Physical Sciences and Mathematics Commons^™

Prospects For Increasing Symbiotic Nitrogen Fixation In Temperate Grasslands, P/ Roger Ball, J R. Crush

IGC Proceedings (1977-2023)

The contribution of some basic plant disciplines, principally physiology and histology, to the improvement of pasture yield and quality is discussed. Studies of temperate pasture grasses have identified several sources of loss of potential yield and have provided selection criteria for increasing yield. Integration of this physiological research with studies of forage consumption on the same pastures is proving a valuable inter- disciplinary approach to defining management options for maximising pasture and/or animal productivity. In tropical grasses, research towards increasing total dry matter yield is perhaps unwarranted but there is need for more co-operative studies involving plant and animal scientists …

Dft Prediction Of Adsorption Properties Of Chrysene On Si(001) Surface, Çağil Kaderoğlu

Turkish Journal of Physics

In this study, DFT calculations were carried out in order to reveal the adsorption properties of chrysene molecule on the Si(001)−(2×1) surface. For this purpose, structural, electronic, and optical properties were calculated for five different adsorption configurations. The results show that the electronic and optical properties vary depending on the adsorption configuration of the molecule. In general, the chrysene molecule was found to reduce the work function of the Si(001)−(2×1) surface and affect its optical absorption and reflection properties in the IR and UV regions. These results indicate that the Chrysene + Si(001) hybrid could be quite useful in molecular …

A Comprehensive Study On The Structural, Electrical And Optical Properties Of Dc Sputtered Molybdenum Thin Films, Fi̇li̇z Keleş, Abdullah Atilgan, Eli̇f Damgaci, Ayşe Seyhan, Abdullah Yildiz

Turkish Journal of Physics

This study investigates the influence of key deposition parameters on the optoelectronic properties of Molybdenum (Mo) films fabricated via DC magnetron sputtering. The results demonstrate that increasing the Argon gas pressure significantly raises the sheet resistance, primarily due to increased porosity and defect formation. In contrast, films deposited at lower pressures exhibit higher conductivity due to their denser morphology. The substrate temperature also plays a critical role in determining crystallographic structure and electrical performance. Mo bilayer films deposited at 250 °C show a much lower sheet resistance compared to room temperature (RT) growth. Additionally, increasing the total film thickness from …

Exact Solution Of The Two-Reflector Optical System Meeting Herschel’S Condition, Giuseppe Orlando

Turkish Journal of Physics

The exact explicit solution of a stigmatic two-reflector optical system under Herschel’s condition is presented Details of how the solution is calculated are reported. A comparison of caustics between an aplanatic optical system and the relevant system meeting Herschel’s condition is also presented. Ray tracing verification on different optical systems validates the obtained results.

On-Chip Ge-Based Refractive Index Sensor Based On Multislot Ge Waveguide, Çi̇çek Boztuğ

Turkish Journal of Physics

On-chip optical sensors have been attracting significant attention of the researchers due to their portability, low cost, and in-situ detection compatibility. The realization of these compact and mass-producible sensors will be possible if all required optical and electronic components are fully integrated on the same chip. However, the main obstacle here is the lack of the room-temperature on-chip light source; therefore, an external source has been utilized for optical sensing applications. Our work introduces a germanium-based optical sensing structure, specifically designed to operate at low refractive indices, with all required optical components integrated on silicon in a CMOS-compatible manner. The …

Charge Asymmetry Phenomenon For The Leptonically Decaying W Γ Pair At Nnlo Qcd, Kadi̇r Saygin

Turkish Journal of Physics

The paper presents for the first time a phenomenological assessment which focuses on the charge asymmetry phenomenon for the W γ production process with subsequent leptonic decay modes W⁺γ → l⁺ν_lγ and W⁻γ → l⁻ν_l^¯γ in proton-proton (pp) collisions. Charge asymmetry for the leptonic W γ process is predicted by the inclusion of genuine next-to-next-to-leading order (NNLO) corrections in the QCD perturbation theory. NNLO predictions of asymmetry distributions are assessed for the key observables, including the decay lepton and photon pseudorapidities η_l and η_γ, …

Shifted Frequency Analysis Hybrid Simulation Algorithm Based On Multi-Rate Asynchronous Coordination, Yankan Song, Libin Wen, Ying Chen, Jinji Xi, Haoyuan Zhang, Li Xiong

