Reference Model Research Articles

Integration of Fog Computing in a Distributed Manufacturing Execution System Under the RAMI 4.0 Framework

Technological progress has driven the integration of new technologies in the field of industrial automation, but a structured framework is often lacking to efficiently guide the transition from traditional industries. This article presents the implementation of advanced technologies on FESTO’s (MPS-500) modular production system, using the reference architectural model for Industry 4.0 (RAMI 4.0) as a guide for scaling. It highlights the importance of the synergy between information technologies (ITs), which enables the development of a multi-level processing system. This system performs concurrent tasks, thus managing execution and manufacturing through an MES based on requests from the cloud. On the other hand, at a lower level, a fog computing system was integrated, which relieves the processing load by distributing processes locally. In addition, matrix mapping was performed to map the integrated technologies within the context of a reference model, allowing a clear alignment between the different levels of the system. The results show a significant reduction in waiting times between batches and operations, which directly improves productivity and offers greater flexibility, that is crucial for SMEs during their growth and scaling process towards Industry 4.0.

Accuracy of full-guided versus half-guided implant procedures carried out with digital implant planning software by students as part of a university curriculum.

This in vitro study investigated whether full-guided (FG) or half-guided (HG) implant placement is more suitable for beginners and to what extent the use of the coDiagnostiX (CDX) (10.5, Straumann Group, Basel, Switzerland) implant planning software proves useful in teaching. Twenty students planned implant positions with CDX which were then placed in a model using printed drill templates in the sense of FG implantation (group 1) and HG implantation (group 2). The implant positions could be compared with those of the reference model, and deviations could be determined. The results were tested for significance using the t-test for independent samples for groups 1 and 2. A total of 32 students subsequently completed a questionnaire about the software. Cronbach's α was also calculated to check the reliability of the questions for the individual subject areas. In both groups the greatest deviation occurred along the y-axis in the vestibular direction. It measured 1.390mm in group 1 and 1.570mm in group 2. Comparing both groups, there were significant deviations along the y-axis (p = .013), along the z-axis (p = .049), and in the total deviation (p = .031). The questionnaire was evaluated in design, with 95% positive answers. In contrast, the evaluation of the area of time and effort resulted in only 55% positive responses. Overall, experience with the software was rated as positive by 74%. Group 1 achieved more accurate results, especially along the y-axis in the vestibular direction. In both groups, the implants were placed too deep. The questionnaire indicated a software with high usability and is therefore very suitable for teaching. If clinically feasible, beginners should prefer full-guided implant placement.

Model Reference Adaptive Control of an Independent Steer-by-Wire System: A Simulation Using a 14-Degree-of-Freedom Vehicle Model

This research delves into how Model Reference Adaptive Control (MRAC) can be applied in an independent Steer-by-Wire (SBW) system, utilising a detailed 14 Degrees of Freedom (DOF) full-vehicle model. This study is all about pushing forward vehicle dynamics and control using SBW technology. This study also has come up with some cutting-edge control algorithms that allow each wheel to be steered independently, which seriously boosts how manoeuvrable and responsive the vehicle is. Through simulations, the study shows that MRAC is a big improvement over traditional control methods where quantitative analysis shows that MRAC reduces yaw rate errors by up to 66.67% compared to Proportional-Integral-Derivative (PID) and 50% compared to Multi-order PID (MOPID). Additionally, in lateral acceleration and sideslip angle controls, MRAC demonstrates a similar reduction in errors, significantly outperforming PID and MOPID with errors maintained well below 10%, proving its worth in predictive control and real-time adaptability to various road conditions and driver intentions. The key finding from this study is that MRAC greatly enhances manoeuvrability and responsiveness compared to standard methods which offer flexibility according to the different driving scenarios. There are notable advancements in vehicle steering systems, which contribute to safer and more efficient driving. In essence, this work marks a significant step forward in automotive steering technology, opening the door to safer and more efficient modern vehicles.

An anti-sense lncRNA of the A-FABP gene regulates the proliferation of hair follicle stem cells via the chi-miR-335-5p/DKK1/β-catenin axis.

