Search Results (325)

Ballistic helmets are individual pieces of armor equipment designed to protect a soldier’s head from projectiles and fragments. Although very common, these helmets are responsible for several casualties due to their significant back face deformation and low ballistic resistance to projectiles. Therefore, to enhance helmet performance, studies have focused on the development of new materials and new ballistic protection solutions. The purpose of this study was to develop and evaluate a new ballistic solution using thermoplastic-based matrices. The first matrix was based on high-density polyethylene (HDPE). The second matrix was based on HDPE modified with exfoliated montmorillonite (MMT). The main manufacturing processes of a thermoplastic-based ballistic helmet are presented, along with its ballistic performance, according to the National Institute of Justice (NIJ) standard 0106.01 and an investigation of its failure mechanisms via a non-destructive technique. All the helmets resulted in level III-A ballistic protection. The postimpact helmets were scanned to evaluate the back face deformation dimensions, which revealed that the global cone deformation was deeper in the HDPE than in the HDPE/MMT helmet. The failure analysis revealed an overall larger deformation area in the HDPE and HDPE/MMT helmet delamination zones in the regions with a large radius of curvature than in the zones with the lowest radius, which is in accordance with previous simulations reported in the literature. Full article

Background/Objectives: Previous evidence has shown that American football headgear (e.g., facemasks, visors/eye shields) differentially impairs reaction time (RT) to visual stimuli, most notably in peripheral fields of view. However, this has only been established with stationary RT testing, which may not translate to gameplay situations that require gross motor skills. Therefore, the purpose of this study was to build upon previous findings to elucidate the effects of various American football headgear on gross motor visuomotor drill performance. Methods: Division 1 NCAA football players (n = 16) with normal/corrected-to-normal vision participated and completed two experiments (EXP), each with differing conditions: EXP1- Varying facemask reinforcement and EXP2- Varying visor/eye shield light transmittance. In EXP1, participants completed an agility test for the following conditions: baseline/no helmet (BL), helmet + light (HL), helmet + medium (HM), and helmet + heavy (HH) face mask reinforcement. In EXP2, participants completed an agility test for the following conditions: baseline/no helmet (BL), helmet + clear visor (HCV), helmet + smoke-tinted visor (HSV), and helmet + mirrored visor (HMV). For each condition in EXP1 and EXP2, participants completed a reactive agility task using a FITLIGHT trainer system where five poles were equipped with a total of ten LED sensors and were placed in a semi-circle 1 m around a center point. Participants were asked to step and reach with their hands to hit each ten lights individually as fast as possible upon illumination. Each reactive agility test was repeated for a total of three attempts. Results: Average reaction time was analyzed and compared between conditions and according to visual fields of interest (e.g., central vs. peripheral). Results from EXP1 showed that compared to BL, reactive agility was worsened by HL (p = 0.030), HM (p = 0.034), and HH (p = 0.003) conditions. No differences between facemask conditions existed for overall performance (p > 0.05). For EXP2, HCV (p < 0.001), HSV (p < 0.001), and HMV (p < 0.001) conditions resulted in worsened reactive agility performance compared to BL. No differences between visor conditions existed for overall performance (p > 0.05). Conclusions: Overall, these findings suggest that American football headgear impairs reactive agility, which could result in worsened game performance and safety. Future studies investigating training strategies to overcome impairments are warranted. Full article

This article addresses manufacturing structures made via injection molding from biodegradable materials. The mentioned structures can be successfully used as energy-absorbing liners of all kinds of sports helmets, replacing the previously used expanded polystyrene. This paper is focused on injection technological tests and tensile tests (in quasi-static and dynamic conditions) of several composites based on a PLA matrix with the addition of other biodegradable softening agents, such as PBAT and TPS (the blends were prepared via melt blending using a screw extruder with mass compositions of 50:50, 30:70, and 15:85). Tensile tests showed a positive strain rate sensitivity of the mixtures and a dependence of the increase in the ratio of the dynamic to static yield stress on the increase in the share of the plastic component in the mixture. Technological tests showed that increasing the amount of the plasticizing additive by 35% (from 50% to 85%) results in a decrease in the minimal thickness of the thin-walled element that can be successfully injection molded by about 32% in the case of PLA/PBAT blends (from 0.22 mm to 0.15 mm) and by about 26% in the case of PLA/TPS blends (from 0.23 mm to 0.17 mm). Next, the thin-walled elements (dimensions of 55 × 55 × 20 mm) were manufactured and evaluated using a spring-loaded drop hammer. The 60 J impact energy was tested in accordance with the EN 1078 standard. The dynamic crushing test included checking the influence of the materials’ temperature (−20, 0, 20, and 40 °C) and the impact velocity. It was proven that the maximum deflection increases with increasing material temperature and an increase in the share of the plastic component in the mixture. The PLA15PBAT85 blend was selected as the most effective material in terms of its use as an energy-absorbing liner for sport helmets. Johnson–Cook and Cowper–Symonds material plasticizing models were constructed. Their use during dynamic FE simulation provided results that were in good agreement with those of the conducted experiment. Full article