Journal of System Simulation

Abstract: Large-scale AC/DC power systems exhibit complex dynamics across multiple time scales, and existing hybrid simulations suffer from interface delays and frequency losses during multi-rate coordination, compromising accuracy. To address this issue, a multi-rate asynchronous coordination method was proposed to construct hybrid simulations using shifted frequency analysis (SFA). Within the multi-area Thevenin equivalence (MATE) framework, the algorithm introduced an interpolation-based asynchronous coordination mechanism, effectively eliminating interface delays; by extending SFA theory and designing a universal interface model, it achieved lossless data exchange between partitions with different rates and model types. Case studies on an AC/DC test system demonstrate that …

Soft Sensor Modeling Based On Improved Transformer In Dual-Stream Framework, Hao Gu, Jiayu Wang, Weili Xiong

Journal of System Simulation

Abstract: Industrial process information is highly nonlinear and dynamic, with long-term dependencies between data, making it difficult to adequately extract time-series features. To address this issue, an improved Transformer-based soft sensor model in a dual-stream framework was proposed. The data were segmented and expanded. The features were extracted in parallel using a dual-stream structure combining a convolutional neural network with a self-attention mechanism and the improved Transformer model. The dual-stream features were fused for soft sensor regression. Residual connections were further introduced to accelerate the convergence speed of the model, and an orthogonal random features-based improved multi-head attention mechanism was …

Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning, Xiuman Liang, Ziliang Liu, Zhendong Liu

Journal of System Simulation

Abstract: To address the low planning efficiency, poor safety, and limited practicability of the RRT algorithm in global path planning within complex three-dimensional environments, which fail to meet the requirements of planning the safe flight path of UAVs, an improved SAC-RRT algorithm was proposed, which fused SAC deep reinforcement learning algorithm and RRT algorithm. A target point bias strategy and a dynamic step size based on the SAC decision-making network were designed to reduce the blindness of RRT. A random point correction process was designed to optimize the position of random points based on actions from the decision network and …

Low-Energy Multi-Robot Path Planning Algorithm Under Hca* Framework, Ning Wang, Jianlin Mao, Dayan Li, Chengyuan Fang, Chengze Qian

Journal of System Simulation

Abstract: To address the energy optimization problem in multi-robot path planning, this paper proposed a multi-robot path planning algorithm based on the energy-guided hierarchical cooperative A* (E-HCA*) algorithm. To address the issue of robot oscillations caused by mutual avoidance at bottlenecks and narrow passages in multi-robot systems, a node expansion method with path length as a secondary feature was introduced, and a greedy suppression strategy under the cooperative A* framework was proposed. A differential-drive robot energy consumption model was established, and an energy-guided heuristic function was constructed by integrating energy metrics into the underlying A* algorithm to guide low-energy path …

Multi-Objective Optimization Of Signal Timing At Intersections Considering Tailpipe Emissions, Xinhuan Ding, Huaqing Wang, Xu Dang

Journal of System Simulation

Abstract: In order to alleviate urban road congestion and improve the traffic and environmental benefits at intersections, a multi-objective timing optimization model with total delay time, total number of stops, capacity, and total tailpipe emission at intersections as optimization objectives was developed. The model incorporated tailpipe emissions into a mathematical optimization model and quantified the mathematical relationship between traffic efficiency indicators and tailpipe emissions by constructing a specific power-based algorithm for measuring total tailpipe emissions. According to the intersection delay time and the number of stops, the total tailpipe emissions could be estimated. Both the NDX crossover operator and the …

Decarbonization Is Happening In U.S. Higher Education, But Carbon Neutrality Goals Alone Are Not Delivering It, Alexander R. Barron, Nubraz F. Khan, Greta Anesko, Emma S. Larsen, Junwen Ding, Shey P. Sanges, Aaron L. Strong

Environmental Science and Policy: Faculty Publications

Carbon neutrality emerged as an overall framework for climate action several decades ago, with the assumption that institutions would follow a carbon management hierarchy - reducing their own emissions (i.e. decarbonization) and relying on high-quality offsets to address any residual emissions. In practice, offsets and other accounting-based emissions reductions have often dominated both discussion and action. However, sector-wide studies examining the balance between decarbonization and offsetting have been limited. Here, we examine 23 U.S. Higher Education Institutions (HEIs) that have declared carbon neutrality and contrast their direct (Scope 1) greenhouse gas emissions reduction efforts with a broader universe of U.S. …

