Search Results (364)

Flexible robotic fish are emerging as a promising class of deep-sea exploration platforms because they combine compliant bodies, low-disturbance fish-like propulsion, and the potential for distributed sensing and autonomy. Unlike conventional biomimetic robotic fish developed mainly for shallow or moderate-depth environments, deep-sea flexible robotic fish must simultaneously address high hydrostatic pressure, low temperature, darkness, limited communication, constrained power supply, and complex near-bottom terrain. This review synthesizes research at the intersection of deep-sea soft robotics, bio-inspired robotic fish, smart-material actuation, pressure-adaptive packaging, multimodal sensing, and autonomous control. The literature is organized around a system-level design chain: biological mechanisms that inspire pressure adaptation and perception, body architectures that distribute pressure and protect electronics, soft actuators that generate fish-like propulsion, and control strategies that enable near-bottom and long-duration tasks. The review highlights that the central challenge is not any single actuator or material, but the co-design of pressure-adaptive bodies, hybrid soft actuation, reliable interfaces, multimodal perception, energy management, and autonomy. To strengthen engineering translation, this revised review further adds design-principle abstraction, actuator-selection guidance, prototype-level comparison, failure-mode analysis, and a computational design workflow. Future research should prioritize long-term reliability tests, standardized deep-sea evaluation protocols, physics-informed modeling, and integrated prototype demonstrations under realistic mission conditions. Full article

Goaf gas drainage and residual-coal spontaneous-combustion prevention are often designed independently, even though both are controlled by the same leakage-flow, oxygen-transport and heat-release fields in a longwall goaf. This decoupled design may reduce methane accumulation while unintentionally enlarging the oxidation zone. Taking the No. 1217 fully mechanized working face of Zhongxing Coal Mine, Shanxi Province, China, as an engineering prototype, this study develops an integrated laboratory-field numerical framework to quantify the drainage-induced evolution of the three zones of spontaneous combustion. Programmed temperature-rise experiments on the No. 2 coal seam were used to determine the oxygen-consumption rate, heat-release intensity and apparent activation energy under oxygen concentrations of 3–21%, yielding a critical oxygen concentration of 5.9%. Bundle-tube monitoring and distributed optical-fiber temperature sensing delineated the in situ three-zone boundaries, and a three-dimensional CFD model coupling porous-media seepage, species transport and Arrhenius-type heat generation was validated against the field data, with most relative errors below 5%. Parametric simulations for buried-pipe depths of 20, 30 and 50 m and negative pressures of 15 and 20 kPa reveal a pronounced asymmetric response: drainage compresses and advances the return-side oxidation zone toward the working face, but drives the inlet-side oxidation zone deeper into the goaf by enhancing oxygen-bearing leakage. Within the investigated parameter space, a buried depth of 30 m and a negative pressure of 20 kPa provide the best compromise, reducing the return-side oxidation-zone width from 32 to 21 m and the upper-corner methane concentration from 6.80% to 0.58%. The results demonstrate that drainage design should be constrained simultaneously by methane dilution and oxidation-zone control, and provide a quantitative basis for coordinating gas extraction with fire prevention in gas-rich, oxidation-prone longwall panels. Full article