Wearing safety helmets at construction sites is a major measure to prevent safety accidents, so it is essential to supervise and ensure that workers wear safety helmets. This requires a high degree of real-time performance. We improved the network structure based on YOLOv7. To enhance real-time performance, we introduced GhostModule after comparing various modules to create a new efficient structure that generates more feature mappings with fewer linear operations. SE blocks were introduced after comparing several attention mechanisms to highlight important information in the image. The EIOU loss function was introduced to speed up the convergence of the model. Eventually, we constructed the efficient model EGS-YOLO. EGS-YOLO achieves a mAP of 91.1%, 0.2% higher than YOLOv7, and the inference time is 13.3% faster than YOLOv7 at 3.9 ms (RTX 3090). The parameters and computational complexity are reduced by 37.3% and 33.8%, respectively. The enhanced real-time performance while maintaining the original high precision can meet actual detection requirements. Full article

(1) Introduction: Since electric scooters were launched in 2017, they have become increasingly popular worldwide and a cause of childhood trauma. (2) Case reports: This paper has a double-fold purpose: it reports two cases of epidural hematomas and compares them with electric scooter-related head trauma in the literature. An overview of the literature on this topic was performed to make such a comparison. Our cases are one of almost 52 cm³ and one of 129 cm^3, both in two eight-year-olds. (3) Discussion: Although usually mild, traumatic brain injuries following e-scooter falls can also be moderate and severe. Reduced helmet use, high speed, and a lack of experience are the perfect set-up for potential severe injuries. Intracranial bleeds are not frequent, and epidural hematomas are rare in such cases, but they can significantly impact the individual, community, and healthcare system. No other medium- or large-sized epidural hematomas were reported in children sustaining electric scooter-related head trauma. (4) Conclusions: Our review parallels the literature and our hospital’s experience. Although there are both similarities and discrepancies between our cases and the literature, mild trauma should not be disregarded, for it may hide serious complications requiring immediate surgery. Full article

Objectives: This study aimed to determine the impact of standing electric scooters on maxillofacial on the Italian territory. Methods: The authors analyzed the epidemiology of the injuries to define electric mobility’s impact on maxillofacial surgery practice. For this retrospective cohort study, data were collected by unifying the standing e-scooter-related fractures database from 10 Italian maxillofacial surgery departments. The reference period considered was from January 2020 to December 2023. The main data considered included age, gender, type of access, time slot of admission, type of admission, alcohol level, helmet use, dynamics of the accident, and area of the fracture. Results: A total of 79 patients were enrolled. The average age of the participants was approximately 31 years. The blood alcohol level was found to be above the Italian norm in 15 cases (19%). Only one patient wore a helmet. The most affected facial third was the middle one with 36 cases (45.5%), followed by the lower one (31, 39.3%). The most recurrent patterns were fractures of the orbito-malar-zygomatic complex (15, 19%), followed by multifocal (bifocal, trifocal) fractures of the mandible (14, 17.5%). Conclusions: This study demonstrated how maxillofacial fractures related to the use of electric scooters are associated with complex patterns, associated with a high rate of post-surgical aftermaths. Full article

Electric vehicle accidents on the road occur frequently, and head injuries are often the cause of serious casualties. However, most electric vehicle riders seldom wear helmets. Therefore, combining target detection algorithms with road cameras to intelligently monitor helmet-wearing has extremely important research significance. Therefore, a helmet-wearing detection algorithm based on the improved YOLOv8n model, PRE-YOLO, is proposed. First, we add small target detection layers and prune large target detection layers. The sophisticated algorithm considerably boosts the effectiveness of data manipulation while significantly reducing model parameters and size. Secondly, we introduce a convolutional module that integrates receptive field attention convolution and CA mechanisms into the backbone network, enhancing feature extraction capabilities by enhancing attention weights within both channel and spatial aspects. Lastly, we incorporate an EMA mechanism into the C2f module, which strengthens feature perception and captures more characteristic information while maintaining the same model parameter size. The experimental outcomes indicate that in comparison to the original model, the proposed PRE-YOLO model in this paper has improved by 1.3%, 1.7%, 2.2%, and 2.6% in terms of precision P, recall R, [email protected], and [email protected]:0.95, respectively. At the same time, the number of model parameters has been reduced by 33.3%, and the model size has been reduced by 1.8 MB. Generalization experiments are conducted on the TWHD and EBHD datasets to further verify the versatility of the model. The research findings provide solutions for further improving the accuracy and efficiency of helmet-wearing detection on complex traffic roads, offering references for enhancing safety and intelligence in traffic. Full article

