Search Results (646)

In high-temperature testing scenarios that rely on contact, fine-wire thermocouples demonstrate commendable dynamic performance. Nonetheless, their thermal inertia leads to notable dynamic nonlinear inaccuracies, including response delays and amplitude reduction. To mitigate these challenges, a novel dynamic error correction approach is introduced, which combines a Continuous Restricted Boltzmann Machine, Deep Belief Network, and Physics-Informed Neural Network (CDBN-PINN). The unique heat transfer properties of the thermocouple’s bimetallic structure are represented through an Inverse Heat Conduction Equation (IHCP). An analysis is conducted to explore the connection between the analytical solution’s ill-posed nature and the thermocouple’s dynamic errors. The transient temperature response’s nonlinear characteristics are captured using CRBM-DBN. To maintain physical validity and minimize noise amplification, filtered kernel regularization is applied as a constraint within the PINN framework. This approach was tested and confirmed through laser pulse calibration on thermocouples with butt-welded and ball-welded configurations of 0.25 mm and 0.38 mm. Findings reveal that the proposed method achieved a peak relative error of merely 0.83%, superior to Tikhonov regularization by −2.2%, Wiener deconvolution by 20.40%, FBPINNs by 1.40%, and the ablation technique by 2.05%. In detonation tests, the corrected temperature peak reached 1045.7 °C, with the relative error decreasing from 77.7% to 5.1%. Additionally, this method improves response times, with the rise time in laser calibration enhanced by up to 31 ms and in explosion testing by 26 ms. By merging physical constraints with data-driven methodologies, this technique successfully corrected dynamic errors even with limited sample sizes. Full article

Karate is divided into two disciplines, Kata (forms) and Kumite (sparring), both of which are strongly influenced by the function of the tibiotarsal joint. However, the performance model differences between the two have not yet been thoroughly explored. The aim of this study is to evaluate the differences in ankle range of motion between Kata and Kumite, investigating the correlations between joint mobility, elastic strength, and Rate of Force Development (RFD). The sample consisted of 36 athletes, of male sex, evenly split between the two disciplines, who underwent a specific training protocol for three months. Three tests were administered: Weight Bearing Lunge, Counter Movement Jump, and Squat Jump. Data were analysed using Pearson’s correlation. In the Kata group, a moderate negative correlation emerged between ankle ROM and elastic strength (R = −0.521), and between ankle ROM and RFD (R = −0.570). In the Kumite group, the correlations were weakly negative: R = −0.261 for elastic strength and R = −0.257 for RFD. Greater ankle mobility, typical of Kata, appears to be associated with lower explosive capabilities, whereas more limited mobility in Kumite correlates with higher reactive strength and a faster rate of force development. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

The inversion of shallow underground vibration fields primarily relies on signals collected by numerous sensors deployed on the surface. However, the accuracy of inversion is affected by the spatial distribution of these sensors. Therefore, under limited measurement points, signal reconstruction at unknown locations remains a critical challenge. To address this problem, we developed an SC-Net-based self-supervised interpolation method for missing wavefield data in shallow subsurface applications. This study utilizes incomplete seismic data acquired in real-world scenarios to train a neural network for seismic data interpolation, thereby expanding the sampled signals required for inversion. Since available seismic data samples are often scarce in practice, we adopt a hybrid training strategy combining simulated and real data. Specifically, a large number of numerically simulated samples are jointly trained with a limited set of real-world measurements. Furthermore, to enhance the robustness of network outputs, we integrate the Mean Teacher model framework and propose a self-supervised learning approach for missing data. Additionally, to enable the network to effectively capture long-range dependencies in both frequency and spatial domains of seismic data, we introduce a dual-branch feature fusion network that jointly models channel-wise and spatial relationships. Finally, in our actual field explosion experiments conducted at the test site, we demonstrated improved accuracy of our method through comparative analysis with several typical interpolation neural networks. Three ablation studies are also designed to demonstrate the effectiveness of the proposed approach. Full article