Hair follicle development relies on both the epithelial-mesenchymal interaction (EMI) and the proliferation of hair follicle stem cells (HFSCs). This intricate process involves numerous regulatory molecules. Increasing evidence suggests that long non-coding RNAs (lncRNAs) play a crucial role in hair follicle development. However, the functions and molecular mechanisms of many lncRNAs in hair follicle development of cashmere goats remain unclear. Based on our previous lncRNA sequencing results in cashmere goats, an unannotated lncRNA differentially expressed at various stages of hair follicle development, named FABP_AS, was detected. Consequently, we aimed at exploring the function and molecular mechanisms of FABP_AS. We constructed a CRISPR/Cas9 knockout system to specifically knock down FABP_AS, providing a reference model for target lncRNA knockout in animal primary cells. Functional experiment results demonstrated that FABP_AS significantly inhibited HFSCs proliferation. Mechanism experiment results revealed that FABP_AS competitively bond to chi-miR-335-5p, promoted DKK1 gene expression, and reduced Wnt/β-catenin signaling pathway activity. In summary, our findings indicated that FABP_AS acted as a miRNA sponge, sequestering chi-miR-335-5p away from the DKK1 gene, thereby suppressing HFSCs proliferation, which would lay the groundwork for a better understanding of the molecular mechanisms of hair follicle development and provide therapeutic targets for hair loss.

A mileage correction method of pipeline inertial measurement for dam internal deformation monitoring

Internal deformation monitoring is of great significance to the safe operation and maintenance of dams. Some scholars have carried out dam internal deformation monitoring by pipeline inertial measurement with a pipeline measurement robot, which has improved the monitoring accuracy and efficiency considerably, compared with the traditional monitoring methods. However, there are still shortcomings in this method. Because the odometer on the pipeline measurement robot will slip or slide, resulting in errors in the mileage of the multiple measurement routes, which hinders the development of the accuracy of pipeline inertial measurement. This paper proposes a mileage correction method based on junction detection to improve the accuracy of pipeline inertial measurement for better dam internal deformation monitoring. The mileage correction method establishes a reference model of pipeline junction location, detects the locations of pipeline junction welds on the measurement routes using two k-mean clustering, and corrects the odometer data based on the difference between the detected and reference locations. We evaluate the enhancement of this mileage correction method on the internal accuracy and deviation values compared with other method for pipeline inertial measurement. The proposed method is validated using measured data from the internal pipeline of Tianchi Dam. The results show that compared with the results directly fused without detection–correction, the method proposed in this paper reduces the root-mean-square error of the results by an average of 30.30% and improves the deviation compared with the reference deformation by an average of 2.3 mm. Therefore, the method has good results and practical applications.

Computational study with neural networks to double diffusion in Prandtl thermal nanofluid flow adjacent to a stretching surface design with numerical treatment

The study focuses on the implementation of neural networks in analyzing the behavior of Prandtl nanofluid near a extending surface in the existence of dual diffusion. The research investigates the merged consequence of thermal and concentration gradients on the fluid flow and heat transfer characteristics using advanced computational techniques. The investigation delves into the phenomenon of double diffusion in the flow of Prandtl nanofluid near a stretching surface (DD-PNSS), employing the Levenberg-Marquardt scheme with Artificial Neural Networks (LMS-ANNs). The application of similarity variables converts the non-linear partial differential equations into non-linear ordinary differential equations. Through the application of the Lobatto IIIa formula in a three-stage process, various data sets are generated for the LMS-ANNs by varying parameters such as the Prandtl fluid parameter ([Formula: see text]), Prandtl number (Pr), Brownian motion parameter (Nb), thermophoresis parameter (Nt), and Dufour-solutal Lewis number (Ld). The range of parameter α is from 2 to 3.5, parameter β from 0.1 to 0.9, Nb is from 0.1 to 0.4, Nt is from 0.1 to 0.7, Le is from 1 to 3 and Ld is from 0.1 to 0.5. The intrinsic behavior of embedded parameters on dimensionless velocity, temperature, solutal concentration, and nanoparticle concentration profiles is graphically evaluated. The proposed LMS-ANNs model is meticulously tested, validated, and trained using a multi-stage approach, and its performance is compared to established references to ensure its reliability. The effectiveness of the suggested LMS-ANNs model is further affirmed through regression analysis, Mean Squared Error (MSE) evaluation, and histogram studies, showcasing an exceptional accuracy level ranging from 10−08 to 10−10, setting it apart from alternative approaches and reference models.

