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The purpose of this paper is to optimize the design of the advanced combat helmet (ACH) currently in use, by its designers in order to attain maximum protection against ballistic impacts (fragments, shrapnel, etc.) and hard-surface/head collisions. Since traumatic brain injury experienced by a significant fraction of the soldiers returning from the recent conflicts is associated with their exposure to blast, the ACH should be redesigned in order to provide the necessary level of protection against blast loads. In the present work, augmentations of the ACH for improved blast protections are considered. These augmentations include the use of a polyurea (a nano-segregated elastomeric copolymer)-based ACH external coating/internal lining.

To demonstrate the efficacy of this approach, instrumented (unprotected, standard-ACH-protected, and augmented-ACH-protected) head-mannequin blast experiments are carried out. These experimental efforts are complemented with the appropriate combined Eulerian/Lagrangian transient non-linear dynamics computational fluid/solid interaction analysis.

The results obtained indicated that: when the extent of peak over-pressure reduction is used as a measure of the blast-mitigation effectiveness, polyurea-based augmentations do not noticeably improve, and sometimes slightly worsen, the performance of the standard ACH; when the extent of specific impulse reduction is used as a measure of the blast-mitigation effectiveness, application of the polyurea external coating to the standard ACH improves the blast-mitigation effectiveness of the helmet, particularly at shorter values of the charge-detonation standoff distance (SOD). At longer SODs, the effects of the polyurea-based ACH augmentations on the blast-mitigation efficacy of the standard ACH are inconclusive; and the use of the standard ACH significantly lowers the accelerations experienced by the skull and the intracranial matter. As far as the polyurea-based augmentations are concerned, only the internal lining at shorter SODs appears to yield additional reductions in the head accelerations.

To the authors’ knowledge, the present work contains the first report of a combined experimental/computational study addressing the problem of blast-mitigation by polyurea-based augmentation of ACH.

The design of the Advanced Combat Helmet (ACH) currently in use was optimized by its designers in order to attain maximum protection against ballistic impacts (fragments, shrapnel, etc.) and hard-surface/head collisions. Since traumatic brain injury experienced by a significant fraction of the soldiers returning from the recent conflicts is associated with their exposure to blast, the ACH should be redesigned in order to provide the necessary level of protection against blast loads. The paper aims to discuss this issue.

In the present work, an augmentation of the ACH for improved blast protection is considered. This augmentation includes the use of a polyurea (a nano-segregated elastomeric copolymer) based ACH external coating. To demonstrate the efficacy of this approach, blast experiments are carried out on instrumented head-mannequins (without protection, protected using a standard ACH, and protected using an ACH augmented by a polyurea explosive-resistant coating (ERC)). These experimental efforts are complemented with the appropriate combined Eulerian/Lagrangian transient non-linear dynamics computational fluid/solid interaction finite-element analysis.

The results obtained clearly demonstrated that the use of an ERC on an ACH affects (generally in a beneficial way) head-mannequin dynamic loading and kinematic response as quantified by the intracranial pressure, impulse, acceleration and jolt.

To the authors’ knowledge, the present work is the first reported combined experimental/computational study of the blast-protection efficacy and the mild traumatic brain-injury mitigation potential of polyurea when used as an external coating on a helmet.

The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry.

The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area.

The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites.

This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.

APPLIED Technology, Middle East and European marketing and technical support representative of PF Industries Inc, will exhibit ground support equipment supplied to airlines worldwide.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

THE twenty‐fifth international air show which is to be held at Lc Bourget, from June 6 to June 16, promises to be the largest and most competitive of any of the Salons which have been organized by the French Union Syndicalc des Industries Aeronautiqucs ct Spatialcs, since the first in 1909. Approximately 400 companies from seventeen countries will present their latest aerospace products on the ground and in the air—the British industry being represented by 65 leading firms.

There has been little thought given in science to the impact of direct brain‐machine interfacing upon the future development of human consciousness. Even less thought has been given to the possibilities for both optimizing and thwarting development in the cyborg child. A neurocognitive model of the evolution of cyborg consciousness is summarized, and from this model grounded speculations are offered pertaining to the future development of the higher cognitive functions in the cyborg child. It will be shown that cybernetic implants are “multistable”; that is, the artificial intelligence (AI) component of the cyborg brain‐machine linkage may function to condition development along ideological lines (the brain conditioned by the “ideological chip”), or may operate to open up neurocognitive development to new and heretofore unrealized limits (the brain’s development optimized by the “guru programme”). Development of the cyborg child may be conditioned in the interests of ideological concerns, or may lead to a consciousness that easily transcends all forms of ideology. Application of the guru programme may foster the emergence of new levels of cognitive complexity and information processing (à la Piagetian and neo‐Piagetian theory) that in turn allows new strategies of adaptation previously beyond human comprehension. The ethical and regulatory problems raised by cyborg technologies are addressed.

Aeronautical & General Instruments Ltd, will exhibit their R 128 recording cameras, designed to photograph the information displayed on the cathode ray tube of an aircraft's reconnaissance radar.

Reports on the MSc group design project of students at the College of Aeronautics, Cranfield University. Details the design of the aircraft systems and their reliability and maintainability.

Accles & Pollock Ltd. of Oldbury, Worcestershire, a TI Steel Tube Division company, will be exhibiting a comprehensive range of precision steel tube and tubular products, including plain, annularly convoluted and thin wall tube, at Farnborough.