Simulation Techniques Research Articles

Design Optimization of a Run-Of-River Small Hydropower Plant: A Case Study of Mabula Kapi Site

Prior to construction, small or large hydropower plants undergo feasibility studies, which involve technical, economic, financial, social, and environmental aspects. Technical studies, particularly, include various engineering disciplines. This study sought to conduct a component of technical feasibility studies, focused on design optimization of the turbine-plant capacity size for a run-of-river case study called Mabula Kapi, on Kaombe River, Serenje, Zambia. The study method used computational modelling, simulation, and optimization techniques, and used secondary data from previous site. A prefeasibility study conducted earlier had underestimated the turbine-plant capacity owing to underestimated discharge data, owing to lack of site gauging station. A less accurate discharge estimation method involving transposition from a gauged similar catchment had been used. A later feasibility-level site hydrological study found improved discharge data. Riding on the improved discharge data, this study found the following optimised plant design parameters based on plant design discharge of 6.20 m3/s: Installed capacity of 10.20 MW; Annual energy production of 42 GWh; Capacity factor of 47.1%; 3 x Pelton wheel of 272 rpm speed, 2.1 m diameter, 2-nozzles of diameter 0.16 m each, 21 buckets of 0.58m width each; and 3 x 4 MVA generators of 50Hz, 22-poles, and 11kV.

Features of the Formation of the Practical Skill of Conducting Basic Cardiopulmonary Resuscitation in Undergraduate Students of Medical Universities

The conducted single-center prospective study of the formation of practical skills for performing basic cardiopulmonary resuscitation demonstrated high results in eduction using phantom simulation techniques. At the same time, the terms and volumes of reduction of the performance skill in the absence of periodic repeated training are determined.

Estimation and Analysis of Trigonometric Models under Bayesian Approach

In this study, we explore three innovative trigonometric models within the Bayesian framework, utilizing the inverse Weibull distribution as our foundation. These models—namely the Sine inverse Weibull, Cosine inverse Weibull, and Tan inverse Weibull—are crafted from distinct distribution families. We employ both maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) simulation techniques to estimate parameters, drawing upon a comprehensive dataset. By scrutinizing posterior samples numerically and graphically, we evaluate the efficacy of our models, generating Bayes estimates for parameters, examining reliability and hazard functions, and establishing credible intervals. Furthermore, we assess the predictive capacity of all three models through posterior predictive checks. We also conduct comparative analyses, pitting our models against competing ones using real-world data. Notably, our results reveal that the proposed trio of models exhibit strikingly similar performance in terms of fitting the data.

Beyond Inverse Dynamics: Methods for Assessment of Individual Muscle Function during Gait.

Three-dimensional motion analysis performed in the modern gait analysis laboratory provides a wealth of information about the kinematics and kinetics of human locomotion, but standard gait analysis is largely restricted to joint-level measures. Three-dimensional joint rotations, joint moments, and joint powers tell us a great deal about gait mechanics, but it is often of interest to know about the roles that muscles play. This narrative review surveys work that has been done, largely over the past four decades, to augment standard gait analysis with muscle-level assessments of function. Often, these assessments have incorporated additional technology such as ultrasound imaging, or complex modeling and simulation techniques. The review discusses measurements of muscle moment arm during walking along with assessment of muscle mechanical advantage, muscle-tendon lengths, and the use of induced acceleration analysis to determine muscle roles. In each section of the review, examples are provided of how the auxiliary analyses have been used to gain potentially useful information about normal and pathological human walking. While this work highlights the potential benefits of adding various measures to gait analysis, it is acknowledged that challenges to implementation remain, such as the need for specialized knowledge and the potential for bias introduced by model choices.

A novel probabilistic load shifting approach for demand side management of residential users

Demand side management is a beneficial set of techniques which leads improving consumption profile of residential users. Practice of dynamic pricing and appropriate shifting of electrical appliances at home are important tasks at that point. In this study, a novel probabilistic load shifting strategy is proposed and its beneficial effects on economic and technical parameters are demonstrated. For that purpose, a survey on energy consumption is applied for residential users and its results are used for creating a probabilistic model. 300 virtual residents are created in this way and a probabilistic simulation technique is developed. Next, the developed simulation technique is applied for creation of the monthly consumption data. Finally, it is demonstrated that electricity bills are decreased significantly when the proposed approach is applied. Besides that, peak-to-average ratio which is calculated by considering all residential users is also decreased as a result. In conclusion, when the results are compared with the ones of conventional constant tariff, time-of-use tariff with three time zones and a deterministic load shifting strategy, it is observed that the best results of economic and technical parameters in question are achieved by using the proposed probabilistic load shifting strategy together with dynamic pricing mechanism involved.

