Search Results (725)

Driven by increasingly stringent requirements for energy saving and emission reduction in non-road machinery, hybrid wheel loaders have attracted growing attention as a practical pathway toward cleaner construction equipment. However, conventional energy management strategies often show limited adaptability to highly transient operating cycles and struggle to balance fuel economy, real-time applicability, and battery charge sustainability. To address these issues, this study proposes an improved sparrow-search-algorithm-based equivalent consumption minimization strategy (ISSA-ECMS) for a series hybrid wheel loader. A quasi-static powertrain model was established, while ISSA was used to optimize both the hyperparameters of a Convolutional Neural Network-Long Short-Term Memory (CNN–LSTM) stage-recognition model and the stage-dependent ECMS parameters. A hidden Markov model (HMM)-based post-processing framework was further introduced to improve temporal consistency in operating-stage recognition. The results show that the optimized ISSA-CNN–LSTM achieved 93.22% accuracy, 93.08% Macro-F1, and 93.21% Weighted-F1, while HMM refinement further improved recognition accuracy from 94.02% to 97.92%. In energy management simulations, ISSA-ECMS maintained the terminal state of charge (SOC) at 50.0069%, reduced fuel consumption by 2.1% and 1.4% compared with conventional ECMS and A-ECMS, respectively, and increased the proportion of engine operating points in the economical region to 77.549%. Compared with dynamic programming, its fuel-consumption increase was only 0.28%, while retaining online applicability. These results demonstrate that the proposed method provides an effective and practical solution for real-time energy management of series hybrid wheel loaders. Full article

This paper introduces a rigorous class of two-dimensional Markov-switching autoregressive moving-average (2D MS-ARMA) models for spatial lattice data exhibiting regime-dependent dynamics. The switching mechanism is governed by a latent causal Markov random field that drives spatial transitions between regime-specific autoregressive and moving-average structures. We provide sufficient conditions for the existence of a strictly stationary solution through the top Lyapunov exponent associated with a sequence of random matrices obtained from a state-space representation constructed along the lexicographic order. For the first-order bidirectional specification, we derive explicit spectral conditions linking stationarity to the regime-dependent spectral radii. Sufficient conditions ensuring the existence of finite second-order moments are also provided. Parameter estimation is carried out using a variational expectation–maximization (VEM) algorithm based on a mean-field approximation of the posterior distribution of the hidden regimes. The E-step yields closed-form coordinate ascent updates, while the M-step relies on gradient-based numerical optimization with derivatives computed via recursive differentiation. Under increasing-domain asymptotics, we discuss the consistency and asymptotic behavior of the variational estimator. The proposed framework fills a methodological gap between classical one-dimensional Markov-switching ARMA models and spatial autoregressive structures by extending regime-switching theory to multi-indexed processes with rigorous probabilistic foundations. It provides a comprehensive basis for statistical inference, model diagnostics, and prediction in spatially heterogeneous environments. Full article

In this paper we present a hybrid convolutional neural network–hidden Markov model framework for detecting solar flare events of intensity greater than or equal to M1.0 from very low frequency signals via their induced sudden ionospheric disturbances. The convolutional neural network processes fixed-length windows of raw very low frequency signals and their temporal derivatives to produce probabilistic flare estimates, which serve as emission probabilities for a two-state hidden Markov model. Viterbi decoding enforces temporal consistency, suppressing spurious fluctuations and yielding physically plausible event sequences. The approach is specifically designed to detect the onset-to-peak interval of flare events and, with further development, could operate in real time for early flare warning. The model was trained and evaluated on very low frequency data from the DHO38 transmitter in Germany to a receiver near Birr, Ireland. Sample-level evaluation achieved a balanced accuracy of 0.819 and a Matthews correlation coefficient of 0.529, while event-level detection reached a peak F1-score of 0.558 for moderate-to-strong flares of intensity greater than or equal to C6.0. These results demonstrate automated, physically consistent detection of solar flares based on sudden ionospheric disturbances, indicating the potential of the proposed approach, when combined across multiple receivers, to act as a low-cost complement to satellite-based monitoring. Full article

