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This paper aims to develop an on-board shock absorber detection method for general aviation aircraft. The effects of common gas and oleo leakage are analyzed in this paper. Based on the principle of landing gear dynamics, it is found that gas leakage and oleo leakage would mainly affect air spring force of shock absorbers in various ways. A rigid-flexible coupled landing gear multi-body system (MBS) model is developed by considering strut flexibility, aiming to offer more accurate simulated responses. A database is developed that considers common leakage faults and typical landing conditions using the developed landing gear model. A deep learning model is proposed in this paper. The proposed model is trained and tested using the database simulated from the rigid-flexible coupling landing gear model. The proposed method demonstrates robust detection performance, achieving over 95% precision for most fault types. This work provides a practical, sensor-efficient solution for real-time health monitoring of landing gear shock absorbers, contributing to improved maintenance strategies and operational safety for general aviation aircraft. As this is a preliminary feasibility study, full validation requires future drop tests or instrumented flight tests.

This study presents an innovative approach for diagnosing gearbox gear faults by enabling numerical vibration data analysis using image-based deep learning models. The Gearbox Fault Diagnosis Data set available on Kaggle was used to collect vibration signals from four different sensors (a1, a2, a3, a4). The maximum, minimum, and mean values of these signals were calculated and normalized within the [0-255] range and then mapped to the red, green, and blue (RGB) color channels, respectively. As a result, 500 images of 256 × 256 pixels were generated for each category. Then, these image representations were used to train a pre-trained U-Net deep learning model for segmentation, with only 10 training epochs. The model achieved a classification accuracy of 99.87% and an mean average precision (mAP) score of 99.74%. These high-performance metrics demonstrate that converting non-visual numerical data into RGB images and analyzing them using convolutional neural networks (CNNs) offers significant advantages over commonly used machine learning and text-based deep learning methods.To the best of our knowledge, this is the first study to classify numerical sensor data with such high accuracy by converting it into a visual format. The proposed method not only advances the field of gearbox fault detection and introduces a new paradigm for solving similar signal-based engineering problems in the literature.

Campylobacter coli is a major zoonotic foodborne pathogen, but its detection through conventional culture-based methods is challenged by intricate taxonomy, fastidious growth requirements, and auxotrophic characteristics. Prompt and precise detection of C. coli is therefore crucial for effective outbreak control and robust food safety monitoring and recall. This study describes the development and evaluation of a GEAR (genome exponential amplification reaction) test for the precise detection of C. coli. The assay combined three key elements: a simplified centrifugation-assisted DNA extraction protocol, thermal cycler-free amplification, and SYBR Green I dye-mediated visualization. Targeting the glyA gene of C. coli, the assay demonstrated high specificity, distinguishing C. coli from a non-coli strain and fourteen other bacterial species associated with foodborne diseases. GEAR amplification was performed at 62 °C for 60 min, achieving an analytical sensitivity of 50 fg/reaction, which was 100-fold higher than real-time PCR (5 pg/reaction) and 1,000-fold higher than end-point PCR (50 pg/reaction) using purified genomic DNA. In artificially contaminated pork samples, the assay detected as low as 250 CFU/g without prior enrichment and 25 CFU/g following a 12-hour enrichment, representing a 10-fold lower detection limit than real-time PCR in the food matrix. With rapid results, operational simplicity, low cost, high sensitivity, and minimal instrumentation requirements, the GEAR assay offers a promising tool for routine C. coli detection in resource-limited food testing laboratories. This represents the first reported application of GEAR for C. coli screening.

For enhancing the tribological behavior of gear-driven systems, TA, AO, GNS, SiCf and Ti are used to prepare Ti-based composite foils that are subsequently combined with Ti2AlNb foils to prepare laminated samples for use in aviation gearing systems. Friction and wear tests are executed to demonstrate the optimum tribological behavior of TAGSTT, coupled with ideal reductions in the friction coefficient (57.89%) and wear rate (60.32%) relative to that of TT. These improved metrics are probably because the SiCf, GNS, TA and AO induce smaller nanocrystals in the lubrication film on the transition layer, which improves the self-healing ability of wear scars, prevents the wear interface from damage, and imparts excellent tribological behavior to the TAGSTT. These results provide a propagable method for the tribological design of drive-gear systems.

