Search Results (281)

The long-distance high-voltage transmission tower-line system, frequently traversing active fault zones, is vulnerable to severe symmetry-breaking damage during earthquakes due to asymmetric permanent ground displacements. However, the seismic performance of such systems, particularly concerning symmetry-breaking effects caused by asymmetric fault displacements, remains inadequately studied. This study investigates the symmetry degradation mechanisms in a 1:40 scaled 500 kV tower-line system subjected to cross-fault ground motions via shaking table tests. The testing protocol incorporates representative fault mechanisms—strike-slip and normal/reverse faults—to systematically evaluate their differential impacts on symmetry response. Measurements of acceleration, strain, and displacement reveal that while acceleration responses are spectrally controlled, structural damage is highly fault-type dependent and markedly asymmetric. The acceleration of towers without permanent displacement was 35–50% lower than that of towers with permanent displacement. Under identical permanent displacement conditions, peak displacements caused by normal/reverse motions exceeded those from strike-slip motions by 50–100%. Accordingly, a fault-type-specific amplification factor of 1.5 is proposed for the design of towers in dip-slip fault zones. These results offer novel experimental insights into symmetry violation under fault ruptures, including fault-specific correction factors and asymmetry-resistant design strategies. However, the conclusions are subject to limitations such as scale effects and the exclusion of vertical ground motion components. Full article

This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

We report high-resolution, cavity-enhanced direct frequency comb Fourier transform spectroscopy of cold acetylene (C₂H₂) molecules in a planar supersonic jet expansion. The experiment is based on a near-infrared frequency comb with a 300 MHz effective repetition rate, matched to a high-finesse enhancement cavity traversing the jet. The rotational and translational cooling of acetylene was achieved via expansion in argon carrier gas through a slit nozzle. By interleaving successive mode-resolved spectra measured at different comb repetition rates, we retrieved full absorption line profiles. Spectroscopic analysis reveals sharp, Doppler-limited transitions corresponding to a jet core rotational temperature below 7 K. Frequency comb and cavity stabilization were achieved through active Pound–Drever–Hall locking and mechanical vibration damping, enabling a spectral precision better than 2 MHz, limited by the vibrations induced by the pumping system. The demonstrated sensitivity reaches a minimum detectable absorption of 7.8 × 10⁻⁷ cm⁻¹ over an 18 m effective path length in the jet core. This work illustrates the potential of cavity-enhanced direct frequency comb spectroscopy for precise spectroscopic characterization of cold supersonic expansions, with implications for studies in molecular dynamics, reaction kinetics, and laboratory astrophysics. Full article

Underwater vessel localization using passive magnetic anomaly sensing is a challenging problem due to the variability in vessel magnetic signatures and operational conditions. Data-based approaches may fail to generalize even to slightly different conditions. Thus, we propose an Attentive Neural Process (ANP) approach, in order to take advantage of its few-shot capabilities to generalize, for robust localization of underwater vessels based on magnetic anomaly measurements. Our ANP models the mapping from multi-sensor magnetic readings to position as a stochastic function: it cross-attends to a variable-size set of context points and fuses these with a global latent code that captures trajectory-level factors. The decoder outputs a Gaussian over coordinates, providing both point estimates and well-calibrated predictive variance. We validate our approach using a comprehensive dataset of magnetic disturbance fields, covering 64 distinct vessel configurations (combinations of varying hull sizes, submersion depths (water-column height over a seabed array), and total numbers of available sensors). Six magnetometer sensors in a fixed circular arrangement record the magnetic field perturbations as a vessel traverses sinusoidal trajectories. We compare the ANP against baseline multilayer perceptron (MLP) models: (1) base MLPs trained separately on each vessel configuration, and (2) a domain-randomized search (DRS) MLP trained on the aggregate of all configurations to evaluate generalization across domains. The results demonstrate that the ANP achieves superior generalization to new vessel conditions, matching the accuracy of configuration-specific MLPs while providing well-calibrated uncertainty quantification. This uncertainty-aware prediction capability is crucial for real-world deployments, as it can inform adaptive sensing and decision-making. Across various in-distribution scenarios, the ANP halves the mean absolute error versus a domain-randomized MLP (0.43 m vs. 0.84 m). The model is even able to generalize to out-of-distribution data, which means that our approach has the potential to facilitate transferability from offline training to real-world conditions. Full article

