Recent innovations in Simio

This paper briefly describes Simio™ simulation software, a simulation modeling framework based on intelligent objects. It then describes a few of the many recent enhancements and innovations including SMORE charts that allow unprecedented insight into your simulation output, sophisticated built-in experimentation that incorporates multi-processor support, optimization, and Risk-based Planning and Scheduling (RPS).

This paper briefly describes Simio™ simulation software, a simulation modeling framework based on intelligent objects. It then describes a few of the many recent enhancements and innovations including SMORE charts that allow unprecedented insight into your simulation output, sophisticated built-in experimentation that incorporates multi-processor support, optimization, and Risk-based Planning and Scheduling (RPS).

Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?

The cause for low maize yields in rain-fed production systems is usually associated with water stress due to perceived suboptimal seasonal precipitation. A modeling study using Agricultural Model Intercomparison and Improvement Project modeling framework was conducted to determine the magnitude of rain-fed potential yield and yield gap of maize in the Wami River sub-basin, Tanzania. Primary and secondary data on soils, weather, management, and crop yields and cultivars were used. Data matrix search technique was used to calibrate CERES-Maize Crop System model against reported yield for each of 168 farms involved in this study. Then the individual farms' simulated yields, actual reported yields, and the resultant yield gaps were aggregated into ward-level averages. Model calibration was robust as there was a very close agreement between reported and simulated yield (R2 = 0.9). Actual yields reported from farm survey ranged from 50 kg ha−1 to 3600 kg ha−1 with an average of 860 kg ha−1. Simulated rain-fed potential yield was between 2073 kg ha−1 and 5443 kg ha−1 and a mean of 4033 kg ha−1. It is apparent therefore that there exists a wide maize yield gap of 79% with current management under rain-fed conditions. This suggests that there is a large scope of improving maize yields under rain-fed conditions. Narrowing the yield gaps would require an intensive soil fertility improvement in the study area.

Situation awareness in complex urban environments is an important component for a successful task fulfillment both in military and civil area of applications. In the first area, the fields of deployment of the members of the North Atlantic Alliance have been changed, in the past two decades, from the originally assigned task of acting as national and allied defense forces within the partners’ own borders to out-of-area missions under conditions of an asymmetric conflict. Because of its complicated structure, urban terrain represents a particular difficulty of military missions such as patrolling. In the civil field of applications, police and rescue forces are also often strongly dependent on a local visibility and accessibility analysis. However, the process of decision-taking within a short time and under enormous pressure can be extensively trained in an environment that is tailored to the concrete situation. The contribution of this work consists of context-based modeling of urban terrain that can be then integrated into simulation software, for example, Virtual Battlespace 2 (VBS2). The input of our procedure is made up by the airborne sensor data, collected either by an active or a passive sensor. The latter is particularly important if the application is time-critical or the area to be explored is small. After description of our procedure for urban terrain modeling with a detailed focus on the recent innovations, the main steps of model integration into simulation software will be presented and two examples of missions for military and civil applications that can be easily created with VBS2 will be given.

The article is devoted to the study of methods and means of determining objects of radio technical intelligence using machine learning technologies and an ontological approach. A naіve Bayesian classifier was used to identify objects of radio technical intelligence. The Naive Bayes classifier is a machine learning algorithm used to classify objects based on probabilities. In this article, a naive Bayesian classifier is used to determine the classes to which objects of radio technical intelligence belong. The classifier uses historical data on object properties to determine the probability that each object belongs to a certain class. For example, based on the properties of the operating frequency range, pulse duration, pulse repetition period, and the number of sources of radio emissions, it is possible to determine the probability that the object belongs to a certain class of radio-technical intelligence. An ontological approach was used to specify the classes to which the objects of radio technical intelligence belong. The ontological approach is used to define classes of objects of radio technical intelligence in order to create a clear and unambiguous model of the subject area. This allows you to structure knowledge about objects, their properties, and relationships, which simplifies further data analysis and allows more accurate classification of new objects. The process of classifying objects in the military field, namely radio-technical intelligence, has been improved by combining the methods of k-nearest neighbors, the naive Bayesian classifier, and the ontological approach, which, unlike the existing methods, before applying the classifier, clustering of objects is carried out in order to take into account the ranges within which features of objects are defined. The analysis of input features showed that the main features for determining the means of radio technical intelligence are: “range of working frequencies”; “impulse duration”; “pulse repetition period”; “the number of sources of radio emissions”. An information system for the classification of radio-technical intelligence tools has been developed, the central component of which is the ontology of radio-technical intelligence tools. Simulation modeling of the work of the developed methods and algorithms was carried out. The choice of software tools for the implementation of the developed methods with the aim of further implementation on various platforms is substantiated. The JavaScript programming language using the JQuery library was used to implement the functional content of the system. The conducted simulation shows a satisfactory result of the developed methods and algorithms.

Impact of generated solar radiation on simulated crop growth and yield

Trends in Simulation and Planning of Manufacturing Companies

This paper introduces a new modeling and inference framework for multivariate and anisotropic point processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields, and from them a family of anisotropic point processes. We give conditions that make the proposed models valid. We also propose a Palm likelihood‐based inference method for this type of point process, circumventing issues of likelihood tractability. Finally we illustrate the utility of the proposed modeling framework by analyzing spatial ecological observations of plants and trees in the Barro Colorado Island data.