Safe physical human-robot interaction (pHRI) with compact, high-ratio actuators remains challenging in the absence of dedicated torque sensing. While high transmission ratios enable lightweight and efficient actuation, they are widely believed to degrade the ability to detect external disturbances through motor-side measurements - yet this limitation has not been rigorously quantified. We introduce a torque-sensorless control framework for a high-ratio, backdrivable Wolfrom gearbox (222:1), combining a double-inertia analytical model with a higher-order sliding-mode disturbance observer (HOSM-DOB). The analytical model yields a closed-form expression for disturbance-detection bandwidth as a function of gear ratio and motor inertia. The HOSM-DOB estimates external torques in real time using only motor-side measurements and is integrated into an impedance controller with active inertia shaping to reduce effective reflected inertia and mitigate impact forces. Experimental validation on a single-degree-of-freedom testbed, including controlled collisions measured with a Pilz Robot Measurement System (PRMS), demonstrates torque estimation errors below 10% for soft contacts. The HOSM-DOB outperforms a classical disturbance observer by 35% in detecting high-frequency impacts. Compliance with ISO/TS 15066 safety limits is confirmed up to a maximum motor speed of 7000 rpm, comparable to existing collaborative robot platforms. The bandwidth analysis reveals a fundamental trade-off between gear ratio, motor inertia, and sensing performance, demonstrating that classical inertia-matching principles do not maximize impact detectability. The results establish that high-ratio, backdrivable actuators - when combined with observer-based control - can achieve both high torque density and safe interaction, providing a viable alternative to direct-drive and harmonic-drive solutions in human-centered robotics.

To overcome the limitations of single-dimensional data and low efficiency in traditional cycloidal gear inspection, a comprehensive machine vision-based method was proposed. A high-precision vision platform was established, and a Sigmoid surface-based edge detection algorithm was employed for sub-pixel edge localization. Logarithmic transformation combined with light intensity compensation was applied to correct saturation-induced errors. The pixel equivalent and compensation coefficient were systematically calibrated using a dot-matrix plate and gauge blocks. A sub-pixel tooth profile model in the physical coordinate system was reconstructed through pixel equivalent calibration, dynamic light intensity compensation, and multi-coordinate transformation. Comparative tests against a coordinate measuring machine (CMM) verified that the point-to-point deviation between the two measurement systems was within 10 μm (maximum 11.62 μm). The inherent tooth profile deviation of the tested cycloidal gears, which reflects the machining quality of workpieces, ranged from 24 μm to 37 μm. Multiple repeated tests prove that the system achieves a repeat positioning accuracy of 0.8 μm. Based on the measurement characteristics, a hybrid analytical method integrating Cartesian and polar coordinate systems was developed, enabling the simultaneous evaluation of critical geometric tolerances, such as the diameters of the center hole and crankshaft hole. The full inspection cycle for cycloidal gears was reduced to 13 s, which demonstrates a substantial efficiency improvement over traditional methods.