Background: Only a limited number of studies have examined the effects of short-term, strength–speed-oriented velocity-based training (VBT) on lower-body power in female junior volleyball players and elite female artistic gymnasts. The present study aimed to investigate the impact of a four-week VBT intervention on jump performance and force–velocity characteristics in these athletes. Methods: Seven junior female volleyball players (age: 17.4 ± 0.9 years; height: 179.4 ± 6.5 cm; weight: 74.01 ± 3.5 kg) (top-league team members), and seven elite female artistic gymnasts (age: 17.6 ± 2.9 years; height: 159.6 ± 7.2 cm; weight: 59.3 ± 6.3 kg) (National Team members) completed two weekly training sessions for four weeks, each consisting of four sets of six repetitions of parallel back squats (PBSs) and hip thrusts (HTs). Training loads were regulated using barbell velocity targets (PBSs: 0.46–0.72 m/s; HTs: 0.36–0.60 m/s). Pre- and post-intervention assessments included loaded (15–60% body mass) and unloaded squat jumps (SJs) and countermovement jumps (CMJs) to determine peak power output, jump height, and force–velocity profiles. Results: Volleyball players showed significant improvements in peak power predominantly during loaded SJs (SJ45%: +5.5%, p < 0.01; SJ60%: +5.7%, p < 0.05), whereas gymnasts exhibited greater gains in loaded CMJs (CMJ60%: +7.7%, p < 0.01). In contrast, unloaded SJ and CMJ performances remained largely unchanged for all athletes. Both groups demonstrated a significantly steeper post-intervention force–velocity profile (p < 0.001), indicating an enhanced capacity to produce force at lower movement velocities under external loading. Conclusions: Strength–speed-oriented VBT was effective in improving power production under loaded conditions but had limited transfer to unloaded jump performance. These findings highlight the necessity of subsequent training blocks emphasizing high-velocity, sport-specific movements to optimize explosive performance. Future studies should further investigate low-velocity-loss training protocols as a potential means of enhancing unloaded jump outcomes. Full article

Introduction: Massive burns, particularly those exceeding 90% total body surface area (TBSA), represent one of the most demanding challenges in critical care and reconstructive surgery. Advances in resuscitation, early excision, and wound coverage techniques have improved survival rates, but despite these advances, mortality remains high, and standardized treatment protocols are lacking. Case Report: We report a case which demonstrates survival and meaningful recovery in an extreme case of massive burns. A 57-year-old woman sustained 92% TBSA burns following a gas explosion at her home. She developed burn shock requiring aggressive fluid resuscitation and vasopressor support. Due to extensive burns and limited donor sites, staged debridement with temporary allograft coverage was performed, followed by Meek micrografting for definitive wound closure. After 197 days in the Burn Unit and an additional three months of rehabilitation, she regained functional independence. Conclusions: While historically considered non-survivable, burns exceeding 90% TBSA are increasingly being successfully treated with multimodal strategies. This case highlights the importance of multidisciplinary care in redefining survival expectations for massive burn patients. As burn care continues to evolve, further research is needed to refine treatment strategies, enhance long-term functional outcomes and standardize protocols for these complex cases. Full article

Handball is a sport that demands explosive movements and unique skills, and its popularity has been rising in recent years. This study evaluated elite handball players’ nutritional supplement (NS) use profiles and the differences in sex, competition level, and competition type based on the Australian Institute of Sport (AIS) criteria. The data collection form contains questions about participants’ sociodemographic characteristics, training details, use of supplements, and related factors. Supplements were classified into A, B, C, and D classes according to the scientific evidence level of the AIS. The study involved 92 elite handball athletes, comprising 48 professionals and 44 amateurs, and included 37 females and 55 males. The most frequently used supplements among participants were magnesium (37.0%) (Group C—AIS), vitamin C (20.7%) (Group B—AIS), whey protein (19.6%), sports bars (19.6%), and vitamin D (19.6%) (Group A—AIS). Regarding sex differences, a significant difference was observed only in Group C supplements, with male players using them more frequently than female players (p < 0.05). Professional athletes exhibited a significantly higher prevalence of supplement use, covering total, Group A, sports foods, performance supplements, and Groups B and C, relative to amateur players (p < 0.05). The results reveal that handball players have limited awareness of NS, emphasizing the need for training and consulting services. Full article

