with the ensemble emergence of individual NAO events occurring on the intraseasonal time scale, one naturally wonders how the intraseasonal processes cause the interannual variability and what the dynamics are underlying the multiscale interaction. Using a novel time-dependent and spatially localized multiscale energetics formalism, this study investigates the dynamical sources for the NAO events with different phases and interannual regimes. For the positive-phase events (NAO+), the intraseasonal-scale kinetic energy (K1) over the North Atlantic sector is significantly enhanced for NAO+ occurring in the negative NAO winter regime (NW), compared to those in the positive winter regime (PW). It is caused by the enhanced inverse cascading from synoptic transients and reduced energy dispersion during the life cycle of NAO+ in NW. For the negative-phase events (NAO−), K1 is significantly larger during the early and decay stages of NAO− in NW than that in PW, whereas the reverse occurs in the peak stage. Inverse cascading and baroclinic energy conversion are primary drivers in the formation of the excessive K1 during the early stage of NAO− in NW, whereas only the latter contributes to the larger K1 during the decay stage of NAO− in NW compared to that in PW. The barotropic transfer from the mean flow, inverse cascading, and baroclinic energy conversion are all responsible for the strengthened K1 in the peak stage of NAO− in PW.

From Shadows to Light: A Swarm Robotics Approach With Onboard Control for Seeking Dynamic Sources in Constrained Environments

From Shadows to Light: A Swarm Robotics Approach With Onboard Control for Seeking Dynamic Sources in Constrained Environments