Search Results (11,519)

This work presents the structural analysis and validation of a sub-250 g FPV drone chassis, emphasizing both theoretical rigor and practical applicability. The novelty of this contribution lies in four complementary aspects. First, the structural philosophy introduces a screwless frame with interchangeable arms, joined through interlocking mechanisms inspired by traditional Japanese joinery. This approach mitigates stress concentrations, reduces weight by eliminating fasteners, and enables rapid arm replacement in the field. Second, validation relies on nonlinear static and transient FEM simulations, explicitly including crash scenarios at 5 m/s, systematically cross-checked with bench tests and instrumented flight trials. Third, unlike most structural studies, the framework integrates firmware (Betaflight), GPS, telemetry, and real flight performance, linking structural reliability with operational robustness. Finally, a practical materials pathway was implemented through a dual-track strategy: PETG for rapid, low-cost prototyping, and carbon fiber composites as the benchmark for production-level performance. Nonlinear transient FEM analyses were carried out using Inventor Nastran under multiple load cases, including maximum motor acceleration, pitch maneuvers, and lateral impact at 40 km/h, and were validated against simplified analytical models. Experimental validation included bench and in-flight trials with integrated telemetry and autonomous features such as Return-to-Home, demonstrating functional robustness. The results show that the prototype flies correctly and that the chassis withstands the loads experienced during flight, including accelerations up to 4.2 G (41.19 m/${\mathrm{s}}^2$), abrupt changes in direction, and high-speed maneuvers reaching approximately 116 km/h. Quantitatively, safety factors of approximately 5.3 under maximum thrust and 1.35 during impact confirm sufficient structural integrity for operational conditions. In comparison with prior works reviewed in this study, the key contribution of this work lies in unifying advanced, crash-resilient FEM simulations with firmware-linked flight validation and a scalable material strategy, establishing a distinctive and comprehensive workflow for the development of sub-250 g UAVs. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Recent research efforts have focused on improving the performance of metal-oxide semiconductor (MOS) gas sensors through their thermal modulation using integrated heaters. This approach allows us to enhance the selectivity of measurements; however, the main challenge with this amelioration lies in interpreting the sensor response, which takes the form of complex patterns that require the application of advanced signal processing techniques. This study introduces a methodology for the quantitative determination of volatile compounds (ethanol and methanol at various concentrations ranging from 31 to 2000 ppm for each of these compounds) in mixtures using a single thermally modulated MOS gas sensor. The recorded responses of the detector were interpreted by combining two signal processing techniques: principal component analysis (PCA) for feature extraction, and artificial neural networks (ANNs) for predicting the levels of the tested volatile components. The proposed methodology demonstrated satisfactory performance achieving R² values at the level of 0.999 across all datasets (learning, test, validation) and low error metrics (RMSE = 11.6–14.4 ppm), thereby confirming the robustness and accuracy of the approach and its applicability in a wide range of fields where rapid, cost-effective, and precise detection of ethanol and methanol is essential. Full article

Aiming to address the frequent design conflicts arising during multi-disciplinary collaboration in the detailed design phase of large cruise ships, coupled with the inadequacy of traditional methods in detecting unknown constraints, this paper proposes a hybrid conflict detection framework integrating interval propagation with intelligent algorithms. First, using the piping design of a cruise ship’s water supply system as a typical scenario, design constraints are categorized into known and unknown sets. For known constraints, the interval propagation algorithm is employed for rapid inference and verification. For unknown constraints that are difficult to express explicitly, an improved particle swarm optimization (IPSO) algorithm is proposed to optimize the parameters of a radial basis function (RBF) neural network, thereby constructing an IPSO-RBF conflict detection model. Case studies demonstrate the interval propagation algorithm’s efficacy in identifying conflicts within water supply pipeline designs. Concurrently, testing against historical design datasets reveals that the IPSO-RBF model outperforms multiple comparative models, including PSO-RBF, AFSA-RBF, etc., in terms of conflict detection accuracy, precision, and recall. This validates the method’s effectiveness and superiority in resolving design conflicts within complex systems for large cruise ships. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

The aramid honeycomb composite material plays an important role in industry. Defects of this material seriously influence its performance. However, conventional detecting tools such as X-ray or computer tomography (CT) imaging, ultrasonic testing, and visual inspection are not able to meet the requirements of fast, safe, and high resolution at the same time. In this study, we numerically use rapid terahertz time−domain spectroscopy (THz-TDS) to identify defects in the aramid paper composite structure effectively. Simulation results demonstrate that THz-TDS technology enables the non-destructive reflection imaging of layered defects in glass fiber covering and glue layers as supporting components within the composite structure, with a spatial resolution of 0.5 mm and a depth range exceeding 10 mm. During the study, the finite difference time domain (FDTD) simulation with a real pulse waveform is achieved, and the defect position can be recognized by the anomaly in the reflection profile when compared with the waveform reflected by non-defect samples. At the same time, it is found that the defect identification ability is obviously affected by the incident position. The numerical research illustrates that the detectable defect is as thick as 0.1 mm and has a diameter of 1 mm. The results will offer valuable guides to the real application of THz-TDS systems in the detection of a similar structure. Full article

