Digital twins (DTs) for tower crane operations support task simulation before execution, prediction of movement trajectories, and hazard alert generation for nearby personnel and surrounding structures. These capabilities improve situational awareness and support operator decision-making in real-time. To implement such DT systems, suitable simulation platforms should balance modeling accuracy, computational requirements, and integration with sensing and control components. This paper benchmarks two widely used platforms—NVIDIA Isaac Sim and Gazebo—focusing on their applicability to resource-constrained, real-time environments. We evaluate GPU resource usage, including processing load, memory consumption, power draw, and overall performance. Results show that while Isaac Sim achieves higher frame rates (82 FPS), it consumes significantly more power (175W), whereas Gazebo, operating at 75 FPS, demonstrates much higher performance per watt, achieving 2.89 FPS/W compared to Isaac Sim’s 0.47 FPS/W. These results suggest Gazebo can have practical advantages for deploying lightweight, energy-efficient DTs in crane operations.

Digital Twin (DT) technology creates virtual replicas of physical systems for monitoring and optimization. This papers investigates the effects of DT simulations on GPU systems, evaluating processing, memory usage, and power consumption. It shows that adjusting rendering quality can improve performance. It also demonstrates the importance of optimizing energy consumption and performance for sustainable deployment, highlighting advancements in GPU technology and energy management.

Advanced technological solutions are needed to improve the control and prediction of incidents, promoting a safer work environment. Cargo handling operations are critical in sectors such as construction, shipping, and manufacturing, but they pose significant safety risks due to the dynamic and unpredictable nature of load motion. Dynamic modeling has emerged as a valuable tool in the industrial context, allowing for the simulation and analysis of various factors over time. This approach provides a detailed understanding of risk elements, such as excessive workload, training deficiencies, and inadequate equipment maintenance. It also facilitates the evaluation of intervention strategies without real risks, such as implementing training programs and new safety procedures. This paper addresses improving the safety and efficiency of cargo handling operations through the integration of kinematic and dynamic systems modeling, Inertial Measurement Unit (IMU)11Intelligent 9-axis absolute orientation sensor from Bosch©sensors, and Robot Operating System (ROS)22https://www.ros.org. The proposed system aims to predict load movement, monitor in real-time, validate and improve prediction accuracy, and enhance safety through predictive maintenance and operational adjustments.

This article reviews computer vision technologies for detecting and tracking objects in industrial cargo handling activities. We have proposed a dataset and a methodology for identifying people, containers, cages, equipment, boxes, and piping, in real-time operation. Our experimental results demonstrate that our artificial neural network model effectively detects and segments objects in non-sparse environments using an annotated industrial image dataset, achieving average precision up to 95% for most classes, including 93% of test instances. This improved perception capability enhances operators' decision-making and accident prevention.

Despite the critical role of fertilizers in modern agriculture, the lack of properly labeled datasets has significantly hindered advancements in automated fertilizer analysis. To address this gap, this paper introduces three novel datasets tailored for the development and validation of fertilizer detection and classification systems. First, a synthetic dataset is generated using a surface simulator that combines images of individual fertilizer grains, providing a highly controlled yet diverse data source for preliminary algorithm testing. Second, a controlled environment dataset is created under optimal yet realistic conditions to offer a balance between controlled experiments and applicability in natural settings. Third, a real-environment dataset is compiled under challenging field conditions, which presents the complexities of real-world agricultural data collection. Together, these datasets not only enhance the training and testing of machine learning models but also pave the way for substantial improvements in precision agriculture by enabling more accurate and efficient fertilizer management. This paper details the creation, characteristics, and potential applications of these datasets, aiming to set a new standard for dataset quality and utility in agricultural research.

Nowadays one important subject considered in the rehabilitation of patients with spasticity is the mathematical modelling of the muscles behavior under injuries and helping in the planning of personalized treatments. With this work it was possible to identify that it is necessary to develop musculoskeletal models focused on the study of patients with spasticity, since there is a difference between the models for the exclusive analysis of healthy patients and those with some type of limitation. It is also identified the importance of the use of parameter estimation in vivo, since the muscle of a cadaver can undergo modifications and degradation. In addition, authors present the integration of robots and humans to contribute to a targeted physiotherapy focused on the limitations of each patient, guaranteeing greater strength and torque in the movements. We introduce and explain the importance of this typo of study in section one and in the second section was developed a literature review to investigate what was the principal lines of research and considerations about musculoskeletal models, doing a classification about the relevance for the material to our job with focus in pacients who have spaticity, because some studies focused only in a model for health people. In the third section was analised the models more useds, the importance of utilization the parameters in vivo, the use of a model who is developed to pacients with spasticity. Doing this, we choose the Giat Model develped in the work of [26] para o trabalho a ser desenvolvido. In the section four we presentate the use of Eletromiography (EMG) and the procedures developed for the tests. Finaly in the section five we presentate the softwares who is using in this type of project and the beneficits of these.