Inverse Calculation Research Articles

Estimation of load for tunnel lining in elastic soil using physics‐informed neural network

AbstractA reverse calculation method termed soil and lining physics‐informed neural network (SL‐PINN) is proposed for the estimation of load for tunnel lining in elastic soil based on radial displacement measurements of the tunnel lining. To achieve efficient and accurate calculations, the framework of SL‐PINN is specially designed to consider the respective displacement characteristics of surrounding soil and tunnel lining. A multistep training method based on the meshless characteristics of SL‐PINN is established to promote calculation efficiency. The multistep training method involves increasing the number of collocation points in each calculation step while decreasing the learning rate after scaling of SL‐PINN. The feasibility of SL‐PINN is verified by numerical simulation data and field data. Compared to other inverse calculation methods, SL‐PINN has lower precision requirements for the measurement instrument with the same level of calculation accuracy.

Indirect load measurement method and experimental verification of floating offshore wind turbine

Accurate load data of wind turbines operating in real sea conditions is crucial for real-time control and offline structural optimization. However, the sheer size of these structures and the complexities of their working environments challenge the direct load measurement through strain gauges and fiber Bragg grating sensors. To address these problems, this paper introduces an indirect measurement method to acquire the thrust and pitch moment loads of the turbine. First, load identification techniques based on the frequency domain separation and aerodynamic damping were applied to decouple the wind and wave loads and establish the inverse load calculation model. Then, numerical simulations were carried out to calibrate and verify this model under a range of steady and unsteady conditions. Preliminary conclusions indicated the strong viability of this method, revealing a mean relative error of 2.32% for thrust and a corresponding equivalent high-frequency error of 24.66% for pitch moment under the designed operating conditions. Finally, a hybrid scaled model was constructed, combining a virtual wind turbine with a physical platform. Experimental results in the wave basin demonstrated the effectiveness of this indirect method in measuring thrust loads of wind turbines, achieving a mean relative error of less than 10%.

Optimized parameterized Uzawa methods for solving complex Helmholtz equations

As we know that, the parameterized Uzawa (PU) method can be very efficient when used to solve the standard saddle point problem, especially, when we have good and accurate estimation of preconditioned Schur complement matrix. In this paper, by taking full use of the special structure of coefficient matrix arising from complex Helmholtz equations, two types of optimized PU (OPU) methods are discussed theoretically and experimentally. Specifically, the convergence factors of these two OPU methods are less than 0.172, and the optimal result of first OPU method can reach 0.0396 with σ1≥σ2>0, which is currently the best theoretical result in the literatures. Moreover, the second OPU method has better computational advantages compared with the first OPU method as it avoids the inverse computation of W at each step, indicating the less CPU time will be costed by the second OPU method. In addition, the optimal parameters involved in our algorithms consist of constants, reducing the computational complexity associated with parameter selection. Finally, numerical results are given, not only show the effectiveness of OPU methods, but also confirm the rationality of theoretical analysis.

Preface

The Sharjah International Conference on Physics of Advanced Materials (SICPAM) was held on April 23–25, 2023, on the beautiful campus of the University of Sharjah. It brought together more than 120 scientists from 21 countries. The list of invited speakers included leading scientists, editors of reputable journals, young researchers, and students in the field of material science from Europe, the USA and Asia.Materials have been the key behind major technological breakthroughs throughout history. Their properties can be engineered and optimized to develop specific applications. The transformation of iron into steel ignited the second industrial revolution and the development of railways before the end of the nineteenth century. Similarly, semiconductors enabled the design of solid-state devices that gave birth to modern electronics and the digital revolution (the third industrial revolution).Today, we are witnessing the birth of the fourth industrial revolution with the fast development of generative AI and the production of the first versions of quantum computers. Quantum materials are emerging as the building blocks of quantum technologies.The conference covered a wide range of materials and some of the advanced techniques that are used to investigate their properties, as well as the computational methods that are emerging as very powerful tools for material design and screening for diverse applications, including energy conversion and storage, gas sensing and biosensing, water treatment and desalination, etc.The conference was an enriching experience and an excellent opportunity for many scientists to develop connections and possible collaborations. We would like to express our high appreciations to the University of Sharjah for hosting the conference and members of the scientific and organizing committees who worked hard to make the event a great success that will remain in our memory for a long time.Our sincere thanks go to the sponsors who generously supported the conference, namely: Sharjah Electricity and Water Authority (SEWA), Sharjah Broadcasting Authority, Sharjah National Oil Corporation (SNOC) and Dubai Electricity and Water Authority (DEWA).Finally, I would like to address my deepest thanks to the co-Editors who worked tirelessly to ensure that all submitted papers are thoroughly reviewed and the referees’ concerns are properly addressed.Prof. Nouar Tabet, Chairman of the Scientific CommitteeList of Editors, Scientific Committee, Organizing Committee are available in the pdf

