Search Results (15,632)

Terrain types significantly affect the dynamics and locomotion performance of hexapod robots during walking. Terrain classification is a key solution to modify gait patterns in different terrains and recognize hazardous conditions. Perceiving the terrain with proprioceptive sensing is a robust and reliable approach in extreme conditions. In this paper, an efficient terrain classification approach for a hexapod robot is proposed. The proposed method combines a deep classification framework including the long short-term memory (LSTM) network and an effective statistical feature extraction. Proprioceptive inertial measurement unit (IMU) data is only used as the sensing system for the robot–terrain interaction. In the feature extraction process, four meaningful characteristic features, namely, the mean, median, Lomb–Scargle periodogram power spectral density (LPSD), and Welch’s power spectral density (WPSD), are extracted from the body orientation data using a sliding-window method. These features are combined and fed into the network to perform the training and testing processes. In the experiments, the proposed method is evaluated with commonly used soft computing and deep learning models. The classification performance for the concrete, pebble, and waxed tile terrains reaches 100% with the proposed method. The overall accuracy, precision, sensitivity, specificity, F1-score, and Matthew correlation coefficient are recorded as 95.45%, 96.36%, 95.28%, 98.86%, 95.49%, and 94.61%, respectively. These results demonstrate that the proposed approach delivers reliable classification performance with a low-cost and easy-to-implement solution. Full article

Water scarcity in semi-arid regions makes efficient irrigation scheduling a priority, yet farm-level adoption of soil-moisture monitoring remains limited by the cost, low portability and installation complexity of commercial sensing systems. This study presents the development and field validation of WaziSense, a low-cost, solar-powered Internet-of-Things (IoT) smart tensiometer, developed within the OSIRRIS platform for soil-water monitoring and irrigation scheduling. The device couples a Watermark granular-matrix sensor and a DS18B20 temperature probe to an ATmega328P microcontroller (Arduino Pro-Mini, 3.3 V, 8 MHz) with long-range LoRa communication and a maximum-power-point-tracking (MPPT) solar-charging stage, logging soil matric potential and soil temperature every 15 min. An open-source edge/cloud stack (WaziGate, WaziApp) retrieves weather forecasts from an open API and runs an automated machine learning (AutoML) regression pipeline that forecasts soil-water dynamics and the time to a user-defined threshold, from which irrigation is scheduled and its applied volume verified by a flow meter. The system was deployed at three bioclimatic sites in Tunisia (durum wheat at Cherfech, citrus at Nabeul, apple at Sbeitla), with tensiometers installed at 20 and 40 cm depths, and validated against commercial 10HS capacitive probes coupled to a ZL6 data logger, with which the co-located readings were significantly correlated (r = 0.81). Calibrated readings showed a strong relationship between soil–water content and soil–water potential (R² = 0.99), and the edge forecasting model reproduced soil–water dynamics on unseen data (Sbeitla apple site, 5-day horizon) with R² = 0.73, RMSE = 0.35, MAE = 0.23 and MPE = 12.52%. With a material cost under about 90 EUR per node and fully open-source hardware and software, WaziSense is one to two orders of magnitude cheaper than commercial monitoring stations, offering an affordable, reproducible and scalable tool for data-driven irrigation in water-limited agriculture. Full article

Nature reserves (NRs) sustain biodiversity and multiple ecosystem services (ESs), yet assessments commonly stop at administrative boundaries and seldom examine how service relationships and associated factors vary across surrounding landscape gradients. We assessed carbon storage (CS), habitat quality (HQ), soil retention (SR), and water yield (WY) in 54 NRs in Jiangxi Province, China, and nested 5 and 10 km surrounding zones for 2000, 2010, and 2020. The InVEST model, a comprehensive ecosystem service index (CESI), correlation analysis, RMSE, and GeoDetector were used to characterize service dynamics, trade-offs/synergies, and spatial associations. CS and HQ increased slightly, whereas SR and WY rose until 2010 and subsequently declined. Mean CESI decreased outward from NR interiors (0.53) to the 5 km (0.50) and 10 km zones (0.49), although WY was higher outside NRs in 2020. CS–HQ and SR–WY were predominantly synergistic, while HQ–SR showed the strongest trade-off. Dominant associated factors and their interactions varied among services and spatial zones. These findings reveal context-dependent cross-boundary differentiation in ecosystem-service patterns, supporting coordinated management of NRs and their surrounding landscapes and service-specific conservation priorities. Full article