Detailed Images of Deep Brain Stimulation Leads Using Micro-CT

Introduction: One of the challenges in directional deep brain stimulation (DBS) is to determine the orientation of implanted electrodes relative to targeted regions. Post-operative images must be aligned with a model of the implanted lead, usually a computer-based model provided by the manufacturer. This paper shows that models can alternatively be obtained by capturing images of individual leads using micro-CT, a high-resolution CT technique. Contrary to computer-aided design models, lead models generated this way provide realistic X-ray contrast and finer details. Methods: We scanned DBS leads from various vendors using a Bruker SkyScan 1276 micro-CT system. To reduce beam-hardening artefacts, samples were scanned at maximum X-ray tube voltage (100 kV) and with copper filtering. Images were made publicly available for download and 3D visualisation. Conclusion: Detailed images of single DBS leads can be generated using standard micro-CT systems. Their use as reference models could improve lead orientation algorithms, in particular those dedicated to X-ray modalities. Furthermore, the possibility to share models online could broaden access for clinical research.

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Resilient and robust control strategy against deception attacks in the platoon of vehicles

AbstractA platoon must ensure the security of conducting missions due to the susceptibility of the vehicles to cyber‐attacks. The deception attack is a common type of cyber‐attack that might be introduced during data exchange through communication channels. Therefore, it can divert each vehicle by injecting uncertainty into the communication link between the agents in the cyber domain. This paper employs a leader–follower consensus system as a reference model to control the platoon vehicles. Accordingly, each agent in the reference model sends its information as a reference trajectory to its corresponding vehicle within the platoon. The vehicles are equipped with a robust local controller, enabling them to follow their desired trajectory in the presence of external disturbances. Furthermore, each vehicle employs an unknown input observer to detect a deception attack while decoupling it from external disturbances. Moreover, by introducing a switching strategy based on mode‐dependent average dwell time to the leader–follower consensus system, a novel resilient control strategy against the deception attack is implemented in the cyber domain. The resultant resilient leader–follower consensus system in the platoon retrieves and restores the attacked vehicle in the platoon to its associated states. The applicability of the proposed method is shown via simulation.

Measuring Supply Chain Performance Using the SCOR Model: A Case Study of CV. XYZ

This research discusses the design of a performance measurement system for supply chain management using the Supply Chain Operations Reference (SCOR) model developed by the Supply Chain Council. The SCOR model focuses on five main activities within a company's supply chain, namely plan, source, make, deliver, and return. All activities are linked with performance attributes such as reliability, responsiveness, flexibility, cost, and assets. The implementation of this model is applied to CV. XYZ, a company that operates in the production of plain and printed T-shirts, which is located in East Java. The research was conducted from January 2024 to September 2024. The results of the study identified that there are 35 performance indicators representing supply chain activities, with the following breakdown: 9 indicators for the plan activity, 7 indicators for the source activity, 6 indicators for the make activity, 7 indicators for the deliver activity, and 6 indicators for the return activity. The Analytical Hierarchy Process (AHP) method was used to weight each activity in the SCOR model. Based on the results, the highest priority was the source activity (0.252), followed by deliver (0.222), plan (0.221), make (0.209), and return (0.095). From the scoring system, it was found that 23 indicators have a green score, 8 indicators have a yellow score, and 4 indicators have a red score. The limitation of this research is that one SCOR activity, "enable," was not analyzed. Therefore, it is recommended that future research analyze all SCOR activities.