Lithium niobate on insulator (LNOI) has emerged as a promising platform for compact, low-loss phase modulators. The extant LNOI studies evaluate device performance almost exclusively through the Pockels effect, treating piezoelectric–photoelastic strain and thermo-optic drift as decoupled channels. Crucially, both mechanisms directly perturb the phase bias of a fiber-optic gyroscope (FOG), rendering them indispensable in sensing-oriented design. This work establishes a unified multiphysics model of an X-cut TFLN ridge phase modulator that self-consistently couples the electro-optic, piezoelectric–photoelastic, thermo-optic, and pyroelectric channels. The contributions of the four mechanisms are quantitatively decomposed under realistic FOG operating conditions, and the slab thickness, ridge-top width, and electrode gap are systematically optimized to balance modulation efficiency against environmental robustness. The co-optimization of the ridge geometry and electrode gap design maintains the EO overlap factor near 0.55, while reducing the half-wave voltage requirement. This results in a half-wave voltage length of V_πL = 1.65 V·cm at a 4.4 μm electrode gap. The optimized geometry and electrode gap (4.4 μm) are essentially temperature-independent: extracted from the Pockels modulation slope, V_πL remains stable at ≈1.65 V·cm (push–pull single-pass; within ~0.3%) across 25~85 °C. Furthermore, an externally imposed substrate temperature rise of 60 K (the upper end of the 25~85 °C FOG operating range) induces a mode-field-weighted thermal residual corresponding to approximately 27% of the Pockels modulation depth at an applied voltage of 5 V. The present study demonstrates that the DC-coupled operation of TFLN sensor-grade modulators is viable across the full FOG temperature range, without dedicated active temperature stabilization, and the residual thermal-bias offset is absorbed by the FOG’s standard closed-loop servo electronics. The results of the study provide quantitative design guidelines for high-performance, environmentally stable TFLN phase modulators in compact FOG systems. Full article

Rapid urbanization in semi-arid cities intensifies heat exposure, air pollution, and land-surface degradation, yet these stressors are often assessed separately. This study develops a scale-aware Urban Environmental Stress (UES) framework for Jaipur, India, using multi-sensor Earth observation data. The framework explicitly addresses indicator redundancy, weighting bias, short time-series interpretation, and temporal comparability. The final primary UES surface uses twelve retained stress-oriented indicators on a 500 m common analysis grid, excludes NDBI because it is algebraically redundant with NDMI when both are computed from the same NIR/SWIR bands, and applies equal weights so that built fraction does not dominate the composite. Entropy weighting is reported only as a sensitivity diagnostic. The resulting UES map identifies high relative stress in Jaipur’s dense urban core and transport-industrial corridors, with lower stress along the Aravalli flank and peri-urban green or water-adjacent areas. The framework is presented as a relative spatial prioritization tool rather than an absolute physical time series; temporal claims are limited to independently reported land-cover and individual-indicator trajectories unless fixed multi-year normalization and fixed weights are applied. Full article

Effective thermal management is crucial for the performance, thermal safety, and lifespan of lithium-ion batteries in electric vehicles (EVs). Thermal management strategies are essential for preventing overheating, thermal imbalance, and the associated risk of thermal runaway. Nanofluids are emerging and attracting considerable attention as potential coolants for high-power energy storage and electronics systems. This review updates and summarizes the most recent advances in nanofluid-based cooling strategies for battery thermal management systems (BTMSs) over the past five years, emphasizing their implications for battery thermal safety. Three main nanofluid-based cooling strategies have been evaluated in depth, including nanofluid-based indirect liquid cooling, nanoparticle-enhanced PCM cooling, and nanofluid-based heat pipe cooling. Various nanofluid formulations, including mono, hybrid, and ternary nanofluids, have been considered and evaluated for their heat dissipation under high charge/discharge and abuse-relevant conditions. Thermal and hydraulic performance characteristics, including maximum temperature, maximum temperature difference, and pressure drop, have been comprehensively evaluated for different nanofluid-based cooling strategies. The findings demonstrated that nanofluids significantly improved heat transfer rates and enhanced temperature control efficiency. In particular, hybrid and ternary nanofluids exhibit superior thermal performance and effectively suppress the escalation of safety-critical temperatures. Beyond summarizing cooling performance, this review further discusses the role of nanofluid-based cooling strategies as functional thermal-control layers within intelligent BTMS architectures. Particular attention is given to their compatibility with sensing networks, BMS-/VCU-level supervisory control, predictive thermal models, actuator responsiveness, fault-warning algorithms, and long-term reliability under realistic driving and fast charging conditions. Therefore, this review provides architecture-oriented insights for developing safe, energy-efficient, and control-ready BTMSs for next-generation high-power and connected EVs. Full article