HYPERSPECTRAL ANOMALY DETECTION WITH AN IMPROVED APPROACH: INTEGRATION OF GO DECOMPOSITION ALGORITHM AND LAPLACIAN MATRIX MODIFIER

In this study, a hyperspectral anomaly detection method based on Laplacian matrix (HADLAP) is proposed. This paper addresses the problem of determining covariance matrix inversion in high-dimensional data and proposes a new approach for identifying anomalies in hyperspectral images (HSIs). The study’s goals are to find anomalous locations in HSIs and to deal with the problem of calculating the inversion of the covariance matrix of high dimensional data. The method is centered on two main concepts. One of them is decomposition process. The other one is detection process. First, HSI data is decomposed as a low rank and sparse matrices. Second, the sparse component of the data is used to build Mahalanobis Distance (MD). In this study, go decomposition (GoDec) algorithm is employed to decompose the data. Then, the distance is calculated by obtained matrix with aim of detection of anomalous pixels in the HSIs. The method differs from previous studies that covariance matrix in the distance is computed with Laplacian matrix and MD. Experiments conducted on three hyperspectral datasets present the superiority and effectiveness of the proposed framework in terms of detection performance with respect to state-of-the-art methods.

Improving target budgeting in a corporate performance management system

Currently the portrayal of the procedure for developing management actions during the planning process in the scientific and professional community does not align with the practice of systematic and consistent plan creation supported by an informational analytical system. Alternatively, the non-formalized decision-making activity of the planner, which involves a situational expert approach, becomes a dependency in the planning process. This study is aimed at developing an analytical approach for implementing the plan reconciliation procedure in the process of corporate performance planning. This will increase the utilization of capabilities of the corporate performance management system and formalize the task of generating managerial actions by adjusting targeted budgeting values using mathematical methods. For this purpose, the standard planning process is enhanced by analytical support units, including the algorithm of inverse calculations of individual key performance indicators (KPI) and an advanced module for scenario modeling. The improved model of target budgeting process presented here delivers automated formation of management actions of the budgeting department and subdivision management, guided towards accomplishing strategic goals. The application of inverse calculations provides a mathematical formulation of the task of calculating indicators of planned key values, and the Sense and Respond (SaR) system allows you to supplement the mathematical formulation with weighting coefficients of key performance indicators calculated algorithmically, relying on the manager’s decisions rather than expert evaluation. The implementation of the approach we developed will improve the quality of planning by the highest priority criteria of operability, accuracy and adaptability due to the consistency and methodology of budgeting with the use of modern information technology.

Inverse Calculation and Regularization Process for the Solar Aspect System (SAS) of HXI Payload on ASO-S Spacecraft