Objectives: Plyometric training is a well-established method for enhancing jump performance in basketball and volleyball athletes but has certain limitations. Kettlebell training may provide a viable alternative as it mimics key biomechanical aspects of jumping, like explosive hip and knee extension during a ballistic hip–hinge pattern. Because evidence remains limited, this study aimed to compare the effects of both training methods. Methods: Thirty-eight volleyball and basketball club athletes (age: 22 (4.3); male = 29, female = 9) completed this study. Countermovement jump (CMJ), squat jump (SJ), drop jump (DJ), body fat percentage (FM), and muscle mass percentage (MM) were assessed pre- and post-intervention. The participants were assigned to one of three groups: a kettlebell training group (KbG), a plyometric training group (PG), or a control group (CG). Both the KbG and PG completed two supervised 25-min training sessions per week for six weeks, while the CG did not engage in any additional training intervention. The level of significance was set at p ≤ 0.05. Results: There were no significant differences in CMJ, SJ, and DJ performance between the groups before the intervention. Significant differences in change between the groups from pre- to post-test were found for the SJ (p = 0.006), but not for the DJ (p = 0.06), CMJ (p = 0.26), FM (p = 0.9), and MM (p = 0.55). Pairwise comparisons revealed significantly greater positive change in the KbG than in the CG for the SJ (p = 0.003) and DJ (p = 0.03). Within-group analyses showed significant improvements in the KbG for the CMJ (p = 0.04), SJ (p < 0.001), and DJ (p = 0.003) performance, whereas FM and MM did not change. Within the PG and CG, no significant change occurred. Conclusions: Kettlebell training effectively improved jump performance and may therefore serve as a valuable component within strength and conditioning programs for basketball and volleyball athletes. Full article

There is a need to develop rapid, in situ methods that require less sample preparation and lower limits of detection for the detection of High Explosives (HEs). Considering that human hair is one of the primary attributes of the human body, its presence can be used to identify possible traces of hair evidence for forensic screenings. Using non-invasive in situ approaches coupled with multivariate analysis (MVA) can enable rapid detection, thereby decreasing analysis time and reducing the cognitive load on analysts, with response times as low as milliseconds or lower. This preliminary study demonstrates the detection of 2,4,6-trinitrotoluene (TNT), 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), and pentaerythritol tetranitrate (PETN) on black, bleached, and natural gray human hair coupled with principal component analysis (PCA). It was possible to discriminate the HE signals from those of the substrates (hair types) on black, gray, and bleached hair by monitoring characteristic peaks for the nitro group’s vibrations of the explosives. Gray hair presented good discrimination for the explosives due to the absence of melanin. The best modes for discriminating HEs from all three hair types were identified using PCA, with data pretreatment based on the first and second derivatives of the algorithms. The classifications were based on the more substantial variation in the NO₂ symmetric vibration for each HE. Full article