Adversarial attacks on reinforcement learning agents for command and control

Given the recent impact of deep reinforcement learning in training agents to win complex games such as StarCraft and DoTA (Defense Of The Ancients)—there has been a surge in research for exploiting learning-based techniques for professional wargaming, battlefield simulation, and modeling. Real-time strategy games and simulators have become a valuable resource for operational planning and military research. However, recent work has shown that such learning-based approaches are highly susceptible to adversarial perturbations. In this paper, we investigate the robustness of an agent trained for a command and control task in an environment that is controlled by an active adversary. The C2 agent is trained on custom StarCraft II maps using the state-of-the-art RL algorithms—Asynchronous Advantage Actor Critic (A3C) and proximal policy optimization (PPO). We empirically show that an agent trained using these algorithms is highly susceptible to noise injected by the adversary and investigate the effects these perturbations have on the performance of the trained agent. Our work highlights the urgent need to develop more robust training algorithms especially for critical arenas like the battlefield.

4D printing of magneto-responsive shape memory nano-composite for stents

Abstract This study focuses on the 4D printing simulation technique of magneto-responsive shape memory nanocomposite stents. A nanocomposite material was created by incorporating polycaprolactone, a shape memory material, with Fe3O4 to enhance magnetic responsiveness and stiffness. Tensile tests were conducted, and the material properties were applied to finite element analysis. Shape memory experiments were also performed to measure the temperature at which shape memory progression occurs due to magnetic response. In the 4D printing simulation, different coefficients of thermal expansion and the measured temperatures were reflected in the sections where shape memory is activated to implement shape memory behavior. The specimen simulation confirmed shape memory behavior progressing from 145 degrees to 3 degrees, while the stent simulation demonstrated satisfactory expansion to a radius of 3 mm. This study proposes a controllable method for implementing shape memory considering temperatures induced by magnetic response, showing potential for various medical device applications.

Global feature capture and spatially-aware neural networks for predicting CO2-flooding performance in heterogeneous low-permeable reservoirs

CO2-flooding can enhance crude oil recovery rates by reducing the viscosity of crude oil, improving the mobility ratio between crude oil and water, and leveraging the swelling effects of crude oil components. Furthermore, it facilitates the sequestration of greenhouse gases, leading to the realization of carbon capture, utilization, and storage (CCUS), which holds significant strategic importance for mitigating climate change, safeguarding ecological environments, and achieving sustainable development. Consequently, predicting CO2-flooding performance in heterogeneous low-permeability reservoirs holds crucial significance in sustainable energy production. However, the drawbacks of numerical simulation techniques, such as extensive computational resource consumption and prolonged simulation durations, have prompted extensive research into the utilization of artificial intelligence technologies. Prevalent approaches often treat the problem as a time series issue, which proves inadequate for reservoirs lacking historical production data. Furthermore, this methodology is unable to fully exploit the complex implicit relationships between various factors and the prediction target. Our study proposes a novel method for predicting the performance, which stands apart from previous approaches by not treating the problem as a time series issue, eliminating the need for historical production data for each prediction, and can predict various production variables such as gas production, oil production, water production, etc. in a universal manner. We combine enhanced MultiScale Non-local Neural Networks with ConvNet to handle sparse well network matrices with injection-production parameters. Additionally, we employ MultiScale convolutional networks with spatial attention mechanism to address formation permeability fields with spatial distribution features. Experimental results demonstrate the superiority of our approach in accuracy, achieving a prediction accuracy of over 97%. This research provides an innovative and flexible method, making significant contributions to the intelligentization and sustainable development of the petroleum industry, aligned with global efforts to both reduce carbon emissions and neutralize the existing ones.

Sparse Convolution-Based 6D Pose Estimation for Robotic Bin-Picking With Point Clouds

Abstract Estimating the orientation and position of objects is a crucial step in robotic bin-picking tasks. The challenge lies in the fact that, in real-world scenarios, a diverse array of objects is often randomly stacked, resulting in significant occlusion. This study introduces an innovative approach aimed at predicting 6D poses by processing point clouds through a two-stage neural network. In the initial stage, a network for scenes with low-textured environments is designed. Its purpose is to perform instance segmentation and provide an initial pose estimation. Entering the second stage, a pose refinement network is suggested. This network is intended to enhance the precision of pose prediction, building upon the output from the first stage. To tackle the challenge of resource-intensive annotation, a simulation technique is employed to generate a synthetic dataset. Additionally, a dedicated software tool has been developed to annotate real point cloud datasets. In practical experiments, our method demonstrated superior performance compared to baseline methods such as PointGroup and Iterative Closest Point. This superiority is evident in both segmentation accuracy and pose refinement. Moreover, practical grasping experiments have underscored the method's efficacy in real-world industrial robot bin-picking applications. The results affirm its capability to successfully address the challenges produced by occluded and randomly stacked objects.