For the ASO-S/HXI payload, the accuracy of the flare reconstruction is reliant on important factors such as the alignment of the dual grating and the precise measurement of observation orientation. To guarantee optimal functionality of the instrument throughout its life cycle, the Solar Aspect System (SAS) is imperative to ensure that measurements are accurate and reliable. This is achieved by capturing the target motion and utilizing a physical model-based inversion algorithm. However, the SAS optical system’s inversion model is a typical ill-posed inverse problem due to its optical parameters, which results in small target sampling errors triggering unacceptable shifts in the solution. To enhance inversion accuracy and make it more robust against observation errors, we suggest dividing the inversion operation into two stages based on the SAS spot motion model. First, the as-rigid-as-possible (ARAP) transformation algorithm calculates the relative rotations and an intermediate variable between the substrates. Second, we solve an inversion linear equation for the relative translation of the substrates, the offset of the optical axes, and the observation orientation. To address the ill-posed challenge, the Tikhonov method grounded on the discrepancy criterion and the maximum a posteriori (MAP) method founded on the Bayesian framework are utilized. The simulation results exhibit that the ARAP method achieves a solution with a rotational error of roughly ±3.″5 (1/2-quantile); both regularization techniques are successful in enhancing the stability of the solution, the variance of error in the MAP method is even smaller—it achieves a translational error of approximately ±18 μm (1/2-quantile) in comparison to the Tikhonov method’s error of around ±24 μm (1/2-quantile). Furthermore, the SAS practical application data indicates the method’s usability in this study. Lastly, this paper discusses the intrinsic interconnections between the regularization methods.

Lookup Table-Based Design of Scalar Multiplication for Elliptic Curve Cryptography

This paper is aimed at using a lookup table method to improve the scalar multiplication performance of elliptic curve cryptography. The lookup table must be divided into two polynomials and requires two iterations of point doubling operation, for which negation operations are needed. It is well known that an inversion operation requires a lot of multiplication time. The advantage of this paper is that we are able to reduce one inverse element calculation for this problem and also improve the basic operations of finite fields through segmentation methods. If the normal basis method is used in the design of the inverse element operation, it must be converted to the normal basis through the standard basis. However, the conversion process requires a lot of matrix operations. Though the anti-element operation has good speed performance, it also increases the computational complexity. Using number theory and grouping methods will greatly improve the performance of inverse element operations. With application of the two-time point doubling operation in the hardware implementation, the developed approach reduces the computing time by 48% as compared with the conventional approach. The computational time of the scalar multiplication using the presented method is further improved by 67% over the traditional algorithm with only an area increase of 12%. Finally, the proposed lookup table-based technique can be utilized for software and hardware implementation, as the developed arithmetic operations are simple and are consistent in their execution.

Evaluation of shale gas reservoir reserves and production capacity based on Arps regression

ABSTRACT At present, global energy conflicts are prominent and the world’s attention is focused on energy security. In order to evaluate the reserves and production capacity of the shale gas, which is a special fossil energy source, the study proposes a reserve and production capacity evaluation design for its reservoirs based on Arps decrement. The study intersects the material balance method and Arps reconciliation decrement method to achieve the evaluation of shale gas reservoir reserves. The shale gas reservoir production capacity was calculated using Integrated Geostatistical and Geomechanical Modeling (IGIG) based on Genliang Guo’s fractured horizontal wells, and Effective Fracture Radius (EFR) formula based on large capsule-shaped flow zones. Experimental data showed that the capacity evaluation method based on the binomial dynamic coefficient inverse calculation of formation pressure can calculate the current unobstructed flow rate and allotted production rate to provide data support for the extraction of shale gas reservoirs.

Comparison of kinematics and joint moments calculations for lower limbs during gait using markerless and marker-based motion capture.

Objective: This study aimed at quantifying the difference in kinematic and joint moments calculation for lower limbs during gait utilizing a markerless motion system (TsingVA Technology, Beijing, China) in comparison to values estimated using a marker-based motion capture system (Nokov Motion Capture System, Beijing, China). Methods: Sixteen healthy participants were recruited for the study. The kinematic data of the lower limb during walking were acquired simultaneously based on the markerless motion capture system (120Hz) and the marker-based motion capture system (120Hz). The ground reaction force was recorded synchronously using a force platform (1,200Hz). The kinematic and force data were input into Visual3D for inverse dynamics calculations. Results: The difference in the lower limb joint center position between the two systems was the least at the ankle joint in the posterior/anterior direction, with the mean absolute deviation (MAD) of 0.74cm. The least difference in measuring lower limb angles between the two systems was found in flexion/extension movement, and the greatest difference was found in internal/external rotation movement. The coefficient of multiple correlations (CMC) of the lower limb three joint moments for both systems exceeded or equaled 0.75, except for the ad/abduction of the knee and ankle. All the Root Mean Squared Deviation (RMSD) of the lower limb joint moment are below 18N·m. Conclusion: The markerless motion capture system and marker-based motion capture system showed a high similarity in kinematics and inverse dynamic calculation for lower limbs during gait in the sagittal plane. However, it should be noted that there is a notable deviation in ad/abduction moments at the knee and ankle.

