Determining Maximum Load Carrying Capacity of 2-link Mechanical Manipulator Using GA and PSO Algorithms

168 Impact of Connecticut’s Good Samaritan Laws in Preventing Opioid Overdose Deaths – An Applied System Dynamics Approach

The aim of this paper is to analyze load‐carrying capacity of redundant free‐floating space manipulators (FFSM) in trajectory tracking task. Combined with the analysis of influential factors in load‐carrying process, evaluation of maximum load‐carrying capacity (MLCC) is described as multiconstrained nonlinear programming problem. An efficient algorithm based on repeated line search within discontinuous feasible region is presented to determine MLCC for a given trajectory of the end‐effector and corresponding joint path. Then, considering the influence of MLCC caused by different initial configurations for the starting point of given trajectory, a kind of maximum payload initial configuration planning method is proposed by using PSO algorithm. Simulations are performed for a particular trajectory tracking task of the 7‐DOF space manipulator, of which MLCC is evaluated quantitatively. By in‐depth research of the simulation results, significant gap between the values of MLCC when using different initial configurations is analyzed, and the discontinuity of allowable load‐carrying capacity is illustrated. The proposed analytical method can be taken as theoretical foundation of feasibility analysis, trajectory optimization, and optimal control of trajectory tracking task in on‐orbit load‐carrying operations.

This paper investigated the eccentricity ratio, relative clearance, and length to diameter ratio under different load carrying capacities and journal rotation speeds, aiming to provide design reference of eccentricity ratio, relative clearance, and length to diameter ratio in water-lubricated bearing design process. The computer fluid dynamics was adopted to analyze eccentricity ratio, relative clearance, and length to diameter ratio of water-lubricated bearing. The cavitation effect of water was considered in the analysis. The simulation results of eccentricity ratio were verified by experiments. In different journal rotation speeds, the relative clearance had little influence on the maximum water film pressure and load carrying capacity. The numerical number of maximum water film pressure and load carrying capacity was in a small range. Designing chart of eccentricity ratio, relative clearance, and length to diameter ratio was provided.

이 연구에서는 고강도 철근콘크리트 외부 보-기둥 접합부의 시공성 및 내진성능을 개선하기 위하여 보-기둥 접합부 영역의 스터럽 및 띠철근 유무에 따라 고인성섬유 복합모르타르를 사용하여 내진성능을 평가하였다. 총 5개의 실험체를 제작하고 실험을 수행하여 내진성능을 평가하였으며, 이 연구의 시험 결과를 근거로 다음과 같은 결론을 얻었다. 기존 고강도 철근콘크리트 내부 보-기둥 접합부의 위험단면 영역을 고인성섬유 복합모르타르로 보강한 결과 재하 전 과정을 통하여 섬유의 가교역할로 인한 균열 분산효과로 인하여 균열 제어 효과가 커서 안정적인 파괴형태 및 내력을 나타내었다. 고강도 철근콘크리트 외부보-기둥 접합부의 시공성 및 내진성능을 개선하기 위하여 고인성섬유 복합모르타르를 사용하여 보강한 실험체(BCJNSP 시리즈)는 스터럽과 띠철근이 제거 되었음에도 안정적인 이력거동을 나타내었고, 최대내력이 전단보강근이 없는 실험체 BCJNS의 1.09∼2.03배로 증가하였다. 그리고 고인성섬유 복합모르타르를 사용하여 보강한 실험체(BCJNSP 시리즈)는 표준실험체 BCJC의 최대내력이 0.92∼0.96배로 거의 비슷하였고, 에너지소산능력은 최대 1.62배로 크게 증가하였다.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System Dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. SD methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity and focuses on feedback processes arising from endogenous system elements. A variety of data sources may be used in SD model development, and parametric sensitivity analysis with SD models can determine priority information needs in feedback-rich systems when data are lacking. Although numerous applications of SD modeling to agriculture exist, the approach is underutilized as a useful tool for research, instruction and programmatic development. This presentation highlights key elements of SD modeling using two examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model, and illustrates the benefits of the SD approach in animal nutrition research and for farm-level nutritional management decisions. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The presentation concludes with recommendations to increase awareness and training is SD methods to enhance its appropriate use in research and instruction.

A dynamic modelling approach to manage the cruise port of call

Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model

Transport is a vast and complex socio-technical system, and despite a clear need to reduce dependence on fossil fuels due to undesirable environmental impacts, it is largely locked into business-as-usual. Systems approaches are a useful way to help make sense of multiple competing influences which may be simultaneously driving change and supporting the status quo. This paper applies qualitative system dynamics modelling to help interpret the results of a Delphi study into global transport transitions, involving 22 international experts in various aspects of transport. The main contribution of the paper is its exploration of the use of system dynamics (SD) modelling to interpret the Delphi findings. SD modelling was used to reveal and elucidate the causal arguments put forward by the expert panel about the factors driving business-as-usual, the factors creating barriers to more sustainable transport systems, and the drivers of change. The SD model is used to explore and expose the key causal patterns at play, and how these interact to both support and hinder change. The resulting model shows the complex, interdependent dynamics involved in supporting the status quo. Even at the relatively high level of analysis reported here, the model is useful in revealing interdependencies between parts of the system, where change in one part may well have knock-on effects elsewhere in the system. In particular the model reveals the strong reinforcing loops that act to minimise the impact of change drivers and thus retain the dominance of automobility. The result is a system that is highly dependent on the continued existence of key reinforcements such as policies that subsidise fossil fuels. From a methodological perspective, the outcomes of the Delphi study provided a rich source of qualitative material which was highly suitable for developing a system dynamics model.