The arbitrary-order hidden Markov model ($\alpha$-HMM) is a nontrivial generalization of the standard HMM, designed to model stochastic processes with higher-order dependences among arbitrarily distant random events. The $\alpha$-HMM admits an efficient Viterbi-style optimal decoding algorithm, making it feasible to discover higher-order dependences among data objects in observed sequential data. Because the $\alpha$-HMM exceeds the expressive power of standard HMMs, fixed $k\mathrm{th}$-order HMMs, and stochastic context-free grammars, effective probabilistic parameter estimation approaches are required to translate this theoretical expressiveness of the $\alpha$-HMM into practical utility. This paper introduces a principled methodology for effective estimation of probabilistic parameters of the $\alpha$-HMM from observed data. In large-scale sequential datasets, higher-order dependencies can vary widely across instances, so a single global parameter set may be inadequate. Instead, an amortized parameter inference approach is proposed for the $\alpha$-HMM, in which an input-conditioned parameter estimator is learned from data and used to infer instance-specific parameters for each input instance to the decoding algorithm. Specifically, the neural parameter estimator is trained using a composite learning objective that is partially enabled by the optimal decoding algorithm. The effectiveness of the proposed parameter estimation method is demonstrated through empirical results of the application of the $\alpha$-HMM in biomolecular structure modeling and prediction. Full article

Accurately quantifying carbon dioxide (CO₂) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO₂ emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO₂ emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO₂ emissions in this region. Correlation analysis reveals a significant positive relationship (p < 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO₂ emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout. Full article

Collecting a large number of dangerous omen scenarios from drivers’ first-person perspective is of great significance for training and improving end-to-end autonomous driving models. In this study, we aim at capturing driver-perspective scenarios when recognizing dangerous omens. Firstly, through the design and implementation of vehicle and virtual driving experiments, the electroencephalogram, electrocardiogram and eye movement data of the subjects are collected. Statistical tests are conducted to analyze the characteristic differences among drivers across three distinct states. It also reveals that the driver can perceive and distinguish the dangerous omen clearly. Secondly, the evolution law of drivers’ perception state is analyzed to accurately judge the time period of drivers’ dangerous omen perception. Thirdly, the Hidden Markov Model is used to build the driver perception state transition model, and then the model is calibrated and verified. Finally, the model is utilized to identify drivers’ dangerous omen perception states and extract the corresponding perspective objective scenarios, which can provide sufficient samples for training end-to-end autonomous driving models. This study is of great significance to enable the capability of vehicles to recognize dangerous omens, advancing end-to-end and other high-level autonomous driving technologies and further securing vehicle safety. Full article

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. Full article

This paper presents a hybrid econometric and machine-learning framework for forecasting GDP that bridges long-run structure with short-run regime dynamics. Using annual World Bank data spanning 1960 to 2024, the framework combines three complementary components: an ARIMA baseline that captures persistence, a three-state Hidden Markov Model (HMM) that provides probabilistic regime identification, and an LSTM-based extension that learns nonlinear patterns associated with regime transitions. Detailed out-of-sample forecasting evidence is reported for five representative countries (the United States, China, Germany, India, and Greece), chosen to illustrate performance across different volatility profiles and economic environments. Across these case studies, the integrated HMM–LSTM approach often delivers lower forecast errors than the benchmark alternatives, although the magnitude of the gains is not uniform across countries. Beyond point forecasting performance, the regime layer yields an interpretable probabilistic representation of business cycle conditions that can support real-time monitoring and early-warning assessment. By combining transparency with adaptability, the proposed framework contributes to the forecasting literature and provides a practical decision-support tool under heightened macroeconomic uncertainty. Full article