Adaptive Cartesian space control of robotic manipulators: A concurrent learning based approach

This work introduces a concurrent learning-based adaptive control design for end-effector tracking and the corresponding stability analysis for robotic manipulators. The presented controller is developed directly in Cartesian space, thereby removing the necessity for inverse kinematics calculations at the position level. The designed adaptive controller ensures global exponential tracking of the end-effector in Cartesian space. Moreover, the developed controller assures globally exponential convergence of uncertain dynamical parameters to their actual values without demanding persistence excitation conditions via a combination of a standard gradient-based adaptation with a novel integral concurrent learning component. The developed integral concurrent learning part operates both real-time output data and the most informative historical data gathered by employing the singular-value maximization algorithm (SVMA) to reduce the size of memory allocation. Lyapunov-based arguments are applied to ensure the exponential stability of the closed-loop system. Numerical studies are performed to depict the feasibility and performance of the proposed design.

High-performance absorber with substitutable materials for short-wave infrared sensing

The optical absorption device plays a crucial role as a component of the infrared astronomical telescope and possesses a significant impact on astronomical observations. A simple metamaterial absorber with substitutable middle materials is made for short-wave infrared sensing. The absorber is designed as a hollow square column, using a patterning approach for the top-layer structure of metamaterials. The absorption characteristics are verified using the impedance matching method, which involves extracting S-parameters and then performing inverse calculations to determine the absorber’s equivalent impedance. The result shows the highest absorption peak is at 3.25 μm, reaching 99.71%, with an impressive average absorption rate of 99.01% between 1.52 and 3.66 μm. The results demonstrate that this absorber shows polarization insensitivity while maintaining high absorption even at large angles of incidence. The distribution of the electromagnetic field within the absorber, the electromagnetic losses within individual layers, and their impact on the absorptive performance are analyzed in detail. Polarization angles, transverse magnetic polarization, and transverse electric polarization are further explored. The parameters of each layer have been discussed. An investigation of the intermediate dielectric layer has been conducted. The proposed absorber shows the potential to achieve exceptional absorption performance under various dielectric conditions, rendering it a promising candidate for use in astronomical observation, medical tests, infrared detection, invisible short-wave infrared systems, radar and various optical devices.

Computer vision-based real-time deflection monitoring of complex and sizeable steel structures

Deflection, as an intuitive index, plays a pivotal role in assessing the load-bearing capability and structural integrity of complex and sizable steel structures. Notwithstanding, the conventional contact measurement is limited to static deflection and necessitates work stands and manual readings. Therefore, it hinders the attainment of dynamic deflection and fails to cater to the engineering demands of real-time and prolonged monitoring. By leveraging the advancements in computer vision technology, we propose an innovative system for real-time deflection monitoring of complex and sizable steel structures, particularly suitable for monotonic deformation induced by prolonged loading. Specifically, the off-axis-based displacement measurement method was adopted to surmount the constraint of requiring the optical axis of the camera to be perpendicular to the target. Additionally, the inverse compositional Gauss-Newton (IC-GN) algorithm and parallel computation based on seed point diffusion were exploited to boost the matching and computing efficiency for attaining real-time monitoring of multi-points. To validate the efficacy of the proposed system, we conducted static load monitoring tests on a Bailey beam, with a height of 29.5 m and length of 16.5 m per span, as part of the Alibaba Jiangsu headquarters project in Nanjing. The collected test data were compared with the results from a laser displacement sensor and the ABAQUS model. The outcomes substantiate that the system is capable of non-contact, expeditious, and straightforward installation, besides achieving high accuracy and real-time deflection monitoring. This system serves as a sophisticated solution for health monitoring in constructing complex and sizeable steel structures and further contributes to realizing construction intelligence.