Rural Tourism has the potential to contribute significantly to the economic development of Malaysia. However, like other forms of tourism, sustainability is its main problem. Responsible Tourism (RT) is a new movement claims that could enhance sustainability in Malaysia tourism industry. A dynamic simulation model has been developed to assist decision makers in RT development. Lundu is chosen as a destination in structuring the causal loop diagram to help understand the dynamic relationship between environment, tourism and local population. A simulation model is designed using mathematical equations in the causal loop diagram. In this model, several scenarios were used to analyze the data obtained and results generated were being analysed. Validation of the model in usability is done by a few tourism research officers. Key indicators were refined to improve the effectiveness and consistency. This study shows that system dynamic modelling is an effective and powerful tool to help understand the complex tourism system by presenting a comprehensive picture. It is hoped that this model will help policy makers in the decision making to achieve desirable outcomes.

A study of regional sustainable development based on GIS/RS and SD model — Case of Hadaqi industrial corridor

An Early Warning System of Water Shortage in Basins Based on SD Model

A computational technique for obtaining the maximum load-carrying capacity for a robotic manipulator with joint elasticity, subject to accuracy and actuator constraints, is described. Full load motions and increased productivity are linked in the industrial applications of many robotic manipulators; the maximum load carrying capacity which can be achieved by a manipulator during a given trajectory is limited by a number of factors. The dynamic properties of a manipulator, its actuator limitations, and joint elasticity (transmissions, reducers, and servo drive system) are probably the most important factors. This paper presents a strategy for determining dynamic load carrying capacity (DLCC), subject to both accuracy and actuator constraints, where a series of cubical bounds centred at the desired trajectory is used in the end-effector oscillation constraint while a typical d.c. motor speed-torque characteristics curve is used in the actuator constraint. The technique which considers the full nonlinear manipulator dynamics, actuator constraints, and accuracy constraints permits the manipulator user to specify the trajectory completely. Finally, a numerical example involving a two-link manipulator with joint flexibility using this method is presented and the results are discussed.

Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD’s capacity to simulate intricate system behaviors with GA’s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD’s ability to capture complex system behaviors with GA’s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD–GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies’ robustness and comparative assessments highlight our approach’s advantages in navigating complex policy spaces. This study introduces a new SD–GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.

This investigation considered the usability of ceramic waste powder (CWP) in altered quantities in reinforced concrete beams (RCBs). In this way, it was aimed to reduce the environmental impacts of concrete by using CWP as a raw material in RCBs. 12 small-scale shear RCBs with the dimensions of 100 × 150 × 1000 mm were tested in this study. The variations of stirrups spacing and CWP ratio were examined in these specimens. The percentages of CWP by weight utilized in RCBs were 10%, 20%, and 30%, and stirrups spacings were adopted as 270 mm, 200 mm, and 160 mm. At the end of the study, it was determined that more than 10% CWP additive negatively affected the RCBs' compressive strength. The load-carrying capacity reduced between 30.3% and 59.4% when CWP increased from 0% to 30% as compared to RCB with stirrups spacing of 270 mm without CWP. However, compared to RCB with stirrups spacings of 200 mm and 160 mm without CWP, there were decreases in the load-carrying capacity as 21.4%–54.3% and 18.6%–54.6%, respectively. While the CWP ratio increased, the specimens with 160 mm, 200 mm, and 270 mm stirrups spacings obtained a lower maximum load value. However, with the increase of the CWP ratio in the specimens with 160 mm stirrups spacing, RCBs reached the maximum load-carrying capacity at an earlier displacement value. When stirrups spacing was selected as 270 mm, it was observed that the maximum load-carrying capacity of RCBs reached at a similar displacement value as the CWP ratio increased. Besides, it was resulted that the bending stiffness of RCBs reduced as the quantity of CWP enhanced. The bending stiffness decreased by 29.1% to 66.4% in the specimens with 270 mm stirrups spacing, 36.3% to 20.2% with 200 mm stirrups spacing, and 10.3% to 36.9% with 160 mm stirrups spacing. As an implication of the experiments, the use of CWP up to 10% in RCBs was realized as an economical and environmental approach and is suggested. There is some evidence to report that making use of CWP may be considered to be ecologically benign. This is due to the fact that reusing CWP may significantly reduce CO2 emissions, save energy, and reduce total power consumption. Furthermore, the experimental results were compared to the analytical calculations.

The aim of this study was to diagnose and classify myopathy and neuropathy using Wavelet coefficients. In this study, EMG signals were obtained from 410 individuals using a needle electrode. The signal was decomposed to the second and third levels of wavelet using Daubechies mother wavelet. 13 features were extracted from each level and 3 optimal feature were selected using PSO and GA algorithms. The results show that the best classification accuracy was 93% using the PSO algorithm and level three Daubechies, and the best features that achieved this were the Mean, zero crossing, standard deviation. The results show that wavelet coefficients have been able to provide better resolution or in other words higher accuracy in diagnosing the disease. Given that neuromuscular diseases often have similar symptoms; the proposed method is useful in their diagnosis.