Tajikistan is highly vulnerable to climate change and depends heavily on agriculture, making soil moisture dynamics critical for water and food security. This study provides a comprehensive assessment of soil moisture variability across four depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) from 2000 to 2021 using NASA’s GLDAS-2 model and remote sensing data for land-air temperature, precipitation, and vegetation to identify key nexus of soil moisture change. Moisture data were converted to volumetric water content (m³/m³) to enable valid cross-layer comparisons. Our findings show that volumetric soil moisture increases with depth, from 0.219 m³/m³ at the surface to 0.293 m³/m³ in the deepest layer. Eastern Tajikistan exhibits higher moisture levels than the west, likely due to differing precipitation patterns. Seasonally, spring replenishes the soil with the highest moisture (0.270 m³/m³ at 0–10 cm), while summer strips it away (0.194 m³/m³ at 0–10 cm), potentially reflecting evapotranspiration losses. A significant warming trend is evident, with mean annual temperature peaking at 4.32 °C in 2016. Precipitation strongly influences upper-layer moisture (correlation: 0.49 at 0–10 cm; 0.44 at 10–40 cm). While annual averages remain stable, seasonal trends reveal significant winter wetting (+0.00043 m³/m³ per year, p < 0.001) and summer drying in the deepest layer, indicating intensifying seasonal contrasts. Vegetation follows a parallel pattern, declining from 2000 to 2010 and recovering thereafter. Greening is observed in 16.74% of the area, concentrated in the western mountains and northern highlands, while only 2.98% shows decline, mostly in small, fragmented patches. These findings highlight the substantial connection between climate, soil moisture, and vegetation in Tajikistan. They also suggest the need for depth-specific and seasonally aware water management strategies in this climate-sensitive region. Managing water here means looking beyond surface averages and thinking in layers, seasons, and geography. Full article

Pinoresinol–lariciresinol reductase (PLR) catalyzes a key reductive step in plant lignan biosynthesis. Although flax (Linum usitatissimum L.) seeds are rich in lignans, the PLR gene family and its relationship with lignan accumulation during seed development remain insufficiently characterized. Here, 18 LuPLR genes were identified from the telomere-to-telomere genome assembly of the flax cultivar ‘Gaosi’ using BLASTP and HMMER searches. Their phylogenetic relationships, gene structures, conserved motifs, chromosomal distribution, duplication and syntenic relationships, promoter cis-acting elements, predicted microRNA targets, and expression profiles were analyzed. Seed lignan content at 5, 10, 20, 30, and 40 days after flowering was quantified by high-performance liquid chromatography, and candidate genes were screened using quantitative real-time PCR and Pearson correlation analysis. The LuPLR genes were unevenly distributed across eight chromosomes and exhibited substantial structural and regulatory diversity. Seed lignan content varied dynamically and reached its highest level at 40 days after flowering. LuPLR10, LuPLR11, and LuPLR16 showed positive correlations with lignan content. Among them, LuPLR10 was prioritized because its developmental expression pattern most closely paralleled lignan accumulation. Subcellular localization analysis indicated that the LuPLR10 protein was predominantly associated with chloroplasts. These findings provide a genomic framework for the flax PLR family and identify LuPLR10 as a priority candidate for further functional investigation. Full article

Conservation laws are core concepts in dynamical system modeling and the study of physical symmetries. Although machine learning has achieved significant progress in discovering physical laws, existing methods often face challenges such as identifying only a single conserved quantity. To bridge this gap, we introduce OAC-Net. By embedding a gradient orthogonality penalty directly into the neural network’s objective function, OAC-Net enables the simultaneous discovery of multiple independent integrals of motion (IOM) directly from raw trajectory data. Unlike previous heuristic approaches, we prove that gradient orthogonality of real-analytic functions implies the functional independence of the learned integrals of motion. The proposed method is validated on several canonical dynamical systems, including the two-dimensional Kepler system. Experimental results show that OAC-Net efficiently and robustly identifies multiple independent integrals of motion, with the learned integrals of motion exhibiting strong correlations with their true physical values. Additionally, ablation studies confirm OAC-Net’s robustness to hyperparameters such as noise strength and orthogonal penalty coefficient. Our approach provides an effective framework for discovering multiple interpretable integrals of motion from complex trajectory data. Full article