In a recent study using 3-D fullwave simulations, it was shown for a nonhuman primate model that a helmet-shaped 3D array of 128 transducer elements can be assembled for neurostimulation in an optimized configuration with the accommodation of an imaging aperture. Considering all acoustic losses, according to this study, for a nonhuman primate skull, the assembly of the proposed transducers was projected to produce sufficient focusing gain in two different focal positions at deep and shallow brain regions, thus providing sufficient acoustic intensity at these distinct focal points for neural stimulation. This array also has the ability to focus on multiple additional brain regions. In the work presented here, we designed and fabricated a single 15 mm diameter capacitive micromachined ultrasonic transducer (CMUT) element operating at 800 kHz central frequency with a 480 kHz 3 dB bandwidth, capable of producing a 190 kPa peak negative pressure (PNP) on the surface. The corresponding projected transcranial spatial peak pulse average intensity (I_SPPA) was 28 Wcm⁻², and the mechanical index (MI) value was 1.1 for an array of 128 of these elements. Full article

The correct wearing of safety helmets and reflective vests is of great significance in construction sites, offices, and civil engineering sites. Aiming to address the issues of low detection accuracy and high algorithm complexity caused by complex background environments in the small target detection of safety helmets and reflective clothing using existing algorithms, an improved algorithm based on YOLOv8n is proposed. Firstly, the SE module is utilized to reduce interference in complex environments. Next, the IOU function is modified to speed up calculations. Then, a lightweight universal upsampling operator (CARAFE) is employed to obtain a larger receptive field. Finally, the Bidirectional Feature Pyramid Network is used to replace the Concat module of the original head layer. Based on these four modifications made to the model, this article names the new model SDCB-YOLO, derived from the initial letters of the four respective modules. The experimental results show that the mAP of the SDCB-YOLO model on the test set reached 97.1%, which is 4.6% higher than YOLOv5s and 3.5% higher than YOLOv8n. Additionally, the model boasts a parameter count of 3,094,304, a computational load of 8.4 GFLOPs, and a model size of 6.13 MB. Compared to YOLOv5s, with a parameter count of 7,030,417, a computational cost of 16.0 GFLOPs, and a model size of 13.79 MB, the SDCB-YOLO model is significantly smaller. When compared to YOLOv8n, with a parameter count of 3,011,628, a computational complexity of 8.2 GFLOPs, and a model size of 6.11 MB, the SDCB-YOLO model’s parameters and model size are only slightly increased, while maintaining a comparable computational load. Therefore, the improved detection algorithm presented in this article not only ensures the lightweight nature of the model but also significantly enhances its detection accuracy. Full article

Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO₂ concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future. Full article

Purpose: Short-track speed skating results in high-energy crashes with an elevated risk of head injury. The goal of this study was to evaluate the resulting kinematics of an anti-rotation helmet technology for speed skating. Methods: Two traditional rigid foam speed-skating helmets (BT and ST) were compared with one anti-rotation speed skating helmet (MIPS). Each helmet was impacted with a pneumatic device across three locations. The resulting linear or rotational accelerations (PLA or PRA) and rotational velocities (PRV) were measured with accelerometers placed on a Hybrid III head form. Additionally, the head impact criterion (HIC) was calculated from accelerations and the brain injury criterion (BrIC) was obtained from rotational velocities. Results: MIPS showed significantly higher values of accelerations (PLA = 111.24 ± 9.21 g and PRA = 8759.11 ± 2601.81 rad/s²) compared with the other helmets at all three impact locations (p < 0.01, ES = 3.00 to 4.11). However, velocities were lowest, but not significantly different, for the MIPS helmet (25.77 ± 1.43 rad/s). Furthermore, all resulting kinematics except peak linear accelerations were significantly different across impact locations. Conclusion: Helmet designs specific to the collision characteristics of speed skating may still be lacking, but would decrease the risk of sport-related concussions. Full article

Thermal Magnetic Resonance (ThermalMR) integrates Magnetic Resonance Imaging (MRI) diagnostics and targeted radio-frequency (RF) heating in a single theranostic device. The requirements for MRI (magnetic field) and targeted RF heating (electric field) govern the design of ThermalMR applicators. We hypothesize that helmet RF applicators (HPA) improve the efficacy of ThermalMR of brain tumors versus an annular phased RF array (APA). An HPA was designed using eight broadband self-grounded bow-tie (SGBT) antennae plus two SGBTs placed on top of the head. An APA of 10 equally spaced SGBTs was used as a reference. Electromagnetic field (EMF) simulations were performed for a test object (phantom) and a human head model. For a clinical scenario, the head model was modified with a tumor volume obtained from a patient with glioblastoma multiforme. To assess performance, we introduced multi-target evaluation (MTE) to ensure whole-brain slice accessibility. We implemented time multiplexed vector field shaping to optimize RF excitation. Our EMF and temperature simulations demonstrate that the HPA improves performance criteria critical to MRI and enhances targeted RF and temperature focusing versus the APA. Our findings are a foundation for the experimental implementation and application of a HPA en route to ThermalMR of brain tumors. Full article