Model Reference Adaptive System of Permanent Magnet Synchronous Motor Based on Current Residual Compensation Without Position Measurement

Model Reference Adaptive System of Permanent Magnet Synchronous Motor Based on Current Residual Compensation Without Position Measurement

Substantial Global Radial Variations of Basalt Content Near the 660‐km Discontinuity

AbstractMid‐ocean ridges generate basalt and harzburgite, which are introduced into the mantle through subduction as a mechanical mixture, contributing to both lateral and radial compositional heterogeneity. The possible accumulation of basalt in the mantle transition zone has been examined, but details of the mantle composition below the 660‐km discontinuity (hereafter d660) remain poorly constrained. In this study, we utilize the subtle waveform details of S660S, the underside shear‐wave reflection off the d660, to interpret the seismic velocity, density, and compositional structure near, and particularly below, the d660. We identify a significant difference in S660S waveform shape in subduction zones compared to other regions. The inversion results reveal globally enriched basalt at the d660, with a notably higher content in subduction zones, consistent with the smaller impedance jump and S660S peak amplitude. The basalt fraction decreases significantly to less than 10% near 800‐km depth, forming a global harzburgite‐enriched layer and resulting in a steep seismic velocity gradient just below the d660, in agreement with 1D global reference models. The striking compositional radial variations near the d660 verify geodynamic predictions and challenge the applicability of homogeneous radial compositional models in the mantle. These variations may also affect the viscosity profile and, consequently, the dynamics at the boundary between the upper and lower mantle.

Direct Fractional-Order Adaptive Control Design for Cascaded Doubly-Fed Induction Generator (CDFIG) in a Wind Energy System

This paper is devoted to a fractional-order model reference adaptive control (FO-MRAC) synthesis for the independent control of the active and reactive power flows in the cascaded doubly fed induction generator (CDFIG) in wind energy systems. The proposed adaptive control law combines a second-order-like fractional reference model and a direct MIT adaptation law using a fractional order integrator. This generator configuration can be an interesting alternative to standard double-output wound rotor induction generators. It is made up of two identical wound rotor induction motors such that their rotors are mechanically and electrically coupled. Using two cascaded induction machines permits the elimination of the brushes and copper rings in the traditional doubly-fed induction generator DFIG, which makes the system more resistant and reduces maintenance costs. In the first step, we propose a classical PI controller synthesis to regulate the active and reactive power produced by CDFIG. Then, the FO-MRAC design is realized and a comparative study based on numerical simulations is performed between the classical regulators PI, MRAC, and FO- MRAC, to demonstrate the superiority of the proposed fractional-order adaptive controller relative to conventional integer order PI and MRAC controllers. These results illustrate the reliability and efficiency of the proposed adaptive control scheme.

Shear tests and meso-scale simulation of cold joint structures in rock-filled concrete: Considering the mutual contact effects between component materials

To realistically reflect the shear performance of the rock-filled concrete (RFC) between layers, this study relies on an RFC dam project in Guizhou Province. Test blocks, measuring 5 m × 4 m × 2 m with cold joints, were cast on site and subjected to shear tests. Additionally, a three-dimensional four-phase meso-model was developed, considering the mutual contact effects between component materials. The mechanical response and damage mechanism of the interlayer were analyzed using the cohesive zone model. The results confirm that the cohesive zone model effectively simulates interlayer cold joint structures. The damage modes of the RFC interlayer include cold joint debonding damage (Mode I) and plastic extrusion damage of self-compacting concrete (Mode II). As the exposed height of the rocks increases, the damage mode shifts from Mode I to Mode II, and higher normal stresses intensify Mode II damage. Meanwhile, the paths of crack extension depend on the distribution of shear-resistant rocks, with weak interfacial transition zones serving as “bridges’’ for crack propagation. The interlayer shear strength is positively correlated with the average exposed height of rocks and normal stresses, with the former having a more significant impact. Finally, the accuracy of the proposed shear strength prediction model for the RFC interlayer was validated by comparing it with results from other studies, providing useful references for meso-numerical modeling of RFC.