Hybridization effectively enhances breeding efficiency and significantly boosts sheep productivity. However, the epigenetic mechanisms underlying the superior production performance of crossbreds remain largely elusive. In this study, Hu sheep were crossbred with Suffolk rams used as the paternal line. We integrated RNA-seq, ATAC-seq, and CUT&Tag (H3K4me3, H3K4me1, H3K27ac, and H3K27me3) techniques to characterize epigenetic regulatory differences in the longissimus dorsi muscle between Hu sheep (HU) and crossbred progeny (SH). Phenotypic and transcriptomic analyses revealed that SH crossbred sheep exhibited superior growth performance (p < 0.05), and the upregulated genes in the Apelin signaling pathway were significantly correlated with eye muscle area (p < 0.05). Utilizing a Hidden Markov Model, we annotated 15 distinct chromatin states in both HU and SH sheep, systematically characterizing the dynamic epigenomic landscapes across the two breeds. In contrast to SH sheep, the genome of HU sheep exhibited enrichment of repressive chromatin modifications typified by H3K27me3. Strong active enhancers (EnhA) were significantly enriched within upregulated genes in SH. A total of 1862 SH-specific and 691 HU-specific EnhA elements were characterized in this study. Motif analysis revealed that SH-specific EnhA were enriched for myogenic MEF2 family motifs (p < 0.05), which promote muscle and vascular development. By integrating multi-omics data, we constructed a putative regulatory network potentially modulated by SH-specific enhancers, identifying CMKLR1, PPARGC1A, and TLE3 as the core hub genes. Collectively, this study provides a robust data resource, identifying candidate genes and regulatory elements associated with crossbreeding-related muscle phenotypes. Full article

Background: PYR/PYL/RCAR proteins are core abscisic acid (ABA) receptors that play essential roles in ABA signal transduction, plant growth and development, and abiotic stress responses. However, the PYL gene family in watermelon (Citrullus lanatus) has not been systematically characterized, limiting our understanding of ABA-mediated stress adaptation in this economically important crop. Methods: A genome-wide analysis was performed to identify ClPYL genes in watermelon using a hidden Markov model search. Phylogenetic relationships were reconstructed using the maximum likelihood method. Segmental duplication events were analyzed using synteny analysis. Conserved motifs, gene structures, and promoter cis-acting elements were characterized using MEME and PlantCARE. Expression profiles under drought, salt, and cold stresses were examined by quantitative real-time PCR (qRT-PCR) with three biological replicates. Results: In this study, 15 ClPYL genes were identified in watermelon through genome-wide analysis. Phylogenetic reconstruction classified these genes into four subfamilies, with subfamily II being exclusively present in cucurbits—a lineage-specific feature not observed in Arabidopsis. Synteny analysis revealed eight segmental duplication events involving members of subfamilies I, III, and IV, while subfamily II members were not associated with these duplications. Members within the same subfamily share similar exon-intron structures and conserved motifs. Promoter analysis revealed that ClPYL genes are enriched with various cis-acting elements associated with hormone signaling and abiotic stress responses. Expression profiling demonstrated that ClPYL genes exhibit diverse and dynamic expression patterns under drought, high-salinity, and cold stresses. Notably, genes such as ClPYL5 under drought, ClPYL02 under salt, and ClPYL15 under cold stress displayed persistent stress-responsive expression. Conclusions: These findings reveal the evolutionary conservation and diversification of the PYL family in watermelon and provide a set of candidate genes for functional studies aimed at dissecting ABA-mediated stress adaptation. This work establishes a genomic framework for developing stress-resilient watermelon varieties through molecular breeding. Full article