Cyanobacterial blooms in lakes and reservoirs pose significant environmental and public health risks. This paper presents an effective exploration strategy to detect them from Autonomous Surface Vehicles (ASVs) equipped with probes, whose sensing trajectories are optimized by an AI-based planner that considers the 3D spatial-temporal evolution of the cyanobacteria concentration obtained by a multiphysics model. The planner, simultaneously working on the AI decision-making and robotic domains, optimizes the surface displacement of the ASV and the depth of its probe by solving a constrained multi-objective optimization problem that minimizes the mission duration and trajectory length, maximizes the possibilities of the probe to overpass areas with high concentration of cyanobacteria, and satisfies operational constraints (such as ASV velocity or acceleration limits, and obstacle avoidance). The optimization is supported by two well-known versions of the Non-Sorted Generic Algorithm (NSGA-II and NSGA-III) and by encoding the trajectories with spline curves whose number of control points can be fixed, progressively increased, or freely manipulated by the algorithm. The performance of different configurations of the planner is tested against six scenarios obtained from different simulations of the multiphysics model (which couples water dynamics and temperature, light transmission, daily vertical migration of the cyanobacteria and their growth). The statistical analysis of the planner results determines that NSGA-III working with variable-length chromosomes and NSGA-II with the progressive increment of spline points as the best configurations for maximizing cyanobacteria detection, and minimizing mission duration and trajectory length. Full article

Underground coal fires are persistent subsurface hazards threatening energy resources. UAV thermal infrared remote sensing provides high-resolution observations of surface thermal anomalies, but these signals may be spatially offset from underlying fire sources. An integrated framework was developed for subsurface temperature-field reconstruction and surface–subsurface correspondence and offset analysis. Surface thermal anomaly centers were extracted using statistical thresholding, adaptive kernel density estimation, and intensity-weighted centroids. Subsurface temperature fields were reconstructed using an MGSM-RBF model that combines multi-Gaussian fire-source representation with residual correction. The framework was applied to the Sandaoba coal fire area using UAV thermal infrared data and 370 borehole temperature measurements from 39 boreholes, covering depths of approximately 0–85 m. Reconstruction accuracy was evaluated using spatially buffered cross-validation and compared with eight baseline methods. MGSM–RBF achieved the best performance, with RMSE = 92.49 $°\mathrm{C}$, MAE = 61.26 $°\mathrm{C}$, and $R^2$ = 0.81. Two surface thermal anomaly centers and three subsurface fire sources were identified, with primary combustion concentrated at 30–55 m depths. Surface anomalies were not vertical projections of subsurface sources. The horizontal offsets were approximately one-fifth to one-third of burial depth, reflecting depth-dependent and multi-source-controlled surface thermal responses. These findings support UAV-based coal fire interpretation and fire-control planning. Full article

Snow Water Equivalent (SWE) is a key variable for evaluating hydrological processes and the impacts of climate change in mountainous regions such as the Qilian Mountains. Passive microwave remote sensing provides large-scale SWE estimates, but its coarse spatial resolution and coverage gaps pose limitations, particularly in complex terrain with heterogeneous snow distribution. This study integrates multi-source data from 2018 to 2024, combining ground-based observations with multiple meteorological factors to develop a high-resolution SWE reconstruction model tailored to the Qilian Mountains. Eight machine learning algorithms—Support Vector Machine (SVM), CatBoost, LightGBM, XGBoost, Random Forest, AdaBoost, ElasticNet, and Bayesian Ridge Regression—were systematically compared, with LightGBM achieving the best performance on the test set. During feature selection, Granger causality inference was applied to screen input variables, resulting in an optimized reconstruction model with a mean absolute error (MAE) of only 1.984 mm, a root mean square error (RMSE) of 4.656 mm, and a coefficient of determination (R²) of 0.973. Model interpretability was enhanced using SHAP (Shapley Additive Explanations), which revealed that snow depth, surface soil temperature and moisture, and precipitation were the primary driving factors, with varying contributions to the model. The model generates SWE reconstruction sequences at 30 min intervals. This high-resolution dataset provides crucial support for studying snow dynamics in complex mountainous regions and contributes to improved water resource management and climate change assessments in the Qilian Mountains. Full article