Decreases in target prices for U.S. government supported commodities in the 1985 and 1990 Farm Bills provided additional stress to many agricultural producers. This analysis examines alternative paths of adjustment for debt-ridden cotton producers in the Trans-Pecos region of Texas in response to such decreases in support. The Erosion Productivity Impact Calculator (EPIC) crop growth simulation model was used to estimate yields under stochastic weather conditions for several cotton (Gossypium hirsutum L.) irrigation strategies. Stochastic dominance analysis was applied to whole farm net returns generated from these simulations to determine the stochastically efficient set of irrigation strategies. Results indicate that the more water-intensive irrigation strategies are universally in the preferred risk efficient set []

The exponential growth of large language models has necessitated the development of sophisticated distributed training frameworks to efficiently manage computational resources, model complexity, and parallelization strategies. This article presents a comprehensive analysis of distributed training architectures for large language models, examining their technical foundations, implementation challenges, and recent innovations. Beginning with a detailed exploration of core parallelization strategies—data parallelism, model parallelism, and pipeline parallelism—the article evaluates how each approach addresses fundamental constraints in training massive neural networks. It then examines leading frameworks, including Megatron-LM, DeepSpeed, and Alpa, highlighting their unique approaches to memory optimization, parallelization automation, and computational efficiency. The article further investigates persistent challenges in distributed training, including communication overhead, memory management limitations, and fault tolerance requirements. Finally, it explores emerging trends in heterogeneous computing and energy efficiency that promise to shape the future development of distributed training systems. Throughout, the article emphasizes how these frameworks and techniques collectively enable the continued scaling of language models while managing the associated computational demands.

This review adopts the use of artificial intelligence based prediction models in enhancing energy efficiency in marine energy generation using tidal power. Traditional forecasting techniques such as hydrodynamic simulation and statistical modeling fail to capture dynamic and non-linear dynamics of aquatic systems. Hence, efficacy of energy output is lost, and risk of operation failure is increased. Recent innovations in artificial intelligence such as machine learning and deep learning capabilities enhance power of modeling, forecasting, and optimization. Techniques such as long short-term memory (LSTM) networks, hybrid wavelet-convolutional neural networks (HW-CNNs), and physics-informed neural networks (PINNs) have significantly increased forecasting precision and versatility compared to conventional techniques. Artificial Intelligence-based models are seen to lower mean absolute percentage error (MAPE) in forecasting of tides and marine power by up to 35% and prediction-based maintenance frameworks lower unplanned downtime by more than 30%. Besides, usage of digital twins which are computerized replicas of physical assets, real-time assimilation of data have increased adaptive control ability by a significant percent, resulting in reduced structural fatigue and operating cost by 15 to 20%. Such contributions are not only technical but environmental and economical such as minimizing ecological disruptions and enhancing financial viability of projects. Overall, Artificial Intelligence-based prediction models are a disruptive methodology of scalable, efficient, and sustainable implementation of marine energy technology using tidal power.

Proper utilization of manufacturing resources is of crucial importance to any manufacturing industry in today’s global arena of competition. Simulation is extremely valuable tool for analyzing complex manufacturing systems. This paper presents a simulation study carried out at a flywheel manufacturing industry. This manufacturing industry required analysis of its manufacturing process in an attempt to increase its throughput and overall productivity. The main objective of this work was to simulate the existing flywheel manufacturing system and to find whether the current layout gives maximum throughput if not to find out the maximum throughput of the facility and to find the current bottlenecks to the throughput. The work provides information about performance of manufacturing system after optimization of certain parameters. A simulation model of existing flywheel manufacturing system was developed using manufacturing simulation tool. The simulation output helped to answer above mentioned questions. A set of feasible modifications to the existing manufacturing process was prepared. These modifications were included in the simulation model and output of the modified model was analyzed. The results show significant amount of improvement in the parameters like throughput and the work in process (WIP).

We first consider an extension of the generalized autoregressive conditional heteroskedasticity (GARCH) model that allows for a more flexible weighting of financial squared-returns for the filtering of volatility. The parameter for the squared-return in the GARCH model is time-varying with an updating function similar to GARCH but with the squared-return replaced by the product of the volatility innovation and its lagged value. This local estimate of the first order autocorrelation of volatility innovations acts as an indicator of the importance of the squared-return for volatility updating. When recent volatility innovations have the same sign (positive autocorrelation), the current volatility estimate needs to adjust more quickly than in a period where recent volatility innovations have mixed signs (negative autocorrelation). The empirical relevance of the accelerated GARCH updating is illustrated by forecasting daily volatility in return series of all individual stocks present in the Standard & Poor’s 500 index. Major improvements are reported for those stock return series that exhibit high kurtosis. The local adjustment in weighting new observational information is generalised to score-driven time-varying parameter models of which GARCH is a special case. It is within this general framework that we provide the theoretical foundations of accelerated updating. We show that acceleration in updating is more optimal in terms of reducing Kullback-Leibler divergence and in comparison to fixed updating. The robustness of our proposed extension is highlighted in a simulation study within a misspecified modelling framework. The score-driven acceleration is also empirically illustrated with the forecasting of US inflation using a model with time-varying mean and variance; we report significant improvements in the forecasting accuracy at a yearly horizon.

Regional yield estimation using a crop simulation model: Concepts, methods, and validation