Impact Of Information Security Awareness Training On Knowledge, Attitude, And Behavior: A K-12 Case Study, Michael S. Robbins, Christopher Robbins

Journal of Cybersecurity Education, Research and Practice

Abstract— Information security breaches remain a serious threat across all sectors, often exploiting human factors rather than technical flaws. This study examines how a structured Information Security Awareness (ISA) training program influences employees’ knowledge of security policies, attitudes towards those policies, and self-reported security behaviors within a K-12 educational environment. A quantitative pre-test/post-test design was employed with 201 staff members (administrators, teachers, and support personnel) in a public school district. Participants completed the Human Aspects of Information Security Questionnaire (HAIS-Q) before and after undergoing an interactive cybersecurity training program. Statistical analysis revealed a significant improvement in information security knowledge, attitudes, …

Multisource Information Fusion Method For Human Gait Perception, Guiliang Chen, Guowei Liu, Yongchao Li, Chao Cai, Zihao Li, Dong Yang

Journal of System Simulation

Abstract: In response to the insufficient gait perception capability during lower limb exoskeleton assistance, a human lower limb gait phase optimization classification model was proposed. A wireless transmission gait information collection system was designed for collecting the required gait phase feature information. Human joint angles were accurately calculated by fusing acceleration and angular velocity information using extended Kalman filtering. Additionally, kernel principal component analysis was applied to reduce dimensionality in conjunction with plantar pressure data. The LSSVM algorithm was employed to classify gait data, and the PSO algorithm was utilized to find the optimal classification parameters. Experimental results demonstrate that …

10.20.2025 Ored Connect, Liz Williamson

ORED Newsletter

• OTC Director Candidate Forum
• ORED Small Grants Program RFP

Optimal Scheduling Of Integrated Energy Systems Considering Source-Load Uncertainty And Linear Carbon Trading, Huaping Zhong, Yubo Fan, Jijun Shui, Danhao Wang, Daogang Peng

Journal of System Simulation

Abstract: In order to overcome the impact of source-load uncertainty on the scheduling of integrated energy systems (IES) and reflect the flexibility of the carbon trading price with the change in trading volume, an optimal scheduling method for integrated energy systems considering source-load uncertainty and linear carbon trading was proposed. The equipment within the IES was modeled, and nonparametric kernel density estimation was used to obtain the probability density function for each time period, generating the set of scenes through Monte Carlo simulation and calculating the probability of each scene. For the time shift of wind and solar output peaks …

Simulation Of Three-Degree-Of-Freedom Internal Mode Sliding Mode Control For Non-Ideal Single-Inductor Dual-Output Boost Converter, Bingli Liu, Jiarong Wu, Lin Yang, Dinglin Yan

Journal of System Simulation

Abstract: To reduce the cross-interference in the single-inductor dual-output (SIDO) Boost converter and to enhance the output accuracy and stability of the system, the parasitic resistances of the circuit components were considered, and a three-degree-of-freedom internal model sliding mode control strategy was proposed for the non-ideal SIDO Boost converter. An affine nonlinear mathematical model of the non-ideal SIDO Boost converter was established, and the nonlinear system was linearized and decoupled into two linear subsystems based on the differential geometry theory. The linear subsystem was designed as a three-degree-of-freedom internal model controller and a sliding mode controller, respectively. The robustness …

A Novel Joint Training Simulation Evaluation Framework And Its Key Techniques, Rusheng Ju, Dongdong Chen, Yunxiu Zeng, Jiyuan Liu, Sihang Qiu, Peng Zhou

Journal of System Simulation

Abstract: To address the challenges of traditional evaluation systems, such as internal module coupling, lack of reusability, and poor adaptability to multi-domain evaluation needs, a three-tier decoupled technical evaluation framework of "data + service + application" was designed. A strategy was proposed for extracting high-value information from massive audio and video data based on key events, resolving the problem of unstructured evaluation data processing. A design method combining general and dedicated evaluation model templates was proposed, improving the general applicability of the evaluation model. An expert knowledge-driven comprehensive integrated discussion and evaluation environment was constructed using qualitative and …