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

Monitoring coastal and estuarine dynamics is crucial for understanding coupled physical, biogeochemical, and human impacts on coastal waters. Motivated by the availability of high spatial resolution ocean color data from the proof-of-concept SeaHawk-HawkEye ocean color CubeSat, this study assesses the capabilities and limitations of satellite remote sensing in capturing shallow water (<10 m) coastal dynamics by integrating in situ measurements with satellite imagery. A Sea Sciences Acrobat collected detailed transects at the mouth of Masonboro Inlet (Wilmington, NC, USA), with “tow-yo” style profiles from the surface to 10 m. It measured conductivity, temperature, and depth (CTD), chlorophyll a (Chl a), turbidity, and dissolved oxygen. Satellite data from SeaHawk-HawkEye, Aqua-MODIS, and Sentinel 3A/3B-OLCI provided extensive spatial coverage, revealing surface-level physical/biological interactions, but were only available 48 h after in situ sampling due to cloud cover during field sampling. Tow-yo profiles elucidated a three-dimensional phytoplankton plume, the spatial extent of which we further characterize with satellite imagery, demonstrating the value of integrating in situ and satellite data. A spatial matchup comparison between data from each satellite and the in situ sensor package revealed significant discrepancies across all satellite sensors analyzed, attributed to differences in sensor resolution, atmospheric correction approaches, and proximity to land/benthos. This study emphasizes key challenges with study design and data interpretation in dynamic nearshore environments. In particular, results suggest that meaningful comparisons of satellite vs. in situ observations in such systems require near-synchronous sampling, careful consideration of spatial scale, and improved characterization of optical complexity. Full article

Top Submerged Lance (TSL) technology is widely used in non-ferrous smelting, yet in-situ bath dynamics remain challenging to quantify because the process operates in a closed, high-temperature, highly turbulent and optically inaccessible environment. The absence of direct diagnostics limits the ability to relate operating conditions to bubble dynamics, gas penetration and bath agitation and constrains validation of multiphase CFD models under realistic conditions. This study introduces a multimodal sensing framework that combines spectral acoustic analysis with lance-mounted inertial motion sensing to characterize dynamic bath behavior across cold-model, laboratory-scale and pilot-scale systems. Water-glycerin experiments establish repeatable acoustic signatures of individual bubble-collapse events, with dominant emission bands in the 300–900 Hz range and higher-frequency components extending into the kilohertz domain. High-temperature laboratory trials using fayalitic slag reproduce these frequency regions while exhibiting depth-dependent attenuation and clear spectral separation between submerged and non-submerged lance operation. Power Spectral Density (PSD) and cumulative spectral power analyses resolve the influence of gas flow rate and lance submersion depth on acoustic spectral power distribution, while inertial measurements capture corresponding increases in vertical lance acceleration associated with back-pressure fluctuations. Pilot-scale trials at 120 Nm³/h air and 13 L/h diesel confirm that shallow lance submersion substantially increases measured acoustic spectral power below 3 kHz, whereas deeper penetration enhances periodic vertical acceleration response measured by the inertial sensor. The combined acoustic-inertial methodology provides a physically interpretable and cross-scale framework for assessing bubble collapse activity, plume interaction and bath agitation under high-temperature TSL conditions. The approach enables frequency-based diagnostics that can be systematically compared with CFD predictions of plume oscillation and collapse-related dynamics. Once baseline frequency ranges are established for a given slag system, the method can support process monitoring and may provide indirect indicators related to changes in surface agitation or foaming tendency, enabling structured data-driven analysis. The framework thus provides a practical bridge between cold-model experiments, high-temperature measurements, multiphase modeling and industrial TSL operation. Full article