Identifying concealed explosives in X-ray backscatter (XRBS) imagery remains a critical challenge, primarily due to low image contrasts, cluttered backgrounds, small object sizes, and limited structural details. To address these limitations, we propose YOLOv11-XRBS, an enhanced detection framework tailored to the characteristics of XRBS images. A dedicated dataset (SBCXray) comprising over 10,000 annotated images of simulated explosive scenarios under varied concealment conditions was constructed to support training and evaluation. The proposed framework introduces three targeted improvements: (1) adaptive architectural refinement to enhance multi-scale feature representation and suppress background interference, (2) a Size-Aware Focal Loss (SaFL) strategy to improve the detection of small and weak-feature objects, and (3) a recomposed loss function with scale-adaptive weighting to achieve more accurate bounding box localization. The experiments demonstrated that YOLOv11-XRBS achieves better performance compared to both existing YOLO variants and classical detection models such as Faster R-CNN, SSD512, RetinaNet, DETR, and VGGNet, achieving a mean average precision (mAP) of 94.8%. These results confirm the robustness and practicality of the proposed framework, highlighting its potential deployment in XRBS-based security inspection systems. Full article

This review critically evaluates the performance of approximately 80 commercially available mobile detectors for explosive identification. The majority of devices utilize Ion Mobility Spectrometry (IMS), Fourier Transform Infrared Spectroscopy (FTIR), or Raman Spectroscopy (RS). IMS-based instruments, such as the M-ION (Inward Detection), typically achieve sensitivities at the ppt level, while other IMS implementations demonstrate detection ranges from low ppb to ppm. Gas Chromatography–Mass Spectrometry (GC–MS) systems, represented by the Griffin™ G510 (Teledyne FLIR Detection), provide detection limits in the ppb range. Transportable Mass Spectrometers (Bay Spec) operate at ppb to ppt levels, whereas Laser-Induced Fluorescence (LIF) devices, such as the Fido X4 (Teledyne FLIR Detection), achieve detection at the nanogram level. Quartz Crystal Microbalance (QCM) sensors, exemplified by the EXPLOSCAN (MS Technologies Inc. 8609 Westwood Center Drive Suite 110, Tysons Corner, VA, USA), typically reach the ppb range. Only four devices employ two orthogonal analytical techniques, enhancing detection reliability and reducing false alarms. Traditional colorimetric tests based on reagent–analyte reactions remain in use, demonstrating the continued relevance of simple yet effective methods. By analyzing the capabilities, limitations, and technological trends of current detection systems, this study underscores the importance of multi-technique approaches to improve accuracy, efficiency, and operational effectiveness in real-world applications. The findings provide guidance for the development and selection of mobile detection technologies for security, defense, and emergency response. Full article

To meet safety regulations in underground coal mines, wireless power transfer (WPT) systems must house both the transmitter and receiver within explosion-proof enclosures. However, eddy currents induced on the surfaces of these non-ferromagnetic metal enclosures significantly hinder magnetic flux coupling, thereby reducing transmission efficiency. This paper proposes a slotting technique applied to explosion-proof enclosures to suppress eddy currents, along with the integration of magnetic flux focusing materials into the coils to enhance coupling. Simulations were conducted to compare three system configurations: (i) a WPT system without enclosures, (ii) a system with solid (unslotted) enclosures, and (iii) a system with slotted enclosures. The results show that solid enclosures reduce efficiency to nearly zero, whereas slotted enclosures restore efficiency to 90% of the baseline system without enclosures. Joule heating remains low in the slotted explosion-proof enclosures, with energy losses of 2.552 J for the transmitter enclosure and 2.578 J for the receiver enclosure. A conservative first-order estimation confirms that the corresponding temperature rise in the enclosure surfaces remains below 50 °C, which is well within the 150 °C limit stipulated by the Chinese National Standard GB 3836.1-2021 (Explosive Atmospheres—Part 1: Equipment General Requirements). These findings confirm effective eddy current suppression and efficiency recovery without compromising explosion-proof safety. The core innovation of this work lies not merely in the physical slotting approach, but in the development of a precise equivalent circuit model that fully incorporates all mutual inductance components representing eddy current effects in non-ferromagnetic explosion-proof enclosures, and its integration into the overall MCR-WPT system circuit. Full article