In foggy weather, outdoor safety helmet detection often suffers from low visibility and unclear objects, hindering optimal detector performance. Moreover, safety helmets typically appear as small objects at construction sites, prone to occlusion and difficult to distinguish from complex backgrounds, further exacerbating the detection challenge. Therefore, the real-time and precise detection of safety helmet usage among construction personnel, particularly in adverse weather conditions such as foggy weather, poses a significant challenge. To address this issue, this paper proposes the DST-DETR, a framework for foggy weather safety helmet detection. The DST-DETR framework comprises a dehazing module, PAOD-Net, and an object detection module, ST-DETR, for joint dehazing and detection. Initially, foggy images are restored within PAOD-Net, enhancing the AOD-Net model by introducing a novel convolutional module, PfConv, guided by the parameter-free average attention module (PfAAM). This module enables more focused attention on crucial features in lightweight models, therefore enhancing performance. Subsequently, the MS-SSIM + ${\mathsf{\ell }}_2$ loss function is employed to bolster the model’s robustness, making it adaptable to scenes with intricate backgrounds and variable fog densities. Next, within the object detection module, the ST-DETR model is designed to address small objects. By refining the RT-DETR model, its capability to detect small objects in low-quality images is enhanced. The core of this approach lies in utilizing the variant ResNet-18 as the backbone to make the network lightweight without sacrificing accuracy, followed by effectively integrating the small-object layer into the improved BiFPN neck structure, resulting in CCFF-BiFPN-P2. Various experiments were conducted to qualitatively and quantitatively compare our method with several state-of-the-art approaches, demonstrating its superiority. The results validate that the DST-DETR algorithm is better suited for foggy safety helmet detection tasks in construction scenarios. Full article

The aim of the study was to determine the changes occurring in the eggs of helmeted guinea fowl (Numida meleagris) from free-range farming in relation to the laying season and storage time. The experimental material consisted of 360 guinea fowl eggs, collected in the first, second and third laying seasons and stored for 7, 14 and 21 days. After each period, physical and physicochemical characteristics of the eggs were determined, as well as the basic chemical composition and mineral content of the albumen and yolk and the yolk fatty acid profile. The age of the guinea fowls affected certain physical parameters of the eggs. The egg weight, shape index and shell thickness increased with the age of the laying hens; however, a decrease in the proportion of shell in the egg was demonstrated. Storage time had a significant effect on egg weight, weight loss during storage and air cell height. Significant differences were found in the chemical composition of guinea fowl eggs depending on the age of the laying hens. Eggs obtained from older laying hens were characterized by higher yolk fat content and lower ash content, while the albumen contained higher water content and lower ash content. During the three-year laying period, changes were observed in the mineral composition of the eggs. The fatty acid profile underwent significant changes; however, no important differences were observed in the total content of SFA, MUFA, PUFA and n-6 fatty acids. Conversely, significant differences were found for n-3 acids and the n-6/n-3 ratio. Eggs in the first and second laying seasons exhibited the most favorable composition. The slow dynamics of changes occurring in successive laying seasons and egg storage time indicated that the raw material studied was safe and could be used by consumers Full article

Positional plagiocephaly is a deformational cranial flattening frequently treated in pediatric neurosurgical practice. Positional maneuvers and orthotic helmet therapy are preferred therapeutic options for moderate-to-severe forms. Treatment response seems to be age-dependent. Nevertheless, predictive data are vague, and cost-efficiency might be a limiting factor for treatment. The purpose of this study was to investigate the early predictive value of sonographic parameters on the efficacy of orthotic helmet therapy through the assessment of changes in skull shape and correlation of the parameters with caliper cephalometry values and with age. A consecutive cohort of 49 patients < 10 months of age, undergoing orthotic helmet therapy for positional plagiocephaly, was recruited prospectively. The authors routinely assessed the patency of the lambdoid sutures by ultrasound and the following additional skull parameters were measured: suture width, adjacent full bone thickness, adjacent cortical bone thickness and occipital angle. Caliper cephalometric values, as well as demographic and clinical data were collected. Retrospective data analysis showed an inverse relation between both cortical and full skull bone thickness and early treatment efficacy, defined by a reduction in the occipital angle. The improvement of sonographic parameters correlated with the development of cranial caliper cephalometry values. In conclusion, the sonographic assessment of skull bone thickness is a safe and cost-effective tool to predict the early efficacy of orthotic helmet therapy in positional plagiocephaly and might, therefore, help the clinician to foresee the potential evolution of the deformity. Full article