A homemade NiCr/NiSi thin-film thermocouple integrated with a PCBN turning tool was developed for real-time temperature monitoring during dry turning of AISI 1045 steel. The study addresses a practical limitation of existing cutting-temperature methods, namely the difficulty of combining local in situ sensing near the cutting edge with a transient thermal analysis framework that can interpret the measured signal under repeatable cutting conditions. The sensor was fabricated on an Al₂O₃ substrate by magnetron sputtering, protected by a SiO₂ layer, and tested at cutting speeds corresponding to spindle speeds of 1000, 1500 and 2000 rpm, with a cutting depth of 0.5 mm, a feed rate of 0.1 mm/rev and cutting times of 30–90 s. A three-dimensional transient heat-conduction model and inverse heat-flux reconstruction were then used to interpret the temperature history. The maximum measured temperature increased from 342 °C to 488 °C, and VB increased from 0.082 mm to 0.295 mm, showing a strong temperature–wear association within the investigated parameter window. Full article

Ocean monitoring is essential for understanding climate change and marine ecosystem dynamics, yet achieving comprehensive global coverage remains a challenge in oceanography. Current technologies face limitations in cost, power, hardware, and depth capacity that restrict widespread monitoring capabilities. Here we show that biohybrid robotic jellyfish (Aurelia aurita) can serve as autonomous vertical ocean profilers by integrating microcontrollers with positively buoyant sensor payloads, achieving controlled vertical-profiling capabilities. Laboratory experiments demonstrated repeatable up–down trajectories, quantified force balance limits, and identified predictable, size-dependent descent swimming speeds. Field deployments in Massachusetts coastal waters and the open ocean off the Florida Keys demonstrated field operation to ocean depths >25 m with successful in situ temperature and depth measurements. To our knowledge, this represents the first biohybrid jellyfish platform to combine autonomous, pressure-triggered vertical profiling with onboard oceanographic sensing in natural marine environments. This approach leverages the global distribution and remarkable swimming efficiency of living jellyfish while eliminating propulsion power requirements by utilizing the animal’s natural swimming capabilities. While further development is required for long-term ocean deployment, this study lays the groundwork for a new class of biohybrid ocean-sensing platforms with advantages in cost, power, and mission flexibility, providing a pathway toward dense sensor networks and increased ocean monitoring observations. Full article

Engineering systems increasingly demand multifunctional and energy-efficient integration within constrained volume and energy budgets. One promising solution is the monolithic integration of components and functions to minimize occupied volume and simplify control interfaces. Paraffin-based phase change material (PCM) actuators provide high mechanical work density and can be coupled with thermoelectric generators (TEGs) for multifunctional operation. However, their dynamic response is typically constrained by the intrinsically low thermal conductivity of PCM materials. This work introduces a quasi-monolithic fabrication method for a fully integrated TEG-PCM system combining standard four-layer printed circuit board (PCB) technology and CNC milling. By constructing the system as a quasi-monolithic block, thermal interface materials are considerably reduced, thereby diminishing parasitic thermal resistance and promoting faster heat transport from the TEG to the PCM cavity. The system is fabricated using CNC milling with high depth resolution enabled by an electrical sensing-via structure. Experimental validation shows a 76% improvement in displacement rate (15.03 µm/s) at half the input power (1 W) compared to a conventional hybrid-assembled TEG-PCM actuator system consisting of a commercial TEG and an aluminum PCM container. The exploitation of the PCM as a thermal flux modulator for energy harvesting has been preliminarily investigated; considering the measured 5 K temperature difference sustained during a simulated short “day–night” cycle, an estimated open-circuit voltage of ∼13.5 mV is expected to be retrieved under load-match conditions. The actuator is compatible with PCB-based power management and thermal routing, enabling scalable incorporation into compact microsystems and multifunctional MEMS devices. Full article