Pinning synchronization of a complex network: Nodes, edges and higher-order edges

Abstract In recent years, the interdisciplinary study of complex networks has become increasingly important in fields ranging from biology and physics to sociology and mathematics. This paper focuses on pinning control, an approach essential for achieving coordinated behavior in dynamic networks. We explore recent advancements in pinning control strategies, explaining theoretical frameworks and simulation techniques. Additionally, we discuss the significance of certain structures within networks across different orders. Finally, We conclude with a summary of key insights and propose our outlook on future research.

Use of Historical Road Incident Data for the Assessment of Road Redesign Potential

Drivers’ safety and overall road functionality are key triggers for deciding on road interventions. Because of the socioeconomical implications of traffic incidents, either fatal or no, continuous research has been dedicated over the previous decades on the assessment of factors contributing to crash potential. Apart from the behavioral aspects of driving, which are commonly studied through simulation and advanced modelling techniques, the road infrastructure status is of equal or even higher significance. In this study, an approach is presented to discuss the road redesign potentials based on the evaluation of network-level historical incident records from road crashes in Greece. Based on total and fatal crash records, the following infrastructure-related aspects were assessed as critical for the discussion of the road redesign potential needs: the status of road’s surface (i.e., dry, wet, etc.), the issue of improving driving conditions near at-grade intersections, the presence and suitability of signage and/or lighting, and the consideration of particular geometric design features. Overall, it is deemed that intervention actions for at least one of these pillars should aim at enhancing the safety and functionality of roadways.

Exposure range matters: considering non-linear associations in the meta-analysis of environmental pollutant exposure using examples of per- and polyfluoroalkyl substances and birth outcomes.

Meta-analysis is a powerful analytic method for summarizing effect estimates across studies. However, conventional meta-analysis often assumes a linear exposure-outcome relationship and does not account for variability over the exposure ranges. In this work, we first used simulation techniques to illustrate that the linear-based meta-analytical approach may result in oversimplistic effect estimation based on three plausible non-linear exposure-outcome curves (S-shape, inverted U-shape, and M-shape). We showed that subgroup meta-analysis that stratifies on exposure levels can investigate non-linearity and identify the consistency of effect magnitudes in these simulated examples. Next, we examined the heterogeneity of effect estimates across exposure ranges in two published linear-based meta-analyses of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) on changes in mean birth weight or risk of preterm birth. The re-analysis found some varying effect sizes and potential heterogeneity when restricting to different PFAS exposure ranges, but findings were sensitive to the cut-off choices used to rank the exposure levels. Finally, we discussed methodological challenges and recommendations for detecting and interpreting potential non-linear associations in meta-analysis. Using meta-analysis without accounting for exposure range could contribute to literature inconsistency for exposure-induced health effects and impede evidence-based policymaking. Therefore, investigating result heterogeneity by exposure range is recommended.

Evaluating the Impact of Universal Health Coverage Policies on Population Health Outcomes Using Big Data Analytics for Comprehensive Policy Assessment

Universal Health Coverage (UHC) aims to eliminate financial barriers to care which is important for everyone’s affordability. Thus, it is helpful to study how UHC affects population health outcomes and where it may be headed in the future. Big Data Analytics provides good tools for scrutinizing those principles and their results on a huge scale. Communities differ in terms of their health outcomes; and health systems are complex. This paper tries to address these among other issues by integrating big data sets that are diverse and analysing them together. As an innovative way of examining universal health coverage initiatives’ impact on populations, researchers propose the Comprehensive Analysis of Fragmented Health Systems (CA-FHS). CA-FHS uses Big Data Analytics to compile and analyse information from many different sources thereby giving an exhaustive breakdown of health outcomes by demography as well as geographic area. This would allow trends or patterns not seen using conventional evaluation tools to be discovered. It encompasses public health, policy-making, as well as healthcare management concerns. In this way, the method may bring out the strengths and weaknesses inherent in the healthcare system such that policies are recommended for change while resource allocation is done for bettering UHC-related consequences internationally. It will enable the evaluation of long-term effects resulting from these projects so that they can meet their goals eventually. The process will involve creating hypothetical policy scenarios and then assessing how these would affect population’s historical health outcomes through use of historical data simulation techniques; thus providing input into potential policy choices at federal level concerning evidence-based recommendations towards achieving improvement in health status.