Design and Test of Seed–Fertilizer Replenishment Device for Wheat Seeder

In view of existing problems, such as the seed and fertilizer supply link for wheat seeders still relying on manual installation and the lack of practical application equipment, a seed–fertilizer replenishment device based on the three-degree-of-freedom mechanical arm and screw conveying principle is designed using the seed box installation and supply as the operation scenario to replace the manual installation process. Combined with the requirements of the seed box replenishment operation, the key parameters of the replenishment robot arm and the screw conveyor auger are determined. Then, the kinematic model of the replenishment robot arm is established based on the modified D-H method, forward and inverse kinematics calculations are performed, and the workspace is analyzed using the Monte Carlo method. Based on this, the robotic arm task path is designed, the fifth-degree polynomial interpolation method is used to complete the trajectory planning, and MATLAB R2016a software is used to simulate the motion trajectories of each joint, verifying the feasibility of the trajectory planning solution. Finally, a prototype is trial-produced and quadratic regression orthogonal testing and response surface analyses are conducted to obtain the optimal working parameters of the replenishment device. The verification test shows that when the angular velocity of the lumbar joint of the replenishment device is 4°/s, the speed of screw conveyor is 90 r/min, and the angle of the big arm is 12°, the conveying loss rate is 3.98%, and the conveying efficiency is 0.833 kg/s. The relative errors with the theoretical optimal values are 4.2% and 2.4%, respectively, both less than 5%. The supply trajectory is reasonable, and the robot arm runs smoothly. This study can provide reference for the design of seed–fertilizer replenishment device for wheat seeders.

The Prevalence of Drug Abuse as it Relates to Psychosocial Reasons among University Athletes in Selected Competitive Sports in Kenya

Purpose: The main objective of this study was to explore the prevalence of drug abuse as it relates to psychosocial reasons that include peer pressure, social environment, emotional state and relationships; among university athletes involved in selected competitive sports.
 Methodology: The study used a mixed methods research design with an intention to capture and utilize both qualitative and quantitative data. The study sample comprised 300 respondents. Out of the 300 students, 173 (57.7%) were male and 127 (42.3%) were female players derived from six selected competitive sports at the universities in Kenya. The selected sports were badminton, tennis, basketball, volleyball, soccer and rugby. Stratified sampling followed by simple random sampling was used to select 10 universities proportionately (4 private and 6 public), the method was the most appropriate to capture universities with women rugby in addition to the five other selected sports. The study sought to investigate the prevalence of drug abuse due to factors such as psychosocial, medicinal and competition. SPSS computer version 20 was used to process data. The data was presented in form of tables.
 Findings: Data indicated clearly that athletes had abused drugs due to various psychosocial reasons. The prevalence per gender was close at 18% and 12.12% for males and females respectively. Peer pressure was regarded as a great contributor to drug abuse among the youth. Another psychosocial indicator is the emotional state of a particular individual. Students, just like other members of society, go through various challenging situations in life. The other indicator regarding drug abuse due to psychosocial factors is depression. Four (4, 40%) of the 10 dean of students interviewed said that students were victims of drug abuse due to depression. They alluded the depression cases mostly to relationship issues and financial challenges while the students are in session. The data was presented in form of Tables.
 Unique Contribution to Theory, Practice and Policy: Social Learning Theory, Stress-Coping Theory and Social Control Theory may be used to anchor future studies. It is recommended that university athletes be sensitized on proper use of sports to relieve stress and maintain emotional balance. Further, only qualified coaches and sports officers should be allowed to mentor and man sports in the universities. According to some heads of sports seven out of the ten (70%) and 4 of the 10 (40%) deans of students interviewed, some coaches may not report cases of drug abuse by athletes. This could be attributed to the fact that the coaches, who mostly are on casual basis, could fear experiencing bad blood with the players thereby threatening their jobs. Trained sports officers and coaches should accompany the university athletes always and act as worthy mentors to the young athletes.