Recent advancements in synchrophasor measurement technologies have introduced an unprecedented level of visibility in power distribution networks (PDNs), providing a high-quality data foundation for the accurate perception of event source locations. However, the high cost and deployment expense pose a significant challenge in balancing system observability and event source location identification (ESLI) accuracy. In this paper, we propose a staged ESLI scheme based on voltage measurement deviation (VMD), which can achieve high-precision ESLI and event current calculations under extremely low-observability conditions, where the measurement devices are deployed only at the head substation and terminal buses. By setting an unknown event injection current and traversing each bus along the target feeder to derive the terminal bus voltage and its outgoing current, an ESLI model based on virtual event current injection (VCI) is constructed, which not only assists in the ESLI task but also confers the solving capability of the event current. Leveraging the event current calculation ability of the ESLI model, a VMD-based staged ESLI algorithm is developed, achieving an ordered and accurate search for the exact location of the event source in a goal-oriented manner. The effectiveness of the developed ESLI algorithm is evaluated on the IEEE 33-bus test system. Experimental results demonstrate that our VMD achieves high-precision ESLI and event current solving in PDNs under extremely low observability, significantly outperforming the state-of-the-art ESLI methods. Full article

Microplastics, synthetic polymer particles measuring less than 5 mm, have become a widespread environmental pollutant, raising concerns over their possible effects on human health. Growing evidence links MPs to vascular aging and cardiovascular disease beyond their ecological toxicity. Upon inhalation, ingestion, or skin contact, microplastics can traverse biological barriers, circulate systemically, and accumulate in vascular tissues. Experimental investigations indicate that MPs, especially polystyrene and polyethylene in nano- and micro-sized forms, induce oxidative stress, mitochondrial dysfunction, and chronic inflammation. These disruptions activate redox-sensitive signaling pathways, such as NF-κB and NLRP3 inflammasome, causing endothelial dysfunction, vascular smooth muscle modulation, and foam cell production, indicating early vascular aging. Animal models and in vitro studies have consistently shown endothelial activation, increased cytokine production, and changes in vascular tone after exposure to MPs. Initial human research has detected microplastics in blood, thrombi, and atherosclerotic plaques, which correlate with negative cardiovascular outcomes and systemic inflammation. Notably, recent research suggests that the gut microbiota and antioxidant systems may play a role in adaptive reactions, although these processes are still not fully understood. MP-induced vascular toxicity is covered in this interdisciplinary review, highlighting molecular pathways, experimental data, and translational gaps. Full article

Cutting beech plywood using abrasive water jet (AWJ) technology represents a significant area of research due to increasing demands for precision, quality, and environmental sustainability in manufacturing processes within the woodworking industry. AWJ technology enables non-contact cutting of materials without causing thermal deformation or mechanical damage, which is crucial for preserving the structural integrity and mechanical properties of the plywood. This article investigates cutting beech plywood using technical methods using an abrasive water jet (AWJ) at 400 MPa pressure, with Australian garnet (80 MESH) as the abrasive material. It examines how abrasive mass flow rate, traverse speed, and material thickness affect AWJ lag, which in turn influences both cutting quality and accuracy. Measurements were conducted with power abrasive mass flow rates of 250, 350, and 450 g/min and traverse speeds of 0.2, 0.4, and 0.6 m/min. Results show that increasing the abrasive mass flow rate from 250 g/min to 350 g/min slightly decreased the AWJ cut width by 0.05 mm, while further increasing to 450 g/min caused a slight increase of 0.1 mm. Changes in traverse speed significantly influenced cut width; increasing the traverse speed from 0.2 m/min to 0.4 m/min widened the AWJ by 0.21 mm, while increasing it to 0.6 m/min caused a slight increase of 0.18 mm. For practical applications, it is recommended to use an abrasive mass flow rate of around 350 g/min combined with a traverse speed between 0.2 and 0.4 m/min when cutting beech plywood with AWJ. This balance minimizes jet lag and maintains high surface quality comparable to conventional milling. For thicker plywood, reducing the traverse speed closer to 0.2 m/min and slightly increasing the abrasive flow should ensure clean cuts without compromising surface integrity. Full article