The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies. Full article

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions. Full article

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments. Full article

Under ground-based observation geometry, infrared radiant-intensity sequences of space objects are jointly influenced by object micromotion, thermal radiation, time-varying viewing conditions, and atmospheric propagation. Existing simulation studies often prescribe the line of sight or simplify the coupling between viewing geometry and atmospheric attenuation, which limits long-duration ground-based sequence analysis. This study develops a physics-informed framework for generating atmosphere-attenuated infrared radiant-intensity sequences of space objects undergoing precession or tumbling. The framework reconstructs observation geometry from azimuth–elevation–range trajectories, updates facet normals through a unified micromotion attitude model, computes visible projected area and transient facet temperature, and incorporates MODTRAN-derived elevation-dependent atmospheric transmittance. Using this framework, we construct IRPeriodic, an eight-class simulated dataset for long-duration univariate time-series classification. We further propose LPD-Net, which integrates large-kernel residual feature extraction, prototype-guided dynamic temporal alignment, and differential periodic representation to capture long-range waveform morphology, sample-dependent temporal correspondence, and segment-level local variation. On IRPeriodic, LPD-Net achieves an accuracy of 0.8618 ± 0.0057, a macro-F1 of 0.8615 ± 0.0061, and a Matthews correlation coefficient of 0.8426 ± 0.0065, outperforming the evaluated neural-network and ROCKET-type baselines. Ablation and synthetic-noise sensitivity analyses indicate that the performance gain is mainly associated with long-context feature extraction, with additional improvements from dynamic alignment and differential periodic statistics. Auxiliary experiments on selected public UCR datasets suggest that the representation is also competitive for univariate time-series classification. These results demonstrate the effectiveness of LPD-Net on the proposed physics-informed benchmark for long-duration ground-based infrared radiant-intensity sequence classification. Full article

Damage identification in instrumented structures is often framed through modal-parameter changes, finite element updating, or supervised classifiers. These approaches are powerful, but they require an explicit structural model, identified modes, labeled damage cases, or expert choices about reference sensors. This paper introduces a new data-driven and training-free approach with limited physical priors, defining a sensor network where each sensor is a node and the edges are defined from the spatial correlation of sensor responses. The idea is to use each sensor time history as the measured structural dynamics feature while damage is localized from the edges, whose correlations change relative to a baseline. The method is demonstrated on a full-scale seven-story reinforced-concrete shear-wall building tested at UC San Diego, considering four progressive earthquake-induced damage states and one brace-modification state. The results are compared with those obtained from a previously published finite element model. The results reveal that this network-based approach localizes the damage states in agreement with previous studies with limited prior requirements and low computational cost. Beyond damage localization, this network representation provides sensor centrality, allowing informative sensors to be selected from data rather than chosen randomly or only from experimental intuitions. For the case study, using this sensor network, we find the most central sensors, those carrying the most information with reduced trial-and-error and reduced expert intervention, and use them to recover the first three natural frequencies as a secondary dynamic check. The results show that spatial correlation networks can screen for damage, localize affected regions, and guide modal parameter extraction without building an FE model. This study opens a research avenue in which network representations of multi-sensor structural dynamics complement traditional modal analysis for structural health monitoring, with dense or heterogeneous sensing systems. Full article

