Browse Topic: Personnel
The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.
New technologies, advanced materials, evolving mission profiles and fast-changing requirements are forcing the aerospace and defense (A&D) industry to dramatically increase the speed of engineering. Companies must design, validate and bring more complex products to market faster than ever, even as software, electronics and autonomy continue to reshape what aircraft, spacecraft and defense systems can do. At the same time, a growing production challenge is emerging. Workforce shortages, supply chain disruption and pressure to reduce cost and cycle time are converging with new demands for greater volume and flexibility. Defense programs are seeing increasing need for larger quantities of lower-cost systems such as drones, while commercial aerospace companies continue to work through backlogs and reinforce their fleets. To keep pace, the industry must accelerate innovation while also scaling production with greater speed, resilience and adaptability.
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
While many manufacturers are enjoying demand for their products and have innovations in the pipeline, their growth strategy can sometimes stall. The issue might be due to workforce shortages, which can evolve from temporary setbacks to long-term challenges. For a variety of industries, helping hands are often hard to find, and it’s an issue that’s been consistent in recent years. Given this, many companies are considering transitioning to automation, including through the use of collaborative robots or cobots, for short.
The outwash flow of a VTOL aircraft in near-ground operation is a serious risk for surrounding objects or personnel, and has been investigated for a long time. The current paper contributes to this topic by revisiting quantitative outwash measurement techniques and evaluation strategies suitable for full-scale flight experiments. An array of purpose-built ultrasonic anemometers, a pitot tube rake, and fiber-film sensors were applied during outwash tests with a hovering Eurocopter EC135, complemented by accompanying numerical simulations with the Vorticity Transport Model (VTM). A focus is set on an analysis of the flow unsteadiness, revealing large fluctuations at low frequencies which require careful data post-processing. The fluctuations limit the application of pitot tubes to measure the mean flow due to their angular sensitivity, and ultrasonic anemometers are recommended as a particularly suitable and convenient measurement method with a sufficient frequency response. Furthermore, the experimental and numerical results are compared to semi-empirical outwash models, and the importance of simultaneous measurements of the ambient wind conditions is demonstrated.
Understanding the fluid flow behavior over and into narrow gaps is crucial for many industrial applications, particularly in the automotive sector. Evaluating the potential of water ingress into narrow pathways and towards components is of great importance to design the water management of such components. The employment of CFD simulations supports the evaluation of potential water ingress into such gaps. Lagrangian based tools are used in a variety of simulation scenarios of fluid flow, especially due to their ability to easily simulate free surfaces with strong curvatures. In our previous work, a validated simulation setup was developed using the meshless simulation tool MESHFREE from Fraunhofer ITWM [8] for simulating water entering small gaps. Especially for industrial use cases, the computation time of several days is too expensive. Thus, we enhanced this approach to a fast and robust CFD simulation that realizes industrial use cases within appropriate time. The development was conducted in two stages. First, the previously validated simulation method was analyzed with respect to different parameters (e.g. parameters influencing time step sizes) in a simplified benchmark case. This allowed for an initial assessment of their effects on computation time, accuracy, and robustness. Second, the improved parameter configuration was then further optimized for industrial applications to maximize performance across these criteria. The results demonstrate great potential in reducing the computation time. These findings will contribute to improving future work on modeling water pathways inside a vehicle.
Proactive Roadway Maintenance System could tap into humanity's altruistic mode. Sue Bai has done the research and will deliver a keynote speech at WCX 2026. The Ohio Department of Transportation is responsible for nearly 49,000 lane miles (79,000 km) of roadway, and one way ODOT knows which ones need attention is to send actual employees out to take actual notes. A recent project, conducted with Honda, could drastically reduce this type of driving busywork, giving ODOT more time - and money - to actually fix things instead of spending time searching for the things that need fixing. Honda worked with DriveOhio, ODOT's smart mobility hub, to further develop the automaker's Proactive Roadway Maintenance System (PRMS). Honda has been testing PRMS since 2021 and completed the two-year Ohio project in January. Technology partners included i-Probe Inc., Parsons, and the University of Cincinnati.