Acid mine drainage, a consequence of exposure of sulfide mining waste to weathering processes, results in significant water, sediment, and soil contamination. This contamination results in acidophilic ecosystems, with low pH values and elevated concentrations of sulfate and potentially toxic elements. The São Domingos mine, an abandoned site in the Iberian Pyrite Belt, lacks remediation measures and has numerous waste dumps, which are a major source of contamination to local water systems. Therefore, this study examines sediment accumulation in five mine dams along the São Domingos stream that traverses the entire mine complex. Decades of sediment and waste transport since mine closure have resulted in dam-clogging processes. The geochemical, mineralogical, and magnetic properties of the sediments were analyzed to evaluate the mineralogical controls on the mobilization of potentially toxic elements. The sediments are dominated by iron oxides, oxyhydroxides, and hydroxysulfates, with jarosite playing a key role in binding high concentrations of iron and toxic elements. However, no considerable correlation was found between potentially toxic elements and magnetic parameters, highlighting the complex behavior of these contaminants in acid mine drainage-affected systems. Full article

Twinning-induced plasticity (TWIP) steels represent a significant development in automotive steel production, characterized by advanced strength and ductility properties. The present study empirically investigated the effects of process parameters on the cutting process and surface quality of TWIP980 steel sheet by abrasive water jet (AWJ) cutting. The cutting experiments were conducted on 1.4 mm thick sheet metal using four different traverse speeds (50, 100, 200, and 400 mm/min) and four different water jet pressures (1500, 2000, 2500, and 3000 bar). Two different abrasive flow rates (300 and 600 g/min) were also utilized. The cut surfaces were characterized in three dimensions with an optical profilometer. The parameters of surface roughness, kerf width, taper angle, and material removal rate (MRR) were determined. Furthermore, microhardness measurements were conducted on the cut surfaces. The optimal surface quality and geometrical accuracy were achieved by applying a combination of parameters, including 3000 bar of pressure, a traverse rate of 400 mm/min, and an abrasive flow rate of 600 g/min. Concurrently, an effective cutting performance with increased MRR and reduced taper angles was achieved under these conditions. The observed increase in microhardness with increasing pressure is attributable to a hardening effect resulting from local plastic deformation. Full article

To investigate how severe wear at No. 12 turnout frogs in an urban rail transit line operating at speeds over 120 km/h on the dynamic performance of the vehicle, a vehicle–frog coupled dynamic model was established by employing the 2021 version of SIMPACK software. Profiles of No. 12 alloy steel frogs and metro wheel rims were measured to simulate wheel–rail interactions as the vehicle traverses the turnout, using both brand-new and worn frog conditions. The experimental results indicate that increased service life deepens frog wear, raises equivalent conicity, and intensifies wheel–rail forces. When a vehicle passes through the frog serviced for over 17 months at the speed of 120 km/h, the maximum derailment coefficient, lateral acceleration of the car body, and lateral and vertical wheel–rail forces increased by 0.14, 0.17 m/s², 9.52 kN, and 105.76 kN, respectively. The maximum contact patch area grew by 35.73%, while peak contact pressure rose by 236 MPa. To prevent dynamic indicators from exceeding safety thresholds and ensure train operational safety, it is recommended that the frog maintenance cycle be limited to 12 to 16 months. Full article