Green tea quality is largely determined by the metabolite profile of fresh leaves. However, the time-of-day-dependent dynamics of these metabolites remain largely unknown in Xinyang Maojian (XYMJ), a premium Chinese green tea. In this study, we performed the first integrative transcriptomic and metabolomic analysis of Camellia sinensis cv. Xinyang 10 shoots (one bud and one leaf) sampled at 7:00, 13:00, and 18:00 under field conditions with light intensities of 19.2, 113, and 5.4 klx, respectively. We identified 525 differentially accumulated metabolites and 19,767 differentially expressed genes exhibiting distinct time-of-day-dependent patterns. Physiological measurements confirmed significant fluctuations in starch, soluble sugars, chlorophyll, relative water content, and polyphenols throughout the daytime. A key finding was a daytime carbon allocation trade-off: primary metabolism (starch biosynthesis, glycolysis, TCA cycle) peaked at 13:00, whereas secondary metabolism (flavonoids, theaflavins, phenolic acids, anthocyanins) dominated at 18:00, supported by strong negative correlations between primary and secondary metabolic modules. Chlorophyll and oligomeric catechins peaked at 7:00; theaflavins at 13:00; and starch, soluble sugars, theanine, and organic acids at 18:00. Light-responsive transcription factors (bZIP, NF-Y, HD-Zip, SPL, ARF, MADS) and other regulators (MYB, AP2/ERF, WRKY, NAC, GRAS, bHLH) exhibited time-specific expression, sequentially modulating flavonoid, caffeine, and theanine biosynthesis, along with specific gene modules including SS3/SS4 (starch synthesis), BAM3 (starch degradation), FBA1 (carbon fixation), CYP72A219 (terpenoid metabolism), and L7A (theaflavin biosynthesis). Evening-harvested leaves accumulated higher levels of theanine (umami) and soluble sugars (sweetness), whereas morning leaves were enriched in astringent catechins and flavonols. This multi-omics dissection of time-of-day-dependent metabolism in XYMJ tea provides a scientific basis for time-of-day harvesting strategies and graded processing of tea products. Full article

Hyperspectral image super-resolution (HSI SR) focuses on enhancing the spatial resolution of HSIs while preserving their inherent spectral information. Existing single-image HSI SR methods still suffer from blurred spatial edges and spectral distortion. Although numerous spatial–spectral enhancement networks can enhance spatial–spectral feature extraction, they often lead to a cumbersome network architecture. To address these issues, we propose a Spatial–Spectral Distillation Mamba Network, called SSDM-Net, for HSI SR, which contains a main reconstruction branch and two training-only auxiliary branches for spatial and spectral knowledge distillation. Specifically, the spatial and spectral auxiliary branches, which are utilized exclusively during training, provide edge-aware guidance and capture spectral correlations, respectively. During training, the spatial–spectral knowledge is transferred to the main branch. During inference, the auxiliary branches are removed, improving reconstruction quality without extra computational burden. In the main branch, a Mamba-based spatial–spectral global enhancement module processes spatial and latent inter-channel sequences using selective scanning whose cost is linear in the processed sequence lengths when the feature dimensions are fixed. In addition, a dynamic loss weighting strategy is developed to balance reconstruction, distillation, and auxiliary losses during optimization. Comprehensive experiments conducted on the CAVE and Houston datasets with three scale factors demonstrate that SSDM-Net produces more accurate reconstruction results than existing representative HSI SR methods. Cross-dataset experiments on the Harvard dataset further suggest that the method can maintain competitive reconstruction performance under the evaluated cross-dataset settings. Full article

Polypropylene (PP) fiber-reinforced soil slopes undergo progressive resistance degradation under wet–dry cycling (WDC), while stochastic seismic loading introduces additional uncertainty, challenging deterministic seismic assessment. This study develops a time-dependent seismic reliability framework integrating the probability density evolution method and the equivalent extreme value event method. The cohesion and internal friction angle of unreinforced soil measured at different WDC states are represented as cross-correlated lognormal random fields and combined with random fiber configurations and weighted nonstationary stochastic ground motions in a nonlinear dynamic model. The main contribution is a unified uncertainty-propagation scheme that incorporates experimentally characterized WDC degradation and multiple uncertainty sources into the evolution of response probability and multilevel first-passage reliability. With increasing WDC number and PGA, the extreme displacement distributions shift toward larger values, accompanied by increased response dispersion, tail risk, and reliability loss. The reliability evolution exhibits three stages, namely initial stability, rapid degradation, and residual convergence, during the 70 s excitation. PP fiber reinforcement improves reliability, although the marginal gain becomes limited when the fiber content exceeds 0.15% under the present numerical conditions. The proposed framework provides a probabilistic basis for the seismic assessment and deformation control of PP fiber-reinforced soil slopes at different WDC degradation states. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article