Macrolides are crucial for the management and treatment of bovine respiratory disease (BRD). However, antimicrobial resistance (AMR) threatens the efficacy of these and other antimicrobials. We developed real-time recombinase polymerase amplification (RPA) assays targeting three clinically relevant macrolide antimicrobial resistance genes (ARGs)—msrE-mphE and erm42—in ≤30 min using extracted DNA. A set of 199 deep nasopharyngeal swabs (DNPS) collected from feedlot calves near the time of arrival were selected based on bacterial culture (BC) results for Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni and antimicrobial susceptibility testing (AST) for tulathromycin, tilmicosin, tildipirosin, or gamithromycin. Samples were also tested for the same targets using RPA and polymerase chain reaction (PCR). In samples that were culture-positive for one or more macrolide-resistant BRD-associated bacteria (n = 101), msrE-mphE and/or erm42 were detected in 95% of cases using RPA. The remaining 98 samples were either culture-negative, or the recovered bacteria were macrolide-susceptible: 43% of these were RPA-positive for at least one macrolide ARG. Together with BC-AST and PCR, Bayesian latent class modelling estimated the clinical sensitivity of RPA for macrolide ARGs to be 95% and specificity to be 58%, with moderate agreement between RPA and BC-AST (κ = 0.52) or PCR (κ = 0.55). The estimated sensitivity of the RPA multiplex assay for the targeted macrolide ARGs was very good, although estimated specificity was limited. However, Sanger sequencing confirmed RPA detection of msrE-mphE in BC-AST/PCR-negative samples (n = 23), reflecting the presence of this locus in non-target bacteria, as well as potential ARG variants among BRD bacteria. These findings support the potential of RPA for rapid ARG detection from extracted DNA. Continued assay optimization and evaluation for detection of respiratory bacteria and ARGs will further enhance its diagnostic utility. Full article

Polymer-dispersed liquid crystal (PDLC), as an electrically controlled dimming material, has broad application prospects in various fields, including smart glass, display technology, and optical devices. However, traditional PDLC materials still face some challenges in practical applications, such as a high driving voltage and insufficient optical contrast, which limit their further application in high-performance optoelectronic devices. In this study, PDLC composite films exhibiting low-voltage operation (23 V), high contrast ratios (135), and rapid response times (T_R ~1.28 ms, T_D ~48 ms) were developed. This was achieved by modifying the chain length of the crosslinking agent and polymer monomer as well as by incorporating molybdenum disulfide (MoS₂) nanosheets. It shows a good regulation ability in the sunlight range (ΔT_sol = 63.92%, ΔT_lum = 73.97%). Simultaneously, the various chemical bonds inside the film and its special network structure enable it to exhibit a good radiative cooling effect. The indoor sunlight simulation tests showed that the indoor temperature decreased by 5 °C. This study provides valuable ideas for the development and preparation of smart windows with high efficiency and energy savings. Full article

Image clustering analysis faces the curse of dimensionality, distance concentration, multimodal landscapes, and rapid diversity loss that challenge meta-heuristics. Meanwhile, the standard Crayfish Optimization Algorithm (COA) has shown notable potential but often suffers from poor convergence speed and premature convergence. To address these issues, this paper introduces a Chaos-initiated and Adaptive Multi-guide Control-based COA (CMCOA). First, a chaotic initialization strategy is employed by explicitly exploiting the reflection symmetry of logistic-map chaotic sequences together with opposition-based learning, which enhances population diversity and facilitates early exploration of promising regions. Second, a fitness-feedback adaptive parameter control mechanism, motivated by the general idea of the MIT rule, is integrated to dynamically balance exploration and exploitation, thereby accelerating convergence while mitigating premature stagnation. Furthermore, a multi-guide stage-switching strategy is designed to avoid being trapped in local optima by promoting adaptive transitions between exploration phases and exploitation phases. CMCOA is benchmarked against competing algorithms on ten challenging test functions drawn from CEC2017, CEC2019, CEC2020, and CEC2022 suites. We also conducted multispectral clustering, where class differences often lie in reflectance magnitude; we adopt Euclidean distance for its efficiency and suitability in capturing such variations. Compared with other algorithms, CMCOA shows faster convergence, higher accuracy, and improved robustness, revealing its broader potential for image analysis tasks. Full article

The accumulation of polyethylene terephthalate (PET) in the environment demands efficient microbial strategies for its degradation. This study evaluates the biodegradation potential of Schizophyllum commune BNT39 toward bis(2-hydroxyethyl) terephthalate (BHET), a major PET intermediate, and PET itself. Clear halos on BHET-agar plates indicated extracellular hydrolytic activity. In liquid culture, thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC) analyses revealed a three-phase degradation profile characterized by rapid BHET hydrolysis, transient dimer accumulation, and subsequent conversion to terephthalic acid (TPA). BHET was reduced by approximately 96% within seven days, while TPA accumulation reached 0.8 mg/mL after 30 days of incubation. Although PET degradation was limited, TPA was consistently detected as the principal product, with no BHET or MHET intermediates. To explore strategies for enhancing enzymatic activity, apple-derived cutin, PET, BHET, and polycaprolactone (PCL) were tested as inducers. Cutin markedly stimulated extracellular enzyme production, suggesting activation of cutinase-like enzymes. Overall, S. commune BNT39 demonstrates the ability to transform PET-related substrates, with cutin emerging as a promising natural stimulant to enhance enzymatic depolymerization. Future studies should focus on enzyme purification, activity profiling, and reaction optimization near PET’s glass transition temperature, where the polymer becomes more accessible for enzymatic attack. Full article