The Domains of Unknown Functions 1664 (DUF1664) genes are a class of genes with unknown functions, and their roles in abiotic stresses responses have not yet been reported. Using the hidden Markov model (HMM) profile of DUF1664 (PF07889) obtained from the Pfam database, along with Arabidopsis thaliana DUF1664 family protein sequences as reference, and verifying complete DUF1664 domains with the NCBI CD-Search online tool, seven DUF1664 family members were identified in the peanut (Arachis hypogaea) genome, designated as AhDUF1664-1A through AhDUF1664-4. Promoter analysis revealed that cis-acting elements in AhDUF1664 genes are associated with growth and development, stress responses, and plant hormone signaling, and these genes exhibit relatively conserved motifs. Functional validation showed that ectopic expression of AhDUF1664-1A enhanced tolerance to salt and drought stresses in Arabidopsis thaliana by modulating the expression of ABA signaling-related genes. Our findings identify the AhDUF1664 gene family in peanut and provide a basis for further investigation into the biological functions of these genes. Full article

The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the “state-flickering” issue inherent in traditional HMMs, we incorporate a “Sticky” parameter into the transition matrix, imposing a temporal penalty on spurious state switching to maintain continuity. Furthermore, we introduce a Dynamic Prior Strategy that adaptively calibrates self-transition probabilities by mapping frequency-domain features of the observed sequence to the model’s parameter space. The proposed decoding process employs a two-pass refinement strategy and the Viterbi algorithm in the logarithmic domain to ensure numerical stability. The model’s efficacy was validated using a high-fidelity dataset of simulated apnea events. This work provides a computationally efficient and mathematically rigorous approach that demonstrates strong potential for long-term respiratory health monitoring. Full article

Can machine learning generate statistically validated alpha in equity markets while adapting to changing market conditions? This study addresses this question by proposing a regime-aware LightGBM framework conditioned on market regimes detected via a rolling Hidden Markov Model, eliminating look-ahead bias. Backtested on 51 NASDAQ-100 constituents (2015–2026), the strategy achieved a portfolio Sharpe ratio of 1.18 (95% CI: [0.53, 1.84]) and outperformed four baseline models. The key findings include the following: (i) cross-asset features (Bitcoin as a leading indicator) contribute the most predictive value; (ii) macroeconomic indicators outweigh traditional technical indicators for high-beta stocks; (iii) the model autonomously adapts its decision logic across regimes, shifting from mean reversion in bear markets to risk appetite monitoring in bull markets. While block bootstrap tests confirm statistical significance ($p<0.001$), the Deflated Sharpe Ratio (0.69) does not reach formal significance after multiple testing correction—an honest finding we report transparently. Full article

MLO (Mildew Resistance Locus O) genes encode seven-transmembrane proteins that function as critical regulators of powdery mildew resistance and abiotic stress responses. Despite their established importance, the MLO gene family in Gossypium hirsutum L. has not been systematically investigated under salt stress conditions. Here, we performed genome-wide identification of 46 GhMLO members using Hidden Markov Model and BLAST searches based on the latest cotton genome assembly. Phylogenetic analysis classified these genes into four distinct subfamilies. Transmembrane topology and conserved domain analyses revealed that all GhMLO proteins contain typical MLO domains and transmembrane structures, maintaining high structural similarity with dicotyledonous model plants. Synteny analysis demonstrated that the expansion of the GhMLO family was primarily driven by segmental and tandem duplications. Integration of transcriptomic data from the COTTONOMICS database revealed tissue-specific expression patterns, with higher transcript abundance in receptacles, stems, and roots, but lower levels in stamens and petals. Salt, drought, and cold stress treatments induced upregulation of GhMLO family members, with most genes showing increased expression over time. RT-qPCR analysis validated that five candidate GhMLO genes were significantly upregulated under salt stress. In summary, this study provides a comprehensive genome-wide characterization of the GhMLO gene family, elucidating their phylogenetic relationships and expression dynamics, which establishes a theoretical basis for identifying key regulatory genes involved in abiotic stress responses and offers novel genetic resources for improving stress tolerance in cotton molecular breeding. Full article

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic. Full article