An Improved Virtual Terrain Generation Method Based On Simplex Noise, Bo Shen, Jianqin Zhang, Shuaibao Ma, Zheng Wen

Journal of System Simulation

Abstract: To address the issues of high computational complexity, slow generation speed, and insufficient realism present in traditional virtual terrain generation methods, this study proposed an improved virtual terrain generation method based on Simplex noise. This method leveraged the advantages of Simplex noise, such as high computational efficiency, low hardware overhead, and more natural randomness, to construct a basic terrain template. A fractal algorithm was introduced to enhance the level of terrain details through the superposition of noises with multiple frequencies and amplitudes. With the integration of a turbulence algorithm, random perturbations and complexity were added to further improve the …

Research On Constrained Programming Of Manipulator Using Rrt* Algorithm And Ellipse Prior, Zhen Yang, Li Su, Zhiyu Cheng

Journal of System Simulation

Abstract: There are problems in the traditional RRT* algorithm using a uniform sampling strategy applied in constrained programming problems, such as inaccurate turning guidance of sampling points and unnecessary node cost comparisons, which lead to an increase in additional time costs. To address these issues, an improved RRT* algorithm was proposed. This algorithm leveraged a heuristic function cost of the projected sampling points to make an ellipse prior judgment on the sampling points. Based on the ellipse prior, the sampling points were judged to determine whether they could optimize the path and shorten the programming time. The geodesics were used …

Robust Identification Of Dual-Rate Sampled Nonlinear Systems Based On Salr Network, Wenbin Jiang, Yuqing Cao, Li Xie, Huizhong Yang

Journal of System Simulation

Abstract: A robust identification algorithm based on the self-join adjacent-feedback loop reservoir (SALR) network was proposed for dual-rate sampled nonlinear systems with complex nonlinear characteristics and measurement outputs containing outliers. The SALR network was applied to describe the nonlinear characteristics of the target system, and wavelet neurons were injected into the reservoir to enhance its memory and nonlinear description capabilities. The identification problem of the nonlinear system was transformed into the identification problem of the network's output weight matrix. The Huber loss function was used to construct the criterion function, and an error threshold was introduced to improve the robustness …

How Fair Workweek Laws Affect Labor Markets: A New York City Case Study, Joseph Pickens, Aaron Sojourner

Upjohn Institute Policy and Research Briefs

No abstract provided.

Distributed Dispatch Method For Distribution Network And Microgrid Considering Diverse Regulating Resources, Yanbo Chen, Jiahao Yin, Tuben Qiang, Haoxin Tian, Yuxin Liang, Zhi Zhang

Journal of System Simulation

Abstract: Traditional centralized optimization-based dispatch methods for distribution networks struggle to balance the interests of multiple stakeholders while ensuring economic efficiency and operational reliability of the system. To address this issue, a distributed dispatch method for distribution network and microgrid considering diverse regulating resources was proposed. The operational models of the distribution network and microgrid were established by comprehensively incorporating active management elements and demand response mechanisms. A fuzzy chance-constrained method was employed to model the uncertainty in renewable energy output, thereby constructing a coordinated optimization dispatch model for distribution network and microgrid under renewable generation uncertainty. An improved goal …

Deep Learning Modeling Of Multi-Scale Characteristics Of Large-Scale Wind Turbine Gearbox, Yang Hu, Zihao Li, Deyi Fu, Ziqiu Song, Fang Fang, Jizhen Liu

Journal of System Simulation

Abstract: To address challenges in characterizing high-frequency vibrations of wind turbine gearboxes, the long computation time of rigid-flexible coupled multi-body dynamics models, and the complexity of configuring gearbox models across multiple scenarios, this study proposed a deep learning modeling method for multi-scale operation using full-condition digital testing. The study proposed a cascaded extended simulation scheme based on stream data-driven OpenFAST and Adams and utilized dynamic mode decomposition technology to construct a multi-scale dataset for the flexible multi-body dynamics characteristics of the gearbox under all operating conditions of the wind turbine. Based on this dataset, a digital surrogate model covering multiple …

Optimization Dispatch Method For High-Proportion Renewable Energy Power Systems Based On Sc-Ppo, Zhongkai Xu, Chenyang Chu, Kai Xie, Ruizhuo Zhao, Wenjun Ke