A new system that brings together real-world sensing and virtual reality would make it easier for building maintenance personnel to identify and fix issues in commercial buildings that are in operation. The system was developed by computer scientists at the University of California San Diego and Carnegie Mellon University.
A 4-rotor uninhabited air vehicle is described, with a primary mission of supporting personnel fighting wildfires. The paper demonstrates the use of technical design tools for a small Uninhabited Aircraft System (sUAS). A description of the design process is provided, including developing requirements, identifying constraints, the software tools employed, and examination of results. The vehicle is capable of delivering more than 20 kg of supplies to a delivery point 10 nm away while penetrating 30 kt winds. The sized vehicle is transportable in a medium-duty pickup truck and can be picked up and moved for ground handling by one or two individuals. The vehicle information will be publicly released for NDARC software users. Future work will examine other requirements, such as maneuvering and gust rejection.
The article is devoted to a comprehensive analysis of the digital transformation of education using the example of a project to train engineering personnel for the innovative transport industry in Russia. Special attention is paid to the introduction of hybrid formats, digital platforms, inclusivity, issues of digital inequality, as well as the experience of the National Research Center of the Russian Federation FSUE NAMI and interaction with leading universities in the country. A comparative analysis with foreign initiatives, including modern AI solutions for inclusive education, is presented, as well as the impact of the project to create educational and methodological centers on the professional motivation of teachers.
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
The U.S. Food and Drug Administration (FDA) has taken a substantial step in its digital modernization strategy with the deployment of agentic artificial intelligence capabilities across all agency employee groups. The move represents an expansion of the agency’s internal AI tools, intended to streamline complex, multi-step processes that support regulatory science, product review, and compliance activities. The deployment strengthens the FDA’s ongoing effort to embed structured, secure, and transparent AI systems into daily workflows, building on the rapid adoption of the LLM-based tool Elsa earlier this year.
Without reliability and signal integrity, aerospace communications risk severe signal degradation and reduced security, posing risks to both personnel and mission-critical data. These challenges are particularly critical for applications that depend on military aircraft, satellite communications, and unmanned aerial vehicles (UAVs). As global demand for real-time data continues to surge, communication infrastructure requires regular maintenance and upgrades to maintain secure and reliable performance.
The Operator’s Field of Vision (FOV) test, conducted in accordance with IS/ISO 5006:2017, is a vital assessment to ensure the safety and operational comfort of personnel operating Construction Equipment Vehicles (CEVs) / Earth-Moving Machinery. IS/ ISO 5006:2017 defines rigorous guidelines for evaluating the operator’s visibility from the driver's seat, with particular emphasis on the Filament Position Centre Point (FPCP), determined from the Seat Index Point (SIP) coordinates. The test includes assessment of masking areas, focusing on the Visibility Test Circle (a 24-meter diameter ground-level circle around the machine), and on the Rectangular Boundary on which a vertical test object is placed at a height specific to the machine type and its operating mass. These parameters are designed to simulate real-world operating conditions. This paper introduces a portable testing setup developed specifically for conducting the Operator’s FOV test as per IS/ISO 5006:2017. The setup facilitates include accurate verification of X and Y coordinates of the SIP, Integration of a high-intensity light system to project and assess masking areas, and Quick & repeatable deployment in field conditions, enhancing usability across various types of CEVs and earth moving machinery. The portable design ensures adaptability, reduces overall testing time, and upholds the accuracy requirements stipulated by IS/ISO 5006:2017. This solution not only enables more efficient visibility assessments but also supports enhanced safety compliance for manufacturers and operators. It presents a cost-effective, field-deployable solution for ensuring visibility requirements in construction equipment vehicles / earth-moving equipment.