Methodology for material flow analysis at the organizational scale

Organizations play a crucial role in facilitating the transition to a circular economy. Implementing circular practices often begins with a material flow analysis (MFA) to identify issues and opportunities. However, existing MFA methodologies focus mainly on territorial applications and lack effectiveness at the organizational level. Conducting MFA at this level presents distinct challenges, requiring insights into dynamic material circulation and effective handling of heterogeneous data. Addressing this gap, this study introduces an innovative methodology tailored to organizational contexts. The developed methodology consists of six steps for conducting MFA while proposing an archetype-based approach capable of analyzing extensive and disparate datasets, coupled with bootstrapping and Monte Carlo simulation techniques, that considerably reduces the complexity of MFA execution. Furthermore, this methodology enables a comprehensive understanding of material flows within the system and provides a straightforward method for estimating uncertainty in mass estimations by incorporating confidence level calculations. A case study from a governmental organization is used to illustrate the proposed methodology.

Evaluation of patient's quality of counselling in the context of diabetes treatment by community pharmacists for Public Health

Providing Patient Counseling (PC) may be beneficial in enhancing disease treatment results, especially for chronic illnesses like Diabetes Mellitus (DbM), which is prevalent in underdeveloped countries. This research aims to examine the counseling and contentment levels of patients who visit diabetes clinics in Community Pharmacies (CP), intending to contribute to the field of Public Health (PH). This research aimed to evaluate the efficacy of PC services offered to individuals with DbM. In this cross-sectional research, a patient simulation technique was used with an organized survey of 560 CPs chosen to offer primary healthcare. The survey has been split into three sections: demographics, PC methods used during prescription collection, and satisfaction assessment of the services rendered. The results revealed that PC was offered for taking medication among patients with type 1 DbM, but it was lacking for patients with type 2 DbM. Patients were provided with insufficient counseling on the potential adverse effects and the need to adopt a healthier lifestyle. The degree of satisfaction was a mere 20.2%, suggesting that there may be a deficiency in PC services. The investigation has shown inadequate PC procedures and low levels of contentment in the services offered by CP to patients with DbM. Given the rising occurrence of DbM and the presence of other medical conditions, it is crucial to provide PCs with DbM.

Design and simulation of a Ka-band frequency doubling gyroklystron amplifier

The design methodology of a Ka-band frequency doubling two cavity gyroklystron has been discussed in this paper. In this study, the amplifier’s design and simulation are accomplished by employing nonlinear analysis and validated using particle-in-cell simulation techniques. The output cavity functions in the TE02 mode at two times the input signal frequency at the second harmonic of the cyclotron frequency, while the input cavity is powered by a 17.5 GHz source and functions in the TE01 mode at the first harmonic of the cyclotron resonance. The device’s performance has been examined both in the presence and in the absence of a beam. A number of factors were taken into account in order to optimize the interaction structure dimension to reach the device’s targeted performance. By adjusting the structural features and electron beam parameters, the device’s RF output power is assessed in an effort to increase efficiency. Simulation results estimate that the device displays a gain of 21.5 dB, an efficiency of 21.6%, a power of 227 kW, and a −3 dB bandwidth of ∼0.3%.

SPECTRAL THEORY AND FUNCTIONAL CALCULI IN THE REFLEXIVE BANACH SPACES

The design and assessment of closed loop control systems must include accurate and stable accuracy analysis and simulation. An overview of the significance of closed loop control systems and its uses in several engineering disciplines is given in this paper. The paper then explores the different techniques used for evaluating the accuracy and stability of closed loop control systems, including steady-state error analysis, stability analysis, and simulation techniques. It also discusses the design and tuning of control algorithms to optimize the performance of the system, and identifies the different factors that can affect the accuracy and stability of closed loop control systems, such as sensor noise, model inaccuracies, and environmental disturbances.

A flowchart for porosity and acoustic impedance mapping using seismic inversion with semi hybrid optimization combining simulated annealing and pattern search techniques

A flowchart for porosity and acoustic impedance mapping using seismic inversion with semi hybrid optimization combining simulated annealing and pattern search techniques

Simulation and Validation Techniques for High-Speed Hardware Systems Using Modern Tools

Simulation and Validation Techniques for High-Speed Hardware Systems Using Modern Tools

Systematic Optimization of Electromagnet Hardware for Electromagnetic Suspension: A Fusion of Simulation and Multi-objective Optimization Techniques

Systematic Optimization of Electromagnet Hardware for Electromagnetic Suspension: A Fusion of Simulation and Multi-objective Optimization Techniques