This study proposes a Fabry–Perot interferometric system and an associated evaluation method for measuring the weight of moving trains. An optical fiber sensor, comprising a sensing fiber and a supporting structure, is securely bonded to the rail foot. As a train traverses the track, the resulting localized bending induces a change in the sensing fiber’s length, which manifests as a quantifiable phase shift in the interference signal. We developed a physical–mathematical model, based on three Gaussian functions, to describe the temporal change in sensing fiber length caused by the passage of a single bogie. This model enables the determination of a proportionality constant to accurately convert the measured phase change into train weight. Model validation was performed using a train set, including a locomotive and four variably loaded wagons, traveling at 15.47 km/h. This system offers a novel and effective approach for real-time train weight monitoring. Full article

Pollen allergies represent a growing public health concern that necessitates enhancements to the network of instruments and modeling calculations in order to facilitate a more profound comprehension of pollen transportation. The Beenose instrument quantifies the light scattered by particles that traverse a laser beam at four angles. This methodology enables the differentiation of pollen particles from other particulate matter, predominantly mineral and carbonaceous in nature, thereby facilitating the retrieval of pollen concentrations. The Beenose instrument has been installed on the tourist balloon known as “Ballon de Paris” in a large park situated in the southwest of Paris, France. The measurement period is from April to November 2024, coinciding with the pollen seasons of trees and grasses. The balloon conducts numerous flights per day, reaching an altitude of 150 m when weather conditions are conducive, which occurs approximately 58% of the time during this period. The data are averaged to produce vertical profiles with a resolution of 30 m. Concentrations of the substance decrease with altitude, although a secondary layer is observed in spring. This phenomenon may be attributed to the presence of emissions from a proximate forest situated at a higher altitude. The average decrease in concentration of 11 ± 8% per 10 m is consistent with the findings of previous studies. The long-term implementation of Beenose measurements on this tourist balloon is intended to enhance the precision of the results and facilitate the differentiation of the various parameters that can influence the vertical transportation of pollen. Full article

Non-invasive Micro-test Technology (NMT) represents a pioneering approach in the study of physiological functions within living organisms. This technology possesses the remarkable capability to monitor the flow rates and three-dimensional movement directions of ions or molecules as they traverse the boundaries of living organisms without sample destruction. The advantages of NMT are multifaceted, encompassing real-time, non-invasive assessment, a wide array of detection indicators, and compatibility with diverse sample types. Consequently, it stands as one of the foremost tools in contemporary plant physiological research. This comprehensive review delves into the applications and research advancements of NMT within the field of plant abiotic stress physiology, including drought, salinity, extreme temperature, nutrient deficiency, ammonium toxicity, acid stress, and heavy metal toxicity. Furthermore, it offers a forward-looking perspective on the potential applications of NMT in plant physiology research, underscoring its unique capacity to monitor the flux dynamics of ions/molecules (e.g., Ca²⁺, H⁺, K⁺, and IAA) in real time, reveal early stress response signatures through micrometer-scale spatial resolution measurements, and elucidate stress adaptation mechanisms by quantifying bidirectional nutrient transport across root–soil interfaces. NMT enhances our understanding of the spatiotemporal patterns governing plant–environment interactions, providing deeper insights into the molecular mechanism of abiotic stress resilience. Full article

Urban Air Mobility (UAM) requires reliable communication and surveillance infrastructures to ensure safe Unmanned Aerial Vehicle (UAV) operations in dense metropolitan environments. However, urban infrastructure is inherently heterogeneous, leading to significant spatial variations in monitoring performance. This study proposes a unified framework that integrates infrastructure readiness assessment with Deep Reinforcement Learning (DRL)-based UAV path planning. Using Singapore as a representative case, we employ a data-driven methodology combining clustering analysis and in situ measurements to estimate the citywide distribution of surveillance quality. We then introduce an infrastructure-aware path planning algorithm based on a Double Deep Q-Network (DQN) with a convolutional architecture, which enables UAVs to learn efficient trajectories while avoiding surveillance blind zones. Extensive simulations demonstrate that the proposed approach significantly improves path success rates, reduces traversal through poorly monitored regions, and maintains high navigation efficiency. These results highlight the potential of combining infrastructure modeling with DRL to support performance-aware airspace operations and inform future UAM governance systems. Full article