Lithium iron phosphate (LFP) batteries have become a popular choice for energy storage, electrified mobility, and plants. All lithium-based batteries produce flammable vent gas as a result of failure through thermal runaway. LFP cells produce less gas by volume than nickel-based cells, but the composition of this gas most often contains less carbon dioxide and more hydrogen. However, when LFP cells fail, they generate lower temperatures, so the vent gas is rarely ignited. Therefore, the hazard presented by a LFP cell in thermal runaway is less of a direct battery fire hazard but more of a flammable gas source hazard. This research identified the constituents and components of the vent gas for different sized LFP prismatic cells when overcharged to failure. This data was used to calculate the maximum homogenous concentration of gas that would be released into a 1.73 m³ test rig and the percentage of the lower explosive limit (LEL). Overcharge experiments were conducted using the same type of cells in the test rig in the presence of remote ignition sources. Ignition and deflagration of the vent gas were possible at concentrations below the theoretical LEL of the vent gas if it was homogeneously mixed. Full article

Objectives: The primary objective was to compare the usage of Hazardous Materials (HazMat) Protective Personal Equipment (PPE) and ordinary PPE when performing basic and advanced health care support maneuvers in a prehospital setting, evaluating the effectiveness of several procedures, defined as the mean success rate of each. The secondary objective was to evaluate the presence of a learning effect, with improvements in the success rate and/or procedure timing. Methods: This was a prospective within-subjects (repeated-measures) study conducted on Emergency Medical Services (EMS) responders within their Chemical-Biological-Radiological-Nuclear-Explosive (CBRNe) training institutional programme. Volunteers performed a trial sequence of eight lifesaving procedures four times. During the first trial sequence, they wore standard clothing; during the three successive trials, they wore full HazMat PPE equipment. The primary outcomes were changes in success rate and time interval across the four trials. Results: A total of 146 EMS responders volunteered for the experiment. Procedure success rates remained high overall, with the most notable initial drop observed for video-assisted intubation (≈−10%). The only statistically significant delay in the first HazMat trial compared with baseline was for intravenous access (median +30 s; p < 0.001). In the two successive HazMat trials, success rates and timings improved, with median values coming close to baseline. However, only 61% of participants completed the entire drill due to tolerance limits of the equipment. Conclusions: HazMat PPE, while physically and ergonomically demanding, has minimal impact on most lifesaving procedures, though it may reduce intubation success and delay intravenous access. Tolerance to prolonged use is a key limitation, but dexterity improves rapidly with brief practice. EMS responders can benefit from continuous training practice, while manufacturers could explore ergonomic and tolerance improvements in their PPE equipment. Full article

Photogrammetry-based 3D reconstruction of the shape of fast-moving objects from image sequences presents a complex yet increasingly important challenge. The 3D reconstruction of a large number of fast-moving objects may, for instance, be of high importance in the study of dynamic phenomena such as impact experiments and explosions. In this context, analyzing the 3D shape, size, and motion trajectory of the resulting fragments provides valuable insights into the underlying physical processes, including energy dissipation and material failure. High-speed cameras are typically employed to capture the motion of the resulting fragments. The high cost, the complexity of synchronizing multiple units, and lab conditions often limit the number of high-speed cameras that can be practically deployed in experimental setups. In some cases, only a single high-speed camera will be available or can be used. Challenges such as overlapping fragments, shadows, and dust often complicate tracking and degrade reconstruction quality. These challenges highlight the need for advanced 3D reconstruction techniques capable of handling incomplete, noisy, and occluded data to enable accurate analysis under such extreme conditions. In this paper, we use a combination of photogrammetry, computer vision, and artificial intelligence techniques in order to improve feature detection of moving objects and to enable more robust trajectory and 3D shape reconstruction in complex, real-world scenarios. The focus of this paper is on achieving accurate 3D shape estimation and motion tracking of dynamic objects generated by impact loading using stereo- or monoscopic high-speed cameras. Depending on the object’s rotational behavior and the number of available cameras, two methods are presented, both enabling the successful 3D reconstruction of fragment shapes and motion. Full article