This study demonstrates the feasibility and performance of a pilot-scale hybrid membrane-aerated biofilm reactor (MABR) equipped with an innovative gear-structured polyethylene membrane. Over 210 days of continuous operation, the integrated system-comprising an anaerobic tank, a hybrid MABR tank, containing 2-16 cassettes within a membrane module, and an aerobic tank-achieved removal efficiencies of 88 ± 12% for BOD, 40 ± 14% for total nitrogen, and 90 ± 14% for ammonia. The total sludge yield was 0.20 kg-TSS/kg-CODMn, significantly lower than that of conventional activated sludge systems. Spatial and temporal variations in oxygen transfer rate (OTR) and efficiency (OTE) were observed across membrane cassettes. The average OTR ranged from 3.51 to 9.63 g-O₂/(m²·day) and was influenced by operating time and membrane surface area, with lower surface areas enhancing OTR. These results highlight the importance of hydrodynamics and cassette configuration in optimizing oxygen supply. Campaign-based assessments revealed low N₂O emission factors of 0.058% on day 100 (29.2 °C) and 0.074% on day 190 (18.5 °C). At 18.5 °C, N₂O emissions were primarily attributed to the aerobic tank, while contributions from the MABR tank decreased over time, likely due to biofilm maturation. Combined microbial analyses revealed dense populations of ammonia-oxidizing bacteria within the membrane biofilm, dominated by comammox Nitrospira, which likely contributed to the observed low N₂O emissions. Overall, gear-structured membranes promoted resilient biofilm formation, reduced sludge production, and minimized N₂O emissions. These findings provide important insights into the design and operation of sustainable and energy-efficient MABR systems for wastewater treatment.

Aiming at the problems of diverse defect types, large-scale differences, and complex background interference in gear surface defect detection, a lightweight model, ASW-YOLO, is proposed based on YOLOv8n. By using an ADown dual downsampling module to compress feature map resolution and preserve fine-grained information. C2f_SE channel attention is introduced to enhance small-scale defect response. The CIoU is replaced with WIoU to optimize multi-scale target localization accuracy. The experiments are conducted on the gear dataset. The comparative experiments show that mAP@0.5 of ASW-YOLO reached 94.8%, an increase of 4.5% compared to YOLOv8n, with a reduction of 9.3% in parameter count and 8.5% in computational complexity. The ablation experiments confirm the effectiveness of the three modules. ASW-YOLO achieves a 4.5% increase in mAP@0.5 and a 6.1% increase in recall compared to YOLOv8n. The generalization experiments demonstrate that the mAP@0.5 fluctuation of ASW-YOLO remains below 2% under strong highlight and striped shadow. Moreover, the model maintains over 85% mAP@0.5 under motion blur. ASW-YOLO balances precision and lightweight, making it suitable for real-time quality monitoring in industry.

In free-space optical (FSO) communication systems, vortex beams carrying orbital angular momentum (OAM) have attracted significant attention owing to their unique helical phase structures. To further expand the degrees of freedom of light fields, we propose a novel structured light field termed the rotating gear vortex beam (RGVB), constructed through the mode superposition of two-dimensional Laguerre-Gaussian (LG) beams. The RGVB exhibits a distinctive rotating gear intensity distribution during propagation while maintaining low divergence. Notably, the beam possesses four independent tunable parameters, significantly enhancing the capacity for high-dimensional information encoding. Numerical simulations demonstrate that within a beam quality factor range of 1 to 15, the RGVB provides 204 available encoding modes, which is approximately 3.19 times that of conventional LG beams. Furthermore, the robust encoding performance of the RGVB is validated by transmitting 64-level grayscale images through simulated atmospheric turbulence. Under varying turbulence strengths (D/r0 ranging from 1 to 4), the RGVB consistently outperforms LG beams, with recognition accuracy improvements reaching up to 25.14% in strong turbulence (D/r0 = 4). The obtained results indicate that RGVBs offer superior encoding capacity and turbulence resilience, suggesting great potential for applications in high-capacity FSO communication.

The paper proposes a new protocol for assessing the impact of abandoned, lost or otherwise discarded fishing gear (ALDFG) involving the acquisition of comparable video images across deep and shallow coralligenous habitats, obtained using Remote Operating Vehicles (ROVs) and scientific divers, respectively. Furthermore, a modified Coralligenous Bioconstruction Quality Index (CBQI) applied to the analysis of diver-collected images was used to assess the ecological quality of coralligenous reefs. The new protocol was tested on three case studies applying the BACI (Before-After/Control-Impact) sampling design. Two shallow and one deep coralligenous reefs affected by ALDFG were compared with nearby unaffected reefs, before the ALDFG removal and one year later. Analysis of the index values showed that the ecological quality of the affected sites increased significantly following the removal action. The proposed protocol may represent a valuable tool to support ALDFG management, and could be applied across a wide range of depths.