The assessment of collision risks is crucial for effective risk control and scientific management of maritime safety. To prevent maritime transportation accidents, an accident causation model has been proposed to analyze risks in maritime transportation systems. The 24-model further analyzes the impact pathways of accident factors in the accident chain and calculates the fit of HOF-related factors. Using Bayesian Networks as a foundation and the 24-model as a tool, a Bayesian Network model for collision risk is constructed by identifying risk factors and determining their correlations, utilizing accident data from Chinese maritime authorities. Utilizing a Bayesian Network to construct a ship collision risk model that couples HOF and calculates conditional probabilities of relevant node occurrences. To explore the coupled relationships between nodes in a network, this study employs the N-K model to construct a safety risk coupling model for ship collision accidents, calculating risk values for different coupling types within the model. Case analysis shows that accidents result from dynamic interaction and linear combination of risk factors. The analysis of experimental results indicates that various accident factors contribute differently to overall maritime risk. Human factors are the direct cause of maritime ship collision accidents. From the perspective of coupled risk, organizational factors, as root influences, are crucial aspects that bridge resource management needs to focus on. The application of this model provides maritime personnel with a novel approach to mitigate the risk of maritime collisions.
Like those in many other industries, truck and off-highway vehicle manufacturers face the challenge of producing quality components and maintaining productive processes while also generating a better bottom line. Improving employee training, simplifying complex operations and implementing better workflows can all help generate efficiencies. While not a new concept, lightweighting - in this case, reducing the weight of parts through the substitution of traditional steel with high-strength, thinner steels - can also be a viable answer to a better vehicle. As a rule of thumb, when manufacturers double the strength of the material through lightweighting, it is possible to reduce the weight of the part by one-third. That weight reduction can then lower the cost per part for greater profitability per piece of equipment and greater annual savings.
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning, one which considers not only the tasks to be performed by the RAS themselves but regards the flow of information to support the observability of the RAS by the operator. GTRI has developed an initial capability for mission planning of mixed motive, heterogeneous, autonomous systems for management of macro level metrics that support the decision making of the operator or user during employment. The work is ongoing, extensible to additional capability sets, and modular to support integration of other autonomous capabilities.
A paper-based diagnostic device can detect COVID-19 and other infectious diseases in under 10 minutes, without the need for sophisticated lab equipment or trained personnel.
This study investigates the evolution of axial and radial velocities in the downwash-outwash region of a counter-rotating coaxial rotor hovering in-ground effect (IGE). The presence of the ground deflects the axial flow of the rotor wake radially outward, with mean radial velocities reaching approximately 2Vh along the ground. Based on the observed velocity profiles, the wake was classified into three distinct regions: the downwash region characterized by maximum wake contraction, the transition region where flow turns from axial to radial, and the outwash region exhibiting wall jet behavior. Results show that increasing inter-rotor spacing d/R and rotor height above ground (z/R)l extends the downwash and transition regions, delaying the onset of radial outwash. Aerodynamic loads on personnel were estimated, showing maximum mean forces and moments of 120N and 120Nm, remaining within safety thresholds for untrained personnel. However, the loads exceeded these limits for heavy-category helicopters with higher disk loading. Instantaneous velocities revealed unsteady behavior driven by tip vortex convection, with peak velocities reaching 2.8Vh and peak loads approximately 50% higher than mean values, as identified using the 98th percentile approach. Frequency analysis confirmed that fluctuations at the rotor frequency dominated the unsteadiness in the rotor wake. These findings provide critical insights for safe rotorcraft operation near ground surfaces and contribute to informed vertiport design and personnel safety guidelines.
A continuous effort to improve reliability and efficiency of processes is at the forefront of any successful business. One methodology that can have a crucial impact in this effort is Lean Six Sigma (LSS), which aims to reduce variability and wasteful activities within a company’s processes, in turn leading to improvements in areas such as customer satisfaction, employee morale, regulatory compliance, and profitability. In the medical device industry, where a seemingly minor error could be life-threatening, LSS can play a pivotal role in patient safety. This article presents a case study illustrating the benefits of LSS for a medical device manufacturing company, as well as one of its key customers.
Lead-filled aprons are currently used for atomic number (Z)-grade radiation shielding in the medical industry to protect personnel from hazardous gamma radiation. These apron garments are made with lead-filled elastomeric sheets encased in polymeric fabrics and are both heavy and bulky to meet necessary shielding requirements. In addition, there are environmental safety concerns surrounding disposal of these garments due to their lead content. An innovator at NASA Langley Research Center has developed a novel method for making thin, lightweight radiation shielding that can be sprayed or melted onto common textiles used in clothing such as cotton, nylon, polyester, Nomex, and Kevlar.
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