Journal of System Simulation

Abstract: The high proportion of renewable energy integration brings significant challenges of randomness, multi-objective coupling, and security constraints to power systems. Traditional model-driven methods have limitations in modeling accuracy and adaptability. To address these issues, this paper proposed a safety-constrained PPO algorithm (SC-PPO). The method included three improvements. A temporal convolutional network was utilized to construct a dynamic state encoder that integrated historical operation, real-time monitoring, and prediction data to form a causal state representation. A hierarchical reward structure was designed, and an adaptive weighting mechanism based on constraint satisfaction degree was introduced to coordinate multi-objective optimization. Physical constraint projection …

Microsimulation Of Infectious Disease Transmission Considering Virus Release, Transmission, And Action, Zhiming Fang, Shengdong Yuan, Ge Huang, Jingqian Yang, Zhongyi Huang

Journal of System Simulation

Abstract: Existing infection risk assessment methods mostly evaluate infection probability through mathematical models or simulation, but they lack analysis of the relationship between air circulation and individual infection probability. This study proposed a risk prediction model for infectious disease transmission based on indoor air circulation. At the microscopic scale, the space was discretized into grid points. By integrating CFD numerical simulation, the entire process of virus droplet release, transmission, and action was fully simulated. The simulation results show that in an obstacle-free room, the error between the total indoor viral load predicted by the model under windless and low wind …

Path Planning For Mobile Robots Based On Improved Rrt-Connect And Dwa Fusion, Yi Luo, Jia Deng

Journal of System Simulation

Abstract: To improve the efficiency and quality of dynamic path planning for mobile robots in complex environments, this paper proposed a path planning algorithm that combined an improved RRT-connect with the DWA. Two expanding random trees were introduced for alternating expansion, and a dynamically restricted sampling area was set to reduce the randomness of the sampling process while ensuring the probability completeness of the algorithm. A target bias adaptive step size strategy was employed to enhance the target orientation of the random tree expansion process. A greedy strategy was adopted to prune redundant nodes in the path and smooth the …

Modeling And Simulation Of Traffic Signal Control Based On Mlp With Improved Gcn-Td3, Deqi Huang, Yating Tu, Zhenhua Zhang, Xin Guo

Journal of System Simulation

Abstract: To address the issues of uneven traffic flow at urban intersections, limited road capacity, and the poor coordination of existing traffic signal control algorithms, a traffic signal control algorithm based on graph convolutional reinforcement learning was proposed. By utilizing a multilayer perceptron, the dynamic features of vehicles and phase information at the controlled intersection and its neighboring intersections were extracted. A graph convolutional neural network was then employed to aggregate these vehicle dynamic features into potential features representing regional traffic. The control strategy was derived through multiple iterations of an improved twin delayed deep deterministic policy gradient (TD3) algorithm. …

Dynamic Order Scheduling For Pick-And-Pass System Considering Workload Balance And Learning Effects, Weihong Liu, Sixiang Zhao, Dali Zhang, Zhenhui Jiang

Journal of System Simulation

Abstract: In e-commerce logistics, the hybrid pick-and-pass systems offer both complexity and flexibility, enabling adaptation to a wider range of order picking scenarios. Therefore, they have been widely used. However, this also complicates the order scheduling problem, particularly when both workload balance and pickers' learning effects need to be considered. Efficiently scheduling orders to reduce picking time under these conditions poses a significant challenge. This study began by constructing a mathematical model for the static scheduling problem with known orders. Based on this model, a simulation model of hybrid pick-and-pass zones was developed, and a scheduling rule incorporating multiple system …

Simulation And Optimization Of Continuous Motion Control Based On Spiking Reinforcement Learning, Xiaode Liu, Yufei Guo, Yuanpei Chen, Jie Zhou, Yuhan Zhang, Weihang Peng, Zhe Ma

Journal of System Simulation

Abstract: To improve the model robustness for multi-degree-of-freedom continuous motion control, an intelligent motion control algorithm was proposed based on the Actor-Critic reinforcement learning framework and spiking neural networks. This algorithm integrateed the Actor network with spiking population coding and enhanced model training performance by introducing feature transformation methods. The Critic network was used to evaluate the effectiveness of the motion control. The results show that, compared to other reinforcement learning algorithms, the average reward value of this method increases by more than 10%. The simulation results validate the effectiveness of the model in improving multi-degree-of-freedom continuous control performance.