This finite element study evaluated the biomechanical effects of 2 different miniscrew positions, anterior vs posterior, in high-pull palatal gear (HPPG) mechanics. The objectives were to analyze 3-dimensional tooth displacement, occlusal plane rotation, and the clinical applicability of each configuration. Finite element analysis simulated HPPG mechanics with a midpalatal miniscrew placed at either the first premolar (anterior HPPG) or the first molar level (posterior HPPG). Approximately 200 g of force was applied from lingual buttons on the bilateral lateral incisors to the miniscrew for retraction. Tooth displacement and occlusal plane rotation were evaluated and compared between the 2 configurations. Both configurations produced significant incisor intrusion. The anterior HPPG showed greater incisor intrusion with less molar intrusion, resulting in a pronounced counterclockwise rotation of the occlusal plane and distinct lingual root displacement of the incisors. Posterior HPPG demonstrated a similar intrusion of the anterior and posterior teeth, with consistent distal displacement of both the crowns and roots throughout the dentition, leading to whole-arch intrusion and distalization with minimal incisor axis change and occlusal plane rotation. The HPPG technique is effective for vertical control of the maxillary dentition. The miniscrew position significantly influences the line of force relative to the center of resistance, producing different occlusal plane rotations. Anterior HPPG induces greater incisor intrusion and counterclockwise occlusal plane rotation, whereas posterior HPPG results in comparable anterior and posterior tooth intrusion with minimal occlusal plane alteration. Miniscrew position should be selected according to the desired occlusal plane modification for optimal esthetic and functional treatment outcomes.

As forces prepare for large scale combat environments (LSCO), military medicine must adapt by building an in-theater system capable of providing prolonged casualty care under direct threat, moving away from reliance on access to specialty providers and rapid evacuation. This new approach needs to address mental health (MH) conditions by equipping front-line personnel with MH skills. This work aimed to evaluate the effectiveness of the BH GEAR training, which teaches MH prevention, identification and management skills to soldiers without prior MH training. Soldiers (n = 545) from seven U.S. Army units participated in the study from October 2022-April 2024, attended the training, and completed pre- and post-training surveys on the same day. The six-hour training provided instruction on conducting MH assessments, signs and symptoms of illnesses, interventions to prevent or manage concerns, and considerations for managing medical evacuations. Analyses examined satisfaction with the training, self-report of skills learned, and changes in scope of practice, knowledge and confidence utilizing the skills. Most participants (89%) rated the training as relevant and useful, and learned how to assess for (93%) and provide interventions for (91%) MH concerns. Post-training, participants experienced significant increases in MH-related scope of work, knowledge, and confidence. This work shows that completion of a one-day training is associated with increased MH skill knowledge and anticipated confidence using these skills with military service members. Integrating this training into military curriculums will increase the availability of support during current and future operations, ultimately sustaining readiness and lessening the impact of mental health challenges.

Molecular profiling is central to pediatric precision oncology, yet only 15-20% of patients benefit from biology-guided treatments. This limitation underscores the urgent need for functional precision medicine, which interrogates therapeutic vulnerabilities using patient-derived models. Despite hurdles in establishing these models, such as limited biopsy material and technical screening barriers, recent advances in tumoroids, patient-derived xenografts and high-throughput platforms offer an unprecedented opportunity to bridge the translational gap. In this review, we describe the evolution of patient-derived models of pediatric brain cancers, their current strengths and weaknesses and the technological innovations redefining the field. Together, these insights provide a road map for integrating functional data into clinical decision-making, ultimately aiming to 'cure more and heal better' children with brain tumors.