Effects of low temperature on near-nozzle breakup and droplet size distribution in airblast kerosene spray

Atomization of low-temperature fuel is encountered in extreme operating conditions of liquid propulsion systems such as cold start and high-altitude relight for aeroengines. Fuel temperature has a great impact on airblast spray characteristics by influencing fuel viscosity and thus the gas–liquid interaction, which raises the demand to clarify the temperature-dependent transition in near-nozzle breakup behavior and the corresponding droplet size distribution. A liquid-centered swirl coaxial injector is tested on the low-temperature swirl spray and combustion test rig at Zhejiang University, using 25 kHz high-speed digital off-axis holography. RP-3 aviation kerosene is atomized under ignition conditions at temperatures of 233, 253, and 301 K, fuel pressures of 0.03 and 0.69 MPa, and air pressure ranging from 0 to 4.0 kPa. Time-resolved near-nozzle dynamics suggest four types of elementary breakup processes: wavy-sheet breakup, pulsating breakup, membrane-type breakup, and nonaxisymmetric Rayleigh breakup. Each process alternately dominates the near field as fuel Reynolds number (Ref) and aerodynamic Weber number (Weg) decrease, corresponding to four primary breakup modes. A mode classification plot is summarized. Spray structures show an extended breakup length and reduced spray cone angle as fuel temperature (Tf) decreases. Increasing air pressure (Pg) promotes spray expansion at 0.03 MPa, but contracts spray cone at 0.69 MPa. Cross-sectional Sauter mean diameter (SMD) distribution indicates a solid-cone spray at 0.03 MPa and a hollow cone spray at 0.69 MPa. Lowering Tf will rise the SMD in the spray center at 0.03 MPa and transform the toroidal SMD distribution at 0.69 MPa into a solid one. Finally, a temperature-related SMD model is derived considering the exponential viscosity–temperature relationship, and a good fit with R2 > 0.95 is achieved. This research aims to deepen the understanding of the effects of low temperature on the transition of near-nozzle atomization characteristics for airblast sprays. Both spray visualization and SMD results provide reference for numerical simulations and near-nozzle spray modeling.

Spatial and temporal analysis of decomposition models in China

This study presents a comprehensive evaluation of 15 decomposition models (12 empirical and 3 atmospheric transmittance models) for estimating diffuse horizontal irradiance at 17 radiation sites in China, using hourly radiation data from 2011 to 2020. Our results show distinct patterns in model performance across different geographical regions, seasons, and sky conditions. The Liu model demonstrates the best overall performance with an RMSE of 78.20 W/m2, while model accuracy shows significant geographical variation, performing best in South and Southeast China (RMSE<70 W/m2) and worst in the Qinghai-Tibet Plateau and Northwest China (RMSE>90 W/m2). Seasonal analysis reveals better performance in winter than in summer, with RMSE differences approaching 40 W/m2, mainly due to the higher proportion of solar elevation angles exceeding 30° in summer. Under different sky conditions (classified by clearness index: 0–0.35 for overcast, 0.35–0.65 for partly cloudy, 0.65–1 for clear skies), most models follow an RMSE pattern of partly cloudy > clear sky > overcast. However, the Reindl2, Boland, DIRINT, and DIRINDEX models deviate from this trend due to their formula structure and sensitivity to atmospheric parameters. To reduce these regional disparities, we propose a new region-specific model selection strategy: the DIRINDEX model for eastern regions, DIRINT for central areas, and Karatasou for western regions. This combined approach reduces the overall RMSE to 73.17 W/m2. This research deepens our understanding of the application of decomposition models in China's complex geographical and climatic conditions, offering valuable references for solar radiation modeling and renewable energy forecasting in diverse climatic regions.

Preliminary investigation of the effects of high-temperature thermal activation on the activity of graphite tailings and the mechanical properties of graphite tailings mortar

The disposal of graphite tailings, a byproduct of graphite mining, poses significant recycling challenges. This study explores eco-friendly cement mortar by partially replacing sand with activated graphite tailings, focusing on waste utilization. Activating the pozzolanic properties of graphite tailings at various thermal activation temperatures converts them into high-quality binder materials. Quantitative assessments of graphite tailings' activity, conducted through ion leaching tests and raw material diffraction analysis, produce an activity index Ka, evaluating their performance under different thermal activation temperatures. Variable testing of substitution rates and activation temperatures examines changes in mechanical properties, microstructure, and pore distribution of the graphite tailings mortar. Optimum results reveal a 30 % substitution rate and a 750°C activation temperature. Graphite tailings, as pozzolanic binder materials, enhance mortar strength by improving particle size distribution, reducing "micro-area bleeding" and "boundary effects". After thermal activation, reactive minerals on the tailings' surface undergo secondary hydration reactions with cementitious products, modifying chemical structures and micro-morphology at the interface. Semi-quantitative XRD analysis and FT-IR spectroscopy characterize the ensuing chemical reactions, unveiling molecular bond and functional group changes. The superior performance of thermally activated graphite tailings primarily stems from increased active SiO2 and Al2O3 post-activation. The active SiO2 and Al2O3 reacts with cementitious products to form C-(A)-S-H gels, filling micro-pores at the tailings-mortar interface, which is the key characteristic that enables graphite tailings to serve as high-quality pozzolanic binder aggregates. A predictive model for compressive strength, utilizing reference models from solid waste mortar concrete, quantifies the positive impact of chemical activation. The theoretical results obtained from this predictive model closely align with the experimental findings.