This paper presents an automated threshold-based multi-channel epileptic seizure detection algorithm designed for low-complexity hardware implementations. The algorithm relies on two discriminative, computationally simple time-domain features, based on power and amplitude variations, that enable accurate and timely detections due to their rapid adaptiveness to fluctuations in neural activity. To ensure long-term functionality and high sensitivity, system thresholds are optimized through an offline calibration process that exploits the statistical analysis of patient-specific inter-ictal and ictal periods. The novelty of the approach lies in its multi-channel decision-making strategy, which enhances reliability against false alarms. The proposed algorithm is tested on multiple datasets to assess its adaptability to different recording conditions, achieving roughly 98% accuracy and over 98% sensitivity on both the EEG CHB-MIT dataset and the iEEG SWEC-ETHZ dataset, with average latencies of 3.37 s and 7.84 s, respectively. These results are comparable to, and in some cases outperform, several published machine-learning-based approaches. On the hardware side, FPGA synthesis highlights the minimal and scalable resource requirements of the proposed architecture, achieved through Time-Division Multiplexing (TDM) of both filtering and feature extraction. When compared to state-of-the-art proposals, the system emerges as an ideal candidate for real-time, resource-constrained hardware implementations. Full article

With the rapid development of offshore oil and gas fields in China, there is an increasing demand for high-efficiency and high-reliability compression equipment. This study presents the design and localization of a 25 MW gas turbine-driven centrifugal compressor unit specifically developed for offshore platforms. Based on performance calculations, the gas turbine and compressor were selected and structurally optimized. A skid-mounted base frame with vibration isolation was designed to adapt to offshore steel deck structures, and a control system was developed and integrated. Performance verification was conducted through risk-based type tests. The results show that the unit demonstrates excellent operational stability, high efficiency, and reliability, fully meeting the requirements of offshore oil and gas applications. This work provides technical support and engineering experience for promoting the localization of key offshore equipment. Full article

Left-sided congestive heart failure (CHF) is a common cause of acute respiratory distress in cats, and echocardiographic assessment of left atrial (LA) size is an important test to differentiate it from respiratory diseases that cause similar clinical signs. Furosemide, a potent loop diuretic, is the first-line therapy for cardiogenic pulmonary edema, but its effect on LA size has not been systematically investigated in cats. Some dyspneic cats are referred after having received high doses of furosemide by the referring veterinarian without prior point-of-care ultrasound (POCUS). This can make the diagnosis of CHF challenging. If furosemide significantly reduces left atrial size, it could potentially lead to misdiagnosis, by erroneously categorizing these cats as not having CHF. This prospective, observational multicenter study enrolled 25 cats with acute left-sided CHF. Point-of-care ultrasound was used to assess LA to aortic ratio (LA:Ao) and maximal LA diameter (LAD) at admission and three hours after furosemide administration. Significant reductions were observed in LA:Ao (2.48 ± 0.35 to 2.17 ± 0.40; p < 0.001), LAD (21.0 ± 2.8 mm to 18.4 ± 3.2 mm; p < 0.001), and respiratory rate (64 ± 30 to 40 ± 14 breaths/min; p < 0.001). Normalization of respiratory rate occurred in 50% of cats, while normalization of maximum LAD occurred in 32%. One cat achieved normalization of LA:Ao. We found that furosemide induced rapid reduction in LA size and respiratory rate in cats with left-sided CHF. Clinicians should be aware that severe LA dilation can be absent in referred dyspneic cats that had already received furosemide. Full article

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion. Full article

Evaluating electric vehicle (EV) motor performance over dynamic drive cycles is essential for accurate energy efficiency prediction and system-level optimization. While conventional steady-state models enable rapid generation of efficiency maps, they can introduce significant errors due to grid interpolation and the omission of transient dynamics. Limited understanding exists regarding how grid coarseness and modeling approach affect the discrepancy between steady-state and time-stepping solutions. This study quantifies these differences for a laboratory-scale induction motor (IM) operating under down-scaled drive cycles, using experimental time-stepping measurements as a reference. Efficiency maps are developed using three methods—analytic modeling, finite element analysis (FEA), and experimental testing—while time-stepping simulations are conducted using an analytic model. The study evaluates both total drive cycle energy efficiency errors and pointwise deviations across the torque–speed envelope for various grid resolutions. Results are compared against laboratory-based time-stepping measurements to identify trade-offs between computational efficiency and accuracy. Additionally, the analysis evaluates the impact of operating point (OP) placement within the grid and temperature variation on the accuracy of efficiency maps. Full article