Rational design of arbitrary topology in three-dimensional space via inverse calculation of phase modulation

Abstract Recent advances in nanotechnology have led to the emergence of metamaterials with unprecedented properties through precisely controlled topologies. To explore metamaterials with nanoscale topologies, interest in three-dimensional nanofabrication methods has grown and led to rapid production of target nanostructures over large areas. Additionally, inverse design methods have revolutionized materials science, enabling the optimization of microstructures and topologies to achieve the desired properties without extensive experimental cycles. This review highlights the recent progress in inverse design methods applied in proximity-field nanopatterning. It introduces novel approaches, such as adjoint methods and particle swarm optimization, to achieve target topologies and high-resolution nanostructures. Furthermore, machine learning algorithms for inverse design are explored, demonstrating the potential efficacy of the phase-mask design. This comprehensive review offers insights into the progress of inverse design using phase modulation to realize target topologies of nanostructures.

Design and experimentation of multi-fruit envelope-cutting kiwifruit picking robot.

Currently kiwifruit picking process mainly leverages manual labor, which has low productivity and high labor intensity, meanwhile, the existing kiwifruit picking machinery also has low picking efficiency and easily damages fruits. In this regard, a kiwifruit picking robot suitable for orchard operations was developed in this paper for kiwifruit grown in orchard trellis style. First, based on the analysis of kiwifruit growth pattern and cultivation parameters, the expected design requirements and objectives of a kiwifruit picking robot were proposed, and the expected workflow of the robot in the kiwifruit orchard environment was given, which in turn led to a multi-fruit envelope-cutting kiwifruit picking robot was designed. Then, the D-H method was used to establish the kinematic Equations of the kiwifruit-picking robot, the forward and inverse kinematic calculations were carried out, and the Monte Carlo method was used to analyze the workspace of the robot. By planning the trajectory of the robotic arm and calculating critical nodes in the picking path, the scheme of trajectory planning of the robot was given, and MATLAB software was applied to simulate the motion trajectory as well as to verify the feasibility of the trajectory planning scheme and the picking strategy. Finally, a kiwifruit picking test bed was set up to conduct picking tests in the form of fruit clusters. The results show that the average time to pick each cluster of fruit was 9.7s, the picking success rate was 88.0%, and the picking damage rate was 7.3%. All the indicators met the requirements of the expected design of the kiwifruit-picking robot.

Efficient Selection Methods in Evolutionary Algorithms

Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the possibility of producing offspring play a huge role in the process of natural evolution. This process is called a natural selection. This mechanism is responsible for eliminating poor population members and gives the possibility of development for good ones. The evolutionary algorithm - an instance of evolution in the computer environment also requires a selection method, a computer version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness and some kind of ``fight'' among individuals. But computer environment is quite different from nature. Computer populations of individuals are usually small, they easily suffer from a premature convergence to local extremes. To avoid this drawback, computer selection methods must have different features than natural selection. In the computer selection methods randomness, fight and competition should be controlled or influenced to operate to the desired extent. Several new methods of individual selection are proposed in this work: several kinds of mixed selection, an interval selection and a taboo selection. Also advantages of passing them into the evolutionary algorithm are shown, using examples based on searching for the maximum α-clique problem and traditional TSP in comparison with traditionally considered as very efficient tournament selection, considered ineffective proportional (roulette) selection and similar classical methods.

Energy demand forecasting of remote areas using linear regression and inverse matrix analysis

Efficient energy demand forecasting is pivotal for addressing energy challenges in remote areas of Bangladesh, where reliable access to energy resources remains a concern. This study proposes an innovative approach that combines linear regression analysis (LRA) and inverse matrix calculation (IMC) to forecast energy demand accurately in these underserved regions. By leveraging historical energy consumption data and pertinent predictors, such as meteorological conditions, population dynamics, economic indicators, and seasonal patterns, the model provides reliable forecasts. The application of the proposed methodology is demonstrated through a case study focused on remote regions of Bangladesh. The results showcase the approach's effectiveness in capturing the intricate dynamics of energy demand and its potential to inform sustainable energy management strategies in these remote areas. This research contributes to the advancement of energy planning and resource allocation in regions facing energy scarcity, fostering a path towards improved energy efficiency and development. These techniques can be applied to estimate short-term electricity demand for any rural or isolated region worldwide.

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).