The Treasure Trove Hidden in Plain Sight: The Utility of GPT-4 in Chest Radiograph Evaluation.

Background Limited statistical knowledge can slow critical engagement with and adoption of artificial intelligence (AI) tools for radiologists. Large language models (LLMs) such as OpenAI's GPT-4, and notably its Advanced Data Analysis (ADA) extension, may improve the adoption of AI in radiology. Purpose To validate GPT-4 ADA outputs when autonomously conducting analyses of varying complexity on a multisource clinical dataset. Materials and Methods In this retrospective study, unique itemized radiologic reports of bedside chest radiographs, associated demographic data, and laboratory markers of inflammation from patients in intensive care from January 2009 to December 2019 were evaluated. GPT-4 ADA, accessed between December 2023 and January 2024, was tasked with autonomously analyzing this dataset by plotting radiography usage rates, providing descriptive statistics measures, quantifying factors of pulmonary opacities, and setting up machine learning (ML) models to predict their presence. Three scientists with 6-10 years of ML experience validated the outputs by verifying the methodology, assessing coding quality, re-executing the provided code, and comparing ML models head-to-head with their human-developed counterparts (based on the area under the receiver operating characteristic curve [AUC], accuracy, sensitivity, and specificity). Statistical significance was evaluated using bootstrapping. Results A total of 43 788 radiograph reports, with their laboratory values, from University Hospital RWTH Aachen were evaluated from 43 788 patients (mean age, 66 years ± 15 [SD]; 26 804 male). While GPT-4 ADA provided largely appropriate visualizations, descriptive statistical measures, quantitative statistical associations based on logistic regression, and gradient boosting machines for the predictive task (AUC, 0.75), some statistical errors and inaccuracies were encountered. ML strategies were valid and based on consistent coding routines, resulting in valid outputs on par with human specialist-developed reference models (AUC, 0.80 [95% CI: 0.80, 0.81] vs 0.80 [95% CI: 0.80, 0.81]; P = .51) (accuracy, 79% [6910 of 8758 patients] vs 78% [6875 of 8758 patients], respectively; P = .27). Conclusion LLMs may facilitate data analysis in radiology, from basic statistics to advanced ML-based predictive modeling. © RSNA, 2024 Supplemental material is available for this article.

Simulating the Cellular Context in Synthetic Datasets for Cryo-Electron Tomography.

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane-bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

Marine spatial planning techniques with a case study on wave-powered offshore aquaculture farms

As emerging marine technologies lead to the development of new infrastructure across the ocean, they enter an environment that existing ecosystems and industries already rely on. Although necessary to provide sustainable sources of energy and food, careful planning will be important to make informed decisions and avoid conflicts. This paper examines several techniques used for marine spatial planning, an approach for analyzing and planning the use of marine resources. Using open-source software including QGIS and Python, the potential for developing offshore aquaculture farms powered by a reference model wave energy converter from the Sandia National Labs, the RM3, along the Northeast coast of the United States is assessed and several feasible sites are identified. The optimal site, located at 43.7°N 68.9°W along the coast of Maine, has a total cost for a 5-pen farm of $56.8M, annual fish yield of 676 tonnes, and a levelized cost of fish of $9.23 per kilogram. Overall trends indicate that the cost greatly decreases with distance to shore due to the greater availability of wave energy and that conflicts and environmental constraints significantly limit the number of feasible sites in this region.